Migration of northern Yellowstone elk: implications of spatial structuring
Bibliographic record
Abstract
Migration can enhance survival and recruitment of mammals by increasing access to higher-quality forage or reducing predation risk, or both.We used telemetry locations collected from 140 adult female elk during 2000-2003 and 2007-2008 to identify factors influencing the migration of northern Yellowstone elk.Elk wintered in 2 semidistinct herd segments and migrated 10-140 km to at least 12 summer areas in Yellowstone National Park (YNP) and nearby areas of Montana.Spring migrations were delayed after winters with increased snow pack, with earlier migration in years with earlier vegetation green-up.Elk wintering at lower elevations outside YNP migrated an average of 13 days earlier than elk at higher elevations.The timing of autumn migrations varied annually, but elk left their summer ranges at about the same time regardless of elevation, wolf numbers, or distance to their wintering areas.Elk monitored for multiple years typically returned to the same summer (96% fidelity, n 5 52) and winter (61% fidelity, n 5 41) ranges.Elk that wintered at lower elevations in or near the northwestern portion of the park tended to summer in the western part of YNP (56%), and elk that wintered at higher elevations spent summer primarily in the eastern and northern parts of the park (82%).Elk did not grossly modify their migration timing, routes, or use areas after wolf restoration.Elk mortality was low during summer and migration (8 of 225 elk-summers).However, spatial segregation and differential mortality and recruitment between herd segments on the northern winter range apparently contributed to a higher proportion of the elk population wintering outside the northwestern portion of YNP and summering in the western portion of the park.This change could shift wolf spatial dynamics more outside YNP and increase the risk of transmission of brucellosis from elk to cattle north of the park.
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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 | 0.001 |
| 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".