Territorial scent-marking and proestrus in a recolonizing wild Gray Wolf (<i>Canis lupus</i>) population in central Wisconsin
Bibliographic record
Abstract
Gray Wolf (Canis lupus) uses scent-marking to communicate breeding status, dominance, and territorial boundaries. Despite its importance for reproduction and pack dynamics, information on scent-marking and proestrus in wild wolf populations is limited to a handful of locations. We estimated the rate of territorial scent-marking and the probability of proestrus in a recolonizing Gray Wolf population near the species southern range extent in eastern North America. An analysis of 221 pack-winters of tracking data show that the incremental addition of one wolf pack increased marking rates by 3.4%, whereas increasing the number of wolves in a pack decreased marking rates by 12.1%. Scent-marking rates subsequently increased from 1.9 times/km during recolonization to 3.0 times/km once the population was saturated. We observed evidence of proestrus from 19 December to 14 March with the highest probability of proestrus occurring around 6 February, after peak marking rates around 26 January. Repeated observations of bloody urinations within individual packs suggest proestrus averages 27.9 days. Our study reveals the role of population growth on territorial behaviours and provides a foundation for studies exploring the role of geographic and temporal variation on territorial and reproductive behaviours in wolves.
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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 | 0.000 | 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) | 0.000 | 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 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".