Comparing Survival and Movements of Non‐Urban and Urban Translocated Mule Deer
Bibliographic record
Abstract
ABSTRACT In many parts of North America, deer (Odocoileus spp.) have adapted to live in urban areas and are a source of negative human‐wildlife interactions. Management strategies such as culling, immunocontraceptives, sterilization, and translocation have been implemented to manage urban deer populations. In the East Kootenay region of southern British Columbia, urban mule deer (Odocoileus hemionus) populations have been increasing, whereas non‐urban mule deer populations have decreased. In 2014 a non‐urban mule deer research project began in the area and in 2016 an urban deer translocation trial was approved in the same region. We fit 121 non‐urban deer with global positioning system (GPS)‐collars and translocated 135 urban mule deer to non‐urban areas, of which 57 were fit with GPS‐collars. We tested if annual survival between urban translocated (i.e., translocated) and non‐urban deer differed, and if translocated deer survival increased in subsequent years after translocation. We also determined if age, body condition, release site, capture area and distance between capture and release sites affected translocated deer survival. We evaluated if translocated deer exhibited different movement behaviors than non‐urban deer by comparing probability of migration, maximum net displacement, home range size, and probability of crossing a paved road. Finally, during our study we observed some translocated deer return to a municipal area after translocation and assessed if any covariates such as age, release site, or capture city could help predict this behavior. Annual survival of translocated deer was 0.48 and was significantly lower than survival of non‐urban deer, which was 0.77. We observed 20 of 57 collared translocated deer return to a town after translocation. Translocated deer had larger net displacements and larger seasonal home range sizes than non‐urban deer. Non‐urban deer were more likely to migrate than translocated deer and crossed fewer paved roads than translocated deer. The management effectiveness of translocation to reduce urban deer densities is mixed because annual survival of translocated deer may be lower than may be acceptable to some stakeholders. Additionally, some translocated deer returned to an urban area, and the large distances traveled by deer after translocation may unintentionally spread disease. © 2020 The Wildlife Society.
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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 | 0.000 | 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, 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".