Literature DB >> 33552747

Examining the utility of DNA barcodes for the identification of tallgrass prairie flora.

Sarah A Herzog1,2, Maribeth Latvis1,2.   

Abstract

PREMISE: The tallgrass prairies of North America are one of the most threatened ecosystems in the world, making efficient species identification essential for understanding and managing diversity. Here, we assess DNA barcoding with high-throughput sequencing as a method for rapid plant species identification.
METHODS: Using herbarium collections representing the tallgrass prairie flora of Oak Lake Field Station, South Dakota, USA, we amplified and examined four common nuclear and plastid barcode regions (ITS, matK, psbA-trnH, and rbcL), individually and in combination, to test their success in identifying samples to family, genus, and species levels using BLAST searches of three databases of varying size.
RESULTS: Concatenated barcodes increased performance, although none were significantly different than single-region barcodes. The plastid region psbA-trnH performed significantly more poorly than the others, while barcodes containing ITS performed best. Database size significantly affected identification success at all three taxonomic levels. Confident species-level identification ranged from 8-44% for the global database, 13-56% for the regional database, and 21-80% for the sampled species database, depending on the barcode used. DISCUSSION: Barcoding was generally successful in identifying tallgrass prairie genera and families, but was of limited use in species-level identifications. Database size was an important factor in successful plant identification. We discuss future directions and considerations for improving the performance of DNA barcoding in tallgrass prairies.
© 2021 Herzog and Latvis. Applications in Plant Sciences is published by Wiley Periodicals LLC on behalf of the Botanical Society of America.

Entities:  

Keywords:  DNA barcode; Northern Great Plains; grassland; high‐throughput sequencing; prairie

Year:  2021        PMID： 33552747      PMCID： PMC7845766          DOI： 10.1002/aps3.11405

Source DB:  PubMed          Journal:  Appl Plant Sci        ISSN： 2168-0450            Impact factor:   1.936


Rapid species identification is vital for understanding the continuing losses in threatened communities (Raven and Miller, 2020). Many monitoring programs have sacrificed identification accuracy due to the time it takes for correct taxa assignment, but in doing so are failing to capture critical information that can be used in further research. Compounding the problem of species identification is the decreasing number of taxonomists in the field (Drew, 2011). Morphological plasticity in populations, cryptic species, and dependency on life stage also hinder efficient species identification (Hebert et al., 2003; Hollingsworth et al., 2016). Although morphology and anatomy are extremely important for species identification, it takes time to assess these plant features and requires an increasingly rare level of expertise. DNA barcoding offers one potential solution, as all that is needed is a small amount of tissue, regardless of developmental stage, to identify the plant. DNA barcoding uses small segments of DNA to identify species and has been effectively used for species‐level identification in many animal and plant groups (Hebert et al., 2003; CBOL Plant Working Group, 2009), invasive species control (Floyd et al., 2010), forensics (Savolainen and Lundeberg, 1999), and regulatory enforcement (Parveen et al., 2016). Although DNA barcoding is unlikely to replace the field identification of species, it is another tool for when morphological features are not available, whether due to disturbance (e.g., grazing or burning), for the analysis of fecal material (Goldberg et al., 2020), or for the verification of morphology‐based identification. Whereas animals have a recognized barcoding region (mitochondrial CO1), a universal barcode for plants has remained elusive. The most effective methods use a combination of gene regions, such as a selection of plastid (matK, rbcL, rpoc1, rpoB, psbA‐trnH, and trnL) and nuclear (internal transcribed spacer [ITS]) regions; however, many of these regions are not universally usable across all plant groups (Kress et al., 2005; Chase et al., 2007; CBOL Plant Working Group, 2009; Hollingsworth et al., 2009). The advent of high‐throughput sequencing (HTS) has opened up even more potential for the application of DNA barcodes. HTS methods result in shorter read lengths (~300 bp) compared with Sanger sequencing, potentially leading to insufficient variation in sequences to correctly classify closely related species and species with unresolved boundaries (Seberg and Petersen, 2009); however, the advantage of HTS is the ability to sequence numerous regions and individuals at once, reducing time and cost inputs. Temperate grasslands are one of the most threatened ecosystems globally (Hoekstra et al., 2005) and could benefit from DNA barcoding as a tool for the rapid identification of taxa. Within the United States, the grasslands of the Great Plains have seen large reductions in area and are continuing to be lost at a relatively higher rate than the Brazilian Amazon Rainforest (World Wildlife Fund, 2018). The tallgrass prairie ecosystems of the Great Plains have been particularly hard hit, with over 99% of pre‐settlement tallgrass prairie having been lost, primarily to row‐crop agriculture and non‐native species planted for grazing (Wright and Wimberly, 2013; Lark et al., 2015; Wright et al., 2017). The rate of loss is especially severe in the Northern Great Plains, with South Dakota having the highest rate of grassland conversion (Wright and Wimberly, 2013; Larkin et al., 2015; Wright et al., 2017). The morphological identification of plant species in tallgrass prairies can be difficult due to disturbance (e.g., grazing or burning), inadequate developmental stage at the time of sampling (e.g., not yet at or past anthesis), and the existence of closely related species that require a taxonomic key and magnification to distinguish (e.g., species in Poaceae, Cyperaceae, and Asteraceae). These challenges have led to observer discrepancies in identification of 10–30% of species in grassland systems compared with a 2–10% error rate in other habitat types (Morrison, 2016). Accelerating the pace of accurate species identification within this ecosystem has the potential to hasten subsequent studies of ecosystem function and biodiversity. As morphological identification is not always feasible or accurate, HTS could be an alternative method allowing many samples to be identified in parallel to remove some of the identification error; however, the high number of closely related species in grasslands of the Northern Great Plains, including many with uncertain boundaries, may render HTS methods ineffective as an identification tool. This study aims to examine the effectiveness of using commonly proposed DNA barcodes as a potential service to identify tallgrass prairie species using HTS methods. Previous studies have found moderate success in grasslands (31–85% species resolution) when using Sanger sequencing to obtain barcode sequences (Braukmann et al., 2017). If HTS approaches can provide confident species identifications, DNA barcoding could be a rapid and cost‐effective tool for the identification of regional flora.

METHODS

Sampling

Leaf material was removed from 286 herbarium samples (C. A. Taylor Herbarium [SDC], South Dakota State University, Brookings, South Dakota, and Oak Lake Field Station herbarium [OLFS], Astoria, South Dakota) based on the OLFS species inventory list (see Appendix 1 for voucher information). Herbarium tissue was used, rather than fresh samples, in an effort to begin documenting historical specimen genetic data for long‐term preservation (as discussed in Raven and Miller, 2020). The OLFS species list consists of 269 species in 63 families, with nearly half of the species in four angiosperm families: Asteraceae, Poaceae, Cyperaceae, and Fabaceae (18%, 13%, 10%, and 8% of the total list, respectively). We prioritized voucher specimens collected at OLFS, choosing the most recent collections for DNA extraction to reduce the amount of degraded DNA and improve sequence amplification (Adams and Sharma, 2010; Staats et al., 2011). For inventoried species lacking vouchers from the OLFS property, we sampled herbarium vouchers from localities near OLFS.

DNA extraction, amplification, and sequencing

From each sample, 0.02–0.03 mg of tissue was used for total genomic DNA extraction using a modified 2× cetyltrimethylammonium bromide (CTAB) approach (Doyle and Doyle, 1987). The DNA extractions were then visualized on agarose gel to assess DNA quality and concentration. Four DNA regions were selected for this study due to their prominence as “universal” plant barcodes: nuclear ITS2, and plastid rbcLa, matK, and psbA‐trnH (see Table 1 for primer sequences and references; psbA‐trnH abbreviated in figures and tables as trnH). These primers were selected due to their ability to amplify across angiosperm families and produce amplicon lengths compatible with the Illumina MiSeq (San Diego, California, USA) HTS platform limit of 300‐bp paired‐end reads (CBOL Plant Working Group, 2009; China Plant BOL Group, 2011; Braukmann et al., 2017).
Table 1

Selected DNA barcoding regions and their primer pairs compatible for high‐throughput sequencing.

RegionPrimerPrimer sequence from 5′ endAmplicon length (bp)References
ITS2 (nuclear)UniPlantF (5′)TGTGAATTGCARRATYCMG300Moorhouse‐Gann et al., 2018
UniPlantR (3′)CCCGHYTGAYYTGRGGTCDC
matK (chloroplast)matK‐1F (5′)ACTGTATCGCACTATGTATCA400–600Bremer et al., 2002
matK‐4R (3′)GCATCTTTTACCCARTAGCGAAG
rbcLa (chloroplast)rbcLa‐F (5′)ATGTCACCACAAACAGAGACTAAAGC550Kress and Erickson, 2007
rbcLa‐R (3′)GTAAAATCAAGTCCACCRCG
psbA‐trnH (chloroplast)psbA3_f (5′)GTTATGCATGAACGTAATGCTC500Sang et al., 1997; Tate and Simpson, 2003
trnHf_05 (3′)CGCGCATGGTGGATTCACAATCC
Selected DNA barcoding regions and their primer pairs compatible for high‐throughput sequencing. We followed a modified 16S Illumina library construction protocol (Illumina, 2013) and optimized the annealing temperatures for each primer set using the OligoAnalyzer Tool (Owczarzy et al., 2008). This process consisted of an initial amplification of target regions using site‐specific primers (“PCR 1”), with an additional adapter sequence tag added to the 5′ end of the synthesized oligonucleotide. These tags acted as a binding site for an additional pair of primers to add an 8‐bp index sequence in a second round of PCR (“PCR 2”; see Appendix 2 for sequences), allowing for the identification of samples after the amplicons were pooled. To construct the library, Phusion Hot Start II High‐Fidelity PCR Master Mix (Thermo Fisher Scientific, Waltham, Massachusetts, USA) and the Nextera XT Index Kit v2 (Illumina) were used. All four amplicons for each specimen were pooled after PCR 1, which gave each individual the same index for later identification while still allowing each region to be identified by primer sequence. Bead cleanup was conducted after PCR 2 to remove unwanted reaction components (e.g., fragments shorter than 50 bp) using HighPrep PCR Clean‐up System magnetic beads (MAGBIO, Gaithersburg, Maryland, USA) in an IntegenX Apollo 324 automated library preparation system (Thermo Fisher Scientific). All samples were pooled after PCR 2 to a concentration of 4 nM based on the concentration values determined using a Qubit 3 Fluorometer (Thermo Fisher Scientific). The pooled library was then sequenced in one run with the Illumina MiSeq platform using 300‐bp paired‐end reads. The data were received through BaseSpace (cloud‐based Illumina software; https://basespace.Illumina.com), pre‐demultiplexed to individual sample, and the index sequences were removed. Raw sequence files were deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive database (BioProject accession PRJNA649768). Pooled reads for each individual were run through Fluidigm2PURC (Blischak et al., 2018) using default settings, which trims the sequences and combines paired reads. Because the data were pre‐demultiplexed using the Illumina software based on the index sequences, we used a custom script to further group sequences by amplicon based on the primer sequences and then remove primers (locus_assigner; F.‐W. Li, Cornell University, personal communication). Consensus sequences were generated and chimeric sequences removed using purc_recluster2 in Fluidigm2PURC, with clustering values of 0.92 and 0.93, and the largest consensus cluster was used for downstream analysis. Sequences identified as fungal contaminants were removed. Cleaned sequences were then concatenated in all possible combinations between the four single‐locus barcodes in Geneious Prime version 2019.2.3 (https://www.geneious.com).

BLAST

In order to evaluate the performance of individual regions and concatenated regions (both hereafter referred to as “barcodes”), we constructed three different sequence databases: (1) the entirety of GenBank sequence data, representing a broad range of species (accessed 4 October 2020; Clark et al., 2016); (2) a regional database using South Dakota and regional tallgrass prairie species occurrence data from the Great Plains Regional Herbarium Network (https://ngpherbaria.org/) and vouchered sequences from GenBank to create a more realistic scenario for the use of DNA barcodes (i.e., no knowledge of species identity a priori) (accessed 4 October 2020); and (3) only species sampled from OLFS, creating a “best‐case scenario” to reduce the amount of closely related species (accessed 4 October 2020). Basic Local Alignment Search Tool (BLAST) (Altschul et al., 1990; Camacho et al., 2009) was used to find the sequences that best match the generated DNA barcode sequences in each of the databases.

BLAST processing

We retrieved each top hit with the highest E‐value from the BLAST searches against our three databases and compared them against our vouchered specimens. If multiple top hits were returned, they were filtered to use the ones with highest percentage identity and bitscore. “Confident” successful identification was recorded for species that were always correctly identified by the top results, “ambiguous” identification was assigned when results contained both correct and incorrect species, and an “incorrect” identification was assigned when none of the top hits contained the correct species. Success was determined for family‐, genus‐, and species‐level identification. Family names were generated and species names updated using the Catalogue of Life: 2019 Annual Checklist (Roskov et al., 2019) with taxize (Chamberlain and Szocs, 2013; Chamberlain et al., 2020) in R (version 0.9.92; R Core Team, 2018) for both BLAST results and the original OLFS species list. To evaluate the confident identification success between barcodes, we used prop.test in the base R package stats (version 3.5.1) to run a pairwise Pearson’s chi‐squared test statistic (Holm [1979] correction method) on the mean confident correct identification levels for each barcode (α = 0.05).

