The spatial scale of habitat selection by red deer
Bibliographic record
Abstract
Accounting for spatial scale is essential for understanding habitat selection, but few studies have used spatial statistics to reveal the characteristic scale at which organisms respond to their environment. We studied habitat selection by GPS-tracked red deer ( Cervus elaphus L., 1758) in the Pyrenees Mountains, France, by applying a geostatistical model that compares autocorrelation of a resource between used and available sites to uncover the scale at which animals assess habitat. Using an artificial landscape, we demonstrated that the model can handle discrete habitat classes. Based on conventional hierarchical analysis, deer selected for open habitat, especially meadow, and avoided coniferous forest, more strongly at the coarse level of the home range than GPS locations. Home ranges exhibited generally lower autocorrelation in elevation and meadow habitat than random locations within the population range, indicative of preference for high habitat heterogeneity. Mean maximum discrepancy in autocorrelation, which was more pronounced at the level of the home range than GPS locations, occurred at 830 m for meadow habitat and at 1511 m for elevation, suggesting that red deer responded to their environment at this scale. Our study demonstrates how spatial statistics can serve as an instructive complement to conventional approaches to habitat selection.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".