DOI: 10.5061/dryad.wstqjq2td
title: Data and code for: Species-specific effects of production practices on genetic diversity in plant reintroduction programs

description: Plant production practices can influence the genetic diversity of
 cultivated plant materials and, ultimately, their potential to adapt to a
 reintroduction site. A common step in the plant production process is the
 application of seed pre-treatment to alleviate physiological seed dormancy
 and successfully germinate seeds. In production settings, the seeds that
 germinate more rapidly may be favored in order to fill plant quotas. In
 this study, we investigated how the application of cold-moist
 stratification treatments with different durations can lead to differences
 in the genetic diversity of the propagated plant materials. Specifically,
 we exposed seeds of three Viola species to two different cold
 stratification durations, and then we analyzed the genetic diversity of
 the resulting subpopulations through double-digestion restriction
 site-associated sequencing (ddRADseq). Our results show that, in two out
 of three species, utilizing a short stratification period will decrease
 the genetic diversity of neutral and expressed loci, likely due to the
 imposition of a genetic bottleneck and artificial selection. We conclude
 that, in some species, the use of minimal stratification practices in
 production may jeopardize the adaptive potential and long-term persistence
 of reintroduced populations and suggest that practitioners carefully
 consider the evolutionary implications of their production protocols. We
 highlight the need to consider the germination ecology of target species
 when selecting the length of dormancy-breaking pre-treatments.
description: <em>Seed
 sourcing</em> In 2019, seeds of
 <em>V. lanceolata</em> and <em>V. sagittata</em>
 were provided by The Nature Conservancy Kankakee Sands Preserve Nursery
 (KS; Indiana, US). At this nursery, beds for both species were established
 around 2015, using seeds collected from four local native populations.
 Seeds from these beds were harvested and used to grow a subsequent
 generation of plants in a greenhouse; then these potted plants were
 transferred outdoors. Seeds harvested from fruits of both chasmogamous and
 cleistogamous flowers were mixed and then were exposed to cold-moist
 stratification for 60 days at 4°C. This seed mix was either used for next
 years’ propagation material or for direct seeding in restorations. In
 addition, each year the nursery staff perform a small amount of wild
 collecting, depending on availability, to augment the levels of genetic
 diversity within the seed mix. Given the propagation practices carried out
 at this nursery, the number of maternal lines in the sourced seed sample
 was not known. The seed of <em>Viola pedatifida</em> was
 provided by the Lake County Forest Preserve Nursery (LCFP; Illinois, US)
 and harvested in 2019 from plants grown in an in-ground nursery production
 bed for one generation from seeds collected from a nearby wild population.
 All seeds were
 stored at +5 °C until July 2019, when 100 randomly chosen seeds of each
 species were surface sterilized and sown into two Petri dishes per cold
 stratification treatment (25 seeds per replicate).
 <em>Cold-moist
 stratification of subpopulations</em> We performed
 surface-sterilization of all seeds by immersing and stirring the seeds
 into a 1% bleach solution for two minutes, followed by two consecutive
 one-minute rinses in sterile water. Seeds were then sown in Petri dishes
 on 1.5% agar medium, sealed with parafilm, and exposed to cold
 stratification treatments at 0-3 °C for different durations: for the short
 stratification, all species were stratified for 42 days, while for the
 long stratification seeds were stratified for 112 days for <em>V.
 sagitatta</em> and 84 days for <em>V. lanceolata</em>
 and <em>V. pedatifida.</em> These stratification lengths were
 determined to ensure a low but sufficient level of germination with short
 cold stratification, and to maximize germination with long stratification,
 based on conversations with practitioners.  At the end of the
 cold stratification periods, Petri dishes were transferred into an
 incubator maintained at 25/15 °C (alternating day/night temperatures,
 12/12 h cycle) and 12/12 photoperiod to trigger germination. From these
 two stratification duration treatments, we selectively generated two ‘cold
 stratification subpopulations’ for each species. For the short
 subpopulation, we randomly selected 20 seeds that germinated within two
 weeks of moving to the incubator. A small proportion of seeds germinated
 while in cold stratification for <em>V. sagittata</em> and
 <em>V. pedatifida</em>, and none for <em>V.
