Identification and interaction of multiple stressors in central European lowland rivers

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. (C) 2017 Elsevier B.V. All rights reserved.