Correlation and persistence of hunting and logging impacts on tropical rainforest mammals
Bibliographic record
Abstract
Humans influence tropical rainforest animals directly via exploitation and indirectly via habitat disturbance. Bushmeat hunting and logging occur extensively in tropical forests and have large effects on particular species. But how they alter animal diversity across landscape scales and whether their impacts are correlated across species remain less known. We used spatially widespread measurements of mammal occurrence across Malaysian Borneo and recently developed multispecies hierarchical models to assess the species richness of medium- to large-bodied terrestrial mammals while accounting for imperfect detection of all species. Hunting was associated with 31% lower species richness. Moreover, hunting remained high even where richness was very low, highlighting that hunting pressure persisted even in chronically overhunted areas. Newly logged sites had 11% lower species richness than unlogged sites, but sites logged >10 years previously had richness levels similar to those in old-growth forest. Hunting was a more serious long-term threat than logging for 91% of primate and ungulate species. Hunting and logging impacts across species were not correlated across taxa. Negative impacts of hunting were the greatest for common mammalian species, but commonness versus rarity was not related to species-specific impacts of logging. Direct human impacts appeared highly persistent and lead to defaunation of certain areas. These impacts were particularly severe for species of ecological importance as seed dispersers and herbivores. Indirect impacts were also strong but appeared to attenuate more rapidly than previously thought. The lack of correlation between direct and indirect impacts across species highlights that multifaceted conservation strategies may be needed for mammal conservation in tropical rainforests, Earth's most biodiverse ecosystems.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".