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Morphological and genome-wide
evidence for natural hybridisation
within the genus Stipa (Poaceae)
Evgenii Baiakhmetov1,2*, Arkadiusz Nowak3,4, Polina D. Gudkova2,5 & Marcin Nobis1*
Hybridisation in the wild between closely related species is a common mechanism of speciation in
the plant kingdom and, in particular, in the grass family. Here we explore the potential for natural
hybridisation in Stipa (one of the largest genera in Poaceae) between genetically distant species
at their distribution edges in Mountains of Central Asia using integrative taxonomy. Our research
highlights the applicability of classical morphological and genome reduction approaches in studies
on wild plant species. The obtained results revealed a new nothospecies, Stipa × lazkovii, which
exhibits intermediate characters to S. krylovii and S. bungeana. A high-density DArTseq assay
disclosed that S. × lazkovii is an F1 hybrid, and established that the plastid and mitochondrial DNA was
inherited from S. bungeana. In addition, molecular markers detected a hybridisation event between
morphologically and genetically distant species S. bungeana and probably S. glareosa. Moreover,
our ndings demonstrated an uncertainty on the taxonomic status of S. bungeana that currently
belongs to the section Leiostipa, but it is genetically closer to S. breviora from the section Barbatae.
Finally, we noticed a discrepancy between the current molecular data with the previous ndings on S.
capillata and S. sareptana.
Hybridisation in the wild between closely related species is a common mechanism of speciation in the plant
kingdom1–7. Due to the prevalence of polyploidy found in angiosperms it has been estimated that around 11%
of owering plants may have arisen through hybridisation events4. In addition, speciation via hybridisation can
lead to an equal ploidy number within parental and newly formed species3. In general, hybridisation is oen
accompanied by introgression and causes gene transfer between species via repeated backcrossing4,8–11. On the
one hand it may have contributed to species diversity and speciation5,12,13, on the other, deleterious consequences
of hybridisation such as decreased tness, genetic assimilation and gene swamping may drive populations toward
the brink of extinction14–16.
In the grass family (Poaceae) hybridisation and introgression are well studied mainly for economically impor-
tant plants, such as wheats17,18, maize19,20, rice21, 22, barley23,24, oats25,26, rye27,28, sugarcanes29,30, and sorghums31,32.
Nowadays new molecular markers and technologies that rst came to the eld of agriculture are becoming widely
used in studies of wild populations with little or no previous genomic information. For instance, genotyping-by-
sequencing (GBS) and GBS-like approaches that were initially developed for maize and barley33 help to detect
hybridisation and introgression events in many wild plant genera34–38.
e genus Stipa L. belongs to the subfamily Pooideae and alongside with Bambusoideae (bamboos), and
Oryzoideae (rices) form the so-called BOP clade39. e BOP species are known as the "cool season" or "pooid"
grasses and all are C3 and distributed in temperate climates40. Following Tzvelev (1974), the genus Stipa includes
six main sections Barbatae Junge, Leiostipa Dumort, Pseudoptilagrostis Tzvelev, Regelia Tzvelev, Stipa, and Smirno-
via Tzvelev41, and comprises over 150 species native to Asia, Europe and North Africa42,43. In its strict sense,
the genus is monophyletic44,45, but subdivisions within the genus are not consistently supported by available
molecular data43,46. Species of the genus are dominants and/or subdominants in steppe plant communities47–50,
can be used for their classication51, and in studies related to climate change52–54. Moreover, the species are of
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great economic importance mainly as pasture and fodder plants, especially in the early phases of development55,
they can be used for soil remediation processes56, and as ornamental plants (e.g. S. capillata L., S. pulcherrima
K. Koch, S. pennata L.).
For decades it has been hypothesised that some Stipa taxa arose via hybridisation57–60. According to our
observations, Stipa hybrids reproduce vegetatively and, less frequently, sexually60. It recently was shown that
hybrids in Stipa can produce fertile pollen grains and therefore are able to backcross with both parental species61.
In addition, based on morphology, a hybrid origin can be attributed to ca. 30% of Stipa species where only in
Middle Asia 23 of 72 species are regarded as nothospecies43. For instance, to such taxa belong S. × czerepanovii
Kotukhov (= S. orientalis Tri n. × S. richteriana Kar. & Kir.); S. × fallax M. Nobis & A. Nowak (S. drobovii (Tzvel.)
Czer. × S. macroglossa P. A. Smirn. subsp. macroglossa); S. × gegarkunii P. A. Smirn. (= S. caucasica Schmalh. × S.
pulcherrima K. Koch); S. × hissarica M. Nobis (= S. lipskyi Roshev. × S. orientalis Trin.); S. × tzveleviana Kotukhov
(= S. orientalis × S. macroglossa subsp. kazachstanica); and S. × zaissanica Kotukhov (= S. orientalis × S. hohenack-
eriana Trin. & Rupr.)43,60,62,63.
Heretofore, all putative hybrid taxa within Stipa were described based exclusively on morphological com-
parison. e only exception is Stipa × heptapotamica Golosk., whose origin has been established using molecular
methods61. Although its parental species Stipa richteriana Kar. & Kir and S. lessingiana Trin. & Rupr. were mor-
phologically distant and aliated to dierent sections Leiostipa and Subbarbatae Tzvelev41,58,64, genetically they
are closely related65,66 and able to hybridise with each other61.
During eld studies in eastern Kyrgyzstan in 2015 and 2017, interesting specimens of Stipa, combining
characters not observed in the previously described taxa, were found on the south shore of Lake Issyk-Kul
(Fig.1). Due to these specimens seeming to be morphologically intermediate between two species from the same
locality, we hypothesised that they can be hybrids between S. krylovii Roshev. and S. bungeana Trin. Although,
traditionally both putative parental taxa were assigned to the section Leiostipa58, they are distant phylogeneti-
cally and belong to two dierent clades61,65. Both of them have wide distribution ranges, Stipa krylovii occurs
in the Russian Far East and Southern Siberia, Mongolia, China, Northern Nepal, Southern Tajikistan, Eastern
Kazakhstan, and Eastern Kyrgyzstan43,67, whereas S. bungeana is distributed in Southern Mongolia, China, and
Eastern Kyrgyzstan68,69 (Fig.1a).
Since hybrids between genetically distant Stipa species have not been observed previously in nature, in the
current study by using integrative taxonomy based on morphology and high density genome wide genotyping-by-
sequencing data, we aim to (1) obtain insight into the extent of hybridisation between S. krylovii and S. bungeana
on macro- and micromorphological levels; (2) assess levels of inter-species gene ow (if present) between the
examined Stipa taxa; (3) analyse the usefulness of SilicoDArT and SNPs markers for genomic studies in Stipa.
Numerical analysis. e factor analysis of mixed data (FAMD) revealed six markedly dierentiated groups
of OTUs in accordance with the taxonomic classication of the examined taxa (Fig.2). e rst three dimen-
sions explained 41.71%, 13.64%, and 10.14%, of the total variability, respectively. e rst dimension is com-
posed, in order of descending contribution, by the quantitative variables AL, Col1L, CL, LG, CvH (Supple-
mentary TableS2, for character abbreviations see Table1). e second dimension is composed, in order of
descending contribution, by the quantitative variables DDL, LHTA, LigIV, WVS, LHD, SL, and the qualitative
variable HTTA (Supplementary TableS2). e third dimension is composed, in order of descending contribu-
tion, by the quantitative variables HLCol2, HLCol1, WCol1, CBW, and the qualitative variable AdSVL (Sup-
plementary TableS2). e two dimensional plot revealed the overlapping of OTUs belonging to S. breviora
and S. bungeana, whereas OTUs of S. sareptana are slightly overlapped with OTUs of S. krylovii and S. capillata
(Fig.2a). A clear dispersal of the OTUs could be seen in the three-dimensional plot, where dierences between
Figure1. Distribution map represents (a) general ranges of S. krylovii (green) and S. bungeana (red) with the
dashed line indicating the hypothetical border, (b) localities of the examined specimens used for the molecular
analysis. e current map is based on Google Maps.
