Phytoplankton community composition can be predicted best in terms of morphological groups

We explored how well the aggregated biovolume of groups of species can be predicted from environmental variables using three different classification approaches: morphology-based functional groups, phylogenetic groups, and functional groups proposed by Reynolds. We assessed the relationships between biovolume of each group and environmental conditions using canonical correlation analyses as well as multiple linear regressions, using data from 211 lakes worldwide ranging from subpolar to tropical regions. We compared the results of these analyses with those obtained for single species following the same protocol. While some species appear relatively predictable, a vast majority of the species showed no clear relationship to the environmental conditions we had measured. However, both the multivariate and the regression analyses indicated that morphology-based groups can be predicted better from environmental conditions than groups based on the other classification methods. This suggests that morphology captures ecological function of phytoplankton well, and that functional groups based on morphology may be the most suitable focus for predicting the composition of communitie