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Record W2997359134

Population genomics of a timberline conifer, subalpine larch (Larix lyallii Parl.)

2019· dissertation· ceb· W2997359134 on OpenAlexaboutno aff
Marie Vance

Bibliographic record

VenueUVic’s Research and Learning Repository (University of Victoria) · 2019
Typedissertation
Languageceb
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLarchMontane ecologyGeographyPopulationForestryBiologyEcologyDemography
DOInot available

Abstract

fetched live from OpenAlex

Subalpine larch (Larix lyallii Parl.) has a narrow ecological niche at timberline in the Cascade Range and the Rocky Mountains of western North America. Demographic factors, including a long generation time (average 500 years) and a late arrival at sexual maturity (100-200 years), make it unlikely that this species will be able to adapt to predicted climate change. A better understanding of genetic structure and genetic diversity is necessary in order to effectively manage this species for future generations. Foliage from 62 populations of subalpine larch was collected in order to elucidate the range-wide population genomics of the species. DNA was extracted and a next-generation sequencing method, restriction site associated DNA sequencing (RAD-seq), was used to generate genome-wide single nucleotide polymorphism (SNP) marker data. Three genetically differentiated clusters were identified via principal components analysis, a discriminant analysis of principal components and Bayesian STRUCTURE analysis: the Cascade Range, the southern Rocky Mountains and the northern Rocky Mountains. A monophyletic group in the central Rocky Mountains was also identified in a dendrogram of genetic distance but this group had weak bootstrap support (49%), meaning genetic differentiation depends on relatively few genetic variants. Genetically differentiated groups should be prioritized for future management and conservation efforts. Negative values of Tajima’s D and preferred demographic scenarios generated by coalescent simulations indicated that 15 populations all have a recent history of expansion. Genetic diversity within these populations was found to be moderate (HO = 0.15 – 0.20), inbreeding coefficients were found to be high (FIS = 0.15 – 0.25) and genetic differentiation among populations was found to be high (average FST = 0.18). These results indicated that fragmentation driven by Holocene warming may have resulted in reduced effective population sizes. Smaller populations experience stronger genetic drift and an increased likelihood of inbreeding, which may hinder an adaptive response to natural selection. Still, parameter estimates for preferred demographic scenarios suggested a minimum effective population size of around 20,000 individuals, which is not considered small by most conservationists. A final study of 18 populations found local adaptation to cold temperature in the northern portion of the species range. In all seasons, populations from the northern Rocky Mountains had significantly higher cold tolerance than populations from the central Canadian Rocky Mountains and the northern Cascades. Winter cold tolerance showed strong clines associated with the frost-free period and degree days below zero. These two climate variables explained 65% of the explainable variance in phenotype when redundancy analysis models were conditioned on geography. Seven SNPs were identified that explained a significant portion of the variance in winter cold tolerance. Range-wide, additional SNPs were identified as FST outliers and/or as significantly correlated with environmental gradients, even after correcting for neutral genetic structure. Together, the results of this work indicate that dispersal, neutral evolutionary processes and natural selection have all played important roles in shaping patterns of genetic variation across the natural range of subalpine larch. All of these factors should be considered during the development of management and conservation strategies for this high-elevation conifer species.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.027
GPT teacher head0.258
Teacher spread0.231 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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