Cumulative effects of dams on migratory fishes across the conterminous United States: Regional patterns in fish responses to river network fragmentation
Bibliographic record
Abstract
Abstract Globally, dams fragment river networks, threatening migratory fishes which require access to distinct habitats to complete their life cycles. Efforts to understand how cumulative effects of multiple dams affect migratory fishes across large regions, such as a country or continent, could help to identify locations for connectivity‐enhancing actions to conserve migratory fishes. To address this, we evaluated cumulative effects of dams on migratory fishes in rivers across nine ecoregions of the conterminous USA. First, using fish data from thousands of sites ( N = 45,989), we summarized ecoregional patterns in assemblages, quantifying the number of migratory species comprising assemblages, showing the prominence of potamodromous species across the large region as well as differences in migratory life history traits among ecoregions. Next, we compared the importance of a set of river network fragmentation metrics that captured influences of multiple dams in networks versus other anthropogenic landscape stressors and natural landscape factors that impact migratory fishes by ecoregion. We found that migratory fishes were more sensitive to cumulative dam effects than other stressors including urbanization and agriculture in the eastern USA. To further identify specific effects of environmental variables on potamodromous fishes, we conducted Boosted Regression Trees analysis in the eastern ecoregions. Our results suggested that the key natural influences on river fishes included catchment area as well as river baseflow and air temperature, suggesting that migratory fishes may be affected by changing climate. Additionally, we found that downstream dams were more influential than other human stressors to potamodromous fishes, underscoring the importance of enhancing connectivity within river networks to conserve migratory fishes. Collectively, our results provide new insights in identifying threats to migratory fish species across the USA, providing information that can aid in conserving this vulnerable but ecologically and socioeconomically important group of fishes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".