Stable pack abundance and distribution in a harvested wolf population
Bibliographic record
Abstract
ABSTRACT Harvesting gray wolves (Canis lupus) could affect the abundance and distribution of packs, but the frequency of change in pack occurrence (i.e., turnover) and relative effects of harvest compared to environmental factors is unclear. We used noninvasive genetic sampling, hunter surveys, and occupancy models to evaluate the effect of harvest on occurrence and turnover of packs in a population of wolves managed with intensive harvest in the Canadian Rocky Mountains, 2012–2014. We tested 2 alternative hypotheses: the abundance and distribution of wolf packs were dynamic because of harvest or the abundance and distribution of wolf packs were generally stable regardless of harvest. We found the mean annual probability for wolf pack occupancy ranged 0.72–0.74 and the estimated distribution of wolf packs was consistent over time, 2012–2014. Our top model indicated wolf pack occupancy was positively associated with forest cover and the probability of detecting a wolf pack was positively associated with the intensity of harvest for wolves in that area. We observed frequent turnover of individuals within packs that were genetically sampled consecutive years but not of entire packs. Because turnover of packs occurred infrequently during our study, we could not reject our hypothesis that occurrence of packs was generally stable in a harvested population of wolves. Our results suggest environmental factors have a stronger effect than harvest on the abundance and distribution of wolf packs in southwestern Alberta, but harvest appears to strongly influence turnover of individuals within packs. We hypothesize local dispersal from within the study area and neighboring packs on the periphery of the study area helped promote pack stability. © 2018 The Wildlife Society.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".