Impacts of hunting on tropical forests in Southeast Asia
Bibliographic record
Abstract
Although deforestation and forest degradation have long been considered the most significant threats to tropical biodiversity, across Southeast Asia (Northeast India, Indochina, Sundaland, Philippines) substantial areas of natural habitat have few wild animals (>1 kg), bar a few hunting-tolerant species. To document hunting impacts on vertebrate populations regionally, we conducted an extensive literature review, including papers in local journals and reports of governmental and nongovernmental agencies. Evidence from multiple sites indicated animal populations declined precipitously across the region since approximately 1980, and many species are now extirpated from substantial portions of their former ranges. Hunting is by far the greatest immediate threat to the survival of most of the region's endangered vertebrates. Causes of recent overhunting include improved access to forests and markets, improved hunting technology, and escalating demand for wild meat, wildlife-derived medicinal products, and wild animals as pets. Although hunters often take common species, such as pigs or rats, for their own consumption, they take rarer species opportunistically and sell surplus meat and commercially valuable products. There is also widespread targeted hunting of high-value species. Consequently, as currently practiced, hunting cannot be considered sustainable anywhere in the region, and in most places enforcement of protected-area and protected-species legislation is weak. The international community's focus on cross-border trade fails to address overexploitation of wildlife because hunting and the sale of wild meat is largely a local issue and most of the harvest is consumed in villages, rural towns, and nearby cities. In addition to improved enforcement, efforts to engage hunters and manage wildlife populations through sustainable hunting practices are urgently needed. Unless there is a step change in efforts to reduce wildlife exploitation to sustainable levels, the region will likely lose most of its iconic species, and many others besides, within the next few years.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".