Fishing for Effective Conservation: Context and Biotic Variation are Keys to Understanding the Survival of Pacific Salmon after Catch-and-Release
Bibliographic record
Abstract
Acute stressors are commonly experienced by wild animals but their effects on fitness rarely are studied in the natural environment. Billions of fish are captured and released annually around the globe across all fishing sectors (e.g., recreational, commercial, subsistence). Whatever the motivation, release often occurs under the assumption of post-release survival. Yet, capture by fisheries (hereafter "fisheries-capture") is likely the most severe acute stressor experienced in the animal's lifetime, which makes the problem of physiological recovery and survival of relevance to biology and conservation. Indeed, fisheries managers require accurate estimates of mortality to better account for total mortality from fishing, while fishers desire guidance on strategies for reducing mortality and maintaining the welfare of released fish, to maximize current and future opportunities for fishing. In partnership with stakeholders, our team has extensively studied the effects of catch-and-release on Pacific salmon in both marine and freshwater environments, using biotelemetry and physiological assessments in a combined laboratory-based and field-based approach. The emergent theme is that post-release rates of mortality are consistently context-specific and can be affected by a suite of interacting biotic and abiotic factors. The fishing gear used, location of a fishery, water temperature, and handling techniques employed by fishers each can dramatically affect survival of the salmon they release. Variation among individuals, co-migrating populations, and between sexes all seem to play a role in the response of fish to capture and in their subsequent survival, potentially driven by pre-capture pathogen-load, maturation states, and inter-individual variation in responsiveness to stress. Although some of these findings are fascinating from a biological perspective, they all create unresolved challenges for managers. We summarize our findings by highlighting the patterns that have emerged most consistently, and point to areas of uncertainty that require further research.
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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 | 0.001 |
| 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".