Human pressures threaten diet-specialized mammal communities
Bibliographic record
Abstract
Environmental change is increasing worldwide and many animal species face anthropogenic threats, especially diet specialists. Yet the degree to which specialist species are currently impacted by environmental change remains poorly understood. We examine how anthropogenic pressures impact dietary specialist species. We calculated indices of diet specialization for mammal species, based on the Gini inequality coefficient, and combined these indices with human pressure data. We then used spatially explicit Mantel tests to examine global patterns in mammal diet specialization. We used a generalized linear mixed model to investigate correlations between the percentage of diet specialist species in mammal communities in an area and its total species richness, human pressure and protection status (mediated through an interaction with the continent). Findings revealed that areas with many diet specialists in mammal communities are also impacted by high human pressure. Additionally, we found that the global protected area system adequately covers habitat for many mammal diet specialists, but has lower effectiveness in South America, Oceania, North America and Europe compared with Africa and Asia. Finally, we identified potential reservoirs for specialist species-places containing many highly diet specialist species and that are subject to less human pressure-which may be important for conservation efforts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".