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the studied species are explained by the third principal axis (Fig.2b and in the interactive three-dimensional plot
available at https ://plot.ly/~eugen ebaya hmeto v/3/). In particular, the third axis dierentiates S. breviora and S.
bungeana as clear non-overlapped clouds of OTUs.
In addition, the notch plots of variables showed signicant dierences between means and the strong evidence
of diering medians within all the taxa for CL; AL demonstrates the dierence within all the taxa except the pair
S. krylovii and S. sareptana; Col1L exhibits the dierence within all the taxa except the pair S. bungeana and S.
breviora; LG indicates the dierence within all the taxa except the pairs S. capillata and S. sareptana, as well
as S. breviora and the putative hybrid (S. bungeana × S. krylovii), here and below named as S. × lazkovii; the SL
variable shows the dierence within all the taxa except the pairs S. × lazkovii and S. capillata, and S. krylovii and
S. sareptana (Supplementary Fig.S1).
Figure2. Factor analysis of mixed data performed on 22 quantitative and three qualitative characters of the six
examined species of Stipa. (a) Plot of the two principal axes. (b) Plot of the three principal axes. e gure was
created using the R-packages factoextra v.1.0.6 (Fig. a), https ://CRAN.R-proje ct.org/packa ge=facto extra /, and
plotly v.4.9.2 (Fig. b), https ://plotl y.com/r/getti ng-start ed/.
Table 1. Morphological characters used in the present study.
Quantitative characters (mm)
Width of blades of vegetative shoots WVS
Length of ligules of the middle cauline leaves LigC
Length of ligules of the internal vegetative shoots LigIV
Length of lower glume LG
Length of anthecium AL
Width of anthecium AW
Length of callus CL
Length of hairs on the dorsal part of callus CdH
Length of hairs on the ventral part of callus CvH
Length of callus base CBL
Width of callus base CBW
Length of hairs on the dorsal line on lemma LHD
Length of hairs on the ventral line on lemma LHV
Distance from the end of dorsal line of hairs to the top of lemma DDL
Distance from the end of ventral line of hairs to the top of lemma DVL
Length of hairs on the top of lemma LHTA
Length of lower segment of awn Col1L
Length of middle segment of awn Col2L
Length of seta SL
Length of hairs on lower segment of awn HLCol1
Length of hairs on middle segment of awn HLCol2
Width of lower segment of awn WCol1
Character of abaxial surface of vegetative leaves (glabrous, with prickles) AbSVL
Character of adaxial surface of vegetative leaves (short hairs, long hairs, mixed) AdSVL
Type of hairs on the top of anthecium (glabrous, poor developed, well developed) HTTA
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Seven notch plots of variables show signicant dierences between means and the strong evidence of dif-
fering medians within S. bungeana, S. krylovii, and their putative hybrid: AL, CL, Col1L, SL, WCol1, LG, and
WVS (Supplementary Fig.S1). At the same time, S. bungeana, S. krylovii and S. × lazkovii share six characters
that have no signicant dierences between their means: CvH, CBL, CBW, DVL, HLCol1, and HLCol2. Further,
S. × lazkovii and S. krylovii share seven characters with no signicant dierences between their means, but dif-
fer with S. bungeana: CdH, DDL, Col2L, LigC, LigIV, AW, LHTA. Finally, only two characters LHD and LHV
have no signicant dierences between means within pairs S. × lazkovii and S. krylovii, and S. × lazkovii and S.
bungeana, but have signicant dierences between means of S. krylovii and S. bungeana (Supplementary Fig.S1).
Micromorphology. e micromorphological examination of Stipa bungeana, S. krylovii and their putative
hybrid revealed the pattern of lemma that is typical for the genus Stipa (Fig.3)45,60,62,70,71. In all three taxa, the
fundamental long cells are rectangular to more or less square in shape. e side walls of long cells are raised
and undulate. Silica bodies are sparse or absent, but if present, they are reniform to ovate, whereas cork cells are
absent. Hooks are frequent and oriented towards the lemma apex, whereas prickles are present mostly near the
lemma apex (Fig.3). Macrohairs are straight or bent near the base, cylindrical and/or string-like, with a bulbous
base and a needle-like apex. ey are organised in seven lines. e lemma apex is scabrous due to abundant
hooks, prickles and short macrohairs (present especially in S. bungeana and in the hybrid), surpassed by a ring
of unequal macrohairs. e pattern of lemma apex shows clearly intermediate character of S. × lazkovii between
the two putative parents (Figs.3a, 3f, 3k).
Figure3. Micromorphological patterns of Stipa krylovii (a-e), S. × lazkovii (f-j) and S. bungeana (k–o): top of
lemma (a, f, k), lemma abaxial surface (b-c, g-h, l-m), adaxial surface of leaf blade (d, i, n), abaxial surface of
leaf blade (e, j, o). Abbreviations: h – hooks, lc – long cells; mh – macrohairs, pr – prickles; sb – silica bodies.
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DArTseq analysis. A total of 137,437 SilicoDArT and 125,850 SNPs markers were obtained using a DArT-
seq high-density assay, of which 76,604 silicoDArT and 19,133 SNPs markers were kept aer the ltering steps.
e rst two axes of principal coordinates analysis (PCoA) explained 77% and 91% of the total genetic diver-
gence within the studied taxa based on the SilicoDArT and SNPs markers, respectively, whereas the third axes
explained only 6.3% and 3% (Fig.4).
In general, based on genetic similarities both markers revealed six markedly dierentiated groups (Fig.4).
Most of the specimens are grouped together accordingly to their taxonomical classications. However, one
sample (ID0494394), which morphologically was somewhat similar to S. breviora, is grouping together with S.
bungeana OTUs and far distant to the rest of OTUs belonging to S. breviora. All S. × lazkovii specimens have
an intermediate position between S. bungeana and S. krylovii, suggesting an admixed origin. In addition, on
the basis of two axes both markers are not allowed to dierentiate two taxa, S. capillata and S. sareptana. On the
other hand, the dierence can be marked in the three-dimensional plot based on SilicoDArT markers (Figs.4b,
the interactive plot available at https ://plot.ly/~eugen ebaya hmeto v/5/), but not in SNPs markers (Fig.4d, https
://plot.ly/~eugen ebaya hmeto v/7/).
Figure4. Principal Coordinates Analysis plot based on genetic distances between samples. (a) Plot of the two
principal axes based on SilicoDArT markers. (b) Plot of the three principal axes based on SilicoDArT markers.
(c) Plot of the two principal axes based on SNPs markers. (d) Plot of the three principal axes based on SNPs
markers. e gure was created using the R-packages ggplot2 v.3.3.0 (Figs a and c), https ://ggplo t2.tidyv erse.
org/, and plotly v.4.9.2 (Figs b and d), https ://plotl y.com/r/getti ng-start ed/.
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A fastSTRU CTU RE analysis of the SilicoDArT markers revealed the most likely number of clusters at K value
of 5 (Fig.5a). For the SNPs markers, the ’best’ K was inferred in fastSTRU CTU RE as K = 4 (Fig.5b). Both analyses
dened S. breviora, S. bungeana, and S. krylovii as clear taxa with the exception of the specimen ID0494394
(Fig.5) that shares 73% of markers with S. bungeana and 27% with probably S. glareosa, indicating their rst
backcross generation progeny (Fig.5a). e last-mentioned taxon was not present in the analyses, however, it
is common in the locality, where the specimen ID0494394 was growing. In case of SNPs markers, the specimen
ID0494394 has 75% of markers with S. bungeana, 19% with S. capillata/S. sareptana, and 6% with S. krylovii,
suggesting a possible hybridisation between these species followed by backcrossing with S. bungeana (Fig.5b).
e fastSTRU CTU RE analyses revealed F1 hybrid specimens between S. krylovii and S. bungeana due to
samples of S. × lazkovii have admixture between these clusters in a range of 55% and 45% for the SilicoDArT
markers (Fig.5a), and 50/50% for the SNPs markers (Fig.5b). e fastSTRU CTU RE output for the SilicoDArT
markers exhibits no dierence between S. capillata and S. sareptana resulting in clustering them together (Fig.5a),
whereas the analysis of the SNPs shows an admixture between S. capillata/S. sareptana and S. krylovii in a range
of 53% and 47%, respectively (Fig.5b).
e results of the UPGMA cluster analyses revealed a clear division of samples into two major clades (Fig.6).
