Dams have varying impacts on fish communities across latitudes: A quantitative synthesis
Bibliographic record
Abstract
Abstract Dams are recognized to impact aquatic biodiversity and ecosystem functions, but the magnitude of effects vary across studies. By using a meta-analytical approach, we examined the effects of impoundment on fish community across three large biomes. The impacts of dams on richness and diversity differed across biomes, with significant declines in the tropics, lower amplitude but similar directional changes in temperate reservoirs, and no changes in boreal reservoirs. Our analysis also showed that non-native species increased significantly in tropical and temperate reservoirs, but not in boreal reservoirs. In contrast, temporal trajectories in fish assemblage metrics were common across regions, with all biomes showing an increase in mean trophic position and in the proportion of generalist species after impoundment. Such changes in fish assemblages may affect food web stability and merit closer study. Across the literature examined, predominant factors or mechanisms that render fish assemblages susceptible to impacts from dams were: 1) the transformation of the lotic environment into a lentic environment; 2) habitat fragmentation and 3) invasive or non-native species. Collectively our results highlight that an understanding of the regional context and a suite of metrics are needed to make robust predictions about how fish will respond to river impoundments.
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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 | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".