RESULTS

After updating the taxonomic names in taxize, we identified 266 distinct species for our 286 samples, as some taxa were combined under the same name and four samples failed to amplify. The rbcLa region had the highest amplification success, with 249 retrieved sequences, while matK amplification was worst, with only 112 sequences retrieved (Fig. 1). We retrieved 178 sequences of ITS2 and 138 sequences of psbA‐trnH. Of our 266 taxa, nine species were not represented in GenBank. Additionally, the coverage of the tested regions in GenBank varied, with 253 of the 266 species represented for the rbcLa region (97%), 246 species covered by ITS2 (94%), 239 by matK (91%), and 123 by psbA‐trnH (47%). Species not in databases were still included in the results because we were looking for an overall view of barcoding success for the identification of the regional flora in the context of a potential barcoding service for regional stakeholders.
Figure 1

Sequence retrieval success of amplicons for 286 tallgrass prairie plant herbarium specimens sampled. psbA‐trnH is listed as trnH on the figure.

Sequence retrieval success of amplicons for 286 tallgrass prairie plant herbarium specimens sampled. psbA‐trnH is listed as trnH on the figure. The GenBank database resulted in 79–98% confident and 0–12% ambiguous family identifications (Fig. 2, Appendix 3). Genus identification rates ranged from 54–78% confident and 0–40% ambiguous. Species identification when using GenBank was the lowest of the three databases, with confident identification rates of 8–44% and ambiguous identification rates of 7–65%. No barcodes yielded a significantly different level of confidence in genus or species identification, but matK and ITS2 gave significantly different rates of confidence in family identification (Figs. 2, 3; see Appendix S1 for table of chi‐squared test results). The matK and rbcLa gene regions were the least successful individual regions for confident species‐level identification (10% and 8%, respectively) using the GenBank database as a reference, followed by psbA‐trnH (23%). The ITS2 region had the highest success (30%) of the single‐region barcodes. The concatenation of ITS2, matK, and psbA‐trnH proved to yield the most successful confident species discrimination at 44%.
Figure 2

Success of identification to family, genus, and species using various barcodes. Barcode names linked with an underscore indicate multi‐region barcodes. OLFS, database of species sampled from Oak Lake Field Station.

Figure 3

Pairwise proportion tests indicate significant differences in the rate of confident identification at the species, genus, and family taxonomic levels for the three tested databases. OLFS, database of species sampled from Oak Lake Field Station.

Success of identification to family, genus, and species using various barcodes. Barcode names linked with an underscore indicate multi‐region barcodes. OLFS, database of species sampled from Oak Lake Field Station. Pairwise proportion tests indicate significant differences in the rate of confident identification at the species, genus, and family taxonomic levels for the three tested databases. OLFS, database of species sampled from Oak Lake Field Station. The regional database resulted in identifications with a significantly higher rate of confidence (average 34%) than GenBank (average 23%). Family identification rates ranged from 75–98% confident, while 0–2% were ambiguous (Fig. 2, Appendix 3). Genus‐level identification rates ranged from 42–90% confident, while 1–20% were ambiguous. Using the regional database, ITS2 was again the best‐performing single‐locus barcode at 48% confident species identifications, followed by matK and rbcLa at 18% and psbA‐trnH at 13%. The ITS2 and matK barcodes performed the best, at 56% each. At the family‐, genus‐, and species‐level identification, psbA‐trnH performed significantly more poorly than the other barcodes (Figs. 2, 3). When using the smallest database containing only sampled species (average confident identification = 57%), we found confident family identification rates of 66–98%, while none were ambiguous (Fig. 2, Appendix 3). Genus identifications ranged from 28–90% confident and 0–6% ambiguous. As in the other databases, ITS2 performed the best as a single‐locus barcode (69% confident species identifications), followed by rbcLa (55%), matK (25%), and psb‐trnH (21%). Combining all four barcodes resulted in the highest confident species identification (80%). Barcodes psbA‐trnH and matK were significantly poorer for making confident identifications than the other barcodes across the three taxonomic levels (Figs. 2, 3). The use of the OLFS database resulted in significantly better confident species identifications than GenBank and the regional database. Breaking down the results by the four most speciose families in our tallgrass prairie community (Asteraceae, Poaceae, Cyperaceae, and Fabaceae, with 49, 35, 25, and 22 specimens, respectively), we found ITS2 and rbcLa had the highest levels of sequence retrieval. The rbcLa barcode was most successful for identifying the Asteraceae (30/49), Fabaceae (20/22), and Cyperaceae (23/25), while ITS2 and psbA‐trnH were most useful for the Poaceae (25/35). The barcode that performed best for confident identifications varied for each family; however, concatenated barcodes containing ITS2 generally performed best (Figs. 4, 5, 6, Appendix 4). Identification success was low (<25%) for these families when using GenBank (except when using ITS2 for Fabaceae). Reducing the database size improved the taxonomic resolution for all four families.
Figure 4

Barcode success for the four most diverse families when using GenBank as the reference database (see Figs. 5 and 6 for other databases and Appendix 4 for more information). Only barcodes for which more than five sequences were obtained are shown.

Figure 5

Barcode success for the four most diverse families when using a regional database as the reference (see Figs. 4 and 6 for other databases and Appendix 4 for more information). Only barcodes for which more than five sequences were obtained are shown.

Figure 6

Barcode success for the four most diverse families when using the database of species sampled from Oak Lake Field Station (OLFS) (see Figs. 4 and 5 for other databases and Appendix 4 for more information). Only barcodes for which more than five sequences were obtained are shown.

Barcode success for the four most diverse families when using GenBank as the reference database (see Figs. 5 and 6 for other databases and Appendix 4 for more information). Only barcodes for which more than five sequences were obtained are shown. Barcode success for the four most diverse families when using a regional database as the reference (see Figs. 4 and 6 for other databases and Appendix 4 for more information). Only barcodes for which more than five sequences were obtained are shown. Barcode success for the four most diverse families when using the database of species sampled from Oak Lake Field Station (OLFS) (see Figs. 4 and 5 for other databases and Appendix 4 for more information). Only barcodes for which more than five sequences were obtained are shown.

DISCUSSION

Our results indicate DNA barcoding is useful for the identification of taxa at the ranks of genus and family across the tested barcodes but demonstrated only low to moderate success at the species level. We found higher success when using smaller, more focused databases, as was expected due to the presence of more closely related species in larger databases than are found regionally, lowering species resolution, which was also reported by Parmentier et al. (2013). Reducing database size is especially helpful when identifying closely related species, such as members of the Asteraceae, Poaceae, Fabaceae, and Cyperaceae, as our identifications greatly improved when using smaller databases for our identification of these families (Figs. 4, 5, 6). We did see reductions in taxa discrimination at the higher taxonomic levels for some barcodes when using the smaller databases, likely due to removal of closely related species (i.e., congeners). Here, we were particularly interested in the ability of DNA barcodes to differentiate plant taxa of the tallgrass prairies of the Northern Great Plains as a possible identification service. As such, using a focused database of only sampled species (as shown in our OLFS‐specific database) would require a priori knowledge of species identity; however, we included it to examine the barcode success rates under a “best‐case” scenario. Our findings indicate that reducing a regional database of state‐wide and regional plant species to a smaller, more focused database would be beneficial if using DNA barcodes as an identification service. Using a regional database is preferable over a large‐scale database such as GenBank. Generally, single‐region barcodes had lower success at species‐level identification than multi‐region barcodes, in accordance with previous studies (Chase et al., 2007; CBOL Plant Working Group, 2009; China Plant BOL Group, 2011). We found significant variation in the ability of a barcode to successfully identify individuals at the species, genus, and family levels. The nuclear ITS2 region consistently performed best in terms of confident species identification, both as a single‐locus barcode and as a member of the top‐performing concatenated barcodes. Plastid regions matK and psbA‐trnH were particularly poor at identifying species when used as single‐region barcodes. The low success rate in species discrimination when using matK contrasts with other studies, where matK was one of the most successful barcoding regions for plant identification (Lahaye et al., 2008; CBOL Plant Working Group, 2009; Braukmann et al., 2017). A major limitation in using matK is the difficulty of finding universal primer pairs (CBOL Plant Working Group, 2009; Hollingsworth et al., 2011). In contrast, although rbcL only has moderate identification success, it amplifies well across taxa, in our study and others, which has led to it being promoted as a good candidate for inclusion in a multi‐region barcode (CBOL Plant Working Group, 2009; China Plant BOL Group, 2011; Hollingsworth et al., 2011). The combination of matK and rbcL has been promoted as one of the most promising universal two‐region plant barcodes (CBOL Plant Working Group, 2009). The failure of this two‐region barcode for species identification in some systems, particularly for closely related taxa (Seberg and Petersen, 2009; Roy et al., 2010; Parmentier et al., 2013), has resulted in the recommendation to include a nuclear‐encoded ribosomal internal transcribed spacer, ITS2 (Chen et al., 2010; China Plant BOL Group, 2011; Hollingsworth et al., 2011). The universal presence of ITS2 across plant taxa and its short length (~350 bp) make it a promising barcode for use in community assessments and HTS approaches for DNA barcoding. Our results correspond with previous studies that ITS performs well at both amplification and taxon discrimination. It is possible to increase identification success for tallgrass prairie species. Likely the best methodology for DNA barcoding of these species would be through the creation of a specific probe set suited for this plant community to increase the amplification success of some taxonomic groups, particularly for regions such as matK as was done by Heckenhauer et al. (2016). A concentrated effort to supplement molecular data from species missing from the database will be beneficial, as will continued effort to collect the genetic information of regional species. Creating a smaller database based on verified vouchered specimens, as we have started to do through our sequencing efforts in this project, will result in fewer potential identification errors than occur when using GenBank (although misidentification levels appear to be low in GenBank, as noted by Leray et al. [2019]). Additionally, HTS makes it more feasible to increase the number of sequenced regions to offset the lower identification success created by using shorter reads, leading to the proposal of whole plastid genome sequencing for species discrimination (Parks et al., 2009; Nock et al., 2011; Steele and Pires, 2011; Kane et al., 2012). Nuclear probe sets such as Angiosperms353, which targets 353 nuclear single protein–coding regions, might be promising, as these amplified regions are variable at shallow taxonomic levels (Johnson et al., 2018; Larridon et al., 2020). The increased number of regions returned with HTS also allows for the inclusion of chloroplast regions, in addition to nuclear, potentially increasing the resolution of species identification. This could be particularly helpful for species with ambiguous species boundaries or historical hybridization events, as chloroplast genomes are generally inherited maternally and nuclear genomes are inherited biparentally (Rieseberg and Soltis, 1991; Soltis and Kuzoff, 1995). Our results indicate there is potential for the use of DNA barcoding to identify tallgrass prairie plant species of the Northern Great Plains using HTS methods, particularly at the family and genus levels; however, species‐level identification with these barcoding regions could be limiting, depending on the resolution needed. The optimization of primers for prairie species and the addition of missing species in a regional database are promising future directions that will likely increase successful identification at these shallow taxonomic scales.

