Focusing on what matters most: Evaluating multiple challenges to stability in recreational fisheries
Bibliographic record
Abstract
Abstract Recreational fisheries were traditionally theorized to self‐regulate in a sustainable feedback loop in which recreational anglers moderate their fishing effort in response to population declines. However, several mechanisms are hypothesized to break down this self‐regulatory process, including recruitment variability and depensatory population dynamics. Although many of these mechanisms of instability have been estimated in empirical systems and explored using modelling, we still do not know the extent to which these mechanisms can (1) erode stability at their observed strength in real systems and (2) interact to dampen or intensify each other's effects. In this study, we synthesize existing data on four of these mechanisms: (1) depensation in the stock‐recruit relationship, (2) recruitment stochasticity, (3) density‐dependent catchability and (4) the strength of anglers' responsiveness to changing catch rates. We report the range of observed values for these four mechanisms in real‐world fisheries and observe their effect on a simplified recreational fishery model. We find that at moderate fishing effort none of the mechanisms was destabilizing enough on its own to collapse the modelled population, but that an angler population that was likely to keep fishing when catch rates approached zero was a key element of interactions that caused collapse. The strongest interaction was between an angler population with this characteristic and a fish population with hyperstable catch rates. Our results highlight the need for more consistent and widespread estimation of utility‐based angler effort functions as well as the importance of interdisciplinary teams that can gather both social and ecological data.
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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 | 0.008 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".