Modeling the impact of moose and wolf management on persistence of woodland caribou.
Bibliographic record
Abstract
Limiting factors of caribou (Rangifer tarandus) populations vary regionally. In tundra environments, this species appears to be regulated by food, either because wolves (Canis lupus) are absent or because migration of caribou allows escape from predation during part of the year. In the boreal forest, the main limiting factors are hunting and predation but because of low caribou densities, no regulation mechanism seems to exist between caribou and wolves. Moose (Alces alces) is the primary prey species of wolves and consequently, if moose abundance increases, wolves should also increase, independently of the caribou population. Thus, caribou could experience high predation rates and be eliminated in high wolf densities. Here we attempted to identify the necessary conditions to maintain caribou numbers in the presence of moose. To do so, we built a deterministic model that simulated the relationship between a caribou population regulated by food competition and limited by predation, a moose population regulated by predation, and a wolf population, the abundance of which is determined by moose abundance. At current hunting rates for caribou and moose in the boreal forest, and in the absence of wolf trapping, the model predicted that the caribou population would be extirpated in ap- proximately 100 years. Wolf trapping was not adequate to conserve the caribou population unless very intensive control was undertaken. In the absence of trapping, cessation of caribou hunting allowed a 3-fold increase in caribou numbers over the long term, if the moose population remained low. According to our model, the best management measure for caribou consisted of maintaining a low moose density through appropriate population and habitat management strategies, which prevented expansion of the wolf population and limited predation on caribou. ALCES VOL. 43: 13-27 (2007)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".