DNA barcoding of freshwater fishes from Brahmaputra River in Eastern Himalaya biodiversity hotspot.

The genetic diversity of freshwater fishes is still anonymous in several drainage systems in northeast India. Moreover, the comparative genetic analysis is largely sporadic to judge their actual diversity and true status. We generated 89 DNA barcodes of 40 morphologically identified fishes collected from two major tributaries of Brahmaputra River. The comparative study revealed that most of the species were clearly discriminated by their estimated genetic distances and monophyletic clustering in Bayesian (BA) tree. Considering the genetic divergence (2%) for species discrimination boundary, the high genetic diversity (2.36-10.73%) was detected in 11 species (Macrognathus pancalus, Channa punctata, Puntius terio, Bangana ariza, Garra arupi, Badis badis, Mystus vittatus, Rita rita, Gagata cenia, Mastacembelus armatus, and Danio dangila), which signified the occurrence of concealed genetic diversity in this ecozone. However, the insignificant genetic distances were also noticed in few reportedly valid species: Channa stiktos and C. ornatipinnis (1.43%); Mystus ngasep, M. rufescens, and M. carcio (0.4%); Glyptothorax trilineatus, G. churamanii, and G. verrucosus (0.4%); Botia almorhae, B. histrionica, B. lohachata, and B. rostrata (0-0.4%); Barilius barilia and B. vagra (0.4%); Batasio merianiensis and B. tengana (1.2%); Puntius chola and P. fraseri (0%), Schistura beavani and S. paucireticulata (0%); hence to validate this species, generation of more barcode data was required from their types or topotypes. The present study would help to develop conservation schemes for the native species and collegiate ecosystem, which associated with the livelihoods of millions of ethnic communities in this region.

Introduction

The northeastern region of India shares two biodiversity hotspots, Eastern Himalaya and Indo-Burma. These two hotspots are rich in biodiversity and considered as a prolific land of various floral and faunal diversity (Myers et al. 2000). The unique biogeography, climatic condition, vegetation, and inland water systems furnished a safe adobe for various indigenous living systems including ichthyofauna in this ecozone. The four river basins, the Bramhaputra, Barak, Chindwin, and Koladyne harbour a tremendous diversity of freshwater fishes. In general, these species not only play a substantial role in the ecosystem but also directly link with humans (as food resources, remedies, hobbies, and recreation purposes) and aid to flourish the country's economy. During the last two decades, ichthyologists exposed deep attention in the survey of piscatorial resources and consequently many new taxa have been discovered from this region (Darshan et al. 2018). However, the comprehensive checklist has emerged in 2012 which listed the potential occurrence of about 422 species from this region (Goswami et al. 2012). Further, the knowledge on fish diversity is exploring rapidly from this region. Almost 89 new species were added to the freshwater fish fauna in northeast India in the last decade (Animal Discoveries 2009–2017; Eschmeyer 2012). Addition to this, due to the Miocene tectonic activity, this region is also reported by a considerable number of endemic fishes (He et al. 2001; Ruber et al. 2004). The status of freshwater fishes in eastern Himalaya was recorded as Critically Endangered (1%), Endangered (3%), Vulnerable (10%), Near Threatened (9%), Least Concern (50%), and Data Deficient (27%) as per IUCN Red List category (Allen et al. 2010). Nevertheless, these organisms face major threats in terms of habitat loss and degradation, untenable fishing practices, development of dams, illegitimate trade, and other anthropogenic activities (Allen et al. 2005; Carrizo et al. 2017). Thus, the rigorous assessment of freshwater fish diversity in each river basin is urgently required for ichthyology and implication of conservation policies (Molur and Walker 1998).

