There is tremendous diversity in species of phytoplankton. Yet one may expect some degree of commonality in the response of similar species to similar conditions. Functional groups are those sets of species that respond similarly to environmental conditions because they have similar properties. The identification of such functional groups can assist model-based prediction of the abundance of phytoplankton as a function of time, space, and environmental conditions. Functional groups can also assist limnologists in the analysis and presentation of field data. We identified functional groups of phytoplankton using a combination of prior knowledge (based on taxonomic divisions and measurable properties) and statistical cluster analysis of long-term, species-level data from three Swiss lakes of different trophic state. For this task, we used the taxonomic division as the basic unit of analysis. Each taxonomic group was subdivided into several further groups by analysing the occurrence pattern of each species of the group and grouping together species with similar patterns. The reasons for the occurrence pattern for each species within a group were then analysed based on the main properties of the species. The results of this analysis were used to merge groups that had similar occurrence for similar reasons across taxonomic boundaries. Groups with different occurrence patterns but similar properties were also merged. This led to suggestions for functional groups at multiple levels of aggregation. The resulting groups were used in a subsequent study for modelling phytoplankton in the three lakes used for this analysis. The general methodology of combining prior knowledge on properties with empirical evidence on occurrence should be useful for finding functional groups of phytoplankton in other lakes as well. Comparisons of studies across lakes can then contribute to the identification of universal functional groups of phytoplankton applicable to a broad class of waters. © 2008 Eawag, Eidgenössische Anstalt für Wasserversorgung, Abwasserreinigung und Gewässerschutz.
Identifying functional groups of phytoplankton using data from three lakes of different trophic state