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Comparison of the Efficiency of Single-Locus Species Delimitation Methods: A Case Study of a Single Lake Fish Population in Comparison against the Barcodes from International Databases

Dmitry P. Karabanov
Alexey A. Kotov
Elena A. Borovikova
Yulia V. Kodukhova
1 and
Xiaowei Zhang
Papanin Institute for Biology of Inland Waters of Russian Academy of Sciences, 152742 Borok, Russia
A.N. Severtsov Institute of Ecology and Evolution of Russian Academy of Sciences, Leninsky Prospect 33, 119071 Moscow, Russia
State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
Authors to whom correspondence should be addressed.
Water 2023, 15(10), 1851;
Submission received: 14 March 2023 / Revised: 26 April 2023 / Accepted: 7 May 2023 / Published: 12 May 2023
(This article belongs to the Special Issue Biogeography and Speciation of Aquatic Organisms)


To date, a rather large set of both mathematical theories for species delimitation, based on single-locus genetic data, and their implementations as software products, has been accumulated. Comparison of the efficiencies of different delineation methods in the task of accumulating and analyzing data with reference to different taxa in different regions, is vital. The aim of this study was to compare the efficiency of fifteen single-locus species delimitation methods using the example of a fish species found in a single lake in European Russia (Lake Plescheyevo) with reference to other sequences of revealed taxa deposited in international databases. We analyzed 186 original COI sequences belonging to 24 haplotypes, and 101 other sequences previously deposited in GenBank and BOLD. Comparison of all 15 alternative taxonomies demonstrated that all methods adequately separate only the genera, while the number of delimited mOTUs differed from 16 (locMin) to 43 (HwM/CoMa). We can assume that the effectiveness of each method is correlated with the number of matches based on Ctax and MatchRatio criteria. The most comparable results were provided by bGMYC, mPTP, STACEY, KoT and ASAP and the most synchronous results were obtained from bGMYC, mPTP, STACEY and ASAP. We believe that these results are maximally realistic in the number of revealed mOTUs. A high genetic diversity, resulting in the existence of several mOTUs and phylogenetic lineages within many species, demonstrates the usefulness of the “polymorphic species” concept, which does not underestimate species richness and does not prevent the rational use and protection of biodiversity.

“What’s the use of their having names,” the Gnat said, “if they won’t answer to them?”
“No use to them,’ said Alice; ‘but it’s useful to the people who name them, I suppose. If not, why do things have names at all?”
Lewis Carroll “Through the Looking-Glass and what Alice found there” (1871).

1. Introduction

The development of objective methods for assessing terrestrial and water biodiversity is an urgent recent task for biological sciences. The urgency of aquatic ecosystem studies is reflected in Agenda 21 of the Declaration on Environment and Development [1], with special attention to continental water bodies. It is specially noted that special attention, during the assessment of biological diversity of aquatic ecosystems, should be paid to issues of inventory and preservation of local and relict forms.
A general trend in modern studies of biodiversity assessment is the wide use of molecular genetic approaches. Moreover, phylogenetic trees have become key to understanding the biodiversity and speciation processes in different organisms [2]. An important circumstance for the success of biodiversity assessment is the development, and widespread implementation, of mathematical models for processing obtained genetic data and further progress in computational methods for phylogenetic reconstructions.
In general, methods of species differentiation and delimitation can be divided into single-locus and multi-locus methods [3]. The most renowned and important single-locus method for assessing local and global biodiversity of animals is the so-called “DNA barcoding”, based on analysis of the nucleotide sequences encoding a 5′ segment of the first subunit of mitochondrial cytochrome oxidase-c mtDNA gene (COI, COX1). “Barcodes” are recognized as universal markers for species-specific identification [4], although some shortcomings of this approach are reasonably criticized by many taxonomists [5,6,7,8,9]. It is known that the introduction of genetic methods in some cases makes animal identification even more difficult and complex as compared to morphological identification [10], and gene trees do not always accurately reflect patterns of biological evolution [11]. However, in many cases, a COI gene sequence allows reliable identification of animal species [12]. In contrast, progress in this area is much more modest for algae, fungi and higher plants [13,14,15,16]. Although work on species identification increasingly requires analysis of a large set of genes, and movement toward genomic technologies [7,17,18], DNA barcoding of animals from different systematic groups is one of the most popular methods for genetic identification of animal species [19]. In general, publications on single-locus delimitation predominate in current scientific literature [20].
However, taxonomy is currently passing through a “splitting” phase, resulting in “taxonomic inflation”, in that individual subspecies, population groups, or even individual populations are ranked as independent species [21]. This inflation directly impacts not only taxonomy, but also conservation and environmental management. On the one hand, assigning species status to local populations permits more optimal conservation actions towards them [22], and a greater number of species identified in a region directly confirms its greater biodiversity value [23]. On the other hand, “splitting” of taxa can also be detrimental to bio-resource management. For example, when a species is unreasonably split into several units, loss of genetic variability, and even extinction, can occur during its captive breeding or metapopulation management [24]. Accurate species identification is very important in revealing recent cases of anthropogenic biological invasions, including cryptic invasions with potentially unknown effects on ecosystems [25], and reduction of the number of false negative and false positive cases of invasion detections among different animal groups [26]. Therefore, it is crucial to identify true species not only on the basis of their general biological significance, but also from the perspective of environmental management and conservation of rare and endangered forms.
A current trend of genetic-based taxon identification is increased automation of species delimitation [20,27,28]. The following different approaches are applied: direct comparison with reference sequences in international databases, such as NCBI GenBank and BOLD [29,30]; distance-based methods and a determination of genetic divergence thresholds [31,32,33]; Generalized Mixed Yule Coalescent models [34,35]; Poisson Tree Processes [36,37]; the multispecies coalescent process [38,39]; analysis of haplotype networks [40], etc.
To date, a rather large set of both mathematical theories for species delimitation and their implementation as software products has been accumulated. Several different methods are used in current studies, but the choice is rarely justified by an applicability analysis with reference to a certain studied material [41]. The “machinist” approach for species delimitation is subject to justified criticism [42,43]. A serious reason for criticism of the “cybertaxonomic” approach is related to the fact that different methods and different delimitation scenarios often produce significantly different results based on the same dataset [44,45]. Some interesting examples of combining different methods in a single system are proposed, such as the single Python iTaxoTools [46] or a large R-script pipeline [47]. However, usually each researcher in his/her study chooses a set of approaches and their particular implementation is mainly based on personal preferences rather than on accurate justification of the approach(es) used. It remains an important task to compare the efficiencies of different delineation methods in accumulating and analyzing data with reference to different taxa in different regions.
The model group for studying biodiversity in continental water bodies is fish, a most important biological water resource for humanity. Barcoding of fish populations has been conducted in many regions of the planet [48,49,50,51,52]. Using the example of fish, a well-studied animal group, it is interesting to compare the efficiencies of different delimitation methods in specific river basins, or even single water bodies, with adequate knowledge of the species composition developed in the course of intensive previous morphological and genetic work.
Lake Plescheyevo, located in the Yaroslavskaya Oblast’ (Yaroslavl Area) of Russia, well meets the requirements as a suitable water body. “Lake Plescheyevo” National Park was originally created to protect the relict population of the Pereslavl vendace Coregonus albula pereslavicus Borisov, 1924 (Actinoptery: Salmonidae), listed in the Red Book of the Russian Federation [53]. In addition, a unique “molluscivorous” form of the roach Rutilus rutilus (Actinoptery: Leuciscidae) is found in the same water body [54]. The fish species composition in Plescheyevo Lake is relatively well studied [55], but total barcoding was never conducted for its fish population.
The aim of this study was to compare the efficiencies of different single-locus species delimitation methods on examples of the fish species found in this single lake in European Russia with reference to other sequences of revealed taxa deposited in international databases.