According to the clustering obtained with the SilicoDArT markers, the rst clade is subdivided into four smaller
clusters, specically, comprising samples of: (1) S. × lazkovii; (2) S. krylovii; (3) S. sareptana; (4) S. capillata
(Fig.6a). e rst two species are genetically closely related to each other and distant to S. sareptana and S.
Figure5. FastSTRU CTU RE results based on (a) SilicoDArT markers for K = 5 and (b) SNPs markers for K = 4.
e gure was created using an in-house R script in RStudio v.1.1.463, https ://rstud io.com/produ cts/rstud io/.
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capillata that together form one sub-cluster. e second clade is composed of three clusters comprising samples
of: (1) S. breviora; (2) S. bungeana; (3) the sample ID0494394 that is genetically closer to S. bungeana than to
S. breviora. e UPGMA cluster analysis of the SNPs markers demonstrated the subdivision of samples into
the same number of clusters as were obtained for the SilicoDArT markers. However, in this case, specimens of
S. × lazkovii are genetically closer to S. bungeana, but not to S. krylovii.
Genetic mapping onto chloroplast genomes of Stipa species and mitochondria of specimens from the Poaceae
family (Supplementary TableS3) revealed 11 SilicoDArT markers assigned to chloroplast DNA and 27 loci
assigned to mitochondrial DNA. e downstream neighbour-joining cluster analysis showed grouping of Stipa
taxa into two main clades (Fig.7). In the rst clade three species could be dened: S. krylovii, S. capillata (boot-
strap support 90%), S. sareptana (bootstrap support 84%), with an exception of the specimen ID0494394 that is
grouped together with S. krylovii (Fig.7). e second clade comprises a group of S. bungeana and S. × lazkovii
(bootstrap support 79%), and the rest of S. breviora specimens with a good bootstrap support of 87% (Fig.7).
All S. × lazkovii samples are grouping alongside with S. bungeana, and one S. bungeana specimen (ID0459867)
is placed outside the main group of S. bungeana and S. × lazkovii with a bootstrap support of 79% (Fig.7).
Figure6. Unweighted Pair Group Method with Arithmetic Mean cluster analyses based on Jaccard’s similarity
coecients generated from (a) SilicoDArT markers and (b) SNPs markers. e gure was created using the
R-package stats v.3.6.2, https ://www.rdocu menta tion.org/packa ges/stats /versi ons/3.6.2/.
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Although many interspecic hybrids have been described in the genus Stipa43,57–59, so far only a single molecular
investigation was performed to verify the origin of one such species, Stipa × heptapotamica61 that appeared to
be a hybrid between genetically closely related species65,66. e current study is the rst report of hybridisa-
tion between two genetically distant Stipa species, S. krylovii and S. bungeana61,65, at their distribution edges in
Mountains of Central Asia (Fig.1a).
Analyses of morphological variation resulted in a clear delimitation of the studied species (Fig.2b). Par-
ticularly, the main morphological characters (Table1) show that species S. capillata, S. sareptana, S. krylovii, S.
bungeana, S. × lazkovii representing the section Leiostipa are quite distant to S. breviora which traditionally has
been aliated to the section Barbatae41. As expected, the hybrid specimens of S. × lazkovii were mostly charac-
terised by intermediate morphological traits between the parental taxa S. krylovii and S. bungeana (Figs.2 and 3,
Supplementary Fig.S1). In addition, some OTUs of S. sareptana were slightly overlapped with OTUs of S. krylovii
and S. capillata. However, S. sareptana and S. krylovii are easy to distinguish based on morphology of leaves
(scabrous in S. sareptana and glabrous in S. krylovii) and the lemma apex (with a poorly developed ring of hairs
in S. sareptana and with a well-developed ring of hairs in S. krylovii)43,58,67. As for S. sareptana and S. capillata,
these taxa can be delimited by characteristics of their vegetative leaves (scabrous in S. sareptana and glabrous in
S. capillata) and characters of lemma (hairs on the top in S. sareptana and glabrous in S. capillata)43,58,67.
Both PCoA and fastSTRU CTU RE analyses conrmed that S. × lazkovii is the F1 hybrid of S. krylovii and S.
bungeana (Figs.4 and 5). In addition, the neighbour-joining cluster analysis identied S. bungeana as the source
of maternal DNA for all hybrid specimens, suggesting unidirectional hybridisation (Fig.7). However, due to the
small sample size, we cannot exclude either an opposite combination or interspecic gene ow through intro-
gression that could exist in nature, especially since in this area of Issyk-Kul Lake populations of both parental
species are extremely large.
Although morphologically S. bungeana is considered as a member of the section Leiostipa58, molecular
analyses demonstrated that it is quite distant from S. krylovii, S. capillata, and S. sareptana from the same sec-
tion (Figs.4, 6 and 7). ese ndings support our previous molecular results for these taxa based on a nuclear
region61,65. In addition, the results of the distance based clustering algorithms UPGMA and NJ revealed that
S. bungeana is closer to S. breviora then to the rest of Leiostipa taxa from the study (Figs.6 and 7). is result
demands further investigations on S. bungeana to establish its proper taxonomic place in the genus Stipa.
Analyses of molecular markers also revealed that the genetic relationships within some studied taxa are
more complex than expected. Firstly, the sample ID0494394, which morphologically was somewhat similar to
S. breviora, appeared to be an introgressive hybrid that shares 73% of markers with S. bungeana and 27%, more
likely, with S. glareosa (Fig.5a). Here, we presume that hybridisation events are happening between S. bungeana
Figure7. Neighbor-joining tree reconstructed based on the SilicoDArT markers derived from chloroplast
and mitochondrial genomes. e bootstrap values > 50% obtained from 10,000 replicates are shown above the
branches. e gure was created using Figtree v1.4.4, https ://tree.bio.ed.ac.uk/sow are/gtr ee/.
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and S. glareosa, because the introgressive hybrid was found on the north shore of Lake Issyk-Kul, where only
three Stipa taxa were recorded (S. bungeana, S. breviora, and S. glareosa). However, due to S. glareosa was
absent in the analyses, a new study focusing on hybridisation should be performed to verify if the gene ow is
a common event within these taxa. Secondly, our research demonstrates the discordance between the results of
fastStructure and PCoA analyses from one side and the UPGMA and NJ from the other. e rst two represent
no or almost no dierence between S. capillata and S. sareptana (Figs.4 and 5). Notwithstanding, the UPGMA
and NJ dendrograms show that genetically these taxa can be delimited (Figs.6 and 7) that supports our previ-
ous molecular investigations on these taxa61,65. However, in the current research S. capillata and S. sareptana are
grouped together, whereas based on the nuclear Intergenic Spacer (IGS) the last taxon is closer to S. krylovii61,65.
Due to the limited number of analysed specimens in the present and previous studies, we believe that a bigger
sample size combining genetics and traditional taxonomy should be undertaken in order to better resolve the
relationship between these species.
Until now the DArTseq approach has been used mostly in commercially important plant species72–78 and its
implication in genomic studies in wild species is still limited79,80. us, the current study highlights the applicabil-
ity of genome reduction approaches such as DArTseq in studies on natural hybridisation in wild, and specically
in a grass genus Stipa. e high density genome wide genotyping-by-sequencing resulted in a total of 137,437
silicoDArT and 125,850 SNPs markers, of which 76,604 silicoDArT and 19,133 SNPs provided robust information
of the Stipa genome in the absence of the reference sequence information. Such number of markers is several
100-fold higher than was achieved in our previous study on natural hybridisation in Stipa61. In particular, by
using inter simple sequence repeat markers (ISSR) we were able to detect only 105 polymorphic bands for the
S. heptapotamica hybrid complex. In addition, dominant markers were used in several genomic studies in Stipa
and resulted in 372 polymorphic ISSR bands for S. bungeana81, 34 polymorphic ISSR bands for S. ucrainica and
S. zalesskii82, 212 polymorphic ISSR bands for S. tenacissima83, 231 polymorphic random amplied polymor-
phic DNA (RAPD) bands for S. krylovii84, 310 polymorphic RAPD bands for S. grandis85, and 504 polymorphic
sequence-related amplied polymorphism bands for S. bungeana81. us, both silicoDArT and SNPs markers
may better suit for genetic diversity studies in Stipa. Furthermore, the current study demonstrated the useful-
ness of silicoDArT markers as a tool to detect chloroplast and mitochondrial loci and thus may help to clarify
the maternal inheritance of hybrid species.
