Urbanisation generates multiple trait syndromes for terrestrial taxa worldwide
Bibliographic record
Abstract
Cities can host significant biological diversity. Yet, urbanisation leads to the loss of habitats and, potentially, to local extinctions. Understanding how multiple taxa respond to urbanisation globally is essential to promote and conserve biodiversity in cities and surrounding landscapes. Using a dataset with site-level occurrence and trait data of 5302 species from six terrestrial fauna taxonomic groups across 379 cities on 6 continents, we show that urbanisation produces taxon-specific changes in trait composition, with traits related to reproductive strategy consistently showing the strongest response. The effect of urbanisation on community trait composition is strongest at the largest spatial scale considered, and more closely linked to landscape composition (% urban) than arrangement (aggregation), although latitude and climatic variables remain a stronger influence. This study did not find evidence in support of a global urban taxa syndrome, but instead we suggest that there are four general urban trait syndromes, with resources associated with reproduction and diet likely to be driving patterns in traits associated with mobility and body size. Functional diversity measures showed a wide range of responses, leading to a shift in trait space that is most likely driven by the distribution and abundance of critical resources, and the urban trait syndrome displayed by individual species within a community. Further research is required to understand the interactions between the four general urban trait syndromes, resource distribution and abundance and changes in functional diversity of taxa at different spatial and temporal scales. Maximising opportunities to support species within taxa groups with different urban trait syndromes should be pivotal in conservation and management programmes within and among cities. This will reduce the likelihood of biotic homogenisation at the taxa level, and helps ensure that urban environments have the ecological capacity to respond to challenges such as climate change, further habitat fragmentation and loss, and other disruptions. These actions are critical if we are to reframe the role of cities in global biodiversity loss.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".