Fish community structure in accordance with environmental signatures in tropical river ecosystem, Eastern Himalayan eco-region

doi:10.21203/rs.3.rs-5340746/v1

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Fish community structure in accordance with environmental signatures in tropical river ecosystem, Eastern Himalayan eco-region

https://doi.org/10.21203/rs.3.rs-5340746/v1

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published 05 Feb, 2025

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Environmental characteristics significantly influence the distribution of fish communities in aquatic ecosystems. This study examined the relationship between fish community structure and ecological characteristics in the Dhansiri River, a tropical river within the Eastern Himalaya eco-region. Sampling was conducted across three seasons (monsoon, pre-monsoon and post-monsoon) at four stations representative of the whole river stretch. Highest number of species was recorded along upper stretch S1 (54), followed by S2 (45), S3 (41) and least in S4 (37). Seasonally, the number of species peaked during monsoon (64), decreased in post-monsoon (59) and was lowest in pre-monsoon (54). Shannon diversity index (H') ranged from 3.327 to 3.750, with higher values upstream and gradually declining downstream. Species diversity was lowest at S4, likely due to poor environmental conditions and high anthropogenic pressure. Cyprinids emerged as the most dominant fish group, with relative family abundance varying from 0-5.63%. Non-metric dimensional scaling indicated a distinct separation of S4 from S1, S2 and S3. Analysis of water quality revealed a pristine nature at S1, with gradual deterioration downstream. Significant relationships were identified between most water quality variables and fish community structure. Principal component analysis showed that pH (20.96%), total alkalinity (13.80%), specific conductivity (9.92%), NO₂(12.19%), and TDS (6.22%) contributed significantly to Dim1, while NO₃ (53.43%) and water temperature (6.05%) influenced Dim2. BIO-ENV analysis reflected that NH₃, NO₃, CO₂, TDS, total alkalinity, pH, specific conductivity, DO and water temperature significantly correlated with fish abundance and community composition. This study provides critical insights into the role of environmental parameters in shaping the fish community structure in a less-explored tropical river of the Eastern Himalayan and offers valuable information for the sustainable management of riverine fish diversity.

Fish assemblage

Ecology

Spatio-temporal diversity

Himalayan River

Sustainable fisheries

Tropical rivers are among the most diverse and dynamic ecosystems on Earth, serving as very important habitats for both local and global biodiversity (Dudgeon 2000). These ecosystems are very important due to their role in supporting an extensive range of species, many of which are endemic and play critical roles in maintaining ecological stability (Vörösmarty et al. 2010). They also provided essential ecosystem services, including riverine fisheries, which serve as a source of food and income for millions of individuals worldwide. Fish community structure refers to composition, abundance, diversity, and interactions among fish species within a particular aquatic ecosystem. They provide key insights into the health and functioning of tropical biodiversity hotspots, such as the rivers of the Eastern Himalaya region. Fish occupy central roles in aquatic food webs as primary consumers, predators and prey, thereby driving energy flow within these ecosystems (Allan and Castillo 2007; Kumari 2024). However, despite their ecological importance, tropical rivers face increasing natural and anthropogenic pressures, jeopardizing their health and the services they provide.

The composition and structure of fish communities are highly sensitive to a wide range of environmental variables, including changes in water temperature, hydrological regimes, water quality, nutrient concentrations, habitat structure and land-use pattern (Reid et al. 2019). Climate change, habitat alteration, introduction of non-native species, pollution, and rising human activities are contributing to the rapid degradation of tropical rivers, threatening the balance of their aquatic communities (Dudgeon et al. 2006). The combined effects of these factors are severely threatening the biodiversity and ecological balance of tropical rivers, putting both the ecosystems and the human populations that depend on them at risk. Of particular concern is the impact of climate change, which alters water temperatures and hydrological patterns, disrupting the natural balance of these ecosystems. Temperature plays a critical role in regulating fish physiology, affecting metabolism, reproduction, and migration, which can lead to shifts in community structure (Comte et al. 2013). Adequate dissolved oxygen (DO) is essential for sustaining metabolic activities, and oxygen depletion significantly stresses fish, impacting growth and survival. Most fish thrive within a pH range of 6.5 to 8.5, with deviations causing stress, impaired gill function, and increased toxicity of compounds like ammonia (Moyle and Light 1996). Stable alkalinity supports these conditions by buffering against sudden pH changes (Palmer et al. 2008). Nutrient concentrations such as nitrite, nitrate, and ammonia influence water quality, and excessive nutrients from agricultural runoff lead to eutrophication, depleting oxygen and causing harmful algal blooms. Elevated carbon dioxide (CO₂) from organic decomposition and pollution reduces oxygen availability, increasing fish stress (Rahel and Olden, 2008).