Taxonomic treatment. Stipa × lazkovii M. Nobis & A. Nowak, nothosp. nov. (Fig.3f-j, Supplementary Figs
S2 and S3). TYPE: Kyrgyzstan, between Kongurlen and Kultor, 17km SW from coast of Issyk-Kul, semidesert, N
42°5′47.07′’ / E 76°39′6.22′’, elev. 1940m, wp. 930, 6 July 2017, M. Nobis, E. Klichowska, A. Wróbel, A. Nowak sn.
(holotype KRA 495,093! (specimen in the middle part of the sheet); isotypes KRA 487,067!, 487,066!, 481,608!).
Diagnosis: Stipa × lazkovii diers from S. krylovii Roshev. by having shorter anthecium (7.3–8.5mm vs.
9.0–11.5), shorter callus (1.8–2.2 vs. 2.3–3.8mm long), shorter glumes (15–17 vs. 18–28mm long) as well as by
having long prickle-hairs below the top of the anthecium (Fig.3). Having long prickle-hairs below the top of the
anthecium Stipa × lazkovii is also similar to S. bungeana, however diers from it by longer anthecium (7.3–8.5
vs. 4.8–6.0mm long), longer callus (over 1.8 vs. up to 1.3mm long), longer glumes (over 15 vs. up to 15mm
long) and narrower leaves (0.5–0.6 vs. 0.6–1.0mm wide).
Description: Plants perennial, densely tued, with a few culms and numerous vegetative shoots; culms
35–55cm tall, 3-noded, glabrous at and below the nodes. Leaves of vegetative shoots: sheaths glabrous, at mar-
gins ciliate; ligules truncate, up to 0.2mm ciliate at margins; blades convolute, up to 25cm long, 0.5–0.6(–0.7)
mm in diameter, adaxial surface densely pubescent with up to 0.1mm long hairs (prickles), adaxial surface
glabrous, rarely very slightly scabrous. Cauline leaves: sheaths glabrous and with white edge, shorter than inter-
nodes; ligules 0.5–5mm long, acute and glabrous; blades glabrous, up to 12cm long. Panicle up to 25cm long
contracted, at base enclosed by sheath of uppermost leaf, branches erect, setulose, single or paired. Glumes
subequal, 15–25mm long, narrowly lanceolate, tapering into long hyaline apex. Anthecium 7.3–8.5mm long
and 0.7–0.9mm wide. Callus 1.8–2.2mm long, densely pilose on ventral and dorsal surfaces, callus base acute,
cuneate, scar elliptic. Lemma pale green, on dorsal surface with abundant hooks and with 7 lines of ascending
hairs, hairs up to 0.5mm long, ventral line of hairs terminates at 1.3–1.7mm below top of lemma and dorsal line
terminates at 1.5–2.2mm below top of lemma; top of lemma scabrous due to hooks and prickles and at apex with
a ring of hairs up to 0.5mm long. Palea equals to lemma in length. Awn 95–118mm long, bigeniculate; lower
segment of column 19–25mm long, twisted, scabrous due to prickles and short hairs up to 0.15; upper segment
of column 11.5–13mm long, twisted, scabrous due to prickles and short hairs up to 0.2mm in long; seta exu-
ous 65–80mm long, hairs in the lower part of the seta 0.1–0.2mm long, gradually decreasing in length towards
apex. Anthers yellow, 4–5mm long, glabrous.
Etymology: e name of the taxon honours prof. dr Georgy A. Lazkov (Academy of Sciences, Bishkek, Kyr-
gyzstan), the eminent botanist, taxonomists and expert of vascular plants of Middle Asian Mountains.
Other specimens studied (paratypes): Kyrgyzstan, western Tian-Shan, Kongurlen Valley, steppe grasslands
near the road, 3km E of Kongurlen settl., to the S of SW part of Issyk-Kul Lake, N 42°5′53.97′’ / E 76°38′37.28′’,
elev. 1945m, wp. 644, 10 July 2015, M. Nobis, A. Nowak sn. (KRA 476,871, 476,870!, 476,869!, WA!).
An identication key to central Asian species of Stipa that have scabrous awns or awns that are throughout
covered by 0.1–0.3mm long hairs is given in Supplementary S4.
SCIENTIFIC REPORTS | (2020) 10:13803 |
Materials and methods
Plant material. Morphological examination is based on plant specimens deposited in the KRA herbarium
(the acronym from iers86). In total, 188 fully developed Stipa samples were studied under a light microscope
SMZ800 (Nikon, Japan) including 40 specimens of S. krylovii, 40 of S. bungeana, 6 of S. × lazkovii, 22 of S. brevi-
ora, 40 of S. capillata, and 40 of S. sareptana.
For molecular analysis, we collected leaves of plants from localities where S. krylovii and S. bungeana grow
together with their putative hybrid, as well as from areas where S. krylovii and S. bungeana grow separately from
each other (Fig.1b). Additionally, we included Stipa taxa that frequently occur in the area near of Issyk-Kul Lake.
In total, we selected 20 specimens of S. krylovii, 20 specimens of S. bungeana, 6 specimens of S. × lazkovii, 10
specimens of S. breviora, 2 specimens of S. capillata, and 2 specimens of S. sareptana. Only one taxon, S. glareosa,
is not presented in the study due to it was not found in the locality of S. × lazkovii. Moreover, S. glareosa belongs
to the section Smirnovia41 and exhibits unique characters (e.g. long and pilose awns with a single geniculation),
which were not observed in any Stipa taxa in this region.
All voucher specimens used in the molecular analysis are preserved at KRA (Supplementary TableS1). e
names of plants were adopted from the WCSP87.
Macromorphological analyses. For the morphometric analyses, 188 specimens were used as operational
taxonomic units (OTUs)88. As a rst step, the Shapiro–Wilk test was used in the R-package MVN89 to assess the
normality of the distribution of each character. e non-parametric Spearman’s correlation coecient was used
in the R-package MVN to examine relations between the studied characters. e 22 most informative quantita-
tive and three qualitative morphological characters, commonly used in keys and taxonomic descriptions were
chosen for the analyses (Table1).
A Factor Analysis of Mixed Data (FAMD)90 was performed in the R-package FactoMineR91 to characterise
variation within and among groups of taxa without a priori taxonomic classication and to extract the variables
that best identied them. e number of principal components included in the analysis was chosen based on
Scree’s test92. e R-package factoextra93 was used to visualise the rst two components, whereas the R-package
plotly94 was chosen to illustrate the rst three.
Notch plots were created in the R-package ggplot295 to explore distributional relationships between each
response variable and the studied taxa (Supplementary Fig.S1). e notched box plots display a condence
interval around the median, which is normally based on the median ± 1.57 × interquartile range/square root of
n. According to this graphical method for data analysis, if the notches of the two boxes do not overlap, there
is "strong evidence" (95% condence) that their medians dier. Additionally, to reveal signicant dierences
between means of particular characters across all examined taxa the nonparametric Kruskal–Wallis test followed
by the Wilcoxon rank sum test for post hoc group comparisons were calculated. To address the multiplicity of
comparison, the Bonferroni method was applied to calculate corrected p-values.
Micromorphological examination. e lemma and lamina micromorphology within Stipa × lazkovii, S.
krylovii, and S. bungeana were examined using scanning electron microscopy (SEM). e dried samples were
coated with a gold layer using a Quorum Q150R S coater (Quorum, UK). e SEM images were obtained by a
scanning electron microscope S-4700 (Hitachi, Japan). Further, we examined the adaxial and abaxial surfaces
of lamina, and ve sets of diagnostic characters of lemma micromorphology: (1) long cells, (2) silica bodies, (3)
hooks, (4) prickles, (5) macrohairs.
