Identifying functional groups of phytoplankton using data from three lakes of different trophic state

Abstract

Abstract. 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.

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

“…All analyses were performed using SPSS for Windows version 15.0. We classified the models for each classification group, and species as wellpredicted when they reached at least 30% of explained variance following Møller and Jennions (2002).…”

“…MBFG, morphology-based functional groups; Reynolds, trait-based functional groups following Reynolds; Phylo, phylogenetic groups; and the species. and reaches high explained variance when compared with general ecological models (Møller and Jennions 2002;McGill et al 2006). One explanation might be that the morphology-based functional groups actually correspond to self-organized groups of similar species, in the sense of the theory of emergent neutrality (Scheffer and Van Nes 2006).…”

“…The idea is to cluster species with common traits and similar responses to environmental changes. Such functional classifications have been proposed for phytoplankton by several authors Salmaso and Padisák 2007;Mieleitner et al 2008). A particularly influential functional classification is the one proposed by based not only on individual functional traits, but also on the ranges of environmental conditions over which the species are found to occur, as well as co-occurrence patterns.…”

Seasonal dynamics of phytoplankton functional groups and its relationship with the environment in river: a case study in northeast China

“…In contrast to phylogenetic groups, functional groups were more effective in evaluating the responses to environmental conditions and changes . Phytoplankton functional groups have been successfully applied to estuary (Costa et al 2009), lake Mieleitner et al 2008) and reservoir ecosystems (Crossetti and Bicudo 2008;Becker et al 2009;Xiao et al 2011). However, few studies have been carried out in rivers (Devercelli 2006).…”

Weekly dynamics of phytoplankton functional groups under high water level fluctuations in a subtropical reservoir-bay

“…Reynolds (1997) defined several phytoplankton functional groups that potentially, and alternately, may dominate or co-dominate in a given environment, based on Grime's (1979) seminal work on terrestrial vegetation and using physiological, morphological and ecological attributes of the phytoplankton species Kruk et al 2002). Nowadays, the phytoplankton functional groups approach uses 38 assemblages according to their sensitivities and tolerances, identified by alpha-numeric codes (Padisák et al 2009) and have been triumphantly applied in estuary, lake and reservoir ecosystems (Costa et al 2009;Mieleitner et al 2008;Becker et al 2009;Crossetti and Bicudo 2008).…”