Indian Himalaya Research Articles

‘All cases are false’: law, gendered violence, and the politics of thickening in Himalayan India

AbstractThis article focuses on Indian women's experiences of filing complaints of gendered violence in order to address two interconnected questions: how are complaints of gendered and sexual violence authenticated as genuine or rejected as dubious before they even reach a courtroom? And how do women who bring these complaints before the law navigate a social field in which what counts as the ‘truth’ might conflict with their own understandings and experiences? Drawing on ethnographic fieldwork in the Central Himalayan state of Uttarakhand in India, this article explores how family, community, and local state officials engaged in a kind of thick description that contextualized women's complaints within rural social relations and political economy. It shows how this politics of thickening often displaced women's individual experiences of violence and served to falsify their complaints. This everyday thickening bears a troubling similarity to the theory and methods of feminist activists and anthropologists, necessitating reflection on how to write ethically about gendered violence without replicating violence. Finally, this article turns attention to how some women decided to take on this politics of thickening through canny adoption of its methods and premises, eventually stretching the limits of the law and unintentionally expanding its scope.

Co-occurrence of microplastics and heavy metals in a freshwater lake system in Indian Himalaya: Distribution and influencing factors

Microplastic (MP) contamination in freshwater systems is a prevalent and persistent environmental issue, yet their occurrence and distribution remain poorly constrained. The present study examines the MPs abundance in sediment and water samples and factors controlling their distribution in the Manasbal Lake, north-western (NW) Himalaya. Additionally, the study investigates the relationship between heavy metals and the distribution of MPs in sediment, as well as their potential interactions. The MPs counts in lake surface sediment and water samples varied from 840 nkg−1 to 4020 nkg−1 and 13 nL−1 to 89 nL−1, respectively. The MPs distribution in Manasbal Lake exhibited spatial heterogeneity, with the greatest abundance observed in the eastern and northeastern areas near lake inlets. Grain size and land use appear to collectively modulate the variability of MPs in Manasbal Lake sediments. Five main MPs types were identified: beads/pellets, fragments, fibres, foams, and films, with beads being the predominant type. Polypropylene, polyethylene, and polystyrene are the dominant constituents of the observed MPs, with domestic sewage suggested as the predominant source for their abundance in the lake. Furthermore, the contamination factor for heavy metals indicated a high level of lead contamination in surface sediments, while copper and cobalt showed moderate contamination near the lake inlet. The SEM-EDS analysis illustrated the presence of toxic elements such as Hg, Zn, Pb, Cr, Cd, Ni, and Cu to the surface of MPs. This study expands the baseline characterization of MPs in freshwater systems and enhances our understanding of the potential sources and factors influencing MP distribution.

Enhancing landslide susceptibility mapping using a positive-unlabeled machine learning approach: a case study in Chamoli, India

IntroductionThe Indian Himalayas' susceptibility to landslides, particularly as a location where climate change effects may be event catalysts, necessitates the development of dependable landslide susceptibility maps (LSM).MethodThis study diverges from traditional binary classification models, framing LSM as a positive-unlabeled learning problem. This approach acknowledges that regions without recorded landslides are not necessarily at low risk but could simply have not experienced landslides yet. The study utilizes novel positive-unlabeled learning-enhanced algorithms—Random Forest, K-Nearest Neighbor, and Decision Tree—to create LSM for Chamoli district, India. Eleven causative factors for landslides are identified, including elevation, aspect, slope, geology, geomorphology, distance to lineament, lithology, NDVI, distance to river, distance to road and residential land use. To address spatial correlation biases, instead of randomly splitting the dataset, the study adopts spatial splitting to get the training and testing datasets.ConclusionThe study reveals that positive-unlabeled learning substantially improves the Area Under Curve and recall, leading to a more conservative LSM compared to binary classification methods. Analysis shows that the southern region of Chamoli exhibits high recall but lower accuracy, suggesting a latent high landslide susceptibility despite a lack of historical landslides in this region. The study also quantifies the impact of human activity on landslide risk, indicating an elevated threat to life and the local economy, especially in Chamoli's southwestern areas.

Phytocannabinoid profile and potency of cannabis resin (hashish) of northwest Himalayas of India.

