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Record W2169312456 · doi:10.11575/prism/15521

Genetic analysis of movement, dispersal and population fragmentation of grizzly bears in southwestern Canada

2003· article· en· W2169312456 on OpenAlexaboutno aff
Michael F. Proctor

Bibliographic record

VenueeScholarship (California Digital Library) · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsBiological dispersalGeographyHabitat fragmentationGrizzly BearsPopulationWildlifeHabitatEcologyHabitat destructionFragmentation (computing)Wildlife corridorNational parkPhilopatryPopulation fragmentationUrsusBiologyGene flowArchaeologyDemography

Abstract

fetched live from OpenAlex

I studied dispersal and inter-population movement of grizzly bears near the southern extent of their North American range in southwestern Canada and northwestern U.S.A. This area represents the interior portion of the southern edge of grizzly bear distribution following 100 years ofrange contraction. Fragmentation, as a result of the increasing human presence, is influencing ecosystems around the globe. I address whether anthropogenic fragmentation has affected grizzly bear populations in this vulnerable area. Human attitudes toward grizzly bears, and large carnivores in general, have experienced a paradigm shift from active persecution towards tolerance and respect. However, major forces underpinning range contraction including human-caused mortality and fragmentation, may be still operating, albeit, more subtly and less intentionally. Checking further range contraction requires specific knowledge of the processes at work. Improvements have been made in managing and monitoring human-caused mortality, however, besides the obviously isolated populations ( e.g. Yellowstone National Park), the status of fragmentation in this region was largely unknown. My goals were to use genetic analyses to explore bear movement and dispersal within and between the relictually occupied mountain ranges in southwestern Canada. I genetically sampled and generated 15-locus micro satellite genotypes for 83 5 bears across approximately 100,000 km2 in immediately adjacent geographic areas. I used population assignment techniques, parentage analysis, cluster analysis, multiple linear regression and several matrices of population genetics. I present evidence of natural and human-caused fragmentation, identify fragmenting forces, establish population and sub-population boundaries in the region, identify small vulnerable sub-populations, describe dispersal behaviour, and discuss factors that make bears susceptible to fragmentation. Female movement was restricted by human transportation and settlement corridors, and male movement appeared to be reduced in some areas. Fragmentation by north/south oriented major human-settled valleys and the major east/west transportation corridors have left much of the area a partially :fragmented set of local sub-populations varying in size and intensity of fragmentation. I found one small isolated population (n < 100) in the southern Selkirk Mountains, several small sub-populations (n < 100), including several "female demographic islands" and several population sub-units that were relatively large (n > 300). Through multiple linear regression, I implicate human settlement patterns, human-caused mortality, and traffic volumes as inhibiting rates of inter-population movement. I also measured dispersal distances of grizzly bears and found that males, on average, dispersed approximately 46 km and females 14 km, a result similar to previous radiotelemetery findings for the region. Because several fragmented sub-units are small, maintaining regional connectivity may be necessary to ensure persistence. Despite grizzly bear vagility, their conservative dispersal behaviour and difficulty in living close to humans, make maintenance of regional connectivity challenging.

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 score0.060
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.186
Teacher spread0.179 · 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

Citations28
Published2003
Admission routes1
Has abstractyes

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