Patterns and causes of demographic variation in a harvested moose population: evidence for the effects of climate and density-dependent drivers
Bibliographic record
Abstract
1. Better understanding of the mechanisms affecting demographic variation in ungulate populations is needed to support sustainable management of harvested populations. While studies of moose Alces alces L. populations have previously explored temporal variation in demographic processes, managers responsible for populations that span large heterogeneous landscapes would benefit from an understanding of how demography varies across biogeographical gradients in climate and other population drivers. Evidence of thresholds in population response to manageable and un-manageable drivers could aid resource managers in identifying limits to the magnitude of sustainable change. 2. Generalized additive models (GAMs) were used to evaluate the relative importance of population density, habitat abundance, summer and winter climatic conditions, primary production, and harvest intensity in explaining spatial variation in moose vital rates in Ontario, Canada. Tree regression was used to test for thresholds in the magnitudes of environmental predictor variables that significantly affected population vital rates. 3. Moose population growth rate was negatively related to moose density and positively related to the abundance of mixed deciduous habitat abundant in forage. Calf recruitment was negatively related to a later start of the growing season and calf harvest. The ratio of bulls to cows was related to male harvest and hunter access, and thresholds were evident in predictor variables for all vital rate models. 4. Findings indicate that the contributions of density-dependent and independent factors can vary depending on the scale of population process. The importance of density dependence and habitat supply to low-density ungulate populations was evident, and management strategies for ungulates may be improved by explicitly linking forest management and harvest. Findings emphasize the importance of considering summer climatic influences to ungulate populations, as recruitment in moose was more sensitive to the timing of vegetation green-up than winter severity. The efficacy of management decisions for harvested ungulates may require regional shifts in targets where populations span bioclimatic gradients. The use of GAMs in combination with recursive partitioning was demonstrated to be an informative analytical framework that captured nonlinear relationships common in natural processes and thresholds that are relevant to population management in diverse systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".