Evolutionary History Mediates Population Response to Rapid Environmental Change through Within-Generational and Transgenerational Plasticity.

AbstractRapid environmental change is affecting many organisms; some are coping well, but many species are in decline. A key mechanism for facilitating success following environmental change is phenotypic plasticity. Organisms use cues to respond phenotypically to environmental conditions; many incorporate recent information (within-generation plasticity) and information from previous generations (transgenerational plasticity). We extend an existing evolutionary model where organisms utilize within-generational plasticity, transgenerational plasticity, and bet hedging to include changes in environmental regime. We show how when rapid evolution of plasticity is not possible, the effect of environmental change (altering the environment mean, variance, or autocorrelation or cue reliability) on population growth rate depends on the population's evolutionary history and past evolutionary responses to historical environmental conditions. We then evaluate the predictions that populations adapted to highly variable environments or with greater within-generational plasticity are more likely to successfully respond to environmental change. We identify when these predictions fail and show that environmental change is most detrimental when previously reliable cues become unreliable. When multiple cues become unreliable, environmental change can cause deleterious effects regardless of the population's evolutionary history. Overall, this work provides a general framework for understanding the role of plasticity in population responses to rapid environmental change.

Integrating animal behaviour into research on multiple environmental stressors: a conceptual framework.

While a large body of research has focused on the physiological effects of multiple environmental stressors, how behavioural and life-history plasticity mediate multiple-stressor effects remains underexplored. Behavioural plasticity can not only drive organism-level responses to stressors directly but can also mediate physiological responses. Here, we provide a conceptual framework incorporating four fundamental trade-offs that explicitly link animal behaviour to life-history-based pathways for energy allocation, shaping the impact of multiple stressors on fitness. We first address how small-scale behavioural changes can either mediate or drive conflicts between the effects of multiple stressors and alternative physiological responses. We then discuss how animal behaviour gives rise to three additional understudied and interrelated trade-offs: balancing the benefits and risks of obtaining the energy needed to cope with stressors, allocation of energy between life-history traits and stressor responses, and larger-scale escape from stressors in space or time via large-scale movement or dormancy. Finally, we outline how these trade-offs interactively affect fitness and qualitative ecological outcomes resulting from multiple stressors. Our framework suggests that explicitly considering animal behaviour should enrich our mechanistic understanding of stressor effects, help explain extensive context dependence observed in these effects, and highlight promising avenues for future empirical and theoretical research.

Enhancing the ecological realism of evolutionary mismatch theory.

Following rapid environmental change, why do some animals thrive, while others struggle? We present an expanded, cue-response framework for predicting variation in behavioral responses to novel situations. We show how signal detection theory can be used when individuals have three behavioral options (approach, avoid, or ignore). Based on this theory, we outline predictions about which animals are more likely to make mistakes around novel conditions (i.e., fall for a trap or fail to use an undervalued resource) and the intensity of that mismatch (i.e., severe versus moderate). Explicitly considering three options provides a more holistic perspective and allows us to distinguish between severe and moderate traps, which could guide management strategies in a changing world.

Matching Behaviours and Rewards.

Matching describes how behaviour is related to rewards. The matching law holds when the ratio of an individual's behaviours equals the ratio of the rewards obtained. From its origins in the study of pigeons working for food in the laboratory, the law has been applied to a range of species, both in the laboratory and outside it (e.g., human sporting decisions). Probability matching occurs when the probability of a behaviour equals the probability of being rewarded. Input matching predicts the distribution of individuals across habitats. We evaluate the rationality of the matching law and probability matching, expose the logic of matching in real-world cases, review how recent neuroscience findings relate to matching, and suggest future research directions.

Rapid environmental change in games: complications and counter-intuitive outcomes.

Human-induced rapid environmental change (HIREC) has recently led to alterations in the fitness and behavior of many organisms. Game theory is an important tool of behavioral ecology for analyzing evolutionary situations involving multiple individuals. However, game theory bypasses the details by which behavioral phenotypes are determined, taking the functional perspective straight from expected payoffs to predicted frequencies of behaviors. In contrast with optimization approaches, we identify that to use existing game theoretic models to predict HIREC effects, additional mechanistic details (or assumptions) will often be required. We illustrate this in relation to the hawk-dove game by showing that three different mechanisms, each of which support the same ESS prior to HIREC (fixed polymorphism, probabilistic choice, or cue dependency), can have a substantial effect on behavior (and success) following HIREC. Surprisingly, an increase in the value of resources can lead to a reduction in payoffs (and vice versa), both in the immediate- and long-term following HIREC. An increase in expected costs also increases expected payoffs. Along with these counter-intuitive findings, this work shows that simply understanding the behavioral payoffs of existing games is insufficient to make predictions about the effects of HIREC.

