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More often than not, rivers are impacted by multiple stressors simultaneously affecting water quality, ecological flow, habitat diversity and ultimately lotic biodiversity. Identifying individual stressors as specific causes of deterioration can help inform water managers about stressor hierarchy and appropriate management options. Here, we investigate whether biological metrics from bioassessment schemes hold diagnostic capabilities to distinguish between the impact of individual stressors. We hypothesise that stressor-specific responses occur, when individual stressors show independent 'modes of action' (i.e. the specific stress-induced changes of environmental factors that modify the ecological niches of the species constituting the biological community). The stress receptors comprised three aquatic organism groups (macrophytes, benthic invertebrates, fish) represented by 437 biological metrics relevant in aquatic bioassessment. The stressor groups under investigation were physico-chemical, hydromorphological and hydrological stress. The data originated from official monitoring programmes with 769 sampling sites located at three broad river types in Western and Central Germany. Linear and non-linear variance partitioning was performed separately for each river type, with the non-linear analysis using a combination of boosted regression tree modeling and variance partitioning. We considered metrics to be potentially stressor-specific, if the corresponding models were explained predominantly by one stressor group. The linear analyses revealed 16 metrics that met our criteria. Subsequent non-linear modeling resulted in two genuinely stressor-specific metrics, both based on invertebrate data: The Index of Biocoenotic Region (specifically indicating hydromorphological stress) and the Relative abundance of alien invertebrate species (specifically indicating physico-chemical stress). We conclude that stressor-specific metrics can be empirically derived based on available monitoring data, and thus help support decision making in environmental management. However, their applicability is restricted to specific regions (e.g. river basin districts) reflecting the case-specific circumstance to which these metrics are conditioned.

The biota of European rivers are affected by a wide range of stressors impairing water quality and hydro-morphology. Only about 40% of Europe's rivers reach 'good ecological status', a target set by the European Water Framework Directive (WFD) and indicated by the biota. It is yet unknown how the different stressors in concert impact ecological status and how the relationship between stressors and status differs between river types. We linked the intensity of seven stressors to recently measured ecological status data for more than 50,000 sub-catchment units (covering almost 80% of Europe's surface area), which were distributed among 12 broad river types. Stressor data were either derived from remote sensing data (extent of urban and agricultural land use in the riparian zone) or modelled (alteration of mean annual flow and of base flow, total phosphorous load, total nitrogen load and mixture toxic pressure, a composite metric for toxic substances), while data on ecological status were taken from national statutory reporting of the second WFD River Basin Management Plans for the years 2010-2015. We used Boosted Regression Trees to link ecological status to stressor intensities. The stressors explained on average 61% of deviance in ecological status for the 12 individual river types, with all seven stressors contributing considerably to this explanation. On average, 39.4% of the deviance was explained by altered hydro-morphology (morphology: 23.2%; hydrology: 16.2%), 34.4% by nutrient enrichment and 26.2% by toxic substances. More than half of the total deviance was explained by stressor interaction, with nutrient enrichment and toxic substances interacting most frequently and strongly. Our results underline that the biota of all European river types are determined by co-occurring and interacting multiple stressors, lending support to the conclusion that fundamental management strategies at the catchment scale are required to reach the ambitious objective of good ecological status of surface waters.

Interactions of multiple stressors in lotic systems have received growing interest and have been analysed in a growing number of studies using experiment and survey data. In this study, we present a protocol to identify, display and analyse stressors of rivers and their interactions (additive, synergistic or antagonistic). We used a dataset of 125 samples of central European lowland rivers comprising hydromorphological, physico-chemical and land use stressor and pressure variables as well as benthic macroinvertebrate traits as biological response variables. To identify and visualise multiple stressor combinations jointly operating in the data set, we applied social network analysis. The main co-occurring stressor combination was fine sediment accumulation (hydromorphological stress) and enhanced phosphorus concentration (nutrient stress). Agricultural (cropland) and urban land use were identified as the main large scale environmental pressures. Stressor interactions were analysed using generalised linear regression modelling (GLM) including pairwise interaction terms. Altogether, 14 macroinvertebrate response variables were tested on six stressor combinations and revealed predominantly additive effects (80% of all significant models with absolute standardised effect sizes >0.1). Significant antagonistic and synergistic interactions occurred in almost 20% of the models. Fine sediment stress was more influential and frequent than nutrient stress. The methodology presented here is standardisable and thus could help inform practitioners in aquatic ecosystem monitoring about prominent combinations of multiple stressors and their interactions. Yet, further understanding of the mechanisms behind the biological responses is required to be able to derive appropriate guidance for management. This applies to rather complex stressors and pressures, such as land use, for which more detailed data (e.g. nutrient concentrations, fine sediment entry, pesticide pollution) is often missing.