Characterizing demographic parameters across environmental gradients: a case study with Ontario moose (<i>Alces alces</i>)
Bibliographic record
Abstract
Population‐level demographic characteristics as estimated by standard logistic growth models (i.e., carrying capacity and intrinsic growth rate) should vary with changes in habitat quality and availability of resources. However, few published studies have tested this hypothesis by comparing population growth rates across broad bioclimatic gradients, and fewer still the carrying capacities of those populations. We used time series data on moose (Alces alces) population densities based on aerial census and hunter harvest data for 34 management units across Ontario to estimate local carrying capacities and intrinsic growth rates. These population parameters were then regressed against associated habitat covariates for each management unit to assess how moose demography changes across a broad gradient of productivity, habitat abundance, and timber harvest. Moose carrying capacity was found to increase with increasing forest productivity as measured by ΔNDVI and the proportion of mixedwood stands in the forest. Both variables are plausibly indicative of high quality forage abundance for moose. Moose carrying capacity decreased with the proportion of forest stands harvested for timber annually, suggesting that immediate removal of forest stands and increased access by hunters temper maximum population size. Maximum rates of population growth by Ontario moose did not vary predictably with any of the landscape covariates tested. These findings contribute to our understanding of changes in demography across broad geographic and bioclimatic gradients and suggest that crude population estimators may be derived based on known habitat preferences and resource availability without a priori knowledge of animal abundance.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".