Linking Habitat Selection, Limiting Factors, and Genetic Resistance to Chronic Wasting Disease in White-Tailed Deer
Bibliographic record
Abstract
Along Saskatchewan’s boreal fringe, white-tailed deer (Odocoileus virginianus) exist at the periphery of their northern range, overlapping with the southern range of Threatened woodland caribou (Rangifer tarandus caribou). Concerns exist that deer may facilitate caribou declines via predator-mediated apparent competition and chronic wasting disease (CWD). My study examined habitat selection, primary limiting factors, and genetic CWD resistance in 39 GPS collared white-tailed deer to identify factors influencing range expansion and risk to caribou as well as pressures influencing selection for CWD resistance. It is the first of its kind to assess genetic CWD resistance in boreal white-tailed deer. Using resource selection functions, I found that deer selected for a mix of natural and anthropogenic habitat features. Second order (landscape) selection was driven primarily by forage and thermal cover, with strongest selection for mixed coniferous-deciduous forest in the winter and spring. Deer selected for deciduous, grassland, and shrubland vegetation throughout the year, as well as areas near agriculture and forestry cutblocks. Females (does) prioritized predator avoidance during fawning and lactation, avoiding roads and young cutblocks—features selected for by males (bucks). At the third order of selection (within home range), both sexes continued to select for forage, including year-round use of baits associated with hunting. Preference for intermediate distances from risk-associated linear features suggests a balance between foraging and predator avoidance. Deer avoided caribou-associated mature coniferous forest but selected for wetlands in the winter and spring. Habitat selection patterns reflected behavioural responses to key limiting factors such as forage availability, thermal cover, and predation risk. Apparent wolf predation accounted for 71.1% of mortalities and annual deer survival was low (39.5%). These limiting factors influence survival outcomes, shaping selective pressures on traits like PRNP genotypes. Frequencies of CWD resistant PRNP alleles were higher than reported elsewhere, with 82.1% of genotypes containing at least one resistant allele. 96GS and 96SS comprised 59.0% of samples. Thus, fine-scale habitat use not only reflects immediate survival trade-offs but may interact with disease-mediated selection, linking behavioural ecology to genetic structure. Strong preference for agriculture, high predation rates, and genetic resistance may mitigate current impacts to caribou. However, future changes to climate, forest disturbance, or predator populations could elevate future risks. This study informs wildlife managers of current and emerging threats deer pose to caribou and underscores the importance of preserving natural risk barriers through wholistic management.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".