Movement parameters of ungulates and scale‐specific responses to the environment
Bibliographic record
Abstract
Summary Most studies of animal movements and habitat selection do not recognize empirically that different components of the environment are important to animals at different scales. Often, availability of habitats is defined at one or more arbitrary spatio‐temporal scales, but use of those habitats is constrained to one scale. Identification of scalar movement is the first step in developing models to explain why animals select or move to certain parts of their range. We used a non‐linear curve‐fitting model of movement rates to identify discontinuities in the scales of movement by woodland caribou Rangifer tarandus caribou collared with global positioning system (GPS) collars. We differentiated intrapatch from interpatch movements, but were unable to distinguish interpatch from migratory‐type movements for most combinations of individual caribou by season. Model fit was stronger for winter than summer movements. We suggest that increased patch heterogeneity during the winter resulted in interseason variation in movements and corresponding model fit. Responses by caribou to the environment were scale‐dependent. When we applied logistic regressions, land‐cover type, energetic costs of movement, and predation risk differentiated the two scales of movement. Intrapatch movements had a lower cost of movement, were associated with cover types where foraging behaviours probably occurred, and were closer to areas of higher predator risk than interpatch movements. Application of the non‐linear model will aid in developing mechanism‐based approaches to studying resource selection and animal behaviour.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".