The limitations of bans when conserving species that are incidentally caught : a case study of India’s seahorses
Bibliographic record
Abstract
Bans on exploitation and trade are increasingly being used in wildlife conservation. However, their effects on small, incidentally caught marine fishes are rarely examined. For my thesis, I evaluate the impact of one such paired ban in India, across different spatial scales, on the conservation of seahorse species and on the fishers who depend on them. In my first two research chapters, I investigated the effects of India’s ban on seahorse capture and trade, at two scales: nationally (Chapter 2) and in Tamil Nadu state (Chapter 3). In Chapter 2, I found that seahorse extraction continued, mostly from non-selective fishing gear such as trawlers and drag-netters. I also found that by far the most seahorses were caught from the state of Tamil Nadu, even though the fishers there were most aware of the ban. In Chapter 3, I found that higher seahorse catches in Tamil Nadu were associated with (i) biogenic habitats and (ii) active and non-selective benthic fishing gear. I also found that despite the ban, seahorse populations in the state appeared to be declining and that illegal trade persisted. In Chapter 4, I explored why fishers continued catching protected species. I found a convergence of factors such as high economic value, ease of catching seahorses in non-selective gear, and fisher exclusion from the decision-making process, all helped to explain poor compliance. In Chapter 5, I showed a pragmatic spatial analysis of the pressures on wild seahorse populations, derived from fisher knowledge, which can be used to evaluate the effectiveness of existing management and guide a path to sustainable exports. My tiered mapping assessments of risk and responses can be deployed in other data-poor situations to help assess the sustainability of exploitation and overcome management paralysis. My thesis illuminates the failures of bans in managing catch and trade of incidentally caught marine fishes and the need instead to constrain indiscriminate fishing pressures like bottom trawling. These findings have implications for other countries considering bans as measures to manage wildlife.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".