Climate change and habitat conversion favour the same species
Bibliographic record
Abstract
Land-use change and climate change are driving a global biodiversity crisis. Yet, how species' responses to climate change are correlated with their responses to land-use change is poorly understood. Here, we assess the linkages between climate and land-use change on birds in Neotropical forest and agriculture. Across > 300 species, we show that affiliation with drier climates is associated with an ability to persist in and colonise agriculture. Further, species shift their habitat use along a precipitation gradient: species prefer forest in drier regions, but use agriculture more in wetter zones. Finally, forest-dependent species that avoid agriculture are most likely to experience decreases in habitable range size if current drying trends in the Neotropics continue as predicted. This linkage suggests a synergy between the primary drivers of biodiversity loss. Because they favour the same species, climate and land-use change will likely homogenise biodiversity more severely than otherwise anticipated.
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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) | 0.000 | 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) | 0.033 | 0.003 |
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; both teacher heads agree on what is shown here.
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".