Identifying mangrove areas for fisheries enhancement; population assessment in a patchy habitat
Bibliographic record
Abstract
ABSTRACT Small‐scale fisheries are an important element of the ecosystem goods and services that mangrove habitats provide, especially to poorer coastal communities that rely most on natural resources, and have similar values to payments for ecosystem services (PES) under carbon‐trading schemes. In advance of fishery‐enhancement trials for the mud crab Scylla olivacea, a mark–recapture study was conducted to estimate population size and turnover in 50 ha of isolated mangrove on Panay Island, Philippines. A total of 811 crabs were released in six sessions with an overall recapture rate of 41.5 ± 3.6%. Population size ranged from 607–1637 individuals. There was a high degree of site‐fidelity, with 45.5% of recaptures in the same sampling areas as releases. Total mortality was 0.79 month‐1, with fishing mortality accounting for 95% of overall mortality. Von Bertalanffy and Gompertz growth models yielded estimates for L∞ (carapace width) of 117.3 ± 14.7 and 110.6 ± 2.1 mm and for k of 2.16 ± 0.74 and 3.25 ± 0.81, respectively. Crab densities of 12–33 individuals ha‐1 in the study area were lower than in other mangrove systems owing to intermittent recruitment, while growth rates indicated no limitation in terms of food supply. The study demonstrates that in specific mangrove habitats that are below carrying capacity, there is potential for fisheries enhancement to sustain or increase direct economic benefits from mangrove ecosystems and hence promote community engagement in broader conservation and PES initiatives. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".