Resource selection at homesites by wolves and eastern coyotes in a <i>Canis</i> hybrid zone
Bibliographic record
Abstract
Abstract We modeled resource selection by wolves (Canis spp.), eastern coyotes (C. latrans), and admixed canids during the pup‐rearing season at den and rendezvous sites (collectively, homesites) within a largely unprotected landscape proposed as the recovery zone for federally and provincially threatened eastern wolves (C. lycaon) in Ontario, Canada. Overall, canids selected wetlands, while avoiding secondary roads and open‐structure rock‐grass habitat patches. Packs with greater wolf ancestry selected wetlands and tertiary roads more strongly, while avoiding mixed conifer‐hardwood forests. Contrary to our prediction, canids with greater coyote ancestry did not establish homesites closer to roads, which likely mitigated their risk of human‐caused mortality during pup‐rearing. Packs exhibited increased selection of wetlands within territories as a function of increasing availability of wetlands. Packs with abundant access to wetlands may prioritize this habitat type to exploit beavers, a valuable prey species during pup‐rearing. Packs with higher pup survival selected hardwood forests and avoided conifer forests more than packs with lower pup survival. This is consistent with our understanding of habitat relations of the main prey species for canids in central Ontario and suggests that selecting prey‐rich habitat types at homesites increases fitness. A proposed goal of eastern wolf recovery is numerical and geographical expansion outside of the population core in Algonquin Provincial Park. Thus, our results provide valuable information for conservation by quantifying resource selection of wolves, coyotes, and hybrids during pup‐rearing and identifying links between fitness and homesite selection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".