Conservation behaviour in action: using fish behaviour to understand and mitigate the impacts of hydropower development
Bibliographic record
Abstract
The impacts that hydropower facilities have on non-anadromous downstream migrants and other resident freshwater fish are increasingly being recognized by environmental managers.The overall goal for my thesis was to apply conservation behaviour and risk analysis approaches to inform decision making for avoiding/mitigating common hydropower-related hazards faced by freshwater fish.Specifically, the thesis considers the risks of injury and mortality from entrainment and exposure to supersaturated total dissolved gasses (TDG).Many studies have quantified entrainment-related mortality and injury, but these studies generated site-specific data.To address this knowledge gap, I conducted a systematic review to quantify the risk associated with common hydropower infrastructure.My results revealed an increased overall injury and mortality risk resulting from entrainment relative to control fish.An increased risk was also revealed for several infrastructure types and fish taxa.To examine the reentrainment risk of a freshwater resident fish, I tracked the movements of salvaged Kokanee salmon in the forebay area of a large hydropower facility.Telemetry data revealed minimal reentrainment risk for salvaged Kokanee at the facility.Several studies have examined spatialtemporal movements of diadromous fish relative to TDG levels, but few have examined resident fish species.To examine the TDG exposure risk of resident fish, TDG was modeled in an impounded hydro-affected river system, and I tracked Rainbow Trout and Mountain Whitefish movement and depth use.Telemetry data revealed patterns in MW reach and depth residency that corresponded to spawning, foraging, and refuge behaviour whereas RT exhibited high site fidelity in one area of the system.The risk assessment revealed that Rainbow Trout had a higher TDG risk exposure relative to Mountain Whitefish, and that risk was highest in both species at locations near one of the hydropower facilities.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".