Determinants of migration trajectory and movement rate in a long-distance terrestrial mammal
Bibliographic record
Abstract
Abstract Animal migrations occur in many taxa and are considered an adaptive response to spatial or temporal variations in resources. Human activities can influence the cost-benefit trade-offs of animal migrations, but evaluating the determinants of migration trajectory and movement rate in declining populations facing relatively low levels of human disturbance can provide new and valuable insights on the behavior of wildlife in natural environments. Here, we used an adapted version of path selection functions and quantified the effects of habitat type, topography, and weather, on 313 spring migrations by migratory caribou (Rangifer tarandus) in northern Québec, Canada, from 2011 to 2018. Our results showed that during spring migration, caribou selected tundra and avoided water bodies, forest, and higher elevation. Higher precipitation and deeper snow were linked to lower movement rates. Weather variables had a stronger effect on the migration trajectories and movement rates of females than males. Duration of caribou spring migration (mean of 48 days) and length (mean of 587 km) were similar in males and females, but females started (22 April) and ended (10 June) spring migrations ca. 6 days earlier than males. Caribou spring migration was influenced by habitat type, topography, and weather, but we also observed that caribou migrations were not spatially constrained. Better knowledge on where and when animals move between their winter and summer ranges can help inform management and land planning decisions. Our results could be used to model future migration trajectories and speed of caribou under different climate change scenarios.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".