Tactical departures and strategic arrivals: Divergent effects of climate and weather on caribou spring migrations
Bibliographic record
Abstract
Abstract The Arctic has been warming rapidly, affecting ecological processes across the region. Caribou and reindeer (Rangifer tarandus) is a keystone Arctic species undergoing declines in many parts of its range, but definitive links between climate and populations remain elusive. The conspicuous and dramatic mass migration of many caribou populations, during which nearly all pregnant females move from wintering ranges to calving grounds shortly before giving birth, may be an important link between climate and caribou populations. The drivers of migration, however, are similarly mysterious. It is unknown, for example, whether caribou respond to immediate phenological cues, anticipate conditions on calving grounds, or are driven by lagged effects related to physical condition. To investigate the drivers of migration, we analyzed movement data from over 1000 individual caribou from seven major herds, spanning 3000 km across Alaska, Yukon, Northwest Territories (NWT), and Nunavut in Canada, from 1995 to 2017. We developed a hierarchical model to estimate migration departure and arrival times, and analyzed these variables against global climate indices and local weather conditions, exploring immediate and lagged effects, as well as snowmelt timing and vegetation indices. We discovered a continent‐wide synchrony in spring migration departure times, driven mainly by large‐scale, ocean‐driven climate indices (Pacific Decadal Oscillation, Arctic Oscillation, and North Atlantic Oscillation). However, we also found that the speed of migration was highly plastic with later migration departure times followed by shorter migration durations. This plasticity made arrival timing independent of departure timing and its respective drivers. Rather, arrival timing depended strongly on weather conditions from the previous summer: cooler and windier summers generally led to earlier arrival at calving grounds the following year. We suggest that maternal body condition, mainly influenced by conditions that limit insect harassment, is a major factor for earlier spring migration arrival timing, and therefore earlier calving and higher survival rates. We place these results in the context of mechanistic links between climate and caribou population dynamics. Long‐term and large‐scale observations of migratory animals can provide insights into the mechanisms by which long‐distance, collective migrants may adapt to dynamic and unpredictable environments.
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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 | 0.001 | 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 | 0.001 |
| 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".