 lanceolata</em>. For the long subpopulation, we discarded any
 individuals that germinated during the cold stratification, as we assumed
 they would pertain to the short or an intermediate subpopulation. By
 discarding seeds that germinated while in cold stratification, we
 simulated a real-world application. Practitioners often stratify seeds in
 moist sand in the refrigerator during the winter to overcome dormancy
 prior to sowing seeds in flats in the spring; seeds that germinate in cold
 stratification are less likely to survive this process, as the radicle can
 easily be damaged when sowing. While the removal of some individuals that
 germinated during cold stratification may limit the full range genotypes
 and, therefore, genetic diversity observed in the long subpopulation, this
 experimental group should still sample a greater portion of the
 theoretical distribution of genotypes in the population compared to the
 short subpopulation and retain greater genetic
 diversity. Following two
 weeks of warm stratification, more seeds had germinated in the long
 stratification treatment than the short
 for <em>V.</em><em> pedatifida</em> (ca. 30%
 germination in short vs. 75% in the long) and <em>V. sagittata
 </em>(ca. 65-80% germination in the short vs. 80-90% in the long).
 Germination was similar between the short and long stratification
 treatments in <em>V. lanceolata</em> (85+%). The randomly
 selected germinated seeds were planted in individual plugs with peat media
 and transferred to a greenhouse with mist (day temperature of 21 °C and
 night temperature of 18 °C, with supplemental lighting from 6 AM to 10 PM
 to provide a long day photoperiod and mist running 3 seconds every 20
 minutes from 4 AM to 11 PM). After about one week at these conditions, the
 seedlings were transplanted to larger pots and transferred to a second
 greenhouse without mist (day temperature of 19 °C and night temperature of
 17 °C with supplemental lighting 6 AM to 10 PM). Here, the plants were
 watered every one to two days, and grown until seed production. From the
 twenty plants grown for each subpopulation, we randomly selected 13-14
 individuals for the genetic analysis. The collection and experimental
 treatment of plants in this study followed relevant institutional,
 national and international guidelines and
 legislation. <em>Genomic
 sequencing, data processing, and statistical
 analyses</em> We genotyped
 individuals using double-digestion restriction site-associated sequencing
 and the STACKS v 2.2 pipeline. First, we extracted genomic DNA from fresh
 leaf tissue using a DNeasy Plant Mini Kit (Qiagen, Venlo, Netherlands).
 Next, we used a modified double-digestion restriction site-associated
 sequencing, or ddRAD-seq, protocol to sample the genome of each species.
 We used EcoRI and MspI restriction enzymes (New England Biolabs, Ipswich,
 MA, USA) to digest genomic DNA, after which we ligated adapters and used
 AMPureXP magnetic beads (Beckman Coulter, Indianapolis, IN, USA) to
 individually size-select fragments between 500-900 bp. We then amplified
 and cleaned up libraries and finally sequenced libraries using pair-end
 150-bp sequencing on an Illumina NovaSeq 6000. We used STACKS v
 2.2<sup> </sup>to call single nucleotide polymorphisms (SNPs)
 de novo for each species. We used a subset of individuals to optimize the
 STACKS parameters -m, -M, and -n. For different combinations of
 parameters, we compared patterns of genetic distance in Multidimensional
 Scaling Plots (MDS), the number of recovered variant sites, and measures
 of genetic diversity – optimal parameters are those that recover many
 SNPs, retain high levels of genetic diversity, and do not drastically
 change genetic relationships between individuals. We selected the default
 STACKS parameters (-m 3, -M 2, and -n 2) for calling SNPs using the
 pipeline denovo_map.pl. For each species, all samples were included as
 being in the same population in the population map, required at least 35%
 of individuals in the population retain a locus for it to be processed
 (-r), a minimum minor allele frequency of 0.05 (--min_maf), and one SNP
 was retained per stack to minimize linkage disequilibrium
 (--write-random-snp). We then generated
 three unique datasets for each species. First, we used
 VCFtools 0.1.14 to attain all loci that passed quality filtering. To
 quality filter SNPs, we exclude the following from the analysis:
 individuals with more than 80% missing data, loci with more than 45%
 missing data (--max-missing), and loci with an average minimum mean depth
 of coverage below 10X (--min-meanDP). Overall, SNPs had low to moderate
