Abundance, demography, and harvesting of water snakes from agricultural landscapes in West Java, Indonesia
Bibliographic record
Abstract
Context Across much of its geographic range, the masked water snake, Homalopsis buccata, is harvested each year in large numbers, questioning the sustainability of that offtake. Aims To quantify abundance and demography of water snakes in anthropogenically disturbed habitats in an area of West Java, where these snakes are subject to intensive harvest. Methods We accompanied professional snake-collectors, and conducted our own surveys of ponds and irrigation canals, to record the numbers and attributes (species, sex, size, etc.) of snakes that were captured using a variety of methods. Key results Snakes of several species were abundant, with mean capture rates of 32 666 snakes km-1 of irrigation canals, and 57 501 snakes km-2 of fishponds (9500 and 43 788 for H. buccata alone). Sex ratios of H. buccata were female-biased in ponds but not irrigation channels. Ponds underlain by deeper mud contained more snakes. Collecting methods varied among habitat types, in a way that reduced collateral risk to commercially farmed fish in ponds. Conclusions These water snakes are extremely abundant in Java, despite high levels of historical and continuing harvest. The inference of low population sizes for H. buccata in Indonesia, as presented in the IUCN Red List, is erroneous. Implications An ability to utilise anthropogenic resource subsidies (in this case, fish farmed in village ponds) allows some native predator species to attain remarkably high abundances, and to withstand intense efforts at harvesting.
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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 | 0.000 | 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) | 0.001 | 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".