Selection of vegetation types and density of bison in an arid ecosystem
Bibliographic record
Abstract
ABSTRACT Understanding species habitat selection and factors that drive selection are key components for conservation. We report the first resource selection functions (RSFs) for bison inhabiting an arid ecosystem and use them with density estimates of bison to estimate the number of bison that could be supported if the bison range were expanded to federal lands in the San Luis Valley of Colorado. We derived RSFs for vegetation types using locations of plains bison collected weekly over 3 years from 2005 to 2007. Bison selected for wet or mesic grassland habitats in all seasons. Wetland selection by bison was predicted to be 18 times greater than that of rabbitbrush vegetation, the reference category, and selection of meadows was predicted to be 11 times greater than that of the rabbitbrush type. Willow‐dominated plant communities were strongly avoided. Cottonwood communities were also avoided, with the exception of some moderate levels of selection in fall. The willow and cottonwood communities have an understory with low biomass of herbaceous species and low productivity in this arid system. Based on the RSFs we predicted that in the San Luis Valley of Colorado up to 2,379 bison could be supported in similar habitats under Fish and Wildlife Service (FWS) jurisdiction, and up to 759 bison could be supported on adjacent National Park Service (NPS) land. This modeling framework provides a conservation tool for the restoration of bison to their historical habitats, and has utility for application to other terrestrial species where assumptions are met. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".