Original Articles

By Prof. Kurt Heininger
Corresponding Author Prof. Kurt Heininger
Department of Neurology, Heinrich Heine University Duesseldorf, - Germany
Submitting Author Prof. Kurt Heininger

Stochasticity, natural selection, cybernetics, bet-hedging, multilevel selection, Law of Requisite Variety, mean geometric fitness

Heininger K. Duality of stochasticity and natural selection: a cybernetic evolution theory. WebmedCentral ECOLOGY 2015;6(2):WMC004796
doi: 10.9754/journal.wmc.2015.004796

This is an open-access article distributed under the terms of the Creative Commons Attribution License(CC-BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submitted on: 22 Feb 2015 09:03:27 PM GMT
Published on: 23 Feb 2015 07:42:40 AM GMT


Orthodox Darwinism assumes that environments are stable. There is an important difference between breeding (Darwin’s role model of evolution) and evolution itself: while in breeding the final goal is preset and constant, adaptation to varying biotic and abiotic environmental conditions is a moving target and selection can be highly fluctuating. Evolution is a cybernetic process whose Black Box can be understood as learning automaton with separate input and output channels. Cybernetics requires a closed signal loop: action by the system causes some change in its environment and that change is fed to the system via information (feedback) that enables the system to change its behavior. The input signal is given by a complex biotic and abiotic environment. Natural selection is the output/outcome of the learning automaton.

Environments are stochastic. Particularly, density- and frequency-dependent coevolutionary interactions generate chaotic and unpredictable dynamics. Stochastic environments coerce organisms into risky lotteries. Chance favors the prepared. The ‘Law of Requisite Variety’ holds that cybernetic systems must have internal variety that matches their external variety so that they can self-organize to fight variation with variation. Both conservative and diversifying bet-hedging are the risk-avoiding and -spreading insurance strategies in response to environmental uncertainty. The bet-hedging strategy tries to cover all bases in an often unpredictable environment where it does not make sense to “put all eggs into one basket”. In this sense, variation is the bad/worst-case insurance strategy of risk-aversive individuals. Variation is pervasive at every level of biological organization and is created by a multitude of processes: mutagenesis, epimutagenesis, recombination, transposon mobility, repeat instability, gene expression noise, cellular network dynamics, physiology, phenotypic plasticity, behavior, and life history strategy. Importantly, variation is created condition-dependently, when variation is most needed – in organisms under stress. The bet-hedging strategy also manifests in a multitude of life history patterns: turnover of generations, reproductive prudence, iteroparity, polyandry, and sexual reproduction.

Cybernetic systems are complex systems. Complexity is conceived as a system’s potential to assume a large number of states, i.e., variety. Complex systems have both stochastic and deterministic properties and, in fact, generate order from chaos. Non-linearity, criticality, self-organization, emergent properties, scaling, hierarchy and evolvability are features of complex systems. Emergent properties are features of a complex system that are not present at the lower level but arise unexpectedly from interactions among the system’s components. Only within an intermediate level of stochastic variation, somewhere between determined rigidity and literal chaos, local interactions can give rise to complexity. Stochastic environments change the rules of evolution. Lotteries cannot be played and insurance strategies not employed with single individuals. These are emergent population-level processes that exert population-level selection pressures generating variation and diversity at all levels of biological organization. Together with frequency and density-dependent selection, lottery- and insurance-dependent selection act on population-level traits.

The duality of stochasticity and selection is the organizing principle of evolution. Both are interdependent. The feedback between output and input signals inextricably intertwines both stochasticity and natural selection, and the individual- and population-levels of selection. Sexual reproduction with its generation of pre-selected variation is the paradigmatic bet-hedging enterprise and its evolutionary success is the selective signature of stochastic environments.Sexual reproduction is the proof of concept that (epi)genetic variation is no accidental occurrence but a highly regulated process and environmental stochasticity is its evolutionary “raison d’être”.Evolutionary biology is plaqued by a multitude of controversies (e.g. concerning the level of selection issue and sociobiology. Almost miraculously, these controversies can be resolved by the cybernetic model of evolution and its implications.

Table of contents


Table of contents

1. Introduction

2. Darwin's role model of evolution

3. The cybernetics of evolution

4. The improbability of evolution

5. The cybernetic learning automaton

6. Black Box theory

   6.1 Input and output of the Back Box

7. Evolutionary outcomes are probabilistic. But why?

8. Chance and necessity

   8.1 Chance

      8.1.1 Mutations

      8.1.2 Genetic drift

   8.2 Necessity

9. Gedankenexperiment: evolution in stable environments

10. The evolutionary signature of stochastic environments

   10.1 Environmental stochasticity

   10.2 Demographic stochasticity

11. Bet-hedging: risk avoidance and risk-spreading in response to uncertainty

   11.1 Molecular biological bet-hedging

      11.1.1 Gene expression noise

      11.1.2 Epigenesis

      11.1.3 Protein promiscuity

      11.1.4 Energy-Ca2+-redox triangle

   11.2 Phenotypic and behavioral bet-hedging

      11.2.1 Canalization and phenotypic plasticity: two sides of the same coin

   11.3 Stress and bet-hedging

12. The gambles of life

   12.1 Lottery and insurance: responses to uncertainty and risk

   12.2 "Decisions" under uncertainty: utility/fitness optimization

13. Evolution is far-sighted

14. Complexity and self-organization: chaos and order

   14.1 Complexity

   14.2 Fractals and 1/f noises

   14.3 Self-organization

      14.3.1 Self-organized criticality

15. Stochasticity and multilevel selection

   15.1 Community selection as an emergent behavior of complex systems

   15.2 Fitness as transgenerational propensity

   15.3 Reproductive fitness in stochastic environments

      15.3.1 Reproductive prudence

16. Life history phenotypes of bet-hedging

   16.1 Turnover of generations: bet-hedging in time?

   16.2 Iteroparity

   16.3 Polyandry

   16.4 Sexual reproduction

17. Stochasticity and selection: duality in evolution

   17.1 The creative conflict between stochastic indeterminism and selective determinism

      17.1.1 Playing dice with controlled odds

18. The blending of ecology and evolution

19. Cutting the Gordian knot of controversies

20. Abbreviations

21. References    

1. Introduction

Although biology has been the theater of numerous controversies, the view that biological processes must be deterministic has almost never been put into question. Since Ancient times with Aristotle and his finalist conception of living beings, since Descartes and his mechanistic theory of life in the 17th century, since Claude Bernard and his physico-chemical description of physiology in the 19th century, until the computational metaphor of a genetic program that was put forth by Monod and Jacob in the 20th century, biology has always been dominated by deterministic theories. One may even say that determinism has always appeared as being in the deep nature of life, and therefore dominant in the explanation of the latter.
Gandrillon et al. (2012)

Evolutionary forces are often divided into two sorts: stochastic and deterministic (Wright, 1955). To date, there is a general agreement that ecological and evolutionary outcomes and the tools that are used to describe them are probabilistic and statistical (Beatty, 1984; Millstein, 1997, 2003; Graves et al., 1999; Glymour, 2001; Shanahan, 2003; Rosenberg, 2004; Colyvan, 2005). However, it has been a contentious issue whether evolution is deterministic or indeterministic (Rosenberg, 1994; Horan, 1994; Brandon & Carson, 1996; Graves et al., 1999; Stamos, 2001; Weber, 2001; Shanahan, 2003). Glymour (2001) concluded that “any complete and correct evolutionary theory must be probabilistic”, a statement that has not been questioned by the advocates of a more deterministic perspective (Rosenberg, 2001). However, the deterministic perspective (e.g. Graves et al., 1999; Rosenberg, 2001) reflects the dictum of Laplace (1825) that randomness is only a measure of our ‘‘ignorance of the different causes involved in the production of events.’’ “The world, it is said, might often look haphazard, but only because we do not know the inevitable workings of its inner springs” (Hacking, 1990, p. 1).

 Contingency may be defined as the outcome of a particular set of concomitant effects that apply in a particular space-time situation and thus determine the outcome of a given event. In most of the epistemological literature, this word has aptly taken the place of the term ‘chance’ or ‘random event’, and in fact, it has a different texture. For example, a car accident can be seen as a chance event, but indeed it is due to the concomitance of many independent factors, like the car speed, the road conditions, the state of the tyres, the alcohol consumption of the driver, etc. These factors all sum together to give the final result, seen as a chance event. The same can be said for a stock-market crash, or the stormy weather of a particular summer day. Interestingly, each of these independent factors can actually be seen per se as a deterministic event, e.g. the bad state of the car tyres determines per se a car sliding off at a curve. The fact, however, that there are so many of these factors, and each with an unknown statistical weight, renders the complete accident unpredictable: a chance event. Change the contingent conditions (perhaps only one of them) and the final result would be quite different: it may happen one week later, or with another driver, or never. As Gould states about biological evolution (Gould, 1989), ‘run the tape again, and the first step from prokaryotic to eucariotic cell may take 12 billion years instead of two. . . ’, implying that the onset of multicellular organisms, including mankind, may not have arisen yet–or may never arise. This is contingency in the clearest form (Luisi, 2003).

Since Darwin the role of Natural Selection in evolution has been under dispute. For Mayr (1980), selection is ‘‘the only direction-giving factor in evolution’’. However, from Kimura’s “Neutral Theory” (1968) and Gould and Lewontin’s “Panglossian paradigm” (1979), the extent to which natural selection is the only creative force of evolution has been questioned. More recently, the literature on evolutionary systems was unclear about the role of natural selection (Laszlo, 1987, 1994; Csányi, 1989; Goonatilake, 1991; Salthe, 1998), culminating in the assertion that there exists no biological equivalent to “laws of motion” by which the evolution of the biosphere can be predicted (Longo et al., 2012; Kauffman, 2013). The past 50 years have seen an increased recognition of sluggish evolution and failures to adapt (Conner, 2001; Kingsolver et al., 2001; Futuyma, 2010). According to Lewin (1980), the existence of constraints meant that natural selection was involved at only one stage of the evolutionary process and thus was not the only essential factor in evolution. It has been speculated that these additional forces may been forces like drift, gene flow, epigenetic inheritance, pleiotropy, and/or developmental, structural and phylogenetic constraints (Gould & Lewontin, 1979; Amundson, 1994, 2001; Futuyma, 2010). Pigliucci (2007) noted that natural selection cannot be the only mechanism of evolution. Natural selection apart, all evolutionary processes are random with respect to adaptation, and therefore tend to degrade it (Barton & Partridge, 2000). In the reformist counterparadigm, one invokes “chance”, “constraints”, and “history” to explain imperfections: some features don’t turn out perfectly, due to statistical noise, in-built limitations, and so on; some features, due to “historical contingency”, are side-effects or vestiges. Selection still governs evolution, as Darwin said, but there are “limits to selection” (Barton & Partridge, 2000).

In this work, I further elaborate the concept of the stochasticity-natural selection duality in evolution that was first presented under the impression that sexual reproduction is a sophisticated bet-hedging enterprise in response to environmental stochasticity (Heininger, 2013).I will argue that from a cybernetic perspective there is compelling evidence for a dualistic creative conflict between stochasticity and selection in evolution. The dictionary definition of stochastic is ‘‘(1) Relating to, or characterized by conjecture; conjectural. (2) Involving or containing a random variable or variables: stochastic calculus. (3) Involving chance or probability: a stochastic stimulation. (4) adj: (statistics) being or having a random variable; ‘‘a stochastic variable’’; ‘‘stochastic processes.’’ The ambiguous implication of chance, randomness and probability reflects the uncertainty and unpredictability of abiotic and biotic systems and the probabilistic character of evolution (Weiss & Buchanan, 2011).


2. Darwin

The Modern Synthesis built on Darwin's two major realizations: (i) that all living organisms are related to one another by common descent; (ii) that a primary explanation for the pattern of diversity of life—and especially for the obvious “fit” of organisms to their environments—is the process that he called natural selection. He recognized the importance of variation for the action of selection (1859, chapters I, II and V). However, he had no idea how this variation arose: “our ignorance of the laws of variation is profound” (1859, p. 149). Yet, understanding the “laws of variation” should have a key role in our understanding of evolution. At the beginning of the 20th century the foundation of the modern theory of evolution posited that evolution is the result of the interplay between two antagonistic mechanisms: natural selection and sources of genetic variation (Campos & Wahl, 2010). In Darwin`s tradition, the Modern Synthesis understood selection as the only driving force in evolution (Mayr, 1980). Genetic variation was considered the result of accidental mutations. But Levinton (1988, p. 494) stated: “Evolutionary biologists have been mainly concerned with the fate of variability in populations, not the generation of variability. ... This could stem from the dominance of population genetic thinking, or it may be due to a general ignorance of the mechanistic connections between the genes and the phenotype. Whatever the reason, the time has come to reemphasize the study of the origin of variation.” During the 27 years since this statement our knowledge of the proximal, molecular biological, generation of variation has been expanded tremendously (see chapter 11.1) but little progress has been made in our understanding of the ultimate evolutionary origin of variation.

Darwin's strongest evidence for the power of natural selection was by analogy with the dramatic success of artificial selection (Darwin, 1859, Chapter 1) and studies since Darwin's time have confirmed his view. What is remarkable is that almost all traits respond to selection, and that selection on large sexual populations causes a sustained response over many generations (Barton & Turelli, 1989; Falconer & Mackay, 1995). But there is an important difference between artificial selection and evolution. In breeding, artificial selection has the goal to improve a certain predefined trait, e.g. oil content in maize (Laurie et al., 2004), milk quantity and quality in dairy cattle breeding (Miglior et al., 2013), a certain morphological trait in pigeons, or, in the laboratory, flight speed in Drosophila (Weber, 1996). The target is pre-defined by the breeder (figure 1B). Importantly, breeding is an iterative process (Hill & Caballero, 1992; Williams & Lenton, 2007) in which the ultimate goal is reached after many generations, but the setting of the ultimate goal and thus the direction of selection remain constant. Here, variation is an often unwanted noise, at least when it does not serve the ultimate target of selection. The breeder has at least two functions: (s)he determines the goal of the breeding operation and selects the individuals for the next round of breeding. In evolution, however, the direction and selective regime are established by the organism’s stochastic, often unpredictable, environment. The evolutionary dynamics consist of a fitness-dominated, directed part caused by selection and a neutral, undirected part due to fluctuations (Frey, 2010). Thus, adaptation to varying biotic and abiotic environmental conditions is determined by a moving target and selection can be highly fluctuating (figure 1C; Siepielski et al., 2009; Bell, 2010).

3. The cybernetics of evolution

There is simply no denying the breathtaking brilliance of the designs to be found in nature. Time and again, biologists baffled by some apparently futile or maladroit bit of bad design in nature have eventually come to see that they have underestimated the ingenuity, the sheer brilliance, the depth of insight to be discovered in one of Mother Nature's creations. Francis Crick......baptized this trend in the name of his colleague Leslie Orgel, speaking of what he calls "Orgel's Second Rule: Evolution is cleverer than you are."
Daniel Dennett, Darwin's Dangerous Idea, 1995

The term cybernetics stems from the Greek κυβερνητης (kybernetes = steersman, governor, pilot, or rudder). Cybernetics had a crucial influence on the birth of various modern sciences: control theory, computer science, information theory, automata theory, artificial intelligence and artificial neural networks, cognitive science, computer modeling and simulation science, dynamical systems, and artificial life. Many concepts central to these fields, such as complexity, self-organization, self-reproduction, autonomy, networks, connectionism, and adaptation, were first explored by cyberneticians during the 1940's and 1950's. Examples include von Neumann's computer architectures, game theory, and cellular automata; Ashby's and von Foerster's analysis of self-organization; Braitenberg's autonomous robots; and McCulloch's artificial neural nets, perceptrons, and classifiers (Heylighen & Joslyn, 2001). Cybernetics is the science of control systems; or, to expand it into Norbert Wiener's own words: "the science of control and communication in the animal and the machine" (Wiener, 1948, Ashby, 1956). According to Beer (1959), there are three main characteristics of a cybernetic system: extreme complexity, probabilism, and self-regulation. Cybernetics is about how to cope with the challenge of ubiquitous complexity (see also chapter 14). Cybernetic systems are characterized by input and output variables and it is essential to distinguish the one from the other (Ashby, 1956). The control of a system requires getting information from the output back to the input of a system and this is called feedback. Cybernetics requires a closed signal loop: action by the system causes some change in its environment and that change is fed to the system via information (feedback) that enables the system to change its behavior. Cybernetic systems are systems with feedback. They are a special class of cause-and-effect (input-output) systems (figure 1A). Patten and Odum (1981) offered a minimalist definition that distinguished cybernetic systems from non-cybernetic systems by the presence of feedback control; in cybernetic systems, ‘‘input is determined, at least in part, by output''. Very small feedbacks may exert very large effects (Patten & Odum, 1981).

All life is cybernetic (Korzeniewski, 2001, 2005; De Silva & Uchiyama, 2007; Nurse, 2008; Abel, 2012). All life depends upon linear digital prescriptive information and cybernetic programming (Abel, 2012). Genetic cybernetics even inspired Turing's, von Neumann's, and Wiener's development of computer science (Turing, 1936; von Neumann, 1950, 1956; von Neumann et al., 1987, 2000; Wiener, 1948, 1961). Evolution is a cybernetic process (e.g. Ashby, 1954; 1956; Schmalhausen, 1960; Waddington, 1961; Corning, 2005; Scott, 2010). In his 1858 essay, A.R. Wallace referred to the evolutionary principle "as exactly like that of the centrifugal governor of the steam engine, which checks and corrects any irregularities almost before they become evident....". For Gregory Bateson (1972, p. 435) "the result will be...a self-corrective system. Wallace, in fact, proposed the first cybernetic model." However, the first account of how a phenotypic change induced by a change in the environment could lead to a change in the inherited genome was provided by Spalding (1837). Spalding's driver of evolution comprised a sequence of learning followed by differential survival of those individuals that expressed the phenotype more efficiently without learning (Bateson, 2012). Fitness-related differential reproduction is the feedback control that drives the cybernetic system.Basically, evolution, like the brain, is seen as an input/output device: ‘‘brain function is ultimately best understood in terms of input/output transformations and how they are produced'' (Ashby, 1954; Mauk, 2000; Maye et al., 2007). Within this conceptual framework, it is intuitive to understand Orgel's Second Rule: "Evolution is cleverer than you are."

4. The improbability of evolution

Mathematical models that mimic biological evolutionary processes have revealed that the traditional view of Darwinian evolution, according to which the most fit of random mutants are selected, faces a major problem (Eden, 1967; Schützenberger, 1967; Bak et al., 1987, 1988; Bak, 1993, 1996; Kauffman, 1995 p. 155ff; Fernández et al., 1998): It is much too slow to account for real evolution. In 1966, mathematicians, physicists and engineers met in Philadelphia (Moorhead & Kaplan, 1967). The mathematicians argued that neo-Darwinism faced a formidable combinatorial problem. Murray Eden illustrated the issue with reference to an imaginary library evolving by random changes to a single phrase: “Begin with a meaningful phrase, retype it with a few mistakes, make it longer by adding letters, and rearrange subsequences in the string of letters; then examine the result to see if the new phrase is meaningful. Repeat until the library is complete” (Eden, 1967). Would such an exercise have a realistic chance of succeeding, even granting it billions of years? In the view of mathematicians, the ratio of the number of functional genes and proteins, on the one hand, to the enormous number of possible sequences corresponding to a gene or protein of a given length, on the other, seemed so small as to preclude the origin of genetic information by a random mutational search. A functional protein one hundred amino acids in length represents an extremely unlikely occurrence. There are roughly 10130 possible amino acid sequences of this length, if one considers only the 20 protein-forming acids as possibilities. In human codes, M. P. Schützenberger (1967) argued, randomness is never the friend of function, much less of progress. When we make changes randomly to computer programs, “we find that we have no chance (i.e. less than 1/101000) even to see what the modified program would compute: it just jams.” Bak (1993, cited in Fernández et al., 1998) described the difficulty: “If, for the sake of argument, we imagine the outer world frozen (for a while) and try to construct from scratch an equally fit species by recourse to engineering techniques rather than by evolution, we will be forced to accept that eons are needed. By starting at a random configuration one certainly will reach a wrong and much less fit maximum. It would be necessary to systematically go through all configurations, involving exponentially large times.” According to Kashtan et al. (2007), computer simulations that mimic natural evolution by incorporating replication, variation (e.g. mutation and recombination) and selection, typically observe a logarithmic slowdown in evolution: longer and longer periods are required for successive improvements in fitness (Lipson et al., 2002; Lenski et al., 2003; Kashtan & Alon, 2005). A similar slowdown is observed in adaptation experiments on bacteria in constant environments (Elena & Lenski, 2003; Dekel & Alon, 2005). Simulations can take many thousands of generations to reach even relatively simple goals, such as Boolean functions of several variables (Lenski et al., 2003; Kashtan & Alon, 2005).

5. The cybernetic learning automaton

Learning is a process of acquiring information, storing it in memory, and using it to modify future behaviors. A learning system is characterized by its ability to improve its behavior with time, in some sense tending towards an ultimate goal (Narendra & Thathachar, 1974). The evolution of learning is a paradigm case of the dual action of environmental stochasticity and natural selection. The ability to learn is a behavioral capacity whose evolution is usually explained through the action of natural selection (e.g. Staddon, 1983; Marler & Terrace, 1984; Bolles & Beecher, 1988; Davey, 1989; Miller & Todd, 1990). However, a vital component of the learning process is also the environment. If the environments were relatively static, there might be little need for learning to evolve. Since some cost is associated with learning (Dukas & Duan, 2000; Mery & Kawecki, 2003, 2004; Burger et al., 2008), in an absolutely fixed environment a genetically fixed pattern of behavior should evolve (“absolute fixity argument”). But if the environment is diverse and unpredictable, innate environment-specific mechanisms are of little use. Unpredictable or variable environments favor the evolution of cognition and learning (Bergman & Feldman, 1995; Richerson & Boyd, 2000; Godfrey-Smith, 2002; Mery & Kawecki, 2002; Brown et al., 2003; Kerr & Feldman, 2003; Heller, 2004; Kotrschal & Taborsky, 2010; Richardson, 2012; Clarin et al., 2013; Tebbich & Teschke, 2014) and cognition/learning is thought to enable organisms to deal with environmental heterogeneity (Godfrey-Smith, 2002; Richardson, 2012). “The function of cognition is to enable the agent to deal with environmental complexity” (Godfrey-Smith, 1996). Learning is an important pathway to flexibility as it allows animals to adjust their behavior to environmental changes. On the other hand, in an absolutely unpredictable environment, where the past and present states of the environment offer no information about the future there is nothing to learn and there is again no driving force for a learning capability to evolve (Bergman & Feldman, 1995). Thus, learning should only be adaptive, if learning rates are sufficiently higher than the rates of environmental change (Dukas, 1998) and should therefore vary with environmental stability and predictability. Similarly, Stephens (1991) argued that the pattern of predictability in relation to an individual’s life history could determine the evolutionary advantage of learning. Within these framework conditions, a stochastic environment encourages the evolution of learning (Levins, 1968; Johnston, 1982; Chalmers, 1990; Stephens, 1991; Bergman & Feldman, 1995; Krakauer & Rodr??guez-Gironés, 1995; Groß et al., 2008; Eliassen et al., 2009). Bergman and Feldman (1995) viewed learning as the ability to construct a representation of the environment and, by proper use of the representation, to predict future states of the environment. This requires some regularity in the environmental signals and the capacity to capture this regularity. Learning is believed to be adaptive because under a wide range of conditions it allows the learner to generate predictions about its environment, and hence to make better decisions, than by using innate knowledge alone (Johnston, 1982; Stephens, 1991; Bergman & Feldman, 1995). Environmental fluctuations early in life are known to enhance the behavioral flexibility of animals with regard to predator avoidance strategies (Braithwaite & Salvanes, 2005; Salvanes et al., 2007), feeding performance (Braithwaite & Salvanes, 2005) and social behavior (Salvanes & Braithwaite, 2005; Salvanes et al., 2007). A possible explanation for these behavioral effects is that variable environments evoke repeated neural stimulations resulting in faster and better learning (Braithwaite & Salvanes, 2005). Several studies showed that neural stimulation over longer periods by exposing animals to enriched environments (e.g., Kempermann et al., 1997; Brown et al., 2003) can enhance brain development (Bredy et al., 2004; Botero et al., 2009), for example through an increased synaptic density (Bredy et al., 2003), and can lead to improved learning abilities and memory capacity (Bredy et al., 2003). For example, the learning abilities of fishes increased in response to experimental variation of environmental quality during ontogeny (Kotrschal & Taborsky, 2010).

Learning automata are adaptive decision-making devices operating on unknown random environments (Narendra & Thathachar, 1974; 1989). The automaton updates its action probabilities in accordance with the inputs received from the environment so as to improve its performance in some specified sense (Narendra & Thathachar, 1974; 1989). The basic operation carried out by a learning automaton is the updating of the action probabilities on the basis of the responses of the environment. The learning automaton has a finite set of actions and each action has a certain probability (unknown to the automaton) of getting rewarded by the controlled system, which is considered as environment of the automaton. The aim is to learn to choose the optimal action (i.e. the action with the highest probability of being rewarded) through repeated interaction on the system. If the learning algorithm is chosen properly, then the iterative process of interacting on the system can be made to result in the selection of the optimal action (Zeng et al., 2000). The learning model that is closest to the evolutionary approach is ‘‘reinforcement learning’’ based on the insight that successful strategies will be reinforced and used more frequently. Reinforcement learning has been successfully applied for solving problems involving decision making under uncertainty (Narendra & Thathachar, 1989; Barto et al., 1983; Zikidis & Vasilakos, 1996; Zeng et al., 2000; Thathachar & Sastry, 2002). (When speaking of ‘decisions’, use of the term is in an evolutionary sense, not implying any conscious rationalization on the part of individual organisms.) In general, a reinforcement learning algorithm conducts a stochastic search of the output space, using only an approximative indication of the “correctness” (reward) of the output value it produced in every iteration. Based on this indication, a reinforcement learning algorithm generates, in each iteration, an error signal giving the difference between the actual and correct response and the adaptive element uses this error signal to update its parameters (Zeng et al., 2000).

6. Black Box theory

“In our daily lives we are confronted at every turn with systems whose internal mechanisms are not fully open to inspection, and which must be treated by the methods appropriate to the Black Box” (Ashby, 1956, p. 86). A Black Box theory treats its object or subject matter as if it were a system devoid of internal structure; it focuses on the system’s behavior and handles the system as a single unit (Bunge, 1967 p. 509). The cybernetic Black Box theory deals with incomplete knowledge about causal mechanisms but deduces knowledge about the system’s properties from the relations between the input- and the corresponding output-characteristics (Ashby, 1956 chapter 6). A complex system usually cannot be studied by decomposing the system into its constituent subsystems, but rather by measuring certain signals generated by the system and analyzing the signals to gain insights into the behavior of the system (Gao et al., 2007). Since it is often difficult to predict the behavior of a complex system, Simon (1981) recommends vicarious system experimentation through simulation, pointing out that this technique may even create new knowledge about system behavior. He is especially keen to demonstrate that system behavior can be predicted even in ignorance of (or with a minimal knowledge of) the system’s structure. In connection with this, he speaks in favor of Black Box theories (Simon, 1981, p. 20): “We knew a great deal about the gross physical and chemical behavior of matter before we had a knowledge of molecules, a great deal about molecular chemistry before we had an atomic theory, and a great deal about atoms before we had any theory of elementary particles?–if indeed we have such a theory today. This skyhook-skyscraper construction of science from the roof down to the yet unconstructed foundations was possible because the behavior of the system at each level depended on only a very approximate, simplified, abstracted characterization of the system at the level next beneath.” Simon also refers to John von Neumann’s research in computer reliability and the problem of organizing a system in such a way that as a whole, it becomes relatively reliable in spite of the possible unreliability of its components (Mattessich, 1982).

6.1 Input and output of the Black Box

Darwin was vague in the meaning of his new concept of “Natural Selection,” using it interchangeably as one of the causes for evolutionary change and as the final outcome (= evolutionary change). But his clearest definition of natural selection (Darwin, 1859 p. 61: “I have called this principle, by which each slight variation, if useful, is preserved, by the term of Natural Selection, in order to mark its relation to man’s power of selection.”) is an outcome definition, not that of a cause (Bock, 2003). First, natural selection is a metaphor, an umbrella term that serves to label and characterize a vast array of specific factors with survival consequences. The generally accepted modern definition of natural selection is that it is an outcome (Fisher, 1930; Endler, 1986; Bock, 2003, 2010; Reese, 2005), and is: ‘‘nonrandom (differential) reproduction of genotypes’’ (e.g., Ehrlich & Holm, 1963, p. 326); or ‘‘nonrandom differential survival or reproduction of classes of phenotypically different entities.’’ (Futuyma, 1986, p. 555). Natural selection is used by most biologists (e.g., Dobzhansky, 1959; Lerner, 1959) quite interchangeably as a cause, a process and an outcome resulting in massive confusion (Endler, 1986; Bock, 2010; MacColl, 2011). However, since evolution is a continuous iterative process where the resultant population of the previous contest becomes the input population to the next contest, Darwin’s and his followers’ ambiguity was not unfounded.

In a cybernetic system, natural selection corresponds to an output. Surprisingly, the input into the process of evolution has been less rigorously defined. Ross Ashby (1956, p. 46) defined the input of cybernetic systems as follows: “With an electrical system, the input is usually obvious and restricted to a few terminals. In biological systems, however, the number of parameters is commonly very large and the whole set of them is by no means obvious. It is, in fact, co-extensive with the set of ‘all variables whose change directly affects the organism’. The parameters thus include the conditions in which the organism lives. In the chapters that follow [in Ashby’s book, KH], the reader must therefore be prepared to interpret the word “input” to mean either the few parameters appropriate to a simple mechanism or the many parameters appropriate to the free-living organism in a complex environment.” Environmental information falls into two categories: signals and environmental factors. The signals are deterministic, that is, once received the consequences are inevitable. The environmental factors are stochastic, that is they generate randomness (Skår & Coveney, 2003). Complex environments have a high degree of uncertainty/stochasticity (Yoshimura & Clark, 1991; Grant & Grant, 2002; Lenormand et al., 2009) or capriciousness (Lewontin, 1966).Uncertainty in environments is a function of (1) degrees of freedom (generally taken as the most basic definition of complexity [Gell-Mann, 1994]); (2) the possible nonlinearity of each variable comprising each degree of freedom, and (3) the possibility that each may change (McKelvey, 2004a).

Stochastic automata operating in an unknown random environment have been proposed as models of learning (Narendra & Thathachar, 1974). These automata update their action probabilities in accordance with the inputs received from the environment and can improve their own performance during operation.Developments in stochastic control theory took into account uncertainties that might be present in the process; stochastic control was effected by assuming that the probabilistic characteristics of the uncertainties are known. Frequently, however, the uncertainties are of a higher order, and even the probabilistic characteristics such as the distribution functions may not be completely known. It is then necessary to make observations on the process as it is in operation and gain further knowledge of the process. A distinctive feature of such problems is that there is little a priori information, and additional information is to be acquired on line. Narendra and Thathachar (1974) illustrated the automaton approach in an example featuring a student and a probabilistic teacher. “A question is posed to the student and a finite set of alternative answers is provided. The student can select one of the alternatives, following which the teacher responds in a binary manner indicating whether the selected answer is wright or wrong. The teacher, however, is probabilistic?–there is a nonzero probability of eliciting either of the two responses for any of the answers selected by the student. The saving feature of the situation is that it is known that the teacher’s negative responses have the least probability for the correct answer. Under these circumstances the interest is in finding the manner in which the student should plan a choice of a sequence of alternatives and process the information obtained from the teacher so that he learns the correct answer.
In stochastic automaton models the stochastic automaton corresponds to the student, and the random environment in which it operates represents the probabilistic teacher. The actions (or states) of the stochastic automaton are the various alternative answers that are provided. The responses of the environment for a particular action of the stochastic automaton are the teacher’s probabilistic responses. The problem is to obtain the optimal action that corresponds to the correct answer.
The stochastic automaton attempts a solution of this problem as follows. To start with, no information as to which one is the optimal action is assumed, and equal probabilities are attached to all the actions. One action is selected at random, the response of the environment to this action is observed, and based on this response the action probabilities are changed. Now a new action is selected according to the updated action probabilities, and the procedure is repeated. A stochastic automaton acting in this manner to improve its performance is referred to as a learning automaton….” (Narendra & Thathachar, 1974).

Stochasticity can take various forms (McNamara et al., 2011): “As Frank and Slatkin (1990) pointed out, stochasticity can be partitioned into variation that affects each member of a lineage independently and variance that is correlated across individuals. The former, referred to as individual variation, is also known as demographic stochasticity in ecology (e.g. Lande, 1988) and as idiosyncratic risk in economics (e.g. Kreps, 1990). At the other extreme, stochasticity that affects all members of a lineage in the same way will be referred to as environmental stochasticity (e.g. Lande, 1988); economists refer to this as aggregate uncertainty (e.g. Kreps, 1990). Organisms typically experience both forms of stochasticity. Examples of environmental stochasticity include large-scale fluctuations in the environment produced by weather or fluctuations in population density. Even within a particular local environment, individuals may have good and bad luck foraging. This good and bad luck constitutes a source of individual stochasticity (Houston & McNamara, 1999).” In the cybernetic model, environmental stochasticity corresponds to the input level and demographic stochasticity to the output level of the evolutionary Black Box.

The whole ecosystem and its subsystems can be described as stochastic automata whose changes of states are given by discrete measures of probability (Gnauck & Straškraba, 1980; Patten & Odum, 1981; Gnauck, 2000). Stochastic automata models in which single sites or groups of sites are chosen for updating at each time are used in several contexts—including in Markov-Chain Monte Carlo and stochastic optimization algorithms (Stewart, 1994; Norris, 1996), and in modeling DNA sequence evolution (Arndt et al., 2002). Environmental stochasticity acts both at the input and output level of the Black Box: (i) the stochastic input leads to the evolution of learning and various risk-aversion behaviors; (ii) the stochastic output results in fluctuating selection, often termed stochastic selection, and selection-independent demographic stochasticity. Uncertainty of outcome refers to incomplete knowledge of outcome probabilities (Knight, 1921; Svetlova & van Elst, ‎2012). Outcomes can be assigned odds but not determined in advance. While natural selection as evolutionary outcome variable is widely accepted, stochasticity as both input and output variable of the Black Box and organizing principle of evolution remains to be shown.

A myriad of studies used statistical tools like Monte Carlo methods, Markov chains, Bayesian statistics, and lottery games to simulate evolutionary processes. Although, there is a classical interpretation of probability which is neutral with respect to determinism/indeterminism: the frequency interpretation, according to which probabilities represent the actual or limiting frequency of an event in a series of like events (Weber, 2001). The cybernetic input-output model of evolution allows to lay the theoretical foundations explaining the probabilistic behavior of the system. Clearly, the fact that the Black Box generates winners and losers (in terms of reproductive output) suggests that some type of lottery unfolds, that stochastic processes play a role within the learning box. The outcome of any evolutionary process is not a single result; it is at best a probability distribution of possible outcomes (Proulx & Adler, 2010). Hence, evolution can be described by a lottery model (Chesson & Warner, 1981; Proulx & Day, 2001; Svardal et al., 2011). Since during the iterative process of evolution the direction of selection can fluctuate, often unpredictably, a winner’s status is not written in stone. The descents of the winners of the evolutionary lottery again are raffle tickets in another round of this evolutionary game.

7. Evolutionary outcomes are probabilistic. But why?

The Oxford Dictionary defines selection as: “The action or fact of carefully choosing someone or something as being the best or most suitable”. According to this definition, selection should have a deterministic outcome. The Oxford Dictionary definition of natural selection is: “The process whereby organisms better adapted to their environment tend to survive and produce more offspring. The theory of its action was first fully expounded by Charles Darwin and is now believed to be the main process that brings about evolution.” The Darwinian concept of natural selection was conceived within a set of Newtonian background assumptions about systems dynamics. Within this conceptual framework the process of natural selection is deterministic (Sober, 1984; Brandon & Carson, 1996; Witting, 2003; Sols, 2014). This is in analogy to the breeder’s deterministic selection, Darwin’s role model of evolution. Sober (1984) elaborates: “When it acts alone, the future frequencies of traits in a population are logically implied by their starting frequencies and the fitness values of the various genotypes” (Sober 1984, p. 110; italics in original). A role for deterministic natural selection is typically inferred when genotypes or phenotypes are similar for independent populations in similar environments: that is, parallel or convergent evolution (Endler, 1986; Schluter, 2000; Langerhans & DeWitt, 2004; Arendt & Reznick, 2008; Losos, 2011; Wake et al., 2011). As another example, specific causes of natural selection are typically inferred through correlations between genotypes or phenotypes and a particular ecological factor (Endler, 1986; Wade & Kalisz, 1990; MacColl, 2011; Hendry et al., 2013), such as diet (e.g., Schluter & McPhail, 1992; Kaeuffer et al., 2012), structural habitat features (e.g., Losos, 2009), predation (e.g., Reznick & Bryga, 1996; Langerhans & DeWitt, 2004), or water flow (e.g., Langerhans, 2008).

There has been a general agreement that ecological and evolutionary outcomes and the tools that are used to describe them are probabilistic and statistical (Glymour, 2001; Millstein, 2003; Shanahan, 2003; Rosenberg, 2004; Colyvan, 2005). Mendelian genetics at first did not sit well with the gradualist assumptions of the Darwinian theory. Eventually, however, Mendelism and Darwinism were fused by reformulating natural selection in statistical terms. This reflected a shift to a more probabilistic set of background assumptions based upon Boltzmannian systems dynamics (Weber & Depew, 1996). This triumph was possible only in the new, more probabilistic scientific climate that, as historians of science have been arguing, began to take shape in the last half of the nineteenth century (Hacking, 1975, 1983, 1990; Krüger et al., 1987; Gigerenzer et al., 1989; Weber & Depew, 1996). It has been a contentious issue what evolutionary factors underlie the statistical character of evolutionary theory. Graves et al. (1999) argue that the probabilism of the theory of evolution is epistemically motivated. For Brandon and Carson, the probabilism of natural selection derives solely from its real-life connection with drift: “natural selection is indeterministic at the population level because (in real life as opposed to certain formal models) it is inextricably connected with random drift” (1996, p. 324; italics in original). One of the most commonly encountered analogies for the process of evolution is that of a blindfolded selector drawing balls from an urn. The metaphor is thought to illuminate the irreducibly probabilistic nature of evolutionary processes (Walsh et al., 2002). Other statistical metaphors abound too: selection is spoken of as “discriminate sampling” (Beatty, 1984). Drift is spoken of variously as “indiscriminate sampling,” or “sampling error”. Accordingly, the issue of what is more important in accounting for evolutionary change, indiscriminate sampling in finite populations, or discriminate sampling is not all-or-none, but a more-or-less issue (Beatty, 1984) due to processes whose relative importance vary with population size. Natural selection, by this way of thinking, is a mere consequence of a statistical property of a population—its variation in fitness (Endler, 1986). Evolutionary theory dealt with the issue of stochasticity, both at the genetic (mutations, recombination) and population (random drift, migration) level. In the tradition of Darwin’s theory, the Modern Synthesis considered either imperfect biological processes or random drift as the sources of stochasticity. Importantly, stochasticity and natural selection are regarded as distinct entities (Millstein, 2002). Figure 2 depicts the linear evolution model as put forward in the Modern Synthesis (e.g. Mayr, 2000). Chance mutations create variation on which natural selection acts. This linear model, however, lacks a feedback loop and is unable to learn.

8. Chance and necessity

In his influential work Le hasard et la nécessité (Essai sur la philosophie naturelle de la biologie moderne) (1970), the French biologist Jacques Monod contrasts chance and natural selection as the two driving mechanisms of evolution (Sols, 2013). “Pure chance, (...) mere chance is at the very roots of the prodigious framework of evolution: today, this central biological notion (...) is the only one which is consistent with the reality shown by observations and experience” (Monod, 1971). The living world is shaped by the interplay of deterministic laws and randomness. It is widely accepted that the evolution of any particular organism or form is a product of the interplay of a great number of historical contingencies (Monod, 1971). Rewind and replay the tape of life again and again, as the now familiar argument goes, and there is no predicting (or reproducing) the outcomes (Gould, 1989). Roses and redwoods, humans and hummingbirds, trilobites and dinosaurs each owe their existence (or demise) to unfathomable combinations of innumerable rolls of the ecological and genetic dice (Carroll, 2001a). Despite the widespread occurrence and attractive mechanistic simplicity of adaptive radiations, the evolutionary outcome of an instance of adaptive radiation cannot generally be predicted with any degree of confidence. The inability to make such a prediction is due in part to an inability to evaluate the relative roles of chance and necessity (Monod, 1971) in promoting divergence (Travisano et al., 1995a, b). Longo et al. (2012) and Kauffman (2013) assert that the interactions between organisms, biological niches and ecosystems are ever changing, intrinsically indeterminate and even unprestatable. Hence, no laws of motion can be formulated for evolution (Longo et al., 2012; Kauffman, 2013). Examples of adaptive radiations suggest that either chance or adaptation can be the dominant factor in shaping the adaptive process and the resulting adaptive radiations (Wahl & Krakauer, 2000; Chan & Moore, 2002).

8.1 Chance

According to Lynch (2007a; b), out of the four major forces in evolution, natural selection, mutation, recombination and drift, the latter three are stochastic in nature. In addition, evolutionary forces are often divided into two sorts: stochastic and deterministic (Wright, 1955).

8.1.1 Mutations

The Modern Synthesis holds that (i) mutations occur independently of the environment, (ii) mutations are due to replication errors, and (iii) mutation rates are constant (Lenski & Mittler, 1993; Brisson, 2003). Currently, biologists usually agree that all genetic mutations occur by “chance” or at “random” with respect to adaptation (Miller, 2005) and the novel allele is subsequently selected for or against. The claim dates back to Darwin’s conception of “spontaneous,” “accidental” or “chance” variation (Darwin, 1859; Darwin & Seward, 1903). The Modern Synthesis later redefined Darwin’s idea as rooted in the phenomenon of genetic mutation following a long period of controversy over the “chance” vs “directed” character of variation (Merlin, 2010). However, in the view of mathematicians, the ratio of the number of functional genes and proteins, on the one hand, to the enormous number of possible sequences corresponding to a gene or protein of a given length, on the other hand, seemed so small as to preclude the origin of genetic information by a random mutational search (see chapter 4).

Due to the high probability that any particular mutation will have deleterious effects, orthodox theory holds that “natural selection of mutation rates has only one possible direction, that of reducing the frequency of mutation to zero” (Williams, 1966). Thus, there should be a strong selective pressure to eliminate mutations altogether. Accordingly, theory indicates that under most conditions, selection puts a premium on the faithful maintenance and transmission of genetic information and is expected to favor alleles that reduce the mutation rate (Karlin & McGregor, 1974; Feldman & Liberman, 1986; Kondrashov, 1995; Sniegowski et al., 2000; Sniegowski, 2004). In fact, DNA replication can have a remarkable fidelity, estimated to produce 10-9 to 10-11 mutations/nucleotide, achieved by multiple mechanisms of error avoidance and correction (Kunkel, 2004). In well-adapted populations in stable environments the rate of mutation will evolve towards lower values (Leigh, 1973; Karlin & McGregor, 1974; Liberman & Feldman, 1986; Drake, 1991; Kunkel, 2004). Theory holds that the combined metabolic and temporal costs of perfection in replication and transcription fidelity (Kimura, 1967; Sniegowski et al., 2000) limit further improvements in replication fidelity and DNA repair (André & Godelle, 2006). Thus, stable environments would favor low mutation rates (anti-mutator genotypes), constrained only by the costs of error-repair mechanisms (Kimura, 1967; Drake, 1991). On the other hand, Eigen (1992) argued that replication error rates established themselves near an error-threshold where the best conditions for evolution exist.

There is cumulative evidence to refute the metabolic-costs-of-replication-fidelity argument. Nature is unforgiving at the edge of life (Kis-Papo et al., 2003). As a consequence of the increasingly narrower adaptive road, a variety of Archaeal extremophiles, compared to mesophiles, evolved a high genomic stability (Mackwan, 2006; White & Grogan, 2008; Kish & DiRuggiero, 2012) with low mutation rates (Battista, 1997; Grogan et al., 2001; Grogan, 2004; Mackwan, 2006; Mackwan et al., 2007; Drake, 2009), very high replication fidelity (Lundberg et al., 1991; Mattila et al., 1991; Cline et al., 1996; Grogan et al., 2001; Dietrich et al., 2002; Berkner & Lipps, 2008; Zhang et al., 2010), and decreased genetic diversity (Kis-Papo et al., 2003; Friedman et al., 2004; Sonjak et al., 2007; de los Ríos et al., 2010; Vinogradova et al., 2011). Since extreme habitats are routinely resource-limited (Waterman, 1999; 2001; Plath et al., 2007; Rampelotto, 2010; Prasad et al., 2011), the reduced mutation rates of extremophiles indicate that the perfection of replication fidelity in mesophiles is not limited by the availability of resources. Since many components of the DNA replication machinery of eukaryotes have evolved from a common ancestor in Archaea (Yutin et al., 2008), the question arises whether this loss of fidelity of the eukaryotic replication machinery has an evolutionary rationale.

8.1.2 Genetic drift

Genetic drift or allelic drift is the change in the frequency of a gene variant (allele) in a population due to chance events (Masel, 2011). Genetic drift may cause gene variants to disappear completely and thereby reduce genetic variation.Genetic drift is the stochastic fluctuation in allele frequencies caused by random differences in the fecundity and survival of individuals.Genetic drift is considered to be the most important of the stochastic forces in the evolution of natural populations (Gillespie, 2001). The term applies to many effects on populations or organisms which are said to be due to “chance” and to factors which are thought to help to produce such effects, e.g. natural disasters or “founder effects”. However, many core senses of random drift make it something which varies inversely with population size. Any strategy with non-zero reproductive fitness can persist over evolutionary time by genetic drift. As the effective population size, Ne, increases, genetic drift becomes weaker because the larger the population, the smaller the proportional impact of each random event that concerns just one individual or a group of individuals. Hence, selection pressure and random drift, whose relative importance for evolution is often disputed in the literature, are equally important, although they act differently: selection promotes evolution, and random drift slows it down (Rouzine et al., 2001). In the cybernetic model of evolution, the genetic changes due to random drift are a property of the output signal that due to the iterative nature of evolution secondarily becomes an input signal.

When there are few copies of an allele, the effect of genetic drift is larger, and when there are many copies the effect is smaller. Vigorous debates occurred over the relative importance of natural selection versus neutral processes, including genetic drift. Ronald Fisher held the view that genetic drift plays at the most a minor role in evolution, and this remained the dominant view for several decades. In 1968, Motoo Kimura rekindled the debate with his neutral theory of molecular evolution, which claims that most instances where a genetic change spreads across a population (although not necessarily changes in phenotypes) are caused by genetic drift.

8.2 Necessity

Genotypic diversity enhances the evolutionary responsiveness and adaptability of populations (Ayala, 1968; Abrams & Matsuda, 1997; Yoshida et al., 2003; Gamfeldt et al., 2005; Reusch et al., 2005; Gamfeldt & Kallstrom, 2007; Becks & Agrawal, 2012; Roze, 2012) and its lack can increase extinction risk (Keller & Waller, 2002). It would be highly adaptive for organisms inhabiting variable environments to modulate mutational dynamics in ways likely to produce necessary adaptive mutations in a timely fashion. Jablonka and Lamb (2005, p.101) wrote: “it would be very strange indeed to believe that everything in the living world is the product of evolution except one thing ? the process of generating new variation!” In his 1905 paper, Einstein proposed that the same random forces that cause the erratic Brownian motion of a particle also underlie the resistance to the macroscopic motion of that particle when a force is applied (Kaneko, 2009; Lehner & Kaneko, 2011). This insight can be generalized to state that the response of a variable to perturbation should be proportional to the fluctuation of that variable in the absence of an applied force (Kubo et al., 1985). In short, the more something varies, the more it will respond to perturbation, irrespective of the precise molecular details. A generalized version of the fluctuation–response relationship can be applied to evolved, dynamical systems (Sato et al., 2003; Kaneko & Furusawa, 2006; Kaneko, 2009). The concept has been confirmed experimentally in unicellular prokaryotes and eukaryotes (Sato et al., 2003; Yomo et al., 2006; Lehner, 2010; Park et al., 2010). Metzgar and Wills (2000) argued that adaptively tuned mutation rates do not require any special foresight. Instead, they must have been selected for repeatedly in the past for their ability to generate genetic change. Mutational tuning does not require the specific generation of adaptive mutations (nonrandomness with respect to function) but rather the concentration of mutations under specific environmental conditions or in particular regions of the genome (nonrandomness with respect to time or location) (Metzgar & Wills, 2000). The literature reveals significant effects of genetic diversity on ecological processes such as primary productivity, population recovery from disturbance, interspecific competition, community structure, and fluxes of energy and nutrients. Thus, genetic diversity can have important ecological consequences at the population, community and ecosystem levels, and in some cases the effects are comparable in magnitude to the effects of species diversity (Gamfeldt et al., 2005; Fussmann et al., 2007; Gamfeldt & Kallstrom, 2007; Lankau & Strauss, 2007; Hughes et al., 2008). Moreover, theoretical and empirical studies suggest that diversity at one level may depend on the diversity at the other (Whitham et al., 2003; Abrams, 2006; Crutsinger et al., 2006; Johnson et al., 2006; Vellend, 2006; Lankau & Strauss, 2007).

9. Gedankenexperiment: evolution in stable environments

The Law of Causality states: Every event must have a cause (Hughes & Lavery, 2004). Therefore scientists explain particular events and general patterns by identifying the causal factors involved. Ordering two or more events in a causal order is crucial for a scientific understanding. Another order of events is their temporal order. While the temporal order is observable, outside of a controlled scientific experiment the causal order is not. This is because a complete causal account specifies the necessary and sufficient conditions for something to occur and both of these conditions involve counterfactual statements (Damer, 1995; Hughes & Lavery, 2004). Counterfactual statements are, by definition, not observable. But they are amenable to thought experiments. Certain disciplines such as evolutionary biology and economics often do not lend themselves to experimentation. Although computer simulation may help to clarify issues (Casti, 1997), it remains the case that we cannot avoid frequent recourse to “thinking our way through” a problem, i.e., to thought experiment (Damper, 2006).

A living organism never enjoys a perfectly stable environment; the system to which it belongs may incur slow or quick changes that will impinge on its well-being and fitness. However, orthodox Darwinism assumes that environments are stable and traditionally, evolutionary models have assumed environmental constancy for simplicity and tractability (Keyfitz, 1977; Rubenstein, 1982; Caswell, 2001; Lee & Doughty, 2003). For example, Maynard Smith's (1982a) often quoted book on Evolutionary Game Theory contains no reference to environmental stochasticity. The concept of environmental variance is almost completely absent from the 40 foundation papers (published from 1887–1971) identified by Real and Brown (1991). Through the 1960s, the word ‘variance’ appeared in the abstract of only about ten papers per thousand published by the Ecological Society of America (Ruel & Ayres, 1999). However, the number of such papers has increased since then to about 50 per thousand during the 1990s. This suggests a growing recognition among ecologists that an explicit consideration of variance is essential to explain many of the important patterns and processes in nature (Ruel & Ayres, 1999).

Environments display a range of instabilities. Across this range of spatiotemporal gradients of instability, adaptive responses show linear trends with regard to the generation of variation that allow to extrapolate these trends to perfectly stable environments. A general pattern emerges:
(i) In well-adapted populations in stable environments the rate of mutation will evolve towards lower values (Leigh, 1973; Karlin & McGregor, 1974; Liberman & Feldman, 1986; Drake, 1991; Kunkel, 2004).
(ii) In more stable environments, phenotypic plasticity is lost or limited because it may incur costs (Levins, 1968; Ghalambor et al., 2006; Schleicherová et al., 2013; Tonsor et al., 2013). In evolving populations of Escherichia coli adapting to a single nutrient in the medium, unused catabolic functions decayed and their diet breadth became narrower and more specialized (Cooper & Lenski, 2000). Adaptive plasticity is lost during long periods of environmental stasis (Masel et al., 2007). If the environment changes only very slowly relative to the generation time of the organism, then genetic specialization is favored over plasticity (Orzack, 1985); in relatively stable environments there is rather a selective pressure for the evolution of instinctive behaviors (Turney, 1996). Theory predicts diminished fitness for highly plastic lines under stabilizing selection, because their developmental instability and variance around the optimum phenotype will be greater compared to nonplastic genotypes. Theory is supported empirically: the most plastic traits exhibited heritabilities reduced by 57% on average compared to nonplastic traits (Tonsor et al., 2013).Conversely, developmental instability increases adaptive evolution in the face of changing environments (Rutherford & Lindquist, 1998; Masel, 2006).Environmental heterogeneity is the main factor for the evolution of a plastic trait (Pigliucci et al., 2006; Fusco & Minelli, 2010).
(iii) In relatively stable environments risk-spreading evolutionary strategies such as bet-hedging (see chapter 11) have a lower fitness advantage and do not pay any more (Philippi & Seger, 1989; Müller et al., 2013).
(iv) Since some cost is associated with learning (Dukas & Duan, 2000; Mery & Kawecki, 2003, 2004; Burger et al., 2008), in an absolutely fixed environment a genetically fixed pattern of behavior should evolve (“absolute fixity argument”). But if the environment is diverse and unpredictable, innate environment-specific mechanisms are of little use. Unpredictable or variable environments favor the evolution of cognition and learning (see chapter 5)
(v) Sexual reproduction is favored in intermediate stressful environments, while stable stressfree ones favor asexuality (Bürger, 1999; Moore & Jessop, 2003; Heininger, 2013) which may explain the high incidence of parthenogenesis in environments such as stable forest soils (Cianciolo & Norton, 2006; Domes et al., 2007).

In summary, stable environments select against evolvability (Altenberg, 2005) as achieved by mutagenesis, phenotypic plasticity, learning, sexual reproduction, and bet-hedging behavior. Thus, in a perfectly stable environment, evolution would virtually come to a hold, finally exploiting all beneficial mutations and maximizing individual fitness.

10. The evolutionary signature of stochastic environments

Natural environments are stochastic (Gard & Kannan, 1976; Halley, 1996; Bell & Collins, 2008; Lei, 2012). In a review of published studies on variation in recruitment, Hairston et al. (1996a) found that reproductive success of long-lived adults varied from year to year by factors up to 333 in forest perennial plants, 4 in desert perennial plants, 591 in marine invertebrates, 706 in freshwater fish, 38 in terrestrial vertebrates, and 2200 in birds. Similarly, the recruitment success of diapausing seeds or eggs varied by factors of up to 1150 in chalk grassland annual and biennial plants, 614 in chapparal perennials, 1150 in freshwater zooplankton, and 31,600 in insects (Ellner, 1997). These figures represent the variation among years when some reproduction occurred; many of the studies also report years in which reproduction failed completely. A life-history model predicted the occurrence of skipped reproduction only for intermediate environmental qualities, with high reproductive investment being optimal at both ends of a gradient of environmental quality (Fischer, 2009). Skipped reproduction is frequently observed in nature (in fish: Bull & Shine, 1979; Engelhard & Heino, 2005; Rideout et al., 2005; Jørgensen & Fiksen, 2006; Jørgensen et al., 2006; in amphibians: Bull & Shine, 1979; Harris & Ludwig, 2004; in reptiles: Bull & Shine, 1979; Brown & Weatherhead, 2004; in birds: Illera & Diaz, 2006). Poor individual condition and/or poor environmental quality are thought of as the main causes for skipped reproduction (Bull & Shine, 1979; Dutil, 1986; Rideout et al., 2005; Illera & Diaz, 2006). In taxa without parental brood care, particularly insects, the number of embryos entering a habitat is usually far in excess of its carrying capacity, and larval survivorship is typically low (e.g., Berryman, 1988; Ohgushi, 1991; Willis & Hendrick, 1992; Tinkle et al., 1993; Duffy, 1994; Dempster & McLean, 1998; Dixon et al., 1999) and unpredictable (Madsen & Shine, 1998; Fincke & Hadrys, 2001; Haugen, 2001; Rollinson & Brooks, 2007). Ecological factors such as deterioration of larval habitats or fluctuations in the density of food, predators, cannibals, or parasites can result in unpredictable windows of offspring survivorship (e.g., Smith, 1987; Newman, 1989; So & Dugeon, 1989; Morin et al., 1990; Messina, 1991; Anholt, 1994; Dixon et al., 1999). In insects, “while lifetime egg production is largely determined by chance” (Thompson, 1990), the numbers of mature offspring produced (fitness) is largely unpredictable (Fincke & Hadrys, 2001) and in natural populations, crucially, is poorly correlated with behavioral observations of mating, particularly for females (Thompson et al., 2011).Instead, the time span between hatching of the first and the last egg within a clutch was detected to be the most appropriate estimate of reproductive success. This was because a larger hatching span increased the likelihood that some larvae encountered a window of opportunity during which the risks of being eaten by larger conspecifics were lower (Fincke & Hadrys, 2001). Similarly, also in the water python Liasis fuscus, time of hatching, and not clutch size, was most predictive of reproductive success (Madsen & Shine, 1998). Unpredictable environmental change can lead to reduced survival, or to extinction of previously well-adapted organisms (Bell & Collins 2008; Simons, 2009). Extinction risk in natural populations depends on stochastic factors that affect individuals, and is estimated by incorporating such factors into stochastic models (Athreya & Karlin, 1971; May, 1973a, b; Gabriel & Bürger, 1992; Lande, 1993; Lynch & Lande, 1993; Ludwig, 1996; Halley & Kunin, 1999; Lande et al., 2003; Sæther et al., 2004a; Kendall & Fox, 2003; Fox et al., 2006; Melbourne & Hasting, 2008).Theory suggests thatenvironmental stochasticity can be comparable to the accumulation of mildly deleterious mutations in causing extinction of populations smaller than a few thousand individuals (Lande, 1994, 1995, 1998).

Stochasticity can be divided into four categories, which include demographic stochasticity, the probabilistic nature of birth and death at the level of individuals (May, 1973a), environmental stochasticity, resulting in the variation in population-level birth and death rates among times or locations (Athreya & Karlin, 1971; May, 1973b), the sex of individuals (Lande et al., 2003; Sæther et al., 2004a), and demographic heterogeneity, the variation in vital rates among individuals within a population (Kendall & Fox, 2003; Fox et al., 2006). Generally, the uncertainty due to abiotic capriciousness is perceived as major source of stochasticity. Variable abiotic environments, however, are also often predictable (Beissinger & Gibbs, 1993). More than 40 years ago, Van Valen’s Red Queen hypothesis (1973a) emphasized the primacy of biotic conflict over abiotic forces in driving selection. According to the Red Queen hypothesis, each adaptation by a species is matched by counteracting adaptations in another interacting species, such that perpetual evolutionary change is required for existence. Despite continued evolution, average relative fitness remains constant: evolution is a zero-sum game (Brockhurst et al., 2014). Thus, for a vast number of biological situations, the salient aspects of the selective environment are biotic (Richardson & Burian, 1992; Venditti et al., 2010; Ezard et al., 2011; Liow et al., 2011; Brockhurst et al., 2014). For example, in human populations pathogens have a higher impact on genetic diversity than climate conditions (Fumagalli et al., 2011).

10.1 Environmental stochasticity

Spatiotemporal, often unpredictable, variation in environmental quality is a salient feature of natural habitats (Wiens, 1976, 2000; Shorrocks & Swingland, 1990; Halley, 1996; Wiens, 2000; Simons, 2002, 2009; Metcalf & Koons, 2007; Bell & Collins, 2008; Doebeli & Ispolatov, 2014). To survive to reproduce, an animal must solve multidimensional problems with components that can vary independently of one another over its lifetime. Furthermore, many of these components are fundamentally unpredictable at the spatial and temporal scales at which organisms operate (Dall, 2010). Environmental variance includes a wide array of event magnitudes – possibly a continuum spanning the spectrum from minor fluctuations occurring over short time scales (seconds or hours) to rare events leading to mass extinction. Event magnitudes are inversely related to their frequency of occurrence. Based on records of sea-level changes and temperature from the deep ocean, Steele (1985) showed that environmental variation in marine environments increases continually with longer time series over timescales from hours to millennia. The implication is that environmental events that are disproportionately influential occur at a relatively low frequency. This general pattern of variance is known as 1⁄f-noise (Halley, 1996). Theoretical and empirical studies have further advanced our understanding of the structure of temporal environmental variance (Ariño & Pimm, 1995; Halley, 1996; Bengtsson et al., 1997; Cyr, 1997; Solé et al., 1997, 1999; McKinney & Frederick, 1999; Plotnick & Sepkoski, 2001).

In the Robertson–Price equation for the evolution of quantitative characters, the effects of environmental stochasticity causing fluctuating selection can be partitioned from the effects of selection due to random variation in individual fitness caused by demographic stochasticity (Engen & Sæther, 2014). A stochastic version of the Price equation reveals the interplay of deterministic and stochastic processes in evolution (Rice, 2008). Demographic stochasticity can cause random variation in selection differentials independent of fluctuating selection caused by environmental variation. Populations continually evolve and interacting species continually coevolve, building a constantly coevolving web of life (Futuyma & Slatkin, 1983; Thompson, 2005, 2009) that is highly dynamic and stochastic (Dieckmann & Law, 1996; Heininger, 2013). For instance, by consuming resources, constructing nests, and excreting waste, organisms modify their environment, creating ecological feedback that alters existing selective pressures and creates others anew (Jones et al., 1994; Odling-Smee et al., 1996, 2003; Wolf et al., 1999; Laland & Sterelny, 2006; Kokko & Lopez-Sepulcre, 2007; van Dyken & Wade, 2012).Individuals from the same or different species impose selection on one another, creating a dynamically changing selective environment that evolves along with the traits that it selects (Futuyma & Slatkin, 1983; Kiester et al., 1984; Dieckmann & Law, 1996; Wolf et al., 1998). Coevolutionary pressures not only include interactions between e.g. symbionts, mutualists, pathogens and hosts, predators and prey, herbivores and plants but also density- and frequency-dependent coevolutionary interactions (Mueller et al., 1991; Doebeli & Ispolatov, 2014). Frequency-dependent selection occurs when ‘the fitness of a genotype (or of an allele) is affected by its frequency within the population’ (Futuyma [1986], p. 166). In some cases, a genotype is fitter when it is rare (negative frequency-dependence); in other cases, a genotype can be fitter when it is common (positive frequency-dependence) (Millstein, 2006). Frequency-dependent selection is believed to be quite common. Futuyma ([1986], p. 166) remarks that “it is likely that there is a frequency-dependent component in virtually all selection that operates in natural populations, for interactions among members of a population affect the selective advantage of almost all traits, and such interactions usually give rise to frequency-dependent effects.” Frequency dependence generates an evolutionary feedback loop, because selection pressures, which cause evolutionary change, change themselves as a population’s phenotype distribution evolves, causing complicated dynamics in models (Altenberg, 1991; Nowak & Sigmund, 1993a; Gavrilets & Hastings, 1995; Schneider, 2008; Priklopil, 2012; Doebeli & Ispolatov, 2014). Density- and frequency-dependence of fitness results in a highly dynamic landscape, a fitness ‘‘sphagnum bog’’ (Rosenzweig, 1978; Bolnick, 2004) that is chaotic and unpredictable (Altenberg, 1991; Priklopil, 2012; Doebeli & Ispolatov, 2014) with intraspecific competition as its key driver (Milinski & Parker, 1991; Doebeli & Dieckmann, 2000).

10.2 Demographic stochasticity

While environmental stochasticity refers to situations where several individuals are affected by a common factor, demographic stochasticity refers to hazards experienced independently by each individual. It is commonly observed that even very large populations may show considerable stochastic fluctuations. In the classical birth-death population models, this is not the case. If the parameters are chosen so that the population size can be very large, these stochastic models will behave almost deterministically by the law of large numbers. The mean value of the contributions to the population change will have variance close to zero due to the assumption of independence. This component of the stochasticity in the growth rate, vanishing when populations become large, is in the literature referred to as demographic stochasticity (Engen et al., 1998). For sufficiently large populations, the risk of extinction from demographic stochasticity is less important than that from either environmental stochasticity or random catastrophes (Lande, 1993). Some authors have discussed the role that demographic stochasticity may have on cycles (Bartlett, 1960; Renshaw, 1991; McKane & Newman, 2005). Murase et al. (2010) showed that demographic stochasticity can significantly alter the predictions arising from models of community assembly over evolutionary time. While mutualistic communities show little dependence on stochastic population fluctuations, predator-prey models show strong dependence on the stochasticity. For a predator-prey model, the noise causes drastic decreases in diversity and total population size. The communities that emerge under influence of the noise consist of species strongly coupled with each other and have stronger linear stability around the fixed-point populations than the corresponding noiseless model.

Theoretical predictions proposing a link between population dynamics and individual variation (e.g. May, 1973a; Lomnicki, 1978; Leigh, 1981; Shaffer, 1981; Lande, 1993; Uchmanski, 1999) are supported by data from natural populations (Dochtermann & Gienger, 2012). Demographic heterogeneity, among-individual variation in vital parameters such as survival and reproduction, is ubiquitous (Stover et al., 2012), resulting from fine-scale spatial habitat heterogeneity (e.g., Gates & Gysel, 1978; Boulding & Van Alstyne, 1993; Menge et al., 1994; Winter et al., 2000; Franklin et al., 2000; Manolis et al., 2002; Bollinger & Gavin, 2004; Landis et al., 2005), unequal allocation of parental care (e.g., Manser & Avey, 2000; Johnstone, 2004), maternal family effect (e.g., Fox et al., 2006; Pettorelli & Durant, 2007), conditions during early development, including birth order effects (e.g., Lindström, 1999), persistent social rank (e.g., von Holst et al., 2002), and genetics (e.g., Yashin et al., 1999; Ducrocq et al., 2000; Gerdes et al., 2000; Casellas et al., 2004; Isberg et al., 2006). The stability of population sizes is related to the probability of extinction (Pimm et al., 1988; Inchausti & Halley, 2003). Individual variability in life-history traits drives demographic stochasticity and extinction risk (Dochtermann & Gienger, 2012). In some cases the presence of life-history variation doubles the persistence time of populations (Conner & White, 1999). In natural populations of water fowl, among-individual variation contributes to more than a threefold change in survival probability (Sedinger & Chelgren, 2007). Similarly, considerable within-population variation in survival probabilities has been observed in corvids (Fox et al., 2006), lagomorphs (Rodel et al., 2004), orthopterans (Ovadia & Schmitz, 2002) and in many other systems. More generally, individual variation in life-history characteristics dramatically influences the likelihood that a population will go extinct (Kokko & Ebenhard, 1996; Kendall & Fox, 2002; Sæther et al., 2004b; Fox, 2005).Selection against demographic stochasticity, favoring reductions in variance rather than a maximization of the mean, has been invoked in the evolution of numerous life-history traits, including offspring size, offspring numbers, hatching synchrony, diapause, seed dormancy, timing of germination, timing of flowering, sex-biased dispersal, etc. (Cohen, 1966; Slatkin, 1974; Gillespie, 1977, Seger & Brockmann, 1987; Yoshimura & Clark, 1991; Lehmann & Balloux, 2007; Guillaume & Perrin, 2009; Childs et al., 2010; Simons, 2011; Gremer & Venable, 2014).

