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Frontiers in Aquaculture ·

Volume 5 - 2026 | https://doi.org/10.3389/faquc.2026.1777546

Mitochondrial marker-based insights into the genetic diversity and population connectivity of the vulnerable (IUCN Red List) snow trout (Schizothorax plagiostomus) from the Tons River, Western Himalaya

  • Pooja Badoni1*corresponding author
  • J. V. S. Rauthan1
  1. Fisheries Research Laboratory, Department of Zoology, D.A.V. (PG) College, Hemvati Nandan Bahuguna Garhwal University, Srinagar, Uttarakhand, India
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Abstract

Advanced molecular tools have a significant role in the growth of sustainable aquaculture systems. Their immense contribution is particularly notable among cold-water fish in the Himalayan regions, where available genetic data is limited. This study aimed to evaluate the specific characteristics, such as genetic diversity and population structure, of vulnerable Schizothorax plagiostomus (Cyprinidae) in the Tons River, a major tributary of the Yamuna River in Uttarakhand. Mitochondrial markers, namely COI and Cytb, were utilised for the analysis. In this study, a total of 40 S. plagiostomus sequences, from concatenated COI and Cytb (762 bp), were analysed. Major findings observed 10 haplotypes in COI and 12 in Cytb, indicating relatively higher diversity in Cytb than in COI. Whereas 20 haplotypes were observed in the concatenated sequences, representing overall moderate genetic diversity among populations. S. plagiostomus is listed as a vulnerable species on the IUCN Red List. This study revealed that despite historical population declines, the species still retains considerable genetic variation. Conserving this existing moderately high genetic variation is important for maintaining the long-term evolutionary potential of this species. This study provides the first mitochondrial baseline data for S. plagiostomus in the Tons River. And also demonstrates how genetic markers function as advanced tools for conservation and sustainable aquaculture development in Himalayan cold-water species. The moderately high mitochondrial diversity observed suggests the presence of diverse maternal lineages. However, further investigation using nuclear markers is required to fully assess genetic diversity and cryptic structure. Furthermore, the study demonstrates the usefulness of mitochondrial markers for assessing genetic diversity.

Keywords

Introduction

Freshwater ecosystems are among the most valuable ecosystems, covering only about 0.01% of the Earth’s total surface (Meinam et al., 2025). Despite their limited extent, they support 68.7% of cold water fisheries, mainly inhabiting the Himalayas (Bharti et al., 2023). However, these systems are increasingly threatened by a range of anthropogenic pressures resulting in widespread population declines of native species and rendering many freshwater fishes endangered (Dudgeon et al., 2006; Sarkar et al., 2008; Borgwardt et al., 2019).

Genus Schizothorax (Heckel, 1838), commonly known as snow trout, belongs to the family Cyprinidae and subfamily Schizothoracinae (Zakiya et al., 2023). These cold-water fishes are characteristic of Himalayan river systems. The genus Schizothorax exhibits a broad geographic distribution spanning the Aral Sea basin (west) (Rozimov et al., 2025), Qinghai–Tibetan Plateau (east) (Ma et al., 2023), Hindukush (southwest) and the Himalayan ranges (south) (Regmi et al., 2023). According to the current global database for fish taxonomy, Eschmeyer’s Catalogue of Fishes (Fricke et al., 2026), the subfamily Schizothoracinae comprises 142 available species names, of which approximately 76 are currently considered valid species distributed across 4 valid genera. This discrepancy underscores why the exact number of valid Schizothorax species remains unresolved, particularly in Central Asia, where taxonomy is highly problematic due to phenotypic plasticity and unresolved phylogenetic relationships (Regmi et al., 2023). Within this genus, S. plagiostomus (Heckel, 1838) remains a key indigenous species throughout the Himalayan river systems of northern India, including Jammu and Kashmir, Himachal Pradesh, and Uttarakhand (Talwar and Jhingran, 1991; Mirza, 1991; Mir et al., 2013).

The Himalayan snow trout, Schizothorax plagiostomus, is the predominated native fish species in the Tons River, which is a glacial-fed and is the longest and largest tributary of the Yamuna River. It originates from the Bandarpunch glacier and merges into the Yamuna River in Uttarakhand. This species exhibits immense ecological and commercial importance due to high nutritional value, strong market demand in local fisheries as well as aquaculture sectors, where it acts as a candidate species (Deng et al., 2013; Jiang et al., 2018; Lu et al., 2024). Despite its economic importance, the population of S. plagiostomus has declined due to overexploitation, habitat destruction, pollution, invasive species, and large-scale anthropogenic activities such as dam construction and the use of unsustainable fishing practices predominant in the hill regions, leading to its classification as Vulnerable by the IUCN (Jan and Daniels, 2022). Therefore, for effective species management, understanding the precise status of population structure is essential. Currently, the snow trout fishery is restricted within the upland coldwater region to small-scale capture operations only (Nautiyal et al., 2019), highlighting the urgent need for effective conservation strategies, captive breeding protocols, and enhancement of stocks through aquaculture. Although artificial breeding of Schizothoracine fishes is considered essential to restore stocks and to support rising food demands, the success of the large-scale hatcheries remains inconsistent, largely due to limited information regarding the reproductive biology and broodstock requirements of these fishes (Agarwal and Singh, 2009).

