, Volume 24, Issue 4, pp 760–769 | Cite as

Analysing chemical-induced changes in macroinvertebrate communities in aquatic mesocosm experiments: a comparison of methods

  • Eduard SzöcsEmail author
  • Paul J. Van den Brink
  • Laurent Lagadic
  • Thierry Caquet
  • Marc Roucaute
  • Arnaud Auber
  • Yannick Bayona
  • Matthias Liess
  • Peter Ebke
  • Alessio Ippolito
  • Cajo J. F. ter Braak
  • Theo C. M. Brock
  • Ralf B. Schäfer


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.


Mesocosms Principal Response Curves Generalised linear models Multivariate analysis Community-level effects 



The authors gratefully acknowledge the contributions from scientists and technicians from the INRA Experimental Unit of Aquatic Ecology and Ecotoxicology and Ecotoxicology and Quality and Aquatic Research Group during mesocosm experiments. These experiments were conducted in programmes funded by the Interface Recherche-Expertise pour l’Evaluation du Risque en Ecotoxicologie coordinated by the Structure Scientifique Mixte INRA–Direction Générale de l’Alimentation (Study no. 8), by the Ministry of Ecology, Sustainable Development and Energy through its “Pesticides” research programme (Study Nos. 3, 4), and by TOTAL S.A. (Study Nos. 10, 11). A. Auber and Y. Bayona PhDs were funded through a grant from The Region Bretagne and INRA and by TOTAL S.A., respectively. The studies conducted at Wageningen UR (Study nos. 1, 7 and 9) were financially supported by the Dutch Ministry of Economic Affairs, as well as the contribution of Paul Van den Brink and Theo Brock to this paper (Project BO-20-002-001). The data sets of these studies are available upon request.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10646_2015_1421_MOESM1_ESM.pdf (667 kb)
Supplementary material 1 (PDF 667 kb)


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Eduard Szöcs
    • 1
    Email author
  • Paul J. Van den Brink
    • 2
    • 3
  • Laurent Lagadic
    • 4
  • Thierry Caquet
    • 4
  • Marc Roucaute
    • 4
  • Arnaud Auber
    • 4
  • Yannick Bayona
    • 4
  • Matthias Liess
    • 5
  • Peter Ebke
    • 6
  • Alessio Ippolito
    • 7
  • Cajo J. F. ter Braak
    • 8
  • Theo C. M. Brock
    • 2
  • Ralf B. Schäfer
    • 1
  1. 1.Institute for Environmental SciencesUniversity Koblenz-LandauLandauGermany
  2. 2.Alterra, Wageningen University and Research CentreWageningenThe Netherlands
  3. 3.Department of Aquatic Ecology and Water Quality ManagementWageningen University, Wageningen University and Research CentreWageningenThe Netherlands
  4. 4.INRA, UMR0985 Ecologie et Santé des Ecosystèmes, Équipe Écotoxicologie et Qualité des Milieux Aquatiques, Agrocampus OuestRennes CedexFrance
  5. 5.Department System EcotoxicologyUFZ – Helmholtz Centre for Environmental ResearchLeipzigGermany
  6. 6.Mesocosm GmbHHomberg (Ohm)Germany
  7. 7.International Centre for Pesticides and Health Risk Prevention (ICPS)University Hospital Luigi SaccoMilanItaly
  8. 8.Biometris, Wageningen UniversityWageningenThe Netherlands

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