Individual outcomes matter in the context of responsible and sustainable catch-and-release practices in recreational fisheries and their management
Bibliographic record
Abstract
Abstract Recreational anglers often engage in catch-and-release (C&R) whereby some of their catch is returned to the water (either to comply with harvest regulations or voluntarily) with the assumption that fish will survive and experience negligible impacts. Despite the assumption that C&R is usually harmless to fish and, thus, helps reduce overall fishing mortality, a large evidence base shows a proportion of released fish will not survive. Even if the event is not lethal, each individual fish will experience some sublethal impact (e.g., injury and stress). There is some debate within the recreational fisheries science and management community regarding the extent to which sublethal impacts or even mortality of individual fish matter, given that fisheries management efforts often focus on whether excessive overall mortality affects population size or quality of angling. Here, we embrace the perspective that individual-level outcomes matter in the context of responsible and sustainable C&R in recreational fisheries and their management. We outline 10 reasons why there is a need to account for individual outcomes of C&R fish to generate resilient fisheries under a changing climate and in the face of other ongoing, increasing, and future threats and stressors. Fostering better handling practices and responsible behaviors within the angling community through education will improve interactions between fish and people while ensuring more successful releases and ecological benefits across fisheries. We acknowledge that cultural norms and values underpin ethical perspectives, which vary among individuals, regions (e.g., rural vs. urban), and geopolitical jurisdictions, and that these can dictate angler behavior and management objectives as well as how individual-level C&R impacts are perceived. Our perspective complements a parallel paper (see Corsi et al., 2025) that argues that individual fish outcomes do not matter unless they create population-level impacts. Creating a forum for discussing and reflecting on alternative viewpoints is intended to help identify common ground where there is opportunity to work collectively to ensure recreational fisheries are managed responsibly and sustainably.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".