Author Contribution

M.L. conceived of the study. S.A.H. performed sample preparation, analyzed the data, and wrote the manuscript with assistance from M.L. All authors approved the final manuscript. APPENDIX S1. P values from pairwise chi‐squared tests between each barcode’s confident correct species identification. Barcodes were compared within each database. Click here for additional data file.
FamilySpeciesCollectionHerbariumBioSample accession
Amaryllidaceae Allium stellatum Mixon s.n.OLFSSAMN15679782
Anacardiaceae Rhus glabra Larson 6925OLFSSAMN15679923
Anacardiaceae Toxicodendron rydbergii Larson 11716OLFSSAMN15679861
Apiaceae Cicuta bulbifera Beauzay 323OLFSSAMN15679813
Apiaceae Cicuta maculata Beauzay 332SDCSAMN15679650
Apiaceae Cryptotaenia canadensis Ode 84‐102OLFSSAMN15679770
Apiaceae Osmorhiza longistylis Stahnke s.n.SDCSAMN15679651
Apiaceae Osmorhiza longistylis Larson 8724OLFSSAMN15679888
Apiaceae Sium suave Beauzay 322SDCSAMN15679727
Apiaceae Zizia aptera Troelstrup s.n.SDCSAMN15679726
Apiaceae Zizia aurea Jensen s.n.SDCSAMN15679725
Apocynaceae Apocynum cannabinum King 94OLFSSAMN15679798
Apocynaceae Apocynum cannabinum Stahnke s.n.SDCSAMN15679724
Apocynaceae Apocynum cannabinum Jensen s.n.SDCSAMN15679723
Apocynaceae Asclepias incarnata Stahnke s.n.SDCSAMN15679722
Apocynaceae Asclepias incarnata Beauzay 327SDCSAMN15679721
Apocynaceae Asclepias syriaca Mixon s.n.OLFSSAMN15679797
Apocynaceae Asclepias verticillata Jensen s.n.SDCSAMN15679720
Apocynaceae Asclepias verticillata Troelstrup s.n.SDCSAMN15679719
Araceae Lemna trisulca Larson 9016OLFSSAMN15679835
Araceae Lemna turionifera Fredrickson s.n.OLFSSAMN15679834
Asparagaceae Maianthemum stellatum Larson 8750OLFSSAMN15679873
Asteraceae Achillea millefolium Beauzay 296OLFSSAMN15679781
Asteraceae Agoseris glauca Pooler 173OLFSSAMN15679779
Asteraceae Ambrosia artemisiifolia Larson 9917OLFSSAMN15679778
Asteraceae Ambrosia psilostachya Larson 9922OLFSSAMN15679777
Asteraceae Artemisia frigida Roemmich 114OLFSSAMN15679776
Asteraceae Artemisia ludoviciana Taylor 7506OLFSSAMN15679774
Asteraceae Artemisia ludoviciana Buckert s.n.SDCSAMN15679775
Asteraceae Bidens frondosa Sargent s.n.OLFSSAMN15679795
Asteraceae Carduus nutans Johnson 419OLFSSAMN15679773
Asteraceae Cirsium arvense Mixon s.n.OLFSSAMN15679772
Asteraceae Cronquistianthus bulliferus Bauer 042OLFSFAILED
Asteraceae Echinacea angustifolia Jensen s.n.SDCSAMN15679715
Asteraceae Echinacea angustifolia Jensen s.n.SDCSAMN15679716
Asteraceae Erigeron canadensis Law 105OLFSSAMN15679771
Asteraceae Erigeron philadelphicus Larson 8980OLFSSAMN15679791
Asteraceae Erigeron strigosus Taylor s.n.OLFSSAMN15679790
Asteraceae Eupatorium perfoliatum Anders 24OLFSSAMN15679759
Asteraceae Euthamia graminifolia Ode s.n.OLFSSAMN15679758
Asteraceae Eutrochium maculatum Beauzay 326SDCSAMN15679714
Asteraceae Grindelia squarrosa Mixon s.n.OLFSSAMN15679789
Asteraceae Helenium autumnale Stahnke s.n.SDCSAMN15679713
Asteraceae Helianthus grosseserratus Larson s.n.OLFSSAMN15679787
Asteraceae Helianthus maximiliani Jensen s.n.SDCSAMN15679712
Asteraceae Helianthus maximiliani Law 73OLFSSAMN15679786
Asteraceae Helianthus nuttallii Larson 11732OLFSSAMN15679788
Asteraceae Heliopsis helianthoides Jensen s.n.SDCSAMN15679711
Asteraceae Liatris aspera Stahnke s.n.SDCSAMN15679709
Asteraceae Liatris punctata Mixon s.n.OLFSSAMN15679785
Asteraceae Lygodesmia juncea Troelstrup s.n.SDCSAMN15679708
Asteraceae Matricaria discoidea Anderson 09OLFSSAMN15679898
Asteraceae Packera paupercula Larson 8920OLFSSAMN15679906
Asteraceae Packera plattensis Jensen s.n.SDCSAMN15679705
Asteraceae Packera pseudaurea Larson 9967OLFSFAILED
Asteraceae Ratibida columnifera Stahnke s.n.SDCSAMN15679707
Asteraceae Ratibida columnifera Jensen s.n.SDCSAMN15679706
Asteraceae Ratibida columnifera Mixon s.n.OLFSSAMN15679925
Asteraceae Rudbeckia laciniata Larson 6912OLFSSAMN15679924
Asteraceae Silphium perfoliatum Mixon s.n.OLFSSAMN15679876
Asteraceae Solidago canadensis Jensen s.n.SDCSAMN15679704
Asteraceae Solidago gigantea Beauzay 337SDCSAMN15679703
Asteraceae Solidago missouriensis Bortem 102OLFSSAMN15679875
Asteraceae Solidago mollis Stahnke s.n.SDCSAMN15679702
Asteraceae Solidago rigida Larson 9926SDCSAMN15679701
Asteraceae Symphyotrichum ericoides Bortnem 110SDCSAMN15679718
Asteraceae Symphyotrichum laeve Stahnke s.n.SDCSAMN15679717
Asteraceae Taraxacum officinale Monteith s.n.OLFSSAMN15679863
Asteraceae Tragopogon dubius Jensen s.n.SDCSAMN15679700
Asteraceae Vernonia fasciculata Larson 11538OLFSSAMN15679848
Asteraceae Vernonia fasciculata Jensen s.n.SDCSAMN15679699
Balsaminaceae Impatiens capensis Beauzay 335SDCSAMN15679676
Boraginaceae Lithospermum canescens Stahnke s.n.SDCSAMN15679678
Boraginaceae Lithospermum canescens Troelstrup s.n.SDCSAMN15679679
Boraginaceae Lithospermum incisum Jensen s.n.SDCSAMN15679680
Boraginaceae Lithospermum incisum Stahnke s.n.SDCSAMN15679677
Boraginaceae Lithospermum onosmodium Beauzay 282OLFSSAMN15679889
Brassicaceae Capsella bursa‐pastoris Taylor 11799OLFSSAMN15679833
Brassicaceae Cardamine bulbosa Steinauer s.n.OLFSSAMN15679832
Brassicaceae Erysimum cheiranthoides Larson 11335OLFSSAMN15679760
Brassicaceae Lepidium densiflorum Law 93OLFSSAMN15679836
Brassicaceae Rorippa palustris McLead s.n.OLFSSAMN15679918
Campanulaceae Lobelia siphilitica Beauzay 330OLFSSAMN15679698
Campanulaceae Lobelia siphilitica Beauzay 330SDCSAMN15679904
Campanulaceae Lobelia spicata Larson s.n.OLFSSAMN15679903
Cannabaceae Celtis occidentalis Ode 12‐27OLFSSAMN15679815
Caprifoliaceae Lonicera tatarica Stahnke s.n.SDCSAMN15679696
Caprifoliaceae Symphoricarpos occidentalis Stahnke s.n.SDCSAMN15679697
Caryophyllaceae Callitriche brutia Larson s.n.OLFSSAMN15679865
Caryophyllaceae Stellaria media Bortem 2OLFSSAMN15679866
Celastraceae Celastrus scandens Larson 11227OLFSSAMN15679816
Ceratophyllaceae Ceratophyllum demersum Larson 6963OLFSSAMN15679814
Comandraceae Comandra umbellata Jensen s.n.SDCSAMN15679684
Commelinaceae Tradescantia bracteata Roberts 72‐5‐28:6OLFSSAMN15679860
Convolvulaceae Calystegia macounii Larson 11358OLFSSAMN15679793
Convolvulaceae Calystegia sepium Pooler 84996OLFSSAMN15679792
Convolvulaceae Convolvus sepium Jensen s.n.SDCSAMN15679695
Cornaceae Cornus sericea Larson 11240OLFSSAMN15679812
Cupressaceae Juniperus virginiana Taylor s.n.OLFSFAILED
Cyperaceae Carex aquatilis Ode 82‐11OLFSSAMN15679830
Cyperaceae Carex blanda Larson 11215OLFSSAMN15679829
Cyperaceae Carex brevior Larson 11132OLFSSAMN15679828
Cyperaceae Carex cristatella Larson 11330SDCSAMN15679694
Cyperaceae Carex emoryi Larson 6393OLFSSAMN15679827
Cyperaceae Carex granularis Larson 11346OLFSSAMN15679826
Cyperaceae Carex gravida Larson 6395OLFSSAMN15679825
Cyperaceae Carex hystericina Larson 11337SDCSAMN15679693
Cyperaceae Carex meadii Ode 83‐57OLFSSAMN15679824
Cyperaceae Carex molesta Larson 9365OLFSSAMN15679822
Cyperaceae Carex pellita Larson 9270OLFSSAMN15679823
Cyperaceae Carex praegracilis Peterson s.n.SDCSAMN15679692
Cyperaceae Carex sartwellii Larson s.n.OLFSSAMN15679821
Cyperaceae Carex sprengelii Larson 11216OLFSSAMN15679820
Cyperaceae Carex stricta Larson 11329SDCSAMN15679690
Cyperaceae Carex tenera Larson 6396OLFSSAMN15679819
Cyperaceae Carex tetanica Larson s.n.OLFSSAMN15679818
Cyperaceae Carex utriculata Larson 9165OLFSSAMN15679817
Cyperaceae Carex vulpinoidea Larson 11345SDCSAMN15679691
Cyperaceae Cyperus odoratus Beauzay 341SDCSAMN15679689
Cyperaceae Eleocharis erythropoda Larson 11639OLFSSAMN15679766
Cyperaceae Eleocharis palustris Sletten 169OLFSSAMN15679765
Cyperaceae Eriophorum angustifolium Larson 11340OLFSSAMN15679761
Cyperaceae Scirpus microcarpus Larson s.n.OLFSSAMN15679910
Cyperaceae Scirpus pallidus Beauzay 257OLFSSAMN15679909
Elaeagnaceae Elaeagnus angustifolia Johnson 420OLFSSAMN15679767
Equisetaceae Equisetum arvense Jensen s.n.SDCSAMN15679688
Fabaceae Amorpha canescens Jensen s.n.SDCSAMN15679675
Fabaceae Amorpha fruticosa Beauzay 317SDCSAMN15679674
Fabaceae Amphicarpa bracteata Larson 6680OLFSSAMN15679811
Fabaceae Astragalus crassicarpus Pooler 186OLFSSAMN15679796
Fabaceae Caragana arborescens Fairlee 54OLFSSAMN15679831
Fabaceae Dalea candida Stahnke s.n.SDCSAMN15679667
Fabaceae Dalea purpurea Stahnke s.n.SDCSAMN15679666
Fabaceae Gleditsia triacanthos Purinton s.n.OLFSSAMN15679841
Fabaceae Lathyrus palustris Pengra P‐16‐17OLFSSAMN15679838
Fabaceae Lathyrus polymorphus Troelstrup s.n.SDCSAMN15679665
Fabaceae Lathyrus venosus Mixon s.n.OLFSSAMN15679837
Fabaceae Medicago lupulina Anderson 17OLFSSAMN15679897
Fabaceae Medicago sativa Larson 11421OLFSSAMN15679896
Fabaceae Melilotus officinalis Johnson s.n.SDCSAMN15679673
Fabaceae Melilotus officinalis Johnson s.n.SDCSAMN15679672
Fabaceae Melilotus officinalis Larson 9041OLFSSAMN15679895
Fabaceae Psoralea argophylla Stahnke s.n.SDCSAMN15679671
Fabaceae Psoralea esculenta Jensen s.n.SDCSAMN15679670
Fabaceae Trifolium pratense Jensen s.n.SDCSAMN15679669
Fabaceae Trifolium pratense Beauzay 276OLFSSAMN15679859
Fabaceae Vicia americana Jensen s.n.SDCSAMN15679668
Fabaceae Dalea purpurea Jensen s.n.SDCSAMN15679664
Fagaceae Quercus macrocarpa Stahnke s.n.OLFSSAMN15679927
Gentianaceae Gentiana andrewsii Troelstrup s.n.SDCSAMN15679687
Gentianaceae Gentiana puberulenta Roemmich 98OLFSSAMN15679842
Grossulariaceae Ribes americanum Larson 8885OLFSSAMN15679922
Grossulariaceae Ribes americanum Pooler 84045OLFSSAMN15679921
Hydrophyllaceae Hydrophyllum virginianum Taylor 7540OLFSSAMN15679840
Iridaceae Sisyrinchium campestre Larson 8733OLFSSAMN15679874
Juncaceae Juncus dudleyi Bettross 51OLFSSAMN15679839
Juncaceae Juncus nodosus Larson 11334SDCSAMN15679657
Lamiaceae Lycopus americanus Beauzay 346SDCSAMN15679663
Lamiaceae Lycopus asper Beauzay 342SDCSAMN15679662
Lamiaceae Lycopus uniflorus Larson 11426OLFSSAMN15679901
Lamiaceae Mentha arvensis Troelstrup s.n.SDCSAMN15679660
Lamiaceae Monarda fistulosa Stahnke s.n.SDCSAMN15679661
