Ecotoxicology

, 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 Szöcs
  • 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
Article

Abstract

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.

Keywords

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

Supplementary material

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

References

  1. Anderson MJ (2006) Distance based tests for homogeneity of multivariate dispersions. Biometrics 62:245–253CrossRefGoogle Scholar
  2. 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
  3. Auber A, Roucaute M, Togola A, Caquet T (2011) Structural and functional effects of conventional and low pesticide input crop-protection programs on benthic macroinvertebrate communities in outdoor pond mesocosms. Ecotoxicology 20:2042–2055CrossRefGoogle Scholar
  4. 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/
  5. 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
  6. Beketov MA, Kattwinkel M, Liess M (2013) Statistics matter: data aggregation improves identification of community-level effects compared to a commonly used multivariate method. Ecotoxicology 22:1–10CrossRefGoogle Scholar
  7. Bolker B, Brooks M, Clark C, Geange S, Poulsen J, Stevens M, White J (2009) Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 24:127–135CrossRefGoogle Scholar
  8. 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
  9. Brock TCM, Hammers-Wirtz M, Hommen U et al (2014) The minimum detectable difference (MDD) and the interpretation of treatment-related effects of pesticides in experimental ecosystems. Environ Sci Pollut Res 22(2):1160–1174. doi:10.1007/s11356-014-3398-2 CrossRefGoogle Scholar
  10. Cañedo-Argüelles M, Bundschuh M, Gutiérrez-Cánovas C, Kefford BJ, Prat N, Trobajo R, Schäfer RB (2014) Effects of repeated salt pulses on ecosystem structure and functions in a stream mesocosm. Sci Total Environ 476–477:634–642CrossRefGoogle Scholar
  11. Caquet T, Hanson M, Roucaute M, Graham D, Lagadic L (2007) Influence of isolation on the recovery of pond mesocosms from the application of an insecticide. II Benthic macroinvertebrate responses. Environ Toxicol Chem 26:1280–1290CrossRefGoogle Scholar
  12. Cuppen JGM, Van den Brink PJ, Camps E, Uil KF, Brock TCM (2000) Impact of the fungicide carbendazim in freshwater microcosms. I. Water quality, breakdown of particulate organic matter and responses of macroinvertebrates. Aquat Toxicol 48:233–250CrossRefGoogle Scholar
  13. Daam MA, Van den Brink PJ, Nogueira AJA (2008) Impact of single and repeated applications of the insecticide chlorpyrifos on tropical freshwater plankton communities. Ecotoxicology 17:756–771CrossRefGoogle Scholar
  14. 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
  15. 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
  16. Ellison AM, Gotelli NJ, Inouye BD, Strong DR (2014) P values, hypothesis testing, and model selection: it’s déjà vu all over again. Ecology 95:609–610CrossRefGoogle Scholar
  17. 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
  18. Ippolito A, Carolli M, Varolo E, Villa S, Vighi M (2012) Evaluating pesticide effects on freshwater invertebrate communities in alpine environment: a model ecosystem experiment. Ecotoxicology 21:2051–2067CrossRefGoogle Scholar
  19. Knauer K, Maise S, Thoma G, Hommen U, Gonzalez-Valero J (2005) Long-term variability of zooplankton populations in aquatic mesocosms. Environ Toxicol Chem 24:1182–1189CrossRefGoogle Scholar
  20. Legendre P, Anderson MJ (1999) Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol Monogr 69:1–24CrossRefGoogle Scholar
  21. Legendre P, Gallagher ED (2001) Ecologically meaningful transformations for ordination of species data. Oecologia 129:271–280CrossRefGoogle Scholar
  22. Legendre P, Legendre L (2012) Numerical ecology. Elsevier, AmsterdamGoogle Scholar
  23. Legendre P, Oksanen J, ter Braak CJF (2011) Testing the significance of canonical axes in redundancy analysis. Methods Ecol Evol 2:269–277CrossRefGoogle Scholar
  24. Liess M, Beketov M (2011) Traits and stress: keys to identify community effects of low levels of toxicants in test systems. Ecotoxicology 20:1328–1340CrossRefGoogle Scholar
  25. Liess M, Beketov MA (2012) Rebuttal related to “Traits and Stress: Keys to identify community effects of low levels of toxicants in test systems” by Liess and Beketov (2011). Ecotoxicology 21:300–303CrossRefGoogle Scholar
