Do short-term ecological alterations affect fish diversity in the long-run? A study from a sub-tropical river in the Eastern Himalayas

Do short-term ecological alterations affect fish diversity in the long-run? 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A study from a sub-tropical river in the Eastern Himalayas Simanku Borah, Pranab Gogoi, Kavita Kumari, Shyamal Chandra Sukla Das, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7347029/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract The present study was carried out to assess the effect of short-term ecological alteration on fish diversity of River Siang, Arunachal Pradesh in the Eastern Himalayas. Fish diversity of six stations along the river (Puging, Yingkiong, Boleng, Komsing, Pasighat and Oiramghat) was assessed across pre-monsoon, monsoon and post-monsoon seasons along with all major physico-chemical attributes of water. We recorded 78 fish species belonging to 48 genera under 17 families. On a spatial scale, the maximum number of species was recorded from Oiramghat (65 species) followed by Pasighat (56 species) along the lower stretch of the river. The family, Cyprinidae formed more than one-fourth (26.92%) of the total number of species followed by Danionidae (21.79%), Bagridae and Sisoridae (7.69% each) and Channidae (6.41%). During 2017-18 mean values of Shannon-Wiener index (Hʹ), Margalef’s richness index (dʹ) and Pielou’s evenness index (Jʹ) ranged from 1.877 to 2.420, 2.446 to 3.369 and 0.727 to 0.769, respectively, while for the period 2018–2019 values ranged from 2.654 to 3.00, 4.351 to 5.638 and 0.865 to 0.886; and from 2.731 to 3.083, 5.032 to 6.607 and 0.858 to 0.887, respectively during 2019-20. A total of 11 water quality variables were analyzed during the period. During 2017-18, water quality of the river was characterized by high turbidity and low transparency values, which improved in subsequent years. Significant variations were recorded in mean values of transparency, turbidity, and total chlorophyll between 2017-18 with 2018-19 and 2019-20 (p < 0.05). Statistical analysis affirmed that water quality attributes like depth, pH, dissolved oxygen and turbidity have strong association with fish community compositions in the river. We have observed that following the short period of environmental degradation, as the river regained its pristine status, fish diversity also improved concurrently, which suggests that short-term ecological degradation do not affect fish diversity in the long-run. Fish diversity environmental variables Siang River Eastern Himalayas Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Spatial orientation of biotic assemblages in an aquatic ecosystem depends on its environmental variables either directly or indirectly as reiterated by Fausch et al. ( 1990 ) for fishes and very recently by Gogoi et al. ( 2021 ) in phytoplankton and by Das et al. ( 2022 ) in macrobenthic invertebrates. Geomorphological characteristics of ecosystems along with biotic and abiotic properties govern fish diversity and community structure both in terms of species abundance and pattern of distribution (Orrego et al., 2009 ; Alexandre and Almeida, 2010). Environmental variables significantly influence fish diversity and distribution in aquatic systems (Schlosser, 1985 ; Grossman et al., 1998 ). Influence of environmental attributes on fish species richness and diversity was also reported upon by Mondal and Bhat ( 2020 ) from rivers and streams of Central and Eastern India. As evident from studies worldwide, fish diversity and community structure in rivers have an intrinsic relationship with habitat conditions (Wang et al., 2006 ; Fialho et al., 2008 ). Fish diversity and distribution at temporal and spatial scales is a dynamic mechanism. In addition to long-term seasonal variation in environment, changes in the short term like diurnal variations in ecological attributes do influence fish community structure and species diversity (Noakes, 1992 ; Axenrot et al., 2004 ). Thus, any change in the habitat and ecological attributes of a system, whether it is sudden or in the long term, will affect fish composition. As a consensus, impaired habitats tend to have lower fish diversity in comparison to pristine ones. River Siang which also goes by the name Dihang is a transboundary watercourse draining the north-east Indian province of Arunachal Pradesh, and serves as the principal headwater of River Brahmaputra (Das and Saikia, 2015 ). Originating from Chema Yungdung Glacier near Kubi, Tibet at 5150 masl the river traverses by the name Yarlung-Tsangpo through Tibet (1,625 km), and enters India as Siang at Gelling in Arunachal Pradesh (Das et al., 2014 ). River Siang runs through the state of Arunachal Pradesh from north to south for 278 km, dissecting the Eastern Himalayas (Bhattacharjya et al., 2017 ). As Siang River enters the plains of Assam, it is joined by two large trans-Himalayan tributaries, Dibang and Lohit, and the combined river is called Brahmaputra (Bhattacharjya et al., 2017 ). The River Brahmaputra flows for about 640 km in an east-west direction effectively splitting the Indian state of Assam into two equal halves from Sadiya to Dhubri (Bhattacharjya et al., 2017 ). At the Indo-Bangladesh border in Dhubri, this west flowing river takes an abrupt southerly turn and enters Bangladesh. The river then flows for about 337 km in Bangladesh and finally drains into Bay of Bengal (Bhattacharjya et al., 2017 ). The northeastern region of India has been identified as one of the major biodiversity hotspots (Chatterjee et al., 2006 ). The rivers and wetlands in the region harbour unique and rich aquatic biodiversity. Despite its importance towards aquatic biodiversity, fisheries and livelihoods of people in northeastern India, detailed studies on the ecology and fish diversity of the Siang River are limited. The only study we could come across was that of Das et al. ( 2017 ), where they studied the fish diversity from the lower part of the Siang River during the years 2012-13 and reported 82 species under 24 families. In recent decades, freshwater ecosystems have been threatened by different environmental and physicochemical factors (Sala et al., 2000 ). It has been observed that aquatic resources and their biodiversity are facing increased stress from natural and anthropogenic factors on a global scale and Siang River is no exception. Researchers have identified anthropogenic pressure along with climate change as some of the major threats to aquatic ecosystems (Hauer et al., 2013 ; Junker et al., 2015 ). Arthington ( 2012 ) attributed the loss of biological diversity in freshwater bodies to anthropogenic activities such as pollution, habitat modifications through dams and sluice gates, habitat degradation, invasive species and over-exploitation. In contrast to marine and terrestrial habitats, impact of anthropogenic pressure on freshwater systems is much higher (Jenkins, 2003 ; Heino et al., 2009 ). Rivers are major repositories of freshwater diversity and decline in fish diversity from these ecosystems due to prolonged deterioration in habitat conditions over the last decades are well-documented across the globe (Diana et al., 2006 ; Yang et al., 2020 ). Himalayan rivers are one of the most scrutinized resources in Indian sub-continent owing to their high hydropower potential and habitat modification in these rivers have endangered valuable freshwater biodiversity of the Himalayas (Das et al., 2020 ). Anthropogenic impact on Indian part of River Brahmaputra have resulted in declining catch of Indian major carps, catfishes, shad, featherbacks while being replaced by small miscellaneous fish group (Borah et al., 2014 ; Yadav et al., 2022 ). Besides fisheries, natural and anthropogenic factors have also impacted the environmental status of Siang River. Once such instance was in 2017, when there was a significant deterioration in the water quality of River Siang. Artificially induced or natural changes in the upper stretch of the river Siang resulted in exceptionally high silt load in its waters. Transparency was reported to be < 4.0 cm (Gogoi et al., 2018) and turbidity of water was abnormally high ranging from 258–405 NTU (Roy et al., 2019 ) as compared to the values (78.1–99.9 NTU) reported by Das et at. (2014). Extreme levels of ecological impairment in aquatic ecosystems can pose threats to nature and to its biodiversity. However, studies conducted by ICAR-Central Inland Fisheries Research Institute (CIFRI) revealed that physico-chemical attributes of water in the river reverted to normal during the subsequent period (2018-19 and 2019-20). In the present study, we tried to analyze the effects of short-term ecological changes on fish diversity of River Siang in the long run. This study holds importance as the information generated can have worldwide applications towards sustainable management of identical riverine ecosystems. This study on fish community structure also bears paramount significance in the context of large hydropower projects which are reportedly being constructed in the headwaters of River Siang in Tibet. Besides, the study can also contribute towards satisfying Sustainable Development Goals (SDGs) (SDG 14 - Life Below Water) of United Nations. Materials and methods Study area We selected six representative sampling stations covering upper, middle and lower stretch of River Siang in Arunachal Pradesh and Assam. Stations viz ., Puging (N 28⁰45'42.3" and E 94⁰54'08.20") and Yingkiong (N 28⁰39'41.96" and E 95⁰00'47.20") in upper stretch; Boleng (N 28⁰21'21.64" and E 95⁰03'18.33") and Komsing (N 28⁰14'1.29"and E 95⁰0'7.79") in middle stretch; Pasighat (N 28⁰05'43.90" and E 95⁰18'13.23") and Oiramghat (N 27⁰50'18.61" and E 95⁰21'54") in lower stretch were selected for our study (Fig. 1 ). The first five stations are located in Arunachal Pradesh at an elevation of 155–1150 masl, while Oiramghat is situated in Assam (104 masl). The river is characterized by a rocky bed in all sampling stations except Oiramghat, where the riverbed is sandy, fast-flowing waters with presence of rapids, runs and riffles. Sampling methodology Water and fish samples were collected from selected sampling locations over three years (2017-20). Sampling was carried out at seasonal intervals viz. , monsoon (June-September), post-monsoon (October-January) and pre-monsoon (February-May). Dissolved oxygen (DO), water temperature, pH, specific conductivity, turbidity, total dissolved solids (TDS), and total chlorophyll were measured using a water quality probe (Model 9829, HANNA®, Romania) (Ongh et al., 2025 ); APHA ( 2017 ) was followed to measure free carbon dioxide (CO 2 ) and total alkalinity (TA); and transparency was measured using Secchi disc following the procedure outlined by Strickland and Parsons ( 1972 ). All these parameters were measured in field for real time and reliable results. Water samples were collected from the subsurface at a depth of 0.5 m using a standard water sampler designed following the ‘Ruttner water sampler’ (Das Sarkar et al., 2019 ). The water samples were then shifted immediately to clean 1 L capacity polyethylene bottles for analysis of various quality parameters in the laboratory (Ongh et al., 2025 ). Fishes were collected from selected sampling locations using traditional bamboo traps (locally known as Dingora , Polo , Sepa, Porang and Atoong ), lift nets (0.5 and 1.0 cm mesh size), gill nets (1.0–6.0 cm mesh size), cast nets (1.0 and 2.0 cm mesh size) and drag nets (1.5 cm mesh size). Fresh fish specimens were identified in the field to the maximum extent possible. Doubtful specimens preserved in 8–10% neutral buffered formalin were brought to the laboratory and identified using published books and manuals (Talwar and Jhingran, 1991 ; Jayaram, 1999 ; Vishwanath et al., 2007 ). Taxonomic classification of fishes was done following Fricke et al. ( 2024 ). Abundance of each species and values of water quality parameters were recorded in standard data collection sheets. Data analysis Data analysis was done for two periods, 2017-20 and 2018-20. For the first, data was analyzed to compare the changes that have occurred during the impaired period when Siang River was under severe environmental stress (2017-18), with the restored period (2018-19 and 2019-20). Analysis was done for the restored period (2018-20), when the river reverted back to its normal environmental state to understand the fish community assemblage and relationship with environmental drivers. One way analysis of variance (ANOVA) and post-hoc test was used to determine significant differences in water quality parameters between degraded period (2017-18) and restored period of 2018-19 and 2019-20. Prior analysis, water quality data except pH were normalized using log(x + 1) transformation (Gogoi et al., 2021 ). To assess the fish diversity, indices like Shannon diversity index (Hʹ) (Shannon-Weiner, 1949), Margalef richness index (dʹ) (Margalef, 1958 ) and Pielou’s evenness index (Jʹ) (Pielou, 1977) were employed and calculated using R 3.5.3 (The R Foundation, 2019) for the period 2017-20. ANOVA was also done to verify difference in diversity indices across stations and seasons. Again, repeated measures of ANOVA was done to verify any significant variation in water quality attributes across stations over three seasons and comparison of mean values was done employing post hoc Duncan's multiple range tests for the restored period 2018-20. Pearson’s correlation (2-tailed) coefficients for water quality attributes for the period 2018-20 were computed using R 3.5.3 (The R Foundation, 2019). Permutational Multivariate Analysis of Variance (PERMANOVA) was also performed to examine the variation (p ≤ 0.05) in water quality and fish abundances across seasons and stations for the period 2018-20, when the riverine environment restored back to its pristine status. To investigate the dominance pattern on a temporal scale (seasons) k -dominance curve was used (Gogoi et al., 2021 ; Ongh et al., 2025 ). Hierarchical cluster analysis, supported by SIMPROF test, was applied to identify statistically significant clusters of sampling stations (Gogoi et al., 2019 ) based on fish abundance and composition. NMDS based on Bray–Curtis similarity protocols was employed to visualize similarities in fish community structure across seasons (Ongh et al., 2025 ). Analysis of similarity (ANOSIM) was carried out to evaluate significant variation in fish assemblages across spatial locations (Gogoi et al., 2021 ), while similarity percentage (SIMPER) analysis was performed to identify fish species contributing most to similarities and dissimilarities between groups (Gogoi et al., 2021 ). To fulfill the criteria of normality assumptions, fish abundance data were subjected to square root transformation prior to analysis (Ongh et al., 2025 ). Relationship between environmental attributes and fish community structure was assessed through BIO–ENV and distance-based linear modelling (DistLM) (Gogoi et al., 2021 ; Ongh et al., 2025 ). Model selection followed the Akaike Information Criterion (AIC) with a stepwise approach (Gogoi et al., 2021 ). The above mentioned statistical analyses were conducted using PRIMER v6.0 (Clarke and Gorley, 2006 ) and were carried out for the period 2018–2020, when river water quality reverted to its normal condition. Results Water quality Of the 11 measured environmental variables, all the parameters except free CO 2 showed significant differences between seasons during 2018-20 (Table 1 ). However, transparency, specific conductivity, DO and TDS did not portray