2. Materials and Methods

2.1. Sampling

Lake Plescheyevo (56.76° N; 38.78° E) is a water body in Central Russia (Figure 1) with a shoreline of ca. 27 km and area of 51.5 km2. The lake has an oval shape, its littoral zone has a depth of up to 3 m and it occupies ca. 20% of the total area of the lake. The town of Pereslavl-Zalessky, with a population of about 40,000, is situated on the south shore of the lake [56]. The lake is connected with the Upper Volga by the relatively small Vyoksa River. However, the latter is impeded by a dam 5 km away from its source for regulation of the flood level; this dam causes considerable isolation of the lake and its fish population from the Volga basin. Lake Plescheyevo has extremely poor ichthyofauna—only 12 species of fish have been identified here by morphological methods [55].
Fish were sampled during the field seasons of 2014–2016 from May to October, by means of gill nets in the littoral zone, in the following three biotopes: in the area of Solomidino village (56.75535° N, 38.74237° E), near the Urev site (56.79983° N, 38.75352° E) and in the area of the Kukhmarka river mouth (56.79542° N, 38.78621° E). A total of 117 gill net catches were processed. Overall net exposure was 760 h. In addition, fish fry in the littoral zone were caught using the minnow seine. All catches were analyzed according to standard methods [57,58]. For molecular genetic analysis, a portion of the caudal fin blade or a piece of skin and muscle behind the dorsal fin was taken from each specimen and fixed in 95% ethanol cooled to −20 °C, while the voucher specimen was fixed in 4% formalin for subsequent morphological analysis. All vouchers are kept in the collection of the Ecology of Fishes of the Papanin Institute for Biology of Inland Waters of the Russian Academy of Sciences, Borok, Russia (see Supplementary Table S1).

