Caribou, water, and ice – fine-scale movements of a migratory arctic ungulate in the context of climate change
Bibliographic record
Abstract
BACKGROUND: Freshwater lakes and rivers of the Northern Hemisphere have been freezing increasingly later and thawing increasingly earlier during the last century. With reduced temporal periods during which ice conditions are favourable for locomotion, freshwater bodies could become impediments to the inter-patch movements, dispersion, or migration of terrestrial animals that use ice-covered lakes and rivers to move across their range. Studying the fine-scale responses of individuals to broad-scale changes in ice availability and phenology would help to understand how animals react to ongoing climate change, and contribute to the conservation and management of endangered species living in northern environments. Between 2007 and 2014, we equipped 96 migratory caribou Rangifer tarandus caribou from the Rivière-aux-Feuilles herd in northern Québec (Canada) with GPS telemetry collars and studied their space use. We measured contemporary (digital MODIS maps updated every 8 days, 2000-2014) and historical (annual observations, 1947-1985) variations in freshwater-ice availability and evaluated the concurrent responses of caribou to these changes. RESULTS: Ice had a positive influence on caribou movement rates and directionality, and caribou selected ice and avoided water when moving across or in the vicinity of large water bodies. When ice was unavailable, caribou rarely swam across (6 % of crossings) and frequently circumvented water bodies for several km. Although ice phenology did not change significantly during our study, climate projections indicated that ice availability could decrease considerably before the end of the century, generating a ~28 % increase in distance travelled by caribou during the early spring and fall migrations. CONCLUSIONS: We demonstrated that ice availability influenced the movements of a migratory arctic ungulate. Warmer air temperatures in the Arctic will undoubtedly modify the phenology of ice forming on freshwater lakes and rivers. If migratory caribou are unable to adjust the timing of their migrations, they could be forced to circumvent unfrozen water bodies more frequently and over broader areas, which may increase the distance, time, and energy they use to reach wintering areas. The long-term conservation of wide-ranging species will ultimately depend on our ability to identify the fine-scale behavioural reactions of individuals to broad-scale changes in climate and land use.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".