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.
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.
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.
Thus far, no study has attempted the selection of a small set of markers that can effectively be used for inference of population structure and ancestry (Ancestry Informative Markers – AIMs) from this dataset.
Here we describe the development of a new approach, PCO-MC, which couples principal coordinate analysis to a clustering procedure for the inference of population structure from multilocus genotype data.
In ecology, precise inference of population structure is necessary to accurately describe the distribution of biodiversity across ecosystems, to improve conservation efforts, and, more generally, to further understanding of the evolutionary process.
Nonetheless, neither differentiation-based diversity (principal coordinates analysis on mean Shannon values, Figure 4), nor probabilistic inference of population structure [20] revealed any indication of more than three distinct genotype spectra within all samples from Waslala.