Understanding the genetic diversity and population structure is important in the sustainable use of economically important species and planning of conservation strategies for critically threatened and endangered species (Sharma et al., 2022). Mitochondrial DNA markers have become a key asset in Conservation biology, widely used to assess genetic diversity, population structure, and both inter and intraspecific variation, gene flow, evolutionary relationships, stock characterisation, and phylogeography (Habib et al., 2012; Sati et al., 2015; Roy et al., 2024). The utility of these markers is largely due to the haploid, maternally inherited, non-recombining nature of mtDNA and its lower effective population size, thus making it more sensitive to the demographic and genetic events (Englbrecht et al., 2000; Whitehead et al., 2003; Domingues et al., 2007). Among these markers, the cytochrome b gene is highly reliable for evolutionary analyses, due to its small size and the high nucleotide substitution rate at synonymous sites (Perdices et al., 2004; Arshad et al., 2009; Kumar et al., 2011). Within the family Cyprinidae, these substitution rates facilitate the identification of biogeographical patterns (Gilles et al., 2001; Durand et al., 2002), resolution of taxonomic ranks (Xiao et al., 2001), and the construction of species-specific primers to identify species (Anucherngchai et al., 2019; Parson et al., 2000). Additionally, the Cytochrome oxidase I marker is used as a standard, authenticated, and reliable genetic marker extensively used for DNA barcoding to identify species accurately (Akhtar and Ali, 2016) and to delineate cryptic species in Schizothorax species (Ma et al., 2020; Rozimov et al., 2025). It is also used for Phylogenetic studies to understand evolutionary relationships within this economically important group of Himalayan fishes (Barat et al., 2012; Purohit et al., 2023; Wang et al., 2025).

While existing literature on S. plagiostomus has primarily focused on the phylogeny (Khan et al., 2017; Purohit et al., 2023), its Sex and condition factor (Magloo et al., 2024), length-weight relationship (Muddasir and Imtiaz, 2016; Sheikh and Ahmed, 2018; Kunal et al., 2021) reproductive biology and ovarian histology (Jan and Ahmed, 2019), food and feeding Habits (Jan et al., 2018), seasonal cyclicity of testicular and ovarian development (Purohit et al., 2022; Sharma et al., 2023), fecundity and gonadosomatic index (Jan et al., 2014), skeletal deformity (Khan et al., 2025), and scale surface topography (Rehman et al., 2025), there is a critical lack of information regarding the genetic diversity and population structure of this species, which results in a major gap that limits informed broodstock development and conservation-oriented aquaculture planning. Therefore, this study aimed to (i) assess the genetic diversity of S. plagiostomus in the Tons River using mitochondrial markers. (ii) to test for the presence of population structure along the Tons River. (iii) assess how mitochondrial DNA diversity can assist in monitoring maternal lineages for conservation management. This study provides a baseline mitochondrial genetic information that may assist future broodstock and conservation planning when integrated with nuclear genetic data.

Methodology

Study area and sampling

A total of 40 Samples of the S. plagiostomus species were collected from 4 different sites (Mori (11), Tiuni (10), Atal (9) and Kwanu (10) along the Tons River, Uttarakhand, between 2023 and 2024 (Table 1, Figure 1). The sample size (n=40) was determined to be sufficient for mitochondrial diversity inference, not fine-scale structure, in accordance with previous studies on Himalayan snow trout (Ali et al., 2014, n=25; Kousar et al., 2025, n=34). To adhere to ethical guidelines and minimize the direct impacts on fish populations, samples were obtained from deceased specimens already harvested by Local artisanal fishers using gillnets for commercial purposes. No animals were sacrificed specifically for this study. To ensure spatial accuracy and avoid mixed-stock bias, sampling was coordinated with local artisanal fishers who utilized fixed gill nets deployed overnight at specific river locations. The GPS coordinates recorded in Table 1 represent these exact net deployment sites. As these small-scale fishers transport their catch directly from the nets to the immediate landing points or local shops, the sampled individuals are directly representative of the local microhabitat and site-specific populations at the recorded coordinates. Sampling involved the excision of a small tissue fragment (Fin clip) from the pectoral fin of the deceased fish, which was immediately preserved in 95% ethanol for genetic assessment. For species Identification, standard literature and the latest Taxonomic reviews were consulted (Talwar and Jhingran, 1991; Jayaram, 2010). Upon collection, the specimen was photographed and labelled for the morphological record- keeping before tissue preservation (Figure 2).

Table 1. Sampling locations of samples collected from the Tons River.

ZoneStateDistrictAreaLongitudeLatitudeN
UpperUttarakhandUttarkashiMori78.0528931.0321911
UpperUttarakhandDehradunTiuni77.8484530.9451010
LowerUttarakhandDehradunAtal77.7706930.611079
LowerUttarakhandDehradunKwanu77.7654930.8253710
Total40

Molecular Workflow for the Fish genetic analysis (detailed in Supplementary Figure 1).

(N) denotes the total number of samples collected per site. All sampling locations are located within the Tons River basin in the state of Uttarakhand, India.

Sampling locations of *S. plagiostomus* in the Tons River. ArcGIS 10.2 (ESRI, CA, USA) was used to draw the map and edited using Adobe Photoshop CS5.1 (Adobe Systems Inc., CA, USA).
Figure 1. Sampling locations of S. plagiostomus in the Tons River. ArcGIS 10.2 (ESRI, CA, USA) was used to draw the map and edited using Adobe Photoshop CS5.1 (Adobe Systems Inc., CA, USA).
A representative organism of the *S. plagiostomus* in the Tons River.
Figure 2. A representative organism of the S. plagiostomus in the Tons River.

Genomic DNA extraction and Quality estimation

The DNA was isolated from fin samples using the DNeasy Blood and Tissue Kit (Qiagen, Inc., USA), and for quality estimation, 0.8% agarose gel electrophoresis was used.

