Conservation physiology in practice: how physiological knowledge has improved our ability to sustainably manage Pacific salmon during up-river migration
Bibliographic record
Abstract
Despite growing interest in conservation physiology, practical examples of how physiology has helped to understand or to solve conservation problems remain scarce. Over the past decade, an interdisciplinary research team has used a conservation physiology approach to address topical conservation concerns for Pacific salmon. Here, we review how novel applications of tools such as physiological telemetry, functional genomics and laboratory experiments on cardiorespiratory physiology have shed light on the effect of fisheries capture and release, disease and individual condition, and stock-specific consequences of warming river temperatures, respectively, and discuss how these findings have or have not benefited Pacific salmon management. Overall, physiological tools have provided remarkable insights into the effects of fisheries capture and have helped to enhance techniques for facilitating recovery from fisheries capture. Stock-specific cardiorespiratory thresholds for thermal tolerances have been identified for sockeye salmon and can be used by managers to better predict migration success, representing a rare example that links a physiological scope to fitness in the wild population. Functional genomics approaches have identified physiological signatures predictive of individual migration mortality. Although fisheries managers are primarily concerned with population-level processes, understanding the causes of en route mortality provides a mechanistic explanation and can be used to refine management models. We discuss the challenges that we have overcome, as well as those that we continue to face, in making conservation physiology relevant to managers of Pacific salmon.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".