City sicker? A meta‐analysis of wildlife health and urbanization
Bibliographic record
Abstract
Urban development can alter resource availability, land use, and community composition, which, in turn, influences wildlife health. Generalizable relationships between wildlife health and urbanization have yet to be quantified and could vary across different measures of health and among species. We present a phylogenetic meta‐analysis of 516 comparisons of the toxicant loads, parasitism, body condition, or stress of urban and non‐urban wildlife populations reported in 106 studies spanning 81 species in 30 countries. We found a small but significant negative relationship between urbanization and wildlife health, driven by considerably higher toxicant loads and greater parasite abundance, greater parasite diversity, and/or greater likelihood of infection by parasites transmitted through close contact. Invertebrates and amphibians were particularly affected, with urban populations having higher toxicant loads and greater physiological stress than their non‐urban counterparts. We also found strong geographic and taxonomic bias in research effort, highlighting future research needs. Our results suggest that some types of health risks are more pronounced for wildlife in urban areas, which could have important implications for conservation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".