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Record W2797720636 · doi:10.7939/r3cn6zc9z

Wolf-Moose Spatial Dynamics in Alberta’s Athabasca Oil Sands Region

2017· article· en· W2797720636 on OpenAlexaboutno aff
Eric W. Neilson

Bibliographic record

VenueUniversity of Alberta Library · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsOil sandsGeologyPhysical geographyGeographyArchaeologyAsphalt

Abstract

fetched live from OpenAlex

The degree to which predator and prey distributions overlap in space influences the probability of encounters between predator and prey, kills of prey, and consequently, how each species’ abundance varies in time and in space. Predator and prey attempt to increase or decrease overlap respectively through movement and habitat selection, processes that are sensitive to habitat heterogeneity. If predator and prey respond differently to novel habitat heterogeneity such as a zone of influence in and around human disturbance, it may provide prey with a refuge or facilitate predator hunting efficiency. Alberta’s Athabasca oils sands region (AOSR) is a region of boreal forest with extensive mining developments and overlapping wolf (Canis lupus) and moose (Alces alces) populations. To assess whether the human disturbance in AOSR has affected wolf-moose spatial overlap, I quantified the degree to which both wolves and moose avoid human disturbance across my study area. I hypothesized that wolves would avoid areas disturbed by human developments and activity, and that this avoidance would be used by moose as a refuge. Wolves and moose both used and selected areas near human disturbance such that no refugia for moose was available due to human disturbance. Further, I found that a higher proportion of moose were killed as the distance to oil sands mines decreased. I also found that wolves selected to move on linear features associated with oil extraction and such selection facilitated faster movement. Wolves did select to move farther away from human habitation and oil sands facilities, but only during the day. There was no relationship between wolf movement speed and proximity to industrial facilities, urban area or oil sands mines. Moose cows, particularly those with calves, strongly avoided areas within their home ranges with a high intensity of wolf use. In addition, moose altered their behaviour both within and between individuals as a function of the local intensity of use by wolves, but only with respect to natural features. Rivers and streams were avoided in areas with more wolf use. Overall, I conclude that human disturbance in AOSR has not generated prey refugia for moose, rather it has provided a marginal advantage for wolves while hunting in proximity to mines.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.169
Teacher spread0.163 · 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 teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2017
Admission routes1
Has abstractyes

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