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. 2026 Aug 4:e70073. Online ahead of print. doi: 10.1002/aps3.70073

Molecular methods for differentiating Phragmites australis subspecies and identification of hybrid stands in the Upper Midwestern United States

Nicholas P Tippery 1,2,✉, Daniel J Larkin 3, Julia Bohnen 3, Douglas L Wendell 4, Neil O Anderson 5, Susan M Galatowitsch 3, Marie Abbey 5, Alan G Smith 5, Andrzej K Noyszewski 5,6
PMCID: PMC13435300  PMID: 42553627

Abstract

Premise

In North America, Phragmites australis (common reed) has drawn a great deal of research attention. Non‐native P. australis subsp. australis is a noxious weed that has locally displaced native P. australis subsp. americanus in some areas. Although morphological features can distinguish the two subspecies, molecular tools often are required to confirm identifications. Additionally, the existence of natural intrasubspecific hybrids presents novel management challenges. Hybrid Phragmites is difficult to detect, and it has become standard practice to apply molecular tools to survey for hybrids.

Methods

We applied several molecular techniques—microsatellite, DArTseq (a type of genotyping‐by‐sequencing), restriction fragment length polymorphism (PCR‐RFLP), and next‐generation sequencing—to characterize P. australis at the landscape scale in Minnesota and Wisconsin and to search for hybrids.

Results

We obtained molecular data for Phragmites plants sampled from 341 stands, ultimately characterizing 98 stands as native and 236 as non‐native. Plants from two adjacent stands in Washington County, Minnesota, were confirmed to be hybrids.

Discussion

These are the first confirmed hybrids from the Upper Midwest/western Great Lakes region. We also discuss the relative cost and effectiveness of the various molecular methods and offer recommendations for future studies.

Keywords: barcoding, common reed, internal transcribed spacer, invasive species, molecular identification, PCR, Phragmites, restriction fragment length polymorphism

Wetlands are critically important ecological habitats worldwide, and invasive plant species are among the most urgent threats to wetland health (Lázaro‐Lobo and Ervin, 2021). In several cases, such as cattail (Typha L., Typhaceae; Travis et al., 2010), watermilfoil (Myriophyllum L., Haloragaceae; Moody and Les, 2007), and floating‐heart (Nymphoides Ség., Menyanthaceae; Harms et al., 2021), sympatry of native and invasive taxa has presented acute challenges for habitat management, such as the need to differentiate morphologically similar plants and identify hybrids (Larkin, 2024). The wetland grass Phragmites australis (Cav.) Trin. ex Steud. (common reed; Poaceae) occurs as both a native subspecies (P. australis subsp. americanus Saltonst., P.M.Peterson & Soreng) and a non‐native subspecies (P. australis subsp. australis) in North America (Saltonstall et al., 2004). The non‐native subspecies is regarded as a noxious invasive weed, and it has spread widely across the continent, displaced the native subspecies from portions of its historic range, altered wetland hydrology, reduced critical habitat for wildlife, and impacted biodiversity and ecosystem functioning across trophic levels (Chambers et al., 1999; Saltonstall, 2002; Able and Ragan, 2003; Windham and Meyerson, 2003; Gratton and Denno, 2006). Sympatry of native and non‐native Phragmites subspecies throughout much of North America further complicates management. Moreover, uncertainty about the origins of a given population can lead to delayed control responses to new invasions or, conversely, to overly aggressive treatments targeting native plants that are presumed to be invasive (Meyerson et al., 2010a; Hunt et al., 2017; Larkin, 2024). In addition, P. australis subsp. americanus × subsp. australis hybrids have been confirmed in several locations in North America, specifically in the Mid‐Atlantic, Northeastern, and Western United States (Saltonstall et al., 2014, 2016; Wu et al., 2015). Thus, it is important to screen for molecular methods that can improve the capacity to differentiate P. australis subspecies and identify hybrids among them, and to apply these tools in regions such as the Upper Midwest, where there is extensive sympatry but no known hybrids.

Potential for hybridization

The potential for hybridization between P. australis subsp. americanus and P. australis subsp. australis was first confirmed through experimental crosses (Meyerson et al., 2010b). Since then, wild hybrids have been genetically verified in Lake Erie (Paul et al., 2010), the Finger Lakes region of New York State (Saltonstall et al., 2014), Chesapeake Bay (Wu et al., 2015), and the Las Vegas, Nevada area (Saltonstall et al., 2016). Further investigation of Las Vegas hybrid plants revealed traits that could increase invasibility, including higher biomass, rhizome viability, and seed production (Williams et al., 2019). Given the role of hybridization as a stimulus for plant invasions in general (Schierenbeck and Ellstrand, 2009) and the exacerbating effects of heterosis (hybrid vigor) in other aquatic plant invasions, such as watermilfoil (LaRue et al., 2013; Glisson and Larkin, 2021), purple loosestrife (Lythrum salicaria L., Lythraceae; Shi et al., 2018), and cattail (Bansal et al., 2019), there is urgency for increased efforts to detect hybrid Phragmites.

Although P. australis subsp. americanus and P. australis subsp. australis widely co‐occur and can hybridize, the reasons for the apparent rarity of hybrids remain uncertain. Possible explanations include the separation of flowering timing, insufficient search effort in regions with high abundance of both subspecies, and limitations of commonly used molecular screening tools. Following the initial evidence for intersubspecific hybrids in North America (Paul et al., 2010; Lambertini et al., 2012), Meyerson et al. (2012) postulated that an increased search effort might uncover additional hybrids in three regions where native and non‐native Phragmites subspecies grow in sympatry: the Southwest, the West Coast, and the Great Lakes region of the United States. Since the time of that study, hybrids have been confirmed in the Southwest (Saltonstall et al., 2016) and the eastern Great Lakes Basin (Saltonstall et al., 2014). Meyerson et al. (2012) and Lambertini et al. (2012) also mentioned that studies using only microsatellite markers could fail to detect hybridization and recommended developing additional nuclear DNA markers, given that evidence for hybridization requires plants containing a combination of markers unique to the nuclear DNA of each respective subspecies.

