Traditional ecological models ignore evolution: phenotypic traits are treated as fixed attributes of species. Conversely, traditional evolutionary models ignore ecology: key parameters such as selection coefficients and population sizes are treated as fixed constants rather than as emergent dynamic outcomes of the interactions between species and their environment. The interplay of ecology and evolution occurs at all spatial and temporal scales. Evolutionary models can be roughly classified into models of microevolution, and models of macroevolution (speciation and extinction). Similarly, ecological models can be roughly divided into ‘microscale’ models that focus on one or a few species at a single site, and those that focus on more species at larger spatial scales. This symposium considers the interplay of ecology and evolution at both ‘micro’ and ‘macro’ scales, and an important question is how to relate micro- and macroscale processes and patterns. Considering ecology and evolution together necessarily demands more complex models than considering either alone, creating a need for well-motivated ways to limit model complexity to a manageable level. All of the speakers address this issue by using data from specific real-world systems to guide theory development and testing, thereby allowing the data to dictate appropriate simplifications.

Results/Conclusions

I highlight four themes that emerge from the symposium. First, ecology and evolution are part of the same dynamical system, and it is frequently misleading to treat either as an exogenous factor. This makes development of tractable models a challenge; assuming a separation of timescales between fast ecology and slow evolution (or slow ecology and fast evolution) is sometimes a useful approximation, but sometimes not. Second, describing genetic details (the distribution of standing variation, the sources of new variants, and the genotype-phenotype map) in a realistic yet tractable fashion is challenging, but the challenge can be met in well-studied model systems. Getting genetic details right is frequently essential. Eco-evolutionary modelers should explore the robustness of their conclusions to alternative assumptions about genetic details. Explaining why simple genetic models (e.g., quantitative genetics) sometimes work despite making unrealistic assumptions is an open question. Third, drift and other sources of stochasticity deserve more consideration in the context of eco-evolutionary models. Stochasticity can qualitatively alter predictions derived from deterministic nonlinear models. Fourth, macroscale dynamics emerge from microscale processes, and so cannot oversimplify these processes. Macroscale models of model systems with well-understood microscale processes are a promising direction for future work.