Structural and socioeconomic features of cities predict migratory bird species richness
Bibliographic record
Abstract
Cities are aggregates of human activities where our decisions shape the environment creating heterogeneity across urban centers that can have significant ecological effects on wildlife. Many bird species are found in cities during the breeding season, which implies they find sufficient resources in cities to support them during this energetically costly time. As populations of many migratory bird species are declining, knowledge of how they are affected by urbanization is needed. Yet, we know little about how the species richness of migratory birds varies across different types of cities. Here we ask if cities' structural and socioeconomic features can predict the species richness of migratory birds that generally select different breeding habitats during the breeding season. We used eBird data from census‐designated urban areas in the US to model the relationship between features of cities (housing density, median income, city age, and commuting time), environmental disturbance (measured by the human footprint index) and species richness by fitting generalized linear models to data. We show that commuting time was the most important factor determining species richness across cities and the rest of the city features were weakly associated with species richness. Overall species were responding to city variation in similar ways. While we expected that cities with more disturbance would have lower species richness, our results indicate that some species are able to tolerate even highly disturbed cities and that cities in certain regions may act as a refuge to birds. This knowledge is important for our general understanding of cities as habitat for birds and how migratory birds respond to across‐city variation during the breeding season.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".