Varied tastes: home range implications of foraging‐patch selection
Bibliographic record
Abstract
Despite evidence of home range behaviour across many taxa, the mechanisms underlying the development of home ranges are still unknown. Recently, models have been developed to explore these mechanisms for both territorial and non‐ territorial species. One such model for a generic forager suggests animal memory and optimal foraging theory as underlying mechanisms driving forager movement and the development of stable home ranges. Although this is a promising model for ungulate home range development, assumptions of the model have yet to be evaluated. Using GPS relocation data from two populations of elk, we explored how foraging patch selection might influence the structure and development of home ranges in elk Cervus elaphus . During the summer growing season, we identified and sampled foraging patches used by elk. Points along elk paths not used for foraging were sampled identically for comparison. We contrasted ‘patch’ and ‘nonpatch’ data points, to identify foraging selection differences across herd, sex and season using a combination of directly sampled and remotely sensed covariates. In general, elk selected patches with higher biomass, cover, slope and lower traffic on the nearest road. These patch‐selection results speak directly to differences between foraging areas and other areas used by elk and demonstrate that both physiographic and anthropocentric features influence foraging patch selection. Our results offer insight as to what defines a valuable foraging patch for elk and how these patches might influence the development and structure of home ranges in a free‐ranging ungulate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".