Habitat Selection and Range Overlap of Moose and White-tailed Deer in the Boreal Plains of Saskatchewan
Bibliographic record
Abstract
In the Boreal Plains of western Canada, human land use has created extensive linear features (roads, trails, transmission corridors) linking nodes of disturbance such as forestry cutovers and wildfire burns. These changes, compounded by climate change, are reshaping predator–prey relationships among moose (Alces alces), white-tailed deer (Odocoileus virginianus), boreal caribou (Rangifer tarandus caribou), and wolves (Canis lupus). White-tailed deer, historically uncommon in boreal forests, have expanded their range over the past 50 years. This expansion raises concerns about apparent competition, where one species increases predation or pathogen pressure on another. Such interactions are well documented between white-tailed deer and caribou; here, I examine potential apparent competition between white-tailed deer and moose in east-central Saskatchewan. I hypothesized that overlap between moose and deer would be greatest in areas of high anthropogenic disturbance, especially during summer and fall when forage is abundant. Both species are early-successional browsers predicted to use regenerating burns and forestry cutovers, while overlap should be minimal in spring and absent in winter due to climatic constraints on deer. To test this, I analyzed resource selection of 65 GPS-collared moose (2023–2025) and quantified seasonal niche overlap with 39 GPS-collared deer (2020–2023). Moose selected forage in clearings created by wildfire and forestry but avoided roads year-round and did not use agriculture. Using latent-selection difference functions, I found that overlap was highest in habitats modified by overlapping linear features and fire. Seasonal overlap peaked during calving and fall, and was lowest in winter. These results have management implications. Current legislation such as Road Corridor Game Preserves prohibits hunting of both moose and deer along some northern roadways. Yet, moose avoid roads while deer use them to penetrate deeper into boreal forests, potentially facilitating northward disease spread (e.g., meningeal worm, chronic wasting disease). Targeted harvest of deer in overlap zones and travel corridors could help limit disease expansion and conserve native cervids. In conclusion, anthropogenic disturbance is promoting seasonal niche overlap between moose and white-tailed deer, creating the potential for apparent competition via shared predators and pathogens. Re-evaluating current land management policies is essential to mitigate risks to boreal cervid populations.
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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 | 0.000 | 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) | 0.000 | 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 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".