Urbanisation dampens the latitude‐diversity cline in ants
Bibliographic record
Abstract
Abstract The increase in species diversity from temperate to tropical regions is one of the most widespread patterns in biogeography. As humans continue to drastically modify natural habitats, land‐use changes such as the development of cities could potentially alter typical latitudinal diversity gradients. Cities could depress or enhance biodiversity through filtering, localised extirpations, or increasing niche availability, respectively. To address these possibilities and the consequences for the latitudinal diversity gradient, we constructed a global dataset of urban species diversity (richness) and community composition across ~60° of absolute latitude and from 63 cities. We focused our study on ants, for which comparable urban and non‐urban diversity data are broadly available. We found that urbanisation significantly dampened the latitude‐diversity cline. The effects of urbanisation varied with latitude: at lower latitudes, cities were relatively species poor and harboured distinct ant communities relative to nearby non‐urban communities. In higher latitude cities, both species richness and community composition were more similar to the surrounding non‐urban ant communities. Our analyses suggest that the strongest impacts of urbanisation on ant diversity may be in the tropics, where biological diversity is already expected to experience the greatest risk of extinction in the face of climate change.
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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 | 0.002 |
| 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 | 0.000 |
| 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".