Spatial overlap, proximity, and habitat use of individual wolves within the same packs
Bibliographic record
Abstract
ABSTRACT Packs are the basic social and breeding groups of wolf ( Canis spp.) populations and are often the sampling unit for wolf research. Researchers commonly assume, either explicitly or implicitly, that telemetry data from ≥1 individual wolf can be used to represent the space use, distribution, presence, and resource selection of the pack. We tested these critical assumptions using Global Positioning System telemetry by directly comparing home range size, probability of spatial overlap, seasonal proximity, and habitat use of individuals within wolf packs in central and northern Ontario, Canada, 2006–2010. Space use was similar and probability of overlap was high for wolves within packs for individual 95% home‐range contours. Variation was greater for 50% contours, indicating that individual wolves may use space differently within territories. Wolves within packs spent more time <100 m and <1 km from each other during winter ( = 66 and 75%, respectively) than during other seasons ( = 33 and 40%, respectively) supporting earlier findings that pack cohesion was highest during winter. Individuals within all packs exhibited differences in the use of specific resource variables when separated by >100 m. Use of habitat variables was more similar when we compared resource selection of individuals within packs using all locations (regardless of proximity), although we still detected differences within most packs. Our results suggest that telemetry data from individual wolves can be used to reliably estimate territories for packs, but that researchers should be cautious when making pack‐level inferences regarding finer scale space and habitat use within home ranges based on data from individual animals. © 2014 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".