Evidence for red fox (<i>Vulpes vulpes</i>) exploitation of anthropogenic food sources along an urbanization gradient using stable isotope analysis
Bibliographic record
Abstract
As urban areas expand, wildlife show adaptations to urban ecosystems. We tested two hypotheses for urban populations of red fox (Vulpes vulpes (Linnaeus, 1758)) in urban areas: the population pressure hypothesis, which posits that urban foxes make do with suboptimal habitat, and the urban island hypothesis, which presumes that urban areas provide high-quality habitat. We investigated habitat quality by investigating anthropogenic food in fox diets across a rural–urban gradient in Lancaster, Pennsylvania (USA). We used stable carbon isotopes because human food can have a distinct stable carbon isotope signature. We collected fox hair and stomach samples from 21 locations and extracted land use and land cover characteristics within a 100 ha buffer area. We found that higher δ13C values in fox hair were positively correlated with impervious surface cover and developed open spaces, key metrics of urbanization, and negatively associated with agricultural land cover, an indicator of rural habitats. Overall, fox hair δ13C was less related to urbanization and more related to the availability of developed open spaces that provide habitat with vegetation cover and access to nearby food sources. Our results suggest that urban habitats are high quality and support the growing literature revealing that certain species may thrive in urban areas.
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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 | 0.000 |
| 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 | 0.001 |
| 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".