DNA extraction, amplication, and DArT sequencing. Isolation of genomic DNA was performed
from dried leaf tissues using a Genomic Mini AX Plant Kit (A&A Biotechnology, Poland). Quality check, quan-
tication and concentration adjustment for sequencing and genotyping were accomplished using a NanoDrop
One (ermo Scientic, USA) and agarose gel electrophoresis visualisation. e concentration of each sample
was adjusted to 50ng/μL. Puried DNA samples (1μg for each sample) were sent to Diversity Arrays Technology
Pty Ltd (Canberra, Australia) for sequencing and marker identication.
DArTseq represents a combination of a DArT complexity reduction methods and next generation sequenc-
ing platforms96–100. e technology is optimised for each organism and application in order to select the most
appropriate complexity reduction method (both the size of the representation and the fraction of a genome
selected for assays). Based on testing several enzyme combinations for complexity reduction Diversity Arrays
Technology Pty Ltd selected the PstI-MseI method for Stipa.
DNA samples were processed in digestion/ligation reactions as described previously97, but replacing a sin-
gle PstI-compatible adaptor with two dierent adaptors corresponding to two dierent Restriction Enzyme
(RE) overhangs. e PstI-compatible adapter was designed to include Illumina owcell attachment sequence,
sequencing primer sequence and "staggered", varying length barcode region, similar to the sequence previously
reported33. Reverse adapter contained owcell attachment region and MseI-compatible overhang sequence. Only
"mixed fragments" (PstI-MseI) were eectively amplied by PCR using an initial denaturation step of 94°C for
1min, followed by 30 cycles with the following temperature prole: denaturation at 94°C for 20s, annealing at
58°C for 30s and extension at 72°C for 45s, with an additional nal extension at 72°C for 7min. Aer PCR
equimolar amounts of amplication products from each sample of the 96-well microtiter plate were bulked
and applied to c-Bot (Illumina, USA) bridge PCR followed by sequencing on Hiseq2500 (Illumina, USA). e
sequencing (single read) was run for 77 cycles.
Sequences generated from each lane were processed using proprietary DArT analytical pipelines. In the pri-
mary pipeline, the fastq les were rst processed to lter away poor quality sequences, applying more stringent
selection criteria to the barcode region compared to the rest of the sequence. In that way the assignments of
SCIENTIFIC REPORTS | (2020) 10:13803 |
the sequences to specic samples carried in the "barcode split" step were very reliable. Approximately 2.5 mln
sequences per barcode/sample were identied and used in marker calling.
DArTseq data analysis. DArTseq produce two types of data: (1) co-dominant single nucleotide polymor-
phisms (SNPs) markers, and (2) dominant SilicoDArT markers that represent the presence or absence of restric-
tion fragments. All molecular analyses with the DArTseq data (SNPs and SilicoDArT) sets were performed aer
ltering steps in the R-package dartR101 with the following parameters: (1) a scoring reproducibility of 100%,
(2) at least 95% loci called (the respective DNA fragment had been identied (= called) in greater than 95% of
all individuals), (3) monomorphic loci were removed, (4) SNPs that shared secondaries (had more than one
sequence tag represented in the dataset) were randomly ltered out to keep only one random sequence tag.
ree approaches were used to analyse genetic structure of the studied taxa: (1) Principal Coordinates Analy-
sis (PCoA), (2) fastSTRU CTU RE analysis, and (3) Unweighted Pair Group Method with Arithmetic Mean
(UPGMA). e PCoA analyses based on Euclidean distance matrices were performed using R-packages dartR
and visualised by using ggplot2 to show the rst two components, and plotly to illustrate the rst three com-
ponents. Genetic structure was then investigated using the fastSTRU CTU RE soware, which implements the
Bayesian clustering algorithm STRU CTU RE, assuming Hardy–Weinberg equilibrium between alleles, in a fast
and resource-ecient manner102. A number of clusters (K-values) ranging from 2 to 10 were tested using the
default convergence criterion of 10−6 and priors. e most likely K-value was estimated with the best choice
function implemented in fastSTRU CTU RE. In case of a range of K values, the true K was determined as a value
between the estimates predicted by fastSTRU CTU RE and based on what made most biological sense. e out-
put matrices for the best K-values were reordered and plotted using an in-house R script in RStudio (Version
1.1.463)103. e threshold of 0.10 < q < 0.90 was applied as the most widely utilised measure for the assessment
of hybridisation104–107. Contributions from each cluster in a range between 45 and 55% were considered as F1
hybrids, while rst‐ and second‐generation backcrosses with one parent were considered at values 0.25 and
0.125, respectively108. e UPGMA cluster analyses based on Jaccard’s distance matrices were performed using
R-packages dartR and visualised with stats109.
Finally, the SilicoDArT tags were used to determine maternal inheritance of the putative hybrid Stipa × laz-
kovii. e trimmed sequences of the parental species S. krylovii and S. bungeana, and the rest of studied taxa
were mapped onto chloroplast genomes of Stipa species and mitochondrions of specimens from the Poaceae
family (Supplementary TableS3) by using Minimap2110. e nal binary data matrix was used to generate a
neighbor-joining tree (NJ) derived from Jaccard’s genetic distances in the ngerprint analysis with missing data
soware v1.31 (FAMD)111 with a set of 10,000 bootstrap replicates. e resulting tree was visualised and edited
using Figtree v1.4.4112.
e datasets used and/or analysed during the current study are available from the corresponding authors upon
Received: 18 February 2020; Accepted: 31 July 2020
1. Grant, V. Plant Speciation 2nd edn. (Columbia University Press, New York, 1981).
2. Abbott, R. J. Plant invasions, hybridization and the evolution of new plant taxa. Trends Ecol. Evol. 7, 401–405. https ://doi.
org/10.1016/0169-5347(92)90020 -C (1992).
3. Rieseberg, L. H. Hybrid origins of plant species. Annu. Rev. Ecol. Syst. 28, 359–389. https ://doi.org/10.1146/annur ev.ecols
4. Arnold, M. L. Evolution rough Genetic Exchange (Oxford University Press, Oxford, 2006).
5. Mallet, J. Hybrid speciation. Nature 446, 279–283. https ://doi.org/10.1038/natur e0570 6 (2007).
6. Abbott, R. J. et al. Hybridization and speciation. J Evol Biol 26, 229–246. https ://doi.org/10.1111/j.1420-9101.2012.02599 .x
7. Goulet, B. E., Roda, F. & Hopkins, R. Hybridization in plants: old ideas. New Techniques. Plant Physiol. 173, 65–78. https ://doi.
8. Rieseberg, L. H. & Wendel, J. F. In Hybrid zones and the evolutionary process (ed. Harrison, R. G.) 70–109 (Oxford University
Press, Oxford, 1993).
9. Mallet, J. Hybridization as an invasion of the genome. Trends Ecol. Evol. 20, 229–237. https ://doi.org/10.1016/j.tree.2005.02.010
10. Hamilton, J. A. & Miller, J. M. Adaptive introgression as a resource for management and genetic conservation in a changing
climate. Conserv. Biol. 30, 33–41. https ://doi.org/10.1111/cobi.12574 (2015).
11. Suarez-Gonzalez, A., Lexer, C. & Cronk, Q. C. B. Adaptive introgression: a plant perspective. Biol. Lett. 14, 20170688. https ://
12. López-Caamal, A. & Tovar-Sánchez, E. Genetic, morphological, and chemical patterns of plant hybridization. Revista Chilena
Hist. Nat. 87, 1–14. https ://doi.org/10.1186/s4069 3-014-0016-0 (2014).
13. Abbott, R. J., Barton, N. H. & Good, J. M. Genomics of hybridization and its evolutionary consequences. Mol. Ecol. 25, 2325–
2332. https ://doi.org/10.1111/mec.13685 (2016).
14. Lepais, O. et al. Species relative abundance and direction of introgression in oaks. Mol. Ecol. 18, 2228–2242. https ://doi.
org/10.1111/j.1365-294X.2009.04137 .x (2009).
15. Gómez, J. M., González-Megías, A., Lorite, J., Abdelaziz, M. & Perfectti, F. e silent extinction: climate change and the potential
hybridization-mediated extinction of endemic high-mountain plants. Biodivers. Conserv. 24, 1843–1857. https ://doi.org/10.1007/
s1053 1-015-0909-5 (2015).
