Factors driving variation in movement rate and seasonality of sympatric ungulates
Bibliographic record
Abstract
Defining biologically relevant seasons is a critical issue in the interpretation of animal space-use studies. Moreover, understanding the effects of extrinsic (e.g., predation risk) and intrinsic (e.g., age and sex) factors on individual differences in seasonal transition dates will deepen our understanding of the mechanisms driving animal movement and potentially population dynamics. We used nonlinear modeling of movement rate over time using global positioning system-collared nonmigratory elk (Cervus elaphus manitobensis) and white-tailed deer (Odocoileus virginianus) in southern Manitoba, Canada, to derive species- and sex-specific seasonal transition dates. In addition, we used variables known to influence timing of migration in migratory populations to explain individual differences in seasonal transition dates. We found ecologically important differences in start and length of seasons between male and female elk and white-tailed deer. Individual differences in seasonal transition dates were large, and could be explained by a combination of intrinsic and extrinsic factors. Age-class of the individual animal and elevation influenced timing of winter, spring, and date of parturition, whereas predation risk from wolves (Canis lupus) influenced onset of spring, summer, and autumn. Our findings suggest that similar extrinsic and intrinsic factors can influence both large-(i.e., migratory) and small-scale movement patterns and can be used effectively to empirically define biologically relevant seasons for sympatric large herbivores.
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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".