Double constrained ordination for assessing biological trait responses to multiple stressors: A case study with benthic macroinvertebrate communities

Benthic macroinvertebrate communities are used as indicators for anthropogenic stress in freshwater ecosystems. To better understand the relationship between anthropogenic stress and changes in macroinvertebrate community composition, it is important to understand how different stressors and species traits are associated, and how these associations influence variation in species occurrence and abundances. Here, we show the capacity of the multivariate technique of double constrained correspondence analysis (dc-CA) to analyse trait-environment relationships, and we compare it with the redundancy analysis method on community weighted mean values of traits (CWM-RDA), which is frequently used for this type of analysis. The analyses were based on available biomonitoring data for macroinvertebrate communities from the Danube River. Results from forward selection of traits and environmental variables using dc-CA analyses showed that aquatic stages, reproduction techniques, dispersal tactics, locomotion and substrate relations, altitude, longitudinal and transversal distribution, and substrate preferendum were significantly related to habitat characteristics, hydromorphological alterations and water quality measurements such as physico-chemical parameters, heavy metals, pesticides and pharmaceuticals. Environmental variables significantly associated with traits using the CWM-RDA method were generally consistent with those found in dc-CA analysis. However, the CWM-RDA does neither test nor explicitly select traits, while dc-CA tests and selects both traits and environmental variables. Moreover, the dc-CA analysis revealed that the set of environmental variables was much better in explaining the community data than the available trait set, a kind of information that can neither be obtained from CWM-RDA nor from RLQ (Environment, Link and Trait data), which is a close cousin of dc-CA but not regression-based. Our results suggest that trait-based analysis based on dc-CA may be useful to assess mechanistic links between multiple anthropogenic stressors and ecosystem health, but more data sets should be analysed in the same manner.