Data from: Machine learning based detection of adaptive divergence of the stream mayfly Ephemera strigata populations

Adaptive divergence is a key mechanism shaping the genetic variation of
natural populations. A central question linking ecology with evolutionary
biology is how spatial environmental heterogeneity can lead to adaptive
divergence among local populations within a species. In this study, using
a genome scan approach to detect candidate loci under selection, we
examined adaptive divergence of the stream mayfly Ephemera strigata in the
Natori River Basin in north eastern Japan. We applied a new machine
learning method (i.e. Random Forest) besides traditional distance-based
redundancy analysis (dbRDA) to examine relationships between environmental
factors and adaptive divergence at non-neutral loci. Spatial
autocorrelation analysis based on neutral loci was employed to examine the
dispersal ability of this species. We conclude the following: 1) E.
strigata show altitudinal adaptive divergence among the populations in the
Natori River Basin; 2) random forest showed higher resolution for
detecting adaptive divergence than traditional statistical analysis; 3)
separating all markers into neutral and non-neutral loci could provide
full insight into parameters such as genetic diversity, local adaptation,
and dispersal ability.