Effects of variability in spacing of coastal marine reserves on fisheries yield and sustainability
Bibliographic record
Abstract
The size and configuration of marine reserves best suited to reaching fisheries and conservation goals are poorly understood. It has been suggested that variable spacing between reserves is essential to the success of a system of marine reserves. We used numerical models to examine the effect of variable spacing on the efficacy of marine reserves for managing a fish species with sedentary adults and planktonic larvae. Variability in reserve spacing affected catch and recruitment relative to values for a uniform configuration of reserves only for populations near collapse even in the presence of a system of reserves. For species with low fishing rates or large marine reserves, variability in spacing had only a minor effect. At high fishing rates and small reserve sizes, variable reserve placement had a positive local effect on catch and recruitment when several reserves fell close to each other. These configurations led to uneven spatial distributions with greater catch and recruitment in areas with a higher concentration of reserves. Variable reserve spacing can offer additional protection to overfished populations along certain sections of the coastline, but concern for spatial homogeneity will argue for a uniform distribution of reserves covering an adequate proportion of the entire coastline.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".