Lamiaceae Scutellaria lateriflora Roemmich 259OLFSSAMN15679907
Lamiaceae Stachys palustris Stahnke s.n.SDCSAMN15679659
Lamiaceae Stachys palustris Larson 11508OLFSSAMN15679867
Liliaceae Lilium philadelphicum Jensen s.n.SDCSAMN15679658
Mazocraeidae Brickellia eupatorioides Stahnke s.n.SDCSAMN15679710
Melanthiaceae Anticlea elegans Mixon s.n.OLFSSAMN15679843
Nyctaginaceae Mirabilis nyctaginea Bortem 23OLFSSAMN15679894
Oleaceae Fraxinus pennsylvanica Ode s.n.OLFSSAMN15679757
Onagraceae Epilobium leptophyllum Millar 37OLFSSAMN15679764
Onagraceae Oenothera biennis Beauzay 334SDCSAMN15679686
Onagraceae Oenothera serrulata Larson 11247OLFSSAMN15679794
Orchidaceae Cypripedium candidum Leoschke 1531OLFSSAMN15679769
Orchidaceae Liparis loeselii Larson 11327OLFSSAMN15679905
Orchidaceae Platanthera aquilonis Larson 11328OLFSSAMN15679878
Orchidaceae Platanthera hyperborea Larson 9140OLFSSAMN15679877
Orobanchaceae Pedicularis lanceolata Ode 00‐21OLFSSAMN15679885
Oxalidaceae Oxalis corniculata Mixon s.n.OLFSSAMN15679887
Oxalidaceae Oxalis violacea Larson 11212OLFSSAMN15679886
Papaveraceae Dicentra cucullaria Larson 6380OLFSSAMN15679768
Phrymaceae Erythranthe glabrata Larson 11325SDCSAMN15679683
Phrymaceae Mimulus ringens Beauzay 351SDCSAMN15679682
Phrymaceae Phryma leptostachya Ode 84‐97OLFSSAMN15679883
Pinaceae Picea glauca Taylor s.n.OLFSSAMN15679881
Pinaceae Pinus ponderosa Lehman 38OLFSSAMN15679880
Plantaginaceae Penstemon albidus Larson 11234OLFSSAMN15679884
Plantaginaceae Plantago major Beauzay 303OLFSSAMN15679879
Plantaginaceae Veronica anagallis‐aquatica Larson 10785OLFSSAMN15679851
Plantaginaceae Veronica peregrina Larson 9999OLFSSAMN15679850
Poaceae Agropyron cristatum Pooler 84027OLFSSAMN15679784
Poaceae Agrostis stolonifera Ode s.n.OLFSSAMN15679783
Poaceae Andropogon gerardii Roemmich 81OLFSSAMN15679810
Poaceae Bouteloua curtipendula Jensen s.n.SDCSAMN15679656
Poaceae Bromus inermis Peterson s.n.SDCSAMN15679655
Poaceae Calamagrostis canadensis Sletten 305OLFSSAMN15679809
Poaceae Calamagrostis stricta Hansen 852OLFSSAMN15679808
Poaceae Cenchrus americanus Beauzay 257OLFSSAMN15679912
Poaceae Echinochloa muricata Beauzay 347SDCSAMN15679653
Poaceae Elymus repens Kanoute 033OLFSSAMN15679803
Poaceae Elymus villosus Genereux s.n.OLFSSAMN15679804
Poaceae Elymus virginicus VanSickle 586OLFSSAMN15679805
Poaceae Glyceria grandis Sletten 303OLFSSAMN15679802
Poaceae Glyceria striata Kjellsen 35OLFSSAMN15679801
Poaceae Hordeum jubatum Orth s.n.OLFSSAMN15679800
Poaceae Koeleria macrantha Kopp 225OLFSSAMN15679799
Poaceae Muhlenbergia cuspidata Kanoute 092OLFSSAMN15679893
Poaceae Nassella viridula Lehmon 179OLFSSAMN15679864
Poaceae Panicum acuminatum Larson 11060OLFSSAMN15679807
Poaceae Panicum oligosanthes Bortnem s.n.SDCSAMN15679654
Poaceae Panicum virgatum King 103OLFSSAMN15679892
Poaceae Panicum wilcoxianum Larson 10982OLFSSAMN15679806
Poaceae Phalaris arundinacea Sletten 136OLFSFAILED
Poaceae Phleum pratense Christner s.n.OLFSSAMN15679891
Poaceae Poa palustris Ode 84‐107OLFSSAMN15679890
Poaceae Poa pratensis Larson 6850SDCSAMN15679652
Poaceae Schizachyrium scoparium VanSickle 536OLFSSAMN15679913
Poaceae Setaria viridis Roemmich 82OLFSSAMN15679911
Poaceae Sorghastrum nutans Larson s.n.SDCSAMN15679728
Poaceae Sphenopholis intermedia Larson 11341OLFSSAMN15679730
Poaceae Sphenopholis intermedia Larson 11341SDCSAMN15679871
Poaceae Sphenopholis obtusata Sletten 379OLFSSAMN15679870
Poaceae Sporobolus compositus Dirks s.n.OLFSSAMN15679869
Poaceae Sporobolus heterolepis Pauly s.n.OLFSSAMN15679868
Poaceae Sporobolus michauxianus Beauzay 325SDCSAMN15679729
Polemoniaceae Phlox pilosa Troelstrup s.n.SDCSAMN15679731
Polygonaceae Persicaria amphibia Beauzay 321SDCSAMN15679732
Polygonaceae Persicaria amphibia Ode s.n.OLFSSAMN15679929
Polygonaceae Persicaria lapathifolia Beauzay 320SDCSAMN15679733
Polygonaceae Persicaria punctata Beauzay 339SDCSAMN15679734
Polygonaceae Polygonum aviculare Larson 11565OLFSSAMN15679930
Polygonaceae Polygonatum biflorum Pooler 84029OLFSSAMN15679931
Polygonaceae Rumex crispus Unkenholz s.n.OLFSSAMN15679920
Polygonaceae Rumex orbiculatus Roberts 73‐8‐19:1OLFSSAMN15679919
Primulaceae Lysimachia ciliata Beauzay 352OLFSSAMN15679900
Primulaceae Lysimachia thyrsiflora Sletten 273OLFSSAMN15679899
Ranunculaceae Anemonastrum canadense Troelstrup s.n.SDCSAMN15679735
Ranunculaceae Anemone cylindrica Stahnke s.n.SDCSAMN15679736
Ranunculaceae Aquilegia canadensis Jensen s.n.SDCSAMN15679738
Ranunculaceae Caltha palustris Troelstrup s.n.SDCSAMN15679739
Ranunculaceae Delphinium carolinianum Jensen s.n.SDCSAMN15679740
Ranunculaceae Pulsatilla patens Stahnke s.n.SDCSAMN15679737
Ranunculaceae Ranunculus hispidus Ode 02‐7OLFSSAMN15679926
Ranunculaceae Ranunculus macounii Jensen s.n.SDCSAMN15679741
Ranunculaceae Thalictrum dasycarpum Larson 6651OLFSSAMN15679862
Rhamnaceae Rhamnus cathartica Stahnke s.n.SDCSAMN15679742
Rosaceae Agrimonia striata Beauzay 349SDCSAMN15679743
Rosaceae Amelanchier alnifolia Stahnke s.n.SDCSAMN15679744
Rosaceae Amelanchier ovalis Reese s.n.SDCSAMN15679745
Rosaceae Fragaria virginiana Monteith s.n.OLFSSAMN15679763
Rosaceae Geum aleppicum Larson 11332SDCSAMN15679746
Rosaceae Geum canadense Larson 11336OLFSSAMN15679762
Rosaceae Geum rossii Jensen s.n.SDCSAMN15679747
Rosaceae Prunus americana Stahnke s.n.SDCSAMN15679748
Rosaceae Prunus serotina Stahnke s.n.SDCSAMN15679749
Rosaceae Rosa arkansana Stahnke s.n.SDCSAMN15679750
Rosaceae Rubus occidentalis Larson 10437OLFSSAMN15679917
Rubiaceae Galium aparine Anderson 02OLFSSAMN15679756
Rubiaceae Galium boreale Jensen s.n.SDCSAMN15679751
Rubiaceae Galium trifidum Larson 9213OLFSSAMN15679754
Rubiaceae Galium triflorum Larson 6882OLFSSAMN15679755
Salicaceae Populus tremuloides Larson 11227OLFSSAMN15679928
Salicaceae Populus ×jackii Larson 1131SDCSAMN15679685
Salicaceae Salix alba Millar 011OLFSSAMN15679916
Salicaceae Salix amygdaloides Larson 7023OLFSSAMN15679915
Salicaceae Salix interior Larson 11264OLFSSAMN15679914
Sapindaceae Aesculus glabra Taylor s.n.OLFSSAMN15679780
Scrophulariaceae Scrophularia lanceolata Mixon s.n.OLFSSAMN15679908
Solanaceae Lycium barbarum Taylor 11723OLFSSAMN15679902
Solanaceae Physalis virginiana Roberts s.n.OLFSSAMN15679882
Typhaceae Sparganium eurycarpum Sletten 210OLFSSAMN15679872
Typhaceae Typha angustifolia Stahnke s.n.SDCSAMN15679681
Typhaceae Typha latifolia Larson 9117OLFSSAMN15679857
Typhaceae Typha ×glauca Larson 11386OLFSSAMN15679858
Ulmaceae Ulmus americana Riley 51OLFSSAMN15679856
Ulmaceae Ulmus pumila Larson 11559OLFSSAMN15679855
Urticaceae Urtica dioica Mixon s.n.OLFSSAMN15679854
Verbenaceae Verbena bracteata Law 103OLFSSAMN15679853
Verbenaceae Verbena hastata Beauzay 319SDCSAMN15679753
Verbenaceae Verbena stricta Jensen s.n.SDCSAMN15679752
Verbenaceae Verbena stricta Pooler 84028OLFSSAMN15679852
Violaceae Viola canadensis Larson 11214OLFSSAMN15679847
Violaceae Viola nephrophylla Larson 9985OLFSSAMN15679846
Violaceae Viola pedatifida Larson 11219OLFSSAMN15679845
Violaceae Viola sororia Larson 7019OLFSSAMN15679844
Vitaceae Vitis riparia Sletten 175OLFSSAMN15679849
IndexIndex adapterPrimer sequence
Index 1 (i7) N701TAAGGCGA
N702CGTACTAG
N703AGGCAGAA
N704TCCTGAGC
N705GGACTCCT
N706TAGGCATG
N707CTCTCTAC
N708CAGAGAGG
N709GCTACGCT
N710CGAGGCTG
N711AAGAGGCA
N712GTAGAGGA
Index 2 (i5) S501TAGATCGC
S502CTCTCTAT
S503TATCCTCT
S504AGAGTAGA
S505GTAAGGAG
S506ACTGCATA
S507AAGGAGTA
S508CTAAGCCT
Locusa Taxon levelDatabaseSequencesConfidentAmbiguousIncorrectNo match
ITSFamilyGenBank178141 (79.21%)20 (11.24%)13 (7.3%)4 (2.25%)
ITSFamilyRegional178159 (89.33%)3 (1.69%)11 (6.18%)5 (2.81%)
ITSFamilyOLFS178159 (89.33%)013 (7.3%)6 (3.37%)
ITSGenusGenBank178124 (69.66%)25 (14.04%)25 (14.04%)4 (2.25%)
ITSGenusRegional178142 (79.78%)10 (5.62%)21 (11.8%)5 (2.81%)
ITSGenusOLFS178146 (82.02%)3 (1.69%)23 (12.92%)6 (3.37%)
ITSSpeciesGenBank17853 (29.78%)59 (33.15%)62 (34.83%)4 (2.25%)
ITSSpeciesRegional17886 (48.31%)33 (18.54%)54 (30.34%)5 (2.81%)
ITSSpeciesOLFS178122 (68.54%)12 (6.74%)38 (21.35%)6 (3.37%)
trnHFamilyGenBank138123 (89.13%)2 (1.45%)13 (9.42%)0
trnHFamilyRegional138104 (75.36%)034 (24.64%)0
trnHFamilyOLFS13892 (66.67%)043 (31.16%)3 (2.17%)
trnHGenusGenBank13894 (68.12%)14 (10.14%)30 (21.74%)0
trnHGenusRegional13859 (42.75%)2 (1.45%)77 (55.8%)0
trnHGenusOLFS13839 (28.26%)096 (69.57%)3 (2.17%)
trnHSpeciesGenBank13832 (23.19%)27 (19.57%)79 (57.25%)0
trnHSpeciesRegional13818 (13.04%)6 (4.35%)114 (82.61%)0
trnHSpeciesOLFS13829 (21.01%)1 (0.72%)105 (76.09%)3 (2.17%)
matKFamilyGenBank112107 (95.54%)05 (4.46%)0
matKFamilyRegional112104 (92.86%)08 (7.14%)0
matKFamilyOLFS11290 (80.36%)022 (19.64%)0
matKGenusGenBank11261 (54.46%)11 (9.82%)40 (35.71%)0
matKGenusRegional11269 (61.61%)1 (0.89%)42 (37.5%)0
matKGenusOLFS11245 (40.18%)067 (59.82%)0
matKSpeciesGenBank11211 (9.82%)16 (14.29%)85 (75.89%)0
matKSpeciesRegional11221 (18.75%)4 (3.57%)87 (77.68%)0
matKSpeciesOLFS11228 (25.00%)084 (75.00%)0
rbcLaFamilyGenBank249220 (88.35%)24 (9.64%)5 (2.01%)0
rbcLaFamilyRegional249240 (96.39%)3 (1.20%)5 (2.01%)1 (0.4%)
rbcLaFamilyOLFS249240 (96.39%)08 (3.21%)1 (0.4%)
rbcLaGenusGenBank249135 (54.22%)99 (39.76%)15 (6.02%)0
rbcLaGenusRegional249181 (72.69%)50 (20.08%)17 (6.83%)1 (0.4%)
rbcLaGenusOLFS249215 (86.35%)14 (5.62%)19 (7.63%)1 (0.4%)
rbcLaSpeciesGenBank24920 (8.03%)164 (65.86%)65 (26.1%)0
rbcLaSpeciesRegional24946 (18.47%)120 (48.19%)82 (32.93%)1 (0.4%)
rbcLaSpeciesOLFS249138 (55.42%)68 (27.31%)42 (16.87%)1 (0.4%)
ITS_trnHFamilyGenBank9684 (87.5%)8 (8.33%)4 (4.17%)0
ITS_trnHFamilyRegional9691 (94.79%)1 (1.04%)4 (4.17%)0
ITS_trnHFamilyOLFS9689 (92.71%)07 (7.29%)0
ITS_trnHGenusGenBank9669 (71.88%)12 (12.5%)15 (15.62%)0
ITS_trnHGenusRegional9675 (78.12%)7 (7.29%)14 (14.58%)0
ITS_trnHGenusOLFS9672 (75%)2 (2.08%)22 (22.92%)0
ITS_trnHSpeciesGenBank9634 (35.42%)20 (20.83%)42 (43.75%)0
ITS_trnHSpeciesRegional9648 (50%)14 (14.58%)34 (35.42%)0