11. Bet-hedging: risk avoidance and risk-spreading in response to uncertainty

Environmental variation has long been recognized as being important in determining evolutionary patterns (Bradshaw, 1965; Levins, 1968) as well as the evolution of life histories (Murphy, 1968; Wilbur et al., 1974; Ellis et al., 2009). Much circumstantial evidence suggests that one of the main effects of natural selection has been the evolution of adaptations, such as behavioral diversification (Oster & Heinrich, 1976; Lapchin, 2002; Donaldson-Matasci et al., 2008; Starrfelt & Kokko, 2012), storage of resources (Bevison et al., 1972; Lee, 1975), increases in body size (Bell, 1971; Jarman, 1974; Boyce, 1979; Peters, 1983), and increases in mobility that buffer animals against the effects of fluctuating environments (Rubenstein, 1982). While both environmental and either phenotypic or life history variability abound, the causal relationship between both, however, may often be difficult to untangle (Lacey et al., 1983; Stearns, 1989a; Halkett et al., 2004; Viney & Reece, 2013) since changes in phenotypes or life histories may also result from processes unrelated to environmental variations or due to other nonadaptive alternatives (Stearns, 1989a; Cooch & Ricklefs, 1994; Halkett et al., 2004).Thus, it is often difficult to tell empirically whether the life history variation is produced randomly, as in bet-hedging, or in response to predictive environmental cues (Philippi, 1993b; Clauss & Venable, 2000; Adondakis & Venable, 2004; Morey & Reznick, 2004; Donaldson-Matasci et al., 2010). In some cases, it may actually be a combination of both mechanisms (Richter-Boix et al., 2006; García-Roger et al., 2014).

Since the early work of Haldane and Jayakar (1963), Kimura (1965), Ewens (1967), Levins (1968) and Lewontin and Cohen (1969), it has become clear that the ability to respond to environmental variability has a selective advantage and models incorporating stochastic fluctuations in fitness are an important part of the population genetic literature (Dempster, 1955; Felsenstein, 1976; Frank & Slatkin, 1990; Jablonka et al., 1995; Lachmann & Jablonka, 1996; Ancel & Fontana, 2000). It is important to realize that, when environmental conditions fluctuate, strategies may be superior that are inferior under constant conditions (Diekmann, 2004). The evolution of learning and memory, for instance, although maladaptive in static environments is the evolutionary signature of stochastic environments (discussed in chapter 5). Ross Ashby (1956) characterized environments and possible adaptive options using the term “variety.” His classic ‘Law of Requisite Variety’ (1956, p. 206) holds that: “variety can destroy variety” (now commonly quoted as “only variety can absorb variety” [Beer, 1966]). His insight was that a system has to have internal variety that matches its external variety so that it can self-organize to deal with and thereby “destroy” or overcome the negative effects on adaptation of imposing environmental constraints and complexity. In biology, this is to say that a species has to have enough internal variance to successfully adapt to whatever resource and competitor tensions imposed by its environment (McKelvey, 2004a). According to a similar vein of thought (“fighting change with change” [Meyers & Bull, 2002]), a “bet-hedging strategy” (Seger & Brockmann, 1987) through the diversification of the population can cover all bases of unpredictable evolutionary scenarios. An analogy with financial problems of risk management has been noticed many times (Lewontin & Cohen, 1969; Real, 1980; Stearns, 2000; Wagner, 2003). Two distinct types of strategy exist to cope with stochastic environments: risk avoidance and risk spreading (den Boer, 1968; Seger & Brockmann, 1987; Yoshimura & Clark, 1993; Einum & Fleming, 2004). Bet-hedging involves betting so as to offset a bet already made (Diamond & Rothschild, 1978). In commerce, hedging may refer to sales of securities against previous purchases of other securities to avert possible loss or, conversely, to buy against previous sales (Boyce et al., 2002). In an uncertain market, a hedging investor can reduce the risk of devastating losses during bad times, but of course, gains during a favorable period would not be as great as if he had taken the risk. By hedging, one may reduce or eliminate risk (Boyce, 1988). Conservative strategies avoid extremes, diversified strategies offer insurance against risks (Boyce et al., 2002).

Risk avoidance, also referred to as conservative bet-hedging, is an individual adaptation. Conservative bet-hedging corresponds to pursuing a relatively slow life history strategy, in which individuals sacrifice offspring quantity forquality by producing a smaller number of offspring than would be optimal over a reproductive lifetime in a stable environment of the same average quality. The conservative strategy involves producing offspring that are reasonably well equipped to handle the range of fluctuating conditions encountered over the organism’s evolutionary history (Ellis et al., 2009). When such offspring perform fairly well across this range, and/or when environmental changes affect an entire population on the timescale of a generation (e.g., years of drought) and thus cannot be handled through niche selection, natural selection tends to favor conservative bet-hedging (Donaldson-Matasci et al., 2008).

By contrast, diversified bet-hedging is a population-level adaptation. It involves “spreading the risk” by increasing phenotypic variation among offspring, and thus increasing the probability that at least some offspring will be suited to whatever environmental conditions occur in the next generation. Diversified bet-hedging can be achieved through maintenance of genetic polymorphisms or through variable expression of phenotypes arising from a monomorphic genetic structure. When any single phenotype performs poorly across the range of changing conditions encountered over evolution (i.e., when generalist strategies fail), and/or when environments vary substantially across individuals in a single generation (enabling diverse organisms to evaluate and select niches that match their phenotypes), selection tends to favor diversified bet-hedging (Donaldson-Matasci et al., 2008). Examples of risk spreading include genetic variation (Ellner, 1996; Sasaki & Ellner, 1997), dispersal of progeny (spatial averaging: Levin et al., 1984; Kisdi, 2002), longevity (Morris et al., 2008), iteroparity (Murphy, 1968; Bulmer, 1985; Orzack & Tuljapurkar, 1989; Wilbur & Rudolf, 2006), brood care (Bonsall & Klug, 2011, Wong et al., 2013), delayed germination of seeds (temporal averaging: Ellner, 1985), phenotypic polymorphism (Levins, 1968; Roughgarden, 1979, p. 272), canalization of genetic, developmental, and environmental perturbations (Pfister, 1998; Proulx & Phillips, 2005), and generalism (Lapchin, 2002; Donaldson-Matasci et al., 2008; Starrfelt & Kokko, 2012). Risk spreading strategies are adaptations at the population level whereby individual members are spread or diversified into different habitats, times or strategies (Yoshimura & Clark, 1993). Organisms adapt their life histories to temporally uncertain environments with life-history delays, such as seed dormancy, variable age at maturity, iteroparity, and they adapt to spatially uncertain environments with dispersal (Wilbur & Rudolf, 2006). Several diversifying traits such as dispersal, dormancy, and seed size variation may selectively interact and are complementary and partially substitutable life-history responses to spatial and temporal environmental uncertainty (Venable & Brown, 1988).For instance, diapause and dispersal are considered as two alternative responses to unfavorable environmental conditions (Southwood, 1977; Hanski, 1988; Bohonak & Jenkins, 2003), so that temporal dispersal via developmental mechanisms (especially diapause) is considered to be functionally equivalent to spatial dispersal (Hairston, 2000; Hairston & Kearns, 2002; Bohonak & Jenkins, 2003). Diversifying bet-hedging creates variation among individuals. Structured variation among individuals in survival and fecundity can reduce demographic stochasticity (Fox & Kendall, 2002; Kendall & Fox, 2002; Fox, 2005; Fox et al., 2006; Stover et al., 2012). At its extreme, it may completely eliminate demographic stochasticity (Kendall & Fox, 2002). This feature may be a manifestation of Ashby’s (1956) ‘Law of Requisite Variety’.

Conservative and diversified bet-hedging are not mutually exclusive, and the same species may display both. Great tits (Parus major) inhabitenvironments characterized by substantial temporal unpredictability. One adaptation shown by them is conservative bet-hedging: Average clutch size (8.53) is below the optimal size (12), given the long-term average quality of their habitat (Boyce & Perrins, 1987). This smaller clutch size has apparently been selected for because, in bad years, individuals laying smaller clutches experience substantially better nesting success. This bad-years effect “reduces the mean and increases the variance in fitness for individuals laying large clutches more than it does for individuals laying smaller clutches” (Boyce & Perrins, 1987). Although these conditions have given rise to conservative bet-hedging, the unpredictability of the great tit’s environment has also favored diversified bet-hedging: adaptive genetic variation in personality, which can be characterized along the Hawk-Dove dimension. As reviewed by Ellis et al. (2006), unpredictable variation in climate cycles strongly affects food supplies and intrasexual competition among great tits, resulting in density-dependent selection for Hawks and Doves, but in opposite directions in good and bad years and in males and females. This covariation between the Hawk-Dove dimension of personality in great tits and fitness in fluctuating environments (Dingemanse et al., 2004) provides an empirical basis for the maintenance of adaptive genetic variation as a diversified bet-hedging strategy. A single trait, e.g. flowering size, can also mediate both a conservative and diversifying bet-hedging response (Childs et al., 2010). Likewise, cooperation as bet-hedging response can be both conservative and diversifying (Fronhofer et al., 2011; Rubenstein, 2011). If the amplitude of environmental fluctuation is small enough to be covered by one phenotype, conservative bet-hedgers can evolve; otherwise diversified bet-hedgers will evolve (Yasui, 1998). It has also been argued that the traditional division between conservative and diversified strategies can be considered a false dichotomy, and is better viewed as two extreme points on a continuum (Starrfelt & Kokko, 2012).Within-generation and between-generation bet-hedging is also a false dichotomy; bet-hedging strategies can occur under any grain of the environment effectively being a combination of between-generation and within-generation characteristics (Starrfelt & Kokko, 2012).

Uncertainty can be overcome by acquiring information about an environment (Stephens, 1987, 1989); risk cannot (Winterhalder et al., 1999). To deal with uncertainty, organisms had to acquire the capacity to learn from past environments to generalize to new environments (Kirschner & Gerhart, 2005; Gerhart & Kirschner, 2007; Parter et al., 2008). Early work in population genetics (Haldane, 1957; Kimura, 1961; Felsenstein, 1971, 1978) and recent analyses of evolution in fluctuating environments (Bergstrom & Lachmann, 2004; Kussell & Leibler, 2005; Donaldson?Matasci et al., 2010; Rivoire & Leibler, 2011) hint at a possible relation between information and fitness. However, evolution does not “know” in advance which evolutionary path will lead to the increase of fitness or how fluctuating, often unpredictable, environments will change (Grant & Grant, 2002). Theoretical studies show that strategies of producing random difference are best when environmental information is poor, absent or too costly to process (Perkins & Swain, 2009). Therefore, prospectively, the best strategy to increase fitness is to take every possible path at every next step. As a result, no configurations should be missed (Fu, 2007). Which configuration is a “fit” one, is finally decided by the survival and reproductive success of the individual. Fluctuating environments can favor the evolution of mixed strategies (Haccou & Iwasa, 1995; McNamara, 1995; Sasaki & Ellner, 1995). To remain in the student-probabilistic teacher scenario (Narendra & Thathachar, 1974; seechapter 6.1), learning would progress most effectively if the student was allowed to give several alternative answers to the uncertainty at hand. Thus, the risk to miss consistently the correct answer(s), resulting in extinction of a population, would be minimized (Simons, 2007, 2008). Via the law of large numbers evolution generated a form of automatic biological insurance against idiosyncratic risk (Robson, 1996). Risk-spreading by bet-hedging can be represented by an evolutionary game (Olofsson et al., 2009).

The question whether all these variation-generating processes are accidental or are selected for amounts to the question whether bet-hedging is a haphazard process or an ESS.Evolutionary game theory (Maynard Smith & Price, 1973; Maynard Smith, 1982a) has become an important way of thinking about evolution in situations in which the fitness of particular phenotypes depends on their frequencies in the population (Parker et al., 1972; Maynard Smith, 1974; Axelrod & Hamilton, 1981; Charnov, 1982; Axelrod, 1984; Nowak & Sigmund, 1993b). The key point in Evolutionary Game Theory (EGT) models is that the success of a strategy is determined by how good the strategy is in the presence of other alternative strategies, and of the frequency that other strategies are employed within a competing population. To create a sufficient amount of winners under all realistic assumptions an evolutionary stable strategy (ESS) must ‘cover all bases’. Both theoretical and experimental approaches demonstrated that in the face of variable and unpredictable environments, bet-hedging is the ESS (Hairston & Munns, 1984; Haccou & Iwasa, 1995; Sasaki & Ellner, 1995; Beaumont et al., 2009; Olofsson et al., 2009; Rees et al., 2010; Ripa et al., 2010; Charpentier et al., 2012; Starrfelt & Kokko, 2012). In fluctuating environments it may be optimal for different individuals of the same genotype to take different actions to spread the risk and ensure the genotype is represented in future generations. It does not make sense to “put all your eggs into one basket”. What is remarkable about EGT being applicable to fluctuating environments is that the players need never physically interact, compete or even communicate; nor there be any frequency-dependent selection (Hutchinson, 1996). In lotteries, spreading of the bets is a must to improve one’s chances to win. Variation is the bet-hedging strategy to cover all bases in an often unpredictable environment.Intriguingly, theoretical modeling suggests that bet-hedging as ESS in stochastically switching systems may have a U-shaped relationship with the frequency at which the environment changes (Müller et al., 2013): (i) in systems with a rapid change, a monomorphic phenotype adapted to the mean environment, (ii) for an intermediate range, a bimorphic bet-hedging phenotype and (iii) in slowly changing environments, a monomorphic phenotype adapted to the current environment are favored. Another analysis indicated that the benefits derived from bet-hedging strategies are much enhanced for higher environmental variabilities (large external noise) and/or for small spatial dimensions (large intrinsic noise). The authors concluded that these circumstances are typically encountered by living systems, thus providing a possible justification for the ubiquitousness of bet-hedging in nature (Hidalgo et al., 2014b).

Stochasticity works as environmental stochasticity at the input level of the cybernetic Black Box and at the output level resulting from stochasticity of (i) evolutionary/molecular effector mechanisms (e.g. random drift, mutagenesis, noisy cellular gene expression) and (ii) risk-spreading response to environmental stochasticity. Both an unpredictable, fluctuating abiotic environment and constantly coevolving web of life (Grant & Grant, 2002; Thompson, 2005, 2009) contribute to stochasticity. Uncertainty can be measured as the variance of a distribution of environmental quality, and adversity as the mean (Andras et al., 2003; Fronhofer et al., 2011). Both adversity and uncertainty have been conceptualized as aspects of environmental ‘risk’ (Daly & Wilson, 2002; Dall, 2010). In response to uncertainty as to which phenotype will have highest fitness in the future, biological systems exert risk minimization by risk avoidance or risk-spreading. In general, decision theory predicts and theoretical studies show that random strategies can outperform deterministic strategies whenever some aspect of the environment is unobserved (Bertsekas, 2005; Perkins & Swain, 2009). In the face of environmental stochasticity, evolution “learned” not to “put all its eggs into one basket” but to be prepared for potential selective scenarios. The environment does not need to be variable or heterogeneous for selection to favor bet-hedging; it simply needs to create risk at all places and times (Stearns, 2000). The probability of Having Descendants Forever has been advocated as complementary to the approaches of maximizing the expected number of offspring or geometric mean growth rate (Meginniss, 1977; Levy, 2010).According to this concept, constant relative risk aversion can be viewed as an evolutionary-developed heuristic aimed to maximize the probability of having descendant forever.

In fluctuating environments it may be optimal for different individuals of the same genotype to take different actions to spread the risk. Risk spreading polymorphism makes sense only for groups - by definition, an individual cannot be polymorphic. The fitness of the genotype is determined by the, perhaps complementary, actions of all individuals of the genotype, and the best action of an individual depends on the states and actions of other population members (McNamara et al., 1995; McNamara, 1998; Török et al., 2004; Simons, 2009). Game-theoretic methods show that multiple strategies will coexist when types compete (Ellner, 1997). Risk minimization strategies are exerted on all levels of biological organization (Cohen, 1966; Gillespie, 1974a; Slatkin, 1974; Tonegawa, 1983; Hairston & Munns, 1984; Seger & Brockmann, 1987; Philippi & Seger, 1989; Frank & Slatkin, 1990; Moxon et al., 1994; Sasaki & Ellner, 1995; Ellner, 1997; Simovich & Hathaway, 1997; Danforth, 1999; Hopper, 1999; Menu et al., 2000; Lips, 2001; Meyers & Bull, 2002; Stumpf et al., 2002; Fox & Rauter, 2003; Friedenberg, 2003; Hopper et al., 2003; Balaban et al., 2004; Einum & Fleming, 2004; Laaksonen, 2004; King & Masel, 2007; Rollinson & Brooks, 2007; Venable, 2007; Acar et al., 2008; Gourbière & Menu, 2009; Olofsson et al., 2009; Simons, 2009, 2011; Childs et al., 2010; Monro et al., 2010; de Jong et al., 2011; Dobrzy?ski et al., 2011; Nicholls, 2011; Charpentier et al., 2012; Gremer et al., 2012; Morrongiello et al., 2012; Starrfelt & Kokko, 2012; Auld & Rubio de Casas, 2013; Brutovsky & Horvath, 2013; Heininger, 2013; Graham et al., 2014; Solopova et al., 2014). There is growing evidence of evolutionary selection for stochastic diversity-generating mechanisms in unicellular and multicellular organisms at a variety of genetic, epigenetic, developmental, and physiological levels (McAdams & Arkin, 1997; True & Lindquist, 2000; Elowitz et al., 2002; Fraser et al., 2004; Raser & O’Shea, 2004; 2005; Kærn et al., 2005; Avery, 2006; Peaston & Whitelaw, 2006; Smits et al., 2006; Lim & van Oudenaarden, 2007; Maamar et al., 2007; Acar et al., 2008; Davidson & Surette, 2008; Freed et al., 2008; Losick & Desplan, 2008; Shahrezaei & Swain, 2008; Lenormand et al., 2009; Dercole et al., 2010; Lidstrom & Konopka, 2010; Huang, 2012).

Biological fluctuations span multiple spatial and temporal scales from fast cellular and sub-cellular processes to more gradual whole-organism multi-cellular dynamics to very slow evolutionary and population-level variability. There is a continuum of bet-hedging strategies from cellular to organismal and ecological levels (Simons, 2002). There is some evidence that macroevolutionary events at or above the species level such as speciation, radiations, or extinctions (Stanley, 1998; Levinton, 2001) could be decoupled from microevolutionary ones(at the population level within species); that macroevolutionary and microevolutionary trends,at least in part, are governed by different principles (Solé et al., 1996a, 1999; Erwin, 2000; Carroll, 2001b; Plotnick & Sepkoski, 2001). The terms “microevolution” and “macroevolution” reflect the controversy (Eldredge & Gould, 1972; Stanley, 1975; Orzack, 1981; Charlesworth et al., 1982; Maynard Smith, 1989; Gould & Eldredge, 1993; Van Valen, 1994; Bennett, 1997; Erwin, 2000; Carroll, 2001; Simons, 2002) over the unity of the process of natural selection operating at different time scales. Bet-hedging theory should be considered as relevant not only to a broad range of microevolutionary studies, but may also be applied hierarchically to macroevolutionary time scales and to all phylogenetic levels. Integral to this perspective is the treatment of environmental variance as a potentially continuous variable, spanning minor fluctuations to catastrophic events, where event frequency and severity are inversely related. Recent empirical and theoretical studies suggesting the prevalence of “reddened” or “1⁄f” temporal spectra thus offer support to the proposed perspective (Simons, 2002).

Recent work shows stochastic switching to be a near universal feature of living systems (Kaern et al., 2005; Smits et al., 2006), arising from little other than molecular noise (Elowitz et al., 2002; Smits et al., 2006; Lim & van Oudenaarden, 2007; Maamar et al., 2007; Freed et al., 2008). In contrast, metazoan bet-hedging usually involves phenotypic diversification among an individual's offspring, such as differences in egg and seed dormancy (Hopper, 1999; Laaksonen, 2004; Evans & Dennehy, 2005; Evans et al., 2007; Venable, 2007; Crean & Marshall, 2009; Simons, 2009) or developmental instability (Simons & Johnston, 1997).

11.1 Molecular biological bet-hedging

Variability in biological populations is the result of many confluent factors. The most basic one is genetic diversity among individual organisms. This genetic diversity is crucial for survival of the species in an ever-changing environment (Tsimring, 2014). It has been proposed (Ferenci & Maharjan, 2014) that heterogeneity of mutational types in populations, e.g. point mutations, deletions, insertions, transpositions and duplications, and their flexible frequency in populations provides a source of risk avoidance and alternative evolutionary strategies. To survive in a dynamic environment, cells are equipped with gene networks that allow growth to continue in spite of changing conditions. However, this flexibility comes at a price, and cells experiencing environmental fluctuations usually do not attain their fastest growth rate. In light of this, it is likely that there are genetic mechanisms that exist because they have been selected for in natural environments, but have little competitive advantage in highly controlled laboratory experiments (Razinkov et al., 2013).

Even genetically identical organisms, such as monoclonal microbial colonies, cloned animals or identical human twins exhibit significant phenotypic variability. Traditionally, this variability was ascribed to environmental fluctuations affecting development of individual organisms (extrinsic noise), but in recent years it has become clear that significant variability persists even when genetically identical organisms are kept under nearly identical conditions (intrinsic noise) (Tsimring, 2014). Mounting experimental evidence suggests that gene expression, both in prokaryotes and eukaryotes, is an inherently stochastic process. Transcription and translation show significantly higher error rates than replication. Stochasticity can be attributed to the randomness of the transcription and translation processes (intrinsic noise), as well as to different environmental conditions or differences in the concentration of transcription factors governing the network on a cellular level (extrinsic noise) (McAdams & Arkin, 1997; Elowitz et al., 2002; Swain et al., 2002; Paulsson, 2004, 2005; Longo & Hasty, 2006; Zhuravel et al., 2010). According to mass spectrometry measurements (Ishihama et al., 2008), the median copy number of all proteins in a single E. coli bacterium is approximately 500, and 75% of all proteins have a copy number of less than 250. The copy numbers of RNAs often number in tens, and the chromosomes (and so the majority of the genes) are usually present in one or two copies. Therefore, the reactions among these species can be prone to significant stochasticity (Tsimring, 2014). Importantly, as Ashby (1956, p. 186) stated: “It must be noticed that noise is in no intrinsic way distinguishable from any other form of variety. Only when some recipient is given, who will state which of the two is important to him, is a distinction between message and noise possible.” The behavior of gene regulatory networks also displays stochastic characteristics which, in several cases, can lead to significant phenotypic variation in isogenic cell populations (Ozbudak et al., 2002; Rao et al., 2002; Cinquemani et al., 2008). For example, experimental observations suggest that stochastic uncertainty may play a crucial role in enhancing the robustness of biochemical processes (Vilar et al., 2002), or may be behind the variability observed in the behavior of biological systems (Kærn et al., 2005; Wolf et al., 2005a; Wu et al., 2005; Blake et al., 2006; Kouretas et al., 2006; Cinquemani et al., 2008). Tsuda and Kawata (2010) constructed an evolutionary model of gene regulatory networks and simulated its evolution under various environmental conditions. The results showed that most features of known gene regulatory networks, particularly robustness and evolvability, evolve as a result of adaptation to unpredictable environmental fluctuations.

11.1.1 Gene expression noise

There are two main sources of uncertainty in the DNA replication process. The first has to do with which origins of replication fire in a particular cell cycle and the second with the times at which they fire (Patel et al., 2006).‘Noise’ has been defined as an empirical measure of stochasticity (Shahrezaei & Swain, 2008). Intriguingly, at least in part, cellular noise is genetically controlled (Raser & O’Shea, 2004). Several studies suggest that gene architecture may also be an important determinant of gene expression noise (MacNeil & Walhout, 2011). The chromatin environment of a gene plays an important role in regulating stochasticity in gene expression. Histone acetylation and DNA methylation significantly affect stochasticity in gene expression, suggesting that cells are able to adjust the variability of the expression of their genes through modification of chromatin marks (Viñuelas et al., 2012). Given that the alteration of chromatin marks is itself subject to the expression of chromatin modifiers, a complex circular causality may provide the cell with many regulation loops and ultimately with a fine-tuning of its phenotype and phenotypic variability (Viñuelas et al., 2012). Genes that are controlled by promoters that possess a TATA box are noisier in their expression (Becksei & Serrano, 2000; Blake et al., 2006; Batada & Hurst, 2007; Maheshri & O'Shea, 2007; Tirosh & Barkai, 2008). A strong TATA box has been shown experimentally to increase noise (Raser & O’Shea, 2005). In contrast, transcription factors known to disrupt chromatin structure correlate with low noise genes. Genes which are constitutively expressed and under an almost constant demand (commonly referred to as house-keeping genes) have below-average levels of gene expression noise. Essential proteins and proteins related to translation, the ribosome, the proteasome, and the secretory pathway exhibit low noise (Fraser et al., 2004; Bar-Even et al., 2006). Sensitivity of gene expression to mutations increases with both increasing trans-mutational target size and the presence of a TATA box (Landry et al., 2007). Genes with greater sensitivity to mutations are also more sensitive to systematic environmental perturbations and stochastic noise. Elevated expression noise may be beneficial and subject to positive selection. Under certain conditions, expression noise increases the evolvability of gene expression by promoting the fixation of favorable expression level-altering mutations. Indeed, yeast genes with higher noise show greater between-strain and between-species divergences in expression. Elevated expression noise is advantageous, is subject to positive selection, and is a facilitator of adaptive gene expression evolution (Zhang et al., 2009). These findings provide a mechanistic basis for gene expression evolvability that can serve as a foundation for realistic models of regulatory evolution. Cell-to-cell variation in genetically identical cells of multicellular organisms is often regulated by active non-genetic mechanisms (Kimble & Hirsh, 1979; Kimble, 1981; Doe & Goodman, 1985; Sternberg & Horvitz, 1986; Priess & Thomson, 1987; Jan & Jan, 1995; Karp & Greenwald, 2003; Hoang, 2004; Colman-Lerner et al., 2005). Observations suggest that the molecular events underlying cellular physiology are subject to fluctuations and have led to the proposal of a stochastic model for gene expression and biochemistry in general (Rao et al., 2002). Other cellular processes influenced by noise include ion-channel gating (White et al., 2000), neural firing (Allen & Stevens, 1994), cytoskeleton dynamics (van Oudenaarden & Theriot, 1999) and motors (Simon et al., 1992). The generation of phenotypic heterogeneity owing to a variable gene expression depends on the genetic circuitry of a system. The specific molecular interactions and/or chemical conversions depicted as links in the conventional diagrams of cellular signal transduction and metabolic pathways are inherently probabilistic, ambiguous, and context-dependent (Kurakin, 2007). Regulatory systems or decisions, in which the outcome of a cellular event is at least partially the result of intrinsic noise, are said to be stochastic (Theise & Harris, 2006; Losick & Desplan, 2008; Eldar & Elowitz, 2010). Single-cell expression profiling experiments of high spatial and temporal resolution revealed stochastic activation of responsive genes (McAdams & Arkin, 1997; Elowitz et al., 2002; Fraser et al., 2004; Levsky et al., 2002; Raser & O’Shea, 2004, 2005).

The level of transcription of any gene is not maintained at a steady level but rather occurs as a series of rapid bursts separated by periods of lower expression (Ross et al., 1994; Newlands et al., 1998; Blake et al., 2003; Golding & Cox, 2004; Golding et al., 2005; Cai et al., 2006; Yu et al., 2006). Such bursts are entirely stochastic and occur at different times for different genes. Simulations of stochastic behavior in dynamically unstable high-dimensional biochemical networks resulted in burstiness (Rosenfed, 2009, 2011). Using a stochastic model of simple feedback networks, Kuwahara & Soyer (2012) found that independent of the specific nature of the environment–fitness relationship, the main outcome of fluctuating selection is the evolution of bistability and stochastic switching in a gene regulatory network. Such emergence occurs as a byproduct of the evolution of evolvability and exploitation of noise by evolution (Kuwahara & Soyer, 2012). Phenotypic heterogeneity is often an outcome of gene expression dynamics involving positive feedback (Ferrell, 2002; Dubnau & Losick, 2006; Smits et al., 2006). The combined effect of positive feedback and noise provide a universal mechanism for generating phenotypic heterogeneity in cell populations (Weinberger et al., 2005; Dubnau & Losick, 2006; Kashiwagi et al., 2006; Smits et al., 2006; Karmakar & Bose, 2007; Leisner et al., 2007; Maamar et al., 2007; Süel et al., 2007; Sureka et al., 2008). Positive feedback induces a swich-like behavior and bistability (Ferrell & Machleder, 1998; Ferrell, 2002; Tyson et al., 2003) and negative feedback represses noise effects (Tyson et al., 2003; Paulsson, 2004; Dublanche et al., 2006; Loewer & Lahav, 2006). Noise in gene expression does not give rise to phenotypic heterogeneity as long as it is suppressed by negative feedback but it becomes important when amplified by a positive feedback loop (Smits et al., 2006; Davidson & Surette, 2008;Sureka et al., 2008). Analysis of transcription in single cells indicated that both alleles of imprinted genes were expressed randomly, but with different probabilities (Jouvenot et al., 1999). The phenomena of monoallelic gene expression (Serizawa et al., 2003), haploinsufficiency (Cook et al., 1998) and phenotypic heterogeneity in isogenic cell populations (Blake et al., 2003) were explained by the inherently stochastic nature of gene expression (Kurakin, 2005a).

Theoretical modeling and empirical analysis of yeast data (Wang & Zhang, 2011) showed that (i) expression noise reduces the mean fitness of a cell by at least 25%, and this reduction cannot be substantially alleviated by gene overexpression; (ii) higher sensitivity of fitness to the expression fluctuations of essential genes than nonessential genes creates stronger selection against noise in essential genes, resulting in a decrease in their noise; (iii) reduction of expression noise by genome doubling offers a substantial fitness advantage to diploids over haploids, even in the absence of sex; (iv) expression noise generates fitness variation among isogenic cells, which lowers the efficacy of natural selection similar to the effect of population shrinkage. Thus, expression noise renders organisms both less adapted and less adaptable. Because expression noise is only one of many manifestations of the stochasticity in cellular molecular processes, the results suggest a much more fundamental role of molecular stochasticity in evolution than is currently appreciated (Wang & Zhang, 2011). If a mutation is neutral, its fixation probability is unaffected by the presence/absence of the fitness noise. The fixation probability increases with the level of fitness noise for deleterious mutations but decreases for beneficial mutations. Fitness noise also affects an allele’s time to fixation just like population shrinkage (Wang & Zhang, 2011).

Simulations show that in gene expression significant fluctuations occur on both short and long length- and timescales (van Zon et al., 2006). The fluctuations on long timescales are predominantly due to protein degradation presumably by dilution, which means that the relaxation rate of this process is on the order of 1 h (Swain et al., 2002; Paulsson, 2004; Rosenfeld et al., 2005). On much shorter length- and timescales gene expression noise is associated with the competition between repressor and RNA polymerase for binding to the promoter. When a repressor molecule dissociates from the DNA, it can rebind very rapidly: possibly on a timescale of milliseconds, or less. This timescale is much shorter than that on which the RNA polymerase binds to the promoter, which is on the order of 0.01–0.1 s. Hence, when a repressor molecule has just dissociated, the probability that a RNA polymerase will bind before the repressor molecule rebinds, is verysmall. A repressor molecule will on average rebind many times before it eventually diffuses away from the promoter and a RNA polymerase molecule, or another repressor molecule, can bind to the promoter. This decreases the effective dissociation rate, which increases the noise in gene expression (van Zon et al., 2006).

In addition to gene expression noise there is substantial transcription infidelity. Comparing RNA sequences from human B cells of 27 individuals to the corresponding DNA sequences from the same individuals, Li et al. (2011) uncovered more than 10,000 exonic sites where the RNA sequences did not match that of the DNA, revealing infidelity of information transmission from DNA to RNA as an additional aspect of genome variation. The number of events varied among individuals by up to sixfold across 27 subjects (Li et al., 2011).Rosenfeld et al. (2005) found that quantitative relations between transcription factor concentrations and the rate of protein production fluctuate dramatically in individual living cells, thereby limiting the accuracy with which genetic transcription circuits can transfer signals.

Related to the concept of bistability is the one of phase variation. Phase variation is a process that results in differential expression of one or more genes and results in two subpopulations within a clonal population: one lacking or having a decreased level of expression of the phase variable gene(s) and the other subpopulation expressing the gene fully (van der Woude & Bäumler, 2004; van der Woude, 2006). In specific cases, phase variation can lead to antigenic variation, for example if phase variation affects expression of a lipopolysaccharide modifying enzyme. A key feature of phase variation is that the ‘On’ and ‘Off’ phenotypes are interchangeable. Thus, a cell with gene expression in the ‘Off’ phase, that is lacking expression, retains its ability to switch to ‘On’ and vice versa. Cell switching is stochastic in the sense that no prediction can be made about which cell in a population will undergo the switch. One stochastic event can set in motion a series of events that influence the frequency of occurrence of other stochastic events. The occurrence of phase variation thus results in a heterogenic and dynamically changing phenotype of a bacterial population (Srikhanta et al., 2005; van der Woude, 2006). The term ‘contingency genes’ is often adopted to describe the class of genes that are expressed in a phase variable manner (Moxon et al., 2006). By the Oxford dictionary ‘contingency’ is defined as ‘a future event, which is possible but can not be predicted with certainty’. An encompassing view on the role of phase variation is that the generation of diverse subpopulations enhances the chance that at least one can overcome a stressful challenge, in essence a ‘bet-hedging’ strategy (van der Woude, 2006).

11.1.2 Epigenesis

DNA methylations are not gene-locus specific and have a substantial stochastic component (Silva et al., 1993; Ushijima et al., 2003; Reiss & Mager, 2007; Raj & van Oudenaarden, 2008; Huang, 2009; Mohn & Schübeler, 2009; Feinberg & Irizarry, 2010; Petronis, 2010). The degree of fidelity in epigenetic transmission is about three orders of magnitude lower than that of DNA sequence (an error rate of 1 in 106 and 1 in 103 for DNA sequences and DNA modification, respectively) (Ushijima et al., 2003; Laird et al., 2004; Riggs & Xiong, 2004; Genereux et al., 2005; Fu et al., 2010; Petronis, 2010). The cardinal signs of epigenetic effects on gene transcription are variable expression of a gene in a population of isogenic individuals (variable expressivity) and/or a mosaic pattern among cells of the same type within an individual (variegation) (Whitelaw & Martin, 2001). Stochastic epigenetic variation plays an important role for adaptation to fluctuating environments: by modifying the geometric mean fitness (see chapter 15.3), variance-modifying genes can change the course of evolution and determine the long-term trajectory of the evolving system (Carja et al., 2013).

11.1.3 Protein promiscuity

There is a growing realization that proteins are not as ‘ligand specific’ as the textbooks, or crystal structures, suggested (Atkins, 2014). It often happens that a polypeptide chain’s free energy conformational space does not have a well-defined minimum; this may result in markedly different structures and chemical behaviors of proteins after folding (Grosberg, 2004). Functional promiscuity (a great number of proteins can interact with a great number of molecular partners) or ‘messiness’ of most enzymes, receptors or other proteins has clear roles in biology (Glasner et al., 2007; Noy, 2007; Basu et al., 2009; Nobeli et al., 2009; Tokuriki & Tawfik, 2009; Khersonsky & Tawfik, 2010; Tawfik, 2010; Giuseppe et al., 2012; Mohamed & Hollfelder, 2013). It has been argued that, because proteins lack specificity, biological molecular interactions are, by themselves, intrinsically stochastic (Bork et al., 2004; Kupiec, 2009; Atkins, 2014). They are subject to large combinatorial possibilities making simple auto-assembly insufficient for the explanation of ontogenesis. This stochastic phenomenon is different from noise. It is not due to solely fluctuations in the concentration of molecules present in small number but to the lack of specificity of proteins causing widespread competition between them for interaction. Taking into account the intrinsically stochastic behavior of proteins necessarily brings genetic determinism into question (Kupiec, 2010).Promiscuous intermediates are highly evolvable and it has been suggested that promiscuity is actually selected as an advantageous trait within the entire proteome in order to ensure evolutionary adaptability. In fact, significant experimental evidence suggests that protein/enzyme promiscuity per se is a trait that is required to optimize evolutionary efficiency, because fewer mutations may be required when starting from a promiscuous template than from a previously optimized enzyme with high specificity (Williams et al., 2007; Chakraborty, 2012; Tokuriki et al., 2012; Dellus-Gur et al., 2013; Díaz Arenas & Cooper, 2013; Atkins, 2014).

11.1.4 Energy-Ca2+-redox triangle

The ultimate carriers of molecular biological stochasticity are the agents of the energy-Ca2+-redox triangle that is modulated e.g. by metabolic stress due to maladaptation (Brookes et al., 2004; Camello-Almaraz et al., 2006; Feissner et al., 2009; Peng & Jou, 2010). Evidence is accumulating that the environment is able to shape the phenotype of organisms not only by the action of intragenerational natural selection but also by transgenerational processes (Jablonka & Lamb, 1995, 2005, 2007; Caporale, 1999, 2003a, b, 2009; Radman et al., 1999; Shapiro, 2011; Heininger, 2013). In a random environment, transgenerational effects deliver higher fitness than either a plastic only or genetic only strategy (Jablonka et al., 1995; Hoyle & Ezard, 2012). The adaptive stress in a given environment determines the metabolic condition of organisms that establishes a feedback loop for the fit between environmental and (epi)genotypic/phenotypic condition (see Heininger, 2013). This flow of information is not coded and specific as from gene to protein but code-free and stochastic. The randomness of the feedback from environment to the genome relies on the simple, codeless messenger agents ATP, Ca2+ and free radicals (Saran et al., 1998), both regulated by and regulating cellular and organismal homeostasis in a feedback triangle (Brookes et al., 2004; Camello-Almaraz et al., 2006; Yan et al., 2006; Feissner et al., 2009; Kowaltowski et al., 2009). Cellular oxidative stress-dependent responses, although undoubtedly programmed, are also highly variable (Heininger, 2012, 2013), at least in part based on the stochasticity of mitochondrial bioenergetic/oxidative events (Hüser et al., 1998; Genova et al., 2003; Passos et al., 2007; Wang et al., 2008). In addition to cellular processes, these agents regulate organismal life history events like development and aging (Heininger, 2012) in response to environmental cues. The regulated stochastic nature of the effectors and the degeneracy of (epi)mutagenic tools (Edelman & Gally, 2001; Whitacre & Bender, 2010) may act both as a source of robustness and evolvability (Heininger, 2013). These stochastic factors allow multiple solutions for a given problem (Lenski & Travisano, 1994; Rosenzweig et al., 1994; Finkel & Kolter, 1999) and therefore have given rise to the huge diversity of evolution with an ever increasing complexity (Adami et al., 2000).

11.2 Phenotypic and behavioral bet-hedging

Bet-hedging is the risk-minimizing response to environmental uncertainty both at the individual and population level (Simovich & Hathaway, 1997; Einum & Fleming, 2004; Marshall et al., 2008; Beaumont et al., 2009; Crean & Marshall, 2009; Gourbière & Menu, 2009; Olofsson et al., 2009; Rajon et al., 2009; Simons, 2009, 2011; Nevoux et al., 2010; Morrongiello et al., 2012). Genetically identical organisms can grow faster by choosing some fraction of their population to be in a phenotype less favored in the current environment, but prepared for potentially different future environments (Beaumont et al., 2009). This can be viewed as an inherently pessimistic strategy of survival: organisms switch to the less favorable phenotype in anticipation of the worst (Forbes, 1991; Friedman et al., 2013).

For the past 30 years, phenotypic plasticity and developmental instability mostly have been dealt with independently, both with regard to theory and empirical study.Yet both are alternative outcomes to selection in a varying environment and might interact with each other (Scheiner, 2014).Developmental instability has been shown to have a genetic basis (e.g., Scheiner et al., 1991; Ros et al., 2004; Ibáñez-Escriche et al., 2008; Shen et al., 2012; Tonsor et al., 2013) and thus can be selected for. As an adaptive response to environmental heterogeneity, developmental instability maximizes the fitness of a lineage by which increasing the phenotypic variation among individuals of that lineage (Starrfelt & Kokko, 2012; Scheiner, 2014).

Phenotypic plasticity, that is, the ability of a genotype to develop different phenotypes in different environments (Stearns 1989a), is an important characteristic that is subject to natural selection (Halkett et al., 2004). Phenotypic plasticity in insects has usually been equated with predictive plasticity, or conditional polyphenism (Walker, 1986), in which a genotype responds to different current environments by producing different phenotypes in a way that maximizes its fitness. The term “conditional polyphenism” describes this form of plasticity corresponding, for a single genotype, to a response to a given current environment by deterministically producing a given phenotype. However, the “decision” made now will generally have consequences on future fitness although the future state of the environment cannot be perfectly predicted on the basis of the current one. Thus, there may be a delay between the instant when the decision is made and the instant when it affects individual fitness, and during this delay the environment may change (Moran, 1992). In such a case, a stochastic decision, called adaptive coin-flipping (Cooper & Kaplan, 1982; Kaplan & Cooper, 1984; Halkett et al., 2004) or stochastic polyphenism (Walker, 1986), can be fitter (Cooper & Kaplan, 1982; Haccou and Iwasa 1995; Menu et al., 2000) and can lead to diversifiedbet hedging (Seger & Brockman, 1987; Hopper, 1999; Menu et al., 2000; Menu & Desouhant, 2002). The term “stochastic polyphenism” describes the form of plasticity corresponding to a response by a single genotype to a given current environment by stochastically (e.g., flipping a coin) producing one phenotype among a set of possible phenotypes (Plantegenest et al., 2004). Because an organism can never perceive its environment with complete accuracy, all decision making is made under some uncertainty, and this frequently leads to a selective advantage for genotypes performing stochastic polyphenism. This form of plasticity, which copes with uncertainty, can thus be expected to be widespread in nature (Walker, 1986; Moran, 1992; Halkett et al., 2004). In models, the phenotypes that are not variable are outcompeted by those able to generate variation and innovations (Fontana & Schuster, 1998; Ancel & Fontana, 2000; Meyers et al., 2005). Fluctuations in the physical environment may even be drivers of evolutionary transitions (Boyle & Lenton, 2006).

The general question of whether one should expect environmental fluctuations to select for a component of randomness in the expression of phenotypic plasticity has received rather little attention (but see Walker, 1986; Haccou and Iwasa, 1995; Van Dooren, 2001; Koops et al., 2003; Halkett et al., 2004; Crean & Marshall, 2009;Simons, 2009; Charpentier et al., 2012). Phenotypic diversification has been hypothesized to be a form of bet-hedging, a survival strategy analogous to stock market portfolio management. From this point of view, ‘selfish’ genotypes diversify assets among multiple stocks (phenotypes) to minimize the long-term risk of extinction and maximize the long-term expected growth rate in the presence of (environmental) uncertainty (Wolf et al., 2005a). Soil and sediment banks of dormant propagules result in overlapping generations and a prolonged generation time for an otherwise short-lived organism. It may also result in the reintroduction of genotypes which may have done poorly in previous years and a constant reshuffling of genotypes with variable past success (Templeton & Levin, 1979; Hairston & DeStasio, 1988; Ellner & Hairston, 1994; Simovich & Hathaway, 1997; Evans & Dennehy, 2005; Evans et al., 2007). Typically in unfavorable environments, some organisms have environmentally induced arrested development at different stages: embryonic diapause (Moriyama & Numata, 2008), larval diapause (Golden & Riddle, 1984), and pupal diapause (Belozerov et al., 2002). Stochastic parsing of viral populations into lytic and lysogenic (or latent) states is believed to have evolved as an adaptive solution to fluctuations in the availability of bacterial hosts (Mittler, 1996; Stumpf et al., 2002).In the phage lambda infection process, which is governed by the lysis–lysogeny decision circuit, only a fraction of infecting phage chooses to lyse the cell. The remainder become dormant lysogens awaiting bacterial stress signals to enter the production phase of their life cycle (Ptashne, 1998).

Dispersal phenotypes could be subject to bet-hedging as well; when an environment consists of niches that become available stochastically for colonization, the optimal genotype produces a mix of dispersing and non-dispersing progeny (Comins et al., 1980). Bet-hedging in the plant kingdom might also be common, as exemplified by the probabilistic germination strategies favored by desert plants subjected to random rain-drought patterns (Satake et al., 2001). In a large range of taxa, including plants, insects, fishes and birds, offspring size variability has been suggested to be of adaptive value in variable habitats (McGinley et al., 1987; Geritz, 1995; Geritz et al., 1999; Moles & Westoby, 2006; Westoby et al., 1996). As egg phenotype is linked to offspring phenotype, increased within-brood variation in egg phenotype can have a selective advantage in unpredictable environments by increasing maternal geometric fitness (Marshall et al., 2008; Crean & Marshall, 2009; Crean et al., 2012). There is less egg size variability, both within and among female brook trouts, when environments are more predictable, and females use variability in egg size to offset the cost of imperfect information when producing smaller eggs (Koops et al., 2003). Even in clonal plants the production of variably sized offspring has been shown to be adaptive to temporal variability in environmental conditions (Charpentier et al., 2012).

Almost all known microbial bet-hedging strategies rely on low-probability stochastic switching of a heritable phenotype by individual cells in a clonal group (Thattai & van Oudenaarden, 2004; van der Woude & Bäumler, 2004; Kussell et al., 2005; Kussell & Leibler, 2005; Wolf et al., 2005a; Avery, 2006; Moxon et al., 2006; Smits et al., 2006; Veening et al., 2008a, b; Beaumont et al., 2009; Fraser & Kaern, 2009; Gordon et al., 2009; Ratcliff & Denison, 2010; de Jong et al., 2011; Libby & Rainey, 2011; Rainey et al., 2011; Levy et al., 2012). The capacity to switch stochastically between heritable phenotypic states is common in bacteria (Libby & Rainey, 2011). Observed initially as variation in the morphology of colonies arising from single clones of certain bacterial pathogens (Andrewes, 1922), adaptive stochastic phenotype switching has been identified e.g. in (i) the case of bacterial persistence. Cells switch stochastically between growing and non-growing (persister) states (Keren et al., 2004) that can be adaptive in the face of periodic encounters with antibiotics despite the cost associated with non-growing cells (Balaban et al., 2004; Kussell & Leibler, 2005; Gefen & Balaban, 2009); (ii)the competence to non-competence switch for natural DNA transformation in the soil bacterium Bacillus subtilis (Maamar et al., 2007). Like the persister state, competence is associated with periods of nongrowth in an otherwise growing population and can be beneficial, despite the cost, provided the population periodically encounters conditions that kill growing cells (Johnsen et al., 2009); (iii) Haemophilus influenzae avoidance of recognition by the host immune response.H. influenzae experiences unpredictable environmental fluctuations in terms of host immune response with varying dynamics and degrees of uncertainty; whether or not bet hedging evolves depends on many factors (Thattai & van Oudenaarden, 2004; Kussell & Leibler, 2005; Kussell et al., 2005; Wolf et al., 2005b; King & Masel, 2007; Acar et al., 2008; Donaldson-Matasci et al., 2010; Gaal et al., 2010), including the existence and reliability of environmental cues (Bull, 1987; Donaldson-Matasci et al., 2008), the capacity of the population to respond via mutation and selection (King & Masel, 2007; Visco et al., 2010), the nature of the fitness landscape (Salathé et al., 2009; Gaal et al., 2010) and the cost–benefit balance of different strategies (Kussell & Leibler, 2005; Kussell et al., 2005; Gaal et al., 2010;Visco et al., 2010). The de novo evolution of a bet-hedging or risk-reducing strategy evolved in bacteria by a selective regime that captured essential features of the host immune response. The experimental regime involved strong frequency-dependent selection realized via dual imposition of an exclusion rule and population bottleneck (Beaumont et al., 2009; Libby & Rainey, 2011; Rainey et al., 2011).

11.2.1 Canalization and phenotypic plasticity: two sides of the same coin

The genotype-phenotype map is the common theme underlying such varied biological phenomena as genetic canalization, developmental constraints, biological versatility, developmental dissociability, and morphological integration (Wagner & Altenberg, 1996). Environmental canalization and phenotypic plasticity represent features of either conservative or diversifying bet-hedging. Environmental canalization is the insensitivity of a phenotype to variation in the environment; in the broad sense, environmental canalization refers to any kind of robustness against environmental perturbations (Waddington 1942, 1957; Roff, 1997; de Visser et al., 2003; Flatt, 2005). For example, the stimulation of a stress response can reduce mutation penetrance in Caenorhabditis elegans. Moreover, this induced mutation buffering varies across isogenic individuals because of interindividual differences in stress signaling (Casanueva et al., 2012). In contrast, phenotypic plasticity is the sensitivity of the phenotype produced by a single genotype to variation in the environment (e.g., Bradshaw, 1965; Stearns 1989b; Roff, 1997). Thus, environmental canalization and phenotypic plasticity describe different aspects of the same phenomenon: the dependency of the phenotype on the environment (Roff, 1997; Ancel & Fontana, 2000; Rutherford, 2000; Debat & David, 2001; de Visser et al., 2003; Proulx & Phillips, 2004). Importantly, canalization and plasticity are not mutually exclusive (Stearns & Kawecki, 1994). With regard to reaction norms, canalization is characterized by flatter, and plasticity by steeper, slopes of environmental sensitivity (Falconer, 1990; de Jong, 1990).Theoretical and empirical studies showed that the level of phenotypic plasticity/canalization in a trait and variation in the slope of reaction norm are under selection and dependent on levels of temporal and spatial environmental heterogeneity (Scheiner, 1993; Ellers & van Alphen, 1997; Pfennig & Murphy, 2002; Price et al., 2003; Hassall et al., 2005; Pigliucci et al., 2006; Winterhalter & Mousseau, 2007; Liefting et al., 2009; Fusco & Minelli, 2010).If environmental change is recurring, predictable, or sufficiently gradual, then adaptive phenotypic plasticity is expected to evolve (Via & Lande, 1985; Thompson, 1991; Leimar et al., 2006; Reed et al., 2010; Beldade et al., 2011; Graham et al., 2014). In a computer simulation model, environmental variation and uncertainty affect whether or not plasticity is favored with different sources of variation—arising from the amount and timing of dispersal, from temporal variation, and even from the genetic architecture underlying the phenotype—having contrasting, interacting, and at times unexpected effects (Scheiner & Holt, 2012).

Fluctuating environments generate traps on the fitness landscape. This phenomenon has been modelled in the context of drug resistance and compensatory mutation (Tanaka & Valckenborgh, 2011). The authors suggested that this phenomenon may be found more widely in nature than in the context of drug resistance and compensatory mutation. For example, cryptic genetic variation can accumulate in a genome, whose selective effects are unveiled when the environment changes (Gibson & Dworkin, 2004; Rouzic & Carlborg, 2008). The dynamics of crossing a fitness valley can be regarded as a stochastic process; the rate of traversal is a function of various parameters underlying the population biology (Stephan, 1996; Carter & Wagner, 2002; Weinreich & Chao, 2005; Durrett & Schmidt, 2008; Gokhale et al., 2009; Weissman et al., 2009; Lynch & Abegg, 2010). In addition to the steepness of the valley—the selective coefficients—the population size has been identified as an important parameter. The Hsp90 protein at the hub of the canalization-plasticity balance

The Hsp90 stress response protein is the major molecular biological hub of the canalization-plasticity balance (Taipale et al., 2010; Heininger, 2013). Hsp90 is an ancient, abundant and nearly ubiquitous protein chaperone that interacts in an ATP-dependent system with more than 100 ‘client proteins’ in the cell, most of which are involved in signaling pathways, including protein kinases, transcription factors and others, and either facilitates their stabilization and activation or directs them for proteasomal degradation. Hsp90 is extremely abundant – constituting ~1% of total protein under normal growth conditions – and these levels may even increase following environmental stress up to tenfold both in prokaryotes and in eukaryotes (Buchner, 1999). Complete loss of Hsp90 function is lethal, as multiple essential pathways are inactivated. By linking genetic variation to phenotypic variation, the Hsp90 protein folding reservoir might promote both stasis and change (Jarosz & Lindquist, 2010). The Hsp90 chaperone system alters relationships between genotypes and phenotypes under conditions of environmental stress (Rutherford & Lindquist, 1998; Sangster et al., 2008; Jarosz & Lindquist, 2010; Chen et al., 2012) and, in so doing, provides at least two routes to the rapid evolution of new traits: (i) Acting as a potentiator, Hsp90’s folding reservoir allows individual genetic variants to immediately create new phenotypes; when the reservoir is compromised, the traits previously created by potentiated variants disappear. (ii) Acting as a capacitor, Hsp90’s excess chaperone capacity buffers the effects of other variants, storing them in a phenotypically silent form; when the Hsp90 reservoir is compromised, the effects of these variants are released, allowing them to create new traits. The loss of Hsp90 function under high stress may be due to its ATP-dependent functioning when ATP becomes limiting energetic stress-dependently (Panaretou et al., 1998; Buchner, 1999; Rutherford et al., 2007). Moreover, ROS-dependent degradation of Hsp90 protein may result in the loss of Hsp90 chaperone function, leading to client protein degradation, possibly by an ADP- and iron-dependent local generation of hydroxyl radicals through a Fenton-type reaction (Beck et al., 2012). Thus, canalization and phenotypic plasticity are two sides of the same coin. Under environmental stress the function of Hsp90 breaks down affecting the odds for a change of the redox-dependent canalization-phenotypic plasticity balance. Hsp90 can be considered one of the key regulators of evolvability (Wagner et al., 1999; Milton et al., 2006; Rutherford et al., 2007).

11.3 Stress and bet-hedging

Adversity has the effect of eliciting talents, which in prosperous circumstances would have lain dormant.
Horace (65BC-6BC)

While the causal relationship between environmental stress and bet-hedging behavior may be hard to establish at the population level, the stress-bethedging relationship is amenable to experimental manipulation at the molecular biological level. And here the picture is nonambiguous: stress causes molecular bet-hedging. Stress is here defined as an environmental condition to which organisms are poorly adapted and that reduces Darwinian fitness (Sibly & Calow, 1989; Zhivotovsky, 1997; Rion & Kawecki, 2007; Heininger, 2013). Environmental stress is one of the most important sources of natural selection, as is witnessed by many specific adaptations evolved to alleviate the consequences of stress (e.g. Hoffman & Parsons, 1991; Randall et al., 1997; Heininger, 2001). Importantly, what is perceived as stressor depends on the evolutionary and ecological history of an organism, a change in the usual environmental conditions for any given life form. A certain environment may be claimed as stressful only if considered with respect to both a given population and the environment in which the population has evolved (Zhivotovsky, 1997; Bijlsma & Loeschcke, 2005). It follows that while a specific condition (e.g. a temperature of 65 °C) may be stressful (or even lethal) to a certain microorganism that normally lives at 37 °C, it will be optimal for growth to a thermophilic organism (Conway de Macario & Macario, 2000). As an extreme example, the deep-sea barophilic hyperthermophile Thermococcus barophilus obviously experiences stress when grown under atmospheric pressure (Marteinsson et al., 1999). Similarly, abundant resources appear to be stressful for human populations that have been selected for their thrifty genotype (Neel, 1962; Editorial, 1989; Hales & Barker, 1992; Allen & Cheer, 1996; Fernández-Sánchez et al., 2011).

Intrapopulation diversity is a mechanism to ensure survival upon exposure to environmental challenges (Booth, 2002; Avery, 2006). Bacillus subtilis responds to environmental stress with an arsenal of probabilistically invoked survival strategies (Msadek, 1999; Maughan & Nicholson, 2004). Only a certain fraction of cells within a population actually embark on a particular pathway. These include synthesis of extracellular polymer-degrading enzymes (Msadek et al., 1993), competence for DNA uptake (Hadden & Nester, 1968; Solomon & Grossman, 1996; Dubnau & Lovett, 2002), motility and chemotaxis (Frederick & Helmann, 1996; Mirel et al., 2000), biofilm and fruiting body formation (Branda et al., 2001), adaptive mutagenesis (Sung & Yasbin, 2002), and cellular differentiation into dormant and resistant spores (Perego & Hoch, 2002; Piggot & Losick, 2002). Random fluctuations in the concentration of certain key regulator molecules (Chung et al., 1994; Grossman, 1995; Dubnau & Lovett, 2002; Perego & Hoch, 2002) among individual cells may result in phenotypic fragmentation of populations. The proximate decision to embark on the different developmental pathways is not encoded in the genome, as none of five populations of B. subtilis showed any response to selection after over 5,000 generations of directional selection for enhanced efficiency of spore formation (Maughan & Nicholson, 2004). Stochastic differentiation into a growth-arrested but stress-resistant state (such as a spore) may optimize survival in an uncertain, frequently stressful environment by segregating two essential tasks: growth in the absence of stress and survival in the presence of stress (Balázsi et al., 2011). Theory has shown that a population of cells capable of random phenotypic switching can have an advantage in a fluctuating environment (Thattai & van Oudenaarden, 2004; Kussell & Leibler, 2005; Wolf et al., 2005a). Recent experiments confirmed these predictions, showing that noise stabilizes molecular network architecture under stress (Bollenbach & Kishony, 2009; Ça?atay et al., 2009), can aid survival in severe stress (Booth, 2002; Blake et al., 2006; Bishop et al., 2007), and optimize survival in specific fluctuating environments (Acar et al., 2008).

Recent findings in yeast suggest an intricate relationship between growth rate, stress resistance and bet-hedging strategies (Levy et al., 2012). As is true in descriptions of bacterial bet-hedging and persistence (Balaban et al., 2004), slow growth is a crucial predictor of stress survival in yeast (Levy et al., 2012). Both bacteria and yeast appear to be maximizing population fitness by balancing fast growth in good conditions with bet-hedging against bad ones (Kussell & Leibler, 2005). Both trehalose content and expression levels of Tsl1, a trehalose-synthesis regulator, are correlated with resistance to various forms of stress, including heat, freezing, desiccation, and high ethanol content (Hottiger et al., 1987; Crowe et al., 1992; Winderickx et al., 1996; Singer & Lindquist, 1998; Kandror et al., 2004; Bandara et al., 2009), and growth rate in yeast (Levy et al., 2012). In contrast to bacteria, where persisters and non-persisters constitute binary growth states that predict survival in an all-or-none fashion (Balaban et al., 2004), populations of yeast might contain a continuum of metastable epigenetic cell states that each confer a different fitness in a given environment (Levy et al., 2012).Moreover, while the vast majority of characterized bacterial two-state systems are thought to interconvert through a stochastic mechanism (Hernday et al., 2003, 2004; Balaban et al., 2004; Fujita & Losick, 2005; Maamar & Dubnau, 2005; Smits et al., 2005; Veening et al., 2005; Maamar et al., 2007), differences in yeast growth and survival appear to be due to a more complex combination of stochastic and deterministic factors (Levy et al., 2012).

Further evidence for the view that microbes are single-celled stockbrokers (Wolf et al., 2005a) comes from observations that stress phenotypes introduce a trade-off between a fitness advantage under stress with a fitness defect under more favorable conditions (Cooper & Lenski, 2000; Kishony & Leibler, 2003). Diversification could be a response to this trade-off ensuring the availability of ‘favored’ phenotypes for growth in each environmental condition. Bet-hedging may involve the production of fewer and larger offspring (conservative) or of variable-sized offspring (diversified). Conservative bet-hedging strategies are recognized by a reduction in individual-level variance in fitness while diversified bet-hedging strategies are recognized by a reduction in between-individual correlations in fitness (Starrfelt & Kokko, 2012). In relatively stable environments bet-hedging strategies have a lower fitness advantage and do not pay any more (Philippi & Seger, 1989; Müller et al., 2013).

Importantly, variation is created condition-dependently, when variation is most needed– in organisms under stress. A variety of cellular noisy processes are rendered even noisier under conditions of stress. Thus, stress elicits increased mutagenesis, increased epimutagenesis, increased recombination, increased transposon mobility, increased repeat instability, increased phenotypic plasticity, and, in organisms that can reproduce both asexually and sexually, increased sexual reproduction (Heininger, 2013).

  1. Biological systems function robustly despite uncertainty due to stochastic phenomena, fluctuating environments, and genetic variation (McAdams & Arkin, 1999; Stelling et al., 2004). The regulation and expression of some genes are highly robust; their expression is controlled by invariable expression programs. For example, in development and differentiation, little deviation is tolerated. However, responses to stress can be more stochastic. Genome-scale studies in yeast have shown that while dose-sensitive genes and proteins forming multicomponent complexes tend to have low gene expression noise (Fraser et al., 2004; Batada & Hurst, 2007; Lehner, 2008), stress-related genes and proteins responding to changes in the environment tend to display high noise (Bar-Even et al., 2006, Newman et al., 2006; Fraser & Kærn, 2009; Stewart-Ornstein et al., 2012). Generally, gene expression noise increases following cellular stress and oxidative stress (Thattai & van Oudenaarden, 2004; Bahar et al., 2006; Neildez-Nguyen et al., 2008) that may lead to random cell fates at random times (Chang et al., 2008; Raj & van Oudenaarden, 2008; Gandrillon et al., 2012). Gene expression noise can be exploited to generate a fitness advantage under stress (Booth, 2002; Avery, 2006; Smits et al. 2006; Lopez-Maury et al., 2008; Losick & Desplan, 2008; Fraser & Kærn 2009; Zhuravel et al., 2010). Severe stress causes a global increase in gene expression noise in Escherichia coli (Guido et al., 2007), and increased extrinsic noise in Bacillus subtilis is used to trigger phenotypic switching in response to stress (Maamar et al., 2007). Mycobacterial survival under stress via the stringent response is dependent on positive feedback- and noise-driven bistability transitions (Sureka et al., 2008, see chapter 11.1.1). Stern et al. (2007) reported that yeast cells adapt to novel challenges by undergoing global transcriptional reprogramming that involves random gene activation, as the changes in expression of most genes were irreproducible in repeat experiments. The authors proposed a general adaptive strategy that would allow cells to overcome a broad range of stress environments by mediating stochastic gene activation (Stern et al., 2007). By broadening the range of environmental stress resistance across a population, added gene expression noise could increase the likelihood that some cells within the population are better able to endure environmental assaults (Booth, 2002; Avery, 2006). Experimental results providing support for this hypothesis were obtained in a study by Bishop et al. (2007) who demonstrated a competitive advantage of stress-resistant yeast mutants under high stress due to increased phenotypic heterogeneity. The most prominent biological examples demonstrating the benefits of stochasticity in phenotypic diversification, including persistence, sporulation and competence, represent bet-hedging strategies. In these systems, stochasticity increases phenotypic diversity in anticipation of a future adversity at the expense of reduced mean fitness (Fraser & Kærn, 2009). These observations suggest two possible stress-response mechanisms where high extrinsic noise plays a constructive role; one where it generates phenotypic diversity by increasing the variability in downstream gene expression, and one where it serves as a stochastic trigger of stress response programs (Zhuravel et al., 2010).

  2. Species-wide depletion of accessible beneficial mutations requires a degree of environmental constancy that is not typical of the earth’s history (Lambeck & Chappell, 2001; Zachos et al., 2001; Eldredge et al., 2005). Genetic diversity is crucial for survival of a species in an ever-changing environment. Thus, error-free DNA repair may be maladaptive in mutagenic or stressful environments (Breivik & Gaudernack, 2004; Ponder et al., 2005; Siegl-Cachedenier et al., 2007; Zhao & Epstein, 2008; Heininger, 2013). Mathematical models suggest that mutation rates adapt up (or down) as the evolutionary demands for novelty in variable environments (genetic innovation) or memory in stable environments (genetic conservation) increase (Bedau & Packard, 2003; Buchanan et al., 2004; Clune et al., 2008; Dees & Bahar, 2010). The optimal genomic mutation rate was found to depend only on the environmental change and its severity (Nilsson & Snoad, 2002; Ancliff & Park, 2009). A generic thermodynamical analysis of genetic information storage yielded the insight that mutation rate depends on availability/utilization of metabolic resources. A lowered ability to employ metabolic resources in mutation suppression increases the minimum effective mutation rate. This predicts transient mutation rate increase as a response to stress (Hilbert, 2011). Even in stable abiotic environments, relatively high mutation rates may be observed for traits subject to cyclical frequency-dependent population dynamics (Allen & Scholes Rosenbloom, 2012). Stress-induced mutation is a collection of molecular mechanisms in bacterial, yeast and animal cells that promote mutagenesis specifically when cells are maladapted to their environment, i.e. when they are stressed. In this sense, stress-induced bacterial and eukaryotic mutagenesis (Sung & Yasbin, 2002; Loewe et al., 2003; Achilli et al., 2004; Tenaillon et al., 2004; Galhardo et al., 2007; Robleto et al., 2007; Pybus et al., 2010; Rosenberg, 2011; Shee et al., 2011a, b; Heininger, 2013) are bet-hedging behaviors.

  3. A major part of epigenetic variation is triggered by (oxidative) stress or changes in the environment (Lertratanangkoon et al., 1997; Finnegan, 2002; Labra et al., 2002; Wada et al., 2004; Rapp & Wendel, 2005; Grant-Downton & Dickinson, 2006; Richards, 2006; Choi & Sano, 2007; Bossdorf et al., 2008; Boyko & Kovalchuk, 2008, 2011; Mason et al., 2008; Jablonka & Raz, 2009; Turner, 2009; Angers et al., 2010; Halfmann & Lindquist, 2010; Verhoeven et al., 2010; Flatscher et al., 2012; Grativol et al., 2012; Heininger, 2013). Epigenetics is closely linked to cellular bioenergetics (Xie et al., 2007; Naviaux, 2008; Smiraglia et al., 2008; Minocherhomji et al., 2012) and is, at least in part, regulated by mtDNA copy number and mitochondrial energetics (Heininger, 2013). Adverse environmental conditions play a key role in transgenerational inheritance. Conditions of stress seem to be particularly important as inducers of heritable epigenetic variation, and lead to changes in epigenetic and genetic organization that are targeted to germline specific genomic sequences (Heininger, 2013). Moreover, if conditions return to their original state, spontaneous back-mutation of epialleles can restore original phenotypes [e.g., in position-effect variegation (Richards, 2006; Flatscher et al., 2012)].