2.2. From DNA Extraction to Phylogenetic Analysis

Sample preparation, total DNA isolation, PCR reactions and Sanger sequencing were performed according to a previously published protocol [52]. Primary analysis of the chromatograms, contig assemblage and sequence editing was made in the Sanger Reads Editor block of the uGENE ver. 46 package (Unipro, Novosibirsk, Russia) [59]. The original sequences were deposited with GenBank (National Center for Biotechnology Information, Bethesda MD, USA) under numbers KT989758-73 and KY228990. Previously obtained COI sequences for fish from this lake were included in the analysis, namely KT254047-49, KT254051, KT254053, KT254055. A sequence GQ328816 [60] of the Siberian sturgeon (Acipenser baerii) was used as an a-priori outgroup. For comparative purposes, for each taxon we took from the NCBI GenBank, by the ‘megablast’ query [61], several closest and several maximally distant (by criterion E-value with 100% Query Cover) sequences of the same taxon with a maximally wide geographic coverage. The sequences unambiguously belonging to the congener taxa were included in the analysis. As a result, we formed a set of 125 sequences for precise taxon identification and delimitation (see Supplementary Table S1). Ambiguous taxonomic marks (sp., cf., complex, clade) were removed from our analysis, because they are useless for accurate species delimitation. As a result, we had sequences from seven families of Actinopterygii. The taxonomy is represented according to the last edition of FishBase, ver. 02/2023 [62].
If not specially described below, all the calculations were made in “Microsoft R-Open and MKL” 64-bit ver. 3.5 (Microsoft Corp., Redmond, WA, USA;, accessed on 15 January 2023), Python 64-bit ver. 3.11 (Python Software Foundation, Dover, DE, USA;, accessed on 15 January 2023), or Java Development Kit 64-bit ver. 19 (Oracle Corp., Redwood Shores, CA, USA;, accessed on 15 January 2023) with necessary packages installed. For this work, we principally used only public and non-commercial open-source software. Simple scripts in “R” for the delimitation is represented in Appendix A.
Sequence alignment was performed using the MAFFT ver. 7.5 [63], with “Translation Align” options, and the FFT-NS-I strategy. Phylogeny was reconstructed based on a whole set of “unlinked” data using [64] maximum likelihood (ML), Bayesian inference (BI) and maximum parsimony (MP).
ModelFinder ver. 1.6 [65] was used to search for the best model of nucleotide substitutions on the web portal of the Center for Integrative Bioinformatics (CIBIV, Vienna, Austria;, accessed on 17 January 2023) [66]. The substitution pattern was identified for each nucleotide position in the codon (1st—TIM3e + G4; 2nd—F81 + F + I; 3th—TN + F + G4), based on the minimum values of the Bayesian information criterion, BIC [67]. It should be noted that the parameters of the BIC model were almost identical to those determined on the basis of the corrected Akaike’s information criterion, AICc, which may indicate high agreement of the calculated models with the real best model [68].
For maximum likelihood (ML) analysis, we used the IQ-TREE ver. 2.2 algorithm [69]. As a branch support test, we used 10 k replicas of the UFboot2 bootstrap test [70]. Topology estimation for ML trees was based on 1 k replicates of SH-aLRT test [71], realized in the Building Phylogenetic Tree block of the uGENE. The ML tree constructed from the initial data was a realized (and not true) phylogenetic tree, and for such a case there is no unambiguous opinion about the correctness of using topological tests to check the monophyly of certain branches. However, in combination with a standard bootstrap, this procedure can be useful for assessing the group monophyly [72].
Maximum parsimony (MP) tree was achieved using a high-speed “New Technology” algorithm TNT ver. 1.5 [73]. As a branch support test, we used a standard bootstrap with 1 k replicas [74].
Reconstruction of the phylogeny using Bayesian inference (BI) was carried out using the BEAST2 ver. 2.7 software package [75] and the recommendations of [76]. Each analysis included six independent runs of MCMC, 50 M generations each, and selection of each 10 k tree. The effectiveness of MCMC on the convergence of the results of all independent runs with the estimated effective sample size (ESS) for all parameters above 200 was carried out with the Tracer ver. 1.7 program [77]. After combining the results of all MCMC runs through LogCombiner ver. 2.7 [78], a consensus tree was computed, based on the maximum confidence clade (MCC) using TreeAnnotator ver. 2.7 [78] with 25% burn-in.

