Assessing the patterns of fish assemblages in the Rupnarayan River, West Bengal in India

Assessing the patterns of fish assemblages in the Rupnarayan River, West Bengal in India | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Assessing the patterns of fish assemblages in the Rupnarayan River, West Bengal in India Harshali Patkar, Vidyadhar Atkore, Gaurav Shinde, Priyankar Chakraborty, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9140684/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tropical rivers, are highly biodiverse yet they face significant threats from human activities, impacting overall their ecological health. We studied fish diversity (richness and abundance) and its relationships with environmental factors, understanding size class distribution and the role of land use land cover in a Rupnarayan river in east India. Using a hierarchical nested design and space-for-time replacement method to sample fishes from January 2024 to March 2024 over a 30 km stretch of the river. We recorded 40 species, comprising 774 individuals from 14 orders and 25 families. Multiple linear regression indicated that channel type, water temperature, river width and depth, and time spent on fish sampling were significantly associated with species richness and abundance. Flow-ecology relationships demonstrated a preference for slower currents among selected species. The fishing gears influences the body size class of fish species. Land Use Land Cover analysis showed that cropland (44%) dominates the study area followed by vegetation (25%), built-up areas (18%) etc. Despite the absence of immediate threats, the Rupnarayan River plays a crucial role in supporting significant riverine biodiversity which may be helpful for future ecological research and conservation efforts in similar tropical rivers in the world. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Environmental factors fish diversity Land Use Land Cover (LULC) streamflow tropical river Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Fish comprise the most diverse group of vertebrates, and exhibit a considerable richness of about 34000 species (Klimpel et al., 2019 ). Fish diversity in the riverine ecosystem has great significance in terms of livelihood and socio-economic importance (Ghorai, 2018 ). Fishes are substantial size consumers and possess long lifespans, that assimilate information about conditions throughout the food web. Their intricate ecological needs make river-dwelling fishes a suitable indicator of habitat conditions (Schiemer 2000 ). Various aspects of their abundance, species composition, age-class distribution, and reproductive patterns are reflective of the overall health of the river ecosystem (Schmutz et al., 2007 ). According to bioassessment research, fishes are important in indicating many characteristics of anthropogenic ecosystem degradation, particularly in rivers and streams (Schmutz et al., 2007 ; Hering et al., 2006 , Poikane et al., 2017 ). Although fish represent the most diverse group of vertebrates, freshwater fish are also the most threatened. After amphibians, fish may be the most vulnerable group of vertebrates (Bruton, 1995 ; Duncan and Lockwood, 2001 ). Increasing anthropogenic pressure in the form of illegal fishing and the use of indiscriminate fishing practices such as dynamiting, fish poisoning, electrofishing and river substate mining have drastically decimated the world’s freshwater fishes (Atkore et al., 2011 ; Hughes, 2021 ). Human activities pose numerous threats to river ecosystems, including damming, urbanisation, and agricultural expansion which adversely affect water quality and biodiversity (Vörösmarty et al., 2010 ) The freshwater ecosystem comprises of numerous community metric to assess the ecosystem health which includes diversity, community structure, and species assemblages in streams and rivers. Fish diversity is a measure of scale. The growth or decline of fish species assemblage within the spatial distribution limit is determined by biotic and abiotic factors (Minns, 1989 ; Olden et al., 2000). And the study of fish species composition and distribution helps in the identification of endangered or threatened species as well as the assessment of their population size and health. Global patterns in fish diversity within tropical rivers are shaped by a complex interaction of environmental, geographical, and anthropogenic factors. Large tropical river basins, such as the Amazon, Congo, and Mekong, have the highest fish diversity due to their extensive and varied aquatic habitats (Van der Sleen and Albert, 2022 ). Environmental gradients, such as temperature, dissolved oxygen, and nutrient levels, significantly influence fish assemblage structures, as seen in the Mekong and Narmada rivers (Chea et al., 2017; Shukla and Bhat, 2017 ). Also, the seasonal variations and local environmental conditions, including stream order and forest cover, are crucial in shaping fish diversity patterns (Shukla and Bhat, 2017 ; Larre-Campuzano et al., 2023 ) Globally, riverine ecosystems are increasingly threatened by hydrological regulation and the rapidly growing demands for irrigation, fisheries and industrial facilities (Davis et al., 2010 ; Suski and Cooke, 2007). So, understanding the interactions between different fish species within community helps to predict the potential impacts of environmental changes and designing effective conservation and management strategies (Mondal and Bhat, 2020 ). Environmental factors such as precipitation, topography, stream width, depth, altitude, temperature variation and scale of the study play crucial roles in shaping fish assemblages (Brown, 2000 ; Askeyev et. al., 2014 ; Dubey et al., 2012 ). India is recognized as a hotspot of freshwater fish diversity and has a substantial degree of endemism that significantly contributes to the World's biological resources (Kottelat and Whitten, 1996 ; Dahanukar et al., 2003 ). Recent estimates indicate that India harbours about 2667 species, securing its position among the top 25 countries globally in terms of endemism. Additionally, India holds the third position in Asia concerning overall freshwater fish diversity (Thakur et al., 2021 ). In India, researchers have drawn to study fish diversity, distribution and highlighted the role of environmental variables and anthropogenic factors in shaping fish diversity. Johnson and Arunachalam ( 2009 ) studied fish diversity in streams of the southern Western Ghats, recording 60 species across four orders, 13 families, and 27 genera, with Cyprinids being the most dominant. They observed that fish diversity varied among streams, with the highest diversity in the third-stream order (Thalayanai), and noted similarities in faunal assemblage among nearby basins. Dwivedi and De ( 2024 ) reported that in the Bhima River basin of the Deccan Plateau – a part of northern WG, fish communities show significant spatial variations. Another study from this region, Mohite and Sawant (2013) noted that the land use pattern transformation and riparian habitat loss, negatively affected fish diversity in the Warna River Basin. Study in rivers of central Western Ghats (States of Karnataka and Goa) found that water chemistry significantly influences fish guild composition and species richness, particularly at the sub-basin scale (Atkore et. al., 2020 ). Similar studies in streams across central and eastern India reveal that water flow is a common driver of fish richness and diversity, with no significant seasonal effects observed. This suggest that abiotic factors consistently influence fish communities across different regions (Mondal and Bhat. 2020). According to Damseth et. al. ( 2024 ), the practice of riverbed mining directly threatens the survival of many aquatic species. Sand mining is a common practice in most parts of the river, which is the major cause of the threat to fish fauna (Rao et al., 2013 ). The previous studies conducted in east India especially in the state of West Bengal did not receive adequate ecological except the few studies such as the study by Kar et al., 2016 documented 45 fish species in the Kangsabati River where fish abundance and richness were influenced by various physicochemical parameters like pH, dissolved oxygen, salinity, and suspended solids (Kar et al ( 2016 ). Another study conducted in 14 major Indian rivers, including Damodar River found that the surface area of river basin and availability of fish habitat were the most influential factors affecting the species richness (Das et al., 2012 ). A study documented the 38 fish species but also found that the disposal of fly ash mixed with hot wastewater from the Kolaghat Thermal Power Station into the Rupnarayan river of West Bengal state had adverse effects on fish diversity (Ghorai et al., 2015 ; Ghorai, 2018 ). Although the Rupnarayan River harbours a rich diversity of riverine fish species, the influence of ecological and anthropogenic parameters on fish assemblage remains inadequately understood and lacks further investigation. In this study we aim to explore the relation between fish diversity and environmental and anthropogenic factors and also assessed the land use and land cover (LULC) along the sampled stretch of Rupnarayan river. Objectives 1. To study the diversity and relative abundance of fish species in the Rupnarayan river, West Bengal. 2. To assess the relationship between fish diversity and environmental factors including stream flow and anthropogenic activities. 3. To evaluate the effect of fishing gears on size-class distribution of select fish species. 