Statistics matter: data aggregation improves identification of community-level effects compared to a commonly used multivariate method
- 319 Downloads
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.
KeywordsMesocosms 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.
- 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
- 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
- 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
- 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
- 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
- Newman MC, Unger MA (2002) Fundamentals of ecotoxicology. CRC Press, Boca RatonGoogle Scholar
- 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. http://vegan.r-forge.r-project.org/
- 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
- The R Development Core Team (2010) R: a language and environment for statistical computing. Reference index version 2.15.2., Vienna, Austria. www.r-project.org