No-take marine reserves can enhance population persistence and support the fishery of abalone
Bibliographic record
Abstract
A critical aspect in the design of a marine reserve (MR) network is its spatial configuration (i.e., the number, size, and spacing of the individual reserves), particularly how these features influence the effect on fisheries. Here, we derived a size-based, spatially explicit, stochastic demographic model to explore how different spatial configurations of MR networks can affect abundance and commercial yield of the green abalone (Haliotis fulgens), taking as a reference case the abalone fishery of Isla Natividad in Baja California Sur (Mexico). Our analysis suggests that a network of MRs can have a positive effect on abalone population abundance and a slightly negative effect on fishery output with respect to traditional maximum sustainable yield (MSY; i.e., with no reserves). Simulations show that maximum catches achievable with MRs are, under the best configuration, ∼2%–14% lower than traditional MSY depending on the total fraction of the fishing grounds protected. In the case of overexploitation, long-term yields can increase following the implementation of MRs. In addition, in the presence of MRs, abundances and yields are much less sensitive to systematic errors in the enforcement of the optimal harvesting rate compared with situations in which MRs are not present. Given the limited dispersal ability of the species, the best outcomes in terms of fishery output would be achieved with very small reserves — around 100 m wide — so to maximize larval export in the fishable areas. Our results indicate appropriately designed MR networks are an effective strategy for meeting both conservation and economic goals under uncertainty. While the size of the existing reserves in Isla Natividad seems adequate to protect the abalone stock, smaller reserves could maximize fishery benefits, although this poses challenges for enforcement.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".