Net effects of multiple stressors in freshwater ecosystems: a meta‐analysis
Bibliographic record
Abstract
The accelerating rate of global change has focused attention on the cumulative impacts of novel and extreme environmental changes (i.e. stressors), especially in marine ecosystems. As integrators of local catchment and regional processes, freshwater ecosystems are also ranked highly sensitive to the net effects of multiple stressors, yet there has not been a large-scale quantitative synthesis. We analysed data from 88 papers including 286 responses of freshwater ecosystems to paired stressors and discovered that overall, their cumulative mean effect size was less than the sum of their single effects (i.e. an antagonistic interaction). Net effects of dual stressors on diversity and functional performance response metrics were additive and antagonistic, respectively. Across individual studies, a simple vote-counting method revealed that the net effects of stressor pairs were frequently more antagonistic (41%) than synergistic (28%), additive (16%) or reversed (15%). Here, we define a reversal as occurring when the net impact of two stressors is in the opposite direction (negative or positive) from that of the sum of their single effects. While warming paired with nutrification resulted in additive net effects, the overall mean net effect of warming combined with a second stressor was antagonistic. Most importantly, the mean net effects across all stressor pairs and response metrics were consistently antagonistic or additive, contrasting the greater prevalence of reported synergies in marine systems. Here, a possible explanation for more antagonistic responses by freshwater biota to stressors is that the inherent greater environmental variability of smaller aquatic ecosystems fosters greater potential for acclimation and co-adaptation to multiple stressors.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.051 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".