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Record W2885667372 · doi:10.7939/r3xd0r573

Population Genomics and Quantitative Genetics of Polar Bears (Ursus maritimus)

2016· article· en· W2885667372 on OpenAlexaboutno aff
René M. Malenfant

Bibliographic record

VenueUniversity of Alberta Library · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsUrsus maritimusGenomicsBiologyPopulationGeneticsEvolutionary biologyGenomeGeneMedicineEcology

Abstract

fetched live from OpenAlex

Polar bears (Ursus maritimus) were among the first large mammals to be assessed for genetic variation in the wild, and they remain a common subject of genetics studies. Although recent advances in genotyping technology have allowed for more accurate determination of population structure and the detection of adaptive variation, most modern research has focused on historical divergence between polar bears and brown bears—a topic with little relevance to current management. The goal of this dissertation is to develop and use large datasets to better describe contemporary genetic variation in polar bears. To this end, I first describe a reanalysis of global polar bear population structure using nuclear microsatellites and mitochondrial DNA. This reanalysis was necessitated by the publication of a study suffering from flaws in design and analysis, most notably non-convergence of BAYESASS, a program used to estimate migration rates. In this reanalysis, I have rectified these errors, and—in contrast to the original study—I show that there is no evidence of strong directional movement in response to recent climate-change-induced loss of sea ice. Second, I describe the development of a custom 9K Illumina Infinium BeadChip for polar bears from restriction-site associated DNA (RAD) and transcriptome sequencing. I show the utility of this chip for sex determination of samples from harvested individuals, and that it gives realistic estimates of population structure and linkage disequilibrium (LD) decay. Third, I perform a more comprehensive Canada-wide population genetic analysis using genotypes from this BeadChip, which provides higher resolution than microsatellites. I confirm the presence of four moderately differentiated genetic clusters of polar bears across the Canadian Arctic, including the Beaufort Sea, the Canadian Arctic Archipelago, Norwegian Bay, and the Hudson Bay Complex. I also confirm the presence of east–west substructure within the Canadian Arctic Archipelago and north–south substructure within the Hudson Bay Complex. Evidence for adaptive differentiation between these clusters is limited. For the two remaining data chapters, I narrow my focus to the Western Hudson Bay management unit, where Environment and Climate Change Canada researchers have conducted mark–recapture studies and collected phenotypic data since 1966. First, I describe the construction of a 4449-individual multigenerational pedigree for Western Hudson Bay bears—among the most extensive pedigrees for any large mammal in the world. I show that inbreeding is rare in this subpopulation, and I document the first known pair of identical twin bears and six new cases of cub adoption. These results are discussed in the context of inclusive fitness theory. Finally, I use this pedigree to estimate the heritability of four routinely measured adult traits: head length, zygomatic breadth, body length, and axillary girth (a measure that is partially dependent on fatness). I then use the BeadChip to perform association studies of these traits. I find moderate heritability (h2 = 0.34–0.48) for strictly skeletal traits and lower heritability (h2 = 0.17) for axillary girth, and I show that variability in these traits is not convincingly affected by any genes of large effect in LD with markers on the BeadChip. Implications for future adaptation are discussed. Collectively, this dissertation represents the most comprehensive assessment of contemporary polar bear genetic variation that has ever been conducted, not only within Western Hudson Bay, but also at the Canadian and circumpolar levels.

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 score0.014
Threshold uncertainty score1.000

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)0.0010.000

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 head0.011
GPT teacher head0.185
Teacher spread0.174 · 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
Published2016
Admission routes1
Has abstractyes

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