Habitat Selection of a Large Herbivore at High Density and Without Predation: Trade-off between Forage and Cover?
Bibliographic record
Abstract
Abstract Although herbivores are generally known to trade off forage in open habitat patches and cover in forested habitat patches, it remains unclear if high population density and low predation risk can modulate the trade-off between forage and cover. We studied a population of white-tailed deer (Odocoileus virginianus) that was at high density and on a large island free of predators to assess the influence of forage and cover on habitat selection under harsh environmental conditions. We fitted 19 female white-tailed deer with global positioning system collars and delineated summer home ranges and core areas. We sampled vegetation in the core areas and in the rest of the home ranges to determine abundance of forage and forest cover within habitat patches, and assessed habitat selection between open and forested habitat patches. At a coarse scale, white-tailed deer preferred open habitat patches over forested ones, suggesting that they adopted a foraging strategy favoring energy intake. At a fine scale, habitat selection was influenced positively by the percentage of ground cover of forbs and deciduous shrubs, but negatively by conifer density. The biomass of preferred plant species, lateral cover, fir regeneration, and distance to the nearest open–forest edge were not strong predictors of habitat selection by deer. We conclude that fine-scale habitat selection by white-tailed deer at high population density and in the absence of predation is mainly determined by forage abundance. These patterns of habitat selection demonstrate that herbivores can adjust their behavior to other limiting factors when predation risk is relaxed.
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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".