| Autor: |
Szöcs, Eduard, Brink, Paul, Lagadic, Laurent, Caquet, Thierry, Roucaute, Marc, Auber, Arnaud, Bayona, Yannick, Liess, Matthias, Ebke, Peter, Ippolito, Alessio, Braak, Cajo, Brock, Theo, Schäfer, Ralf |
| Předmět: |
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| Zdroj: |
Ecotoxicology; May2015, Vol. 24 Issue 4, p760-769, 10p |
| Abstrakt: |
Mesocosm experiments that study the ecological impact of chemicals are often analysed using the multivariate method 'Principal Response Curves' (PRCs). Recently, the extension of generalised linear models (GLMs) to multivariate data was introduced as a tool to analyse community data in ecology. Moreover, data aggregation techniques that can be analysed with univariate statistics have been proposed. The aim of this study was to compare their performance. We compiled macroinvertebrate abundance datasets of mesocosm experiments designed for studying the effect of various organic chemicals, mainly pesticides, and re-analysed them. GLMs for multivariate data and selected aggregated endpoints were compared to PRCs regarding their performance and potential to identify affected taxa. In addition, we analysed the inter-replicate variability encountered in the studies. Mesocosm experiments characterised by a higher taxa richness of the community and/or lower taxonomic resolution showed a greater inter-replicate variability, whereas variability decreased the more zero counts were encountered in the samples. GLMs for multivariate data performed equally well as PRCs regarding the community response. However, compared to first axis PRCs, GLMs provided a better indication of individual taxa responding to treatments, as separate models are fitted to each taxon. Data aggregation methods performed considerably poorer compared to PRCs. Multivariate community data, which are generated during mesocosm experiments, should be analysed using multivariate methods to reveal treatment-related community-level responses. GLMs for multivariate data are an alternative to the widely used PRCs. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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