  26. McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: a comment on distance-based redundancy analysis. Ecology 82:290–297CrossRefGoogle Scholar
  27. Nakagawa S, Schielzeth H (2013) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4:133–142CrossRefGoogle Scholar
  28. Newman MC, Clements WH (2008) Ecotoxicology : a comprehensive treatment. CRC Press, Boca RatonGoogle Scholar
  29. O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods Ecol Evol 1:118–122CrossRefGoogle Scholar
  30. 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
  31. 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
  32. 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/
  33. 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
  34. Roessink I, Arts GHP, Belgers JDM, Bransen F, Maund SJ, Brock TCM (2005) Effects of lambda-cyhalothrin in two ditch microcosm systems of different trophic status. Environ Toxicol Chem 24:1684–1696CrossRefGoogle Scholar
  35. Sanchez-Bayo F, Goka K (2012) Evaluation of suitable endpoints for assessing the impacts of toxicants at the community level. Ecotoxicology 21:667–680CrossRefGoogle Scholar
  36. Sanderson H, Laird B, Brain R, Wilson CJ, Solomon KR (2009) Detectability of fifteen aquatic micro/mesocosms. Ecotoxicology 18:838–845CrossRefGoogle Scholar
  37. Schäfer RB, Bundschuh M, Focks A, von der Ohe PC (2013) Letter to the Editor. Environ Toxicol Chem 32:734–735CrossRefGoogle Scholar
  38. Simpson GL (2013) Permute: functions for generating restricted permutations of data. R package version 0.8-0. http://CRAN.R-project.org/package=permute
  39. Stroup WW (2014) Rethinking the analysis of non-normal data in plant and soil science. Agron J 106:1–17CrossRefGoogle Scholar
  40. Szöcs E, Kefford BJ, Schäfer RB (2012) Is there an interaction of the effects of salinity and pesticides on the community structure of macroinvertebrates? Sci Total Environ 437:121–126CrossRefGoogle Scholar
  41. R Core Team (2013) R: a language and environment for statistical computing. Vienna, Austria. http://www.R-project.org/
  42. Van den Brink PJ (2013) Assessing aquatic copulation and community-level risks of pesticides. Environ Toxicol Chem 32:972–973CrossRefGoogle Scholar
  43. Van den Brink PJ, ter Braak CJF (1998) Multivariate analysis of stress in experimental ecosystems by principal response curves and similarity analysis. Aquat Ecol 32:163–178CrossRefGoogle Scholar
  44. Van den Brink PJ, ter Braak CJF (1999) Principal response curves: analysis of time-dependent multivariate responses of biological community to stress. Environ Toxicol Chem 18:138–148CrossRefGoogle Scholar
  45. Van den Brink PJ, ter Braak CJF (2012) Response to “Traits and stress: keys to identify community effects of low levels of toxicants in test systems” by Liess and Beketov (2011). Ecotoxicology 21:297–299CrossRefGoogle Scholar
  46. Van den Brink PJ, van Wijngaarden RPA, Lucassen WGH, Brock TCM, Leeuwangh P (1996) Effects of the insecticide Dursban 4E (active ingredient chlorpyrifos) in outdoor experimental ditches: II. Invertebrate community responses and recovery. Environ Toxicol Chem 15:1143–1153CrossRefGoogle Scholar
  47. Van den Brink PJ, Hattink J, Bransen F, van Donk E, Brock TCM (2000) Impact of the fungicide carbendazim in freshwater microcosms. II. Zooplankton, primary producers and final conclusions. Aquat Toxicol 48:251–264CrossRefGoogle Scholar
  48. Van Leeuwen CJ, Vermeire TG (2007) Risk assessment of chemicals: an introduction. Springer, NetherlandsCrossRefGoogle Scholar
  49. Wang M, Riffel M (2011) Making the right conclusions based on wrong results and small sample sizes: interpretation of statistical tests in ecotoxicology. Ecotoxicol Environ Saf 74:684–692CrossRefGoogle Scholar
  50. Wang Y, Naumann U, Wright ST, Warton DI (2012) mvabund—an R package for model-based analysis of multivariate abundance data. Methods Ecol Evol 3:471–474CrossRefGoogle Scholar
  51. Warton D (2011) Regularized sandwich estimators for analysis of high-dimensional data using generalized estimating equations. Biometrics 67:116–123CrossRefGoogle Scholar
  52. Warton DI, Wright ST, Wang Y (2012) Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol Evol 3:89–101CrossRefGoogle Scholar
  53. Zuur A, Ieno E, Elphick C (2010) A protocol for data exploration to avoid common statistical problems. Methods Ecol Evol 1:3–14CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Eduard Szöcs
    • 1
  • 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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