Turbine entrainment and passage of potadromous fish through hydropower dams: Developing conceptual frameworks and metrics for moving beyond turbine passage mortality
Bibliographic record
Abstract
Abstract Potadromous fishes are vulnerable to involuntary entrainment through hydropower turbines. However, turbines can also provide a downstream passage route for potadromous fish. Here, we review evidence for turbine entrainment and passage in potadromous fish, and evaluate the effects of these processes on upstream and downstream populations. We develop conceptual frameworks and metrics to quantify vulnerability to turbine entrainment removals, and to quantify the efficiency of turbines as a downstream passage route. We highlight factors that influence these processes and provide case‐studies demonstrating their applicability. We found that juvenile potadromous fish are being entrained through turbines at rates high enough to impact upstream populations. Given that juvenile passage survival is often high, we argue that turbines provide an important downstream passage route for potadromous fish. We show that entrainment vulnerability is likely a function of interactions between in‐reservoir fish behaviour, habitat configuration and operations and thus not well captured by passage mortality estimates. Similarly, we show that while passage mortality can limit downstream passage efficiency, passage success is also dependent on reservoir and forebay navigation, along with survival and fitness in the downstream river. We advocate for a shift in focus away from estimates of passage mortality and injury, which have previously accounted for the majority of turbine passage research. Instead, we recommend an approach that focusses on quantification of the factors that influence downstream passage efficiency and entrainment vulnerability. Moreover, we highlight the need to better understand the broader scale impacts of these events on upstream and downstream populations.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".