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

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.