Owing to the advancement of taxonomic and biodiversity research, the utility of molecular tools, especially DNA barcoding has successfully evidenced to determine the fish diversity worldwide including India (Hubert et al. 2008; April et al. 2011; Chakraborty and Ghosh 2014; Khedkar et al. 2014; Chen et al. 2015). The notable studies on freshwater fishes through DNA barcoding have been elucidated by estimating the diversity of catfish species from Barak drainage (Bhattacharjee et al. 2012) and ornamental fishes from both Brahmaputra and Barak drainage (Dhar and Ghosh 2015). This supplementary technique was manifested to discriminate the closely related species as well as resolve taxonomic ambiguities (Laskar et al. 2013, 2018). This additional tool was also corroborated to update the checklist of ichthyofauna from Ranganadi River in Assam state, and intensification of freshwater fish database from India (Nagpure et al. 2015; Kaushik and Bordoloi 2016). A significant study was also endeavouring to evaluate the genetic diversity of fishes from Diphlu River inside Kaziranga National Park (Laskar et al. 2019). Further, the large-scale attempt has been accomplished in recent past to generate the barcode data of 109 freshwater fishes from Indo-Myanmar biodiversity hotspot; considering Barak, Brahmaputra, Chindwin, and Karnafuli Rivers (Barman et al. 2018). However, the publicly available barcode data of freshwater fishes are still inadequate from each drainage system. Due to the inaccessibility in hilly terrain, lack of colloquial skill with native ethnic communities, and other legislative concerns; the survey and assessment of fishes are largely sporadic in the inland waters of Arunachal Pradesh, northeast India. These northeastern-most state is mainly encompassed by three major hills, Miri, Abor, and Mishmi as well as contiguous with three neighbouring nations, Bhutan, China, and Myanmar. Although political boundaries visibly demarcated this state from other countries, the streams of the underlying riverine system naturally share bio-resources including freshwater fishes. Nevertheless, the invasion of non-native species and their impacts on the native ichthyofauna has obtained less attention in the globe including northeast India (Collins et al. 2012). Moreover, the indigenous inhabitants of this region have frequently used ichthyofauna as folklore therapeutics and ethnozoological practices (Chinlampianga et al. 2013). Hence, the present study is aimed to rapidly evaluate the fish species diversity, hunted by the local tribes, from two focal tributaries (Siang and Dibang) of the Brahmaputra River and generate the DNA barcode data of taxonomically identified species. Besides, the comparative analysis of the genetic data within the native freshwater fishes has meagerly examined from northeast India. These open approaches will help to (i) recognize the fish genetic diversity in two drainage systems of the Brahmaputra River, (ii) enhance the ichthyofauna checklist of the state as well as from the region, and (iii) enrich the global DNA barcode library. The present study will be helpful to formulate the strategies for sustainable management and conservation of ichthyofauna bio-resources.

Materials and methods

Taxon sampling and morphological identification

A total of 89 freshwater fish samples were collected from the local tribes during the fishing activities in five different localities (28.15N 95.66E, 28.19N 95.55E, 28.08N 95.33E, 28.27N 95.19E, and 28.14N 95.10E) in two main streams of the Brahmaputra River, Siang and Dibang at two districts East Siang and Lower Dibang Valley in Arunachal Pradesh (Figure 1a). Desire size fish samples were preserved in 70% alcohol for further taxonomic and molecular study. The morphological identification and nomenclature were confirmed by the available taxonomic keys in recently published literature, books, and existing information at Catalog of Fishes (Animal Discoveries 2009–2017; Eschmeyer 2012; Vishwanath et al. 2014). A meagre amount of muscle tissue was collected from each sample using sterile forceps, scissors, and surgical blades for DNA analysis. The voucher specimens were properly tagged and preserved in the National Zoological Collections (NZCs) of the Freshwater Fish Section, Zoological Survey of India (ZSI), Kolkata.

(a) Map with red dots showing the collection localities in Arunachal Pradesh, northeast India. (b) Pruned Bayesian (BA) tree by partial mtCOI gene inferred the delimitation of the studied freshwater fishes in northeast India. Lateral and dorsal images of the representative fish species were merged beside the BA tree. (c) Pruned Bayesian (BA) tree by partial mtCOI gene inferred the delimitation of the studied freshwater fishes in northeast India. Lateral and dorsal images of the representative fish species were merged beside the BA tree.