The identification of a hybrid Phragmites population in the eastern Great Lakes region, combined with the relatively low effort that has been expended to detect hybrids in the western Great Lakes or Upper Midwestern United States, makes the latter regions a priority for hybrid surveillance. There have been prior studies in Illinois and Wisconsin, USA, that employed molecular identification across many localities and both subspecies (a combined total of 167 stands) and found no evidence of hybridization; however, both studies relied on microsatellite markers (Fant et al., 2016; Tippery et al., 2020). In contrast with some parts of North America, where the native subspecies has been nearly extirpated (Saltonstall, 2002), Wisconsin and neighboring Minnesota still have ample native Phragmites, along with expanding populations of invasive Phragmites (EDDMapS, 2025). In addition to unintentional introductions, both states also have constructed wastewater treatment “reed beds” for dewatering biosolids that were planted with P. australis subsp. australis, falsely presumed to only produce sterile seed (Minnesota Department of Agriculture, 2025).

Molecular data approaches

The native and invasive P. australis subspecies can be distinguished morphologically on the basis of stem, leaf sheath, and ligule characteristics, but such identification can be challenging for non‐experts. Resource managers in some regions prefer the confidence associated with molecular identification, and putative hybrids require molecular confirmation (Saltonstall et al., 2004; Meyerson et al., 2012). Several molecular methods have been developed to identify Phragmites subspecies and hybrids. These include PCR followed by sequencing or restriction digest, microsatellite markers, random amplified polymorphic DNA (RAPD), and amplified fragment length polymorphism (AFLP) (Chu et al., 2011; Lambertini et al., 2012). Recently, Wendell et al. (2025) introduced a relatively simple PCR‐restriction fragment length polymorphism (PCR‐RFLP) test that can distinguish native Phragmites subspecies and identify their F1 hybrids, thereby expanding the toolkit for hybrid detection.

An additional approach that, to our knowledge, has not been used to test for Phragmites hybridization is DArTseq, a high‐throughput genotyping‐by‐sequencing approach (Sansaloni et al., 2011). DArTseq uses restriction enzymes to reduce genomic complexity and then generates large numbers of single‐nucleotide polymorphisms (SNPs) via restriction fragment sequencing. This method has gained popularity as a cost‐effective approach for identifying genome‐wide molecular markers. It is also easy to use: researchers do not have to perform their own laboratory work, and bioinformatic analyses are accessible through an R package known as dartR (Gruber et al., 2018; R Core Team, 2025). In the context of invasive plant genetics, DArTseq analyses have shown that some Phalaris arundinacea L. (reed canarygrass; Poaceae) plants in Minnesota previously thought to be of non‐native European origin were native genotypes (Noyszewski et al., 2021). DArTseq also has been used to detect hybrids and determine geographic origins of European willow species (Salix L.; Salicaceae) (Vašut et al., 2024) and to investigate relationships among invasive knotweed species and hybrids in North America (Reynoutria Houtt.; Polygonaceae) (Drazan, 2022).

In other plant groups, the nuclear internal transcribed spacer (ITS) region has been a common source of molecular data for confirming hybrids between species and subspecies (e.g., Les et al., 2009; Tippery et al., 2024). Sequencing of the nuclear ITS region has been applied only sparingly in Phragmites, however, and reference sequences are available only for plants from China (Yao et al., 2017), Iraq (Al‐Shebly et al., unpublished; Hassan, unpublished), Korea (Lee et al., unpublished), and a single plant each from Canada (Telfer et al., 2015) and the United States (Hsiao et al., 1999). Preliminary efforts in the present study to amplify the ITS region in North American Phragmites plants encountered difficulties, perhaps explaining why so few sequences have been published to date. It is possible that the PCR primers that are conventionally used to amplify the ITS region in plants are a poor match for Phragmites, or data collection could be impaired by competing molecular signal from endophyte DNA (Clay et al., 2016) or complications due to polyploidy (Wang et al., 2021, 2024). If it were possible to sequence the ITS region in Phragmites, it would offer a relatively simple means of testing for hybridization.

In this study, we aimed to evaluate the relative merits of various molecular methods for identifying Phragmites subspecies and hybrids. We sampled 341 Phragmites stands across Minnesota and Wisconsin and applied multiple molecular methods to identify samples to subspecies and to detect evidence of hybrid genotypes. Because of the relative ease of obtaining DArTseq data, we used this method as an initial screen to identify putative hybrids, after which we applied PCR‐RFLP and PCR paired with next‐generation sequencing of the nuclear ITS region to confirm or reject hybrid status. Ultimately, we confirmed two adjacent stands of hybrid Phragmites in Minnesota (within a single wetland complex), the first known cases from the Upper Midwest.

METHODS

This study was designed to evaluate the relative effectiveness and efficiency of different methods for identifying Phragmites subspecies and hybrids. Four main methods were used. Our first approach was to analyze microsatellite data using methods originally developed by Saltonstall (2003a). The second strategy used the DArTseq method, which allowed for bulk sample processing and yielded genome‐wide evidence of genetic variability (Sansaloni et al., 2011). Following the microsatellite and DArTseq approaches, which were applied to the largest number of samples, we also applied two methods to confirm the presence of hybrids in a subset of samples. The first of these methods employed PCR‐RFLP techniques to visualize key differences among Phragmites haplotypes in the nuclear NRT2 and PaGT4 loci (Wendell et al., 2025) and the plastid trnT‐trnL and rbcL‐psaI gene regions (Saltonstall, 2002, 2003a). The second confirmation method involved amplifying and sequencing the nuclear ITS region with newly designed PCR primers, followed by next‐generation sequencing. The relative costs of each method, quantified in terms of both monetary expense and time investment, were estimated by the authors based on their prior experience with these methods.

Specimen collection

Tissue samples from Phragmites plants included in this study were collected between 2017 and 2024. Many of the sampled Phragmites stands were originally identified via a crowdsourcing effort initiated by the University of Minnesota (UMN) to assess the status of invasive Phragmites in Minnesota. Subsequent collection efforts by UMN and the Wisconsin Department of Natural Resources yielded many additional samples. Initial identification of specimens as either native or invasive Phragmites was based primarily on morphological characteristics of the stems, leaf sheaths, and ligules. Wisconsin voucher specimens were deposited in the University of Wisconsin–Whitewater (UWW) herbarium. A representative sample of the Minnesota voucher specimens was deposited in the UMN Herbarium at the Bell Museum (Appendix S1). Leaf tissue samples were placed in envelopes and stored dry prior to testing.

