Integrating marine protected areas with catch regulation
Bibliographic record
Abstract
Previous models of marine protected areas (MPAs) have generally assumed that there were no existing regulations on catch and have frequently shown that MPAs, by themselves, can be used to maintain both sustainable fish stocks and sustainable harvests. We explore the impact of implementing an MPA in a spatially structured model of a single-species fish stock that is regulated by total allowable catch (TAC). We find that when a stock is managed at maximum sustainable yield, or is overfished, implementation of an MPA will require a reduction in TAC to avoid increased fishing pressure on the stock outside the MPA. In both cases, catches will be lower as a result of overlaying an MPA on existing fisheries management. Only when the stock is so overfished that it is headed towards extinction does an MPA not lead to lower catches. In a TAC-regulated fishery, even if the stock is overfished, MPA implementation may not improve overall stock abundance or increase harvest unless catch is simultaneously reduced in the areas outside the MPA. Models that consider differential adult and larval dispersal need to be explored to see if these results are found with the more complex biology of a two-stage model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".