DOI: 10.5061/dryad.c6p8q62
title: Data from: Genomic signatures of environmental selection despite near-panmixia in summer flounder

description: Rapid environmental change is altering the selective pressures experienced
 by marine species. While adaptation to local environmental conditions
 depends on a balance between dispersal and natural selection across the
 seascape, the spatial scale of adaptation and the relative importance of
 mechanisms maintaining adaptation in the ocean are not well understood,
 Here, using population assignment tests, Approximate Bayesian Computation
 (ABC), and genome scans with double-digest restriction-site associated DNA
 sequencing data, we evaluated population structure and locus-environment
 associations in a commercially important species, summer flounder
 (Paralichthys dentatus), along the U.S. east coast. Based on 1137 single
 nucleotide polymorphisms across 232 individuals spanning nearly 1900 km,
 we found no indication of population structure across Cape Hatteras, North
 Carolina (FST = 0.0014) or of isolation by distance along the coast using
 individual relatedness. ABC estimated the probability of dispersal across
 the biogeographic break at Cape Hatteras to be high (95% credible
 interval: 7-50% migration). However, we found 15 loci whose allele
 frequencies were associated with at least one of four environmental
 variables. Of those, 11 were correlated with bottom temperature. For
 summer flounder, our results suggest continued fisheries management as a
 single population and identify likely response mechanisms to climate
 change. Broadly, our findings suggest that spatial balancing selection can
 manifest in adaptive divergence on regional scales in marine fish despite
 high dispersal, and that these conditions likely result in the widespread
 distribution of adaptive alleles and a high potential for future genetic
 adaptation in response to changing environmental conditions. In the
 context of a rapidly changing world, a landscape genomics perspective
 offers a useful approach for understanding the causes and consequences of
 genetic differentiation.
description: All summer flounder SNPs
 following bioinformatics filtersVCF file containing
 ddRADseq data for summer flounder. This file contains all SNPs remaining
 following bioinformatics filters.SNP.DP3g95maf05.FIL.FIL.recode.updated.vcf1137 summer flounder SNPsVCF file containing ddRADseq data for summer flounder. This file contains the first SNP on each contig, resulting in 1137 SNPs. These SNPs were used in the main analyses.SNP.DP3g95maf05.FIL.FIL.recode.updated.onesnp.vcfStructure formatted file made from SNP.DP3g95maf05.FIL.FIL.recode.updated.onesnp.vcfThis file is formatted for input in the program STRUCTURE. It contains 1137 SNPs across 241 summer flounder. The first column identifies an individual fish. There are two rows for every fish. The second column is a population identifier. Columns 3-1139 are SNPs. Values of 1-4 indicate nucleotides (A = 1, T = 2, G = 3, C = 4). Missing data are indicated by -9.structure_input_Nov_11_2015.strStructure formatted file containing 1137 SNPs across 232 fish in main analysesThis file is formatted for input into the program STRUCTURE. It contains 1137 SNPs across 232 summer flounder. These were the individuals used in the main analyses, and this file was used to create the input files for BayEnv2. The first column identifies an individual fish. There are two rows for every fish. The second column is a population identifier. Columns 3-1139 are SNPs. Values of 1-4 indicate nucleotides (A = 1, T = 2, G = 3, C = 4). Missing data are indicated by -9.structure_input_232forbayenv.strSummer flounder metadataThis files contains metadata for the 232 summer flounder used in the main analyses. Data include the individual ID used for sequencing, capture date, sex, total length (cm), maturity status, collecting agency, latitude, longitude, calculated distance from a southern point, depth of capture, bottom temperature at capture, bottom salinity at capture and population grouping for BayEnv2 analysis.pade_metadata.txtest file for generating simulations in fastsimcoalOne of two files used to generate simulations in fastsimcoal for ABC analysis. The other necessary file is: migrationABC_DNA1millionby100.tpl.migrationABC_5729_2_0.5dispersal.esttpl file for generating simulations in fastsimcoalOne of two files used to generate simulations in fastsimcoal for ABC analysis. The other necessary file is: migrationABC_5729_2_0.5dispersal.est.migrationABC_DNA1millionby100.tplScript that uses the .est and .tpl files to generate fastsimcoal simulationsThis script can be used to submit many fastsimcoal jobs to a computing cluster. It uses the parameters in the migrationABC_DNA1millionby100.tpl and migrationABC_5729_2_0.5dispersal.est files to generate 500 coalescent simulations each time it is submitted.run_fsc.sh