Changes in mesopredator-community structure in response to urbanization
Bibliographic record
Abstract
Common raccoons (Procyon lotor (L., 1758)), Virginia opossums (Didelphis virginiana Kerr, 1792), and striped skunks (Mephitis mephitis (Schreber, 1776)) are common urban inhabitants, yet their relative demographic response to urbanization is unknown. Urbanization often affects community structure, and understanding these effects is essential in rapidly changing landscapes. We examined mesopredator-community structure in small and large patches of natural habitat surrounded by urban, suburban, or rural matrices. We created generalized logit models using road-survey and livetrapping data to examine effects of surrounding land use on proportions of opossums and skunks relative to raccoons, while accounting for effects of season and year and their interactions. For large sites, the land use × season model was chosen for both data sets, and occurrence of opossums and skunks relative to raccoons was higher at the rural site (P < 0.001 for all tests). For small sites, the land-use model best fit the road-survey data, with a higher occurrence of skunks relative to raccoons at the rural site (χ2 = 21.06, df = 1, P < 0.001). However, the season model best fit the trapping data for small sites. Our data indicated that raccoons exhibited a greater demographic response to urbanization, suggesting that they exploit anthropogenic resources more efficiently. Although numerous reasons exist for disparity in anthropogenic-resource use, differences in intraspecific tolerance and the role of learning in foraging behaviors were best supported by our observations.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".