To Jump or Not to Jump: Mule Deer and White-tailed Deer Crossing Decisions
Bibliographic record
Abstract
Wildlife meet energetic requirements for maintenance, reproduction and survival by considering the physiological, biotic, and abiotic factors that regulate energetic costs. These can include demographic, climatic and anthropogenic factors. The purpose of this study is to investigate fence crossing decisions of mule deer (Odocoileus hemionus) and white-tailed deer (Odocoileus virginianus) and determine what factors influence their crossing decisions. I hypothesize that deer will choose to cross under a fence rather than jump over if it’s more energetically beneficial, based on measured physical and abiotic attributes. Data from remote cameras was collected and analyzed from three study areas; two in Southeastern Alberta, Canada and one in Northcentral Montana. Using a Before-After-Control-Impact (BACI) design, cameras were set up along fence lines within each study area. I recorded individual’s species type, age, sex and crossing decision. I also recorded the season, bottom and top wire height, snow presence, and the modification-type of the fence. I used logistic regression to model the probability of deer crossing under a fence versus jumping over it based on important fence and environmental characteristics. My results show that males and white-tail deer are less likely to cross under than females and mule deer. Both species are more likely to cross under during the summer and fall in reference to spring. Deer are less likely to cross under during the winter than in spring, however it was not statistically significant (P-value>0.05). As the bottom and top wire heights increase, deer are more likely to cross under. Snow presence, modification-type, and before/after periods were not included in the model because they were found to be statistically insignificant. Understanding the determinants behind either crawling under or jumping over a fence and how energetic requirements are associated with this decision is important to discerning animal movement for management and conservation practices.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".