Broad-scale resource selection and food habits of a recently reintroduced elk population in Missouri
Bibliographic record
Abstract
Since being extirpated from eastern North America, elk (Cervus elaphus) have been reintroduced in 10 eastern states and 1 Canadian province. However, little is known about the habitat needs of eastern elk populations. Our objectives were to determine broad-scale resource selection and food habits of the recently reintroduced elk population in Missouri. To achieve these objectives, we placed GPS collars on all adult animals prior to their release. To determine elk resource selection, we defined nine resource attributes using GIS layers. We modeled resource selection using a hierarchical Bayesian discrete choice model. Elk selection for forage openings (fields cultivated to provide forage for wildlife) was overwhelmingly greater than for all other landscape features. Elk also selected other attributes associated with open lands including glades, pastures, and low canopy cover. We determined seasonal diet selection of elk in Missouri by comparing use (diet composition) with forage availability. We measured diet composition through the microhistological analysis of feces. We determined forage availability through vegetation sampling at stratified random points. Elk selected grains and cool-season grasses over all other forage classes. Legumes were the most highly consumed forage class by elk. Approximately half of the elk diet was composed of plants cultivated in forage openings. The availability of open lands is a critical resource for elk in forest dominated landscapes. Managers of elk in similar ecosystems should ensure the availability of open lands is sufficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".