Climate warming is a ubiquitous stressor in freshwater ecosystems, yet its interactive effects with other stressors are poorly understood. We address this by testing the ability of three contrasting null models to predict the joint impacts of warming and a second stressor using a new database of 296 experimental combinations. Despite concerns that stressors will interact to cause synergisms, we found that net impacts were best explained by the effect of the stronger stressor (the dominance null model), especially if it was associated with human land use. Prediction accuracy depended on stressor identity and the magnitude of asymmetry between their effects. These findings suggest we can often effectively forecast impacts of multiple stressors by focusing on the stronger stressor, as habitat alteration and contamination often override the biological consequences of higher temperatures in freshwater ecosystems.

Predicting the impacts of multiple stressors is important for informing ecosystem management, but is impeded by a lack of a general framework for predicting whether stressors interact synergistically, additively, or antagonistically. Here we use process-based models to study how interactions generalise across three levels of bio-logical organisation (physiological, population, and community) for a simulated two-stressor experiment on a seagrass model system. We found that the same underlying processes could result in synergistic, additive or antagonistic interactions, with interaction type depending on initial conditions, experiment duration, stressor dynamics, and consumer presence. Our results help explain why meta-analyses of multiple stressor experimental results have struggled to identify predictors of consistently non-additive interactions in the natural environment. Experiments run over longer temporal scales, with treatments across gradients of stressor magnitude, are needed to identify the processes that underpin how stressors interact and provide useful predictions to management.

Climate warming is an important stressor in freshwater ecosystems, yet its interactive effects with other environmental changes are poorly understood. We address this challenge by testing the ability of three contrasting null models to predict the joint impacts of warming and a second stressor using a new database of 296 experimental combinations. Despite concerns that stressors will interact to cause synergisms, we found that net impacts were best explained by the effect of the worst stressor (the dominance null model). When this stressor’s impact was at least 50% greater than that of the second, the dominance model was most accurate in 62% of responses. Prediction accuracy depended on the identity of the stressors and declined at higher levels of biological organisation. Together these findings suggest we can often effectively forecast impacts of multiple stressors by focusing on the degree of asymmetry that exists among their independent impacts.

Freshwater biodiversity is declining dramatically, and the current biodiversity crisis requires defining bold goals and mobilizing substantial resources to meet the challenges. While the reasons are varied, both research and conservation of freshwater biodiversity lag far behind efforts in the terrestrial and marine realms. We identify fifteen pressing global needs to support informed global freshwater biodiversity stewardship. The proposed agenda aims to advance freshwater biodiversity research globally as a critical step in improving coordinated action towards its sustainable management and conservation.