Effects of Noncompliance on the Success of Alternative Designs of Marine Protected‐Area Networks for Conservation and Fisheries Management
Bibliographic record
Abstract
Abstract: Studies examining the efficacy of marine protected areas (MPAs) rarely consider the potential for noncompliance. Violation of MPAs will typically occur near boundaries, so perimeter‐to‐area ratios will be important determinants of actual protection, suggesting that MPAs should be larger and likely fewer. If larval dispersal is highly localized, however, MPAs will need to be more numerous, widespread, and likely smaller in order to replenish many fished areas. Thus, there is a discord between the MPA network that would best achieve external replenishment and that which would maximize compliance. I investigated these competing criteria with a spatially structured model of a hypothetical marine fishery exploiting a sedentary reef‐dwelling organism. With full compliance, a network of several small MPAs protects a population of similar size to that in a single large MPA and produces higher fishery yield across a range of fishing mortality rates. As noncompliance increases, however, the protected population in the network of several small MPAs approaches zero, whereas the single, large MPA population declines much less. Furthermore, at high levels of fishing mortality and noncompliance, yield with the network of several small MPAs begins to mirror that with no MPAs and drops below the yield with the single large MPA. Temporal variability in both the protected population size and yield are similar between the two designs with full compliance, but the single large MPA provides much greater stability in both metrics at high fishing mortality rates as noncompliance increases. My results highlight the important effects of noncompliance in realized MPA benefits and can explain why observed and expected effects might differ. Moreover, my results support a call for increased attention to rates of noncompliance and their ecological effects and greater collaboration among natural scientists, social scientists, managers, and stakeholders in understanding and altering illegal behavior.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".