Weather conditions and variation in timing of spring and fall migrations of migratory caribou
Bibliographic record
Abstract
Species that make long-distance migrations face changes in the phenology of natural processes linked to global climate changes.Mismatch between the onset of resources and arrival on breeding grounds or changes in the conditions faced during migration such as early snowmelt in northern environments could have severe impacts on migrant populations.We investigated the impact of local weather and broad-scale climate and of the availability of forage resources on timing of spring and fall migrations of migratory caribou (Rangifer tarandus) from the Rivière-George and Rivière-aux-Feuilles herds in northern Québec and Labrador, Canada.We tested the effect of local weather using data provided by the Canadian Regional Climate Model, a large-scale climate index, snow and ice cover, and the Normalized Difference Vegetation Index on departure and arrival dates of 377 spring migrations and 499 fall migrations of female caribou.Since 2000, except for the spring arrival, migrations tended to occur earlier.Spring arrival was delayed when caribou encountered mild temperatures and abundant precipitation during migration, as early snowmelt may increase cost of movements.At greater population sizes, caribou seemed to limit the time spent on summer range by arriving later and departing earlier, possibly to limit competition for summer forage.During fall, caribou adjusted their migration to conditions en route because they arrived earlier if November was snowy and mild, possibly to limit the costs of moving through deep snow.Like numerous migrant species, most caribou herds are declining, and it is crucial to assess which environmental factors affect migrant populations.Our study contributes to the understanding of the impact of local weather conditions and climate change on migratory land mammals.
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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 | 0.000 | 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 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".