Predicting defaunation: accurately mapping bushmeat hunting pressure over large areas
Bibliographic record
Abstract
Unsustainable hunting is emptying forests of large animals around the world, but current understanding of how human foraging spreads across landscapes has been stymied by data deficiencies and cryptic hunter behaviour. Unlike other global threats to biodiversity like deforestation, climate change and overfishing, maps of wild meat hunters' movements-often based on forest accessibility-typically cover small scales and are rarely validated with real-world observations. Using camera trapping data from rainforests across Malaysian Borneo, we show that while hunter movements are strongly correlated with the accessibility of different parts of the landscape, accessibility measures are most informative when they integrate fine-scale habitat features like topography and land cover. Measures of accessibility naive to fine-scale habitat complexity, like distance to the nearest road or settlement, generate poor approximations of hunters' movements. In comparison, accessibility as measured by high-resolution movement models based on circuit theory provides vastly better reflections of real-world foraging movements. Our results highlight that simple models incorporating fine-scale landscape heterogeneity can be powerful tools for understanding and predicting widespread threats to biodiversity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".