From tropical shelters to temperate defaunation: The relationship between agricultural transition stage and the distribution of threatened mammals
Bibliographic record
Abstract
Abstract Aim Agriculture is a key threat to biodiversity; however, its relationship with biodiversity patterns is understudied. Here, we evaluate how the extent, intensity and history of croplands relate to the global distribution of threatened mammals. We propose two hypotheses to explain these relationships: shelter, which predicts that threatened species concentrate in areas with low human land use; and threat, according to which threatened species should concentrate in areas of high human land use. Location Global. Time period c. 6000 BC – AD 2014. Major taxa studied Terrestrial mammals. Methods We used boosted regression trees (BRT) that include spatial autocorrelation to investigate the relationship between the proportion of threatened terrestrial mammals [as defined by the International Union for Conservation of Nature (IUCN) Red List] and multiple metrics describing agricultural extent, intensity and history derived from remote sensing data and statistical projections. Data were analysed with a grain size of c. 110 km × 110 km at both global and biogeographical‐realm scales. Results Agricultural extent and intensity were the most relevant indicator types, with specific metrics important for each realm. Forest cover (extent) was identified as important in several regions. Tropical regions in early agricultural transition stages (e.g. frontier landscapes) were consistent with the shelter hypothesis, whereas patterns found for regions in later stages (e.g. intensified agricultural landscapes) were mostly found in temperate regions and agreed with the threat hypothesis. Main conclusions These results highlight the need to consider multiple land‐use indicators when addressing threats to biodiversity and to separately assess areas with divergent human and ecological histories in global‐scale studies. Different relationships associated with different agricultural transition stages suggest that high concentrations of threatened species may have contrasting meanings in different regions worldwide. We propose a new unifying hypothesis following a cyclic relationship along agricultural transition stages resulting in alternating negative and positive relationships between agriculture and threatened species richness.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".