Evaluating the Mechanisms of Landscape Change on White‐Tailed Deer Populations
Bibliographic record
Abstract
ABSTRACT Understanding how landscape change influences the distribution and densities of species, and the consequences of these changes, is a central question in modern ecology. The distribution of white‐tailed deer ( Odocoileus virginianus ) is expanding across North America, and in some areas, this pattern has led to an increase in predators and consequently higher predation rates on woodland caribou ( Rangifer tarandus caribou )—an alternate prey species that is declining across western Canada. Understanding the factors influencing deer distribution has therefore become important for effective conservation of caribou in Canada. Changing climate and anthropogenic landscape alteration are hypothesized to facilitate white‐tailed deer expansion. Yet, climate and habitat alteration are spatiotemporally correlated, making these factors difficult to isolate. Our study evaluates the relative effects of snow conditions and human‐modified habitat (habitat alteration) across space on white‐tailed deer presence and relative density. We modeled deer response to snow depth and anthropogenic habitat alteration across a large latitudinal gradient (49° to 60°) in Alberta, Canada, using motion‐sensitive camera data collected in winter and spring from 2015 to 2019. Deer distribution in winter and spring were best explained by models including both snow depth and habitat alteration. Sites with shallower snow had higher deer presence regardless of latitude. Increased habitat alteration increased deer presence in the northern portion of the study area only. Winter deer density was best explained by snow depth only, whereas spring density was best explained by both habitat alteration and the previous winter's snow depth. Our results suggest that limiting future habitat alteration or restoring habitat can alter deer distribution, thereby potentially slowing or reversing expansion, but that climate plays a significant role beyond what managers can influence. © 2020 The Wildlife Society.
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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 | 0.000 | 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 teacher head, 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".