16. Mota, M. R., Pinheiro, F., Leal, B. S. S., Wendt, T. & Palma-Silva, C. e role of hybridization and introgression in maintain-
ing species integrity and cohesion in naturally isolated inselberg bromeliad populations. Plant Biol. 21, 122–132. https ://doi.
SCIENTIFIC REPORTS | (2020) 10:13803 |
17. Parisod, C., Denod, C., Sarr, A., Arrigo, N. & Felber, F. Genome-specic introgression between wheat and its wild relative
Aegilops triuncialis. J. Evol. Biol. 26, 223–228. https ://doi.org/10.1111/jeb.12040 (2013).
18. Cheng, H. et al. Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome
Biol. 20, 136. https ://doi.org/10.1186/s1305 9-019-1744-x (2019).
19. Huord, M. B. et al. e genomic signature of crop-wild introgression in maize. PLoS Genet. 9, e1003477. https ://doi.org/10.1371/
journ al.pgen.10034 77 (2013).
20. Gonzalez-Segovia, E. et al. Characterization of introgression from the teosinte Zea mays ssp. mexicana to Mexican highland
maize. PeerJ 7, e6815. https ://doi.org/10.7717/peerj .6815 (2019).
21. Xia, H. B., Wang, W., Xia, H., Zhao, W. & Lu, B. R. Conspecic crop-weed introgression inuences evolution of weedy rice (Oryza
sativa f. spontanea) across a geographical range. PLoS ONE 6, e16189. https ://doi.org/10.1371/journ al.pone.00161 89 (2011).
22. Civán, P. & Brown, T. A. Role of genetic introgression during the evolution of cultivated rice (Oryza sativa L.). BMC Evol. Biol.
18, 57. https ://doi.org/10.1186/s1286 2-018-1180-7 (2018).
23. Molnár-Láng, M. & Linc, G. Wheat-barley hybrids and introgression lines. In Alien Introgression in Wheat: Cytogenetics, Molecu-
lar Biology, and Genomics (eds Molnár-Láng, M. et al.) 315–345 (Springer, Berlin, 2015).
24. Pankin, A. & von Kor, M. Co-evolution of methods and thoughts in cereal domestication studies: a tale of barley (Hordeum
vulgare). Curr. Opin. Plant Biol. 36, 15–21. https ://doi.org/10.1016/j.pbi.2016.12.001 (2017).
25. Yan, H. et al. High-density marker proling conrms ancestral genomes of Avena species and identies D-genome chromosomes
of hexaploid oat. eor. Appl. Genet. 129, 2133. https ://doi.org/10.1007/s0012 2-016-2762-7 (2016).
26. Liu, Q., Lin, L., Zhou, X., Peterson, P. M. & Wen, J. Unraveling the evolutionary dynamics of ancient and recent polyploidization
events in Avena (Poaceae). Sci. Rep. 7, 41944. https ://doi.org/10.1038/srep4 1944 (2017).
27. Martis, M. M. et al. Reticulate evolution of the rye genome. Plant Cell 25, 3685–3698. https ://doi.org/10.1105/tpc.113.11455 3
28. Hagenblad, J., Oliveira, H. R., Forsberg, N. E. G. & Leino, M. W. Geographical distribution of genetic diversity in Secale landrace
and wild accessions. BMC Plant Biol. 16, 23. https ://doi.org/10.1186/s1287 0-016-0710-y (2016).
29. Almaraj, V. A. & Balasundaram, N. On the taxonomy of the members of ’Saccharum complex’. Genet. Resour. Crop. Evol. 53,
35–41. https ://doi.org/10.1007/s1072 2-004-0581-1 (2006).
30. Pachakkil, B. et al. Cytogenetic and agronomic characterization of intergeneric hybrids between Saccharum spp. hybrid and
Erianthus arundinaceus. Sci. Rep. 9, 1748. https ://doi.org/10.1038/s4159 8-018-38316 -6 (2019).
31. Barnaud, A. et al. A weed-crop complex in sorghum: the dynamics of genetic diversity in a traditional farming system. Am. J.
Bot. 96, 1869–1879. https ://doi.org/10.3732/ajb.08002 84 (2009).
32. Ohadi, S., Hodnett, G., Rooney, W. & Bagavathiannan, M. Gene ow and its consequences in Sorghum spp. Crit. Rev. Plant Sci.
36, 367–385. https ://doi.org/10.1080/07352 689.2018.14468 13 (2018).
33. Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379.
https ://doi.org/10.1371/journ al.pone.00193 79 (2011).
34. Hamlin, J. A. & Arnold, M. L. Determining population structure and hybridization for two iris species. Ecol. Evol. 4, 743–755.
https ://doi.org/10.1002/ece3.964 (2014).
35. Qi, L. et al. Genotyping-by-sequencing uncovers the introgression alien segments associated with sclerotinia basal stalk rot
resistance from wild species-I. Helianthus argophyllus and H. petiolaris. Frontiers in genetics 7, 219. https ://doi.org/10.3389/fgene
36. Schilling, M. P., Gompert, Z., Li, F. W., Windham, M. D. & Wolf, P. G. Admixture, evolution, and variation in reproductive
isolation in the Boechera puberula clade. BMC Evol. Biol. 18, 61. https ://doi.org/10.1186/s1286 2-018-1173-6 (2018).
37. Edet, O. U., Gora, Y. S. A., Nasuda, S. & Tsujimoto, H. DArTseq-based analysis of genomic relationships among species of tribe
Triticeae. Sci. Rep. 8, 16397. https ://doi.org/10.1038/s4159 8-018-34811 -y (2018).
38. Hodkinson, T. R., Perdereau, A., Klaas, M., Cormican, P. & Barth, S. Genotyping by sequencing and plastome analysis nds
high genetic variability and geographical structure in Dactylis glomerata L. in Northwest Europe despite lack of ploidy variation.
Agronomy 9, 342. https ://doi.org/10.3390/agron omy90 70342 (2019).
39. Saarela, J. M. et al. A 250 plastome phylogeny of the grass family (Poaceae): Topological support under dierent data partitions.
PeerJ 6, 4299. https ://doi.org/10.7717/peerj .4299 (2018).
40. Soreng, R. J. et al. A worldwide phylogenetic classication of the Poaceae (Gramineae) II: An update and a comparison of two
2015 classications. J. Syst. Evol. 55, 259–290. https ://doi.org/10.1111/jse.12262 (2017).
41. Tzvelev, N. N. Notulae de tribu Stipeae Dum. (fam. Poaceae) in URSS. Novosti Sistematiki Vyssih Rastenij 11, 4–20 (1974).
42. Nobis, M. Taxonomic revision of the Central Asiatic Stipa tianschanica complex (Poaceae) with particular reference to the
epidermal micromorphology of the lemma. Folia Geobot. 49, 283–308. https ://doi.org/10.1007/s1222 4-013-9164-2 (2014).
43. Nobis, M., Gudkova, P., Nowak, A., Sawicki, J. & Nobis, A. A revision of the genus Stipa (Poaceae) in Middle Asia, including a
key to species identication, an annotated checklist and phytogeographic analysis. Ann. Mo. Bot. Gard. 105, 1–63. https ://doi.
org/10.3417/20193 78 (2020).
44. Hamasha, H. R., von Hagen, K. B. & Röser, M. Stipa (Poaceae) and allies in the Old World: molecular phylogenetics realigns
genus circumscription and gives evidence on the origin of American and Australian lineages. Plant Syst. Evol. 298, 351–367.
https ://doi.org/10.1007/s0060 6-011-0549-5 (2012).
45. Romaschenko, K. et al. Systemat ics and evolution of the needle grasses (Poaceae: Pooideae: Stipeae) based on analysis of multiple
chloroplast loci, ITS, and lemma micromorphology. Taxon 61, 18–44. https ://doi.org/10.1002/tax.61100 2 (2012).
