DOI: 10.5061/dryad.k0p2ngf59
title: Taxonomic revision of Delphinium (Ranunculaceae) in the south-east of European Russia

description: Morphological and phylogenetic (nrITS barcode) analyses are conducted to
 clarify the taxonomic status of 10 Delphinium species (D. cuneatum, D.
 dyctiocarpum, D. duhmbergii, D. elatum, D. litwinowii, D. pubiflorum, D.
 puniceum, D. sergii, D. subcuneatum, D. uralense) grown in the south-east
 of European Russia. The morphometric analysis is carried out with 22
 quantitative and 32 qualitative parameters. Based on all parameters, the
 PCoA supports the differentiation of D. puniceum, D. sergii, D. uralense
 and D. pubiflorum whereas other studied taxa remain undistinguished.
 Furthermore, the Random forest analysis and the MrBayes phylogenetic
 analysis of ITS sequences confirm the species independence of D. puniceum
 belonging to the Diedropetala section and D. elatum, D. uralense, D.
 dyctiocarpum and D. pubiflorum belonging to the Delphinastrum section.
 Finally, recursive partitioning is performed to develop the dichotomous
 key which can be used to differentiate between Delphinium species in the
 territory under study. In conclusion, we stress that the definition of
 species belonging to the Delphinastrum section is hindered by the presence
 of numerous intermediate or hybrid forms, on the one hand, and the impact
 of climatic conditions on the display of morphological (and specifically,
 taxonomically significant) traits, on the other.
description: <i>Morphometric
 analysis</i> Morphometric analysis was
 conducted for 790 specimens of 46 populations of 10
 <i>Delphinium</i> species (<i>D. cuneatum, D.
 </i><i>dyctiocarpum</i><i>, D.
 duhmbergii</i>, <i>D. elatum, D. litwinowii</i>,
 <i>D. pubiflorum</i>, <i>D. puniceum, D.
 sergii</i>, <i>D. subcuneatum, D. uralense</i>) grown in
 the south-east of European Russia (Astrakhan, Volgograd, Voronezh,
 Orenburg, Penza, Rostov, Samara, Saratov, Tambov and Ulyanovsk Provinces,
 and the Republics of Kalmykia, Mordovia and Bashkortostan) (Fig. 1, Table
 2). Voucher specimens from each population were deposited in the SARBG
 herbarium (Botanical Garden of Saratov State University, Saratov, Russia).
 The relatively small number of populations subject to the research was due
 to their scarcity throughout the territory in question, especially in the
 steppe regions. As a working hypothesis, we
 accepted Tzvelev’s understanding (2001) of the taxonomic structure of the
 genus. In order
 to maintain homogeneity in morphological data assessment, we limited the
 analysis to mature plants (Sharma, 2011). The generative growth stage was
 identified following Fyodorov’s recommendations (2003). The analysis included from 10 to
 30 specimens of each population. Some populations were represented by a
 smaller number of specimens due to the general small number of a
 population or the small proportion of mature specimens. Whenever a
 specimen had a gap in morphometric data in the final matrix, the missing
 quantitative value was filled in by the mean variable value. The
 proportion of missing data was below 1 %. The analysis involved 54 – 32
 qualitative and 22 quantitative – morphometric parameters. As for the
 qualitative parameters, values were assigned to each specimen based on a
 parameter display (ESM 1). The values of the quantitative parameters of a
 specimen’s flower, bracts, bracteoles and leaves were measured and listed
 in ESM 2 and 3. The analyzed qualitative
 parameters considered significant for the differentiation between the
 species of the <i>Delphinium</i> genus (Tzvelev, 2001) were as
 follows: pubescence of stem, inflorescence axis, pedicles, sepals (inside
 and outside), ovary and fruits; color of nectar petals and, for the
 <i>Diedropetala</i> section, sepals; form of blade base;
 divergence of lowest blade lobes; gap between lowest blade lobes; degree
 of leaf dissection; shape of bracts and bracteoles. In addition to the
 listed qualitative parameters, we analyzed a number of other qualitative
 (pubescence of spur and petiole) and quantitative parameters (plant
 height; shoot length; number of shoot leaves; number of paraclades; length
 of blade; length of non-dissected part of blade; width of blade; width of
 base of middle segment of central lobe; width of base of central lobe)
 (Kashin et al., 2019). For all statistic calculations, R
 environment was used. The whole dataset comprised 790 samples. The
 Shapiro-Wilk test was conducted to check the normality of the data. To
 analyze the dataset, several multidimensional approaches were applied.
 Non-metric multidimensional scaling
 (nMDS) (Ripley, 1996) with the Gower distance was carried out
 with all quantitative and qualitative parameters. First, we performed the principal
 coordinate analysis (PCoA); hulls were constructed. The distances between
 the hull centers as well as the degree of hull overlap were measured.