2.3. Different Methods of mOTUs Delimitation

We performed a comparative analysis of almost all available and widely used methods of molecular operational taxonomic units, mOTUs [79] realized in different software packages.
Traditional identification of vouchers. Primary identification of our vouchers was based on morphological characters, and, then, sequences were compared to the reference sequences in two international databases: NCBI GenBank [30] and The Barcode of Life Data System [29]. A search of GenBank was conducted through the high speed HS-BLASTN [80] query. The exact identification was represented according to the NCBI Taxonomy Database [81]. Species identification in BOLD was performed through the ‘bold_identification’ script [82] with the demonstration of 10 best fits in automatic mode. Note that just the initial author’s identifications were used for the GenBank and BOLD sequences, without the possibility to check accurateness and correspondence to recent fish taxonomy.
Approach based on the population genetic theory. Our analysis included calculations of the K (the average pair distance between putative species-level clades) over Theta (estimation of the genetic diversity) rate, performed with the “KoT—K over Theta” ver. 1 package [31]. To prevent excessive splitting, we set up a stricter (>6) K/Theta threshold value, used for species delimitation with p < 0.01. It is known that the population genetic method, and, in particular “K over Theta”, depends excessively on nucleotide diversity, volume of studied material, population structure and even peculiarities in the migrations within the taxon [72]. Use of the standard value of K/Theta < 4 leads to identification as species of some clades which are apparently not species. We increased the threshold value to K/Theta < 6 as a way to resolve the problem of excessive “splitting” in the method [83].
The threshold-based delimitation methods. Analysis based on a direct calculation of the genetic divergence [4], keeping in mind the threshold value of 2.1%, was performed with the TaxonDNA/SpeciesIdentifier ver. 1.8 program [33]. The second approach [84], based on the “barcode gap” concept and formalized in the “Local Minima” function (locMin), was realized with the ‘spider’ package [85]. A simple script in “R” for the locMin calculation and the delimitation using this approach is represented in Appendix A. Our calculated threshold of 3.2% agreed well with the literature data [32] and was used for the “species gaps” findings in further automatic methods of the delimitation, as follow: ABGD, Automatic Barcode Gap Discovery [86] and ASAP, Assemble Species by Automatic Partitioning [87] through the ‘asapy’ and ‘abgdpy’ scripts of the iTaxoTools ver. 0.1 package [46], based on “simple” p-distances as the most preferable for barcoding purposes [88].
It is known that among the “barcoding gap” methods, a key moment is genetic threshold determination, which is based on some empirical observations. The level of 2.1–2.5% is most frequently used for fishes [89], but we need to note that this method is relatively effective for the COI locus only, with a great number of sequences deposited in GenBank. Using the SpeciesIdentifier, we needed to take into consideration that the threshold could be different in different genera, and even in families [90]. An automatic search for the “gaps” has some limitations, both in ABGD and ASAP, which could be partly resolved if the threshold is calculated separately using the locMin algorithm [91], although the latter tends to give thresholds that are too conservative for the discrimination of slightly different species.
Method of the Generalized Mixed Yule Coalescent (GMYC). If different variants of the GMYC realization led to similar delimitation results [35], we selected a “classical” scheme in the ‘splits’ package [92]. Bayesian implementation was performed with the ‘bgmyc’ package [93]. Our analysis followed an algorithm successively applied by us for invertebrates [94]. The cladogenesis threshold was determined to be p < 0.01 to prevent the possibility of redundant “splitting”. The scripts for “R” for the calculation of GMYC and bGMYC are represented in Appendix A. Note that a correct realization of ‘bgmyc’ is better performed in “R” ver. 3.x (The R Foundation for Statistical Computing, Vienna, Austria) while some bugs are possible in more recent versions.
GMYC has recently become one of the most frequently used delimitation methods, and just ‘splits’ realization is widely used in frames of the “traditional model” [34,92], because of its “just add water” approach. However, it is known that such delimitation results in excessive “splitting” [95]. Moreover, GMYC has a limitations in its objective representation of data and biological uncertainty of models [96]. Bayes realization in the ‘bgmyc’ package has a large set of adjusted parameters and works better with incomplete and strongly diverse datasets [93]. A strict criterion of p < 0.01 must be used to prevent false positive results [97].
Tree analysis based on the Poisson tree processes (PTP). Initial Python codes for realization of PTP and Bayesian implementation of PTP are published at their GitHub repositories: (b) PTP at (accessed on 20 January 2023) and mPTP at (accessed on 20 January 2023). In all cases, we used the ML and kept the outgroup [27] as the input tree, and all other values were set up according to general recommendations for trees with a smaller (<50) number of taxa [36,37].
PTP is a powerful delimitation method, which has traditional (PTP) and Bayesian (bPTP) realizations [36], and also has the most powerful multi-rate version (mPTP) [37]. Note that mPTP works well just with mitochondrial genes demonstrating no incomplete lineage sorting and hybridization consequences.
Delimitation using the multispecies coalescent model. Our analysis was performed with the additional packages of BEAST2: “Species Tree And Classification Estimation, Yarely”— STACEY ver. 1.3 [38] and “SPEciEs DEliMitatiON”—SPEEDEMON ver. 1.1 [39]. All calculations were made in BEAST2 with the parameters were as in the phylogenetic analysis (see Section 2.2). Formation of the input XML files and their analysis and post-processing were performed according to the manufacturer recommendations.
The main advantage of delimitation using GMYC is that we did not need an a-priori species identification and an input tree. In SPEEDEMON (in contrast to STACEY), the Yule–skyline collapse model is used, with the speciation rate changing in time [39]. Two models are made for a multi-locus analysis, and they are also applicable to the single-locus delimitation [98]. However, we needed to take into consideration the point of view [99] that multi-species coalescences in reality delimit the population structure instead of real biological species, and we needed to be very critical in our interpretation of such delimitations.
Haplotype network analysis. Our analysis was performed with the HaplowebMaker/CoMa ver. 1 [40], and the initial code. Java-realization, available at (accessed on 20 January 2023), operated as a pipeline, allowing construction of the haplotype network so as to obtain the partition matrix summarizing the partitions inferred from each marker, transforming them to a conspecificity matrix and, finally, visualizing the results. The Median Joining algorithm [100] was used for the network construction, with the default values of all parameters.
The haplotype network analysis has an advantage over the tree analysis due to its taking into consideration, and visualizing, many alternative relationships between haplotypes and their groupings into larger structures. Such an analysis is very important for revealing the centers and means of organism dispersion, population and demographic models and the geographic limits of species distribution [101,102]. Selection of mOTUs based on networks in HaplowebMaker/CoMa, as with any other methods of phylogeographic analysis, is more acceptable for the determination of monophyletic clades and lineages than species identification and delimitation. The results of any phylogeographic reconstruction depend on the volume of studied material and adequate representation of the distribution range by samples. Moreover, cases of recent biological invasions make such an analysis more complicated [103]. Therefore, it is necessary to be very critical in using haplotype networks for species delimitation.
Approach to compare different delimitation methods. A qualitative comparison of alternative partitions was performed in the LIMES ver. 1.3 [104] and CoMa ver. 1 [40] programs. The algorithms of LIMES are analogous to those of CoMa, but the former provides the possibility to calculate several indices and to conduct a comparative analysis of different delimitation methods i.e., Ctax [105] and MatchRatio [106]. The conspecificity matrices integrating the results from 15 different methods of species delimitation was visualized in “R” package ‘heatmap3’ [107].
As in Box’s aphorism “all models wrong” [108], here, we followed a “caution” taxonomic strategy in the interpretation of genetic data [41], whereby “in most contexts it is better to fail to delimit species than it is to falsely delimit entities that do not represent actual evolutionary lineages”. The delimitations with a minimal number of taxa were preferable to us, while a higher number of mOTUS was interpreted as excessive splitting.
All original materials, namely DNA sequences, alignments, phylogenetic trees and images, used in this study are publicly available in the Supplementary Material and the Open Science Framework repository (, accessed on 9 May 2023).

