Genetic structure
Four complementary analyses were conducted to characterize the pattern
of genetic structure within E. coccineum using the PG_dataset.
First, we calculated the pairwise FST values among all sampling
localities using the function stamppFst (), using 1000 bootstrap
replicates across loci and 95% of confidence intervals with the R
package StAMPP v.1.5.1 (Pembleton et al., 2013). Second, a principal
component analysis (PCA) was performed using the glPCA () function
with the R package adegenet v.2.1.1 (Jombart, 2008). Only the first
three components were retained to estimate genetic groups. Third, a
model-based STRUCTURE clustering analysis was performed using STRUCTURE
v.2.3.4. (Falush et al., 2003). Ten independent simulations allowing
admixture were run for each K (K=1-10), each with 200.000 Markov chain
Monte Carlo replicates (MCMC) and 100.000 samples discared as burnin.
STRUCTURE HARVESTER
(http://taylor0.biology.ucla.edu/structureHarvester/ ) was used to
determine the optimum K based on L(K) and ΔK parameters (Zhang et al.,
2018). Fourth, to describe the relationships between individuals and
genetic groups among E. coccineum , the Nei’s genetic distance
between individuals was calculated within the R package StAMPP v.1.5.1
(Pembleton et al., 2013). Unrooted Neighbour-Joining (NJ) distances
trees were constructed using the individual Nei’s distance matrix, as
implemented by the R package ape v .5.3 (Paradis and Schliep, 2018).