In total, we sampled 341 stands of Phragmites, primarily in Minnesota (252) and Wisconsin (72), in addition to reference material from a small number of stands in California (6), Michigan (1), Nevada (7), Utah (1), and Washington State (1), including previously documented P. australis subsp. americanus × subsp. australis hybrids (Saltonstall et al., 2016) (Appendix S1). Minnesota sampling localities were distributed throughout the state, representing each of the four Department of Natural Resources administrative units (https://www.dnr.state.mn.us/aboutdnr/regions.html): Central (67.6% of Minnesota samples), Northeast (5.0%), Northwest (10.8%), and Southern (16.6%). The Central unit includes the Minneapolis–St. Paul metropolitan region, where approximately 75% of documented invasive Phragmites stands in the state are located (EDDMapS, 2025). Stands were defined as noncontiguous patches of Phragmites that were separated from one another by at least 1 km or by an anthropogenic structure such as a highway. In addition to the Minnesota and Wisconsin samples, we also included, as controls, tissue samples for plants from Nevada, California, Michigan, Utah, and Washington State that had been previously confirmed as native, non‐native, or hybrid using DNA evidence (Saltonstall et al., 2016; Williams et al., 2019).

Microsatellites

Novel microsatellite data were generated using the methods reported by Tippery et al. (2020). Specifically, six loci (PaGT4, PaGT8, PaGT11, PaGT13, PaGT14, PaGT16) were amplified using original primers (Saltonstall, 2003b), and an additional two loci (PaGT9 and PaGT12) were amplified using modified primers (Meyerson et al., 2010b). Out of the 10 microsatellite loci that were published originally (Saltonstall, 2003b; Meyerson et al., 2010b), three loci (PaGT4, PaGT9, PaGT16) have alleles that are diagnostic for distinguishing between native and non‐native subspecies in the study region (Tippery et al., 2020), and these were targeted preferentially. An oligonucleotide “tail” sequence was appended to each forward primer so that fluorescently labeled DNA probes could anneal to amplicons during PCR (Culley et al., 2013).

PCR reactions were run using the Phire Hot Start II DNA Polymerase (Thermo Fisher Scientific, Waltham, Massachusetts, USA), with an initial denaturation step of 98°C for 30 s followed by 40 cycles of 98°C for 5 s, 55°C for 5 s, and 72°C for 25 s, and a final extension step of 72°C for 60 s. Samples were then diluted 1:10 in water, and 1.0 μL of the dilution was combined with 16 μL of Hi‐Di formamide (Thermo Fisher Scientific) and 0.08 μL of GeneScan 500 LIZ (Thermo Fisher Scientific) size standard. Fragment sizes were determined using an ABI 3730xl DNA Analyzer (Thermo Fisher Scientific) and scored using the Fragman package ver. 1.0.9 (Covarrubias‐Pazaran et al., 2016) in R.

Newly obtained microsatellite data were analyzed together with prior data for Wisconsin Phragmites plants (Tippery et al., 2020). The combined microsatellite data for Phragmites plants in Minnesota and Wisconsin represented 775 individuals from 112 stands (mean: 6.9 individuals per stand). Genetic diversity among stands was evaluated using principal component analysis (PCA) via the dudi.pca function in the ade4 package ver. 1.7.23 for R (Dray and Dufour, 2007), after converting data into the genind format using the df2genind function from the adegenet package ver. 2.1.11 (Jombart, 2008).

DArTseq

We submitted 1098 samples for DArTseq analysis. The plants were collected from 364 stands (Figure 1, Appendix S1), and between one and 27 individuals were analyzed from each stand (mean: 3.0 individuals per stand). Dried leaf samples were sent to Diversity Arrays Technology (Canberra, Australia) for DNA extraction and processing. DNA was analyzed using the DArTseq proprietary genome‐scale sequencing method (Sansaloni et al., 2010, 2011) at the “low density” (LD) SNP coverage that has been used in a wide range of invasive plant genera, such as Phalaris (Noyszewski et al., 2021) and Reynoutria (Drazan, 2022).

Figure 1.

Figure 1

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Map of collection localities in Minnesota and Wisconsin. Colors and shapes indicate whether plants at each site were identified as native P. australis subsp. americanus (orange circles), non‐native P. australis subsp. australis (dark blue squares), a mixture of native and non‐native plants in the same locality (light blue diamonds), or hybrid plants (green triangles). Shapes are also identified with text referring to the stand identifiers in Appendix S1. Maps are depicted using the U.S. National Atlas Equal Area projection (EPSG: 9311; https://epsg.io/). (A) Overview of sites in both states. Background shading indicates administrative regions for the Minnesota Department of Natural Resources (https://www.dnr.state.mn.us/aboutdnr/regions.html) and Wisconsin Department of Natural Resources (https://data-wi-dnr.opendata.arcgis.com/datasets/dnr-regions/about). The base map was built using country and provincial boundaries from geoBoundaries (https://www.geoboundaries.org/; Runfola et al., 2020), U.S. county boundaries are from the 2024 Cartographic Boundary Files available from the United States Census Bureau (https://www.census.gov/geographies/mapping-files/time-series/geo/cartographic-boundary.html), and water body boundaries were obtained from the World Wildlife Fund Global Lakes and Wetlands Database (https://www.worldwildlife.org/our-work/science/global-lakes-and-wetlands-database/). Maps were visualized using the packages sf ver. 1.0.21 (Pebesma, 2018; Pebesma and Bivand, 2023) and ggplot2 ver. 3.5.2 (Wickham, 2016), with scale bar added via the annotation_scale function in the ggspatial package ver. 1.1.10 (Dunnington, 2025). (B) Detailed map of the Twin Cities in Minnesota (Minneapolis and Saint Paul), showing more precisely the locality of newly determined hybrids. Hybrid stands P419 and P420 are located at the same highway intersection, on the east and west sides of the highway, respectively. Highway data were obtained from the United States Census Bureau TIGER/Line Shapefile, 2023, State, Minnesota, Primary and Secondary Roads (https://catalog.data.gov/dataset/tiger-line-shapefile-2023-state-minnesota-primary-and-secondary-roads).

The DArTseq method generated raw data for 15,666 SNPs. The gl.report.callrate and gl.filter.callrate functions in the dartR package ver. 2.9.9.5 in R (Gruber et al., 2018; Mijangos et al., 2022) were used to select and apply an optimal “call rate” (i.e., the proportion of samples for which the genotype call is non‐missing). This was achieved by examining histograms of loci and individuals and selecting a call rate that retained a large number of individuals while excluding low‐quality loci. After removing loci and individuals having call rates <0.6 (i.e., >40% missing data), the number of SNPs decreased from 15,666 to 7707 and the number of individuals decreased from 1098 to 956.

