Spatial genetic patterns
We investigated spatial genetic patterns and restrictions to gene flow
in A. cephalotes , using the software ARLEQUIN version 3.5.2.2
(Excoffier & Lischer, 2010) to perform alternative hierarchical
Analyses of Molecular Variance (AMOVA). First, the populations were
classified into two regions (Pacific and Andean), separated by the
western range of the Colombian Andes, in order to study the effect of
this range as a major geographic barrier to gene flow affecting the
distribution of genetic variation in the data (isolation by barrier,
IBB). Second, the populations were classified into three regions
(Pacific, Andean 1, and Andean 2) defined by the climatic conditions
mediated by the Andes mountains (isolation by environment, IBE;
Supplementary table ST1). In the AMOVA, we estimated the associatedF ST for microsatellites and the genetic
distance-based Φ -statistic for mtCOI (Excoffier, Smouse,
& Quattro, 1992; Meirmans, 2006; Meirmans & Hedrick, 2011), both
globally and between all pairs of populations. The significance of the
variance components and associated FST andΦ indices were calculated using 10,000 permutations.
We further investigated the structure of the A. cephalotesnuclear data using two clustering analyses. First, we used a model-based
Bayesian clustering method implemented in the software STRUCTURE v.
2.3.4 (Pritchard, Stephens, & Donnelly, 2000), which estimates the
number of genetic clusters (K ) independent of spatial sampling.
Analyses were performed using one individual per nest, with and without
admixture, for correlated allele frequencies. A burn-in of 50,000 and
500,000 sampling generations were implemented for K ranging from
1 to 12, with 10 iterations for each value of K . Evanno’s method
(Evanno, Regnaut, & Goudet, 2005), implemented in STRUCTURE HARVESTER
(Earl & vonHoldt, 2012), was used to estimate the optimal number of
clusters from the STRUCTURE output (Supplementary figure SF1) and the
results were visualized using CLUMPAK (Kopelman, Mayzel, Jakobsson,
Rosenberg, & Mayrose, 2015).
Second, discriminant analysis of principal components (DAPC) was
performed using a principal component analysis (PCA) prior to the
discriminant analysis (DA) (Jombart, Devillard, & Balloux, 2010). The
DA partitions genetic variation, maximizing differences between clusters
while minimizing within-cluster variation. We performed a DAPC analysis
in the R package ADEGENET (Jombart et al., 2010) using one individual
per nest for microsatellite data and a polymorphic nucleotide positions
matrix for mtCOI . The function ‘dapc ’ was used to estimate
all available principal components (PCs), and to determine the optimal
number of PCs used based on cumulative variance.