Catch and trade bans for seahorses can be negated by non‐selective fisheries
Bibliographic record
Abstract
Abstract All seahorse species (genus Hippocampus) are listed under Schedule I of India's Wild Life Protection Act, making all capture and trade of seahorses illegal. In the more than 15 years since the ban, little work has been done to assess its effects on seahorse conservation. Between 2015 and 2017, fisheries and trade surveys were conducted along the south‐east coast of India, in the state of Tamil Nadu, historically known to be a hub for seahorse catches and trade. Seahorses were primarily landed as bycatch, although in greater quantities by traditional drag nets than as trawl bycatch. Total annual catches were estimated between 4.98 million and 13.64 million seahorses, 87% of which were caught by active non‐selective gear. Generalized additive models revealed that seahorse catch per unit effort had non‐linear relations with depth and latitude, and were higher in biogenic habitats, with active, bottom‐used, and non‐selective gears (e.g. trawls). The illegal nature of the trade in seahorses hampered an understanding of trade routes and trade volumes. Catch estimates indicated that 11.21–30.31 tonnes of seahorses probably entered trade, yet interviews with traders only documented trade of about 1.6 tonnes. Fishers reported a decreasing availability of seahorses. Since most seahorses come from bycatch in persistent fisheries that are not directly affected by the ban on seahorse capture, this decline is likely to represent a population decline. A fishery and trade ban for incidentally caught species, particularly in a poorly regulated fishery, appears to add little conservation value. There needs to be a shift in the management approach, moving from a ban towards spatial and temporal restrictions, and toward enforcing existing fishery regulations.
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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".