ITS_trnHSpeciesOLFS9661 (63.54%)4 (4.17%)31 (32.29%)0
ITS_matKFamilyGenBank7566 (88%)3 (4%)6 (8%)0
ITS_matKFamilyRegional7568 (90.67%)07 (9.33%)0
ITS_matKFamilyOLFS7570 (93.33%)05 (6.67%)0
ITS_matKGenusGenBank7548 (64%)7 (9.33%)20 (26.67%)0
ITS_matKGenusRegional7564 (85.33%)1 (1.33%)10 (13.33%)0
ITS_matKGenusOLFS7556 (74.67%)019 (25.33%)0
ITS_matKSpeciesGenBank7518 (24%)17 (22.67%)40 (53.33%)0
ITS_matKSpeciesRegional7542 (56%)12 (16%)21 (28%)0
ITS_matKSpeciesOLFS7547 (62.67%)2 (2.67%)26 (34.67%)0
ITS_rbcLaFamilyGenBank164133 (81.1%)21 (12.8%)10 (6.1%)0
ITS_rbcLaFamilyRegional164152 (92.68%)3 (1.83%)9 (5.49%)0
ITS_rbcLaFamilyOLFS164155 (94.51%)09 (5.49%)0
ITS_rbcLaGenusGenBank164113 (68.9%)32 (19.51%)19 (11.59%)0
ITS_rbcLaGenusRegional164133 (81.1%)12 (7.32%)19 (11.59%)0
ITS_rbcLaGenusOLFS164141 (85.98%)6 (3.66%)17 (10.37%)0
ITS_rbcLaSpeciesGenBank16443 (26.22%)71 (43.29%)50 (30.49%)0
ITS_rbcLaSpeciesRegional16472 (43.9%)42 (25.61%)50 (30.49%)0
ITS_rbcLaSpeciesOLFS164116 (70.73%)21 (12.8%)27 (16.46%)0
matK_rbcLaFamilyGenBank109103 (94.5%)3 (2.75%)3 (2.75%)0
matK_rbcLaFamilyRegional109105 (96.33%)2 (1.83%)2 (1.83%)0
matK_rbcLaFamilyOLFS109106 (97.25%)03 (2.75%)0
matK_rbcLaGenusGenBank10960 (55.05%)19 (17.43%)30 (27.52%)0
matK_rbcLaGenusRegional10982 (75.23%)20 (18.35%)7 (6.42%)0
matK_rbcLaGenusOLFS10995 (87.16%)4 (3.67%)10 (9.17%)0
matK_rbcLaSpeciesGenBank10913 (11.93%)36 (33.03%)60 (55.05%)0
matK_rbcLaSpeciesRegional10928 (25.69%)53 (48.62%)28 (25.69%)0
matK_rbcLaSpeciesOLFS10963 (57.8%)31 (28.44%)15 (13.76%)0
matK_trnHFamilyGenBank5753 (92.98%)04 (7.02%)0
matK_trnHFamilyRegional5752 (91.23%)05 (8.77%)0
matK_trnHFamilyOLFS5744 (77.19%)013 (22.81%)0
matK_trnHGenusGenBank5739 (68.42%)018 (31.58%)0
matK_trnHGenusRegional5740 (70.18%)1 (1.75%)16 (28.07%)0
matK_trnHGenusOLFS5728 (49.12%)029 (50.88%)0
matK_trnHSpeciesGenBank5715 (26.32%)4 (7.02%)38 (66.67%)0
matK_trnHSpeciesRegional5713 (22.81%)2 (3.51%)42 (73.68%)0
matK_trnHSpeciesOLFS5721 (36.84%)036 (63.16%)0
rbcLa_trnHFamilyGenBank126114 (90.48%)4 (3.17%)8 (6.35%)0
rbcLa_trnHFamilyRegional126121 (96.03%)05 (3.97%)0
rbcLa_trnHFamilyOLFS126119 (94.44%)07 (5.56%)0
rbcLa_trnHGenusGenBank12682 (65.08%)27 (21.43%)17 (13.49%)0
rbcLa_trnHGenusRegional12691 (72.22%)22 (17.46%)13 (10.32%)0
rbcLa_trnHGenusOLFS126100 (79.37%)7 (5.56%)19 (15.08%)0
rbcLa_trnHSpeciesGenBank12629 (23.02%)40 (31.75%)57 (45.24%)0
rbcLa_trnHSpeciesRegional12632 (25.4%)47 (37.3%)47 (37.3%)0
rbcLa_trnHSpeciesOLFS12673 (57.94%)28 (22.22%)25 (19.84%)0
ITS_matK_rbcLaFamilyGenBank7365 (89.04%)2 (2.74%)6 (8.22%)0
ITS_matK_rbcLaFamilyRegional7366 (90.41%)07 (9.59%)0
ITS_matK_rbcLaFamilyOLFS7368 (93.15%)05 (6.85%)0
ITS_matK_rbcLaGenusGenBank7344 (60.27%)8 (10.96%)21 (28.77%)0
ITS_matK_rbcLaGenusRegional7361 (83.56%)3 (4.11%)9 (12.33%)0
ITS_matK_rbcLaGenusOLFS7364 (87.67%)1 (1.37%)8 (10.96%)0
ITS_matK_rbcLaSpeciesGenBank7313 (17.81%)21 (28.77%)39 (53.42%)0
ITS_matK_rbcLaSpeciesRegional7335 (47.95%)19 (26.03%)19 (26.03%)0
ITS_matK_rbcLaSpeciesOLFS7356 (76.71%)7 (9.59%)10 (13.7%)0
ITS_matK_trnHFamilyGenBank4140 (97.56%)01 (2.44%)0
ITS_matK_trnHFamilyRegional4140 (97.56%)01 (2.44%)0
ITS_matK_trnHFamilyOLFS4140 (97.56%)01 (2.44%)0
ITS_matK_trnHGenusGenBank4131 (75.61%)010 (24.39%)0
ITS_matK_trnHGenusRegional4137 (90.24%)2 (4.88%)2 (4.88%)0
ITS_matK_trnHGenusOLFS4130 (73.17%)011 (26.83%)0
ITS_matK_trnHSpeciesGenBank4118 (43.9%)4 (9.76%)19 (46.34%)0
ITS_matK_trnHSpeciesRegional4122 (53.66%)7 (17.07%)12 (29.27%)0
ITS_matK_trnHSpeciesOLFS4127 (65.85%)014 (34.15%)0
ITS_rbcLa_trnHFamilyGenBank9183 (91.21%)6 (6.59%)2 (2.2%)0
ITS_rbcLa_trnHFamilyRegional9188 (96.7%)1 (1.1%)2 (2.2%)0
ITS_rbcLa_trnHFamilyOLFS9187 (95.6%)04 (4.4%)0
ITS_rbcLa_trnHGenusGenBank9167 (73.63%)13 (14.29%)11 (12.09%)0
ITS_rbcLa_trnHGenusRegional9173 (80.22%)8 (8.79%)10 (10.99%)0
ITS_rbcLa_trnHGenusOLFS9176 (83.52%)4 (4.4%)11 (12.09%)0
ITS_rbcLa_trnHSpeciesGenBank9132 (35.16%)23 (25.27%)36 (39.56%)0
ITS_rbcLa_trnHSpeciesRegional9141 (45.05%)18 (19.78%)32 (35.16%)0
ITS_rbcLa_trnHSpeciesOLFS9163 (69.23%)9 (9.89%)19 (20.88%)0
matK_rbcLa_trnHFamilyGenBank5651 (91.07%)1 (1.79%)4 (7.14%)0
matK_rbcLa_trnHFamilyRegional5654 (96.43%)02 (3.57%)0
matK_rbcLa_trnHFamilyOLFS5655 (98.21%)01 (1.79%)0
matK_rbcLa_trnHGenusGenBank5639 (69.64%)2 (3.57%)15 (26.79%)0
matK_rbcLa_trnHGenusRegional5645 (80.36%)7 (12.5%)4 (7.14%)0
matK_rbcLa_trnHGenusOLFS5648 (85.71%)2 (3.57%)6 (10.71%)0
matK_rbcLa_trnHSpeciesGenBank5616 (28.57%)8 (14.29%)32 (57.14%)0
matK_rbcLa_trnHSpeciesRegional5618 (32.14%)17 (30.36%)21 (37.5%)0
matK_rbcLa_trnHSpeciesOLFS5634 (60.71%)14 (25%)8 (14.29%)0
ITS_matK_rbcLa_trnHFamilyGenBank4039 (97.5%)01 (2.5%)0
ITS_matK_rbcLa_trnHFamilyRegional4039 (97.5%)01 (2.5%)0
ITS_matK_rbcLa_trnHFamilyOLFS4039 (97.5%)01 (2.5%)0
ITS_matK_rbcLa_trnHGenusGenBank4031 (77.5%)09 (22.5%)0
ITS_matK_rbcLa_trnHGenusRegional4035 (87.5%)3 (7.5%)2 (5%)0
ITS_matK_rbcLa_trnHGenusOLFS4036 (90%)1 (2.5%)3 (7.5%)0
ITS_matK_rbcLa_trnHSpeciesGenBank4016 (40%)5 (12.5%)19 (47.5%)0
ITS_matK_rbcLa_trnHSpeciesRegional4020 (50%)8 (20%)12 (30%)0
ITS_matK_rbcLa_trnHSpeciesOLFS4032 (80%)3 (7.5%)5 (12.5%)0
FamilyLocusa Taxon levelDatabaseSequencesConfidentAmbiguousIncorrectNo match
AsteraceaeITSFamilyGenBank2825 (89.29%)3 (10.71%)00
AsteraceaeITSFamilyRegional2828 (100%)000
AsteraceaeITSFamilyOLFS2828 (100%)000
AsteraceaeITSGenusGenBank2821 (75%)6 (21.43%)1 (3.57%)0
AsteraceaeITSGenusRegional2826 (92.86%)2 (7.14%)00
AsteraceaeITSGenusOLFS2826 (92.86%)1 (3.57%)1 (3.57%)0
AsteraceaeITSSpeciesGenBank286 (21.43%)17 (60.71%)5 (17.86%)0
AsteraceaeITSSpeciesRegional2815 (53.57%)11 (39.29%)2 (7.14%)0
AsteraceaeITSSpeciesOLFS2823 (82.14%)3 (10.71%)2 (7.14%)0
AsteraceaetrnHFamilyGenBank3231 (96.88%)01 (3.12%)0
AsteraceaetrnHFamilyRegional3231 (96.88%)01 (3.12%)0
AsteraceaetrnHFamilyOLFS3231 (96.88%)01 (3.12%)0
AsteraceaetrnHGenusGenBank3224 (75%)1 (3.12%)7 (21.88%)0
AsteraceaetrnHGenusRegional3220 (62.5%)1 (3.12%)11 (34.38%)0
AsteraceaetrnHGenusOLFS3215 (46.88%)017 (53.12%)0
AsteraceaetrnHSpeciesGenBank3210 (31.25%)4 (12.5%)18 (56.25%)0
AsteraceaetrnHSpeciesRegional328 (25%)2 (6.25%)22 (68.75%)0
AsteraceaetrnHSpeciesOLFS3211 (34.38%)021 (65.62%)0
AsteraceaematKFamilyGenBank3535 (100%)000
AsteraceaematKFamilyRegional3535 (100%)000
AsteraceaematKFamilyOLFS3535 (100%)000
AsteraceaematKGenusGenBank357 (20%)6 (17.14%)22 (62.86%)0
AsteraceaematKGenusRegional3515 (42.86%)1 (2.86%)19 (54.29%)0
AsteraceaematKGenusOLFS357 (20%)028 (80%)0
AsteraceaematKSpeciesGenBank351 (2.86%)3 (8.57%)31 (88.57%)0
AsteraceaematKSpeciesRegional353 (8.57%)1 (2.86%)31 (88.57%)0
AsteraceaematKSpeciesOLFS355 (14.29%)030 (85.71%)0
AsteraceaerbcLaFamilyGenBank4336 (83.72%)7 (16.28%)00
AsteraceaerbcLaFamilyRegional4340 (93.02%)3 (6.98%)00
AsteraceaerbcLaFamilyOLFS4343 (100%)000
AsteraceaerbcLaGenusGenBank4312 (27.91%)30 (69.77%)1 (2.33%)0
AsteraceaerbcLaGenusRegional4321 (48.84%)20 (46.51%)2 (4.65%)0
AsteraceaerbcLaGenusOLFS4335 (81.4%)3 (6.98%)5 (11.63%)0
AsteraceaerbcLaSpeciesGenBank432 (4.65%)36 (83.72%)5 (11.63%)0
AsteraceaerbcLaSpeciesRegional434 (9.3%)26 (60.47%)13 (30.23%)0
AsteraceaerbcLaSpeciesOLFS4315 (34.88%)20 (46.51%)8 (18.6%)0
AsteraceaeITS_trnHFamilyGenBank2222 (100%)000
AsteraceaeITS_trnHFamilyRegional2222 (100%)000
AsteraceaeITS_trnHFamilyOLFS2222 (100%)000
AsteraceaeITS_trnHGenusGenBank2216 (72.73%)3 (13.64%)3 (13.64%)0
AsteraceaeITS_trnHGenusRegional2217 (77.27%)3 (13.64%)2 (9.09%)0
AsteraceaeITS_trnHGenusOLFS2214 (63.64%)1 (4.55%)7 (31.82%)0
AsteraceaeITS_trnHSpeciesGenBank226 (27.27%)4 (18.18%)12 (54.55%)0
AsteraceaeITS_trnHSpeciesRegional227 (31.82%)7 (31.82%)8 (36.36%)0
AsteraceaeITS_trnHSpeciesOLFS2211 (50%)2 (9.09%)9 (40.91%)0
AsteraceaeITS_matKFamilyGenBank2019 (95%)1 (5%)00
AsteraceaeITS_matKFamilyRegional2020 (100%)000
AsteraceaeITS_matKFamilyOLFS2020 (100%)000
AsteraceaeITS_matKGenusGenBank208 (40%)4 (20%)8 (40%)0
AsteraceaeITS_matKGenusRegional2020 (100%)000
AsteraceaeITS_matKGenusOLFS209 (45%)011 (55%)0
AsteraceaeITS_matKSpeciesGenBank202 (10%)6 (30%)12 (60%)0
AsteraceaeITS_matKSpeciesRegional2011 (55%)7 (35%)2 (10%)0
AsteraceaeITS_matKSpeciesOLFS208 (40%)1 (5%)11 (55%)0
AsteraceaeITS_rbcLaFamilyGenBank2421 (87.5%)3 (12.5%)00
AsteraceaeITS_rbcLaFamilyRegional2424 (100%)000
AsteraceaeITS_rbcLaFamilyOLFS2424 (100%)000
AsteraceaeITS_rbcLaGenusGenBank2419 (79.17%)4 (16.67%)1 (4.17%)0
AsteraceaeITS_rbcLaGenusRegional2423 (95.83%)1 (4.17%)00
AsteraceaeITS_rbcLaGenusOLFS2422 (91.67%)1 (4.17%)1 (4.17%)0
AsteraceaeITS_rbcLaSpeciesGenBank246 (25%)13 (54.17%)5 (20.83%)0
AsteraceaeITS_rbcLaSpeciesRegional2413 (54.17%)9 (37.5%)2 (8.33%)0
AsteraceaeITS_rbcLaSpeciesOLFS2419 (79.17%)3 (12.5%)2 (8.33%)0
AsteraceaematK_rbcLaFamilyGenBank3535 (100%)000
AsteraceaematK_rbcLaFamilyRegional3533 (94.29%)2 (5.71%)00
AsteraceaematK_rbcLaFamilyOLFS3535 (100%)000
AsteraceaematK_rbcLaGenusGenBank358 (22.86%)8 (22.86%)19 (54.29%)0
AsteraceaematK_rbcLaGenusRegional3516 (45.71%)17 (48.57%)2 (5.71%)0
AsteraceaematK_rbcLaGenusOLFS3527 (77.14%)3 (8.57%)5 (14.29%)0
AsteraceaematK_rbcLaSpeciesGenBank353 (8.57%)4 (11.43%)28 (80%)0
AsteraceaematK_rbcLaSpeciesRegional354 (11.43%)19 (54.29%)12 (34.29%)0
AsteraceaematK_rbcLaSpeciesOLFS3512 (34.29%)15 (42.86%)8 (22.86%)0
AsteraceaematK_trnHFamilyGenBank2424 (100%)000
AsteraceaematK_trnHFamilyRegional2424 (100%)000
AsteraceaematK_trnHFamilyOLFS2424 (100%)000
AsteraceaematK_trnHGenusGenBank2414 (58.33%)010 (41.67%)0
AsteraceaematK_trnHGenusRegional2416 (66.67%)1 (4.17%)7 (29.17%)0
AsteraceaematK_trnHGenusOLFS2410 (41.67%)014 (58.33%)0
AsteraceaematK_trnHSpeciesGenBank246 (25%)1 (4.17%)17 (70.83%)0
AsteraceaematK_trnHSpeciesRegional246 (25%)1 (4.17%)17 (70.83%)0
AsteraceaematK_trnHSpeciesOLFS248 (33.33%)016 (66.67%)0
AsteraceaerbcLa_trnHFamilyGenBank2929 (100%)000
AsteraceaerbcLa_trnHFamilyRegional2929 (100%)000
AsteraceaerbcLa_trnHFamilyOLFS2929 (100%)000
AsteraceaerbcLa_trnHGenusGenBank2922 (75.86%)4 (13.79%)3 (10.34%)0