Predicting evolutionarily stable strategies from functional responses of Sonoran Desert annuals to precipitation.

For many decades, researchers have studied how plants use bet-hedging strategies to insure against unpredictable, unfavourable conditions. We improve upon earlier analyses by explicitly accounting for how variable precipitation affects annual plant species' bet-hedging strategies. We consider how the survival rates of dormant seeds (in a 'seed bank') interact with precipitation responses to influence optimal germination strategies. Specifically, we incorporate how response to resource availability (i.e. the amount of offspring (seeds) generated per plant in response to variation in desert rainfall) influences the evolution of germination fractions. Using data from 10 Sonoran Desert annual plants, we develop models that explicitly include these responses to model fitness as a function of precipitation. For each of the species, we identify the predicted evolutionarily stable strategies (ESSs) for the fraction of seeds germinating each year and then compare our estimated ESS values to the observed germination fractions. We also explore the relative importance of seed survival and precipitation responses in shaping germination strategies by regressing ESS values and observed germination fractions against these traits. We find that germination fractions are lower for species with higher seed survival, with lower reproductive success in dry years, and with better yield responses in wet years. These results illuminate the evolution of bet-hedging strategies in an iconic system, and provide a framework for predicting how current and future environmental conditions may reshape those strategies.

Predicting Habitat Choice after Rapid Environmental Change.

Decisions made while searching for settlement sites (e.g., nesting, oviposition) often have major fitness implications. Despite numerous case studies, we lack theory to explain why some species are thriving while others are making poor habitat choices after environmental change. We develop a model to predict (1) which kinds of environmental change have larger, negative effects on fitness, (2) how evolutionary history affects susceptibility to environmental change, and (3) how much lost fitness can be recovered via readjustment after environmental change. We model the common scenario where animals search an otherwise inhospitable matrix, encountering habitats of varying quality and settling when finding a habitat better than a threshold quality level. We consider decisions and fitness before environmental change, immediately following change (assuming that animals continue to use their previously adaptive decision rules), and after optimal readjustment (e.g., via learning or evolution). We find that decreases in survival per time step searching and declines in habitat quality or availability generally have stronger negative effects than reduced season duration. Animals that were adapted to good conditions remained choosy after conditions declined and thus suffered more from environmental change than those adapted to poor conditions. Readjustment recovered much of the fitness lost through a reduction in average habitat quality but recovered much less following reductions in habitat availability or survival while searching. Our model offers novel predictions for empiricists to test as well as suggestions for prioritizing alternative mitigation steps.

Prey Responses to Exotic Predators: Effects of Old Risks and New Cues.

Exotic predators can have major negative impacts on prey. Importantly, prey vary considerably in their behavioral responses to exotic predators. Factors proposed to explain variation in prey response to exotic predators include the similarity of new predators to familiar, native predators, the prevalence and diversity of predators in a prey's past, and variation in a prey's innate ability to discriminate between predators and safety. While these factors have been put forth verbally in the literature, no theory exists that combines these hypotheses in a common conceptual framework using a unified behavioral model. Here, we formalize existing verbal arguments by modeling variation in prey responses to new predators in a state-dependent detection theory framework. We find that while some conventional wisdom is upheld, novel predictions emerge. As expected, prey respond poorly to exotic predators that do not closely resemble familiar predators. Furthermore, a history with more abundant or diverse native predators can lessen effects of some exotic predators on prey; however, under some conditions, the opposite prediction emerges. Also, prey that evolved in situations where they easily discriminate between safe and dangerous situations can be more susceptible to novel predators.

Foraging for foundations in decision neuroscience: insights from ethology.

Modern decision neuroscience offers a powerful and broad account of human behaviour using computational techniques that link psychological and neuroscientific approaches to the ways that individuals can generate near-optimal choices in complex controlled environments. However, until recently, relatively little attention has been paid to the extent to which the structure of experimental environments relates to natural scenarios, and the survival problems that individuals have evolved to solve. This situation not only risks leaving decision-theoretic accounts ungrounded but also makes various aspects of the solutions, such as hard-wired or Pavlovian policies, difficult to interpret in the natural world. Here, we suggest importing concepts, paradigms and approaches from the fields of ethology and behavioural ecology, which concentrate on the contextual and functional correlates of decisions made about foraging and escape and address these lacunae.

The erroneous signals of detection theory.