  4. Increased recombination has been observed in response to stress (fitness-associated recombination) (Plough, 1917, 1921; Grell, 1971, 1978; Zhuchenko et al., 1986; Parsons, 1988; Gessler & Xu, 2000; Hadany & Beker, 2003a, b; Schoustra et al., 2010; Zhong & Priest, 2011), including genetic stress (Tedman-Aucoin & Agrawal, 2012; Stevison, 2012; Heininger, 2013). Recombination may allow a population to keep up with environmental changes by producing appropriate novel allelic combinations (Robson et al., 1999; Manos et al., 2000; Carja et al., 2013). Work on the evolution of recombination rates in heterogeneous environments suggests that fluctuating selection may favor increased recombination when the direction of selection changes appropriately over time (Charlesworth, 1976, 1993; Lenormand & Otto, 2000; Otto & Michalakis, 2007).

  5. Likewise, compelling evidence demonstrates that stress increases mutability of simple sequence repeats (SSRs) (Jackson, 1998; Wang et al., 1999, Nevo et al., 2005; Heininger, 2013), mobilization of transposable elements (Capy et al., 2000; Pericone et al., 2002; Heininger, 2013) and prion-driven phenotypic diversity (Halfmann et al., 2010). An immanent feature of SSRs is their high mutability, which leads to both sequence and length polymorphism (Kelkar et al., 2008; Pumpernik et al., 2008), the latter being at least one order of magnitude greater than the former (Borstnik & Pumpernik, 2002; Pumpernik et al., 2008). The SSR mutation rates (10-2 to 10-6 events per locus per generation) are very high, as compared with the rates of point mutations at coding gene loci (Li et al., 2002; Ellegren, 2004). SSRs encode their own mutability through the unit size, length, and purity of the repeat tract (King et al., 1997; Ellegren, 2004; King & Kashi, 2007; Legendre et al., 2007). In 296 Escherichia coli genes related to repair, recombination and physiological adaptations to different stresses, Rocha et al. (2002) observed a significant high number of SSRs capable of inducing phenotypic variability by slipped-mispair during DNA, RNA or protein synthesis. Overrepresentation of SSRs in stress response genes may be a bacterial strategy to increase versatility under stressful conditions.

All these molecular processes jointly increase the stochasticity of the genotype-phenotype mapping under stressful conditions and variable environments (Altenberg, 1995; Wagner & Altenberg, 1996; El-Samad & Madhani, 2011; Martin et al., 2011). Overall, abiotic and biotic environmental conditions to which organisms are poorly adapted and that reduce Darwinian fitness elicit bet-hedging behavior increasing cellular noise, (epi)genetic and phenotypic diversity.

12. The gambles of life

Uncovering the mysteries of natural phenomena that were formerly someone else’s ‘noise’ is a recurring theme in science.
Bedard & Georges, 2000

12.1 Lottery and insurance: responses to uncertainty and risk

A growing consensus suggests that ecological and economic theories are ultimately indistinguishable (Boulding, 1978; Hirschliefer, 1977; Real & Caraco, 1986; Noë & Hammerstein, 1994; Gandolfi et al., 2002; Orr, 2007; Yaari & Solomon, 2010; Okasha & Binmore, 2012). Malthus’ (1803) argument that the growth rate of a population would tend to outpace the growth rate of output implied, for Darwin, an inevitable struggle for existence and, hence, natural selection of the fittest. Somewhat less well-known is the influence of Adam Smith (1776), whose “Invisible Hand” seems to have been a fundamental and pervasive inspiration for Darwin. Unfettered self-interested utility or profit maximization became, for Darwin, the struggle for reproductive success. The efficiency achieved by the market became the prodigious adaptation and balance evident in nature (Gould, 1993, p. 148–151; Robson, 2001).

There is a deep analogy between rational choice theory, particularly as it applies to games of strategy, and evolutionary theory. In a standard rational choice scenario, an agent is faced with a choice between a set of options; the aim of the theory is to say which choice is optimal. As Skyrms (1996, 2000) notes, it is easy to transpose such a scenario to an evolutionary context. Instead of thinking of a conscious agent trying to choose between the options, we can think of natural selection as doing the choosing, favoring the option with the greatest Darwinian fitness. Just as, according to traditional rational choice theory, the rational person favors the option that maximizes her/his expected utility, so natural selection favors the option that confers the greatest expected reproductive success on its bearer. Expected utility is thus analogous to expected number of offspring; the maximization of the former by rational agents is analogous to the maximization of the latter by natural selection. Just as rational choice theorists argue that much human behavior can be understood as an attempt to maximize expected utility, so evolutionary theorists argue that much animal behavior can be understood as an attempt to maximize reproductive output (Okasha, 2007).

Natural selection is often viewed as a statistical process, maximizing the expected or mean reproductive success of individuals carrying a certain gene or genotype (Darwin, 1859; Fisher, 1930). The expected reproductive success is then called ‘mean’ fitness. In this sense, standard theory can be referred to as a ‘statistical’ theory of natural selection. In order to analyze the optimality of a phenotypic trait based on mean fitness, most traditional theories of natural selection almost invariably assume constant and predictable environments. However, for almost all organisms in the wild, environments are variable and unpredictable (Yoshimura & Clark, 1993). In order to understand the basic properties of uncertainty, a probabilistic perspective for natural selection, a synthetic orintegrated view of the effects of uncertainty on natural selection is warranted. Stochastic environments are the raffle boxes in the lotteries of life. Organisms have no choice other than to try their luck in these lotteries. On the other hand, insurance is the risk-sharing strategy of risk-averse agents that have to compete in lotteries.

It is a common observation that people exhibit risk-aversion when making some choices while also exhibiting risk-preference in other cases. People buy both insurance and lottery tickets.The standard explanation for this behavior begins with Friedman and Savage (1948), who suggested that the typical von Neumann-Morgenstern utility function is concave over low values of wealth but then becomes convex over higher values. People/animals with such utility/fitness functions would seek insurance protection against downside risk, while at the same time buying lottery tickets that promise a small probability of a large increase in wealth (Robson &Samuelson, 2009).

In economics, one way to take into account this effect was to declare that what is to be maximized is not the wealth itself but rather the “utility function” (von Neumann & Morgenstern, 1944). The case where the “utility function“ is the logarithm of the wealth reduces to considering the geometric mean rather than the arithmetic mean. Thus, the use of this utility function may be interpreted as a way to take into account the fact that in general a strategy is applied repeatedly for long spans of time such that the frequency of the events approach their probability. Some of the behavioral anomalies studied over the years (Allais, 1953; Kahneman & Tversky, 1979; Thaler, 1994) can be related to the subtle difference between the expectation for one game and the probability for longer series of events (Yaari & Solomon, 2010).

12.2 "Decisions" under uncertainty: utility/fitness optimization

Yoshimura et al. (2009) classified environmental uncertainty into three categories based on the level of integration: (i) short-term temporal change experienced by an individual (individual level within a generation), (ii) phenotypic variation among individuals (population level within a generation) and (iii) population fluctuation across generations due to long-term environmental changes (cross-generationlevel). Knight (1921) made his famous distinction between risk and uncertainty by explaining that risk is ordinarily used in a loose way to refer to any sort of uncertainty viewed from the standpoint of the unfavorable contingency, and uncertainty similarly with reference to favorable outcomes. Uncertainty can be overcome by acquiring information about an environment (Stephens, 1987, 1989); risk cannot (Winterhalder et al., 1999). A large body of literature now has shown taxonomic ubiquitous risk-sensitive behavioral capacities (Stephens, 1981; Real & Caraco, 1986; Stephens & Krebs, 1986; Ellner & Real, 1989; Cartar & Dill, 1990; McNamara & Houston, 1992; Bernstein, 1996; Kacelnik & Bateson, 1996; Smallwood, 1996; Winterhalder et al., 1999; Dall & Johnstone, 2002; Shafir et al., 2005; Stephens et al., 2007; Ydenberg, 2007; Houston et al., 2011; Mayack & Naug, 2011; Ratikainen, 2012; Ito et al., 2013). When people and animals are faced with dicey decisions, a well-documented trend holds (Bernoulli, 1738/1954; Pratt, 1964; Arrow, 1965; Caraco & Chasin, 1984; Yoshimura & Shields, 1987; Hintze et al., 2013; Ito et al., 2013): If the stakes are sufficiently high, they are risk averse. Risk averseness is usually described as a resistance to accept a deal with risky payoff as opposed to one that is less risky or even safe, even when the expected value of the safer bargain is lower. The principle is similar to risk aversion in utility theory (Menezes & Hanson, 1970): the cost of a negative deviation from the mean is larger than the benefit of an equivalent positive deviation (Philippi & Seger, 1989). Risk-sensitive behavior is variance-sensitive behavior (Smallwood, 1996; Ydenberg, 2007; Mayack & Naug, 2011; Ratikainen, 2012). The logic of risk-sensitive foraging is captured by the energy-budget rule of Stephens (1981). It can be illustrated using foragers facing two options with equal mean rewards but different variance (Rao & Sejnowski, 2003). Real and co-workers (Real et al., 1990; Real, 1991) performed a series of experiments on bumble bees foraging on artificial flowers whose colors, blue and yellow, predicted the delivery of nectar. They examined how bees respond to the mean and variability of this delivery in a foraging version of a stochastic two-armed-bandit problem. All the blue flowers contained 2 μl of nectar, 1/3 of the yellow flowers contained 6 μl, and the remaining 2/3 of the yellow flowers contained no nectar at all. In practice, 85% of the bees’ visits were to the constant-yield blue flowers despite the equivalent mean return from the more variable yellow flowers. When the contingencies for reward were reversed, the bees switched their preference for flower color within one to three visits to flowers. Real and co-workers further demonstrated that the bees could be induced to visit the variable and constant flowers with equal frequency if the mean reward from the variable flower type was made sufficiently high. This experimental finding shows that bumble bees, like honeybees, can learn to associate color with reward. Further, color and odor learning in honeybees has approximately the same time course as the shift in preference described above for bumble bees (Gould, 1987). It also indicates that under the conditions of a foraging task, bees prefer less variable rewards and compute the reward availability in the short term. This is a behavioral strategy used by a variety of animals under similar conditions for reward (Krebs et al., 1978; Real et al., 1990; Real, 1991), suggesting a common set of constraints in the underlying neural substrate.

The sensitivity to variance in food reward of small seed-eating birds (Caraco et al., 1980; Caraco, 1981, 1982, 1983; Caraco & Chasin, 1984; Caraco & Lima, 1985), shrews (Barnard & Brown, 1985), warblers (Moore & Simm, 1986), and hummingbirds (Stephens & Paton, 1986) is apparently affected by the probability of meeting daily energetic requirements. When the forager expects not to meet its energetic requirement, it should prefer the more uncertain alternative and hence be risk-prone. However, when it is doing well and expects not to fall short of its energetic requirement, it should avoid uncertainty and be risk-averse. Since stored reserves affect an organism’'s expectation of meeting its food requirement, reserves should be a determinant of foraging risk-sensitivity. Thus, bumble bees can be both risk-averse (preferring constant flowers) and risk-prone (preferring variable flowers), depending on the status of their colony energy reserves. Diet choice in bumble bees appears to be sensitive to the “target value” of a colony-level energetic requirement (Cartar & Dill, 1990). Animals on a negative energy budget increase their preferences for risk, while animals on a positive energy budget are typically risk-averse (Rubenstein, 1982). Animals adapted to living in unpredictable conditions are unlikely to benefit from risk-seeking strategies, and instead are expected to reduce energetic demands while maintaining risk-aversion (Kahneman & Tversky, 1979; Stephens & Krebs, 1986; McNamara & Houston, 1992; Kacelnik & Bateson, 1996; Platt & Huettel, 2008; MacLean et al., 2012).However, if faced with a scenario in which the less variant food supply will not meet an animal’s expected energetic needs for survival, the animal should switch to a higher-risk food source that affords a greater chance of survival (Caraco et al., 1980; Caraco, 1981; Stephens, 1981). In other words, if the rate of energy gain associated with the less variant, ‘‘safe’’ food supply falls short of that needed for survival, adopting a risk-seeking strategy offers the only chance of survival and should become the favored strategy.Because of their risk-taking attitude, lower-ranking individuals are more likely to innovate (Sigg, 1980; Katzir, 1983; Reader & Laland, 2001; Brosnan & Hopper, 2014). The malleability of these preferences may be evolutionarily advantageous, and important for maximizing chances of survival during brief periods of energetic stress (MacLean et al., 2012).The theory predicts, for example, that prey should select habitats that minimize the ratio of predation rate to growth rate (Werner & Gilliam, 1984). On the other hand, when food is scarce animals should increase their activity and use of risky habitats, thus increasing growth but also predation rates (Houston et al. 1993; Werner & Anholt, 1993; Mangel & Stamps, 2001). Studies in the laboratory (e.g. Gilliam & Fraser, 1987; Anholt & Werner, 1995, 1998) and in the field (Biro et al. 2003a, b) support these predictions and show substantial increases in prey activity, use of risky habitats and greater predation mortality with declines in food abundance (Biro et al., 2004).Predators select against high growth rates and risk-taking behavior in prey populations (Biro et al., 2004).

The fitness maximization problem arises when individuals have to choose among risky alternatives. Idiosyncratic risk, respectively uncertainty, is risk or uncertainty to which only specific agents are exposed, in contrast to systematic or aggregate risk/uncertainty that is faced by all agents in the market. For example, the weather is a standard example of aggregate risk—a very harsh winter may kill all members of a population. In evolution, often risk is a combination of a systemic component stemming primarily from the impact of unfavorable weather events and of an idiosyncratic component depending on individual characteristics. Lotteries may be either idiosyncratic or aggregate (or both) in nature. An idiosyncratic lottery is defined to be one where the realizations are statistically independent across individuals. A lottery is aggregate if individuals share outcomes.Curry (2001) showed that lotteries that involve idiosyncratic risk have differing implications for fitness from lotteries that involve aggregate uncertainty. This stems from the fact that nature selects for the gene with the highest growth rate within a population.When lotteries are purely idiosyncratic in nature, then reproductive value is equal to the individual's expected offspring. This is not true, however, for a lottery that has an aggregate component. An example given by Curry (2001) illustrated the difference between the two types of lotteries. The growth rate associated with an idiosyncratic lottery A is higher than that of an aggregate lottery B, even though B stochastically dominates A. It would seem that there would be strong selection for a gene that could make appropriate distinctions between lotteries that are idiosyncratic in nature and ones that involve an aggregate component.

Various theorists have addressed the role of variable environments, uncertainty and risk for the optimization of reproductive strategies. The common motif of these models is that individuals will randomize their strategies. Cooper and Kaplan (1982) have demonstrated that when lotteries are aggregate, the optimal decision rule involves randomization. That is, when the environment is stochastic, a gene may spread through the population faster when agents of the same genotype take different lotteries. This type of phenotypic variation was called “adaptive coin-flipping”, “intra-genotypic strategy-mixing” (Cooper & Kaplan, 1982; Kaplan & Cooper, 1984; Cooper, 1989) or “stochastic polyphenism” (Walker, 1986). Strangely, Cooper and Kaplan (1982) termed this “coin-flipping altruism”: “It is as though each individual of the superior strategy-mixing genotype were practising a form of “coin-flipping altruism” by assuming the risk of getting stuck with the personally inferior strategy... True, this is not the customary kind of altruism in which the altruist renders some tangible service to other individuals. It nonetheless represents a sacrifice of immediate individual fitness for the sake of the long term advantage of the genotype” (Cooper & Kaplan, 1982). According to this logic every participant of a lottery, by buying a lottery ticket, commits an act of altruism towards the eventual winner(s) of the lottery. Likewise, clients of insurance companies in which the insured event does not occur would act altruistically versus the clients in which the insured event occurs. And are gamblers at the casino and/or stock traders (Statman, 2002; Gao & Lin, 2011; Liao, 2013) when they lose altruists towards the winners?

With a fixed environment, the type of individual maximizing expected offspring is selected. In other words, the evolutionarily most successful attitude to risk is risk neutrality in offspring (Robson, 1996). Uncertainty due to a random environment has distinct evolutionary consequences from risk given the environment. The risks in the evolutionary environment are unlikely to have been purely idiosyncratic. Fluctuations in the weather or abundance of predators, epidemics, and failures of food sources are all bound to have a common effect on death rates. With a random environment, the type selected is strictly less averse to idiosyncratic risk than to risk which is correlated across all individuals (Robson, 1996). This criterion incorporates an essential distinction between idiosyncratic risk given the environment and aggregate uncertainty concerning the environment itself, implying a greater aversion toward the latter. Insurance is a population-level risk-sharing strategy of risk-averse agents turning idiosyncratic risk into aggregate risk. Via the law of large numbers evolution generated a form of automatic biological insurance against idiosyncratic risk, whereas this insurance is inoperative in the same sense against aggregate uncertainty (Robson, 1996). The distribution of types of agents changes in response to generated rewards ? this occurs through the standard replicator dynamic. In particular, preference types that do well, increase in relative frequency (To, 1999). The fundamental feature of multiplicative processes is the fact that the expected gain of the players taking part in this iterative process depends in a crucial way on the number of players considered (number of independent realizations) and the number of time steps that the game is played. For long times (the number of time steps played in the game), the expected wealth of the players follows the geometric mean and not the arithmetic mean of the game, keeping in mind that geometric mean ≤ arithmetical mean (Yaari & Solomon, 2010). The use of this function may be interpreted as a way to take into account the fact that in general a strategy is applied repeatedly for long spans of time such that the frequency of the events approach their probability (Yaari & Solomon, 2010).

13. Evolution is far-sighted

Evolution is not teleological in the sense that its processes or actions are for the sake of an end, i.e., the Greek “telo” or final cause. Clearly, once an organism has survived and/or reproduced one can point to its various attributes and say “yes, that attribute appears to have contributed to the organism’s survival/reproduction". However, that is no more evidence of “foresightedness” than a lottery winner saying “I chose these lottery numbers (or bought those particular scratch-off tickets) because I knew they would be winners". This is known as the “fallacy of affirming the consequent” (also called post hoc, ergo propter hoc argumentation) and is logically inadmissible in the natural sciences (MacNeill, 2009). `Backwards causation', by which some future state or event influences (‘causes’) an action in the present or past, is often characteristic of teleological arguments. The Modern Synthesis took pride in having discouraged such thinking (Mayr, 1992). In the tradition of the Modern Synthesis it has been argued that mutations must be random because natural selection cannot “assist the process of evolutionary change,” since “selection lacks foresight, and no one has described a plausible way to provide it” (Dickinson & Seger, 1999). Such an evolutionary strategy was called a raffle or lottery (Stockley et al., 1997; Parker et al., 2010) and would correspond to a “random trial” approach: genetic change would arise at random, independent of its functional consequences and natural selection would decide about its fitness value. However, even in a raffle competition an increased number of lottery tickets, as in sperm competition games (Parker, 1990), would increase the chances of a winner. Obviously, environments are uncertain and unpredictable. In consequence, evolution can have no foresight (Grant & Grant, 2002). However, like a chess player that takes several potential moves of his opponent into account, evolution is able to anticipate at least some of the more plausible “moves” of a stochastic environment and “takes them into account”, covering some of the more plausible bases.

The Baldwin effect, independently forwarded by Baldwin (1896), Lloyd Morgan (1896), and Osborn (1896), but largely so called because of Baldwin’s influential book (Baldwin, 1902), states that the ability of individuals to learn can guide and accelerate the evolutionary process (Hinton & Nowlan, 1987; French & Messinger, 1994; Weber & Depew, 2003; Sznajder et al., 2012; Weber, 2013). Currently, this principle is widely used in evolutionary computing and evolutionary algorithms (Ackley & Littman, 1991; Mitchell & Forrest, 1994; Bull, 1999; Eiben & Smith, 2008; Paenke et al., 2009a).

The Baldwin effect consists of the following two steps (Turney et al., 1996): In the first step, lifetime learning gives individual agents chances to change their phenotypes. If the learned traits are useful to agents and result in increased fitness, they will spread in the next populationdue to fitness-related differential reproduction. This step means the synergy between learning and evolution. In the second step, if the environment is sufficiently stable, the evolutionary path finds innate traits that can replace learned traits, because of the cost of learning. This step is known as genetic assimilation (Arita & Suzuki, 2000). Mathematical models suggest that learning would speed up the adaptation process by providing more explicit information about the environment in the genotype (Sendhoff & Kreutz, 1999; Arita & Suzuki, 2000). Learning alters the shape of the search space in which evolution operates and thereby provides good evolutionary paths towards sets of co-adapted alleles. Hinton and Nowlan (1987) demonstrated that this effect allows learning organisms to evolve much faster than their non-learning counterparts, even though the characteristics acquired by the phenotype are not communicated to the genotype. With this feedback control adaptation proceeds by “trial and error” (Ashby, 1954). “Random trial” and “trial and error” approaches differ in an important variable: feedback of outcome. “Random trial” lacks the feedback loop: it either cannot find out whether the trial was a success or failure or it is completely unable to learn from this knowledge. The “trial and error” approach has a feedback loop that identifies “errors”. In evolution, the feedback loop occurs through Charles Darwin’s natural selection-mediated preferential reproduction of the fit (Ackley & Littman, 1991) and Alfred R. Wallace’s elimination of the unfit (Smith, 2012a, b). A learning system is able to draw its lesson from the error(s) and make its next trial less random. Learning from “trial and error” systems leads to “educated guess” approaches that are less random-driven but use past experience to navigate future direction and thereby limit the search space and increase the likelihood that some of the problem solutions generated will be useful (Jablonka & Lamb, 2007; Heininger, 2013). Learning may generate selection in favor of conspicuous novel traits faster, and for a wider range of traits, than genetically based sensory biases (Zuk et al., 2014).

Altenberg (2005) compiled a list of short-sighted adaptations:

• cheating, defection, and other antisocial behavior,

• meiotic drive (Lewontin, 1962),

• parthenogenesis (Griffiths & Butlin, 1995),

• overpopulation (Wynne-Edwards, 1962),

• imprudent predation (Rosenzweig, 1972) and other forms of habitat over-exploitation–the ‘tragedy of the commons’ (Hardin, 1968),

• cannibalism (Hamilton, 1970),

• cancer (the organism being the population) (Nunney, 1999; Stoler et al., 1999),

• adaptation to temporally unreliable resources (Kauffman & Johnsen, 1991),

• viable but infectious pathogen carrier states (Kirchner & Roy, 1999),

• evolution of endosymbionts to the detriment of host (Wallace, 1999).

He argued that when the trait confers short-term individual advantage but long-term population disadvantage, under a hierarchically structured population, evolvability evolves to be suppressed (Altenberg, 2005). Several processes have evolved that can tune the evolvability and far-sightedness of organisms:
(i) Condition-dependent mutagenesis
(ii) Epigenetic conditioning of mutations
(iii) Behavioral conditioning of new traits (Zuk et al., 2014).

A multitude of transgenerational processes indicate that evolution is far-sighted: evolution favors processes whose outcomes are robust and sustainable: in learning and memory past experience guides future actions (Kirschner & Gerhart, 2005; Gerhart & Kirschner, 2007; Parter et al., 2008); bet-hedging is a forward-looking response to past environmental unpredictability (Simons, 2009, 2011); demographic stochasticity and the tragedy of the commons is prevented by a multitude of processes establishing prudent reproduction (Goodnight et al., 2008, Heininger, 2013); evolution “cares” for future generations by curtailing the reproductive potential and lifespan of the current generation (Heininger, 2012); sexual reproduction is the paradigmatic bet-hedging process that creates pre-selected variation (Heininger, 2013).

14. Complexity and self-organization: chaos and order

…a fully adequate theory of evolution must encompass both self-organization and selection.
Corning, 1995, p. 112

14.1 Complexity

To begin with, the term complex is a relative one. Individual organisms may use relatively simple behavioral rules to generate structures and patterns at the collective level that are relatively more complex than the components and processes from which they emerge. Systems are complex not because they involve many behavioral rules and large numbers of different components but because of the nature of the system’s global response. Complexity and complex systems generally refer to a system of interacting units that displays global properties not present at the lower level. These systems may show diverse responses that are often sensitively dependent on both the initial state of the system and nonlinear interactions among its components. Ever since the pioneering discovery by May (1974, 1976) in the seventies that simple rules can lead to complex dynamics including chaos, ecological chaos has been a subject of intense research.

There are two quite different but complementary meanings of the term “complexity.” The term is used both toindicate randomness and structure.A correct understanding of complexity reveals that both are required elements of complex systems. A large number of cases demonstrate that structural complexity arises from the dynamical interplay of tendencies to order and tendencies to randomness (Crutchfield & Machta, 2011). Complexity, in Ashby’s sense, is essentially conceived as a system’s potential to assume a large number of states, and we also have a measure for it: variety, the number of states a system can assume (Schwaninger, 2004). The founding idea of complexity science was Prigogine’s juxtapositioning of the 1st and 2nd Laws of Thermodynamics so as to explain the emergence of dissipative structures (Stengers, 2004). Implicit in this was his questioning of the reversibility of time and the centrality of equilibrium in “normal” science (Prigogine & Stengers, 1984; Prigogine, 1997). Complexity science - really ‘order-creation science’ – is founded on theories explicitly aimed at explaining order creation rather than accounting for classical physicists’ traditional concerns about explaining equilibrium (McKelvey, 2001, 2004a, b). Systems that originate in response to, and are maintained by, the optimizing imperative of the 2nd Law of Thermodynamics are sometimes called dissipative structures (Nicolis & Prigogine, 1989) or complex adaptive systems (Levin, 1995). Prigogine (1997) asserted that like weather systems, organisms are unstable systems existing far from thermodynamic equilibrium. Instability resists standard deterministic explanation. Instead, due to sensitivity to initial conditions, unstable systems can only be explained statistically, that is, in terms of probability. Ilya Prigogine (1997) argued that complexity is non-deterministic, and there is no way whatsoever to precisely predict the future. There is the changing role of models. Math is good for equilibrium modeling. However, math models can’t handle order creation. Agent-based computational models are essential for modeling order creation (McKelvey, 2004a). The complex system approach is neither holistic nor reductionist but asserts that ecological relationships between patterns and processes span multiple scales of organization (Proulx, 2007). Many key concepts are often associated to the complex system approach: non-linearity, emergence, criticality, scaling, hierarchy and evolvability to list a few (Milne, 1998; Brown et al., 2002). On the other hand, the literature on nonlinear systems often mentions self-organization, emergent properties, and complexity as well as dissipative structures and chaos (Glansdorf & Prigogine, 1971; Nicolis & Prigogine, 1989; Prigogine, 1997). Since these nonlinear interactions involve amplification or cooperativity, complex behaviors may emerge even though the system components may be similar and follow simple rules (Camazine et al., 2001). Emergent properties are features of a system that arise unexpectedly from interactions among the system’s components. An emergent property cannot be understood simply by examining in isolation the properties of the system’s components, but requires a consideration of the interactions among the system’s components (Kauffman, 1993; Kelso, 1995; Camazine et al., 2001; Corning, 2002). An ideal gas in a vessel of a macroscopic size is a large system because it contains 6 x 1023 molecules per mole. This system, however, cannot be regarded as complex since all the elements interact by simple laws of classical or quantum mechanics that are uniformly applicable to all the events of interaction. One may call a system complex either if there is a wide variety of interactions between the system’s components, or if the system consists of a large number of distinctly different subsystems interacting with each other, or both (Rosenfeld, 2009). Complex biological systems manifest a large variety of emergent phenomena among which prominent roles belong to self-organization and swarm intelligence. On the other hand, emergence is what self-organizing processes produce (Corning, 2002). In fact, natural selection may well be an emergent phenomenon of the complex system “life” (Kauffman, 1993; Kelso, 1995; Weber & Depew, 1996; Weber, 1998; Hoelzer et al., 2006). Complex adaptive systems also require stochastic factors, e.g. noise and fluctuations (Gros, 2008). It is only with an intermediate level of stochastic variation, somewhere between determined rigidity and literal chaos that local interactions give rise to complexity (Johnson, 2001; Theise, 2004; Theise & Harris, 2006).

A complex system constantly changes, largely through three different types of transition (Manson, 2001): First, a key characteristic of a complex system is self-organization, the property that allows it to change its internal structure in order to better interact with its environment. Self-organization allows a system to learn through piecemeal changes in internal structure.
Second, a system becomes dissipative when outside forces or internal perturbations drive it to a highly unorganized state before suddenly crossing into one with more organization (Schieve & Allen, 1982). Economies can be dissipative when confronting large shifts in the nature of their relationships with the environment. Introduction of new technologies, such as in the industrial revolution, can spur radical change in the internal structure of an economy (Harvey & Reed, 1994). The work of Holling (1978, 1995) illustrates how small disturbances such as pest infestations or fire can trigger large-scale redistribution of resources and connectivity within the internal structure of an ecosystem.
Third, the term self-organized criticality refers to the ability of complex systems to balance between randomness and stasis. Instead of occasionally weathering a crisis, a system can reach a critical point where its internal structure lies on the brink of collapsing without actually doing so (Bak & Chen, 1991). Self-organized criticality is a form of self-organization where the rate of internal restructuring is almost too rapid for the system to accommodate but necessary for its eventual survival (Scheinkman & Woodford, 1994). Research on self-organized criticality is largely restricted to ecological and biogeophysical systems (e.g., Andrade et al., 1995; Correig et al., 1997) but there is a growing body of work on urban and economic systems (Sanders, 1996; Allen, 1997).

14.2 Fractals and 1/f noises

A particular class of complex systems are scale independent (Bak, 1996; Gisiger, 2001). A classical example of such systems in physics is the earth’s crust (Gutenberg, 1949; Turcotte, 1992). It is a well established fact that a photograph of a geological feature, such as a rock or a landscape, is useless if it does not include an object that defines the scale: a coin, a person, trees, buildings, etc. This fact is described as scale invariance: a geological feature stays roughly the same as we look at it at larger or smaller scales. In other words, there are no patterns there that the eye can identify as having a typical size. The same patterns roughly repeat themselves on a whole range of scales. This property can manifest itself with fractals (spatial scale invariance), flicker noise or 1/f-noise where f denotes the frequency of a signal (temporal scale invariance) and power laws (scale invariance in the size and duration of events in the dynamics of the system). The patterns displayed by many natural systems do not allow for a simple description using Euclidean geometry: they present scale-invariance; that is, no characteristic length measure can be obtained from them. Therefore, when observed at different resolutions, they display the same pattern. The common feature of self-similar behavior is the presence of scaling laws (West et al., 1997; Gisiger, 2001) (also known as power laws). A wide variety of physical systems show power-law correlations in space (fractals) (Mandelbrot, 1982; Pietronero & Toscatti, 1986; Aharony & Feder, 1989) or time (1/f noises) (Voss, 1978; Weissman, 1988).

The Chaos Game shows that local randomness and global determination can coexist to create an orderly, self-similar structure called a fractal (Peters, 1994, p. 10–17). Fractals have the property of self-similarity in that the parts are in some way related to the whole. Fractal geometry is symbolized in the self-similar patterns of the Sierpinski triangle, which can be generated with an algorithm that has both a random and a lawful element (Carr, 2004). Natural beauty in mountains, plants, and snowflakes reveals a fractal geometry characterized by the complex interplay between randomness (symbolized by dice) and global determinism (which loads the dice) (Mandelbrot, 1983). Nature offers many examples of fractal statistics: branching in our lungs and in plants; variations in the flooding of the Nile river, of rainfall, of tree-rings and the intricate vein structure of leaves. Overall, evolution has a fractal geometry (Green, 1991; Burlando, 1993; Halley, 1996; Rikvold & Zia, 2003). The fluctuations of the stock market also obey fractal statistics (Peters, 1994). Because fractals involve long-range correlations, they also reflect some key features of how living systems are organized and how they evolve in time. The implications for evolution are very important, because cooperative effects emerging from the interactions can lead to new, sometimes counterintuitive, results. If fractal structures and self-similar fluctuations are so common, perhaps some universal dynamical processes are at work.

1/f noise represents the fluctuations of some physical quantity about its steady-state value. It is found in a wide variety of quite different systems (Voss & Clarke, 1978), and in some cases has been shown to be an equilibrium property (Voss & Clarke, 1976). Among ecologists, there has been a growing recognition of the importance of long-term correlations in environmental time series. Recent empirical evidence points to ever-increasing environmental variance through time (Steele, 1985; Pimm & Redfearn, 1988; Ariño & Pimm, 1995; Bengtsson et al., 1997; Solé et al., 1997). The diversity of a desert ecosystem, for example, will be influenced by numerous small changes each day. Some rare events, such as desert storms, will have longer-lasting influence. The family of 1/f-noises – fluctuations defined in terms of the different timescales present – is a useful approach to this problem. White noise and the random walk, the two currently favored descriptions of environmental fluctuations, lie at extreme ends of this family of processes. A true random process or white noise has no correlations in time. Recent analyses of data, results of models, and examination of basic 1/f-noise properties, suggest that pink 1/f-noise, which lies midway between white noise and the random walk, might be the best null model of environmental variation (Halley, 1996). There is strong evidence that background abiotic fluctuations have 1/f-noise spectra (Mandelbrot & Wallis, 1969; Steele, 1985), though there may be significant differences between terrestrial and marine environments (Steele, 1985; Vasseur & Yodzis, 2004). A white noise represents the maximum rate of information transfer, but a 1/f noise, with its scale-independent correlations, seems to offer the best compromise between efficient information transfer and immunity to errors on all scales (Voss, 1992). Spectral density measurements of individual DNA base positions suggest the ubiquity of low-frequency 1/fβ noise and long-range fractal correlations as well as prominent short-range periodicities (Voss, 1992). Lévy flights, a special class of Markov processes, are scale invariant and often associated with power-laws described in many systems (Cole, 1995; Viswanathan et al., 1999; Martin et al., 2001). Lévy-like search strategies were revealed in analyses of a variety of behaviors from plankton to humans (Berg, 1993; Viswanathan et al., 1996, 2001; Bartumeus et al., 2003; Barabasi, 2005; Brockmann et al., 2006; Reynolds & Frye, 2007; Reynolds & Rhodes, 2009; Humphries et al., 2010). The models simulating these behaviors combine a multitude of stochastic processes by deterministic rules (Maye et al., 2007). In addition to the inevitable noise component, a nonlinear signature suggesting deterministic endogenous processes (i.e., an initiator) is involved in generating behavioral variability. It is this combination of chance and necessity that renders individual behavior so notoriously unpredictable (Maye et al., 2007).

14.3 Self-organization

A basic feature of diverse systems is the means by which they acquire their order and structure (Camazine et al., 2001; Ben-Jacob, 2003). In self-organizing systems, pattern formation occurs through interactions internal to the system, without intervention by external directing influences. Haken (1977, p. 191) illustrated this crucial distinction with an example based on human activity: “Consider, for example, a group of workers.We then speak of organization or, more exactly, of organized behavior if each worker acts in a well-defined way on given external orders, i.e., by the boss. We would call the same process as being self-organized if there are no external orders given but the workers work together by some kind of mutual understanding.” (Because the “boss” does not contribute directly to the pattern formation, it is considered external to the system that actually builds the pattern.) Systems lacking self-organization can have order imposed on them in many different ways, not only through instructions from a supervisory leader but also through various directives such as blueprints or recipes, or through pre-existing patterns in the environment (templates).

Critical to understanding Camazine’s et al. (2001) definition of self-organization is the meaning of the term pattern. As used here, pattern is a particular, organized arrangement of objects in space or time. Examples of biological pattern include a school of fish, a raiding column of army ants, the synchronous flashing of fireflies, and the complex architecture of a termite mound. To understand how such patterns are built, it is important to note that in some cases the building blocks are living units—fish, ants, nerve cells, etc.—and in others they are inanimate objects such as bits of dirt and fecal cement that make up the termite mound. In each case, however, a system of living cells or organisms builds a pattern and succeeds in doing so with no external directing influence, such as a template in the environment or directions from a leader. Instead, the system’s components interact to produce the pattern, and these interactions are based on local, not global, information. In a school of fish, for instance, each individual bases its behavior on its perception of the position and velocity of its nearest neighbors, rather than knowledge of the global behavior of the whole school. Similarly, an army ant within a raiding column bases its activity on local concentrations of pheromone laid down by other ants rather than on a global overview of the pattern of the raid (Camazine et al., 2001).

It seems that the philosopher Kant was the first to define life as a “self-organized, self-reproducing” process (Karsenti, 2008). Through pure reasoning, he defined life as the emergence of functions by self-organization. He said that in an organism, every part owes its existence and origin to that of the other parts, with the functions that are attributed to a complete living organ or organism emerging from the properties of the parts and of the whole. He defined this complex state of living matter as a self-organized end (Kant, 1790; Van de Vijver, 2006; Fox Keller, 2007).This led him to question the validity of using the causality principle of classical physics to explain life, and to suggest that a new kind of science would be required to study how purpose and means are intricately connected (Kant, 1790).

The cybernetician W. Ross Ashby (1962) proposed what he called “the principle of self-organization”. He noted that a dynamical system, independently of its type or composition, always tends to evolve towards a state of equilibrium, or what would now be called an attractor. This reduces the uncertainty we have about the system’s state, and therefore the system’s statistical entropy. This is equivalent to self-organization (Heylighen, 2001). The resulting equilibrium can be interpreted as a state where the different parts of the system are mutually adapted. Heinz von Foerster (1960) formulated the principle of “order from noise”. In a similar vein of thought, self-organization was proposed to generate “order through fluctuations” (Prigogine & Stengers, 1984) or “order through disorder” (Saetzler et al., 2011). Von Foerster noted that, paradoxically, the larger the random perturbations (“noise”) that affect a system, the more quickly it will self-organize (produce “order”).Another reason for this intrinsic robustness is that self-organization thrives on randomness, fluctuations or “noise”. In fact, self-organizational phenomena depend deeply on stochastic processes (Weber & Depew, 1996). The polarity of randomness and law characterizes the self-creating natural world (Carr, 2004). Non-linear systems have in general several attractors. When a system resides in between attractors, it will be in general a chance variation, called “fluctuation” in thermodynamics, that will push it either into the one or the other of the attractors. Without the initial random movements, the spins would never have discovered an aligned configuration. It is this intrinsic variability or diversity that makes self-organization possible. Just as there is an optimal level of stochastic perturbation favoring adaptive responses of thermodynamic self-organization systems (Helbing & Vicsek, 1999), there is an optimal rate of mutation that favors adaptive evolution under natural selection (Iwasaki & Yonezawa, 1999).

A logical consequence of complexity science as ‘order-creation science’ (McKelvey, 2004a, b) is that order could be generated through evolution by a synergy between natural selection and self-organizing processes (Solé et al., 1999).A diverse group of researchers in mathematics, physics, and several branches of biology have argued that self-organization should be placed alongside natural selection as a complementary mechanism of evolution (Nicolis & Prigogine, 1977; Conrad, 1983; Kauffman, 1993, 1995; Corning, 1995; Camazine et al., 2001; Heylighen, 2001; Richardson, 2001; Denton et al., 2003; Kurakin, 2005b, 2007; Hoelzer et al., 2006; Newman et al., 2006; Karsenti, 2008; Wills, 2009). Using computer models, Michael Conrad (1983) showed that natural selection will work together with self-organization to “smooth out fitness landscapes”, thereby reducing the large differences in a Wrightian fitness landscape to shallow saddles. In this way self-organization plays a role even on highly gradualistic, Fisher-like assumptions. Natural selection, and the adaptations it brings about, necessarily occur within a veritable ocean of stochastic and self-organizational events and processes. Stochastic, selective, and self-organizational processes are empirically and causally intertwined in the evolution of living systems (Weber & Depew, 1996). Self-organization fails to emerge in completely determined systems (planets in motion, billiard balls) and completely random ones (molecules in a gas). It is only with an intermediate level of stochastic variation, somewhere between determined rigidity and literal chaos that local interactions give rise to complexity (Johnson, 2001; Theise, 2004; Theise & Harris, 2006).It is at the boundary between order and chaos at which evolvability is maximized. The highly ordered regime is one in which perturbations generate minimal overall change. In other words, there is minimal variation. The chaotic regime is one in which perturbations have unpredictable effects: there is no correlation between the initial and perturbed states. There is no heritability. The “edge of chaos” that is, in the narrow domain between frozen constancy (equilibrium) and turbulent, chaotic activity, is simply the region in which there is heritable variation. Heritable variation is necessary for evolution. Systems at the “edge of chaos” would be “evolvable,” because it is only here that there is heritable variation (Kauffman, 1993, 1995; Richardson, 2001).

14.3.1 Self-organized criticality

It is becoming increasingly clear that many complex systems have critical thresholds—so-called tipping points—at which the system shifts abruptly from one state to another (Scheffer et al., 2009). In medicine, spontaneous systemic failures such as asthma attacks (Venegas et al., 2005) or epileptic seizures (Litt et al., 2001; McSharry et al., 2003) occur; in global finance, there is concern about systemic market crashes (Kambhu et al., 2007; May et al., 2008); in the Earth system, abrupt shifts in ocean circulation or climate may occur (Lenton et al., 2008); and catastrophic shifts in rangelands, fish populations or wildlife populations may threaten ecosystem services (Scheffer et al., 2001; Millennium Ecosystem Assessment, 2005). Cooperation is an emergent behavior of complex systems (Miramontes & DeSouza, 2014; Heininger, 2015): under adverse environmental conditions unicellular microorganisms display multicellular behavior (Juhas et al., 2005; Hooshangi & Bentley, 2008).

The mechanism by which complex systems tend to maintain on this critical edge has been called self-organized criticality (Bak et al., 1987; Bak, 1996; Jensen, 1998). The system’s behavior on this edge is typically governed by a “power law”: large adjustments are possible, but are much less probable than small adjustments. Self-organized criticality (SOC) is stated as follows: large, far from equilibrium, complex systems, formed by many interacting parts, spontaneously evolve towards the critical point. SOC was originally introduced (Bak et al., 1987) as an approach to understand 1/f-noise as well as the apparent abundance of power laws in nature, which is generally accepted as the sign of scale-invariance. The idea is that under very general circumstances driven stochastic processes develop into a scale-invariant state without the explicit tuning of parameters, contrary to what one would expect from equilibrium critical phenomena (Stanley, 1971; Pruessner, 2004). SOC systems can self-tune to a balanced (critical) state, precisely at the transition between a (subcritical) regime of inactivity and one of (supercritical) runaway activity. Sightings of SOC have been reported in every conceivable and inconceivable area of science (Clauset et al., 2009), including sociology (Roberts & Turcotte, 1998; Bentley & Maschner, 2000), financial markets (Lux & Marchesi, 1999), computer science (Gorshenev & Pis'mak, 2004; Cook et al., 2005), computer network traffic (Fukuda et al., 2000; Valverde & Solé, 2002), engineering (Carreras et al., 2004), and biology (Bak & Sneppen, 1993; Sepkoski, 1993; Sneppen et al., 1995; Solé et al., 1999; Bornholdt & Rohlf, 2000; Camazine et al., 2001; Nykter et al., 2008, Ribeiro et al., 2010; Mora & Bialek, 2011; Furusawa & Kaneko, 2012; Longo et al., 2012; Krotov et al., 2014).

A simple metaphor of an SOC process is provided by a sandpile (Bak et al., 1987; Bak, 1996). If additional sand grains are randomly added on top of a sand pile then inevitably an instance will occur when local steepness of the slope surpasses a certain critical threshold thus causing local failure of structural stability. The excess of material will cascade into adjacent areas of the pile causing their failures as well. Thus an avalanche will occur, shifting the entire sandpile into a new stable state. What is fundamentally important in this process is that a random local event quickly propagates through the entire system, thus establishing long-range correlations within the system. SOC exemplifies an emergent phenomenon of system-wide organized behavior resulting from purely mechanistic reasons, i.e. from member-to-member local interactions without any intelligent organizing force (Rosenfeld, 2013). This new state cannot be anticipated from the properties of individual units.

In physics, fractal structures in space and time are known to emerge in the proximity of some types of phase transition (Binney et al., 1992; Solé et al., 1996b). The classic example is a magnetic material. A small piece of iron can tug on a paper clip at room temperature, but when heated to a high temperature no magnetic power is observed.The atoms that form the iron are themselves like small magnets. Each atom only interacts with its nearest neighbors and their natural tendency is to align spontaneously into small domains with the same orientation. At high temperature the coupling between nearest atoms breaks down because of thermal perturbations and, therefore, the atoms can have anypolarity (up or down). But suddenly,when the material is cooled down, order spontaneously shows up. There is a critical temperature at which globalmagnetization appears and bothfractal-spatial and fractal-temporal features arise. These transitions are described by an ‘order parameter’ which is zero at the disordered phase and positive otherwise (Solé et al., 1999).

The hypothesis that tuning a biological system to a critical state would render it somehow optimal has a long history (Langton, 1990). The underlying idea is that a system tuned to criticality presents a richer dynamical repertoire, being therefore able to react (i.e. process information) to a wider range of challenges (environmental or other) (Ribeiro et al., 2010). The experimental evidence in this direction ranges from gene expression patterns in response to stimulation of single macrophages (Nykter et al., 2008) to collective ant foraging (Beekman et al., 2001). Gene regulatory networks operate in a critical regime, i.e. close to a phase transition between ordered and chaotic dynamics (Serra et al., 2004, 2007; Shmulevich et al., 2005; Balleza et al., 2008; Nykter et al., 2008; Chowdhury et al., 2010; Torres-Sosa et al., 2012).Criticality is profoundly linked to evolvability. Critical dynamics, and hence the developmental trade-off in genetic networks, naturally emerge as a robust byproduct of the evolutionary processes that select for evolvability and optimize the evolutionary trade-off. Furthermore, the emergence of criticality occurs without fine-tuning of parameters or imposing explicit selection criteria regarding specific network properties (Torres-Sosa et al., 2012).Complex adaptive or evolutionary systems can be much more efficient in coping with diverse heterogeneous environmental conditions when operating at criticality. Analytical as well as computational evolutionary and adaptive models vividly illustrate that a community of such systems dynamically self-tune close to a critical state as the complexity of the environment increases while they remain noncritical for simple and predictable environments (Hidalgo et al., 2014a).The capability to perform complex computations, which turns out to be the fingerprint of living systems, is enhanced in “machines” operating near a critical point (Langton, 1990; Kauffman, 1993; Bertschinger & Natschläger, 2004), i.e. at the border between two distinct phases: a disordered phase, in which perturbations and noise propagate unboundedly—thereby corrupting information transmission and storage—and an ordered phase where changes are rapidly erased, hindering flexibility and plasticity. The marginal, critical situation provides a delicate compromise between these two impractical tendencies, an excellent tradeoff between reproducibility and flexibility (Beggs, 2008; Chialvo, 2010; Shew & Plenz, 2013) and, on larger time scales, between robustness and evolvability (Wagner, 2005, 2008).

At the molecular level, an example of a critical point in biological systems can be exemplified by the dynamics of RNA viruses (Solé et al., 1999; Domingo et al., 2001, 2012). The understanding of how these entities evolve and adapt must take into account their extreme variability, caused by the error-prone RNA polymerase activity and the lack of proofreading mechanisms (Nowak, 1992; Heininger, 2013). Instead of a given single sequence, there is a cloud of mutants around the so-called ‘master’ sequence. This cloud is known as a quasispecies, a term first coined by Manfred Eigen (Eigen, 1971; Eigen et al., 1988).RNA viruses adapt to a changing environment by making use of their variability. Selection pressures by the immune system force the virus quasispecies to evolve (Kamp et al., 2003). The quasispeciesmodel is consistent with this observation,but something defeats our intuition:there is a critical mutation rate beyondwhich heredity breaks down. This is referred to as the error catastrophe, and it is nothing but a phase transition point, which poses serious limitations tothe virus complexity. Available molecular data confirm the theory: RNA viruses do replicate close to the error catastrophe (Swetina & Schuster, 1982; Schuster, 1994; Cottry et al., 2001; Anderson et al., 2004). Eigen (1992) argued that virus replication error rates established themselves near an error-threshold where the best conditions for evolution exist.

Sexual reproduction displays another SOC phenomenon. Sexual reproduction uses a variety of stressors to create variation and to select the most resilient gametes and offspring from this variation (Heininger, 2013). Oxidative stress is an inherent feature of gametogenesis in all taxa. Importantly, from lower to higher taxa there is a substantially incremental use of this general principle. As evidenced by a male mutagenic bias, particularly male gametogenesis balances at the verge of mutational error catastrophe. The phenotype of the transition to error catastrophe is characterized by infertility.

In the light of the SOC theory, the variation-creating processes (discussed in chapters 11.1 and 11.2) and their tuning under stress (see chapter 11.3) can be interpreted as selected-for phenomena in self-organizing systems at the threshold of criticality.

15. Stochasticity and multilevel selection

…that an opinion has been widely held is no evidence whatever that it is not utterly absurd….
Bertrand Russell (1929)

In what follows, ‘individual’ refers to an individual organism, whereas a population refers to ‘a group of conspecific organisms that occupy a more or less well-defined geographic region and exhibit reproductive continuity from generation to generation’ (Futuyma [1986], pp. 554–5).Even though a population is composed of individual organisms, it is important to distinguish between properties that apply to individual organisms and properties that characterize the relationships among organisms—that is, properties that apply to populations. For example, individual organisms have properties such as color, shape and length. Populations, on the other hand, have properties such as size (defined as the number of individuals), frequency (defined as the proportion of individuals of one type or another) and growth rate (defined as the rate of change in the number of individuals in the population). Thus, in a sense, population-level properties are properties that arise only given the collection and interaction of individuals (Millstein, 2006). There are a variety of evolutionary phenomena that depend on population-level processes. Moreover, diversity and variation are population-level properties. Frequency- and density-dependent selection depend on population-level selection because the outcome of selection (the change in gene or genotype frequencies from one generation to the next) are determined by population-level parameters: the frequency of genotypes within a population or the density of the population (Millstein, 2006). Stochastic environments change the rules of evolution. Lotteries cannot be played and insurance strategies not employed with single individuals. These are emergent population-level processes that exert population-level selection pressures generating variation and diversity at all levels of biological organization. Together with frequency and density-dependent selection, lottery- and insurance-dependent selection act on population-level traits.

Recent discussions by philosophers of science regarding natural selection have given conflicting answers to a pair of questions: first, is natural selection a causal process or is it a purely statistical aggregation? And second, is natural selection at the population level or at the level of individuals? Walsh et al. (2002) and Matthen & Ariew (2002) argued that natural selection is purely statistical and on the population level, whereas Bouchard & Rosenberg (2004) maintained that natural selection is causal and on the individual level. Millstein (2006) argued for a third possibility: natural selection is indeed a causal process, but it operates at the population level. What causes the conceptual confusion is the feedback control of the cybernetic system (figure 1C). This involves the replacement of the open, linear, chain of cause and effect familiar in most science by a circular causality, a closed feedback loop that implies the merging of causes and effects, the confluence of output and input signals. The system, however, cannot be understood properly without conceptually distinguishing input and output signals. Importantly, the output signal is a population-level signal including density- and frequency-dependent phenomena that feeds back to the individual-level input signal, inextricably intertwining the individual and population levels of selection.

Until the 1960s, it was a routine assumption that selection acts not only on the individual, but also on the group level (Corning, 1997). This idea goes back to Charles Darwin (1871), who wrote “There can be no doubt that a tribe including many members who [. . .] were always ready to give aid to each other and to sacrifice themselves for the common good, would be victorious over other tribes; and this would be natural selection.”The Modern Synthesis was also compatible with group selection of various kinds. For instance, Sewall Wright coined the term “interdemic selection”, i.e. selection between discrete breeding groups, or “demes”, and developed what he called a “shifting balance” model, which he believed was of the utmost importance in producing evolutionary changes (Wright, 1968-1978). Julian Huxley (1966) thought that ritual fighting behavior evolved because escalated fighting would ‘militate against the survival of the species’. Ernst Mayr, likewise, speaks of evolutionary change as a population-level phenomenon, meaning that populations and species are the ultimate units of evolutionary change, not individuals. Mayr also developed what he called the “founder principle”, which envisions small, reproductively isolated groups as a significant source of evolutionary innovation (Mayr, 1963, 1976). A theoretical “punctuated equilibrium” (Corning, 1997) occurred in 1962 with V.C. Wynne-Edwards’ subsequently much-maligned book Animal Dispersion in Relation to Social Behaviour (Wynne-Edwards, 1962). Peter A. Corning (1997) vividly described the rancorous theoretical debate whose protagonists were William D. Hamilton (1964), George C. Williams (1966), John Maynard Smith (1973, 1976), and Richard Dawkins (1976) resulting in a wholesale rejection of the concept of group selection. Wynne-Edwards became a pariah in evolutionary biology and has been routinely chastised for his heresy ever since (Corning, 1997).

A very large proportion of the literature pertaining to group selection consists of theoretical papers. The general conclusion has been that, although group selection is possible, it cannot override the effects of individual selection within populations except for a highly restricted set of parameter values. Since it is unlikely that conditions in natural populations would fall within the bounds imposed by the models, group selection, by and large, has been considered an insignificant force for evolutionary change (Wade, 1978a). David Sloan Wilson, Elliott Sober and a growing number of other workers has been attempting to resurrect group selection on a new foundation. What Wilson calls “trait group selection” (Wilson 1975, 1980; Wilson & Sober 1989, 1994) refers to a model in which there may be linkages (a “shared fate”) between two or more individuals (genotypes) in a randomly breeding population, such that the linkage between the two becomes a unit of differential survival and reproduction (Corning, 1997). Compatible with this concept, in game theory being applicable to fluctuating environments the players need never physically interact, compete or even communicate (Hutchinson, 1996). John Maynard Smith (1982b) developed a similar model, which he dubbed “synergistic selection”, in recognition of the fact that it implies a functional interdependency. According to Corning (1997), functional synergy explains the evolution of cooperation in nature, not the other way around. In other words, functional groups (in the sense of functionally integrated “teams” of cooperators of various kinds) have been important units of evolutionary change at all levels of biological organization; “functional group selection” is thus a ubiquitous aspect of the evolutionary process (Corning, 1997). George Price (1970, 1972) provided an elegant formalization that showed, among other things, how the force of natural selection acting on genes can be partitioned into ‘group-level’ and ‘individual-level’ components. Unfortunately, the insight derived from Price’s simple demonstration did not spread very far outside of theoretical evolutionary biology and failed to impede the spread of the belief that group-selectionist-thinking is somehow logically flawed, wrong-headed, or merely wishful thinking. This untutored dismissal of group selection has slowed progress in understanding a variety of evolutionary processes (Henrich, 2004). However, with the exception of some orthodox Darwinists (e.g. Dawkins, 2012), multilevel selection (i.e., individual and group selection combined) has now received broad support (e.g. Gould, 2002; Okasha, 2006; Bijma et al., 2007; Godfrey-Smith, 2009; Calcott & Sterelny, 2011; Nowak & Highfield, 2011; Edward O. Wilson, 2012).

Maynard Smith (1976) argued that “For group selection, the division into groups which are partially isolated from one another is an essential feature. […]... that the extinction of some groups and the ‘reproduction’ of others are essential features of evolution by group selection. If groups are the units of selection, then they must have the properties of variation, multiplication, and heredity required if natural selection is to operate on them.” Early theorists (e.g. Williams, 1966; Maynard Smith, 1976) made evolutionarily unrealistic assumptions about group selection. In the light of empirical studies of group selection with laboratory populations of the flour beetle, Tribolium (Wade, 1976, 1977), Wade (1978a) argued that the models have a number of assumptions in common which are inherently unfavorable to the operation of group selection. (Keep in mind: “A mathematical model is only as good as its assumptions.” [Maynard Smith & Brookfield, 1983]). In their group selection experiments with Impatiens capensis, Stevens et al. (1995) showed that groups need not be discrete entities (Goodnight, 2005). Rather, groups were defined by interactions and their effect on fitness (Stevens et al., 1995). Within this framework, coevolution, e.g. of host-parasite and prey-predator communities (Gilpin, 1975; Levin & Pimentel, 1981), convergent evolution (Orians & Paine, 1983), the guild concept (Simberloff & Dayan, 1991), and community and ecosystem phenotypes (Whitham et al., 2003, 2006) are no longer conceptual orphans.

One of the cases, illustrating the biased choice of model assumptions, concerns the treatise of bet-hedging by theoreticians. There are two distinct forms of bet-hedging: (i) between-generation and (ii) within-generation. Current theory predicts that bet-hedging is far more likely to be a successful evolutionary strategy when the bets are hedged over several generations, than in a within-generation scenario (Yasui, 1998; Hopper et al., 2003). According to theory, the time scale of between-generation bet-hedging ensures that all individuals with a given phenotype suffer the same fate – circumstances such as drought exert homogenous pressure on all members of a population. Under within-generation bet-hedging, however, individuals with the same phenotype are subject to heterogeneous selection pressure – predation, for example, will affect some individuals but not others. An important consequence of this difference is that conditions favoring the evolution of within-generation bet hedging are very restricted. While a single lineage may realize increased fitness via within-generation bet-hedging, this fitness advantage varies inversely with population size and becomes vanishingly small at even modest population sizes (Yasui, 1998; Hopper et al., 2003). Most students of evolution are trained to focus on costs and benefits at the individual level, and tend to seek adaptive explanations for individual traits such as bet-hedging (e. g., Grafen, 1999). Although this focus is often successful, it leads astray in the case of within-generation bet-hedging. Only by assessing the fitness effects of a trait in the context of whole populations can one accurately identify traits that can and cannot be favored by within-generation bet-hedging (Hopper et al., 2003).Polyandry (Yasui, 1998; see chapter 16.3), conspecific brood parasitism (see below), prolonged diapause (Menu & Desouhant, 2002; see chapter or cooperation (Fronhofer et al., 2011; Rubenstein, 2011) are within-generation bet-hedging phenotypes that have a much higher prevalence than theory would predict.

In the case of conspecific brood parasitism, a within-generation bet-hedging behavior, the biased choice of model assumptions has been refuted by long-term field data (Pöysä & Pesonen, 2007). Conspecific brood parasitism (CBP) is a taxonomically widespread alternative reproductive tactic in which a female lays eggs in the nest or egg group of a conspecific that provides all subsequent parental care (de Valpine & Eadie, 2008; Lyon & Eadie, 2008). CBP is particularly widespread among birds, being documented in at least 234 species and is particularly prevalent in Anseriformes where it has been reported in 76 of the 161 species (Payne, 1977; Yom-Tov, 1980, 2001). Moreover, it occurs in several other animal taxa, including fishes (e.g., Sato, 1986; Wisenden, 1999), amphibians (e.g., Summers & Amos, 1997), and insects (e.g., Eickwort, 1975; Eberhard, 1986; Müller et al., 1990; Zink, 2000, 2003; García-González & Gomendio, 2003; Loeb, 2003; Tallamy, 2005). Because conspecifics provide the only hosts for brood parasites, obligate parasitism cannot become fixed in a population. Further, the advantages of parasitic laying are likely to be greatest when the frequency of parasitism is low and many host nests are available containing few parasitic eggs; the advantages will decrease as frequency of parasitism increases and more host nests contain many parasitic eggs (de Valpine & Eadie, 2008). One of the earliest hypotheses to explain the occurrence and evolution of CBP was that by spreading eggs among nests, parasites can increase the likelihood that at least some offspring will escape predation and survive to independence, also known as the “risk spreading” hypothesis (e.g., Rubenstein, 1982; Petrie & Møller, 1991). Specifically, on the basis of a simulation model, Rubenstein (1982) reached the conclusion that laying eggs in several nests to avoid predation has a selective advantage over laying all the eggs in one nest. Indeed, considering that nest predation is the major source of nesting mortality in birds (Ricklefs, 1969; Nilsson, 1984; Martin 1988; Wesolowski and Tomialoj?, 2005) and plays an important role in the life-history evolution of birds (Bosque & Bosque, 1995; Martin 1995; Martin & Clobert, 1996; Sæther, 1996; Julliard et al., 1997; Martin et al., 2000; Ghalambor & Martin, 2001), risk spreading is an appealing explanation for the evolution and occurrence of CBP (Pöysä & Pesonen, 2007). However, assuming that nests are predated at random and that parasites lay eggs randomly with respect to nest predation risk, Bulmer (1984) found that different egg-distribution strategies produced the same mean fitness when entire clutches were affected by stochastic events. Empirical field work in a well-studied model species of CBP, the common goldeneye (Bucephala clangula), revealed that, at least in some species, these assumptions are not valid. Pöysä and coworkers (Pöysä 1999, 2003, 2006; Pöysä et al., 2001, 2014) found that nests are not predated at random and that parasites use risk assessment and preferentially lay in safe nests. By taking these findings into account, model simulations revealed that the selective advantage of parasitic egg laying related to nest predation is much higher than previously thought (Pöysä & Pesonen, 2007). Likewise, by modeling mean fitness under a variety of egg-distribution strategies with only partial nest predation (as is often observed in nature), Roy Nielsen et al. (2008) found that higher fitness resulted from distributing eggs among multiple nests.

Cooperation is also a within-generation bet-hedging response that can be both conservative and diversifying (Fronhofer et al., 2011; Rubenstein, 2011).Typically, individual fitness and population fitness are in conflict. While selfish behavior is favored by individual selection, cooperation can evolve in many models of multilevel/group selection (Eshel, 1972; Uyenoyama, 1979; Slatkin, 1981; Leigh, 1983; Wilson, 1983; Boyd & Richerson, 1990, 1992, 2002; Binmore, 1992, 1994a, b; van Baalen & Rand, 1998; Bergstrom, 2002; Goodnight, 2005; Killingback et al., 2006; Traulsen & Nowak, 2006; Nowak et al., 2010; see Heininger, 2015).

With Starrfelt and Kokko (2012) I think that the distinction between within- and between-generation bet-hedging is flawed. Ignoring such artificial distinctions, a recent model (Ratcliff et al., 2015) examined how key characteristics of risk and organismal ecology affect the fitness consequences of variation in diversification rate. In 1000-patch metapopulations the spatial and temporal dynamics of uncertainty were modeled. Either small (10 individuals) or large (104 individuals) carrying capacities, resulted in maximum global population sizes of 104 or 107 individuals, respectively. A single unpredictable event varied in scale from population-wide (e.g., a landscape-level process like unpredictable season length) to local (e.g., chance of nest discovery by a predator). Similarly, risk affected populations randomly in time or occurred in correlated series. Rapid diversification was strongly favored when the risk faced has a wide spatial extent, with a single disaster affecting a large fraction of the population. This effect was especially great in small populations subject to frequent disaster. In contrast, when risk was correlated through time, slow diversification was favored because it allows adaptive tracking of disasters that tend to occur in series. Naturally evolved diversification mechanisms in diverse organisms facing a broad array of environmental risks largely supported these results. The theory explained the prevalence of slow stochastic switching among microbes and rapid, within-clutch diversification strategies among plants and animals (Ratcliff et al., 2015).

In contrast to what theoretical models suggest, group selection concepts have strong empirical support. When resources are limited, adult productivity in experimental populations of the flour beetle Tribolium was found to be strongly and negatively correlated with time to extinction of populations (MacDonald & Stoner, 1968, Nathanson, 1975; Wade, 1977). Thus, individual fitness, measured as relative reproductive rate, and population fitness, measured as persistence, may be in conflict. Wynne-Edwards (1962) discussed this possibility in detail and suggested that interpopulation selection may have led to the evolution of controls on individual reproductive interests. Multilevel selection analyses find that sizes appear as a competitive/selfish trait, favored in individual selection but selected against in group selection (Stevens et al., 1995; Aspi et al., 2003; Donohue, 2004; Weinig et al., 2007; Boege, 2010; Dudley et al., 2013). Plant height or elongation often appear as competitive traits in multilevel selection studies; they were selected to increase by individual selection and decrease by group selection in four studies (Stevens et al., 1995; Donohue, 2003, 2004; Weinig et al., 2007), selected to increase by individual and group selection in Silene (Aspi et al., 2003) and increase under individual selection only in another study (Boege, 2010). These opposing forces of selection result in an ecological process frequently observed in plants: a constant seed yield regardless of planting density (Goodnight et al., 1992; Stevens et al., 1995; Goodnight & Stevens, 1997; Donohue, 2003; Weinig et al., 2007). More specifically, individual selection can reasonably be expected to prevail in lower-density stands and favor large individual size, while selection at the group level may predominate in higher-density stands and act to reduce individual size (Weinig et al., 2007). In Tribolium, the ecological mechanisms of interspecies competition are the same, for the most part, as those of intraspecies competition (Park et al. 1964, 1965, 1974, Teleky 1980). The evolutionary interests of individuals within populations may be different from, and possibly opposed to, the evolutionary interests of populations (Wade, 1980a; Goodnight, 1985). Empirical studies have confirmed that group selection can be effective in situations when individual selection is not (Craig, 1982; Goodnight, 1985, 1990) and leads to faster evolutionary change than individual selection alone (Wade, 2003). Genetically-based interactions between individuals will not respond to individual selection but will respond to group selection (Griffing, 1977; 1981a, b). These findings support Wade’s (1978) suggestion that higher level selection can act on sources of genetic variance that is not available to lower levels.

15.1 Community selection as an emergent behavior of complex systems

As dicussed previously (see chapter 14.1) complexity and complex systems generally refer to a system of interacting units that display global properties not present at the lower level. In their group selection experiments with Impatiens capensis, Stevens et al. (1995) showed that groups need not be discrete entities (Goodnight, 2005). Rather, groups are defined by interactions and their effect on fitness (Stevens et al., 1995). Although quantitative genetics has successfully been applied to many traits, it does not provide a general theory accounting for interaction among individuals and selection acting on multiple levels. Consequently, current quantitative genetic theory fails to explain why some traits do not respond to selection among individuals, but respond greatly to selection among groups (Bijma et al., 2007). Emergent properties are features of a complex system that are not present at the lower level but arise unexpectedly from interactions among the system’s components. An emergent property cannot be understood simply by examining in isolation the properties of the system’s components, but requires a consideration of the interactions among the system’s components (Kauffman, 1993; Kelso, 1995; Camazine et al., 2001; Corning, 2002). Based on this insight, group selection is the emergent behavior of complex systems. I prefer the term “community selection” instead of “group selection” because it has the connotation of “commonality”, e.g. common ecological factors (Wilson & Swenson, 2003). Communities are defined by shared interactions and common selective pressures (Ehrlich & Raven, 1964; Lubchenco & Gaines, 1981; Goodnight, 1990a, b).This is in accord with the group selection models of Wilson and Sober (Wilson 1975, 1980; Wilson & Sober 1989, 1994) in which there may be linkages (a “shared fate”) between two or more individuals (genotypes) in a randomly breeding population.

