Impacts of climate change on the seasonal distribution of migratory caribou
Bibliographic record
Abstract
Abstract Arctic ecosystems are especially vulnerable to global climate change as temperature and precipitation regimes are altered. An ecologically and socially highly important northern terrestrial species that may be impacted by climate change is the caribou, Rangifer tarandus. We predicted the current and potential future occurrence of two migratory herds of caribou [Rivière George herd (RG) and Rivière‐aux‐Feuilles (RAF) herd] under a Canadian General Circulation Model climate change scenario, across all seasons in the Québec–Labrador peninsula, using climatic and habitat predictor variables. Argos satellite‐tracking collars have been deployed on 213 caribou between 1988 and 2003 with locations recorded every 4–5 days. In addition, we assembled a database of climate (temperature, precipitation, snowfall, timing and length of growing season) and habitat data obtained from the SPOT VEGETATION satellite sensor. Logistic regression models indicated that both climatic and physical habitat variables were significant predictors of current migratory caribou occurrence. Migratory caribou appeared to prefer regions with higher snowfall and lichen availability in the fall and winter. In the summer, caribou preferred cooler areas likely corresponding to a lower prevalence of insects, and they avoided disturbed and recently burnt areas. Climate change projections using climate data predicted an increased range for the RAF herd and decreased range for the RG herd during 2040–2069, limiting the herds to northeastern regions of the Québec–Labrador peninsula. Direct and indirect consequences of climate change on these migratory caribou herds possibly include alteration in habitat use, migration patterns, foraging behaviour, and demography, in addition to social and economic stress to arctic and subarctic native human populations.
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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 | 0.001 |
| 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) | 0.001 | 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".