Genomic DNA extraction, amplification and sequencing

Genomic DNA extraction, PCR, and purification were carried out from each tissue sample following standard protocol with published primer pairs (Ward et al. 2005). PCR was performed using Veriti® Thermal Cycler (Applied Bio systems, Foster City, CA). The purified PCR products were cycle sequenced cleaned following by published protocol and sequenced bi-directionally using ABI-3730 DNA analyzer at the Zoological Survey of India, Kolkata (Tyagi et al. 2017).

Sequence quality control measures and data acquisition

The forward and reverse chromatograms (>600 bp) of the partial mitochondrial cytochrome c oxidase subunit 1 (mtCOI) gene were obtained for each studied sample. The consensus sequence of each sample was annotated carefully by the alignment of both forward and reverse complement of reverse sequence. The online tool, nucleotide BLAST (https://blast.ncbi.nlm.nih.gov) was used to check the insertion and deletion within the annotated sequences, and amino acid array of vertebrate mitochondrial genome was assured through online NCBI-ORF finder (https://www.ncbi.nlm.nih.gov/orffinder/). The availability of the DNA barcode data of the known Indian freshwater fish species was screened and total 140 sequences were retrieved from GenBank database, which were published in peer reviewed journals (Table S2). The database sequence of Apristurus ampliceps (order Carcharhiniformes) was also acquired from GenBank to use as out-group. Further, the list of sampled freshwater fishes (40 species) and their congeners (105 species) known from northeast India were finalized for comparative genetic analysis following the published literatures (Goswami et al. 2012; Barman et al. 2018).

Genetic divergence and cluster analysis

To form a final dataset, all the generated and publicly available database sequences were aligned through ClustalX software (Thompson et al. 1997). Further, to evade the incongruous outcomes in genetic distance and tree analysis, the dataset was prepared to equal length of 609 bp. The genetic divergence within and between each taxonomic group was calculated in MEGAX (Kumar et al. 2018). The best fit model for this dataset was estimated using Mr. MODELTEST v2with lowest BIC (Bayesian Information Criterion) score (Nylander 2004). The Bayesian (BA) tree was constructed in Mr. Bayes 3.1.2by selecting nst = 6 for GTR + G + I model and four (one cold and three hot) metropolis-coupled Markov Chain Monte Carlo (MCMC), was run for 10,000,000 generations with 25% burn in with trees saving at every 100 generations (Ronquist and Huelsenbeck 2003). The MCMC analysis was used to generate the convergence metrics, till the standard deviation (SD) of split frequencies reached under 0.01 and the potential scale reduction factor (PSRF) for all parameters approached 1.0. To represent the generated BA tree, the web based iTOL tool (https://itol.embl.de/) was used (Letunic and Bork 2007).

Results and discussion

DNA barcoding and delimitation of taxa

The present study generated 89 DNA barcode sequences of 40 morphologically identified species from two major streams of Brahmaputra River in Arunachal Pradesh (Figure 1b,c; Table S1). All the studied morphospecies were closely related to the publicly available published database sequences of the same species and well discriminated within seven orders by partial mtCOI gene and reciprocal monophyly (Figure 1b,c). The genetic distances were 11.7%, 12.9%, 20.2%, 22.1%, and 22.5% in Synbranchiformes, Perciformes, Siluriformes, Anabantiformes, and Cypriniformes, respectively. The genetic distances between the above said orders were ranging from 24.4% to 30.0%. The present dataset deals with 229 DNA barcode data on 145 species within 32 genera reported from northeast India. Excluding the singleton species, the genetic distances within the genus were ranging from 3.21% (Paracanthocobitis) to 18.98% (Channa). The previous molecular study suggested that the variety of snakehead species is higher in Eastern Himalaya due to the concealed diversity and endemism or the fusion of more well-defined lineages (Conte-Grand et al. 2017). The resulted high genetic distances within the genus Channa reflected hidden species diversity in the studied region. Further, the genetic distances between the genera were ranged from 12.4% to 44.9%. Considering the previous thoughts on K2P distances is greater than 2% for accurate species differentiation in vertebrates (Johns and Avise 1998; Hebert et al. 2004), the present dataset observed high genetic distances within 11 species; Macrognathus pancalus (2.36%), Channa punctata (2.80%), Puntius terio (2.80%), Bangana ariza (3.20%), Garra arupi (3.45%), Badis badis (4.87%), Mystus vittatus (6.20%), Rita rita (6.93%), Gagata cenia (7.03%), Mastacembelus armatus (7.49%), and Danio dangila (10.73%). Although numerous studies has been intended to establish the actual barcode gap for species delimitation (Meyer and Paulay 2005; Steinke et al. 2009), the genetic distances for evaluating the barcode gap were inconsistent relating to the sampling strategies of different taxa (Meier et al. 2008). Hence, by observing the high genetic distances within the species, we assumed that the studied species may have possible cryptic diversity or more hidden species diversity from this region.

