There’s no place like home — site fidelity by female moose (<i>Alces alces</i>) in central Ontario, Canada
Bibliographic record
Abstract
Site fidelity is thought to provide increased fitness through familiarity with the distribution of forage, protective cover, breeding and offspring rearing sites, and predators. For moose (Alces alces (Linnaeus, 1758)), previous research has documented fidelity at varying spatial scales. Our objective was to build on this knowledge and assess fidelity by adult female moose in two areas of central Ontario, Canada (Algonquin Provincial Park (APP) and Wildlife Management Unit 49 (WMU49)). We used global positioning system data to generate mean weekly locations for collared moose, then measured the distance between paired weekly locations among consecutive years to evaluate site fidelity. We tested for effects of study area, biological season, moose age, and reproductive status using generalized linear mixed models. Moose demonstrated stronger site fidelity in WMU49, an area with more anthropogenic disturbance, than the protected area, APP. Fidelity was weakest in the winter, but was similar among other seasons and was independent of maternal age and the presence of a calf. Our study highlights the need to consider the scale of site fidelity relative to habitat management. Actions aimed at supporting moose populations might benefit more by protecting habitat classes selected by moose rather than specific sites used by individuals.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".