Table S2.

Demographic models show equivocal evidence for growth among the P. falciparum ancestral-like subpopulation

Model	θ	ηD* (0.001–1.0)	ηG* (1–100)	T* (0.01–5.0)	Log likelihood	AIC
Static	396.183	—	—	—	−27.336	54.67
NegG	396.188	0.999	—	0.308	−27.337	58.67
PosG-Epoch	297.831	—	1.515	1.262	−24.747	53.49
PosG-Exp	240.479	—	1.943	4.998	−24.736	53.47
BG	272.881	0.999	1.714	3.457	−24.737	55.47

Log-likelihoods and AIC were determined for the following five models as seen in Fig. 4: constant population size (Static), population decline (NegG), two-epoch increase (PosG-Epoch), exponential increase (PosG-Exp), and bottleneck with subsequent exponential growth (BG). Models were fit to the P. falciparum central (i.e., ancestral) population, because it comprises a single population for comparison with P. vivax scenarios. Output variables determined included (i) θ, the ancestral mutation rate which is a proxy for the N_eff; (ii) η_D, the factor of population contraction; (iii) η_G, the factor of population expansion; and (iv) T, the time in the past of change in population. Best-fit (i.e., lowest AIC) parameter sets were selected from 100 model-fit replicates. AIC aids in scenario selection by penalizing highly parameterized models while seeking to minimize information loss. The extent of information loss between the best-fit and other models is described by $e^{((AI{C}_{min}-AI{C}_i)/2)}$, where AIC_min is AIC for the best-fit model (i.e., lowest AIC), and AIC_i represents AIC for the ith model.

^*Ranges explored for optimization are provided for optimizable parameters.