Genetic diversity among stands was evaluated using PCA via the gl.pcoa function in the dartR package, which employs functions developed for the adegenet package (Jombart, 2008; Jombart et al., 2010; Jombart and Ahmed, 2011). Following PCA, individuals were classified as subspecies or hybrids, and these categories were treated as “populations” in an analysis to determine the number of private alleles, using the gl.report.pa function in dartR. In pairwise comparisons among “populations,” private alleles are defined as being present in only one population and not the other (Hinds et al., 2005). The gl.report.pa function also reports the allele frequency differences between pairs of “populations” (Berner, 2019). Observed heterozygosity within each “population” was determined using the gl.report.heterozygosity function in dartR.

PCR‐RFLP of nuclear markers

Following DArTseq analysis, we selected two individuals from each of 29 stands that had been identified as potential hybrids for additional molecular testing via genotyping using the PCR‐RFLP method. For PCR‐RFLP, DNA was purified from Phragmites samples using a NucleoSpin Plant II Mini kit (Macherey‐Nagel, Allentown, Pennsylvania, USA). Genotypes for two previously identified nuclear DNA polymorphisms that differentiate P. australis subsp. americanus from other subspecies, namely an indel in the NRT2 gene that can be cut by the restriction enzyme XapI in plants of the native subspecies (Wendell et al., 2021) and a SNP linked to the PaGT4 microsatellite that can be cut by the restriction enzyme BstUI in plants of the native subspecies, were assayed by PCR‐RFLP as reported previously (Wendell et al., 2025).

Plastid haplotypes

Plastid haplotypes of the samples that were identified as hybrids by PCR‐RFLP were determined by sequencing the trnT‐trnL and rbcL‐psaI spacer regions of the plastid genome and comparing them against non‐hybrid reference sequences (Saltonstall et al., 2016). Plastid data are generally interpreted to reflect the maternal lineage of inheritance, although heteroplasmy (i.e., more than one plastid sequence type) has been documented in Phragmites (Lambertini, 2016). Target sequences were amplified by PCR using Invitrogen Platinum SuperFi II Green PCR Master Mix (Thermo Fisher Scientific) with the primers trnT‐Pa and trnL‐Pa for the trnT‐trnL region and rbcL‐Pa and psaI‐Pa for the rbcL‐psaI region (Wendell et al., 2025). Amplicons were treated with ExoSAP‐IT Express reagent (Thermo Fisher Scientific) followed by sequencing reactions using the BigDye Terminator v. 1.1 Cycle Sequencing Kit (Thermo Fisher Scientific). Sequences were determined using a SeqStudio Genetic Analyzer (Thermo Fisher Scientific).

Nuclear ITS sequencing

In addition to using the PCR‐RFLP method, we also applied PCR and sequencing of the ITS region for the same group of suspected hybrids. We sequenced the ITS region of 41 Phragmites individuals, including suspected hybrids and control plants that had been confirmed previously as P. australis subsp. americanus, P. australis subsp. australis, hybrids between these two subspecies, or P. australis subsp. berlandieri (E.Fourn.) Saltonst. & Hauber. Initial attempts to sequence the nuclear ITS region in Phragmites plants using conventional PCR primers (White et al., 1990; Hsiao et al., 1999; Cheng et al., 2016) were unsuccessful. Therefore, novel primers were designed using published sequences of Poaceae for reference (Lee et al., 2015): PhragITS2R (5′‐GAACAACTCRTGTYGCCCACC‐3′), PhragITS3F (5′‐AAATGCGATACCTGGTGTGAATTGC‐3′), PhragITS4R (5′‐ATTCACACCAGGTATCGCATTTTGC‐3′), and PhragITS5F (5′‐GGATCATTGTYGTGACCCTGACC‐3′). Ultimately, the primer pair PhragITS5F/PhragITS2R was used for most samples. PCR reactions used the Phire Hot Start II DNA Polymerase (Thermo Fisher Scientific), with an initial denaturation step of 98°C for 30 s followed by 35 cycles of 98°C for 5 s, 55°C for 5 s, and 72°C for 25 s, and a final extension step of 72°C for 60 s. PCR products were cleaned using 0.1 volumes of 3 M ammonium acetate and four volumes of 100% ethanol, followed by a single wash with 80% ethanol. Next‐generation sequencing was conducted by Eurofins Genomics using Oxford Nanopore Technologies (Oxford, United Kingdom) with the Ligation Sequencing Kit V14 followed by primer‐free sequencing via the Flongle Flow Cell R10.4.1. Sequences were assembled into contigs using Geneious Prime ver. 2025.1.3 (Biomatters, Auckland, New Zealand). Contigs and previously published Phragmites sequences (GenBank accession numbers AF019810, FJ766175, HQ6000491, HQ600499, KP057062–KP057069, KP711061, KX282324–KX282325, LC085881, MF063629, MH703315, MZ753933, ON107262, ON885250–ON885252, OP600622) were aligned together using the pairwise alignment tool in Mesquite ver. 4.02 (Maddison and Maddison, 2025).

Prior to phylogenetic analysis, the data matrix was trimmed to include a representative subset of samples (i.e., by removing some sequences that were identical to others). Phylogenetic analysis of the nuclear ITS sequence alignment was conducted in BEAST ver. 1.10.4 (Suchard et al., 2018) with the HKY model of evolution (Hasegawa et al., 1985), using 10 million generations of Markov chain Monte Carlo (MCMC), sampled every 5000 generations, and trees were summarized after the first 25% of trees were discarded as burn‐in. Relationships among sequences were also visualized using the program SplitsTree ver. 6.3.25 (Huson and Bryant, 2006), using uncorrected p‐distances and the neighbor‐net method (Bryant and Moulton, 2004). The phylogeny was rooted using a sequence of Molinia caerulea (L.) Moench (GenBank MT796500), which was determined to be among the most similar sequences outside of the genus Phragmites.

RESULTS

Microsatellites

Of the eight microsatellite loci considered in this study, individuals were represented by a mean of 6.8 loci. For any one locus, the data completeness ranged from 66–95%. The PCA of microsatellite data differentiated P. australis subsp. americanus samples from P. australis subsp. australis on the first principal component axis (PC1) (Figure 2). PC1 explained 47% of variance among samples, and PC2 explained 9.3% of variance. Samples from Minnesota and Wisconsin were intermingled, showing no obvious genetic differentiation by state. Several individuals occupied an intermediate space between native and non‐native subspecies, potentially consistent with them being intersubspecific hybrids. However, these individuals, particularly samples identified as P. australis subsp. americanus, had a larger proportion of missing data (Figure 2).

