Resource selection of a recently translocated elk population in Missouri
Bibliographic record
Abstract
ABSTRACT Elk (Cervus canadensis) have been successfully translocated to 11 states in the United States and 1 Canadian province in eastern North America. Availability of suitable habitat is an important factor in determining the success of relocations, but there is limited information on habitat selection of elk in eastern deciduous forests. Our objective was to determine resource selection of male and female elk recently translocated in the Ozark Mountains, Missouri, USA. We placed global positioning system (GPS) collars on all translocated adult elk. We modeled seasonal resource selection as a function of 9 habitat‐related covariates using a hierarchical Bayesian discrete choice model. Forage openings (cultivated fields providing forage for wildlife), glades, and cool‐season grasslands (pastures) had high probabilities of elk use. Areas with vegetation type heterogeneity, low canopy cover, and far from paved roads but close to 2‐track roads also had high probabilities of elk use. The availability of open lands, such as glades, pastures, and forage openings, appears to be important for elk in all seasons in forest‐dominated landscapes and may help encourage site fidelity following translocation and reduce conflict with private property as the population becomes established. Managers of elk populations in similar ecosystems should ensure sufficient availability of open lands, which might be met through maintenance of forage openings and restoration of natural open lands. Additionally, restoration efforts can benefit from post‐translocation monitoring that allows managers to improve upon externally derived habitat models through a process of adaptive restoration planning and habitat management. © 2018 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".