TABLE 2.

AUC with 95% C.I. for ABC and BayesFst methods under different scenarios

10μθ	σθ	npop	ρZ	N	s		AUC (ABC)	AUC (BayesFst)
4	0	6	0.05	400	0.02	0.02	0.498 [0.47, 0.525]	0.499 [0.471, 0.527]
4	0	6	0.05	400	0.02	0.1	0.509 [0.485, 0.533]	0.499 [0.472, 0.525]
4	0	6	0.05	400	0.1	0.02	0.646 [0.615, 0.677]	0.657 [0.624, 0.691]
4	0	6	0.05	4000	0.02	0.02	0.82 [0.79, 0.85]	0.811 [0.781, 0.841]
4	0	6	0.05	400	0.1	0.1	0.887 [0.869, 0.905]	0.887 [0.867, 0.906]
4	0	6	0.05	4000	0.02	0.1	0.935 [0.923, 0.947]	0.94 [0.927, 0.952]
8	0.5	6	0.05	4000	0.1	0.17	0.95 [0.939, 0.96]	0.875 [0.852, 0.897]
4	0	3	0.05	4000	0.1	0.1	0.953 [0.942, 0.964]	0.965 [0.955, 0.974]
8	0.5	6	0.05	4000	0.1	0.1	0.958 [0.947, 0.969]	0.886 [0.863, 0.908]
4	0	6	0.05	4000	0.1	0.1	0.959 [0.948, 0.971]	0.932 [0.917, 0.947]
0.4	0	6	0.05	4000	0.1	0.1	0.962 [0.95, 0.974]	0.972 [0.962, 0.982]
4	0	6	0.01	4000	0.1	0.1	0.974 [0.958, 0.989]	0.983 [0.971, 0.996]
4	0	6	0.05	4000	0.1	0.1	0.974 [0.966, 0.982]	0.981 [0.975, 0.988]
4	0	6	0.1	4000	0.1	0.1	0.977 [0.972, 0.983]	0.985 [0.981, 0.989]
4	0	6	0.05	4000	0.1	0.02	0.989 [0.984, 0.994]	0.992 [0.988, 0.996]

Nμ, scaled mutation rate; σ_μ, standard deviation of mutation rate across loci (on log₁₀ scale); npop, number of populations; ρ_Z, proportion of loci under selection; N, subpopulation size; s, selection coefficient. As noted in the text, for population j is drawn from a beta distribution with parameters (, ). The immigration rate, in the terminology of our model, is then computed by .