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To investigate the spatial admixture in our phylogeographic analyses and statistically measure trait associations in the phylogeny we used the parsimony score (PS) and the association index (AI), as implemented in BaTS [58].
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Therefore, the data strongly suggest that spatial mixture among separate genetic clusters was common, but individual admixture much less so in this system.
Indeed, several recent studies have incorporated admixture and spatial and temporal variability of larval sources in the explanation [ 50, 55, 66] and it is widely accepted that population genetic structure can be affected by temporal variability in recruitment sources and rates [ 55, 67- 69].
The spatial distribution of the ADMIXTURE components (K = 6, see Results) was visualized by plotting the fraction of each component in the analyzed populations on a map with the interpolation plugin of the QGIS software, using the Inverse Distance Weighting method with a distance coefficient of 3 (QGIS Development Team 2014).
These spatial interpolations use color mixing to predict admixture proportions across space in order to better visualize the spatial partitioning of genetic differentiation.
Geostatistical methods were used to analyze spatial pattern of variation in admixture and SES.
We generated RADseq data and applied phylogenetic methods, hierarchical Bayesian clustering, PCA and population differentiation with admixture analyses to characterize spatial genetic structure across the species range.
Although both models underestimate the spatial and temporal complexity of the true admixture history of African-Americans, these approximations enable us to illustrate the way in which estimates of the sex bias depend on assumptions about the admixture model.
Early migrations of modern humans (i.e., Out of Africa) are believed to have started approximately 100 ka (Jobling et al. 2004) with complex spatial demographic phenomena (e.g., expansions, contractions, admixture events) being particularly responsible for much of the variation identified between present-day human populations.
We used the conditional autoregressive (CAR) Gaussian model of admixture with linear trend surface, setting the spatial interaction parameter to 0.6.
We introduce models that leverage the spatial structure of genetic variation using HMMs for the admixture process to achieve high accuracy in localizing the recent ancestry of a given individual on a geographical map.
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