Synergistic effects of climate change and agricultural land use on mammals
Bibliographic record
Abstract
Although climate change and the expansion of agriculture are two of the primary threats to global biodiversity, they are usually considered independently. Here, I show that climate change and agricultural expansion interact synergistically in their impacts on mammals across mega‐biodiverse Southeast Asia. Rising temperatures do not directly reduce niche availability for most species but do trigger a major altitudinal expansion in the cultivation zone for oil palm ( Elaeis guineensis ), a cold‐intolerant crop tree that is currently restricted to tropical lowlands. The resulting replacement of native forests would reduce mammal ranges by 47–67% by 2070, given a low‐ or high‐carbon‐emissions trajectory, respectively. This reduction is 3–4 times the magnitude of that predicted from land‐use change without considering climatic effects. The synergistic interaction between climate change and land‐use change greatly outweighs the impact on biodiversity of either factor alone or their additive combination.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".