Influence of climate and human land use on the distribution of white-tailed deer (<i>Odocoileus</i><i>virginianus</i>) in the western boreal forest
Bibliographic record
Abstract
Understanding the factors that drive species distributions is emerging as an important tool in wildlife management, under unprecedented changes in species ranges. While invasion ecologists have long studied the impact of human land use on species’ distributions, and models developed more recently to explain changes in species range boundaries have been largely parameterized by climate variables, few authors have considered climate and land-use factors together to explain species distribution. The purpose of this study was to test two main competing hypotheses involving human land use and climate effects on white-tailed deer (Odocoileus virginianus (Zimmermann, 1780)) distribution, which has expanded into the boreal ecosystem in recent decades. Using a species distribution modeling approach with data from boreal Alberta, we found that climate, as measured by an index of winter severity, was the most important individual factor determining current white-tailed deer distribution in boreal Alberta. Human land use (as measured by total land-use footprint) acted to substantially increase white-tailed deer presence but only in areas with more severe winter conditions. We use our findings to recommend where limiting or reclaiming the industrial footprint may be most beneficial to limiting white-tailed deer distribution.
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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 | 0.001 |
| 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".