Flexible migration by woodland caribou in Ontario, Canada
Bibliographic record
Abstract
Abstract Recent studies of ungulate movement ecology suggest that seasonal movement tactics often vary within a population. The forest‐tundra and forest‐dwelling ecotypes of woodland caribou (Rangifer tarandus caribou) in Ontario, Canada, are traditionally presumed to differ in migratory strategy; however, their potential for facultative migration, the practice of interannual switching between migratory and non‐migratory strategies, has yet to be explored. Understanding facultative migration, and any inherent variation and influences could help improve habitat management. We acquired global positioning system (GPS) telemetry‐based movement data from 109 radio‐collared caribou across the Hudson Bay lowlands between 2009 and 2019. We compared the data with estimates of vegetation density, snow cover, and human disturbance to identify environmental influences associated with the probability and magnitude of migration. We also compared seasonal resource selection between migratory and sedentary individuals. Caribou demonstrated plasticity in migration, with evidence of facultative migration by the forest‐tundra and the forest‐dwelling ecotypes. Variation in migration was likely a combination of local adaptation and acute response to changing environmental factors, particularly snow. Probability and distance of migration were positively correlated with snow, while distance also showed spatial dependency. Plasticity in migration has significant implications in relation to future shifts in climate and should be considered in relevant predictive analyses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".