Comparative DNA barcodes analysis

In recent years, the generation of barcode data of freshwater fishes has massively contributed in the global GenBank database from different riverine systems, lakes, and reservoirs (Díaz et al. 2016; Lakra et al. 2016). Indeed, this rapid enrichment of database helps to identify the species promptly without prolonged appraisal through morpho-taxonomy (Hebert et al. 2003; Tautz et al. 2003; Ward et al. 2009). However, several technical issues such as erroneous annotation, and biased towards the publicly available sequences, are the major impediments for accurate species identification (Moritz and Cicero 2004; Shen et al. 2013). Further, the DNA barcodes of erroneously identified species or biological contamination often yield wrong similarity search result in open access Nucleotide Basic Local Alignment Search Tool (BLASTn) search (https://blast.ncbi.nlm.nih.gov) (Ebach and Holdrege 2005). Several DNA barcodes neither contained any collateral supplementary information like vouchers IDs, coordinates for collection localities nor published electronically. Hence, dealing with those open access sequences is often misleading during the dataset preparation and comparative analysis. Keeping all those obstacles in mind, the database sequences were retrieved and incorporated in the present comparative analysis. The northeast India is reported by seven genera each with single species, Bangana devdevi Chanda nama, Mastacembelus armatus, Glossogobius giuris, Gymnostomus ariza, Rita rita, and Securicula gora, which showed distinct genetic discrepancies as compared to other species. The species Bangana ariza is recently synonymized under Gymnostomus (Yang et al. 2012) and however showed sister cladding with Bangana devdevi in the BA tree.

The genus Channa is known by 15 species from northeast India (Eschmeyer 2012; Lalramliana Knight et al. 2018). Eleven species of Channa (C. aurantimaculata, C. aurantipectoralis, C. barca, C. bleheri, C. gachua, C. marulius, C. orientalis, C. pardalis, C. punctata, C. stewartii, and C. striata) showed 4.1% to 28.4% genetic distances with monophyletic clustering in BA tree. Further, C. stiktos from Kaladan River drainage, Mizoram is morphologically closer and showed low genetic divergence (1.43%) with the Burmese species, C. ornatipinnis and sufficient genetic distinctiveness (18.4–26.7%) with northeast Indian species. The recently described species, C. bipuli showed clear genetic distances (4.8–23%) and distinct clade with the northeast Indian species. The genus Badis is known by nine species from northeast India (Darshan et al. 2018; Eschmeyer 2012). Among them, four species (B. assamensis, B. badis, B. chittagongis, and B. tuivaiei) showed 3.2–18.9% genetic distance and close relationship in BA tree. The genus Macrognathus is known by four species from northeast India. Among them, two species, M. aral and M. pancalus maintained 17.7% genetic distance and closely assemblage in BA tree. The genus Amblyceps is known by eight species from northeast India (Darshan et al. 2016). Among them, four species (A. apangi, A. arunchalensis, A. laticeps, and A. mangois) showed 9.7–17.2% genetic distances and monophyletic clustering in BA tree.

