Contender pressure versus resource dispersion as predictors of territory size of coyotes (<i>Canis latrans</i>)
Bibliographic record
Abstract
Many studies have proposed resource dispersion as the main determinant of territory size in coyotes ( Canis latrans Say, 1823), but few have considered contender pressure as an alternative hypothesis. We tested for differences in rates of intra-territorial visitation, movement, and extra-territorial excursions between two populations of coyotes with large differences in territory sizes. We collected fine-scale (15 min) movement data of coyotes in southeastern Texas and south-central Idaho. Both populations were active for similar lengths of each day, but coyotes in Idaho had territories 10× larger, moved 2× faster, traveled 2× farther daily, and made extra-territorial excursions 3× less. Even with increased movement rates, coyotes in Idaho traversed territories slower than coyotes in Texas as predicted by the contender pressure hypothesis. We propose that in regions with high resource abundance, territory size of coyotes is determined by contender pressure and an inability to defend larger areas. Conversely, in low-resource areas, territory sizes are determined more by prey abundance and dispersion because intrusion rates are reduced given the lower density of conspecifics.
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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 | 0.000 | 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".