Cannabis is one of the most consumed illicit drugs and the potency of cannabis products is of note due to health-related concerns. Hand-rubbed hashish is the ancient technique of extracting psychoactive resin from cannabis plants and is practiced in the Indian Himalayas. This study establishes the cannabinoid profile and potency of hand-rubbed hashish collected from 20 regions of the northwest Himalayas. Fifty-eight hashish samples were analyzed using a validated high-performance liquid chromatography-diode array detection (HPLC-DAD) method. Ten cannabinoids were quantified including acidic (THCA & CBDA), and neutral compounds (CBDV, THCV, CBD, CBG, CBN, Δ9-THC, Δ8-THC, and CBC). The mean concentration (w/w%) of Δ9-THC is 26%; THCA is 15% and THCTotal is 40% is observed in the studied hashish samples. The majority (70%) of the hashish samples were categorized in chemotype I with the THC:CBD:CBN ratio of 91:3:4, and the remaining 30% were categorized under chemotype II with the ratio of 76:15:8. Diverse qualities of hashish are produced in the studied regions as per the seed, plant selection, and skills of manual rubbing, which results in potency variations. The average difference between the least and highest potent hand-rubbed hashish of a region is 27 w/w% (THCTotal). The other studied non-psychoactive cannabinoids have a mean w/w% of <5%, followed by 6% of CBDA. It is concluded that the cultivated and wild cannabis fields in the northwest Himalayas belong to the drug-type cannabis subspecies. Hand-rubbed hashish holds traditional significance and impacts the current policies of legislation.

Studies on sex ratio, condition factor and patterns of phenotypic estimation in stock identification of snow trout, Schizothorax esocinus Heckel, 1838 inhabiting the colder Indian Himalayan region

Schizothorax esocinus, a nutrient-rich food fish offered in Asian countries, is rapidly declining due to severe fishing pressure and pollution in its wild environment, requiring active conservation strategies. Our study examined 196 specimens to elucidate critical biological parameters including sex ratio, length-weight relationships (LWRs), Fulton's condition factor (K) and morphometric-meristic parameters. The general sex ratio was not substantially departed from the predicted value of 1 : 1, with the exception for the month of May, where χ2 = 4.15 which is greater than the calculated value of 3.84. The correlation between the length and weight was reported as Log W = 0.134209 + 2.5218 log L for males, Log W = 0.19173 + 2.6751 log L for females and Log W = 0.162116 + 2.594 for combined sexes. Schizothorax esocinus demonstrated a negative allometric growth as indicated by the allometric parameter b for the LWR. The two sexes showed no significant differences in morphometric measures. So, this study included combined-sex regression analysis. These comprehensive findings highlight the critical need for conservation efforts in India and neighbouring countries to preserve this valuable fish stock for future generations.

Interplanted crops and rainfall variability regulate soil erosion in rainfed maize of the Indian Himalayas

AbstractSoil erosion is a serious threat to agriculture and environmental sustainability in the risk‐prone rainfed Himalayan ecosystem. Hence, runoff and soil loss mitigation ability of four maize‐based intensified systems; maize (Zea mays) + turmeric (Curcuma longa), maize + ginger (Zingiber officinale), maize + colocasia (Colocasia esculenta), and maize + sweet potato (Ipomoea batatas) were tested against the sole maize. The study recorded 41 erosive events during the 2020–2022 monsoon season. A principal component analysis (PCA) was performed to transform correlated rainfall parameters into orthogonal principal components. A two‐dimensional biplot analysis examined the relationship between PCs, rainfall events, and rainfall parameters. The rainfall events were divided into three regimes based on cluster analysis using PC. Rainfall regime 1 was characterized by low rainfall, I30, rainfall erosivity (E), and moderate rainfall duration (D), regime2 with moderate rainfall, short duration, high I30, and E, and regime3 with high D, I30, and rainfall erosivity. The soil loss of regime‐3 was 9.29 and 3.24 times higher than regime‐1 and regime‐2, respectively. Cover crops reduced runoff by 21.5%–69.3% and soil loss by 54.1%–77.0% over control. Redundancy analysis (RDA) showed that rainfall parameters (I30, I60, D, and E) had a significant direct influence across the systems, explaining 37.7%–54.9% of response variable variance, with E being the most influential (47.4%–54.9%), indicating varied impacts on runoff and soil loss. The sweet potato was best suited with maize as an intercrop to minimize soil erosion and maximize the system's profitability.

