, Volume 22, Issue 10, pp 1516–1525 | Cite as

Statistics matter: data aggregation improves identification of community-level effects compared to a commonly used multivariate method

  • Mikhail A. BeketovEmail author
  • Mira Kattwinkel
  • Matthias Liess


The identification of the effects of toxicants on biological communities is hampered by the complexity and variability of communities. To overcome these challenges, the trait-based SPEAR approach has been developed. This approach is based on (i) identifying the vulnerable taxa using traits and (ii) aggregating these taxa into a group to reduce the between-replicate differences and scattered low-abundance distribution, both of which are typical for biological communities. This approach allows for reduction of the noise and determination of the effects of toxicants at low concentrations in both field and mesocosm studies. However, there is a need to quantitatively investigate its potential for mesocosm data evaluations and application in the ecological risk assessment of toxicants. In the present study, we analysed how the aggregation of the sensitive taxa can facilitate the identification of the effects. We used empirical data from a long-term mesocosm experiment with stream invertebrates and an insecticide as well as a series of simulated datasets characterised by different degrees of data matrix saturation (corresponding to different sampling efforts), numbers of replicates, and between-replicate differences. The analyses of both the empirical and simulated data sets revealed that the taxa aggregation approach allows for the detection of effects at a lower saturation of the data matrices, smaller number of replicates, and higher between-replicate differences when compared to the multivariate statistical method redundancy analysis. These improvements lead to a higher sensitivity of the analysed systems, as long-term effects were detected at lower concentrations (up to 1,000 times). These outcomes suggest that methods based on taxa aggregation have a strong potential for use in mesocosm data evaluations because mesocosm studies are usually poorly replicated, have high between-replicate variability, and cannot be exhaustively sampled due to technical and financial constraints.


Mesocosms Community-level effects Sensitivity Risk assessment Statistical noise Trait-based approaches 



The study was supported by the European Union (Project INTERACT, Marie Curie IIF contract no. MIF1- CT-2006-021860) and the Helmholtz Association of German Research Centres (Project ECOLINK, HRJRG-025). We thank two anonymous reviewers for many constructive suggestions.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary material

10646_2013_1138_MOESM1_ESM.pdf (58 kb)
Supplementary material 1 (PDF 57 kb)
10646_2013_1138_MOESM2_ESM.r (4 kb)
Supplementary material 1 (R 5 kb)


