Morphology captures function in phytoplankton : a large-scale analysis of phytoplankton communities in relation to their environment

Abstract

Predicting phytoplankton community dynamics in detail seems an overwhelming task as there are so many species, and a myriad of combinations of potential conditioning factors. Furthermore, even with full knowledge of all aspects of species biology intrinsic chaos in communities may make detailed prediction fundamentally impossible. Aggregated estimators of phytoplankton communities may work to predict overall community responses to varying environmental conditions. However, phytoplankton species differ strongly in their effect on ecosystem functioning and ecosystem services. Therefore, it is important to consider community composition rather than just biomass. This thesis focuses on the question whether species might be clustered in groups that are reasonably homogeneous in a functional sense, and might be better predictable from environmental conditions than individual species. To answer this question we first explored the factors that affect richness and biomass at the species level and then evaluated how well trait-based groups of species capture function and may be predicted from environmental conditions. We used a large data base including more than 700 species from 200 lakes in different climate zones and continents