DOI: 10.5061/dryad.12jm63z1h
title: Data from: Diversity among rare and common congeneric plant species from the Garry oak and Okanagan shrub-steppe ecosystems in British Columbia: implications for conservation

description: Using universal non-coding chloroplast DNA markers (cpDNA), we
 investigated genetic diversity and genetic structure in four rare and
 common plant species pairs inhabiting threatened ecosystems (Garry Oak and
 Okanagan shrub-steppe) in British Columbia. The species found in
 the Garry oak ecosystem are: Sanicula
 bipinnatifida (purple sanicle; Apiaceae; rare), Sanicula
 crassicaulis (Pacific sanicle; Apiaceae; common),
 and Balsamorhiza deltoidea (deltoid balsamroot;
 Asteraceae; rare). The species found in the Okanagan shrub-steppe
 ecosystem are: Balsamorhiza sagittata (arrowleaf
 balsamroot; Asteraceae; common), Orthocarpus
 barbatus (Grand Coulee owl-clover; Orobanchaceae;
 rare), Orthocarpus luteus (yellow owl-clover;
 Orobanchaceae; common), Phacelia ramosissima (branching
 phacelia; Hydrophyllaceae; rare), and Phacelia
 linearis (thread-leaved phacelia; Hydrophyllaceae;
 common). Eight cpDNA regions were sequenced for each study
 species. Sequences were aligned and concatenated within each species, and
 single nucleotide polymorphisms (SNPs) were used to analyze patterns of
 regional genetic diversity and phylogeographic structure within genera and
 species. Results include: total gene diversity (Ht), nucleotide diversity
 (π), number of private alleles, haplotype networks, isolation by distance,
 and analysis of molecular variance.   
description: <em>Sample Collection</em>
 We sampled single individuals from a total
 of 95 populations. Seventy-three samples were leaf excisions,
 taken with permission from herbarium specimens representing populations in
 western North America including the provinces/states British Columbia,
 California, Idaho, Oregon, Nevada, and Washington. In addition, we collected 22 fresh leaf
 samples from well-documented and previously vouchered populations of rare
 species in regional, provincial, and national parks in British
 Columbia. We placed fresh leaf tissue
 in paper envelopes inside a sealed plastic bag containing silica gel
 desiccant and stored them at room temperature. We assigned each of the 95
 samples to northern or southern regional categories based on the location
 of the population sampled in relation to the last glacial maximum (LGM) of
 the Cordilleran ice sheet.
 <em>DNA
 extraction and sequencing</em>
 We ground 10 mg of dried leaf tissue (herbarium) or lyophilized
 fresh tissue of each sample using a Qiagen Tissuelyser II (Qiagen,
 Valencia, CA), and extracted DNA with a NucleoSpin® Plant II kit
 (Machery-Nagel, Bethlehem, PA, USA) with an extended incubation time of 1
 hour at 65ºC, recommended for dry herbarium samples. We analyzed DNA
 concentration and quality with a Nanodrop 2000c (Fisher Scientific,
 Toronto, ON, Canada) and visualized using agarose gel
 electrophoresis. We amplified eight
 non-coding cpDNA regions for all samples using primer sequences published
 by Shaw et al., 2014 (See Table 1 below). We conducted polymerase chain
 reactions (PCR) in 50 ul reaction volumes consisting of 0.2 mM dNTP (New
 England Biolabs, Ipswich, MA, USA), 1X PCR buffer (Stratagene, La Jolla,
 CA, USA), 2.5 units of Paq5000 (Stratagene), 50 pmol each forward and
 reverse primers (Integrated DNA Technologies, Skokie, IL, USA), 0.5 ug/ul
 BSA, 1 mM MgCl2 with 10 ng DNA template. We used the following PCR
 protocol for amplification: 80ºC for 5 min, 30 cycles of 95ºC for 1 min,
 50ºC for 1 min (ramp + 0.3ºC/sec), 65ºC for 5 min followed by a final
 extension of 65ºC for 5 min. We purified PCR products using Wizard® PCR
 Preps DNA Purification System (Promega) and sequenced with Thermo
 Sequenase cycle sequencing kit (Thermo Fisher Scientific, Waltham, MA,
 USA) on a T100 Thermal Cycler (Bio-Rad, Hercules, CA, USA) with the
 following conditions: 95ºC for 3 min, cycled 24 times at 95ºC for 30 sec,
 58ºC for 30 sec, and 70ºC for 30 sec. We loaded sequencing products on a
 5% polyacrylamide gel (UBC Genetic Data Centre protocol) and ran
 electrophoresis for 10 h on a LI-COR 4200 automated sequencer (LI-COR
 Inc., Lincoln, NE, USA). We visualized sequences using eSeq DNA Sequencing
 and Analysis software version 2.0 (LI-COR Inc., Nebraska,
 USA). We inspected chromatograms for quality and trimmed them
 using Geneious v. 10.2.6 (Biomatters, Inc. San Diego, CA, USA). Herbarium
 samples generally produced shorter reads and lower sequence quality than
 fresh samples and required extensive trimming. As a result, it was often
 not possible to assemble our forward and reverse sequences into a single
 consensus sequence.  Our initial analysis of sequences from herbarium
 samples showed that unidirectional sequences generated with forward
