Isolation by distance and isolation by environment
To better understand the drivers potencially explaining the observed
patterns of spatial genetic variation in E. coccineum , we
performed distance-based redundancy analysis (db-RDA) and partial db-RDA
at the individual level on the complete PG_dataset and on loci
potentially influenced by selection. RDA is a multiple linear regression
method performed between a matrix of dependent variables and matrices of
independent variables, which has been demonstrated as more appropriate
than other methods (i.e., Mantel test) when multiple variables are
analyzed to identify drivers of population genetic structure (Nadeu et
al., 2016; Orsini et al., 2013).
A dependent matrix, calculated with a Bray-Curtis dissimilarity index
(Bray and Curtis, 1957), of co-dominant variant call format of each
individual was used as a response variable. Predictor matrices were
geographic distance (IBD), environmental disparity (IBE) and degree of
shared co-ancestry (IBA). As environmental data, we used seven
environmental variables with low level of covariation (see Supplementary
table 1). For the distance matrix, we used a Principal Coordinates of
Neighbourhood Matrix (PCNM), using a truncation threshold of 0.05 for
long distances with the function pcnm() within the R package vegan
v.2.5.7 (Oksanen et al., 2007). PCNM is commonly used to transform
spatial distances into rectangular data matrices suitable for
constrained ordination or regression analyses (Borcard and Legendre
2002). Populations Q-values obtained with STRUCTURE K = 2, which
separated populations into North and Center-South ancestry linages (see
results for more details), were used as co-ancestry variable. Prior to
the analysis, all three independent matrices were scaled to a mean of
zero and variance of one with the scale() function in R.
Among populations variation in E. coccineum was partitioned into
components explained by geographic distance (IBD), ecological gradients
(IBE) and co-ancestry (IBA), or their combination (i.e., constrained by
the effects of the remaining two independent matrices), using the
vartpart() function within R package vegan v.2.5.7 (Oksanen et al.,
2007). Significance of each partition was tested with the anova.cca()
function through 1,000 permutations on the R package vegan v.2.5.7
(Oksanen et al., 2007).