Signal detection theory has influenced the behavioural sciences for over 50 years. The theory provides a simple equation that indicates numerous 'intuitive' results; e.g. prey should be more prone to take evasive action (in response to an ambiguous cue) if predators are more common. Here, we use analytical and computational models to show that, in numerous biological scenarios, the standard results of signal detection theory do not apply; more predators can result in prey being less responsive to such cues. The standard results need not apply when the probability of danger pertains not just to the present, but also to future decisions. We identify how responses to risk should depend on background mortality and autocorrelation, and that predictions in relation to animal welfare can also be reversed from the standard theory.

Predicting behavioural responses to novel organisms: state-dependent detection theory.

Human activity alters natural habitats for many species. Understanding variation in animals' behavioural responses to these changing environments is critical. We show how signal detection theory can be used within a wider framework of state-dependent modelling to predict behavioural responses to a major environmental change: novel, exotic species. We allow thresholds for action to be a function of reserves, and demonstrate how optimal thresholds can be calculated. We term this framework 'state-dependent detection theory' (SDDT). We focus on behavioural and fitness outcomes when animals continue to use formerly adaptive thresholds following environmental change. In a simple example, we show that exposure to novel animals which appear dangerous-but are actually safe-(e.g. ecotourists) can have catastrophic consequences for 'prey' (organisms that respond as if the new organisms are predators), significantly increasing mortality even when the novel species is not predatory. SDDT also reveals that the effect on reproduction can be greater than the effect on lifespan. We investigate factors that influence the effect of novel organisms, and address the potential for behavioural adjustments (via evolution or learning) to recover otherwise reduced fitness. Although effects of environmental change are often difficult to predict, we suggest that SDDT provides a useful route ahead.

Adaptive Use of Information during Growth Can Explain Long-Term Effects of Early Life Experiences.

Development is a continuous process during which individuals gain information about their environment and adjust their phenotype accordingly. In many natural systems, individuals are particularly sensitive to early life experiences, even in the absence of later constraints on plasticity. Recent models have highlighted how the adaptive use of information can explain age-dependent plasticity. These models assume that information gain and phenotypic adjustments either cannot occur simultaneously or are completely independent. This assumption is not valid in the context of growth, where finding food results both in a size increase and learning about food availability. Here, we describe a simple model of growth to provide proof of principle that long-term effects of early life experiences can arise through the coupled dynamics of information acquisition and phenotypic change in the absence of direct constraints on plasticity. The increase in reproductive value from gaining information and sensitivity of behavior to experiences declines across development. Early life experiences have long-term impacts on age of maturity, yet-due to compensatory changes in behavior-our model predicts no substantial effects on reproductive success. We discuss how the evolution of sensitive windows can be explained by experiences having short-term effects on informational and phenotypic states, which generate long-term effects on life-history decisions.

Adaptive learning can result in a failure to profit from good conditions: implications for understanding depression.

BACKGROUND AND OBJECTIVES: Depression is a major medical problem diagnosed in an increasing proportion of people and for which commonly prescribed psychoactive drugs are frequently ineffective. Development of treatment options may be facilitated by an evolutionary perspective; several adaptive reasons for proneness to depression have been proposed. A common feature of many explanations is that depressive behaviour is a way to avoid costly effort where benefits are small and/or unlikely. However, this viewpoint fails to explain why low mood persists when the situation improves. We investigate whether a behavioural rule that is adapted to a stochastically changing world can cause inactivity which appears similar to the effect of depression, in that it persists after the situation has improved. METHODOLOGY: We develop an adaptive learning model in which an individual has repeated choices of whether to invest costly effort that may result in a net benefit. Investing effort also provides information about the current conditions and rates of change of the conditions. RESULTS: An individual following the optimal behavioural strategy may sometimes remain inactive when conditions are favourable (i.e. when it would be better to invest effort) when it is poorly informed about the current environmental state. Initially benign conditions can predispose an individual to inactivity after a relatively brief period of negative experiences. CONCLUSIONS AND IMPLICATIONS: Our approach suggests that the antecedent factors causing depressed behaviour could go much further back in an individual s history than is currently appreciated. The insights from our approach have implications for the ongoing debate about best treatment options for patients with depressive symptoms.

The evolution of decision rules in complex environments.

Models and experiments on adaptive decision-making typically consider highly simplified environments that bear little resemblance to the complex, heterogeneous world in which animals (including humans) have evolved. These studies reveal an array of so-called cognitive biases and puzzling features of behaviour that seem irrational in the specific situation presented to the decision-maker. Here we review an emerging body of work that highlights spatiotemporal heterogeneity and autocorrelation as key properties of most real-world environments that may help us understand why these biases evolved. Ecologically rational decision rules adapted to such environments can lead to apparently maladaptive behaviour in artificial experimental settings. We encourage researchers to consider environments with greater complexity to understand better how evolution has shaped our cognitive systems.