Figure 2.

Figure 2

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Principal component analysis of microsatellite data. Samples are colored by subspecies, with shapes indicating the state in the United States where they were collected. Shapes with a gray circle behind them represent individuals lacking data for more than four loci.

DArTseq

PCA of DArTseq data also separated plants by subspecies on the PC1 axis (Figure 3). The majority of genetic variation (66%) was attributable to PC1, whereas PC2 accounted for 1.9% of the variation. The samples of previously identified hybrids that were included in our analysis fell near the midpoint of the PC1 axis, consistent with patterns that were observed previously in PCA of microsatellite data (Paul et al., 2010; Saltonstall et al., 2014, 2016). Samples from Washington County, Minnesota (P419 and P420; Appendix S1), were similarly located in a central position on the PC1 axis, distinct from samples of P. australis subsp. australis or P. australis subsp. americanus plants. Based on their proximity to previously identified hybrids from Nevada and their distance from plants of the two subspecies, plants with intermediate PC1 values (i.e., greater than 0 and less than 20) were subsequently identified as hybrids. After making determinations based on PC1 values, 247 individuals were identified as P. australis subsp. americanus, 688 as P. australis subsp. australis, and 11 as hybrids. The hybrids included seven previously identified hybrids from Nevada (Saltonstall et al., 2016) and four newly determined hybrids from Washington County, Minnesota.

Figure 3.

Figure 3

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Principal component analysis of DArTseq data. Samples are colored by subspecies, with shapes indicating the state in the United States where they were collected. Samples identified as hybrids include previously determined hybrid individuals from Nevada and newly identified hybrids from Minnesota.

The private allele analysis identified 1055 alleles that were private to P. australis subsp. americanus and 3529 private to P. australis subsp. australis. Five alleles were private to plants identified as hybrids. Observed heterozygosity was 0.012 among P. australis subsp. americanus plants, 0.069 among P. australis subsp. australis plants, and 0.257 among hybrids.

PCR‐RFLP of nuclear markers

Plants from two of the stands that were genotyped using PCR‐RFLP were heterozygous for alleles that are otherwise specific to P. australis subsp. americanus or P. australis subsp. australis. Both the NRT2Δ4 and the PaGT4 alleles were heterozygous for four individuals from Washington County, Minnesota (P419 and P420; Appendix S1), thus the plants were identified as hybrids. Other plants that originally were suspected of being hybrids based on DArTseq analysis were identified by PCR‐RFLP as homozygous for alleles of P. australis subsp. americanus. The potential hybrid status of these individuals had been identified during an initial evaluation of DArTseq data. Upon further review, these putative hybrid samples that were not confirmed as such also had low call rates, and therefore they were excluded from the more refined version of the DArTseq analysis.

Plastid haplotypes

Sequencing the plastid trnT‐trnL and rbcL‐psaI regions for the hybrid individuals from stands P419 and P420 produced sequences that were identical to the “M” haplotype that commonly occurs in North American P. australis subsp. australis (Saltonstall, 2003a). This result is consistent with the hybrids having the non‐native subspecies as their maternal parent. In contrast, a nearby stand identified as P. australis subsp. americanus (P418, Appendix S1) was attributable to haplotype E, the most prominent haplotype for native Phragmites (Saltonstall, 2003a). We did not detect any evidence of heteroplasmy in the analyzed samples (Lambertini, 2016).

Nuclear ITS sequencing

Next‐generation sequencing of the ITS region recovered between one and eight distinct sequence variants from any one individual. Read lengths from the Oxford Nanopore sequencing were between 250–600 nucleotides, with 50–250× coverage. Phylogenetic analysis placed the North American ITS sequences among plants that were previously identified as either P. australis or P. japonicus Steud. (Figure 4). The most common variants for P. australis plants were identified as ITS‐A, ITS‐H, ITS‐J, and ITS‐M (note that these letters were assigned arbitrarily and have no relationship with the haplotype codes used for plastid regions; Saltonstall, 2003a). Additionally, the sequence variants were divided into subtypes with codes ITS‐H1, ITS‐H2, etc. Plants that were identified as the native P. australis subsp. americanus in the PCR‐RFLP analysis yielded a single type of ITS sequence (the ITS‐A type), whereas up to six ITS variants were commonly encountered for non‐native P. australis subsp. australis (the ITS‐H, ITS‐J, and ITS‐M types). A small number of P. australis subsp. australis plants that had been confirmed to have the plastid haplotype P (Saltonstall, 2002) produced distinct ITS sequences that were distinct from the haplotype M plants, and these were labeled as ITS‐R, ITS‐S, and ITS‐W. ITS sequences for P. australis subsp. berlandieri plants also were distinct from all other sequences and were assigned the labels ITS‐V and ITS‐X.

Figure 4.

Figure 4

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Phylogeny of Phragmites, built using a representative sample of sequence diversity from the nuclear ITS region. Distinct sequence types are identified using letters (ITS‐A, ITS‐H, etc.), and “haplotype” refers to the plastid haplotype (Saltonstall, 2002). Newly obtained sequences are shown in boldface and use the identification codes that are listed in Appendix S1. Previously published sequences are shown with their GenBank accession numbers and countries of origin. Species are Phragmites australis (with subspecies indicated) unless indicated otherwise. One example of a newly determined hybrid (P420C) is included.

Native P. australis subsp. americanus plants with the ITS‐A sequence type were monophyletic, and the next most similar sequences were previously reported from Iraq and Korea. There were no published sequences that closely matched the newly obtained P. australis subsp. americanus sequences. The majority of sequences we obtained for P. australis subsp. australis were attributable to plastid haplotype M (Saltonstall, 2002) and resolved to three separate clades. For the most part, the sequences we recovered for P. australis subsp. americanus and P. australis subsp. australis were not monophyletic with Phragmites ITS sequences that had been reported previously; however, several sequences from Iraq (GenBank accession numbers ON885250–ON885252; Jameel and Kadhim, unpublished) were monophyletic with the ITS‐M type of newly obtained sequence. Previously reported sequences from Canada (GenBank KT695247; Telfer et al., 2015) and the United States (GenBank AF019810; Hsiao et al., 1999) were not closely related to any of the newly reported sequences.

