Quantifying species-level responses to urbanization in North American birds
Bibliographic record
Abstract
Species vary in their responses to urbanization – most species avoid urban habitats, some tolerate these environments, and very few thrive in them. To better characterize the extent to which species vary in their responses to urbanization (hereafter urban responses), we developed several methods to quantify these responses at a continental scale across all birds. Using open access community science-derived data from the eBird Status and Trends Products and two different types of high-resolution geospatial data that quantify urbanization of landscapes, we calculated urban response indices for 476 species with breeding ranges that overlap large cities in Canada or the United States. We used six different calculations to characterize species-level urban response indices, allowing us to assess how each species’ relative abundance during the breeding season varied with estimates of urbanization. We assessed correlations among these six indices, then compressed them into a single principal component (multivariate urban response index) that captured variation in urban responses among species. We demonstrated the accuracy of our multivariate urban response index using 24 species that are well characterized in their tolerance or avoidance of urban habitat, as well as with previously published, independent urban response estimates for 99 species. We found a significant phylogenetic signal in the multivariate urban response index for younger lineages but not among deeper lineages, suggesting that traits associated with urban responses are not highly conserved. Our study provides some of the most precise estimates of species' responses to urbanization to date.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".