AsteraceaerbcLa_trnHGenusRegional2921 (72.41%)5 (17.24%)3 (10.34%)0
AsteraceaerbcLa_trnHGenusOLFS2920 (68.97%)1 (3.45%)8 (27.59%)0
AsteraceaerbcLa_trnHSpeciesGenBank298 (27.59%)5 (17.24%)16 (55.17%)0
AsteraceaerbcLa_trnHSpeciesRegional298 (27.59%)5 (17.24%)16 (55.17%)0
AsteraceaerbcLa_trnHSpeciesOLFS2913 (44.83%)7 (24.14%)9 (31.03%)0
AsteraceaeITS_matK_rbcLaFamilyGenBank2019 (95%)1 (5%)00
AsteraceaeITS_matK_rbcLaFamilyRegional2020 (100%)000
AsteraceaeITS_matK_rbcLaFamilyOLFS2020 (100%)000
AsteraceaeITS_matK_rbcLaGenusGenBank209 (45%)4 (20%)7 (35%)0
AsteraceaeITS_matK_rbcLaGenusRegional2020 (100%)000
AsteraceaeITS_matK_rbcLaGenusOLFS2019 (95%)01 (5%)0
AsteraceaeITS_matK_rbcLaSpeciesGenBank202 (10%)6 (30%)12 (60%)0
AsteraceaeITS_matK_rbcLaSpeciesRegional2011 (55%)7 (35%)2 (10%)0
AsteraceaeITS_matK_rbcLaSpeciesOLFS2017 (85%)1 (5%)2 (10%)0
AsteraceaeITS_matK_trnHFamilyGenBank1717 (100%)000
AsteraceaeITS_matK_trnHFamilyRegional1717 (100%)000
AsteraceaeITS_matK_trnHFamilyOLFS1717 (100%)000
AsteraceaeITS_matK_trnHGenusGenBank1711 (64.71%)06 (35.29%)0
AsteraceaeITS_matK_trnHGenusRegional1715 (88.24%)1 (5.88%)1 (5.88%)0
AsteraceaeITS_matK_trnHGenusOLFS177 (41.18%)010 (58.82%)0
AsteraceaeITS_matK_trnHSpeciesGenBank174 (23.53%)1 (5.88%)12 (70.59%)0
AsteraceaeITS_matK_trnHSpeciesRegional176 (35.29%)4 (23.53%)7 (41.18%)0
AsteraceaeITS_matK_trnHSpeciesOLFS176 (35.29%)011 (64.71%)0
AsteraceaeITS_rbcLa_trnHFamilyGenBank2020 (100%)000
AsteraceaeITS_rbcLa_trnHFamilyRegional2020 (100%)000
AsteraceaeITS_rbcLa_trnHFamilyOLFS2020 (100%)000
AsteraceaeITS_rbcLa_trnHGenusGenBank2016 (80%)1 (5%)3 (15%)0
AsteraceaeITS_rbcLa_trnHGenusRegional2017 (85%)1 (5%)2 (10%)0
AsteraceaeITS_rbcLa_trnHGenusOLFS2016 (80%)1 (5%)3 (15%)0
AsteraceaeITS_rbcLa_trnHSpeciesGenBank205 (25%)2 (10%)13 (65%)0
AsteraceaeITS_rbcLa_trnHSpeciesRegional207 (35%)4 (20%)9 (45%)0
AsteraceaeITS_rbcLa_trnHSpeciesOLFS2013 (65%)2 (10%)5 (25%)0
AsteraceaematK_rbcLa_trnHFamilyGenBank2424 (100%)000
AsteraceaematK_rbcLa_trnHFamilyRegional2424 (100%)000
AsteraceaematK_rbcLa_trnHFamilyOLFS2424 (100%)000
AsteraceaematK_rbcLa_trnHGenusGenBank2415 (62.5%)1 (4.17%)8 (33.33%)0
AsteraceaematK_rbcLa_trnHGenusRegional2417 (70.83%)5 (20.83%)2 (8.33%)0
AsteraceaematK_rbcLa_trnHGenusOLFS2418 (75%)1 (4.17%)5 (20.83%)0
AsteraceaematK_rbcLa_trnHSpeciesGenBank247 (29.17%)2 (8.33%)15 (62.5%)0
AsteraceaematK_rbcLa_trnHSpeciesRegional247 (29.17%)4 (16.67%)13 (54.17%)0
AsteraceaematK_rbcLa_trnHSpeciesOLFS2411 (45.83%)7 (29.17%)6 (25%)0
AsteraceaeITS_matK_rbcLa_trnHFamilyGenBank1717 (100%)000
AsteraceaeITS_matK_rbcLa_trnHFamilyRegional1717 (100%)000
AsteraceaeITS_matK_rbcLa_trnHFamilyOLFS1717 (100%)000
AsteraceaeITS_matK_rbcLa_trnHGenusGenBank1713 (76.47%)04 (23.53%)0
AsteraceaeITS_matK_rbcLa_trnHGenusRegional1716 (94.12%)01 (5.88%)0
AsteraceaeITS_matK_rbcLa_trnHGenusOLFS1715 (88.24%)02 (11.76%)0
AsteraceaeITS_matK_rbcLa_trnHSpeciesGenBank175 (29.41%)1 (5.88%)11 (64.71%)0
AsteraceaeITS_matK_rbcLa_trnHSpeciesRegional177 (41.18%)3 (17.65%)7 (41.18%)0
AsteraceaeITS_matK_rbcLa_trnHSpeciesOLFS1713 (76.47%)04 (23.53%)0
CyperaceaeITSFamilyGenBank76 (85.71%)1 (14.29%)00
CyperaceaeITSFamilyRegional77 (100%)000
CyperaceaeITSFamilyOLFS77 (100%)000
CyperaceaeITSGenusGenBank76 (85.71%)1 (14.29%)00
CyperaceaeITSGenusRegional77 (100%)000
CyperaceaeITSGenusOLFS77 (100%)000
CyperaceaeITSSpeciesGenBank71 (14.29%)2 (28.57%)4 (57.14%)0
CyperaceaeITSSpeciesRegional72 (28.57%)2 (28.57%)3 (42.86%)0
CyperaceaeITSSpeciesOLFS74 (57.14%)1 (14.29%)2 (28.57%)0
CyperaceaetrnHFamilyGenBank52 (40%)03 (60%)0
CyperaceaetrnHFamilyRegional52 (40%)03 (60%)0
CyperaceaetrnHFamilyOLFS5004 (80%)1 (20%)
CyperaceaetrnHGenusGenBank52 (40%)03 (60%)0
CyperaceaetrnHGenusRegional52 (40%)03 (60%)0
CyperaceaetrnHGenusOLFS5004 (80%)1 (20%)
CyperaceaetrnHSpeciesGenBank501 (20%)4 (80%)0
CyperaceaetrnHSpeciesRegional5005 (100%)0
CyperaceaetrnHSpeciesOLFS5004 (80%)1 (20%)
CyperaceaematKFamilyGenBank1001 (100%)0
CyperaceaematKFamilyRegional1001 (100%)0
CyperaceaematKFamilyOLFS1001 (100%)0
CyperaceaematKGenusGenBank1001 (100%)0
CyperaceaematKGenusRegional1001 (100%)0
CyperaceaematKGenusOLFS1001 (100%)0
CyperaceaematKSpeciesGenBank1001 (100%)0
CyperaceaematKSpeciesRegional1001 (100%)0
CyperaceaematKSpeciesOLFS1001 (100%)0
CyperaceaerbcLaFamilyGenBank2423 (95.83%)01 (4.17%)0
CyperaceaerbcLaFamilyRegional2423 (95.83%)01 (4.17%)0
CyperaceaerbcLaFamilyOLFS2423 (95.83%)01 (4.17%)0
CyperaceaerbcLaGenusGenBank2422 (91.67%)1 (4.17%)1 (4.17%)0
CyperaceaerbcLaGenusRegional2422 (91.67%)1 (4.17%)1 (4.17%)0
CyperaceaerbcLaGenusOLFS2423 (95.83%)01 (4.17%)0
CyperaceaerbcLaSpeciesGenBank2405 (20.83%)19 (79.17%)0
CyperaceaerbcLaSpeciesRegional242 (8.33%)6 (25%)16 (66.67%)0
CyperaceaerbcLaSpeciesOLFS248 (33.33%)3 (12.5%)13 (54.17%)0
CyperaceaeITS_trnHFamilyGenBank11 (100%)000
CyperaceaeITS_trnHFamilyRegional11 (100%)000
CyperaceaeITS_trnHFamilyOLFS11 (100%)000
CyperaceaeITS_trnHGenusGenBank11 (100%)000
CyperaceaeITS_trnHGenusRegional11 (100%)000
CyperaceaeITS_trnHGenusOLFS11 (100%)000
CyperaceaeITS_trnHSpeciesGenBank11 (100%)000
CyperaceaeITS_trnHSpeciesRegional11 (100%)000
CyperaceaeITS_trnHSpeciesOLFS11 (100%)000
CyperaceaeITS_matKFamilyGenBank11 (100%)000
CyperaceaeITS_matKFamilyRegional11 (100%)000
CyperaceaeITS_matKFamilyOLFS11 (100%)000
CyperaceaeITS_matKGenusGenBank11 (100%)000
CyperaceaeITS_matKGenusRegional11 (100%)000
CyperaceaeITS_matKGenusOLFS11 (100%)000
CyperaceaeITS_matKSpeciesGenBank11 (100%)000
CyperaceaeITS_matKSpeciesRegional11 (100%)000
CyperaceaeITS_matKSpeciesOLFS11 (100%)000
CyperaceaeITS_rbcLaFamilyGenBank76 (85.71%)1 (14.29%)00
CyperaceaeITS_rbcLaFamilyRegional77 (100%)000
CyperaceaeITS_rbcLaFamilyOLFS77 (100%)000
CyperaceaeITS_rbcLaGenusGenBank76 (85.71%)1 (14.29%)00
CyperaceaeITS_rbcLaGenusRegional77 (100%)000
CyperaceaeITS_rbcLaGenusOLFS77 (100%)000
CyperaceaeITS_rbcLaSpeciesGenBank71 (14.29%)2 (28.57%)4 (57.14%)0
CyperaceaeITS_rbcLaSpeciesRegional72 (28.57%)2 (28.57%)3 (42.86%)0
CyperaceaeITS_rbcLaSpeciesOLFS75 (71.43%)1 (14.29%)1 (14.29%)0
CyperaceaematK_rbcLaFamilyGenBank1001 (100%)0
CyperaceaematK_rbcLaFamilyRegional11 (100%)000
CyperaceaematK_rbcLaFamilyOLFS11 (100%)000
CyperaceaematK_rbcLaGenusGenBank1001 (100%)0
CyperaceaematK_rbcLaGenusRegional11 (100%)000
CyperaceaematK_rbcLaGenusOLFS11 (100%)000
CyperaceaematK_rbcLaSpeciesGenBank1001 (100%)0
CyperaceaematK_rbcLaSpeciesRegional1001 (100%)0
CyperaceaematK_rbcLaSpeciesOLFS1001 (100%)0
CyperaceaematK_trnHFamilyGenBank1001 (100%)0
CyperaceaematK_trnHFamilyRegional1001 (100%)0
CyperaceaematK_trnHFamilyOLFS1001 (100%)0
CyperaceaematK_trnHGenusGenBank1001 (100%)0
CyperaceaematK_trnHGenusRegional1001 (100%)0
CyperaceaematK_trnHGenusOLFS1001 (100%)0
CyperaceaematK_trnHSpeciesGenBank1001 (100%)0
CyperaceaematK_trnHSpeciesRegional1001 (100%)0
CyperaceaematK_trnHSpeciesOLFS1001 (100%)0
CyperaceaerbcLa_trnHFamilyGenBank42 (50%)02 (50%)0
CyperaceaerbcLa_trnHFamilyRegional43 (75%)01 (25%)0
CyperaceaerbcLa_trnHFamilyOLFS43 (75%)01 (25%)0
CyperaceaerbcLa_trnHGenusGenBank42 (50%)02 (50%)0
CyperaceaerbcLa_trnHGenusRegional43 (75%)01 (25%)0
CyperaceaerbcLa_trnHGenusOLFS43 (75%)01 (25%)0
CyperaceaerbcLa_trnHSpeciesGenBank401 (25%)3 (75%)0
CyperaceaerbcLa_trnHSpeciesRegional402 (50%)2 (50%)0
CyperaceaerbcLa_trnHSpeciesOLFS42 (50%)02 (50%)0
CyperaceaeITS_matK_rbcLaFamilyGenBank11 (100%)000
CyperaceaeITS_matK_rbcLaFamilyRegional11 (100%)000
CyperaceaeITS_matK_rbcLaFamilyOLFS11 (100%)000
CyperaceaeITS_matK_rbcLaGenusGenBank11 (100%)000
CyperaceaeITS_matK_rbcLaGenusRegional11 (100%)000
CyperaceaeITS_matK_rbcLaGenusOLFS11 (100%)000
CyperaceaeITS_matK_rbcLaSpeciesGenBank11 (100%)000
CyperaceaeITS_matK_rbcLaSpeciesRegional11 (100%)000
CyperaceaeITS_matK_rbcLaSpeciesOLFS11 (100%)000
CyperaceaeITS_matK_trnHFamilyGenBank11 (100%)000
CyperaceaeITS_matK_trnHFamilyRegional11 (100%)000
CyperaceaeITS_matK_trnHFamilyOLFS11 (100%)000
CyperaceaeITS_matK_trnHGenusGenBank11 (100%)000
CyperaceaeITS_matK_trnHGenusRegional11 (100%)000
CyperaceaeITS_matK_trnHGenusOLFS11 (100%)000
CyperaceaeITS_matK_trnHSpeciesGenBank11 (100%)000
CyperaceaeITS_matK_trnHSpeciesRegional11 (100%)000
CyperaceaeITS_matK_trnHSpeciesOLFS11 (100%)000
CyperaceaeITS_rbcLa_trnHFamilyGenBank11 (100%)000
CyperaceaeITS_rbcLa_trnHFamilyRegional11 (100%)000
CyperaceaeITS_rbcLa_trnHFamilyOLFS11 (100%)000
CyperaceaeITS_rbcLa_trnHGenusGenBank11 (100%)000
CyperaceaeITS_rbcLa_trnHGenusRegional11 (100%)000
CyperaceaeITS_rbcLa_trnHGenusOLFS11 (100%)000
CyperaceaeITS_rbcLa_trnHSpeciesGenBank11 (100%)000
CyperaceaeITS_rbcLa_trnHSpeciesRegional11 (100%)000
CyperaceaeITS_rbcLa_trnHSpeciesOLFS11 (100%)000
CyperaceaematK_rbcLa_trnHFamilyGenBank1001 (100%)0
CyperaceaematK_rbcLa_trnHFamilyRegional11 (100%)000
CyperaceaematK_rbcLa_trnHFamilyOLFS11 (100%)000
CyperaceaematK_rbcLa_trnHGenusGenBank1001 (100%)0
CyperaceaematK_rbcLa_trnHGenusRegional11 (100%)000
CyperaceaematK_rbcLa_trnHGenusOLFS11 (100%)000
CyperaceaematK_rbcLa_trnHSpeciesGenBank1001 (100%)0
CyperaceaematK_rbcLa_trnHSpeciesRegional1001 (100%)0
CyperaceaematK_rbcLa_trnHSpeciesOLFS1001 (100%)0
CyperaceaeITS_matK_rbcLa_trnHFamilyGenBank11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHFamilyRegional11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHFamilyOLFS11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHGenusGenBank11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHGenusRegional11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHGenusOLFS11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHSpeciesGenBank11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHSpeciesRegional11 (100%)000
CyperaceaeITS_matK_rbcLa_trnHSpeciesOLFS11 (100%)000
FabaceaeITSFamilyGenBank1514 (93.33%)1 (6.67%)00
FabaceaeITSFamilyRegional1514 (93.33%)01 (6.67%)0
FabaceaeITSFamilyOLFS1514 (93.33%)001 (6.67%)
FabaceaeITSGenusGenBank1511 (73.33%)1 (6.67%)3 (20%)0
