Relative abundance of coyotes (<i>Canis latrans</i>) influences gray fox (<i>Urocyon cinereoargenteus</i>) occupancy across the eastern United States
Bibliographic record
Abstract
Gray fox (Urocyon cinereoargenteus (Schreber, 1775)) populations in portions of the eastern United States have experienced declines whose trajectories differ from those of other mesocarnivore populations. One hypothesis is that gray fox declines may result from interspecific interactions, particularly competition with abundant coyotes (Canis latrans Say, 1823). Alternatively, gray foxes may respond negatively to increased urbanization and reduced forest cover. To evaluate these hypotheses, we used single-species occupancy models of camera trap data to test the effects of habitat covariates, such as the amount of urbanization and forest, on coyote and gray fox occupancy. Additionally, we test the effect of an index based on an N-mixture model of the number of coyotes at each camera trap site on gray fox occupancy. Results indicate that occupancy probabilities of coyote and gray fox relate positively to the amount of forest, but they provided no evidence urban cover impacts gray foxes. Additionally, gray fox occupancy was negatively related to the index of the number of coyotes at each site. Our models support the idea that interactions with coyotes impact gray fox occupancy across the eastern United States. These results illustrate how large-scale studies can relate mechanisms identified within specific landscapes to phenomena observed at larger scales.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".