Interpreting selection when individuals interact

The long-lasting legacy of reproduction: lifetime reproductive success shapes expected genetic contributions of humans after 10 generations

An individual's lifetime reproductive success (LRS) measures its realized genetic contributions to the next generation, but how well does it predict this over longer periods? Here we use human genealogical data to estimate expected individual genetic contributions (IGC) and quantify the degree to which LRS, relative to other fitness proxies, predicts IGC over longer periods. This allows an identification of the life-history stages that are most important in shaping variation in IGC. We use historical genealogical data from two non-isolated local populations in Switzerland to estimate the stabilized IGC for 2230 individuals approximately 10 generations after they were born. We find that LRS explains 30% less variation in IGC than the best predictor of IGC, the number of grandoffspring. However, albeit less precise than the number of grandoffspring, we show that LRS does provide an unbiased prediction of IGC. Furthermore, it predicts IGC better than lifespan, and accounting for offspring survival to adulthood does not improve the explanatory power. Overall, our findings demonstrate the value of human genealogical data to evolutionary biology and suggest that reproduction-more than lifespan or offspring survival-impacts the long-term genetic contributions of historic humans, even in a population with appreciable migration.

The importance of distinguishing individual differences in 'social impact' versus 'social responsiveness' when quantifying indirect genetic effects on the evolution of social plasticity

Social evolution and the dynamics of social interactions have previously been studied under the frameworks of quantitative genetics and behavioural ecology. In quantitative genetics, indirect genetic effects of social partners on the socially plastic phenotypes of focal individuals typically lack crucial detail already included in treatments of social plasticity in behavioural ecology. Specifically, whilst focal individuals (e.g. receivers) may show variation in their 'responsiveness' to the social environment, individual social partners (e.g. signallers) may have a differential 'impact' on focal phenotypes. Here we propose an integrative framework, that highlights the distinction between responsiveness versus impact in indirect genetic effects for a range of behavioural traits. We describe impact and responsiveness using a reaction norm approach and provide statistical models for the assessment of these effects of focal and social partner identity in different types of social interactions. By providing such a framework, we hope to stimulate future quantitative research investigating the causes and consequences of social interactions on phenotypic evolution.

The social evolution of individual differences: Future directions for a comparative science of personality in social behavior

Personality is essential for understanding the evolution of cooperation and conflict in behavior. However, personality science remains disconnected from the field of social evolution, limiting our ability to explain how personality and plasticity shape phenotypic adaptation in social behavior. Researchers also lack an integrative framework for comparing personality in the contextualized and multifaceted behaviors central to social interactions among humans and other animals. Here we address these challenges by developing a social evolutionary approach to personality, synthesizing theory, methods, and organizing questions in the study of individuality and sociality in behavior. We critically review current measurement practices and introduce social reaction norm models for comparative research on the evolution of personality in social environments. These models demonstrate that social plasticity affects the heritable variance of personality, and that individual differences in social plasticity can further modify the rate and direction of adaptive social evolution. Future empirical studies of frequency- and density-dependent social selection on personality are crucial for further developing this framework and testing adaptive theory of social niche specialization.

Social animal models for quantifying plasticity, assortment, and selection on interacting phenotypes

Both assortment and plasticity can facilitate social evolution, as each may generate heritable associations between the phenotypes and fitness of individuals and their social partners. However, it currently remains difficult to empirically disentangle these distinct mechanisms in the wild, particularly for complex and environmentally responsive phenotypes subject to measurement error. To address this challenge, we extend the widely used animal model to facilitate unbiased estimation of plasticity, assortment and selection on social traits, for both phenotypic and quantitative genetic (QG) analysis. Our social animal models (SAMs) estimate key evolutionary parameters for the latent reaction norms underlying repeatable patterns of phenotypic interaction across social environments. As a consequence of this approach, SAMs avoid inferential biases caused by various forms of measurement error in the raw phenotypic associations between social partners. We conducted a simulation study to demonstrate the application of SAMs and investigate their performance for both phenotypic and QG analyses. With sufficient repeated measurements, we found desirably high power, low bias and low uncertainty across model parameters using modest sample and effect sizes, leading to robust predictions of selection and adaptation. Our results suggest that SAMs will readily enhance social evolutionary research on a variety of phenotypes in the wild. We provide detailed coding tutorials and worked examples for implementing SAMs in the Stan statistical programming language.

A Synthesis of Game Theory and Quantitative Genetic Models of Social Evolution

Two popular approaches for modeling social evolution, evolutionary game theory and quantitative genetics, ask complementary questions but are rarely integrated. Game theory focuses on evolutionary outcomes, with models solving for evolutionarily stable equilibria, whereas quantitative genetics provides insight into evolutionary processes, with models predicting short-term responses to selection. Here we draw parallels between evolutionary game theory and interacting phenotypes theory, which is a quantitative genetic framework for understanding social evolution. First, we show how any evolutionary game may be translated into two quantitative genetic selection gradients, nonsocial and social selection, which may be used to predict evolutionary change from a single round of the game. We show that synergistic fitness effects may alter predicted selection gradients, causing changes in magnitude and sign as the population mean evolves. Second, we show how evolutionary games involving plastic behavioral responses to partners can be modeled using indirect genetic effects, which describe how trait expression changes in response to genes in the social environment. We demonstrate that repeated social interactions in models of reciprocity generate indirect effects and conversely, that estimates of parameters from indirect genetic effect models may be used to predict the evolution of reciprocity. We argue that a pluralistic view incorporating both theoretical approaches will benefit empiricists and theorists studying social evolution. We advocate the measurement of social selection and indirect genetic effects in natural populations to test the predictions from game theory and, in turn, the use of game theory models to aid in the interpretation of quantitative genetic estimates.

