What can be learned from fishers’ perceptions for fishery management planning? Case study insights from Sainte-Marie, Madagascar
Bibliographic record
Abstract
Local support is critical to the success and longevity of fishery management initiatives. Previous research suggests that how resource users perceive ecological changes, explain them, and cope with them, influences local support. The objectives of this study were two-fold. First, we collated local fishers' knowledge to characterize the long-term socio-ecological dynamics of the small-scale fishery of Sainte-Marie Island, in Madagascar. Second, we empirically assessed the individual- and site-level factors influencing support for fishery restrictions. Our results indicate that fishers observed a decline in fish abundance and catch sizes, especially in nearshore areas; many also perceived a reduction in fish sizes and the local disappearance of species. To maintain their catches, most fishers adapted by fishing harder and further offshore. Accordingly, fishers identified increased fishing effort (number of fishers and gear evolution) as the main cause of fishery changes. Collectively, our results highlight that the transition from a subsistence to commercial fishery, and resulting changes in the relationship between people and the fisheries, was an underlying driver of fishery changes. Additionally, we found that gender, membership to local associations, coping mechanisms, and perceptions of ecological health, were all interlinked and significantly associated with conservation-oriented attitudes. Conservation-oriented attitudes, however, were not associated with fishers' willingness to decrease fishing. In the short-term, area-based restrictions could contribute to building support for conservation. In the long-term, addressing the underlying causes of the decline will necessitate collaborations among the various groups involved to progressively build livelihood flexibility. Collectively, our study provides additional insights on the individual- and site-level factors influencing support for fishery restrictions. It also highlights the importance of dialoguing with fishers to ensure that fishery management plans are adapted to the local context.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".