Uncertainty during migration: the role of predictable resources in unpredictable conditions in determining movement in Woodland Caribou (<i>Rangifer tarandus</i>)
Bibliographic record
Abstract
Abstract Environmental and climatic variation drive animal migration. Animals must adjust their behavioral strategies, for example, habitat selection, to match best variation in resources whose value likely varies with conditions such as weather. For example, climate change makes processes such as snowmelt and the emergence of vegetation less predictable at the scale of months or weeks. Furthermore, climate change makes meteorological conditions unpredictable—or dynamic—at the scale of days and hours. The profitability of selecting any particular resource may vary according to local meteorological condition. We studied the impact of dynamic weather conditions on fine-scale movement strategies and resource selection during spring migration of adult female Woodland Caribou (Rangifer tarandus). We assessed the role played by static resources such as open, lichen, and closed forest habitats and their role in a context-dependent of dynamic weather conditions, including temperature, precipitation, and changing snow water equivalent. We tested the role of static resources and dynamic conditions in three contexts: (1) whether caribou were encamped or moving using hidden Markov models; state-specific habitat selection for, and the interaction between, resources and conditions while (2) encamped and (3) moving. Weather conditions influenced the probability of moving or staying encamped, and the probability of selecting for a resource, which likely reflects the profitability of a given resource in a particular condition. The probability of staying encamped in the forest increases as temperature increases, and the probability of selecting an open area is higher when precipitation is lower. We highlight how meteorological conditions modify the selection of a static resource and likely the profitability of a given resource. The resource utility to a consumer is increasingly susceptible to climate change-induced effects.
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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 | 0.004 |
| 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 | 0.001 | 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".