The influence of habitat patch attributes on coyote group size and interaction in a fragmented landscape
Bibliographic record
Abstract
From February 2000 to January 2002, I investigated correlates of landscape fragmentation with coyote (Canis latrans Say, 1823) group size and resulting effects on within-group spatial interaction in west-central Indiana, USA, to determine whether habitat patch dispersion and attributes mediated group maintenance and persistence. Twenty-one radio-collared coyotes were assigned to 15 social groups; group territories were then classified as having dispersed (n = 10) or aggregated (n = 5) resource patches. Group size was larger in territories with aggregated patches and was directly correlated to forest area and inversely correlated to corridor area (top-ranked model: group size = β0+ forest area – corridor area; AICc = –2.12, ΔAICc = 0.0, ωi = 0.67). Territories with aggregated patches had proportionally more forest (mean = 0.41, SE = 0.02) and less corridor (mean = 0.01, SE = 0.002) habitats than territories with dispersed patches (forest area: mean = 0.11, SE = 0.01; corridor area: mean = 0.03, SE = 0.002). Within-group spatial interaction was not influenced by patch dispersion. I suggest that differences in territory and group sizes relative to patch dispersion reflect the complex combination of environmental pressures present in human-dominated landscapes and their potential to perturb canid social organization.
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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 | 0.002 |
| 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".