The genus Batasio is known by three species from northeast India (Darshan et al. 2018; Eschmeyer 2012). Batasio affinis showed 13% and 13.5% genetic distances with B. merianiensis and B. tengana, respectively. However, significantly low genetic divergence (1.2%) was revealed between two species, B. merianiensis (KY290036) and B. tengana (KY867679). The genus Sperata is known by three species from northeast India. Among them, two species S. aor and S. seenghala were well discriminated from each other with 9.8% genetic distance. The genus Mystus is known by 10 species from northeast India. All the congeners of Mystus were showed 0.4–24.7% genetic distances and monophyletic clustering in BA tree. However, M. ngasep (MG736522) and M. rufescens (KX245093) as well as M. carcio (KT896740) and M. horai (FJ170791) revealed insignificant genetic distance (0.4%) with each other, which need further reevaluation. The genus Gagata is known by four species from northeast India. Later on, G. gasawyuh was synonymized with G. dolichonema which is reported to be distributed in Chindwin and Tenesserim basins. The other three species, G. cenia, G. gagata, and G. sexualis revealed sufficient genetic distances ranging from 13.2% to 19.6% and showed monophyletic clustering in BA tree.

The genus Glyptothorax is known by 21 species from northeast India (Darshan et al. 2018). Later on, five species, G. churamanii, G. dikrongensis, G. jayarami, G. pasighatensis, and G. verrucosus were described from northeast India (Arun Kumar 2016). Two Glyptothorax specimens (NMHS FES 65 and NMHS FES 94) were collected from the same type locality in Siang River of Arunachal Pradesh and showed similarity with G. pasighatensis. The subsequent taxonomic revision further synonymized G. coheni with G. saisii, distributed in the Subarnarekha River of eastern India; and G. chindwinica with G. burmanicus, distributed in Irrawaddy and Salween River drainages in China and Myanmar. The examined genetic distance was negligible between G. indicus (MH087037) and G. telchitta (KU667402) as well as G. churamanii (KF598811) and G. verrucosus (KJ909466). Further, G. trilineatus (KU667332) showed 0.4% genetic distance with G. churamanii and G. verrucosus in the present dataset. Excluding the low genetic distances of above-mentioned Glyptothorax congeners, the genetic distances were ranging from 2.4% to 19.3%. However, all the Glyptothorax species showed monophyletic clustering in BA tree as suggested earlier (Jiang et al. 2011).

The genus Botia is known by five species from northeast India (Goswami et al. 2012). Among them B. dario (MG736510) showed 7.9–8.3% genetic distances and monophyletic clustering in BA tree with other four species (B. almorhae, B. histrionica, B. lohachata, and B. rostrata). However, the publicly available database sequences of four species; B. almorhae (KY867676), B. histrionica (KY847870), B. lohachata (KY867674), and B. rostrata (KY847869) showed low genetic distance (0–0.4%) in the present dataset, which suggests further investigation. The genus Paracanthocobitis is known by three species from northeast India. Paracanthocobitis adelaideae and P. zonalternans were distributed in the Salween River drainage, Thailand and Myanmar. However, these two species were also reported in northeast India. The generated DNA barcode sequences of these two species (KX576654 and MG736493) showed 15.9% genetic distance and monophyletic clustering in BA tree. The genus Lepidocephalichthys is known by eight species from northeast India. Later on, L. manipurensis was synonymized with L. micropogon and L. menoni was synonymized with L. annandalei (Eschmeyer 2012). Three species, L. annandalei, L. goalparensis, and L. guntea showed 20.1–21% genetic distances and monophyletic clustering in BA tree.

The genus Salmostoma is known by four species from northeast India (Goswami et al. 2012). The present study collected two Salmostoma species, S. bacalia (NMHS FES 11) and S. phulo (NMHS FES 28 and NMHS FES 103) and showed monophyletic clustering with 13% genetic distance. The genus Barilius is known by 15 species from northeast India. Among them, nine species, B. bakeri, B. barna, B. barnoides, B. chatricensis, B. dogarsinghi, B. lairokensis, B. ngawa, B. shacra, and B. tileo, were transferred to genus Opsarius (Eschmeyer 2012). Later on, two species, B. arunachalensis and B. profundus were discovered from the D’Ering Memorial Wild Life Sanctuary, Arunachal Pradesh, and Koladyne River, Mizoram, respectively. In the present dataset, we compared the genetic distinctiveness of four Barilius species, B. barila, B. bendelisis, B. gatensis, and B. vagra, which showed 2–21.9% genetic distances with monophyletic cladding in BA tree. Further, the comparative analysis also noted insignificant genetic distance (0.4%) between two species, B. barilia and B. vagra.

