DOI: 10.5061/dryad.6q573n600
title: Genomic time-series data show that gene flow maintains high genetic diversity despite substantial genetic drift in a butterfly species

description: Effective population size affects the efficacy of selection, rate of
 evolution by drift, and neutral diversity levels. When species are
 subdivided into multiple populations connected by gene flow, evolutionary
 processes can depend on global or local effective population sizes. Theory
 predicts that high levels of diversity might be maintained by gene flow,
 even very low levels of gene flow, consistent with species long-term
 effective population size, but tests of this idea are mostly lacking.
 Here, we show that Lycaeides butterfly populations maintain low
 contemporary (variance) effective population sizes (e.g., ~200
 individuals) and thus evolve rapidly by genetic drift. In contrast,
 populations harbored high levels of genetic diversity consistent with an
 effective population size several orders of magnitude larger. We
 hypothesized that the differences in the magnitude and variability of
 contemporary versus long-term effective population sizes were caused by
 gene flow of sufficient magnitude to maintain diversity but only subtly
 affect evolution on generational time scales. Consistent with this
 hypothesis, we detected low but non-trivial gene flow among populations.
 Furthermore, using short-term population-genomic time-series data, we
 documented patterns consistent with predictions from this hypothesis,
 including a weak but detectable excess of evolutionary change in the
 direction of the mean (migrant gene pool) allele frequencies across
 populations, and consistency in the direction of allele frequency change
 over time. The documented decoupling of diversity levels and short-term
 change by drift in Lycaeides has implications for our understanding of
 contemporary evolution and the maintenance of genetic variation in the
 wild.
description: A genotyping-by-sequencing approach was used to generate the DNA
 sequence data. These were then aligned to the <em>Lycaeides
 melissa</em> genome. Variant (SNP) calling was done using samtools
 and bcftools. This, followed by quality filtering, resulted in the
 included vcf file. Genotypes were then inferred from genotype likelihoods
 using the (ad)mixture model in Entropy. Allele frequencies for each
 population and year were estimated using an expectation-maximization
 algorithm.
description: This data set contains the following files.
 G_SAM_sub.txt = A comma-delimited text file with the genotype
 estimates from Entropy. There is one row per individual (1536) and one
 column per SNP (12886). These are non-integer Bayesian point estimates of
 the number of non-reference allele copies. indIds.txt =
 IDs for the individuals in G_SAM_sub.txt. These are in the same order as
 the individuals in the genotype file. Each row gives an individuals
 population ID followed by the collection year.
 p_combined_SAM_sub.txt = A text file with allele frequency
 estimates. There is one row per SNP (12886) and one column for each
 population and year. These are in alphanumeric order: BCR 2013, BCR 2015,
 BCR 2017, BNP 2013, BNP 2015, BNP 2017, BTB 2013, BTB 2014, BTB 2015, BTB
 2017, GNP 2013, GNP 2015, GNP 2017, HNV 2013, HNV 2014, HNV 2015, HNV
 2017, MRF 2013, MRF 2015, MRF 2017, PSP 2013, PSP 2015, PSP 2017, RNV
 2013, RNV 2014, RNV 2017, SKI 2013, SKI 2014, SKI 2015, SKI 2017, USL
 2013, USL 2015, USL 2017.
 morefilter_filtered2x_lyc_timeseries_samtbcft_vcf.gz = This is
 the filtered and compressed vcf (variant) file from samtools/bcftools.
 This includes the full set of SNPs from samtools/bcftools, not just those
 also called by GATK.