Impacts of run‐of‐river hydropower on coho salmon (<i>Oncorhynchus kisutch</i>): the role of density‐dependent survival
Bibliographic record
Abstract
Abstract Predicting whether anthropogenic sources of mortality have negative consequences at the level of population dynamics is challenged by mechanisms like density‐dependent survival that can amplify or offset the loss of individuals from anthropogenic disturbances. Run‐of‐river (RoR) hydropower is a growing industry that can cause frequent mortality of salmonid fry through rapid reductions in streamflow, leading to stranding on dewatered shores. However, whether individual‐level impacts reduce population growth rates or increase local extinction risk is difficult to predict. We used a stochastic stage‐structured matrix model to evaluate how the timing and magnitude of anthropogenic flow fluctuations impacted population abundance and extinction risk of coho salmon ( Oncorhynchus kisutch ), which spend up to 1.5 yr in many streams regulated by RoR hydropower. We additionally assessed how the timing (spring, winter) and strength (weak, moderate, high) of natural density‐dependent bottlenecks experienced by salmon in freshwaters tempers or amplifies the potential for RoR‐induced mortality to scale to emergent population dynamics. We compared population sizes and the 45‐yr probability of quasi‐extinction under 12 scenarios that varied the frequency (0–20 events per year) and magnitude (1–10% mortality per event) of RoR‐induced flow fluctuations, as well as the timing and strength of density‐dependent bottlenecks occurring during the first year in freshwater. We found that even mild flow fluctuations by RoR hydropower can impact coho salmon population dynamics, especially if density dependence is weak or occurs early in freshwater residency (spring). When density dependence was strong and during winter, the potential for population‐level impact was lessened, but populations still declined by 13–42% when RoR‐induced mortality was severe (5–10%) or frequent (10–20 events/yr). We conclude that strong density‐dependent survival bottlenecks could partially mitigate the loss of fry from anthropogenic flow fluctuations, especially if bottlenecks occur late in freshwater residency, but not for all intensities of flow fluctuations. Even with strong density dependence in winter, our models predict declining populations by up to 70% under strong and very frequent flow fluctuations, which should serve to caution those tasked with regulating flows in streams affected by RoR hydropower.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".