Environmental variables
Temperature and precipitation
variables have been widely assessed as robust predictors for
environmental adaptation in eucalypts for traits and genetic variants
(Correia et al., 2018; Aspinwall et al., 2019; Pritzkow et al., 2020).
We tested a total of five climate variables. Two of these represent
extreme temperature and precipitation variables and we predicted they
would drive patterns of adaptation: maximum temperature of the warmest
month (TMAX) and precipitation of the warmest quarter
(PWQ). Three other temperature and precipitation
variables were selected as independent climatic factors (based on
principal component analysis (PCA) and Pearson’s correlation
coefficients) and are known to be important for local adaptation in
eucalypts (Queirós et al., 2020; Rocha et al., 2020): minimum
temperature of the coldest month (TMIN), mean annual
precipitation (PMA), and temperature seasonality
(TSEAS). Climatic data for all populations was
downloaded from the 19 variables
in the WorldClim v2 database (Fick & Hijmans, 2017) at a spatial
resolution of 30 arcsec. Climate data for each population was extracted
using the R package raster from the geo-located GPS coordinates
of the sampled populations. PCA of environmental variables was performed
with R package ade4 and a Pearson’s correlation coefficient
matrix was calculated between all 19 climate variables using thecor function.