Integrating physiology and life history to improve fisheries management and conservation
Bibliographic record
Abstract
Abstract Knowledge of life‐history traits is increasingly recognized as an important criterion for effective management and conservation. Understanding the link between physiology and life history is an important component of this knowledge and in our view is particularly relevant to understanding marine and freshwater fishes. Such linkages (i.e. the life‐history/physiology nexus) have been recently advocated for avian systems and here we explore this concept for fish. This paper highlights the gap in fisheries literature with regard to understanding the relationship between physiology and life history, and proposes ways in which this integration could improve fisheries management and conservation. We use three case studies on different fishes (i.e. the Pacific salmon, the grouper complex and tuna) to explore these issues. The physiological structure and function of fish plays a central role in determining stock response to exploitation and changes in the environment. Physiological measures can provide simple indicators necessary for cost‐effective monitoring in the evaluation of fisheries sustainability. The declining state of world fisheries and the need to develop and implement restoration strategies, such as hatchery production or protected areas, provides strong incentive to better understand the influence of physiology on population and reproductive dynamics and early life history. Physiology influences key population‐level processes, particularly those dealing with reproduction, which must be incorporated into the design and successful implementation of specific and broadscale initiatives (e.g. aquatic protected areas and bycatch reduction). Suggestions are made for how to encourage wider application of the physiology/life‐history link, in fisheries management and conservation, as well as more broadly in education and research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".