  1. Beketov MA, Liess M (2008a) An indicator for effects of organic toxicants on lotic invertebrate communities: Independence of confounding environmental factors over an extensive river continuum. Environ Pollut 156(3):980–987. doi: 10.1016/j.envpol.2008.05.005 CrossRefGoogle Scholar
  2. Beketov MA, Liess M (2008b) Variability of pesticide exposure in a stream mesocosm system: macrophyte-dominated vs. non-vegetated sections. Environ Pollut 156(3):1364–1367. doi: 10.1016/j.envpol.2008.08.014 CrossRefGoogle Scholar
  3. Beketov MA, Liess M (2008c) Acute and delayed effects of the neonicotinoid insecticide thiacloprid on seven freshwater arthropods. Environ Toxicol Chem 27(2):461–470. doi: 10.1897/07-322r.1 CrossRefGoogle Scholar
  4. Beketov MA, Schafer RB, Marwitz A, Paschke A, Liess M (2008) Long-term stream invertebrate community alterations induced by the insecticide thiacloprid: effect concentrations and recovery dynamics. Sci Total Environ 405(1–3):96–108. doi: 10.1016/j.scitotenv.2008.07.001 CrossRefGoogle Scholar
  5. Beketov MA, Foit K, Schafer RB, Schriever CA, Sacchi A, Capri E, Biggs J, Wells C, Liess M (2009) SPEAR indicates pesticide effects in streams—comparative use of species- and family-level biomonitoring data. Environ Pollut 157(6):1841–1848. doi: 10.1016/j.envpol.2009.01.021 CrossRefGoogle Scholar
  6. Beketov MA, Kefford BJ, Schafer RB, Liess M (2013) Pesticides reduce regional biodiversity of stream invertebrates. Proc Natl Acad Sci USA 110(27):11039–11043. doi: 10.1073/pnas.1305618110 CrossRefGoogle Scholar
  7. Campbell PJ, Arnold DJS, Brock TCM, Grandy NJ, Heger W, Heimbach F (1999) Guidance document on higher-tier aquatic risk assessment for pesticides (HARAP). SETAC Press, BrusselsGoogle Scholar
  8. De Jong FMW, Brock TCM, Foekema EM, Leeuwangh P (2008) Guidance for summarizing and evaluating aquatic micro- and mesocosm studies, RIVM Report 601506009/2008. A guidance document of the Dutch platform for the Assessment of Higher Tier Studies, RIVM, BilthovenGoogle Scholar
  9. EFSA, European Food Safety Authority (2013) Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters. EFSA J 11(7):3290Google Scholar
  10. European Commission (2009) European Commission Directive 2009/128/EC of the European Parliament and of the Council of 21 October 2009 establishing a framework for Community action to achieve the sustainable use of pesticides, EUGoogle Scholar
  11. Fleeger JW, Carman KR, Nisbet RM (2003) Indirect effects of contaminants in aquatic ecosystems. Sci Total Environ 317(1–3):207–233. doi: 10.1016/s0048-9697(03)00141-4 CrossRefGoogle Scholar
  12. Giddings JM, Brock TCM, Heger W, Heimbach F, Maund SJ, Norman SM (2002) Community-level aquatic system studies, interpretation criteria (CLASSIC). SETAC Press, PensacolaGoogle Scholar
  13. 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(5):1182–1189. doi: 10.1897/04-010r.1 CrossRefGoogle Scholar
  14. Knillmann S, Stampfli NC, Noskov YA, Beketov MA, Liess M (2012) Interspecific competition delays recovery of Daphnia spp. populations from pesticide stress. Ecotoxicology 21(4):1039–1049. doi: 10.1007/s10646-012-0857-8 CrossRefGoogle Scholar
  15. Leps J, Smilauer P (2003) Multivariate analysis of ecological data using CANOCO. University Press, CambridgeCrossRefGoogle Scholar
  16. Liess M, Beketov M (2011) Traits and stress: keys to identify community effects of low levels of toxicants in test systems. Ecotoxicology 20(6):1328–1340. doi: 10.1007/s10646-011-0689-y CrossRefGoogle Scholar
  17. 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(2):300–303. doi: 10.1007/s10646-011-0840-9 CrossRefGoogle Scholar
  18. Liess M, von der Ohe PC (2005) Analyzing effects of pesticides on invertebrate communities in streams. Environ Toxicol Chem 24(4):954–965. doi: 10.1897/03-652.1 CrossRefGoogle Scholar
  19. Liess M, Schulz R, Liess MHD, Rother B, Kreuzig R (1999) Determination of insecticide contamination in agricultural headwater streams. Water Res 33(1):239–247. doi: 10.1016/s0043-1354(98)00174-2 CrossRefGoogle Scholar
  20. Newman MC, Unger MA (2002) Fundamentals of ecotoxicology. CRC Press, Boca RatonGoogle Scholar
  21. Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MH, Wagner H (2012). Vegan: Community Ecology Package. R package version 2.0-5.
  22. Sanderson H, Laird B, Brain R, Wilson CJ, Solomon KR (2009) Detectability of fifteen aquatic micro/mesocosms. Ecotoxicology 18(7):838–845. doi: 10.1007/s10646-009-0327-0 CrossRefGoogle Scholar
  23. Schäfer RB, Kefford B, Metzeling L, Liess M, Burgert S, Marchant R, Pettigrove V, Goonan P, Nugegoda D (2011) A trait database of stream invertebrates for the ecological risk assessment of single and combined effects of salinity and pesticides in South-East Australia. Sci Total Environ 409(11):2055–2063. doi: 10.1016/j.scitotenv.2011.01.053 CrossRefGoogle Scholar
  24. Schäfer RB, von der Ohe PC, Rasmussen J, Kefford BJ, Beketov MA, Schulz R, Liess M (2012) Thresholds for the effects of pesticides on invertebrate communities and leaf breakdown in stream ecosystems. Environ Sci Technol 46(9):5134–5142. doi: 10.1021/es2039882 CrossRefGoogle Scholar
  25. Stampfli NC, Knillmann S, Liess M, Beketov MA (2011) Environmental context determines community sensitivity of freshwater zooplankton to a pesticide. Aquat Toxicol 104(1–2):116–124. doi: 10.1016/j.aquatox.2011.04.004 CrossRefGoogle Scholar
  26. Stampfli NC, Knillmann S, Liess M, Noskov YA, Schafer RB, Beketov MA (2013) Two stressors and a community—effects of hydrological disturbance and a toxicant on freshwater zooplankton. Aquat Toxicol 127:9–20. doi: 10.1016/j.aquatox.2012.09.003 CrossRefGoogle Scholar
  27. The R Development Core Team (2010) R: a language and environment for statistical computing. Reference index version 2.15.2., Vienna, Austria.
  28. 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(2):138–148. doi: 10.1897/1551-5028(1999)018<0138:prcaot>;2 CrossRefGoogle Scholar
  29. 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(2):297–299. doi: 10.1007/s10646-011-0825-8 CrossRefGoogle Scholar
  30. Van den Brink PJ, Hartgers EM, Fettweis U, Crum SJH, van Donk E, Brock TCM (1997) Sensitivity of macrophyte-dominated freshwater microcosms to chronic levels of the herbicide linuron. I Primary producers. Ecotoxicol Environ Saf 38:13–24CrossRefGoogle Scholar
  31. Van Wijngaarden RPA, Brock TCM, Van den Brink PJ (2005) Threshold levels for effects of insecticides in freshwater ecosystems: a review. Ecotoxicology 14(3):355–380. doi: 10.1007/s10646-004-6371-x CrossRefGoogle Scholar
  32. 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(4):684–692. doi: 10.1016/j.ecoenv.2010.10.019 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Mikhail A. Beketov
    • 1
    Email author
  • Mira Kattwinkel
    • 2
  • Matthias Liess
    • 1
  1. 1.Department System EcotoxicologyUFZ – Helmholtz Centre for Environmental ResearchLeipzigGermany
  2. 2.Department System Analysis, Integrated Assessment and ModellingEawag: Swiss Federal Institute of Aquatic Science and TechnologyDuebendorfSwitzerland

Personalised recommendations