Detection of adaptive divergence in populations of the stream mayflyEphemera strigatawith machine learning

Abstract

1Adaptive divergence is a key mechanism shaping the genetic variation of natural populations. A central 1 2 question linking ecology with evolutionary biology concerns the role of environmental heterogeneity in 1 3 determining adaptive divergence among local populations within a species. In this study, we examined 1 4 adaptive the divergence among populations of the stream mayfly Ephemera strigata in the Natori River 1 5Basin in northeastern Japan. We used a genome scanning approach to detect candidate loci under 1 6 selection and then applied a machine learning method (i.e. Random Forest) and traditional distance-1 7 based redundancy analysis (dbRDA) to examine relationships between environmental factors and 1 8 adaptive divergence at non-neutral loci. We also assessed spatial autocorrelation at neutral loci to 1 9 quantify the dispersal ability of E. strigata. Our main findings were as follows: 1) random forest shows a 2 0 higher resolution than traditional statistical analysis for detecting adaptive divergence; 2) separating 2 1 markers into neutral and non-neutral loci provides insights into genetic diversity, local adaptation and 2 2 dispersal ability and 3) E. strigata shows altitudinal adaptive divergence among the populations in the 2 3 Natori River Basin.2 4