Article Figures & Data
Figures
- Figure 1.—
Random tessellation of a unit square into two spatial domains through a colored Voronoi tiling. Left, realization of a Poisson point process with Voronoi tessellation induced. Right, partition obtained after union of tiles belonging to the same population (coded as two colors).
- Figure 2.—
Illustration of the need for a tessellation prior allowing nonconnected components.
- Figure 3.—
Relationship between drift factor and FST. Results are from simulated data. The drifts d1 and d2 were equal and sampled from a Beta(2, 20) prior (x-axis).
- Figure 4.—
Estimated number of populations K̂. Each histogram shows estimates over 50 different simulated data sets with K = 1, 2, 5, 9, first using the spatial F-model (left) and then using the spatial D-model (right) as a prior for the allele frequencies. The vertical dashed line depicts the true value.
- Figure 5.—
Empirical relationship between FST and the number of populations K̂ estimated using the spatial F-model (circle) and the spatial D-model (triangles) as a prior in the MCMC inference. The 50 simulated data sets are made of K = 2 populations, with L = Jl=1,…,L = 10.
- Figure 6.—
Examples of simulated spatial organization of 100 individuals (black dots) into two populations (coded as two colors) with various levels of spatial dependence. This level is controlled by parameter m (number of Voronoi tiles). The nuclei of the tiles are not depicted for clarity.
- Figure 7.—
Maps of posterior probabilities, simulated data set A. The dashed green line depicts the true sine-shaped line of discontinuity. FST = 0.16, L = Jl=1,…,L = 10.
- Figure 8.—
Delineation of lines of discontinuities from the dispersion δ(s) of the posterior probability π[c(s)|t, z].
- Figure 9.—
Maps of posterior probabilities π(c(s)|t, z) in the presence of one migrant on both sides of the line of discontinuity. The migrants from the upper to lower population and from the lower to upper population are depicted by triangles and circles, respectively.
- Figure 10.—
Maps of the posterior probability π[c(s) = k|t, z] when coordinates si are blurred by a uniform noise. First row, wrong coordinates, assumed true; second row, wrong coordinates, assumed wrong; third row, true coordinates, assumed wrong; fourth row, true coordinates, assumed true. FST = 0.08, L = Jl=1,…,L = 10. Dashed black lines are the true borders.
- Figure 11.—
Posterior distribution of the number of populations for the wolverine data.
- Figure 12.—
Maps of the posterior probability to belong to each population for the wolverine data. Unit of axis is kilometers.
- Figure 13.—
Map of the mode of the posterior probability to belong to each class for the wolverine data. Large character numbers indicate population labels. Arrows indicate putative migrants.
Tables
- TABLE 1
Average false classification rates (in percentage) for all simulated data sets and subsamples with various levels of genetic and spatial structure
Structure Spatial Nonspatial Genetic Spacial F-model D-model F-model D-model Results with 10 loci All All 1.8 2.6 3.8 3.3 FST < 0.04 All 7.8 14.2 15 13.5 FST < 0.06 All 4.7 7.6 9 8.5 FST > 0.11 All 0.3 0.3 0.2 0.2 All m < 12 2.3 1.9 11.4 6 All m < 25 1.7 1.8 6.8 4.4 All m > 80 2.2 3 2.8 3 FST < 0.06 m < 25 2.7 5.3 11.8 9.5 FST < 0.04 m < 12 3.5 1 24 16.7 Results with 3 loci All All 11.3 12.5 17.5 17.5 The level of genetic and spatial structure increases with FST and decreases with m, respectively. Results are shown from 1000 simulated data sets of 100 individuals in two populations, with L = Jl=1,…,L = 10 and L = 3, Jl=1,…,L = 10.