4. To quantify land use and land cover along the study stretch of Rupnarayan river. Materials and Methods Study Area The study was conducted in the Rupnarayan River a tributary of the Hooghly River and is situated in the state of West Bengal, northeastern India. Originating as the Dhaleshwari (Dhalkisor) in the foothills of the Chota Nagpur plateau northeast of Purulia, the river follows a meandering southeasterly course. The union of the Dwarkeswar and Shilabati near the Ghatal town of Paschim Medinipur district got its name as the Rupnarayan River. The Rupnarayan River holds significance as the primary watercourse in both Purba Medinipur and Paschim Medinipur districts. Stretching approximately 80 km, it is a tidal freshwater zone without a forest patch along its course. The river is not wide near the confluence of Dwarkeshwar and Shilabati but gradually widening after receiving discharges from Mundeshwari channel near Pansiuli (Khanakul block, Hooghly district) The river receives a very high discharge by the Mundeshwari stream/ channel near Gopiganj and Bakshi, with the bank from Bakshi to Gadiara (Howrah district) situated at 45 km before joining the Hooghly River at Gadiara. (Das, 2023 ). Field surveys The systematic fish sampling was carried out during January 2024 to March 2024 following the hierarchical nested study design where in the river habitat (main channel, meander, pool etc) was nested in the stream segment of the main channel (Frissell et. al., 1986). The space for time replacement was used across the 30 km stretch from Ghatal to Mankur to capture environmental gradients, allowing us to infer broader ecological trends within a single sampling period. Different sets of fishing nets (4-gill nets of different mesh sizes, viz., 14 mm, 30 mm, 60mm, and 70 mm, were deployed for a minimum of 2 hours at each selected sampling point. The cast nets of 3 different mesh sizes, i.e., 11 mm, 25 mm, and 30 mm, were thrown up to 30 times) for fish sampling at the same sampling point within a river segment. The catch per unit effort (CPUE) was estimated as number of individuals of fish collected by different fishing gears per site per day. Basic morphometric measurements like total length (cm), standard length (cm), and weight (g) were measured for individual fish. Fishes were kept in the glass tanks, and photographed using a Nikon P900 camera. Soon after the measurements, all the fishes were released unharmed back to the site except a few individuals of complex groups (loaches, and catfishes). We recorded the geographic coordinates of each sampling unit using Garmin eTrex 30x handheld GPS (Table 1 ). At each site, we also recorded key river characteristics such as water temperature (°C), water depth (m), flow (m/s), width of the channel (m), salinity (ppt) using refractometer (SIMMANS), bank habitat type, channel type, and weather condition. We noted down the anthropogenic activities such as sand mining, agriculture, embankment, boat traffic that might influence the fish diversity directly or indirectly. The fish species were identified using standard taxonomic literature (Talwar and Jhingran, 1991 ; Jayram, 1999; 2010) and Eschmeyer’s catalog (Van der Laan et al 2014 ). Table 1 Sampling sites with coordinates, LULC types, and disturbances. Site Latitude Longitude Elevation (m) LULC Type Disturbance Ghatal 22.6707 87.77328 6.33 Built up Area, Vegetation Fishing activity Harispur 22.6537 87.7921 1.93 Vegetation Fishing activity Ghoradaha 22.637 87.80174 5.09 Crop Land Fishing activity, Embankment, Sand mining Garerghat 22.6333 87.81481 0 Crop Land Not detected Ranichak 22.6163 87.82433 6.18 Crop Land Fishing activity Dhaldanga 22.6051 87.83861 5.33 Crop Land Not detected Kaijuri 22.5884 87.85595 8.03 Vegetation Not detected Benai 22.5743 87.84722 0 Built up Area, Crop Land Embankment Dakshin Bhatora 22.5535 87.84724 7.32 Built up Area, Crop Land Sand mining Kultikari 22.5415 87.86021 5.37 Built up Area Not detected Dudhkomra 22.5298 87.87785 4.27 Built up Area Embankment, Sand mining Mankur 22.5176 87.89637 1.9 Built up Area Embankment Land Use and Land Cover classification To create the LULC map, we downloaded Sentinel-2 imagery from March 2023 (10 m resolution) from the Copernicus Open Access Hub ( https://scihub.copernicus.eu/ ). ArcMap 10.8 software was used to completed the entire mapping process. The study area was then extracted by clipping the image to required boundary. For classification, training samples were collected for different land cover types, such as vegetation, crop land, water bodies and built-up areas. A supervised classification approach using the Maximum Likelihood Classifier in ArcGIS was applied to categorised the land cover, and compared with Google Earth Pro (Ver 7.3.6) and analysed to understand the spatial distribution of different land cover types (Purkis and V Klemas, 2011). Ethical guidelines We followed standard ethical guidelines to study fish as per the (American Fisheries Society 2014) and The Committee for Control and Supervision of Experiments on Animals (CCSEA), Government of India. Additionally, we also sought ethical permission from the West Bengal Forest Department (Letter No. 3659/WL/4R-28/2016, dated- 27/12/2023) before commissioning the study. All fishes were captured using conventional fishing gears at every sampling site. They were measured (total body length in cm), weighed, photographed and released unharmed at the site of capture except a few complex groups which were euthanized using clove oil and preserved in 70% ethanol. Statistical Analysis Assessing species diversity is an important aspect in ecology. Recently, Chao et al (2015; 2022) and Hsieh et al ( 2016 ) have assessed the species diversity based on Hill numbers using interpolation and extrapolation (iNEXT) method. We analysed fish diversity at two scales, one at small scale (n = 36 sites), representing individual sampling points, and at large scale (n = 12), representing 2.5 km river reaches by using iNEXT in R (Hsieh et al 2024 ; R core Team, 2023 ). Based on exploratory data analysis, we used a large-scale dataset to perform multiple regression using fish richness and fish abundance as response variables against environmental data (predictor variables) across sampling sites. We followed the model selection procedure for both richness and abundance data (Burnham and Anderson 2004 ). For species richness and fish abundance, we built ten models each. Of them, a top model was selected based on Akaike Information Criteria (AICc) (Johnson and Omland 2004 ). We used the sum of Akaike weights of each candidate model in which each predictor appeared. We chose the model with the delta AIC value to indicate that the model describes the data well compared with the best-fit model (Burnham and Anderson 2004 ). The model averaging using AICc was done to select the best predictor model that explains fish richness and fish abundance. Also, we evaluated basic flow-ecology relationships for a few select fish species. Subsequently, we explored the basic relationship between the body size and fishing gears. All statistical analysis and modelling were performed using the R software (R Core Team 2023 ; Hammer et al., 2001). Results Fish Diversity and Distribution A total of 774 individuals were recorded across 12 sites (Mean abundance 64.5 individuals/Site), representing 40 species (mean richness 3.33 species/site) distributed in 14 orders and 25 families. Among the total collected fish species, the order Cypriniformes (54%, n = 414) was dominant, followed by Gobiiformes (27%, n = 210) and Perciformes (6%, n = 44). There were 38 species 95% classified as Least Concerned (LC), 1 species (2.5%) as Data Deficient (DD), and 1 species (2.5%) as Not Evaluated (NE) (Table 2 ) Table 2 Fish species and conservation status (IUCN) in Rupnarayan River. Order Family Scientific Name No of Individuals IUCN Status Acanthuriformes Leiognathidae Equulites cf. elongatus (Günther 1874) 1 NE Anabantiformes Badidae Badis badis (Hamilton 1822) 1 LC Channidae Channa punctata (Bloch 1793) 3 LC Nandidae Nandus nandus (Hamilton 1822) 1 LC Osphronemidae Trichogaster bejeus (Hamilton 1822) 6 LC Beloniformes Adrianichthyidae Oryzias cf. dancena (Hamilton 1822) 1 LC Belonidae Xenentodon cancila (Hamilton 1822) 1 LC Clupeiformes Ehiravidae Corica soborna (Hamilton 1822) 3 LC Dorosomatidae Gonialosa manmina (Hamilton 1822) 12 LC Dorosomatidae Gudusia chapra (Hamilton 1822) 17 LC Dorosomatidae Tenualosa illisha (Hamilton 1822) 3 LC Engraulidae Setipinna phasa (Hamilton 1822) 14 LC Cypriniformes Cyprinidae Amblypharyngodon mola (Hamilton, 1822) 6 LC Cyprinidae Labeo calbasu (Hamilton 1822) 5 LC Danionidae Laubuka laubuca (Hamilton 1822) 2 LC Danionidae Opsarius cf. barna (Hamilton 1822) 1 LC Cyprinidae Osteobrama cotio (Hamilton 1822) 1 LC Cyprinidae Pethia ticto (Hamilton 1822) 17 LC Cyprinidae Puntius chola (Hamilton 1822) 12 LC Cyprinidae Puntius sophore (Hamilton 1822) 147 LC Danionidae Salmostoma bacaila (Hamilton 1822) 221 LC Cyprinidae Labeo bata (Hamilton 1822) 2 LC Gobiiformes Gobiidae Apocryptes bato (Hamilton 1822) 186 LC Butidae Butis butis (Hamilton 1822) 1 LC Gobiidae Glossogobius giuris (Hamilton 1822) 13 LC Gobiidae Mugilogobius cf. mertoni (Weber 1911) 1 LC Gobiidae Odontamblyopus rubicundus (Hamilton 1822) 8 LC Butidae Prionobutis cf. microps (Weber 1907) 1 LC Mugiliformes Mugilidae Rhinomugil corsula (Hamilton 1822) 26 LC Ambassidae Chanda nama (Hamilton 1822) 19 LC Ambassidae Parambassis ranga (Hamilton 1822) 8 LC Perciformes Platycephalidae Platycephalus indicus (Linnaeus 1758) 17 DD Carangiformes Cynoglossidae Cynoglossus cynoglossus (Hamilton 1822) 5 LC Siluriformes Sisoridae Erethistes pusillus (Müller & Troschel 1849) 1 LC Horabagridae Pachypterus atherinoides (Bloch 1794) 1 LC Ailiidae Silonia silondia (Hamilton 1822) 1 LC Acanthuriformes Sparidae Acanthopagrus berda (Fabricius 1775) 1 LC Synbranchiformes Mastacembelidae Macrognathus pancalus (Hamilton 1822) 6 LC Syngnathiformes Syngnathidae Microphis cf. cuncalus (Hamilton 1822) 1 LC Tetraodontiformes Tetraodontidae Leiodon cutcutia (Hamilton 1822) 1 LC The species diversity accumulated at 800 individuals both for small and large scale (Fig. 2 ) and this was also true for sample coverage plot whereas the species diversity increased sharply at the small scale than large scale (Fig. 2 ). Maximum number of species and individuals were recorded in Kaijuri sampling site, while low number of species and individuals were recorded in Mankur site. Effect of environmental variables on fish richness and abundance Multiple regression analysis of fish richness and abundance against environmental data showed that the fish richness was best explained by the channel type, time spent (hour per site), and water temperature in the Rupnarayan river (Table 3 , 4 ). The effect size plots suggest that the fish richness was higher at the narrow channel, meander, and confluence (Fig. 3 ) and it increased with time spent for fishing (no. of hours spent for fishing). Whereas fish richness show decline with increasing temperature. Table 3 The candidate model tested for predicting fish richness in Rupnarayan River. Model terms AICc dAICC. df weight Richness ~ Channel type + Elevation + Time spent + Water temperature + Flow 79.9 0 9 0.512 Richness ~ Elevation + Width + Depth 80.7 0.8 5 0.34 Richness ~ Tidal type + Time spent 83.4 3.4 4 0.092 Richness ~ Time spent + Width + Depth 84.6 4.6 5 0.051 Table 4 Coefficient estimates of explanatory variables for the best multiple linear regression models for species richness in the study area. Variables Beta estimates Standard error 2.5% 97.50% p-value Multiple R2 Adjusted R2 p-value Intercept -10.4 27.44 -86.6 65.8 0.72 0.84 0.52 0.13 Channel type narrow channel, confluence -1.68 4.18 -13.3 9.93 0.7 Channel type narrow channel, meander* -20.97 6.61 -39.3 -2.59 0.03 Channel type narrow channel, meander, confluence -4.85 6.05 -21.7 11.94 0.46 Elevation -1.28 1.15 -4.48 1.91 0.32 Time spent* 7.6 2.15 1.28 12.25 0.02 Water temperature -0.99 0.8 -3.23 1.24 0.28 Flow -10.86 18.6 -62.5 40.79 0.59 Fish abundance in the Rupnarayan River is best predicted by time spent for fishing, and tide type which is supported by the model selection (Table 5 , 6 ). The effect size plot suggests that, the fish abundance was strongly positively associated with time spent (Fig. 4 ) and higher during the high tide as compared to low tide. Table 5 Model selection based on AICc showing top one model for fish abundance. Model terms AICc dAICC. df Weight Abundance ~ Tidal type + Time spent 97.9 0 4 0.531 Abundance ~ Time spent + Width + Depth 98.3 0.4 5 0.434 Abundance ~ Elevation_ + Width + Depth 103.4 5.5 5 0.034 Abundance ~ Number of irrigation pump + number of fishing activity + Sand mining 110.6 12.7 5 < 0.001 Abundance ~ Channel type + Flow 120.2 22.3 6 < 0.001 Abundance ~ Number of fishing activity + Channel type 120.8 23 6 < 0.001 Table 6 Coefficient estimates of explanatory variables for the best multiple linear regression models for species abundance. Variables Beta estimates Standard error 2.5% 97.50% P value Multiple R 2 Adjusted R 2 p-value Intercept* -71.82 27.04 -133 -10.64 0.02 0.87 0.84 < 0.0001 Tidal type Low -12.44 7.97 -30.5 5.58 0.15 Time spent*** 16.5 2.73 10.3 22.68 0.0001 Response of common fishes to the stream flow The relation of flow and, selected a key dominant species such as Apocryptes bato (Hamilton 1822), Puntius sophore (Hamilton 1822), Salmostoma bacaila (Hamilton 1822) consistently declined with the rise of streamflow (Fig .5) whereas other rare species such as Chanda Nama (Hamilton 1822) and Platycephalus indicus (Linnaeus 1758) declined similarly except Gudusia chapra (Hamilton 1822) which showed a slight increase with the rise of flow (Fig. 6 ). The gear size influence on size class distribution In this study the overall size class distribution of all captured fishes was 2.8 cm (1.6 cm to 25.2 cm). The largest fish sampled was Glossogobius guiris (25.2 cm) whereas the smallest fish sampled was Badis badis (2.9 cm). The fishing gear used in sampling clearly shows the variation in fish size classes. Hand net primarily tended to captures smaller-sized fish, with median size below 5 cm, showing a narrow range with minimal variation. Gill nets tend to capture fish within the 7.5 to 10 cm range, indicating preference for medium-sized individuals. Cast nets, on the other hand, also capture fish in similar range but show slightly lower median values compared to gill nets. The distinct separation between hand nets and the other two suggests that each gear type plays a specific role in targeting different fish sizes (Fig. 8 ). Land use and Land cover Analysis The LULC analysis revealed that 44% of the total area is dominated by cropland, amounting to 16.38 km 2 . Vegetation covers 25% of the region with an area of 9.76 km 2, while built-up area accounts for 18%, totalling 6.86 km 2 . River system constitutes 12% of the land, and pond occupy 1% of the region, with and area of 0.20 km 2 (Table 7 , Fig. 9 ). Table 7 Land Use Land Cover distribution along Rupnarayan River. Built up Area Sum of Area (km 2 ) Percentage (%) 6.86 18 Crop Land 16.38 44 Ponds 0.20 1 River 4.38 12 Vegetation 9.76 25 Grand Total 37.58 100 Normalised Difference Vegetation Index (NDVI) Analysis : The vegetation distribution map (Fig. 10 ) represents the spatial changes along the Rupnarayan River based on NDVI analysis. The dense riparian vegetation is shown by the high NDVI values (0.43 to 0.67), which are displayed in green. The medium regions 0.16 to 0.43 (yellow) form a buffer zone between dense vegetation and anthropologically impacted areas whereas the low NDVI values − 0.17 to 0.16 (red) are scattered and fragmented patches, representing agricultural and urban settlements. Discussion Fish diversity patterns and associated environmental factors The tropical rivers are highly rich in its biodiversity (Dudgeon 2006; Dudgeon, 2000; Zhou and Li, 2024). The Rupnarayan River one of the tributaries of Ganga exhibited a very high fish diversity with 40 species. The family Cyprinidae is the most dominant in this study area and also in the southeast Asian streams (Lowe McConnell,1987). Numerous studies demonstrated that the diversity patterns in streams and rivers are shaped by variety of factors at different scales (Olden et al 2001; Oberdorff et al., 2011). At the large scale, the relative difference between the number of individuals and species among sites indicated a consistent pattern across spatial scales. While, at smaller scale species diversity tends to more variable and shaped by local factors such as habitats (Gorman and Karr 1978 . At smaller spatial scale demonstrated that the fish diversity was largely driven by microhabitat features (pools, run, riffles) river substrate composition (Gorman and Karr 1978 ). But at the river basin or catchment level, fish diversity was governed by hydrology, river characteristics, and human activities (Olden et al 2001; Atkore et al 2020 ). Hydrodynamics and tidal type also influence native fish assemblages (Huntsman et al 2023; (Caroline et al., 2023; Ma et al., 2024 ). The present study found that channel type, elevation, water temperature, flow, and time spent for fish sampling and tidal type driven the fish diversity patterns. However, we did not encounter any non-native species but presence of these species could not be ruled out. (Sandilyan, 2022 ). This study found a negative relationship between diversity and channel depth, width, contrasting with past research suggesting larger, diverse habitats increase species diversity (Angermeier and Schlosser, 1989 ; Taylor et al., 2006 ). In this study, fish diversity showed a negative relationship with water temperature and stream flow, suggesting these variables shapes fish assemblage in tropical regulated rivers (Mondal and Bhat, 2020 ; Bice et al., 2014 ). Optimum water temperature is essential for physiological reactions and impacts fish growth and abundance (Chapman, 1996; Chaudhary et al., 2020 ). Stream flow variation is important in governing the distribution as well as reproductive behaviour of fish species in tropical rivers (Poff et al 1997; Fang-Fang Li et al 2019 ). A few dominant species such as Apocryptes bato (Hamilton 1822) , Puntius sophore (Hamilton 1822), Salmostoma bacaila (Hamilton 1822) showed negative responses to the rise in the streamflow condition whereas other species Gudusia chapra (Hamilton 1822) showed positive or neutral response to the flow condition suggesting different species may adapt or behave differently to the flow condition during their life cycle (Mims & Olden, 2012 ; Stratford et al., 2016 ). We could not establish a relationship between flow and fish reproductive behaviour due to the time constraints of the study but long-term studies should explore such patterns in the future. Of the habitat studied, narrow channel confluence and meander influenced the fish richness than the other channels suggesting surface dweller fishes preferred confluence habitat may be due to meet their demand for the fresh dissolved oxygen and as a migration route to other habitats at the confluence and refugia or completion of their life cycle at the meanders (Stoffers et al 2022 ; Liu et al 2024 ). Generally, large-body size fishes tend to prefer slower water bodies with deeper pools whereas smaller to medium-sized fishes prefer shallower and fast to medium streamflow (Akbaripasand et al., 2011 ). Such insights are more important in hydrological studies. The flow regulation especially below the dam during the summer season, significantly affects the migratory species when flow is reduced significantly (Vaidya et al., 2008 ). Although Rupnarayan is a free-flowing tributary of the Hooghly River, the flow rate was not disrupted due to the absence of hydrological barriers. We observed that, during the high salinity period (afternoon in each day at 1:55 hrs), fish were higher whereas during at lower salinity period (morning 07:15 hrs), fish were lower in their relative abundances. A previous study in Slocun River estuary, U. S. A also reported that salinity levels influenced species composition and diversity of fish species (Hoff and Ibara, 1977 ). The sand mining, embankment, and agriculture are some of the main threats to the river habitat and its fauna (Paukert 2008; Fischer 2012). Although this study was of a short duration but the results indicates that the fish diversity showed a negative relationship with sand mining and, the number of fishing activities. Out of 36 sites sampled three sites were exposed to sand mining activities. At two sites manual sand mining was present and at one site near Mankur, mechanized sand mining was observed. The higher intensity of sand mining, and water withdrawals using a greater number of irrigation pumps may further threaten the fish life of this river. Land use changes, particularly the conversion of land for agriculture and human habitation, pose significant threats to riverine ecosystems. In the Marapanim River watershed, Brazil's deforestation for agriculture and urban development has led to the loss of 1,614.72 km² of forest, negatively affecting water-related ecosystem services such as carbon sequestration and soil moisture regulation (Beltrão & Gomes, 2023 ). Similarly, agricultural expansion in the Tonle Sap Floodplain, Cambodia, has resulted in a drastic decline in scrubland and grassland, contributing to habitat loss, decreased fish productivity, and a 12% reduction in carbon stocks (Mahood et al., 2020 ). This study shows that in 2024, cropland dominates 44% of the total study area, while built-up areas account for 18%, leaving only 25% of the landscape covered by vegetation in the Rupnarayan River region. These findings align with broader patterns of land use change observed in other river basins, such as the Upper Yamuna Basin, which has also experienced rapid urbanisation and agricultural expansion, leading to a decline in green spaces and increasing pollution levels in the river (Neenu & Kansal, 2024 ). The Rupnarayan River hold a significant fish diversity. Future work should consider studying the long-tern influence of environmental, temporal and anthropogenic factors along with parameters of water quality to monitor the health of the river. Declarations Author Contribution HP – Data collection, analysis and written the original draft, VA- Designed, Conceptualised, Supervised, Analysis, edited the original manuscript, GS – Data collection. PC-Data collection, edited the manuscript, SR-Data collection, QQ-Designed, Conceptualised, Supervised, Analysis, edited the original manuscript, VK- Designed, Conceptualised, Supervised, edited the original manuscript. Acknowledgements We sincerely thank Director, Dean, Registrar and Research Coordinators of the Wildlife Institute of India’s Salim Ali Centre for Ornithology and Natural History (SACON) for their encouragement and support. This study was conducted under the CAMPA Dolphin Project of WII which provided the necessary research permissions and financial support for the entire duration of this study. We thank West Bengal Forest Department for granting the necessary research permission to carry out the study (Letter No. 3659/WL/4R-28/2016, dated- 27/12/2023). We are deeply grateful to the fishermen, Mr Raju Koyal, Mr Kishno Jadhav, and Mr Bilas Dolai, whose hard work made this research possible. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9140684","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":612572630,"identity":"c3d60844-f19e-4db6-878c-9c3b697c90ff","order_by":0,"name":"Harshali Patkar","email":"","orcid":"","institution":"Bhavan’s College, Andheri","correspondingAuthor":false,"prefix":"","firstName":"Harshali","middleName":"","lastName":"Patkar","suffix":""},{"id":612572631,"identity":"29bab420-090b-4740-81f1-4c078be05930","order_by":1,"name":"Vidyadhar 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Bengal.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/b23b002c09e5a5724fbf0f3b.png"},{"id":105585447,"identity":"1b55753e-5585-4e3b-9a20-28763cfffc5b","added_by":"auto","created_at":"2026-03-27 15:21:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130062,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Sample size-based rarefaction and extrapolation sampling curve at two scales, (b) Depict the sample completeness curves and (c) figures indicate coverage-based rarefaction and extrapolation sampling curve at two scales in Rupnarayan river.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/44719ff5c3a9f7c99c305c08.png"},{"id":105585439,"identity":"4e79bc88-23ba-47eb-b59f-47edc095deaa","added_by":"auto","created_at":"2026-03-27 15:21:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134857,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect size plots for the model best predicting fish richness by channel types.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/6644d1c087847cdd6dee0eb4.png"},{"id":105728183,"identity":"d631b1c4-e8d4-4a9a-be7f-751921c6ce89","added_by":"auto","created_at":"2026-03-30 11:10:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116199,"visible":true,"origin":"","legend":"\u003cp\u003eThe higher fish abundance is predicted by high tide than low tide.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/547eb878b5ba171c341a6150.png"},{"id":105585443,"identity":"3016e7ae-cf6e-4555-b2ee-88ab5160e289","added_by":"auto","created_at":"2026-03-27 15:21:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90465,"visible":true,"origin":"","legend":"\u003cp\u003eThe select common fish species declined with stream flow.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/e94cd4c6a77ff15de6fc591e.png"},{"id":105728533,"identity":"22e1d6bb-ca03-4800-bcec-0cc047ffafdc","added_by":"auto","created_at":"2026-03-30 11:12:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81225,"visible":true,"origin":"","legend":"\u003cp\u003eThe select rare fish species showed negative relationship with stream flow except \u003cem\u003eGaducia chapra\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/b44658f04b03ad92e6f950ff.png"},{"id":105728153,"identity":"e0efd15a-2a78-4feb-8459-af21237b9dfd","added_by":"auto","created_at":"2026-03-30 11:10:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":212782,"visible":true,"origin":"","legend":"\u003cp\u003eSize class distribution of fish species across the sampling sites.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/1a3fe5d56bd01e04bc3692f5.png"},{"id":105728573,"identity":"80b78155-79f9-47f9-ad17-4651af923784","added_by":"auto","created_at":"2026-03-30 11:12:10","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":112354,"visible":true,"origin":"","legend":"\u003cp\u003eThe gear size influenced the fish size class distribution.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/49530d826534b79227853c38.png"},{"id":105728140,"identity":"be79cd5b-0ac2-4525-ac33-85b747774489","added_by":"auto","created_at":"2026-03-30 11:10:06","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":361138,"visible":true,"origin":"","legend":"\u003cp\u003eLULC classification of Rupnarayan River in West Bengal.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/992980fd674ef6478bcbbf72.png"},{"id":105585446,"identity":"7bc8c701-b1ad-422e-8d26-0af0f57e9725","added_by":"auto","created_at":"2026-03-27 15:21:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":366303,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation Distribution along Rupnarayan River.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/52a120c3e2930240cc9d043f.png"},{"id":108806766,"identity":"0d40168c-99bb-475f-adef-8af4095aee82","added_by":"auto","created_at":"2026-05-08 15:29:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2482867,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9140684/v1/7e414afd-f235-4ee8-9eed-4368586fff82.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the patterns of fish assemblages in the Rupnarayan River, West Bengal in India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFish comprise the most diverse group of vertebrates, and exhibit a considerable richness of about 34000 species (Klimpel et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Fish diversity in the riverine ecosystem has great significance in terms of livelihood and socio-economic importance (Ghorai, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Fishes are substantial size consumers and possess long lifespans, that assimilate information about conditions throughout the food web. Their intricate ecological needs make river-dwelling fishes a suitable indicator of habitat conditions (Schiemer \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Various aspects of their abundance, species composition, age-class distribution, and reproductive patterns are reflective of the overall health of the river ecosystem (Schmutz et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). According to bioassessment research, fishes are important in indicating many characteristics of anthropogenic ecosystem degradation, particularly in rivers and streams (Schmutz et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Hering et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Poikane et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough fish represent the most diverse group of vertebrates, freshwater fish are also the most threatened. After amphibians, fish may be the most vulnerable group of vertebrates (Bruton, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Duncan and Lockwood, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Increasing anthropogenic pressure in the form of illegal fishing and the use of indiscriminate fishing practices such as dynamiting, fish poisoning, electrofishing and river substate mining have drastically decimated the world\u0026rsquo;s freshwater fishes (Atkore et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hughes, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Human activities pose numerous threats to river ecosystems, including damming, urbanisation, and agricultural expansion which adversely affect water quality and biodiversity (V\u0026ouml;r\u0026ouml;smarty et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe freshwater ecosystem comprises of numerous community metric to assess the ecosystem health which includes diversity, community structure, and species assemblages in streams and rivers. Fish diversity is a measure of scale. The growth or decline of fish species assemblage within the spatial distribution limit is determined by biotic and abiotic factors (Minns, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Olden et al., 2000). And the study of fish species composition and distribution helps in the identification of endangered or threatened species as well as the assessment of their population size and health.\u003c/p\u003e \u003cp\u003eGlobal patterns in fish diversity within tropical rivers are shaped by a complex interaction of environmental, geographical, and anthropogenic factors. Large tropical river basins, such as the Amazon, Congo, and Mekong, have the highest fish diversity due to their extensive and varied aquatic habitats (Van der Sleen and Albert, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Environmental gradients, such as temperature, dissolved oxygen, and nutrient levels, significantly influence fish assemblage structures, as seen in the Mekong and Narmada rivers (Chea et al., 2017; Shukla and Bhat, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Also, the seasonal variations and local environmental conditions, including stream order and forest cover, are crucial in shaping fish diversity patterns (Shukla and Bhat, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Larre-Campuzano et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eGlobally, riverine ecosystems are increasingly threatened by hydrological regulation and the rapidly growing demands for irrigation, fisheries and industrial facilities (Davis et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Suski and Cooke, 2007). So, understanding the interactions between different fish species within community helps to predict the potential impacts of environmental changes and designing effective conservation and management strategies (Mondal and Bhat, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Environmental factors such as precipitation, topography, stream width, depth, altitude, temperature variation and scale of the study play crucial roles in shaping fish assemblages (Brown, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Askeyev et. al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dubey et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIndia is recognized as a hotspot of freshwater fish diversity and has a substantial degree of endemism that significantly contributes to the World's biological resources (Kottelat and Whitten, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Dahanukar et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Recent estimates indicate that India harbours about 2667 species, securing its position among the top 25 countries globally in terms of endemism. Additionally, India holds the third position in Asia concerning overall freshwater fish diversity (Thakur et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn India, researchers have drawn to study fish diversity, distribution and highlighted the role of environmental variables and anthropogenic factors in shaping fish