If two Newtonian forces act on a single body, say gravitation and friction, then the effects of their actions are separable. One can attribute some aspect of the final motion as due to friction, the other to gravity (Mitchell, 2009). To get the overall effect in this case the vector sum of the forces is used to predict the motion that will result from the simultaneous action of gravity and friction. Vector addition is in physics a general method for combining the effects of independent forces on the motion of a body (Mitchell, 2009). Natural selection has the attributes of a vector: force and direction (Sober, 1984). Accordingly, I envisage a selective pressure as a vector force (Sober, 1984; Eldredge, 2003) acting on an organism (but see Matthen & Ariew, 2002). Like Sober (1984), I use vectors not in the Newtonian sense but as a metaphor to illustrate evolutionary processes. Of course, the vectors acting on the individuals are not independent. In evolution, organisms that interact with each other mutually affect the strength and direction of their selection vectors and coevolve. Convergent selective pressures on individuals, visualized as a bundle of vectors that point into the same direction, should create a force field and momentum of coordinated movement. The coordinated movement of units within communities can be found both at the cellular and behavioral level. Cells performing collective migration share many cell biological characteristics with independently migrating cells but, by affecting one another mechanically and via signaling, these cell groups are subject to additional regulation and constraints (Rørth, 2009). Thus, for collective migration, the relevant cell biology is that of a single migratory cell plus the features added by the community effects. A characteristic of collective behavior of cells found in a wound monolayer is the emergence of leaders and followers and coordination between the movement vector of one cell and its neighbors (Poujade et al., 2007; Vitorino & Meyer, 2008). Likewise, the coordinated movement of a school of fish, a raiding column of army ants, the synchronous flashing of fireflies, are emergent behaviors of complex systems (see chapter 14.3). But how could the vector forces elicit the coordinated, heritable, movement in communities of individuals? Clearly these “movers” have to be exchanged at the level of hereditary units. In fact, individuals are not that individual as is often assumed. Their individuality depends on the unique assortment of genetic modules (von Mering et al., 2003; Pereira-Leal et al., 2006). However, individuals of sexually reproducing taxa are more or less transiently assorted entities in a network of exchanged modules from a population pool. Recombination is the glue that keeps them together and that exchanges the “vectors” that drive communities into certain directions. Genetic vectors are organized in modules (Donadio et al., 1991). Intriguingly, the modularity of metablic networks of organisms (Parter et al., 2007; Kreimer et al., 2008) and other biological systems (He et al., 2009; Lorenz et al., 2011) appears to be an evolutionary signature of variable environments. Moreover, the modular organization greatly accelerates evolution (Kashtan et al., 2007). The directional forces that are determined by ecological pressures and genomic constraints, give rise to community-level processes such as coevolution, cooperation, mutualism and symbiosis. Even convergent evolution, the concordant response of distinct communities, can be explained by the vector model.

The evolutionary reality of community-level processes that ensure the sustainability of ecosystems cannot be explained by selection at the level of selfish individuals. Broadly defined, synergy refers to the combined (cooperative) effects that are produced by two or more particles, elements, parts or organisms – effects that are not otherwise attainable (Corning, 1983, 1995, 1996, 1997, 2005). Motive forces, as visualized by vectors, drive bodies into certain directions. Natural selection is a kinetic force. Community selection can be visualized by more or less parallel vectors that act on groups of individuals representing synkinetic selection.

Both the time frame and scale of environmental fluctuations that become selectively relevant are altered in community selection vs. individual selection. Groups experience a stronger selection pressure than individuals for homeostasis with respect to reproductively limiting variables, because their greater longevity exposes them more often to suboptimal physical conditions, and greater physical size means they encompass a larger fraction of any resource/nutrient gradient. Groups achieve homeostasis by differentiation into microcosms with specialist functions, e.g. cell types. Such differentiation is more limited in individuals due to their smaller size and shorter lifespan. Hence tolerance of fluctuation in certain physical variables is proposed to be weaker in individuals than in groups (Boyle & Lenton, 2006).

15.2 Fitness as transgenerational propensity

Fitness is often estimated as r, the instantaneous rate of increase (Clutton-Brock, 1988), or R0, the net reproductive rate, or simply the total number of offspring produced in an individual’s lifespan (Clutton-Brock, 1988). It is often assumed that a simple estimate of fitness is all that is needed to understand the selection pressures operating in a particular system. The organisms’ environments play a fundamental role in determining their fitness and hence the action of natural selection. Attempts to produce a general characterization of fitness and natural selection are incomplete without the help of general conceptions of what conditions are included in the environment. Thus there is a “problem of the reference environment”—more particularly, problems of specifying principles which pick out those environmental conditions which determine fitness (Abrams, 2009a). In constant environments natural selection leads to each individual organism maximizing its expected number of descendants left far in the future. If there are no environmental fluctuations, population fitness is maximized and measured by the arithmetic mean number of surviving descendants. In evolutionary computation, the Genetic Algorithm is based on the “survival of the fittest” principle and simulates natural evolution on computer systems to solve complex problems. Individuals are selected and reproduced according to a fitness performance criterion. The fitter the individual, the higher are its chances to produce offspring. Since the process is biased towards the regions of the solution space which enclose the fittest individuals, the evolving population gradually loses diversity and converges. After a population has converged, it is very difficult to readapt to a new optimum when the environment changes (Cobb & Grefenstette, 1993; Simões & Costa, 2002; Bui et al., 2005). Thus, premature convergence is a problem for the Genetic Algorithm as it gradually loses its exploratory ability during the evolutionary process under an oversimplified “survival of the fittest” principle.In stochastic environments (see chapter 10), the evolutionary fate of a genotype can change from generation to generation (Abrams, 2009a). The propensity definition of fitness takes the transgenerational stochasticity of fitness into account. Objective probabilistic dispositions are known as propensities (this use of the term was originated by Popper [1959]). Within the philosophy of biology, the most widely accepted modern definition of evolutionary fitness is probabilistic propensity, which holds that a trait confers fitness on an organism if that trait has the probabilistic propensity of increasing the organism's (reproductively viable) offspring (Brandon, 1978; 1990; Mills & Beatty, 1979; Burian 1983; Richardson & Burian, 1992; Millstein, 2002; Pence & Ramsey, 2013). However, various inconsistencies and implausibilities of this concept are unresolved (Bouchard & Rosenberg, 2004; Abrams, 2009b; Ariew & Ernst, 2009).

Natural environments continuously undergo changes that alter the fitness landscapes, displacing populations towards suboptimal fitness regions. R.A. Fisher thought that environmental changes are so ubiquitous that, as he once said, Wright's peaks and valleys are more like the undulating wave crests and troughs of an ocean than a mountainous landscape. He believed that a population rarely, if ever, finds itself in a position where no allele frequency change could increase its fitness (Crow, 1987). The static concepts of fitness and fitness landscapes (Wright, 1931, 1932; Gavrilets, 2004; Svensson & Calsbeek, 2012) have been supplemented by dynamic concepts (Wilke et al., 2001a; Mustonen & Lässig, 2009). Dubbed fitness seascapes (Mustonen & Lässig, 2010), they take the ever changing nature of environmental conditions into account. The dynamical approach leads to a quantitative measure of adaptation called fitness flux, which counts the excess of beneficial over deleterious genomic change (Mustonen & Lässig, 2009). Dobzhansky (1950), in a seminal statement on adaptation to diverse environments, wrote ‘Changeable environments put the highest premium on versatility rather than on perfection in adaptation’.

Typically, individual fitness and population fitness are in conflict. While selfish behavior is favored by individual selection, cooperation is favored by population-level selection (van Baalen & Rand, 1998; Traulsen & Nowak, 2006; see Heininger, 2015).This insight is at variance with the individual-as-maximising-agent paradigm of orthodox Darwinism (Grafen, 1999). The individual-as-maximising-agent does not make sense even if looking at the level of an individual, because an individual may be displaying a behavior that is not adapted to the environment. But, it makes sense at the level of the population because the population is displaying a range of behaviors making it always adapted to the environment. Therefore, while the individual is not the most fit, the population is (Dubravcic, 2013). This has been shown in bacteria that change between fast growing/antibiotic sensitive and slow growing/antibiotic resistance states (Balaban et al., 2004), B. subtilis expressing sporulating and non-sporulating state (Veening et al., 2008a, b), plant seeds that germinate at different time points (Simons, 2009), etc. (see chapter 11).

In a constantly changing and resource-limited environment, fitness is defined by reproduction rather than survival of the individual. In fact, survival is only evolutionarily relevant in the tautological sense of “survive to reproduce”. Lonesome George, “the rarest living creature” according to the Guinness Book of World Records, the apparent sole survivor of the now probably extinct Geochelone abingdoni species of giant Galápagos tortoises from Pinta Island left no offspring and, although surviving for approx. 100 years, had an overall Darwinian fitness of zero. Therefore, I advocate to delete the term survival altogether from fitness definitions. A trait that enhances an organism’s viability, but renders it sterile, has an overall fitness of zero. This includes transgenerational processes such as the mutation “grandchildless” in Drosophila (Boswell et al., 1991) and C. elegans mutations that end in sterility not one or two, but dozens of generations later (Ahmed & Hodgkin, 2000). On the other hand, a trait that slightly reduces viability while augmenting fertility, may be very fit overall (Sober, 2001). There are short-term and long-term aspects to fitness (Beatty & Finsen, 1989; Sober, 2001; Pence & Ramsey, 2013). This distinction is not trivial. In fact, short-term reproductive success may threaten the evolutionary success of a geno-/phenotype, by placing too great a demand on available resources (Beatty & Finsen, 1989). Accordingly, a prudent resource management is routinely observed in wild populations (see chapter 15.3.1). Clearly, a population is doomed that although able to reproduce a million times is unable to sufficiently protect its offspring against e.g., predators, until reproductive maturity. In many taxa, the survival of offspring is dependent on parental care, which is defined as any trait that enhances the fitness of offspring and originated/is maintained for this function (Smiseth et al., 2012). Parental care is common across animal taxa and increases offspring survival and/or quality in a range of species (Clutton-Brock, 1991; Smiseth et al., 2012).

Current concepts of fitness put much emphasis on the representation of genes in the next generation. Taking into account that evolution is an iterative process, long-term concepts of fitness (Thoday, 1953; Cooper, 1984; Beatty & Finsen, 1989; Sober, 2001; McNamara et al., 2011; Pence & Ramsey, 2013) suggest that fitness should be defined as the probability of leaving descendants in the long run. Asymmetric fitness curves combined with temporal environmental fluctuations can lead to strategies that appear to be suboptimal in the short-term, but are in fact optimal in the long run (Ruel & Ayres, 1999; Martin & Huey, 2008). The reason for the apparent departure from optimality is that deviations to the right of the fitness peak reduce fitness more than equivalent deviations to the left do. Gillespie (1973a, b), Hartl and Cook (1973) and Karlin and Liberman (1974, 1975) first showed that the evolution of a system under temporal fluctuations is determined not only by expected fitness in a given generation, but also by the degree of variation in fitness over time, and established the geometric mean fitness principle (Lande, 2008; Frank, 2011). It states that in a random environment, alleles that increase the geometric mean fitness can invade a randomly mating population at equilibrium.

The obvious reason to be suspicious of the idea that variability has been fine-tuned in order to maximize the evolutionary potential of populations is that it suggests a teleological view of evolution. Natural selection cannot adapt a population for future contingencies any more than an effect can precede its cause, so any future utility of the capacity to generate variation can have no influence on the maintenance of that capacity in the present. As Sydney Brenner supposedly remarked many years ago, it would make no sense for a population in an early geological period to retain a feature that was useless merely because it might “come in handy in the Cretaceous!” Teleology need not be invoked to support evolvability arguments, however. A history of environmental uncertainty could favor a population with increased variability over others because such a population is more successful at adapting. Sniegowski and Murphy (2006) called this the evolvability-as-adaptation hypothesis. In fact, there is a great amount of evidence suggesting that evolvability itself is a selectable trait and hence, evolvability evolves (Wagner & Altenberg, 1996; Turney, 1999; Partridge & Barton, 2000; Bedau & Packard, 2003; Woods et al., 2003; Earl & Deem, 2004; Jones et al., 2007; Colegrave & Collins, 2008; Crombach & Hogeweg, 2008; Draghi & Wagner, 2008; Pigliucci, 2008; Palmer & Feldman, 2011; Pavlicev et al., 2011; Woods et al., 2011). Both temporal and spatial environmental variation can select for evolvability (van Nimwegen et al., 1999; Wilke et al., 2001b; Siegal & Bergman, 2002; de Visser et al., 2003; Wagner, 2008; Palmer & Feldman, 2011) and can speed up evolution (Kashtan et al., 2007; Parter et al., 2008; Draghi & Wagner, 2009).

Increasing evolvability implies an accelerating evolutionary pace (Turney, 1999). For evolvability to increase, environmental change must occur within certain bounds. If there is too little change, there is no advantage to evolvability. If there is too much change, evolution cannot move fast enough to track the changes (Turney, 1999). RNA virus genotypes with similar fitness may differ in their evolvability (Burch & Chao, 2000; McBride et al., 2008). To understand what determines the long-term fate of different clones, each carrying a different set of beneficial mutations, Woods and co-workers (2011) “replayed” evolution by reviving an archived population of Escherichia coli from a long-term evolution experiment and compared the fitness and ultimate fates of four genetically distinct clones. The expected scenario was that eventual winners (EW) clones were already more fit than eventual losers (EL) clones at generation 500, but competition experiments showed that actually the opposite was the case. Surprisingly, two clones with beneficial mutations that would eventually take over the population after 1,500 generations had significantly lower competitive fitness after 500 generations than two clones with mutations that later went extinct. Replaying the experiment many times starting with the 500-generation EWs and ELs showed that the EWs indeed beat the ELs most of the time. Likewise, E. coli strains with larger fitness defects due to deleterious mutations are more evolvable than wild-type clones in terms of both the beneficial mutations accessible in their immediate mutational neighborhoods and integrated over evolutionary paths that traverse multiple beneficial mutations (Barrick et al., 2010).

15.3 Reproductive fitness in stochastic environments

The assumption that expected or within-generation fitness is maximized by natural selection is simply wrong.
Simons, 2002

In stable environments (see chapter 9), the default setting of orthodox Darwinism, short-term fitness predicts long-term fitness. Hence current concepts of fitness put much emphasis on the individual’s representation of genes in the next generation. However, theory predicts that the fitness of a life-history strategy may be considerably different in a random environment compared with a constant environment or in populations with and without density dependence (Tuljapurkar 1989, 1990a, b; Mueller et al., 1991; Kawecki, 1993; Mylius & Diekmann, 1995). Therefore, finding that a particular life-history strategy is maladaptive may be the result of oversimplistic assumptions about the ecology of the study population. In particular, when there is density-dependent regulation in a population, the fitness of one life history may depend on other life histories present in the population. In stochastic environments, the variance of selection, or more generally the entire probability distribution of fitness, becomes a critical factor of selection (Yoshimura & Shields, 1987). Dempster (1955) introduced the model in which temporal fluctuations in reproductive success for competing genotypes favor the genotype with the highest geometric-mean reproductive success. Ever since, the standard criterion for evaluating the fitness of genotypes in stochastic environments is the geometric mean of the growth rates (geometric mean fitness) (Dempster, 1955; Cohen, 1966; Lewontin & Cohen, 1969; Kuno, 1981; Klinkhamer et al., 1983; Metz et al., 1983; Frank & Slatkin, 1990; Yoshimura & Clark, 1991; Yoshimura & Jansen, 1996; Hopper, 1999; Simons, 2009; Yoshimura et al., 2009). Geometric mean fitness is a concept widely used in ecology and evolutionary biology to understand persistence of populations in fluctuating environments (Lewontin & Cohen, 1969; Levins, 1969; Gillespie, 1974a, b; Kuno, 1981; Yoshimura & Jansen, 1996; Jansen & Yoshimura, 1998).

Risk-sensitive reproductive strategies may reduce the average (arithmetic mean) of individual reproductive output, while yet maximizing the population geometric mean; this trade-off in terms of average reproduction is ‘bet hedging’ (Gillespie, 1973, 1974a; Slatkin, 1974; Seger & Brockmann, 1987; Philippi & Seger, 1989). Risk-sensitive behavior is variance-sensitive behavior (Smallwood, 1996; Ydenberg, 2007; Mayack & Naug, 2011; Ratikainen, 2012). However, the notion of a ‘sacrifice’ of expected fitness for geometric mean fitness is deceptive. There is no detrimental effect of maximizing the geometric mean fitness and, hence, no trade-off between the mean and variance in fitness exists; the assumption that expected or within-generation fitness is maximized by natural selection is simply wrong (Simons, 2002). There is typically a trade-off between the number of surviving offspring and their quality (i.e. their reproductive value) so that in general fitness is not maximized by maximizing the mean number of surviving offspring (McNamara et al., 2011; Heininger, 2013). A rigorous definition of bet-hedging includes lower expected arithmetic mean fitness, as well as greater expected geometric mean fitness (Seger & Brockmann, 1987; Simons, 2002, 2009). Bet-hedging involves a trade-off between the mean and variance of fitness. If the environment varies temporally, phenotypes with low variances of fitness may be favored over alternatives with higher variances and higher mean fitnesses (Philippi & Seger, 1989). This reduction in among-generation variation in fitness (yielding a higher geometric mean) forms the basis of bet-hedging theory: bet-hedgers, reducing variance in fitness, don’t necessarily do best all the time, but they perform most consistently and are therefore favored by selection (Cohen, 1966; Roff, 1992). Thus, in stochastic environments, individual fitness maximization regimes are replaced by population-level fitness maximization strategies that yield suboptimal fitness results for individuals (Cohen, 1966; Ellner, 1986; McNamara 1995; 1998; McNamara et al., 1995; Yoshimura & Jansen, 1996).Geometric mean fitness is a typical example of such a selection criterion under environmental stochasticity over many generations (Lewontin & Cohen, 1969; Yoshimura & Clark, 1991).The total resources available to the population limit reproductive success. Density-dependent competition causes the reproductive success of each type to be influenced by the reproduction of other types. For that reason, one cannot simply multiply the reproductive successes of each type independently and then compare the long-term geometric means. Instead, each bout of density-dependent competition causes interactions between the competing types. Those interactions depend on frequency (Frank, 2011).

When the fitness of a genotype varies over generations, the appropriate measure of its relative growth rate is its geometric mean fitness, rather than its arithmetic mean fitness. Lewontin and Cohen (1969) presented a formal argument showing the absurdity of the use of the arithmetic mean fitness under environmental variability: even when expected fitness approaches infinity, the probability of extinction in a variable environment may rise to one. Fitness, like return on investment, is determined by a multiplicative process (Dempster, 1955; Gillespie, 1974a)—that of reproduction—and bet hedging increases the geometric-mean fitness (the nth root of the product of n fitness values) by reducing fitness variance over generations (Gillespie, 1977). If fitness for a given genotype is zero in generation z (i.e. goes extinct in that generation), then the fitness of that genotype across generations x, y, z is not the arithmetic mean of the fitness of these three generations, but zero. If the numbers vary, then the geometric mean is always less than the arithmetic mean; in general, the geometric mean becomes smaller as the numbers being averaged become more variable. Thus the geometric mean fitness of a genotype can be increased by reducing the variance of its fitness (over generations), even if the reduction of variance also entails a reduction of the arithmetic mean. When fitness fluctuates through time and the fluctuations are modest, the identity of the allele that predominates in a population depends on both the mean and the variance in fitness. Consequently, if two alleles have the same (arithmetic) mean fitness through time, the allele that ‘wins’ is the one with the smaller variance in fitness. Thus, it is advantageous for alleles to avoid large fluctuations in fitness. When there are no fluctuations in fitness through time (constant environments), the geometric mean fitness collapses to the arithmetic mean fitness (Orr, 2009).

Jensen’s inequality (1906), a mathematical property of nonlinear functions (Ruel & Ayres, 1999), provides a fundamental tool for understanding and predicting consequences of variance, but it is only just beginning to be explicitly acknowledged in the primary literature (Stockhoff, 1993; Smallwoood, 1996; Anderson et al., 1997; Karban et al., 1997; Ruel & Ayres, 1999; Martin & Huey, 2008; Lof et al., 2012). Asymmetric fitness curves are probably common given that many ecological and physiological processes affecting fitness are likely to exhibit skewness, particularly with respect to temperature (Gilchrist, 1995; Martin & Huey, 2008; Dell et al., 2011). Asymmetric fitness curves combined with temporal environmental fluctuations can lead to strategies that appear to be suboptimal in the short-term, but are in fact optimal in the long run (Ruel & Ayres, 1999; Martin & Huey, 2008). The reason for the apparent departure from optimality is that deviations to the right of the fitness peak reduce fitness more than equivalent deviations to the left do.

15.3.1 Reproductive prudence

The tragedy of the commons (a situation where individual competition reduces the resource over which individuals compete, resulting in lower overall fitness for all members of a group or population) provides a useful analogy allowing to understand why shared resources tend to become overexploited (Hardin, 1968). The logic of the tragedy of the commons predicts that individual good will be maximized with disastrous consequences for the population. Overexploitation of resources can result in reduced per capita birth rates or increased mortality and thereby provides an upper limit to population size (Hairston et al., 1960; Arcese & Smith, 1988). If there are time-lags involved, this mechanism might also result in periodic oscillations around a ‘carrying capacity’ (McCauley et al., 1999). The tragedy of the commons analogy has become increasingly used to explain why, in principle, selfish individuals in a multitude of parasite, animal and plant populations evolved means to avoid the overexploitation of limited collective resources (Frank, 1995; Gersani et al., 2001; Falster & Westoby, 2003; Foster, 2004; Wenseleers & Ratnieks, 2004; Rankin & López-Sepulcre, 2005; Kerr et al., 2006; Rankin & Kokko, 2006; Mideo & Day, 2008; Carter et al., 2014). Factors such as high relatedness in social groups (Wenseleers & Ratnieks, 2004), diminishing returns (Foster, 2004), policing and repression of competition (Frank, 1995, 1996a; Hartmann et al., 2003; Ratnieks & Wenseleers, 2005; Kentzoglanakis et al., 2013), altruism (Frank, 1996b; van Baalen, 2002; Lion & van Baalen, 2008), reputation (Milinski et al., 2002), pleiotropy (Foster et al., 2004), plasticity (Fischbacher et al., 2012; Cavaliere & Poyatos, 2013) or control of population density (Hauert et al., 2006; Kokko & Rankin, 2006; Rankin, 2007; Frank, 2010) have been argued to constrain the evolution of overexploitative behavior, and thus reduce the potential for a tragedy of the commons to arise in such populations. Geometric mean fitness criterion

In unstable environments, the geometric mean is always lower than the arithmetic mean (see also chapter 15.3). In fluctuating environments, when geometric mean fitness is maximized, individual optimization fails (Cohen, 1966; Ellner, 1986; McNamara 1995; 1998; McNamara et al., 1995; Yoshimura & Jansen, 1996). Under the geometric mean criterion, behavior appears to be determined largely by a worst case scenario; behavior may appear suboptimal under the perspective of normal or average conditions (Yoshimura & Clark, 1991; Yoshimura & Jansen, 1996). In other words, in unpredictable environments it is better to do on average bad but stable as opposed to sometimes good and sometimes bad (Starrfelt, 2011). Except under extreme environmental conditions, mammalian litters (Murie & Dobson, 1987; Risch et al., 1995) and avian clutches (Perrins, 1965; Klomp, 1970; Murray, 1979; Lessells, 1986; Murphy & Haukioja, 1986; Boyce & Perrins, 1987; Vander Werf, 1992) larger than those that are observed in nature might result in increased fecundity, with little if any cost of reproduction in terms of parental survival. However, in unusually bad years such large clutches might be disastrous, in terms of parental survival (Yoshimura & Clark, 1991; Yoshimura & Shields, 1992). According to David Lack’s (1947, 1954) brood reduction hypothesis, asynchronous hatching facilitates adaptive brood reduction when environmental conditions are poor, and thus maximizes the number of fledglings produced under such circumstances (Forbes, 1991; Amundsen & Slagsvold, 1998). An illustrative example was given by Philippi & Seger (1989): “Suppose that years are ‘good’ or ‘bad’ with equal probability, and that the wild type produces, on average, 9 offspring in good years and 1 offspring in bad years, for an average of 5. Now introduce a mutant that produces 5 offspring in good years and 3 offspring in bad years, for an average of only 4. Despite its lower mean fitness, the mutant quickly goes to fixation because its geometric mean fitness (3.87) is much higher than that of the wild type (3.0) and its variance lower. The mutant’s best performance is much worse than the wild type’s best, but its worst is better, and this is the key to its success.” Evidence for this prudent reproduction is found in all taxa. Viruses

In a study, groups of bacteria and bacteria-infecting viruses were grown in 96 separate wells on plates. “Migration” between the groups was executed by a robot transferring small quantities of liquid between wells according to prespecified schemes. Under biologically plausible migration schemes, “prudent” virus strains were able to outcompete more “rapacious” strains, despite their selective disadvantage within each group. Prudent phage dominate when migration is spatially restricted, while rapacious phage evolve under unrestricted migration (Kerr et al., 2006). Microbes

One characteristic of bacteria is that microbial growth yields are often 50% less than the optimal yield (Westerhoff et al., 1983).There is an inevitable thermodynamic trade-off between growth rate and yield among heterotrophic organisms (Pfeiffer et al., 2001; Novak et al., 2006). Two opposing ecological strategies exist at either end of the growth rate/yield spectrum: a fast-growing, low yield competitive strategy and a slow-growing, high yield cooperative strategy (Pfeiffer et al., 2001; Kreft & Bonhoeffer, 2005). Metabolic pathways are faced with a trade-off between the rate and yield of ATP production. Simple evolutionary models argue that this trade-off generates a fundamental social conflict in microbial populations: average fitness in a population is highest if all individuals exploit common resources efficiently, but individual reproductive rate is maximized by consuming common resources at the highest possible rate (MacLean & Gudelj, 2006; MacLean, 2008). For microbes, the cooperative, slow, efficient growth strategy is more successful in spatially structured environments such as biofilms (Pfeiffer et al., 2001; Kreft, 2004; Kreft & Bonhoeffer, 2005; MacLean & Gudelj, 2006).

Gene expression noise is a selected-for trait, particularly to increase survival in stressful conditions (see chapters 11.1.1 and 11.3). Within the conceptual framework of traditional Darwinism it is hard to understand that gene expression noise in yeast reduces the mean fitness of a cell by at least 25%, and this reduction cannot be substantially alleviated by gene overexpression (Wang & Zhang, 2011). However, within the framework of the cybernetic model of evolution this trade-off between growth in benign conditions and survival in stressful conditions makes perfect sense. This trade-off illustrates that the geometric mean fitness criterion can also be applied to microbes (Beaumont et al., 2009; Ratcliff & Denison, 2010).

From an evolutionary perspective, mechanistic coupling between transmission and virulence strongly shapes the life history of parasites (Day, 2002; Frank & Schmid-Hempel, 2008). A fundamental property underlying many perspectives on the evolution of virulence is a link or ‘trade-off’ between the virulence of an infection and the reproductive capacity of the parasite (Anderson & May, 1982; May & Anderson, 1983; Ewald, 1987; Bull, 1994; Frank, 1996b). The most commonly assumed mechanism for this trade-off is that virulence is an unavoidable consequence of parasite reproduction in the host and hence that higher parasite reproduction results in higher virulence. A parasite's fitness improves with increases in its reproductive capacity, but is diminished by high virulence because virulence debilitates the host's ability to transmit the parasite (Messenger et al., 1999). Highest parasite fitness is thus achieved as a compromise, the exact optimum depending on the shape of the trade-off surface. Comparisons across parasites evolved in nature and from selection experiments are consistent with trade-offs (Bull et al., 1991; Diffley et al., 1987; Dearsly et al., 1990; Day et al., 1993; Herre, 1993; Ebert, 1994; Ewald, 1994; Ebert & Mangin, 1997; Turner et al., 1998; Mackinnon & Read, 1999; Messenger et al., 1999; Paul et al., 2004; Salvaudon et al., 2005; de Roode et al., 2008; Mackinnon et al., 2008). Many processes such as pathogen adaptation to within-host competition, interactions with the immune system and shifting transmission routes, will all be interrelated to virulence-transmission trade-off making sweeping evolutionary predictions harder to obtain (Alizon et al., 2009). If host immunity is short-lived, or if it is imperfect, the level of host exploitation should increase. Only parasites causing diseases with long-lived immunity are likely to be prudent in space (Lion & Boots, 2010).

Testing Cohen’s (1966) classic bet-hedging model using the fungus Neurospora crassa, Graham et al. (2014) allowed ascospore dormancy fraction in N. crassa to evolve under five experimental selection regimes that differed in the frequency of unpredictable ‘bad years’. The straightforward prediction of the model is that, by the geometric-mean principle, dormancy fraction should evolve to equal the probability of occurrence of a bad year. By contrast, the prediction of the arithmetic-mean principle is the evolution of zero dormancy (immediate germination) under a broad range of ecological scenarios; namely if the probability of a good year is greater than 0.5 (Graham et al., 2014). Results were consistent with bet-hedging theory: final dormancy fraction in 12 genetic lineages across 88 independently evolving samples was proportional to the frequency of bad years, and evolved both upwards and downwards as predicted from a range of starting dormancy fractions (Graham et al., 2014). Prolonged dormancy

In many insect species (Waldbauer, 1978; Ushatinskaya, 1984; Tauber et al., 1986; Danks, 1987, 1992; Hanski, 1988; Menu, 1993a, b; Menu & Debouzie, 1993; Roux et al., 1997; Danforth, 1999; Menu et al., 2000) but also in other organisms such as plants (e.g. Venable & Lawlor, 1980; Venable, 1989; Philippi, 1993a, 1993b; Clauss & Venable, 2000), crustaceans (Ellner & Hairston, 1994; Hairston et al., 1995, 1996b) and tropical fishes (Wourms, 1972), life cycle duration varies within the population. Certain individuals of the same generation reproduce after 1 year and others after 2 or more years because of prolonged dormancy. Diapause is a genetically programmed developmental response that occurs at a specific stage for each species that allows synchronization of the life cycle with seasonal variations in the environment (Tauber et al., 1986; Danks, 1987, 1992). Interestingly, diapause lasting more than 1 year, namely ‘‘prolonged’’ or ‘‘extended’’ diapause, is not exceptional for insect species (Danks, 1987, 1992; Hanski, 1988). It usually occurs in populations whose seasonal resources fluctuate unpredictably in abundance and availability (Hanski, 1988). Species undergoing prolonged dormancy (diapause or quiescence) usually reside in arid or semiarid areas (Nakamura & Ae, 1977; Sims, 1983; Powell, 1987, 1989, 2001; Danforth, 1999; Tauber & Tauber, 2002) as well as in regions of the arctic zone (Danks, 2004). Nonetheless, prolonged dormancy has also been reported in temperate zone species (Barnes, 1952; Neilson, 1962; Prentiss, 1976; Shapiro, 1979, 1980; Annila, 1982; Hedlin et al., 1982; Tzanakanis et al., 1991; Levine et al., 1992; Menu, 1993a, b; Menu & Debouzie, 1993; Higaki & Ando, 1999; Maeto & Ozaki, 2003; Higaki, 2005; Matsuo, 2006; Wang et al., 2006; Chirumamilla et al., 2008). Prolonged diapause is a within-generation bet-hedging phenotype (Menu & Desouhant, 2002). As noted by Hutchinson (1996), “Biologists who are used to thinking in terms of maximisation of individual fitness are often perturbed that a seed (or an insect) should agree not to germinate (emerge as adult) immediately when its own chances of reproducing are lower if it spends a year in dormancy.” Individuals that express prolonged dormancy are exposed to increased mortality risks and they postpone reproduction, both of which may result in fitness costs (Danks, 1987; Leather et al., 1993; Hairston, 1998).In an experimental study, prolonged dormancy did not affect adult longevity but both lifetime fecundity and oviposition were significantly decreased (Moraiti et al., 2012). Social insects

Several studies reported a survival advantage of multiple foundress colonies compared with single foundress colonies of the wasp genera Polistes (Metcalf & Whitt, 1977; Gibo, 1978; Tibbetts & Reeve, 2003), Belonogaster (Keeping & Crewe, 1987; Tindo et al. , 1997a), and Ropalidia (Shakarad & Gadagkar, 1995), Allodapine bees (Hogendoorn & Zammit, 2001) and social shrimps (Duffy, 2002). In the primitively eusocial wasp species Belonogaster juncea juncea multiple foundress colonies were significantly more successful than single foundress colonies in producing at least one adult (Tindo et al., 2008). The total productivity of the colonies increased significantly with the number of associated foundresses, but the productivity per capita did not. No single foundress colony (out of 13) reached the sexual phase, while eight (out of 36; 21.6%) multiple foundress colonies did. The increase in total productivity as a function of group size is in line with previous findings reported on primitively eusocial species (Michener, 1964; Shakarad & Gadagkar, 1995; Tindo et al., 1997b; Tibbetts & Reeve, 2003). On the other hand, the decreasing per capita productivity concomitant with an increasing number of females noted in the study of Tindo et al. (2008) illustrates Michener’s paradox (1964) in primitively eusocial insects (Michener, 1964; Noonan, 1981; Strassmann et al., 1988; Shakarad & Gadagkar, 1995; Gadagkar, 1996; Hogendoorn & Zammit, 2001; Seppä et al., 2002; Soucy et al., 2003). The coefficient of variance of the per capita productivity significantly decreased with group size, as Wenzel and Pickering (1991) noted in the model they created to explain the paradox (Tindo et al., 2008). Wenzel and Pickering (1991) suggested that individuals in larger groups might trade lower per capita productivity for less variability and greater predictability. Prudent predators

Predator-prey systems (and related host-pathogen systems) have been studied theoretically for decades. Most of the studies have focused on the role of environmental stochasticity, the relevance of nonlinear interactions or of spatial effects, to explain the mechanism of cycling (Nisbet & Gurney, 1982; Renshaw, 1991; Kaitala et al., 1996; Aparicio & Solari, 2001; Bjørnstad & Grenfell, 2001; Pascual et al., 2001; Pascual & Mazzega, 2003). The “prudent predator” concept (Slobodkin, 1961, 1974; Goodnight et al., 2008) has elucidated evolutionary outcomes of predator-prey interactions and provided evolutionary mechanisms to resolve the tragedy of the commons dilemma. A predator here is defined as any species that consumes or exploits another species in order to survive and reproduce, including pathogens, parasites, parasitoids, grazers and browsers, as well as “true” predators. The classical model of predator-prey dynamics, the Lotka-Volterra equation, predicts that under most conditions predator populations, like prey populations, due to overexploitation of resources, go through a series of oscillations between feast and famine, at each cycle approaching the brink of extinction (Holland, 1995; Mitteldorf, 2010). The paradox is that in the natural world, we know that predator and prey stably coexist in nature even when heritable variation exists for traits involved in predator attack rates (e.g. Forsman & Lindell, 1993; Virol et al., 2003; Palkovacs & Post, 2008). The overexploitation of resources can only be prevented by conservation of the resource by prudent reproduction (Slobodkin, 1961, 1974; Goodnight et al., 2008). Evidence for the evolutionary merit of reproductive prudence comes from multiple experimental studies in various taxa that is supported by theoretical models (Gilpin, 1975; Nathanson, 1975; Wilson, 1978; Wade, 1980a; Holmes, 1983; Walker, 1984; Rand et al., 1995; Savill & Hogeweg, 1998; Sober & Wilson, 1998; Boots & Sasaki, 2000; Haraguchi & Sasaki, 2000; Rauch et al., 2002, 2003; Werfel & Bar-Yam, 2004; Kerr et al., 2006; Goodnight et al., 2008; MacLean, 2008; Borrello, 2012; Carter et al., 2014). Reproductive prudence of cells arose as a necessary prerequisite of multicellularity (Buss, 1987; Maynard Smith & Szathmáry, 1995; Frank & Nowak, 2004). Cancerogenesis can be regarded as a violation of this reproductive prudence resulting in the tragedy of the commons (Nunney, 1999; Stoler et al., 1999).

…that an opinion has been widely held is no evidence whatever that it is not utterly absurd….
Bertrand Russell (1929)

16. Life history phenotypes of bet-hedging

16.1 Turnover of generations: bet-hedging in time?

Turnover of generations may be considered as transgenerational bet-hedging. Traditional concepts of aging (the “evolutionary theories of aging”) do not take into account the stochasticity of environments. In fluctuating environments it cannot be expected that fitness of individuals is optimal over longer intervals. Theoretical studies on variability in life cycle duration both in plants (Philippi, 1993a, 1993b; Clauss & Venable, 2000) and insects (Danforth, 1999; Menu et al., 2000) proposed bet-hedging as an explanationof such variability. As adaptation to environmental unpredictability, diapause cycle length must be expressed as a responsiveness to unpredictive proximate environmental factors (i.e. factors without predictive value for the decision at hand) (Menu 1993; Menu & Debouzie, 1993; Menu et al., 2000; Menu & Desouhant, 2002).

Life history traits of long-lived vertebrates constrain the ability of populations to respond to environmental perturbations resulting in chronic increases in mortality (Heppell et al., 2000; Fordham et al., 2007) because compensatory responses are thought to be limited and recovery is slow (Musick et al., 2000). A ‘slow–fast’ continuum in life histories exists for a range of taxa (Heininger, 2012), including mammals (Heppell et al., 2000), birds (Sæther et al., 1996), reptiles (Webb et al., 2002) and sharks (Smith et al., 1998), and a species’ position along this continuum influences how populations will respond to change in a demographic trait (Sæther & Bakke, 2000). Intriguingly, organisms that live either in more stable environments such as the deep sea or are more resilient to environmental perturbations and hence can tolerate a broader range of environmental conditions (such as endothermic organisms) have greater longevities (Finch, 1990).

16.2 Iteroparity

To understand how environmental fluctuations shape the evolution of life histories, stochastic demography has to be used (Tuljapurkar, 1990b; Caswell, 2001; Tuljapurkar et al., 2009). Distributing reproduction in time has been visited by many studies since Cole (1954) coined the terms “semelparity” and “iteroparity”. Cole (1954) viewed iteroparity as a paradox because semelparity, a single bout of reproduction, should always be favored in a constant environment by the compounding nature of exponential growth. In a constant environment, Cole’s paradox boils down to a question of lifetime reproductive success; the type producing most offspring over a lifetime will come to dominate (Mylius & Diekmann, 1995; Zeineddine & Jansen, 2009). As discussed in chapter 10, reproductive success can be highly variable (Hairston et al., 1996a). Cole’s paradox has been resolved by numerous models demonstrating that variation in reproductive success favors iteroparity (Murphy, 1968; Gadgil & Bossert, 1970; Schaffer, 1974; Wilbur et al., 1974; Bell, 1976, 1980; Goodman, 1984; Bulmer, 1985; Orzack, 1985, 1993; Bradshaw, 1986; Roerdink, 1987; Orzack & Tuljapurkar, 1989, 2001; Fox, 1993; Charlesworth, 1994; Cooch & Ricklefs, 1994; Erikstad et al., 1998; Benton & Grant, 1999; Brommer et al., 2000; Ranta et al., 2000a, 2000b, 2002; Katsukawa et al., 2002; Wilbur & Rudolf, 2006).Thus, life history theory holds that in the face of annual resource variability, organisms should shift from semelparous to iteroparous reproductive patterns (Murphy, 1968; Bulmer 1985, Orzack & Tuljapurkar, 1989); and furthermore, under certain circumstances they should evolve a longer lifespan and reduced annual reproduction (Stearns, 1976; Gillespie, 1977; Roff, 2002; Nevoux et al., 2010). By this theory, bet-hedging evolves to reduce the probability of investing too much in reproduction during resource-poor years, which may ultimately result in null fitness. However, for logistical reasons, the theoretical prediction that environmental variability will lead to the evolution of longer life span (Murphy, 1968; Roff, 2002) has rarely been tested or detected in wild populations (Roff, 2002; Nevoux et al., 2010).

16.3 Polyandry

Whereas for males reproductive success is expected to increase linearly with the number of mates, the advantages of multiple mating for females are less clear (Yasui, 1997; Jennions & Petrie, 2000). Mating can be costly to females in terms of time and energy, or because of increased risk of predation, injury or infection (Daly, 1978; Chapman et al., 1995; Blanckenhorn et al., 2002). Polyandry (multiple female mating) is common in a wide variety of animal taxa (Birkhead, 2000; Jennions & Petrie, 2000; Uller & Olsson, 2008). The evolutionary rationale for this behavior may differ between species and a multitude of mutually non-exclusive theories have been forwarded to explain its occurrence. For instance, polyandry may represent the combined effect of mate-encounter frequency and conflict over mating rates between males and females driven by large male benefits and relatively small female costs resulting in “convenience polyandry” (DiBattista et al., 2008; Uller & Olsson, 2008). On the other hand, polyandry may be another within-generation bet-hedging behavior (Yasui, 1998; Hopper et al., 2003; Sarhan & Kokko, 2007). There are two ways in which polyandry could be favored by bet-hedging (Jennions & Petrie, 2000). First, females may only be able to distinguish broad categories of males due to perceptual errors in assessment; or there may only be a few discrete levels of signaling by males, despite continuous variation in male quality (Johnstone, 1994). Second, there may be temporal fluctuations in the environment that lead to variable selection on fitness-enhancing traits under natural selection (e.g. Jia & Greenfield, 1997). As such, females cannot identify the male with the best viability genes for the future. In both cases, females can reduce the variance in mate quality by mating with several males whom they perceive to be broadly genetically suitable as mates (so-called ‘genetic bet-hedging’; Watson, 1991). Genetic bet-hedging (Gillespie 1973a, 1974a, 1975, 1977; Seger & Brockman, 1987; Hopper, 1999) could explain polyandry, especially when females mate indiscriminately (Yasui, 1998, 2001; Fox & Rauter, 2003). Another advantage may be a form of diversified bet-hedging akin to not putting all your eggs in one basket (Kaplan & Cooper, 1984). Benefits of polyandry may include genetic bet-hedging against environmental uncertainty, mating with costly males and genetic incompatibility (Loman et al., 1988; Watson, 1991; Zeh & Zeh, 1996; Newcomer et al., 1999; Jennions & Petrie, 2000; Yasui, 2001; Fox & Rauter, 2003; Lorch & Chao, 2003; Mäkinen et al., 2007; Byrne & Roberts, 2012). Field studies of vertebrates suggest, and laboratory experiments on invertebrates confirm, that even when males provide no material benefits, polyandry can enhance offspring survival and fitness (Madsen et al., 1992, 2005; Tregenza & Wedell, 1998, 2000, 2002; Jennions & Petrie, 2000; Zeh & Zeh, 2001, 2006; Garant et al., 2005; Ivy & Sakaluk, 2005; Fisher et al., 2006; Sarhan & Kokko, 2007; Byrne & Whiting, 2011; Aguirre & Marshall, 2012; Reding, 2014; Garcia-Gonzalez et al., 2015). Genetic variation among sexually produced siblings could also reduce the likelihood of disease and predation (Wolfe, 1985; Zhu et al., 2000; Jokela et al., 2009; Aguirre & Marshall, 2012).

Female birds may benefit indirectly from extra-pair mating by enhancing the genetic quality of their offspring, through good genes or genetic compatibility effects (Jennions & Petrie, 2000; Kokko, 2001; Griffith et al., 2002; Neff & Pitcher, 2005). Supporting this idea, recent studies have identified a range of fitness-related traits for which extra-pair offspring are superior to their within-pair half-siblings (Hasselquist et al., 1996; Kempenaers et al., 1997; Sheldon et al., 1997; Johnsen et al., 2000; Charmantier et al., 2004; Schmoll et al., 2005; Freeman-Gallant et al., 2006; Garvin et al., 2006; Bouwman et al., 2007; O’Brien & Dawson, 2007; Dreiss et al., 2008; Fossøy et al., 2008; Losdat et al., 2011). A recent study (Gohli et al., 2013) found that more promiscuous species of passerine birds had higher nucleotide diversity at autosomal introns, but not at Z-chromosome introns. In more promiscuous species, major histocompatibility complex class IIB alleles had higher sequence diversity, and therefore should recognize a broader spectrum of pathogens. The results suggest that female promiscuity in passerine birds targets a multitude of autosomal genes for their nonadditive, compatibility benefits. Also, as immunity genes seem to be of particular importance, interspecific variation in female promiscuity among passerine birds may have arisen in response to the strength of pathogen-mediated selection (Gohli et al., 2013).

16.4 Sexual reproduction

The immune system maintains a living system’s organization by destroying parasitic bodies, such as bacteria or cancer cells. It achieves this by producing antibodies that attach themselves to the alien bodies and thus neutralize them. To find the right type of antibodies, the immune system simply produces an astronomical variety of different antibody shapes. However, only the ones that “fit” the invaders are selected and reproduced in large quantities (Heylighen, 2001). Thus, Abel (2012) thought that the only system that seems to waste energy deliberately exploring randomness is the immune system. To prepare for exposure to an indefinite array of possible antigens, the immune system must be prepared to deal with any possible new combination of viral, bacterial, mycotic, or other parasitic invasion. The immune system is unique in its continuing perusal of potential genetic sequence space and three dimensional phase space (Abel, 2012). Likewise, however, in a stochastic environment the best strategy to increase fitness is to take every possible path at every next step. As a result, no configurations should be missed (Fu, 2007). Thus environmental stochasticity elicits bet-hedging as risk-spreading response resulting in (epi)genetic, developmental, phenotypic, physiological and behavioral variation on which selection can act (figure 1C).

Several theoretical models indicate that sexual reproduction is selected for in variable environments (Hines & Moore, 1981; Weinshall, 1986; Roughgarden, 1991; Robson et al., 1999). Sexual reproduction is the ultimate bet-hedging enterprise and its evolutionary success the selective signature of stochastic environments (Heininger, 2013). Sexual reproduction subjects an extremely large variety of germline cells that are organized like a quasispecies to a cascade of selective regimes before the most resilient (Holling, 1996) are released and exposed to natural selection (Heininger, 2013).With these features, gametogenesis in sexually reproducing organisms is characterized as complex self-organized system as described by Heylighen (2001): “The system needs a fitness criterion for choosing the best action for the given circumstances. The most straightforward method is to let the environment itself determine what is fit: if the action maintains the basic organization, it is, otherwise it is not. This can be dangerous, though, since trying out an inadequate action may lead to the destruction of the system. Therefore, complex systems such as organisms or minds have evolved internal models of the environment. This allows them to try out a potential action “virtually”, in the model, and use the model to decide on its fitness. The model functions as a vicarious selector, which internally selects actions acting for, or in anticipation of, external selection. This “shortcut” makes the selection of actions much more reliable and efficient. It must be noted, though, that these models themselves at some stage must have evolved to fit the real environment; otherwise they cannot offer any reliable guidance. Usually, such models are embodied in a separate subsystem, such as the genome or the brain” (Heylighen, 2001).

The sex-stress relationship is nonlinear and is described by approximation as inverted “U”-shaped: sex is favored in intermediate stressful environments, while stable stress-free and extreme stressful environments favor asex (Moore & Jessop, 2003). Constant conditions favor asexuality (Bürger, 1999) which may explain the high incidence of parthenogenesis in environments such as stable forest soils (Cianciolo & Norton, 2006; Domes et al., 2007). Evolutionary models based on the asexual and sexual replication pathways in Saccharomyces cerevisiae suggested that sexual replication can eliminate genetic variation in a static environment, as well as lead to faster adaptation in a dynamic environment (Gorodetsky & Tannenbaum, 2008).

A change of environmental conditions that reduce Darwinian fitness may increase (i) mutagenesis, (ii) epimutagenesis, (iii) recombination rate, (iv) mutability of simple sequence repeats, and (v) mobilization of transposable elements, all of which, when acting on the germline, increase heritable (epi)genetic variation (Heininger, 2013). Sexual reproduction regulates these processes and, by changing the balance of sexual mutagenesis-selection cascades, modulates the (epi)genetic variation-selection balance. Theoretical models suggest that fluctuating selection is an important factor in maintaining genetic polymorphism (Korol et al., 1996, Kirzhner et al., 1998; Bürger & Gimelfarb, 2002). Likewise, empirical studies of cyclical and fluctuating selection suggest an association between temporal environmental heterogeneity and the amount of genetic variation (Kondrashov & Yampolsky, 1996; Korol et al., 1996). These environments and their associated stochastic generation of variation appear to have an evolutionary rationale: fighting variation with variation (Ashby, 1956; Meyers & Bull, 2002) creating lottery tickets for the raffle of life. On the other hand, sexual reproduction as evolutionarily highly successful strategy highlights an eminent characteristic of evolution: it pays off to diversify and be prepared for the unlikely event. And: generation of variation is no happenstance outcome but a highly regulated process and environmental stochasticity is its evolutionary “impetus”.

17. Stochasticity and selection: duality in evolution

The paradigm of calculability, determinism and monocausality dominated the sciences until the beginning of the 20th century. Since the end of the 19th century, however, monocausal approaches in many different sciences started to collapse. Even in pure mathematics and logics, problems with the calculability of the universe arose (e.g. Russell´s paradox). Hilberts program failed with Kurt Gödel´s proof. At the level of physics, many different problems (e.g. ultraviolet catastrophe, wave-particle duality) led to the development of new physics (Brunner & Klauninger, 2003). Niels Bohr, the ‘‘father of quantum mechanics,’’ indicated that the complementarity predicted and observed in quantum mechanical investigations ? such as the wave-particle duality of light and all quanta ? was not limited to the quantum realm, but was a more broadly applicable (perhaps universal) concept, which should have correlates in the study of living things (Bohr, 1937; Roll-Hansen, 2000; McKaughan, 2005). Like the wave paradigm could not explain a variety of physical properties of light, explaining evolution by natural selection as only organizing principle has created various implausibilities. As it stands, it is accepted that it makes sense to use stochastic models in population genetics. But why should a selection-only process be stochastic? It is agreed that natural selection has its limits (Barton & Partridge, 2000). But so far these limits have been explained by e.g. genetic architecture, genetic drift, historical contingency or developmental constraints.

Evolution is both the result of random events at all levels of organization of life and of constraints that canalize it, in particular by excluding, by selection, incompatible random explorations. So, ergodic explorations are restricted or prevented both by selection and the history of the organism (Longo et al., 2012). Mayr (2000) wrote: “Darwin settled the several-thousand year-old argument among philosophers over chance or necessity. Change on the earth is the result of both, the first step being dominated by randomness, the second by necessity.       only the first step in natural selection, the production of variation, is a matter of chance. The character of the second step, the actual selection, is to be directional.” According to Mayr (1980), selection is ‘‘the only direction-giving factor in evolution’’. On the other hand, Monod (1971, p. 112-113) argued that “chance alone is at the source of every innovation, of all creation in the biosphere. Pure chance, absolutely free but blind, at the very root of the stupendous edifice of evolution: ...It is today the sole conceivable hypothesis, the only one that squares with observed and tested fact.” Figure 2 depicts the linear evolution model as put forward in the Modern Synthesis (e.g. Mayr, 2000). This linear model contains the elements of selection and chance but lacks a feedback loop and, hence, is unable to learn. Moreover, the model failed to recognize the interaction of stochasticity and selection. The confusion caused by this failure led to the perception of natural selection as a statistical process (see chapter 15).

Darwin already realized that variation is an essential commodity in evolution but he was unaware of its cause. The Modern Synthesis regarded variation as the result of accident, happenstance and imperfection. The Modern Synthesis draws a non-cybernetic picture of evolution. As outlined by Mayr (see above), stochasticity and natural selection are distinct entities, chance and necessity. Natural selection and random drift can be distinguished from one another (Millstein, 2002; Pfeifer, 2005; but see Abrams, 2007). The cybernetic theory, as advocated here, links both by feedback control: input (environmental stochasticity) determines output (natural selection) and input, at least in part, is determined by output. Importantly, the output signal is a population-level signal including density- and frequency-dependent phenomena that feed back to the individual level of the input signal, inextricably intertwining both stochasticity and natural selection and the individual and population levels of selection. Similarly, in Newtonian mechanics space and time are distinct entities. In Einstein’s Relativity Theory, both are no longer separated but an integrated entity in a four-dimensional continuum of space and time. Intriguingly, the stochasticity-selection duality seems analogous to thewave-particle duality. Schrödinger’s concept of ‘entanglement’ between the states of particles is the key to wave–particle duality (Knight, 1998). ‘Entanglement’, is a peculiar but basic feature of quantum mechanics introduced by Erwin Schrödinger in 1935. Individual quantum-mechanical entities need have no well-defined state; they may instead be involved in collective, correlated (‘entangled’) states with other entities, where only the entire superposition carries information. That may apply to a set of particles, or to two or more properties of a single particle. Likewise, the entangled state of the stochasticity-selection duality can be conceptually disentangled by cybernetic modeling but is phenomenologically an entity.

It is textbook knowledge that selection needs variation to work on. The fundamental question, however, is whether variation is the result of accident and chance or whether it evolved as a means to cover all bases in response to the unpredictability of life. Ashby’s Law of Requisite Variety formulated the conceptual framework to understand how internal variety of a system has to match its external variety. That variation arises at all levels of biological organization such as the genetic, epigenetic, cellular network, developmental, physiological, behavioral and life-history level, that it is malleable in response to stress (when it is most needed) and that sexual reproduction evolved as tool creating pre-selected variation, is evidence Ashby’s Law succinctly describes the cybernetic behavior of evolution.

17.1 The creative conflict between stochastic indeterminism and selective determinism

All processes in Nature are fundamentally stochastic. Poisson tried to model mathematically how one could have stable probabilities of mass phenomena even when the probabilities for individuals are not constant. The law of large numbers teaches that absolute regularity emerges in a long run of draws. He did indeed prove that under certain restrictions, even when the probability at repeated trials is variable, in the long run the average relative frequency does converge on p, the average probability for individual trials (Hacking, 1983) The name “law of large numbers” is still used loosely in probability theory, although there are now so many different theorems that one needs better names, which usually clump around what is called the central limit theorem.The law of large numbers is true for systems at equilibrium, where one can generally expect for a system with N degrees of freedom the relative magnitude of fluctuations to scale as 1/N. However, when the system is driven out of equilibrium, the central limit theorem does not always apply, and even macroscopic systems can exhibit anomalously large (giant) fluctuations (Keizer, 1987; Tsimring, 2014). In the duality of stochasticity and selection, variation is recognized as the result of a multitude of processes, resulting in a bet-hedging response to stochasticity. Ross Ashby’s ‘Law of Requisite Variety’ (1956, p. 206) is the organizing principle of the stochasticity-selection duality. Stochastic environments coerce organisms into lotteries. But today’s winners can be tomorrow’s losers, particularly following natural disaster or epidemic outbreaks. Insurance is a population-level risk-sharing strategy of risk-averse agents buffering against idiosyncratic risk. Via the law of large numbers and bet-hedging, evolution generated a form of automatic biological insurance against idiosyncratic risk (Robson, 1996).

In essence, stochasticity and selection work against each other within the limits of total chaos and complete order, the two extremes where evolution can no longer work. Stochasticity contributes to maladaptation or limits adaptation (Travisano et al., 1995a; Hereford, 2009; Lenormand et al., 2009). On the other hand, stochasticity and selection are interdependent. None can prevail without depriving evolution of its very basis. Selection could not work without the stochastic phenomenon of variation; and stochasticity needs the ordering power of selection to create the complex structures of self-organization (Bak et al., 1987, 1988). Intriguingly, part of the stochasticity is created by selection itself, e.g. through bet-hedging strategies, coevolutionary cycles, density- and frequency-dependent selection, or niche construction (Meyers & Bull, 2002). On the other hand, stochasticity drives variation and variation is the raw material for selection to work on. Theoretical models suggest that fluctuating selection is an important factor in maintaining genetic polymorphism (Korol et al., 1996, Kirzhner et al., 1998; Bürger & Gimelfarb, 2002). Likewise, empirical studies of cyclical and fluctuating selection suggest an association between temporal environmental heterogeneity and the amount of genetic variation (Kondrashov & Yampolsky, 1996; Korol et al., 1996). Lévy-like search strategies were revealed in analyses of a variety of behaviors from plankton to humans (Viswanathan et al., 1996, 2001; Bartumeus et al., 2003; Barabasi, 2005; Brockmann et al., 2006; Reynolds & Frye, 2007; Reynolds & Rhodes, 2009; Humphries et al., 2010). The models simulating these behaviors combine a multitude of stochastic processes by deterministic rules (Maye et al., 2007).In addition to the inevitable noise component, a nonlinear signature suggesting deterministic endogenous processes (i.e., an initiator) is involved in generating behavioral variability. It is this combination of chance and necessity that renders individual behavior so notoriously unpredictable (Maye et al., 2007).

Although within wide boundaries, stochasticity and selection have to be balanced. Evolutionary biology already acknowledged mutation-selection equilibrium as evolutionary phenomenon; it is time to realize that there is a stochasticity-selection balance. Too much stochasticity would be detrimental for learning: if the cybernetic feedback concerning fitness effects would not behave with a certain stability and change too irregularly, learning would be impaired. Fortunately, with respect to living organisms, nature is capricious rather than completely random (Lewontin, 1961, 1966). There is a variable degree of ecological predictability: demographic cycles due to e.g. predator/prey interactions, seasons with their cyclicity of resource availability, circadian cycles, tides, etc. Bet-hedging only is favored in an intermediate range of environmental stochasticity. As the environment becomes more stable or more chaotic, bet-hedging strategies have a lower fitness advantage (Philippi & Seger, 1989; Müller et al., 2013). Stochasticity is ambiguous (e.g. beneficial, neutral and deleterious mutations) with regard to outcome while selection filters and directs the ambiguity. And learning attenuates the randomness. Selection is the stabilizing force that brings order into the chaos and provides the feedback for learning to occur. Both stochasticity and selection render evolution opportunistic.

The stochasticity-determinism duality is not adequately reflected by existing models. Modifying Dobzhansky’s notorious quote, Lynch (2007a) wrote: “Nothing in evolution makes sense except in light of population genetics”. However, in 1961 Lewontin did not consider population genetics an “adequate theory of evolutionary dynamics. On the contrary, the theory of population genetics, as complete as it may be in itself, fails to deal with many problems of primary importance for an understanding of evolution.” In this paper, Lewontin (1961) suggested that the modern theory of games (von Neumann & Morgenstern, 1944, 1953) may be useful in finding exact answers to problems of evolution not covered by the theory of population genetics. A first application of game theory to evolutionary issues was the work of Maynard Smith and Price (1973) on animal conflicts and their concept of an “evolutionarily stable strategy” (ESS). The vast body of theoretical work based on the concept of an ESS, however, often disregards environmental stochasticity. For example, Maynard Smith's often quoted book (Maynard Smith, 1982a) contains no reference to stochasticity. Many models in evolutionary game theory (EGT) involve infinite populations with a deterministic evolutionary dynamic. While these idealizations may provide a good starting point for reasons of mathematical tractability, there are important limitations to them. The methodological focus on equilibria (specifically the ESS) in EGT has resulted in missing important features of evolutionary systems that can only be captured by dynamical analysis (Huttegger & Zollman, 2012). An important feature of EGT models is repetition. If the games were not repeated, these EGT models would not be able to provide any insight into adaptive behaviors and strategies due to the dynamic nature of the mechanisms of evolution. But even standard dynamical analysis has strong idealizations such as infinite populations and deterministic evolution. These kind of idealizations can miss possible explanations, for example regarding the evolution of cooperation (Smead, 2008; Forber & Smead, 2014). Importantly, evolution “plays” both within-generation and trans-generation games. At each game repetition population make-up in turn is determined by the results of all of the previous contests before the present contest- it is a continuous iterative process where the resultant population of the previous contest becomes the input population to the next contest. As stochastic process (Lenormand et al., 2009; Kupiec et al., 2012) evolution can be described by lottery models (Chesson & Warner, 1981; Proulx & Day, 2001; Svardal et al., 2011).

17.1.1 Playing dice with controlled odds

Albert Einstein once said, “I am convinced the Old One [God] does not play dice” (Jammer, 1999, p. 222). New evidence about the fractal geometry of nature, chaos, and complexity challenges these negative statements about the statistical nature of the physical world (Gleick, 1987). Thus, chaos theorist Joseph Ford remarked: “God plays dice with the universe, but they’re loaded dice” (Gleick, 1987, p. 314).

The indeterminism-determinism interaction is best illustrated by processes at the cellular level. Cell fate decisions are often controlled by both stochastic and deterministic features (Losick & Desplan, 2008; MacArthur et al., 2009; Balázsi et al., 2011; Snijder & Pelkmans, 2011). Thus, genetically homogeneous populations adopt distinct fates ? cell fate decisions are stochastic by virtue of the feedback architecture of genetic networks (Smits et al., 2006; Davidson & Surette, 2008) or deterministically linked to the cell cycle or even a combination of both. Examples of cellular-population heterogeneity include differentiation of progenitor hematopoietic stem cells (Mayani et al., 1993), non-genetic individuality in bacterial chemotaxis (Spudich & Koshland, 1976), and epigenetic inheritance and incomplete penetrance of transgenes in mice (Morgan et al., 1999). As a result, bacteria and cells determine their fate by “playing dice with controlled odds” (Ben-Jacob & Schultz, 2010). Constrained randomness, intermediate between rigid determinism and complete disorder is what is usually seen (Theise & Harris, 2006). Specific environmental or genetic cues may bias the process, causing certain cellular fates to be more frequently chosen (as when tossing identically biased coins). Still, the outcome of cellular decision making for individual cells is a priori unknown (Balázsi et al., 2011). Sexual reproduction is the paradigm of this controlled stochastic strategy. The huge (epi)genetic variation that is created by stochastic epimutagenesis and mutagenesis is contained by selection cascades that engender pre-selected variation (Heininger, 2013).

18. The blending of ecology and evolution

In my opinion, the greatest error which I have committed has been not allowing sufficient weight to the direct action of the environment, for example, food and climate, independently of natural selection. When I wrote The Origin, and for some years afterwards, I could find little good evidence of the direct action of the environment; now there is a large body of evidence.
Charles Darwin (1876) in a letter to Moritz Wagner

In most natural populations, the reproductive potential far exceeds the environmental opportunity, and natural selection proceeds by culling to what the habitat can support (King, 1967). As Smith (2012a) put it: “In some respects natural selection is a quite simple theory, arrived at through the logical integration of three propositions (the presence of variation within natural populations, an absolutely limited resources base, and procreation capacities exceeding mere replacement numbers) whose individual truths can hardly be denied.” The resulting struggle for existence is the engine that drives evolution. Haeckel (1866) defined ecology as the science of the struggle for existence (Cooper, 2003). Thus, from early on, ecology and evolution have been intertwined. In this vein of thought Van Valen (1973b) described evolution as “the control of development by ecology”. Calls for an ‘integrative’ understanding of biological processes keep being repeated in the literature, from Dobzhansky’s (1973) famous quote “Nothing in biology makes sense except in the light of evolution” to current, more focused statements that evolution itself only makes sense when viewed in its ecological context (Coulson et al., 2006; Saccheri & Hanski, 2006; Johnson & Stinchcombe, 2007; Metcalf & Pavard, 2007; Pelletier et al., 2007; 2009; Kokko & López-Sepulcre, 2007; Blute, 2008; Grant & Grant, 2008; Bassar et al., 2010; Matthews et al., 2011; Schoener, 2011). The repeated call for an integrative view of ecology and evolution only reflects the still existing division between ecology and evolution despite Grant and Grant's (2008) dictum: “Nothing in evolutionary biology makes sense except in the light of ecology.” Notwithstanding some recent relevant studies, the importance of the evolution-to-ecology pathway across systems is still considered unknown (Schoener, 2011). The feedbacks between ecological and evolutionary changes are now known to be bidirectional (Post & Palkovacs, 2009; Schoener, 2011; Miner et al., 2012). Thus, “Nothing in biology makes sense except in the light of an integrated perspective of both ecology and evolution”. The stochasticity-natural selection duality finally blends ecology and evolution into each other. Einstein introduced the concept of space-time as a single entity. Stochasticity and natural selection interact on a variety of levels (Abrams, 2007), and, in fact, form a single entity.

19. Cutting the Gordian knot of controversies

Evolutionary theory is the arena of a multitude of controversies.Particularly, the level of selection issue and sociobiology have seen rancorous theoretical debates.

In chapter 15 it has been argued that environmental stochasticity changes the rules of evolution. Darwinian tradition with its assumption of constant environments emphasizes the role of individual selection. Stochastic environments coerce individuals into lotteries. The risk-averse individuals, on the other hand, employ the risk-sharing strategy of insurance. Risk-sharing can only be done in groups, the larger the better. Thus, stochastic environments turn individual selection into multilevel selection.

At the heart of the debate in sociobiology is how cooperation and altruism can persist in the face of cheating (Wade & Breden, 1980; Hamilton & Taborsky, 2005; Bijma et al., 2007). Some have suggested that the solution to this problem is the level of selection (Slatkin & Wade, 1978; Wade, 1978a; Wilson & Sober, 1994; Keller, 1999; Goodnight, 2005; Wilson, 2005; Nowak et al., 2010). In both biology and the human sciences, social groups are sometimes treated as adaptive units, whose organization cannot be reduced to the individual level. In this view, group-level adaptations can evolve only by aprocess of natural selection acting at the group level (Wilson & Sober, 1994). This group-level view is opposed by a more individualistic one that treats social organization as a by-product of self-interest, suggesting that altruism can evolve through individual selection depending on the degree of relatedness within a group (Hamilton, 1964; Wade, 1978b, 1980b; Michod, 1982). More recent approaches treat multilevel selection as a continuum, in which fitnesses of individuals depend on both individual and group properties, of which pure group selection and individual selection are limiting cases (Keller, 1999). I elaborated a theory explaining the ecology-driven pattern of social interactions based on the insight that environmental stochasticity favors the evolution of cooperation as bet-hedging behavior (Heininger, 2015).

Another conundrum of evolutionary biology and population genetics is the coexistence of two basic observations (Walsh & Blows, 2009; Leffler et al., 2012): in natural populations genetic variation is found in almost all traits (Mousseau & Roff, 1987; Houle, 1991, 1992, 1998; Hill & Caballero, 1992; Lynch & Walsh, 1998) in the presence of strong stabilizing natural and sexual selection (Haldane, 1949; Clarke, 1979; Endler, 1986; Kingsolver et al., 2001; Hereford et al., 2004; Johnson & Barton, 2005). These two observations are in direct conflict as stabilizing selection should deplete genetic variation (Bürger & Gimelfarb, 1999; Tomkins et al., 2004; Johnson & Barton, 2005; Walsh & Blows, 2009). In general, maintenance of genetic variation is linked with environmental heterogeneity (Hedrick, 1986; Futuyma & Moreno, 1988; Wilson, 1994; MacDonald, 1995; Ellis et al., 2006). Thus, genetic variation in populations is the evolutionary footprint of temporally and spatially stochastic environments (Antonovics, 1971; Gillespie, 1973b; Hedrick et al., 1976; Hedrick, 1986; Mitchell-Olds, 1995; Sasaki & Ellner, 1995, 1997; Ellner, 1996; Bürger & Gimelfarb, 2002; Leimar, 2005; Heininger, 2013).Moreover, the biodiversity of species is mainly supported by habitat heterogeneity and niche partitioning. As a result, a positive relationship between species richness and habitat heterogeneity is predicted (Hutchinson, 1957; MacArthur, 1972; Petren, 2001; Kallimanis et al., 2008; de Souza Júnior et al., 2014).

A variety of other evolutionary controversies and conundrums can be resolved by the stochastic environment paradigm as was discussed in Heininger (2013).

20. Abbreviations

EGT: evolutionary game theory

ESS: evolutionarily stable strategy

SOC: self-organized criticality





21. References

Abel DL (2012) Is life unique? Life 2: 106–134.

Abrams M (2007) How do natural selection and random drift interact? Philos Sci 74: 666–679.

