An unresolved controversy regarding social behaviors is exemplified when natural selection might lead to behaviors that maximize fitness at the social-group level but are costly at the individual level. Except for the special case of groups of clones, we do not have a general understanding of how and when group-optimal behaviors evolve, especially when the behaviors in question are flexible. To address this question, we develop a general model that integrates behavioral plasticity in social interactions with the action of natural selection in structured populations. We find that group-optimal behaviors can evolve, even without clonal groups, if individuals exhibit appropriate behavioral responses to each other's actions. The evolution of such behavioral responses, in turn, is predicated on the nature of the proximate behavioral mechanisms. We model a particular class of proximate mechanisms, prosocial preferences, and find that such preferences evolve to sustain maximum group benefit under certain levels of relatedness and certain ecological conditions. Thus, our model demonstrates the fundamental interplay between behavioral responses and relatedness in determining the course of social evolution. We also highlight the crucial role of proximate mechanisms such as prosocial preferences in the evolution of behavioral responses and in facilitating evolutionary transitions in individuality. Disciplines Behavior and Ethology | Biology | Population Biology CommentsAt the time of publication, author Erol Akçay was affiliated with the University of Tennessee. Currently, he is a faculty member at the Department of Biology at the University of Pennsylvania. Online enhancement: appendix.abstract: An unresolved controversy regarding social behaviors is exemplified when natural selection might lead to behaviors that maximize fitness at the social-group level but are costly at the individual level. Except for the special case of groups of clones, we do not have a general understanding of how and when group-optimal behaviors evolve, especially when the behaviors in question are flexible. To address this question, we develop a general model that integrates behavioral plasticity in social interactions with the action of natural selection in structured populations. We find that group-optimal behaviors can evolve, even without clonal groups, if individuals exhibit appropriate behavioral responses to each other's actions. The evolution of such behavioral responses, in turn, is predicated on the nature of the proximate behavioral mechanisms. We model a particular class of proximate mechanisms, prosocial preferences, and find that such preferences evolve to sustain maximum group benefit under certain levels of relatedness and certain ecological conditions. Thus, our model demonstrates the fundamental interplay between behavioral responses and relatedness in determining the course of social evolution. We also highlight the crucial role of proximate mechanisms such as prosocial preferences in the evolution of behavioral respons...

Models have made numerous contributions to evolutionary biology, but misunderstandings persist regarding their purpose. By formally testing the logic of verbal hypotheses, proof-of-concept models clarify thinking, uncover hidden assumptions, and spur new directions of study. thumbnail image credit: modified from the Biodiversity Heritage Library

Uncertain environments pose a tremendous challenge to populations: The selective pressures imposed by the environment can change so rapidly that adaptation by mutation alone would be too slow. One solution to this problem is given by the phenomenon of stochastic phenotype switching, which causes genetically uniform populations to be phenotypically heterogenous. Stochastic phenotype switching has been observed in numerous microbial species and is generally assumed to be an adaptive bet-hedging strategy to anticipate future environmental change. We use an explicit population genetic model to investigate the evolutionary dynamics of phenotypic switching rates. We find that whether or not stochastic switching is an adaptive strategy is highly contingent upon the fitness landscape given by the changing environment. Unless selection is very strong, asymmetric fitness landscapes-where the cost of being maladapted is not identical in all environments-strongly select against stochastic switching. We further observe a threshold phenomenon that causes switching rates to be either relatively high or completely absent, but rarely intermediate. Our finding that marginal changes in selection pressures can cause fundamentally different evolutionary outcomes is important in a wide range of fields concerned with microbial bet hedging.

The evolution of social traits remains one of the most fascinating and feisty topics in evolutionary biology even after half a century of theoretical research. W.D. Hamilton shaped much of the field initially with his 1964 papers that laid out the foundation for understanding the effect of genetic relatedness on the evolution of social behavior. Early theoretical investigations revealed two critical assumptions required for Hamilton's rule to hold in dynamical models: weak selection and additive genetic interactions. However, only recently have analytical approaches from population genetics and evolutionary game theory developed sufficiently so that social evolution can be studied under the joint action of selection, mutation, and genetic drift. We review how these approaches suggest two timescales for evolution under weak mutation: (i) a short-term timescale where evolution occurs between a finite set of alleles, and (ii) a long-term timescale where a continuum of alleles are possible and populations evolve continuously from one monomorphic trait to another. We show how Hamilton's rule emerges from the short-term analysis under additivity and how non-additive genetic interactions can be accounted for more generally. This short-term approach reproduces, synthesizes, and generalizes many previous results including the one-third law from evolutionary game theory and risk dominance from economic game theory. Using the long-term approach, we illustrate how trait evolution can be described with a diffusion equation that is a stochastic analogue of the canonical equation of adaptive dynamics. Peaks in the stationary distribution of the diffusion capture classic notions of convergence stability from evolutionary game theory and generally depend on the additive genetic interactions inherent in Hamilton's rule. Surprisingly, the peaks of the long-term stationary distribution can predict the effects of simple kinds of non-additive interactions. Additionally, the peaks capture both weak and strong effects of social payoffs in a manner difficult to replicate with the short-term approach. Together, the results from the short and long-term approaches suggest both how Hamilton's insight may be robust in unexpected ways and how current analytical approaches can expand our understanding of social evolution far beyond Hamilton's original work.

