Functional classifications and their application in phytoplankton ecology

• Published in: Freshwater biology 2015-04, Vol.60 (4), p.603-619
• Main Authors: Salmaso, Nico, Naselli-Flores, Luigi, Padisák, Judit
• Format: Article
• Language: English
• Subjects: Algae; ecological indicators; ecological redundancy; Ecology; functional classifications; phytoplankton; Plankton; taxonomical classifications
• ISSN: 0046-5070; 1365-2427
• DOI: 10.1111/fwb.12520

Summary Ecologists often group organisms based on similar biological traits or on taxonomic criteria. However, the use of taxonomy in ecology has many drawbacks because taxa may include species with very different ecological adaptations. Further, similar characters may evolve independently in different lineages. In this review, we examine the main criteria that have been used in the identification of nine modes of classifying phytoplankton non‐taxonomically. These approaches are based purely on morphological and/or structural traits, or on more complex combinations including physiological and ecological features. Different functional approaches have proved able to explain some fraction of the variance observed in the spatial and temporal distribution patterns of algal assemblages, although their effectiveness varies greatly, depending on the number and characteristics of functional traits used. The attribution of functional traits to single species or broad groups of species has allowed a few classifications (e.g. Functional Groups, FG) to be used in the assessment of ecological status. We stress that the misuse of functional classifications (by applying them under conditions other than those intended) can have serious consequences for interpreting ecological processes. Assigning functional traits or groups cannot be considered a surrogate for the knowledge of species or ecotypes, and the use of specific traits must always be justified and circumscribed within the limits of ecological questions and hypotheses. An important future challenge will be to integrate advances in molecular genetics, metabolomics and physiology with more conventional traits; this will form the basis of the next generation of functional classifications.