Environmental parameters like specific conductivity and total dissolved solids (TDS) reflect pollution levels, affecting fish osmoregulation and reducing biodiversity in sensitive species (Dudgeon et al. 2006). Additionally, reduced transparency from sedimentation or algal blooms decreases primary productivity and disrupts visual predators, while limited depth affects habitat availability for certain species (Reid et al. 2019). The introduction of non-native species, either through human activity or as a result of changing environmental conditions, poses another significant threat to the integrity of tropical river ecosystems (Palmer et al. 2008). These invasive species often outcompete native species for resources, leading to declines in biodiversity and alterations in ecosystem function (Dudgeon et al. 2006). Therefore, monitoring and assessing fish community structures provide valuable information for fisheries management, conservation efforts, and ecosystem health assessments. The structure of fish communities is a reliable indicator of environmental stress (Barrella and Petrere 2003), and specific community compositions can reflect the degree of habitat degradation (Wichert and Rapport 1998). Environmental and anthropogenic factors (Mondal and Bhat 2020), biotic factors (Robinson and Tonn 1989), abiotic factors (Debnath et al. 2022), trophic interactions and life-history traits have significantly influenced fish and other biotic community structures in aquatic systems worldwide. Anthropogenic influences have altered water flow and habitat quality for many freshwater fish species, considerably contributing to decline in freshwater biodiversity (Wilkinson et al. 2018; Zeng et al. 2022).

Northeast India covers an ecologically diverse region, encompassing the seven states of Arunachal Pradesh, Assam, Manipur, Meghalaya, Mizoram, Nagaland, and Tripura. This region is located within Eastern Himalayan biodiversity hotspot and shares borders with Bhutan, Myanmar, and Bangladesh. Its unique geographical location, combined with varied topography, has resulted in creation of numerous distinct ecosystems. Rivers and water bodies in Northeast India are crucial components of its biodiversity. The mighty Brahmaputra River and its tributaries, including the Dhansiri River, play a vital role in supporting aquatic life, such as endangered Ganges river dolphin, freshwater turtles and numerous fish species. The Dhansiri River, a significant waterway of the Brahmaputra basin, flows through Nagaland and Assam. It originates from Laisong Peak in Wokha district of Nagaland and traverses various regions before joining Brahmaputra at Golaghat district, Assam. Its diverse habitats provide favourable conditions for numerous fish species to thrive. The Nagaland Pollution Control Board (NPCB) has designated lower stretch of the Dhansiri (near Dimapur town) as highly polluted and categorized it under Priority I due to high BOD levels (30 mg/l) (Anon 2019). This part of the river has been severely stressed by immense anthropogenic pressures in recent years due to domestic and municipal sewage dumping. Dhansiri is one of many rivers in the biodiversity hotspot of North East India that has yet to receive much scientific attention regarding its fish diversity and ecology.

Previously, several studies have been conducted on the Dhansiri River, mostly focusing on land use, land cover changes and geomorphological aspects, such as channel sinuosity, erosion and sediment dynamics using remote sensing and geographical information system (GIS) techniques (Kotoky et al. 2012; Mili and Acharjee 2014; Barman and Goswami 2015). However, research on the fish community has been limited. Acharjee et al. (2012) reported 34 species under 5 orders, 25 families and 24 genera from the Dimapur stretch of Dhansiri River. A study by Daimari (2020) identified 15 fish species from the river, representing 4 orders and 8 families. These studies primarily focused on species composition and abundance, lacking a comprehensive understanding of how environmental factors influence fish communities. Understanding fish assemblage structure in relation to environmental variables in rivers remains a challenge due to limited knowledge on the subject (Mondal and Bhat 2020). This study aims to investigate how specific environmental factors influence fish community structures in the Eastern Himalayan tropical river ecosystem, seeking to identify patterns in species distribution and biodiversity linked to distinct environmental signatures. The present study holds significance as it addresses these knowledge gaps, providing valuable insights into ecosystem functioning and conservation needs, which will aid in the sustainable riverine fisheries management. Additionally, the research helps address the impacts of climate change on fish communities, providing valuable insights for adaptation strategies. The study also contributes to protecting endangered species, ensuring their survival amid growing environmental challenges. Preserving fish community structure can ensure long-term health and sustainability of our riverine ecosystems in context of changing scenarios and ever-increasing stressors.

