Disentangling the effects of multiple stressors on freshwater macroinvertebrates: A quantitative analysis of experimental studies
Bibliographic record
Abstract
• Additive effects of multiple stressors were common in their impact on freshwater macroinvertebrates. • Fine sediment increases and EPT taxa were often associated with interactive effects. • Antagonistic and reversal effects were common between fine sediment increase and flow decrease or nutrient increase. • Synergistic interactions between fine sediment and temperature often impacted EPT richness. Multiple stressors in freshwater ecosystems usually interact and produce synergistic, antagonistic, reversal or additive effects on organisms. However, the understanding of the interactive types of stressors on macroinvertebrate assemblages is still limited. We synthesized 1,087 experimental treatment–control observations extracted from 32 publications to quantify the individual and combined effects of 14 stressors on macroinvertebrates (abundance and richness) and to determine the effect types of paired-stressor interaction. We find that multiple stressors acted mostly through additive effects (84.85%) on freshwater macroinvertebrates. Among the non-additive interactions, antagonistic (7.18%) and reversal (6.41%) effects were more common, while synergistic effects were relatively rare (1.55%). Notably, these interactions often occurred in EPT taxa, with synergistic interactions between increases in fine sediment and temperature often occurring for EPT richness. Additionally, antagonistic and reversal effects were commonly observed between fine sediment increase and either flow decrease or nutrient increase. Our study provides a quantitative basis for accounting for the effect types of multiple stressors in freshwater macroinvertebrate-based biodiversity assessment and ecosystem management. Our study also highlights the consideration should be given to the variability in interactions resulting from different combinations of stressors and emphasizes how realized effects may differ among taxonomic groups.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".