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Record W7106012324 · doi:10.7939/83233

Diet and habitat selection of gray wolves (Canis lupus) during the boreal caribou (Rangifer tarandus caribou) calving season in the southern Northwest Territories

2025· dissertation· en· W7106012324 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsPredationHabitatUngulateTaigaTrophic levelThreatened speciesBorealForagingWoodland caribou

Abstract

fetched live from OpenAlex

Understanding the dietary habits of a species is crucial for studying ecosystem dynamics, and it becomes particularly important to understand the role of predators when prey populations become threatened. Prey are affected by predators via direct predation, indirectly through trophic cascades, and ultimately through the element of fear which influences the behaviour of prey. As an opportunistic predator, the gray wolf (Canis lupus) will often consume prey that are both numerous and vulnerable; however, wolves also adapt their diet and focus hunting efforts on ungulate prey in most of their range. Wolves are territorial, have large yearly home ranges, and become central place foragers during the denning period when pups are less mobile, often seeking habitats of higher quality to access valuable resources. The distribution of prey, den site selection, shelter, and anthropogenic features are all factors that may influence both diet and habitat selection of wolves. Therefore, my main objectives were to: 1) examine the diet of wolves in the southern Northwest Territories, Canada during the boreal caribou (Rangifer tarandus caribou) calving period as they are considered a threatened species and 2) develop habitat selection models using resource selection functions for wolves during both the denning period and annually. For my diet analyses, I used two methods: macroscopic and genetic approaches to examine the variation in diet at den sites from 2016-2020. Beaver (Castor canadensis) was the main prey in both frequency of occurrence and biomass consumed from the macroscopic analysis. White-tailed deer (Odocoileus virginianus) was the main prey by frequency of occurrence and moose (Alces alces) composed the largest portion by biomass with the genetic analysis. Habitat selection models were developed using data combined from 16 wolves tracked by satellite telemetry from 2016-2021. I analysed habitat selection in two groups of wolves: those associated with a den and those not associated with a den. For denning wolves, the top-performing model was the combined model, indicating selection for broadleaf-dense forests, non-vegetated areas, mixed wood-dense forests, steeper slopes, lower densities of linear features, proximity to roads, proximity to water, lower elevations, and avoidance of human settlements. For non-denning wolves, the top-performing model was also the combined model, indicating selection for mixed-wood-open forests, broad-leaf-open forests, mixed-wood-dense forests, higher densities of linear features, steeper slopes, proximity to human settlements, lower elevations, proximity to water, and proximity to roads. Although the diet analysis did not indicate that boreal caribou is highly prevalent prey for wolves in the southern NT, my habitat selection analysis during the denning period did align with preferred caribou habitat, suggesting spatial overlap between wolves and caribou that could increase encounter rates. This study will inform wildlife managers about habitat selection by wolves and possible impacts on prey.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.763
Threshold uncertainty score0.471

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.160
Teacher spread0.157 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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