diversity. Johnson and Arunachalam (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) studied fish diversity in streams of the southern Western Ghats, recording 60 species across four orders, 13 families, and 27 genera, with Cyprinids being the most dominant. They observed that fish diversity varied among streams, with the highest diversity in the third-stream order (Thalayanai), and noted similarities in faunal assemblage among nearby basins. Dwivedi and De (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that in the Bhima River basin of the Deccan Plateau \u0026ndash; a part of northern WG, fish communities show significant spatial variations. Another study from this region, Mohite and Sawant (2013) noted that the land use pattern transformation and riparian habitat loss, negatively affected fish diversity in the Warna River Basin. Study in rivers of central Western Ghats (States of Karnataka and Goa) found that water chemistry significantly influences fish guild composition and species richness, particularly at the sub-basin scale (Atkore et. al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar studies in streams across central and eastern India reveal that water flow is a common driver of fish richness and diversity, with no significant seasonal effects observed. This suggest that abiotic factors consistently influence fish communities across different regions (Mondal and Bhat. 2020). According to Damseth et. al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), the practice of riverbed mining directly threatens the survival of many aquatic species. Sand mining is a common practice in most parts of the river, which is the major cause of the threat to fish fauna (Rao et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe previous studies conducted in east India especially in the state of West Bengal did not receive adequate ecological except the few studies such as the study by Kar et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e documented 45 fish species in the Kangsabati River where fish abundance and richness were influenced by various physicochemical parameters like pH, dissolved oxygen, salinity, and suspended solids (Kar et al (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Another study conducted in 14 major Indian rivers, including Damodar River found that the surface area of river basin and availability of fish habitat were the most influential factors affecting the species richness (Das et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). A study documented the 38 fish species but also found that the disposal of fly ash mixed with hot wastewater from the Kolaghat Thermal Power Station into the Rupnarayan river of West Bengal state had adverse effects on fish diversity (Ghorai et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ghorai, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although the Rupnarayan River harbours a rich diversity of riverine fish species, the influence of ecological and anthropogenic parameters on fish assemblage remains inadequately understood and lacks further investigation. In this study we aim to explore the relation between fish diversity and environmental and anthropogenic factors and also assessed the land use and land cover (LULC) along the sampled stretch of Rupnarayan river.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003cspan\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1. To study the diversity and relative abundance of fish species in the Rupnarayan river, West Bengal.\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e2. To assess the relationship between fish diversity and environmental factors including stream flow and anthropogenic activities.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e3. To evaluate the effect of fishing gears on size-class distribution of select fish species.\u003cbr\u003e\u003c/span\u003e\u003cspan\u003e4. To quantify land use and land cover along the study stretch of Rupnarayan river.\u003c/span\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Rupnarayan River a tributary of the Hooghly River and is situated in the state of West Bengal, northeastern India. Originating as the Dhaleshwari (Dhalkisor) in the foothills of the Chota Nagpur plateau northeast of Purulia, the river follows a meandering southeasterly course. The union of the Dwarkeswar and Shilabati near the Ghatal town of Paschim Medinipur district got its name as the Rupnarayan River. The Rupnarayan River holds significance as the primary watercourse in both Purba Medinipur and Paschim Medinipur districts. Stretching approximately 80 km, it is a tidal freshwater zone without a forest patch along its course. The river is not wide near the confluence of Dwarkeshwar and Shilabati but gradually widening after receiving discharges from Mundeshwari channel near Pansiuli (Khanakul block, Hooghly district) The river receives a very high discharge by the Mundeshwari stream/ channel near Gopiganj and Bakshi, with the bank from Bakshi to Gadiara (Howrah district) situated at 45 km before joining the Hooghly River at Gadiara. (Das, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField surveys\u003c/h3\u003e\n\u003cp\u003eThe systematic fish sampling was carried out during January 2024 to March 2024 following the hierarchical nested study design where in the river habitat (main channel, meander, pool etc) was nested in the stream segment of the main channel (Frissell et. al., 1986). The space for time replacement was used across the 30 km stretch from Ghatal to Mankur to capture environmental gradients, allowing us to infer broader ecological trends within a single sampling period. Different sets of fishing nets (4-gill nets of different mesh sizes, viz., 14 mm, 30 mm, 60mm, and 70 mm, were deployed for a minimum of 2 hours at each selected sampling point. The cast nets of 3 different mesh sizes, i.e., 11 mm, 25 mm, and 30 mm, were thrown up to 30 times) for fish sampling at the same sampling point within a river segment. The catch per unit effort (CPUE) was estimated as number of individuals of fish collected by different fishing gears per site per day.\u003c/p\u003e \u003cp\u003eBasic morphometric measurements like total length (cm), standard length (cm), and weight (g) were measured for individual fish. Fishes were kept in the glass tanks, and photographed using a Nikon P900 camera. Soon after the measurements, all the fishes were released unharmed back to the site except a few individuals of complex groups (loaches, and catfishes).\u003c/p\u003e \u003cp\u003eWe recorded the geographic coordinates of each sampling unit using Garmin eTrex 30x handheld GPS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At each site, we also recorded key river characteristics such as water temperature (\u0026deg;C), water depth (m), flow (m/s), width of the channel (m), salinity (ppt) using refractometer (SIMMANS), bank habitat type, channel type, and weather condition. We noted down the anthropogenic activities such as sand mining, agriculture, embankment, boat traffic that might influence the fish diversity directly or indirectly. The fish species were identified using standard taxonomic literature (Talwar and Jhingran, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Jayram, 1999; 2010) and Eschmeyer\u0026rsquo;s catalog (Van der Laan et al \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSampling sites with coordinates, LULC types, and disturbances.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLULC Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDisturbance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhatal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.6707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.77328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuilt up Area, Vegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFishing activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHarispur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.6537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.7921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFishing activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGhoradaha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.80174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFishing activity, Embankment, Sand mining\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarerghat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.6333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.81481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanichak\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.6163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.82433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFishing activity\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhaldanga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.6051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.83861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaijuri\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.85595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenai\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.84722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuilt up Area, Crop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEmbankment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDakshin Bhatora\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.84724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuilt up Area, Crop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSand mining\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKultikari\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.86021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNot detected\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDudhkomra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.87785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEmbankment, Sand mining\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMankur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.5176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.89637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEmbankment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eLand Use and Land Cover classification\u003c/h3\u003e\n\u003cp\u003eTo create the LULC map, we downloaded Sentinel-2 imagery from March 2023 (10 m resolution) from the Copernicus Open Access Hub (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scihub.copernicus.eu/\u003c/span\u003e\u003cspan address=\"https://scihub.copernicus.