 read depth coverage and moderate to high percentage of missing data for
 individuals and sites for all species. This group of high-quality SNPs
 comprises the putatively ‘neutral SNPs’ dataset for each species, which we
 used to verify that individuals from the short and long subpopulations do
 not comprise distinct genetic clusters for each species. We evaluated
 population genetic structure using the first two axes of a scaled and
 centered principal components analysis (PCA) to evaluate genetic structure
 within each species using the package adegenet() in R v. 4.2.2 (R Core
 Team 2022). In addition, we used the pairwise.neifst() function in the
 hierfstat package in R v. 4.2.2 (R Core Team 2022) to measure Nei’s
 F<sub>ST</sub> between the short and long subpopulations of
 each species.  We then subset the
 neutral SNPs datasets to obtain two additional datasets. The second
 dataset that we generated for each species was the ‘annotated SNPs’
 dataset. We generated a FASTA file for the neutral SNPs dataset which we
 ran through the National Center for Biotechnology Information’s (NCBI)
 database using the MegaBLAST® search program within the order of Violales
 (National Center for Biotechnology Information 1988). The annotated SNPs
 are those that had a match, or hit, with an annotated sequence in the NCBI
 database and was associated with a specific protein (i.e., hits to
 uncharacterized loci, chloroplast or mitochondrial loci that were not
 related to a protein were excluded). If the top hit for a locus was not
 associated with a protein, the second best hit that was associated with a
 protein was selected. The third dataset for each species was the ‘shared,
 annotated SNPs’ dataset. As we assumed physiological responses associated
 with short dormancy would be shared across taxa, we compared the NCBI
 database matches associated with proteins between species. For these
 shared annotated SNPs, we investigated the gene ontology for those
 proteins and determined the broad functional category of each protein
 based on the molecular function and biological processes listed in the
 PANTHER Classifications and UniProt databases as well as the literature.
 The three datasets used in these analyses are subsets of one another
 meaning that each dataset represents a unique group of SNPs.
 For each species
 we evaluated differences in genetic diversity between the short and long
 subpopulations using both the neutral SNPs datasets and the annotated SNPs
 datasets. Here, we are specifically interested in variation and diversity
 of alleles, rather than how alleles are arranged into genotypes. As such,
 for each locus, we measured gene diversity, or H<sub>S</sub>,
 which considers the frequency of alleles as well as their evenness in the
 population. We used the basic.stats() function in the hierfstat package in
 R v. 4.2.2 (R Core Team 2022) to measure gene diversity for the short and
 long subpopulations of each species. To evaluate how the selection of
 short versus long moist cold stratification influences gene diversity, we
 tested for differences in H<sub>S</sub> using a paired
 Wilcoxon rank sum test with Bonferroni correction using the function
 wilcox_test() in the package rstatix v.0.7.2. For all comparisons the
 percent change in H<sub>S</sub> was then calculated in loci in
 the long moist cold stratification subpopulation to the short
 subpopulation (e.g. [H<sub>Slong</sub>/
 H<sub>Sshort</sub>] / H<sub>Slong</sub>). Loci
 whose percent change was above 2 standard deviations above the mean
 percent change were then identified, suggesting that the change is greater
 than would be expected by chance and is therefore putatively a significant
 increase in percent change. If a locus was fixed for homozygosity (i.e.,
 H<sub>S</sub>=0) in the short subpopulation but non-zero in
 the long subpopulation, then the percent change was considered as a
 significant increase from long to short. If a locus was not present in at
 least two subpopulations, it was excluded from all
 analyses. In addition, we
 measured rarified allelic richness (A<sub>R</sub>), or the
 rarefied count of alleles, for loci in each subpopulation using the
 allelic.richness() function in the hierfstat package. For each species we
 rarefied A<sub>R </sub>according to the subpopulation with the
 smallest sample size. We tested for differences in
 A<sub>R</sub> using a paired Wilcoxon rank sum test with
 Bonferroni correction using the function wilcox_test() in the package
 rstatix v.0.7.2. If a locus was not present in two or more subpopulations,
 it was excluded from all analyses.
