Managing wolves (<i>Canis</i><i> lupus</i>) to recover threatened woodland caribou (<i>Rangifer</i><i> tarandus caribou</i>) in Alberta
Bibliographic record
Abstract
Across Canada, woodland caribou (Rangifer tarandus caribou (Gmelin, 1788)) populations are declining because of human-induced changes to food webs that are resulting in apparent competition-induced increases in predator-caused caribou mortality. We tested the hypothesis that wolf (Canis lupus L., 1758) population reduction could reverse declines in a woodland caribou population following a BACI (before-after-control-impact) design conducted over a 12-year period in west-central Alberta, Canada. We monitored annual survival for 172 adult female caribou and calf recruitment from 2000 through 2012 and conducted a provincial government delivered wolf population reduction program annually during the winters of 2005–2006 to 2012 (inclusive) in an area centered on the Little Smoky range. Wolf removal translated to a 4.6% increase in mean population growth rate of the Little Smoky population mostly through improvements in calf recruitment. In contrast, the Red Rock Prairie Creek control population exhibited a 4.7% decline. Although the wolf population reduction program appeared to stabilize the Little Smoky population, it did not lead to population increase, however, with λ remaining approximately equal to 1. Therefore, we recommend, if required, predation management be combined with effective habitat conservation and long-term planning to effect the recovery of species, such as woodland caribou, which are declining as a result of habitat-mediated apparent competition.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".