2.3 Population diversity, differentiation, and divergence
Each population was described by calculating haplotype frequencies,
inter-haplotype distances, haplotype diversity and nucleotide diversity
(π), as well as expected and observed heterozygosity for nuclear markers
using DNAsp v.5.10.01 (Librado & Rozas 2009) and Genetix v4.05.2
(Belkhir et al. 2004). We evaluated signals for departures from
neutrality or demographic changes by estimating Tajima’s D (Tajima 1989)
and Fu’s Fs (Fu 1997) for each locus, with Arlequin v.3.1 (Excoffier et
al. 2005). Differentiation among populations was estimated by performing
AMOVAs, and calculating pairwise F ST and
ΦST and the population average pairwise differences
DXY, using Arlequin. For AMOVAs, samples were stratified
into five groups, corresponding to the five nominal lineages
(lherminieri , boydi and baroli in the Atlantic,nicolae and bailloni in the Indian Ocean), and populations
(i.e. sampling localities; Fig. 1) within these groups. The matrix of
genetic distances among all pairs of haplotypes was computed using the
K2P model of substitution for concatenated mitochondrial markers, and
TN93 for concatenated nuclear markers, as determined using jModelTest2.
We used a Mantel test to measure the level of correlation among genetic
distances and geographic distances (Smouse et al. 1986). Geographic
distance was calculated as the shortest distance between two populations
without crossing land. Statistical significance (AMOVAs, PairwiseF ST and Mantel tests) was estimated using 1000
permutations. To visualize relationships among lineages, we inferred
NeighborNet networks using SplitsTree v 4.14.2 (Huson & Bryant 2006),
with different dataset combinations: all markers independently,
concatenated mitochondrial markers, concatenated nuclear markers.