The genus Danio is known by three species (D. dangila, D. jaintianensis, and D. rerio) from northeast India (Goswami et al. 2012). Later on, two new species, D. assamila and D. meghalayensis were discovered from the Brahmaputra River drainage in northeastern India (Eschmeyer 2012). The comparative DNA barcode analysis of remaining four species revealed 5.3–14.4% genetic distances between each other and showed monophyletic clustering in BA tree. The genus Devario is known by 10 species from northeast India including D. assamensis (Goswami et al. 2012). However, the current status of D. assamensis is uncertain because subsequently it was mentioned as a junior synonym of D. aequipinnatus (Kullander et al. 2017). Later on, one species, D. deruptotalea was discovered from Chindwin River drainage, Manipur (Eschmeyer 2012). The comparative analysis of seven species (D. aequipinnatus, D. annandalei, D. anomalus, D. deruptotalea, D. devario, D. regina, and D. yuensis) revealed 3.5–15% genetic distances between each other and showed monophyletic cladding in BA tree. Further, the DNA barcode data of three species (D. acuticephala, D. horai, and D. naganensis) is lacking in the GenBank database. The genus Chagunius is known by two species (C. chagunio and C. nicholsi) from northeast India. The generated and database sequences of both the species showed 12.7% genetic distance and monophyletic clustering in BA tree.

The genus Puntius is known by 24 species from northeast India (Goswami et al. 2012). Later on, most of the species are synonymized under four genera (Pethia, Naziritor, Dawkinsia, and Systomus) (Eschmeyer 2012). The present comparative study deals with four species, P. chola, P. fraseri, P. sophore, and P. terio. Among them, the generated and database sequences of P. chola (NMHS FES 13, NMHS FES 35, NMHS FES 40, KJ936779) and P. fraseri (JX260949) showed negligible genetic distances; however, P. sophore and P. terio showed 9.1–13.5% genetic distance between all the four species. All the studied species showed monophyletic clustering in BA tree. The genus Garra is known by 20 species from northeast India. Later on, 30 new species were also discovered from Bramhaputra, Chindwin, Karnaphuli, and Koladyne River basin in northeast India (Moyon and Arunkumar 2018). Further, the recent morphology and DNA barcode based assessment reported the range expansion of G. qiaojiensis from the Upper Irrawaddy River basin, Yunnan, China in northeast India (Barman et al. 2018). Based on the generated sequences and available database sequences, the comparative analysis was performed with 13 species (G. annandalei, G. arupi, G. dampaensis, G. flavatra, G. gotyla, G. kempi, G. lamta, G. lissorhynchus, G. litanensis, G. mcclellandi, G. nasuta, G. paralissorhynchus, and G. qiaojiensis). The genetic distances (4–23.2%) clearly discriminated all the studied Garra species with monophyletic clustering in BA tree.

