DOI: 10.5061/dryad.hx3ffbgh1
title: DaRT-seq raw data of Eucalyptus spp for the genetic assessment of the value of restoration planting within an endangered eucalypt woodland

description: Assessment of woodland restoration often focusses on stand demographics,
 but genetic factors likely influence long-term stand viability. We
 examined the genetic composition of Yellow Box (Eucalyptus melliodora)
 trees in endangered Box-Gum Grassy Woodland in SE Australia, some 30 years
 after planting with seeds of reportedly local provenance. Using DArT
 sequencing for 1406 SNPs, we compared genetic diversity and population
 structure of planted E. melliodora trees with remnant bushland trees,
 paddock trees, and natural recruits. Genetic patterns imply that natural
 stands and paddock trees had historically high gene flow (among group
 pairwise FST = 0.04–0.10). Genetic diversity was highest among relictual
 paddock trees (He = 0.17), while diversity of revegetated trees was
 identical to natural bushland trees (He = 0.14). Bayesian clustering
 placed the revegetated trees into six genetic groups with four
 corresponding to genotypes from paddock trees, indicating that revegetated
 stands are mainly of genetically diverse, local provenance. Natural
 recruits were largely derived from paddock trees with some contribution
 from planted trees. A few trees have likely hybridised with other local
 eucalypt species which are unlikely to compromise stand integrity. We show
 that paddock trees have high genetic diversity and capture historic
 genetic variety and provide important foci for natural recruitment of
 genetically diverse and outcrossed seedlings.
description: <em><strong>Sample
 collection</strong></em> We collected a
 total of 221 samples of <em>Eucalyptus melliodora</em> trees
 from 14 sites across the central valley and the uncleared valley walls of
 the Warrumbungle National Park in NSW (31°17’S, 149°00’E). We selected
 samples from six remnant populations on the edge of valley (n=60; termed
 “natural stands”), together with samples from all of the relictual paddock
 trees in the valley (n=36; termed “paddock”), from two populations of
 recruits, each one growing near one of the paddock trees (n=48; termed
 “recruits”), and from five populations of planted trees that were planted
 at different years in the restoration project (n=77; termed
 “planted”). We selected samples from remnant
 populations on the valley walls at a distance &gt;1 km from plantings
 on the uncleared slopes of the valley or separate to the central valley,
 with a preference for larger (likely older) trees, to increase the
 probability that these trees pre-date the germination of planted trees and
 hence provide a better reference for remnant <em>E.
 melliodora</em> within the region. We identified relictual paddock
 trees and planted trees from aerial photos taken prior to the
 establishment of the reserve. Relictual trees were easily relocated in the
 cleared central valley as they are large (20-40 m tall) and often have
 associated woody debris, while planted trees were smaller than the
 relictual ones (6-15 m tall) and were generally planted in rows, often
 with adjacent timber posts. The five planted populations were planted in
 different years, with population 11 planted in 1995, population 12 in
 1998, and population 14 planted in 1992, while the dates that the other
 two planted populations (10 and 13) were planted are unknown. Recruits
 were smaller still (0.5-5 m tall) and occurred in scattered locations,
 often in a ‘shadow’ surrounding adult paddock trees.
 <em><strong>DArT
 genotyping</strong></em> We used DArTseq
 services for both DNA extraction from leaf material, and sequencing of
 SNPs (Single Nucleotide Polymorphisms) using a high-density microarray
 developed for eucalypts (Sansaloni et al. 2010, Petroli et al. 2012).
 Extraction of genomic DNA was conducted in accordance with a modified CTAB
 protocol produced by Diversity Array Technologies.
 Complexity reduction was conducted through a PstI/TaqI based
 method developed by Sansaloni et al. (2010), using enzymatic breakdown to
 select for more active genomic regions, and importantly, remove repeat
 sequences typical of polyploidy genomes such as eucalyptus.
 Next Generation Sequencing (NGS), was used to detect SNP
 polymorphisms across the genome of each sample against a library developed
 for eucalypts (Sansaloni et al. 2010) detecting dominant, biallelic SNP
 loci. Descriptive polymorphic SNP loci were sequenced across the genome of
 all leaf samples, using the DArTseq microarray at a ‘high intensity’ run.
 These SNP loci are bi-allelic, with results measuring the presence or
 absence of a polymorphism (SNP) at a specific SNP site on the genome.
 Sequencing was carried out on an Illumina HiSeq 2,500 using 75-cycle
 single end reads. Raw reads were processed using DArT's proprietary
 variant calling pipeline, DArTsoft-14.
 <em><strong>Data
 analysis</strong></em> We filtered the
 primary dataset at a stringent level to ensure only high-quality markers
 were retained, and therefore genotypes were accurate.  Loci were filtered
 in DartR and the final dataset included only those loci that had a read
 depth of ≥ 20, a call rate of ≥ 0.9, a minor allele frequency of ≥ 0.05,
 and a reproducibility rate of ≥ 0.99. To analyse
 population structure, we calculated pairwise FST in Dart R (Gruber et al.
 2018), and conducted a Bayesian cluster analysis in the software
 ParallelStructure 2.3.4 on the CIPRES portal (Miller et al. 2015) to
 identify the number of genetic clusters in the dataset. We used the
 admixture model of ancestry with correlated allele frequencies, a burn-in
 length of 80,000 followed by 120,000 MCMC reps after burn-in, and we
 conducted 5 iterations each of K=1 to K=10. To estimate the number of
 genetic clusters, we used the Puechmaille method (Puechmaille 2016) on
 StructureSelector (Li & Liu 2018), which calculates MedMeaK,
 MaxMeaK, MedMedK, and MaxMedK. While StructureSelector also presents the
 results of ΔK using the Evanno method (Evanno et al. 2005), these results
 were discarded because ΔK is strongly affected by uneven sample sizes and
 shouldn’t be used to infer the number of subpopulations when sampling is
 uneven (Puechmaille 2016). In contrast, the four new estimators of the
 number of clusters used in the Puechmaille method are much less sensitive
 to uneven sampling regimes, and have been shown to consistently outperform
 other methods of genetic cluster membership across all simulations
 (Puechmaille 2016). To estimate genetic diversity and
 mating system, we calculated observed heterozygosity and expected
 heterozygosity under Hardy-Weinberg equilibria, and F (inbreeding
 coefficient) in GenAlEx 6.51b (Peakall & Smouse 2012). To detect
 hybrids, we compared the genotypes of a subset of <em>E.
 melliodora</em> trees with other trees in the study site. Included
 in this analysis were 61 samples of <em>E. melliodora</em>
 selected randomly from all populations in the main dataset (except
 recruits) including natural stands, paddock trees, and planted trees,
 together with 42 samples of <em>E. albens</em>, and 11 samples
 from a range of other species including <em>E. crebra</em> and
 <em>E. blakelyi</em> (labelled “other spp”). Loci were
 filtered as described above, and a genetic cluster analysis was conducted
 in Structure, using the same parameters described above.