 primers were polymorphic, so to reduce sequencing costs due to limited
 budget we used only forward primers to generate sequence data for fresh
 tissue specimens and subsequent analysis of all samples (the exception
 is <em>rpl32-trnL</em><strong> </strong>for which
 we used reverse primers). We aligned sequences within each species and cpDNA region
 using the MUSCLE alignment algorithm (Edgar 2004) in Geneious with default parameters. We inspected
 alignments for ambiguous base calls, with special attention to polymorphic
 sites, and trimmed to the length of the shortest sequence for each region
 within each species to minimize missing data. We concatenated sequence
 data for the eight primer regions for each individual prior to
 analysis. <em>Sequence analysis</em>
 We exported concatenated sequence alignments
 for each species in FASTA format and imported them as ‘DNAbin’ objects
 into R v. 4.0.4 (R Core team 2019) using the <em>read.dna()</em> function from
 the ape package (Paradis and Schliep 2019).  We excluded insertions and deletions (indels) from
 analysis, as the majority were present in only a single individual within
 each species and may have been the result of inaccurate base calls due to
 low sequence quality generated from herbarium samples. Unless otherwise
 specified, missing data were handled using the default options in the R
 functions described below; in most cases, R functions use pairwise
 deletion of sites with missing values. We extracted single nucleotide
 polymorphisms (SNPs) from DNAbin objects and stored them as ‘genind’
 objects for use with the analysis packages adegenet (Jombart 2008), poppr (Kamvar et al. 2014), and hierfstat (Goudet 2005). To describe patterns of genetic diversity in our dataset
 we calculated nucleotide diversity (π) for each species using
 the pegas package (Paradis 2010) and total gene diversity (Ht) using
 the <em>basic.stats()</em> function (with “diploid=FALSE”
 setting) from the hierfstat package. Both metrics account for the number
 of samples sequenced to produce unbiased estimates of diversity. To assess
 whether our results were biased by unequal sampling, we used linear models
 to test for a relationship between diversity measures and sample size
 within species using the <em>lm()</em> function in
 R. To estimate the uniqueness of each species and differences
 between northern and southern populations, we calculated the number of private alleles using
 the <em>private_alleles()</em> function in poppr and by
 calculating the proportion of private alleles per individual in each
 species and each geographic region.
 We used paired t-tests to determine if rare
 species had lower diversity (proportion of polymorphism, proportion of
 private alleles per individual, π, and Ht) than their common congeners. We
 also tested if populations in the north had lower diversity (proportion of
 private alleles per individual, π, and Ht) than populations in the south
 within Garry oak and Okanagan shrub-steppe ecosystems separately. We
 relied on the probability from a one-tailed distribution as we
 hypothesized that diversity might only be lower (not higher) in the
 northern populations or among rare species. Means are presented with
 standard errors. We
 further assessed the degree of genetic differentiation between northern
 (recently glaciated) and southern regional categories within species,
 using analysis of molecular variance (AMOVA). We performed AMOVA using
 the <em style="font-family:'Times New Roman' , serif;text-indent:36pt;">poppr.amova()</em> function
 in poppr, and tested for
 significant differentiation using the <em style="font-family:'Times New Roman' , serif;text-indent:36pt;">randtest() </em>function<em style="font-family:'Times New Roman' , serif;text-indent:36pt;"> </em>(with 1000 randomizations).  We applied the additional options “within=FALSE” (disable calculation of within-individual variation as cpDNA data are haploid) and “cutoff=0.1” (allow loci with 10% missing data) to <em style="font-family:'Times New Roman' , serif;text-indent:36pt;">poppr.amova()</em>. To visualize relatedness among samples and genetic structure within species, we constructed haplotype networks from DNAbin objects using the <em>haplotype()</em> and <em>haploNet()</em> functions in pegas. To help us better understand the effects of geographically limited dispersal on the degree of genetic differentiation, we assessed isolation by distance (IBD) within species. We calculated pairwise genetic and geographic distances between all individuals within species using the base R <em style="font-family:'Times New Roman' , serif;font-size:12pt;">dist()</em> function, and tested for significance of IBD using Mantel tests with the <em style="font-family:'Times New Roman' , serif;font-size:12pt;">mantel.randtest()</em> function (1000 replicates) in ade4.        
description: Please refer to README.txt file