A framework for high‐throughput eco‐evolutionary simulations integrating multilocus forward‐time population genetics and community ecology

Abstract

Abstract The evolutionary dynamics of quantitative traits in a species depends on demographic structure (e.g. age‐ and size‐dependent vital rates, migration across subpopulations, mate preferences, and the presence or absence of overlapping generations), which in turn can depend on interspecific interactions with other evolving species. Furthermore, biologists increasingly appreciate that evolutionary change in a single species can substantively affect broader ecological processes, such as community assembly and ecosystem functioning. Models integrating insights from classical population and quantitative genetics, on the one hand, and community ecology theory, on the other, are therefore critical to elucidating the interplay between ecological and evolutionary processes. However, few modelling approaches integrate ecological and genetic details into a comprehensive framework. Such models are needed to account for the reciprocal feedback between evolutionary change and ecological dynamics, and develop effective responses to anthropogenic disturbances on natural communities, improve agricultural practices and manage global health risks such as emerging pathogens. Here we introduce an open‐source, multi‐species forward‐time population genetics simulator and framework for rapidly simulating eco‐evolutionary dynamics. Our approach permits building models that can account for alternative genetic architectures, non‐standard population dynamics and demographic structures, including those driven by interspecific interactions with other evolving species and the spatial dynamics of metacommunities. By integrating these processes into a common framework, we aim to facilitate the development of models that can further elucidate eco‐evolutionary dynamics. While multi‐species, forward‐time population genetic models can be computationally expensive, we show how our framework leverages relatively economical graphics cards installed in modern desktops. We illustrate the versatility and general applicability of our approach for two very different case studies: antagonistic coevolution in space, and the evolution of life‐history traits in response to resource dynamics in physiologically structured populations. We find that our analyses can run up to c. 200 times faster on a single commodity graphics card than on a single CPU core, comparable to the performance gains on small‐to‐medium sized computer clusters. Our approach therefore substantively reduces implementation difficulties to integrating ecological and evolutionary theory.