Colonization of Non-Traditional Range in Dispersing Elk, <em>Cervus elaphus nelsoni</em>, Populations
Bibliographic record
Abstract
As ungulate populations increase in density on traditional range, resulting increases in intraspecific competition can encourage dispersal of some individuals to new areas. Such areas, although lower in density of conspecifics, might present unfamiliar arrays and types of habitat that could require altered patterns of home range and habitat use by dispersers. However, the specific adaptations employed by dispersers in such circumstances are not well documented or understood. I investigated three cases of range expansion by Elk (Cervus elaphus nelsoni) populations experiencing population growth on traditional ranges in south-central Montana, USA. Each source population produced a group that dispersed to non-traditional areas. Compared to source populations, dispersing groups increased average size of home ranges, changed patterns of use in core areas of home ranges, and used habitats differently than Elk on traditional range. Dispersing groups demonstrated fidelity to new ranges equal to that of source populations, but their seasonal tenure on non-traditional range was strongly linked to environmental conditions, especially rainfall. Dispersal of groups increased the overall range of the population and its range of habitat use. In growing populations of Elk, managers should determine if dispersing groups exist and whether they should be protected to establish new populations in marginal areas or be reduced to limit potential Elk-landowner conflicts.
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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 | 0.001 | 0.001 |
| 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".