Species interactions cause non‐additive effects of multiple environmental stressors on communities
Bibliographic record
Abstract
Abstract Cumulative ecological impacts of chronic, extreme, and often novel, anthropogenic environmental changes (i.e., stressors) often differ from the sum of their individual effects. Uncertainty over the causes of such non‐additivity among multiple stressors confounds forecasts of their net ecological impact. Although stressors can interact directly within the environment to mediate their combined effects on communities, species interactions likely also play key roles. Here, we use a simulation model to explore how species interactions cause community responses (changes in species richness and total biomass) to paired stressors to differ from what we would expect based on the individual effects of each stressor (the additive effect). We demonstrate how interspecific interactions cause communities to respond non‐additively to stressors, and how this depends on whether these interactions are negative or positive and whether the stressors have positive or negative impacts on the community property of interest. When pairwise species interactions involve at least one negative interaction (i.e., competition or predation), stressors combine to have greater than expected negative impacts (e.g., species or biomass loss) and less than expected positive impacts (e.g., biomass increases). In contrast, reciprocally positive interactions between species (i.e., facilitation) generally cause stressors to have additive, or slightly less than additive, net effects on species richness and community biomass. While species interactions determine the nature of the combined impact of multiple stressors (i.e., greater than or less than expected), species co‐tolerance and stressor timing (i.e., sequential vs simultaneous application) only modify the magnitude of this effect. These findings highlight how interactions among species can contribute to non‐additive responses by communities to environmental change, in addition to those caused by interactions among stressors themselves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".