Analysing chemical-induced changes in macroinvertebrate communities in aquatic mesocosm experiments: a comparison of methods
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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.
KeywordsMesocosms 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.
- Anderson MJ, Crist TO, Chase JM, Vellend M, Inouye BD, Freestone AL, Sanders NJ, Cornell HV, Comita LS, Davies KF, Harrison SP, Kraft NJB, Stegen JC, Swenson NG (2011) Navigating the multiple meanings of beta diversity: a roadmap for the practicing ecologist. Ecol Lett 14:19–28CrossRefGoogle Scholar
- Bates D, Maechler M, Bolker B, Walker S (2014) lme4: linear mixed-effects models using Eigen and S4. R package version 1.1-0. https://github.com/lme4/lme4/
- Bayona Y, Roucaute M, Cailleaud K, Lagadic L, Bassères A, Caquet T (2014) Effect of thiram and a petroleum distillate on freshwater macroinvertebrate communities in outdoor stream and pond mesocosms: I Study design, chemical fate and structural responses. Ecotoxicology (submitted)Google Scholar
- Brock TCM, Arts GHP, ten Hulscher TEM, de Jong FMV, Luttik R, Roex EWM, Smit CE, van Vliet PJM (2011) Aquatic effect assessment for plant protection products; Dutch proposal that addresses the requirements of the Plant Protection Product Regulation and Water Framework Directive. Alterra Report 2235, Alterra, WageningenGoogle Scholar
- De Jong FMW, Brock TCM, Foekema EM, Leeuwangh P (2008) Guidance for summarizing and evaluating aquatic micro- and mesocosm studies. RIVM Report 601506009/2008, RIVMGoogle Scholar
- EFSA PPR (2013) Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters. EFSA Panel on Plant Protection Products and their Residues (PPR). Parma, Italy. EFSA J 11(7):3290Google Scholar
- Giddings JM, Brock TCM, Heger W, Heimbach F, Maund SJ, Norman S, Ratte H-T, Schäfers C, Streloke M (eds) (2002) Community-level aquatic system studies—interpretation criteria (CLASSIC). SETAC, PensacolaGoogle Scholar
- Legendre P, Legendre L (2012) Numerical ecology. Elsevier, AmsterdamGoogle Scholar
- Newman MC, Clements WH (2008) Ecotoxicology : a comprehensive treatment. CRC Press, Boca RatonGoogle Scholar
- OECD (2006a) Guidance document on simulated freshwater lentic field tests (outdoor microcosms and mesocosms). No. 53 in Series on testing and assessment. OECD, ParisGoogle Scholar
- OECD (2006b) current approaches in the statistical analysis of ecotoxicity data: a guidance to application. No. 54 in Series on testing and assessment. OECD, ParisGoogle Scholar
- Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2013) Vegan: Community Ecology Package. R package version 2.1-41. http://vegan.r-forge.r-project.org/
- Poisot TE, Mounce R, Gravel D (2013) Moving toward a sustainable ecological science: don’t let data go to waste! Ideas Ecol Evol 6:11–19Google Scholar
- Simpson GL (2013) Permute: functions for generating restricted permutations of data. R package version 0.8-0. http://CRAN.R-project.org/package=permute
- R Core Team (2013) R: a language and environment for statistical computing. Vienna, Austria. http://www.R-project.org/