DOI: 10.5061/dryad.cvdncjtd8
title: Selection over small and large spatial scales in the face of high gene flow

description: Local adaptation represents the balance of selection and gene flow.
 Increasingly, studies find that adaptation can occur on spatial scales
 much smaller than the scale of dispersal, resulting in balanced
 polymorphisms within populations. However, in many cases we lack
 information on how this microgeographic adaptation might facilitate or
 hinder larger scale environmental heterogeneity, for example across
 latitude. Marine systems present a special case, as many marine species
 have high dispersal capacity so that dispersal ‘neighborhoods’ may
 encompass environmental heterogeneity over both extremely small and
 extremely large spatial scales. Here, we leverage fine-scale sampling
 across the California range of the Pacific purple urchin
 (Strongylocentrotus purpuratus), a species with previous evidence of both
 local adaptation and extremely high gene flow. We find that despite
 complete absence of neutral population structure, satellite-based sea
 surface temperature and tidal zone drive genetic differences among
 populations, suggesting that balanced polymorphisms can lead to adaptation
 across both large scale (latitudinal) and small scale (subtidal v.
 intertidal) scales. In fact, some of the same genetic variants
 differentiate populations at both spatial scales, potentially because both
 environmental parameters are related to temperature. Further, we find that
 genes that are expressed at a single tissue or life history stage are more
 divergent than expected across both latitudinal and tidal zone
 comparisons, suggesting that these genes have specific functions that
 might generate phenotypic variation important for local adaptation.
 Together these results suggest that even in populations with little
 population structure,  genetic variation can be sorted across
 even small spatial scales, potentially resulting in local adaptation
 across a complex environmental mosaic.
description: # Selection Over Small and Large Spatial Scales in the Face of High Gene
 Flow README contains necessary files for rerunning analyses discussed in
 this paper ## Description of the data and file structure Directories and
 their contents. Any NAs represent missing data. ## data ```
 6.filtered_goodinds.recode.vcf: vcf file for 114 individuals and 991,002
 SNPs 6.filtered_goodinds_thin.recode.vcf: thinned to 19,081 SNPs
 CORRECTED_SNPS_qvalues_114ind.csv: data for Outflank * CHROM= chromosome *
 POS= position * FST_site= FST values associated with site * FST_tidal= FST
 values associated with tidal zone (intertidal v. subtidal) * FST_NS= FST
 values associated with northern v. southern sites * NS_qvalues= qvalues
 associated with northern v. southern sites * Tidal_qvalues= qvalues
 associated with tidal zone (intertidal v. subtidal) * Site_qvalues=
 qvalues associated with site. ``` ## metadata ``` sites_data.csv: site
 information *site= site name *site_code= 2-3 letter code for each site
 *long,lat= longitude and latitude for each site *tide= tidal zone
 *color,color2= colors assigned to sites for figures *site_name=how site
 name will be shown in figures new_sites_data.csv all columns matching with
 sites_data with new column "shapes" for differentiating between
 intertidal and subtidal sites Urchin_metadata_114inds_SORTED.csv: sample
 information, any section with "NA" represents data that was not
 provided by the collector(s) of that sample *Number=number for sample
 *CCGP_code=code for each site *M_Number, other, stuff, id= additional
 numbers for differentiating between samples and sample sites *Lat,
 Long=lat/long coordinates for sample sites
 *Individuals.per.site..erase.this.column.after.corroborating.information.=
 number of individuals collected *General.notes= more site name information
 *FieldNumber=acronym for site if applicable *Instant.ID=species ID
 *Collector=who collected the samples *DateCollected= date samples were
 collected *Phylum, Class, Order, Family, Genus, Species= Echinodermata,
 Echinoidea, Echinoida, Strongylocentrotidae, Strongylocentrotus,
 purpuratus for all samples *Depth= depth of sampling site *Habitat=
 general description of habitat where samples were collected
 *Habitat_tidal= categorization of sites in the intertidal v. subtidal
 (sub) *north_south=categorization of sites in north v. south
 *site_name=concise full name of site *site_code=2-3 letter code for each
 site ``` ## Rmarkdown * Combine_*Cands*_Urch114.Rmd: candidate genes from
 RDA and LFMM (the input data for these scripts are created in the
 RDA_Urchin114_final.Rmd, and LFMM_Urch114_final.Rmd, respectively). *
 ConStruct.Rmd: Construct analyses for K = 1- 5 and Figure generation for
 K=2 * GE_comparison.R: Gene expression comparison and figure generation *
 LFMM_Urch114_final.Rmd: LFMM analyses * outflank_urchin.Rmd: Outflank
 analyses and figure generation * pca_urchin.Rmd: PCA analyses *
 RDA_Urch114_final.Rmd: RDA analyses * Site Map.R: Site map for figure 1 *
 TopGO.R: Top Gene Ontology values ## Shellscripts * Dups.sh: this script
 removes PCR duplicates * gatk1.sh: step one of gatk, GenomicsDBImport *
 gatk2.sh: step two of gatk, GenotypeGVCFs * gatk3.sh: step three of gatk,
 GatherVcfs, SelectVariants, VariantFiltration, filtering with vcftools *
 Gvcf_array.sh: slurm array script that combines gvcf files with
 HaplotypeCaller in gatk * mapping.sh: slurm array script maps each set of
 reads per sample to the reference genome * rg.sh: adds read groups to bam
 files ## Sharing/Access information Data files listed above