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Interactions between stressors in freshwater ecosystems, including those associated with climate change and nutrient enrichment, are currently difficult to detect and manage. Our understanding of the forms and frequency of occurrence of such interactions is limited; assessments using field data have been constrained as a result of varying data forms and quality. To address this issue, we demonstrate a statistical approach capable of assessing multiple stressor interactions using contrasting data forms in 3 European catchments (Loch Leven Catchment, UK: assessment of phytoplankton response in a single lake with time series data; Pinios Catchment, Greece: macroinvertebrate response across multiple rivers using spatial data; and Lepsämänjoki Catchment, Finland: phytoplankton response across multiple rivers using spatiotemporal data). Statistical models were developed to predict the relative and interactive effects of climate change and nutrient enrichment sensitive indicators (stressors) on indicators of ecological quality (ecological responses) within the framework of linear mixed effects models. In all catchments, indicators of nutrient enrichment were identified as the primary stressor, with climate change-sensitive indicators causing secondary effects (Loch Leven: additive, total phosphorus [TP] × precipitation; Pinios: additive, nitrate × dissolved oxygen; Lepsämänjoki: synergistic, TP × summer water temperature), the intensity of which varied between catchments and along the nutrient stressor gradient. Simple stressor change scenarios were constructed for each catchment and used in combination with mechanistic models to explore potential management responses. This approach can be used to explore the need for multiple stressor management in freshwaters, helping practitioners navigate a complex world of environmental change.

Assessing multiple stressor effects to inform climate change management responses in three European catchments