Tracking snowmelt to jump the green wave: phenological drivers of migration in a northern ungulate
Bibliographic record
Abstract
In northern climates, spring is a time of rapid environmental change: for migrating terrestrial animals, melting snow facilitates foraging and travel, and newly emergent vegetation provides a valuable nutritional resource. These changes result in selection on the timing of important life-history events such as migration and parturition occurring when high-quality resources are most abundant. We examined the timing of female caribou (Rangifer tarandus, n = 94) migration and parturition in five herds across 7 yr in Newfoundland, Canada, as a function of two measures of environmental change-snowmelt and vegetation green-up. We generated resource selection functions to test whether caribou selected for areas associated with snowmelt and green-up during migration and following calving. We found that caribou migrated approximately 1 wk prior to snowmelt, with the flush of emergent vegetation occurring during the weeks following parturition. The results indicate that caribou "jump" the green wave of emergent forage and do so by tracking the receding edge of melting snow, likely reducing movement and foraging costs related to snow cover. Our research further broadens the ecological scope of resource tracking in animals. We demonstrate that resource tracking extends beyond resources directly related to foraging to those related to movement. We also show that snowmelt provides an environmental cue that may provide a buffer against changing environmental conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".