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The importance of metacommunity ecology for environmental assessment research in the freshwater realm

Authors

  • Jani Heino

    Corresponding author
    1. Department of Biology, University of Oulu, PO Box 3000, FI-90014 Oulu, Finland
    • Finnish Environment Institute, Natural Environment Centre, Ecosystem Change Unit, PO Box 413, FI-90014 Oulu, Finland
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(E-mail: jani.heino@environment.fi).

Abstract

Most bioassessment programs rest on the assumption that species have different niches, and that abiotic environmental conditions and changes therein determine community structure. This assumption is thus equivalent to the species sorting perspective (i.e. that species differ in their responses to environmental variation) in metacommunity ecology. The degree to which basing bioassessment on the species sorting perspective is reasonable is likely to be related to the spatial extent of a study and the characteristics of the organism groups (e.g. dispersal ability) with which the effects of anthropogenic changes are assessed. Recent findings in metacommunity research have stressed that community structure is determined not only by local abiotic environmental conditions but also by biotic interactions and dispersal-related effects. For example, dispersal limitation may prevent community structure recovery from the effects of a putative stressor, as organisms may not be able to disperse to all sites in a region. Mass effects (i.e. the presence of species in environmentally suboptimal sites due to high dispersal rates from environmentally suitable sites) may, in turn, obscure the effects of a stressor, as dispersal from source sites (e.g. an unaltered site) allows persistence at sink sites (e.g. an anthropogenically altered site). Better bioassessment should thus take both niche- and dispersal-related processes simultaneously into consideration, which can be accomplished by explicitly modelling spatial location as a proxy for dispersal effects. Such an integrated approach should be included in bioassessment programs using general multivariate approaches, predictive modelling, and multimetric indices.

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