Adaptive Fisher Behavior Alone Can Induce Tipping Points and Stock Collapse: A Synthesis of Bioeconomic Theory That Applies to All Capture Fisheries Under Open Access
Bibliographic record
Abstract
The sustainability of capture fisheries faces threats from tipping points, regime shifts, and alternative stable states. Examples like the Atlantic cod (Gadus morhua) collapse in Newfoundland and more recently the Western Baltic Sea highlight the potential for abrupt stock declines and slow recovery even after a moratorium on harvest. The mechanisms triggering tipping points and regime shifts, especially the role of adaptive behavior of fishers, however, lacks comprehensive understanding. In this paper, a systematic literature review on tipping points in commercial and recreational fisheries is presented. This portion of the synthesis revealed a limited knowledge base biased toward studies explaining regime shifts via harvest-induced ecological mechanisms or interactions of ecological and social processes. The explicit role of adaptive behavior of fishers, however, was found to be underexplored. To address this gap, single-species biomass models with density-dependent fish population growth were extended and linked to diverse benefit functions that reflected realistic behavioral responses of commercial, subsistence, and recreational fisheries under open access. The bioeconomic synthesis revealed that adaptive human behavior alone can trigger tipping points and stock collapse, even under fully compensatory single-species fish population dynamics. Specifically, non-linear utility functions that integrate stock sizes into fishing attractiveness to humans via expected catches drive behavioral feedbacks that can lead to crossing of tipping points. The fishing skill-to-cost ratio – a measure of expected benefits-over-costs – critically dictates tipping point occurrence. High catch efficiency and generally large benefits received while targeting low stock sizes maintains effort and fosters tipping points, signifying how technological advancements, gear improvements, reduced costs (e.g., through subsidies) and utility attached to effort alone (e.g., in recreational fisheries) may all contribute to fisheries destabilization. Tipping and depensation can happen even in the absence of hyperstability in catch rates. In recreational fisheries, aversion to fish harvests, e.g., because fishing becomes boring at high catch and harvest rates, further impacts tipping potential, by reducing the responsiveness of effort at low stock sizes. Tipping points only occur at intermediate fisher numbers: when numbers are low, stable high fish biomasses exist, when numbers are too high, stocks are always collapsed. These results collectively indicate how the combination of type of capture fishery, fisher preferences, technology, costs, and number of fishers dictate the presence or absence of tipping points via adaptive fisher behavior alone. The bioeconomic synthesis that is presented highlights the key role of effort investment behavior of fishers in shaping tipping points in open-access fisheries across commercial, recreational, and subsistence fisheries.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".