Resilient recreational fisheries or prone to collapse? A decade of research on the science and management of recreational fisheries
Bibliographic record
Abstract
Abstract Are recreational fisheries resilient to harvest or prone to collapse? This paper reviews research published since that question was posed by Post et al. (2002, Fisheries 27, 6–17). A number of patterns and processes have been identified that suggest understanding the risk of collapse requires knowledge of the fishing effort response, degree of depensation in the fishery and the life history of the harvested species. Processes involving the behaviour of fish, behaviour of anglers and management responses to declining quality can all impact the degree of resilience of recreational fisheries and their risk of collapse. The spatial context of an individual fishery can be important as they are often embedded in lake districts and joined by mobile anglers so their local dynamics are not independent from other fisheries. Typical regulations that restrict the behaviour of individual anglers in open‐access fisheries can provide some resilience but cannot prevent collapse if the fishing effort is too high. Many uncertainties remain related to the occurrence and intensity of the key processes and therefore adopting an adaptive experimental management approach might be the most useful approach to minimise the risk of collapse in recreational fisheries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".