DOI: 10.5061/dryad.w3r22810z
title: Data from: Demographic history and inbreeding in two declining sea duck species inferred from whole genome sequence data

description: Anthropogenic impact has transitioned from threatening already rare
 species to causing significant declines in once numerous organisms.
 Long-tailed duck (Clangula hyemalis) and velvet scoter (Melanitta fusca)
 were once important quarry sea duck species in NW Europe, but recent
 declines resulted in their reclassification as Vulnerable on the IUCN Red
 List. We sequenced and assembled genomes for both species and resequenced
 15 individuals of each. Using analyses based on site frequency spectra and
 sequential Markovian coalescence, we found long-tailed duck to show more
 historical demographic stability, whereas velvet scoter was affected
 particularly by the Last (Weichselian) Glaciation. This likely reflects
 long-tailed duck breeding continuously across the Arctic, with cycles of
 glaciation primarily shifting breeding areas south or north without major
 population declines, whereas the more restricted southern range of velvet
 scoter would lead to significant range contraction during glaciations.
 Both species showed evidence of declines over the past thousand years,
 potentially reflecting anthropogenic pressures with the recent decline
 indicating an accelerated process. Analysis of Runs of Homozygosity (ROH)
 showed low but non-trivial inbreeding, with FROH from 0.012 to 0.063 in
 long-tailed duck and ranged from 0 to 0.047 in velvet scoter. Lengths of
 ROH suggested that this was due to ongoing background inbreeding rather
 than recent declines. Overall, despite demographically important declines
 this has not yet led to strong inbreeding and genetic erosion, and the
 most pressing conservation concern may be the risk of density dependent
  (Allee) effects. We recommend monitoring of inbreeding using ROH
 analysis as a cost-efficient method to track future developments to
 support effective conservation of these species.
description: <strong>A. Sampling</strong> A
 total of 16 individuals were sampled of each species in overwintering
 areas in Denmark, in each case encompassing 13 males and three females.
 The sampled individuals were obtained in Denmark from Danish hunters
 between December 2017 and January 2019, during the open hunting seasons as
 part of a separate study run by the Danish Hunters’ Association and Aarhus
 University on the distribution and feeding ecology of <em>C.
 hyemalis</em> and <em>M. fusca</em>. Individuals were
 stored at -18°C and kept frozen until samples of muscle tissue were
 collected. Tissue samples were then stored in 96% ethanol at -18°C until
 DNA extraction. <strong>B. DNA extraction and
 whole genome resequencing</strong> DNA was
 extracted from 15 individuals of each species using the
 E.Z.N.A.<sup>®</sup> Tissue DNA Kit (OMEGA, Bio-tek, CA, USA)
 following the manufacturer's recommendations. Whole genome
 resequencing was outsourced to BGI and involved 150 bp paired-end
 sequencing on the DNBseq platform, aimed at providing sequencing depths of
 20X. <strong>C. Mapping and SNP
 calling</strong> Raw reads were trimmed with
 Sickle v1.33 (Joshi & Fass 2011) and then mapped to their
 corresponding reference genomes (GenBank accession: <em>C.
 hyemalis</em>: GCA_029619115.1 and <em>M. fusca</em>:
 GCA_029620185.1) using BWA-mem v0.7.17 (Li & Durbin 2009). The
 software Picard tools v2.26.3 (Broad Institute 2019) was then used to
 sort, mark, and remove duplicated reads in the alignments prior to
 genotyping calls. SNP calling was conducted using the
 <em>mpileup </em>and <em>call</em> functions
 implemented in BCFtools v1.13 (Danecek<em> et al.</em> 2021)
 to obtain sample-specific SNPs with a minimum mapping quality of 30.
