Environmental and individual drivers of animal movement patterns across a wide geographical gradient
Bibliographic record
Abstract
Within the rapidly developing field of movement ecology, much attention has been given to studying the movement of individuals within a subset of their population's occupied range. Our understanding of the effects of landscape heterogeneity on animal movement is still fairly limited as it requires studying the movement of multiple individuals across a variety of environmental conditions. Gaining deeper understanding of the environmental drivers of movement is a crucial component of predictive models of population spread and habitat selection and may help inform management and conservation. In Ontario, woodland caribou (Rangifer tarandus caribou) occur along a wide geographical gradient ranging from the boreal forest to the Hudson Bay floodplains. We used high-resolution GPS data, collected from 114 individuals across a 450000 km(2) area in northern Ontario, to link movement behaviour to underlying local environmental variables associated with habitat permeability, predation risk and forage availability. We show that a great deal of observed variability in movement patterns across space and time can be attributed to local environmental conditions, with residual individual differences that may reflect spatial population structure. We discuss our results in the context of current knowledge of movement and caribou ecology and highlight potential applications of our approach to the study of wide-ranging animals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".