46. Kellogg, E. A. Subfamily Pooideae. In e families and genera of vascular plants (ed. Kubitzki, K.) 199–229 (Springer, Berlin,
47. Yunatov, A. A. Main patterns of the vegetation cover of the Mongolian People’s Republic. Proc. Mongolian Commission 39, 233
48. Dashnyam, B. Flora and vegetation of Eastern Mongolia 78–115 (Mongolian Academy of Sciences, Mongolia, 1974).
49. Lavrenko, E. M., Karamasheva, Z. V. & Nikulina, R. I. Eurasian steppe 143 (Nauka, Nauka, 1991).
50. Nowak, A., Nowak, S., Nobis, A. & Nobis, M. Vegetation of feather grass steppes in the western Pamir Alai Mountains (Tajikistan,
Middle Asia). Phytocoenologia 46, 295–315. https ://doi.org/10.1127/phyto /2016/0145 (2016).
51. Danzhalova, E. V. et al. Indicators of Pasture Digression in Steppe Ecosystems of Mongolia. Explor. into Biol. Resour. Mong. 12,
52. Yang, Y. Q. et al. Transcriptome analysis reveals diversied adaptation of Stipa purpurea along a drought gradient on the Tibetan
Plateau. Funct. Integr. Genomics 15, 295–307. https ://doi.org/10.1007/s1014 2-014-0419-7 (2015).
53. Lv, X., He, Q. & Zhou, G. Contrasting responses of steppe Stipa ssp. to warming and precipitation variability. Ecol. Evol. 9,
9061–9075. https ://doi.org/10.1002/ece3.5452 (2019).
54. Schubert, M., Grønvold, L., Sandve, S. R., Hvidsten, T. R. & Fjellheim, S. Evolution of cold acclimation and its role in niche
transition in the temperate grass subfamily pooideae. Plant Physiol. 180, 404–419. https ://doi.org/10.1104/pp.18.01448 (2019).
55. Maevsky, V. V. & Amerkhanov, H. H. e note of Poaceae species from former USSR ora, recommended as fodder for agricul-
tural production. Bull. Bot. Garden Saratov State Univ. 6, 80–83 (2007).
SCIENTIFIC REPORTS | (2020) 10:13803 |
56. Brunetti, G., Soler-Rovira, P., Farrag, K. & Senesi, N. Tolerance and accumulation of heavy metals by wild plant species grown
in contaminated soils in Apulia region, Southern Italy. Plant Soil. 318, 285–298. https ://doi.org/10.1007/s1110 4-008-9838-3
57. Smirnov, P. A. Stiparum Armeniae minus cognitarum descriptiones. Byull. Moskovsk. Obshch. Isp. Prir. Otd. Biol. 75, 113–115
58. Tzvelev, N. N. Zlaki USSR (Nauka Press, Nauka, 1976).
59. Kotukhov, Y. A. Synopsis of feather grass (Stipa L.) and false needlegrasses (Ptilagrostis Griseb.) the eastern of Kazakhstan (e
Kazakh Altai, Zaisan valley and Prialtayskie ranges). Botanicheskie Issledovaniya Sibiri i Kazakhstana 8, 3–16 (2002).
60. Nobis, M. Taxonomic revision of the Stipa lipskyi group (Poaceae: Stipa section Smirnovia) in the Pamir Alai and Tian-Shan
Mountains. Plant Syst. Evol. 299, 1307–1354. https ://doi.org/10.1007/s0060 6-013-0799-5 (2013).
61. Nobis, M. et al. Hybridisation, introgression events and cryptic speciation in Stipa (Poaceae): a case study of the Stipa hepta-
potamica hybrid-complex. Perspect. Plant Ecol. Evol. Syst. 39, 125457. https ://doi.org/10.1016/j.ppees .2019.05.001 (2019).
62. Nobis, M. & Gudkova, P. D. Taxonomic notes on feather grasses (Poaceae: Stipa) from eastern Kazakhstan with typication of
seven names and one new combination. Phytotaxa 245, 31–42. https ://doi.org/10.11646 /phyto taxa.245.1.3 (2016).
63. Nobis, M. et al. Stipa ×fallax (Poaceae: Pooideae: Stipeae), a new natural hybrid from Tajikistan, and a new combination in Stipa
drobovii. Phytotaxa 303, 141–154. https ://doi.org/10.11646 /phyto taxa.303.2.4 (2017).
64. Tzvelev, N. N. Some notes on the grasses (Poaceae) of the Caucasus. Botanical Zhurnal 78, 83–95 (1993).
65. Krawczyk, K., Nobis, M., Nowak, A., Szczecińska, M. & Sawicki, J. Phylogenetic implications of nuclear rRNA IGS variation in
Stipa L. (Poaceae). Sci. Rep. 7, 11506. https ://doi.org/10.1038/s4159 8-017-11804 -x (2017).
66. Krawczyk, K., Nobis, M., Myszczyński, K., Klichowska, E. & Sawicki, J. Plastid superbarcodes as a tool for species discrimination
in feather grasses (Poaceae: Stipa). Sci. Rep. 8, 1924. https ://doi.org/10.1038/s4159 8-018-20399 -w (2018).
67. Gudkova, P. D., Olonova, M. V. & Feoktisov, D. S. e comparison of ecologo-climatic niches of two species feather grass Stipa
sareptana A.K. Becker and S. krylovii Roshev. (Poaceae). Ukrainian J. Ecol. 7, 263–269. https ://doi.org/10.15421 /2017_115 (2017).
68. Lu, S. L. & Wu, Z. L. On the geographical distribution of the genus Stipa L., China. Acta Phytotaxon. Sin. 34, 242–253 (1996).
69. Wu, Z. L. & Phillips, S. M. Stipa. In Flora of China (eds Wu, Z. Y. et al.) 196–203 (Science Press, Beijing, 2006).
70. Olonova, M. V., Barkworth, M. E. & Gudkova, P. D. Lemma micromorphology and the systematics of Siberian species of Stipa
(Poaceae). Nordic J. Bot. 34, 319–328. https ://doi.org/10.1111/njb.00881 (2016).
71. Barkworth, M. E. & Everett, J. Evolution in the Stipeae: identication and relationships of its monophyletic taxa. In Grass sys-
tematics and evolution (eds Soderstrom, T. R. et al.) 251–264 (Smithsonian Institution Press, Washington, 1987).
72. Xia, L. et al. DArT for high-throughput genotyping of cassava (Manihot esculenta) and its wild relatives. eor. Appl. Genetics
110, 1092–1098. https ://doi.org/10.1007/s0012 2-005-1937-4 (2005).
73. Akbari, M. et al. Diversity arrays technology (DArT) for high-throughput proling of the hexaploid wheat genome. eor. Appl.
Genetics 113, 1409–1420. https ://doi.org/10.1007/s0012 2-006-0365-4 (2006).
74. Mace, E. S. et al. DArT markers: diversity analyses and mapping in Sorghum bicolor. BMC Genom. 9, 26. https ://doi.
75. Simko, I., Eujayl, I. & van Hintum, T. J. Empirical evaluation of DArT, SNP, and SSR marker-systems for genotyping, clustering,
and assigning sugar beet hybrid varieties into populations. Plant Sci. 184, 54–62. https ://doi.org/10.1016/j.plant sci.2011.12.009
76. Alam, M., Neal, J., O’Connor, K., Kilian, A. & Topp, B. Ultra-high-throughput DArTseq-based silicoDArT and SNP markers for
genomic studies in macadamia. PLoS ONE 13, e0203465. https ://doi.org/10.1371/journ al.pone.02034 65 (2018).
77. Abu Zaitoun, S. Y., Jamous, R. M., Shtaya, M. J., Eid, I. S. & Ali-Shtayeh, M. S. Characterizing Palestinian snake melon (Cucumis
melo var exuosus) germplasm diversity and structure using SNP and DArTseq markers. BMC Plant Biol. 18, 246. https ://doi.
org/10.1186/s1287 0-018-1475-2 (2018).
78. Bello, E. B. et al. Genetic diversity analysis of selected sugarcane (Saccharum spp. hybrids) varieties using DArT-Seq technology.
Philippine J. Sci. 148, 103–114 (2019).
79. Rutherford, S. et al. Speciation in the presence of gene ow: population genomics of closely related and diverging Eucalyptus
species. Heredity 121, 126–141. https ://doi.org/10.1038/s4143 7-018-0073-2 (2018).
