DOI: 10.5061/dryad.51c59zw62
title: Defining Relictual Biodiversity: Conservation Units in Speckled Dace (Leuciscidae: Rhinichthys osculus) of the Greater Death Valley Ecosystem

description: The tips in the tree of life serve as foci for conservation and
 management, yet clear delimitations are masked by inherent variance at the
 species-population interface. Analyses using thousands of nuclear loci can
 potentially sort inconsistencies, yet standard categories applied to this
 parsing are themselves potentially conflicting and/or subjective [e.g.,
 DPS (distinct population segments); DUs (Diagnosable Units-Canada); MUs
 (management units); SSP (subspecies); ESUs (Evolutionarily Significant
 Units); UIEUs (uniquely identified evolutionary units)]. One potential
 solution for consistent categorization is to create a comparative
 framework by accumulating statistical results from independent studies and
 evaluating congruence among data sets. Our study illustrates this approach
 in speckled dace (Leuciscidae: Rhinichthys osculus) endemic to two basins
 (Owens and Amargosa) in the Death Valley ecosystem. These fish persist in
 the Mojave Desert as isolated Plio-Pleistocene relicts and are of
 conservation concern, but lack formal taxonomic descriptions/designations.
 Double-digest RAD (ddRAD) methods identified 14,355 SNP loci across 10
 populations (N=140). Species delimitation analyses [multispecies
 coalescent (MSC) and unsupervised machine learning (UML)] delineated four
 putative ESUs. FST outlier loci (N=106) were juxtaposed to uncover the
 potential for localized adaptations. We detected one hybrid population
 that resulted from upstream reconnection of habitat following contemporary
 pluvial periods, whereas remaining populations represent relics of ancient
 tectonism within geographically-isolated springs and groundwater-fed
 streams. Our study offers three salient conclusions: A blueprint for a
 multi-faceted delimitation of conservation units; a proposed mechanism by
 which criteria for intraspecific biodiversity can be potentially
 standardized; and a strong argument for the proactive management of
 critically-endangered Death Valley ecosystem fishes.
description: Whole genomic DNA was extracted using several methods: Gentra
 Puregene DNA Purification Tissue kit; QIAGEN DNeasy Blood and Tissue Kit;
 QIAamp Fast DNA Tissue Kit; and CsCl-gradient. Extracted DNA was
 visualized on 2.0% agarose gels and quantified with a Qubit 2.0
 fluorometer (Thermo Fisher Scientific, Inc.). Library preparation followed
 a double digest Restriction-Site Associated DNA (ddRAD) protocol
 (Peterson, Weber, Kay, Fisher, & Hoekstra, 2012). Barcoded samples
 (100 ng DNA each) were pooled in sets of 48 following Illumina adapter
 ligation, then size-selected at 375-425 bp (Chafin, Martin, Mussmann,
 Douglas, & Douglas, 2018) using the Pippin Prep System (Sage
 Science). Size-selected DNA was subjected to 12 cycles of PCR
 amplification using Phusion high-fidelity DNA polymerase (New England
 Bioscience) following manufacturer protocols. Subsequent quality checks to
 confirm successful library amplification were performed via Agilent 2200
 TapeStation and qPCR. Final libraries were pooled in sets of three per
 lane and subjected to 100bp single-end sequencing (Illumina HiSeq 2000,
 University of Wisconsin Biotechnology Center; and HiSeq 4000, University
 of Oregon Genomics & Cell Characterization Core
 Facility). Libraries were de-multiplexed and filtered
 for quality using process_radtags
 (Stacks v1.48;
 Catchen, Hohenlohe, Bassham, Amores, & Cresko, 2013). All reads
 with uncalled bases or Phred quality scores &lt; 10 were discarded.
 Reads with ambiguous barcodes that otherwise passed quality filtering were
 recovered when possible (= 1 mismatched nucleotide). A clustering
 threshold of 0.85 (Eaton, 2014) was used for <i>de novo</i>
 assembly of ddRAD loci in pyRAD v3.0.66.
 Reads with &gt; 4 low quality bases (Phred quality score &lt; 20)
 were removed. A minimum of 15 reads was required to call a locus for an
 individual. A filter was applied to remove putative paralogs using
 standard methods for their identification in ddRAD data (Eaton, 2014;
 McKinney, Waples, Seeb, & Seeb, 2017) by discarding loci with
 heterozygosity &gt; 0.6 and those containing &gt; 10 heterozygous
 sites. The resulting data were filtered (BCFtools; Li,
 2011) as a means of retaining a single biallelic SNP from each locus, as
 present in at least 33% of individuals (hereafter referred to as
 ‘SNP-all’). The 33% cutoff was designed to minimize potential bias in
 missing data for ingroup samples, given the unbalanced basin sampling
 (e.g., Owens N=50 <i>versus</i> Amargosa N=80). Our desire in
 this effort was to prevent the more numerous Amargosa samples from
 dictating which SNPs were recovered during alignment, genotyping, and
 filtering (Eaton, Spriggs, Park, & Donoghue, 2017; Huang &
 Knowles, 2016).
description: Raw reads are available through the NCBI Sequence Read Archive
 under BioProject ID PRJNA598959. The files listed below
 were input files for the various programs used in this manuscript. All are
 present in the file spd_dv_input_files.tar.gz, along with a readme.txt
 file which provides this same information.
 dv_split_03a.xml = input for BFD<br> dv_split_04.xml =
 input for BFD<br> dv_split_05a.xml = input for BFD<br>
 dv_split_05b.xml = input for BFD<br> dv_split_06a.xml = input for
 BFD<br> dv_split_06b.xml = input for BFD<br> dv_split_06c.xml
 = input for BFD<br> dv_split_07a.xml = input for BFD<br>
 dv_split_07b.xml = input for BFD<br> dv_split_07c.xml = input for
 BFD<br> dv_split_08.xml = input for BFD<br>
 dv_split_amargosa_v_owens.xml = input for BFD<br>
 dv_split_lahontan_v_dv.xml = input for BFD<br>
 spd_dv.filt2.min4.no_RUP.phy.nex = input for SVDquartets (PAUP)<br>
 spd_dv.filt2.min4.phy = input for HyDe<br>
 spd_dv.filt2.min4.phy.no_atratulus.str.genepop = input for
 lositan<br> spd_dv.filt2.no_RUP.vcf.cf = input for PoMo
 (IQ-TREE)<br> spd_dv.filt2.recode.strct_in.str.arp = input for
 Arlequin after spd_dv.filt2.vcf was filtered by ADMIXPIPE<br>
 spd_dv.filt2.vcf = VCF file output by BCFTools after filtering; also
 served as input for ADMIXPIPE<br>
 spd_dv.filt2.vcf.select.recode.strct_in.str.arp = file of SNPs under
 selection input for Arlequin after being filtered by ADMIXPIPE<br>
 spd_dv.filt2.vcf.select.vcf = SNPs under selection; input for
 ADMIXPIPE<br> spd_dv.vcf = unfiltered VCF file output by
 pyRAD<br> subsampled.no_atratulus.phy.str = input for machine
 learning algorithms<br> unlinked.txt.newhybrids = input for
 newhybrids