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). ArcMap 10.8 software was used to completed the entire mapping process. The study area was then extracted by clipping the image to required boundary. For classification, training samples were collected for different land cover types, such as vegetation, crop land, water bodies and built-up areas. A supervised classification approach using the Maximum Likelihood Classifier in ArcGIS was applied to categorised the land cover, and compared with Google Earth Pro (Ver 7.3.6) and analysed to understand the spatial distribution of different land cover types (Purkis and V Klemas, 2011).\u003c/p\u003e\n\u003ch3\u003eEthical guidelines\u003c/h3\u003e\n\u003cp\u003e We followed standard ethical guidelines to study fish as per the (American Fisheries Society 2014) and The Committee for Control and Supervision of Experiments on Animals (CCSEA), Government of India. Additionally, we also sought ethical permission from the West Bengal Forest Department (Letter No. 3659/WL/4R-28/2016, dated- 27/12/2023) before commissioning the study. All fishes were captured using conventional fishing gears at every sampling site. They were measured (total body length in cm), weighed, photographed and released unharmed at the site of capture except a few complex groups which were euthanized using clove oil and preserved in 70% ethanol.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAssessing species diversity is an important aspect in ecology. Recently, Chao et al (2015; 2022) and Hsieh et al (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) have assessed the species diversity based on Hill numbers using interpolation and extrapolation (iNEXT) method. We analysed fish diversity at two scales, one at small scale (n\u0026thinsp;=\u0026thinsp;36 sites), representing individual sampling points, and at large scale (n\u0026thinsp;=\u0026thinsp;12), representing 2.5 km river reaches by using iNEXT in R (Hsieh et al \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; R core Team, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on exploratory data analysis, we used a large-scale dataset to perform multiple regression using fish richness and fish abundance as response variables against environmental data (predictor variables) across sampling sites. We followed the model selection procedure for both richness and abundance data (Burnham and Anderson \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For species richness and fish abundance, we built ten models each. Of them, a top model was selected based on Akaike Information Criteria (AICc) (Johnson and Omland \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). We used the sum of Akaike weights of each candidate model in which each predictor appeared. We chose the model with the delta AIC value to indicate that the model describes the data well compared with the best-fit model (Burnham and Anderson \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The model averaging using AICc was done to select the best predictor model that explains fish richness and fish abundance. Also, we evaluated basic flow-ecology relationships for a few select fish species. Subsequently, we explored the basic relationship between the body size and fishing gears. All statistical analysis and modelling were performed using the R software (R Core Team \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hammer et al., 2001).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFish Diversity and Distribution\u003c/p\u003e \u003cp\u003eA total of 774 individuals were recorded across 12 sites (Mean abundance 64.5 individuals/Site), representing 40 species (mean richness 3.33 species/site) distributed in 14 orders and 25 families. Among the total collected fish species, the order Cypriniformes (54%, n\u0026thinsp;=\u0026thinsp;414) was dominant, followed by Gobiiformes (27%, n\u0026thinsp;=\u0026thinsp;210) and Perciformes (6%, n\u0026thinsp;=\u0026thinsp;44). There were 38 species 95% classified as Least Concerned (LC), 1 species (2.5%) as Data Deficient (DD), and 1 species (2.5%) as Not Evaluated (NE) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFish species and conservation status (IUCN) in Rupnarayan River.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFamily\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScientific Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo of Individuals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIUCN Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcanthuriformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeiognathidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eEquulites\u003c/em\u003e cf. \u003cem\u003eelongatus\u003c/em\u003e (G\u0026uuml;nther 1874)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eAnabantiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBadidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eBadis badis\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChannidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChanna punctata\u003c/em\u003e (Bloch 1793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNandidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eNandus nandus\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOsphronemidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTrichogaster bejeus\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBeloniformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdrianichthyidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOryzias\u003c/em\u003e cf. \u003cem\u003edancena\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelonidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eXenentodon cancila\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eClupeiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEhiravidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCorica soborna\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDorosomatidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGonialosa manmina\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDorosomatidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGudusia chapra\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDorosomatidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTenualosa illisha (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEngraulidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSetipinna phasa\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eCypriniformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAmblypharyngodon mola\u003c/em\u003e (Hamilton, 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLabeo calbasu\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDanionidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLaubuka laubuca\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDanionidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOpsarius\u003c/em\u003e cf. \u003cem\u003ebarna\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOsteobrama cotio\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePethia ticto\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePuntius chola\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePuntius sophore\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDanionidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSalmostoma bacaila\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCyprinidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLabeo bata\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eGobiiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eApocryptes bato\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eButidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eButis butis\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGlossogobius giuris\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMugilogobius\u003c/em\u003e cf. \u003cem\u003emertoni\u003c/em\u003e (Weber 1911)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGobiidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eOdontamblyopus rubicundus\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eButidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePrionobutis\u003c/em\u003e cf. \u003cem\u003emicrops\u003c/em\u003e (Weber 1907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMugiliformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMugilidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRhinomugil corsula\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbassidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eChanda nama\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmbassidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eParambassis ranga\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerciformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlatycephalidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePlatycephalus indicus\u003c/em\u003e (Linnaeus 1758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarangiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCynoglossidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCynoglossus cynoglossus\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSiluriformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSisoridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eErethistes pusillus\u003c/em\u003e (M\u0026uuml;ller \u0026amp; Troschel 1849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHorabagridae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePachypterus atherinoides\u003c/em\u003e (Bloch 1794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAiliidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSilonia silondia\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcanthuriformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSparidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAcanthopagrus berda\u003c/em\u003e (Fabricius 1775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSynbranchiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMastacembelidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMacrognathus pancalus\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSyngnathiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSyngnathidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMicrophis\u003c/em\u003e cf. \u003cem\u003ecuncalus\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTetraodontiformes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTetraodontidae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLeiodon cutcutia\u003c/em\u003e (Hamilton 1822)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe species diversity accumulated at 800 individuals both for small and large scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and this was also true for sample coverage plot whereas the species diversity increased sharply at the small scale than large scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Maximum number of species and individuals were recorded in Kaijuri sampling site, while low number of species and individuals were recorded in Mankur site.