Operational Forest-Fire Spread Forecasting Using the WRF-SFIRE Model

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km2) was validated against the satellite-based burnt area (1.07 km2) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting.

Effects of Digital Elevation Models on Spatial Characterisation of Landslides in the Kalka-Shimla Region of the Indian Himalayas

Effects of Digital Elevation Models on Spatial Characterisation of Landslides in the Kalka-Shimla Region of the Indian Himalayas

Integrating real-time sensor data for improved hydrogeotechnical modelling in landslide early warning in Western Himalaya

This study presents a comprehensive investigation into developing a real-time monitoring framework for a localized landslide early warning system (LEWS), focusing on the hydrological dynamics of an unsaturated slope near the Joshimath Badrinath Highway in Uttarakhand, India. Considering the steepness of the slope and its adjacency to the highway, continuous monitoring and stability assessment are crucial. The framework encompasses primary phases of monitoring, modelling, forecasting, and warning. Monitoring collected hydrological and meteorological data, used for modelling and calibration. Validation using Taylor diagrams ensured accuracy by comparing predicted and monitored data. The calibrated hydrological model guided slope stability modelling to identify instability factors. A machine learning algorithm detected potential instability. Forecasting predicted unstable periods, triggering warnings if the factor of safety (Fs) values drop below 1.5. The study provided important insights into slope stability, highlighting the significance of vegetation parameters in accurately assessing slope stability using the Fs. Calibration of the hydrogeological model, particularly considering rainfall, climate, and vegetation data, improved the alignment between predicted and observed volumetric water content (VWC), especially in shallower depths. However, modelling hydrological dynamics at greater depths remains challenging, emphasizing the need for refined approaches. Machine learning techniques, specifically a Random Forest model, achieve high accuracy in predicting Fs, identifying VWC at 0.3-m and 3-m depths as pivotal variables. Temporal evaluation suggests a 12-month simulation as optimal, showing consistent performance across depths. Overall, the study advances slope stability modelling and offers insights into sustainable slope management practices, highlighting the complex interplay between climate, vegetation, and hydrological dynamics in unsaturated slopes. This approach lays a foundation for effective LEWS in Indian Himalayan states.

Home range and seasonal movement of Nanorana vicina (Stoliczka, 1872) (Anura: Dicroglossidae) in Himalayan streams, India

The spatial movement pattern is a poorly known aspect of amphibian ecology, especially in the Indian Himalayan region. Thus, we studied the home range and movement of Nanorana vicina,, an obligate stream frog endemic to the Western Himalayas. We radio-tagged 16 individuals and tracked their movement for an average of 63 days in two streams of Binog Wildlife Sanctuary, Uttarakhand. Our results showed that the highest mean movement of a tagged frog was 5.9 ± 3.26 meters (m) in 13 days during monsoon. Most of the movements (90 %) were restricted within the stream. Movement varied significantly across seasons, with the highest movement during the monsoon (2.71 ± 0.46 m) and the lowest during the post-monsoon (0.78 ± 0.14 m). The Dunn's Test revealed that post-monsoon movement was significantly lower than in both pre-monsoon and monsoon periods. The minimum convex polygon showed a range of 1.61 m2 to 43.16 m2 and 15.78 m2 to 684.99 m2 for 50% and 90% MCP, respectively. No significant difference was found in the weights of frogs before tagging and after removing tags (n = 10, p &gt; 0.05). Himalayan streams are subjected to rapid anthropogenic and climate-induced changes. Less vagile and highly philopatric species, such as N. vicina, might be much more vulnerable to such changes. Thus, the study highlights the importance of the stream and the riparian zone for the conservation of this stream frog.