PCR Amplification and DNA Sequencing

PCR was performed in 10 µL of a reaction mixture, containing 5 µL of Master mix (QIAGEN Multiplex PCR Kit, Germany), 1 µL of BSA/miliQ (Hi-media), 0.25 µL of each primer, with the remaining 2.5 µL of RNase-free water, and 1 µL of template DNA. The primer pair used to amplify the mitochondrial Cytochrome c oxidase I (COI) gene consisted of F: 5’-TCA ACC AAC CAC AAA GAC ATT GGC AC-3’ as a Forward Primer and R: 5’-TAG ACT TCT GGG TGG CCA AAG AAT CA-3’ (Ward et al., 2005) as a Reverse primer (Table 2). While for the Cytb gene, the forward primer F: 5’-CCA TCC AAC ATC TCA GCA TGA City AA-3’ and the reverse primer R: 5’-GCC CCT CAG AAT GAT ATT TGT CCT CA-3’ (Kocher et al., 1989) were used. PCR amplification was carried out in a PCR with a Thermocycler. The conditions under which the PCR was conducted include an initial denaturation at 95 °C for 15 min, followed by 35 cycles of denaturation at 95 °C for 30 sec, at annealing temp. of 55 °C for 45 sec and elongation at 72 °C for 1 min 30 sec, followed by a final extension at 72 °C for 10 min. 2% agarose gel electrophoresis was used to check the PCR-amplified products and to estimate the size of the amplicons. DNA bands were compared to a 100bp DNA ladder (Gene Bioscience). For visualisation of the amplicons, a UV transilluminator was used. The PCR-amplified products were subsequently purified using the QIAquick PCR Purification Kit (Qiagen, UK). The Exo-Sap-treated cleaned PCR products were subjected to cycle sequencing using the BrilliantDye™ Terminator (v3.1) Cycle Sequencing Kit. The purified sequencing products were analysed using the ABI 5300XL Genetic Analyser (Applied Biosystems, USA).

Table 2. Details of mitochondrial markers for the genetic diversity assessment in S. plagiostomus.

Mitochondrial markerForward (F) and reverse (R) primersExpected amplicon size (bp)
COI F1 R1F:5’-TCA ACC AAC CAC AAA GAC ATT GGC AC-3’∼ 450
(Ward et al., 2005)R:5’-TAG ACT TCT GGG TGG CCA AAG AAT CA-3’
Cytb F (L14841)F: 5’-CCA TCC AAC ATC TCA GCA TGA Analyzer AA-3’∼ 312
Cytb R (H15149)R: 5’-GCC CCT CAG AAT GAT ATT TGT CCT CA-3’
(Kocher et al. (1989)

Data analysis

The generated DNA sequence data were manually examined and edited using BioEdit version 4.1 (Hall, 1999). Nucleotide base quality was assessed using Finch TV version 1.4. and bases with Phred quality score above 20 (QV20+) were considered for further analysis. For Species identification, the NCBI-BLAST tool (National Centre for Biotechnology Information) (https://blast.ncbi.nlm.nih.gov/) was used before the assessment of genetic variation. And the generated sequences were submitted to the NCBI (with Accession numbers PX717346, PX716950 - PX716958, PX760168 - PX760170, PX760281 - PX760285, PX760406-PX760416, PX853912 - PX853914, PX856273 - PX856279, for COI and For Cytb (with accession numbers PX966980, PZ258567, PZ320721-PZ320731, PZ338670 - PZ338696). Mitochondrial markers Cytochrome c oxidase subunit I (COI) with 450 bp and Cytochrome b (Cytb) with 312 bp were utilized. COI was selected for its standardized utility in species identification and high conservation across lineages, while Cytb was selected for its high sensitivity in detecting intraspecific variation and better ability for resolving cryptic species in the genus Schizothorax (Ma et al., 2020). To enhance the resolution of genetic diversity estimates, the sequences were concatenated into a single alignment of total length (762 bp) to increase the number of polymorphic sites, thereby providing an estimation of haplotype diversity (h) and nucleotide diversity (π) than would be possible with a single locus. However, concatenation increases resolution but does not overcome the inherent limitations of single-genome (mitochondrial) inference. The parameters such as polymorphism sites (s), haplotype diversity (h), nucleotide diversity (π), and number of haplotypes (h) were calculated using DnaSP version 5.2 (Librado and Rozas, 2009). Further, to visualise the spatial distribution of the haplotypes, a median joining network was generated using the PopART version 6.1 (Leigh et al., 20155). Random method of haplotype accumulation was performed in R software, to estimate the expected number of haplotypes in the studied population employing the ‘HaploAccum’ function from the ‘spider’ package with 1,000 permutations (Brown et al., 2012). AMOVA was performed using Arlequin v3.5.1.3 (Excoffier and Lischer, 2010) to determine population structure within and between populations. In contrast, DnaSP version 5.2 (Librado and Rozas, 2009) was used to estimate Pairwise Genetic differentiation, Tajima’s D, Fu’s Fs, the Raggedness Statistic (r), the R2 Statistic and mismatch distributions. To test for the isolation by distance (IBD; Rousset, 1997), the Mantel test (Mantel, 1967) was performed using the “vegan” package in the R software, where the correlation between the matrix of Linearized pairwise genetic distances (F<sub>ST/</sub>(1-F<sub>ST</sub>)) (Slatkin, 1993) and the matrix of Logarithmic geographic distances between the population pairs were assessed. The statistical significance of the Mantel test was determined using 9,999 permutations, and to visualize the linear relationship between the two distance matrices, Reduced Major Axis (RMA) regression was used. Bayesian skyline plots (BSP) (Drummond et al., 2005) were generated using BEAST 1.6.1 (Drummond and Rambaut 2007) for concatenated sequences with 100 million generations to explore historical demographic trends; however, results should be interpreted cautiously due to reliance on assumed substitution rates and mtDNA-only data. A strict molecular clock, using the K80 model, was applied with a general 2% per million years (equivalent to 1 × 10–8 substitutions/site/year per lineage) as established in bony fishes (Bermingham, 1997) for the concatenated sequences. For monitoring the convergence of sampling processes, Tracer v1.7.2 (Rambaut et al., 2018) was used, ensuring all effective sample size (ESS) values exceeded 200, followed by the generation of the BSP. To infer the evolutionary relationships among the identified haplotypes, a Neighbour-Joining (NJ) Method was used to construct the tree using MEGA 11 (Tamura et al., 2021) (Saitou and Nei, 1987). The Kimura 2-parameter (K2P) distance model was employed to calculate genetic distances, which are in units of the number of base substitutions per site. The rate variation among sites was modelled with a gamma distribution (shape parameter = 1). The reliability of the tree topology was assessed using bootstrap analysis with 1,000 replicates (Felsenstein, 1985). The resulting phylogenetic tree was further refined and annotated for publication using the Interactive Tree of Life (iTOL v6) online tool (Letunic and Bork, 2024).

