DOI: 10.5061/dryad.2j27b
title: Data from: Demographic model selection using random forests and the site frequency spectrum

description: Phylogeographic data sets have grown from tens to thousands of loci in
 recent years, but extant statistical methods do not take full advantage of
 these large data sets. For example, approximate Bayesian computation (ABC)
 is a commonly used method for the explicit comparison of alternate
 demographic histories, but it is limited by the “curse of dimensionality”
 and issues related to the simulation and summarization of data when
 applied to next-generation sequencing (NGS) data sets. We implement here
 several improvements to overcome these difficulties. We use a Random
 Forest (RF) classifier for model selection to circumvent the curse of
 dimensionality and apply a binned representation of the multidimensional
 site frequency spectrum (mSFS) to address issues related to the simulation
 and summarization of large SNP data sets. We evaluate the performance of
 these improvements using simulation and find low overall error rates
 (~7%). We then apply the approach to data from Haplotrema vancouverense, a
 land snail endemic to the Pacific Northwest of North America. Fifteen
 demographic models were compared, and our results support a model of
 recent dispersal from coastal to inland rainforests. Our results
 demonstrate that binning is an effective strategy for the construction of
 a mSFS and imply that the statistical power of RF when applied to
 demographic model selection is at least comparable to traditional ABC
 algorithms. Importantly, by combining these strategies, large sets of
 models with differing numbers of populations can be evaluated.
description: Barcodes_Grp2_Mar2016_MEC-17-0128Barcodes associated with Grp2_i03_Mar2016.fastq.gz.Barcodes_Grp1_Mar2016_MEC-17-0128Barcodes associated with Grp1_i12_Mar2016.fastq.gz.Barcodes_Grp3_Mar2016_MEC-17-0128Barcodes associated with Grp3_i04_Mar2016.fastq.gz.Barcodes_Grp4_Mar2016_MEC-17-0128Barcodes associated with Grp4_i05_Mar2016.fastq.gz.Barcodes_Grp5_Mar2016_MEC-17-0128Barcodes associated with Grp5_i06_Mar2016.fastq.gz.Barcodes_Grp1_Nov2015_MEC-17-0128Barcodes associated with Grp1_i06_Nov2015.fastq.gz.i4_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi4-V1T-1_S45_L006_R1_001.fastq.gz.i5_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi5-V1T-1_S46_L006_R1_001.fastq.gz.i6_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi6-V1T-1_S47_L006_R1_001.fastq.gz.i7_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi7-V1T-1_S48_L006_R1_001.fastq.gz.i8_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi8-V1T-1_S49_L006_R1_001.fastq.gz.i12_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi12-V1T-1_S50_L006_R1_001.fastq.i3_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi3-V1T-1_S44_L006_R1_001.fastq.gz.i2_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi2-V1T-1_S43_L006_R1_001.fastq.gz.i1_barcodes_July2016_MEC-17-0128Barcodes associated with ZBCi1-V1T-1_S42_L006_R1_001.fastq.gz.ZBCi8-V1T-1_S49_L006_R1_001.fastqZBCi2-V1T-1_S43_L006_R1_001.fastqZBCi1-V1T-1_S42_L006_R1_001.fastqZBCi3-V1T-1_S44_L006_R1_001.fastqZBCi4-V1T-1_S45_L006_R1_001.fastqZBCi5-V1T-1_S46_L006_R1_001.fastqZBCi6-V1T-1_S47_L006_R1_001.fastqZBCi7-V1T-1_S48_L006_R1_001.fastqGrp1_i06_Nov2015.fastqGrp2_i03_Mar2016.fastqGrp1_i12_Mar2016.fastqGrp5_i06_Mar2016.fastqGrp3_i04_Mar2016.fastqGrp4_i05_Mar2016.fastqZBCi12-V1T-1_S50_L006_R1_001.fastqparams_exExample of a params file used in pyramid for this study.Haplo_July2016_77Samples_p60.unlinked_snpspyRAD output: unlinked snpsHaplo_July2016_77Samples_p60.snpspyRAD output: SNPsHaplo_July2016_77Samples_p60.allelespyRAD output: allelesHaplo_July2016_77Samples_p60.locipyRAD output: loci