Physiological and genomic insights into a psychrotrophic drought-tolerant bacterial consortium for crop improvement in cold, semiarid regions

The agricultural land in the Indian Himalayan region (IHR) is susceptible to various spells of snowfall, which can cause nutrient leaching, low temperatures, and drought conditions. The current study, therefore, sought an indigenous psychrotrophic plant growth-promoting (PGP) bacterial inoculant with the potential to alleviate crop productivity under cold and drought stress. Psychrotrophic bacteria preisolated from the night-soil compost of the Lahaul Valley of northwestern Himalaya were screened for phosphate (P) and potash (K) solubilization, nitrogen fixation, indole acetic acid (IAA) production, siderophore and HCN production) in addition to their tolerance to drought conditions for consortia development. Furthermore, the effects of the selected consortium on the growth and development of wheat (Triticum aestivum L.) and maize (Zea mays L.) were assessed in pot experiments under cold semiarid conditions (50 % field capacity). Among 57 bacteria with P and K solubilization, nitrogen fixation, IAA production, siderophore and HCN production, Pseudomonas protegens LPH60, Pseudomonas atacamensis LSH24, Psychrobacter faecalis LUR13, Serratia proteamaculans LUR44, Pseudomonas mucidolens LUR70, and Glutamicibacter bergerei LUR77 exhibited tolerance to drought stress (-0.73 MPa). The colonization of wheat and maize seeds with these drought-tolerant PGP strains resulted in a germination index >150, indicating no phytotoxicity under drought stress. Remarkably, a particular strain, Pseudomonas sp. LPH60 demonstrated antagonistic activity against three phytopathogens Ustilago maydis, Fusarium oxysporum, and Fusarium graminearum. Treatment with the consortium significantly increased the foliage (100 % and 160 %) and root (200 % and 133 %) biomasses of the wheat and maize plants, respectively. Furthermore, whole-genome sequence comparisons of LPH60 and LUR13 with closely related strains revealed genes associated with plant nutrient uptake, phytohormone synthesis, siderophore production, hydrogen cyanide (HCN) synthesis, volatile organic compound production, trehalose and glycine betaine transport, cold shock response, superoxide dismutase activity, and gene clusters for nonribosomal peptide synthases and polyketide synthetases. With their PGP qualities, biocontrol activity, and ability to withstand environmental challenges, the developed consortium represents a promising cold- and drought-active PGP bioinoculant for cereal crops grown in cold semiarid regions.

Assessing Machine Learning and Statistical Methods for Rock Glacier‐Based Permafrost Distribution in Northern Kargil Region

ABSTRACTEstimating permafrost distribution in high‐mountain areas is challenging. In these situations, rock glaciers, provide valuable insights into permafrost distribution and are often used as proxies for identifying permafrost occurrence. Integrating various climatological and topographical conditioning factors with rock glaciers enables inferring the distribution of permafrost in these environments. This study utilized three machine learning models such as random forest (RF), support vector (SVM), and artificial neural network (ANN), and one statistical model, namely, the frequency ratio (FR), to assess the permafrost probability over the northern Kargil region of Indian Himalayas. Among 198 rock glaciers identified through high‐resolution images from Google Earth, 70% are used as training dataset, rest 30% as testing dataset. The study considered eight factors: slope, aspect, elevation, curvature, mean annual land surface temperature (MA‐LST), mean annual normalized difference snow index (MA‐NDSI), mean annual normalized difference water index (MA‐NDWI), and lithology for mapping. Furthermore, the SHapley Additive exPlanations (SHAP) test assessed the variable importance for model performance. The results revealed that the RF model performs best for permafrost probability mapping, followed by the SVM, FR, and ANN models. The study also found that 11% of the total geographic area has a high and very high probability of permafrost occurrence.

