Pathways to social evolution and their evolutionary feedbacks

Abstract

In the context of social evolution, the ecological drivers of selection are the phenotypes of other individuals. The social environment can thus evolve, potentially changing the adaptive value for different social strategies. Different branches of evolutionary biology have traditionally focused on different aspects of these feedbacks. Here, we synthesize behavioral ecology theory concerning evolutionarily stable strategies when fitness is frequency dependent with quantitative genetic models providing statistical descriptions of evolutionary responses to social selection. Using path analyses, we review how social interactions influence the strength of selection and how social responsiveness, social impact, and non‐random social assortment affect responses to social selection. We then detail how the frequency‐dependent nature of social interactions fits into this framework and how it imposes selection on traits mediating social responsiveness, social impact, and social assortment, further affecting evolutionary dynamics. Throughout, we discuss the parameters in quantitative genetics models of social evolution from a behavioral ecology perspective and identify their statistical counterparts in empirical studies. This integration of behavioral ecology and quantitative genetic perspectives should lead to greater clarity in the generation of hypotheses and more focused empirical research regarding evolutionary pathways and feedbacks inherent in specific social interactions.

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

“…This equivalence may be accurate in some cases, but there are several scenarios in which the two diverge (McGlothlin et al 2014;Hadfield and Thomson 2017). As our results show, one such case arises when strong nonlinear fitness effects exist (see also Westneat 2012;Araya-Ajoy et al 2020). We urge empiricists to exercise caution when mapping selection gradients to cost and benefit terms from evolutionary games, particularly when the correlational selection gradient γ N S is nonzero.…”

“…Throughout, we assume that individuals interact in dyads, but our results can easily be extended to larger groups. Our treatment builds upon previous work by Taylor (1996), Taylor and Frank (1996), Queller (2011), Van Cleve (2017), Araya-Ajoy et al (2020), and others, but we provide additional insights into the estimation of parameters in wild populations.…”

“…Previous interacting phenotype models have obscured the contribution of nonlinear fitness effects, which has been cited as one of their primary limitations (Westneat 2012; Araya-Ajoy et al 2020). As noted by Araya-Ajoy et al (2020), one of the most important consequences of nonlinear fitness effects is that they can lead to evolutionary feedback. In other words, because the strength of selection may change with the trait mean in the presence of synergy, the rate of evolution of a social phenotype may potentially increase each generation.…”

“…An alternative approach (Queller 1985, 2011; Westneat 2012; Araya-Ajoy et al 2020) partitions selection to include a separate gradient ( β I ) that estimates the interactive (i.e., multiplicative) effect of focal and social traits ( zz ′). In this partitioning, the three selection gradients take on expectations that are independent of the mean, with , , and .…”

Estimating Nonlinear Selection on Behavioral Reaction Norms

“…season, population density, resource abundance) and individuals' RN parameters (Wright et al 2019). Similar considerations apply to social contexts addressed by evolutionary game theory, in which frequency-dependent fitness functions, such as cooperative strategies with diminishing returns or threshold effects as a function of partners' strategies (McNamara and Leimar 2020), will be observed through interactive selection effects (Araya-Ajoy, Westneat, and Wright 2020;Martin and Jaeggi 2021;Queller 2011). When adaptive individual variation is maintained through state-dependent calibration or feedback processes (e.g.…”