Understanding environmental patterns of canid predation on white-tailed deer (<i>Odocoileus virginianus</i>)
Bibliographic record
Abstract
The outcome of encounters between predators and prey affects predation rates and ultimately population dynamics. Determining how environmental features influence predation rates helps guide conservation and management efforts. We studied where gray wolves (Canis lupus Linnaeus, 1758) and coyotes (Canis latrans Say, 1823) killed white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) in northern Wisconsin, USA. We monitored 499 white-tailed deer for cause-specific mortality between 2011 and 2014 using VHF radio collars. We investigated the locations of 125 deer mortalities and determined that 63 were canid (wolf or coyote) kill sites. We analyzed spatial patterns of kill sites using resource selection functions in a model selection framework, incorporating environmental variables including vegetative cover, human development, snow depth, and water. We found no evidence that vegetative cover or human development affected predation risk; however, we did find that increasing snow depth resulted in increased relative predation risk. This finding is consistent with existing research on the influence of snow cover on white-tailed deer survival. Our results suggest that understanding the spatial and temporal patterns of white-tailed deer predation requires a better understanding of snow depth variation in space and time. As climate change scenarios predict changes in snowfall throughout the northern hemisphere, understanding the effect on predator–prey spatial dynamics will be important for management and conservation efforts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".