In addition to plants identified as P. australis, the ITS phylogeny also includes previously published sequences identified as P. japonicus. The P. japonicus sequences were not reciprocally monophyletic with the P. australis sequences, regardless of how the tree might be rooted. The specimens that were identified by PCR‐RFLP analysis as hybrids (i.e., P419 and P420; Appendix S1) produced ITS sequences that matched those of the P. australis subsp. americanus sequence (type ITS‐A) as well as multiple P. australis subsp. australis sequence types (types ITS‐H and ITS‐J).

The SplitsTree diagram (Appendix S2) depicts an unrooted phylogeny of ITS sequence types and illustrates the degree of differentiation for each type. Some sequence variants (e.g., ITS‐M) are less differentiated from one another and from previously published sequences, whereas other sequence types (e.g., ITS‐H) are distantly related to any other kind of sequence. As shown on the ITS phylogeny, the ITS sequences for any plant type (e.g., P. australis subsp. australis haplotype M) are not monophyletic; instead, sequence variants across species and subspecies are intermingled. Among the newly generated ITS sequences, only the P. australis subsp. americanus plants were found to have a single type of sequence (i.e., the ITS‐A type), whereas we recovered two or three main variant types (e.g., ITS‐J, ITS‐M) and multiple subtypes (e.g., ITS‐J1, ITS‐J2) from all other Phragmites taxa.

DISCUSSION

We sampled Phragmites plants across Minnesota and Wisconsin and applied multiple molecular methods to identify samples to subspecies and search for evidence of hybrid genotypes. Ultimately, we confirmed the presence of two adjacent stands of hybrid Phragmites in Washington County, Minnesota, within the Twin Cities (Minneapolis–Saint Paul) metropolitan region. Because of their proximity, the otherwise low prevalence of hybrids, and the propensity for vegetative propagation in Phragmites (Brisson et al., 2010), it is likely that the two stands represent a single hybrid origin.

Molecular data have been instrumental for understanding Phragmites diversity and ecology in North America, and the methods for identifying subspecies and hybrids have evolved along with technological advancements that enable new kinds of data. The original evidence for native and non‐native subspecies relied on PCR‐RFLP analysis of plastid genes (Saltonstall, 2002). This evidence was later enriched through the development of microsatellite markers that provide information about nuclear loci and intrasubspecific variation (Saltonstall, 2003b). Evidence of hybridization in Phragmites requires molecular confirmation, and studies to date have largely relied on microsatellite data for this purpose (Meyerson et al., 2010b; Paul et al., 2010; Saltonstall et al., 2014, 2016; Wu et al., 2015). Prior studies in Illinois and Wisconsin have used microsatellite data to study genetic diversity and survey for hybrids, but no hybrids were located in these studies (Fant et al., 2016; Tippery et al., 2020). The Minnesota hybrids from the present study were not sampled in this prior work.

There have been significant recent advances in next‐generation sequencing, which can deliver thousands of variable nucleotide sites without a dramatic increase in time or effort compared with traditional methods (Egan et al., 2012). In this study, we investigated the DArTseq method as a potential tool for identifying specimens, confirming hybrids, and quantifying intrasubspecific variation. We also used next‐generation sequencing to obtain sequences for the ITS region following PCR. By doing so, we avoided some of the challenges that often accompany Sanger sequencing of polymorphic loci, such as failure to recover minor sequences or to cleanly isolate multiple sequences (Song et al., 2012).

Broad survey using DArTseq

The most extensive data in our study, in terms of both the amount of DNA sequence information and the number of sampled plants, were collected using the DArTseq method. DArTseq was selected because it enables the acquisition of data from thousands of variable nucleotide sites while requiring relatively little laboratory effort. Moreover, the monetary cost for a project involving several hundred samples was comparable to that of methods that evaluate 10 or fewer informative DNA loci per sample.

During this study, we observed the importance of filtering the DArTseq data by call rate to exclude loci and individuals that were represented by too few data for robust analysis. In preliminary analyses that included a larger proportion of raw data, more than 20 individuals were suspected of being intersubspecific hybrids; however, after filtering by call rate using the criteria described above, only the P419 and P420 plants remained consistent with hybrid status. Filtering by call rate has the advantage of removing low‐quality data and reducing false‐positive hybridization calls, but it also results in samples being excluded from the final analysis. Thus, it is important to obtain multiple replicates from each sampled stand and to include plant material of sufficient quality to support molecular work.

The DArTseq analysis provided evidence of hybridization via the PCA plot, which placed hybrid samples in an intermediate location between the P. australis subsp. americanus and P. australis subsp. australis points on the PC1 axis (Figure 3). The identification of plants as hybrids was also supported by including plants from a known hybrid stand identified by other researchers (Saltonstall et al., 2016). Subsequent examination of the allele data confirmed that hybrids were represented by a larger proportion of heterozygous SNPs, as would be expected.

The DArTseq method offers great opportunities for future study, beyond simply identifying subspecies and hybrids. With its ability to evaluate thousands of polymorphic loci across the genome, it vastly exceeds the small number of microsatellite loci that are available for Phragmites. Potential future applications of the DArTseq method could be to determine fine‐scale dynamics of invasive population introductions, to identify source localities in the native range of P. australis subsp. australis, and to evaluate population genetics of both native and non‐native plants. Population genetic considerations such as genetic diversity, introgression, and the relative reliance on sexual or asexual reproduction all could be studied using available tools (Mahaman Mourtala et al., 2025; Sina et al., 2025). The current DArTseq dataset for Phragmites could be used to investigate such factors, either on its own or following augmentation with additional samples from Eurasia or North America.

Confirmation via PCR‐RFLP

Genetic identification of Phragmites plants using PCR‐RFLP, which was the first method to be developed (Saltonstall, 2003a), remains one of the fastest and least expensive (Table 1). Originally developed for plastid gene regions (trnT‐trnL and rbcL‐psaI spacers), the PCR‐RFLP method has been expanded in recent years to also identify nuclear genotypes (Wendell et al., 2021, 2025). Development of PCR‐RFLP assays for two nuclear markers, the NRT2 gene (Wendell et al., 2021) and the PaGT4 microsatellite locus (Wendell et al., 2025), has enabled rapid identification of hybrids. Moreover, because the NRT2 and PaGT4 loci are located on different nuclear chromosomes (GenBank accession number PRJEB64034; Darwin Tree of Life Project Consortium, 2022; Wendell et al., 2025), they enable independent genetic confirmations and thereby provide a robust method for identifying hybrids or pure subspecies. In this study, we used PCR‐RFLP to confirm the hybrids that were suspected based on the DArTseq analysis and to assign maternal parentage, which was also verified using Sanger sequencing of plastid gene regions. There was no apparent evidence of heteroplasmy in any of the analyzed samples, but it is possible that the Sanger sequencing method was insufficient to detect sequence variants. Targeted methods such as cloning (Lambertini, 2016), quantitative PCR (Frey et al., 2005), or next‐generation sequencing (Lee et al., 2020) would be preferable if any samples were suspected of being heteroplasmic. Because of its relative simplicity and demonstrated efficacy in identifying subspecies and hybrids, the PCR‐RFLP method could be a viable alternative to more data‐intensive methods like microsatellites and DArTseq.