significant differences (p > 0.05) between pre-monsoon and monsoon. Similarly, water variables (pH, TA and depth) were not found to be significantly different (p > 0.05) between monsoon and post-monsoon. Turbidity, total chlorophyll values between pre-monsoon and post-monsoon also showed no significant difference. Karl Pearson’s Correlation matrix revealed a significant negative correlation of water temperature against DO (r=-0.895; p < 0.01), transparency (r=-0.789; p < 0.01), specific conductivity (r=-0.611; p < 0.011), and positive correlation with turbidity (r = 0.843; p < 0.01). DO showed significant positive correlation with variables such as transparency (r = 0.735; p < 0.05) and specific conductivity (r = 0.660; p < 0.01). Similarly, turbidity was negatively correlated with transparency, specific conductivity and DO (Table 2 ). On the whole, PERMANOVA analysis revealed significant spatial (F = 1.909; p = 0.012) and temporal (F = 14.603; p = 0.001) variations in water quality attributes of River Siang. Table 1 Mean seasonal variation of water variables (2018-20) and a comparative assessment between annual periods (2017-18, 2018-19 and 2019-20) Water quality 2018-20 2017-18 2018-19 2019-20 PRM MON POM Water temperature (˚C) 21.63 ± 1.07 a 24.41 ± 0.80 b 13.34 ± 1.99 c 19.21 ± 4.31 a 22.51 ± 1.73 b 17.08 ± 5.75 a Depth (m) 7.96 ± 5.71 a 12.97 ± 9.85 b 9.34 ± 7.11 b 11.27 ± 8.09 9.78 ± 7.48 10.41 ± 7.81 Transparency (cm) 22.75 ± 2.16 a 21.27 ± 2.13 a 52.66 ± 8.64 b 6.81 ± 7.24 a 22.07 ± 3.77 b 42.39 ± 19.90 c Specific Conductivity (µS/cm) 259.75 ± 21.37 a 269.59 ± 14.44 a 380.5 ± 54.81 b 262.78 ± 77.35 a 263.72 ± 40.31 a 342.84 ± 79.88 b pH 7.28 ± 0.14 a 7.28 ± 0.26 b 8.16 ± 0.28 b 7.46 ± 0.50 a 8.03 ± 0.96 b 7.13 ± 0.50 a DO (mg L − 1 ) 8.71 ± 0.36 a 8.38 ± 0.37 a 10.17 ± 0.45 b 8.69 ± 0.71 b 8.58 ± 0.45 a 9.60 ± 1.00 b Free CO 2 (mgL − 1 ) 1.48 ± 1.25 1.41 ± 0.43 1.22 ± 0.63 1.86 ± 0.75 a 1.49 ± 1.26 b 1.26 ± 0.88 ab TA (mg L − 1 ) 39.68 ± 3.04 a 49.91 ± 5.29 b 46.08 ± 4.12 b 47.04 ± 5.67 43.12 ± 9.49 47.33 ± 4.91 TDS (mg L − 1 ) 177.57 ± 31.02 a 178.83 ± 12.73 a 242.33 ± 41.40 b 171.09 ± 48.52 a 178.77 ± 38.90 a 220.39 ± 53.73 b Turbidity (NTU) 45.4 ± 4.11 a 48.7 ± 3.43 b 25.86 ± 2.37 a 177.76 ± 88.65 b 46.57 ± 5.28 a 32.99 ± 11.22 a Total chlorophyll (mg m − 3 ) 128.08 ± 65.32 a 40.12 ± 16.72 b 135.95 ± 103.51 a 65.70 ± 58.36 b 98.54 ± 70.85 a 104.23 ± 126.35 a *Values are expressed as mean ± SD; values with different superscript are significantly different (p < 0.05) Table 2 Intra-relationship between water variables and total fish abundance, diversity indices (n = 36) WT Dep Tran Cond pH DO Free CO 2 TA TDS Tur T Chl Hʹ dʹ Jʹ Fish Abun WT 1 Dep 0.013 1 Tran -0.789** 0.01 1 Cond -0.611** -0.285 0.585** 1 pH 0.008 -0.022 -0.188 -0.197 1 DO -0.895** -0.012 0.735** 0.660** -0.088 1 Free CO 2 0.192 -0.216 -0.099 -0.035 0.135 -0.301 1 TA -0.017 -0.052 0.191 0.079 0.199 -0.01 0.074 1 TDS -0.481** -0.385* 0.503** 0.903** -0.106 0.529** 0.050 0.139 1 Tur 0.843** 0.037 -0.929** -0.705** 0.088 -0.777** 0.023 -0.129 -0.621** 1 T Chl -0.351* -0.170 0.261 0.061 0.092 0.351* -0.078 -0.047 0.016 -0.240 1 Hʹ -0.350* -0.401* 0.409* 0.239 -0.245 0.186 0.206 -0.044 0.265 -0.417* 0.245 1 dʹ -0.138 -0.712** 0.182 0.338* -0.259 0.055 0.136 -0.077 0.373* -0.260 0.197 0.793** 1 Jʹ 0.014 0.804** 0.036 -0.360* 0.017 -0.024 -0.091 -0.027 -0.398* 0.106 -0.145 -0.309 -0.773** 1 Fish Abun -0.252 -0.596** 0.276 0.460** -0.221 0.195 0.176 -0.028 0.474** -0.396* 0.244 0.714** 0.858** -0.691** 1 **. Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed); WT, Water Temperature; Dep, Depth; Trans, Transparency; Cond, Specific Conductivity; DO, Dissolved Oxygen; TA, Total Alkalinity; TDS, Total Dissolved Solids; Tur, Turbidity; T Chl, Total Chlorophyll; Fish Abun, Fish Abundance Comparison of water quality parameters between 2017-18, 2018-19 and 2019-20 showed significant differences across the years. Water temperature and pH during 2018-19 were significantly different from 2017-18 and 2019-20; transparency showed significant difference across the three year period (p < 0.05); specific conductivity in 2019-20 was significantly different from the other two years; free CO 2 showed significant difference between 2017-18 and 2018-19, while its value in 2019-20 was not significantly different with the other two years; mean TDS values between 2017-18 and 2018-19 showed no significant difference, but both the values were significantly different from 2019-20; turbidity and total chlorophyll in 2017-18 was found to be significantly different from the remaining two years (Table 1 ). Two physical variables, transparency (mean, 6.81 ± 7.24 cm) and turbidity (mean, 177.76 ± 88.65 NTU) altered drastically during 2017-18. Although DO values during 2018-19 were significantly different from 2017-18 and 2019-20, drastic changes in DO concentration was not observed in the river. TA and water depth did show any significant difference in the study period. Fish diversity Fish community comprising of 78 species belonging to 48 genera under 17 families were recorded during our survey from 2017–2020. Spatial analysis shows that the highest number of species was reported from Oiramghat (65 species) followed by Pasighat (56 species) along lower stretch of the river. Seasonal species richness in the studied stations is shown in Fig. 2 . Species diversity was comparatively higher in post-monsoon, followed by pre-monsoon and least in monsoon. Cyprinidae, being the most dominant family contributing more than one-fourth (26.92%) of the total number of species reported followed by Danionidae (21.79%), Bagridae, Sisoridae (7.69% each) and Channidae (6.41%). Percentage contribution of different families to the total number of species reported is shown in Fig. S1 (S = Supplementary). Fish species Barilius barila , B. vagra , Opsarius bendelisis , O. barna , Cabdio morar , Tariqilabeo latius , Pethia conchonius , Puntius sophore , P. chola were the most abundant species, while Labeo catla , Hemibagrus menoda , Pterocryptis gangelica , Labeo rohita and Cirrhinus mrigala were least dominant in terms of abundance. However, fish species diversity and abundance were found to be low during the impaired period. A total of 31 species under 10 families were reported from the river during 2017-18 (impaired period), which increased to 60 species under 15 families in 2018-19 and 78 species under 17 families in 2019-20. The major contributors during 2017-18 were Barilius vagra, Opsarius bendelisis, O. sharca, Tariqilabeo latius, Bangana dero, Tor tor, T. putitora and Labeo dyocheilus. Details of species recorded from Siang during our study along with their IUCN status (IUCN, 2024 ) are given in Table S1 . It is observed that out of the total 78 species recorded, 60 species are categorised as Least Concern (LC), 6 Near Threatened (NT), 5 Vulnerable (VU), 3 Data Deficient (DD), 3 Not Evaluated (NE) and 1 species ( Tor putitora ) as Endangered (EN). Small indigenous fishes dominated the fish diversity of the river, contributing 75.64% of the total number of species recorded. We documented ten migratory fish species and a single exotic carp ( Ctenopharyngodon idella ) during our survey (Table S1 ). Mean values of Shannon-Wiener index (Hʹ), Margalef’s richness index (dʹ) and Pielou’s evenness index (Jʹ) was found to range from 1.877 to 3.083; 2.4468 to 6.607 and from 0.727 to 0.887, respectively during the study period. During 2017-18, mean values of Hʹ, dʹ and Jʹ were highest during monsoon and lowest in post-monsoon. While during 2018-19 and 2019-20, values of Hʹ and dʹ were highest during post-monsoon and of Jʹ in monsoon (Table S2). Across stations, mean values of Hʹ and dʹ were highest downstream of the river in Oiramghat and Pasighat, while values of Jʹ were on the higher side along the middle (Boleng and Komsing) and upper stretch (Puging and Yingkiong) (Table S3). Fish diversity was on the lower side during 2017-18 as evidenced from low Hʹ values (1.877 ± 0.434 to 2.420 ± 0.230) indicating low fish diversity, while values of Hʹ (> 2.654 ± 0.329) increased significantly during 2018-19 and 2019-20. ANOVA revealed a significant difference in the values of H, dʹ and Jʹ across seasons for the period 2017-18, 2018-19 and 2019-20 (p < 0.05) (Table S2). Assessment showed that Hʹ and dʹ had a strong positive relationship (r = 0.793; p ≤ 0.01), while dʹ and Jʹ had a strong negative relationship (r=-0.773; p ≤ 0.01) (Table 2 ). PERMANOVA analysis with Monte Carlo simulation showed a significant difference in fish abundance between seasons (F = 19.453; p = 0.001), and stations (F = 33.244; p = 0.001) (Table S4). Nature of similarity Hierarchical agglomerative cluster analysis (SIMPROF test) based on the Bray-Curtis similarity matrix showed two distinct clusters comprising Puging, Yingkiong, Boleng and Komsing (Cluster I), and Oiramghat and Pasighat (Cluster II) (Fig. 3 ). The clusters were found to be statistically insignificant (p > 0.05). Within Cluster I, Puging formed a separate sub-cluster from the other three stations with 68% similarity in community composition, while Yingkiong, Boleng and Komsing had 79.28% similarity. In the case of Cluster II, stations Pasighat and Oiramghat portrayed 64.17% similarity. Overall, the stations exhibited 46% similarity in the fish assemblages. ANOSIM revealed significant differences (r = 0.843; p = 0.01) in fish assemblage pattern between the groups (Group I and II). NMDS (Fig. 4 ) revealed that monsoon season were distinctively separated as compared to pre-monsoon and post-monsoon with some of the samples during post-monsoon and pre-monsoon depicting a similar nature of fish abundance. Cumulative dominance curve ( k -dominance) derived for seasons implied that there was no significant difference in dominance of fish species across seasons with a similar dominance pattern observed throughout the study period (Fig. 5 ). However, in the present study, it was seen that the k -dominance curve for monsoon was slightly higher than the other two seasons. Higher the curve and more quickly it reaches 100% value, less the fish diversity. Grouping of fish species based on SIMPER test showed that in Group I, B. barila (15.25%), O. barna (13.74%), B. vagra (10.30%), O. bendelesis (7.09%), O. siangi (6.52%), T. putitora (6.22%), L. dyocheilus (5.17%), T . tor (4.02%), B. dero (3.69%), Raiamas bola (3.32%) and Semiplotus semiplotus (3.18%) were the main contributors with average similarity 56.31%. Similarly, for group II, average similarity was 53.39%, and C. morar (15.30%) was the major contributor followed by P. sophore (8.30%), B. barila (7.73%), Pethia ticto (6.33%), O. barna (6.28%), T. latius (5.92%), P. conchonius (5.55%), P. chola (5.22%), O. bendelesis (4.81), B. vagra (3.87%) and R. bola (2.10%). The average dissimilarity between groups (Group I & II) was 71.41%, and the major fish species responsible for the difference in abundance pattern is shown in Table 3 . Table 3 SIMPER Routine based on the fish abundance showing the percentage contribution in the clustered groups SIMPER test Species Group 1 (Yin, Bol, Kom) Group 2 (Pas, Oir) Average dissimilarity = 71.41% Average abundance Average abundance Average dissimilarity Contribution% Cumulative% C. morar 0.00 59 9.58 13.42 13.42 T. latius 5.94 48.08 8.2 11.49 24.91 P. sophore 0.00 25.42 4.43 6.2 31.11 B. barila 17.5 35.42 3.68 5.15 36.26 P. ticto 0.00 21.67 3.62 5.07 41.33 P. chola 1.44 20.33 3.02 4.23 45.56 B. vagra 10.67 20.33 2.99 4.19 49.76 O. barna 14.22 27.42 2.63 3.68 53.43 P. conchonius 4.56 19.08 2.43 3.4 56.83 A. coilia 0 13.67 2.14 3 59.83 B. benedelisis 7.72 18.33 1.8 2.52 62.35 A. mola 0.00 10.17 1.58 2.21 64.56 C. nama 0.00 6.33 1.17 1.64 66.19 C. jaya 2.39 8.83 1.16 1.62 67.81 B. dero 4.89 8.67 1.09 1.53 69.34 S. sarana 0.00 7.25 1.06 1.49 70.83 E. vacha 1.28 6.17 1.05 1.47 72.3 O. siangi 7.56 6.25 1.02 1.42 73.72 L. dyocheilus 5.28 4.75 0.96 1.34 75.06 P. baculis 0.00 4.92 0.92 1.29 76.36 T. putitora 6.17 3 0.81 1.13 77.49 D. aequipinnatus 1.5 5.75 0.75 1.04 78.53 D. devario 1.06 5.08 0.72 1.01 79.54 Yin = Yingkiong; Bol = Boleng; Kom = Komsing; Pas = Pasighat; Oir = Oiramghat Relationship of environmental variables and fish community Analysis revealed that water depth and turbidity have a significant negative relationship with fish abundance (p ≤ 0.01), while specific conductivity and TDS have a strong positive relationship with fish abundance (p ≤ 0.01). Water temperature, pH, and total alkalinity also had a negative correlation with fish abundance, but the relationship was statistically insignificant. Similarly, DO had an insignificant positive relationship with fish abundance (Table 2 ). BIO-ENV module revealed that environmental attributes viz . depth of water, DO and transparency have significant correlation with abundance and composition of fishes in River Siang. Highest correlation value p = 0.413 was obtained in the case of water depth, followed by combinations of depth and transparency; depth, DO and transparency (Table 4 ). Table 4 BIO-ENV analysis observed in fish assemblage compared with water parameters (pooled data) No. of variables Correlation selection Spearman correlation (p < 0.01) 1 Depth 0.413 2 Depth, Transparency 0.331 3 Depth, DO, Transparency 0.316 2 Depth, DO 0.301 3 Water temperature, Depth, Transparency 0.300 3 Depth, Transparency, Total chlorophyll 0.298 2 Depth, Total chlorophyll 0.298 4 Depth, DO, Transparency, Total chlorophyll 0.296 4 Depth, DO, CO 2 , TA 0.295 2 Water temperature, Depth 0.287 Marginal test (DistLM) was performed to assess the correlation between fish abundance and individual environmental attribute. Analysis indicated significant relationship (p ≤ 0.05) of fish abundance with temperature, depth, transparency, free CO 2 and turbidity. Sequential tests further affirmed significant relationship (p ≤ 0.05) with pH and DO including depth and turbidity (Table 5 ). DistLM explained four environmental attributes ( i.e. depth, pH, DO and turbidity) that could explain fish abundance and distribution in Siang River during the course of our study (Table 6 ). Fitted model showed r 2 value (0.498) and AICc (244.73), which represented a suitable model for predicting the best explained variables for fish species distribution in Siang River. Seasonally, the main influencing factors along the river continuum were dissolved oxygen, pH and TA during pre-monsoon, specific conductivity, transparency and TDS during post-monsoon, and water temperature, turbidity and depth during monsoon (Fig. 4 ). Table 5 Marginal and sequential tests (distance-based linear model, DistLM) of environmental variables and fish abundance Marginal test Variable Sum of Squares Pseudo-F p Temperature 4245.6 3.4474 0.023 Depth 12398 12.501 0.001 Transparency 3262.4 2.5883 0.047 Specific Conductivity 2567.2 2.0042 0.104 pH 2291.6 1.7778 0.131 DO 3280 2.6033 0.06 Free CO 2 3792 3.0461 0.038 TA 1854.3 1.4243 0.201 TDS 3205.5 2.5398 0.065 Tur 4179.2 3.3881 0.017 Total Chlorophyll 2180.1 1.687 0.132 Sequential tests Variables Sum of Squares Pseudo-F p Depth 12398 12.501 0.001 Turbidity 3279 4.0029 0.016 DO 2330.8 3.0255 0.032 pH 2330.9 3.1261 0.013 Table 6 Distance-based linear model (DistLM) analysis of variables included in the most parsimonious model for the relationship between fish abundance and environmental variables Axis % Explained variation out of fitted model % Explained variation out of total variation Individual Cumulative Individual Cumulative 1 Depth 70.19 70.19 35.01 35.01 2 pH 14.08 84.49 7.38 42.39 3 DO 10.0 94.98 4.99 47.38 4 Turbidity 5.02 100 2.5 49.88 Discussion Environmental