Differential associations of five riverine organism groups with multiple stressors.

The decline of river and stream biodiversity results from multiple simultaneous occuring stressors, yet few studies explore responses explore responses across various taxonomic groups at the same locations. In this study, we address this shortcoming by using a coherent data set to study the association of nine commonly occurring stressors (five chemical, one morphological and three hydraulic) with five taxonomic groups (bacteria, fungi, diatoms, macro-invertebrates and fish). According to studies on single taxonomic groups, we hypothesise that gradients of chemical stressors structure community composition of all taxonomic groups, while gradients of hydraulic and morphological stressors are mainly related to larger organisms such as benthic macro-invertebrates and fish. Organisms were sampled over two years at 20 sites in two catchments: a recently restored urban lowland catchment (Boye) and a moderately disturbed rural mountainous catchment (Kinzig). Dissimilarity matrices were computed for each taxonomic group within a catchment. Taxonomic dissimilarities between sites were linked to stressor dissimilarities using multivariable Generalized Linear Mixed Models. Stressor gradients were longer in the Boye, but did in contrast to the Kinzig not cover low stress intensities. Accordingly, responses of the taxonomic groups were stronger in the Kinzig catchment than in the recently restored Boye catchment. The discrepancy between catchments underlines that associations to stressors strongly depend on which part of the stressor gradient is covered in a catchment. All taxonomic groups were related to conductivity. Bacteria, fungi and macro-invertebrates change with dissolved oxygen, and bacteria and fungi with total nitrogen. Morphological and hydraulic stressors had minor correlations with bacteria, fungi and diatoms, while macro-invertebrates were strongly related to fine sediment and discharge, and fish to high flow peaks. The results partly support our hypotheses about the differential associations of the different taxonomic groups with the stressors.

Of causes and symptoms: using monitoring data and expert knowledge to diagnose the causes of stream degradation.

Ecological status assessment under the European Water Framework Directive (WFD) often integrates the impact of multiple stressors into a single index value. This hampers the identification of individual stressors being responsible for status deterioration. As a consequence, management measures are often disentangled from assessment results. To close this gap and to support river basin managers in the diagnosis of stressors, we linked numerous macroinvertebrate assessment metrics and one diatom index with potential causes of ecological deterioration through Bayesian belief networks (BBNs). The BBNs were informed by WFD monitoring data as well as regular consultation with experts and allow to estimate the probabilities of individual degradation causes based upon a selection of biological metrics. Macroinvertebrate metrics were shown to be stronger linked to hydromorphological conditions and land use than to water quality-related parameters (e.g., thermal and nutrient pollution). The modeled probabilities also allow to order the potential causes of degradation hierarchically. The comparison of assessment metrics showed that compositional and trait-based community metrics performed equally well in the diagnosis. The testing of the BBNs by experts resulted in an agreement between model output and expert opinion of 17-92% for individual stressors. Overall, the expert-based validation confirmed a good diagnostic potential of the BBNs; on average 80% of the diagnosed causes were in agreement with expert judgement. We conclude that diagnostic BBNs can assist the identification of causes of stream and river degradation and thereby inform the derivation of appropriate management decisions.