Abrams M (2009a) What determines biological fitness? The problem of the reference environment. Synthese 166: 21–40.

Abrams M (2009b) The unity of fitness. Philos Sci 76: 750–761.

Abrams PA (2006) Adaptive change in the resource-exploitation traits of a generalist consumer: the evolution and coexistence of generalists and specialists. Evolution 60: 427–439.

Abrams PA, Matsuda H (1997) Prey adaptation as a cause of predator–prey cycles. Evolution 51: 1742–1750.

Acar M, Mettetal JT, van Oudenaarden A (2008) Stochastic switching as a survival strategy in fluctuating environments. Nat Genet 40: 471–475.

Achilli A, Matmati N, Casalone E, Morpurgo G, Lucaccioni A, et al. (2004) The exceptionally high rate of spontaneous mutations in the polymerase delta proofreading exonuclease-deficient Saccharomyces cerevisiae strain starved for adenine. BMC Genet 5: 34.

Ackley DH, Littman ML (1991) Interactions between learning and evolution. In: Langton CG, Farmer JD, Rasmussen S, Taylor CE, eds. Artificial Life II. Reading, MA: Addison-Wesley. pp 487–509.

Adami C, Ofria C, Collier TC (2000) Evolution of biological complexity. Proc Natl Acad Sci USA 97: 4463–4468.

Adondakis S, Venable D (2004) Dormancy and germination in a guild of Sonoran Desert annuals. Ecology 85: 2582–2590.

Aguirre JD, Marshall DJ (2012) Does genetic diversity reduce sibling competition? Evolution 66: 94–102.

Aharony A, Feder J (1989) Fractals in physics, essays in honour of B. B. Mandelbrot. Physica D 38: 192–197.

Ahmed S, Hodgkin J (2000) MRT-2 checkpoint protein is required for germline immortality and telomere replication in C. elegans. Nature 403: 159–164.

Alizon S, Hurford A, Mideo N, Van Baalen M (2009) Virulence evolution and the trade-off hypothesis: history, current state of affairs and the future. J Evol Biol 22: 245–259.

Allais M (1953) Le comportement de l'homme rationnel devant le risque: Critique des postulats et axiomes de l'école Américaine. Econometrica 21: 503–546.

Allen B, Scholes Rosenbloom DI (2012) Mutation rate evolution in replicator dynamics. Bull Math Biol 74: 2650–2675.

Allen C, Stevens CF (1994) An evaluation of causes for unreliability of synaptic transmission. Proc Natl Acad Sci USA 91: 10380–10383.

Allen JS, Cheer SM (1996) The non-thrifty genotype. Curr Anthropol 37: 831–842.

Allen PM (1997) Cities and regions as self-organizing systems: Models of complexity. London, UK: Gordon and Beech Scientific Publishers.

Altenberg L (1991) Chaos from linear frequency-dependent selection. Am Nat 138: 51–68.

Altenberg L (1995) Genome growth and the evolution of the genotype-phenotype map. In: Banzhaf W, Eeckman FH, eds. Evolution and biocomputation: computational models of evolution. Berlin, Germany: Springer Verlag. pp 205–259.

Altenberg L (2005) Evolvability suppression to stabilize far-sighted adaptations. Artif Life 11: 427–443.

Amundsen T, Slagsvold T (1998) Hatching asynchrony in great tits: a bet-hedging strategy? Ecology 79: 295–304.

Amundson R (1994) Two concepts of constraint: Adaptationism and the challenge from developmental biology. Philos Sci 61: 556–578.

Amundson R (2001) Adaptation, development, and the quest for common ground. In: Orzack SH, Sober E, eds. Adaptationist and optimality. New York, NY: Cambridge University Press. pp 303–334.

Ancel L, Fontana W (2000) Plasticity, evolvability and modularity in RNA. J Exp Zool 288: 242–283.

Ancliff M, Park JM (2009) Maximum, minimum, and optimal mutation rates in dynamic environments. Phys Rev E Stat Nonlin Soft Matter Phys 80: 061910.

Anderson JP, Stephens DW, Dunbar SR (1997) Saltatory search: a theoretical analysis. Behav Ecol 8: 307–317.

Anderson JP, Daifuku R, Loeb LA (2004) Viral error catastrophe by mutagenic nucleosides. Annu Rev Microbiol 58: 183–205.

Anderson RM, May RM (1982) Coevolution of hosts and parasites. Parasitology 85: 411–426.

Andrade Jr JS, Wainer I, Moreira JE (1995) Self-organized criticality in the El Niño Southern oscillation. Physica A 215: 331–338.

Andras P, Roberts G, Lazarus J (2003) Environmental risk, cooperation and communication complexity. In: Alonso EKD, ed. Adaptive agents and multi-agent systems. Berlin, Germany: Springer-Verlag. pp 49–65.

André JB, Godelle B (2006) The evolution of mutation rate in finite asexual populations. Genetics 172: 611–626.

Andrewes FW (1922) Studies in group-agglutination. I. The Salmonella group and its antigenic structure. J Path Bacteriol 25: 505–521.

Angers B, Castonguay E, Massicotte R (2010) Environmentally induced phenotypes and DNA methylation: how to deal with unpredictable conditions until the next generation and after. Mol Ecol 19: 1283–1295.

Anholt BR (1994) Cannibalism and early instar survival in a larval damselfly. Oecologia 99: 60–65.

Anholt BR, Werner EE (1995) Interaction between food availability and predation mortality mediated by adaptive behavior. Ecology 76: 2230–2234.

Anholt BR, Werner EE (1998) Predictable changes in predation mortality as a consequence of changes in food availability and predation risk. Evol Ecol 12: 729–738.

Annila E (1982) Diapause and population fluctuations in Megastigmus specularis Walley and M. spermotrophus Wachtl (Hymenoptera: Torymidae). Ann Entomol Fenn 48: 33–36.

Antonovics J (1971) The effects of a heterogeneous environment on the genetics of natural populations. Am Sci 59: 592–599.

Aparicio JP, Solari HG (2001) Sustained oscillations in stochastic systems. Math Bioscience 169: 15–25.

Arcese P, Smith JNM (1988) Effects of population density and supplemental food on reproduction in song sparrows. J Anim Ecol 57: 119–136.

Arendt J, Reznick D (2008) Convergence and parallelism reconsidered: what have we learned about the genetics of adaptation? Trends Ecol Evol 23: 26–32.

Ariew A, Ernst Z (2009) What fitness can’t be. Erkenntnis 71: 289–301.

Ariño A, Pimm SL (1995) On the nature of population extremes. Evol Ecol 9: 429–443.

Arita T, Suzuki R (2000) Interactions between learning and evolution: the outstanding strategy generated by the Baldwin effect. Artif Life 7: 196–205.

Arndt PF, Burge CB, Hwa T (2002) DNA sequence evolution with neighbor-dependent mutation. In: Meyer G, et al., eds. Proceedings of the 6th International Conference on Computational Molecular Biology (RECOMB02). pp 32–38.

Arrow KJ (1965) Aspects of the theory of risk-bearing. Helsinki, Finland: Yrjö Jahnssonin Säätiö.

Ashby WR (1954) Design for a brain. New York, NY: John Wiley & Sons.

Ashby WR (1956) An introduction to cybernetics. London, UK: Chapman & Hall.

Ashby WR (1962) Principles of the self-organizing system. In: von Foerster H, Zopf GW, eds. Principles of self-organization: Transactions of the University of Illinois symposium. New York, NY: Pergamon. pp 255–278.

Aspi J, Jakalaniemi A, Tuomi J, Siikamaki P (2003) Multilevel phenotypic selection on morphological characters in a metapopulation of Silene tatarica. Evolution 57: 509–517.

Athreya KB, Karlin S (1971) On branching processes with random environments: extinction probabilities. Ann Math Stat 42: 1499–1520.

Atkins WM (2014) Biological messiness vs. biological genius: Mechanistic aspects and roles of protein promiscuity. J Steroid Biochem Mol Biol http://dx.doi.org/10.1016/j.jsbmb.2014.09.010

Auld JR, Rubio de Casas R (2013) The correlated evolution of dispersal and mating-system traits. Evol Biol 40: 185–193.

Avery SV (2006) Microbial cell individuality and the underlying sources of heterogeneity. Nat Rev Microbiol 4: 577–587.

Axelrod R (1984) The evolution of cooperation New York, NY: Basic Books.

Axelrod R, Hamilton WD (1981) The evolution of cooperation. Science 211: 1390–1396.

Ayala FJ (1968) Genotype, environment, and population numbers. Science 162: 1453–1459.

Baer B, Schmid-Hempel P (1999) Experimental variation in polyandry affects parasite loads and fitness in a bumble-bee. Nature 397: 151–154.

Bahar R, Hartmann CH, Rodriguez KA, Denny AD, Busuttil RA, Dolle ME, Calder RB, Chisholm GB, Pollock BH, Klein CA, Vijg J (2006) Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 441: 1011–1014.

Bak P (1993) In: Stein W, Varela FJ, eds. Thinking about Biology, Santa Fe Institute Studies in the Sciences of Complexity Vol. III. Reading, MA: Addison-Wesley. pp 255–268.

Bak P (1996) How nature works: The science of self-organized criticality. New York, NY: Copernicus.

Bak P, Tang C, Wiesenfeld K (1987) Self-organized criticality: an explanation for 1/f noise. Phys Rev Lett 59: 381–384.

Bak P, Tang C, Wiesenfeld K (1988) Self-organized criticality. Phys Rev A 38: 364–374.

Bak P, Chen K (1991) Self-organized criticality. Sci Am 264: 46–53.

Bak P, Sneppen K (1993) Punctuated equilibrium and criticality in a simple model of evolution. Phys Rev Lett 71: 4083–4086.

Balaban NQ (2011) Persistence: mechanisms for triggering and enhancing phenotypic variability. Curr Opin Genet Dev 21: 768–775.

Balaban NQ, Merrin J, Chait R, Kowalik L, Leibler S (2004) Bacterial persistence as a phenotypic switch. Science 305: 1622–1625.

Balázsi G, van Oudenaarden A, Collins JJ (2011) Cellular decision making and biological noise: from microbes to mammals. Cell 144: 910–925.

Baldwin JM (1896) A new factor in evolution. Am Nat 30: 441–451.

Baldwin JM (1902) Development and evolution. London, UK: Macmillan.

Balleza E, Alvarez-Buylla ER, Chaos A, Kauffman S, Shmulevich I, et al. (2008) Critical dynamics in genetic regulatory networks: examples from four kingdoms. PLoS ONE 3: e2456.

Bandara A, Fraser S, Chambers PJ, Stanley GA (2009) Trehalose promotes the survival of Saccharomyces cerevisiae during lethal ethanol stress, but does not influence growth under sublethal ethanol stress. FEMS Yeast Res 9: 1208–1216.

Barabasi AL (2005) The origin of bursts and heavy tails in human dynamics. Nature 435: 207–211.

Bar-Even A, Paulsson J, Maheshri N, Carmi M, O'Shea E, Pilpel Y, Barkai N (2006) Noise in protein expression scales with natural protein abundance. Nat Genet 38: 636–643.

Barnard CJ, Brown CAJ (1985) Risk-sensitive foraging in common shrews (Sorex araneus L.). Behav Ecol Sociobiol 16: 161–164.

Barnes HF (1952) Studies of fluctuations in insect populations. XII. Further evidence of prolonged larval life in the wheat-blossom midges. Ann Appl Biol 39: 370–373.

Barrick JE, Kauth MR, Strelioff CC, Lenski RE (2010) Escherichia coli rpoB mutants have increased evolvability in proportion to their fitness defects. Mol Biol Evol 27: 1338–1347.

Barto AG, Sutton RS, Anderson CW (1983) Neurolike adaptive elements that can solve difficult learning control problems. IEEE Trans Syst Man Cybern 13: 834–846.

Barton NH, Turelli M (1989) Evolutionary quantitative genetics: how little do we know? Annu Rev Genet 23: 337–370.

Barton N, Partridge L (2000) Limits to natural selection. BioEssays 22: 1075–1084.

Bartumeus F, Peters F, Pueyo S, Marrase C, Catalan J (2003) Helical Levy walks: Adjusting searching statistics to resource availability in microzooplankton. Proc Natl Acad Sci USA 100: 12771–12775.

Bassar RD, Lopez-Sepulcre A, Walsh MR, Turcotte MM, Torres-Mejia M, Reznick DN (2010) Bridging the gap between ecology and evolution: integrating density regulation and life-history evolution. Ann N Y Acad Sci 1206: 17–34.

Basu MK, Poliakov E, Rogozin IB (2009) Domain mobility in proteins: functional and evolutionary implications. Brief Bioinform 10: 205–216.

Batada NN, Hurst LD (2007) Evolution of chromosome organization driven by selection for reduced gene expression noise. Nat Genet 39: 945–949.

Bateson G (1972) Steps to an ecology of mind. San Francisco, CA: Chandler Publishing Co.

Bateson P (2012) The impact of the organism on its descendants. Genet Res Int 2012: 640612.

Beatty J (1984) Chance and natural selection. Philos Sci 51: 183–211.

Beatty J, Finsen S (1989) Rethinking the propensity interpretation -- a peek inside Pandora’s box. In: Ruse M, ed. What the philosophy of biology is. Dordrecht, the Netherlands: Kluwer Publishers. pp 17–30.

Beaumont HJ, Gallie J, Kost C, Ferguson GC, Rainey PB (2009) Experimental evolution of bet hedging. Nature 462: 90–93.

Beck R, Dejeans N, Glorieux C, Creton M, Delaive E, et al. (2012) Hsp90 is cleaved by reactive oxygen species at a highly conserved N-terminal amino acid motif. PLoS One 7: e40795.

Becks L, Agrawal AF (2012) The evolution of sex is favoured during adaptation to new environments. PLoS Biol 10: e1001317.

Becksei A, Serrano L (2000) Engineering stability in gene networks by autoregulation. Nature 405: 590–593.

Bedard AJ Jr, Georges TM (2000) Atmospheric infrasound. Phys Today 53: 32–37.

Bedau MA, Packard NH (2003) Evolution of evolvability via adaptation of mutation rates. BioSystems 69: 143–162.

Beekman M, Sumpter DJT, Ratnieks FLW (2001) Phase transition between disordered and ordered foraging in Pharaoh’s ants. Proc Natl Acad Sci USA 98: 9703–9706.

Beer S (1959) What has cybernetics to do with operational research? Oper Res Quart10: 1–21.

Beer S (1966) Decision and control. Chichester, UK: Wiley.

Beggs JM (2008) The criticality hypothesis: How local cortical networks might optimize information processing. Phil Trans A Math Phys Eng Sci 366: 329–343.

Beissinger SR, Gibbs JP (1993) Are variable environments stochastic? A review of methods to quantify environmental predictability. In: Yoshimura J, Clark CW, eds. Adaptation in stochastic environments. Berlin, Germany: Springer. pp 132–146.

Beldade P, Mateus ARA, Keller RA (2011) Evolution and molecular mechanisms of adaptive developmental plasticity. Mol Ecol 20: 1347–1363.

Bell G (1976) On breeding more than once. Am Nat 110: 57–77.

Bell G (1976) The costs of reproduction and their consequences. Am Nat 116: 45–76.

Bell G (2010) Fluctuating selection: the perpetual renewal of adaptation in variable environments. Phil Trans R Soc B 365: 87–97.

Bell G, Collins S (2008) Adaptation, extinction and global change. Evol Appl 1: 3–16.

Bell RHV (1971) A grazing ecosystem in the Serengeti. Sci Am 225: 355–363.

Belozerov VN, Fourie LJ, Kok DJ (2002) Photoperiodic control of developmental diapause in nymphs of prostriate ixodid ticks (Acari: Ixodidae). Exp Appl Acarol 28: 163–168.

Bengtsson J, Baillie SR, Lawton J (1997) Community variability increases with time. Oikos 78: 249–256.

Ben-Jacob E (2003) Bacterial self–organization: co–enhancement of complexification and adaptability in a dynamic environment. Phil Trans R Soc Lond A 361: 1283–1312.

Ben-Jacob E, Schultz D (2010) Bacteria determine fate by playing dice with controlled odds. Proc Natl Acad Sci USA 107: 13197–13198.

Bennett KD (1997) Evolution and ecology: the pace of life. Cambridge, UK: Cambridge University Press.

Bentley RA, Maschner HDG (2000) Subtle nonlinearity in popular album charts. Adv Complex Syst 2: 197–208.

Benton TG, Grant A (1999) Elasticity analysis as an important tool in evolutionary and population ecology. Trends Ecol Evol 14: 467–471.

Berg HC (1993) Random walks in biology. Princeton, NJ: Princeton University Press.

Berg OG (1978) A model for the statistical fluctuations of protein numbers in a microbial population. J Theor Biol 71: 587–603.

Bergman A, Feldman MW (1995) On the evolution of learning: representation of a stochastic environment. Theor Popul Biol 48: 251–276.

Bergstrom CT, Lachmann M (2004) Shannon information and biological fitness. In: IEEE Information Theory Workshop 2004. Piscataway, NJ: IEEE. pp 50–54.

Bergstrom TC (2002) Evolution of social behavior: individual and group selection. J Econ Perspect 16: 67–88.

Berkner S, Lipps G (2008) Mutation and reversion frequencies of different Sulfolobus species and strains. Extremophiles 12: 263–270.

Bernoulli D (1954) Exposition of a new theory on the measurement of risk. Econometrica 22: 23–36. (Original work published 1738).

Bernstein EL (1996) Against the Gods: The remarkable story of risk. New York, NY: John Wiley and Sons.

Berryman AA, ed. (1988) Dynamics of forest insect populations: patterns, causes, implications. New York, NY: Plenum Press.

Bertschinger N, Natschläger T (2004) Real-time computation at the edge of chaos in recurrent neural networks. Neural Comput 16: 1413–1436.

Bertsekas DP (2005) Dynamic Programming and Optimal Control, Vol. 1. Nashua, New Hampshire: Athena Scientific.

Bevison AA, Lee RF, Nevenzel JC (1972) Wax esters: major marine metabolic energy sources. Biochem Soc Symp 35: 175–187.

Bijlsma R, Loeschcke V (2005) Environmental stress, adaptation and evolution: an overview. J Evol Biol 18: 744–749.

Bijma P, Muir WM, Van Arendonk JAM (2007) Multilevel selection 1: quantitative genetics of inheritance and response to selection. Genetics 175: 277–288.

Binmore K (1992) Fun and games. Lexington, MA: D.C. Heath.

Binmore K (1994a) Game theory and the social contract. I. Playing fair. Cambridge, MA: MIT Press.

Binmore K (1994b) Game theory and the social contract. II. Just playing. Cambridge, MA: MIT Press.

Binney JJ, Dowrick NJ, Fisher AJ, Newman MEJ (1992) The theory of critical phenomena: an introduction to the renormalization group. New York, NY: Oxford University Press.

Birkhead TR (2000) Promiscuity: an evolutionary history of sperm competition and sexual conflict. London, UK: Faber & Faber.

Biro PA, Post JR, Parkinson EA (2003a) From individuals to populations: risk-taking by prey fish mediates mortality in whole-system experiments. Ecology 84: 2419–2431.

Biro PA, Post JR, Parkinson EA (2003b) Population consequences of behaviour: density-dependent risk-taking mediates mortality in young fish cohorts. J Anim Ecol 72: 546–555.

Biro PA, Abrahams MV, Post JR, Parkinson EA (2004) Predators select against high growth rates and risk-taking behaviour in domestic trout populations. Proc R Soc Lond B 271: 2233–2237.

Bishop AL, Rab FA, Sumner ER, Avery SV (2007) Phenotypic heterogeneity can enhance rare-cell survival in ‘stress-sensitive’ yeast populations. Mol Microbiol 63: 507–520.

Bjørnstad ON, Grenfell BT (2001) Noisy clockwork: time series analysis of population fluctuations in animals. Science 293: 638–643.

Blake WJ, Kærn M, Cantor CR, Collins JJ (2003) Noise in eukaryotic gene expression. Nature 422: 633–637.

Blake WJ, Balázsi G, Kohanski MA, Isaacs FJ, Murphy KF, Kuang Y, et al. (2006) Phenotypic consequences of promoter-mediated transcriptional noise. Mol Cell 24: 853–865.

Blanckenhorn WU, Hosken DJ, Martin OY, Reim C, Teuschl Y, Ward PI (2002) The costs of copulating in the dung fly Sepsis cynipsea. Behav Ecol 13: 353–358.

Blute M (2008) Is it time for an updated ‘eco-evo-devo’ definition of evolution by natural selection? Spontaneous Generations 2: 1–5.

Bock WJ (2003) Ecological aspects of the evolutionary processes. Zool Sci 20: 279–289.

Bock WJ (2010) Multiple explanations in Darwinian evolutionary theory. Acta Biotheor 58: 65–79.

Boege K (2010) Induced responses to competition and herbivory: natural selection on multi-trait phenotypic plasticity. Ecology 91: 2628–2637.

Bohonak AJ, Jenkins DG (2003) Ecological and evolutionary significance of dispersal by freshwater invertebrates. Ecol Lett 6: 783–796.

Bohr N (1937) Causality and complementarity. Philos Sci 4: 289–298.

Bollenbach T, Kishony R (2009) Quiet gene circuit more fragile than its noisy peer. Cell 139: 460–461.

Bolles RC, Beecher MD, eds. (1988) Evolution and learning. Hillsdale, NJ: Erlbaum.

Bollinger EK, Gavin TA (2004) Responses of nesting bobolinks (Dolichonyx oryzivorus) to habitat edges. Auk 121: 767–776.

Bolnick DI (2004) Can intraspecific competition drive disruptive selection? An experimental test in natural populations of sticklebacks. Evolution 58: 608–618.

Bonsall MB, Klug H (2011) The evolution of parental care in stochastic environments. J Evol Biol 24: 645–655.

Booth IR (2002) Stress and the single cell: intrapopulation diversity is a mechanism to ensure survival upon exposure to stress. Int J Food Microbiol 78: 19–30.

Boots M, Sasaki A (2000) The evolutionary dynamics of local infection and global reproduction in host-parasite interactions. Ecol Lett 3: 181–185.

Bork P, Jensen LJ, von Mering C, Ramani AK, Lee I, Marcotte EM (2004) Protein interaction networks from yeast to human. Curr Opin Struct Biol 14: 292–299.

Bornholdt S, Rohlf T (2000) Topological evolution of dynamical networks: Global criticality from local dynamics. Phys Rev Lett 84: 6114–6117.

Borrello ME (2012) Evolutionary restraints: the contentious history of group selection. Chicago, IL: University of Chicago Press.

Borstnik B, Pumpernik D (2002) Tandem repeats in protein coding regions of primate genes. Genome Res 12: 909–915.

Bosque C, Bosque MT (1995) Nest predation as a selective factor in the evolution of developmental rates in altricial birds. Am Nat 145: 234–260.

Bossdorf O, Richards CL, Pigliucci M (2008) Epigenetics for ecologists. Ecol Lett 11: 106–115.

Boswell RE, Prout ME, Steichen JC (1991) Mutations in a newly identified Drosophila melanogaster gene, mago nashi, disrupt germ cell formation and result in the formation of mirror-image symmetrical double abdomen embryos. Development 113: 373–384.

Botero CA, Boogert JB, Vehrencamp SL, Lovette IJ (2009) Climatic patterns predict the elaboration of song displays in mockingbirds. Curr Biol 19: 1–6.

Bouchard F, Rosenberg A (2004) Fitness, probability and the principles of natural selection. Brit J Phil Sci 55: 693–712.

Boulding E, Van Alstyne K (1993) Mechanisms of differential survival and growth of two species of Littorina on wave-exposed and on protected shores. J Exp Mar Biol Ecol 169: 139–166.

Boulding K (1978) Ecodynamics. Beverly Hills, CA: Sage.

Bouwman KM, Van Dijk RE, Wijmenga JJ, Komdeur J (2007) Older male reed buntings are more successful at gaining extrapair fertilizations. Anim Behav 73: 15–27.

Boyce MS (1979) Seasonality and patterns of natural selection for life histories. Am Nat 114: 569–583.

Boyce MS (1988) Bet hedging in avian life histories. In: Ouellet H, ed. Acta XIX Congressus Internationalis Ornithologici. Ottawa, Canada: National Museum of Natural Sciences, Ottawa, Ontario. pp 2131–2139.

Boyce MS, Perrins CM (1987) Optimizing Great Tit clutch size in a fluctuating environment. Ecology 68: 142–153.

Boyce MS, Kirsch EM, Servheen C (2002) Bet-hedging applications for conservation. J Biosci 27(Suppl. 2): 385–392. 

Boyd R, Richerson P (1990) Group selection among alternative evolutionarily stable strategies. J Theor Biol 145: 331–342.

Boyd R, Richerson PJ (1992) Punishment allows the evolution of cooperation (or anything else) in sizable groups. Ethol Sociobiol 13: 171–195.

Boyd R, Richerson PJ (2002) Group beneficial norms can spread rapidly in structured populations. J Theor Biol 215: 287–296.

Boyko A, Kovalchuk I (2008) Epigenetic control of plant stress response. Environ Mol Mutagen 49: 61–72.

Boyko A, Kovalchuk I (2011) Genome instability and epigenetic modification—heritable responses to environmental stress? Curr Opin Plant Biol 14: 260–266.

Boyle RA, Lenton TM (2006) Fluctuation in the physical environment as a mechanism for reinforcing evolutionary transitions. J Theor Biol 242: 832–843.

Bradshaw AD (1965) Evolutionary significance of phenotypic plasticity in plants. Adv Genet 13: 115–155.

Bradshaw WE (1986) Variable iteroparity as a life-history tactic in the pitcher-plant mosquito Wyemyia smithii. Evolution 80: 471–478.

Braithwaite VA, Salvanes AGV (2005) Environmental variability in the early rearing environment generates behaviourally flexible cod: implications for rehabilitating wild populations. Proc R Soc B-Biol Sci 272: 1107–1113.

Branda SS, Gonzalez-Pastor JE, Sigal BY, R. Losick R, Kolter R (2001) Fruiting body formation by Bacillus subtilis. Proc Natl Acad Sci USA 98: 11621–11626.

Brandon RN (1978) Adaptation and evolutionary theory. Stud Hist Philos Sci 9: 181–206.

Brandon RN (1990), Adaptation and environment. Princeton, NJ: Princeton University Press.

Brandon R, Carson S (1996) The indeterministic character of evolutionary theory: No ‘no hidden variables proof’ but no room for determinism either. Philos Sci 63: 315–337.

Bredy TW, Humpartzoomian RA, Cain DP, Meaney MJ (2003) Partial reversal of the effect of maternal care on cognitive function through environmental enrichment. Neuroscience 118: 571–576.

Bredy TW, Zhang TY, Grant RJ, Diorio J, Meaney MJ (2004) Peripubertal environmental enrichment reverses the effects of maternal care on hippocampal development and glutamate receptor subunit expression. Eur J Neurosci 20: 1355–1362.

Breivik J, Gaudernack G (2004) Resolving the evolutionary paradox of genetic instability: a cost-benefit analysis of DNA repair in changing environments. FEBS Lett 563: 7–12.

Brisson D (2003) The directed mutation controversy in an evolutionary context. Crit Rev Microbiol 29: 25–35.

Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439: 462–465.

Brommer J, Kokko H, Pietiäinen H (2000) Reproductive effort and reproductive values in periodic environments. Am Nat 155: 454–472.

Brookes PS, Yoon Y, Robotham JL, Anders MW, Sheu SS (2004) Calcium, ATP, and ROS: a mitochondrial love-hate triangle. Am J Physiol Cell Physiol 287: C817–C833.

Brosnan SF, Hopper LM (2014) Psychological limits on animal innovation. Anim Behav 92: 325–332.

Brown C, Davidson T, Laland K (2003) Environmental enrichment and prior experience of live prey improve foraging behaviour in hatchery-reared Atlantic salmon. J Fish Biol 63: 187–196.

Brown GP, Weatherhead PJ (2004) Sexual abstinence and the cost of reproduction in adult male water snakes, Nerodia sipedon. Oikos 104: 269–276.

Brown JH, Gupta VK, Li BL, Milne BT, Restrepo C, West GB (2002) The fractal nature of nature: power laws, ecological complexity and biodiversity. Phil Trans R Soc Lond B Biol Sci 357: 619–626.

Brunner KA, Klauninger B (2003) An integrative image of causality and emergence. In: Arshinov V, Fuchs C, eds. Causality, Emergence, Self-Organisation. Moscow, Russia: NIA-Priroda. pp 23–35.

Brutovsky B, Horvath D (2013) Structure of intratumor heterogeneity: is cancer hedging its bets? arXiv preprint arXiv:1307.0607.

Buchanan A, Triant M, Bedau M (2004) The flexible balance of evolutionary novelty and memory in the face of environmental catastrophes. Artificial Life IX. Cambridge, MA: MIT Press. pp 297–302.

Buchner J (1999) Hsp90 & Co. - a holding for folding. Trends Biochem Sci 24: 136–141.

Bui LT, Abbass HA, Branke J (2005) Multiobjective optimization for dynamic environments. In: Proceedings of the IEEE Congress on evolutionary computation, vol 3. pp 2349–2356.

Bull JJ (1987) Evolution of phenotypic variance. Evolution 41: 303–315.

Bull JJ (1994) Virulence. Evolution 48: 1423–1435.

Bull JJ, Shine R (1979) Iteroparous animals that skip opportunities for reproduction. Am Nat 114: 296–303.

Bull JJ, Molineux IJ, Rice WR (1991) Selection of benevolence in a host-parasite system. Evolution 45: 875–882.

Bull L (1999) On the Baldwin effect. Artif Life 5: 241–246.

Bulmer MG (1984) Risk avoidance and nesting strategies. J Theor Biol 106: 529–535.

Bulmer MG (1985) Selection for iteroparity in a variable environment. Am Nat 126: 63–71.

Bunge M, ed. (1967) Scientific research-The search for system. Vol. I. New York, NY: Springer-Verlag.

Burch CL, Chao L (2000) Evolvability of an RNA virus is determined by its mutational neighbourhood. Nature 406: 625–628.

Burger JM, Kolss M, Pont J, Kawecki TJ (2008) Learning ability and longevity: a symmetrical evolutionary trade-off in Drosophila. Evolution 62: 1294–1304.

Bürger R (1999) Evolution of genetic variability and the advantage of sex and recombination in changing environments. Genetics 153: 1055–1069.

Bürger R, Gimelfarb A (1999) Genetic variation maintained in multilocus models of additive quantitative traits under stabilizing selection. Genetics 152: 807–820.

Bürger R, Gimelfarb A (2002) Fluctuating environments and the role of mutation in maintaining quantitative genetic variation. Genet Res 80: 31–46.

Burian RM (1983) Adaptation. In: Grene M, ed. Dimensions of Darwinism. Cambridge, UK: Cambridge University Press. pp 286–314.

Burlando B (1993) The fractal geometry of evolution. J Theor Biol 163: 161–172.

Busch CR, DiRuggiero J (2010) MutS and MutL Are Dispensable for maintenance of the genomic mutation rate in the halophilic archaeon Halobacterium salinarum NRC-1. PLoS ONE 5: e9045.

Buss LW (1987) The evolution of individuality. Princeton, NJ: Princeton University Press.

Byrne PG, Whiting MJ (2011) Effects of simultaneous polyandry on offspring fitness in an African tree frog. Behav Ecol 22: 385–391.

Byrne PG, Roberts JD (2012) Evolutionary causes and consequences of sequential polyandry in anuran amphibians. Biol Rev 87: 209–228.

Çagatay T, Turcotte M, Elowitz MB, Garcia-Ojalvo J, Süel GM (2009) Architecture-dependent noise discriminates functionally analogous differentiation circuits. Cell 139: 512–522.

Cai L, Friedman N, Xie XS (2006) Stochastic protein expression in individual cells at the single molecule level. Nature 440: 358–362.

Calcott B, Sterelny K, eds. (2011) The major transitions in evolution revisited. Cambridge, MA: MIT Press.

Camazine S, Deneubourg J, Franks NR, Sneyd J, Theraulaz G, Bonabeau E (2001) Self-organization in biological systems. Princeton, NJ: Princeton University Press.

Camello-Almaraz C, Gomez-Pinilla PJ, Pozo MJ, Camello PJ (2006) Mitochondrial reactive oxygen species and Ca2+ signaling. Am J Physiol Cell Physiol 291: C1082–1088.

Campos PR, Wahl LM (2010) The adaptation rate of asexuals: deleterious mutations, clonal interference and population bottlenecks. Evolution 64: 1973–1983.

Caporale LH (1999) Chance favors the prepared genome. Ann NY Acad Sci 870: 1–21.

Caporale LH (2003a) Darwin in the genome: molecular strategies in biological evolution. New York, NY: McGraw-Hill.

Caporale LH (2003b) Natural Selection and the emergence of a mutation phenotype: An update of the evolutionary synthesis considering mechanisms that affect genome variation. Annu Rev Microbiol 57: 467–485.

Caporale LH (2009) Putting together the pieces: evolutionary mechanisms at work within genomes. BioEssays 31: 700–702.

Capy P, Gasperi G, Biémont C, Bazin C (2000) Stress and transposable elements: co-evolution or useful parasites? Heredity 85: 101–106.

Caraco T (1981) Energy budgets, risk and foraging preferences in dark-eyed juncos (Junco hyemalis). Behav Ecol Sociobiol 8: 213–217.

Caraco T (1982) Aspects of risk-aversion in foraging white-crowned sparrows. Anim Behav 30: 719–727.

Caraco T (1983) White-crowned sparrows (Zonotrichia leucophrys): foraging preferences in a risky environment. Behav Ecol Sociobiol 12: 63–69.

Caraco T, Martindale S, Whittam TS (1980) An empirical demonstration of risk-sensitive foraging preferences. Anim Behav 28: 820–830.

Caraco T, Chasin M (1984) Foraging preferences: response to reward skew. Anim Behav 32: 76–85.

Caraco T, Lima SL (1985) Foraging juncos—interaction of reward mean and variability. Anim Behav 33: 216–224.

Carja O, Liberman U, Feldman MW (2013) Evolution with stochastic fitnesses: A role for recombination. Theor Popul Biol 86: 29–42.

Carr PH (2004) Does God play dice? Insights from the fractal geometry of nature. Zygon 39: 933–940.

Carreras BA, Newman DE, Dobson I, Poole AB (2004) Evidence for self-organized criticality in a time series of electric power system blackouts. IEEE T Circuits I 51: 1733–1740.

Carroll SB (2001a) Chance and necessity: the evolution of morphological complexity and diversity. Nature 409: 1102–1109.

Carroll SB (2001b) The big picture. Nature 409: 669.

Cartar RV, Dill LM (1990) Why are bumble bees risk-sensitive foragers? Behav Ecol Sociobiol 26: 121–127.

Carter AJR, Wagner GP (2002) Evolution of functionally conserved enhancers can be accelerated in large populations: a population-genetic model. Proc Biol Sci 269: 953–960.

Carter LM, Schneider P, Reece SE (2014) Information use and plasticity in the reproductive decisions of malaria parasites. Malaria J 13: 115.

Casanueva MO, Burga A, Lehner B (2012) Fitness trade-offs and environmentally induced mutation buffering in isogenic C. elegans. Science 335: 82–85.

Casellas J, Noguera JL, Varona L, Sánchez A, Arqué M, Piedrafita J (2004) Viability of Iberian x Meishan F2 newborn pigs. II. Survival analysis up to weaning. J Anim Sci 82: 1925–1930.

Casti JL (1997) Would-be worlds: how simulation is changing the frontiers of science. New York, NY: John Wiley.

Caswell H (2001) Matrix population models: construction, analysis and interpretation, 2nd edn. Sunderland, MA: Sinauer.

Cavaliere M, Poyatos JF (2013) Plasticity facilitates sustainable growth in the commons. J R Soc Interface 10: 20121006.

Chakraborty S (2012) An automated flow for directed evolution based on detection of promiscuous scaffolds using spatial and electrostatic properties of catalytic residues. PLoS ONE 7: e40408.

Chalmers DJ (1990) The evolution of learning: an experiment in genetic connectionism. In: Touretzky DS, Elman JL, Sejnowski TJ, Hinton GE, eds. Proceedings of the 1990 Connectionist Models Summer School. San Mateo, CA: Morgan Kaufmann.

Chan KMA, Moore BR (2002) Whole-tree methods for detecting differential diversification rates. Syst Biol 51: 855–865.

Chang HH, Hemberg M, Barahona M, Ingber DE, Huang S (2008) Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453: 544–547.

Chapman T, Liddle LF, Kalb JM, Wolfner MF, Partridge L (1995) Cost of mating in Drosophila melanogaster females is mediated by male accessory gland products. Nature 373: 241–244.

Charlesworth B (1976) Recombination modification in a fluctuating environment. Genetics 83: 181–195.

Charlesworth B (1993) The evolution of sex and recombination in a varying environment. J Heredity 84: 345–350.

Charlesworth B (1994) Evolution in age-structured populations. 2nd ed. New York, NY: Cambridge University Press.

Charlesworth B, Lande R, Slatkin M (1982) A neo-Darwinian commentary on macroevolution. Evolution 36: 474–498.

Charmantier A, Blondel J, Perret P, Lambrechts MM (2004) Do extra-pair paternities provide genetic benefits for female blue tits Parus caeruleus? J Avian Biol 35: 524–532.

Charnov RL (1982) The theory of sex allocation. Princeton, NJ: Princeton University Press.

Charpentier A, Anand M, Bauch CT (2012) Variable offspring size as an adaptation to environmental heterogeneity in a clonal plant species: integrating experimental and modelling approaches. J Ecol 100: 184–195.

Chen G, Bradford WD, Seidel CW, Li R (2012) Hsp90 stress potentiates rapid cellular adaptation through induction of aneuploidy. Nature 482: 246–250.

Chesson PL, Warner RR (1981) Environmental variability promotes coexistence in lottery competitive systems. Am Nat 117: 923–943.

Chialvo DR (2010) Emergent complex neural dynamics. Nat Phys 6: 744–750.

Childs DZ, Metcalf CJ, Rees M (2010) Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants. Proc Biol Sci 277: 3055–3064.

Chirumamilla A, Yocum GD, Boetel MA, Dregseth RJ (2008) Multi-year survival of sugarbeet root maggot (Tetanops myopaeformis) larvae in cold storage. J Insect Physiol 54: 691–699.

Choi CS, Sano H (2007) Abiotic-stress induces demethylation and transcriptional activation of a gene encoding a glycerophosphodiesterase-like protein in tobacco plants. Mol Genet Genomics 277: 589–600.

Chowdhury S, Lloyd-Price J, Smolander O-P, Waici WCV, Hughes TR, et al. (2010) Information propagation within the genetic network of Saccharomyces cerevisiae. BMC Syst Biol 4: 143.

Chung JD, Stephanopoulos G, Ireton K, Grossman AD (1994) Gene expression in single cells of Bacillus subtilis: evidence that a threshold mechanism controls the initiation of sporulation. J Bacteriol 176: 1977–1984.

Cianciolo JM, Norton RA (2006) The ecological distribution of reproductive mode in oribatid mites as related to biological complexity. Exp Appl Acarol 40: 1–25.

Cinquemani E, Milias-Argeitis A, Summers S, Lygeros J (2008) Stochastic dynamics of genetic networks: modelling and parameter identification. Bioinformatics 24: 2748–2754.

Clarin TMA, Ruczy?ski I, Page RA, Siemers BM (2013) Foraging ecology predicts learning performance in insectivorous bats. PLoS ONE 8: e64823.

Clarke B (1979) Evolution of genetic diversity. Proc R Soc Lond B 205: 453–474.

Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51: 661–703.

Clauss MJ, Venable DL (2000) Seed germination in desert annuals: An empirical test of adaptive bet-hedging. Am Nat 155: 168–186.

Cline J, Braman JC, Hogrefe HH (1996) PCR fidelity of Pfu DNA polymerase and other thermostable DNA polymerases. Nucleic Acids Res 24: 3546–3551.

Clune J, Misevic D, Ofria C, Lenski RE, Elena SF, Sanjuán R (2008) Natural selection fails to optimize mutation rates for long-term adaptation on rugged fitness landscapes. PLoS Comput Biol 4: e1000187.

Clutton-Brock TH, ed. (1988) Reproductive success. Chicago, IL: University of Chicago Press.

Clutton-Brock TH (1991) The evolution of parental care. Princeton, NJ: Princeton University Press.

Cobb HG, Grefenstette JJ (1993) Genetic algorithms for tracking changing environments. In: Proceedings of the 5th international conference on genetic algorithms. Citeseer. pp 523–530.

Cohen D (1966) Optimizing reproduction in a randomly varying environment. J Theor Biol 12: 119–129.

Cole BJ (1995) Fractal time in animal behavior - the movement activity of Drosophila. Anim Behav 50: 1317–1324.

Cole LC (1954) The population consequences of life history phenomena. Q Rev Biol 29: 103–137.

Colegrave N, Collins S (2008) Experimental evolution: experimental evolution and evolvability. Heredity 100: 464–470.

Colman-Lerner A, Gordon A, Serra E, Chin T, Resnekov O, Endy D, et al. (2005) Regulated cell-to-cell variation in a cell-fate decision system. Nature 437: 699–706.

Colyvan M (2005) Probability and ecological complexity. Biol Philos 20: 869–879.

Comins HN, Hamilton WD, May RM (1980) Evolutionarily stable dispersal strategies. J Theor Biol 82: 205–230.

Conner JK (2001) How strong is natural selection? Trends Ecol Evol 16: 215–217.

Conner MM, White GC (1999) Effects of individual heterogeneity in estimating the persistence of small populations. Nat Resour Model 12: 109–127.

Conrad M (1983) Adaptability. New York, NY: Plenum Press.

Conway de Macario E, Macario AJL (2000) Stressors, stress, and survival: Overview. Front Biosci 5: d780–786.

Cooch EG, Ricklefs RE (1994) Do variable environments significantly influence optimal reproductive effort in birds? Oikos 69: 447–459.

Cook DL, Gerber AN, Tapscott SJ (1998) Modeling stochastic gene expression: implications for haploinsufficiency. Proc Natl Acad Sci USA 95: 15641–15646.

Cook S, Harrison R, Wernick P (2005) A simulation model of self-organising evolvability in software systems. In: Proc. of the 2005 IEEE International Workshop on Software Evolvability. Budapest, Hungary. pp 17–22.

Cooper GJ (2003) The science of the struggle for existence. On the foundations of ecology. Cambridge, UK: Cambridge University Press.

Cooper VS, Lenski RE (2000) The population genetics of ecological specialization in evolving E. coli populations. Nature 407: 736–739.

Cooper WS (1984) Expected time to extinction and the concept of fundamental fitness. J Theor Biol 107: 603–629.

Cooper WS (1989) How evolutionary biology challenges the classical theory of rational choice. Biol Philos 4: 457–481.

Cooper WS, Kaplan RH (1982) Adaptive “coin-flipping”: A decision-theoretic examination of natural selection for random individual variation. J Theor Biol 94: 135–151.

Corning PA (1983) The Synergism Hypothesis: a theory of progressive evolution. New York, NY: McGraw-Hill.

Corning PA (1995) Synergy and self-organization in the evolution of complex systems. Syst Res 12: 89–121.

Corning PA (1996) The co-operative gene: on the role of synergy in evolution. Evol Theor 11: 183–207.

Corning PA (1997) Holistic Darwinism: “Synergistic selection” and the evolutionary process. J Soc Evol Syst 20: 363–400.

Corning PA (2002) The re-emergence of “emergence”: a venerable concept in search of a theory. Complexity 7: 18–30.

Corning PA (2005) Holistic Darwinism: Synergy, cybernetics and the bioeconomics of evolution. Chicago, IL: University of Chicago Press.

Correig AM, Urquizu M, Vila J (1997) Aftershock series of event February 18, 1996: An interpretation in terms of self-organized criticality. J Geophys Res 102: 27407–27420.

Cottry S, Cameron CE, Andino R (2001) RNA virus error catastrophe: direct molecular test by using ribavirin. Proc Natl Acad Sci USA 98: 6895–6900.

Coulson T, Benton TG, Lundberg P, Dall SRX, Kendall BE (2006) Putting evolutionary biology in the ecological theatre: a demographic framework mapping genes to communities. Evol Ecol Res 8: 1155–1171.

Craig DM (1982) Group selection versus individual selection: An experimental analysis. Evolution 36: 271–282.

Crean AJ, Marshall DJ (2009) Coping with environmental uncertainty: dynamic bet hedging as a maternal effect. Phil Trans R Soc Lond B Biol Sci 364: 1087–1096.

Crean AJ, Dwyer JM, Marshall DJ (2012) Fertilization is not a new beginning: the relationship between sperm longevity and offspring performance. PLoS ONE 7: e49167.

Crombach A, Hogeweg P (2008) Evolution of evolvability in gene regulatory networks. PLoS Comput Biol 4: e1000112.

Crow JF (1987) Population genetics history: a personal view. Annu Rev Genet 21: 1–22.

Crowe JH, Hoekstra FA, Crowe LM (1992) Anhydrobiosis. Annu Rev Physiol 54: 579–599.

Crutchfield JP, Machta J (2011) Introduction to focus issue on “Randomness, structure, and causality: Measures of complexity from theory to applications”. Chaos 21: 037101.

Crutsinger GM, Collins MD, Fordyce JA, Gompert Z, Nice CC, Sanders NJ (2006) Plant genotypic diversity predicts community structure and governs an ecosystem process. Science 313: 966–968.

Csányi V (1989) Evolutionary systems and society: A general theory. Durham, NC: Duke University Press.

Curry PA (2001) Decision making under uncertainty and the evolution of interdependent preferences. J Econ Theory 98: 357–369.

Cyr H (1997) Does inter-annual variability in population density increase with time? Oikos 79: 549–558.

Davey G (1989) Ecological learning theory. New York, NY: Routledge.

Dall SRX (2010) Managing risk: the perils of uncertainty. In: Westneat DF, Fox CW, eds. Evolutionary behavioral ecology. Oxford, UK: Oxford University Press. pp 194–206.

Dall SRX, Johnstone RA (2002) Managing uncertainty: information and insurance under the risk of starvation. Phil Trans R Soc Lond Ser B 357: 1519–1526.

Daly M (1978) The cost of mating. Am Nat 112: 771–774.

Daly M, Wilson M (2002) Two special issues on risk. Evol Hum Behav 23: 1–2.

Damer TE (1995) Attacking faulty reasoning: A practical guide to fallacy-free arguments. 3rd edn. Belmont, CA: Wadsworth Publishing.

Damper RI (2006) Thought experiments can be harmful. The Pantaneto Forum, Issue 26. http://www.pantaneto.co.uk.

Danforth BN (1999) Emergence dynamics and bet hedging in a desert bee, Perdita portalis. Proc R Soc Lond Ser B 266: 1985–1994.

Danks HV (1987) Insect dormancy: an ecological perspective. Monograph Series no. 1. Biological Survey of Canada. Ottawa, Canada: National Museum of Natural Science.

Danks HV (1992) Long life cycle in insects. Can Entomol 124: 167–187.

Danks HV (2004) Seasonal adaptation in Arctic insects. Integr Comp Biol 44: 85–94.

Darwin CR (1859, 6th edn. 1872) On the origin of species. London, UK: John Murray.

Darwin C (1871) The descent of man. London, UK: Murray.

Darwin F, Seward AC, eds. (1903) More Letters of Charles Darwin. Volume I. London, UK: John Murray.

Davidson CJ, Surette MG (2008) Individuality in bacteria. Annu Rev Genet 42: 253–268.

Dawkins R (1976) The selfish gene. Oxford, UK: Oxford University Press.

Dawkins R (2012) The descent of Edward Wilson. Prospect Magazine 195: 1–59.

Day KP, Karamalis F, Thompson J, Barnes DA, Peterson C, Brown H, et al. (1993) Genes necessary for expression of a virulence determinant and for transmission of Plasmodium falciparum are located on a 0.3-megabase region of chromosome 9. Proc Natl Acad Sci USA 90: 8292–8296.

Day T (2002) Virulence evolution via host exploitation and toxin production in spore-producing pathogens. Ecol Lett 5: 471–476.

Dearsly AL, Sinden RE, Self IA (1990) Sexual development in malarial parasites, gametocyte production, fertility and infectivity to the mosquito vector. Parasitology 100: 359–368.

Debat V, David P (2001) Mapping phenotypes: canalization, plasticity and developmental stability. Trends Ecol Evol 16: 555–561.

Dees ND, Bahar S (2010) Mutation size optimizes speciation in an evolutionary model. PLoS ONE 5: e11952.

de Jong G (1995) Phenotypic plasticity as a product of selection in a variable environment. Am Nat 145: 493–512.

de Jong IG, Haccou P, Kuipers OP (2011) Bet hedging or not? A guide to proper classification of microbial survival strategies. Bioessays 33: 215–223.

Dekel E, Alon U (2005) Optimality and evolutionary tuning of the expression level of a protein. Nature 436: 588–592.

Dell AI, Pawar S, Savage VM (2011) Systematic variation in the temperature dependence of physiological and ecological traits. Proc Natl Acad Sci USA 108: 10591–10596.

Dellus-Gur E, Toth-Petroczy A, Elias M, D.S. Tawfik DS (2013) What makes a protein fold amenable to functional innovation? Fold polarity and stability trade-offs. J Mol Biol 425: 2609–2621.

de los Ríos A, Valea S, Ascaso C, Davila A, Kastovsky J, et al. (2010) Comparative analysis of the microbial communities inhabiting halite evaporites of the Atacama Desert. Int Microbiol 13: 79–89.

Dempster ER (1955) Maintenance of genetic heterogeneity. Cold Spring Harb Symp Quant Biol 20: 25–32.

Dempster JP, McLean IFG, eds. (1998) Insect populations: in theory and in practice. London, UK: Kluwer Academic Press.

den Boer PJ (1968) Spreading of risk and stabilization of animal numbers. Acta Biotheor 18: 165–194.

Denton MJ, Dearden PK, Sowerby SJ (2003) Physical law not natural selection as the major determinant of biological complexity in the subcellular realm: New support for the pre-Darwinian conception of evolution by natural law. Biosystems 71: 297–303.

Dercole F, Ferriere R, Rinaldi S (2010) Chaotic Red Queen coevolution in three-species food chains. Proc Biol Sci 277: 2321–2330.

de Roode JC, Yates AJ, Altizer S (2008) Virulence-transmission trade-offs and population divergence in virulence in a naturally occurring butterfly parasite. Proc Natl Acad Sci USA 105: 7489–7494.

De Silva AP, Uchiyama S (2007) Molecular logic and computing. Nat Nano 2: 399–410.

de Souza Júnior MB, Ferreira FF, de Oliveira VM (2014) Effects of the spatial heterogeneity on the diversity of ecosystems with resource competition. Physica A 393: 312–319.

de Valpine P, Eadie JM (2008) Conspecific brood parasitism and population dynamics. Am Nat 172: 547–562.

de Visser JA, Hermisson J, Wagner GP, Ancel Meyers L, Bagheri-Chaichian H, Blanchard JL, et al. (2003) Perspective: evolution and detection of genetic robustness. Evolution 57: 1959–1972.

Diamond PA, Rothschild M (1978) Uncertainty in economics: reading and exercises. New York, NY: Academic Press.

Díaz Arenas C, Cooper TF (2013) Mechanisms and selection of evolvability: experimental evidence. FEMS Microbiol Rev 37: 572–582.

DiBattista JD, Feldheim KA, Gruber SH, Hendry AP (2008) Are indirect genetic benefits associated with polyandry? Testing predictions in a natural population of lemon sharks. Mol Ecol 17: 783–795.

Dickinson WJ, Seger J (1999) Cause and effect in evolution. Nature 399: 30.

Dieckmann U, Law R (1996) The dynamical theory of coevolution: a derivation from stochastic ecological processes. J Math Biol 34: 579–612.

Diekmann O (2004) A beginner’s guide to adaptive dynamics. In: Rudnicki R, ed. Mathematical modelling of population dynamics. Banach Center Publication, Institute of Mathematics, Polish Academy of Sciences, Vol. 63. pp 47–86.

Dietrich J, Schmitt P, Zieger M, Preve B, Rolland JL, Chaabihi H, Gueguen Y (2002) PCR performance of the highly thermostable proof-reading B-type DNA polymerase from Pyrococcus abyssi. FEMS Microbial Lett 217: 89–94.

Diffley P, Scott JO, Mama K, Tsen TNR (1987) The rate of proliferation among African trypanosomes is a stable trait that is directly related to virulence. J Trop Med Hyg 36: 533–540.

Dingemanse NJ, Both C, Drent PJ, Tinbergen JM (2004) Fitness consequences of avian personalities in a fluctuating environment. Proc R Soc B 271: 847–852.

Dixon PA, Milicich MJ, Sugihara G (1999) Episodic fluctuations in larval supply. Science 283: 1528–1530.

Dobrzy?ski M, Bernatowicz P, Kloc M, Kubiak J (2011) Evolution of bet-hedging mechanisms in cell cycle and embryo development stimulated by weak linkage of stochastic processes. In: Kubiak JZ, ed. Cell cycle in development, vol 53. Results and problems in cell differentiation. Berlin, Germany: Springer. pp 11–30.

Dobzhansky T (1950) Evolution in the tropics. Am Sci 38: 209–221.

Dobzhansky T (1959) Variation and evolution. Proc Am Philos Soc 103: 252–263.

Dobzhansky T (1973) Nothing in biology makes sense except in the light of evolution. Am Biol Teach 35: 125–129.

Dochtermann NA, Gienger CM (2012) Individual variability in life-history traits drives population size stability. Curr Zool 58: 358–362.

Doe CQ, Goodman CS (1985) Early events in insect neurogenesis. II. The role of cell interactions and cell lineage in the determination of neuronal precursor cells. Dev Biol 111: 206–219.

Doebeli M, Dieckmann U (2000) Evolutionary branching and sympatric speciation caused by different types of ecological interactions. Am Nat 156: S77–S101.

Doebeli M, Ispolatov I (2014) Chaos and unpredictability in evolution. Evolution 68: 1365–1373.

Domes K, Scheu S, Maraun M (2007) Resources and sex: soil re-colonization by sexual and parthenogenetic oribatid mites. Pedobiologia 51: 1–11.

Domingo E, Biebricher CK, Eigen M, Holland JJ (2001) Quasispecies and RNA virus evolution: principles and consequences. Georgetown, TX: Landes Bioscience.

Domingo E, Sheldon J, Perales C (2012) Viral quasispecies evolution. Microbiol Mol Biol Rev 76: 159–216.

Donadio S, Staver MJ, McAlpine JB, Swanson SJ, Katz L (1991) Modular organization of genes required for complex polyketide biosynthesis. Science 252: 675–679.

Donaldson-Matasci MC, Lachmann M, Bergstrom CT (2008) Phenotypic diversity as an adaptation to environmental uncertainty. Evol Ecol Res 10: 493–515.

Donaldson-Matasci MC, Bergstrom CT, Lachmann M (2010) The fitness value of information. Oikos 119: 219–230.

Donohue K (2003) The influence of neighbour relatedness on multilevel selection in the Great Lakes sea rocket. Am Nat 162: 77–92.

Donohue K (2004) Density-dependent multilevel selection in the Great Lakes sea rocket. Ecology 85: 180–191.

Draghi J, Wagner GP (2008) Evolution of evolvability in a developmental model. Evolution 62: 301–315.

Draghi J, Wagner GP (2009) The evolutionary dynamics of evolvability in a gene network model. J Evol Biol 22: 599–611.

Drake JW (1991) A constant rate of spontaneous mutation in DNA-based microbes. Proc Natl Acad Sci USA 88: 7160–7164.

Drake JW (2009) Avoiding dangerous missense: thermophiles display especially low mutation rates. PLoS Genet 5: e1000520.

Dreiss AN, Silva N, Richard M, Moyen F, Thery M, Møller AP, Danchin E (2008) Condition-dependent genetic benefits of extrapair fertilization in female blue tits Cyanistes caeruleus. J Evol Biol 21: 1814–1822.

Dublanche Y, Michalodimitrakis K, Kümmerer N, Foglierini M, Serrano L (2006) Noise in transcription negative feedback loops: simulation and experimental analysis. Mol Syst Biol 2: doi:10.1038/msb4100081.

Dubnau D, Lovett CM Jr (2002) Transformation and recombination. In: Sonenshein AL, Hoch JA, Losick R, eds. Bacillus subtilis and its closest relatives: from genes to cells. Washington, DC: ASM Press. pp 453–471.

Dubnau D, Losick R (2006) Bistability in bacteria. Mol Microbiol 61: 564–572.

Dubravcic D (2013) Quantitative evolutionary analysis of the life cycle of social amoebae. PhD thesis. Paris, France: Université René Descartes-Paris V.

Ducrocq V, Besbes B, Protais M (2000) Genetic improvement of laying hens viability using survival analysis. Genet Sel Evol 32: 23–40.

Dudley SA, Murphy GP, File AL (2013) Kin recognition and competition in plants. Funct Ecol 27: 898–906.

Duffy JE, Morrison CL, Macdonald KS (2002) Colony defence and behavioural differentiations in the eusocial shrimp Synalpheus regalis. Behav Ecol Sociobiol 51: 488–495.

Duffy WG (1994) Demographics of Lestes disjunctus (Odonata: Zygoptera) in a riverine wetland. Can J Zool 72: 910–917.

Dukas R (1998) Evolutionary ecology of learning. In: Dukas R, ed. Cognitive ecology: the evolutionary ecology of information processing and decision making. Chicago, IL: University of Chicago Press. pp 129–174.

Dukas R, Duan JJ (2000) Potential fitness consequences of associative learning in a parasitoid wasp. Behav Ecol 11: 536–543.

Durrett R, Schmidt D (2008) Waiting for two mutations: with applications to regulatory sequence evolution and the limits of Darwinian evolution. Genetics 180: 1501–1509.

Dutil JD (1986) Energetic constraints and spawning interval in the anadromous Arctic charr (Salvelinus alpinus). Copeia 1986: 945–955.

Earl DJ, Deem MW (2004) Evolvability is a selectable trait. Proc Natl Acad Sci USA 101: 11531–11536.

Eberhard WG (1986) Possible mutualism between females of the subsocial membracid Polyglypta dispar (Homoptera). Behav Ecol Sociobiol 19: 447–453.

Ebert D (1994) Virulence and local adaptation of a horizontally transmitted parasite. Science 265: 1084–1086.

Ebert D, Mangin KL (1997) The influence of host demography on the evolution of virulence of a microsporidian gut parasite. Evolution 51: 1828–1837.

Edelman GM, Gally JA (2001) Degeneracy and complexity in biological systems. Proc Natl Acad Sci USA 98: 13763–13768.

Eden M (1967) Inadequacies of neo-Darwinian evolution as a scientific theory. In: Moorhead PS, Kaplan MM, eds. Mathematical challenges to the neo-Darwinian interpretation of evolution. Philadelphia, PA: Wistar Institute Press. pp 109–111.

Editorial (1989) Thrifty genotype rendered detrimental by progress? Lancet 2: 839–840.

Ehrlich PR, Holm RW (1963) The process of evolution. New York, NY: McGraw-Hill.

Ehrlich PR, Raven PH (1964) Butterflies and plants: a study in coevolution. Evolution 18: 586–608.

Eiben AE, Smith JE (2008) Introduction to Evolutionary Computing. Berlin, Germany: Springer.

Eickwort GC (1975) Gregarious nesting of the mason bee Hoplitis anthocopoides and the evolution of parasitism and sociality among megachilid bees. Evolution 29: 142–150.

Eigen M (1971) Self-organization of matter and evolution of biological macromolecules. Naturwissenschaften 58: 465–526.

Eigen M (1992) Steps towards life: a perspective on evolution. Oxford, UK: Oxford University Press.

Eigen M, McCaskill J, Schuster P (1988) Molecular quasi-species. J Phys Chem 92: 6881–6891

Einum S, Fleming IA (2004) Environmental unpredictability and offspring size: conservative versus diversified bet-hedging. Evol Ecol Res 6: 443–455.

Eldar A, Elowitz M (2010) Functional roles for noise in genetic circuits. Nature 467: 167–173.

Eldredge N (2003) Human triangles: genes, sex and economics in human evolution. In: Scher SJ, Rauscher F, eds. Evolutionary psychology: alternative approaches. New York, NY: Springer-Verlag. pp 91–110.

Eldredge N, Gould SJ (1972) Punctuated equilibrium: an alternative to phyletic gradualism. In: Schopf TJM, ed. Models in paleobiology. San Francisco, CA: Freeman, Cooper. pp 82–115.

Eldredge N, Thompson JN, Brakefield PM, Gavrilets S, Jablonski D, Jackson JBC, et al. (2005) The dynamics of evolutionary stasis. Paleobiology 31: 133–145.

Elena SF, Lenski RE (2003) Evolution experiments with microorganisms: the dynamics and genetic bases of adaptation. Nat Rev Genet 4: 457–469.

Eliassen S, Jørgensen C, Mangel M, Giske J (2009) Quantifying the adaptive value of learning in foraging behavior. Am Nat 174: 478–489.

Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5: 435–445.

Ellers J, van Alphen JJM (1997) Life history evolution in Asobara tabida: plasticity in allocation of fat reserves to survival and reproduction. J Evol Biol 10: 771–785.

Ellis BJ, Jackson JJ, Boyce WT (2006) The stress response systems: Universality and adaptive individual differences. Dev Rev 26: 175–212.

Ellis BJ, Figueredo AJ, Brumbach BH, Schlomer GL (2009) Fundamental dimensions of environmental risk: the impact of harsh versus unpredictable environments on the evolution and development of life history strategies. Hum Nat 20: 204–268.

Ellner S (1985) ESS germination strategies in randomly varying environments. I. Logistic-type models. Theor Popul Biol 28: 50–79.

Ellner S (1986) Germination dimorphisms and parent-offspring conflict in seed germination. J Theor Biol 123: 173–185.

Ellner S (1996) Environmental fluctuations and the maintenance of genetic diversity in age or stage-structured populations. Bull Math Biol 58: 103–128.

Ellner SP (1997) You bet your life: Life-history strategies in fluctuating environments. In: Othmer HG, Adler HG, Lewis MA, Dallon JC, eds. Case studies in mathematical modeling: Ecology, physiology and cell biology. Upper Saddle River, NJ: Prentice Hall pp 3–24.

Ellner S, Real LA (1989) Optimal foraging models for stochastic environments: Are we missing the point? Comment Theor Biol 1: 129–158.

Ellner S, Hairston NG Jr (1994) Role of overlapping generations in maintaining genetic variation in a fluctuating environment. Am Nat l43: 403–417.

Elowitz MB, Levine AJ, Siggia ED, Swain PS (2002) Stochastic gene expression in a single cell. Science 297: 1183–1186.

El-Samad H, Madhani HD (2011) Can a systems perspective help us appreciate the biological meaning of small effects? Dev Cell 21: 11–13.

Endler JA (1986) Natural selection in the wild. Princeton, NJ: Princeton University Press.

Engelhard GH, Heino M (2005) Scale analysis suggests frequent skipping of the second reproductive season in Atlantic herring. Biol Lett 1: 172–175.

Engen S, Bakke Ø, Islam A (1998) Demographic and environmental stochasticity–concepts and definitions. Biometrics 54: 840–846.

Engen S, Sæther BE (2014) Evolution in fluctuating environments: decomposing selection into additive components of the Robertson-Price equation. Evolution 68: 854–865.

Erikstad KE, Fauchald P, Tveraa T, Steen H (1998) On the cost of reproduction in long-lived birds: the influence of environmental variability. Ecology 79: 1781–1788.

Erwin DH (2000) Macroevolution is more than repeated rounds of microevolution. Evol Dev 2: 78–84.

Eshel I (1972) On the neighbor effect and the evolution of altruistic traits. Theor Popul Biol 3: 258–277.

Evans MEK, Dennehy JJ (2005) Germ banking: bethedging and variable release from egg and seed dormancy. Q Rev Biol 80: 431–451.

Evans ME, Ferrière R, Kane MJ, Venable DL (2007) Bet hedging via seed banking in desert evening primroses (Oenothera, Onagraceae): demographic evidence from natural populations. Am Nat 169: 184–194.

Ewald PW (1987) Transmission modes and evolution of the parasitism-mutualism continuum. Ann N Y Acad Sci 503: 295–306.

Ewald PW (1994) The evolution of infectious disease. Oxford, UK: Oxford University Press.

Ewens WJ (1967) The probability of survival of a new mutant in a fluctuating environment. Heredity 22: 438–443.

Falconer DS (1990) Selection in different environments: effects on environmental sensitivity (reaction norm) and on mean performance. Genet Res 56: 57–70.

Falconer DS, Mackay TFC (1995) Introduction to quantitative genetics. London, UK: Longman.

Falster DS, Westoby M (2003) Plant height and evolutionary games. Trends Ecol Evol 18: 337–343.

Feinberg AP, Irizarry RA (2010) Stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc Natl Acad Sci USA 107: 1757–1764.

Feissner RF, Skalska J, Gaum WE, Sheu SS (2009) Crosstalk signaling between mitochondrial Ca2+ and ROS. Front Biosci 14: 1197–1218.

Feldman MW, Liberman U (1986) An evolutionary reduction principle for genetic modifiers. Proc Natl Acad Sci USA 83: 4824–4827.

Felsenstein J (1971) On the biological significance of the cost of gene substitution. Am Nat 105: 1–11.

Felsenstein J (1976) The theoretical population genetics of variable selection and migration. Annu Rev Genet 10: 253–280.

Felsenstein J (1978) Macro-evolution in a model ecosystem. Am Nat 112: 177–195.

Ferenci T, Maharjan R (2014) Mutational heterogeneity: A key ingredient of bet-hedging and evolutionary divergence? BioEssays DOI: 10.1002/bies.201400153.

Fernández J, Plastino A, Diambra L, Mostaccio C (1998) Dynamics of coevolutive processes. Phys Rev E 57: 5897–5903.

Fernández-Sánchez A, Madrigal-Santillán E, Bautista M, Esquivel-Soto J, Morales-González A, et al. (2011) Inflammation, oxidative stress, and obesity. Int J Mol Sci 12: 3117–3132.

Ferrell JE Jr (2002) Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. Curr Opin Cell Biol 14: 140–148.

Ferrell JE Jr, Machleder EM (1998) The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280: 895–898.

Fincke OM, Hadrys H (2001) Unpredictable offspring survivorship in the damselfly, Megaloprepus coerulatus, shapes parental behavior, constrains sexual selection, and challenges traditional fitness estimates. Evolution 55: 762–772.

Finkel SE, Kolter R (1999) Evolution of microbial diversity during prolonged starvation. Proc Natl Acad Sci USA 96: 4023–4027.

Finnegan EJ (2002) Epialleles - a source of random variation in times of stress. Curr Opin Plant Biol 5: 101–106.

Fischbacher U, Gächter S, Quercia S (2012) The behavioral validity of the strategy method in public good experiments. J Econ Psychol 33: 897–913.

