Moose-wolf dynamics and the natural regulation of moose populations
Bibliographic record
Abstract
Long term data indicate that naturally regulated moose populations in southwestern Quebec stabilize at a density of ≈ 0.40 animal-km⁻². To test population regulation by forage competition, moose body condition was investigated near this equilibrium density (0.37=H) and at 2 lower densities (≈0.23=M, 0.17=L). Measurements of head length, cranial breadth, heart weight, and kidney weight were collected from 443 moose killed during the autumn harvesting seasons of 1981 and 1982. There was no indication that body condition was poorer at the high moose density, and hence no evidence that competition for forage explains the regulatory process. To test population regulation by predators, moose-wolf dynamics were studied at the same 3 moose densities (partial data in area L). In areas H and M, packs averaged 5.7 and 3.7 individuals, year-long territory sizes averaged 390 and 255 km2, and interstices between territories represented ≈0% and 30% of the available area, respectively. The wolf population in area M, as compared to area H, suffered from a higher mortality rate due to malnutrition and lethal intraspecific combat, and from a lower success in producing pups. Analysis of summer scats and winter feeding observations indicated a greater use of alternative food resources at lower moose densities. Each pack killed on average 5.3, 1.8, and 1.1 moose 100 days in areas H, M, and L, respectively. January wolf densities were respectively 1.38, 0.82, and 0.36 animals 100 km⁻². Year-long predation rates proved to be density-dependent, increasing with moose density from 6.1 to 19.3% of the postnatal populations. I conclude that moose populations in southwestern Quebec are regulated by predators at a density where competition for forage offers no detrimental effect. Preliminary results of a wolf removal experiment in area H support this hypothesis. A review of the natural regulation of moose populations is presented. I support the concept that wolf predation can have an important regulatory effect at low moose densities (<0.5-1.0 animal km⁻²) but also a depensatory (inversely density-dependent) effect at higher densities. Moose populations that are regulated by predators appear to be more stable than those regulated at high densities by forage competition.
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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 | 0.001 |
| 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 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".