Machine‐learning‐based detection of adaptive divergence of the stream mayfly Ephemera strigata populations

Abstract

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 northeastern 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: (a) E. strigata show altitudinal adaptive divergence among the populations in the Natori River Basin; (b) random forest showed higher resolution for detecting adaptive divergence than traditional statistical analysis; and (c) separating all markers into neutral and non‐neutral loci could provide full insight into parameters such as genetic diversity, local adaptation, and dispersal ability.

Abstract

In this study, using a genome scan approach to detect candidate loci under selection, we detected altitudinal adaptive divergence among the populations of the stream mayfly Ephemera strigata in the Natori River Basin in northeastern Japan. The applied machine‐learning method (i.e., random forest) showed high higher resolution for detecting adaptive divergence than traditional statistical analysis.