Result

Genetic diversity of S. plagiostomus

Sequence variation and genetic diversity were assessed using the concatenated mitochondrial DNA sequences (COI and Cytb) of S. plagiostomus. A total of 40 sequences were analyzed, comprising 450 bp of the COI gene and 312 bp of the Cytb gene, resulting in a total concatenated length of 762bp. Genetic diversity parameters, including polymorphic sites, haplotype diversity (h), nucleotide diversity (π) and number of haplotypes were assessed. The Results revealed 10 haplotypes in COI, 12 in Cytb and 20 in concatenated sequences (COI+Cytb) (Figure 3). Furthermore, 9 Polymorphic sites were identified in COI, 15 in Cytb, and 24 in concatenated sequences. For the individual markers, COI exhibited diversity indices of (h = 0.441 ± 0.099, and π = 0.00158 ± 0.00042), while Cytb showed (h = 0.681 ± 0.077, and π = 0.00422 ± 0.00088), indicating relatively moderate genetic diversity in Cytb compared to COI. Whereas for the concatenated mitochondrial sequences (COI + Cytb), diversity indices of (h = 0.799 ± 0.066 and π = 0.00266 ± 0.00044) were reported (Tables 3, 4; Figure 4), indicating moderately high genetic diversity (Grant and Bowen, 1998; Bowen et al., 2001). The haplotype network for the concatenated sequences (COI+Cytb) revealed a star-like topology (Figure 5). The most common haplotype was Hap_1, present in 14 individuals (35%) and shared across all four sampled populations (Mori, Tiuni, Atal, and Kwanu), followed by Hap_3 in 4 individuals (10%) distributed in Mori and Atal, Hap_12 in 3 individuals (7.5%) distributed in Tiuni and Kwanu, and Hap_6 in 2 individuals (5%) restricted to Atal only. This distribution suggests genetic connectivity and historical gene flow across the sampling sites. The remaining 16 haplotypes (Hap_2, Hap_4, Hap_5, Hap_7-11, Hap_13-20) representing 80% of the total unique haplotypes are private singletons, each represented by only 1 individual (2.5% each). These private singleton haplotypes likely represent recent mutations occurring within the last few generations, suggesting that the observed localized variation in Tiuni (Hap_16) and Kwanu (Hap_10) is a relatively contemporary phenomenon rather than a result of long-term isolation. This pattern of haplotype distribution highlights genetic connectivity and variation across the sampled sites, with Hap_1 acting as the ancestral hub.

Distribution map of Haplotype inferred from concatenated *COI+Cytb* gene of MtDNA in *S. plagiostomus* on the 4 different sites, Mori, Tiuni, Kwanu, and Atal of the Tons River. Each colour represents a unique haplotype. The proportion of colour in a population represents the frequency of particular haplotypes in that population. ArcGIS 10.2 (ESRI, CA, USA).
Figure 3. Distribution map of Haplotype inferred from concatenated COI+Cytb gene of MtDNA in S. plagiostomus on the 4 different sites, Mori, Tiuni, Kwanu, and Atal of the Tons River. Each colour represents a unique haplotype. The proportion of colour in a population represents the frequency of particular haplotypes in that population. ArcGIS 10.2 (ESRI, CA, USA).
Haplotype and nucleotide diversity of *S. plagiostomus* in the Tons River.
Figure 4. Haplotype and nucleotide diversity of S. plagiostomus in the Tons River.
A median-joining network of *S. plagiostomus* haplotypes where haplotypes **(a)** (H1-H10) in *COI*, **(b)** (H1-H12) in *Cytb*, and **(c)** (H1-H20) in concatenated Sequences were labelled. The lines connecting the haplotypes represent genetic differences (mutations) between them, and the number of crossbars on the lines indicates the number of mutational steps. Tiuni, Mori, Atal, and Kwanu represent different sites along the river Tons.
Figure 5. A median-joining network of S. plagiostomus haplotypes where haplotypes (a) (H1-H10) in COI, (b) (H1-H12) in Cytb, and (c) (H1-H20) in concatenated Sequences were labelled. The lines connecting the haplotypes represent genetic differences (mutations) between them, and the number of crossbars on the lines indicates the number of mutational steps. Tiuni, Mori, Atal, and Kwanu represent different sites along the river Tons.

Table 3. Genetic diversity indices for S. plagiostomus based on mtDNA sequences, where N, number of samples; PI, number of Parsimony sites; SV, number of singleton sites; VS, number of variable sites; and H, number of haplotypes.

