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.