Table 1.
Probability (tsimulated<tobserved) | |||||
---|---|---|---|---|---|
Test quantity (t) | Observed value | Scenario 1 | Scenario 2 | Scenario 3 | |
Test quantities | NAL_S | 13.6000 | 0.7275 | 0.2871 | 0.6235 |
corresponding | NAL_1 | 3.4000 | 0.7542 | 0.9865 (*) | 0.4252 |
to thesummary | NAL_2 | 3.6500 | 0.6455 | 0.4102 | 0.4761 |
statistics used | HET_S | 0.8429 | 0.5621 | 0.2471 | 0.4488 |
to discriminate | HET_1 | 0.5151 | 0.4938 | 0.9890 (*) | 0.4339 |
among | HET_2 | 0.5725 | 0.9125 | 0.9188 | 0.8221 |
scenarios and | MGW_S | 0.8242 | 0.3593 | 0.7656 | 0.5230 |
compute | MGW_1 | 0.4072 | 0.3782 | 0.6713 | 0.4524 |
parameter | MGW_2 | 0.4834 | 0.6117 | 0.8499 | 0.7297 |
posterior | FST_S_1 | 0.2170 | 0.7882 | 0.0371 (*) | 0.8105 |
distributions | FST_S_2 | 0.2050 | 0.6180 | 0.4606 | 0.6052 |
FST_2_3 | 0.1761 | 0.0001 (***) | 0.9580 (*) | 0.6289 | |
Test quantities | VAR_S | 21.7561 | 0.7476 | 0.2538 | 0.6209 |
corresponding | VAR_1 | 9.3385 | 0.4861 | 0.3561 | 0.3598 |
to summary | VAR_2 | 9.5277 | 0.5232 | 0.1792 | 0.3748 |
statistics NOT | LIK_1_S | 38.5648 | 0.7867 | 0.4503 | 0.7240 |
used to | LIK_1_2 | 31.7504 | 0.0001 (***) | 1.0000 (***) | 0.7162 |
discriminate | LIK_2_1 | 32.1075 | 0.0001 (***) | 0.9850 (*) | 0.7836 |
among | H2P_S_1 | 0.7734 | 0.6563 | 0.8411 | 0.6115 |
scenarios and | H2P_S_2 | 0.7993 | 0.9231 | 0.8239 | 0.8664 |
compute | H2P_1_2 | 0.6020 | 0.0315 (*) | 0.9975 (**) | 0.7193 |
parameter | DAS_S_1 | 0.1329 | 0.2298 | 0.4582 | 0.2639 |
posterior | DAS_S_2 | 0.1099 | 0.0559 | 0.1681 | 0.0816 |
distributions | DAS_1_2 | 0.3402 | 1.0000 (***) | 0.0001 (***) | 0.2529 |
Evolutionary scenarios 1, 2 and 3 are detailed in Figure 3. The single "pseudo-observed" test data set analyzed here was simulated under scenario 3. The probability (tsimulated <tobserved) given for each test quantities (t) was computed from 10,000 data sets simulated from the posterior distributions of parameters obtained under a given scenario. Corresponding tail-area probabilities, or p-values, of the test quantities (t) can be easily obtained as Prob(tsimulated <tobserved) and 1.0 - Prob (tsimulated <tobserved) for Prob (tsimulated <tobserved) ≤ 0.5 and > 0.5, respectively [22]. The test quantities correspond to the summary statistics used to discriminate among scenarios and compute the posterior distributions of parameters or to other statistics. NAL_i = mean number of alleles in population i, HET_i = mean expected heterozygosity in population i [38], MGW_i = mean ratio of the number of alleles over the range of allele sizes [54], FST_i_j = FST value between populations i and j [39], VAR_i = mean allelic size variance in population i, LIK_i_j = mean individual assignment likelihoods of population i assigned to population j [22], H2P_i_j = mean expected heterozygosity pooling samples from populations i and j, DAS_i_j = shared allele distance between populations i and j [55]. Populations i and j correspond to populations S, 1 or 2 in Figure 3. *, **, *** = tail-area probability < 0.05, < 0.01 and < 0.001, respectively. Significant tail-area probabilities after applying the false discovery rate correction method of Benjamini and Hochberg [43] are given in bold italic characters.