Primer(N)(VS)(SV)(PI)
COI40972
Cytb4015114
COI and Cytb4024186

Table 4. Genetic diversity parameters for S. plagiostomus populations based on COI, Cytb, and concatenated COI+Cytb sequence data set.

PrimerSites(N)(H)Haplotype diversity (h) (Mean ± SD)Nucleotide diversity (π) (Mean ± SD)
*COI*Mori1110.000 ± 0.0000.00000 ± 0.00000
Tiuni1030.378 ± 0.1810.00133 ± 0.00073
Atal930.556 ± 0.1650.00136 ± 0.00047
Kwanu1060.778 ± 0.1370.00360 ± 0.00087
Total40100.441 ± 0.0990.00158 ± 0.00042
*Cyt b*Mori1140.600 ± 0.1540.00338 ± 0.00141
Tiuni1060.778 ± 0.1370.00563 ± 0.00192
Atal930.556 ± 0.1650.00196 ± 0.00068
Kwanu1070.867 ± 0.1070.00627 ± 0.00164
Total40120.681 ± 0.0770.00422 ± 0.00088
*COI and Cyt b*Mori1140.600 ± 0.1540.00138 ± 0.00058
Tiuni1070.867 ± 0.1070.00309 ± 0.00082
Atal950.722 ± 0.1590.00160 ± 0.00047
Kwanu1090.978 ± 0.0540.00470 ± 0.00087
Total40200.799 ± 0.0660.00266 ± 0.00044

N - total number of Samples collected at each specific location, H - total number of haplotypes, h, haplotype diversity; π, nucleotide diversity; SD, Standard deviation; COI, Cytochrome c oxidase I; Cytb, Cytochrome b.

Haplotype accumulation curve

To evaluate the adequacy of the sampling effort for S. plagiostomus populations, a Haplotype Accumulation Curve was generated from concatenated (COI+Cyt b) sequences in Figure 6. Twenty unique haplotypes were identified, and the curve reached an asymptotic state at approximately twenty haplotypes. indicating that most of the major haplotype diversity within the available dataset had been captured. Additional sampling would likely reveal substantially more mitochondrial haplotypes.

Haplotype accumulation curve for *S. plagiostomus*, in concatenated sequences, to show a relationship between the number of sequences and the discovery of new haplotypes. The x-axis shows the total samples sequenced, while the y-axis represents the cumulative count of haplotypes identified with each additional randomly sampled individual (1,000 permutations). Vertical bars indicate the 95% confidence interval. The dashed line shows the number of sequences in this study (n=40).
Figure 6. Haplotype accumulation curve for S. plagiostomus, in concatenated sequences, to show a relationship between the number of sequences and the discovery of new haplotypes. The x-axis shows the total samples sequenced, while the y-axis represents the cumulative count of haplotypes identified with each additional randomly sampled individual (1,000 permutations). Vertical bars indicate the 95% confidence interval. The dashed line shows the number of sequences in this study (n=40).

Analysis of molecular variance

Analysis of molecular variance (AMOVA) revealed that genetic variation in S. plagiostomus among populations is much lower than genetic variation within populations in concatenated (COI + Cyt b), with a Fixation index (F*ST*) value of -0.100 with p < 0.989 (Table 5).

Table 5. AMOVA for the S. plagiostomus population based on concatenated mitochondrial DNA Sequences.

Source of variationdfSum of SquaresVariance Components (σ2)% varianceFixation index (F*ST*)
Among Populations31.739-5.741-10.01-0.100,p < 0.989
Within populations362.2726.311110.01
Total392.2895.737100

*Negative variance components are statistical artefacts interpreted as zero.

Pairwise genetic differentiation (F<sub>ST</sub>)

The pairwise (F*ST*) analysis revealed negligible or Low genetic differentiation among the four studied populations (MO, TI, AT, and KU). Pairwise (F*ST*) values ranged from (-0.033) to (0.008), indicating an absence of population structuring (Figure 7, Table 6).

Representation of pairwise *F<sub>ST</sub>* values among the four studied populations of *S. plagiostomus*. Values on the arrows indicate the degree of genetic differentiation between populations (MO, TI, AT, and KU.
Figure 7. Representation of pairwise F<sub>ST</sub> values among the four studied populations of S. plagiostomus. Values on the arrows indicate the degree of genetic differentiation between populations (MO, TI, AT, and KU.

Table 6. Pairwise Genetic differentiation (F*ST*) values between S. plagiostomus populations based on concatenated (COI + Cyt b) sequences.

Population 1Population 2Pairwise Genetic Differentiation (F<sub>ST</sub>)
Mori (MO)Tiuni (TI)-0.019
Mori (MO)Atal (AT)-0.006
Mori (MO)Kwanu (KU)0.008
Tiuni (TI)Atal (AT)-0.032
Tiuni (TI)Kwanu (KU)-0.015
Atal (AT)Kwanu (KU)-0.033

Pairwise genetic distance

Statistical analyses of Pairwise genetic distances among the four populations (MO, TI, AT, and KU) based on concatenated sequences also revealed low levels of divergence ranging from 0.00149 to 0.00386. The lowest divergence was observed between MO and AT (0.00149), while the highest occurred between TI and KU (0.00386) (Table 7, Figure 8).

Relationship between genetic and geographic distances among the four studied *S. plagiostomus* populations (MO, TI, AT, KU). Genetic distance is expressed as linearized (*F<sub>ST</sub>/*(1-*Fs<sub>T</sub>*)) and plotted against the natural log of geographic distance. Each blue data point (n = 4) represents a pairwise population comparison. The dashed red line indicates the reduced major axis (RMA) regression.
Figure 8. Relationship between genetic and geographic distances among the four studied S. plagiostomus populations (MO, TI, AT, KU). Genetic distance is expressed as linearized (F<sub>ST</sub>/(1-Fs<sub>T</sub>)) and plotted against the natural log of geographic distance. Each blue data point (n = 4) represents a pairwise population comparison. The dashed red line indicates the reduced major axis (RMA) regression.

