Human‐dominated land uses favour species affiliated with more extreme climates, especially in the tropics
Bibliographic record
Abstract
Rapid human population growth has driven conversion of land for uses such as agriculture, transportation and buildings. The removal of natural vegetation changes local climate, with human‐dominated land uses often warmer and drier than natural habitats. Yet, it remains an open question whether land‐use changes influence the composition of ecological assemblages in a direction consistent with the mechanism of local climatic change. Here, we used a global database of terrestrial vertebrates (mammals, birds, reptiles and amphibians) to test whether human‐dominated land uses systematically favour species with distinctive realised climatic niches. We 1) explored the responses of community‐average temperature and precipitation niches to different types of land use, 2) quantified the abundances of species with distinctive climatic niches across land uses and 3) tested for differences in emergent patterns in communities from tropical versus temperate latitudes. We found that, in comparison to species from undisturbed natural habitats, the average animal found in human‐altered habitats lives in areas with higher maximum and lower minimum temperatures and higher maximum and lower minimum precipitation levels. We further found that tropical assemblages diverged more strongly than temperate assemblages between natural and human‐altered habitats, possibly because tropical species are more sensitive to climatic conditions. These results strongly implicate the role of land‐use change in favouring species affiliated with more extreme climatic conditions, thus systematically reshaping the composition of terrestrial biological assemblages. Our findings have the potential to inform species' vulnerability assessments and highlight the importance of preserving local climate refugia.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".