 Also, morphometric and geographic data were subject to the correlation
 analysis with the nMDS axes. Second, the Random forest
 analysis (Liaw, Wiener, 2002) was applied to all data subsets; the earlier
 predicted species identities were taken into account. For the analysis, we
 used the “classification and training” method described in Shipunov and
 Efimov (2015). Third, and finally, we conducted
 recursive partitioning (Venables, Ripley, 2002) – another training method
 which analyzes the available classification to develop binary trees for
 the remaining dataset. The structure of such trees resembles a dichotomous
 key (Therneau et al., 2014). Therefore, we used the results of our
 recursive partitioning to generate the dichotomous key that can be used to
 define <i>Delphinium</i> species in the territory under
 study. <i>Molecular
 analysis</i> DNA was extracted from the
 perianth leaves and leaflets previously dried in silica gel. DNA was
 extracted using the NucleoSpin® Plant II kit (MACHEREY-NAGEL, Germany)
 according to the manufacturer’s protocol. 10 non-coding and potentially
 highly-variable cpDNA regions:
 <i>ndh</i>C<i>−trn</i>V, <i>ndh</i>F,
 <i>pet</i>A<i>−psb</i>J,
 <i>psb</i>E<i>−pet</i>L,
 <i>trn</i>T<i>−psb</i>D (Dong et al., 2012),
 <i>trn</i>C<i>−pet</i>N (Lee, Wen, 2004) and
 <i>trn</i>L–<i>trn</i>F,
 <i>trn</i>H<i>−psb</i>A,
 <i>trn</i>S<i>−trn</i>G,
 <i>mat</i>K<i> </i>(Jabbour et al., 2012) – were
 amplified with specimens of 10 <i>Delphinium</i> species from
 pure populations (containing no intermediate forms). The whole dataset was
 analyzed using the inter-gene transcribed spacer of ribosomal DNA (ITS1,
 5.8S and ITS2) which was amplified with the NNC-18S10 and C26A primers
 (Wen, Zimmer, 1996). The polymerase chain reaction
 (PCR) was carried out in 50 µl reaction volumes. The reaction mixture
 contained 10 µl ready-to-use PCR mix MaGMix (200 µM of each dNTP, 1.5 mM
 MgCl2, 1.5 U SmarTaqDNA-polymerase and buffer; Dialat Ltd., Moscow,
 Russian Federation), 35 µl of deionized water, 3.4 pmol of each primer and
 5 µl of template DNA. The PCR was conducted in the Mastercycler gradient
 thermocycler (Eppendorf, Germany). The program was as follows: preliminary
 denaturation during 5 minutes at 95° C, then 35 cycles of 30 seconds each
 at 95° C, 30 seconds at 55° C and 2 minutes at 72° C, then final
 elongation during 10 minutes at 72° C. PCR products were cleaned with
 agarose gel and eluted with the NucleoSpin® Gel and PCR Clean-up kit
 (MACHEREY-NAGEL, Germany). Sequencing was performed in the
 ABI PRISM 3130 XL sequencer using the BIG DYE TERMINATOR kit ver. 3.1
 according to the manufacturer’s protocol from the SINTOL Company (Moscow,
 Russian Federation). Forward and reverse sequences
 were edited and manually aligned in the BioEdit 7.0.5.3 program (Hall,
 1999). The obtained DNA sequences were submitted to the GenBank (accession
 numbers MT137556 - MT137651;
 MT154543-MT154615). The analysis also included sequences of
 several <i>Delphinium</i> specimens imported from the GenBank
 as outgroups. The
 matrices were analyzed by the Maximum Likelihood method (ML) using the
 Tamura-Nei model in the MEGA ver. X program (Kumar, 2018). The
 evolutionary direction of sequence changes was inferred by the outgroup
 comparison. All most parsimonious trees were summarized by the strict
 consensus method. Bootstrap analyses (1000 replicates) were performed to
 assess support of the clades (Felsenstein, 1985). Bayesian phylogenetic
 analyses were performed with MrBayes 3.1.23 (Ronquist, Huelsenbeck, 2003).
 The sequence evolution model was evaluated by the Akaike Information
 Criterion (AIC) in jModeltest 3.7 (Darriba et al., 2012). Two independent
 analyses with four Markov chains were run for 10 million generations,
 sampling a tree every 100 generations. 25% of initial trees were discarded
 as burn-in. The remaining 250000 trees were combined into a single data
 set, and a majority-rule consensus tree obtained. Bayesian posterior
 probabilities were calculated for that tree in MrBayes 3.1.23.
 Also, the statistical
 parsimony analysis was performed according to the algorithm described in
 Templeton et al. (1992) and carried out in TCS v. 1.21 (Clement et al.,
 2000). In general, the method evaluates the unrooted haplotype network and
 all the links between haplotypes with 95% probability. Only nucleotide
 substitutions were taken into account; indels were treated as the missing
 data. The analysis of molecular variance (AMOVA) was
 conducted in the ARLEQUIN 3.5 program (Excoffier, Lischer, 2010) based on
 the pairwise comparison of specimens with 1000 permutations. To evaluate
 the dispersion between the species and species groups, we used the
 F<sub>CT </sub>(inter-group dispersion) and
 F<sub>SC</sub> (inter-population intra-group dispersion)
 values which were to be maximum and minimum respectively. The correlation
 between genetic and geographical distances between populations was
 evaluated by the Mantel test.
description: Some populations were represented by a smaller number of
 specimens due to the general small number of a population or the small
 proportion of mature specimens. Whenever a specimen had a gap in
 morphometric data in the final matrix, the missing quantitative value was
 filled in by the mean variable value. The proportion of missing data was
 below 1%.