Longest sage‐grouse migratory behavior sustained by intact pathways
Bibliographic record
Abstract
ABSTRACT Expanding human footprints across the globe are affecting animal migrations by altering their use of traditional routes and exposing them to increased costs of travel. Sustaining migratory behavior among populations requires knowledge of animal movement and space use during this critical life stage. We studied the migratory movements in a population of greater sage‐grouse (Centrocercus urophasianus), an imperiled species endemic to the North American sagebrush steppe, with the longest‐known obligate and annual migration for a grouse: a 240‐km journey between Saskatchewan, Canada, and north central Montana, USA. Remote tracking revealed that stepping‐stone behavior in sage‐grouse is analogous to that of migratory big game populations, which frequent stopover sites along varied and individual routes. Within migratory routes, sage‐grouse chose pathways with gently rolling grasslands and sagebrush flats. Sage‐grouse avoided cropland, making punctuated movements across a 6‐km river valley dominated by cropland and trees. During autumn migration (Oct–Nov), individuals typically spent a day at an average of 9 different stopovers while traveling 41–126 km in 14 days. Sage‐grouse were faithful to winter ranges and timing of autumn migration but demonstrated adaptive behavior in response to heavy snowfalls with no effect on survival. Birds made a mass exodus north in spring (Mar–Apr) to their respective breeding ranges, traveling up to 160 km in 18 days. Migratory pathways were stitched together by an array of public and private lands, with large and intact landscapes composed of native vegetation forming the common thread. Conservation easements and public lands policy that preclude temperate grasslands from being fragmented will be critical in maintaining the largest migratory event observed among gallinaceous species. © 2017 The Wildlife Society.
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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".