Genetic diversity analysis of native sheep population of Eastern Indian Himalayas using microsatellite DNA markers

Genetic diversity analysis of native sheep population of Eastern Indian Himalayas using microsatellite DNA markers

Morphological and histological analysis of six Lamiaceae species from the North-Western Indian Himalaya

Morphological and histological analysis of six Lamiaceae species from the North-Western Indian Himalaya

Biomass, productivity and monetary efficiency of various cropping systems in the Almora district of the Indian Himalaya

ABSTRACT Uttarakhand hills are blessed with mesmerizing beauty but low crop productivity because of which the farmers are diverting from agriculture. A research investigation was conducted in Dantola village, Almora District, Uttarakhand, India, to understand the dynamics and performance of hill agro-ecosystems by estimating biomass, productivity and monetary efficiency of the existing cropping systems. Total five systems were selected which consist of six monocrops, three mixed crops, two agri-silviculture systems, one agri-horticulture system and silvi-hortipasture system. System-wise, higher biomass was observed in agri-silviculture system with an average value of 134.3 tonnes/ha. Farmyard manure was found to be the major source of nutrients supply to the crops. The monetary efficiency of various cropping systems revealed that the total returns were highest in the agri-horticulture system (USD 8805.76/ha) followed by horsegram+sorghum (USD 1620.49/ha) and urdbean (USD 1448.65/ha). When the net benefit from various cropping systems was analysed, maximum net returns were observed in agri-horticulture (USD 5867.57 lakh/ha), followed by horsegram+sorghum (mixed cropping) and urdbean. Overall, agri-silviculture systems provided higher biomass while legumes, agri-horticulture system and silvi-hortipasture systems were found to be the most efficient in terms of production and monetary efficiency and therefore can be recommended to the farming communities.

Spatial Variation in Hydrosedimentary Characteristics of the Alaknanda River Basin in the Indian Himalayas: A Field Study

Spatial Variation in Hydrosedimentary Characteristics of the Alaknanda River Basin in the Indian Himalayas: A Field Study

Conservation and management of Himalayan biodiversity in climate change perspectives

Diverse agro-ecosystems are the bedrock of life-sustaining goods and services, ranging from water and energy to food production and the facilitation of energy and nutrient cycling. These ecosystems not only benefit humanity but also play a crucial role in supporting the balance of ecosystems worldwide. Biodiversity conservation and management are paramount as they directly impact the suitability and resilience of ecosystems, ultimately affecting the livelihoods of both human and non-human species. The Indian Himalayan Region (IHR) boasts extraordinary biodiversity; however, efforts to conserve and harness its bio-resources for technological advancements or useful products lag behind many other nations. Climate change exacerbates these challenges, affecting every aspect of agriculture and biodiversity across tropical and subtropical regions. In response to this urgent context, there is a pressing need to review existing conservation and management strategies while identifying areas for improvement. Analyzing the diverse requirements and methods of conserving biodiversity involves prioritizing the sustenance of natural processes and features inherent in our natural resources and associated ecosystems. Recognizing that natural solutions exist within these resources, landscapes, and ecosystems is pivotal. Addressing these challenges requires concerted efforts and institutional synergy among various stakeholders, including researchers, local communities, resource managers, and policymakers. Collaboration is essential to conserve biodiversity while leveraging existing or newly created opportunities and resources within ecosystems. Furthermore, revising and updating legal frameworks, policy guidelines, and other instruments are crucial steps to ensure the sustained protection of biodiversity and ecosystems. This includes safeguarding the livelihoods and food production avenues of local communities, thereby fostering sustainable development practices that prioritize both human well-being and environmental integrity.. KEYWORDS :Himalayan Biodiversity, Climate change, Conservation and Management.

Tracing the green footprints: a bibliometric analysis of biodiversity conservation in the Himalayas

ABSTRACT Mountains cover 24% of the land surface area and are home to 12% of the population of the world. The mountains within the Indian Himalayan region account for 16.2% (500, 000 km2) of India’s total land area and while there is an age-old tradition of biodiversity conservation in this region, the area is under stress due to human population growth and climate change. Therefore, this region requires conservation management activities, however, our study has shown a substantial decrease in publications in this area between 2004 and 2019. In this study, we completed a bibliometric analysis to explore biodiversity protection in the Himalayas. The analysis revealed valuable information regarding current research trends as well as research gaps. This study uses bibliometric analysis to examine the changing landscape of biodiversity protection in the Himalayas. The study analysed 425 journal papers published from 2004 to 2023. This article contributes to understanding studies on Himalayan management challenges, including endangered species conservation, environmental protection, ecosystem dynamics, and conservation management. We hope this paper inspires scientists to complete research in the areas lacking data such that a baseline can be established and policies can be developed to support biodiversity conservation in the Indian Himalayas.

