Scale-dependent drivers of ungulate patch use along a temporal and spatial gradient of snow depth
Bibliographic record
Abstract
Anthropogenic changes in landscape composition or configuration have the potential to increase the abundance of generalist species, often resulting in cascading effects on other trophic levels and ecosystem function. The selection or utilization of individual patches of habitat, however, may vary in both time and space, as a result of patch and landscape attributes as well as dynamic abiotic factors (i.e., snowfall). We hypothesized that the use of high-quality habitat would be most strongly influenced by snow at local scales and by composition and the configuration of the landscape at greater spatial scales. To test this, we examined white-tailed deer ( Odocoileus virginianus (Zimmermann, 1780)) selection of eastern hemlock ( Tsuga canadensis (L.) Carrière) patches as winter habitat over a 3-year period. Patch-level attributes were poor predictors of patch use in areas with strong gradients in abiotic factors relevant to the energetics of movement or forage acquisition. Additionally, heterogeneity in the greater landscape (3000 m radius buffer) served to increase use, while homogeneity appeared to decrease use. Our results suggest that interactions between deer migratory behavior, interannual variation in winter severity, and landscape context might provide spatial and temporal refugia for hemlock regeneration; a better understanding of which could aide conservation and restoration.
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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 | 0.001 | 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".