Table 7. Pairwise Genetic distance values between S. plagiostomus populations based on concatenated (COI+Cytb) sequences.

MOTIATKU
MO–
TI0.00221–
AT0.001490.00229–
KU0.003090.003860.00307–

Results of isolation-by-distance (IBD) analysis with the Mantel test for the populations of S. plagiostomus revealed weak or negligible correlation between Linearized genetic distances and logarithmic geographic distances (Mantel r = 0.063, P = 0.417), which does not follow a simple isolation-by-distance (IBD) model. The relationship was statistically non-significant, suggesting that geographic distance is not the primary driver of genetic differentiation among these four populations..

Population demography

Tazima’s D test (Tajima, 1989), Fu’s Fs test (Fu, 1997) as implemented in DNASP version 5.2 (Librado and Rozas, 2009) were used to test the hypothesis of neutrality of the marker. Negative values of Tazima’s D (-2.163 with P<0.05) and Fu’s Fs -16.905 with P < 0.001) were observed in a concatenated sequence (Table 8). The pairwise distribution among sequences was also obtained using a historical demographic expansion model. Unimodal mismatch distribution was reported in the studied S. plagiostomus populations (Figure 9), which is a characteristic of a population that has undergone a recent expansion. To verify these demographic inferences, a Bayesian Skyline Plot (BSP) was reconstructed (Figure 10).

Mismatch distribution analysis of concatenated *COI+Cyt b* sequences of *S. plagiostomus.* The abscissa indicates the number of pairwise differences between compared sequences, and the ordinate represents the observed (Blue solid line with markers) and expected (green dotted line) frequency for each value under the sudden expansion model performed with the DnaSP version 5.2 software.
Figure 9. Mismatch distribution analysis of concatenated COI+Cyt b sequences of S. plagiostomus. The abscissa indicates the number of pairwise differences between compared sequences, and the ordinate represents the observed (Blue solid line with markers) and expected (green dotted line) frequency for each value under the sudden expansion model performed with the DnaSP version 5.2 software.
Bayesian Skyline Plot (BSP) illustrating the historical demographic trend of *S. plagiostomus*. The x-axis represents time in substitutions per site (from the present at 0.0 towards the past), and the y-axis represents the effective population size (N<sub>e</sub>) on a logarithmic scale. The solid dark line indicates the median estimate of the effective population size, while the shaded blue area represents the 95% Highest Posterior Density (HPD) interval.
Figure 10. Bayesian Skyline Plot (BSP) illustrating the historical demographic trend of S. plagiostomus. The x-axis represents time in substitutions per site (from the present at 0.0 towards the past), and the y-axis represents the effective population size (Ne) on a logarithmic scale. The solid dark line indicates the median estimate of the effective population size, while the shaded blue area represents the 95% Highest Posterior Density (HPD) interval.

Table 8. Fu’s F<sub>S</sub>, Tajima’s D, (r), R<sub>2</sub>, P: significant value for S. plagiostomus.

PrimerNHFu’s FS(P value)Tajima’s D (P value)Raggedness Statistic (r)R2 Statistic
concatenated COI+Cyt b4020-16.905P < 0.001-2.163P < 0.050.0136P = 0.0010.0387P < 0.001

Phylogenetic relationships

The Neighbour- joining phylogenetic tree of S. plagiostomus haplotypes exhibited very low bootstrap support, with most internal nodes having (< 50%) (Figure 11). While the Tree visually partitions haplotypes into four coloured clusters, the lack of statistical robustness indicates a lack of genetic structure across the MO, AT, TI and KU sampling sites.

Neighbour joining (NJ) phylogenetic tree of *S. plagiostomus*, based on haplotypes of concatenated sequences *(COI+Cyt b).* The numbers above the branches represent the bootstrap value obtained in the NJ analyses based on 1,000 replications. MEGA11 software was used to construct the tree, and for visualisation, iTOL software ([https://itol.embl.de/](https://itol.embl.de/)) was used. As branches exhibited low bootstrap support (<50%), they are excluded from the figure.
Figure 11. Neighbour joining (NJ) phylogenetic tree of S. plagiostomus, based on haplotypes of concatenated sequences (COI+Cyt b). The numbers above the branches represent the bootstrap value obtained in the NJ analyses based on 1,000 replications. MEGA11 software was used to construct the tree, and for visualisation, iTOL software (https://itol.embl.de/) was used. As branches exhibited low bootstrap support (<50%), they are excluded from the figure.

Discussion

Species persistence is directly linked to genetic variation, which is crucial for the survivability of a species in the long term as well as for the evolutionary capacity of a population and therefore holds great importance for conservation (Bouzat, 2010; Epstein et al., 2016; Funk et al., 2019; Sahoo et al., 2019; Chen et al., 2020). Ecosystems continue to degrade due to the anthropogenic influences, which lead to a decline in the species population (Cowie et al., 2022) and thus loss of genetic diversity (Shaw et al., 2025). It is also estimated that genetic diversity is probably declining faster than species diversity under growing threats (Manel et al., 2020). Understanding the genetic makeup of the fish species can help us in providing crucial information and a baseline for planning conservation and developing management strategies for fish populations (Pallavi et al., 2012; Jayasankar, 2018).