3. Results

3.1. Phylogenetic Analysis

We performed genotyping of 186 fish specimens, 24 haplotypes (21 unique) having been deposited in GenBank. In total, 24 original sequences and 101 other sequences from the GenBank and BOLD were included in the analysis. A preliminary identification of each specimen, based on morphological characters, is represented in Supplementary Table S1.
Due to the full topological consistencies of the ML, BI and MP trees, we represented only the BI tree (Figure 2), with main branch support calculated by means of different methods.
Different approaches gave somewhat different support of deeper branches, although families and genera were, in general, well-supported by all methods. Lower values of the bootstrap could be explained by a low number of parsimony-informative sites in our dataset. A relatively high support was characteristic for the genera with the exclusion of all genera of Leuciscidae, but different species had different supports. Genetic clusters were fully consistent with the traditional morphospecies in Gymnocephalus, Perca, Lota, Abramis, Blicca. The Coregonus and Esox demonstrated some internal clusters, although their supports were low. It was possible to define supported intraspecies phylogenetic lineages within the species from the genera Carassius, Leusiscus, Tinca, Rutilus (Figure 2).

3.2. Comparison of Different Approaches to mOTU Delimitation

The results of 15 different methods of delimitation, based on the same genetic dataset, are represented in Figure 2 and Figure 3 and Supplementary Figures S1 and S2. Please take into consideration that the CoMa heatmap, represented in Figure 3, visualizes the conspecificity of delimitation methods rather than relationships between the taxa. All methods allowed unambiguous identification of all morphospecies inhabiting the lake, although some were represented by several OTUs. Different methods of the delimitation suggested different numbers of mOTUs (presumable biological species): from 16 (locMin) to 43 (HwM/CoMa) (Figure 2).
After comparison with our original sequences, we concluded that most sequences in Genbank and BOLD were correctly identified by their depositors, with the exception of three species of the genus CoregonusC. albula, C. sardinella and C. autumnalis. Note that BOLD referred to one-third more taxa relative to the more conservative GenBank (33 vs. 25). KoT delimitation suggested a lower taxonomic diversity (only 19 mOTUs). In fact, these mOTUS were consistent with traditional morphospecies with the exception of the genera Leuciscus, where L. idus and L. leuciscus were not differentiated, and Tinca with T. tinca were represented by two phylogenetic lineages. The methods based on the “barcoding gap” gave differing results in that SpId and ABGD suggested many more mOTUs (26 and 22, respectively) than locMin and ASAP (17 mOTUs). In the last case, the mOTUs were consistent with traditional morphospecies, with the exception of the genus Coregonus (as above Figure 2). “Classical” GMYC excessively split the clades (27 mOTUs), while Bayes realization provided only 19 mOTUs. The same differences were observed for the Poisson tree processes, whereby the mPTP approach provided 19 and the bPTP approach provided 31. The former supported mainly the “traditional” morphospecies while the latter also supported a separation of some recently described taxa and some other independent phylogenetic lineages (see Section 4). Both variants on the coalescent-based analysis separated a similar number of mOTUs (18 and 20), the differences between them concerned the genera Esox and Coregonus (as above Figure 2). Finally, HaplowebMaker provided the maximum number of mOTUs — 43, which was an unacceptable splitting. Comparison of all 15 alternative taxonomies, based on the Ctax and MatchRatio criteria (Table 1), allowed us to conclude that they adequately separated only the genera and most differentiated species.
We concluded that using a full set of these methods in a single study is redundant. We assumed that the effectiveness of each method correlated with the number of matches in each analysis, so bGMYC, mPTP, STACEY, KoT and ASAP provided the most comparable results (Figure 3), and the most synchronous results were obtained from bGMYC, STACEY, ASAP and mPTP (Table 1). We believe that these results are maximally realistic in the number of revealed mOTUs.

