While the wolf is away: Modelling the dynamics of a moose population in a protected area
Bibliographic record
Abstract
Populations at high density can threaten the ecological integrity of ecosystems through cascading effects. When such situations arise, management practices must be guided by sufficient knowledge of the biological mechanisms at play. Simulation models are powerful tools for acquiring such knowledge. The moose (Alces alces americana) is a species that recently became overabundant in some areas of eastern North America, sometimes requiring specific management measures. While numerous models exist for moose population dynamics, few of them are adapted to high density populations like the one in Forillon National Park (Quebec, Canada), a protected area in which the moose's apical predator (grey wolf Canis lupus) is absent. We developed a sex- and age-structured population model respecting these conditions that we parameterized using pattern-oriented modelling. The most plausible sequence of vital rates identified exhibited strong negative density dependence in survival, reproduction and dispersal. Predation by alternative predators, black bears (Ursus americanus) and coyotes (Canis latrans), caused substantial mortality of calves each year. Contrary to other areas in northeastern North America, winter tick only had a slight effect on calf survival, except when moose density approached carrying capacity. The variations in the population’s sex ratio were mainly explained by a sex-biased dispersal. Our study provides new insights concerning the dynamics of high-density ungulate populations in the absence of their apical predator, and our modelling approach helped to shed light on new methodological challenges and opportunities. We also present a comprehensive process to build a complex population model and parameterize it while using scarce data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".