Demographic implications of a moderately high haplotype but low nucleotide diversity

The present study aimed to assess genetic diversity, population structure, and evolutionary relationships of S. plagiostomus within and among populations from the Tons rivers, the largest and longest tributary of the Yamuna River, using concatenated (COI+Cyt b) markers. In Populations with small sample sizes and threatened species, haplotypes can be distinguished with the help of mitochondrial genes, due to the high rate of evolution (Habib et al., 2012; Das et al., 2024). Therefore, in this study, the concatenated (COI + Cyt b) gene has generated 20 haplotypes for the vulnerable S. plagiostomus. Among these 4 populations, the Kwanu population has reported the highest number of haplotypes (9), followed by the Tiuni (7) and Atal populations (5). This high frequency of rare haplotypes is supported by a distinct star-like topology, which is consistent with a scenario of historical demographic expansion. Thus, for the genetic characterisation of the population and assessing the extent of genetic diversity in fish species, both the haplotype diversity (h) and nucleotide diversity (π) play an important role (Zhao et al., 2006; Liu, 2017; Zhang et al., 2017, 2020; Fang et al., 2022). Genetic diversity levels were categorized based on the thresholds established by Grant and Bowen (1998) and Bowen et al. (2001), where Low: (h < 0.5), Moderate: (0.5 < h < 0.7), and High: (h > 0.7). And to infer the demographic history of the populations, the relationship between haplotype diversity (h) and nucleotide diversity (π) was used following the criteria established by Grant and Bowen (1998). The combinations of these two indices were categorized into four levels. The combination of Low h and low π indicates the Founder effect or severe population bottleneck, whereas a high h and low π is suggestive of rapid population growth and post-bottleneck expansion following a period of low effective population size. A combination of Low h and high π reflects geographic isolation and subsequent differentiation, whereas a High h and high π combination indicates a stable, large population.

In this study, moderately high haplotype diversity with low nucleotide diversity was revealed in concatenated (COI+Cytb) sequences despite population decline, which is consistent with a scenario of historical demographic expansion, following a low effective population size, which would lead to the accumulation of diverse haplotypes more rapidly (Luhariya et al., 2012), though alternative explanations, such as purifying selection cannot be excluded. In contrast, nucleotide diversity (π) requires a comparatively longer timeframe to increase. Among the 4 populations, the Kwanu population (KU) shows high genetic diversity due to its high haplotype number and haplotype diversity as analysed by concatenated (COI+Cytb) markers, while the Mori (MO) population suggested lower genetic diversity.

AMOVA was used to examine the distribution of genetic diversity within and among S. plagiostomus populations. The results revealed a negative F<sub>ST</sub> value, which is considered as Zero (- F<sub>ST</sub>= 0), indicating no suggested genetic differentiation among populations, with most variation occurring within populations, suggesting high gene flow along the Tons River. This lack of genetic structure, combined with high levels of variation within populations, and observed star-like Topology is consistent with a pattern of recent demographic expansion from a common ancestral pool, suggesting mitochondrial panmixia.

Pairwise genetic differentiation (F*ST*) (Reynolds et al., 1983) measures population structure (Kitada et al., 2021) in conservation biology (Palsbøll et al., 2007; Schwartz et al., 2007), ecology and evolutionary biology (Jorde et al., 2015; Rougemont et al., 2020). Pairwise Genetic differentiation (F<sub>ST</sub>) is a measure of the genetic divergence between pairs of populations. F<sub>ST</sub> was interpreted according to the Qualitative thresholds proposed by Wright (Wright, 1931, 1978), which are as follows: 0 < F<sub>ST</sub>< 0.05: low differentiation; 0.05 < F<sub>ST</sub> < 0.15: moderate differentiation; 0.15 < F<sub>ST</sub> < 0.25: high differentiation; F<sub>ST</sub> > 0.25: very high differentiation. Analysis of the pairwise F<sub>ST</sub> in all the 4 populations (MO, TI, AT, and KU) ranged from -0.033 to 0.008. All values fall substantially below the 0.05 threshold, which indicates low genetic differentiation among the 4 populations. These results suggest high levels of gene flow and historical connectivity across these sites within the Tons River, thereby revealing a lack of genetic structure across all populations. This connectivity also explains the corresponding results of the Mantel test, where the lack of an apparent Isolation by Distance (IBD) pattern may be attributed to the limited number of populations sampled (n = 4), which reduces the statistical power of the Mantel test to detect spatial genetic structuring. With fewer pairwise comparisons available, the test is less likely to reach the standard threshold of significance (P < 0.05) unless the correlation is exceptionally strong.

The Neutrality tests (Fu’s Fs test and Tajima’s D) and mismatch distribution analysis are most commonly used to study demographic history (Guo et al., 2014; Lau et al., 2018). To discover genes that do not evolve neutrally, Tajima’s D, a population genetic test statistic, was calculated. Neutrality tests revealed overall negative and statistically significant values for Tajima’s D and Fu’s Fs, indicating a marked deviation from the neutral theory of evolution, suggesting a population expansion event or the effects of purifying selection. However, such signals can also arise from selective pressures on the mitochondrial genome, which can mimic demographic signatures (Galtier et al., 2000). This was further supported by the mismatch distribution analysis, where a unimodal pattern represented by a single peak in the frequency of pairwise differences, which is consistent with the scenario of historical demographic expansion (Marcia et al., 2013) over that of selective sweeps for all the populations. The Demographic history was also statistically supported by a Harpending’s Raggedness index (r = 0.0136, P = 0.001) and Ramos -Onsins and Rozas’s R2 statistic (R2 = 0.0387, P < 0.001). These parameters indicate a unimodal mismatch distribution, which supports the inference of a recent and rapid expansion model.