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eEffect of environmental variables on fish richness and abundance\u003c/h3\u003e\n\u003cp\u003eMultiple regression analysis of fish richness and abundance against environmental data showed that the fish richness was best explained by the channel type, time spent (hour per site), and water temperature in the Rupnarayan river (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The effect size plots suggest that the fish richness was higher at the narrow channel, meander, and confluence (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and it increased with time spent for fishing (no. of hours spent for fishing). Whereas fish richness show decline with increasing temperature.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe candidate model tested for predicting fish richness in Rupnarayan River.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAICc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edAICC.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eweight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRichness\u0026thinsp;~\u0026thinsp;Channel type\u0026thinsp;+\u0026thinsp;Elevation\u0026thinsp;+\u0026thinsp;Time spent\u0026thinsp;+\u0026thinsp;Water temperature\u0026thinsp;+\u0026thinsp;Flow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e79.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.512\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichness\u0026thinsp;~\u0026thinsp;Elevation\u0026thinsp;+\u0026thinsp;Width\u0026thinsp;+\u0026thinsp;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichness\u0026thinsp;~\u0026thinsp;Tidal type\u0026thinsp;+\u0026thinsp;Time spent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRichness\u0026thinsp;~\u0026thinsp;Time spent\u0026thinsp;+\u0026thinsp;Width\u0026thinsp;+\u0026thinsp;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficient estimates of explanatory variables for the best multiple linear regression models for species richness in the study area.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta estimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultiple R2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted R2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-86.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel type narrow channel, confluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel type narrow channel, meander*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-20.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-39.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChannel type narrow channel, meander, confluence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWater temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFish abundance in the Rupnarayan River is best predicted by time spent for fishing, and tide type which is supported by the model selection (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The effect size plot suggests that, the fish abundance was strongly positively associated with time spent (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and higher during the high tide as compared to low tide.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel selection based on AICc showing top one model for fish abundance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAICc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edAICC.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbundance\u0026thinsp;~\u0026thinsp;Tidal type\u0026thinsp;+\u0026thinsp;Time spent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e97.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.531\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u0026thinsp;~\u0026thinsp;Time spent\u0026thinsp;+\u0026thinsp;Width\u0026thinsp;+\u0026thinsp;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u0026thinsp;~\u0026thinsp;Elevation_ + Width\u0026thinsp;+\u0026thinsp;Depth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u0026thinsp;~\u0026thinsp;Number of irrigation pump\u0026thinsp;+\u0026thinsp;number of fishing activity\u0026thinsp;+\u0026thinsp;Sand mining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e110.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u0026thinsp;~\u0026thinsp;Channel type\u0026thinsp;+\u0026thinsp;Flow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbundance\u0026thinsp;~\u0026thinsp;Number of fishing activity\u0026thinsp;+\u0026thinsp;Channel type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e120.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCoefficient estimates of explanatory variables for the best multiple linear regression models for species abundance.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta estimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.50%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMultiple R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-71.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-10.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTidal type Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-12.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime spent***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResponse of common fishes to the stream flow\u003c/b\u003e The relation of flow and, selected a key dominant species such as \u003cem\u003eApocryptes bato\u003c/em\u003e (Hamilton 1822), \u003cem\u003ePuntius sophore\u003c/em\u003e (Hamilton 1822), \u003cem\u003eSalmostoma bacaila\u003c/em\u003e (Hamilton 1822) consistently declined with the rise of streamflow (Fig .5) whereas other rare species such as \u003cem\u003eChanda Nama\u003c/em\u003e (Hamilton 1822) \u003cem\u003eand Platycephalus indicus\u003c/em\u003e (Linnaeus 1758) declined similarly except \u003cem\u003eGudusia chapra (Hamilton 1822)\u003c/em\u003e which showed a slight increase with the rise of flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eThe gear size influence on size class distribution\u003c/h3\u003e\n\u003cp\u003eIn this study the overall size class distribution of all captured fishes was 2.8 cm (1.6 cm to 25.2 cm). The largest fish sampled was \u003cem\u003eGlossogobius guiris\u003c/em\u003e (25.2 cm) whereas the smallest fish sampled was \u003cem\u003eBadis badis\u003c/em\u003e (2.9 cm). The fishing gear used in sampling clearly shows the variation in fish size classes. Hand net primarily tended to captures smaller-sized fish, with median size below 5 cm, showing a narrow range with minimal variation. Gill nets tend to capture fish within the 7.5 to 10 cm range, indicating preference for medium-sized individuals.\u003c/p\u003e \u003cp\u003eCast nets, on the other hand, also capture fish in similar range but show slightly lower median values compared to gill nets. The distinct separation between hand nets and the other two suggests that each gear type plays a specific role in targeting different fish sizes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLand use and Land cover Analysis\u003c/h2\u003e \u003cp\u003eThe LULC analysis revealed that 44% of the total area is dominated by cropland, amounting to 16.38 km\u003csup\u003e2\u003c/sup\u003e. Vegetation covers 25% of the region with an area of 9.76 km\u003csup\u003e2,\u003c/sup\u003e while built-up area accounts for 18%, totalling 6.86 km\u003csup\u003e2\u003c/sup\u003e. River system constitutes 12% of the land, and pond occupy 1% of the region, with and area of 0.20 km\u003csup\u003e2\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand Use Land Cover distribution along Rupnarayan River.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBuilt up Area\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.86\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePonds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrand Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eNormalised Difference Vegetation Index (NDVI) Analysis\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe vegetation distribution map (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e) represents the spatial changes along the Rupnarayan River based on NDVI analysis. The dense riparian vegetation is shown by the high NDVI values (0.43 to 0.67), which are displayed in green. The medium regions 0.16 to 0.43 (yellow) form a buffer zone between dense vegetation and anthropologically impacted areas whereas the low NDVI values\u0026thinsp;\u0026minus;\u0026thinsp;0.17 to 0.16 (red) are scattered and fragmented patches, representing agricultural and urban settlements.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFish diversity patterns and associated environmental factors\u003c/p\u003e \u003cp\u003eThe tropical rivers are highly rich in its biodiversity (Dudgeon 2006; Dudgeon, 2000; Zhou and Li, 2024). The Rupnarayan River one of the tributaries of Ganga exhibited a very high fish diversity with 40 species. The family Cyprinidae is the most dominant in this study area and also in the southeast Asian streams (Lowe McConnell,1987). Numerous studies demonstrated that the diversity patterns in streams and rivers are shaped by variety of factors at different scales (Olden et al 2001; Oberdorff et al., 2011). At the large scale, the relative difference between the number of individuals and species among sites indicated a consistent pattern across spatial scales. While, at smaller scale species diversity tends to more variable and shaped by local factors such as habitats (Gorman and Karr \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1978\u003c/span\u003e. At smaller spatial scale demonstrated that the fish diversity was largely driven by microhabitat features (pools, run, riffles) river substrate composition (Gorman and Karr \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1978\u003c/span\u003e). But at the river basin or catchment level, fish diversity was governed by hydrology, river characteristics, and human activities (Olden et al 2001; Atkore et al \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Hydrodynamics and tidal type also influence native fish assemblages (Huntsman et al 2023; (Caroline et al., 2023; Ma et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The present study found that channel type, elevation, water temperature, flow, and time spent for fish sampling and tidal type driven the fish diversity patterns. However, we did not encounter any non-native species but presence of these species could not be ruled out. (Sandilyan, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This study found a negative relationship between diversity and channel depth, width, contrasting with past research suggesting larger, diverse habitats