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In population genetics, it has been widely used for inference of population evolutionary histories.
For inference of population structure from a PC plot, a generic clustering algorithm may be appropriate [20].
For our purposes, the SNP selection algorithm differs in that it identifies the maximum number of mutually "independent" SNPs for the inference of population structure.
The inference of population structure applied here can only give information on the number of genotype spectra that are discernible in the existing data set.
The PCO-MC is a method, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multi-locus genotype data.
Our results suggest that chorusing alone is a poor proxy for inference of population stability and highlight a need for reassessment of widely-used amphibian monitoring protocols.
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The approach that we have developed does not rely on accurate inference of populations or subpopulations and explicitly quantifies a measure of genome constellation diversity for each location while accounting for differences in sampling intensity across space.
Thus, ENM uncertainties would challenge objective choices, which are required for inferences of population genetics.
Inferences of population structure were derived with both a Bayesian and a hierarchical clustering method.
We found that subpopulation bias in the selection of SNP loci can affect inferences of population history.
PCA-based inferences of population relationships are largely congruent across types of ascertainment bias, even when ascertainment bias is strong.
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