4. Discussion

In many cases, routine genetic species identification includes only a comparison of original sequences with some reference sequences in the international databases. It is necessary to take into consideration that the latter could be contaminated by DNA of other organisms [109] or represent initially incorrectly identified taxa [110]. Sometimes the barcoding results need to be specially “de-coded” [8]. The situation is also made more complicated by hybridization, whereby a specimen belongs to a certain morphospecies taxon, while sequences of its mitochondrial genes belong to a second species [111,112]. Finally, difficulties in using a “simple” comparison with references for species identification could be related to an unstable group taxonomy, personal preferences of previous authors and the aforementioned “taxonomic inflation” [24]. We recommend a comparison with the NCBI GenBank and BOLD reference barcodes as the first step in sequence identification only, instead of being a good method for species delimitation. For the latter, a combination of several advanced methods based on different approaches is necessary.
Above, we describe a particular situation when the results of different delimitation methods contradicted each other. Such differences could be partly explained by the known shortcomings of each method (see Section 2.3 above) but the rate of such differences probably varies between different taxa due to differences in their genetic patterns.
Different methods could suggest a very different number of mOTUs in a genetic dataset. In our case, as usual, a better correspondence of delimitation by different algorithms was observed in well-studied groups with a relatively simple taxonomic structure. A mismatch of one or more methods in a particular group is a signal for taxonomists to pay closer attention to it [20]. In our dataset, the mOTUs in the genera Gymnocephalus, Perca, Lota, Alburnus, Abramis, Blicca almost each time corresponded well to the traditional morphospecies (Supplementary Table S1). Separation of several mOTUs within some other taxa could be explained by their disjunctive distribution ranges resulting from dispersion from isolated glacial refugia during the Pleistocene age, as in the case of Tinca tinca [113]. The genetic pattern of the Carassius auratus complex is probably related to a distribution range disjunction and a subsequent anthropogenic introduction of the Far Eastern lineage to Europe [114].
At the same time, some conclusions on the species delimitation do not depend on the approach. Remarkably, most methods did not support separation of such traditional fish morphospecies as Leuciscus leuciscus and L. idus (see initial identifications of the sequences from GenBank and BOLD in our tree, Figure 2). Most methods did not support a separation of several taxa within Esox lucius and Rutilus rutilus, in contrast to the opinions of some recent investigators [115,116]. Finally, within the genus Coregonus we observed low genetic differentiation [117] between traditionally separated [118] species, such as European (C. albula) and Siberian (C. sardinella) vendace. Only the Arctic cisco (C. autumnalis) represented a separate mOTU (Figure 2). Most delimitations supported the opinion [119] on the inexpediency of accepting C. albula and C. sardinella as separate species.
We demonstrated again that different mathematical methods suggest separation of different mOTU numbers. It should be noted that results of any phylogeographic reconstructions depend on the volume of studied material and adequate representation of all the distribution range by samples, moreover, cases of recent biological invasions make such analysis more complicated [120].
To date, about 30 different species concepts have been proposed, based on different criteria, and some of them apparently contradict each other [121]. In general, species and mOTUs are different matters [122], although in many publications (especially in cases of cryptic species) they are accepted as identical [123], and such identity at least requires a special study [124]. According to the “phylogenetic species” concept, any population of a non-hybrid origin could be regarded as a species [125]. We think that a more adequate solution for species delimitation is to return to the “polymorphic” or “polytypic” species [126,127,128]. These two concepts are similar in species interpretation. The polymorphic species [127] unites several allopatric and sympatric population groups, while the polytypic species is a complex unit subdivided into subspecies and geographic races [128]. In any case, the animal groups are regarded as morphs or subspecies in cases of the absence of reproductive isolation, and as independent species in cases of its presence. Species polymorphism is not doubted by any investigators [129], and phylogenetic lineages, morphs and races are well known in different animals [94,130,131,132]. There are no problems in using such concepts for interpretation of genetic data, with phylogenetic lineages providing the species polymorphism instead of being separate taxa in the understanding of the International Code of Zoological Nomenclature [133].
Acceptance of the local populations as separate species is a way to simplify legislative implementation (including any red lists) and fundraising for their protection [22]. Based on such approach, we can regard some phylogenetic lines of vendace from Plescheyevo Lake as a separate species (it does not contradict the genetic data [134]) and based on some delimitation methods giving a larger number of mOUTs but we believe that this is not a scientific approach [135]. In our case, just the vendace population from Plescheyevo Lake is included in the Red Book of Russian Federation [53], while this species is not protected in all other territories of the country.
We need to remember that DNA barcoding is not a fully universal method, and, in some cases, it is not able to differentiate “good” species well supported by nuclear phylogenies, due to complex reasons not discussed in our paper, see [136]. Only a combination of different methods (study of mitochondrial, nuclear genes and morphology) could lead to adequate understanding of the biodiversity in different water bodies.

5. Conclusions

In this study we report on the DNA barcoding of all mass fish species in Plescheyevo Lake and we used these barcodes, plus those from international databases, to compare 15 different methods of species delimitation. Based on the fits between different methods, we conclude that the effectiveness of different methods strongly differ, but bGMYC, STACEY, ASAP with locMin and mPTP provided the most synchronous results. Moreover, it is necessary to take into consideration the opportunities and limitations of each method in each particular case. A-priori reasons to use a particular method are not well-formulated to date, and for an accurate and comprehensive estimation of biodiversity, we need to use a combination of several methods based on different approaches. High genetic diversity results in the existence of several mOTUs and phylogenetic lineages within many species and demonstrates the usefulness of the “polymorphic species” concept, which does not underestimate species richness and does not prevent the rational use and protection of biodiversity.

Supplementary Materials

The following supporting information can be downloaded at:, Table S1: Samples used in this study; Figure S1: result of CoMa by 15 methods; Figure S2: result of CoMa by 4 methods.

Author Contributions

Conceptualization, D.P.K. and A.A.K.; methodology, D.P.K.; software, D.P.K.; validation, D.P.K., A.A.K. and X.Z.; formal analysis, D.P.K.; investigation, D.P.K., E.A.B. and A.A.K.; resources, D.P.K.; data curation, D.P.K. and Y.V.K.; writing—original draft preparation, D.P.K., E.A.B. and A.A.K.; writing—review and editing, D.P.K., A.A.K. and X.Z.; visualization, D.P.K. and Y.V.K.; supervision, A.A.K. and X.Z.; project administration, Y.V.K. All authors have read and agreed to the published version of the manuscript.


This work was carried out as part of IBIW RAS State Assignment no. 121051100104-6.

Data Availability Statement

All materials examined in this study are openly available at the facilities listed, and by the catalogue numbers in the Section 2 above. Original sequences are deposited in the NCBI GenBank, acc. nos. KT989758-73, KY228990, KT254047-49, KT254051, KT254053, KT254055. All vouchers are kept at the collection of the Ecology of Fishes of Papanin Institute for Biology of Inland Waters of Russian Academy of Sciences, Borok, Russia. Data are available at Open Science Framework project: Karabanov, D. P., & Kotov, A. A. 2023. OSF. Dataset., accessed on 9 May 2023.