parameters River water quality is crucial owing to its ecological, economic and societal significance. Our study observed remarkable variation in water variables, particularly transparency and water turbidity along the river continuum between two annual periods. Unnatural change of water (heavy load of slag and sediments) during 2017-18, either due to natural phenomena or anthropogenic causes, led to drastic changes in the physical and chemical characteristics of water in River Siang. Muddy and sticky suspended particles increased water turbidity, and conversely low transparency. Water turbidity in the river rose manyfold (> 170 NTU) during 2017-18, more than ten times higher than permissible limit of World Health Organization (< 10 NTU) (WHO, 2005). Secchi disc transparency also decreased to < 7 cm during the period. Total Chlorophyll concentration was also impacted as evident from the low values of 2017–18, compared to post event period (2018-19 and 2019-20), which may be attributed to presence of suspended sediments in water. However, magnitude of DO did not fluctuate significantly over the periods. DO was found to be well above hypoxia level and higher than permissible limit of 5 mgL − 1 (BIS, 2012 ). Surface air-water interaction owing to high flow velocity of the river and photosynthetic processes might have contributed towards high oxygen content in water (Levinton, 2001). Estimated DO cocentration (mean, 8.71 ± 0.36–10.17 ± 0.45 mgL –1 ) was comparatively higher than earlier record of 6.3–8.03 mgL –1 (Das et al., 2014 ). Water pH remained alkaline through out our study period, a contrast to earlier observations of Das et al. ( 2014 ). Alkaline nature of water (pH > 7.0) indicated that river water remained well buffered and biodegradable organic matter was abundant in water column (Rahman et al., 2013; Basu et al., 2021 ). Majority of water variabels exhibited positive correlation with pH, demonstarting the significant role of pH in physicochemical profile of river water. Negative correlation between turbidity and transparency is quite obvious, supported by numerous studies (Das et al., 2014 ; Sharma and Singh, 2018 ). TDS along the river continuum was within prescribed limits of Bureau of Indian Standards (BIS, 2012 ). Significant variation in dissolved solids between seasons with higher value during monsoon and post-monsoon seasons may be attributed to ingestion of allochthonous materials and carbonate deposits. While comparing the values of TDS and total alkalinity with previous record of 5.83–7.33 mgL − 1 and 70.4–77.2 mgL − 1 , respectively (Das et al. 2014 ), slightly lower total alkalinity and high TDS values were observed during the study period. Similar, low alkalinity values has been reported by researchers from high altitude Himalayan waters (Yaqoob et al., 2007 ; Sharma et al., 2016 ; Sharma and Singh, 2018 ). Specific conductivity is an indirect estimate of TDS in water (Sharma and Kumar, 2017 ). In correlation with TDS, specific conductivity values were also found to be on the higher side in our study area (259.75 ± 21.37 to 380.5 ± 54.81 µS/cm). This is in conformation with several earlier studies from high altitude aquatic systems (Saini et al., 2008 ; Singh et al., 2014 ; Sharma and Kumar, 2017 ). Fish species diversity and compositions As observed in our study, the dominance of Cyprinid group of fishes was also reported by Das et al. ( 2017 ). Only 31 species under 10 families were reported from Siang in 2017-18, which increased to 60 species under 15 families in 2018-19 and 78 species under 17 families in 2019-20, which suggest that short term ecological degradation of the river do not seem to hamper the fish diversity in the long run. Poor environment quality during 2017-18 might have forced fish populations to migrate in the adjoining tributaries and connected streams. As the environment improved, these fish populations returned to their original habitat. The available fish diversity observed in Siang during 2019-20 is comparable with large river systems of India as reported by Sarkar et al. ( 2010 ) and Shukla and Bhat ( 2017 ) and was found to be similar with the report of Das et al. ( 2017 ). Studies on fish diversity of freshwater ecosystems of South Asian region revealed Cyprinids as the single most dominant group (Bhat, 2003 ). Higher plasticity, versatility and ability to colonize diverse environments have resulted in Cyprinids emerging as the dominant group in tropical rivers (Johnson and Arunachalam, 2009 ). Similar findings have also been reported by Ongh et al. ( 2025 ) from Dhansiri, an important tributary of the Brahmaputra. Diversity indices in general are reflection of both variety of species found in an ecosystem and their relative abundance (Ongh et al., 2025 ). Shannon diversity index is one of the most used tools to quantify species richness either in a specific habitat or across different habitats (Clarke and Warwick, 2001). In addition to Shannon index, other indices like Margalef richness index and Pielou’s evenness index are also employed by researchers for estimating the diversity of biotic communities. Margalef richness index (Margalef, 1958 ) is a measure of biodiversity that evaluates the number of species in an ecological community. In contrast, Pielou’s evenness index (Pielou, 1966 ) measures how evenly individuals are distributed in a community. Values for Pielou’s index range from 0 to 1, where 0 indicates complete dominance by a single species, and 1 denote perfect evenness, which means all species are occurring in equal abundance (Pielou, 1966 ). During 2017-18, fish diversity was on the lower side as evident from low Hʹ values (1.877 ± 0.434 to 2.420 ± 0.230), while values of Hʹ improved significantly during 2018-19 (2.654 ± 0.329 to 3.000 ± 0.196) and 2019-20 (2.731 ± 0.265 to 3.083 ± 0.190). Similar pattern was noticed both in Margalef richness index and Pielou’s evenness index with lower values during 2017-18 and higher values in the subsequent years. Further, ANOVA showed that values of Hʹ, dʹ and Jʹ was found to be significantly different (p < 0.05) across seasons and stations in most of the cases for the periods 2017-18 and 2018-19 and 2019-20. Taylor ( 2000 ) acknowledged that relationship exist between water quality and fish abundance. Habitat alteration has been listed as one of the major factors driving fish species decline (Moyle and Leidy, 1992 ). High silt load in the river, resulting in very low transparency (6.81 ± 7.24 cm) and high turbidity of water (177.76 ± 88.65 NTU) during the period was recorded in our study. As turbidity of water reduced and environmental properties of the river improved in subsequent years (2018-19 and 2019-20), fish diversity and abundance also enhanced. Elevated levels of turbidity were found to be negatively associated with fish species richness, diversity and abundance (Lunt and Smee, 2020 ). Turbidity values of > 20 NTU in freshwater ecosystems can affect visual foraging behavior of fishes and thereby decrease prey capture rate and competitive interactions (Hazelton and Grossman, 2009 ). Migration in fish is influenced by abiotic variables and besides spawning, fishes undertake feeding and refuge-seeking migrations (Deng and Demisse, 2022). In the present case, as environmental attributes of the river Siang deteriorated, fishes migrated to tributaries like Simang, Siyom etc. and adjoining water bodies and again migrated back to the main river once the ecology of the river reverted to normal condition leading to increase in species richness and diversity. Diversity and species richness in Siang improved post 2017-18, indicating restoration of the environment back to its original state. The assessment for restored period (2018-20) showed a significant positive relationship between Shannon index and species richness (r = 0.793; p ≤ 0.01), consistent with Negi and Mamgain ( 2013 ) in the Tons River. Temporal analysis indicated higher fish species richness and diversity post-monsoon, followed by pre-monsoon and lowest during monsoon, aligning with Surachita and Palita ( 2022 ). Habitat type significantly impacts fish diversity, with diverse habitats promoting more diversity (Martinez et al., 2018 ). Monsoon run-off leads to habitat homogeneity, while diverse micro-habitats in post- and pre-monsoon seasons foster higher species diversity. Cumulative dominance curve ( k -dominance) in the present study, obtained by plotting the percentage total of individuals for each species against (log) species rank (Clarke, 1990 ), also showed monsoon season as least diverse. Non-metric dimensional scaling (NMDS) on a seasonal scale showed that monsoon is separated from pre-monsoon and post-monsoon. NMDS gives an ordination plot based on similarity profile. Clarke ( 1993 ) stated that lower stress value in NMDS reflects better fitting in ordination plot. Stress values are categorized as, values < 0.05 is excellent; <0.10, good; 0.20, not acceptable (Clarke, 1993 ). In case of River Siang, stress value of 0.08 indicates good fitting of distances/dissimilarities in the ordination plot. River ecosystems generally exhibit an increasing trend in fish species diversity and abundance from higher to lower altitudes (Weber and Peter, 2007). Fish species diversity increases from higher to lower altitudes, with Oiramghat and Pasighat reporting the highest species numbers, consistent with Bhatt et al. ( 2013 ). Topography, habitat availability, water flow, and environmental stability are crucial for fish diversity in hilly rivers (Hashemi et al., 2015 ). Hill-stream fishes exhibit structural adaptations to their environment, with adaptive species replacing sensitive ones (Hilburn et al., 2023 ). Similarity profile analysis (SIMPROF) showed distinct separation between upper/middle stretches and lower stretches of the river, with 46% similarity in species abundance and distribution, likely due to habitat variation. Hill-stream fishes tend to be habitat specialists (Gebrekiros, 2016 ), with Cyprinids displaying versatility and occupying diverse habitats (Johnson and Arunachalam, 2009 ), as observed by Das et al. ( 2020 ). Influence of environmental variables on fish community structure Environmental gradients along spatio-temporal scales influence natural habitats (Fischer and Paukert, 2008), impacting fish species richness (Elías et al., 2020). Seasonal flow regime changes affect water quality and quantity, altering fish community structure and abundance (Rowe et al., 2009 ). Many researchers have highlighted the underlying relationship between fish community structure and physicochemical traits in aquatic systems (Taylor, 2000 ; Magalhães et al., 2002 ). Environmental variables significantly impact fish communities, either directly (Debnath et al., 2022 ; Das et al., 2025 ) or indirectly by influencing food matrix. Our study observed that water variables influenced fish community compositions across seasons and stations. BIO-ENV analysis showed the highest correlation of fish abundance with water depth, followed by combinations of depth and transparency, depth, DO, and transparency, and depth and DO, aligning with Lakra et al. ( 2010 ) and Rosso et al. ( 2010 ). However, the observed association was weak (< 0.413), indicating other factors could impact fish abundance. We found significant negative relationships between water depth and turbidity with fish abundance (p ≤ 0.01) and positive relationships with specific conductivity and TDS (p ≤ 0.01) in the Siang River, consistent with Lunt and Smee ( 2020 ), Hazelton & Grossman ( 2009 ) and Matern et al. ( 2021 ). Our DistLM model identified depth, pH, DO, and turbidity as key environmental variables affecting fish assemblages. Habitat traits such as water temperature, specific conductivity, TDS, water depth, transparency, turbidity, pH, and DO are crucial in determining fish assemblage (Shahnawaz et al., 2007; Escalera and Zambrano, 2010 ; Lakra et al., 2010 ; Rosso et al., 2010 ; Vieira and Tejerina-Garro, 2020 ), supporting our findings. Finally, taking the findings of our study into account, we can conclude that short-term ecological changes, especially physico-chemical parameters, do not have a long-term effect on fish diversity in a large fluvial ecosystem with many tributaries/ rivulets such as Siang River. Conclusion Freshwater biodiversity is declining at an unprecedented pace given the threats both from natural and human-induced sources. Considering the rapid changes in dynamics of our natural ecosystems, it has become essential for us to gain vital insights into resilience of aquatic ecosystems and the role of environmental factors in shaping fish communities. This will help us to formulate effective conservation and management strategies. Present study in one of the mega-biodiversity hotspots of the world can contribute to our knowledge and understanding towards sustainable fisheries management and help in achieving SDGs. Declarations Competing interest: Authors declare no conflicts of interest in the research activity and in data presented in the manuscript. Further the authors declare that they have no relevant financial or non-financial interests to disclose. Funding: The authors declare that no funds, grants, or other support were received during the study or during preparation of this manuscript. Availability of data and materials: The data generated in the present study has been submitted to ICAR-Central Inland Fisheries Research Institute data repository and can be obtained from the Institute through proper channel and with due permission from competent authority. Conflict of interest: The authors declare that the research was conducted in the absence of any commercial and financial relationships that could be construed as a potential conflict of interest. Consent to participate: Due consent has been taken from all authors prior to preparation of the manuscript and all have agreed to participate in the manuscript preparation. Consent to publish: All authors have given their consent to submit the manuscript for publication. Ethics approval: Procedures and activities performed during the study period involving animals were in agreement with ethical standards of the institution. The sampling was performed after due approval from Institute Research Committee (IRC) of ICAR-Central Inland Fisheries Research Institute. Ethical responsibilities of Authors: All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors. Statement on ‘Authors contributions’: All authors contributed in preparing the manuscript through designing of study, sampling methodology, data collection, data analysis, drafting and revision of the manuscript. Authors’ contribution Name of author Contribution S. Borah Conceptualization; Methodology; Sampling; Investigation; Data generation; Writing original draft P. Gogoi Conceptualization; Sampling; Data generation; Data analysis; Manuscript revision K. Kumari Sampling; Data generation; Manuscript revision S.C.S. Das Sampling; Data generation; Manuscript revision A. Kakati Sampling; Data generation B.C. Ray Sampling; Data generation B.K. Bhattacharjya Sample analysis; Supervision S.K. Das Sample analysis; Manuscript revision S. Samanta Project administration; Supervision; Manuscript revision Vettath Raghavan Suresh Project administration; Supervision, editing Sullip Kumar Majhi Manuscript revision B.K. Das Overall guidance; Supervision; Validation Acknowledgement Authors are grateful to the Director, ICAR- Central Inland Fisheries Research Institute , Barrackpore for providing necessary facilities to carry out the research work. The authors are also thankful to the fisher community of river Siang, Aruanchal Pradesh, India. References Alexandre, C. M., & d Almeida, P. R. (2010). The impact of small physical obstacles on the structure of freshwater fish assemblages. River Research and Applications , 26 (8), 977-994. APHA (2017). Standard Methods for the Examination of Water and Wastewater , 23 rd edn . American Water Works Association, Water Environment Federation, Washington, DC, USA. Arthington, A. H. (2012). Environmental flows: saving rivers in the third millennium , Vol. 4. University of California Press. Axenrot, T., Didrikas, T., Danielsson, C., & Hansson, S. (2004). 