80. Ivanizs, L. et al. Unlo cking the genetic diversity and population structure of a wild gene source of wheat, Aegilops biuncialis Vis.,
and its relationship with the heading time. Front Plant Sci. 10, 1531. https ://doi.org/10.3389/fpls.2019.01531 (2019).
81. Yu, J., Jing, Z. B. & Cheng, J. M. Genetic diversity and population structure of Stipa bungeana, an endemic species in Loess Plateau
of China, revealed using combined ISSR and SRAP markers. Genet. Mol. Res. 13, 1097–1108. https ://doi.org/10.4238/2014.Febru
82. Kopylov-Guskov, Y. O. & Kramina, T. E. Investigating of Stipa ucrainica и Stipa zalesskii (Poaceae) from Rostov Oblast using
morphological and ISSR analyses. Bull. Moscow Soc. Nat. Biol. Ser. 119, 46–53 (2014).
83. Boussaid, M., Benito, C., Harche, M., Naranjo, T. & Zedek, M. Genetic variation in natural populations of Stipa tenacissima from
Algeria. Biochem. Genet. 48, 857–872. https ://doi.org/10.1007/s1052 8-010-9367-7 (2010).
84. Zhao, N. X., Gao, Y. B., Wang, J. L. & Ren, A. Z. Genetic diversity and population dierentiation of the dominant species Stipa
krylovii in the Inner Mongolia Steppe. Biochem. Genet. 44, 513–526. https ://doi.org/10.1007/s1052 8-006-9054-x (2006).
85. Zhao, N. X., Gao, Y. B., Wang, J. L., Ren, A. Z. & Xu, H. RAPD diversity of Stipa grandis populations and its association with
some ecological factors. Acta Ecol. Sin. 26, 1312–1319. https ://doi.org/10.1016/S1872 -2032(06)60023 -1 (2006).
86. iers, B. Index Herbariorum: a Global Directory of Public Herbaria and Associated Sta. New York Botanical Garden’s Virtual
Herbarium https ://sweet gumny bg.org/scien ce/ih (2018).
87. WCSP. World Checklists of Selected Plant Families https ://wcsp.scien ce.kew.org/home.do (2019).
88. Sokal, R. R. & Sneath, P. H. A. Principles of Numerical Taxonomy (W.H. Freeman, San Francisco, 1963).
89. Korkmaz, S., Goksuluk, D. & Zararsiz, G. Mvn: an r package for assessing multivariate normality. R J. 6, 151–162. https ://doi.
org/10.32614 /rj-2014-031 (2014).
90. Pagès, J. Analyse Factorielle de Donnees Mixtes. Revue Statistique Appliquee 4, 93–111 (2004).
91. Husson, F., Josse, J., Le, S. & Mazet, J. FactoMineR: multivariate exploratory data analysis and data mining https ://facto miner
92. Cattell, R. B. e scree test for the number of factors. Multivariate Behav Res. 1, 245–276 (1966).
93. Kassambara, A. Factoextra: E xtract and Visualize the Results of Multivariate Data Analyses https ://rdrr.io/cran/facto extra (2015).
94. Sievert, C. et al. plotly: create interactive web graphics via ’plotly.js’ https ://rdrr.io/cran/plotl y (2017).
95. Wickham, H. ggplot2: elegant graphics for data analysis (Springer, New York, https ://ggplo t2.tidyv erse.org (2016).
96. Sansaloni, C. et al. Diversity arrays technology (DArT) and next-generation sequencing combined: genome-wide, high through-
put, highly informative genotyping for molecular breeding of Eucalyptus. BMC Proc. 5, 54. https ://doi.org/10.1186/1753-6561-
97. Kilian, A. et al. Diversity arrays technology: a generic genome proling technology on open platforms. Methods Mol. Biol. 888,
67–89. https ://doi.org/10.1007/978-1-61779 -870-2_5 (2012).
98. Courtois, B. et al. Genome-wide association mapping of root traits in a Japonica rice panel. PLoS ONE 8, e78037. https ://doi.
org/10.1371/journ al.pone.00780 37 (2013).
SCIENTIFIC REPORTS | (2020) 10:13803 |
99. Cruz, V. M. V., Kilian, A. & Dierig, D. A. Development of DArT marker platforms and genetic diversity assessment of the US col-
lection of the new oilseed crop lesquerella and related species. PLoS ONE 8, e64062. https ://doi.org/10.1371/journ al.pone.00640
100. Raman, H. et al. Genome-wide delineation of natural variation for pod shatter resistance in Brassica napus. PLoS ONE 9, e101673.
https ://doi.org/10.1371/journ al.pone.01016 73 (2014).
101. Gruber, B., Georges, A., Berry, O. & Unmack, P. dartR: importing and analysing snp and Silicodart data generated by genome-wide
restriction fragment analysis https ://cran.rproj ect.org/web/packa ges/dartR /index .html (2017).
102. Raj, A., Stephens, M. & Pritchard, J. K. fastSTRU CTU RE: variational inference of population structure in large SNP data sets.
Genetics 197, 573–589. https ://doi.org/10.1534/genet ics.114.16435 0 (2014).
103. RStudio Team. RStudio: Integrated Development for R www.rstud io.com (2016).
104. Burgarella, C. et al. Detection of hybrids in nature: application to oaks (Quercus suber and Q. ilex). Heredity 102, 442–452. https
105. Winkler, K. A., Pamminger-Lahnsteiner, B., Wanzenböck, J. & Weiss, S. Hybridization and restricted gene ow between native
and introduced stocks of Alpine whitesh (Coregonus sp.) across multiple environments. Mol. Ecol. 20, 456–472. https ://doi.
org/10.1111/j.1365-294X.2010.04961 .x (2011).
106. Dierking, J. et al. Anthropogenic hy br idization between endangered migratory and commercially harvested stationary whitesh
taxa (Coregonus spp.). Evol. Appl. 7, 1068–1083. https ://doi.org/10.1111/eva.12166 (2014).
107. Malde, K. et al. Whole genome resequencing reveals diagnostic markers for investigating global migration and hybridization
between minke whale species. BMC Genom. 18, 76. https ://doi.org/10.1186/s1286 4-016-3416-5 (2017).
108. Beugin, M. P., Gayet, T., Pontier, D., Devillard, S. & Jombart, T. A fast likelihood solution to the genetic clustering problem.
Methods Ecol. Evol. 9, 1006–1016. https ://doi.org/10.1111/2041-210X.12968 (2018).
109. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
https ://www.R-proje ct.org/ (2019).
110. Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100. https ://doi.org/10.1093/bioin
forma tics/bty19 1 (2018).
111. Schlüter, P. M. & Harris, S. A. Analysis of multilocus ngerprinting data sets containing missing data. Mol. Ecol. Notes 6, 569–572.
https ://doi.org/10.1111/j.1471-8286.2006.01225 .x (2006).
112. Rambaut, A. Figtree v1.4.4 https ://tree.bio.ed.ac.uk/sow are/gtr ee (2018).
e study was supported by a RSF grant (project no. 19-74-10067); partially by a DS grant of the Jagiellonian Uni-
versity (DS/MND/WB/IB/1/2018) and by the National Science Centre, Poland (grant no. 2018/29/B/NZ9/00313).
e authors would like to thank Dr. Ewelina Klichowska and Anna Wróbel (Jagiellonian University, Kraków)
for their help in conducting the eld studies. We would like to express our gratitude to Daria Kareva (Tomsk
State University, Russia) for preparing the line drawing of the nothospecies. Finally, we thank two anonymous
reviewers for providing valuable comments on the manuscript.
E.B. and M.N. designed and supervised the study. M.N. and A.N. performed the eld studies. M.N. collected
the specimens for the morphological and molecular analyses and performed the micromorphological analysis
on SEM. E.B. and P.D.G. performed the macromorphological analysis. E.B. performed the molecular analysis.
E.B. and M.N. analysed all the data and wrote the manuscript with the other authors’ helps. All authors revised
the dra, provided comments, and approved the nal version of the manuscript.
e authors declare no competing interests.
Supplementary information is available for this paper at https ://doi.org/10.1038/s4159 8-020-70582 -1.
Correspondence and requests for materials should be addressed to E.B.orM.N.
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