 Subsequently, the dataset was filtered using VCFutils.pl  and VCFtools
 v0.1.16 (Danecek et al. 2011) to remove sites with extremely high or low
 depth (thresholds determined visually from depth distribution of SNPs;
 Supplementary Material Fig. S1), indels and monomorphic sites. We changed
 individual genotypes with low read depth (&lt;10) or low quality
 (&lt;15) to missing and filtered to retain only biallelic SNPs with a
 QUAL score ≥ 30 and &lt;10% missing genotypes. Finally, we discarded
 any SNPs not in Hardy-Weinberg equilibrium using the R script VCF_HWF
 (https://github.com/shenglin-liu/VCF_HWF/blob/main/VCF_HWF.r). Additionally, we performed SNP calling with the -v parameter in BCFtools <em>call </em>disabled to produce an ‘all sites’ data set containing both polymorphic and monomorphic sites. The data set was subsequently filtered to exclude indels, sites with extreme depth or low genotype quality and SNPs out of HWE as described previously. No filtering for missing data was performed.
description: # Data from: Demographic history and inbreeding in two declining sea duck
 species inferred from whole genome sequence data
 [https://doi.org/10.5061/dryad.w3r22810z](https://doi.org/10.5061/dryad.w3r22810z) ## Description of the data and file structure The data encompasses multiple VCF files generated from whole-genome resequencing of two sea duck species, the velvet scoter *(Melanitta fusca)* and long-tailed duck *(Clangula hyemalis).* **1A) LTD_raw.vcf.gz** Raw variant calls from WGS data from long-tailed duck. **2A) LTD.filtered.max2.QUAL30.minDP10.minGQ15.miss0.9.variant.sansSexChr.HWE.vcf** Variant calls from file 1A) filtered based on quality, depth, missing data and HWE. Contains only bi-allelic SNPs of QUAL&gt;30, only genotypes with read depth &gt;10 and genotype quality &gt;15, and only sites in Hardy-Weinberg equilibrium, located on autosomal scaffolds and with &lt;10% missing data and pooled read depth &gt;250 and &lt;470. **3A) LTD.filtered.max2.QUAL30.minDP10.minGQ15.miss0.9.variant.sansSexChr.HWE.mac2.vcf** Variant calls from file 2A) furthermore filtered to exclude singletons. **4A) LTD.filtered.max2.QUAL30.minDP10.minGQ15.miss0.9.variant.sansSexChr.HWE.mac2.MappabilityMask.100biggest.vcf** Variant calls from file 3A) furthermore filtered to retain only sites with high mappability located on the 100 biggest scaffolds. **5A) LTD_allSites.max2.minDP10.minGQ15.HWE.vcf.gz** File containing both variant and invariant sites filtered based quality, depth, and HWE, as described for file 2A. No filtering for missing data. **1B) VSD_raw.vcf.gz** Raw variant calls from WGS data from velvet scoter. **2B) VSD.filtered.max2.QUAL30.minDP10.minGQ15.miss0.9.variant.sansSexChr.HWE.vcf** Variant calls from file 1B) filtered based on quality, depth, missing data and HWE. Contains only bi-allelic SNPs of QUAL&gt;30, only genotypes with read depth &gt;10 and genotype quality &gt;15, and only sites in Hardy-Weinberg equilibrium, located on autosomal scaffolds and with &lt;10% missing data and pooled read depth &gt;200 and &lt;420. **3B) VSD.filtered.max2.QUAL30.minDP10.minGQ15.miss0.9.variant.sansSexChr.HWE.mac2.vcf** Variant calls from file 2B) furthermore filtered to exclude singletons. **4B) VSD.filtered.max2.QUAL30.minDP10.minGQ15.miss0.9.variant.sansSexChr.HWE.mac2.MappabilityMask.100biggest.vcf** Variant calls from file 3B) furthermore filtered to retain only sites with high mappability located on the 100 biggest scaffolds. **5B) VSD_allSites.max2.minDP10.minGQ15.HWE.vcf.gz** File containing both variant and invariant sites filtered based quality, depth, and HWE, as described for file 2B. No filtering for missing data.