2.6 Influence of host plants and geographic distance on genetic
divergence
We analyzed the influence of geographic distance and host plants on
genetic divergence among H. pungens complex populations from the
native range. Non-native populations in these analyses were excluded
because samples collected in these locations reached the areas by human
mediated dispersal and would distort the analyses. We used a combination
of correlation tests and multiple regression analyses of distance
matrices to analyze the importance of geographic distance and host plant
use on genetic divergence.
In these analyses, we used pairwise Fst linearized values at population
level based either on nuclear SNPs or mtDNA datasets to construct the
matrix of genetic distances. To construct the geographic distance
matrix, we used the coordinates (latitude and longitude) of each
population (Table S1). Host plant distance matrix was built considering
host plant families where mealybugs were collected: Cactaceae,
Portulacaceae, and Amaranthaceae. Thus, we constructed a binary matrix
with three categories (the host plant families) considering patterns of
host plant use.
To analyze the influence of geographic distance and host plant use on
genetic divergence we employed two Mantel tests (Mantel, 1967), first
considering geographic and genetic matrices and second host plant and
genetic distance matrices. We then conducted a partial Mantel test
(Smouse, Jeffrey, Long, Robert, & Sokal, 1986) to evaluate the
influence of host plants on genetic divergence, using pairwise Fst and
host plant distance matrix, while controlling for geographic distance
(Legendre & Fortin, 2010). For all correlation analyses we used the R
package vegan v2.4.4 (Oksanen et al., 2007) and 9,999
permutations to assess statistical significance. We further tested the
influence of geographic distance and host plant use on genetic
divergence by performing matrix regressions using the function MRM from
the package ecodist v2.0.1 (Goslee & Urban, 2007), and ran 9,999
permutation to assess the significance of regression coefficients.