Pursuit and escape drive fine-scale movement variation during migration in a temperate alpine ungulate
Bibliographic record
Abstract
Climate change reduces snowpack, advances snowmelt phenology, drives summer warming,alters growing season precipitation regimes, and consequently modifies vegetation phenologyin mountain systems. Altitudinal migrants cope with seasonal variation in such conditions bymoving between seasonal ranges at different elevations, but vertical movements may becomplex and are often not unidirectional during the spring migratory season. We uncoverdrivers of vertical movement variation in an endangered alpine specialist, Sierra Nevadabighorn sheep. We used integrated step-selection analysis to determine factors that promotevertical movements, and factors that drive selection of destinations after vertical movements.Our results reveal that high temperatures consistently drive uphill movements, and providesome evidence for the contribution of precipitation events to downhill movements.Furthermore, bighorn select destinations that have a high relative index of forage growth andmaximize delay since snowmelt. These results indicate that although Sierra bighorn seek outforaging opportunities related to landscape phenology, they compensate for short-termenvironmental stressors by undertaking brief vertical movements. Migrants may therefore beimpacted by future warming and increased storm frequency or intensity, both in terms of theirfine-scale vertical movements, and in terms of tradeoffs between forage access and predationrisk.
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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 | 0.000 |
| 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".