Species interactions limit the occurrence of urban-adapted birds in cities
Bibliographic record
Abstract
Urbanization represents an extreme transformation of more natural systems. Populations of most species decline or disappear with urbanization, and yet some species persist and even thrive in cities. What determines which species persist or thrive in urban habitats? Direct competitive interactions among species can influence their distributions and resource use, particularly along gradients of environmental challenge. Given the challenges of urbanization, similar interactions may be important for determining which species persist or thrive in cities; however, their role remains poorly understood. Here, we use a global dataset to test among three alternative hypotheses for how direct competitive interactions and behavioral dominance may influence the breeding occurrence of birds in cities. We find evidence to support the competitive interference hypothesis: behaviorally dominant species were more widespread in urban habitats than closely related subordinate species, but only in taxa that thrive in urban environments (hereafter, urban adapted), and only when dominant and subordinate species overlapped their geographic ranges. This result was evident across diverse phylogenetic groups but varied significantly with a country's level of economic development. Urban-adapted, dominant species were more widespread than closely related subordinate species in cities in developed, but not developing, countries; countries in economic transition showed an intermediate pattern. Our results provide evidence that competitive interactions broadly influence species responses to urbanization, and that these interactions have asymmetric effects on subordinate species that otherwise could be widespread in urban environments. Results further suggest that economic development might accentuate the consequences of competitive interactions, thereby reducing local diversity in cities.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".