Comparing habitat quality within and between environments using giving up densities: an example based on the winter habitat of white‐tailed deer <i>Odocoileus virginianus</i>
Bibliographic record
Abstract
We previously developed a model, based on the precepts of optimal patch use, to compare habitat quality both within and between environments. Here we illustrate the use of this model in a study estimating quality of winter habitats (deer yards) of white‐tailed deer Odocoileus virginianus near the northern limit of their range by following their foraging behaviour. We compare giving up densities (GUDs), the amount of food remaining in a patch when a forager ceases foraging there, with and without the presence of supplemental food in order to draw inferences about the relative quality either of habitats within an environment or of distinct environments. We use our model to evaluate the impact of alterations to the winter habitat of deer at two distinct sites that differed in their level of predation risk and food availability. The first site, the Mont Rigaud deer yard, was surrounded by farm land and gradually‐expanding suburbs. Predators were rare and food was available in winter either in farm fields or around private homes but deer browsing in the past had left little food in the forest. At the second site, the Calumet deer yard, deer experienced a higher predation risk and did not have access to supplemental food from farm fields or private homes. However, past browsing by deer had not drastically reduced food in the forest. We offered food to deer in four habitats per site (forest, forest edge, clearing, clearing edge) with four to six replicates per site and measured the GUDs after 24 h. Analysis of these data, interpreted according to our model, suggests that deer are more sensitive to metabolic costs at Mont Rigaud and food availability at Calumet; predation risk does not alter deer behaviour between the two sites. Within habitats, deer at Mont Rigaud reacted to clearings as though they imposed higher metabolic costs than the forest. They also reacted to an interaction in which missed food costs influenced GUD only when metabolic costs were not too high. Thus our model appears to provide a convenient tool for comparing habitat quality both within and between environments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".