Understanding how stressors combine to affect population abundances and trajectories is a fundamental ecological problem with increasingly important implications worldwide. Generalizations about interactions among stressors are challenging due to different categorization methods and how stressors vary across species and systems. Here, we propose using a newly introduced framework to analyze data from the last 25 years on ecological stressor interactions, e.g. combined effects of temperature, salinity, and nutrients on population survival and growth. We contrast our results with the most commonly-used existing method—analysis of variance (ANOVA)—and show that ANOVA assumptions are often violated and have inherent limitations for detecting interactions. Moreover, we argue that rescaling—examining relative rather than absolute responses—is critical for ensuring that any interaction measure is independent of the strength of single-stressor effects. In contrast, non-rescaled measures—like ANOVA—find fewer interactions when single-stressor effects are weak. After re-examining 840 two-stressor combinations, we conclude that antagonism and additivity are the most frequent interaction types, in strong contrast to previous reports that synergy dominates yet supportive of more recent studies that find more antagonism. Consequently, measuring and re-assessing the frequency of stressor interaction types is imperative for a better understanding of how stressors affect populations.

This dataset includes the raw metadata collected from our literature search as well as the output from our meta-analysis using Rescaled Bliss Independence. A guide to the column names is included at the top of our dataset.