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K., & Jhingran, A. G. (1991). Inland fishes of India and adjacent countries (Vol. 1&2, pp. 1158 pp). Oxford and IBH Publishing Co., Pvt. Ltd, New Delhi. Taylor, C. M. (2000). A large-scale comparative analysis of riffle and pool fish communities in an upland stream system. Environmental Biology of Fishes , 58 , 89–95. Vieira, T. B., & Tejerina-Garro, F. L. (2020). Relationships Between Environmental Conditions And Fish Assemblages In Tropical Savanna Headwater Streams. Scientific Reports , 10 , 2174. https://doi.org/10.1038/s41598-020-59207-9 Vishwanath, W., Lakra, W. S., & Sarkar, U. K. (2007). Fishes of North East India . ICAR-National Bureau of Fish Genetic Resources, Lucknow. Wang, L., Seelbach, P. W., & Hughes, R. M. (2006). Introduction to landscape influences on stream habitats and biological assemblages. American Fisheries Society Symposium , 48, 1–23. Weber, C., Peter, A., & Zanini, F. (2007). Spatio-temporal analysis of fish and their habitat: a case study on a highly degraded Swiss river system prior to extensive rehabilitation. Aquatic Sciences , 69 , 162-172. Yadav, A. K., Borah, S., Das, K. K., Raman, R. K., Das, P., & Das, B. K. (2022). Modeling of Hilsa ( Tenualosa ilisha ) landings in the lower stretch of Brahmaputra River (Assam, India) under time-series framework. ScienceAsia , 48 , 367-372. Yang, B., Dou, M., Xia, R., Kuo, Y. M., Li, G., & Shen, L. (2020). Effects of hydrological alteration on fish population structure and habitat in river system: A case study in the mid-downstream of the Hanjiang River in China. Global Ecology and Conservation , 23 , e01090. Yaqoob, K. U., Pandit, A. K., Wani, S. A. (2007). Comparative physicochemical limnology of three lakes of Kashmir Himalaya. In Proceedings of Taal: The 12th World Lake Conference (pp. 1922‒1927). International Lake Environment Committee Foundation, Japan. Additional Declarations No competing interests reported. Supplementary Files MSSiangSupplementary24.01.2025.docx Cite Share Download PDF Status: Published Journal Publication published 28 Mar, 2026 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 01 Sep, 2025 Editor assigned by journal 01 Sep, 2025 Submission checks completed at journal 01 Sep, 2025 First submitted to journal 11 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7347029","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508804930,"identity":"98b87744-558b-4444-8acb-8053565a82e8","order_by":0,"name":"Simanku 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Centre","correspondingAuthor":false,"prefix":"","firstName":"Sullip","middleName":"Kumar","lastName":"Majhi","suffix":""},{"id":508804942,"identity":"0bf4a796-2d81-4428-a2af-1935f527f5e2","order_by":11,"name":"Basanta Kumar Das","email":"","orcid":"","institution":"ICAR-Central Inland Fisheries Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Basanta","middleName":"Kumar","lastName":"Das","suffix":""}],"badges":[],"createdAt":"2025-08-11 13:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7347029/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7347029/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-026-15235-y","type":"published","date":"2026-03-28T16:11:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93675733,"identity":"37ba3c58-e9cb-4186-a67c-5cfa51bd8380","added_by":"auto","created_at":"2025-10-16 11:04:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":604458,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area, Siang River, Arunachal Pradesh\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/89ff021d3ccaaed77e5e64ee.png"},{"id":93675731,"identity":"7db88b06-32e2-41d8-b546-3897f9b491dc","added_by":"auto","created_at":"2025-10-16 11:04:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":554176,"visible":true,"origin":"","legend":"\u003cp\u003eSpatio-temporal variation in species richness across river continuum\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/e5721f3c7a37eabd4b6bf7c0.png"},{"id":93676675,"identity":"9fc0f82e-53c6-4d34-a2ea-978f9c7eb01b","added_by":"auto","created_at":"2025-10-16 11:12:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":82669,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical group average (SIMRPOF test) along river continuum based on the fish abundance (Pug = Puging; Yin = Yingkiong; Bol = Boleng; Kom = Komsing; Pas = Pasighat; Oir = Oiramghat)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/6b89f7c8d5297df976f1bd2a.png"},{"id":93675732,"identity":"5e45d59f-e28f-45a5-98cc-43ac2ece5cc1","added_by":"auto","created_at":"2025-10-16 11:04:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":154187,"visible":true,"origin":"","legend":"\u003cp\u003eNMDS based on Bray-Curtis similarity of samples in Siang River. Water variables overlaid as vectors to understand the association between water variables and fish assemblage in the studied locations (1 denotes Pre-monsoon (PRM); 2 = Monsoon (MON) and 3 = Post-monsoon (POM)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/02da3f15923f2fe0a49e78d4.png"},{"id":93676674,"identity":"608b5a42-68d2-4a6a-aa56-e434bf6d37e5","added_by":"auto","created_at":"2025-10-16 11:12:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":66307,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative dominance curve of fish species abundance across seasons (PRM = pre-monsoon; MON = monsoon; POM = post-monsoon)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/df126474a9bc8860e6bdff09.png"},{"id":105754975,"identity":"b6b7a59a-0ab7-4b7f-9b41-c4250c1135ba","added_by":"auto","created_at":"2026-03-30 16:23:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3040783,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/815a19e5-da3b-4cf0-ae46-8bbb3bf3c251.pdf"},{"id":93675728,"identity":"1c3075ba-4cf3-4a9f-b36a-f0b9ac2aa3eb","added_by":"auto","created_at":"2025-10-16 11:04:41","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":83728,"visible":true,"origin":"","legend":"","description":"","filename":"MSSiangSupplementary24.01.2025.docx","url":"https://assets-eu.researchsquare.com/files/rs-7347029/v1/f3bf31dac896c6f0dbc84aed.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Do short-term ecological alterations affect fish diversity in the long-run? A study from a sub-tropical river in the Eastern Himalayas","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSpatial orientation of biotic assemblages in an aquatic ecosystem depends on its environmental variables either directly or indirectly as reiterated by Fausch et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) for fishes and very recently by Gogoi et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in phytoplankton and by Das et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in macrobenthic invertebrates. Geomorphological characteristics of ecosystems along with biotic and abiotic properties govern fish diversity and community structure both in terms of species abundance and pattern of distribution (Orrego et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Alexandre and Almeida, 2010). Environmental variables significantly influence fish diversity and distribution in aquatic systems (Schlosser, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Grossman et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Influence of environmental attributes on fish species richness and diversity was also reported upon by Mondal and Bhat (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) from rivers and streams of Central and Eastern India. As evident from studies worldwide, fish diversity and community structure in rivers have an intrinsic relationship with habitat conditions (Wang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Fialho et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Fish diversity and distribution at temporal and spatial scales is a dynamic mechanism. In addition to long-term seasonal variation in environment, changes in the short term like diurnal variations in ecological attributes do influence fish community structure and species diversity (Noakes, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Axenrot et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Thus, any change in the habitat and ecological attributes of a system, whether it is sudden or in the long term, will affect fish composition. As a consensus, impaired habitats tend to have lower fish diversity in comparison to pristine ones.\u003c/p\u003e\u003cp\u003eRiver Siang which also goes by the name Dihang is a transboundary watercourse draining the north-east Indian province of Arunachal Pradesh, and serves as the principal headwater of River Brahmaputra (Das and Saikia, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Originating from Chema Yungdung Glacier near Kubi, Tibet at 5150 masl the river traverses by the name Yarlung-Tsangpo through Tibet (1,625 km), and enters India as Siang at Gelling in Arunachal Pradesh (Das et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). River Siang runs through the state of Arunachal Pradesh from north to south for 278 km, dissecting the Eastern Himalayas (Bhattacharjya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). As Siang River enters the plains of Assam, it is joined by two large trans-Himalayan tributaries, Dibang and Lohit, and the combined river is called Brahmaputra (Bhattacharjya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The River Brahmaputra flows for about 640 km in an east-west direction effectively splitting the Indian state of Assam into two equal halves from Sadiya to Dhubri (Bhattacharjya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). At the Indo-Bangladesh border in Dhubri, this west flowing river takes an abrupt southerly turn and enters Bangladesh. The river then flows for about 337 km in Bangladesh and finally drains into Bay of Bengal (Bhattacharjya et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe northeastern region of India has been identified as one of the major biodiversity hotspots (Chatterjee et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The rivers and wetlands in the region harbour unique and rich aquatic biodiversity. Despite its importance towards aquatic biodiversity, fisheries and livelihoods of people in northeastern India, detailed studies on the ecology and fish diversity of the Siang River are limited. The only study we could come across was that of Das et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), where they studied the fish diversity from the lower part of the Siang River during the years 2012-13 and reported 82 species under 24 families. In recent decades, freshwater ecosystems have been threatened by different environmental and physicochemical factors (Sala et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). It has been observed that aquatic resources and their biodiversity are facing increased stress from natural and anthropogenic factors on a global scale and Siang River is no exception. Researchers have identified anthropogenic pressure along with climate change as some of the major threats to aquatic ecosystems (Hauer et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Junker et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Arthington (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) attributed the loss of biological diversity in freshwater bodies to anthropogenic activities such as pollution, habitat modifications through dams and sluice gates, habitat degradation, invasive species and over-exploitation. In contrast to marine and terrestrial habitats, impact of anthropogenic pressure on freshwater systems is much higher (Jenkins, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Heino et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Rivers are major repositories of freshwater diversity and decline in fish diversity from these ecosystems due to prolonged deterioration in habitat conditions over the last decades are well-documented across the globe (Diana et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yang et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Himalayan rivers are one of the most scrutinized resources in Indian sub-continent owing to their high hydropower potential and habitat modification in these rivers have endangered valuable freshwater biodiversity of the Himalayas (Das et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnthropogenic impact on Indian part of River Brahmaputra have resulted in declining catch of Indian major carps, catfishes, shad, featherbacks while being replaced by small miscellaneous fish group (Borah et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yadav et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Besides fisheries, natural and anthropogenic factors have also impacted the environmental status of Siang River. Once such instance was in 2017, when there was a significant deterioration in the water quality of River Siang. Artificially induced or natural changes in the upper stretch of the river Siang resulted in exceptionally high silt load in its waters. Transparency was reported to be \u0026lt;\u0026thinsp;4.0 cm (Gogoi et al., 2018) and turbidity of water was abnormally high ranging from 258\u0026ndash;405 NTU (Roy et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) as compared to the values (78.1\u0026ndash;99.9 NTU) reported by Das et at. (2014). Extreme levels of ecological impairment in aquatic ecosystems can pose threats to nature and to its biodiversity. However, studies conducted by ICAR-Central Inland Fisheries Research Institute (CIFRI) revealed that physico-chemical attributes of water in the river reverted to normal during the subsequent period (2018-19 and 2019-20). In the present study, we tried to analyze the effects of short-term ecological changes on fish diversity of River Siang in the long run. This study holds importance as the information generated can have worldwide applications towards sustainable management of identical riverine ecosystems. This study on fish community structure also bears paramount significance in the context of large hydropower projects which are reportedly being constructed in the headwaters of River Siang in Tibet. Besides, the study can also contribute towards satisfying Sustainable Development Goals (SDGs) (SDG 14 - Life Below Water) of United Nations.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eWe selected six representative sampling stations covering upper, middle and lower stretch of River Siang in Arunachal Pradesh and Assam. Stations \u003cem\u003eviz\u003c/em\u003e., Puging (N 28⁰45'42.3\" and E 94⁰54'08.20\") and Yingkiong (N 28⁰39'41.96\" and E 95⁰00'47.20\") in upper stretch; Boleng (N 28⁰21'21.64\" and E 95⁰03'18.33\") and Komsing (N 28⁰14'1.29\"and E 95⁰0'7.79\") in middle stretch; Pasighat (N 28⁰05'43.90\" and E 95⁰18'13.23\") and Oiramghat (N 27⁰50'18.61\" and E 95⁰21'54\") in lower stretch were selected for our study (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The first five stations are located in Arunachal Pradesh at an elevation of 155\u0026ndash;1150 masl, while Oiramghat is situated in Assam (104 masl). The river is characterized by a rocky bed in all sampling stations except Oiramghat, where the riverbed is sandy, fast-flowing waters with presence of rapids, runs and riffles.