Considering the Role of Adaptive Evolution in Models of the Ocean and Climate System.

Numerical models have been highly successful in simulating global carbon and nutrient cycles in today's ocean, together with observed spatial and temporal patterns of chlorophyll and plankton biomass at the surface. With this success has come some confidence in projecting the century‐scale response to continuing anthropogenic warming. There is also increasing interest in using such models to understand the role of plankton ecosystems in past oceans. However, today's marine environment is the product of billions of years of continual evolution—a process that continues today. In this paper, we address the questions of whether an assumption of species invariance is sufficient, and if not, under what circumstances current model projections might break down. To do this, we first identify the key timescales and questions asked of models. We then review how current marine ecosystem models work and what alternative approaches are available to account for evolution. We argue that for timescales of climate change overlapping with evolutionary timescales, accounting for evolution may to lead to very different projected outcomes regarding the timescales of ecosystem response and associated global biogeochemical cycling. This is particularly the case for past extinction events but may also be true in the future, depending on the eventual degree of anthropogenic disruption. The discipline of building new numerical models that incorporate evolution is also hugely beneficial in itself, as it forces us to question what we know about adaptive evolution, irrespective of its quantitative role in any specific event or environmental changes. Key Points: Marine microbes are key drivers of global biogeochemical cycles and have an immense capacity for evolutionEarth system models do not currently include microbial evolution and thus may miss key feedbacks in a fully coupled systemUnderstanding these feedbacks requires that ecology, evolution, and environment are dynamically represented and linked in models [ABSTRACT FROM AUTHOR]