Accounting for marine reserves using spatial stock assessments
Bibliographic record
Abstract
Some fish stock assessments are conducted in regions that contain no-take marine reserves (NTMRs). NTMRs are expected to lead to spatial heterogeneity in fish biomass by allowing a buildup of biomass inside their borders while fishing pressure occurs outside. Stock assessments do not typically account for spatial heterogeneity caused by NTMRs, which may lead to biased estimates of biomass. Simulation modeling is used to analyze the ability of several stock assessment configurations to estimate current biomass after the implementation of a single, large NTMR. Age-structured spatial operating models with three patterns of ontogenetic movement are used to represent the “true” population dynamics. Results show that assessing populations as a single stock with use of fishery catch-rate data and without accounting for the NTMR results in severe underestimation of biomass for two of the movement patterns. Omitting fishery catch-rate data or allowing time-varying dome-shaped selectivity after NTMR implementation leads to improved estimates of current biomass, but severe bias in estimated trends in biomass over time. Performing separate assessments for fished areas and NTMRs leads to improved estimation performance in the absence of movement among assessment areas, but can severely overestimate biomass otherwise. Performing a spatial assessment with estimation of movement parameters among areas was found to be the best way to assess a species, even when movement patterns were unknown. However, future work should explore the performance of spatial assessments when catchability varies among areas.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".