Supporting Methods 1 (SM1) DIY-ABC Parameters and Settings
Approximate Bayesian Computation (ABC) implemented in the software DIYABC v2.0
(Cornuet et al. 2013) was used for analysis of our microsatellite dataset to compare alternate
biogeographic scenarios for founder/bottleneck events. In all scenarios, the population
divergences matched the population structure observed in the mitochondrial phylogeny shown in
Fig. 1. From each scenario, 1x106 datasets were simulated from parameter values drawn from the
prior distribution for each scenario: the priors for the demographic (effective population size,
time of splitting) and (microsatellite) mutation parameters are available on Dryad. The summary
statistics (mean number of alleles, mean genic diversity, mean size variance, and mean GarzaWilliamson’s M) were calculated for each of the four populations (Meru, Buffalo Springs/Isiolo,
Mbuvu, Mtito Andei/Kathekani). For pairwise population comparisons, the summary statistics
included the mean number of alleles, mean genic diversity, mean size variance, Fst, and the
classification index (Rannala & Mountain 1997). The 5000 simulated datasets most similar to the
observed data were identified through logistic regression and used to estimate the relative
posterior probability (with 95% confidence intervals) of each scenario. The most-likely scenario
was the one with the highest posterior probability value and non-overlapping 95% confidence
intervals. Confidence in the most-likely scenario was evaluated by comparing it with datasets
simulated for each competing scenario (top 3 scenarios with the highest posterior probabilities).
The posterior density distributions of the effective population size (Ne) of each group were
estimated from 1% of the closest datasets simulated according to the most-likely scenario.
References
Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes.
Proceedings of the National Academy of Sciences, 94, 9197–9201.
Supporting Methods 2 (SM2) Hierarchical Clustering
STRUCTURE analyses based on the ΔK method (Evanno et al. 2005) supported a K = 2,
representing distinct groups on either side of the Tana River (Fig. 3a), splitting the northern
localities from the central and southern populations, and corresponding to the first major break in
the mitochondrial phylogeny (Fig. 1). A second round of structure analyses assigned individuals
of Groups 1 (North) and 2 (Central and South) to one of two subgroups respectively (Fig. 3a),
each reflective of the next level of mitochondrial breaks (Fig. 1). Group 1 (north of Tana River)
includes: a) Subgroup 1a: all individuals from Meru and b) Subgroup 1b: all samples from
Buffalo Springs and Isiolo. Group 2 was divided into the following subgroups: a) Subgroup 2a:
all samples from the central region between the Tana River and Athi River (Mbuvu), and b)
Subgroup 2b: all individuals south of the Athi River (Kathekani and Mtito Andei).
The final round of hierarchical structure analyses yielded different estimates of K for
each subgroup (Fig. 3a). Subgroup 1a (Meru) was assigned to three different clusters, but did
not coincide with the three mitochondrial haplotypes. Two colonies (T & W) had all individuals
assigned to a single cluster; two colonies (RRR & CCCC) had the majority of individuals
assigned to the same cluster with one individual assigned to a different cluster (CCCC-9693, a
suspected breeding male that also had a different dloop haplotype than the rest of CCCC animals
sampled); and one colony (YY) had mixture of individuals assigned to each of the three clusters
with some individuals suggesting admixture. Subgroup 1b was split into two groups that reflect
their localities and mitochondrial haplotypes, Buffalo Springs and Isiolo. For Subgroup 2a, ΔK
supports K = 2 with individuals admixed for two clusters. Due to limitations of the Evanno
method (ΔK), evaluation of K = 1 is not possible. Inspection of the ln(K) at K = 2 (-118.31)
revealed a large variance (s2 = 13.78) with K = 1 (-133.62) falling just outside the 95%
confidence interval, supporting K = 1 (Pritchard et al. 2000). Within Subgroup 2b, the Evanno
method (ΔK) supported four clusters, although there is no clear pattern to the clustering and with
individuals assigned to all clusters. Oversampling of multiple individuals per colony may infer
population structure that does not exist (Magle et al. 2010; Tucker et al. 2013). Most of the loci
are fixed within these colonies, with many individuals sharing the same MLG (Table 1). Based
on this, we chose K=1 for the Kathekan and Mtito Andei cluster. Based these hierarchical
methods, the total number of clusters identified across all samples was seven.
References
Rannala B, Mountain JL (1997) Detecting immigration by using multilocus genotypes.
Proceedings of the National Academy of Sciences, 94, 9197–9201.