Population structure
We characterized population genetic structure using the SNP dataset with
no missing data. We performed PCA and DAPC with the Adegenet 2.1.1
package (Jombart, 2008) in R. For the DAPC analysis, we first conductedK -means clustering and selected the number of clusters based on
the lowest Bayesian Information Criterion (BIC) value. We performed
cross-validation with the function xvalDapc to determine the number of
PCs to retain by calculating the lowest root mean squared error. We then
ran DAPC, retaining 20 PCs and 2 discriminant functions.
We used the Bayesian clustering algorithm in the program STRUCTURE
v2.3.4 to infer the number of population clusters (K ) and the
proportion of individual membership assigned to each cluster
(qk ). We used a burn-in of 500,000 steps followed
by 1,000,000 recorded steps, tracking the probability of the data givenK (LnP(D)) to ensure that we ran the program long enough for the
values to stabilize. We used the admixture model, no location priors,
and assumed correlated allele frequencies (Falush, Stephens, &
Pritchard, 2003). We performed a simulation with K from 1 to 7
with 10 replicates each and identified meaningful K values using
the ΔK method (Evanno, Regnaut, & Goutdet, 2005) implemented in
STRUCTURE HARVESTER v0.6.94 (Earl & vonHoldt, 2012). We combined
replicate runs using CLUMPP v1.1.2 (Jakobsson & Rosenberg, 2007).
Using the SNP dataset with 10% missing data, we performed an AMOVA and
calculated pairwise F ST values between
populations identified by STRUCTURE in GenAlEx v6.5 (Peakall & Smouse,
2012), with 10,000 permutations to generate the null distribution. To
investigate local spatial structure, we performed a Mantel test using
ade4 v1.7 (Dray & Dufour, 2007) in R on both the full and unrelated
dataset. We tested for a correlation between pairwise genotypic distance
and Euclidean geographic distance with 9,999 permutations to generate
the null distribution. We also generated a Mantel correlogram to test
for spatial autocorrelation between pairs of treeshrews at different
distance classes using GenAlEx. We first calculated pairwise linear
geographic and genotypic distances, and then used the ‘Spatial’ option
with 9,999 permutations. We defined 7 distance classes (0.2, 1.0, 2.0,
5.0, 10.0, 15.0, and 18.0 km) based on Sturges’s Rule (Sturges, 1926),
chosen to ensure sufficient comparisons within each class. Finally, we
calculated the average, median, and maximum geographic distances between
pairs of individuals in each kinship class corresponding to first,
second, third-order, and distant relatives (Table 1). To test for
significant differences between the means in each kinship class, we
performed a one-way ANOVA in R with a Tukey Honest Significant
Differences test and a Bonferroni correction for multiple comparisons
(Combs, Puckett, Richardson, Mims, & Munshi-South, 2018).