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling methodology\u003c/h3\u003e\n\u003cp\u003eWater and fish samples were collected from selected sampling locations over three years (2017-20). Sampling was carried out at seasonal intervals \u003cem\u003eviz.\u003c/em\u003e, monsoon (June-September), post-monsoon (October-January) and pre-monsoon (February-May). Dissolved oxygen (DO), water temperature, pH, specific conductivity, turbidity, total dissolved solids (TDS), and total chlorophyll were measured using a water quality probe (Model 9829, HANNA\u0026reg;, Romania) (Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e); APHA (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) was followed to measure free carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) and total alkalinity (TA); and transparency was measured using Secchi disc following the procedure outlined by Strickland and Parsons (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). All these parameters were measured in field for real time and reliable results. Water samples were collected from the subsurface at a depth of 0.5 m using a standard water sampler designed following the \u0026lsquo;Ruttner water sampler\u0026rsquo; (Das Sarkar et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The water samples were then shifted immediately to clean 1 L capacity polyethylene bottles for analysis of various quality parameters in the laboratory (Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Fishes were collected from selected sampling locations using traditional bamboo traps (locally known as \u003cem\u003eDingora\u003c/em\u003e, \u003cem\u003ePolo\u003c/em\u003e, \u003cem\u003eSepa, Porang\u003c/em\u003e and \u003cem\u003eAtoong\u003c/em\u003e), lift nets (0.5 and 1.0 cm mesh size), gill nets (1.0\u0026ndash;6.0 cm mesh size), cast nets (1.0 and 2.0 cm mesh size) and drag nets (1.5 cm mesh size). Fresh fish specimens were identified in the field to the maximum extent possible. Doubtful specimens preserved in 8\u0026ndash;10% neutral buffered formalin were brought to the laboratory and identified using published books and manuals (Talwar and Jhingran, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Jayaram, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Vishwanath et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Taxonomic classification of fishes was done following Fricke et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Abundance of each species and values of water quality parameters were recorded in standard data collection sheets.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eData analysis was done for two periods, 2017-20 and 2018-20. For the first, data was analyzed to compare the changes that have occurred during the impaired period when Siang River was under severe environmental stress (2017-18), with the restored period (2018-19 and 2019-20). Analysis was done for the restored period (2018-20), when the river reverted back to its normal environmental state to understand the fish community assemblage and relationship with environmental drivers. One way analysis of variance (ANOVA) and post-hoc test was used to determine significant differences in water quality parameters between degraded period (2017-18) and restored period of 2018-19 and 2019-20. Prior analysis, water quality data except pH were normalized using log(x\u0026thinsp;+\u0026thinsp;1) transformation (Gogoi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo assess the fish diversity, indices like Shannon diversity index (Hʹ) (Shannon-Weiner, 1949), Margalef richness index (dʹ) (Margalef, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1958\u003c/span\u003e) and Pielou\u0026rsquo;s evenness index (Jʹ) (Pielou, 1977) were employed and calculated using R 3.5.3 (The R Foundation, 2019) for the period 2017-20. ANOVA was also done to verify difference in diversity indices across stations and seasons.\u003c/p\u003e\u003cp\u003eAgain, repeated measures of ANOVA was done to verify any significant variation in water quality attributes across stations over three seasons and comparison of mean values was done employing post hoc Duncan's multiple range tests for the restored period 2018-20. Pearson\u0026rsquo;s correlation (2-tailed) coefficients for water quality attributes for the period 2018-20 were computed using R 3.5.3 (The R Foundation, 2019). Permutational Multivariate Analysis of Variance (PERMANOVA) was also performed to examine the variation (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) in water quality and fish abundances across seasons and stations for the period 2018-20, when the riverine environment restored back to its pristine status.\u003c/p\u003e\u003cp\u003eTo investigate the dominance pattern on a temporal scale (seasons) \u003cem\u003ek\u003c/em\u003e-dominance curve was used (Gogoi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Hierarchical cluster analysis, supported by SIMPROF test, was applied to identify statistically significant clusters of sampling stations (Gogoi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) based on fish abundance and composition. NMDS based on Bray\u0026ndash;Curtis similarity protocols was employed to visualize similarities in fish community structure across seasons (Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Analysis of similarity (ANOSIM) was carried out to evaluate significant variation in fish assemblages across spatial locations (Gogoi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), while similarity percentage (SIMPER) analysis was performed to identify fish species contributing most to similarities and dissimilarities between groups (Gogoi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To fulfill the criteria of normality assumptions, fish abundance data were subjected to square root transformation prior to analysis (Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Relationship between environmental attributes and fish community structure was assessed through BIO\u0026ndash;ENV and distance-based linear modelling (DistLM) (Gogoi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Model selection followed the Akaike Information Criterion (AIC) with a stepwise approach (Gogoi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The above mentioned statistical analyses were conducted using PRIMER v6.0 (Clarke and Gorley, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and were carried out for the period 2018\u0026ndash;2020, when river water quality reverted to its normal condition.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eWater quality\u003c/h2\u003e\u003cp\u003eOf the 11 measured environmental variables, all the parameters except free CO\u003csub\u003e2\u003c/sub\u003e showed significant differences between seasons during 2018-20 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, transparency, specific conductivity, DO and TDS did not portray significant differences (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between pre-monsoon and monsoon. Similarly, water variables (pH, TA and depth) were not found to be significantly different (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) between monsoon and post-monsoon. Turbidity, total chlorophyll values between pre-monsoon and post-monsoon also showed no significant difference. Karl Pearson\u0026rsquo;s Correlation matrix revealed a significant negative correlation of water temperature against DO (r=-0.895; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), transparency (r=-0.789; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), specific conductivity (r=-0.611; p\u0026thinsp;\u0026lt;\u0026thinsp;0.011), and positive correlation with turbidity (r\u0026thinsp;=\u0026thinsp;0.843; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). DO showed significant positive correlation with variables such as transparency (r\u0026thinsp;=\u0026thinsp;0.735; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and specific conductivity (r\u0026thinsp;=\u0026thinsp;0.660; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, turbidity was negatively correlated with transparency, specific conductivity and DO (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On the whole, PERMANOVA analysis revealed significant spatial (F\u0026thinsp;=\u0026thinsp;1.909; p\u0026thinsp;=\u0026thinsp;0.012) and temporal (F\u0026thinsp;=\u0026thinsp;14.603; p\u0026thinsp;=\u0026thinsp;0.001) variations in water quality attributes of River Siang.\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\u003eMean seasonal variation of water variables (2018-20) and a comparative assessment between annual periods (2017-18, 2018-19 and 2019-20)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eWater quality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e2018-20\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2017-18\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2018-19\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e2019-20\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePRM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMON\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePOM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater temperature (˚C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.99\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.21\u0026thinsp;\u0026plusmn;\u0026thinsp;4.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.73\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.08\u0026thinsp;\u0026plusmn;\u0026thinsp;5.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepth (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.96\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.97\u0026thinsp;\u0026plusmn;\u0026thinsp;9.85\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.78\u0026thinsp;\u0026plusmn;\u0026thinsp;7.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.41\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransparency (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e22.75\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.66\u0026thinsp;\u0026plusmn;\u0026thinsp;8.64\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.77\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.39\u0026thinsp;\u0026plusmn;\u0026thinsp;19.90\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecific Conductivity (\u0026micro;S/cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e259.75\u0026thinsp;\u0026plusmn;\u0026thinsp;21.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e269.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e380.5\u0026thinsp;\u0026plusmn;\u0026thinsp;54.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e262.78\u0026thinsp;\u0026plusmn;\u0026thinsp;77.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e263.72\u0026thinsp;\u0026plusmn;\u0026thinsp;40.31\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e342.84\u0026thinsp;\u0026plusmn;\u0026thinsp;79.88\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDO (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFree CO\u003csub\u003e2\u003c/sub\u003e (mgL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e39.68\u0026thinsp;\u0026plusmn;\u0026thinsp;3.04\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.08\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e47.04\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e43.12\u0026thinsp;\u0026plusmn;\u0026thinsp;9.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47.33\u0026thinsp;\u0026plusmn;\u0026thinsp;4.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDS (mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177.57\u0026thinsp;\u0026plusmn;\u0026thinsp;31.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178.83\u0026thinsp;\u0026plusmn;\u0026thinsp;12.73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e242.33\u0026thinsp;\u0026plusmn;\u0026thinsp;41.40\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e171.09\u0026thinsp;\u0026plusmn;\u0026thinsp;48.52\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e178.77\u0026thinsp;\u0026plusmn;\u0026thinsp;38.90\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e220.39\u0026thinsp;\u0026plusmn;\u0026thinsp;53.73\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurbidity (NTU)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.4\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e177.76\u0026thinsp;\u0026plusmn;\u0026thinsp;88.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.57\u0026thinsp;\u0026plusmn;\u0026thinsp;5.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.99\u0026thinsp;\u0026plusmn;\u0026thinsp;11.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal chlorophyll (mg m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e128.08\u0026thinsp;\u0026plusmn;\u0026thinsp;65.32\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.12\u0026thinsp;\u0026plusmn;\u0026thinsp;16.72\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e135.95\u0026thinsp;\u0026plusmn;\u0026thinsp;103.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.70\u0026thinsp;\u0026plusmn;\u0026thinsp;58.