Table 1.

Approximate cost (in USD) and time to complete various types of genetic diversity analyses described in this study.a

Method Per‐sample consumables cost (plastics, reagents) Per‐sample analysis cost Total cost Per‐sample labor
PCR‐RFLP $20 Negligible $20 1 h
Plastid haplotype $20 $10 $30 1 h
Microsatellite $20 $5 $25 1 h
DArTseq $10 $10 $20 0.1 h
PCR and next‐generation sequencing $20 $20 $40 1 h
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a

Costs do not include laboratory setup expenses, such as equipment purchases (e.g., thermal cycler, centrifuge, electrophoresis rig). The estimated costs also do not include labor costs, but this figure could be calculated based on the required time. Labor investments typically decrease per sample when multiple samples are run concurrently, so the estimates are for batches of samples. Many analyses can be run in smaller batches (e.g., eight samples), whereas other methods (e.g., DArTseq) must be run in batches of 94, and much of the labor is done by the company providing the analysis. Analysis costs include off‐site labor.

Nuclear ITS sequence data

The nuclear ITS region is abundantly used across angiosperms to identify species and hybrids (e.g., Les et al., 2009; Tippery et al., 2024). However, there are surprisingly few ITS sequences reported for Phragmites, perhaps indicating that this region is less easily amplified and sequenced in the genus. Nevertheless, ITS sequences were available in GenBank for plants from Korea, Kuwait, Iraq, and one each from Canada and the United States. We reliably obtained ITS sequence data by designing new primers and using a next‐generation sequencing platform.

The phylogenetic perspective of ITS diversity in Phragmites is novel and merits further study. One facet that should be investigated is the presence of 2–8 types of ITS sequences in individuals of the non‐native subspecies. The ITS region is a desirable phylogenetic marker in many plant groups because multiple copies tend to homogenize into a single sequence type through concerted evolution (Álvarez and Wendel, 2003). When disparate copies exist, they can serve as evidence of recent hybridization (e.g., Les et al., 2009; Tippery et al., 2024). The relatively recent availability of next‐generation sequencing has made it possible to recover sequence variants that might have been overlooked in Sanger sequencing studies (Song et al., 2012). Our results for Phragmites are similar to those reported for other angiosperms, in which major and minor sequence variants were recovered across many plant groups (Song et al., 2012). The existence of not only ITS sequence variants, but also multiple subtypes within each variant group (e.g., ITS‐J1 and ITS‐J2) also deserves further consideration. The subtypes could function as alleles, yet we observed curiously low sequence variation among subtypes for the same major variant.

Initial attempts to sequence ITS using published PCR primers (ITSL + ITS4) yielded numerous bacterial and fungal sequences (data not shown); therefore, it was necessary to design primers that preferentially amplify the ITS region in Phragmites. Bacterial and fungal endophytes are widespread across plant taxa (Harrison and Griffin, 2020), and care should be taken to ensure they do not contaminate phylogenetic analyses.

The newly obtained ITS phylogeny of Phragmites does not enable a clear understanding of the evolution and inheritance of sequence variants. Nevertheless, it does offer an independent tool for identifying subspecies and hybrids. Some sequence variants may predate the ancestral split between P. australis and P. japonicus, as sequences attributed to these species are not reciprocally monophyletic. Moreover, the sequences recovered from various subspecies of P. australis were also not monophyletic. A previous study using AFLP already called into question the monophyly of P. australis and P. japonicus (Lambertini et al., 2006). Phragmites australis plants, including native and non‐native subspecies in North America, are commonly tetraploid (Clevering and Lissner, 1999; Hansen et al., 2007), and polyploidy potentially could confound efforts to reconstruct a phylogeny (Rothfels, 2021). The phylogenetic pattern of having multiple sequence variants and polyphyletic taxa is consistent with the evolution of gene copies that were generated through polyploidy (McKain et al., 2016), and it may be possible to achieve a clearer species‐ and subspecies‐level perspective by sequencing a larger number of nuclear gene regions (Wang et al., 2021, 2024).

Cost and efficiency

There are relative advantages and disadvantages to the various Phragmites identification methods presented herein, as a function of the costs of reagents and analyses and the time required for molecular analyses (Table 1). The approximate cost of conducting microsatellite analyses includes the costs of running multiple PCR reactions per sample. Potential cost savings include methods to run multiple alleles simultaneously in a “multiplex” PCR (Henegariu et al., 1997) and to use “tailed” primers that allow fewer fluorescent‐labeled primers to be purchased (Culley et al., 2013). Samples frequently can be analyzed in a 96‐well format, with each well containing the PCR products from multiple reactions. Nevertheless, the analysis cost for fluorescent‐labeled fragments is relatively low, and samples can be analyzed within one week.

Compared with the microsatellite approach, the DArTseq method required less effort to prepare samples, because the service provider (Diversity Arrays Technology) performed the DNA extraction and sequencing for a relatively small fee (Table 1). The cost of DArTseq analysis is based upon submitting samples in 96‐well plates; therefore, a larger number of samples must be analyzed together to achieve the stated costs. The DArTseq method requires more than one month to obtain data, because samples must be shipped internationally and then processed in a queue with samples from other customers.

The PCR‐RFLP method is among the fastest and least expensive methods (Table 1), because it requires just three main steps (DNA extraction, PCR, restriction enzyme digest) (Saltonstall, 2003a; Wendell et al., 2025). The cost of plastid haplotyping is comparable to that of the PCR‐RFLP method, with both methods employing PCR but differing in whether they require nucleotide sequencing or restriction digest (Table 1). Both PCR‐RFLP and haplotype sequencing can be conducted within one week.

The laboratory costs of sequencing the ITS region are comparable to the costs of haplotype sequencing, except that next‐generation sequencing is more expensive than Sanger sequencing. However, the Sanger sequencing method was ineffective for this study because it cannot discriminate among the multiple ITS sequence variants that were commonly encountered. It could be possible to sequence ITS variants using Sanger sequencing, for example, if the variants were subcloned into bacteria. However, the additional time and reagent cost of subcloning would make the process more expensive than next‐generation sequencing.