FabaceaeITSGenusRegional1511 (73.33%)04 (26.67%)0
FabaceaeITSGenusOLFS1511 (73.33%)03 (20%)1 (6.67%)
FabaceaeITSSpeciesGenBank157 (46.67%)4 (26.67%)4 (26.67%)0
FabaceaeITSSpeciesRegional159 (60%)1 (6.67%)5 (33.33%)0
FabaceaeITSSpeciesOLFS159 (60%)1 (6.67%)4 (26.67%)1 (6.67%)
FabaceaetrnHFamilyGenBank44 (100%)000
FabaceaetrnHFamilyRegional44 (100%)000
FabaceaetrnHFamilyOLFS44 (100%)000
FabaceaetrnHGenusGenBank44 (100%)000
FabaceaetrnHGenusRegional4004 (100%)0
FabaceaetrnHGenusOLFS4004 (100%)0
FabaceaetrnHSpeciesGenBank4004 (100%)0
FabaceaetrnHSpeciesRegional4004 (100%)0
FabaceaetrnHSpeciesOLFS4004 (100%)0
FabaceaematKFamilyGenBank1313 (100%)000
FabaceaematKFamilyRegional1313 (100%)000
FabaceaematKFamilyOLFS1313 (100%)000
FabaceaematKGenusGenBank1311 (84.62%)02 (15.38%)0
FabaceaematKGenusRegional138 (61.54%)05 (38.46%)0
FabaceaematKGenusOLFS135 (38.46%)08 (61.54%)0
FabaceaematKSpeciesGenBank131 (7.69%)3 (23.08%)9 (69.23%)0
FabaceaematKSpeciesRegional132 (15.38%)011 (84.62%)0
FabaceaematKSpeciesOLFS133 (23.08%)010 (76.92%)0
FabaceaerbcLaFamilyGenBank2020 (100%)000
FabaceaerbcLaFamilyRegional2019 (95%)001 (5%)
FabaceaerbcLaFamilyOLFS2019 (95%)001 (5%)
FabaceaerbcLaGenusGenBank2011 (55%)6 (30%)3 (15%)0
FabaceaerbcLaGenusRegional2016 (80%)1 (5%)2 (10%)1 (5%)
FabaceaerbcLaGenusOLFS2016 (80%)1 (5%)2 (10%)1 (5%)
FabaceaerbcLaSpeciesGenBank202 (10%)13 (65%)5 (25%)0
FabaceaerbcLaSpeciesRegional205 (25%)10 (50%)4 (20%)1 (5%)
FabaceaerbcLaSpeciesOLFS208 (40%)8 (40%)3 (15%)1 (5%)
FabaceaeITS_trnHFamilyGenBank33 (100%)000
FabaceaeITS_trnHFamilyRegional33 (100%)000
FabaceaeITS_trnHFamilyOLFS33 (100%)000
FabaceaeITS_trnHGenusGenBank33 (100%)000
FabaceaeITS_trnHGenusRegional33 (100%)000
FabaceaeITS_trnHGenusOLFS33 (100%)000
FabaceaeITS_trnHSpeciesGenBank32 (66.67%)1 (33.33%)00
FabaceaeITS_trnHSpeciesRegional33 (100%)000
FabaceaeITS_trnHSpeciesOLFS33 (100%)000
FabaceaeITS_matKFamilyGenBank88 (100%)000
FabaceaeITS_matKFamilyRegional88 (100%)000
FabaceaeITS_matKFamilyOLFS88 (100%)000
FabaceaeITS_matKGenusGenBank86 (75%)02 (25%)0
FabaceaeITS_matKGenusRegional86 (75%)02 (25%)0
FabaceaeITS_matKGenusOLFS86 (75%)02 (25%)0
FabaceaeITS_matKSpeciesGenBank82 (25%)3 (37.5%)3 (37.5%)0
FabaceaeITS_matKSpeciesRegional84 (50%)1 (12.5%)3 (37.5%)0
FabaceaeITS_matKSpeciesOLFS84 (50%)1 (12.5%)3 (37.5%)0
FabaceaeITS_rbcLaFamilyGenBank1514 (93.33%)1 (6.67%)00
FabaceaeITS_rbcLaFamilyRegional1515 (100%)000
FabaceaeITS_rbcLaFamilyOLFS1515 (100%)000
FabaceaeITS_rbcLaGenusGenBank1511 (73.33%)1 (6.67%)3 (20%)0
FabaceaeITS_rbcLaGenusRegional1512 (80%)03 (20%)0
FabaceaeITS_rbcLaGenusOLFS1512 (80%)03 (20%)0
FabaceaeITS_rbcLaSpeciesGenBank156 (40%)5 (33.33%)4 (26.67%)0
FabaceaeITS_rbcLaSpeciesRegional1510 (66.67%)1 (6.67%)4 (26.67%)0
FabaceaeITS_rbcLaSpeciesOLFS1510 (66.67%)1 (6.67%)4 (26.67%)0
FabaceaematK_rbcLaFamilyGenBank1313 (100%)000
FabaceaematK_rbcLaFamilyRegional1313 (100%)000
FabaceaematK_rbcLaFamilyOLFS1313 (100%)000
FabaceaematK_rbcLaGenusGenBank1311 (84.62%)02 (15.38%)0
FabaceaematK_rbcLaGenusRegional1311 (84.62%)02 (15.38%)0
FabaceaematK_rbcLaGenusOLFS1311 (84.62%)02 (15.38%)0
FabaceaematK_rbcLaSpeciesGenBank132 (15.38%)5 (38.46%)6 (46.15%)0
FabaceaematK_rbcLaSpeciesRegional133 (23.08%)7 (53.85%)3 (23.08%)0
FabaceaematK_rbcLaSpeciesOLFS134 (30.77%)7 (53.85%)2 (15.38%)0
FabaceaematK_trnHFamilyGenBank33 (100%)000
FabaceaematK_trnHFamilyRegional33 (100%)000
FabaceaematK_trnHFamilyOLFS33 (100%)000
FabaceaematK_trnHGenusGenBank33 (100%)000
FabaceaematK_trnHGenusRegional33 (100%)000
FabaceaematK_trnHGenusOLFS31 (33.33%)02 (66.67%)0
FabaceaematK_trnHSpeciesGenBank3003 (100%)0
FabaceaematK_trnHSpeciesRegional3003 (100%)0
FabaceaematK_trnHSpeciesOLFS3003 (100%)0
FabaceaerbcLa_trnHFamilyGenBank44 (100%)000
FabaceaerbcLa_trnHFamilyRegional44 (100%)000
FabaceaerbcLa_trnHFamilyOLFS44 (100%)000
FabaceaerbcLa_trnHGenusGenBank44 (100%)000
FabaceaerbcLa_trnHGenusRegional44 (100%)000
FabaceaerbcLa_trnHGenusOLFS44 (100%)000
FabaceaerbcLa_trnHSpeciesGenBank4004 (100%)0
FabaceaerbcLa_trnHSpeciesRegional42 (50%)2 (50%)00
FabaceaerbcLa_trnHSpeciesOLFS42 (50%)2 (50%)00
FabaceaeITS_matK_rbcLaFamilyGenBank88 (100%)000
FabaceaeITS_matK_rbcLaFamilyRegional88 (100%)000
FabaceaeITS_matK_rbcLaFamilyOLFS88 (100%)000
FabaceaeITS_matK_rbcLaGenusGenBank86 (75%)02 (25%)0
FabaceaeITS_matK_rbcLaGenusRegional86 (75%)02 (25%)0
FabaceaeITS_matK_rbcLaGenusOLFS86 (75%)02 (25%)0
FabaceaeITS_matK_rbcLaSpeciesGenBank82 (25%)3 (37.5%)3 (37.5%)0
FabaceaeITS_matK_rbcLaSpeciesRegional84 (50%)1 (12.5%)3 (37.5%)0
FabaceaeITS_matK_rbcLaSpeciesOLFS84 (50%)1 (12.5%)3 (37.5%)0
FabaceaeITS_matK_trnHFamilyGenBank22 (100%)000
FabaceaeITS_matK_trnHFamilyRegional22 (100%)000
FabaceaeITS_matK_trnHFamilyOLFS22 (100%)000
FabaceaeITS_matK_trnHGenusGenBank22 (100%)000
FabaceaeITS_matK_trnHGenusRegional22 (100%)000
FabaceaeITS_matK_trnHGenusOLFS22 (100%)000
FabaceaeITS_matK_trnHSpeciesGenBank22 (100%)000
FabaceaeITS_matK_trnHSpeciesRegional22 (100%)000
FabaceaeITS_matK_trnHSpeciesOLFS22 (100%)000
FabaceaeITS_rbcLa_trnHFamilyGenBank33 (100%)000
FabaceaeITS_rbcLa_trnHFamilyRegional33 (100%)000
FabaceaeITS_rbcLa_trnHFamilyOLFS33 (100%)000
FabaceaeITS_rbcLa_trnHGenusGenBank33 (100%)000
FabaceaeITS_rbcLa_trnHGenusRegional33 (100%)000
FabaceaeITS_rbcLa_trnHGenusOLFS33 (100%)000
FabaceaeITS_rbcLa_trnHSpeciesGenBank32 (66.67%)1 (33.33%)00
FabaceaeITS_rbcLa_trnHSpeciesRegional33 (100%)000
FabaceaeITS_rbcLa_trnHSpeciesOLFS33 (100%)000
FabaceaematK_rbcLa_trnHFamilyGenBank33 (100%)000
FabaceaematK_rbcLa_trnHFamilyRegional33 (100%)000
FabaceaematK_rbcLa_trnHFamilyOLFS33 (100%)000
FabaceaematK_rbcLa_trnHGenusGenBank33 (100%)000
FabaceaematK_rbcLa_trnHGenusRegional33 (100%)000
FabaceaematK_rbcLa_trnHGenusOLFS33 (100%)000
FabaceaematK_rbcLa_trnHSpeciesGenBank3003 (100%)0
FabaceaematK_rbcLa_trnHSpeciesRegional31 (33.33%)2 (66.67%)00
FabaceaematK_rbcLa_trnHSpeciesOLFS31 (33.33%)2 (66.67%)00
FabaceaeITS_matK_rbcLa_trnHFamilyGenBank22 (100%)000
FabaceaeITS_matK_rbcLa_trnHFamilyRegional22 (100%)000
FabaceaeITS_matK_rbcLa_trnHFamilyOLFS22 (100%)000
FabaceaeITS_matK_rbcLa_trnHGenusGenBank22 (100%)000
FabaceaeITS_matK_rbcLa_trnHGenusRegional22 (100%)000
FabaceaeITS_matK_rbcLa_trnHGenusOLFS22 (100%)000
FabaceaeITS_matK_rbcLa_trnHSpeciesGenBank22 (100%)000
FabaceaeITS_matK_rbcLa_trnHSpeciesRegional22 (100%)000
FabaceaeITS_matK_rbcLa_trnHSpeciesOLFS22 (100%)000
PoaceaeITSFamilyGenBank2922 (75.86%)6 (20.69%)1 (3.45%)0
PoaceaeITSFamilyRegional2929 (100%)000
PoaceaeITSFamilyOLFS2929 (100%)000
PoaceaeITSGenusGenBank2915 (51.72%)7 (24.14%)7 (24.14%)0
PoaceaeITSGenusRegional2920 (68.97%)3 (10.34%)6 (20.69%)0
PoaceaeITSGenusOLFS2921 (72.41%)2 (6.9%)6 (20.69%)0
PoaceaeITSSpeciesGenBank294 (13.79%)8 (27.59%)17 (58.62%)0
PoaceaeITSSpeciesRegional2911 (37.93%)3 (10.34%)15 (51.72%)0
PoaceaeITSSpeciesOLFS2915 (51.72%)5 (17.24%)9 (31.03%)0
PoaceaetrnHFamilyGenBank3332 (96.97%)01 (3.03%)0
PoaceaetrnHFamilyRegional3332 (96.97%)01 (3.03%)0
PoaceaetrnHFamilyOLFS3332 (96.97%)01 (3.03%)0
PoaceaetrnHGenusGenBank3317 (51.52%)10 (30.3%)6 (18.18%)0
PoaceaetrnHGenusRegional3314 (42.42%)1 (3.03%)18 (54.55%)0
PoaceaetrnHGenusOLFS338 (24.24%)025 (75.76%)0
PoaceaetrnHSpeciesGenBank333 (9.09%)16 (48.48%)14 (42.42%)0
PoaceaetrnHSpeciesRegional332 (6.06%)3 (9.09%)28 (84.85%)0
PoaceaetrnHSpeciesOLFS337 (21.21%)026 (78.79%)0
PoaceaerbcLaFamilyGenBank3434 (100%)000
PoaceaerbcLaFamilyRegional3434 (100%)000
PoaceaerbcLaFamilyOLFS3434 (100%)000
PoaceaerbcLaGenusGenBank349 (26.47%)23 (67.65%)2 (5.88%)0
PoaceaerbcLaGenusRegional3414 (41.18%)16 (47.06%)4 (11.76%)0
PoaceaerbcLaGenusOLFS3424 (70.59%)6 (17.65%)4 (11.76%)0
PoaceaerbcLaSpeciesGenBank342 (5.88%)22 (64.71%)10 (29.41%)0
PoaceaerbcLaSpeciesRegional344 (11.76%)14 (41.18%)16 (47.06%)0
PoaceaerbcLaSpeciesOLFS3414 (41.18%)15 (44.12%)5 (14.71%)0
PoaceaeITS_trnHFamilyGenBank2721 (77.78%)5 (18.52%)1 (3.7%)0
PoaceaeITS_trnHFamilyRegional2727 (100%)000
PoaceaeITS_trnHFamilyOLFS2727 (100%)000
PoaceaeITS_trnHGenusGenBank2713 (48.15%)6 (22.22%)8 (29.63%)0
PoaceaeITS_trnHGenusRegional2718 (66.67%)2 (7.41%)7 (25.93%)0
PoaceaeITS_trnHGenusOLFS2718 (66.67%)1 (3.7%)8 (29.63%)0
PoaceaeITS_trnHSpeciesGenBank274 (14.81%)7 (25.93%)16 (59.26%)0
PoaceaeITS_trnHSpeciesRegional2712 (44.44%)2 (7.41%)13 (48.15%)0
PoaceaeITS_trnHSpeciesOLFS2713 (48.15%)2 (7.41%)12 (44.44%)0
PoaceaeITS_rbcLaFamilyGenBank2925 (86.21%)4 (13.79%)00
PoaceaeITS_rbcLaFamilyRegional2929 (100%)000
PoaceaeITS_rbcLaFamilyOLFS2929 (100%)000
PoaceaeITS_rbcLaGenusGenBank2914 (48.28%)12 (41.38%)3 (10.34%)0
PoaceaeITS_rbcLaGenusRegional2919 (65.52%)4 (13.79%)6 (20.69%)0
PoaceaeITS_rbcLaGenusOLFS2923 (79.31%)2 (6.9%)4 (13.79%)0
PoaceaeITS_rbcLaSpeciesGenBank294 (13.79%)12 (41.38%)13 (44.83%)0
PoaceaeITS_rbcLaSpeciesRegional299 (31.03%)4 (13.79%)16 (55.17%)0
PoaceaeITS_rbcLaSpeciesOLFS2916 (55.17%)6 (20.69%)7 (24.14%)0
PoaceaerbcLa_trnHFamilyGenBank3232 (100%)000
PoaceaerbcLa_trnHFamilyRegional3232 (100%)000
PoaceaerbcLa_trnHFamilyOLFS3232 (100%)000
PoaceaerbcLa_trnHGenusGenBank3213 (40.62%)13 (40.62%)6 (18.75%)0
PoaceaerbcLa_trnHGenusRegional3216 (50%)12 (37.5%)4 (12.5%)0
PoaceaerbcLa_trnHGenusOLFS3223 (71.88%)5 (15.62%)4 (12.5%)0
PoaceaerbcLa_trnHSpeciesGenBank324 (12.5%)16 (50%)12 (37.5%)0
PoaceaerbcLa_trnHSpeciesRegional325 (15.62%)14 (43.75%)13 (40.62%)0
PoaceaerbcLa_trnHSpeciesOLFS3216 (50%)10 (31.25%)6 (18.75%)0
PoaceaeITS_rbcLa_trnHFamilyGenBank2724 (88.89%)3 (11.11%)00
PoaceaeITS_rbcLa_trnHFamilyRegional2727 (100%)000
PoaceaeITS_rbcLa_trnHFamilyOLFS2727 (100%)000
PoaceaeITS_rbcLa_trnHGenusGenBank2712 (44.44%)9 (33.33%)6 (22.22%)0
PoaceaeITS_rbcLa_trnHGenusRegional2718 (66.67%)3 (11.11%)6 (22.22%)0
PoaceaeITS_rbcLa_trnHGenusOLFS2721 (77.78%)2 (7.41%)4 (14.81%)0
PoaceaeITS_rbcLa_trnHSpeciesGenBank274 (14.81%)11 (40.74%)12 (44.44%)0
PoaceaeITS_rbcLa_trnHSpeciesRegional2710 (37.04%)4 (14.81%)13 (48.15%)0
PoaceaeITS_rbcLa_trnHSpeciesOLFS2715 (55.56%)4 (14.81%)8 (29.63%)0
  38 in total

