Under cover: the nuanced influence of functional properties of cover on resource selection by pygmy rabbits (<i>Brachylagus idahoensis</i>)
Bibliographic record
Abstract
Abstract Animals at risk of predation select habitat that enhances security from predators. Two properties of cover related to security are concealment (i.e., habitat structure that blocks an individual from detection by others) and visibility (i.e., visual information accessible relative to habitat structure). Although these properties are often negatively correlated, they are not always inverse; animals in habitat with heterogeneous structure may be able to select for both. We investigated habitat use by pygmy rabbits (Brachylagus idahoensis) at 2 scales (patch and microsite) to evaluate the influence of both structural properties of cover and visual properties (concealment and visibility) on habitat use by prey. We contrasted vegetation structure at paired used and unused patches. At each patch, we measured concealment and viewshed (i.e., visibility) in 3 orientations (i.e., aerial, terrestrial, and overall) and structural density using lidar. We also measured heights of the 3 tallest shrubs. Additionally, within used patches, we assessed the density of fecal pellets as an index of intensity of use and also measured distance to nearest burrow. At the patch scale, rabbits selected for structural properties of cover (dense vegetation and tall shrubs), but not visual properties of cover. Pygmy rabbits more intensively used microsites associated with high terrestrial concealment and in proximity to burrows. Our results suggest that pygmy rabbits may perceive greater threat from terrestrial as opposed to aerial predators at both scales, and they also indicate a nuanced relationship between properties of cover and habitat use.
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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 | 0.000 | 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".