Although much previous work describes evolutionary mechanisms that promote or stabilize different social behaviors, we still have little understanding of the factors that drive animal behavior proximately. Here we present a modeling approach to answer this question. Our model rests on motivations to achieve objectives as the proximate determinants of behavior. We develop a two-tiered framework by first modeling the dynamics of a social interaction at the behavioral time scale and then find the evolutionarily stable objectives that result from the outcomes these dynamics produce. We use this framework to ask whether "other-regarding" motivations, which result from a kind of nonselfish objective, can evolve when individuals are engaged in a social interaction that entails a conflict between their material payoffs. We find that, at the evolutionarily stable state, individuals can be other-regarding in that they are motivated to increase their partners' payoff as well as their own. In contrast to previous theories, we find that such motivations can evolve because of their direct effect on fitness and do not require kin selection or a special group structure. We also derive general conditions for the evolutionary stability of other-regarding motivations. Our conditions indicate that other-regarding motivations are more likely to evolve when social interactions and behavioral objectives are both synergistic.A nimal behavior is determined both by proximate mechanisms that dictate an animal's actions in real time and by evolutionary forces that shape these proximate mechanisms. Even though the evolutionary dynamics of social behavior have been extensively studied (1-4), proximate mechanisms of behavior and how they interface with evolutionary forces remain poorly understood (4). In recent years, some models have integrated a proximate mechanism with an evolutionary analysis (5, 6). Furthermore, an explicitly two-tiered approach with potentially cooperative behavioral dynamics embedded in an evolutionary dynamic has been proposed (7) as necessary to understand the evolution of social behavior. We contribute to this literature by developing a unified framework for modeling the evolution of a specific type of behavioral interaction based on a well-defined proximate mechanism.Our proximate mechanism is based on the notion that animals are motivated to achieve certain objectives. Goal-seeking behavior has been a recurring theme in animal behavior and has been an integral part of earlier ethological thinking (e.g. 8, 9). However, this idea lost its prominence after the emergence of modern behavioral ecology, which focuses mainly on the fitness consequences of behavior (see, for example, page 6 of ref. 10). In addition, proximate models of behavior based on goal-seeking have focused mostly on nonsocial behaviors such as foraging (9) and have rarely considered social interactions. Here, we study goal-seeking behavior in the context of a social interaction by developing a model of a pair of interacting animals whose motivations ...

By consuming and producing environmental resources, organisms inevitably change their habitat. The consequences of such environmental modifications can be detrimental or beneficial not only to the focal organism but also to other organisms sharing the same environment. Social evolution theory has been very influential in studying how social interactions mediated by public goods or bads evolve by emphasising the role of spatial structure. The environmental dimensions driving these interactions, however, are typically abstracted away. Here we propose a new, environmentally-mediated taxonomy of social behaviours where organisms are categorised by their production or consumption of environmental factors that help or harm others in the environment. We discuss microbial examples of our classification and highlight the importance of environmental intermediates more generally.

How should fitness be measured to determine which phenotype or "strategy" is uninvadable when evolution occurs in a group-structured population subject to local demographic and environmental heterogeneity? Several fitness measures, such as basic reproductive number, lifetime dispersal success of a local lineage, or inclusive fitness have been proposed to address this question, but the relationships between them and their generality remains unclear. Here, we ascertain uninvadability (all mutant strategies always go extinct) in terms of the asymptotic per capita number of mutant copies produced by a mutant lineage arising as a single copy in a resident population ("invasion fitness"). We show that from invasion fitness uninvadability is equivalently characterized by at least three conceptually distinct fitness measures: (i) lineage fitness, giving the average individual fitness of a randomly sampled mutant lineage member; (ii) inclusive fitness, giving a reproductive value weighted average of the direct fitness costs and relatedness weighted indirect fitness benefits accruing to a randomly sampled mutant lineage member; and (iii) basic reproductive number (and variations thereof) giving lifetime success of a lineage in a single group, and which is an invasion fitness proxy. Our analysis connects approaches that have been deemed different, generalizes the exact version of inclusive fitness to class-structured populations, and provides a biological interpretation of natural selection on a mutant allele under arbitrary strength of selection.

We compiled three independent data sets of bird species occurrences in northeastern Colorado to test how predicted species richness compared to a combined analysis using all the data. The first data set was a georeferenced regional museum data set from two major repositories -the Denver Museum of Nature, and the Science and University of Colorado Museum. The two national survey data sets were the Breeding Bird Survey (summer), and the Great Backyard Bird Count (winter). Resulting analyses show that the museum data sets give richness estimates closest to the combined data set while exhibiting a skewed abundance distribution, whereas survey data sets do not accurately estimate overall richness even though they contain far more records. The combined data set allows the strengths of one data set to augment weaknesses in others. It is likely some museum data sets display skewed abundance distributions due to collectors' potentially self-selecting under-represented species over common ones.