Moose Calving Strategies in Interior Montane Ecosystems
Bibliographic record
Abstract
Abstract Parturient ungulates are relatively more sensitive to predation risk than other individuals and during other times of the year. Selection of calving areas by ungulates may be ultimately related to trade-offs between minimizing risk of predation and meeting nutritional needs for lactation. We used digital and field data to examine selection of calving areas by 31 global positioning system–collared moose (Alces alces) in southeastern British Columbia. We examined movements 12 days before and after calving, and analyzed habitat selection at 2 scales of comparison: the immediate calving area to the extended calving area (100 ha), and the extended calving area to the surrounding home range. Maternal moose exhibited 1 of 2 distinct elevational strategies for calving area selection during the days leading up to calving: 16 moose were climbers and 15 were nonclimbers. Climbers moved a mean of 310 m higher in elevation to calve, whereas nonclimbers showed little change in elevation. Hourly movements by all maternal females increased 2- to 3-fold in the 1–4 days before calving and were generally directional, such that all calving areas were outside of areas used during the 12 days before calving. At the broad scale, elevation was the strongest predictor of the extended calving area within the home range. At the fine scale, climbers selected areas with reduced tree density, reduced forage, and increased distance from water, whereas nonclimbers selected areas with increased forage, decreased distance from water, and decreased slope. Beyond the obvious elevation difference between climbers and nonclimbers, moose appeared to exhibit 2 distinct calving strategies in mountainous ecosystems. A functional explanation for the 2 strategies may be that climbers moved into areas where forage quantity and quality were relatively low, but where risk of predation (mainly by grizzly bears [Ursus arctos]) also was reduced. Nonclimber moose calved in areas with higher forage values, and appeared to select areas at the finer scale to reduce predation risk (e.g., association with water and reduced tree density for visibility).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".