DOI: 10.5061/dryad.ffbg79d39
title: Data from: Genetic-environment associations explain genetic differentiation and variation between western and eastern North Pacific Rhinoceros Auklet (Cerorhinca monocerata) breeding colonies

description: Animals are strongly connected to the environments they live in and may
 become adapted to local environments. Examining genetic-environment
 associations of key indicator species, like seabirds, provide greater
 insights into the forces that drive evolution in marine systems. Here we
 examined a RADseq dataset of 19,213 SNPs for 99 Rhinoceros Auklets
 (Cerorhinca monocerata) from five western Pacific and ten eastern Pacific
 breeding colonies. We used partial-redundancy analyses to identify
 candidate adaptive loci and to quantify the effects of environmental
 variation on population genetic structure. We identified 262 candidate
 adaptive loci, which accounted for 3.0% of the observed genetic variation
 among western Pacific and eastern Pacific breeding colonies. Genetic
 variation was more strongly associated with pH and maximum current
 velocity, than maximum sea surface temperature. Genetic-environment
 associations explain genetic differences between western and eastern
 Pacific populations, however, genetic variation within the western and
 eastern Pacific Ocean populations appears to follow a pattern of
 isolation-by-distance. This study represents a first to quantify the
 relationship between environmental and genetic variation for this widely
 distributed marine species and provides greater insights into the
 evolutionary forces that act on marine species.
description: DNA was extracted from blood samples using a salting out
 extraction protocol (for samples from the eastern Pacific, Miller, Dykes
 & Polesky, 1988) or a Qiagen DNAeasy kit (for samples from western
 Pacific). Genomic DNA was used to construct nextRAD
 genotyping-by-sequencing libraries (SNPsaurus, LLC) using the
 <em>Sbf1</em> enzyme as described by Baird et al. (2008).
 Genomic DNA was first fragmented with Nextera reagent (Illumina, Inc),
 which also ligates short adapter sequences to the ends of the fragments.
 The Nextera reaction was scaled for fragmenting 15 ng of genomic DNA,
 although 20 ng of genomic DNA was used for input to compensate for the
 amount of degraded DNA in the samples and to increase fragment sizes.
 Fragmented DNA was then amplified for 27 cycles at an annealing
 temperature of 74 <sup>o</sup>C, with one of the primers
 matching the adapter and extending ten nucleotides into the genomic DNA
 with the selective sequence GTGTAGAGCC. Only those fragments starting with
 that sequence can be hybridized by the selective sequence of the primer
 and efficiently amplified. This protocol resulted in a final library
 fragment size of 450 bp (Etter et al. 2011). The nextRAD libraries were
 sequenced on an Illumina NovaSeq 6000 with one lane of single-end 150 bp
 reads. All genomic library preparations and sequencing were completed at
 the University of Oregon. Sequences were demultiplexed
 and then trimmed to 122 bp by SNPsaurus using the SNPsaurus pipeline with
 the bbduk package (BBMaptools, http://sourceforge.net/projects/bbmap/). Next, we assembled reference loci by collecting 10 million high quality reads, evenly from all of the samples (~70, 000 reads per individual were used), and excluding loci with fewer than seven or more than 700 reads. This range of seven to 700 represents a standardized number calculated by SNPsaurus to retain as many loci as possible without compromising the quality of the data with low quality reads. Overall mean depth of the reference genome was 65x. Loci that met the previously stated criteria were then aligned to the assembled reference genome using custom script from SNPsaurus (SNPsaurus, LLC). For the <em>de novo</em> alignment, we mapped 152,204,819 of the original 289,864,865 single-end reads to the <em>de novo</em> reference genome using an identity threshold of 0.95 using bbmap (BBMap tools). Genotype calling was done using the callvariants tool (BBMap tools), with the following settings (multisample=t rarity=0.05 minallelefraction=0.05 usebias=f ow=t nopassdot=f minedistmax=5 minedist=5 minavgmapq=15 minreadmapq=15 minstrandratio=0.0 strandedcov=t).  The genotype data were converted to a VCF file where we filtered the data to remove loci with a minimum frequency of less than 3%, a Q-score below 20, and removed all individuals with greater than 60% missing data (an additional 13 individuals did not meet this criterion and were omitted from all analyses). The average percentage of missing data was much lower than this original threshold (mean=5.8% missing data; median = 3.3% missing data), although we included three individuals with 40% missing data because they grouped with other individuals from the same population. To ensure that relatedness did not skew our results we calculated relatedness among individuals in Genodive 3.04 (Miermans, 2020). Relatedness among individuals from the same population was &lt;0.08, with exception to one pair that had a relatedness of 0.26, suggesting that one set of full siblings from Teuri were present in our data. Given the low level of relatedness among our data, we retained all samples in our analyses. We retained all of the 19,213 SNPs following the filtering for our examination of genetic-environment associations.
description: # Genetic-environment associations explain genetic differentiation and
 variation between western and eastern North Pacific Rhinoceros Auklet
 (Cerorhinca monocerata) breeding colonies This README file is associated
 with the article Genetic-environment associations explain genetic
 differentiation and variation between western and eastern North Pacific
 Rhinoceros Auklet (Cerorhinca monocerata) breeding colonies. The readme
 here is associated with the **RHAUenvVar.csv** data file. These data were
 used to conduct the redundancy analyses and detect associations between
 loci and environmental variables.  IID= Individual ID Lat=Latitude
 Long=Longitude salt.max/min= Maximum and minimum measured salinity value
 (probably in PSU or ppt) cv.max/min=maximum and minimum current velocity
 (m/s) temp.max/min=maximum and minimum sea surface temperature (degree C)
 ph=pH salt.mean=Mean salinity value Res.PC1=Resistance analysis principal
 component 1 Res.PC2=Resistance analysis principal component 2 pg.PCA1=
 principal coordinate 1 for genetic data pg.PCA2=principal coordinate 2 for
 genetic data Coord1 A=derived coordinate Coord2 A=second derived
 coordinate Additionally I have attached the vcf file with all of the SNP
 data. The VCF file associated with these data is **auklets.vcf** The VCF
 file is a VCFv4.2 file. The first 13 lines of the vcf characterize the
 attributes of the vcf file and follow standard format. The next 101499
 lines are the loci/contig names corresponding to where SNPs are located
 the raw genetic data is contained on the lines that follow from line
 101513. This raw data is needed to complete all population genetic
 analyses.