Spatial scales of habitat selection decisions: implications for telemetry‐based movement modelling
Bibliographic record
Abstract
Movement influences a myriad of ecological processes operating at multiple spatial and temporal scales. Yet our understanding of animal movement is limited by the resolution of data that can be obtained from individuals. Traditional approaches implicitly assume that movement decisions are made at the spatial and temporal scales of observation, although this scale is typically an artifact of data‐gathering technology rather than biological realism. To address this limitation, we used telemetry‐based movement data for caribou Rangifer tarandus in Newfoundland, Canada, and compared movement decisions estimated at the temporal resolution of GPS relocations (2 h) to a novel model describing directional movement to areas reachable over an extended period. We showed that this newer model is a better predictor of movement decisions by caribou, with decisions made at the scale of ∼2 km, including the strong avoidance of dense coniferous forest, an outcome not detectable at the scale of GPS relocations. These results illustrate the complexity of factors affecting animal movement decisions and the analytical challenges associated with their interpretation. Our novel modelling framework will help support increased accuracy in predictive models of animal space‐use, and thereby aid in determining biologically meaningful scales for collecting movement and habitat data.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".