The authors are grateful to R.J. Shiel for linguistic corrections in an earlier draft, M.I. Malin for his help to the authors, to D.D. Pavlov, M.I. Bazarov and A.I. Tsvetkov for fish sampling help and D.G. Seleznev for help in operations with “R” software packages. Specially, we would like to thank A.A. Makhrov and M.V. Mina for valuable discussion of the species concepts. The material was collected with the support of the Administration of “Plescheyevo Lake” National Park as part of the project “Comprehensive study of Lake Pleshcheyevo ecosystem”.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Appendix A

Simple “R” scripts.
## local Minima species delimitation
# Based on help by V.Machado with colleagues
# Function locMin second
localMinima2 <- function(distobj)
den <- density(distobj)
a <- rep(NA, length(den$y) - 2)
for (i in 2:(length(den$y) - 1)) a[i - 1] <- den$y[i - 1] > den$y[i] & den$y[i + 1] > den$y[i]
den$localMinima <- den$x[which(a)]
den$ <- deparse(substitute(distobj))
den$call <- paste(“density.default(“, den$, ”)”, sep = “”)
# Read DNA align
data <- as.matrix(as.DNAbin(“C:/data/test.nex”)))
# Make dist matrix
dist <- dist.dna(data, model = “raw”, pairwise.deletion = TRUE)
# Make spp
spp <- dimnames(data)[[1]]
# Run localMinima calc
lmin <- localMinima2(dist)
# Run clusters calc
clu <- tclust(dist, threshold = lmin$localMinima[1])
# Print results of delimitation
cat(“locMin is”, lmin$localMinima[1], “\n”)
cat(“Results of locMin delimitation”, “\n”)
print(lapply(clu, function(x) spp[x]))
## GMYC species delimitation
# Based on help by F.Michonneau
# Read NEXUS file with consensus tree (i.g. BEAST2) into ‘tree’ = “C:/data/test.tree”) -> tree
# Re-root tree to the outgroup “outgroup_name” (If you do not set a priori root in BEAST2)
tree.root = NULL
tree.root = root(tree, c(“outgroup_name”))
# Remove outgroups. It is not necessary, but instead ‘trees.noroot’ must use ‘tree’
tree.noroot = NULL
tree.noroot = drop.tip(tree.root, c(“outgroup_name”))
# Run ‘splits’ calculation
gmyc <- gmyc(tree.noroot)
# Result of GMYC species delimitation
# Result of GMYC: list of species
cat(“GMYC species list:”, “\n”)
spec.list(gmyc) -> spp
## bGMYC species delimitation
# Based on help by N.Reid
# NOTA BENE! The ‘bGMYC’ analysis correctly works only in the “R” v.3.x
# Read NEXUS file with multiple trees (i.g. BEAST2) into ‘trees’. = “C:/data/test.trees”) -> trees
# Read 100 (of some) random trees from ‘trees’ into ‘tree’.
tree <- sample(trees, size = 100)
# Re-root all trees to the outgroup “outgroup_name” (If you do not set a priori root in BEAST2)
trees.root = NULL
for (i in 1:length(tree))
trees.root[[i]] = root(tree[[i]], c(“outgroup_name”))
# Remove outgroups. It is not necessary, but instead ‘trees.noroot’ must use ‘trees.root’
trees.noroot = NULL
for (i in 1:length(trees.root))
trees.noroot[[i]] = drop.tip(trees.root[[i]], c(“outgroup_name”))
# Test bGMYC in single tree. Input parameter list—see Reid’s ‘bGMYC’ instruction
bgmyc.singlephy(trees.noroot[[1]], mcmc = 10,000, burnin = 100, thinning = 10, t1 = 2, t2 = 25, start = c(1,1,25)) -> result.single
# Visual checks the ‘singlephy’ result. You can change the parameters of the model, see ‘bGMYC’ manual for details
# Validation of the model, good at values “Coalescence to Yule”> 0
checkrates(result.single) -> bgmycrates
# Run bGMYC for all trees with corrected the parameters. It may take a long time!
result.multi <- bgmyc.multiphylo(trees.noroot, mcmc = 50,000, burnin = 10,000, thinning = 25, t1 = 2, t2 = 35, start = c(1,1,25))
result.probmat <- spec.probmat(result.multi)
plot(result.probmat, trees.noroot[[1]])
# Check the result and the “heat map” (with 1st tree, or some)
result.probmat <- spec.probmat(result.multi)
plot(result.probmat, trees.noroot[[1]])
# Print the result of species delimitation (with 1% significance level, or some)
out <- bgmyc.point(result.probmat, 0.01)
cat(“Results of bGMYC delimitation”, “\n”)
## Simple heatmap
# Based on help by Y.Spori & J.-F.Flot
# You need CoMa output as *.tsv or the same matrix file
# Read the table and make the input matrix
table <- read.table(“C:/data/CoMa(4).tsv”, header = TRUE)
matrix <- as.matrix(table)
# Show result. You can repeat and change the parameters, see Zhao’s ‘heatmap3′ instruction
result <- heatmap3(matrix, cexRow = 0.2, cexCol = 0.2, revC = TRUE, col = colorRampPalette(c(“blue”, “red”))(100), method = “ward.D”, hclustfun = hclust, symm = TRUE)
# Generate a PDF output file
pdf(file = ‘C:/data/CoMa(4).pdf’)
heatmap3(matrix, cexRow = 0.2, cexCol = 0.2, revC = TRUE, col = colorRampPalette(c(“blue”, “red”))(100), method = “ward.D”, hclustfun = hclust, symm = TRUE)