Fischer B, Taborsky B, Dieckmann U (2009) Unexpected patterns of plastic energy allocation in stochastic environments. Am Nat 173: E108–E120.

Fisher DO, Double MC, Blomberg SP, Jennions MD, Cockburn A (2006) Postmating sexual selection increases lifetime fitness of polyandrous females in the wild. Nature 444: 89–92.

Fisher RA (1930) The genetical theory of natural selection. Oxford, UK: Clarendon Press.

Flatscher R, Frajman B, Schönswetter P, Paun O (2012) Environmental heterogeneity and phenotypic divergence: can heritable epigenetic variation aid speciation? Genet Res Int 2012: 698421.

Flatt T (2005) The evolutionary genetics of canalization. Q Rev Biol 80: 287–316.

Fontana W, Schuster P (1998) Continuity in evolution: on the nature of transitions. Science 280: 1451–1455.

Forber P, Smead R (2014) An evolutionary paradox for prosocial behavior. J Philos 111: 151–166.

Forbes LS (1991) Insurance offspring and brood reduction in a variable environment: the costs and benefits of pessimism. Oikos 62: 325–332.

Fordham DA, Georges A, Brook BW (2007) Demographic response of snake-necked turtles correlates with indigenous harvest and feral pig predation in tropical northern Australia. J Anim Ecol 76: 1231–1243.

Forsman A, Lindell LE (1993) The advantage of a big head-swallowing performance in adders, Vipera berus. Funct Ecol 7: 183–189.

Fossøy F, Johnsen A, Lifjeld JT (2008) Multiple genetic benefits of female promiscuity in a socially monogamous passerine. Evolution 62: 145–156.

Foster KR (2004) Diminishing returns in social evolution: the not-so-tragic commons. J Evol Biol 17: 1058–1072.

Foster KR, Shaulsky G, Strassmann JE, Queller DC, Thompson CRL (2004) Pleiotropy as a mechanism to stabilize cooperation. Nature 431: 693–696.

Fox CW, Rauter CM (2003) Bet-hedging and the evolution of multiple mating. Evol Ecol Res 5: 273–286.

Fox GA (1993) Life history evolution and demographic stochasticity. Evol Ecol 7: 1–14.

Fox GA (2005) Extinction risk of heterogeneous populations. Ecology 86: 1191–1198.

Fox GA, Kendall BE (2002) Demographic stochasticity and the variance reduction effect. Ecology 83: 1928–1934.

Fox GA, Kendall BE, Fitzpatrick JW, Woolfenden GE (2006) Consequences of heterogeneity in survival probability in a population of Florida scrub-jays. J Anim Ecol 75: 921–927.

Fox Keller E (2007) Contenders for life at the dawn of the twenty-first century: approaches from physics, biology and engineering. Interdiscip Sci Rev 32: 113–122.

Frank SA (1995) Mutual policing and repression of competition in the evolution of cooperative groups. Nature 377: 520–522.

Frank SA (1996a) Policing and group cohesion when resources vary. Anim Behav 52: 1163–1169.

Frank SA (1996b) Models of parasite virulence. Q Rev Biol 71: 37–78.

Frank SA (2010) The trade-off between rate and yield in the design of microbial metabolism. J Evol Biol 23: 609–613.

Frank SA (2011) Natural selection. I. Variable environments and uncertain returns on investment. J Evol Biol 24: 2299–2309.

Frank SA, Slatkin M (1990) Evolution in a variable environment. Am Nat 136: 244–260.

Frank SA, Nowak MA (2004) Problems of somatic mutation and cancer. BioEssays 26: 291–299.

Frank SA, Schmid-Hempel P (2008) Mechanisms of pathogenesis and the evolution of parasite virulence. J Evol Biol 21: 396–404.

Franklin AB, Anderson DR, Gutiérrez RJ, Burnham KP (2000) Climate, habitat quality, and fitness in Northern Spotted Owl populations in northwestern California. Ecol Monogr 70: 539–590.

Fraser D, Kærn M (2009) A chance at survival: gene expression noise and phenotypic diversification strategies. Mol Microbiol 71: 1333–1340.

Fraser HB, Hirsh AE, Giaever G, Kumm J, Eisen MB (2004) Noise minimization in eukaryotic gene expression. PLoS Biol 2: e137.

Frederick K, Helmann JD (1996) FlgM is a primary regulator of σD activity, and its absence restores motility to a sinR mutant. J Bacteriol 178: 7010–7013.

Freed NE, Silander OK, Stecher B, Böhm A, Hardt WD, Ackermann M (2008) A simple screen to identify promoters conferring high levels of phenotypic noise. PLoS Genet 4: e1000307.

Freeman-Gallant CR, Wheelwright NT, Meiklejohn KE, Sollecito SV (2006) Genetic similarity, extrapair paternity, and offspring quality in Savannah sparrows (Passerculus sandwichensis). Behav Ecol 17: 952–958.

French R, Messinger A (1994) Genes, phenes and the Baldwin effect: Learning and evolution in a simulated population. Artif Life 4: 277–282.

Frey E (2010) Evolutionary game theory: Theoretical concepts and applications to microbial communities. Physica A 389: 4265–4298.

Friedenberg NA (2003) Experimental evolution of dispersal in spatiotemporally variable microcosms. Ecol Lett 6: 953–959.

Friedman G, McCarthy S, Rachinskii D (2013) Hysteresis can grant fitness in stochastically varying environment. arXiv preprint arXiv: 1311.4919.

Friedman M, Savage LJ (1948). The utility analysis of choices involving risk. J Polit Econ 56: 279–304.

Friedman R, Drake JW, Hughes AL (2004) Genome-wide patterns of nucleotide substitution reveal stringent functional constraints on the protein sequences of thermophiles. Genetics 167: 1507–1512.

Fronhofer EA, Pasurka H, Mitesser O, Poethke HJ (2011) Scarce resources, risk sensitivity, and egalitarian resource sharing. Evol Ecol Res 13: 253–267.

Fu AQ, Genereux DP, Stöger R, Laird CD, Stephens M (2010) Statistical inference of transmission fidelity of DNA methylation patterns over somatic cell divisions in mammals. Ann Appl Stat 4: 871–892.

Fujita M, Losick R (2005) Evidence that entry into sporulation in Bacillus subtilis is governed by a gradual increase in the level and activity of the master regulator Spo0A. Genes Dev 19: 2236–2244.

Fukuda K, Takayasu H, Takayasu M (2000) Origin of critical behavior in Ethernet traffic. Physica A 287: 289–301.

Fumagalli M, Sironi M, Pozzoli U, Ferrer-Admettla A, Pattini L, et al. (2011) Signatures of environmental genetic adaptation pinpoint pathogens as the main selective pressure through human evolution. PLoS Genet 7: e1002355.

Furusawa C, Kaneko K (2012) Adaptation to optimal cell growth through self-organized criticality. Phys Rev Lett 108: 208103.

Fusco G, Minelli A (2010) Phenotypic plasticity in development and evolution: facts and concepts. Phil Trans R Soc B Biol Sci 365: 547–556.

Fussmann GF, Loreau M, Abrams PA (2007) Eco-evolutionary dynamics of communities and ecosystems. Funct Ecol 21: 465–477.

Futuyma DJ (1986) Evolutionary biology, 2nd edn. Sunderland, MA: Sinauer Associates.

Futuyma DJ (2010) Evolutionary constraint and ecological consequences. Evolution 64: 1865–1884.

Futuyma DJ, Slatkin M (1983) Coevolution. Sunderland, MA: Sinauer Associates.

Futuyma DJ, Moreno G (1988) The evolution of ecological specialization. Annu Rev Ecol Syst 20: 207–233.

Gaal B, Pitchford JW, Wood AJ (2010) Exact results for the evolution of stochastic switching in variable asymmetric environments. Genetics 184: 1113–1119.

Gabriel W, Bürger R (1992) Survival of small populations under demographic stochasticity. Theor Popul Biol 41: 44–71.

Gadagkar R (1996) The evolution of eusociality, including a review of the social status of Ropalidia marginata. In: Turillazzi S, West-Eberhard MJ, eds. Natural history and evolution of paper-wasps. Oxford, UK: Oxford University Press. pp 248–271.

Gadgil M, Bossert WH (1970) Life historical consequences of natural selection. Am Nat 104: 1–24.

Galhardo RS, Hastings PJ, Rosenberg SM (2007) Mutation as a stress response and the regulation of evolvability. Crit Rev Biochem Mol Biol 42: 399–435.

Gamfeldt L, Wallen J, Jonsson PR, Berntsson KM, Havenhand JN (2005) Increasing intraspecific diversity enhances settling success in a marine invertebrate. Ecology 86: 3219–3224.

Gamfeldt L, Kallstrom B (2007) Increasing intraspecific diversity increases predictability in population survival in the face of perturbations. Oikos 116: 700–705.

Gandolfi AE, Sachko Gandolfi A, Barash DP (2002) Economics as an evolutionary science: from utility to fitness. New Brunswick, NJ: Transaction Publishers.

Gandrillon O, Kolesnik-Antoin D, Kupiec JJ, Beslon G (2012) Chance at the heart of the cell. Prog Biophys Mol Biol 110: 1–4.

Gao J, Cao Y, Tung WW, Hu J (2007) Multiscale analysis of complex time series: integration of chaos and random fractal theory, and beyond. Hoboken, NJ: John Wiley & Sons.

Gao X, Lin TC (2011) Do individual investors trade stocks as gambling? Evidence from repeated natural experiments. Unpublished working paper, University of Hong Kong.

Garant D, Dodson JJ, Bernatchez L (2005) Offspring genetic diversity increases fitness of female Atlantic salmon (Salmo salar). Behav Ecol Sociobiol 57: 240–244.

García-González F, Gomendio M (2003) A field test of the intraspecific brood parasitism hypothesis in the golden egg bug (Phyllomorpha laciniata). Behav Ecol Sociobiol 53: 332–339.

Garcia-Gonzalez F, Yasui Y, Evans JP (2015) Mating portfolios: bet-hedging, sexual selection and female multiple mating. Proc R Soc B 282: 20141525.

García?Roger EM, Serra M, Carmona MJ (2014) Bet?hedging in diapausing egg hatching of temporary rotifer populations–A review of models and new insights. Int Rev Hydrobiol 99: 96–106.

Gard TC, Kannan D (1976) On a stochastic differential equation modeling of prey-predator evolution. J Appl Probab 13: 429–443.

Garvin JC, Abroe B, Pedersen MC, Dunn PO, Whittingham LA (2006) Immune response of nestling warblers varies with extra-pair paternity and temperature. Mol Ecol 15: 3833–3840.

Gates JE, Gysel LW (1978) Avian nest dispersion and fledging success in field-forest ecotones. Ecology 59: 871–883.

Gavrilets S (2004) Fitness landscapes and the origin of species. Princeton, NJ: Princeton University Press.

Gavrilets S, Hastings A (1995) Intermittency and transient chaos from simple frequency-dependent selection. Proc R Soc Lond B 261: 233–238.

Gefen O, Balaban NQ (2009) The importance of being persistent: heterogeneity of bacterial populations under antibiotic stress. FEMS Microbiol Rev 33: 704–717.

Geisel N (2011) Constitutive versus responsive gene expression strategies for growth in changing environments. PLoS ONE 6: e27033.

Gell-Mann M (1994) The Quark and the Jaguar. New York, NY: Freeman.

Genereux DP, Miner BE, Bergstrom CT, Laird CD (2005) A population-epigenetic model to infer site-specific methylation rates from double-stranded DNA methylation patterns. Proc Natl Acad Sci USA 102: 5802–5807.

Genova ML, Bianchi C, Lenaz G (2003) Structural organization of the mitochondrial respiratory chain. Ital J Biochem 52: 58–61.

Gerdes LU, Jeune B, Ranberg KA, Nybo H, Vaupel JW (2000) Estimation of apolipoprotein E genotype-specific relative mortality risks from the distribution of genotypes in centenarians and middle-aged men: Apolipoprotein E gene is a “frailty gene,” not a “longevity gene”. Genet Epidemiol 19: 202–210.

Gerhart J, Kirschner M (2007) The theory of facilitated variation. Proc Natl Acad Sci USA 104 Suppl 1: 8582–8589.

Geritz SAH (1995) Evolutionary stable seed polymorphism and small-scale spatial variation in seedling density. Am Nat 146: 685–707.

Geritz SAH, van der Meijden E, Metz JAJ (1999) Evolutionary dynamics of seed size and seedling competitive ability. Theor Popul Biol 55: 324–343.

Gersani M, Brown JS, O’Brien EE, Maina GM, Abramsky Z (2001) Tragedy of the commons as a result of root competition. J Ecol 89: 660–669.

Gessler DDG, Xu SZ (2000) Meiosis and the evolution of recombination at low mutation rates. Genetics 156: 449–456.

Ghalambor CK, Martin TE (2001) Fecundity-survival tradeoffs and parental risk-taking in birds. Science 292: 494–497.

Ghalambor CK, Huey RB, Martin PR, Tewksbury JJ, Wang G (2006) Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr Comp Biol 46: 5–17.

Gibo DL (1978) The selective advantage of foundress associations in Polistes fuscatus (Hymenoptera: Vespidae): a field study of the effects of predation on productivity. Can Entomol 110: 519–540.

Gibson G, Dworkin I (2004) Uncovering cryptic genetic variation. Nat Rev Genet 5: 681–690.

Gigerenzer G, Swijtink Z, Porter T, Daston L, Beatty J, Krüger L (1989) The empire of chance. Cambridge, UK: Cambridge University Press.

Gilchrist GW (1995) Specialists and generalists in changing environments. 1. Fitness landscapes of thermal sensitivity. Am Nat 146: 252–270.

Gillespie JH (1973a) Natural selection with varying selection coefficients - a haploid model. Genet Res 21: 115–120.

Gillespie JH (1973b) Polymorphism in random environments. Theor Popul Biol 4: 193–195.

Gillespie JH (1974a) Natural selection for within-generation variance in offspring number. Genetics 76: 601–606.

Gillespie JH (1974b) Polymorphism in patchy environments. Am Nat 108: 145–151.

Gillespie JH (1975) Natural-selection for within-generation variance in offspring number. II. Discrete haploid models. Genetics 81: 403–413.

Gillespie JH (1977) Natural selection for variances in offspring numbers: a new evolutionary principle. Am Nat 111: 1010–1014.

Gillespie JH (2001) Is the population size of a species relevant to its evolution? Evolution 55: 2161–2169.

Gilliam JF, Fraser DF (1987) Habitat selection under predation hazard: test of a model with foraging minnows. Ecology 68: 1856–1862.

Gilpin M (1975) Group selection in predator-prey communities. Princeton, NJ: Princeton University Press.

Gisiger T (2001) Scale invariance in biology: coincidence or footprint of a universal mechanism? Biol Rev Camb Philos Soc 76: 161–209.

Giuseppe M, Luigia M, Elena P, Yan F, Luigi M (2012) Enzyme promiscuity in the hormone-sensitive lipase family of proteins. Protein Pept Lett 19: 144–154.

Glansdorf P, Prigogine I (1971) Structure, stability and fluctuations. New York, NY: Interscience.

Glasner ME, Gerlt JA, Babbitt PC (2007) Mechanisms of protein evolution and their application to protein engineering. Adv Enzymol Relat Areas Mol Biol 75: 193–239.

Gleick J (1987) Chaos: Making a new science. New York, NY: Penguin.

Glymour B (2001) Selection, indeterminism, and evolutionary theory. Philos Sci 68: 518–535.

Gnauck A (2000) Fundamentals of ecosystem theory from general systems analysis. In: Jørgensen SE, Müller F (eds) Handbook of ecosystem theories and management. Lewis, Dordrecht. pp 75–88.

Gnauck A, Straškraba M (1980) Theoretical concepts of ecosystem models. ISEM J 2: 71–80.

Godfrey-Smith P (1996) Complexity and the function of mind in nature. Cambridge, UK: Cambridge University Press.

Godfrey-Smith P (2002) Environmental complexity and the evolution of cognition. In: Sternberg R, Kaufmann J, eds. The evolution of intelligence. London, UK: Lawrence Erlbaum Associates. pp 233–249.

Godfrey-Smith P (2009) Darwinian populations and natural selection. Oxford, UK: Oxford University Press.

Gohli J, Anmarkrud JA, Johnsen A, Kleven O, Borge T, Lifjeld JT (2013) Female promiscuity is positively associated with neutral and selected genetic diversity in passerine birds. Evolution 67: 1406–1419.

Gokhale CS, Iwasa Y, Nowak MA, Traulsen A (2009) The pace of evolution across fitness valleys. J Theor Biol 259: 613–620.

Golden JW, Riddle DL (1984) The Caenorhabditis elegans dauer larva: developmental effects of pheromone, food, and temperature. Dev Biol 102: 368–378.

Golding I, Cox EC (2004) RNA dynamics in live Escherichia coli cells. Proc Natl Acad Sci USA 101: 11310–11315.

Golding I, Paulsson J, Zawilski SM, Cox EC (2005) Real-time kinetics of gene activity in individual bacteria. Cell 123: 1025–1036.

Goodman D (1984) Risk spreading as an adaptive strategy in iteroparous life histories. Theor Popul Biol 25: 1–20.

Goodnight CJ (1985) The influence of environmental variation on group and individual selection in a cress. Evolution 39: 545–558.

Goodnight CJ (1990a) Experimental studies of community evolution. I: The response to selection at the community level. Evolution 44: 1614–1624.

Goodnight CJ (1990b) Experimental studies of community evolution. II: The ecological basis of the response to community selection. Evolution 44: 1625–1636.

Goodnight CJ (2005) Multilevel selection: the evolution of cooperation in non-kin groups. Popul Ecol 47: 3–12.

Goodnight CJ, Schwartz JM, Stevens L (1992) Contextual analysis of models of group selection, soft selection, hard selection, and the evolution of altruism. Am Nat 140: 743–761.

Goodnight CJ, Stevens L (1997) Experimental studies of group selection: what do they tell us about group selection in nature? Am Nat 150(S1): S59–S79.

Goodnight C, Rauch E, Sayama H, de Aguiar MAM, Baranger M, Bar-Yam Y (2008) Evolution in spatial predator-prey models and the “prudent predator”: the inadequacy of steady-state organism fitness and the concept of individual and group selection. Complexity 13: 23–44.

Goonatilake S (1991) The evolution of information: Lineages in gene, culture and artefact. London UK: Pinter Publishers

Gordon AJE, Halliday JA, Blankschien MD, Burns PA, Yatagai F, Herman C (2009) Transcriptional infidelity promotes heritable phenotypic change in a bistable gene network. PLoS Biol 7: e1000044.

Gorodetsky P, Tannenbaum E (2008) A dual role for sex? arXiv:0809.0029v1 [q-bio.PE].

Gorshenev AA, Pis'mak YM (2004) Punctuated equilibrium in software evolution. Phys Rev E 70: 067103.

Gould JL (1987) Instinct and learning in honey bee foraging. In: Kamil AC, Krebs JR, Pulliam HR, eds. Foraging behavior. New York, NY: Plenum. pp 479–496.

Gould SJ (1989) Wonderful life: the Burgess shale and the nature of history. New York, NY: WW Norton & Co.

Gould SJ (1993) Eight little piggies. New York, NY: Norton.

Gould SJ (2002) The structure of evolutionary theory. Cambridge, MA: Belknap Press of Harvard University Press.

Gould SJ, Eldredge N (1993) Punctuated equilibrium comes of age. Nature 366: 223–227.

Gourbière S, Menu F (2009) Adaptive dynamics of dormancy duration variability: evolutionary trade-off and priority effect lead to suboptimal adaptation. Evolution 63: 1879–1892.

Grafen A (1999) Formal Darwinism, the individual-as-maximising-agent analogy, and bet-hedging. Proc R Soc Ser B 266: 799–803.

Graham JK, Smith ML, Simons AM (2014) Experimental evolution of bet hedging under manipulated environmental uncertainty in Neurospora crassa. Proc R Soc B 281: 20140706.

Grant PR, Grant BR (2002) Unpredictable evolution in a 30-year study of Darwin’s finches. Science 296: 707–711.

Grant P, Grant R (2008) How and why species multiply: the radiation of Darwin's finches. Princeton, NJ: Princeton University Press.

Grant-Downton RT, Dickinson HG (2006) Epigenetics and its implications for plant biology 2. The ‘epigenetic epiphany’: epigenetics, evolution and beyond. Ann Bot 97: 11–27.

Grativol C, Hemerly AS, Ferreira PC (2012) Genetic and epigenetic regulation of stress responses in natural plant populations. Biochim Biophys Acta 1819: 176–185.

Graves L, Horan BL, Rosenberg A (1999) Is indeterminism the source of the statistical character of evolutionary theory? Philos Sci 66: 140–157.

Green DM (1991) Chaos, fractals and nonlinear dynamics in evolution and phylogeny. Trends Ecol Evol 6: 333–337.

Grell RF (1971) Heat-induced exchange in the fourth chromosome of diploid females of Drosophila melanogaster. Genetics 69: 523–527.

Grell RF (1978) A comparison of heat and interchromosomal effects on recombination and interference in Drosophila melanogaster. Genetics 89: 65–77.

Gremer JR, Crone EE, Lesica P (2012) Are dormant plants hedging their bets? Demographic consequences of prolonged dormancy in variable environments. Am Nat 179: 315–327.

Gremer JR, Venable DL (2014) Bet hedging in desert winter annual plants: optimal germination strategies in a variable environment. Ecol. Lett. 17: 380–387.

Griffing B (1977) Selection for populations of interacting genotypes. In: Pollak E, Kempthorne O, Baily TB, eds. Proceedings of the International Congress on Quantitative Genetics. Ames, IA: Iowa State University. pp 413–434.

Griffing B (1981a) A theory of natural selection incorporating interaction among individuals. I. The modeling process. J Theor Biol 89: 635–658.

Griffing B (1981b) A theory of natural selection incorporating interaction among individuals. II. Use of random groups of inbred individuals. J Theor Biol 89: 679–690.

Griffith SC, Owens IPF, Thuman KA (2002) Extra-pair paternity in birds: A review of interspecific variation and adaptive function. Mol Ecol 11: 2195–2212.

Griffiths HI, Butlin RK (1995) A timescale for sex versus parthenogenesis: Evidence from subfossil ostracods. Proc R Soc Lond Ser B Biol Sci 260: 65–71.

Grogan DW (2004) Stability and repair of DNA in hyperthermophilic Archaea. Curr Issues Mol Biol 6: 137–144.

Grogan DW, Carver GT, Drake JW (2001) Genetic fidelity under harsh conditions: Analysis of spontaneous mutation in the thermoacidophilic archaeon Sulfolobus acidocaldarius. Proc Natl Acad Sci USA 98: 7928–7933.

Gros C (2008) Complex and adaptive dynamical systems: a primer. Heidelberg, Germany: Springer-Verlag.

Groß R, Houston AI, Collins EJ, McNamara JM, Dechaume-Moncharmont FX, Franks NR (2008) Simple learning rules to cope with changing environments. J R Soc Interface 5: 1193–1202.

Grosberg A (2004) Statistical mechanics of protein folding: some outstanding problems. In: Attig N, Binder K, Grubmuller H, Kremer K, eds. Computational soft matter: from synthetic polymers to proteins, Lecture Notes. John von Neumann Institute for Computing. pp 375–400.

Grossman AD (1995) Genetic networks controlling the initiation of sporulation and the development of genetic competence in Bacillus subtilis. Annu Rev Genet 29: 477–508.

Guido NJ, Lee P, Wang X, Elston TC, Collins JJ (2007) A pathway and genetic factors contributing to elevated gene expression noise in stationary phase. Biophys J 93: L55–L57.

Guillaume F, Perrin N (2009) Inbreeding load, bet hedging, and the evolution of sex-biased dispersal. Am Nat 173: 536–541.

Gutenberg B (1949) Seismicity of the earth and associated phenomena. Princeton, NJ: Princeton University Press.

Haccou P, Iwasa Y (1995) Optimal mixed strategies in stochastic environments. Theor Popul Biol 47: 212–243.

Hacking I (1975) The emergence of probability: A philosophical study of early ideas about probability and statistical inference. Cambridge, UK: Cambridge University Press.

Hacking I (1983) Nineteenth century cracks in the concept of determinism. J Hist Ideas 44: 455–475.

Hacking I (1990) The taming of chance. Cambridge, UK: Cambridge University Press.

Hadany L, Beker T (2003a) Fitness-associated recombination on rugged adaptive landscapes. J Evol Biol 16: 862–870.

Hadany L, Beker T (2003b) On the evolutionary advantage of fitness-associated recombinaton. Genetics 165: 2167–2179.

Hadden C, Nester EW (1968) Purification of competent cells in the Bacillus subtilis transformation system. J Bacteriol 95: 876–885.

Hairston NG Jr (1998) Time travelers: what’s timely in diapause research? Arch Hydrobiol Spec Issues. Adv Limnol 52: 1–15.

Hairston NG (2000) Temporal dispersal: ecological and evolutionary implications of prolonged egg diapause. Am Zool 40: 1039–1040.

Hairston NG, Smith FE, Slobodkin LB (1960) Community structure, population control, and competition. Am Nat 94: 421–425.

Hairston NG Jr, Munns WR Jr (1984) The timing of copepod diapause as an evolutionarily stable strategy. Am Nat 123: 733–751.

Hairston NG Jr, DeStasio BT Jr (1988) Rate of evolution slowed by a dormant propagule pool. Nature 336: 239–242.

Hairston NG, Van Brunt RA, Kearns CM, Engstrom DR (1995) Age and survivorship of diapausing eggs in a sediment egg bank. Ecology 76: 1706–1711.

Hairston NG Jr, Ellner S, Kearns CM (1996a) Overlapping generations: the storage effect and the maintenance of biotic diversity. In: Rhodes Jr OE, Chesser RK, Smith MH, eds. Population dynamics in ecological space and time. Chicago, IL: University of Chicago Press. pp 109–145.

Hairston NG, Kearns CM, Ellner SP (1996b) Phenotypic variation in a zooplankton egg bank. Ecology 77: 2382–2392.

Hairston NG, Kearns CM (2002) Temporal dispersal: ecological and evolutionary aspects of zooplankton egg banks and the role of sediment mixing. Integr Comp Biol 42: 481–491.

Haken H (1977) Synergetics. An introduction. Nonequilibrium phase transitions and self-organization in physics, chemistry, and biology. Berlin, Germany: Springer.

Haldane JBS (1949) Disease and evolution. Ricerca Sci 19 (Suppl. 1): 68–76.

Haldane JBS (1957) The cost of natural selection. J Genet 55: 511–524.

Haldane JBS, Jayakar SD (1963) Polymorphism due to selection of varying direction. J Genet 58: 237–242.

Hales CN, Barker DJ (1992) Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia 35: 595–601.

Halfmann R, Lindquist S (2010) Epigenetics in the extreme: prions and the inheritance of environmentally acquired traits. Science 330: 629–632.

Halfmann R, Alberti S, Lindquist S (2010) Prions, protein homeostasis, and phenotypic diversity. Trends Cell Biol 20: 125–133.

Halkett F, Harrington R, Hullé M, Kindlmann P, Menu F, Rispe C, Plantegenest M (2004) Dynamics of production of sexual forms in aphids: theoretical and experimental evidence for adaptive “coin-flipping” plasticity. Am Nat 163: E112–E125.

Halley JM (1996) Ecology, evolution and 1/f-noise. Trends Ecol Evol 11: 33–37.

Halley JM, Kunin WE (1999) Extinction risk and the 1/f family of noise models. Theor Popul Biol 56: 215–230.

Hamilton IM, Taborsky M (2005) Contingent movement and cooperation evolve under generalized reciprocity. Proc R Soc B Biol Sci 272: 2259–2267.

Hamilton WD (1964) The genetical evolution of social behaviour, I & II. J Theor Biol 7: 1–52.

Hamilton WD (1970) Selfish and spiteful behaviour in an evolutionary model. Nature 228: 1218–1220.

Hanski I (1988) Four kinds of extra long diapause in insects: a review of theory and observations. Ann Zool Fenn 25: 37–53.

Haraguchi Y, Sasaki A (2000) The evolution of parasite virulence and transmission rate in a spatially structured population. J Theor Biol 203: 85–96.

Hardin G (1968) The tragedy of the commons. Science 162: 1243–1248.

Harris RN, Ludwig PM (2004) Resource level and reproductive frequency in female four-toed salamanders, Hemidactylium scutatum. Ecology 85: 1585–1590.

Hartl DL, Cook RD (1973) Balanced polymorphisms or quasineutral alleles. Theor Popul Biol 4: 163–172.

Hartmann A, Wantia J, Torres JA, Heinze J (2003) Worker policing without genetic conflicts in a clonal ant. Proc Natl Acad Sci USA 100: 12836–12840.

Harvey DL, Reed MH (1994) The evolution of dissipative social systems. J Soc Evol Syst 17: 371–411.

Hassall M, Helden A, Goldson A, Grant A (2005) Ecotypic differentiation and phenotypic plasticity in reproductive traits of Armadillidium vulgare (Isopoda: Oniscidea). Oecologia 143: 51–60.

Hasselquist D, Bensch S, von Schantz T (1996) Correlation between male song repertoire, extra-pair paternity and offspring survival in the great reed warbler. Nature 381: 229–232.

Hauert C, Holmes M, Doebeli M (2006) Evolutionary games and population dynamics: maintenance of cooperation in public goods games. Proc Biol Sci 273: 3131–3132.

Haugen L (2002) Privation and uncertainty in the small nursery of Peruvian tadpoles: larval ecology shapes parental mating system. PhD Thesis. Norman, OK: University of Oklahoma.

He J, Sun J, Deem MW (2009) Spontaneous emergence of modularity in a model of evolving individuals and in real networks. Phys Rev E 79: 031907.

Hedlin AF, Miller GE, Ruth DS (1982) Induction of prolonged diapause in Barbara colfaxiana (Lepidoptera: Olethreutidae): correlations with cone crops and weather. Can Entomol 114: 465–471.

Hedrick PW (1986) Genetic polymorphism in heterogeneous environments: a decade later. Annu Rev Ecol Syst 17: 535–566.

Hedrick PW, Ginevan ME, Ewing EP (1976) Genetic polymorphism in heterogeneous environments. Annu Rev Ecol Syst 7: 1–32.

Heininger K (2001) The deprivation syndrome is the driving force of phylogeny, ontogeny and oncogeny. Rev Neurosci 12: 217–287.

Heininger K (2012) The germ-soma conflict theory of aging and death: Obituary to the “evolutionary theories of aging”. WebmedCentral AGING 3: WMC003275.

Heininger K (2013) The mutagenesis-selection-cascade theory of sexual reproduction. WebmedCentral REPRODUCTION 4: WMC004367.

Heininger K (2015) Duality of stochasticity and natural selection shape the ecology-driven pattern of social interactions: the fall of Hamilton’s rule. WebmedCentral ECOLOGY 6: WMC004804.

Helbing D, Vicsek T (1999) Optimal self-organization. New J Phys 1: 13.1–13.17.

Heller D (2004) An evolutionary approach to learning in a changing environment. J Econ Theor 114: 31–55.

Hendry AP, Kaeuffer R, Crispo E, Peichel CL, Bolnick DI (2013) Evolutionary inferences from the analysis of exchangeability. Evolution 67: 3429–3441.

Henrich J (2004) Cultural group selection, coevolutionary processes and large-scale cooperation. J Econ Behav Organ 53: 3–35.

Heppell SS, Caswell H, Crowder LB (2000) Life histories and elasticity patterns: perturbation analysis for species with minimal demographic data. Ecology 81: 654–665.

Hereford J (2009) A quantitative survey of local adaptation and fitness trade-offs. Am Nat 173: 579–588.

Hereford J, Hansen TF, Houle D (2004) Comparing strengths of directional selection: How strong is strong? Evolution 58: 2133–2143.

Hernday AD, Braaten BA, Low DA (2003) The mechanism by which DANN adenine methylase and PapI activate the pap epigenetic switch. Mol Cell 12: 947–957.

Hernday AD, Braaten BA, Broitman-Maduro G, Engelberts P, Low DA (2004) Regulation of the pap epigenetic switch by CpxAR: phosphorylated CpxR inhibits transition to the phase ON state by competition with Lrp. Mol Cell 16: 537–547.

Herre EA (1993) Population structure and the evolution of virulence in nematode parasites of fig wasps. Science 259: 1442–1445.

Heylighen F (2001) The science of self-organization and adaptivity. In: Kiel LD, ed. Knowledge management, organizational intelligence and learning, and complexity. The Encyclopedia of Life Support Systems. Oxford, UK: EOLSS Publishers. pp 253–280.

Heylighen F, Joslyn C (2001) Cybernetics and second-order cybernetics. In: Meyers RA, ed. Encyclopedia of physical science & technology, vol. 4. 3rd edn. New York, NY: Academic Press. pp 155–170.

Hidalgo J, Grilli J, Suweis S, Muñoz MA, Banavar JR, Maritan A (2014a) Information-based fitness and the emergence of criticality in living systems. Proc Natl Acad Sci USA 111: 10095–10100.

Hidalgo J, Pigolotti S, Muñoz MA (2014b) Bet-hedging strategies are maximally efficient in low-dimensional noisy environments. arXiv:1412.1066v1 [q-bio.PE] 1 Dec 2014.

Higaki M (2005) Effect of temperature on the termination of prolonged larval diapause in the chestnut weevil Curculio sikkimensis (Coleoptera: Curculionidae). J Insect Physiol 51: 1352–1358.

Higaki M, Ando Y (1999) Seasonal and altitudinal adaptations in three katydid species: ecological significance of initial diapause. Entomol Sci 2: 1–11.

Hilbert L (2011) Shifting gears: Thermodynamics of genetic information storage suggest stress-dependence of mutation rate, which can accelerate adaptation. arXiv:1104.1930v1 [physics.bio-ph].

Hill WG, Caballero A (1992) Artificial selection experiments. Annu Rev Ecol Syst 23: 287–310.

Hines WGS, Moore WS (1981) An analysis of sex in random environments. Adv Appl Prob 13: 342–352.

Hinton GE, Nowlan SJ (1987) How learning can guide evolution. Complex Syst 1: 495–502.

Hintze A, Olson RS, Adami C, Hertwig R (2013) Risk aversion as an evolutionary adaptation. arXiv preprint arXiv:1310.6338.

Hirschliefer J (1977) Economics from a biological perspective. J Law Econ 20: 1–53.

Hoang T (2004) The origin of hematopoietic cell type diversity. Oncogene 23: 7188–7198.

Hoelzer GA, Smith E, Pepper JW (2006) On the logical relationship between natural selection and self-organization. J Evol Biol 19: 1785–1794.

Hoffman AA, Parsons PA (1991) Evolutionary genetics and environmental stress. Oxford, UK: Oxford University Press.

Hogendoorn K, Zammit J (2001) Benefits of cooperative breeding through increased colony survival in an allodapine bee. Insect Soc 48: 392–397.

Holland JH (1995) Hidden order. How adaptation builds complexity. New York, NY: Perseus Books.

Holling CS, ed. (1978) Adaptive environmental assessment and learning. London, UK: Wiley.

Holling CS (1995) Sustainability: the cross-scale dimension. In: Murasinghe M, Shearer W, eds. Defining and measuring sustainability. Washington, DC: The World Bank. pp 65–75.

Holling CS (1996) Engineering resilience versus ecological resilience. In: Schulze P, ed. Engineering within ecological constraints. Washington, DC: National Academies Press. pp 31–44.

Holmes JC (1983) Evolutionary relationships between parasitic helminths and their hosts. In: Futuyma DJ, Slatkin M, eds. Coevolution. Sunderland, MA: Sinauer. pp 161–185.

Hooshangi S, Bentley WE (2008) From unicellular properties to multicellular behavior: bacteria quorum sensing circuitry and applications. Curr Opin Biotech 19: 550–555.

Hopper KR (1999) Risk-spreading and bet-hedging in insect population biology. Annu Rev Entomol 44: 535–560.

Hopper KR, Rosenheim JA, Prout T, Oppenheim SJ (2003) Within-generation bet hedging: a seductive explanation. Oikos 101: 219–222.

Horan BL (1994) The statistical character of evolutionary theory Philosophy of Science 61: 76–95.

Hottiger T, Boller T, Wiemken A (1987) Rapid changes of heat and desiccation tolerance correlated with changes of trehalose content in Saccharomyces cerevisiae cells subjected to temperature shifts. FEBS Lett 220: 113–115.

Houle D (1991) Genetic covariances of fitness correlates: what genetic correlations are made of and why it matters. Evolution 45: 630–648.

Houle D (1992) Comparing evolvability and variability of quantitative traits. Genetics 130: 195–204.

Houle D (1998) How should we explain variation in the genetic variance of traits? Genetica 102/103: 241–253.

Houston AI, McNamara JM, Hutchinson JMC (1993) General results concerning the trade-off between gaining energy and avoiding predation. Phil Trans R Soc Lond B 341: 375–397.

Houston AI, McNamara JM (1999) Models of adaptive behaviour. Cambridge, UK: Cambridge University Press.

Houston AI, Higginson AD, McNamara JM (2011) Optimal foraging for multiple 15 nutrients in an unpredictable environment. Ecol Lett 14: 1101–1107.

Hoyle RB, Ezard TH (2012) The benefits of maternal effects in novel and in stable environments. J R Soc Interface 9: 2403–2413.

Huang S (2009) Reprogramming cell fates: reconciling rarity with robustness. Bioessays 31: 546–560.

Huang S (2012) The molecular and mathematical basis of Waddington's epigenetic landscape: a framework for post-Darwinian biology? Bioessays 34: 149–157.

Hughes AR, Inouye BD, Johnson MTJ, Underwood N, Vellend M (2008) Ecological consequences of genetic diversity. Ecol Lett 11: 609–623.

Hughes W, Lavery J (2004) Critical thinking. An introduction to the basic skills. Toronto, Canada: Broadview Press Ltd.

Humphries NE, Queiroz N, Dyer JR, Pade NG, Musyl MK, Schaefer KM, et al. (2010). Environmental context explains Lévy and Brownian movement patterns of marine predators. Nature 465: 1066–1069.

Hüser J, Rechenmacher CE, Blatter LA (1998) Imaging the permeability pore transition in single mitochondria. Biophys J 74: 2129–2137.

Hutchinson GE (1957) Concluding remarks. Cold Spring Harb Symp Quant Biol 22: 415–427.

Hutchinson JMC (1996) Evolution in fluctuating environments: a game with kin. Trends Ecol Evol 11: 230–232.

Huttegger SM, Zollman KJS (2012) The limits of ESS methodology. In: Okasha S, Binmore K, eds. Evolution and rationality: decisions, cooperation and strategic behavior. Cambridge, UK: Cambridge University Press. pp 67–83.

Huxley JS (1966) Introduction: a discussion on ritualization of behaviour in animals and man. Phil Trans R Soc B 251: 249–271.

Ibáñez-Escriche N, Sorensen D, Waagepetersen R, Blasco A (2008) Selection for environmental variation: a statistical analysis and power calculations to detect response. Genetics 180: 2209–2226.

Illera JC, Diaz M (2006) Reproduction in an endemic bird of a semiarid island: a food-mediated process. J Avian Biol 37: 447–456.

Inchausti P, Halley J (2003) On the relation between temporal variability and persistence time in animal populations. J Anim Ecol 72: 899–908.

Isberg SR, Thomson PC, Nicholas FW (2006) Quantitative analysis of production traits in saltwater crocodiles (Crocodylus porosus): III. Juvenile survival. J Anim Breed Genet 123: 44–47.

Ishihama Y, Schmidt T, Rappsilber J, Mann M, Hartl FU, Kerner MJ, Frishman D (2008) Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics 9: 102.

Ito H, Uehara T, Morita S, Tainaka KI, Yoshimura J (2013) Foraging behavior in stochastic environments. J Ethol 31: 23–28.

Ivy TM, Sakaluk SK (2005) Polyandry promotes enhanced offspring survival in decorated crickets. Evolution 59: 152–159.

Iwasaki Y, Yonezawa F (1999) A simple model of asymmetric quasi-species: network sustained by emergent high mutation rates. Physica D 126: 160–170.

Jablonka E, Oborny B, Molnar I, Kisdi E, Hofbauer J, Czaran T (1995) The adaptive advantage of phenotypic memory in changing environments. Phil Trans R Soc Lond B 350: 133–141.

Jablonka E, Lamb M (1995) Epigenetic inheritance and evolution: the Lamarckian dimension. Oxford, UK: Oxford University Press.

Jablonka E, Lamb MJ (2005) Evolution in four dimensions: genetic, epigenetic, behavioral, and symbolic variation in the history of life. Cambridge, MA: MIT Press.

Jablonka E, Lamb MJ (2007) Précis of evolution in four dimensions. Behav Brain Sci 30: 353–392.

Jablonka E, Raz G (2009) Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q Rev Biol 84: 131–176.

Jackson AL (1998) Induction of microsatellite instability by oxidative DNA damage. Proc Natl Acad Sci USA 95: 12468–12473.

Jammer M (1999) Einstein and religion. Princeton, NJ: Princeton University Press.

Jan YN, Jan LY (1995) Maggot’s hair and bug’s eye: role of cell interactions and intrinsic factors in cell fate specification. Neuron 14: 1–5.

Jansen VA, Yoshimura J (1998) Populations can persist in an environment consisting of sink habitats only. Proc Natl Acad Sci USA 95: 3696–3698.

Jarman PJ (1974) The social organisation of antelope in relation to their ecology. Behaviour 48: 215–269.

Jarosz DF, Lindquist S (2010) Hsp90 and environmental stress transform the adaptive value of natural genetic variation. Science 330: 1820–1824.

Jennions MD, Petrie M (2000) Why do females mate multiply? A review of the genetic benefits. Biol Rev Camb Philos Soc 75: 21–64.

Jensen HJ (1998) Self-organized criticality. Cambridge, UK: Cambridge University Press.

Jensen JL (1906) Sur les fonctions convexes et les inégualités entre les valeurs moyennes. Acta Math 30: 175–193.

Jia F, Greenfield MD (1997) When are good genes good? Variable outcomes of female choice in wax moths. Proc R Soc Lond Ser B 264: 1057–1063.

Johnsen A, Andersen V, Sunding C, Lifjeld JT (2000) Female bluethroats enhance offspring immunocompetence through extra-pair copulation. Nature 406: 296–299.

Johnsen PJ, Dubnau D, Levin BR (2009) Episodic selection and the maintenance of competence and natural transformation in Bacillus subtilis. Genetics 181: 1521–1533.

Johnson MTJ, Lajeunesse MJ, Agrawal AA (2006) Additive and interactive effects of plant genotypic diversity on arthropod communities and plant fitness. Ecol Lett 9: 24–34.

Johnson MTJ, Stinchcombe JR (2007) An emerging synthesis between community ecology and evolutionary biology. Trends Ecol Evol 22: 250–257.

Johnson S (2001) Emergence. New York NY: Scribner.

Johnson T, Barton N (2005) Theoretical models of selection and mutation on quantitative traits. Phil Trans R Soc Lond B 360: 1411–1425.

Johnston TD (1982) Selective costs and benefits in the evolution of learning. Adv Stud Behav 12: 65–106.

Johnstone RA (1994) Honest signalling, perceptual error and the evolution of 'all-or-nothing' displays. Proc R Soc Lond Ser B 256: 169–175.

Johnstone RA (2004) Begging and sibling competition: how should offspring respond to their rivals? Am Nat 163: 388–406.

Jokela J, Dybdahl MF, Lively CM (2009) The maintenance of sex, clonal dynamics, and host-parasite coevolution in a mixed population of sexual and asexual snails. Am Nat 174: S43–S53.

Jones AG, Arnold SJ, Bürger R (2007) The mutation matrix and the evolution of evolvability. Evolution 61: 727–745.

Jones CG, Lawton JH, Shachak M (1994) Organisms as ecosystem engineers. Oikos 69: 373–386.

Jørgensen C, Fiksen Ø (2006) State-dependent energy allocation in cod (Gadus morhua). Can J Fish Aquat Sci 63: 186–199.

Jørgensen C, Ernande B, Fiksen Ø, Dieckmann U (2006) The logic of skipped spawning in fish. Can J Fish Aquat Sci 63: 200–211.

Jouvenot Y, Poirier F, Jami J, Paldi A (1999) Biallelic transcription of Igf2 and H19 in individual cells suggests a post-transcriptional contribution to genomic imprinting. Curr Biol 9: 1199–1202.

Juhas M, Eberl L, Tümmler B (2005) Quorum sensing: the power of cooperation in the world of Pseudomonas. Environ Microbiol 7: 459–471.

Julliard R, McCleery RH, Clobert J, Perrins CM (1997) Phenotypic adjustment of clutch size due to nest predation in the great tit. Ecology 78: 394–404.

Kacelnik A, Bateson M (1996) Risky theories–The effects of variance on foraging decisions. Am Zool 36: 402–434.

Kærn M, Elston TC, Blake WJ, Collins JJ (2005) Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet 6: 451–464.

Kaeuffer R, Peichel CL, Bolnick DI, Hendry AP (2012) Parallel and nonparallel aspects of ecological, phenotypic, and genetic divergence across replicate population pairs of lake and stream stickleback. Evolution 66: 402–418.

Kahneman D, Tversky A (1979) Prospect theory—analysis of decision under risk. Econometrica 47: 313–327.

Kaitala V, Ranta E, Lindström J (1996) Cyclic population dynamics and random perturbations. J Anim Ecol 65: 249–251.

Kallimanis AS, Mazaris AD, Tzanopoulos J, Halley JM, Pantis JD, Sgardelis SP (2008) How does habitat diversity affect the species–area relationship. Global Ecol Biogeogr 17: 532–538.

Kambhu J, Weidman S, Krishnan N (2007) New Directions for Understanding Systemic Risk: A Report on a Conference Cosponsored by the Federal Reserve Bank of New York and the National Academy of Sciences. Washington, DC: The National Academies Press.

Kamp C, Wilke CO, Adami S, Bornholdt S (2003) Viral evolution under the pressure of an adaptive immune system - optimal mutation rates for viral escape. Complexity 8: 28–33.

Kandror O, Bretschneider N, Kreydin E, Cavalieri D, Goldberg AL (2004) Yeast adapt to near-freezing temperatures by STRE/Msn2,4-dependent induction of trehalose synthesis and certain molecular chaperones. Mol Cell 13: 771–781.

Kaneko K (2009) Relationship among phenotypic plasticity, phenotypic fluctuations, robustness, and evolvability; Waddington’s legacy revisited under the spirit of Einstein. J Biosci 34: 529–542.

Kaneko K, Furusawa C (2006) An evolutionary relationship between genetic variation and phenotypic fluctuation. J Theor Biol 240: 78–86.

Kant I (1790) Kritik der Urteilskraft. Berlin, Germany: L. Heimann’s Verlag.

Kaplan RH, Cooper WS (1984) The evolution of developmental plasticity in reproductive characteristics: an application of the ‘adaptive coin-flipping’ principle. Am Nat 123: 393–410.

Karban R, Agrawal AA, Mangel M (1997) The benefits of induced defenses against herbivores. Ecology 78: 1351–1355.

Karlin S, McGregor J (1974) Towards a theory of the evolution of modifier genes. Theor Popul Biol 5: 59–103.

Karlin S, Liberman U (1974) Random temporal variation in selection intensities: case of large population size. Theor Popul Biol 6: 355–382.

Karlin S, Liberman U (1975) Random temporal variation in selection intensities: one-locus two-allele model. J Math Biol 2: 1–17.

Karmakar R, Bose I (2007) Positive feedback, stochasticity and genetic competence. Phys Biol 4: 29–37.

Karp X, Greenwald I (2003) Post-transcriptional regulation of the E/Daughterless ortholog HLH-2, negative feedback, and birth order bias during the AC/VU decision in C. elegans. Genes Dev 17: 3100–3111.

Karsenti E (2008) Self-organization in cell biology: A brief history. Nat Rev Mol Cell Biol 9: 255–262.

Kashiwagi A, Urabe I, Kaneko K, Yomo T (2006) Adaptive response of a gene network to environmental changes by fitness-induced attractor selection. PLoS ONE 1: e49.

Kashtan N, Alon U (2005) Spontaneous evolution of modularity and network motifs. Proc Natl Acad Sci USA 102: 13773–13778.

Kashtan N, Noor E, Alon U (2007) Varying environments can speed up evolution. Proc Natl Acad Sci USA 104: 13711–13716.

Katsukawa Y, Katsukawa T, Matsuda H (2002) Indeterminate growth is selected by a trade-off between high fecundity and risk avoidance in stochastic environments. Popul Ecol 44: 265–272.

Kauffman SA (1993) The origin of order: self-organization and selection in evolution. Oxford, UK: Oxford University Press.

Kauffman S (1995) At home in the universe. Oxford, UK: Oxford University Press.

Kauffman S (2013) Evolution beyond Newton, Darwin and entailing law. In: Henning BG, Scarfe AC, eds. Beyond mechanism: putting life back into biology. Plymouth, UK: Lexington Books. pp 1–24.

Kauffman SA, Johnsen S (1991) Coevolution to the edge of chaos: Coupled fitness landscapes, poised states, and coevolutionary avalanches. J Theor Biol 149: 467–506.

Kawecki TJ (1993) Age and size at maturity in a patchy environment: fitness maximisation versus evolutionary stability. Oikos 66: 309–317.

Keeping MG, Crewe R (1987) The ontogeny and evolution of foundress associations in Belonogaster petiolata (Hymenoptera: Vespidae). In: Eder J, Rembold H, eds. Chemistry and biology of social insects. München, Germany: Verlag J. Pepeny. pp 383–384.

Keizer J (1987) Statistical thermodynamics of nonequilibrium processes. Berlin, Germany: Springer.

Kelkar YD, Tyekucheva S, Chiaromonte F, Makova KD (2008) The genome-wide determinants of human and chimpanzee microsatellite evolution. Genome Res 18: 30–38.

Keller L (1999) Levels of Selection in Evolution. Princeton, NJ: Princeton University Press.

Keller LF, Waller DM (2002) Inbreeding effects in wild populations. Trends Ecol Evol 17: 230–241.

Kelso JAS (1995) Dynamic patterns: the self-organization of brain and behavior. Cambridge, MA: MIT Press.

Kempenaers B, Verheyen GR, Dhont A (1997) Extra-pair paternity in the blue tit Parus caeruleus: Female choice, male characteristics, and offspring quality. Behav Ecol 8: 481–492.

Kempermann G, Kuhn HG, Gage FH (1997) More hippocampal neurons in adult mice living in an enriched environment. Nature 386: 493–495.

Kendall BE, Fox GA (2002) Variation among individuals and reduced demographic stochasticity. Conserv Biol 16: 109–116.

Kendall BE, Fox GA (2003) Unstructured individual variation and demographic stochasticity. Conserv Biol 17: 1170–1172.

Kentzoglanakis K, García López D, Brown SP, Goldstein RA (2013) The evolution of collective restraint: policing and obedience among non-conjugative plasmids. PLoS Comput Biol 9: e1003036.

Keren I, Kaldalu N, Spoering A,Wang Y, Lewis K (2004) Persister cells and tolerance to antimicrobials. FEMS Microbiol Lett 230: 13–18.

Kerr B, Feldman MW (2003) Carving the cognitive niche: optimal learning strategies in homogeneous and heterogeneous environments. J Theor Biol 220: 169–188.

Kerr B, Neuhauser C, Bohannan BJ, Dean AM (2006) Local migration promotes competitive restraint in a host–pathogen 'tragedy of the commons'. Nature 442: 75–78.

Keyfitz N (1977) Introduction to the mathematics of population. Reading, MA: Addison-Wesley.

Khersonsky O, Tawfik DS (2010) Enzyme promiscuity: a mechanistic and evolutionary perspective. Annu Rev Biochem 79: 471–505.

Kiester AR, Lande R, Schemske DW (1984) Models of coevolution and speciation in plants and their pollinators. Am Nat 124: 220–243.

Killingback T, Bieri J, Flatt T (2006) Evolution in group-structured populations can resolve the tragedy of the commons. Proc R Soc Lond B 273: 1477–1481.

Kimble J (1981) Alterations in cell lineage following laser ablation of cells in the somatic gonad of Caenorhabditis elegans. Dev Biol 87: 286–300.

Kimble J, Hirsh D (1979) The postembryonic cell lineages of the hermaphrodite and male gonads in Caenorhabditis elegans. Dev Biol 70: 396–417.

Kimura M (1961) Natural selection as process of accumulating genetic information in adaptive evolution. Genet Res 2: 127–140.

Kimura M (1965) A stochastic model concerning the maintenance of genetic variability in quantitative characters. Proc Natl Acad Sci USA 54: 731–737.

Kimura M (1967) On the evolutionary adjustment of spontaneous mutation rates. Genet Res 9: 23–34.

Kimura M (1968) Evolutionary rate at the molecular level. Nature 217: 624–626.

King DG, Soller M, Kashi Y (1997) Evolutionary tuning knobs. Endeavour 21: 36–40.

King DG, Kashi Y (2007) Mutability and evolvability: indirect selection for mutability. Heredity 99: 123–124.

King JL (1967) Continuously distributed factors affecting fitness. Genetics 55: 483–492.

King OD, Masel J (2007) The evolution of bet-hedging adaptations to rare scenarios. Theor Popul Biol 72: 560–575.

Kingsolver JG, Hoekstra HE, Hoekstra JM, Berrigan D, Vignieri SN, et al. (2001) The strength of phenotypic selection in natural populations. Am Nat 157: 245–261.

Kirchner JW, Roy BA (1999) The evolutionary advantages of dying young: Epidemiological implications of longevity in metapopulations. Am Nat 154: 140–159.

Kirschner M, Gerhart JC (2005) The plausibility of life. London, UK: Yale University Press.

Kirzhner VM, Korol AB, Nevo E (1998) Complex limiting behavior of multilocus systems in cyclical environments. J Theor Biol 190: 215–225.

Kisdi É (2002) Dispersal: risk spreading versus local adaptation. Am Nat 159: 579–596.

Kisdi É, Meszéna G (1993) Density dependent life history evolution in fluctuating environments. In: Yoshimura J, Clark CW, eds. Adaptation in stochastic environments. Berlin, Germany: Springer. pp 26–62.

Kish A, DiRuggiero J (2012) DNA replication and repair in halophiles. In: Vreeland RH, ed. Advances in understanding the biology of halophilic microorganisms. Dordrecht, the Netherlands: Springer Science. pp 163–198.

Kishony R, Leibler S (2003) Environmental stresses can alleviate the average deleterious effect of mutations. J Biol 2: 14.

Kis-Papo T, Kirzhner V, Wasser SP, Nevo E (2003) Evolution of genomic diversity and sex at extreme environments: fungal life under hypersaline Dead Sea stress. Proc Natl Acad Sci USA 100: 14970–14975.

Klinkhamer PGL, deJong TJ, Metz JAJ (1983) An explanation for low dispersal rates: a simulation experiment. Neth J Zool 33: 532–541.

Klomp H (1970) The determination of clutch size in birds. A review. Ardea 58: 1–124.

Knight FH (1921) Risk, uncertainty and profit. Boston, MA: Houghton Mifflin.

Knight P (1998) Quantum mechanics: where the weirdness comes from. Nature 395: 12–13.

Ko MS (1991) A stochastic model for gene induction. J Theor Biol 153: 181–194.

Kokko H (2001) Fisherian and ‘‘good genes’’ benefits of mate choice: How (not) to distinguish them. Ecol Lett 4: 322–326.

Kokko H, Ebenhard T (1996) Measuring the strength of demographic stochasticity. J Theor Biol 183: 169–178.

Kokko H, Rankin DJ (2006) Lonely hearts or sex in the city? Density-dependent effects in mating systems. Phil Trans R Soc B 361: 319–334.

Kokko H, López-Sepulcre A (2007) The ecogenetic link between demography and evolution: can we bridge the gap between theory and data? Ecol Lett 10: 773–782.

Kondrashov AS (1995) Modifiers of mutation-selection balance: general approach and the evolution of mutation rates. Genet Res 66: 53–69.

Kondrashov AS, Yampolsky LY (1996) High genetic variability under the balance between symmetric mutation and fluctuating stabilizing selection. Genet Res 68: 157–164.

Koops MA, Hutchings JA, Adams BK (2003) Environmental predictability and the cost of imperfect information: influences on offspring size variability. Evol Ecol Res 5: 29–42.

Korol AB, Kirzhner VM, Ronin YI, Nevo E (1996) Cyclical environmental changes as a factor maintaining genetic polymorphism. 2. Diploid selection for an additive trait. Evolution 50: 1432–1441.

Korzeniewski B (2001) Cybernetic formulation of the definition of life. J Theor Biol 209: 275–286.

Korzeniewski B (2005) Confrontation of the cybernetic definition of a living individual with the real world. Acta Biotheor 53: 1–28.

Kotrschal A, Taborsky B (2010) Environmental change enhances cognitive abilities in fish. PLoS Biol 8: e1000351.

Kouretas P, Koutroumpas K, Lygeros J, Lygerou Z (2006) Stochastic hybrid modeling of biochemical processes. In: Cassandras C, Lygeros J, eds. Stochastic hybrid systems. Boca Raton, FL: CRC Press. pp 221–248.

Kowaltowski AJ, de Souza-Pinto NC, Castilho RF, Vercesi AE (2009) Mitochondria and reactive oxygen species. Free Radic Biol Med 47: 333–343.

Krakauer DC, Rodriguez-Gironés MA (1995) Searching and learning in a random environment. J Theor Biol 177: 417–429.

Krebs JR, Kacelnik A, Taylor P (1978) Test of optimal sampling by foraging great tits. Nature 275: 27–31.

Kreft JU (2004) Biofilms promote altruism. Microbiology 150: 2751–2760.

Kreft JU, Bonhoeffer S (2005) The evolution of groups of cooperating bacteria and the growth rate versus yield trade-off. Microbiology 151: 637–641.

Kreimer A, Borenstein E, Gophna U, Ruppin E (2008) The evolution of modularity in bacterial metabolic networks. Proc Natl Acad Sci USA 105: 6976–6981.

Kreps DM (1990) A course in microeconomic theory. Princeton, NJ: Princeton University Press.

Krotov D, Dubuis JO, Gregor T, Bialek W (2014) Morphogenesis at criticality. Proc Natl Acad Sci USA 111: 3683–3688.

Krüger L, Daston L, Heidelberger M (1987) The probabilistic revolution. 2 Vols. Cambridge, MA: MIT Press.

Kubo R, Toda M, Hashitsume N (1985) Statistical physics II. Berlin, Germany: Springer.

Kunkel TA (2004) DNA replication fidelity. J Biol Chem 279: 16895–16898.

Kuno E (1981) Dispersal and the persistence of populations in unstable habitats: a theoretical note. Oecologia 49: 123–126.

Kupiec JJ (2009) The origin of individuals. Singapore: World Scientific.

Kupiec JJ (2010) On the lack of specificity of proteins and its consequences for a theory of biological organization. Prog Biophys Mol Biol 102: 45–52.

Kurakin A (2005a) Self-organization vs Watchmaker: stochastic gene expression and cell differentiation. Dev Genes Evol 215: 46–52.

Kurakin A (2005b) Self-organization versus Watchmaker: Stochastic dynamics of cellular organization. Biol Chem 386: 247–254.

Kurakin A (2007) Self-organization versus Watchmaker: ambiguity of molecular recognition and design charts of cellular circuitry. J Mol Recognit 20: 205–214.

Kussell E, Leibler S (2005) Phenotypic diversity, population growth, and information in fluctuating environments. Science 309: 2075–2078.

Kussell E, Kishony R, Balaban NQ, Leibler S (2005) Bacterial persistence: a model of survival in changing environments. Genetics 169: 1807–1814.

Kuwahara H, Soyer OS (2012) Bistability in feedback circuits as a byproduct of evolution of evolvability. Mol Syst Biol 8: 564.

Laaksonen T (2004) Hatching asynchrony as a bet-hedging strategy: An offspring diversity hypothesis. Oikos 104: 616–620.

Labra M, Ghiani A, Citterio S, Sgorbati S, Sala F, et al. (2002) Analysis of cytosine methylation pattern in response to water deficit in pea root tips. Plant Biol 4: 694–699.

Lacey EP, Real L, Antonovics J, Heckel DG (1983) Variance models in the study of life histories. Am Nat 122: 114–131.

Lachmann M, Jablonka E (1996) The inheritance of phenotypes: an adaptation to fluctuating environments. J Theor Biol 181: 1–9.

Lack D (1947) The significance of clutch size. Ibis 89: 302–352.

Lack D (1954) The natural regulation of animal numbers. Oxford, UK: Clarendon Press.

Laird CD, Pleasant ND, Clark AD, Sneeden JLS, Hassan KMA, et al. (2004) Hairpin-bisulfite PCR: assessing epigenetic methylation patterns on complementary strands of individual DNA molecules. Proc Natl Acad Sci USA 101: 204–209.

Laland KN, Odling-Smee J, Feldman MW (2000) Niche construction, biological evolution, and cultural change. Behav Brain Sci 23: 131–175.

Laland KN, Sterelny K (2006) Seven reasons (not) to neglect niche construction. Evolution 60: 1751–1762.

Lambeck K, Chappell J (2001) Sea level change through the last glacial cycle. Science 292: 679–686.

Lande R (1988) Genetics and demography in biological conservation. Science 241: 1455–1460.

Lande R (1993) Risks of population extinction from demographic and environmental stochasticity and random catastrophes. Am Nat 142: 911–927.

Lande R (1994) Risk of population extinction from fixation of new deleterious mutations. Evolution 48: 1460–1469.

Lande R (1995) Mutation and conservation. Conserv Biol 9: 782–791.

Lande R (1998) Anthropogenic, ecological and genetic factors in extinction and conservation. Res Popul Ecol 40: 259–269.

Lande R (2008) Adaptive topography of fluctuating selection in a Mendelian population. J Evol Biol 21: 1096–1105.

Lande R, Engen S, Sæther BE (2003) Stochastic population dynamics in ecology and conservation. Oxford, UK: Oxford University Press.

Landis RM, Gurevitch J, Fox GA, Fang W, Taub DR (2005) Variation in recruitment and early demography in Pinis ridida following crown fire in the pine barrens of Long Island, New York. J Ecol 93: 607–617.

Landry CR, Lemos B, Rifkin SA, Dickinson WJ, Hartl DL (2007) Genetic properties influencing the evolvability of gene expression. Science 317: 118–121.

Langerhans RB (2008) Predictability of phenotypic differentiation across flow regimes in fishes. Integr Comp Biol 48: 750–768.

Langerhans RB, DeWitt TJ (2004) Shared and unique features of evolutionary diversification. Am Nat 164: 335–349.

Langton CG (1990) Computation at the edge of chaos: phase transitions and emergent computation. Physica D 42: 12–37.

Lankau RA, Strauss SY (2007) Mutual feedbacks maintain both genetic and species diversity in a plant community. Science 317: 1561–1563.

Lapchin L (2002) Host-parasitoid association and diffuse coevolution: when to be a generalist? Am Nat 160: 245–254.

Laplace PS (1825) Essai philosophique sur les probabilités. Paris, France: Gauthier-Villars.

Laszlo E (1987) Evolution; The grand synthesis. Boston, MA: Shambala Publications Inc.

Laszlo E (1994) An introduction to general evolutionary theory. J Biol Syst 2: 105–110.

Laurie CC, Chasalow SD, LeDeaux JR, McCarroll R, Bush D, et al. (2004) The genetic architecture of response to long-term artificial selection for oil concentration in the maize kernel. Genetics 168: 2141–2155.

Leather SR, Walters KFA, Bale JS (1993) The ecology of insect overwintering. Cambridge, UK: Cambridge University Press.

Lee MS, Doughty P (2003) The geometric meaning of macroevolution. Trends Ecol Evol 18: 263–266.

Lee RF (1975) Lipids in the mesopelagic copepod, Gaussia princeps. Wax ester utilization during starvation. Comp Biochem Physiol B 50: 1–4.

Leffler EM, Bullaughey K, Matute DR, Meyer WK, Ségurel L, et al. (2012) Revisiting an old riddle: What determines genetic diversity levels within species? PLoS Biol 10: e1001388.

Legendre M, Pochet N, Pak T, Verstrepen KJ (2007) Sequence-based estimation of minisatellite and microsatellite repeat variability. Genome Res 17: 1787–1796.

Lehmann L, Balloux F (2007) Natural selection on fecundity variance in subdivided populations: kin selection meets bet hedging. Genetics 176: 361–377.

Lehner B (2010) Genes confer similar robustness to environmental, stochastic, and genetic perturbations in yeast. PLoS ONE 5: e9035.

Lehner B, Kaneko K (2011) Fluctuation and response in biology. Cell Mol Life Sci 68: 1005–1010.

Lei J (2012) Stochastic modeling in systems biology. J Adv Math Appl 1: 76–88.

Leigh EG (1973) The evolution of mutation rates. Genetics 73(Suppl.): 1–18.

Leigh EG Jr (1981) The average lifetime of a population in a varying environment. J Theor Biol 90: 213–239.

Leigh EG Jr (1983) When does the good of the group override the advantage of the individual? Proc Natl Acad Sci USA 80: 2985–2989.

Leimar O (2005) The evolution of phenotypic polymorphism: Randomized strategies versus evolutionary branching. Am Nat 165: 669–681.

Leimar O, Hammerstein P, Van Dooren TJ (2006) A new perspective on developmental plasticity and the principles of adaptive morph determination. Am Nat 167: 367–376.

Leisner M, Stingl K, Rädler JO, Maier B (2007) Basal expression rate of comK sets a ‘switching-window’ into the K-state of Bacillus subtilis. Mol Microbiol 63: 1806–1816.

Lenormand T, Otto SP (2000) The evolution of recombination in a heterogeneous environment. Genetics 156: 423–438.

Lenormand T, Roze D, Rousset F (2009) Stochasticity in evolution. Trends Ecol Evol 24: 157–165.

Lenski RE, Mittler JE (1993) The directed mutation controversy and neo-Darwinism. Science 259: 188–194.

Lenski RE, Travisano M (1994) Dynamics of adaptation and diversification: a 10,000-generation experiment with bacterial populations. Proc Natl Acad Sci USA 91: 6808–6814.

Lenski RE, Ofria C, Pennock RT, Adami C (2003) The evolutionary origin of complex features. Nature 423: 139–144.

Lenton TM, Held H, Kriegler E, Hall JW, Lucht W, Rahmstorf S, Schellnhuber HJ (2008) Tipping elements in the Earth’s climate system. Proc Natl Acad Sci USA 105: 1786–1793.

Lerner IM (1959) The concept of natural selection: a centennial view. Proc Am Philos Soc 103: 173–182.

Lertratanangkoon K, Wu CJ, Savaraj N, Thomas ML (1997) Alterations of DNA methylation by glutathione depletion. Cancer Lett 120: 149–156.

Lessells CM (1986) Brood size in Canada Geese: a manipulation experiment. J Anim Ecol 55: 669–689.

Levin SA (1995) Ecosystems and the biosphere as complex adaptive systems. Ecosystems 1: 431–436.

