Habitat selection as a reduction in habitat variance
Bibliographic record
Abstract
When organisms select habitat, they may lessen the environmental variance they experience relative to their surroundings. Such discrepancies in variance could reveal organisms’ habitat preferences and perceptual ranges, particularly when examined across spatial and temporal scales. To test whether habitat variance might provide such understanding, we applied geostatistics to the variance in availability and use of habitat by GPS-collared adult female white-tailed deer ( Odocoileus virginianus ) in central Ontario, Canada. First, to quantify use and availability, we measured vegetation at the locations used by deer during May–June 2022, as well as in the general environment, then applied principal components analysis (PCA) to capture the major gradients in vegetation conditions. Second, to identify habitat selection, we tested for differences between use and availability in both the means and variance of vegetation characteristics. Finally, to depict how variance changed across scales, we constructed spatial and temporal variograms. Based on the first two axes of the PCA, we found that deer selected greater abundance in forage and lower variation in canopy closure. Across space, the selection for reduced variance in canopy appeared largely independent of scale (50–1000 m), implying that the perception capacity of deer may exceed this range. Across time, deer exhibited rising variance in forage abundance at short lags (4–12 h), resembling the periods (6–10 h) when movements were more linear. Deer thus selected for lower variance of habitat without selecting for disproportionate levels of habitat. We propose that selection for diminished variance is a fundamental property of habitat selection, whose scale-dependence might be uncovered with geostatistics. • Selecting habitat can lower environmental variance relative to an animal's surroundings. • In summer, deer selected more abundant forage and lower variance in canopy cover. • Such narrowing of environmental variance is a primary feature of habitat selection.
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) | 0.002 | 0.001 |
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".