Comparisons with other Himalayan and Central Asian snow trout studies

The moderately high genetic diversity reported in S. plagiostomus from the Tons River aligns with the pattern observed in other closely related species of the genus Schizothorax across the Himalayan and Central Asian river systems. Our results are comparable with the values of high genetic diversity (h=0.928 and π =0.00778), reported in S. kozlovi in the Upper Yangtze River (including the Yalong, Jinsha and Wujiang Rivers) (Zhang et al., 2017), and in S. curvilabiatus in the Yarlung Zangbo River (Lower reaches) with haplotype diversity (h) of 0.945 in Cytb, 0.951 in Dloop, and 0.889 in COI (Su et al., 2026).

Guo et al. (2016) also reported high haplotype and low nucleotide diversity values in the western populations of S. o’connori in the Yarlung Tsangpo River (YLTR). Similar patterns of high genetic diversity in the related species of Schizothorax in the Lanchang River, and S. lissolabiatus (h=0.863 ± 0.010 and π=0.0028 ± 0.0001) in the Nujiang River (upper Salween), were reported by (Chen et al., 2016, 2017), further supporting the notion of past population expansion events However, high haplotype and nucleotide diversity were revealed in the Gola River, Ranibagh population of S. richardsonii (h = 1.00000, π = 0.00475 for ATPase6/8 and for COI, h = 0.83333, π = 0.00354) (Ali et al., 2014).

Conservation implications grounded in population genetics theory.

The lack of genetic structure and high gene flow observed indicates that S. plagiostomus should be managed as a single Management Unit (MU) based on mitochondrial data only. There is no need for site-specific management within the Tons River. Conservation efforts must prioritise the maintenance of river connectivity to ensure unimpeded migration. Furthermore, to prevent the loss of unique local maternal lineages, we recommend against inter-basin stocking and suggest that any future supplementation programs utilize broodstock exclusively from the Tons River.

Limitations

As the COI of the 658 bp fragment is considered a universal marker for species identification, for “DNA barcode” (Hebert et al., 2003). This study included relatively short sequence lengths for the mitochondrial markers (450bp for COI and 312 bp for Cytb), so the use of truncated fragments may result in a reduction of phylogenetically informative sites, potentially affecting the resolution of the species or the precision of genetic diversity metrics (Tobe et al., 2010). Also, the Evolutionary rates can vary across different regions of a single gene, so these partial sequences may not fully capture the genetic richness that would be evident in full- length gene sequences or Whole mitogenomes. (Macken et al., 2023).

While mtDNA is a robust tool for assessing historical demography, phylogeographic patterns and matrilineal lineages (Avise, 2000), its utility is inherently constrained. Because markers such as COI and Cytb represent a single, non-recombining, uniparental locus. They may fail to infer whole-genome population structure, sex biased dynamics, specifically male- mediated dispersal (Brunelli et al., 2010; Toews and Brelsford, 2012; Dupont et al., 2022; Giannelli et al., 2025), and contemporary gene flow (Galtier et al., 2009). Future research incorporating bi-parental nuclear markers, such as SNPs or microsatellites, is essential to provide higher resolution needed to detect contemporary hybridisation, recent bottlenecks and individual-level kinship (Sunnucks, 2000; Dessi et al., 2022) In the absence of nuclear data, our findings offer a conservative estimate of genetic isolation, serving as a necessary precursor to more intensive genomic surveys required for comprehensive broodstock management and the definitive identification of Management Units (MUs) (Petit and Excoffier, 2009). Given the current lack of baseline genetic data for this species, this mitochondrial approach provides critical insights for maternal population history, informing future management strategies involving nuclear-based assessments.

Conclusion

In this study, the hypothesis of population structuring was tested using AMOVA; however, the results do not support genetic structuring. This study provides a critical baseline for the maternal genetic structure of S. plagiostomus in the Tons River, where Kwanu shows higher mitochondrial haplotype diversity, which may contribute to maternal line diversity when combined with nuclear and phenotypic data. Consequently, it serves as a primary reservoir for sourcing diverse maternal lines for future conservation or aquaculture initiatives. However, as these results are useful for maintaining maternal line diversity, they should be viewed as a preliminary step. Future conservation programs must integrate this mitochondrial data with phenotypic evaluations and nuclear markers to resolve fine-scale genomic structure and long-term evolutionary potential of the S. plagiostomus in this region.

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Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/faquc.2026.1777546/full#supplementary-material

Supplementary material Supplementary material at the source

Footnotes

Edited by: Neelesh Kumar, Rani Lakshmi Bai Central Agricultural University, India

Reviewed by: Soumya Prasad Panda, Central Inland Fisheries Research Institute (ICAR), India

Bismay Sahoo, Central Institute of Freshwater Aquaculture (ICAR), India

Akbarjon Rozimov, Institute of Zoology Chinese Academy of Sciences, China

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Ethics approval

Ethical review and approval were not required for the study on animals because all samples were obtained from deceased fish from commercial fishing landing sites. No animals were sacrificed specifically for this research.

Author contributions

PB: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Software, Visualization, Writing – original draft, Writing – review & editing. JR: Supervision, Validation, Writing – original draft, Writing – review & editing.

Acknowledgements

The authors thank the Department of Zoology for providing the infrastructure that made this study possible. Special thanks are extended to the local fishing communities and vendors at the various fish landing sites for their cooperation during the sample collection process.

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Citation

Badoni, P. & Rauthan, J. V. S. (2026). Mitochondrial marker-based insights into the genetic diversity and population connectivity of the vulnerable (IUCN Red List) snow trout (Schizothorax plagiostomus) from the Tons River, Western Himalaya. Frontiers in Aquaculture, Volume 5. https://doi.org/
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*Correspondence: Pooja Badoni

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