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98.54\u0026thinsp;\u0026plusmn;\u0026thinsp;70.85\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e104.23\u0026thinsp;\u0026plusmn;\u0026thinsp;126.35\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Values are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; values with different superscript are significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\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\u003eIntra-relationship between water variables and total fish abundance, diversity indices (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTran\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCond\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFree CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eTDS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eTur\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eT Chl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eHʹ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003edʹ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eJʹ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003eFish Abun\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDep\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c10\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHʹ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.350*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.401*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.409*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.417*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003edʹ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.712**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.338*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.373*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.793**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJʹ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.804**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.036\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.360*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.398*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e-0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e-0.309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e-0.773**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFish Abun\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.596**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.460**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.474**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.396*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.714**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e0.858**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e-0.691**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"16\"\u003e**. Correlation is significant at the 0.01 level (2-tailed); *. Correlation is significant at the 0.05 level (2-tailed); WT, Water Temperature; Dep, Depth; Trans, Transparency; Cond, Specific Conductivity; DO, Dissolved Oxygen; TA, Total Alkalinity; TDS, Total Dissolved Solids; Tur, Turbidity; T Chl, Total Chlorophyll; Fish Abun, Fish Abundance\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eComparison of water quality parameters between 2017-18, 2018-19 and 2019-20 showed significant differences across the years. Water temperature and pH during 2018-19 were significantly different from 2017-18 and 2019-20; transparency showed significant difference across the three year period (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); specific conductivity in 2019-20 was significantly different from the other two years; free CO\u003csub\u003e2\u003c/sub\u003e showed significant difference between 2017-18 and 2018-19, while its value in 2019-20 was not significantly different with the other two years; mean TDS values between 2017-18 and 2018-19 showed no significant difference, but both the values were significantly different from 2019-20; turbidity and total chlorophyll in 2017-18 was found to be significantly different from the remaining two years (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Two physical variables, transparency (mean, 6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24 cm) and turbidity (mean, 177.76\u0026thinsp;\u0026plusmn;\u0026thinsp;88.65 NTU) altered drastically during 2017-18. Although DO values during 2018-19 were significantly different from 2017-18 and 2019-20, drastic changes in DO concentration was not observed in the river. TA and water depth did show any significant difference in the study period.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eFish diversity\u003c/h2\u003e\u003cp\u003eFish community comprising of 78 species belonging to 48 genera under 17 families were recorded during our survey from 2017\u0026ndash;2020. Spatial analysis shows that the highest number of species was reported from Oiramghat (65 species) followed by Pasighat (56 species) along lower stretch of the river. Seasonal species richness in the studied stations is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Species diversity was comparatively higher in post-monsoon, followed by pre-monsoon and least in monsoon. Cyprinidae, being the most dominant family contributing more than one-fourth (26.92%) of the total number of species reported followed by Danionidae (21.79%), Bagridae, Sisoridae (7.69% each) and Channidae (6.41%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ePercentage contribution of different families to the total number of species reported is shown in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (S\u0026thinsp;=\u0026thinsp;Supplementary). Fish species \u003cem\u003eBarilius barila\u003c/em\u003e, \u003cem\u003eB. vagra\u003c/em\u003e, \u003cem\u003eOpsarius bendelisis\u003c/em\u003e, \u003cem\u003eO. barna\u003c/em\u003e, \u003cem\u003eCabdio morar\u003c/em\u003e, \u003cem\u003eTariqilabeo latius\u003c/em\u003e, \u003cem\u003ePethia conchonius\u003c/em\u003e, \u003cem\u003ePuntius sophore\u003c/em\u003e, \u003cem\u003eP. chola\u003c/em\u003e were the most abundant species, while \u003cem\u003eLabeo catla\u003c/em\u003e, \u003cem\u003eHemibagrus menoda\u003c/em\u003e, \u003cem\u003ePterocryptis gangelica\u003c/em\u003e, \u003cem\u003eLabeo rohita\u003c/em\u003e and \u003cem\u003eCirrhinus mrigala\u003c/em\u003e were least dominant in terms of abundance. However, fish species diversity and abundance were found to be low during the impaired period. A total of 31 species under 10 families were reported from the river during 2017-18 (impaired period), which increased to 60 species under 15 families in 2018-19 and 78 species under 17 families in 2019-20. The major contributors during 2017-18 were \u003cem\u003eBarilius vagra, Opsarius bendelisis, O. sharca, Tariqilabeo latius, Bangana dero, Tor tor, T. putitora\u003c/em\u003e and \u003cem\u003eLabeo dyocheilus.\u003c/em\u003e Details of species recorded from Siang during our study along with their IUCN status (IUCN, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) are given in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. It is observed that out of the total 78 species recorded, 60 species are categorised as Least Concern (LC), 6 Near Threatened (NT), 5 Vulnerable (VU), 3 Data Deficient (DD), 3 Not Evaluated (NE) and 1 species (\u003cem\u003eTor putitora\u003c/em\u003e) as Endangered (EN). Small indigenous fishes dominated the fish diversity of the river, contributing 75.64% of the total number of species recorded. We documented ten migratory fish species and a single exotic carp (\u003cem\u003eCtenopharyngodon idella\u003c/em\u003e) during our survey (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMean values of Shannon-Wiener index (Hʹ), Margalef\u0026rsquo;s richness index (dʹ) and Pielou\u0026rsquo;s evenness index (Jʹ) was found to range from 1.877 to 3.083; 2.4468 to 6.607 and from 0.727 to 0.887, respectively during the study period. During 2017-18, mean values of Hʹ, dʹ and Jʹ were highest during monsoon and lowest in post-monsoon. While during 2018-19 and 2019-20, values of Hʹ and dʹ were highest during post-monsoon and of Jʹ in monsoon (Table S2). Across stations, mean values of Hʹ and dʹ were highest downstream of the river in Oiramghat and Pasighat, while values of Jʹ were on the higher side along the middle (Boleng and Komsing) and upper stretch (Puging and Yingkiong) (Table S3). Fish diversity was on the lower side during 2017-18 as evidenced from low Hʹ values (1.877\u0026thinsp;\u0026plusmn;\u0026thinsp;0.434 to 2.420\u0026thinsp;\u0026plusmn;\u0026thinsp;0.230) indicating low fish diversity, while values of Hʹ (\u0026gt;\u0026thinsp;2.654\u0026thinsp;\u0026plusmn;\u0026thinsp;0.329) increased significantly during 2018-19 and 2019-20. ANOVA revealed a significant difference in the values of H, dʹ and Jʹ across seasons for the period 2017-18, 2018-19 and 2019-20 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table S2). Assessment showed that Hʹ and dʹ had a strong positive relationship (r\u0026thinsp;=\u0026thinsp;0.793; p\u0026thinsp;\u0026le;\u0026thinsp;0.01), while dʹ and Jʹ had a strong negative relationship (r=-0.773; p\u0026thinsp;\u0026le;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). PERMANOVA analysis with Monte Carlo simulation showed a significant difference in fish abundance between seasons (F\u0026thinsp;=\u0026thinsp;19.453; p\u0026thinsp;=\u0026thinsp;0.001), and stations (F\u0026thinsp;=\u0026thinsp;33.244; p\u0026thinsp;=\u0026thinsp;0.001) (Table S4).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNature of similarity\u003c/h3\u003e\n\u003cp\u003eHierarchical agglomerative cluster analysis (SIMPROF test) based on the Bray-Curtis similarity matrix showed two distinct clusters comprising Puging, Yingkiong, Boleng and Komsing (Cluster I), and Oiramghat and Pasighat (Cluster II) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The clusters were found to be statistically insignificant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Within Cluster I, Puging formed a separate sub-cluster from the other three stations with 68% similarity in community composition, while Yingkiong, Boleng and Komsing had 79.28% similarity. In the case of Cluster II, stations Pasighat and Oiramghat portrayed 64.17% similarity. Overall, the stations exhibited 46% similarity in the fish assemblages.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eANOSIM revealed significant differences (r\u0026thinsp;=\u0026thinsp;0.843; p\u0026thinsp;=\u0026thinsp;0.01) in fish assemblage pattern between the groups (Group I and II). NMDS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) revealed that monsoon season were distinctively separated as compared to pre-monsoon and post-monsoon with some of the samples during post-monsoon and pre-monsoon depicting a similar nature of fish abundance. Cumulative dominance curve (\u003cem\u003ek\u003c/em\u003e-dominance) derived for seasons implied that there was no significant difference in dominance of fish species across seasons with a similar dominance pattern observed throughout the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, in the present study, it was seen that the \u003cem\u003ek\u003c/em\u003e-dominance curve for monsoon was slightly higher than the other two seasons. Higher the curve and more quickly it reaches 100% value, less the fish diversity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eGrouping of fish species based on SIMPER test showed that in Group I, \u003cem\u003eB. barila\u003c/em\u003e (15.25%), \u003cem\u003eO. barna\u003c/em\u003e (13.74%), \u003cem\u003eB. vagra\u003c/em\u003e (10.30%), \u003cem\u003eO. bendelesis\u003c/em\u003e (7.09%), \u003cem\u003eO. siangi\u003c/em\u003e (6.52%), \u003cem\u003eT. putitora\u003c/em\u003e (6.22%), \u003cem\u003eL. dyocheilus\u003c/em\u003e (5.17%), \u003cem\u003eT\u003c/em\u003e. \u003cem\u003etor\u003c/em\u003e (4.02%), \u003cem\u003eB. dero\u003c/em\u003e (3.69%), \u003cem\u003eRaiamas bola\u003c/em\u003e (3.32%) and \u003cem\u003eSemiplotus semiplotus\u003c/em\u003e (3.18%) were the main contributors with average similarity 56.31%. Similarly, for group II, average similarity was 53.39%, and \u003cem\u003eC. morar\u003c/em\u003e (15.30%) was the major contributor followed by \u003cem\u003eP. sophore\u003c/em\u003e (8.30%), \u003cem\u003eB. barila\u003c/em\u003e (7.73%), \u003cem\u003ePethia ticto\u003c/em\u003e (6.33%), \u003cem\u003eO. barna\u003c/em\u003e (6.28%), \u003cem\u003eT. latius\u003c/em\u003e (5.92%), \u003cem\u003eP. conchonius\u003c/em\u003e (5.55%), \u003cem\u003eP. chola\u003c/em\u003e (5.22%), \u003cem\u003eO. bendelesis\u003c/em\u003e (4.81), \u003cem\u003eB. vagra\u003c/em\u003e (3.87%) and \u003cem\u003eR. bola\u003c/em\u003e (2.10%). The average dissimilarity between groups (Group I \u0026amp; II) was 71.41%, and the major fish species responsible for the difference in abundance pattern is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\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\u003eSIMPER Routine based on the fish abundance showing the percentage contribution in the clustered groups\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSIMPER test\u003c/p\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup 1\u003c/p\u003e\u003cp\u003e(Yin, Bol, Kom)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGroup 2\u003c/p\u003e\u003cp\u003e(Pas, Oir)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eAverage dissimilarity\u0026thinsp;=\u0026thinsp;71.41%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage abundance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAverage abundance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAverage dissimilarity\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eContribution%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. morar\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eT. latius\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e24.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP. sophore\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eB. barila\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e36.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP. ticto\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e41.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP. chola\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eB. vagra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eO. barna\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e53.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP. conchonius\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e56.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA. coilia\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eB. benedelisis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eA. mola\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e64.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. nama\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eC. jaya\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e67.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eB. dero\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e69.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eS. sarana\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e70.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eE. vacha\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e72.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eO. siangi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eL. dyocheilus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eP. baculis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eT. putitora\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eD. aequipinnatus\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e78.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eD. devario\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e79.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eYin\u0026thinsp;=\u0026thinsp;Yingkiong; Bol\u0026thinsp;=\u0026thinsp;Boleng; Kom\u0026thinsp;=\u0026thinsp;Komsing; Pas\u0026thinsp;=\u0026thinsp;Pasighat; Oir\u0026thinsp;=\u0026thinsp;Oiramghat\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eRelationship of environmental variables and fish community\u003c/h3\u003e\n\u003cp\u003eAnalysis revealed that water depth and turbidity have a significant negative relationship with fish abundance (p\u0026thinsp;\u0026le;\u0026thinsp;0.01), while specific conductivity and TDS have a strong positive relationship with fish abundance (p\u0026thinsp;\u0026le;\u0026thinsp;0.01). Water temperature, pH, and total alkalinity also had a negative correlation with fish abundance, but the relationship was statistically insignificant. Similarly, DO had an insignificant positive relationship with fish abundance (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). BIO-ENV module revealed that environmental attributes \u003cem\u003eviz\u003c/em\u003e. depth of water, DO and transparency have significant correlation with abundance and composition of fishes in River Siang. Highest correlation value p\u0026thinsp;=\u0026thinsp;0.413 was obtained in the case of water depth, followed by combinations of depth and transparency; depth, DO and transparency (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\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\u003eBIO-ENV analysis observed in fish assemblage compared with water parameters (pooled data)\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=\"left\" 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\"\u003e\u003cp\u003eNo. of variables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCorrelation selection\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpearman correlation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, Transparency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.331\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, DO, Transparency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.316\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, DO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater temperature, Depth, Transparency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, Transparency, Total chlorophyll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, Total chlorophyll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.298\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, DO, Transparency, Total chlorophyll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.296\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth, DO, CO\u003csub\u003e2\u003c/sub\u003e, TA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater temperature, Depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.287\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\u003eMarginal test (DistLM) was performed to assess the correlation between fish abundance and individual environmental attribute. Analysis indicated significant relationship (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) of fish abundance with temperature, depth, transparency, free CO\u003csub\u003e2\u003c/sub\u003e and turbidity. Sequential tests further affirmed significant relationship (p\u0026thinsp;\u0026le;\u0026thinsp;0.05) with pH and DO including depth and turbidity (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). DistLM explained four environmental attributes (\u003cem\u003ei.e.