increase species diversity (Angermeier and Schlosser, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, fish diversity showed a negative relationship with water temperature and stream flow, suggesting these variables shapes fish assemblage in tropical regulated rivers (Mondal and Bhat, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bice et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Optimum water temperature is essential for physiological reactions and impacts fish growth and abundance (Chapman, 1996; Chaudhary et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Stream flow variation is important in governing the distribution as well as reproductive behaviour of fish species in tropical rivers (Poff et al 1997; Fang-Fang Li et al \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A few dominant species such as \u003cem\u003eApocryptes bato (Hamilton 1822)\u003c/em\u003e, \u003cem\u003ePuntius sophore (Hamilton 1822), Salmostoma bacaila (Hamilton 1822)\u003c/em\u003e showed negative responses to the rise in the streamflow condition whereas other species \u003cem\u003eGudusia chapra (Hamilton 1822)\u003c/em\u003e showed positive or neutral response to the flow condition suggesting different species may adapt or behave differently to the flow condition during their life cycle (Mims \u0026amp; Olden, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stratford et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). We could not establish a relationship between flow and fish reproductive behaviour due to the time constraints of the study but long-term studies should explore such patterns in the future. Of the habitat studied, narrow channel confluence and meander influenced the fish richness than the other channels suggesting surface dweller fishes preferred confluence habitat may be due to meet their demand for the fresh dissolved oxygen and as a migration route to other habitats at the confluence and refugia or completion of their life cycle at the meanders (Stoffers et al \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Liu et al \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Generally, large-body size fishes tend to prefer slower water bodies with deeper pools whereas smaller to medium-sized fishes prefer shallower and fast to medium streamflow (Akbaripasand et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Such insights are more important in hydrological studies. The flow regulation especially below the dam during the summer season, significantly affects the migratory species when flow is reduced significantly (Vaidya et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Although Rupnarayan is a free-flowing tributary of the Hooghly River, the flow rate was not disrupted due to the absence of hydrological barriers. We observed that, during the high salinity period (afternoon in each day at 1:55 hrs), fish were higher whereas during at lower salinity period (morning 07:15 hrs), fish were lower in their relative abundances. A previous study in Slocun River estuary, U. S. A also reported that salinity levels influenced species composition and diversity of fish species (Hoff and Ibara, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1977\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe sand mining, embankment, and agriculture are some of the main threats to the river habitat and its fauna (Paukert 2008; Fischer 2012). Although this study was of a short duration but the results indicates that the fish diversity showed a negative relationship with sand mining and, the number of fishing activities. Out of 36 sites sampled three sites were exposed to sand mining activities. At two sites manual sand mining was present and at one site near Mankur, mechanized sand mining was observed. The higher intensity of sand mining, and water withdrawals using a greater number of irrigation pumps may further threaten the fish life of this river.\u003c/p\u003e \u003cp\u003eLand use changes, particularly the conversion of land for agriculture and human habitation, pose significant threats to riverine ecosystems. In the Marapanim River watershed, Brazil's deforestation for agriculture and urban development has led to the loss of 1,614.72 km\u0026sup2; of forest, negatively affecting water-related ecosystem services such as carbon sequestration and soil moisture regulation (Beltr\u0026atilde;o \u0026amp; Gomes, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarly, agricultural expansion in the Tonle Sap Floodplain, Cambodia, has resulted in a drastic decline in scrubland and grassland, contributing to habitat loss, decreased fish productivity, and a 12% reduction in carbon stocks (Mahood et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study shows that in 2024, cropland dominates 44% of the total study area, while built-up areas account for 18%, leaving only 25% of the landscape covered by vegetation in the Rupnarayan River region. These findings align with broader patterns of land use change observed in other river basins, such as the Upper Yamuna Basin, which has also experienced rapid urbanisation and agricultural expansion, leading to a decline in green spaces and increasing pollution levels in the river (Neenu \u0026amp; Kansal, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Rupnarayan River hold a significant fish diversity. Future work should consider studying the long-tern influence of environmental, temporal and anthropogenic factors along with parameters of water quality to monitor the health of the river.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eHP \u0026ndash; Data collection, analysis and written the original draft, VA- Designed, Conceptualised, Supervised, Analysis, edited the original manuscript, GS \u0026ndash; Data collection. PC-Data collection, edited the manuscript, SR-Data collection, QQ-Designed, Conceptualised, Supervised, Analysis, edited the original manuscript, VK- Designed, Conceptualised, Supervised, edited the original manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe sincerely thank Director, Dean, Registrar and Research Coordinators of the Wildlife Institute of India\u0026rsquo;s Salim Ali Centre for Ornithology and Natural History (SACON) for their encouragement and support. This study was conducted under the CAMPA Dolphin Project of WII which provided the necessary research permissions and financial support for the entire duration of this study. We thank West Bengal Forest Department for granting the necessary research permission to carry out the study (Letter No. 3659/WL/4R-28/2016, dated- 27/12/2023). We are deeply grateful to the fishermen, Mr Raju Koyal, Mr Kishno Jadhav, and Mr Bilas Dolai, whose hard work made this research possible. We express our profound gratitude to the field assistants, Mr Shamshul Hoque and Mr Deb Prasad Dolai, for their valuable support during fish sampling and market surveys.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available from the corresponding author on a reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkbaripasand, A., Nichol, EC, Lokman, PM and Closs, GP (2011) \u0026apos;Microhabitat use of a native New Zealand galaxiid fish, Galaxias fasciatus\u0026apos;, New Zealand Journal of Marine and Freshwater Research, 45: 1, 135 -144, DOI: 10.1080/00288330.2010.544044.\u003c/li\u003e\n\u003cli\u003eAngermeier, P. L., and Schlosser, I. J. (1989). Species-Area Relationship for Stream Fishes. Ecology, 70(5), 1450\u0026ndash;1462. https://doi.org/10.2307/1938204\u003c/li\u003e\n\u003cli\u003eAskeyev, O., Askeyev, I., Askeyev, A., Monakhov, S., and Yanybaev, N. (2014). 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Nature, 467(7315), 555\u0026ndash;561. https://doi.org/10.1038/nature09440.\u003c/li\u003e\n\u003cli\u003eZuleica Yael Marchetti, Priscilla Gail Minotti, Ramonell, C., Facundo Schivo, and Kandus, P. (2016). NDVI patterns as indicator of morphodynamic activity in the middle Paran\u0026aacute; River floodplain. \u003cem\u003eGeomorphology\u003c/em\u003e, \u003cem\u003e253\u003c/em\u003e, 146\u0026ndash;158. https://doi.org/10.1016/j.geomorph.2015.10.003.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental factors, fish diversity, Land Use Land Cover (LULC), streamflow, tropical river","lastPublishedDoi":"10.21203/rs.3.rs-9140684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9140684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTropical rivers, are highly biodiverse yet they face significant threats from human activities, impacting overall their ecological health. We studied fish diversity (richness and abundance) and its relationships with environmental factors, understanding size class distribution and the role of land use land cover in a Rupnarayan river in east India. Using a hierarchical nested design and space-for-time replacement method to sample fishes from January 2024 to March 2024 over a 30 km stretch of the river.\u003c/p\u003e \u003cp\u003eWe recorded 40 species, comprising 774 individuals from 14 orders and 25 families. Multiple linear regression indicated that channel type, water temperature, river width and depth, and time spent on fish sampling were significantly associated with species richness and abundance. Flow-ecology relationships demonstrated a preference for slower currents among selected species. The fishing gears influences the body size class of fish species.\u003c/p\u003e \u003cp\u003eLand Use Land Cover analysis showed that cropland (44%) dominates the study area followed by vegetation (25%), built-up areas (18%) etc. Despite the absence of immediate threats, the Rupnarayan River plays a crucial role in supporting significant riverine biodiversity which may be helpful for future ecological research and conservation efforts in similar tropical rivers in the world.\u003c/p\u003e","manuscriptTitle":"Assessing the patterns of fish assemblages in the Rupnarayan River, West Bengal in India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 15:20:47","doi":"10.21203/rs.3.rs-9140684/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4fdae55a-c6ed-434c-87c7-f3614cc9d50d","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T18:49:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-07T06:42:41+00:00","index":38,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65178749,"name":"Biological sciences/Ecology"},{"id":65178750,"name":"Earth and environmental sciences/Ecology"},{"id":65178751,"name":"Earth and environmental sciences/Environmental sciences"}],"tags":[],"updatedAt":"2026-05-07T18:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 15:20:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9140684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9140684","identity":"rs-9140684","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}