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Record W2061176940 · doi:10.14430/arctic304

Habitat and Movement Ecology of Grizzly Bears in the Mackenzie Delta, NWT

2009· article· en· W2061176940 on OpenAlexvenueaboutno aff
Mark A. Edwards

Bibliographic record

VenueARCTIC · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsGrizzly BearsEcologyDeltaHabitatGeographyMovement (music)OceanographyGeologyUrsusBiologyPhysics

Abstract

fetched live from OpenAlex

IN THE 1960S AND 1970S, the Mackenzie Delta region of the Northwest Territories in Canada’s Western Arctic was on the brink of an oil and gas “boom”; however, pipeline construction was delayed following Thomas Berger’s recommendation for a 10-year moratorium so that Native land claims could be settled. Today, the Mackenzie Delta is the proposed site for the new Mackenzie Gas Project, which will include an increase in the number of exploration and production wells and the construction of a pipeline and gathering system with associated facilities, as well as airfields and winter and all-weather roads, and result in landscape-level changes (Imperial Oil Resources Ventures Limited, 2004; Cizek and Montgomery, 2005). Wildlife managers and the affected communities are concerned that sensitive species like the barren-ground grizzly bear (Ursus arctos) could be adversely affected by increasing oil and gas development. Historically, grizzly bear declines in North America have resulted from the fragmentation of habitats by human settlements, roads, agriculture, human intolerance, and inadequate planning in the early stages that precede development (Servheen et al., 1999). Wildlife managers lack the current information on the ecology of this Arctic population of grizzlies needed for effective mitigation of the effects of disturbance caused by hydrocarbon development. Low density, high mobility, and large home ranges describe Arctic grizzly bear populations (Ferguson and McLoughlin, 2000). When compared to other large carnivores, grizzlies are considered to have a lower ecological resilience, which is characterized by low population density, low fecundity, and low dispersal ability through developed areas (Weaver et al., 1996). Low resilience suggests that grizzlies are especially vulnerable to development-related disturbance. The sensitivity of the species makes it difficult for population numbers to increase in multi-use landscapes where the cumulative impacts of industry, subsistence and sport hunting, problem and defence kills, and recreational activities are the norm. The Mackenzie Gas Project will transect areas occupied by grizzly bears within the Inuvialuit Settlement Region, which is also at the northernmost edge of their geographical range. At these northern latitudes, grizzly bears must accumulate enough energy reserves to last the 6–7 months of winter dormancy (Nagy et al., 1983). We do not know what effects a pipeline will have on the grizzlies of the Mackenzie Delta, but it could make it more difficult for them to meet their resource needs given a short active 5–6 month period (Nagy et al., 1983). Harding and Nagy (1980) predicted that hydrocarbon development in the region could be detrimental to grizzly bears because of the loss of available resources, and that mortality from problem bear-human interaction could result in population decline. The primary goals of my project are to collect baseline information on grizzly bear ecology before pipeline construction begins, to describe annual and seasonal home range size and distribution, and to identify important habitats. The information gained will form the foundation for model development to assess the affect of oil and gas–related activities on grizzly bears. Major project objectives are 1) to describe habitat selection patterns, 2) to quantify movement patterns, and 3) to incorporate these patterns into a scenario-based modelling approach to assess the response of grizzly bears to pipeline-related development.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.301

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 communication0.0000.000
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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2009
Admission routes2
Has abstractyes

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