description: This README file was generated on 2023-09-06 by Zoe Diaz-Martin. GENERAL
 INFORMATION 1. Title of Dataset: Reproductive strategies and their
 consequences for divergence, gene flow, and genetic diversity in three
 taxa of Clarkia 2. Author Information A. Corresponding Author Contact
 Information Name: Zoe Diaz-Martin Institution: Spelman College Address:
 Atlanta, GA USA Email: SHARING/ACCESS
 INFORMATION 1. Licenses/restrictions placed on the data: CC0 1.0 Universal
 (CC0 1.0) Public Domain 2. Links to publications that cite or use the
 data: Diaz-Martin, Zoe, De Vitis, Marcello, et al. Species-specific
 effects of production practices on genetic diversity in plant
 reintroduction programs. Evolutionary Applications. 1. Links to other
 publicly accessible locations of the data: None 2. Links/relationships to
 ancillary data sets: None 3. Was data derived from another source? No A.
 If yes, list source(s): NA 4. Recommended citation for this dataset:
 Diaz-Martin, Zoe, De Vitis, Marcello, et al. (Forthcoming 2023). Data and
 Code for: Species-specific effects of production practices on genetic
 diversity in plant reintroduction programs [Dataset]. Dryad. DATA &
 FILE OVERVIEW 1. File List: A) *.annotated.gen B) *.annotated.PerChang.csv
 C) *.annotated.vcf D) *.blast.alignment.txt E) *.filtered.fa F)
 *.neutral.PerChang.csv G) *.neutral.vcf H) *.neutralsnps.gen 1.
 Relationship between files, if important: * is a placeholder for the
 species code, therefore each species will have one file listed above. The
 species codes are: PED = Viola pedatifida; SAG = Viola sagittata; LAN =
 Viola lanceolata. The .vcf files are the quality filtered data and were
 used to generate the data in subsequent files. 2. Additional related data
 collected that was not included in the current data package: We have not
 included the raw, unprocessed data files 3. Are there multiple versions of
 the dataset? No A. If yes, name of file(s) that was updated: NA i. Why was
 the file updated? NA ii. When was the file updated? NA
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.annotated.gen This is a GenePop formatted
 file for the quality filtered single nucleotide polymorphisms (SNPs) that
 had a match in the NCBI database.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.annotated.PerChang.csv 1. Number of
 variables: 3 2. Number of cases/rows: PED = 475; SAG = 312; LAN = 197 3.
 Variable List: * Perchang: A categorical assignment of the direction of
 percent change between annotated paired loci in gene diversity (Hs) from
 short to long. Catergorical codes: Pos = Positive; Sig = Significantly
 positive; DEC = Decrease; NegZ = No change * Short: Gene diversity (Hs)
 for locus in short subpopulation * Long: Gene diversity (Hs) for locus in
 long subpopulation
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.annotated.vcf This is a .vcf formatted
 file for the quality filtered single nucleotide polymorphisms (SNPs) that
 had a match in the NCBI database.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.blast.alignment.txt Output from MegaBlast
 alignment for all loci.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.filtered.fa Fasta formatted file for all
 quality filtered loci. This is the input file for MegaBlast.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.neutral.PerChang.csv 1. Number of
 variables: 3 2. Number of cases/rows: PED = 2821; SAG = 1904; LAN = 1355
 3. Variable List: * Perchang: A categorical assignment of the direction of
 percent change between neutral paired loci in gene diversity (Hs) from
 short to long. Catergorical codes: Pos = Positive; Sig = Significantly
 positive; DEC = Decrease; NegZ = No change * Short: Gene diversity (Hs)
 for locus in short subpopulation * Long: Gene diversity (Hs) for locus in
 long subpopulation
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.neutral.vcf This is a .vcf formatted file
 for the quality filtered single nucleotide polymorphisms (SNPs) that did
 not have a match in the NCBI database.
 \#########################################################################
 DATA-SPECIFIC INFORMATION FOR: *.neutralsnps.gen This is a GenePop
 formatted file for the quality filtered single nucleotide polymorphisms
 (SNPs) that did not have a match in the NCBI database. CODE / SOFTWARE
 Code.R : This is a script for the program R. This script generates the
 analyses used for testing differences in measures of genetic diversity
 within species between short and long subpopulations. This code will also
 generate figures in the manuscript. USAGE NOTES The files containing
 genetic data in .vcf format can be viewed using the Integrative Genomics
 Viewer (IGV; ).
 All data files in this submission can also be viewed in a standard text
 editor.