\u003c/em\u003e depth, pH, DO and turbidity) that could explain fish abundance and distribution in Siang River during the course of our study (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Fitted model showed r\u003csup\u003e2\u003c/sup\u003e value (0.498) and AICc (244.73), which represented a suitable model for predicting the best explained variables for fish species distribution in Siang River. Seasonally, the main influencing factors along the river continuum were dissolved oxygen, pH and TA during pre-monsoon, specific conductivity, transparency and TDS during post-monsoon, and water temperature, turbidity and depth during monsoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eMarginal and sequential tests (distance-based linear model, DistLM) of environmental variables and fish abundance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eMarginal test\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePseudo-F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4245.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.4474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransparency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3262.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.047\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecific Conductivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2567.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2291.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.7778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFree CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0461\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1854.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.4243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.201\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3205.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.5398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.065\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4179.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.3881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Chlorophyll\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2180.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.132\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSequential tests\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSum of Squares\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePseudo-F\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTurbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2330.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.0255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2330.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.1261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\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\u003eDistance-based linear model (DistLM) analysis of variables included in the most parsimonious model for the relationship between fish abundance and environmental variables\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=\"left\" 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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eAxis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e% Explained variation out of fitted model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e% Explained variation out of total variation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCumulative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIndividual\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCumulative\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDepth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e47.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTurbidity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e49.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental parameters\u003c/h2\u003e\u003cp\u003eRiver water quality is crucial owing to its ecological, economic and societal significance. Our study observed remarkable variation in water variables, particularly transparency and water turbidity along the river continuum between two annual periods. Unnatural change of water (heavy load of slag and sediments) during 2017-18, either due to natural phenomena or anthropogenic causes, led to drastic changes in the physical and chemical characteristics of water in River Siang. Muddy and sticky suspended particles increased water turbidity, and conversely low transparency. Water turbidity in the river rose manyfold (\u0026gt;\u0026thinsp;170 NTU) during 2017-18, more than ten times higher than permissible limit of World Health Organization (\u0026lt;\u0026thinsp;10 NTU) (WHO, 2005). Secchi disc transparency also decreased to \u0026lt;\u0026thinsp;7 cm during the period. Total Chlorophyll concentration was also impacted as evident from the low values of 2017\u0026ndash;18, compared to post event period (2018-19 and 2019-20), which may be attributed to presence of suspended sediments in water. However, magnitude of DO did not fluctuate significantly over the periods. DO was found to be well above hypoxia level and higher than permissible limit of 5 mgL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (BIS, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Surface air-water interaction owing to high flow velocity of the river and photosynthetic processes might have contributed towards high oxygen content in water (Levinton, 2001). Estimated DO cocentration (mean, 8.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u0026ndash;10.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 mgL\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) was comparatively higher than earlier record of 6.3\u0026ndash;8.03 mgL\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (Das et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Water pH remained alkaline through out our study period, a contrast to earlier observations of Das et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Alkaline nature of water (pH\u0026thinsp;\u0026gt;\u0026thinsp;7.0) indicated that river water remained well buffered and biodegradable organic matter was abundant in water column (Rahman et al., 2013; Basu et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMajority of water variabels exhibited positive correlation with pH, demonstarting the significant role of pH in physicochemical profile of river water. Negative correlation between turbidity and transparency is quite obvious, supported by numerous studies (Das et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma and Singh, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). TDS along the river continuum was within prescribed limits of Bureau of Indian Standards (BIS, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Significant variation in dissolved solids between seasons with higher value during monsoon and post-monsoon seasons may be attributed to ingestion of allochthonous materials and carbonate deposits. While comparing the values of TDS and total alkalinity with previous record of 5.83\u0026ndash;7.33 mgL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 70.4\u0026ndash;77.2 mgL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively (Das et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), slightly lower total alkalinity and high TDS values were observed during the study period. Similar, low alkalinity values has been reported by researchers from high altitude Himalayan waters (Yaqoob et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sharma and Singh, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Specific conductivity is an indirect estimate of TDS in water (Sharma and Kumar, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In correlation with TDS, specific conductivity values were also found to be on the higher side in our study area (259.75\u0026thinsp;\u0026plusmn;\u0026thinsp;21.37 to 380.5\u0026thinsp;\u0026plusmn;\u0026thinsp;54.81 \u0026micro;S/cm). This is in conformation with several earlier studies from high altitude aquatic systems (Saini et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sharma and Kumar, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eFish species diversity and compositions\u003c/h2\u003e\u003cp\u003eAs observed in our study, the dominance of Cyprinid group of fishes was also reported by Das et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Only 31 species under 10 families were reported from Siang in 2017-18, which increased to 60 species under 15 families in 2018-19 and 78 species under 17 families in 2019-20, which suggest that short term ecological degradation of the river do not seem to hamper the fish diversity in the long run. Poor environment quality during 2017-18 might have forced fish populations to migrate in the adjoining tributaries and connected streams. As the environment improved, these fish populations returned to their original habitat. The available fish diversity observed in Siang during 2019-20 is comparable with large river systems of India as reported by Sarkar et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Shukla and Bhat (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and was found to be similar with the report of Das et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Studies on fish diversity of freshwater ecosystems of South Asian region revealed Cyprinids as the single most dominant group (Bhat, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Higher plasticity, versatility and ability to colonize diverse environments have resulted in Cyprinids emerging as the dominant group in tropical rivers (Johnson and Arunachalam, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Similar findings have also been reported by Ongh et al. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) from Dhansiri, an important tributary of the Brahmaputra.\u003c/p\u003e\u003cp\u003eDiversity indices in general are reflection of both variety of species found in an ecosystem and their relative abundance (Ongh et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Shannon diversity index is one of the most used tools to quantify species richness either in a specific habitat or across different habitats (Clarke and Warwick, 2001). In addition to Shannon index, other indices like Margalef richness index and Pielou\u0026rsquo;s evenness index are also employed by researchers for estimating the diversity of biotic communities. Margalef richness index (Margalef, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1958\u003c/span\u003e) is a measure of biodiversity that evaluates the number of species in an ecological community. In contrast, Pielou\u0026rsquo;s evenness index (Pielou, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) measures how evenly individuals are distributed in a community. Values for Pielou\u0026rsquo;s index range from 0 to 1, where 0 indicates complete dominance by a single species, and 1 denote perfect evenness, which means all species are occurring in equal abundance (Pielou, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1966\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDuring 2017-18, fish diversity was on the lower side as evident from low \u003cem\u003eHʹ\u003c/em\u003e values (1.877\u0026thinsp;\u0026plusmn;\u0026thinsp;0.434 to 2.420\u0026thinsp;\u0026plusmn;\u0026thinsp;0.230), while values of \u003cem\u003eHʹ\u003c/em\u003e improved significantly during 2018-19 (2.654\u0026thinsp;\u0026plusmn;\u0026thinsp;0.329 to 3.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.196) and 2019-20 (2.731\u0026thinsp;\u0026plusmn;\u0026thinsp;0.265 to 3.083\u0026thinsp;\u0026plusmn;\u0026thinsp;0.190). Similar pattern was noticed both in Margalef richness index and Pielou\u0026rsquo;s evenness index with lower values during 2017-18 and higher values in the subsequent years. Further, ANOVA showed that values of Hʹ, dʹ and Jʹ was found to be significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) across seasons and stations in most of the cases for the periods 2017-18 and 2018-19 and 2019-20. Taylor (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) acknowledged that relationship exist between water quality and fish abundance.\u003c/p\u003e\u003cp\u003eHabitat alteration has been listed as one of the major factors driving fish species decline (Moyle and Leidy, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). High silt load in the river, resulting in very low transparency (6.81\u0026thinsp;\u0026plusmn;\u0026thinsp;7.24 cm) and high turbidity of water (177.76\u0026thinsp;\u0026plusmn;\u0026thinsp;88.65 NTU) during the period was recorded in our study. As turbidity of water reduced and environmental properties of the river improved in subsequent years (2018-19 and 2019-20), fish diversity and abundance also enhanced. Elevated levels of turbidity were found to be negatively associated with fish species richness, diversity and abundance (Lunt and Smee, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Turbidity values of \u0026gt;\u0026thinsp;20 NTU in freshwater ecosystems can affect visual foraging behavior of fishes and thereby decrease prey capture rate and competitive interactions (Hazelton and Grossman, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMigration in fish is influenced by abiotic variables and besides spawning, fishes undertake feeding and refuge-seeking migrations (Deng and Demisse, 2022). In the present case, as environmental attributes of the river Siang deteriorated, fishes migrated to tributaries like Simang, Siyom etc. and adjoining water bodies and again migrated back to the main river once the ecology of the river reverted to normal condition leading to increase in species richness and diversity. Diversity and species richness in Siang improved post 2017-18, indicating restoration of the environment back to its original state.