Adaptive Evolution of Resource Use, Phenotypic Diversity, and Productivity of Phytoplankton Communities

Sammanfattning: There is growing concern that the worldwide loss in biodiversity will harm the stability of the ecosystems, and thereby, the carrying capacity and critical ecosystem services the biosphere provides. Phytoplankton (microalgae) in lakes and oceans are arguably the most important primary producers. They are responsible for approximately half of the earth's primary production. However, there is little research into what influences the biodiversity of phytoplankton communities and of those studies the mechanisms for coexistence of phytoplankton have so far almost exclusively been studied on ecological time scales. We, therefore, explored how biodiversity and biomass (a proxy to primary production) of phytoplankton communities respond to co-varied environmental drivers over evolutionary time scales. We model adaptive evolution of phytoplankton' resource use, with a non-dimensionalized model of negatively buoyant phytoplankton inhabiting a partially mixed one-dimensional water column using reaction-advection-diffusion equations. We show that a number of environmental drivers have novel effects on biodiversity and biomass on evolutionary timescales. In contrast with previous non-evolutionary work, we found that decreasing light attenuation or increasing resource use efficiency can result in decreased biomass of plankton communities and nutrient-poor environments. One novel driver of species diversity was the combination of low rates of diffusion with relatively intermediate rates of sinking promote species diversity. Furthermore, we show that the phytoplankton turnover rate affects environmental heterogeneity and is, therefore, a contributing driver to species diversity.The evolution of half saturation constants can produce a variety of biodiversity-ecosystem function patterns as two positive, one unimodal, and one negative association were found when comparing biodiversity-ecosystem function. Collectively, our analyses suggest that environmental drivers can have substantially different effects over evolutionary timescales than those effects ecological modeling has previously shown.