Modelling fishing location choice and spatial behaviour of fishers near a marine protected area
Bibliographic record
Abstract
Purpose This study investigates whether higher catch rates near a marine protected area (MPA), and/or in other fishing areas within a choice set, attract more fishers. A survey conducted in the fishing grounds near an MPA located in south east of Mauritius in the Indian Ocean shows concentration of fishers in regions with lower catch rates. This contrasts with the predictions of the “fishing the line” hypothesis and the ideal free distribution (IFD) that fishers are likely to be attracted near the MPA with higher resource abundance. Design/methodology/approach Using the random utility model as the framework and the random parameter logit (RPL) model, the study attempts to explain spatial behaviour of fishers. Expected catch and catch variability are modelled using the Just and Pope (JP) production function. The study also estimates effort elasticities with respect to expected catch and catch variability and simulates the relocation of effort from area closure. Findings The paper concludes that higher catch does attract fishers but is a partial and very restrictive explanation of fishers' behaviour. The “fishing the line” hypothesis does hold to some extent, but it should not be taken for granted that rising catch rates in adjacent waters will increase fishing pressure. The paper concludes that factors such as catch variability, distance from homeport to fishing ground, potential physical risk and attitudes towards risk of fishers affect spatial behaviour of fishers and should be considered for the placement and size of MPAs. The study also finds that the responsiveness of effort to catch rates is lowest in areas which are already heavily fished and easily accessible. Practical implications The identification of fishing areas as complements (when fishing in one area increases fishing effort in another) and substitutes is valuable information for determining the placement and size of an MPA. A larger reserve is likely to have more displacement effect in this case than a smaller one. Therefore, a small or a network of a small reserve may be appropriate. The premise to select the site and size of the reserve is to avoid overconcentration of fishers in alternative fishing areas, which can be vulnerable to excessive fishing and unintended effects from fishers. Originality/value The paper contributes to an understanding of fishing behaviour and its impact on the configuration of marine reserves. It discusses the importance of effort elasticities to determine the placement and size of an MPA. Studies on this topic are very scanty in the Indian Ocean region. It also shows the application of location choice model, the RPL model and the JP production function in the fisheries sector for a small island.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".