Longitudinal assessment of extreme climate events in Kinnaur district, Himachal Pradesh, north-western Himalaya, India.

It is vital to keep an eye on changes in climatic extremes because they set the stage for current and potential future climate, which usually have a reasonable adverse impact on ecosystems and society. The present study examines the variability and trends in precipitation and temperature across seasons in the Kinnaur district, offering valuable insights into the complex dynamics ofthe Himalayan climate. Using Climatic Research Unit gridded Time Series (CRU TS) datasets from 1951 to 2021, the study analyzes the data to produce 28 climate indices based on India Meteorological Department (IMD) convention indices and Expert Team on Climate Change Detection and Indices (ETCCDI). Although there may be considerable variation in climate indices in terms of absolute values within different products, there is consensus in both long-term trends and inter-annual variability. Analysis shows that even within a small area, there is variability in the magnitude and direction of historic temperature trends. Initially, the datawere subjected to rigorous quality control procedures, which involved identifying anomalies. Statistical analysis like trend analysis, employing Mann-Kendall test and Sen's slope estimator, reveal significant (p < 0.05) increase in consecutive dry days (CDD) at 0.03days/year and decrease in consecutive wet days (CWD) at 0.02days/year. Notably, the frequency of heavy precipitation occurrences showed an increasing trend. Changes in precipitation in the Western Himalaya are driven by a complex interplay of orographic effects, monsoonal dynamics, atmospheric circulation patterns, climate change, and localized factors such as topography, atmospheric circulation patterns, moisture sources, land-sea temperature contrasts, and anthropogenic influences. Moreover, in case of temperature indices, there is significant increasing trend observed. Temperature indices indicate a significant annual increase in warm nights (TN90p) at 0.06%/year and warm days (TX90p) at 0.11%/year. Extreme temperature events have been trending upward, with monthly daily maximum temperature (TXx) increasing by 1.5°C yearly. This study enhances our comprehension of the global warming phenomenon and underscores the importance of acknowledging alterations in the water cycle and their repercussions on hydrologic resources, agriculture, and livelihoods in the cold desert of the northwestern Indian Himalaya.

Genomic analysis and phylogenetic characterization of Himalayan snow trout, Schizothorax esocinus based on mitochondrial protein-coding genes.

Mitochondrial DNA (mtDNA) has become a significant tool for exploring genetic diversity and delineating evolutionary links across diverse taxa. Within the group of cold-water fish species that are native to the Indian Himalayan region, Schizothorax esocinus holds particular importance due to its ecological significance and is potentially vulnerable to environmental changes. This research aims to clarify the phylogenetic relationships within the Schizothorax genus by utilizing mitochondrial protein-coding genes. Standard protocols were followed for the isolation of DNA from S. esocinus. For the amplification of mtDNA, overlapping primers were used, and then subsequent sequencing was performed. The genetic features were investigated by the application of bioinformatic approaches. These approaches covered the evaluation of nucleotide composition, codon usage, selective pressure using nonsynonymous substitution /synonymous substitution (Ka/Ks) ratios, and phylogenetic analysis. The study specifically examined the 13 protein-coding genes of Schizothorax species which belongs to the Schizothoracinae subfamily. Nucleotide composition analysis showed a bias towards A + T content, consistent with other cyprinid fish species, suggesting evolutionary conservation. Relative Synonymous Codon Usage highlighted leucine as the most frequent (5.18%) and cysteine as the least frequent (0.78%) codon. The positive AT-skew and the predominantly negative GC-skew indicated the abundance of A and C. Comparative analysis revealed significant conservation of amino acids in multiple genes. The majority of amino acids were hydrophobic rather than polar. The purifying selection was revealed by the genetic distance and Ka/Ks ratios. Phylogenetic study revealed a significant genetic divergence between S. esocinus and other Schizothorax species with interspecific K2P distances ranging from 0.00 to 8.87%, with an average of 5.76%. The present study provides significant contributions to the understanding of mitochondrial genome diversity and genetic evolution mechanisms in Schizothoracinae, hence offering vital insights for the development of conservation initiatives aimed at protecting freshwater fish species.