Social Selection and the Evolution of Maladaptation

Evolution by natural selection is often viewed as a process that inevitably leads to adaptation or an increase in population fitness over time. However, maladaptation, an evolved decrease in fitness, may also occur in response to natural selection under some conditions. Social selection, which arises from the effects of social partners on fitness, has been identified as a potential cause of maladaptation, but we lack a general rule identifying when social selection should lead to a decrease in population mean fitness. Here we use a quantitative genetic model to develop such a rule. We show that maladaptation is most likely to occur when social selection is strong relative to nonsocial selection and acts in an opposing direction. In this scenario, the evolution of traits that impose fitness costs on others may outweigh evolved gains in fitness for the individual, leading to a net decrease in population mean fitness. Furthermore, we find that maladaptation may also sometimes occur when phenotypes of interacting individuals negatively covary. We outline the biological situations where maladaptation in response to social selection can be expected, provide both quantitative genetic and phenotypic versions of our derived result, and suggest what empirical work would be needed to test it. We also consider the effect of social selection on inclusive fitness and support previous work showing that inclusive fitness cannot suffer an evolutionary decrease. Taken together, our results show that social selection may decrease population mean fitness when it opposes individual-level selection, even as inclusive fitness increases.

Evolutionary demographic models reveal the strength of purifying selection on susceptibility alleles to late-onset diseases

Assessing the role played by purifying selection on a susceptibility allele to late-onset disease (SALOD) is crucial to understanding the puzzling allelic spectrum of a disease, because most alleles are recent and rare. This fact is surprising because it suggests that alleles are under purifying selection while those that are involved in post-menopause mortality are often considered neutral in the genetic literature. The aim of this article is to use an evolutionary demography model to assess the magnitude of selection on SALODs while accounting for epidemiological and sociocultural factors. We develop an age-structured population model allowing for the calculation of SALOD selection coefficients (1) for a large and realistic parameter space for disease onset, (2) in a two-sex model in which men can reproduce in old age and (3) for situations in which child survival depends on maternal, paternal and grandmaternal care. The results show that SALODs are under purifying selection for most known age-at-onset distributions of late-onset genetic diseases. Estimates regarding various genes involved in susceptibility to cancer or Huntington's disease demonstrate that negative selection largely overcomes the effects of drift in most human populations. This is also probably true for neurodegenerative or polycystic kidney diseases, although sociocultural factors modulate the effect of selection in these cases. We conclude that neutrality is probably the exception among alleles that have a deleterious effect in old age and that accounting for sociocultural factors is required to understand the full extent of the force of selection shaping senescence in humans.

Green beards in the light of indirect genetic effects

The green-beard effect is one proposed mechanism predicted to underpin the evolution of altruistic behavior. It relies on the recognition and the selective help of altruists to each other in order to promote and sustain altruistic behavior. However, this mechanism has often been dismissed as unlikely or uncommon, as it is assumed that both the signaling trait and altruistic trait need to be encoded by the same gene or through tightly linked genes. Here, we use models of indirect genetic effects (IGEs) to find the minimum correlation between the signaling and altruistic trait required for the evolution of the latter. We show that this correlation threshold depends on the strength of the interaction (influence of the green beard on the expression of the altruistic trait), as well as the costs and benefits of the altruistic behavior. We further show that this correlation does not necessarily have to be high and support our analytical results by simulations.

Opposite responses to selection and where to find them

We generally expect traits to evolve in the same direction as selection. However, many organisms possess traits that appear to be costly for individuals, while plant and animal breeding experiments reveal that selection may lead to no response or even negative responses to selection. We formalize both of these instances as cases of "opposite responses to selection." Using quantitative genetic models for the response to selection, we outline when opposite responses to selection should be expected. These typically occur when social selection opposes direct selection, when individuals interact with others less related to them than a random member of the population, and if the genetic covariance between direct and indirect effects is negative. We discuss the likelihood of each of these occurring in nature and therefore summarize how frequent opposite responses to selection are likely to be. This links several evolutionary phenomena within a single framework.

A guide to using a multiple-matrix animal model to disentangle genetic and nongenetic causes of phenotypic variance

Non-genetic influences on phenotypic traits can affect our interpretation of genetic variance and the evolutionary potential of populations to respond to selection, with consequences for our ability to predict the outcomes of selection. Long-term population surveys and experiments have shown that quantitative genetic estimates are influenced by nongenetic effects, including shared environmental effects, epigenetic effects, and social interactions. Recent developments to the "animal model" of quantitative genetics can now allow us to calculate precise individual-based measures of non-genetic phenotypic variance. These models can be applied to a much broader range of contexts and data types than used previously, with the potential to greatly expand our understanding of nongenetic effects on evolutionary potential. Here, we provide the first practical guide for researchers interested in distinguishing between genetic and nongenetic causes of phenotypic variation in the animal model. The methods use matrices describing individual similarity in nongenetic effects, analogous to the additive genetic relatedness matrix. In a simulation of various phenotypic traits, accounting for environmental, epigenetic, or cultural resemblance between individuals reduced estimates of additive genetic variance, changing the interpretation of evolutionary potential. These variances were estimable for both direct and parental nongenetic variances. Our tutorial outlines an easy way to account for these effects in both wild and experimental populations. These models have the potential to add to our understanding of the effects of genetic and nongenetic effects on evolutionary potential. This should be of interest both to those studying heritability, and those who wish to understand nongenetic variance.