DNA barcoding data hinted paraphyly of some taxa

The genus Parambasis is known by three species (P. lala, P. ranga, and P. waikhomi) from northeast India. These three species were distinctly separated by 16.2–19.9% genetic distances. However, the BA tree showed ambiguous clustering; with generated and database sequences of P. lala with distinct clade while P. ranga (KJ936704) and P. waikhomi (MG736567) showed close assemblage with C. nama clade. Hence, the congeners of Parambasis represented paraphyletic clades in the present dataset. The genus Schistura is known by 26 species from northeast India (Goswami et al. 2012). Among them eight species S. arunachalensis, S. corica, S. inglisi, S. minutus, S. montanus, S. multifasciatus, S. prashadi, and S. tigrinum are considered as S. tirapensis, Nemacheilus corica, S. rupecula, S. minuta, S. montana, S. multifasciata, Mustura prashadi, and S. tigrina respectively. In recent past, six new species (S. koladynensis, S. maculosa, S. mizoramensis, S. nebeshwari, S. paucireticulata, and S. scyphovecteta) were discovered from Pharsih, Tuivai, Tuirivang, and Koladyne River in Mizoram state of northeast India and one species, S. larketensis was discovered from a limestone cave, east Jaintia hills, Meghalaya (Eschmeyer 2012). The collected specimen (NMHS FES 27 and NMHS FES 86) from Arunachal Pradesh was identified as S. rupecula, which is distributed in India and Nepal. The present dataset represents the comparative analysis of 18 Schistura species. Most of the species showed cohesive clustering in BA tree; except S. savona and S. maculosa, which exhibited close assemblage with Paracanthocobitis clade. Hence, the congeners of Schistura represented paraphyletic clade, which needs further confirmation with more sampling and other genetic markers. The genetic distance was negligible for two species, S. beavani (KY853032) and S. paucireticulata (MG736503). Excluding these two species, the remaining 16 species (S. corica, S. khugae, S. koladynensis, S. mizoramensis, S. multifasciata, S. nagaensis, S. nebeshwari, S. prashadi, S. reticulata, S. reticulofasciata, S. rupecula, S. scaturigina, S. scyphovecteta, S. sikmaiensis, S. tirapensis, and S. vinciguerrae) showed 2.4–22.7% genetic distance with monophyletic cladding in BA tree. The genus Osteobrama is known by four species (O. belangeri, O. cotio, O. cunma, and O. feae) from northeast India. The barcode sequences of Osteobrama congeners are clearly discriminated by genetic distances (12.5–23.9%). Further, O. belangeri (MG736385) showed ambiguous cladding with other three species and depicted paraphyletic clade in BA tree.

Conclusion

Traditional taxonomy is largely assessed to discover and naming of the organisms since Linnaeus time. Over time, scientists proposed various alternative biological species concept, especially phylogenetic theory to recognize the diversity and evolutionary thoughts (Avise 1994; Bickford et al. 2007). However, the high degrees of phenotypic similarity in cryptic species, sibling species, and sexual dimorphism have posed challenges in morphotaxonomy (Hebert et al. 2003; Tyagi et al. 2017). Nonetheless, to measure the Environmental Impact Assessments (EIA) under the Convention on Biological Diversity (CBD), promotion of social benefit sharing, and amendment of sustainable schemes for animal welfare, the assessment of biodiversity is rapidly encountering in the last few decades (Gopal 2015). As a result, researchers have frequently unwrapped the faunal diversity with overlooked or overemphasized morphological characters (Hebert et al. 2004; Collins and Cruickshank 2013). At this juncture, DNA barcoding tool with different species delimitation methods play a pivotal role to resolve several taxonomic dilemmas and provide truthful conceptions in systematics research (Laskar et al. 2013; Yang et al. 2015). This supplementary technique has also equally assisted in taxonomic revision by synonymizing many taxa as well as clarifies the anecdotal thoughts of species distribution (Bergsten et al. 2012; Stuckas et al. 2013). Hence, to validate the taxonomic status of the reported freshwater fish species from the northeastern region, the comparative genetic analysis could have fundamentals for evolutionary studies, implications for biogeography and conservation planning. Most of the studied congeners from northeast India were distinctly discriminated by the estimated genetic distances and tree analysis. However, for in-depth evidence of paraphyletic clade resulted in the congeners of Osteobrama Parambasis, and Schistura; we suggested more sampling and generation of DNA data of multiple molecular markers of all extant congeners from northeast India and other known range distributions. The present comparative analysis further revealed very low genetic distance between few species of Channa, Barilius, Batasio, Botia, Glyptothorax, Mystus, Puntius, and Schistura; which need further confirmation by assessing their topotypes through integrated approaches. The DNA barcode data of taxonomically identified freshwater fishes strengthen the global database for prompt species identification. In addition, the present study also helps to validate the checklist of ichthyofauna from Arunachal Pradesh as well as from northeast India. Collectively, the present genetic analysis of freshwater fishes would be helpful to recognize the diversity, detection of invasive species, as well as formulation of strategies for sustainable management and conservation.

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