Restoring historical moose densities results in fewer wolves killed for woodland caribou conservation
Bibliographic record
Abstract
Abstract Woodland caribou ( Rangifer tarandus caribou ) are declining across much of their distribution in Canada in response to habitat alteration, leading to unsustainable predation, particularly by wolves ( Canis lupus ). Habitat alteration can benefit the primary prey species of wolves (moose [ Alces alces ] and deer [ Odocoileus spp.]) by creating early seral conditions that contain more of their preferred food types. This increase in primary prey populations results in elevated wolf abundance and heightened predation pressure on caribou. In response to the elevated wolf populations and the risks to caribou, managers have reduced wolf abundance in key areas. Ecological theory suggests that reducing wolf abundance would release moose from the top‐down effects of wolf predation, potentially allowing moose populations to grow. Elevated moose abundance thus has the potential to cause wolf populations to rebound quickly each year following reductions, suggesting a possible link between moose abundance and the number of wolves killed for caribou conservation. To test this idea we used a unique management situation in British Columbia and Alberta, Canada, where lethal wolf removals were annually conducted across specific southern mountain caribou population ranges and, in some places, moose populations were concurrently reduced via liberalized hunting. We used indices of moose abundance and wolf removal data to test the hypothesis that reducing moose populations to a historical abundance target by hunting leads to fewer wolves killed for caribou conservation. After controlling for habitat quality, wolves removed per km 2 was 3.2 times lower in areas with reduced moose density ( = 1.55 wolves/1,000 km 2 ± 0.33 [SE]) than in those without reduced moose density ( = 5.02 wolves/1,000 km 2 ± 0.52). However, the average number of wolves removed per year decreased under both conditions. After 9 years, there was a 35% reduction in the predicted difference in the annual removal between areas with and without moose reduction. Our results suggest that policies that do not reduce or stabilize moose abundance will result in the removal of more wolves to increase caribou abundance. Like wolf reductions, moose reductions can also be controversial and affect local harvesters. Thus, understanding the consequences of actions that support caribou recovery is essential to supporting evidence‐based policy discussions.
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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 | 0.000 |
| 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) | 0.002 | 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".