“…However, the number of SNPs obtained and the ability to detect genes underlying local adaptation from the abovementioned methods may be influenced due to the differences in library preparation, SNP densities, and the bioinformatics parameters applied to SNP filtering ( Hoban et al, 2016 ; Lowry et al, 2017 ; McKinney et al, 2017 ). As more and more forest tree genomes have been published (e.g., Table 1 in Ingvarsson et al, 2016 ) and sequencing costs fall, whole-genome resequencing is thriving and becoming an option for landscape genomics studies ( Lin et al, 2018 ; Zhu et al, 2020 ), which can provide unprecedented marker density and determine other genetic variation such as structural variants and mutations in regulatory elements, increasing power for the detection of local adaptation and providing novel insights into the role of selection, recombination, and gene flow in promoting or impairing local adaptation to new habitats compared with reduced-representation methods ( Fuentes-Pardo and Ruzzante, 2017 ; Bourgeois and Warren, 2021 ). In addition, the degrees of linkage disequilibrium (LD) in the studied species will also influence the power of detecting adaptive SNPs.…”