Levin S, Pimentel D (1981) Selection of intermediate rates of increase in parasite-host systems. Am Nat 117: 308–315.

Levin SA, Cohen D, Hastings A (1984) Dispersal strategies in patchy environments. Theor Popul Biol 26: 165–191.

Levine E, Oloumi-Sadeghi H, Ellis CR (1992) Thermal requirements, hatching patterns, and prolonged diapause in Western corn rootworm (Coleoptera: Chrysomelidae) eggs. J Econ Entomol 85: 2425–2432.

Levins R (1968) Evolution in changing environments. Princeton, NJ: Princeton University Press.

Levins R (1969) The effect of random variations of different types on population growth. Proc Natl Acad Sci USA 62: 1061–1065.

Levinton J (1988) Genetics, paleontology, and macroevolution. Cambridge, UK: Cambridge University Press.

Levsky JM, Shenoy SM, Pezo RC, Singer RH (2002) Single-cell gene expression profiling. Science 297: 836–840.

Levsky JM, Singer RH (2003) Gene expression and the myth of the average cell. Trends Cell Biol 13: 4–6.

Levy MS (2010) Evolution of risk aversion: The "Having Descendants Forever" approach. Available at SSRN 1688265.

Levy SF, Ziv N, Siegal ML (2012) Bet hedging in yeast by heterogeneous, age-correlated expression of a stress protectant. PLoS Biol 10: e1001325.

Lewin R (1980) Evolutionary theory under fire. Science 210: 883–887.

Lewontin R (1961) Evolution and the theory of games. J Theor Biol 1: 382–403.

Lewontin RC (1962) Interdeme selection controlling a polymorphism in the house mouse. Am Nat 96: 65–78.

Lewontin RC (1966) Is nature probable or capricious? BioScience 16: 25–27.

Lewontin RC (2000) The triple helix: Gene, organism, and environment. Cambridge, MA: Harvard University Press.

Lewontin RC, Cohen D (1969) On population growth in a randomly varying environment. Proc Natl Acad Sci USA 62: 1056–1060.

Li M, Wang IX, Li Y, Bruzel A, Richards AL, et al. (2011) Widespread RNA and DNA sequence differences in the human transcriptome. Science 333: 53–58.

Li YC, Korol AB, Fahima T, Beiles A, Nevo E (2002) Microsatellites: genomic distribution, putative functions and mutational mechanisms: a review. Mol Ecol 11: 2453–2465.

Liao CM (2013) Risk taking begets risk taking: evidence from casino openings and investor portfolios. Unpublished working paper. University of Toronto.

Libby E, Rainey PB (2011) Exclusion rules, bottlenecks and the evolution of stochastic phenotype switching. Proc R Soc Lond B Biol Sci 278: 3574–3583.

Lidstrom ME, Konopka MC (2010) The role of physiological heterogeneity in microbial population behavior. Nat Chem Biol 6: 705–712.

Liefting M, Hoffmann AA, Ellers J (2009) Plasticity versus environmental canalization: population differences in thermal responses along a latitudinal gradient in Drosophila serrata. Evolution 63: 1954–1963.

Lim HN, van Oudenaarden A (2007) A multistep epigenetic switch enables the stable inheritance of DNA methylation states. Nat Genet 39: 269–275.

Lindström J (1999) Early development and fitness in birds and mammals. Trends Ecol Evol 14: 343–348.

Lion S, van Baalen M (2008) Self-structuring in spatial evolutionary ecology. Ecol Lett 11: 277–295.

Lion S, Boots M (2010) Are parasites “prudent” in space? Ecol Lett 13: 1245–1255.

Liow LH, Van Valen L, Stenseth NC (2011) Red Queen: from populations to taxa and communities. Trends Ecol Evol 26: 349–358.

Lips K (2001) Reproductive trade-offs and bet-hedging in Hyla calypsa, a neotropical treefrog. Oecologia 128: 509–518.

Lipson H, Pollack JB, Suh NP (2002) On the origin of modular variation. Evolution 56: 1549–1556.

Litt B, Esteller R, Echauz J, D’Alessandro M, Shor R, Henry T, et al. (2001) Epileptic seizures may begin hours in advance of clinical onset: a report of five patients. Neuron 30: 51–64.

Lloyd Morgan C (1896) On modification and variation. Science 4: 733–740.

Loeb MLG (2003) Evolution of egg dumping in a subsocial insect. Am Nat 161: 129–142.

Loewe L, Textor V, Scherer S (2003) High deleterious mutation rate in stationary phase of Escherichia coli. Science 302: 1558–1560.

Loewer A, Lahav G (2006) Cellular conference call: external feedback affects cell-fate decisions. Cell 124: 1128–1130.

Lof ME, Reed TE, McNamara JM, Visser ME (2012) Timing in a fluctuating environment: environmental variability and asymmetric fitness curves can lead to adaptively mismatched avian reproduction. Proc Biol Sci 279: 3161–3169.

Loman J, Madsen T, Håkansson T (1988) Increased fitness from multiple matings, and genetic heterogeneity: a model of a possible mechanism. Oikos 52: 69–72.

Lomnicki A (1978) Individual-differences between animals and natural regulation of their numbers. J Anim Ecol 47: 461–475.

Longo D, Hasty J (2006) Dynamics of single-cell gene expression. Mol Syst Biol 2: 64. doi:10.1038/msb4100110.

Longo G, Montévil M, Kauffman S (2012) No entailing laws, but enablement in the evolution of the biosphere. Proceedings of the Fourteenth International Conference on Genetic and Evolutionary Computation Conference Companion. pp 1379–1392.

Lopez-Maury L, Marguerat S, Bahler J (2008) Tuning gene expression to changing environments: from rapid responses to evolutionary adaptation. Nat Rev Genet 9: 583–593.

Lorch PD, Chao L (2003) Selection for multiple mating in females due to mates that reduce female fitness. Behav Ecol 14: 679–686.

Lorenz DM, Jeng A, Deem MW (2011) The emergence of modularity in biological systems. Phys Life Rev 8: 129–160.

Losdat S, Helfenstein F, Saladin V, Richner H (2011) Higher in vitro resistance to oxidative stress in extra-pair offspring. J Evol Biol 24: 2525–2530.

Losick R, Desplan C (2008) Stochasticity and cell fate. Science 320: 65–68.

Losos JB (2009) Lizards in an evolutionary tree: ecology and adaptive radiation of Anoles. Berkeley, CA: University of California Press.

Losos JB (2011) Convergence, adaptation, and constraint. Evolution 65: 1827–1840.

Lubchenco J, Gaines SD (1981) A unified approach to marine plant-herbivore interactions. I. Populations and communities. Annu Rev Ecol Syst 12: 405–437.

Ludwig D (1996) The distribution of population survival times. Am Nat 147: 506–526.

Luisi PL (2003) Contingency and determinism. Phil Trans R Soc Lond A 361: 1141–1147.

Lundberg KS, Shoemaker DD, Adams MWW, Short JM, Sorge JA, et al. (1991) High-fidelity amplification using a thermostable DNA polymerase isolated from Pyrococcus furiosus. Gene 108: 1–6.

Lux T, Marchesi M (1999) Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397: 498–500.

Lynch M (2007a) The origins of genome architecture. Sunderland, MA: Sinauer Associates.

Lynch M (2007b) The frailty of adaptive hypotheses for the origins of organismal complexity. Proc Natl Acad Sci USA 104 Suppl 1: 8597–8604.

Lynch M, Lande R (1993) Evolution and extinction in response to environmental change. In: Kareiva PM, Kingsolver JG, Huey RB, eds. Biotic interactions and global change. Sunderland, MA: Sinauer Associates. pp 234–250.

Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sunderland, MA: Sinauer Associates.

Lynch M, Abegg A (2010) The rate of establishment of complex adaptations. Mol Biol Evol 27: 1404–1414.

Lyon BE, Eadie JM (2008) Conspecific brood parasitism in birds: a life-history perspective. Annu Rev Ecol Evol Syst 39: 343–363.

Maamar H, Dubnau D (2005) Bistability in the Bacillus subtilis K-state (competence) system requires a positive feedback loop. Mol Microbiol 56: 615–624.

Maamar H, Raj A, Dubnau D (2007) Noise in gene expression determines cell fate in Bacillus subtilis. Science 317: 526–529.

MacArthur BD, Ma'ayan A, Lemischka IR (2009) Systems biology of stem cell fate and cellular reprogramming. Nat Rev Mol Cell Biol 10: 672–681.

MacArthur RH (1972) Geographical ecology. New York, NY: Harper & Row.

MacColl ADC (2011) The ecological causes of evolution. Trends Ecol Evol 26: 514–522.

MacDonald DT, Stoner L (1968) Population fitness in Tribolium. II. Population characteristics influencing the capacity for survival. Am Nat 102: 323–336.

MacDonald KB (1995) Evolution, the Five Factor Model, and levels of personality. J Pers 63: 525–567.

Mackinnon MJ, Read AF (1999) Genetic relationships between parasite virulence and transmission in the rodent malaria Plasmodium chabaudi. Evolution 53: 689–703.

Mackinnon MJ, Gandon S, Read AF (2008) Virulence evolution in response to vaccination: the case of malaria. Vaccine 26S: C42–C52.

Mackwan RR (2006) Genetic fidelity in extremophiles. Doctoral dissertation. Cincinnati, OH: University of Cincinnati.

Mackwan RR, Carver GT, Drake JW, Grogan DW (2007) An unusual pattern of spontaneous mutations recovered in the halophilic archaeon Haloferax volcanii. Genetics 176: 697–702.

MacLean EL, Mandalaywala TM, Brannon EM (2012) Variance-sensitive choice in lemurs: constancy trumps quantity. Anim Cogn 15: 15–25.

MacLean RC (2008) The tragedy of the commons in microbial populations: insights from theoretical, comparative and experimental studies. Heredity 100: 471–477.

MacLean RC, Gudelj I (2006) Resource competition and social conflict in experimental populations of yeast. Nature 441: 498–501.

MacNeil LT, Walhout AJM (2011) Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res 21: 645–657.

MacNeill A (2009) http://evolutionlist.blogspot.com/2009/03/are-mechanisms-that-produce-phenotypic.html

Madsen T, Shine R, Loman J, Hakansson T (1992) Why do female adders copulate so frequently? Nature 335: 440–441.

Madsen T, Shine R (1998) Quantity or quality? Determinants of maternal reproductive success in tropical pythons (Liasis fuscus). Proc R Soc B Biol Sci 265: 1521–1525.

Madsen T, Ujvari B, Olsson M, Shine R (2005) Paternal alleles enhance female reproductive success in tropical pythons. Mol Ecol 14: 1783–1787.

Maeto K, Ozaki K (2003) Prolonged diapause of specialist seed-feeders makes predator satiation unstable in masting of Quercus crispula. Oecologia 137: 392–398.

Maheshri N, O'Shea EK (2007) Living with noisy genes: how cells function reliably with inherent variability in gene expression. Annu Rev Biophys Biomol Struct 36: 413–434.

Mäkinen T, Panova M, André C (2007) High levels of multiple paternity in Littorina saxatilis: hedging the bets? J Hered 98: 705–711.

Malthus TR (1803) An Essay on the Principles of Population. London, UK: J. Johnson.

Mandelbrot BB, Wallis JR (1969) Some long-run properties of geophysical records. Water Resour Res 5: 321–340.

Mandelbrot BB (1983) The fractal geometry of nature. New York, NY: W. H. Freeman.

Mangel M, Stamps J (2001) Trade-offs between growth and mortality and the maintenance of individual variation in growth. Evol Ecol Res 3: 583–593.

Manolis JC, Andersen DE, Cuthbert FJ (2002) Edge effect on nesting success of ground nesting birds near regenerating clearcuts in a forest-dominated landscape. Auk 119: 955–970.

Manos H, Liberman U, Feldman MW (2000) On the product mean fitness and population growth in sexual and asexual populations. Evol Ecol Res 2: 525–545.

Manser MB, Avey G (2000) The effect of pup vocalisations on food allocation in a cooperative mammal, the meerkat (Suricata suricatta). Behav Ecol Sociobiol 48: 429–437.

Manson SM (2001) Simplifying complexity: a review of complexity theory. Geoforum 32: 405–414.

Marler P, Terrace HS, eds. (1984) The biology of learning. New York, NY: Springer-Verlag.

Marshall DJ, Bonduriansky R, Bussière LF (2008) Offspring size variation within broods as a bet-hedging strategy in unpredictable environments. Ecology 89: 2506–2517.

Marteinsson VT, Reysenbach AL, Birrien J-L, Prieur D (1999) A stress protein is induced in the deep-sea barophilic hyperthermophile Thermococcus barophilus when grown under atmospheric pressure. Extremophiles 3: 277–282.

Martin JR, Faure P, Ernst R (2001) The power law distribution for walking-time intervals correlates with the ellipsoid-body in Drosophila. J Neurogenet 15: 205–219.

Martin LB, Liebl AL, Trotter JH, Richards CL, McCoy K, McCoy MW (2011) Integrator networks: illuminating the black box linking genotype and phenotype. Integr Comp Biol 51: 514–527.

Martin TE (1988) Processes organizing open-nesting bird assemblages: competition or nest predation? Evol Ecol 2: 37–50.

Martin TE (1995) Avian life history evolution in relation to nest sites, nest predation, and food. Ecol Monogr 65: 101–127.

Martin TE, Clobert J (1996) Nest predation and avian life history evolution in Europe versus North America: a possible role of humans? Am Nat 147: 1028–1046.

Martin TE, Scott J, Menge C (2000) Nest predation increases with parental activity: separating nest site and parental activity effects. Proc R Soc B Biol Sci 267: 2287–2293.

Martin TL, Huey RB (2008) Why ‘suboptimal’ is optimal: Jensen’s inequality and ectotherm thermal preferences. Am Nat 171: E102–E118.

Masel J (2006) Cryptic genetic variation is enriched for potential adaptations. Genetics 172: 1985–1991.

Masel J (2011) Genetic drift. Curr Biol 21: R837–R838.

Masel J, King OD, Maughan H (2007) The loss of adaptive plasticity during long periods of environmental stasis. Am Nat 169: 38–46.

Mason G, Noris E, Lanteri S, Acquadro A, Accotto GP, Portis E (2008) Potentiality of methylation-sensitive amplification polymorphism (MSAP) in identifying genes involved in tomato response to tomato yellow leaf curl Sardinia virus. Plant Mol Biol Rep 26: 156–173.

Matsuo Y (2006) Cost of prolonged diapause and its relationship to body size in a seed predator. Funct Ecol 20: 300–306.

Mattessich R (1982) The systems approach: Its variety of aspects. J Assoc Inf Sci Tec 33: 383–394.

Matthen M, Ariew A (2002) Two ways of thinking about fitness and natural selection. J Philos 99: 55–83.

Matthews B, Narwani A, Hausch S, Nonaka E, Peter H, Yamamichi M, Sullam KE, Bird KC, Thomas MK, Hanley TC, Turner CB (2011) Toward an integration of evolutionary biology and ecosystem science. Ecol Lett 14: 690–701.

Mattila P, Korpela J, Tenkanen T, Pitkanen K (1991) Fidelity of DNA synthesis by the Thermococcus litoralis DNA polymerase - an extremely heat stable enzyme with proofreading activity. Nucleic Acids Res 19: 4967–4973.

Maughan H, Nicholson WL (2004) Stochastic processes influence stationary-phase decisions in Bacillus subtilis. J Bacteriol 186: 2212–2214.

Mauk MD (2000) The potential effectiveness of simulations versus phenomenological models. Nat Neurosci 3: 649–651.

May RM (1973a) Stability and complexity in model ecosystems. Princeton, NJ: Princeton University Press.

May RM (1973b) Stability in randomly fluctuating versus deterministic environments. Am Nat 107: 621–650.

May RM (1974) Biological populations with non-overlapping generations: stable points, stable cycles and chaos. Science 186: 645–647.

May RM (1976) Simple mathematical models with very complicated dynamics. Nature 261: 459–467.

May RM, Anderson RM (1983) Epidemiology and genetics in the coevolution of parasites and hosts. Proc R Soc Lond B 219: 281–313.

May RM, Levin SA, Sugihara G (2008) Ecology for bankers. Nature 451: 893–895.

Mayack C, Naug D (2011) A changing but not an absolute energy budget dictates risk-sensitive behaviour in the honeybee. Anim Behav 82: 595–600.

Mayani H, Dragowska W, Lansdorp PM (1993) Lineage commitment in human hemopoiesis involves asymmetric cell division of multipotent progenitors and does not appear to be influenced by cytokines. J Cell Physiol 157: 579–586.

Maye A, Hsieh C-h, Sugihara G, Brembs B (2007) Order in spontaneous behavior. PLoS ONE 2: e443.

Maynard Smith J (1974) The theory of games and the evolution of animal conflicts. J Theor Biol 47: 209–221.

Maynard Smith J (1976) Group selection. Q Rev Biol 51: 277–283.

Maynard Smith J (1982a) Evolution and the theory of games. Cambridge, UK: Cambridge University Press.

Maynard Smith J (1982b) The evolution of social behavior - A classification of models. In: King’s College Sociobiology Group, eds. Current problems in sociobiology. Cambridge, UK: Cambridge University Press. pp 28–44.

Maynard Smith J (1989) Did Darwin get it right? New York, NY: Chapman & Hall.

Maynard Smith J, Price GR (1973) The logic of animal conflict. Nature 246: 15–18.

Maynard Smith J, Brookfield JFY (1983) Models of evolution [and discussion]. Proc R Soc Lond Ser B Biol Sci 219: 315–325.

Maynard Smith J, Szathmáry E (1995) The major transitions in evolution. Oxford, UK: W.H. Freeman/Spektrum.

Mayr E (1963) Animal species and evolution. Cambridge, MA: Harvard University Press.

Mayr E (1976) Evolution and the diversity of life: Selected essays. Cambridge, MA: Harvard University Press.

Mayr E (1980) Some thoughts on the history of the evolutionary synthesis. In: Mayr E, Provine W, eds. The evolutionary synthesis. Cambridge, MA: Harvard University Press. pp 1–48.

Mayr E (1992) The idea of teleology. J Hist Ideas 53: 117–135.

Mayr E (2000) Darwin’s influence on modern thought. Sci Am 79–83.

McAdams HH, Arkin A (1997) Stochastic mechanisms in gene expression. Proc Natl Acad Sci USA 94: 814–819.

McAdams HH, Arkin A (1999) It’s a noisy business! Genetic regulation at the nanomolar scale. Trends Genet 15: 65–69.

McBride RC, Ogbunugafor CB, Turner PE (2008) Robustness promotes evolvability of thermotolerance in an RNA virus. BMC Evol Biol 8: 231.

McCauley E, Nisbet RM, Murdoch WW, de Roos AM, Gurney WSC (1999) Large-amplitude cycles of Daphnia and its algal prey in enriched environments. Nature 402: 653–656.

McGinley MA, Temme DH, Geber MA (1987) Parental investment in offspring in variable environments: theoretical and empirical considerations. Am Nat 130: 370–398.

McKane AJ, Newman TJ (2005) Predator-prey cycles from resonant amplification of demographic stochasticity. Phys Rev Lett 94: 218102.

McKaughan DJ (2005) The influence of Niels Bohr on Max Delbrück: Revisiting the hopes inspired by ‘‘light and life.’’ Isis 96: 507–529.

McKelvey B (2001) What is complexity science? It is really order-creation science. Emergence 3: 137–157.

McKelvey B (2004a) Complexity science as order-creation science: New theory, new method. E:CO 6: 2–27.

McKelvey B (2004b) Toward a 0th law of thermodynamics: Order creation complexity dynamics from physics and biology to bioeconomics. J Bioecon 6: 65–96.

McKinney ML, Frederick DL (1999) Species-time curves and population extremes: ecological patterns in the fossil record. Evol Ecol Res 1: 641–650.

McNamara JM (1995) Implicit frequency dependence and kin selection in fluctuating environments. Evol Ecol 9: 185–203.

McNamara JM (1998) Phenotypic plasticity in fluctuating environments: consequences of the lack of individual optimization. Behav Ecol 9: 642–648.

McNamara JM, Houston AI (1992) Risk-sensitive foraging: A review of the theory. Bull Math Biol 54: 355–378.

McNamara JM, Webb JN, Collins EJ (1995) Dynamic optimization in fluctuating environments. Proc R Soc Lond B 261: 279–284.

McNamara JM, Trimmer PC, Eriksson A, Marshall JA, Houston AI (2011) Environmental variability can select for optimism or pessimism. Ecol Lett 14: 58–62.

McSharry PE, Smith LA, Tarassenko L (2003) Prediction of epileptic seizures: are nonlinear methods relevant? Nature Med 9: 241–242.

Meginniss JR (1977) Alternatives to the expected utility rule. Unpublished dissertation, Chicago, IL: University of Chicago.

Melbourne BA, Hasting A (2008) Extinction risk depends strongly on factors contributing to stochasticity. Nature 454: 100–103.

Menezes CF, Hanson DL (1970) On the theory of risk aversion. Int Econ Rev 11: 481–487.

Menge BA, Berlow EL, Blanchette CA (1994) The keystone species concept - variation in interaction strength in a rocky intertidal habitat. Ecol Monogr 64: 249–286.

Menu F (1993a) Strategies of emergence in the chestnut weevil Curculio elephas (Coleoptera: Curculionidae). Oecologia 96: 383–390.

Menu F (1993b) Diapause development in the chestnut weevil Curculio elephas. Entomol Exp Appl 69: 91–96.

Menu F, Debouzie D (1993) Coin-flipping plasticity and prolonged diapause in insects: example of the chestnut weevil Curculio elephas (Coleoptera: Curculionidae). Oecologia 93: 367–373.

Menu F, Roebuck JP, Viala M (2000) Bet-hedging diapause strategies in stochastic environments. Am Nat 155: 724–734.

Menu F, Desouhant E (2002) Bet-hedging for variability in life cycle duration: bigger and later-emerging chestnut weevils have increased probability of a prolonged diapause. Oecologia 132: 167–174.

Mery F, Kawecki TJ (2002) Experimental evolution of learning ability in fruit flies. Proc Natl Acad Sci USA 99: 14274–14279.

Mery F, Kawecki TJ (2003) A fitness cost of learning ability in Drosophila melanogaster. Proc R Soc B 270: 2465–2469.

Mery F, Kawecki TJ (2004) An operating cost of learning in Drosophila melanogaster. Anim Behav 68: 589–598.

Messenger SL, Molineux IJ, Bull JJ (1999) Virulence evolution in a virus obeys a trade off. Proc R Soc Lond Ser B Biol Sci 266: 397–404.

Messina FJ (1991) Life-history variation in a seed beetle: adult egg-laying vs. larval competitive ability. Oecologia 85: 447–455.

Metcalf CJ, Koons DN (2007) Environmental uncertainty, autocorrelation and the evolution of survival. Proc Biol Sci 274: 2153–2160.

Metcalf CJE, Pavard S (2007) Why evolutionary biologists should be demographers. Trends Ecol Evol 22: 205–212.

Metcalf RA, Whitt GS (1977) Relative inclusive fitness in the social wasp, Polistes metricus. Behav Ecol Sociobiol 2: 353–360.

Metzgar D, Wills C (2000) Evidence for adaptive evolution of mutation rates. Cell 101: 581–584.

Meyers LA, Bull JJ (2002) Fighting change with change: adaptive variation in an uncertain world. Trends Ecol Evol 17: 551–557.

Meyers LA, Ancel FD, Lachmann M (2005) Evolution of genetic potential. PLoS Comput Biol 1: e32.

Michener CD (1964) Reproductive efficiency in relation to colony size in hymenopterous societies. Insect Soc 11: 317–342.

Michod RE (1982) The theory of kin selection. Annu Rev Ecol Syst 13: 23–55.

Mideo N, Day T (2008) On the evolution of reproductive restraint in malaria. Proc Biol Sci 275: 1217–1224.

Miglior F, Loker S, Shanks RD (2013) Dairy cattle breeding. In: Christou P, Savin R, Costa-Pierce BA, Misztal I, Whitelaw CBA, eds. Sustainable food production. New York, NY: Springer. pp 740–746.

Milinski M, Parker GA (1991) Competition for resources. In: Krebs JR, Davies NB, eds. Behavioural ecology: an evolutionary approach. Oxford, UK: Blackwell. pp 137–168.

Milinski M, Semmann D, Krambeck HJ (2002) Reputation helps solve the ‘tragedy of the commons’. Nature 415: 424–426.

Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: Synthesis report. Washington, DC: Island Press.

Miller GF, Todd PM (1990) Exploring adaptive agency I: Theory and methods for simulating the evolution of learning. In: Touretzky DS, Elman JL, Sejnowski TJ, Hinton GE, eds. Proceedings of the 1990 Connectionist Models Summer School. San Mateo, CA: Morgan Kaufmann. pp 65–80.

Miller JH (2005) Perspective on mutagenesis and repair: the standard model and alternate modes of mutagenesis. Crit Rev Biochem Mol Biol 40: 155–179.

Mills SK, Beatty JH (1979) The propensity interpretation of fitness. Philos Sci 46: 263–286.

Millstein RL (1997) The chances of evolution: An analysis of the roles of chance in microevolution and macroevolution. PhD Thesis. Minneapolis, MN: University of Minnesota.

Millstein RL (2002) Are random drift and natural selection conceptually distinct? Biol Philos 17: 33–53.

Millstein RL (2003) Interpretations of probability in evolutionary theory. Philos Sci 70: 1317–1328.

Millstein RL (2006) Natural selection as a population-level causal process. Brit J Philos Sci 57: 627–653.

Milne BT (1998) Motivation and benefits of complex systems approaches in ecology. Ecosystems 1: 449–456.

Milton CC, Ulane CM, Rutherford S (2006) Control of canalization and evolvability by Hsp90. PloS ONE 1: e75.

Miner BE, De Meester L, Pfrender ME, Lampert W, Hairston NG (2012) Linking genes to communities and ecosystems: Daphnia as an ecogenomic model. Proc R Soc B Biol Sci 279: 1873–1882.

Minocherhomji S, Tollefsbol TO, Singh KK (2012) Mitochondrial regulation of epigenetics and its role in human diseases. Epigenetics 7: 326–334.

Miramontes O, DeSouza O (2014) Social evolution: new horizons. arXiv preprint arXiv:1404.6267.

Mirel DB, Estacio WF, Mathieu M, Olmsted E, Ramirez J, Márquez-Magaña LM (2000) Environmental regulation of Bacillus subtilis σD-dependent gene expression. J Bacteriol 182: 3055–3062.

Mitchell M, Forrest S (1994) Genetic algorithms and artificial life. Artif Life 1: 267–289.

Mitchell-Olds T (1995) The molecular basis of quantitative genetic variation in natural populations. Trends Ecol Evol 10: 324–328.

Mitteldorf J (2010) Evolutionary origins of aging. In: Fahy GM, West MD, Coles LS, Harris SB, eds. The future of aging. Pathways to human life extension. Dordrecht, NL: Springer. pp 87–126.

Mittler JE (1996) Evolution of the genetic switch in temperate bacteriophage. I. Basic theory. J Theor Biol 179: 161–172.

Mohamed MF, Hollfelder F (2013) Efficient, crosswise catalytic promiscuity among enzymes that catalyze phosphoryl transfer. Biochim Biophys Acta 1834: 417–424.

Mohn F, Schübeler D (2009) Genetics and epigenetics: stability and plasticity during cellular differentiation. Trends Genet 25: 129–136.

Moles AT, Westoby M (2006) Seed size and plant strategy across the whole life cycle. Oikos 113: 91–105.

Monod J (1971) Chance and necessity. New York, NY: Knopf.

Monro K, Sinclair-Taylor T, Marshall DJ (2010) Selection on offspring size among environments: the roles of environmental quality and variability. Funct Ecol 24: 676–684.

Moore FE, Simm PA (1986) Risk-sensitive foraging by a migratory bird (Dendroica coronata). Experientia 42: 1054–1056.

Moore IT, Jessop TS (2003) Stress, reproduction, and adrenocortical modulation in amphibians and reptiles. Horm Behav 43: 39–47.

Moorhead PS, Kaplan MM, eds. (1967) Mathematical challenges to the neo-Darwinian interpretation of evolution. Philadelphia, PA: Wistar Institute Press.

Mora T, Bialek W (2011) Are biological systems poised at criticality? J Stat Phys 144: 268–302.

Moraiti CA, Nakas CT, Papadopoulos NT (2012) Prolonged pupal dormancy is associated with significant fitness cost for adults of Rhagoletis cerasi (Diptera: Tephritidae). J Insect Physiol 58: 1128–1135.

Moran NA (1992) The evolutionary maintenance of alternative phenotypes. Am Nat 139: 971–989.

Morey SR, Reznick DN (2004) The relationship between habitat permanence and larval development in California spadefoot toads: Field and laboratory comparisons of developmental plasticity. Oikos 104: 172–190.

Morgan HD, Sutherland HG, Martin DI, Whitelaw E (1999) Epigenetic inheritance at the agouti locus in the mouse. Nat Genet 23: 314–318.

Morin PJ, Lawler SP, Johnson FA (1990) Ecology and breeding phenology of larval Hyla andersonii: the disadvantages of breeding late. Ecology 71: 1590–1598.

Moriyama M, Numata H (2008) Diapause and prolonged development in the embryo and their ecological significance in two cicadas, Cryptotympana facialis and Graptopsaltria nigrofuscata. J Insect Physiol 54: 1487–1494.

Morris WF, Pfister CA, Tuljapurkar S, Haridas CV, Boggs CL, Boyce MS, et al. (2008) Longevity can buffer plant and animal populations against changing climatic variability. Ecology 89: 19–25.

Morrongiello JR, Bond NR, Crook DA, Wong BB (2012) Spatial variation in egg size and egg number reflects trade-offs and bet-hedging in a freshwater fish. J Anim Ecol 81: 806–817.

Mousseau TA, Roff DA (1987) Natural selection and the heritability of fitness components. Heredity 59: 181–198.

Moxon ER, Rainey PB, Nowak MA, Lenski RE (1994) Adaptive evolution of highly mutable loci in pathogenic bacteria. Curr Biol 4: 24–33.

Moxon R, Bayliss C, Hood D (2006) Bacterial contingency loci: The role of simple sequence DNA repeats in bacterial adaptation. Annu Rev Genet 40: 307–333.

Msadek T (1999) When the going gets tough: survival strategies and environmental signaling networks in Bacillus subtilis. Trends Microbiol 7: 201–207.

Msadek T, Kunst F, Rappaport G (1993) Two-component regulatory systems. In: Sonenshein AL, Hoch JA, Losick R, eds. Bacillus subtilis and other gram-positive bacteria: biochemistry, physiology, and molecular genetics. Washington, DC: ASM Press. pp 729–745.

Mueller L, Guo P, Ayala F (1991) Density-dependent natural selection and trade-offs in life history traits. Science 253: 433–435.

Müller J, Hense BA, Fuchs TM, Utz M, Pötzsche C (2013) Bet-hedging in stochastically switching environments. J Theor Biol 336: 144–157.

Müller JK, Eggert AK, Dressel J (1990) Intraspecific brood parasitism in the burying beetle, Necrophorus vespilloides (Coleoptera: Silphidae). Anim Behav 40: 491–499.

Murase Y, Shimada T, Ito N, Rikvold PA (2010) Effects of demographic stochasticity on biological community assembly on evolutionary time scales. Phys Rev E 81: 041908.

Murie JO, Dobson FS (1987) The costs of reproduction in female Columbian ground squirrels (Spermophilus columbianus). Oecologia 73: 1–6.

Murphy EC, Haukioja E (1986) Clutch size in nidicolous birds. In: Johnston RF, ed. Current ornithology, Vol. 4. New York, NY: Plenum. pp 141–180.

Murphy GI (1968) Pattern in life-history and the environment. Am Nat 102: 391–403.

Murray Jr BG (1979) Population dynamics. New York, NY: Academic Press.

Musick JA, Burgess G, Cailliet G, Carmhi M, Fordham S (2000) Management of sharks and their relatives (Elasmobranchii). Fisheries 25: 9–13.

Mustonen V, Lässig M (2009) From fitness landscapes to seascapes: Non-equilibrium dynamics of selection and adaptation. Trends Genet 25: 111–119.

Mustonen V, Lässig M (2010) Fitness flux and ubiquity of adaptive evolution. Proc Natl Acad Sci USA 107: 4248–4253.

Mylius SD, Diekmann O (1995) On evolutionarily stable life-histories, optimization and the need to be specific about density-dependence. Oikos 74: 218–224.

Nakamura I, Ae SA (1977) Prolonged pupal diapause of Papilio alexanor. Arid zone adaptation directed by larval host plant. Ann Entomol Soc Am 70: 481–484.

Narendra KS, Thathachar MAL (1974) Learning automata-a survey. IEEE Trans Syst Man Cybern SMC-4: 323–334.

Narendra KS, Thathachar MAL (1989) Learning automata: an introduction. Englewood Cliffs, NJ: Prentice-Hall.

Nathanson M (1975) The effect of resource limitation on competing populations of flour beetles, Tribolium spp. (Coleoptera, Tenebrionidae). Bull Entomol Res 65: 1–12.

Naviaux RK (2008) Mitochondrial control of epigenetics. Cancer Biol Ther 7: 1191–1193.

Neel JV (1962) Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? Am J Hum Genet 14: 353–362.

Neff BD, Pitcher TE (2005) Genetic quality and sexual selection: an integrated framework for good genes and compatible genes. Mol Ecol 14: 19–38.

Neilson WTA (1962) Effects of temperature on development of overwintering pupae of the apple maggot, Rhagoletis pomonella (Walsh). Can Entomol 94: 924–928.

Nevo E, Beharav A, Meyer RC, Hackett CA, Forster BP, Russell JR, Powell W (2005) Genomic microsatellite adaptive divergence of wild barley by microclimatic stress in ‘Evolution Canyon’, Israel. Biol J Linn Soc 84: 205–224.

Nevoux M, Forcada J, Barbraud C, Croxall J, Weimerskirchi H (2010) Bet-hedging response to environmental variability, an intraspecific comparison. Ecology 91: 2416–2427.

Newcomer SD, Zeh JA, Zeh DW (1999) Genetic benefits enhance the reproductive success of polyandrous females. Proc Natl Acad Sci USA 96: 10236–10241.

Newlands S, Levitt LK, Robinson CS, Karpf AC, Hodgson VR, Wade RP, Hardeman EC (1998) Transcription occurs in pulses in muscle fibers. Genes Dev 12: 2748–2758.

Newman JR, Ghaemmaghami S, Ihmels J, Breslow DK, Noble M, DeRisi JL, Weissman JS (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441: 840–846.

Newman RA (1989) Developmental plasticity of Scaphiopus couchii tadpoles in an unpredictable environment. Ecology 70: 1175–1787.

Newman SA, Forgacs G, Muller GB (2006) Before programs: The physical origination of multicellular forms. Int J Dev Biol 50: 289–299.

Nicholls H (2011) Uncertainty principle: How evolution hedges its bets. New Sci 2794: 28–31.

Nicolis G, Prigogine I (1977) Self-organization in nonequilibrium systems: from dissipative structures to order through fluctuations. New York, NY: John Wiley & Sons.

Nicolis G, Prigogine I (1989) Exploring complexity: An introduction. New York, NY: W. H. Freeman and Company.

Nilsson M, Snoad N (2002) Optimal mutation rates in dynamic environments. Bull Math Biol 64: 1033–1043.

Nilsson SG (1984) The evolution of nest-site selection among holenesting birds: the importance of nest predation and competition. Ornis Scandinavica 15: 167–175.

Nisbet R, Gurney W (1982) Modelling fluctuating populations. New York, NY: Wiley.

Nobeli I, Favia AD, Thornton JM (2009) Protein promiscuity and its implications for biotechnology. Nat Biotechnol 27: 157–167.

Noë R, Hammerstein P (1994) Biological markets: Supply and demand determine the effect of partner choice in cooperation, mutualism and mating. Behav Ecol Sociobiol 35: 1–11.

Noonan KM (1981) Individual strategies of inclusive-fi tness-maximizing in Polistes fuscatus foundresses. In: Alexander RD, Tinkle DW, eds. Natural selection and social behavior: recent research and theory. New York, NY: Chiron. pp 18–44.

Norris JR (1996) Markov Chains. Cambridge University Press, New York.

Novak M, Pfeiffer T, Lenski RE, Sauer U, Bonhoeffer S (2006) Experimental tests for an evolutionary trade-off between growth rate and yield in E. coli. Am Nat 168: 242–251.

Nowak M (1992) What is a quasispecies? Trends Ecol Evol 7: 118–121.

Nowak M, Sigmund K (1993a) Chaos and the evolution of cooperation. Proc Natl Acad Sci USA 90: 5091–5094.

Nowak MA, Sigmund K (1993b) A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner’s Dilemma game. Nature 364: 56–58.

Nowak MA, Tarnita CE, Wilson EO (2010) The evolution of eusociality. Nature 466: 1057–1062.

Nowak MA, Highfield R (2011) SuperCooperators: altruism, evolution, and why we need each other to succeed. New York, NY: Free Press.

Noy N (2007) Ligand specificity of nuclear hormone receptors: sifting through promiscuity. Biochemistry 46: 13461–13467.

Nunney L (1999) Lineage selection and the evolution of multistage carcinogenesis. Proc R Soc Lond Ser B Biol Sci 266: 493–498.

Nurse P (2008) Life, logic and information. Nature 454: 424–426.

Nykter M, Price ND, Aldana M, Ramsey SA, Kauffman SA, Hood LE, et al. (2008) Gene expression dynamics in the macrophage exhibit criticality. Proc Natl Acad Sci USA 105: 1897–1900.

O’Brien EL, Dawson RD (2007) Context-dependent genetic benefits of extra-pair mate choice in a socially monogamous passerine. Behav Ecol Sociobiol 61: 775–782.

Odling-Smee FJ, Laland KN, Feldman MW (1996) Niche construction. Am Nat 147: 641–648.

Odling-Smee FJ, Laland KN, Feldman MW (2003) Niche construction: the neglected process in evolution. Princeton, NJ: Princeton University Press.

Ohgushi T (1991) Lifetime fitness and evolution of reproductive pattern in the herbivorous lady beetle. Ecology 72: 1110–2122.

Okasha S (2006) Evolution and the levels of selection. New York, NY: Oxford University Press.

Okasha S (2007) Rational choice, risk aversion, and evolution. J Philos 104: 217–235.

Okasha S, Binmore K (2012) Evolution and rationality: decisions, co-operation, and strategic behaviour. Cambridge, UK: Cambridge University Press.

Olofsson H, Ripa J, Jonzén N (2009) Bet-hedging as an evolutionary game: the trade-off between egg size and number. Proc Biol Sci 276: 2963–2969.

Orians GH, Paine RT (1983) Convergent evolution at the community level. In: Futuyma DJ, Slatkin M, eds. Coevolution. Sunderland, MA: Sinauer. pp 431–458.

Orr HA (2007) Absolute fitness, relative fitness, and utility. Evolution 61: 2997–3000.

Orr HA (2009) Fitness and its role in evolutionary genetics. Nat Rev Genet 10: 531–539.

Orzack SH (1981) The modern synthesis is partly Wright. Paleobiology 7: 128–134.

Orzack SH (1985) Population dynamics in variable environments. V. The genetics of homeostasis revisited. Am Nat 125: 550–572.

Orzack SH (1993) Life history evolution and population dynamics in variable environments: some insights from stochastic demography. Lect Notes Biomath 98: 63–104.

Orzack SH, Tuljapurkar S (1989) Population dynamics in variable environments. VII. The demography and evolution of iteroparity. Am Nat 133: 901–923.

Orzack SH, Tuljapurkar S (2001) Reproductive effort in variable environments, or environmental variation is for the birds. Ecology 82: 2659–2665.

Osborn HF (1896) A mode of evolution requiring neither natural selection nor the inheritance of acquired characteristics. Trans NY Acad Sci 15: 141–142.

Otto SP, Michalakis Y (2007) The evolution of recombination in changing environments. Trends Ecol Evol 13: 145–151.

Ovadia O, Schmitz OJ (2002) Linking individuals with ecosystems: Experimentally identifying the relevant organizational scale for predicting trophic abundances. Proc Natl Acad Sci USA 99: 12927–12931.

Ozbudak EM, Thattai M, Kurtser I, Grossman AD, van Oudenaarden A (2002) Regulation of noise in the expression of a single gene. Nat Genet 31: 69–73.

Paenke I, Kawecki TJ, Sendhoff B (2009a) The influence of learning on evolution: A mathematical framework. Artif Life 15: 227–245.

Paenke I, Jin, Y, Branke J (2009b) Balancing population-and individual-level adaptation in changing environments. Adapt Behav 17: 153–174.

Palkovacs EP, Post DM (2008) Eco-evolutionary interactions between predators and prey: can predator-induced changes to prey communities feed back to shape predator foraging traits? Evol Ecol Res 10: 699–720.

Palmer ME, Feldman MW (2011) Spatial environmental variation can select for evolvability. Evolution 65: 2345–2356.

Panaretou B, Prodromou C, Roe SM, O'Brien R, Ladbury JE, et al. (1998) ATP binding and hydrolysis are essential to the function of the Hsp90 molecular chaperone in vivo. EMBO J 17: 4829–4836.

Park H, Pontius W, Guet CC, Marko JF, Emonet T, Cluzel P (2010) Interdependence of behavioural variability and response to small stimuli in bacteria. Nature 468: 819–823.

Park T, Leslie PH, Mertz DB (1964) Genetic strains and competition in populations of Tribolium. Physiol Zool 37: 97–162.

Park T, Mertz DB, Grodzinski W, Prus T (1965) Cannibalistic predation in populations of flour beetles. Physiol Zool 38: 289–321.

Park T, Ziegler JR, Ziegler DL, Mertz DB (1974) The cannibalism of eggs by Tribolium larvae. Physiol Zool 47: 37–58.

Parker GA (1990) Sperm competition games: raffles and roles. Proc R Soc Lond B 242: 120–126.

Parker GA, Baker RR, Smith VGF (1972) The origin and evolution of gamete dimorphism and the male-female phenomenon. J Theor Biol 36: 529–553.

Parker GA, Immler S, Pitnick S, Birkhead TR (2010) Sperm competition games: sperm size (mass) and number under raffle and displacement, and the evolution of P2. J Theor Biol 264: 1003–1023.

Parsons PA (1988) Evolutionary rates: effects of stress upon recombination. Biol J Linn Soc 35: 49–68.

Parter M, Kashtan N, Alon U (2007) Environmental variability and modularity of bacterial metabolic networks. BMC Evol Biol 7: 169.

Parter M, Kashtan N, Alon U (2008) Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comput Biol 4: e1000206.

Partridge L, Barton NH (2000) Natural selection: evolving evolvability. Nature 407: 457–458.

Pascual M, Mazzega P, Levin SA (2001) Oscillatory dynamics and spatial scale: the role of noise and unresolved pattern. Ecology 82: 2357–2369.

Pascual M, Mazzega, P (2003) Quasicycles revisited: apparent sensitivity to initial conditions. Theor Popul Biol 64: 385–395.

Passos JF, Saretzki G, Ahmed S, Nelson G, Richter T, et al. (2007) Mitochondrial dysfunction accounts for the stochastic heterogeneity in telomere-dependent senescence. PLoS Biol 5: e110.

Patel PK, Arcangioli B, Baker SP, Bensimon A, Rhind N (2006) DNA replication origins fire stochastically in the fission yeast. Mol Biol Cell 17: 308–316.

Patten BC, Odum EP (1981) The cybernetic nature of ecosystems. Am Nat 118: 886–895.

Paul REL, Lafond T, Müller-Graf CDM, Nithiuthai S, Brey PT, Koella JC (2004) Experimental evaluation of the relationship between lethal or non-lethal virulence and transmission success in malaria parasite infections. BMC Evol Biol 4: 30.

Paulsson J (2004) Summing up the noise in gene networks. Nature 427: 415–418.

Paulsson J (2005) Prime movers of noisy gene expression. Nat Genet 37: 925–926.

Pavlicev M, Cheverud JM, Wagner GP (2011) Evolution of adaptive phenotypic variation patterns by direct selection for evolvability. Proc R Soc B Biol Sci 278: 1903–1912.

Payne RB (1977) The ecology of brood parasitism in birds. Annu Rev Ecol Syst 8: 1–28.

Pearl J (2000) Causation: models, reasoning, and inference. Cambridge, UK: Cambridge University Press.

Pelletier F, Clutton-Brock T, Pemberton J, Tuljapurkar S, Coulson T (2007) The evolutionary demography of ecological change: linking trait variation and population growth. Science 315: 1571–1574.

Pelletier F, Garant D, Hendry AP (2009) Eco-evolutionary dynamics. Phil Trans R Soc B Biol Sci 364: 1483–1489.

Pence CH, Ramsey G (2013) A new foundation for the propensity interpretation of fitness. Brit J Phil Sci 64: 851–881.

Peng TI, Jou MJ (2010) Oxidative stress caused by mitochondrial calcium overload. Ann N Y Acad Sci 1201: 183–188.

Perego M, Hoch JA (2002) Two-component systems, phosphorelays, and regulation of their activities by phosphatases. In: Sonenshein AL, Hoch JA, Losick R, eds. Bacillus subtilis and its closest relatives: from genes to cells. Washington, DC: ASM Press. pp 473–481.

Pereira-Leal JB, Levy ED, Teichmann SA (2006) The origins and evolution of functional modules: lessons from protein complexes. Phil Trans R Soc B Biol Sci 361: 507–517.

Pericone CD, Bae D, Shchepetov M, McCool T, Weiser JN (2002) Short-sequence tandem and nontandem DNA repeats and endogenous hydrogen peroxide production contribute to genetic instability of Streptococcus pneumoniae. J Bacteriol 184: 4392–4399.

Perkins TJ, Swain PS (2009) Strategies for cellular decision-making. Mol Syst Biol 5: 326.

Perrins CM (1965) Population fluctuations and clutch size in the Great Tit, Parus major L. J Anim Ecol 34: 601–647.

Peters EE (1994) Fractal market analysis: applying chaos theory to investment and economics. New York, NY: John Wiley.

Peters RH (1983) The ecological implications of body size. Cambridge, UK: Cambridge University Press.

Petren K (2001) The concepts of the habitat and the niche. In: Levin SA, ed. Encyclopedia of biodiversity. New York, NY: Academic Press. pp 303–315.

Petrie M, Møller AP (1991) Laying eggs in others’ nests: intraspecific brood parasitism in birds. Trends Ecol Evol 6: 315–320.

Petronis A (2010) Epigenetics as a unifying principle in the aetiology of complex traits and diseases. Nature 465: 721–727.

Pettorelli N, Durant SM (2007) Family effects on early survival and variance in long-term reproductive success of female cheetahs. J Anim Ecol 76: 908–914.

Pfeifer J (2005) Why selection and drift might be distinct. Philos Sci 72: 1135–1145.

Pfeiffer T, Schuster S, Bonhoeffer S (2001) Cooperation and competition in the evolution of ATP-producing pathways. Science 292: 504–507.

Pfennig DW, Murphy PJ (2002) How fluctuating competition and phenotypic plasticity mediate species divergence. Evolution 56: 1217–1228.

Pfister CA (1998) Patterns of variance in stage-structured populations: evolutionary predictions and ecological implications. Proc Natl Acad Sci USA 95: 213–218.

Philippi T (1993a) Bet-hedging germination of desert annuals: beyond the first year. Am Nat 142: 474–487.

Philippi T (1993b) Bet-hedging germination of desert annuals: variation among populations and maternal effects in Lepidium lasiocarpum. Am Nat 142: 488–507.

Philippi T, Seger J (1989) Hedging one’s evolutionary bets, revisited. Trends Ecol Evol 4: 41–44.

Pietronero L, Toscatti E, eds. (1986) Fractals in physics. Amsterdam, The Netherlands: North-Holland Publishing.

Piggot PJ, Losick R (2002) Sporulation genes and intercompartmental regulation. In: Sonenshein AL, Hoch JA, Losick R, eds. Bacillus subtilis and its closest relatives: from genes to cells. Washington, DC: ASM Press. pp 483–517.

Pigliucci M (2007) Do we need an extended evolutionary synthesis? Evolution 61: 2743–2749.

Pigliucci M (2008) Is evolvability evolvable? Nat Rev Genet 9: 75–82.

Pigliucci M, Murren CJ, Schlichting CD (2006) Phenotypic plasticity and evolution by genetic assimilation. J Exp Biol 209: 2362–2367.

Pimm SL, Redfearn A (1988) The variability of population densities. Nature 334: 613–614.

Pimm SL, Jones HL, Diamond J (1988) On the risk of extinction. Am Nat 132: 757–785.

Plantegenest M, Outreman Y, Goubault M, Wajnberg E (2004) Parasitoids flip a coin before deciding to superparasitize. J Anim Ecol 73: 802–806.

Plath M, Tobler M, Riesch R, García de León FJ, Giere O, Schlupp I (2007) Survival in an extreme habitat: the roles of behavior and energy limitation. Naturwissenschaften 94: 991–996.

Platt ML, Huettel SA (2008) Risky business: the neuroeconomics of decision making under uncertainty. Nat Neurosci 11: 398–403.

Plotnick RE, Sepkoski Jr JJ (2001) A multiplicative multifractal model for originations and extinctions. Paleobiology 27: 126–139.

Plough HH (1917) The effect of temperature on crossing over in Drosophila. J Exp Biol 24: 147–209.

Plough HH (1921) Further studies on the effect of temperature on crossing over. J Exp Biol 32: 187–202.

Ponder RG, Fonville NC, Rosenberg SM (2005) A switch from high-fidelity to error-prone DNA double-strand break repair underlies stress-induced mutation. Mol Cell 19: 791–804.

Popper KR (1959) The propensity interpretation of probability. Br J Philos Sci 10: 25–42.

Post DM, Palkovacs EP (2009) Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Phil Trans R Soc B 364: 1629–1640.

Poujade M, Grasland-Mongrain E, Hertzog A, Jouanneau J, Chavrier P, et al. (2007) Collective migration of an epithelial monolayer in response to a model wound. Proc Natl Acad Sci USA 104: 15988–15993.

Powell JA (1987) Records of prolonged diapause in Lepidoptera. J Res Lepid 25: 83–109.

Powell JA (1989) Synchronized, mass-emergences of a yucca moth, Prodoxus yinversus (Lepidoptera: Prodoxidae) after 16 and 17 years in diapause. Oecologia 81: 490–493.

Powell JA (2001) Longest insect dormancy: Yucca moth larvae (Lepidoptera: Prodoxidae) metamorphose after 20, 25 and 30 years in diapause. Ann Entomol Soc Am 94: 677–680.

Pöysä H (1999) Conspecific nest parasitism is associated with inequality in nest predation risk in the common goldeneye (Bucephala clangula). Behav Ecol 10: 533–540.

Pöysä H (2003) Parasitic common goldeneye (Bucephala clangula) females lay preferentially in safe neighbourhoods. Behav Ecol Sociobiol 54: 30–35.

Pöysä H (2006) Public information and conspecific nest parasitism in goldeneyes: targeting safe nests by parasites. Behav Ecol 17: 459–465.

Pöysä H, Ruusila V, Milonoff M, Virtanen J (2001) Ability to assess nest predation risk in secondary hole-nesting birds: an experimental study. Oecologia (Berlin) 126: 201–207.

Pöysä H, Pesonen M (2007) Nest predation and the evolution of conspecific brood parasitism: from risk spreading to risk assessment. Am Nat 169: 94–104.

Pöysä H, Eadie JM, Lyon BE (2014) Conspecific brood parasitism in waterfowl and cues parasites use. Wildfowl Special Issue 4: 192–219.

Prasad S, Manasa P, Buddhi S, Singh SM, Shivaji S (2011) Antagonistic interaction networks among bacteria from a cold soil environment. FEMS Microbiol Ecol 78: 376–385.

Pratt J (1964) Risk aversion in the small and in the large. Econometrica 32: 122–136.

Prentiss JB (1976) Time variations of pupal stage of Eupackardia calleta (Saturniidae). J Lepid Soc 30: 187.

Price G (1970) Selection and covariance. Nature 227: 520–521.

Price G (1972) Extensions of covariance selection mathematics. Ann Hum Genet 35: 485–490.

Price TD, Qvarnström A, Irwin DE (2003) The role of phenotypic plasticity in driving genetic evolution. Proc R Soc Lond B 270: 1433–1440.

Priess JR, Thomson JN (1987) Cellular interactions in early C. elegans embryos. Cell 48: 241–250.

Prigogine I (1997) The end of certainty: Time, chaos, and the new laws of nature. New York, NY: The Free Press.

Prigogine I, Stengers I (1984) Order out of chaos: Man’s new dialogue with nature. New York, NY: Bantam Books.

Priklopil T (2012) Chaotic dynamics of allele frequencies in condition-dependent mating systems. Theor Popul Biol 82: 109–116.

Proulx R (2007) Ecological complexity for unifying ecological theory across scales: A field ecologist’s perspective. Ecol Complex 4: 85–92.

Proulx SR, Day T (2001) What can invasion fitness tell us about evolution under stochasticity in finite populations? Selection 2: 2–15.

Proulx SR, Phillips PC (2005) The opportunity for canalization and the evolution of genetic networks. Am Nat 165: 147–162.

Proulx SR, Adler FR (2010) The standard of neutrality: still flapping in the breeze? J Evol Biol 23: 1339–1350.

Pruessner G (2004) Studies in self-organised criticality. Thesis. London, UK: Imperial College London.

Ptashne MA (1998) Genetic switch: phage lambda and higher organisms. Cambridge, MA: Blackwell Scientific Publications.

Pumpernik D, Oblak B, Borstnik B (2008) Replication slippage versus point mutation rates in short tandem repeats of the human genome. Mol Genet Genomics 279: 53–61.

Pybus C, Pedraza-Reyes M, Ross CA, Martin H, Ona K, et al. (2010) Transcription-associated mutation in Bacillus subtilis cells under stress. J Bacteriol 192: 3321–3328.

Radman M, Matic I, Taddei F (1999) Evolution of evolvability. Ann NY Acad Sci 870: 146–155.

Rainey PB, Beaumont HJ, Ferguson GC, Gallie J, Kost C, et al. (2011) The evolutionary emergence of stochastic phenotype switching in bacteria. Microb Cell Fact 10 Suppl 1: S14.

Raj A, van Oudenaarden A (2008) Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135: 216–226.

Rajon E, Venner S, Menu F (2009) Spatially heterogeneous stochasticity and the adaptive diversification of dormancy. J Evol Biol 22: 2094–2103.

Rampelotto PH (2010) Resistance of microorganisms to extreme environmental conditions and its contribution to astrobiology. Sustainability 2: 1602–1623.

Rand DA, Keeling M, Wilson HB (1995) Invasion, stability and evolution to criticality in spatially extended, artificial host-pathogen ecologies. Proc R Soc Lond Ser B 259: 55–63.

Randall D, Burggren W, French K (1997) Eckert animal physiology: mechanisms and adaptations, 4th edn. New York, NY: W.H. Freeman & Company.

Rankin DJ (2007) Resolving the tragedy of the commons: the feedback between intraspecific conflict and population density. J Evol Biol 20: 173–180.

Rankin DJ, López-Sepulcre A (2005) Can adaptation lead to extinction? Oikos 111: 616–619.

Rankin DJ, Kokko H (2006) Sex, death and tragedy. Trends Ecol Evol 21: 225–226.

Ranta E, Tesar D, Alaja S, Kaitala V (2000a) Does evolution of iteroparous and semelparous reproduction call for spatially structured systems? Evolution 54: 145–150.

Ranta E, Kaitala V, Alaja S, Tesar D (2000b) Nonlinear dynamics and the evolution of semelparous and iteroparous reproductive strategies. Am Nat 155: 294–300.

Ranta E, Tesar D, Kaitala V (2002) Environmental variability and semelparity vs. iteroparity as life histories. J Theor Biol 217: 391–396.

Rao CV, Wolf DM, Arkin AP (2002) Control, exploitation and tolerance of intracellular noise. Nature 420: 231–237.

Rao RPN, Sejnowski TJ (2003) Self-organizing neural systems based on predictive learning. Phil Trans R Soc Lond Ser A 361: 1149–1175.

Rapp RA, Wendel JF (2005) Epigenetics and plant evolution. New Phytol 168: 81–91.

Raser JM, O'Shea EK (2004) Control of stochasticity in eukaryotic gene expression. Science 304: 1811–1814.

Raser JM, O’Shea EK (2005) Noise in gene expression: origins, consequences, and control. Science 309: 2010–2013.

Ratcliff WC, Denison RF (2010) Individual-level bet hedging in the bacterium Sinorhizobium meliloti. Curr Biol 20: 1740–1744.

Ratcliff WC, Hawthorne P, Libby E (2015) Courting disaster: How diversification rate affects fitness under risk. Evolution 69: 126–135.

Ratikainen II (2012) Foraging in a variable world: adaptations to stochasticity. PhD Thesis. Trondheim, Norway: Norwegian University of Science and Technology.

Ratnieks FLW, Wenseleers T (2005) Policing insect societies. Science 307: 54–56.

Rauch EM, Sayama H, Bar-Yam Y (2002) The relationship between measures of fitness and time scale in evolution. Phys Rev Lett 88: 228101/1–228101/4.

Rauch EM, Sayama H, Bar-Yam Y (2003) Dynamics and genealogy of strains in spatially extended host-pathogen systems. J Theor Biol 221: 655–664.

Razinkov IA, Baumgartner BL, Bennett MR, Tsimring LS, Hasty J (2013) Measuring competitive fitness in dynamic environments. J Phys Chem B 117: 13175–13181.

Reader SM, Laland KN (2001) Primate innovation: sex, age, and social rank differences. Int J Primatol 22: 787–805.

Real LA (1980) Fitness, uncertainty, and the role of diversification in evolution and behavior. Am Nat 115: 623–638.

Real LA (1991) Animal choice behavior and the evolution of cognitive architecture. Science 253: 980–986.

Real L, Caraco T (1986) Risk and foraging in stochastic environments. Annu Rev Ecol Syst 17: 371–390.

Real LA, Ellner S, Harder LD (1990) Short-term energy maximization and risk-aversion in bumblebees: a reply to Possingham et al. Ecology 71: 1625–1628.

Real LA, Brown JH (1991) Foundations of ecology. Chicago, IL: University of Chicago Press.

Reding L (2014) Increased hatching success as a direct benefit of polyandry in birds. Evolution DOI: 10.1111/evo.12553.

Reed TE, Waples RS, Schindler DE, Hard JJ, Kinnison MT (2010) Phenotypic plasticity and population viability: the importance of environmental predictability. Proc Biol Sci 277: 3391–3400.

Rees M, Jessica C, Metcalf E, Childs DZ (2010) Bet-hedging as an evolutionary game: the trade-off between egg size and number. Proc Biol Sci 277: 1149–1151.

Reese HW (2005) A conceptual analysis of selectionism: Part I & II. Behav Dev Bull 1: 8–16.

Reiss D, Mager DL (2007) Stochastic epigenetic silencing of retrotransposons: does stability come with age? Gene 390: 130–135.

Renshaw E (1991) Modelling biological populations in space and time. Cambridge, UK: Cambridge University Press.

Reusch TBH, Ehlers A, Haemmerli A, Worm B (2005) Ecosystem recovery after climatic extremes enhanced by genotypic diversity. Proc Natl Acad Sci USA 102: 2826–2831.

Reynolds A, Frye M (2007) Free-flight odor tracking in Drosophila is consistent with an optimal intermittent scale-free search. PLoS ONE 2: e354.

Reynolds AM, Rhodes CJ (2009) The Lévy flight paradigm: random search patterns and mechanisms. Ecology 90: 877–887.

Reznick DN, Bryga HA (1996) Life-history evolution in guppies (Poecilia reticulata: Poeciliidae). 5. Genetic basis of parallelism in life histories. Am Nat 147: 339–359.

Ribeiro TL, Copelli M, Caixeta F, Belchior H, Chialvo DR, et al. (2010) Spike avalanches exhibit universal dynamics across the sleep-wake cycle. PLoS ONE 5: e14129.

Rice SH (2008) A stochastic version of the Price equation reveals the interplay of deterministic and stochastic processes in evolution. BMC Evol Biol 8: 262.

Richards EJ (2006) Inherited epigenetic variation—revisiting soft inheritance. Nat Rev Genet 7: 395–401.

Richardson K (2012) Heritability lost; intelligence found. Intelligence is integral to the adaptation and survival of all organisms faced with changing environments. EMBO Rep 13: 591–595.

Richardson RC (2001) Complexity, self-organization and selection. Biol Philos 16: 655–683.

Richardson RC, Burian RM (1992) A defense of propensity interpretations of fitness. In: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992, Vol. 1. pp 349–362.

Richerson PJ, Boyd R (2000) Climate, culture, and the evolution of cognition. In: Heyes C, Huber L, eds. Evolution of cognition. Cambridge, MA: MIT Press. pp 329–346.

Richter-Boix A, Llorente GA, Montori A (2006) A comparative analysis of the adaptive developmental plasticity hypothesis in six Mediterranean anuran species along a pond permanency gradient. Evol Ecol Res 8: 1139–1154.

Ricklefs RE (1969) An analysis of nesting mortality in birds. Smithson Contrib Zool 9: 1–48.

Rideout RM, Rose GA, Burton MPM (2005) Skipped spawning in female iteroparous fishes. Fish Fisheries 6: 50–72.

Riggs AD, Xiong Z (2004) Methylation and epigenetic fidelity. Proc Natl Acad Sci USA 101: 4–5.

Rikvold PA, Zia RKP (2003) Punctuated equilibria and 1/f noise in a biological coevolution model with individual-base dynamics. Phys Rev E 68: 031913.

Rion S, Kawecki TJ (2007) Evolutionary biology of starvation resistance: what we have learned from Drosophila. J Evol Biol 20: 1655–1664.

Ripa J, Olofsson H, Jonzén N (2010) What is bet-hedging, really? Proc Biol Sci 277: 1153–1154.

Risch TS, Dobson FS, Murie JO (1995) Is mean litter size the most productive? A test in Columbian ground squirrels. Ecology 76: 1643–1654.

Rivoire O, Leibler S (2011) The value of information for populations in varying environments. J Stat Phys 142: 1124–1166.

Roberts DC, Turcotte DL (1998) Fractality and self-organized criticality of wars. Fractals 6: 351–357.

Robleto EA, Yasbin R, Ross C, Pedraza-Reyes M (2007) Stationary phase mutagenesis in B. subtilis: a paradigm to study genetic diversity programs in cells under stress. Crit Rev Biochem Mol Biol 42: 327–339.

Robson AJ (1996) A biological basis for expected and non-expected utility. J Econ Theory 68: 397–424.

Robson AJ (2001) The biological basis of economic behavior. J Econ Lit 39: 11–33.

Robson AJ, Bergstrom CT, Pritchard JK (1999) Risky business: sexual and asexual reproduction in variable environments. J Theor Biol 197: 541–556.

Robson AJ, Samuelson L (2009) The evolution of time preference with aggregate uncertainty. Am Econ Rev 99: 1925–1953.

Rocha EPC, Matic I, Taddei F (2002) Over-representation of repeats in stress response genes: a strategy to increase versatility under stressful conditions? Nucleic Acids Res 30: 1886–1894.

Rodel HG, Bora A, Kaetzke P, Khaschei M, Hutzelmeyer H, et al. (2004) Over-winter survival in subadult European rabbits: Weather effects, density dependence, and the impact of individual characteristics. Oecologia 140: 566–576.

Roerdink JBTM (1987) The biennial life strategy in a random environment. J Math Biosci 26: 199–216.

Roff DA (1992) The evolution of life histories. New York, NY: Chapman & Hall.

Roff DA (1997) Evolutionary quantitative genetics. New York, NY: Chapman & Hall.

Roff DA (2002) Life history evolution. Sunderland, MA: Sinauer Associates.

Roll-Hansen N (2000) The application of complementarity to biology: From Niels Bohr to Max Delbrück. Hist Stud Phys Biol Sci 30: 417–442.

Rollinson N, Brooks RJ (2007) Proximate constraints on reproductive output in a northern population of Painted Turtles: An empirical test of the bet-hedging paradigm. Can J Zool 85: 177–184.

Rørth P (2009) Collective cell migration. Annu Rev Cell Dev Biol 25: 407–429.

Ros M, Sorensen D, Waagepetersen R, Dupont-Nivet M, SanCristobal M, Bonnet JC, et al. (2004) Evidence for genetic control of adult weight plasticity in the snail Helix aspersa. Genetics 168: 2089–2097.

Rosenberg A (1994) Instrumental Biology or the Disunity of Science. Chicago: University of Chicago Press.

Rosenberg A (2001) Discussion note: Indeterminism, probability, and randomness in evolutionary theory. Philos Sci 68: 536–544.

Rosenberg SM (2011) Stress-induced loss of heterozygosity in Candida: a possible missing link in the ability to evolve. mBio2(5). pii: e00200-11.

Rosenfeld N, Young JW, Alon U, Swain PS, Elowitz M (2005) Gene regulation at the single-cell level. Science 307: 1962–1965.

Rosenfeld S (2009) Patterns of stochastic behavior in dynamically unstable high-dimensional biochemical networks. Gene Regul Syst Bio 3: 1–10.

Rosenfeld S (2011) Mathematical descriptions of biochemical networks: stability, stochasticity, evolution. Prog Biophys Mol Biol 106: 400–409.

Rosenfeld S (2013) Global consensus theorem and self-organized criticality: unifying principles for understanding self-organization, swarm intelligence and mechanisms of carcinogenesis. Gene Regul Syst Bio 7: 23–39.

Rosenzweig ML (1972) Evolution of the predator isocline. Evolution 27: 84–94.

Rosenzweig ML (1978) Competitive speciation. Biol J Linn Soc 10: 275–289.

Rosenzweig RF, Sharp RR, Treves DS, Adams J (1994) Microbial evolution in a simple unstructured environment: genetic differentiation in Escherichia coli. Genetics 137: 903–917.

Ross IL, Browne CM, Hume DA (1994) Transcription of individual genes in eukaryotic cells occurs randomly and infrequently. Immunol Cell Biol 72: 177–186.

Roughgarden J (1979) Theory of population genetics and evolutionary ecology: an introduction. New York, NY: Macmillan.

Roughgarden J (1991) The evolution of sex. Am Nat 138: 823–842.

Rousset F (2003) A minimal derivation of convergence stability measures. J Theor Biol 221: 665–668.

Roux G, Roques A, Menu F (1997) Effect of temperature and photoperiod on diapause development in a Douglas fir seed chalcid, Megastigmus spermotrophus. Oecologia 111: 172–177.

Rouzic AL, Carlborg O (2008) Evolutionary potential of hidden genetic variation. Trends Ecol Evol 23: 33–37.

Rouzine IM, Rodrigo A, Coffin JM (2001) Transition between stochastic evolution and deterministic evolution in the presence of selection: general theory and application to virology. Microbiol Mol Biol Rev 65: 151–185.

