Impacts on population indices if scientific surveys are excluded from marine protected areas
Bibliographic record
Abstract
Abstract Marine protected areas (MPAs) are increasingly common worldwide, typically restricting fishing activities. However, MPAs may also limit scientific surveys that impact benthic habitat. We combine a historical data degradation approach and simulation to investigate the effects on population indices of excluding surveys from MPAs. Our approach quantifies losses in precision, inter-annual accuracy, trend accuracy, and power to detect trends, as well as correlates of these effects. We apply this approach to a proposed MPA network off western Canada, examining 43 groundfish species observed by four surveys. Survey exclusion particularly impacted less precise indices, species well-represented in MPAs, and those whose density shifted in or out of MPAs. Redistributing survey effort outside MPAs consistently improved precision but not accuracy or trend detection—sometimes making estimates more precise about the ‘wrong’ index. While these changes may not qualitatively alter stock assessment for many species, in some cases, ∼30 percentage point reductions in power to detect simulated 50% population declines suggest meaningful impacts are possible. If survey restrictions continue expanding, index integrity could further degrade, eventually compromising the management of exploited populations. Regulating surveys within MPA boundaries therefore requires careful consideration to balance MPA objectives with the need for reliable monitoring.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".