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Predation plays a role in preventing the evolution of ever more complicated sexual displays, because such displays often increase an individual’s predation risk. Sexual selection theory, however, omits a key feature of predation in modeling costs to sexually selected traits: Predation is density dependent. As a result of this density dependence, predator–prey dynamics should feed back into the evolution of sexual displays, which, in turn, feeds back into predator–prey dynamics. Here, we develop both population and quantitative genetic models of sexual selection that explicitly link the evolution of sexual displays with predator–prey dynamics. Our primary result is that predation can drive eco-evolutionary cycles in sexually selected traits. We also show that mechanistically modeling the cost to sexual displays as predation leads to novel outcomes such as the maintenance of polymorphism in sexual displays and alters ecological dynamics by muting prey cycles. These results suggest predation as a potential mechanism to maintain variation in sexual displays and underscore that short-term studies of sexual display evolution may not accurately predict long-run dynamics. Further, they demonstrate that a common verbal model (that predation limits sexual displays) with widespread empirical support can result in unappreciated, complex dynamics due to the density-dependent nature of predation. 

Abstract Sexual selection has a rich history of mathematical models that consider why preferences favor one trait phenotype over another (for population genetic models) or what specific trait value is preferred (for quantitative genetic models). Less common is exploration of the evolution of choosiness or preference strength: i.e., by how much a trait is preferred. We examine both population and quantitative genetic models of the evolution of preferences, specifically developing “baseline models” of the evolution of preference strength during the Fisher process. Using a population genetic approach, we find selection for stronger and stronger preferences when trait variation is maintained by mutation. However, this force is quite weak and likely to be swamped by drift in moderately-sized populations. In a quantitative genetic model, unimodal preferences will generally not evolve to be increasingly strong without bounds when male traits are under stabilizing viability selection, but evolve to extreme values when viability selection is directional. Our results highlight that different shapes of fitness and preference functions lead to qualitatively different trajectories for preference strength evolution ranging from no evolution to extreme evolution of preference strength. 

Abstract Despite the well known scale‐dependency of ecological interactions, relatively little attention has been paid to understanding the dynamic interplay between various spatial scales. This is especially notable in metacommunity theory, where births and deaths dominate dynamics within patches (the local scale), and dispersal and environmental stochasticity dominate dynamics between patches (the regional scale). By considering the interplay of local and regional scales in metacommunities, the fundamental processes of community ecology—selection, drift, and dispersal—can be unified into a single theoretical framework. Here, we analyze three related spatial models that build on the classic two‐species Lotka–Volterra competition model. Two open‐system models focus on a single patch coupled to a larger fixed landscape by dispersal. The first is deterministic, while the second adds demographic stochasticity to allow ecological drift. Finally, the third model is a true metacommunity model with dispersal between a large number of local patches, which allows feedback between local and regional scales and captures the well studied metacommunity paradigms as special cases. Unlike previous simulation models, our metacommunity model allows the numerical calculation of equilibria and invasion criteria to precisely determine the outcome of competition at the regional scale. We show that both dispersal and stochasticity can lead to regional outcomes that are different than predicted by the classic Lotka–Volterra competition model. Regional exclusion can occur when the nonspatial model predicts coexistence or founder control, due to ecological drift or asymmetric stochastic switching between basins of attraction, respectively. Regional coexistence can result from local coexistence mechanisms or through competition‐colonization or successional‐niche trade‐offs. Larger dispersal rates are typically competitively advantageous, except in the case of local founder control, which can favor intermediate dispersal rates. Broadly, our models demonstrate the importance of feedback between local and regional scales in competitive metacommunities and provide a unifying framework for understanding how selection, drift, and dispersal jointly shape ecological communities.