Roy Nielsen CL, Parker PG, Gates RJ (2008) Partial clutch predation, dilution of predation risk, and the evolution of intraspecific nest parasitism. Auk 125: 679–686.

Roze D (2012) Disentangling the benefits of sex. PLoS Biol 10: e1001321.

Rubenstein DI (1982) Risk, uncertainty and evolutionary strategies. In: King's College Sociobiology Group, ed. Current problems in sociobiology. Cambridge, UK: Cambridge University Press. pp 91–111.

Rubenstein DR (2011) Spatiotemporal environmental variation, risk aversion, and the evolution of cooperative breeding as a bet-hedging strategy. Proc Natl Acad Sci USA 108(Suppl. 2): 10816–10822.

Ruel JJ, Ayres MP (1999) Jensen’s inequality predicts effects of environmental variation. Trends Ecol Evol 14: 361–366.

Rutherford SL (2000) From genotype to phenotype: buffering mechanisms and the storage of genetic information. BioEssays 22: 1095–1105.

Rutherford SL, Lindquist S (1998) Hsp90 as a capacitor for morphological evolution. Nature 396: 336–342.

Rutherford S, Hirate Y, Swalla BJ (2007) The Hsp90 capacitor, developmental remodeling, and evolution: the robustness of gene networks and the curious evolvability of metamorphosis. Crit Rev Biochem Mol Biol 42: 355–372.

Saccheri I, Hanski I (2006) Natural selection and population dynamics. Trends Ecol Evol 21: 341–347.

Sæther BE (1996) Evolution of avian life histories: does nest predation explain it all? Trends Ecol Evol 11: 311–312.

Sæther BE, Ringsby TH, Røskaft E (1996) Life history variation, population process and priorities in species conservation: towards a reunion of research paradigms. Oikos 77: 217–226.

Sæther B, Bakke O (2000) Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81: 642–653.

Sæther BE, Engen S, Lande R, Møller AP, Bensch S, Hasselquist D, et al. (2004a) Time to extinction in relation to mating system and type of density regulation in populations with two sexes. J Anim Ecol 73: 925–934.

Sæther BE, Engen S, Møller AP, Weimerskirch H, Visser ME, et al. (2004b) Life-history variation predicts the effects of demographic stochasticity on avian population dynamics. Am Nat 164: 793–802.

Saetzler K, Sonnenschein C, Soto AM (2011) Systems biology beyond networks: Generating order from disorder through self-organization. Semin Cancer Biol 21: 165–174.

Salathé M, Van Cleve J, Feldman MW (2009) Evolution of stochastic switching rates in asymmetric fitness landscapes. Genetics 182: 1159–1164.

Salthe SN (1998) The role of natural selection theory in understanding evolutionary systems. In: Van de Vijver G, Salthe SN, Delpos M, eds. Evolutionary systems: Biological and epistemological perspectives on selection and self-organization. Dordrecht, The Netherlands: Kluwer Academic Publishers. pp 13–20.

Salvanes AGV, Braithwaite VA (2005) Exposure to variable spatial information in the early rearing environment generates asymmetries in social interactions in cod (Gadus morhua). Behav Ecol Sociobiol 59: 250–257.

Salvanes AGV, Moberg O, Braithwaite VA (2007) Effects of early experience on group behaviour in fish. Anim Behav 74: 805–811.

Salvaudon L, Heraudet V, Shykoff JA (2005) Parasite-host fitness trade-offs change with parasite identity: genotypespecific interactions in a plant-pathogen system. Evolution 59: 2518–2524.

Sanders L (1996) Dynamic modelling of urban systems. In: Fischer M, Scholten HJ, Unwin D, eds. Spatial analytical perspectives on GIS. London, UK: Taylor & Francis. pp 229–244.

Sangster TA, Salathia N, Undurraga S, Milo R, Schellenberg K, et al. (2008) HSP90 affects the expression of genetic variation and developmental stability in quantitative traits. Proc Natl Acad Sci USA 105: 2963–2968.

Saran M, Michel C, Bors W (1998) Radical functions in vivo: a critical review of current concepts and hypotheses. Z Naturforsch [C] 53: 210–227.

Sarhan A, Kokko H (2007) Multiple mating in the Glanville fritillary butterfly: a case of within-generation bet hedging? Evolution 61: 606–616.

Sasaki A, Ellner S (1995) The evolutionarily stable phenotype distribution in a random environment. Evolution 49: 337–350.

Sasaki A, Ellner S (1997) Quantitative genetic variance and covariance maintained by fluctuating selection with overlapping generations. Evolution 51: 682–696.

Satake A, Sasaki A, Iwasa Y (2001) Variable timing of reproduction in unpredictable environments:adaption of flood plain plants. Theor Popul Biol 60: 1–15.

Sato K, Ito Y, Yomo T, Kaneko K (2003) On the relation between fluctuation and response in biological systems. Proc Natl Acad Sci USA 100: 14086–14090.

Sato T (1986) A brood parasitic catfish of mouthbrooding cichlid fishes in Lake Tanganyika. Nature 323: 58–59.

Savill NJ, Hogeweg P (1998) Spatially induced speciation prevents extinction: the evolution of dispersal distance in oscillatory predator-prey models. Proc R Soc Lond Ser B 265: 25–32.

Schaffer WM (1974) Optimal reproductive effort in fluctuating environments. Am Nat 108: 783–790.

Scheffer M, Carpenter S, Foley JA, Folke C, Walker B (2001) Catastrophic shifts in ecosystems. Nature 413: 591–596.

Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V, et al. (2009) Early-warning signals for critical transitions. Nature 461: 53–59.

Scheiner SM (1993) Genetics and evolution of phenotypic plasticity. Annu Rev Ecol Syst 24: 35–68.

Scheiner SM (2014) Bet-hedging as a complex interaction among developmental instability, environmental heterogeneity, dispersal, and life-history strategy. Ecol Evol 4: 505–515

Scheiner SM, Caplan RL, Lyman RF (1991) The genetics of phenotypic plasticity. III. Genetic correlations and fluctuating asymmetries. J Evol Biol 4: 51–68.

Scheiner SM, Holt RD (2012) The genetics of phenotypic plasticity. X. Variation versus uncertainty. Ecol Evol 2: 751–767.

Scheinkman JA, Woodford M (1994) Self-organized criticality and economic fluctuations. Am Econ Rev 84: 417–421.

Schieve WC, Allen PM, eds. (1982) Self-organization and dissipative structures: applications in the physical and social sciences. Austin, TX: University of Texas Press.

Schleicherová D, Sella G, Lorenzi MC (2013) Do stable environments select against phenotypic plasticity in hermaphroditic sex allocation? Ital J Zool 80: 358–363.

Schluter D (2000) The ecology of adaptive radiation. Oxford, UK: Oxford University Press.

Schluter D, McPhail JD (1992) Ecological character displacement and speciation in sticklebacks. Am Nat 140: 85–108.

Schmalhausen II (1960) Evolution and cybernetics. Evolution 14: 509–524.

Schmoll T, Dietrich V, Winkel W, Epplen JT, Schurr F, Lubjuhn T (2005) Paternal genetic effects on offspring fitness are context dependent within the extra-pair mating system of a socially monogamous passerine. Evolution 59: 645–657.

Schneider KA (2008) Maximization principles for frequency-dependent selection I: the one-locus two-allele case. Theor Popul Biol 74: 251–262.

Schoener TW (2011) The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331: 426–429.

Schoustra S, Rundle HD, Dali R, Kassen R (2010) Fitness-associated sexual reproduction in a filamentous fungus. Curr Biol 20: 1350–1355.

Schuster P (1994) How do RNA molecules and viruses explore their worlds? In: Cowan GA, Pines D, Meltzer D, eds. Complexity: metaphors, models and reality. Reading, MA: Addison-Wesley. pp 383–418.

Schützenberger M (1967) Algorithms and neo-Darwinian theory. In: Moorhead PS, Kaplan MM, eds. Mathematical challenges to the neo-Darwinian interpretation of evolution. Philadelphia, PA: Wistar Institute Press. pp 73–75.

Schwaninger M (2004) What can cybernetics contribute to the conscious evolution of organizations and society? Syst Res Behav Sci 21: 515–527.

Scott B (2010) Introduction to Gordon Pask’s “The Cybernetics of evolutionary processes and of self-organising systems”. J Sociocybernetics 8: 25.

Sedinger JS, Chelgren ND (2007) Survival and breeding advantages of larger black brant Branta bernicla nigricans goslings: Within- and among-cohort variation. Auk 124: 1281–1293.

Seger J, Brockmann HJ (1987) What is bet-hedging? In: Harvey P, Partridge L, eds. Oxford Surveys in Evolutionary Biology, vol. 4, New York, NY: Oxford University Press. pp 182–211.

Seger J, Stubblefield JW (1996) Optimization and adaptation. In: Rose MR, Lauder GV, eds. Adaptation. London, UK: Academic Press. pp 93–123.

Sepkoski Jr JJ (1993) Ten years in the library: New data confirm paleontological patterns. Paleobiology 19: 43–51.

Seppä P, Queller DC, Strassmann JE (2002) Reproduction in foundress associations of the social wasp, Polistes carolina: conventions, competition, and skew. Behav Ecol 13: 531–542.

Serizawa S, Miyamichi K, Nakatani H, Suzuki M, Saito M, Yoshihara Y, Sakano H (2003) Negative feedback regulation ensures the one receptor-one olfactory neuron rule in mouse. Science 302: 2088–2094.

Serra R, Villani M, Semeria A (2004) Genetic network models and statistical properties of gene expression data in knock-out experiments. J Theor Biol 227: 149–157.

Serra R, Villani M, Graudenzi A, Kauffman SA (2007) Why a simple model of genetic regulatory network describes the distribution of avalanches in gene expression data. J Theor Biol 246: 449–460.

Shafir S, Menda G, Smith BH (2005) Caste-specific differences in risk sensitivity in honeybees, Apis mellifera. Anim Behav 69: 859–868.

Shaffer ML (1981) Minimum viable population sizes for species conservation. BioScience 31: 131–134.

Shahrezaei V, Swain PS (2008) The stochastic nature of biochemical networks. Curr Opin Biotechnol 19: 369–374.

Shakarad M, Gadagkar R (1995) Colony founding in the primitively eusocial wasp Ropalidia marginata (Hymenoptera: Vespidae). Ecol Entomol 20: 273–282.

Shanahan T (2003) The evolutionary indeterminism thesis. Bioscience 53: 163–169.

Shapiro AM (1979) The phenology of Pieris napi microstriata (Lepidoptera: Pieridae) during and after the 1975–77 California drought, and its evolutionary significance. Psyche 86: 1–10.

Shapiro AM (1980) Egg-load assessment and carryover diapause in Anthocharis (Pieridae). J Lepid Soc 34: 307–315.

Shapiro JA (2011) Evolution: A view from the 21st century. Upper Saddle River, NJ: FT Press Science.

Shee C, Ponder R, Gibson JL, Rosenberg SM (2011a) What limits the efficiency of double-strand break-dependent stress-induced mutation in Escherichia coli? J Mol Microbiol Biotechnol 21: 8–19.

Shee C, Gibson JL, Darrow MC, Gonzalez C, Rosenberg SM (2011b) Impact of a stress-inducible switch to mutagenic repair of DNA breaks on mutation in Escherichia coli. Proc Natl Acad Sci USA 108: 13659–13664.

Sheldon BC, Merilä J, Qvarnström A, Gustafsson L, Ellegren H (1997) Paternal genetic contribution to offspring condition predicted by size of male secondary sexual character. Proc R Soc Lond B Biol Sci 264: 297–302.

Shen X, Pettersson M, Rönnegård L, Carlborg Ö (2012) Inheritance beyond plain heritability: variance-controlling genes in Arabidopsis thaliana. PLoS Genet 8: e1002839.

Shew WL, Plenz D (2013) The functional benefits of criticality in the cortex. Neuroscientist 19: 88–100.

Shmulevich I, Kauffman SA, Aldana M (2005) Eukaryotic cells are dynamically ordered or critical but not chaotic. Proc Natl Acad Sci U S A 102: 13439–13444.

Shorrocks B, Swingland R (1990) Living in a patchy environment. Oxford, UK: Oxford University Press.

Siegal ML, Bergman A (2002) Waddington’s canalization revisited: developmental stability and evolution. Proc Natl Acad Sci USA 99: 10528–10532.

Siegl-Cachedenier I, Munoz P, Flores JM, Klatt P, Blasco MA (2007) Deficient mismatch repair improves organismal fitness and survival of mice with dysfunctional telomeres. Genes Dev 21: 2234–2247.

Siepielski AM, DiBattista JD, Carlson SM (2009) It’s about time: the temporal dynamics of phenotypic selection in the wild. Ecol Lett 12: 1261–1276.

Sigg H (1980) Differentiation of female positions in hamadryas one-male-units. Z Tierpsychol 53: 265–302.

Silva AJ, Ward K, White R (1993) Mosaic methylation in clonal tissue. Dev Biol 156: 391–398.

Simberloff D, Dayan T (1991) The guild concept and the structure of ecological communities. Annu Rev Ecol Syst 22: 115–143.

Simões A, Costa E (2002) Using genetic algorithms to deal with dynamic environments: A comparative study of several approaches based on promoting diversity. In: Langdon WB et al., eds. Genetic and evolutionary computation - GECCO ’02. New York, NY: Morgan Kaufmann Publishers. pp 9–13.

Simon HA (1981) The Sciences of the Artificial, 2nd ed. Cambridge, MA: MIT Press.

Simon SM, Peskin CS, Oster GF (1992) What drives the translocation of proteins? Proc Natl Acad Sci USA 89: 3770–3774.

Simons AM (2002) The continuity of microevolution and macroevolution. J Evol Biol 15: 688–701.

Simons AM (2007) Selection for increased allocation to offspring number under environmental unpredictability. J Evol Biol 20: 813–817.

Simons AM (2008) One big, and many small reasons that direct selection on offspring number is still open for discussion. J Evol Biol 21: 642–645.

Simons AM (2009) Fluctuating natural selection accounts for the evolution of diversification bet hedging. Proc R Soc B Biol Sci 276: 1987–1992.

Simons AM (2011) Modes of response to environmental change and the elusive empirical evidence for bet hedging. Proc R Soc B 278: 1601–1609.

Simons AM, Johnston MO (1997) Developmental instability as a bet-hedging strategy. Oikos 80: 401–406.

Simovich MA, Hathaway SA (1997) Diversified bet-hedging as a reproductive strategy of some ephemeral pool anostracans (Branchiopoda). J Crustacean Biol 17: 38–44.

Sims SR (1983) Prolonged diapause and pupal survival of Papilio zelicaon Lucas (Lepidoptera: Papilionidae). J Lepid Soc 37: 29–31.

Singer MA, Lindquist S (1998) Multiple effects of trehalose on protein folding in vitro and in vivo. Mol Cell 1: 639–648.

Skår J, Coveney PV (2003) Developmental and physiological circuits: dissecting complexity. A report on a talk given by Dr Leroy Hood. Phil Trans R Soc Lond Series A 361: 1313–1317.

Skyrms B (1996) The evolution of the social contract. Cambridge, UK: Cambridge University Press.

Skyrms B (2000) Game theory, rationality, and evolution of the social contract. J Conscious Stud 7: 269–284.

Slatkin M (1974) Hedging one’s evolutionary bets. Nature 250: 704–705.

Slatkin M (1981) Estimating levels of gene flow in natural populations. Genetics 99: 323–335.

Slatkin M, Wade MJ (1978) Group selection on a quantitative character. Proc Natl Acad Sci USA 75: 3531–3534.

Slobodkin LB (1961) Growth and regulation of animal populations. New York, NY: Holt, Rinehart and Winston.

Slobodkin LB (1974) Prudent predation does not require group selection. Am Nat 108: 665–678.

Smallwood PD (1996) An introduction to risk sensitivity: The use of Jensen's inequality to clarify evolutionary arguments of adaptation and constraint. Am Zool 36: 392–401.

Smead R (2008) The evolution of cooperation in the Centipede Game with finite populations. Philos Sci 75: 157–177.

Smiraglia DJ, Kulawiec M, Bistulfi GL, Gupta SG, Singh KK (2008) A novel role for mitochondria in regulating epigenetic modification in the nucleus. Cancer Biol Ther 7: 1182–1190.

Smiseth PT, Kölliker M, Royle NJ (2012) What is parental care? In: Royle NJ, Smiseth PT, Kölliker M, eds. The evolution of parental care. Oxford, UK: Oxford University Press. pp 1–17.

Smith A (1776) The wealth of nations. London, UK: Strahan and Cadell.

Smith CH (2012a) Natural selection: A concept in need of some evolution? Complexity 17: 8–17.

Smith CH (2012b) Alfred Russel Wallace and the elimination of the unfit. J Biosci 37: 203–205.

Smith DC (1987) Adult recruitment in chorus frogs: effects of size and date at metamorphosis. Ecology 68: 344–350.

Smith SE, Au DW, Show C (1998) Intrinsic rebound potentials of 26 species of pacific sharks. Mar Freshwater Res 49: 663–678.

Smits WK, Eschevins CC, Susanna KA, Bron S, Kuipers OP, et al. (2005) Stripping Bacillus: ComK auto-stimulation is responsible for the bistable response in competence development. Mol Microbiol 56: 604–614.

Smits WK, Kuipers OP, Veening JW (2006) Phenotypic variation in bacteria: the role of feedback regulation. Nat Rev Microbiol 4: 259–271.

Sneppen K, Bak P, Flyvbjerg H, Jensen MH (1995) Evolution as a self-organized critical phenomenon. Proc Natl Acad Sci USA 92: 5209–5213.

Sniegowski P (2004) Evolution: bacterial mutation in stationary phase. Curr Biol 14: R245–R246.

Sniegowski PD, Gerrish PJ, Johnson T, Shaver A (2000) The evolution of mutation rates: separating causes from consequences. Bioessays 22: 1057–1066.

Sniegowski PD, Murphy HA (2006) Evolvability. Curr Biol 16: R831–R834.

Snijder B, Pelkmans L (2011) Origins of regulated cell-to-cell variability. Nat Rev Mol Cell Biol 12: 119–126.

So PM, Dugeon D (1989) Life-history responses of larviparous Boettcherisca formosensis (Diptera: Sarcophagidae) to larval competition for food, including comparisons with oviparous Hemipyrellia ligurriens (Calliphoridae). Ecol Entomol 14: 349–356.

Sober E (1984) The nature of selection: Evolutionary theory in philosophical focus. Cambridge, MA: MIT Press.

Sober E (2001) The two faces of fitness. In: Singh R, Paul D, Crimbas C, Beatty J, eds. Thinking about Evolution: Historical, Philosophical and Political Perspectives. Cambridge, UK: Cambridge University Press. pp 309–321.

Sober E, Wilson DS (1998) Unto others: The evolution and psychology of unselfish behavior. Cambridge, MA: Harvard University Press.

Solé RV, Bascompte J, Manrubia SC (1996a) Extinctions: bad genes or weak chaos? Proc R Soc Lond Ser B 263: 1407–1413.

Solé RV, Manrubia SC, Luque B, Delgado J, Bascompte J (1996b) Phase transitions and complex systems: Simple, nonlinear models capture complex systems at the edge of chaos. Complexity 1: 13–26.

Solé RV, Manrubia SC, Benton M, Bak P (1997) Self-similarity of extinction statistics in the fossil record. Nature 388: 764–767.

Solé RV, Manrubia SC, Benton M, Kauffman S, Bak P (1999) Criticality and scaling in evolutionary ecology. Trends Ecol Evol 14: 156–160.

Solomon JM, Grossman AD (1996) Who’s competent and when: regulation of natural genetic competence in bacteria. Trends Genet 12: 150–155.

Solopova A, van Gestel J, Weissing FJ, Bachmann H, Teusink B, Kok J, Kuipers OP (2014) Bet-hedging during bacterial diauxic shift. Proc Natl Acad Sci USA 111: 7427–7432.

Sols F (2014) Uncertainty, incompleteness, chance, and design. In: Carreira MM, Gonzalo JA, eds. Intelligible design. A realistic approach to the philosophy and history of science. Hackensack, NJ: World Scientific. pp 98–118.

Sonjak S, Frisvad JC, Gunde-Cimerman N (2007) Genetic variation among Penicillium crustosum isolates from arctic and other ecological niches. Microb Ecol 54: 298–305.

Soucy SL, Giray T, Roubik DW (2003) Solitary and group nesting in the orchid bee Euglossa hyacinthina (Hymenoptera, Apidae). Insect Soc 50: 248–255.

Southwood TRE (1977) Habitat, the templet for ecological strategies. J Anim Ecol 46: 337–365.

Spalding DA (1837) Instinct with original observations on young animals. Macmillan’s Magazine 27: 282–293.

Spudich JL, Koshland DE Jr (1976) Non-genetic individuality: chance in the single cell. Nature 262: 467–471.

Srikhanta YN, Maguire TL, Stacey KJ, Grimmond SM, Jennings MP (2005) The phasevarion: a genetic system controlling coordinated, random switching of expression of multiple genes. Proc Natl Acad Sci USA 102: 5547–5551.

Staddon JER (1983) Adaptive behavior and learning. Cambridge, UK: Cambridge University Press.

Stamos DN (2001) Quantum indeterminacy and evolutionary biology. Philos Sci 68: 164–184.

Stanley HE (1971) Introduction to phase transitions and critical phenomena. New York, NY: Oxford University Press.

Stanley SM (1975) A theory of evolution above the species level. Proc Natl Acad Sci USA 72: 646–650.

Starrfelt J (2011) Intragenomic bet-hedging. Genetica 139: 1019–1021.

Starrfelt J, Kokko H (2012) Bet-hedging—a triple trade-off between means, variances and correlations. Biol Rev 87: 742–755.

Statman M (2002) Lottery players/stock traders. Financ Anal J 58: 14–21.

Stearns SC (1976) Life-history tactics: a review of the ideas. Q Rev Biol 51: 3–47.

Stearns SC (1989a) The evolution of life histories. New York, NY: Oxford University Press.

Stearns SC (1989b) The evolutionary significance of phenotypic plasticity. BioScience 39: 436–445.

Stearns SC (2000) Daniel Bernoulli (1738): evolution and economics under risk. J Biosciences 25: 221–228.

Stearns SC, Kawecki TJ (1994) Fitness sensitivity and the canalization of life-history traits. Evolution 48: 1438–1450.

Steele JH (1985) A comparison of terrestrial and marine ecological systems. Nature 313: 355–358.

Stelling J, Sauer U, Szallasi Z, Doyle FJ III, Doyle J (2004) Robustness of cellular functions. Cell 118: 675–685.

Stengers I (2004) The challenge of complexity: Unfolding the ethics of science - In memoriam Ilya Prigogine. E:CO 6: 92–99.

Stephan W (1996) The rate of compensatory evolution. Genetics 144: 419–426.

Stephens DW (1981) The logic of risk-sensitive foraging preferences. Anim Behav 29: 628–629.

Stephens DW (1987) On economically tracking a variable environment. Theor Popul Biol 32: 15–25.

Stephens DW (1989) Variance and the value of information. Am Nat 134: 128–140.

Stephens DW (1991) Change, regularity, and value in the evolution of animal learning. Behav Ecol 2: 77–89.

Stephens DW, Krebs JR (1986) Foraging theory. Princeton, NJ: Princeton University Press.

Stephens DW, Paton SR (1986) How constant is the constant of risk-aversion? Anim Behav 34: 1659–1667.

Stephens DW, Brown JS, Ydenberg RC, eds. (2007) Foraging: behavior and ecology. Chicago, IL: Chicago University Press.

Stern S, Dror T, Stolovicki E, Brenner N, Braun E (2007) Genome-wide transcriptional plasticity underlies cellular adaptation to novel challenge. Mol Sys Biol 3: 106.

Sternberg PW, Horvitz HR (1986) Pattern formation during vulval development in C. elegans. Cell 44: 761–772.

Stevens L, Goodnight CJ, Kalisz S (1995) Multilevel selection in natural populations of Impatiens capensis. Am Nat 145: 513–526.

Stevison LS (2012) Male-mediated effects on female meiotic recombination. Evolution 66: 905–911.

Stewart WJ (1994) Introduction to the numerical simulations of Markov Chains. Princeton University Press, Princeton NJ.

Stewart-Ornstein J, Weissman JS, El-Samad H (2012) Cellular noise regulons underlie fluctuations in Saccharomyces cerevisiae. Mol Cell 45: 483–493.

Strassmann JE, Queller DC, Hughes CR (1988) Predation and the evolution of sociality in the paper wasp, Polistes bellicosus. Ecology 69: 1497–1505.

Stockhoff BA (1993) Diet heterogeneity: implications for growth of a generalist herbivore, the gypsy moth. Ecology 74: 1939–1949.

Stockley P, Gage MJG, Parker GA, Møller AP (1997) Sperm competition in fishes: the evolution of testis size and ejaculate characteristics. Am Nat 149: 933–954.

Stoler DL, Chen N, Basik M, Kahlenberg MS, Rodriguez-Bigas MA, Petrelli NJ, Anderson GR (1999) The onset and extent of genomic instability in sporadic colorectal tumor progression. Proc Natl Acad Sci USA 96: 15121–15126.

Stover JP, Kendall BE, Fox GA (2012) Demographic heterogeneity impacts density-dependent population dynamics. Theor Ecol 5: 297–309.

Stumpf MPH, Laidlaw Z, Jansen VAA (2002) Herpes viruses hedge their bets. Proc Natl Acad Sci USA 99: 15234–15237.

Süel GM, Kulkarni RP, Dworkin J, Garcia-Ojalvo J, Elowitz MB (2007) Tunability and noise dependence in differentiation dynamics. Science 315: 1716–1719.

Summers K, Amos W (1997) Behavioral, ecological, and molecular genetic analyses of reproductive strategies in the Amazonian dart-poison frog, Dendrobates ventrimaculatus. Behav Ecol 8: 260–267.

Sung HM, Yasbin RE (2002) Adaptive, or stationary-phase, mutagenesis, a component of bacterial differentiation in Bacillus subtilis. J Bacteriol 184: 5641–5653.

Sureka K, Ghosh B, Dasgupta A, Basu J, Kundu M, et al. (2008) Positive feedback and noise activate the stringent response regulator rel in mycobacteria. PLoS ONE 3: e1771.

Svardal H, Rueffler C, Hermisson J (2011) Comparing environmental and genetic variance as adaptive response to fluctuating selection. Evolution 65: 2492–2513.

Svensson EI, Calsbeek R, eds. (2012) The adaptive landscape in evolutionary biology. Oxford, UK: Oxford University Press.

Svetlova E, van Elst H (2012) How is non-knowledge represented in economic theory? arXiv:1209.2204v1 [q-n.GN].

Swain PS, Elowitz MB, Siggia ED (2002) Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci USA 99: 12795–12800.

Swetina J, Schuster P (1982) Self-replication with errors: a model for polynucleotide replication. Biophys Chem 16: 329–345.

Sznajder B, Sabelis MW, Egas M (2012) How adaptive learning affects evolution: reviewing theory on the Baldwin effect. Evol Biol 39: 301–310.

Taipale M, Jarosz DF, Lindquist S (2010) HSP90 at the hub of protein homeostasis: emerging mechanistic insights. Nat Rev Mol Cell Biol 11: 515–528.

Tallamy DW (2005) Egg dumping in insects. Annu Rev Entomol 50: 347–370.

Tanaka MM, Valckenborgh F (2011) Escaping an evolutionary lobster trap: drug resistance and compensatory mutation in a fluctuating environment. Evolution 65: 1376–1387.

Tauber MJ, Tauber CA, Masaki S (1986) Seasonal adaptations of insects. Oxford, UK: Oxford University Press.

Tauber MJ, Tauber CA (2002) Prolonged dormancy in Leptinotarsa decemlineata (Say) (Coleoptera: Chrysomelidae): a ten-year field study with implications for crop rotation. Environ Entomol 31: 499–504.

Tawfik DS (2010) Messy biology and the origins of evolutionary innovations. Nat Chem Biol 6: 692–696.

Tebbich S, Teschke I (2014) Coping with uncertainty: Woodpecker finches (Cactospiza pallida) from an unpredictable habitat are more flexible than birds from a stable habitat. PLoS ONE 9: e91718.

Tedman-Aucoin K, Agrawal AF (2012) The effect of deleterious mutations and age on recombination in Drosophila melanogaster. Evolution 66: 575–585.

Teleky SB (1980) Egg cannibalism in Tribolium as a model of interference competition. Res Popul Ecol 21: 217–227.

Templeton AR, Levin DA (1979) Evolutionary consequences of seed pools. Am Nat 114: 232–249.

Tenaillon O, Denamur E, Matic I (2004) Evolutionary significance of stress-induced mutagenesis in bacteria. Trends Microbiol 12: 264–270.

Thaler RH (1994) Quasi rational economics. New York, NY: Russell Sage Foundation Publications.

Thathachar MAL, Sastry PS (2002) Varieties of learning automata: an overview. IEEE Trans Syst Man Cybern 32: 711–722.

Thattai M, van Oudenaarden A (2004) Stochastic gene expression in fluctuating environments. Genetics 167: 523–530.

Theise ND (2004) Perspective: Stem cells react! Cell lineages as complex adaptive systems. Exp Hematol 32: 25–27.

Theise ND, Harris R (2006) Postmodern biology: (Adult)( stem) cells are plastic, stochastic, complex, and uncertain. Handbook of Experimental Pharmacology 174: 389–408.

Thoday JM (1953) Components of fitness. Symp Soc Exp Biol 7: 97–113.

Thompson DJ (1990) The effects of survival and weather on lifetime egg production in a model damselfly. Ecol Entomol 15: 455–462.

Thompson DJ, Hassall C, Lowe CD, Watts PC (2011) Field estimates of reproductive success in a model insect: behavioural surrogates are poor predictors of fitness. Ecol Lett 14: 905–913.

Thompson JD (1991) Phenotypic plasticity as a component of evolutionary change. Trends Ecol Evol 6: 246–249.

Thompson JN (2005) The geographic mosaic of coevolution. Chicago, IL: University of Chicago Press.

Thompson JN (2009) The coevolving web of life. Am Nat 173: 125–140.

Tibbetts EA, Reeve HK (2003) Benefits of foundress associations in the paper wasp Polistes dominulus: increased productivity and survival but not assurance of fitness returns. Behav Ecol 14: 510–514.

Tindo M, D’Agostino P, Francescato E, Dejean A, Turillazzi S (1997a) Associative colony foundation in the tropical wasp Belonogaster juncea juncea (Hymenoptera: Vespidae). Insect Soc 44: 365–377.

Tindo M, Turillazzi S, Dejean A (1997b) Behavioral role differentiation in the primitively eusocial wasp Belonogaster juncea juncea (Hymenoptera: Vespidae). J Insect Behav 10: 571–580.

Tindo M, Kenne M, Dejean A (2008) Advantages of multiple foundress colonies in Belonogaster juncea juncea L.: greater survival and increased productivity. Ecol Entomol 33: 293–297.

Tinkle DW, Dunham AE, Congdon JD (1993) Life history and demographic variation in the lizard Sceloporus graciosus: a long-term study. Ecology 74: 2413–2429.

Tirosh I, Barkai N (2008) Two strategies for gene regulation by promoter nucleosomes. Genome Res 18: 1084–1091.

To T (1999) Risk and evolution. Econ Theory 13: 329–343.

Tokuriki N, Tawfik DS (2009) Protein dynamism and evolvability. Science 324: 203–207.

Tokuriki N, Jackson CJ, Afriat-Jurnou L, Wyganowski KT, Tang R, Tawfik DS (2012) Diminishing returns and tradeoffs constrain the laboratory optimization of an enzyme. Nat Commun 3: 1257.

Tomkins JL, Radwan J, Kotiaho JS, Tregenza T (2004) Genic capture and resolving the lek paradox. Trends Ecol Evol 19: 323–328.

Tonegawa S (1983) Somatic generation of antibody diversity. Nature 302: 575–581.

Tonsor SJ, Elnaccash TW, Scheiner SM (2013) Developmental instability is genetically correlated with phenotypic plasticity, constraining heritability and fitness. Evolution 67: 2923–2935.

Török J, Hegyi G, Tóth L, Könczey R (2004) Unpredictable food supply modifies costs of reproduction and hampers individual optimization. Oecologia 141: 432–443.

Torres-Sosa C, Huang S, Aldana M (2012) Criticality is an emergent property of genetic networks that exhibit evolvability. PLoS Comput Biol 8: e1002669.

Traulsen A, Nowak MA (2006) Evolution of cooperation by multilevel selection. Proc Natl Acad Sci USA 103: 10952–10955.

Travisano M, Mongold JA, Bennet AF, Lenski RE (1995a) Experimental tests of the roles of adaptation, chance, and history in evolution. Science 267: 87–90.

Travisano M, Vasi F, Lenski RE (1995b) Long-term experimental evolution in Escherichia coli III: Variation among replicate populations in correlated responses to novel environments. Evolution 49: 189–200.

Tregenza T, Wedell N (1998) Benefits of multiple mates in the cricket Gryllus bimaculatus. Evolution 52: 1726–1730.

Tregenza T, Wedell N (2000) Genetic compatibility, mate choice and patterns of parentage: invited review. Mol Ecol 9: 1013–1027.

Tregenza T, Wedell N (2002) Polyandrous females avoid costs of inbreeding. Nature 415: 71–73.

True HL, Lindquist SL (2000) A yeast prion provides a mechanism for genetic variation and phenotypic diversity. Nature 407: 477–483.

Tsimring LS (2014) Noise in biology. Rep Prog Phys 77: 026601.

Tsuda ME, Kawata M (2010) Evolution of gene regulatory networks by fluctuating selection and intrinsic constraints. PLoS Comput Biol 6: e1000873.

Tuljapurkar S (1989) An uncertain life: demography in random environments. Theor Popul Biol 35: 227–294.

Tuljapurkar S (1990a) Delayed reproduction and fitness in variable environments. Proc Natl Acad Sci USA 87: 1139–1143.

Tuljapurkar S (1990b) Population dynamics in variable environments. Lecture Notes in Biomathematics, vol. 85. New York, NY: Springer.

Tuljapurkar S, Gaillard JM, Coulson T (2009) From stochastic environments to life histories and back. Phil Trans R Soc B 364: 1499–1509.

Turcotte DL (1992) Fractals and chaos in geology and geophysics. Cambridge, UK: Cambridge University Press.

Turing AM (1936) On computable numbers, with an application to the Entscheidungsproblem. J Math 58: 345–363.

Turner BM (2009) Epigenetic responses to environmental change and their evolutionary implications. Phil Trans R Soc Lond B 364: 3403–3418.

Turner PE, Cooper VS, Lenski RE (1998) Trade-off between horizontal and vertical modes of transmission in bacterial plasmids. Evolution 52: 315–329.

Turney PD (1996) Myths and legends of the Baldwin effect. In: Proc 13th Int Conf Mach Learning, Bari, Italy. pp 135–142.

Turney PD (1999) Increasing evolvability considered as a large scale trend in evolution. In: Marrow P, Shackleton M, Fernandez-Villacanas JL, Ray T, eds. Proceedings of the 1999 Genetic and Evolutionary Computation Conference (GECCO-99) Workshop Program. Orlando, FL. pp 43–46.

Turney P, Whitley D, Anderson RW (1996) Evolution, learning, and instinct: 100 years of the Baldwin Effect. Evol Comput 4: 4–8.

Tyson JJ, Chen KC, Novak B (2003) Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr Opin Cell Biol 15: 221–231.

Tzanakanis ME, Karakassis EJ, Tsaklidis G, Karabina EC, Argalavini IC, Arabatzis G (1991) Diapause termination in the almond seed wasp, Eurytoma amygdali Enderlein (Hym., Eurytomidae), in northern Greece and under certain photoperiods and temperatures. J Appl Entomol 111: 86–98.

Uchmanski J (1999) What promotes persistence of a single population: An individual-based model. Ecol Model 115: 227–241.

Uller T, Olsson M (2008) Multiple paternity in reptiles: patterns and processes. Mol Ecol 17: 2566–2580.

Ushatinskaya RS (1984) A critical review of the superdiapause in insects. Ann Zool 21: 3–30.

Ushijima T, Watanabe N, Okochi E, Kaneda A, Sugimura T, Miyamoto K (2003) Fidelity of the methylation pattern and its variation in the genome. Genome Res 13: 868–874.

Uyenoyama M (1979) Evolution of altruism under group selection in large and small populations in fluctuating environments. Theor Popul Biol 15: 58–85.

Valverde S, Solé RV (2002) Self-organized critical traffic in parallel computer networks. Physica A 312: 636–648.

van Baalen M (2002) Contact networks and the evolution of virulence. In: Dieckmann U, Metz JAJ, Sabelis MW, Sigmund K, eds. Adaptive dynamics of infectious diseases: In pursuit of virulence management. Cambridge studies in adaptive dynamics. Cambridge, UK: Cambridge University Press. pp 85–103.

van Baalen M, Rand DA (1998) The unit of selection in viscous populations and the evolution of altruism. J Theor Biol 193: 631–648.

Vander Werf E (1992) Lack's clutch size hypothesis: an examination of the evidence using meta-analysis. Ecology 73: 1699–1705.

van der Woude MW (2006) Re-examining the role and random nature of phase variation. FEMS Microbiol Lett 254: 190–197.

van der Woude MW, Bäumler AJ (2004) Phase and antigenic variation in bacteria. Clin Microbiol Rev 17: 581–611.

Van de Vijver G (2006) Self-organization and emergence in life sciences. Dordrecht, The Netherlands: Springer.

Van Dooren TJM (2001) Reaction norms with bifurcations shaped by evolution. Proc R Soc Lond B 268: 279–287.

van Nimwegen E, Crutchfield JP, Huynen M (1999) Neutral evolution of mutational robustness. Proc Natl Acad Sci USA 96: 9716–9720.

van Oudenaarden A, Theriot JA (1999) Cooperative symmetry-breaking by actin polymerization in a model for cell motility. Nature Cell Biol 1: 493–499.

Van Valen L (1973a) A new evolutionary law. Evol Theory 1: 1–30.

Van Valen L (1973b) Festschrift. Science 180: 488.

Van Valen LM (1994) Concepts and the nature of selection by extinction: is generalization possible? In: Glen W, ed. The mass-extinction debates: how science works in a crisis. Stanford, CA: Stanford University Press. pp. 200–216.

van Zon JS, Morelli MJ, Tânase-Nicola S, ten Wolde PR (2006) Diffusion of transcription factors can drastically enhance the noise in gene expression. Biophys J 91: 4350–4367.

Vasseur DA, Yodzis P (2004) The color of environmental noise. Ecology 85: 1146–1152.

Veening JW, Hamoen LW, Kuipers OP (2005) Phosphatases modulate the bistable sporulation gene expression pattern in Bacillus subtilis. Mol Microbiol 56: 1481–1494.

Veening JW, Smits WK, Kuipers OP (2008a) Bistability, epigenetics, and bet-hedging in bacteria. Annu Rev Microbiol 62: 193–210.

Veening JW, Stewart EJ, Berngruber TW, Taddei F, Kuipers OP, Hamoen LW (2008b) Bethedging and epigenetic inheritance in bacterial cell development. Proc Natl Acad Sci USA 105: 4393–4398.

Vellend M (2006) The consequences of genetic diversity in competitive communities. Ecology 87: 304–311.

Venable DL (1989) Modelling the evolutionary ecology of seed banks. In: Leck MA, Parker VT, Simpson RL, eds. Ecology of soil seed banks. San Diego, CA: Academic Press. pp 67–87.

Venable DL (2007) Bet hedging in a guild of desert annuals. Ecology 88: 1086–1090.

Venable DL, Lawlor L (1980) Delayed germination and dispersal in desert annuals: escape in space and time. Oecologia 46: 272–282.

Venable DL, Brown JS (1988) The selective interactions of dispersal, dormancy, and seed size as adaptations for reducing risk in variable environments. Am Nat 131: 360–384.

Venditti C, Meade A, Pagel M (2010) Phylogenies reveal new interpretation of the Red Queen. Nature 463: 349–352.

Venegas JG, Winkler T, Musch G, Melo MFV, Layfield D, Tgavalekos N, et al. (2005) Self-organized patchiness in asthma as a prelude to catastrophic shifts. Nature 434: 777–782.

Verhoeven KJF, Jansen JJ, van Dijk PJ, Biere A (2010) Stress-induced DNA methylation changes and their heritability in asexual dandelions. New Phytol 185: 1108–1118.

Via S, Lande R (1985) Genotype-environment interaction and the evolution of phenotypic plasticity. Evolution 39: 505–522.

Vilar JM, Kueh HY, Barkai N, Leibler S (2002) Mechanisms of noise-resistance in genetic oscillators. Proc Natl Acad Sci USA 99: 5988–5992.

Viney M, Reece SE (2013) Adaptive noise. Proc R Soc B 280: 20131104. http://dx.doi.org/10.1098/rspb.2013.1104

Vinogradova O, Darienko T, Pavli?ek T, Nevo E (2011) Cyanoprokaryotes and algae of Arubota'im salt cave (Mount Sedom, Dead Sea area, Israel). Nova Hedwigia 93: 107–124.

Viñuelas J, Kaneko G, Coulon A, Beslon G, Gandrillon O (2012) Towards experimental manipulation of stochasticity in gene expression. Prog Biophys Mol Biol 110: 44–53.

Virol JA, Mahoney SP, Mawhinney K (2003) Phenotypic variation in skull size and shape between newfoundland and mainland populations of North American black bears, Ursus americanus. Can Field Nat 117: 236–244.

Visco P, Alled RJ, Majumdar SN, Evans MR (2010) Switching and growth for microbial populations in catastrophic responsive environments. Biophys J 98: 1099–1108.

Viswanathan GM, Afanasyev V, Buldyrev SV, Murphy EJ, Prince PA, et al. (1996) Lévy flight search patterns of wandering albatrosses. Nature 381: 413–415.

Viswanathan GM, Buldyrev SV, Havlin S, da Luz MG, Raposo EP, et al. (1999) Optimizing the success of random searches. Nature 401: 911–914.

Viswanathan GM, Afanasyev V, Buldyrev SV, Havlin S, da Luz MGE, et al. (2001) Lévy flights search patterns of biological organisms. Physica A 295: 85–88.

Vitorino P, Meyer T (2008) Modular control of endothelial sheet migration. Genes Dev 22: 3268–3281.

von Foerster H (1960) On self-organising systems and their environments. In: Yovitts MC, Cameron S, eds. Self-organising systems. London, UK: Pergamon Press. pp 31–50.

von Holst D, Hutzelmeyer H, Kaetzke P, Khaschei M, Rödel HG, Schrutka H (2002) Social rank, fecundity and lifetime reproductive success in wild european rabbits (Oryctolagus cuniculus). Behav Ecol Sociobiol 51: 245–254.

von Mering C, Zdobnov EM, Tsoka S, Ciccarelli FD, Pereira-Leal JB, Ouzounis CA, Bork P (2003) Genome evolution reveals biochemical networks and functional modules. Proc Natl Acad Sci USA 100: 15428–15433.

von Neumann J (1950) Functional operators. Princeton, NJ: Princeton University Press.

von Neumann J (1956) The general and logical theory of automata. In: Newman JR, ed. The world of mathematics, Vol 4. New York, NY: Simon and Schuster.

von Neumann J, Morgenstern O (1944) Theory of games and economic behavior. 1st edn. Princeton, NJ: Princeton University Press.

von Neumann J, Morgenstern O (1953) Theory of games and economic behavior. 3rd edn. Princeton, NJ: Princeton University Press.

von Neumann J, Aspray W, Burks AW (1987) Papers of John von Neumann on computing and computer theory. Cambridge, MA: MIT Press.

von Neumann J, Churchland PM, Churchland PS (2000) The computer and the brain, 2nd edn. New Haven, CT: Yale University Press.

Voss RF (1978) Linearity of 1/f noise mechanisms. Phys Rev Lett 40: 913–916.

Voss RF (1992) Evolution of long-range fractal correlations and 1/f noise in DNA base sequences. Phys Rev Lett 68: 3805–3808.

Voss RF, Clarke J (1976) Flicker (1/f) noise: Equilibrium temperature and resistance fluctuations. Phys Rev B 13: 556–573.

Voss RF, Clarke J (1978) “1/f noise’’ in music: music from 1/f noise. J Accoust Soc Am 63: 258–263.

Wada Y, Miyamoto K, Kusano T, Sano H (2004) Association between up-regulation of stress-responsive genes and hypomethylation of genomic DNA in tobacco plants. Mol Genet Genomics 271: 658–666.

Waddington CH (1942) Canalization of development and the inheritance of acquired characters. Nature 150: 563–565.

Waddington CH (1957) The strategy of the genes. London, UK: George Allen & Unwin.

Waddington CH (1961) The nature of life. London, UK: Allen & Unwin.

Wade MJ (1976) Group selection among laboratory populations of Tribolium. Proc Natl Acad Sci USA 73: 4604–4607.

Wade MJ (1977) An experimental study of group selection. Evolution 31: 134–153.

Wade MJ (1978a) A critical review of the models of group selection. Q Rev Biol 53: 101–114.

Wade MJ (1978b) Kin selection: a classical approach and a general solution. Proc Natl Acad Sci USA 75: 6154–6158.

Wade MJ (1980a) Group selection, population growth rate, and competitive ability in the flour beetles, Tribolium spp. Ecology 61: 1056–1064.

Wade MJ (1980b) An experimental study of kin selection. Evolution 34: 844–855.

Wade MJ (2003) Community genetics and species interactions. Ecology 84: 583–585.

Wade MJ, Breden F (1980) The evolution of cheating and selfish behaviour. Behav Evol Sociobiol 7: 167–172.

Wade MJ, Kalisz S (1990) The causes of natural selection. Evolution 44: 1947–1955.

Wake DB, Wake MH, Specht CD (2011) Homoplasy: from detecting pattern to determining process and mechanism of evolution. Science 331: 1032–1035.

Wagner A (2003) Risk management in biological evolution. J Theor Biol 225: 45–57.

Wagner A (2005) Robustness and evolvability in living systems. Princeton, NJ: Princeton University Press.

Wagner A (2008) Robustness and evolvability: a paradox resolved. Proc Biol Sci 275: 91–100.

Wagner GP, Altenberg L (1996) Complex adaptations and the evolution of evolvability. Evolution 50: 967–976.

Wagner GP, Chiu CH, Hansen TF (1999) Is Hsp90 a regulator of evolvability? J Exp Zool 285: 116–118.

Wahl LM, Krakauer DC (2000) Models of experimental evolution: The role of genetic chance and selective necessity. Genetics 156: 1437–1448.

Waldbauer GP (1978) Phenological adaptation and the polymodal emergence patterns of insects. In: Dingle H, ed. Evolution of insect migration and diapause. Berlin, Germany: Springer. pp 127–144.

Walker TJ (1984) Do populations self-regulate? In: Huffaker CB, Rabb RL, eds. Ecological Entomology. New York, NY: John Wiley & Sons. pp 531–558.

Walker TJ (1986) Stochastic polyphenism: coping with uncertainty. Fla Entomol 69: 46–62.

Wallace AR (1858) On the tendency of species to form varieties; and on the perpetuation of varieties and species by natural means of selection. III. On the tendency of varieties to depart indefinitely from the original type. J Proc Linn Soc Lond 3: 53–62.

Walsh B, Blows MW (2009) Abundant genetic variation + strong selection = multivariate genetic constraints: a geometric view of adaptation. Annu Rev Ecol Evol Syst 40: 41–59.

Walsh DM, Lewens T, Ariew A (2002) The trials of life: natural selection and random drift. Philos Sci 69: 452–473.

Wang GE, Humayun MZ, Taylor DE (1999) Mutation as an origin of genetic variability in Helicobacter pylori. Trends Microbiol 7: 488–493.

Wang W, Fang H, Groom L, Cheng A, Zhang W, et al. (2008) Superoxide flashes in single mitochondria. Cell 134: 279–290.

Wang XP, Xue FS, Hua A, Ge F (2006) Effects of diapause duration on future reproduction in the cabbage beetle, Colaphellus bowringi: positive or negative? Physiol Entomol 31: 190–196.

Wang Z, Zhang J (2011) Impact of gene expression noise on organismal fitness and the efficacy of natural selection. Proc Natl Acad Sci USA 108: E67–E76.

Waterman TH (1999) The evolutionary challenges of extreme environments (part 1). J Exp Zool 285: 326–359.

Waterman TH (2001) The evolutionary challenges of extreme environments (part 2). J Exp Zool 291: 130–168.

Watson PJ (1991) Multiple paternity as genetic bet-hedging in female sierra dome spiders, Linyphia litigiosa (Linyphiidae). Anim Behav 41: 343–360.

Webb JK, Brook BW, Shine R (2002) What makes a species vulnerable to extinction? Comparative life-history traits of two sympatric snakes. Ecol Res 17: 59–67.

Weber BH (1998) Towards a general biology: emergence of life and information from the perspective of complex systems dynamics. In: Van de Vijver G, Salthe SN, Delpos M, eds. Evolutionary systems: Biological and epistemological perspectives on selection and self-organization. Dordrecht, The Netherlands: Kluwer Academic Publishers. pp 59–66.

Weber BH (2013) The Baldwin Effect in an extended evolutionary synthesis. In: Henning BG, Scarfe AC, eds. Beyond mechanism: putting life back into biology. Plymouth, UK: Lexington Books. pp 251–258.

Weber BH, Depew DJ (1996) Natural selection and self-organization. Biol Philos 11: 33–65.

Weber BH, Depew DJ (2003) Evolution and learning: the Baldwin Effect reconsidered. Cambridge, MA: MIT Press.

Weber KE (1996) Large genetic change at small fitness cost in large populations of Drosophila melanogaster selected for wind tunnel flight: rethinking fitness surfaces. Genetics 144: 205–213.

Weber M (2001) Determinism, realism, and probability in evolutionary theory. Philos Sci 68 Suppl: S213–S224.

Weinberger LS, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV (2005) Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 Tat fluctuations drive phenotypic diversity. Cell 122: 169–182.

Weinig C, Johnston JA, Willis CG, Maloof JN (2007) Antagonistic multilevel selection on size and architecture in variable density settings. Evolution 61: 58–67.

Weinreich DM, Chao L (2005) Rapid evolutionary escape by large populations from local fitness peaks is likely in nature. Evolution 59: 1175–1182.

Weinshall D (1986) Why is a two-environment system not rich enough to explain the evolution of sex? Am Nat 128: 736–750.

Weiss KM, Buchanan AV (2011) Is life law-like? Genetics 188: 761–771.

Weissman DB, Desai MM, Fisher DS, Feldman MW (2009) The rate at which asexual populations cross fitness valleys. Theor Popul Biol 75: 286–300.

Weissman MB (1988) 1/f noise and other slow, nonexponential kinetics in condensed matter. Rev Mod Phys 60: 537–571.

Wenseleers T, Ratnieks FLW (2004) Tragedy of the commons in Melipona bees. Proc R Soc Lond B 271: S310–S312.

Wenzel JW, Pickering J (1991) Cooperative foraging, productivity, and the central limit theorem. Proc Natl Acad Sci USA 88: 36–38.

Werfel J, Bar-Yam Y (2004) The evolution of reproductive restraint through social communication. Proc Natl Acad Sci USA 101: 11019–11024.

Werner EE, Gilliam JF (1984) The ontogenetic niche shift and species interactions in size-structured populations. Annu Rev Ecol Syst 15: 393–425.

Werner EE, Anholt BR (1993) Ecological consequences of the trade-off between growth and mortality rates mediated by foraging activity. Am Nat 142: 242–272.

Wesolowski T, Tomialoj? L (2005) Nest sites, nest depredation, and productivity of avian broods in a primeval temperate forest: do the generalizations hold? J Avian Biol 36: 361–367.

West GB, Brown JH, Enquist BJ (1997) A general model for the origin of allometric scaling laws in biology. Science 276: 122–126.

Westerhoff HV, Hellingwerf KJ, van Dam K (1983) Thermodynamic efficiency of microbial growth is low but optimal for maximum growth rate. Proc Natl Acad Sci USA 80: 305–309.

Westoby M, Leishman MR, Lord JM (1996) Comparative ecology of seed size and seed dispersal. Phil Trans R Soc B 351: 1309–1318.

Whitacre JM, Bender A (2010) Degeneracy: a design principle for achieving robustness and evolvability. J Theor Biol 263: 143–153.

White JA, Rubinstein JT, Kay AR (2000) Channel noise in neurons. Trends Neurosci 23: 131–137.

White MF, Grogan DW (2008) DNA stability and repair. In: Robb FT, Antranikian G, Grogan DW, Driessen AJ, ed. Thermophiles: biology and technology at high temperatures. Boca Raton, FL: CRC Press. pp 179–188.

Whitelaw E, Martin DI (2001) Retrotransposons as epigenetic mediators of phenotypic variation in mammals. Nat Genet 27: 361–365.

Whitham TG, Young WP, Martinsen GD, Gehring CA, Schweizer JA, et al. (2003) Community and ecosystem genetics: a consequence of the extended phenotype. Ecology 84: 559–573.

Whitham TG, Bailey JK, Schweitzer JA, Shuster SM, Bangert RK, LeRoy CJ, et al. (2006) A framework for community and ecosystem genetics: from genes to ecosystems. Nat Rev Genet 7: 510–523.

Wiener N (1948) Cybernetics: control and communication in the animal and in the machine. Cambridge, MA: MIT Press.

Wiener N (1961) Cybernetics, its control and communication in the animal and the machine, 2nd edn. Cambridge, MA: MIT Press.

Wiens JA (1976) Population responses to patchy environments. Annu Rev Ecol Syst 7: 81–120.

Wiens JA (2000) Ecological heterogeneity: an ontogeny of concepts and approaches. In: Hutchings MJ, John E, Stewart AJ, eds. The ecological consequences of environmental heterogeneity. Oxford, UK: Blackwell. pp 9–31.

Wilbur HM, Tinkle DW, Collins JP (1974) Environmental certainty, trophic level, and resource availability in life history evolution. Am Nat 108: 805–817.

Wilbur HM, Rudolf VH (2006) Life?history evolution in uncertain environments: bet hedging in time. Am Nat 168: 398–411.

Wilke CO, Ronnewinkel C, Martinetz T (2001a) Dynamic fitness landscapes in molecular evolution. Phys Rep 349: 395–446.

Wilke CO, Wang JL, Ofria C, Lenski RE, Adami C (2001b) Evolution of digital organisms at high mutation rates leads to survival of the flattest. Nature 412: 331–333.

Williams GC (1966) Adaptation and natural selection. Princeton, NJ: Princeton University Press.

Williams GJ, Zhang C, Thorson JS (2007) Expanding the promiscuity of a natural-product glycosyltransferase by directed evolution. Nat Chem Biol 3: 657–662.

Williams HT, Lenton TM (2007) Artificial selection of simulated microbial ecosystems. Proc Natl Acad Sci USA 104: 8918–8923.

Willis LD Jr, Hendrick AC (1992) Life history, growth, survivorship, and production of Hydropysche slossonae in Mill Creek, Virginia. J N Am Benthol Soc 11: 290–303.

Wills PR (2009) Informed generation: Physical origin and biological evolution of genetic codescript interpreters. J Theor Biol 257: 345–358.

Wilson DS (1975) A general theory of group selection. Proc Natl Acad Sci USA 72: 143–146.

Wilson DS (1978) Prudent predation: a field study involving three species of tiger beetles. Oikos 31: 128–136.

Wilson DS (1980) The natural selection of populations and communities. Menlo Park, CA: Benjamin/Cummings.

Wilson DS (1983) The group selection controversy: history and current status. Annu Rev Ecol Syst 14: 159–187.

Wilson DS (1994) Adaptive genetic variation and human evolutionary psychology. Ethol Sociobiol 15: 219–235.

Wilson DS, Sober E (1989) Reviving the superorganism. J Theor Biol 136: 337–356.

Wilson DS, Sober E (1994) Reintroducing group selection to the human behavioral sciences. Behav Brain Sci 17: 585–608.

Wilson DS, Swenson W (2003) Community genetics and community selection. Ecology 84: 586–588.

Wilson EO (2005) Kin selection as the key to altruism: its rise and fall. Soc Res 72: 159–166.

Wilson EO (2012) The social conquest of earth. New York, NY: W.W. Norton & Company.

Winderickx J, de Winde JH, Crauwels M, Hino A, Hohmann S, et al. (1996) Regulation of genes encoding subunits of the trehalose synthase complex in Saccharomyces cerevisiae: novel variations of STRE-mediated transcription control? Mol Gen Genet 252: 470–482.

Winter M, Johnson DH, Faaborg J (2000) Evidence for edge effects on multiple levels in tallgrass prairie. Condor 102: 256–266.

Winterhalder B, Lu F, Tucker B (1999) Risk-senstive adaptive tactics: models and evidence from subsistence studies in biology and anthropology. J Archaeol Res 7: 301–348.

Winterhalter WE, Mousseau TA (2007) Patterns of phenotypic and genetic variation for the plasticity of diapause incidence. Evolution 61: 1520–1531.

Wisenden BD (1999) Alloparental care in fishes. Rev Fish Biol Fisher 9: 45–70.

Witting L (2003) Major life-history transitions by deterministic directional natural selection. J Theor Biol 225: 389–406.

Wolf DM, Vazirani VV, Arkin AP (2005a) Diversity in times of adversity: probabilistic strategies in microbial survival games. J Theor Biol 234: 227–253.

Wolf DM, Vazirani VV, Arkin AP (2005b) A microbial modified prisoner’s dilemma game: how frequency-dependent selection can lead to random phase variation. J Theoret Biol 234: 255–262.

Wolf JB, Brodie III ED, Cheverud JM, Moore AJ, Wade MJ (1998) Evolutionary consequences of indirect genetic effects. Trends Ecol Evol 13: 64–69.

Wolf JB, Brodie III ED, Moore AJ (1999) Interacting phenotypes and the evolutionary process. II. Selection resulting from social interactions. Am Nat 153: 254–266.

Wolfe MS (1985) The current status and prospects of multiline cultivars and variety mixtures for disease resistance. Annu Rev Phytopathol 23: 251–273.

Wong JWY, Meunier J, Kölliker M (2013) The evolution of parental care in insects: the roles of ecology, life history and the social environment. Ecol Entomol 38: 123–137.

Woods RJ, Barrick JE, Cooper TF, Shrestha U, Kauth MR, Lenski RE (2011) Second-order selection for evolvability in a large Escherichia coli population. Science 331: 1433–1436.

Wourms JP (1972) The developmental biology of annual fishes. III. Pre-embryonic diapause of variable duration in the eggs of annual fishes. J Exp Zool 182: 389–414.

Wright S (1931) Evolution in Mendelian populations. Genetics 16: 97–159.

Wright S (1932) The roles of mutation, inbreeding, crossbreeding, and selection in evolution. In: Proceedings of the 6th international congress on genetics. Menasha, WI: Brooklyn Botanic Gardens. pp 355–366.

Wright S (1955) Classification of the factors of evolution. Cold Spring Harbor Symp Quant Biol 20: 16–24.

Wright S (1968-1978) Evolution and the genetics of populations: A treatise. 4 vols. Chicago, IL: University of Chicago Press.

Wu L, Burnett JC, Toettcher JE, Arkin AP, Schaffer DV (2005) Stochastic gene expression in a lentiviral positive-feedback loop: HIV-1 tat fluctuations drive phenotypic diversity. Cell 122: 169–182.

Wynne-Edwards VC (1962) Animal dispersion in relation to social behavior. New York, NY: Hafner.

Xie CH, Naito A, Mizumachi T, Evans TT, Douglas MG, Cooney CA et al. (2007) Mitochondrial regulation of cancer associated nuclear DNA methylation. Biochem Biophys Res Commun 364: 656–661.

Yaari G, Solomon S (2010) Cooperation evolution in random multiplicative environments. Eur Phys J B 73: 625–632.

Yan Y, Wei CL, Zhang WR, Cheng HP, Liu J (2006) Cross-talk between calcium and reactive oxygen species signaling. Acta Pharmacol Sin 27: 821–826.

Yashin AI, Iachine IA, Harris JR (1999) Half of the variation in susceptibility to mortality is genetic: Findings from Swedish twin survival data. Behav Genet 29: 11–19.

Yasui Y (1997) A “good-sperm” model can explain the evolution of costly multiple mating by females. Am Nat 149: 573–584.

Yasui Y (1998) The ‘genetic benefits’ of female multiple mating reconsidered. Trends Ecol Evol 13: 246–250.

Yasui Y (2001) Female multiple mating as a genetic bet-hedging strategy when mate choice criteria are unreliable. Ecol Res 16: 605–616.

Ydenberg RC (2007) Provisioning. In: Stephens DW, Brown JS, Ydenberg RC, eds. Foraging: behavior and ecology. Chicago, IL: Chicago University Press. pp 273–303.

Yomo T, Sato K, Ito Y, Kaneko K (2006) Responses of fluctuating biological systems. Lect Notes Comput Sci 3853: 107–112.

Yom-Tov Y (1980) Intraspecific nest parasitism in birds. Biol Rev 55: 93–108.

Yom-Tov Y (2001) An updated list and some comments on the occurrence of intraspecific nest parasitism in birds. Ibis 143: 133–143.

Yoshida T, Jones LE, Ellner SP, Fussman GF, Hairston NG Jr (2003) Rapid evolution drives ecological dynamics in a predator-prey system. Nature 424: 303–306.

Yoshimura J, Shields WM (1987) Probabilistic optimization of phenotype distributions: a general solution for the effects of uncertainty on natural selection? Evol Ecol 1: 125–138.

Yoshimura J, Clark CW (1991) Individual adaptations in stochastic environments. Evol Ecol 5: 173–192.

Yoshimura J, Shields WM (1992) Components of uncertainty in clutch-size optimization. Bull Math Biol 54: 445–464.

Yoshimura J, Clark CW (1993) Introduction: historical remarks. In: Yoshimura J, Clark CW, eds. Adaptation in stochastic environments. Berlin, Germany: Springer. pp 1–7.

Yoshimura J, Jansen VAA (1996) Evolution and population dynamics in stochastic environments. Res Popul Ecol 38: 165–182.

Yoshimura J, Tanaka Y, Togashi T, Iwata S, Tainaka K (2009) Mathematical equivalence of geometric mean fitness with probabilistic optimization under environmental uncertainty. Ecol Model 220: 2611–2617.

Yoshimura J, Ito H, Miller III DG, Tainaka KI (2013) Dynamic decision-making in uncertain environments I. The principle of dynamic utility. J Ethol 31: 101–105.

Yu J, Xiao J, Ren X, Lao K, Xie XS (2006) Probing gene expression in live cells, one protein molecule at a time. Science 311: 1600–1603.

Yutin N, Makarova KS, Mekhedov SL, Wolf YI, Koonin EV (2008) The deep archaeal roots of eukaryotes. Mol Biol Evol 25: 1619–1630.

Zachos J, Pagani M, Sloan L, Thomas E, Billups K (2001) Trend, rhythms, and aberrations in global climates 65 Ma to present. Science 292: 686–693.

Zeh JA, Zeh DW (1996) The evolution of polyandry I: intragenomic conflict and genetic incompatibility. Proc R Soc B 263: 1711–1717.

Zeh JA, Zeh DW (2001) Reproductive mode and the genetic benefits of polyandry. Anim Behav 61: 1051–1063.

Zeh JA, Zeh DW (2006) Outbred embryos rescue inbred half-siblings in mixed-paternity broods of live-bearing females. Nature 439: 201–203.

Zeineddine M, Jansen VAA (2009) To age, to die: parity, evolutionary tracking and Cole's paradox. Evolution 63: 1498–1507.

Zeng X, Zhou J, Vasseur C (2000) A strategy for controlling nonlinear systems using a learning automaton. Automatica 36: 1517–1524.

Zhang L, Lou H, Guo L, Zhan Z, Duan Z, Guo X, Huang L (2010) Accurate DNA synthesis by Sulfolobus solfataricus DNA polymerase B1 at high temperature. Extremophiles 14: 107–117.

Zhang Z, Qian W, Zhang J (2009) Positive selection for elevated gene expression noise in yeast. Mol Syst Biol 5: 299.

Zhao Y, Epstein RJ (2008) Programmed genetic instability: a tumor-permissive mechanism for maintaining the evolvability of higher species through methylation-dependent mutation of DNA repair genes in the male germ line. Mol Biol Evol 25: 1737–1749.

Zhivotovsky LA (1997) Environmental stress and evolution: a theoretical study. EXS 83: 241–254.

Zhong W, Priest N (2011) Stress-induced recombination and the mechanism of evolvability. Behav Ecol Sociobiol 65: 493–502.

Zhu YY, Chen HR, Fan JH, Wang YY, Li Y, Chen JB, Fan JX, et al. (2000) Genetic diversity and disease control in rice. Nature 406: 718–722.

Zhuchenko AA, Korol AB, Gavrilenko TA, Kibenko TY (1986) The correlation between the stability of the genotype and the change in its recombination characteristics under temperature influences. Genetika 22: 966–974.

Zhuravel D, Fraser D, St-Pierre S, Tepliakova L, Pang WL, Hasty J, Kærn M (2010) Phenotypic impact of regulatory noise in cellular stress-response pathways. Syst Synth Biol 4: 105–116.

Zikidis KC, Vasilakos AV (1996) ASAFES2: a novel-fuzzy architecture for fuzzy computing, based on functional reasoning. Fuzzy Set Syst 83: 63–84.

Zink AG (2000) The evolution of intraspecific brood parasitism in birds and insects. Am Nat 155: 395–405.

Zink AG (2003) Intraspecific brood parasitism as a conditional reproductive tactic in the treehopper Pubilia concave. Behav Ecol Sociobiol 54: 406–415.

Zuk M, Bastiaans E, Langkilde T, Swanger E (2014) The role of behaviour in the establishment of novel traits. Anim Behav 92: 333–344.

Source(s) of Funding

No source of funding

Competing Interests

No competing interests

0 reviews posted so far

5 comments posted so far

Please use this functionality to flag objectionable, inappropriate, inaccurate, and offensive content to WebmedCentral Team and the authors.


Author Comments
0 comments posted so far


What is article Popularity?

Article popularity is calculated by considering the scores: age of the article
Popularity = (P - 1) / (T + 2)^1.5
P : points is the sum of individual scores, which includes article Views, Downloads, Reviews, Comments and their weightage

Scores   Weightage
Views Points X 1
Download Points X 2
Comment Points X 5
Review Points X 10
Points= sum(Views Points + Download Points + Comment Points + Review Points)
T : time since submission in hours.
P is subtracted by 1 to negate submitter's vote.
Age factor is (time since submission in hours plus two) to the power of 1.5.factor.

How Article Quality Works?

For each article Authors/Readers, Reviewers and WMC Editors can review/rate the articles. These ratings are used to determine Feedback Scores.

In most cases, article receive ratings in the range of 0 to 10. We calculate average of all the ratings and consider it as article quality.

Quality=Average(Authors/Readers Ratings + Reviewers Ratings + WMC Editor Ratings)