Optimal behaviour can violate the principle of regularity.

Understanding decisions is a fundamental aim of behavioural ecology, psychology and economics. The regularity axiom of utility theory holds that a preference between options should be maintained when other options are made available. Empirical studies have shown that animals violate regularity but this has not been understood from a theoretical perspective, such decisions have therefore been labelled as irrational. Here, I use models of state-dependent behaviour to demonstrate that choices can violate regularity even when behavioural strategies are optimal. I also show that the range of conditions over which regularity should be violated can be larger when options do not always persist into the future. Consequently, utility theory--based on axioms, including transitivity, regularity and the independence of irrelevant alternatives--is undermined, because even alternatives that are never chosen by an animal (in its current state) can be relevant to a decision.

On evolutionary explanations of cognitive biases.

Apparently irrational biases such as overconfidence, optimism, and pessimism are increasingly studied by biologists, psychologists, and neuroscientists. Functional explanations of such phenomena are essential; we argue that recent proposals, focused on benefits from overestimating the probability of success in conflicts or practising self-deception to better deceive others, are still lacking in crucial regards. Attention must be paid to the difference between cognitive and outcome biases; outcome biases are suboptimal, yet cognitive biases can be optimal. However, given that cognitive biases are subjectively experienced by affected individuals, developing theory and collecting evidence on them poses challenges. An evolutionary theory of cognitive bias might require closer integration of function and mechanism, analysing the evolution of constraints imposed by the mechanisms that determine behaviour.

On the evolution and optimality of mood States.

Moods can be regarded as fluctuating dispositions to make positive and negative evaluations. Developing an evolutionary approach to mood as an adaptive process, we consider the structure and function of such states in guiding behavioural decisions regarding the acquisition of resources and the avoidance of harm in different circumstances. We use a drift diffusion model of decision making to consider the information required by individuals to optimise decisions between two alternatives, such as whether to approach or withdraw from a stimulus that may be life enhancing or life threatening. We show that two dimensions of variation (expectation and preparedness) are sufficient for such optimal decisions to be made. These two dispositional dimensions enable individuals to maximize the overall benefits of behavioural decisions by modulating both the choice made (e.g., approach/withdraw) and decision speed. Such a structure is compatible with circumplex models of subjectively experienced mood and core affect, and provides testable hypotheses concerning the relationships that occur between valence and arousal components of mood in differing ecological niches. The paper is therefore a useful step toward being able to predict moods (and the effect of moods) using an optimality approach.

Generalized optimal risk allocation: foraging and antipredator behavior in a fluctuating environment.

Animals live in complex environments in which predation risk and food availability change over time. To deal with this variability and maximize their survival, animals should take into account how long current conditions may persist and the possible future conditions they may encounter. This should affect their foraging activity, and with it their vulnerability to predation across periods of good and bad conditions. Here we develop a comprehensive theory of optimal risk allocation that allows for environmental persistence and for fluctuations in food availability as well as predation risk. We show that it is the duration of good and bad periods, independent of each other, rather than the overall proportion of time exposed to each that is the most important factor affecting behavior. Risk allocation is most pronounced when conditions change frequently, and optimal foraging activity can either increase or decrease with increasing exposure to bad conditions. When food availability fluctuates rapidly, animals should forage more when food is abundant, whereas when food availability fluctuates slowly, they should forage more when food is scarce. We also show that survival can increase as variability in predation risk increases. Our work reveals that environmental persistence should profoundly influence behavior. Empirical studies of risk allocation should therefore carefully control the duration of both good and bad periods and consider manipulating food availability as well as predation risk.

Does natural selection favour the Rescorla-Wagner rule?

A fundamental question relating to animal behaviour is how animals learn; in particular, how they come to associate stimuli with rewards. Numerous empirical findings can be explained by assuming that animals use some mechanism similar to the Rescorla-Wagner learning rule, which is a relatively simple and highly general method of updating the associative strength between different stimuli. However, the Rescorla-Wagner rule is often not optimal, which raises the question of why a rule with such properties should have evolved. We consider the evolution of learning rules in a simple environment where there exists an optimal rule of similar complexity to the Rescorla-Wagner rule. We show that because the Rescorla-Wagner rule is less sensitive to changes in its parameters than the optimal rule, there is a wider range of parameter values over which the rule structure is initially viable. Consequently, the Rescorla-Wagner rule can be favoured by natural selection, ahead of other rules which are more accurate.