Management implications

Our research reflects a tension between the need to quickly identify hybrids—given the ecological risks they have been shown to pose elsewhere in North America (Williams et al., 2019)—and the need to ensure scientific rigor. The hybrid plants that were confirmed in this study had been noted previously for having intermediate morphological characteristics. One of us (D.J.L.) had visited the site in winter 2016 with state agency staff, and the group was struck by the unusual appearance of Phragmites plants there. It was nearly 10 years later that its hybrid status was finally confirmed with molecular data. Similarly, Saltonstall et al. (2014) confirmed a Phragmites hybrid following the observation of its intermediate morphology and unusual herbivory five years earlier. These anecdotes underscore that, despite extraordinary advancements in genetic methods, classic field biology remains indispensable for invasive species surveillance.

The identified hybrid population in Minnesota is part of a wetland complex where invasive Phragmites is being controlled using herbicides and has been targeted for management. More broadly, our confirmation of hybrid Phragmites, and the many hundreds of samples that this required, highlight the need to broaden surveillance for hybrids. There are undoubtedly undiscovered hybrids on the landscape, but their very low prevalence makes them difficult to detect. It would be ideal to have a stable clearinghouse for genetic analyses of Phragmites samples that resource managers could use to confirm invasive status and additionally screen for hybrid genotypes. This would greatly increase crowdsourcing capacity for hybrid detections and overcome the geographic and temporal constraints of start‐stop funding for individual research projects (e.g., Hunt et al., 2017; Tippery et al., 2020).

CONCLUSIONS

There are numerous molecular methods that can be used to identify Phragmites plants and confirm hybrids. Methods that can be performed quickly and inexpensively to produce unambiguous results will be preferred. We found all methods examined in this study to be effective for specimen identification, and the ultimate decision about which method to use can be made individually by researchers. The DArTseq method produces the largest amount of molecular data that could become useful for investigating population genetic processes. The described methods for sequencing the nuclear ITS region provide a novel approach for identifying specimens and evaluating sequences in a phylogenetic context.

AUTHOR CONTRIBUTIONS

N.P.T., D.J.L., J.B., A.K.N., N.O.A., A.G.S., and S.M.G. conceived the study. N.P.T., A.K.N., M.A., and D.L.W. obtained and analyzed molecular data. J.B. and N.P.T. coordinated specimen collection in Minnesota and Wisconsin, respectively. N.P.T. and D.J.L. wrote the initial draft. All authors contributed to writing the manuscript and approved the final version.

Supporting information

Appendix S1: Molecular data for Phragmites australis plants. Spreadsheet tabs provide separate data for DArTseq, microsatellite, restriction PCR, ITS sequencing, and a summary tab for populations.

APS3-9999-e70073-s001.xlsx (237.1KB, xlsx)

Appendix S2: SplitsTree chart of ITS sequence relationships. Distinct sequence types are identified using letters (ITS‐A, ITS‐H, etc.), with subtypes indicated by numbers (ITS‐H1, ITS‐H2, etc.). Newly obtained sequences are shown in boldface and reference the identification codes in Appendix  S1. Previously published sequences are shown with their GenBank accession numbers and country of origin. Species are Phragmites australis, with clusters identified to subspecies, unless indicated otherwise; “haplotype” refers to the plastid haplotype (Saltonstall,  2002).

APS3-9999-e70073-s002.pdf (587.7KB, pdf)

ACKNOWLEDGMENTS

This research was supported by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Minnesota Aquatic Invasive Species Research Center, with additional support from the USDA National Institute of Food and Agriculture through the Minnesota Agricultural Experiment Station, as well as the Wisconsin Department of Natural Resources. We are grateful to the many individuals who helped to collect plant specimens for study: Katelin Anderson, Christine Bohn, Casimir Curney, Shannon Davis‐Faust, Mike Davy, Ken Dolata, José Estrada, Debbie Feirer, Steven Garske, Scott Goodwin, Chris Hamerla, Emily Heald, Marci Hess, Catherine Higley, Sarah Johnson, Shannon Kirby, Jodi Lepsch, Kyle Luther, Cody MacDonald, Ryan Parchim, Jeanne Scherer, Ian Shackleford, Rod Sharka, Alex Smith, Mark Sundeen, Jen Wahls, Carolyn Weber‐Starling, Alan Wirt, and Brock Woods. Wisconsin specimens were collected with the generous assistance of Maureen Kalscheur (Wisconsin Department of Natural Resources). Minnesota specimens were collected by University of Minnesota researchers, Conservation Corps Minnesota Iowa crews, and many agency staff and citizen volunteers through a crowdsourcing effort organized by University of Minnesota research staff. We also thank Adam Lambert and Kristin Saltonstall for providing tissue samples from confirmed native, non‐native, and hybrid Phragmites and Alan Franck for supplying tissue of Gulf Coast Phragmites. We appreciate the helpful comments provided on our draft manuscript by Carla Lambertini, an anonymous reviewer, and the journal editorial staff.

DATA AVAILABILITY STATEMENT

Summary data for the specimens are provided in the Supporting Information. Original sequence data are available on GenBank (ITS sequence data; accession numbers PX928794–PX928857) and FigShare (DArTseq and microsatellite data; Tippery et al., 2026).

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Appendix S1: Molecular data for Phragmites australis plants. Spreadsheet tabs provide separate data for DArTseq, microsatellite, restriction PCR, ITS sequencing, and a summary tab for populations.

APS3-9999-e70073-s001.xlsx (237.1KB, xlsx)

Appendix S2: SplitsTree chart of ITS sequence relationships. Distinct sequence types are identified using letters (ITS‐A, ITS‐H, etc.), with subtypes indicated by numbers (ITS‐H1, ITS‐H2, etc.). Newly obtained sequences are shown in boldface and reference the identification codes in Appendix  S1. Previously published sequences are shown with their GenBank accession numbers and country of origin. Species are Phragmites australis, with clusters identified to subspecies, unless indicated otherwise; “haplotype” refers to the plastid haplotype (Saltonstall,  2002).

APS3-9999-e70073-s002.pdf (587.7KB, pdf)

Data Availability Statement

Summary data for the specimens are provided in the Supporting Information. Original sequence data are available on GenBank (ITS sequence data; accession numbers PX928794–PX928857) and FigShare (DArTseq and microsatellite data; Tippery et al., 2026).

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