Spatial home-range overlap and temporal interaction in eastern coyotes: the influence of pair types and fragmentation
Bibliographic record
Abstract
No data exist regarding the linkage between the dispersion of critical resources and the spatial distribution of eastern coyotes (Canis latrans). From February 2000 to January 2002, we investigated landscape-level correlates of fragmentation with coyote spacing patterns and interaction in west-central Indiana to determine whether habitat fragmentation may influence spatiotemporal home-range overlap. Eleven pairs of coyotes (four malefemale, four malemale, three femalefemale) displayed spatial overlap in portions of their home-range utilization distributions; seven pairs interacted temporally. Percent home-range overlap of space-sharing pairs averaged 55%. Area of forested habitat within the overlap zone, pair type, and mean squared difference of nearest-neighbor distances between forested patches explained substantial amounts of variation in percent home-range overlap (R2= 0.83, P < 0.001). Extent of temporal interaction differed by pair type, as malemale pairs interacted substantially more than malefemale and femalefemale pairs. Five (two malemale, three malefemale) of seven temporally interacting pairs exhibited simultaneous attraction to the overlap zone. The complex combination of environmental pressures present in human-dominated landscapes may facilitate spatiotemporal home-range overlap in coyotes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".