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Figure 1. Geographic position of Lake Plescheyevo in relation to European Russia and hydrographic network of the Upper Volga basin.
Figure 1. Geographic position of Lake Plescheyevo in relation to European Russia and hydrographic network of the Upper Volga basin.
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Figure 2. BI tree for mitochondrial COI locus. Node supports are posterior probabilities indicated as coloration; branch supports are shown by numbers (SH-aLRT, ML Ufboot2 and MP bootstrap). Grey columns on the right side indicate the results of delimitation by 15 different methods (the number of operational taxonomic units is shown on the line “n”) plus a consensus LIMES taxonomy supported at least three among four methods. Original sequences are marked by «*».
Figure 2. BI tree for mitochondrial COI locus. Node supports are posterior probabilities indicated as coloration; branch supports are shown by numbers (SH-aLRT, ML Ufboot2 and MP bootstrap). Grey columns on the right side indicate the results of delimitation by 15 different methods (the number of operational taxonomic units is shown on the line “n”) plus a consensus LIMES taxonomy supported at least three among four methods. Original sequences are marked by «*».
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Figure 3. Conspecificity matrices obtained using CoMa, integrating the results from 15 different methods of species delimitation (bars on the right side). It was obtained after clustering the raw output using ‘heatmap3’. High conspecificity scores are shown in red and low conspecificity scores are shown in blue. The results from four methods (ASAP, bGMYC, mPTP. STACEY) are shown in black squares.
Figure 3. Conspecificity matrices obtained using CoMa, integrating the results from 15 different methods of species delimitation (bars on the right side). It was obtained after clustering the raw output using ‘heatmap3’. High conspecificity scores are shown in red and low conspecificity scores are shown in blue. The results from four methods (ASAP, bGMYC, mPTP. STACEY) are shown in black squares.
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Table 1. Results of the comparison of 15 alternative delimitations. Values of Ctax are represented above the diagonal; MatchRatio (%) are below the diagonal. Bold type marks the values above 75%. Methods of species delimitation: GB—NCBI GenBank database; BN—“BINs”, Barcode Index Numbers, Barcode of Life Data Systems; KoT—“K over Theta” population genetics; SI—“TaxonDNA/SpeciesIdentifier” distance; LM—“local Minima” by ‘spider’; AB—“ABGD”, Automatic Barcode Gap Discovery; AS—“ASAP”, Assemble Species by Automatic Partitioning; YC—“GMYC”, mixed Yule-coalescent by ‘splits’; bYC—“bGMYC”, Bayesian Yule-coalescent by ‘bgmyc’; PT—“PTP”, Poisson Tree Processes model; bPT—“bPTP”, Bayesian Poisson Tree Processes; mPT—“mPTP”, multi-rate Poisson Tree Processes; STC—coalescence “STACEY”, Species Tree And Classification Estimation, Yarely; SPD—coalescence “SPEEDEMON”, SPEciEs DEliMitatiON; HwM—“Haploweb Maker”, haplotype webs and nets analysis.
Table 1. Results of the comparison of 15 alternative delimitations. Values of Ctax are represented above the diagonal; MatchRatio (%) are below the diagonal. Bold type marks the values above 75%. Methods of species delimitation: GB—NCBI GenBank database; BN—“BINs”, Barcode Index Numbers, Barcode of Life Data Systems; KoT—“K over Theta” population genetics; SI—“TaxonDNA/SpeciesIdentifier” distance; LM—“local Minima” by ‘spider’; AB—“ABGD”, Automatic Barcode Gap Discovery; AS—“ASAP”, Assemble Species by Automatic Partitioning; YC—“GMYC”, mixed Yule-coalescent by ‘splits’; bYC—“bGMYC”, Bayesian Yule-coalescent by ‘bgmyc’; PT—“PTP”, Poisson Tree Processes model; bPT—“bPTP”, Bayesian Poisson Tree Processes; mPT—“mPTP”, multi-rate Poisson Tree Processes; STC—coalescence “STACEY”, Species Tree And Classification Estimation, Yarely; SPD—coalescence “SPEEDEMON”, SPEciEs DEliMitatiON; HwM—“Haploweb Maker”, haplotype webs and nets analysis.
GB 6068696267676775676468717947
BN52 47464247454147474447485542
KoT5035 728386796980866089848540
SI553762 607747672847772687652
LM44297443 71945883715083887936
AB5136836758 768177917086818247
AS523667479162 6289765389948438
YC58336172427345 69818769656755
bYC6842685874638361 776080948543
PT513688756386677368 7086818247
bPT50254870345738794860 60576364
mPT5035846274788357688348 848540
STC604376508270915392754176 8940
SPD67537765677176557776437784 45
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Karabanov, D.P.; Kotov, A.A.; Borovikova, E.A.; Kodukhova, Y.V.; Zhang, X. Comparison of the Efficiency of Single-Locus Species Delimitation Methods: A Case Study of a Single Lake Fish Population in Comparison against the Barcodes from International Databases. Water 2023, 15, 1851.

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Karabanov DP, Kotov AA, Borovikova EA, Kodukhova YV, Zhang X. Comparison of the Efficiency of Single-Locus Species Delimitation Methods: A Case Study of a Single Lake Fish Population in Comparison against the Barcodes from International Databases. Water. 2023; 15(10):1851.

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Karabanov, Dmitry P., Alexey A. Kotov, Elena A. Borovikova, Yulia V. Kodukhova, and Xiaowei Zhang. 2023. "Comparison of the Efficiency of Single-Locus Species Delimitation Methods: A Case Study of a Single Lake Fish Population in Comparison against the Barcodes from International Databases" Water 15, no. 10: 1851.

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