Northward bound: the distribution of white-tailed deer in Ontario under a changing climate
Bibliographic record
Abstract
Context Global climatic changes are increasingly producing observable shifts in species distributions. It is widely believed that the northern distribution of white-tailed deer (Odocoileus virginianus) in North America is limited by cold winter temperatures and deep snow. Under all climate change scenarios, it is likely that the adverse effects of winter will diminish, which may result in a northward expansion of the distribution of white-tailed deer. Aims The goal of this project was to quantify the drivers of white-tailed deer distribution identified from a set of climate and land cover variables. We wanted to forecast changes to the northern limit on white-tailed deer distribution under several climate change scenarios. Methods We used an occupancy-modelling approach to identify the variables or combination of variables that best estimated the occupancy of white-tailed deer across a 140-site camera-observation network operating from 2013 to 2015. We validated our model using data from a mammal atlas from 1993. We used available data from climate change scenarios to predict and map changes to the northern limit of white-tailed deer distribution for three time horizons up to 2100. Key results Our models indicated that both climate and land cover had a determining influence on the northern limit of white-tailed deer distribution in our study area. Variables describing winter climate, in particular temperature and snow depth, were most closely associated with the northern edge of white-tailed deer distribution, and land cover variables added explanatory power. Our predictions suggested that white-tailed deer distribution will expand northward, given the retreat of severe winters. Conclusions White-tailed deer distribution is controlled by land-based habitat indicators and limited at a northern boundary by the severity of winter climate. Current CO2 emission scenarios indicate that winter conditions will no longer limit the northern distribution of white-tailed deer in our study area by the year 2100. Implications Under all climate change scenarios, the influx of white-tailed deer to new northern environments will likely impact the dynamics of other wildlife populations. The management of species such as moose (Alces alces) and caribou (Rangifer tarandus caribou) in these regions must anticipate the disruptive potential of white-tailed deer.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".