\u003c/p\u003e\u003cp\u003eThe assessment for restored period (2018-20) showed a significant positive relationship between Shannon index and species richness (r\u0026thinsp;=\u0026thinsp;0.793; p\u0026thinsp;\u0026le;\u0026thinsp;0.01), consistent with Negi and Mamgain (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) in the Tons River. Temporal analysis indicated higher fish species richness and diversity post-monsoon, followed by pre-monsoon and lowest during monsoon, aligning with Surachita and Palita (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Habitat type significantly impacts fish diversity, with diverse habitats promoting more diversity (Martinez et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Monsoon run-off leads to habitat homogeneity, while diverse micro-habitats in post- and pre-monsoon seasons foster higher species diversity. Cumulative dominance curve (\u003cem\u003ek\u003c/em\u003e-dominance) in the present study, obtained by plotting the percentage total of individuals for each species against (log) species rank (Clarke, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), also showed monsoon season as least diverse. Non-metric dimensional scaling (NMDS) on a seasonal scale showed that monsoon is separated from pre-monsoon and post-monsoon. NMDS gives an ordination plot based on similarity profile. Clarke (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) stated that lower stress value in NMDS reflects better fitting in ordination plot. Stress values are categorized as, values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is excellent; \u0026lt;0.10, good; \u0026lt;0.20, useable; \u0026gt;0.20, not acceptable (Clarke, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). In case of River Siang, stress value of 0.08 indicates good fitting of distances/dissimilarities in the ordination plot.\u003c/p\u003e\u003cp\u003eRiver ecosystems generally exhibit an increasing trend in fish species diversity and abundance from higher to lower altitudes (Weber and Peter, 2007). Fish species diversity increases from higher to lower altitudes, with Oiramghat and Pasighat reporting the highest species numbers, consistent with Bhatt et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Topography, habitat availability, water flow, and environmental stability are crucial for fish diversity in hilly rivers (Hashemi et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Hill-stream fishes exhibit structural adaptations to their environment, with adaptive species replacing sensitive ones (Hilburn et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similarity profile analysis (SIMPROF) showed distinct separation between upper/middle stretches and lower stretches of the river, with 46% similarity in species abundance and distribution, likely due to habitat variation. Hill-stream fishes tend to be habitat specialists (Gebrekiros, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), with Cyprinids displaying versatility and occupying diverse habitats (Johnson and Arunachalam, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), as observed by Das et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eInfluence of environmental variables on fish community structure\u003c/h2\u003e\u003cp\u003eEnvironmental gradients along spatio-temporal scales influence natural habitats (Fischer and Paukert, 2008), impacting fish species richness (El\u0026iacute;as et al., 2020). Seasonal flow regime changes affect water quality and quantity, altering fish community structure and abundance (Rowe et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Many researchers have highlighted the underlying relationship between fish community structure and physicochemical traits in aquatic systems (Taylor, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Magalh\u0026atilde;es et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Environmental variables significantly impact fish communities, either directly (Debnath et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Das et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) or indirectly by influencing food matrix. Our study observed that water variables influenced fish community compositions across seasons and stations. BIO-ENV analysis showed the highest correlation of fish abundance with water depth, followed by combinations of depth and transparency, depth, DO, and transparency, and depth and DO, aligning with Lakra et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Rosso et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, the observed association was weak (\u0026lt;\u0026thinsp;0.413), indicating other factors could impact fish abundance. We found significant negative relationships between water depth and turbidity with fish abundance (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) and positive relationships with specific conductivity and TDS (p\u0026thinsp;\u0026le;\u0026thinsp;0.01) in the Siang River, consistent with Lunt and Smee (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Hazelton \u0026amp; Grossman (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Matern et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our DistLM model identified depth, pH, DO, and turbidity as key environmental variables affecting fish assemblages. Habitat traits such as water temperature, specific conductivity, TDS, water depth, transparency, turbidity, pH, and DO are crucial in determining fish assemblage (Shahnawaz et al., 2007; Escalera and Zambrano, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lakra et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Rosso et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Vieira and Tejerina-Garro, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), supporting our findings.\u003c/p\u003e\u003cp\u003eFinally, taking the findings of our study into account, we can conclude that short-term ecological changes, especially physico-chemical parameters, do not have a long-term effect on fish diversity in a large fluvial ecosystem with many tributaries/ rivulets such as Siang River.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eFreshwater biodiversity is declining at an unprecedented pace given the threats both from natural and human-induced sources. Considering the rapid changes in dynamics of our natural ecosystems, it has become essential for us to gain vital insights into resilience of aquatic ecosystems and the role of environmental factors in shaping fish communities. This will help us to formulate effective conservation and management strategies. Present study in one of the mega-biodiversity hotspots of the world can contribute to our knowledge and understanding towards sustainable fisheries management and help in achieving SDGs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interest:\u0026nbsp;\u003c/strong\u003eAuthors declare no conflicts of interest in the research activity and in data presented in the manuscript. Further the authors declare that they have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the study or during preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThe data generated in the present study has been submitted to ICAR-Central Inland Fisheries Research Institute data repository and can be obtained from the Institute through proper channel and with due permission from competent authority.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare that the research was conducted in the absence of any commercial and financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e Due consent has been taken from all authors prior to preparation of the manuscript and all have agreed to participate in the manuscript preparation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e All authors have given their consent to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eProcedures and activities performed during the study period involving animals were in agreement with ethical standards of the institution. The sampling was performed after due approval from Institute Research Committee (IRC) of ICAR-Central Inland Fisheries Research Institute.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical responsibilities of Authors:\u0026nbsp;\u003c/strong\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026quot;Ethical responsibilities of Authors\u0026quot; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement on \u0026lsquo;Authors contributions\u0026rsquo;:\u0026nbsp;\u003c/strong\u003eAll authors contributed in preparing the manuscript through designing of study, sampling methodology, data collection, data analysis, drafting and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"637\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eName of author\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eContribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eS. Borah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eConceptualization; Methodology;\u0026nbsp;Sampling; Investigation; Data generation; Writing original draft\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eP. Gogoi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eConceptualization; Sampling; Data generation; Data analysis; Manuscript revision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eK. Kumari\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSampling; Data generation; Manuscript revision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eS.C.S. Das\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSampling; Data generation; Manuscript revision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eA. Kakati\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSampling; Data generation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eB.C. Ray\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSampling; Data generation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eB.K. Bhattacharjya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSample analysis; Supervision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eS.K. Das\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSample analysis; Manuscript revision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eS. Samanta\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eProject administration; Supervision; Manuscript revision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eVettath Raghavan Suresh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eProject administration; Supervision, editing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eSullip Kumar Majhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eManuscript revision\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eB.K. Das\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 308px;\"\u003e\n \u003cp\u003eOverall guidance; Supervision; Validation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are grateful to the Director, ICAR-\u003cem\u003e\u0026nbsp;Central Inland Fisheries Research Institute\u003c/em\u003e, Barrackpore for providing necessary facilities to carry out the research work. The authors are also thankful to the fisher community of river Siang, Aruanchal Pradesh, India.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlexandre, C. M., \u0026amp; d Almeida, P. R. (2010). The impact of small physical obstacles on the structure of freshwater fish assemblages. \u003cem\u003eRiver Research and Applications\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(8), 977-994.\u003c/li\u003e\n\u003cli\u003eAPHA (2017). \u003cem\u003eStandard Methods for the Examination of Water and Wastewater\u003c/em\u003e,\u003cem\u003e 23\u003csup\u003erd\u003c/sup\u003e edn\u003c/em\u003e. American Water Works Association, Water Environment Federation, Washington, DC, USA.\u003c/li\u003e\n\u003cli\u003eArthington, A. H. (2012). \u003cem\u003eEnvironmental flows: saving rivers in the third millennium\u003c/em\u003e, Vol. 4. University of California Press.\u003c/li\u003e\n\u003cli\u003eAxenrot, T., Didrikas, T., Danielsson, C., \u0026amp; Hansson, S. (2004). 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International Lake Environment Committee Foundation, Japan.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fish diversity, environmental variables, Siang River, Eastern Himalayas","lastPublishedDoi":"10.21203/rs.3.rs-7347029/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7347029/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe present study was carried out to assess the effect of short-term ecological alteration on fish diversity of River Siang, Arunachal Pradesh in the Eastern Himalayas. Fish diversity of six stations along the river (Puging, Yingkiong, Boleng, Komsing, Pasighat and Oiramghat) was assessed across pre-monsoon, monsoon and post-monsoon seasons along with all major physico-chemical attributes of water. We recorded 78 fish species belonging to 48 genera under 17 families. On a spatial scale, the maximum number of species was recorded from Oiramghat (65 species) followed by Pasighat (56 species) along the lower stretch of the river. The family, Cyprinidae formed more than one-fourth (26.92%) of the total number of species followed by Danionidae (21.79%), Bagridae and Sisoridae (7.69% each) and Channidae (6.41%). During 2017-18 mean values of Shannon-Wiener index (Hʹ), Margalef\u0026rsquo;s richness index (dʹ) and Pielou\u0026rsquo;s evenness index (Jʹ) ranged from 1.877 to 2.420, 2.446 to 3.369 and 0.727 to 0.769, respectively, while for the period 2018\u0026ndash;2019 values ranged from 2.654 to 3.00, 4.351 to 5.638 and 0.865 to 0.886; and from 2.731 to 3.083, 5.032 to 6.607 and 0.858 to 0.887, respectively during 2019-20. A total of 11 water quality variables were analyzed during the period. During 2017-18, water quality of the river was characterized by high turbidity and low transparency values, which improved in subsequent years. Significant variations were recorded in mean values of transparency, turbidity, and total chlorophyll between 2017-18 with 2018-19 and 2019-20 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Statistical analysis affirmed that water quality attributes like depth, pH, dissolved oxygen and turbidity have strong association with fish community compositions in the river. We have observed that following the short period of environmental degradation, as the river regained its pristine status, fish diversity also improved concurrently, which suggests that short-term ecological degradation do not affect fish diversity in the long-run.\u003c/p\u003e","manuscriptTitle":"Do short-term ecological alterations affect fish diversity in the long-run? A study from a sub-tropical river in the Eastern Himalayas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 11:04:37","doi":"10.21203/rs.3.rs-7347029/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-02T02:55:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-01T10:21:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-01T10:20:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2025-08-11T13:39:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b396a19a-9535-4aa1-8ad4-cf42c65b66f4","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:18:27+00:00","versionOfRecord":{"articleIdentity":"rs-7347029","link":"https://doi.org/10.1007/s10661-026-15235-y","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2026-03-28 16:11:08","publishedOnDateReadable":"March 28th, 2026"},"versionCreatedAt":"2025-10-16 11:04:37","video":"","vorDoi":"10.1007/s10661-026-15235-y","vorDoiUrl":"https://doi.org/10.1007/s10661-026-15235-y","workflowStages":[]},"version":"v1","identity":"rs-7347029","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7347029","identity":"rs-7347029","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}