1.  Biological identifications through DNA barcodes.

Authors:  Paul D N Hebert; Alina Cywinska; Shelley L Ball; Jeremy R deWaard
Journal:  Proc Biol Sci       Date:  2003-02-07       Impact factor: 5.349

2.  Ultra-barcoding in cacao (Theobroma spp.; Malvaceae) using whole chloroplast genomes and nuclear ribosomal DNA.

Authors:  Nolan Kane; Saemundur Sveinsson; Hannes Dempewolf; Ji Yong Yang; Dapeng Zhang; Johannes M M Engels; Quentin Cronk
Journal:  Am J Bot       Date:  2012-02-01       Impact factor: 3.844

Review 3.  Biodiversity assessment: state-of-the-art techniques in phylogenomics and species identification.

Authors:  P Roxanne Steele; J Chris Pires
Journal:  Am J Bot       Date:  2011-02-25       Impact factor: 3.844

4.  Recent land use change in the Western Corn Belt threatens grasslands and wetlands.

Authors:  Christopher K Wright; Michael C Wimberly
Journal:  Proc Natl Acad Sci U S A       Date:  2013-02-19       Impact factor: 11.205

5.  Forensic evidence based on mtDNA from dog and wolf hairs.

Authors:  P Savolainen; J Lundeberg
Journal:  J Forensic Sci       Date:  1999-01       Impact factor: 1.832

6.  Testing the Efficacy of DNA Barcodes for Identifying the Vascular Plants of Canada.

Authors:  Thomas W A Braukmann; Maria L Kuzmina; Jesse Sills; Evgeny V Zakharov; Paul D N Hebert
Journal:  PLoS One       Date:  2017-01-10       Impact factor: 3.240

7.  Validation of the ITS2 region as a novel DNA barcode for identifying medicinal plant species.

Authors:  Shilin Chen; Hui Yao; Jianping Han; Chang Liu; Jingyuan Song; Linchun Shi; Yingjie Zhu; Xinye Ma; Ting Gao; Xiaohui Pang; Kun Luo; Ying Li; Xiwen Li; Xiaocheng Jia; Yulin Lin; Christine Leon
Journal:  PLoS One       Date:  2010-01-07       Impact factor: 3.240

8.  How effective are DNA barcodes in the identification of African rainforest trees?

Authors:  Ingrid Parmentier; Jérôme Duminil; Maria Kuzmina; Morgane Philippe; Duncan W Thomas; David Kenfack; George B Chuyong; Corinne Cruaud; Olivier J Hardy
Journal:  PLoS One       Date:  2013-04-02       Impact factor: 3.240

9.  How many loci does it take to DNA barcode a crocus?

Authors:  Ole Seberg; Gitte Petersen
Journal:  PLoS One       Date:  2009-02-25       Impact factor: 3.240

10.  New universal ITS2 primers for high-resolution herbivory analyses using DNA metabarcoding in both tropical and temperate zones.

Authors:  Rosemary J Moorhouse-Gann; Jenny C Dunn; Natasha de Vere; Martine Goder; Nik Cole; Helen Hipperson; William O C Symondson
Journal:  Sci Rep       Date:  2018-06-04       Impact factor: 4.379
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