Study area and sampling station description

The study was conducted on the Dhansiri River, one of the largest tributaries of the Brahmaputra. Dhansiri is a perennial river with a total length of 352 km (Long: 25°54’45” N, 93°44’30” E and Lat: 25°91’25” N, 93°74’16” E). It originates from Laishong Peak in the Barail range of Nagaland and flows from south to north before joining Brahmaputra at Dhansirimukh, Assam. Samples were collected during 2020-21 from four designated sampling stations, each representing different stretches of the river. The stations were selected based on their ecological significance and geographic distribution along the river’s course. Intangki National Park (INP) (S1; upper stretch, 25°43'19.5" N and 93°32'35.4" E), is situated within a protected forest area and characterized by minimal anthropogenic disturbance. Mangalmukh (S2; middle stretch, 25°43'19.3" N and 93°32'35.7" E), is characterized by a mixed forested landscape with moderate human activities. Doyapur (S3; middle stretch, 25°46'9.4" N and 93°35'54.3" E), lies further downstream with increasing agricultural and human activities. Domukhia (S4; lower stretch, 25°57'26" N and 93°46'10" E), is influenced by urbanization and agricultural runoff (Fig. 1). Samples were collected across three distinct seasons viz., post-monsoon (POM), pre-monsoon (PRM) and monsoon (MON) to account for seasonal variations in fish community structure and environmental conditions.

Fish diversity

Experimental fishing was carried out at each station using various gear types, including gill nets (2 no.; 10 m in length; 1m width; 25 mm and 30 mm mesh size), cast nets (3 no.; 2.4 m length and 3 m width; 10 mm and 20 mm mesh size), scoop nets, hooks and lines, and traditional bamboo traps, to assess fish diversity. Gill nets were set along the current in deeper pools and kept overnight. Other fishing gear was operated during early morning hours for approximately 2–3 hours. Fish samples collected were cleaned, labeled and preserved in 8–10% formalin and transported to laboratory for identification. Identification of fish was done following Talwar and Jhingran (1991) and Jayaram (1999). Taxonomic status and systematic position of fishes were cross-verified using the Catalog of Fishes (Fricke et al. 2023). Conservation status of fish species was assessed based on the IUCN Red List of Threatened Species (IUCN 2023).

Environmental parameters

A total of 12 environmental variables viz. water temperature (WT), depth, transparency, pH, dissolved oxygen (DO), total dissolved solids (TDS), CO₂, specific conductivity, total alkalinity (TA), nitrate (NO₃), nitrite (NO₂) and ammonia (NH₃) was systematically measured to assess water quality. Water samples were collected from surface and sub-surface (up to 0.5 m depth), homogenized and immediately transferred to pre-rinsed polyethylene bottles (1 L capacity). WT, pH, DO and specific conductivity were measured in field using a multi-parameter water quality meter ((Model 9829, HANNA, Romania) to ensure immediate and accurate in-situ readings. All the other remaining water quality parameters were analyzed in the laboratory following standard procedures (APHA 2017). Transparency was measured with a Seechi disc, following the method of Strickland and Parsons (1972). Depth measurements were taken manually to ensure precision.

Statistical analysis

The assessment of fish community structure and diversity employed a range of indices calculated from species abundance data. These indices included the Shannon-Wiener index (H′), the Margalef richness index (d), Pielou’s evenness index (J’), Simpson’s dominance (λ) and Simpson’s diversity (D) (Table 1). All indices were calculated using Primer (Ver. 7.0). Cumulative dominance was also assessed using Primer (Ver. 7.0) (Clarke and Gorley 2015). Relative abundance was calculated for each family across seasons and stations in MS Excel, using the following formula:

Relative abundance = (Abundance (no.) in each family/Total abundance (no.)) × 100.

To analyse species composition patterns, non-metric dimensional scaling (NMDS) was employed. NMDS particularly effective in visualizing community structure by reducing high-dimensional datasets, was applied to explore dissimilarity patterns within the dataset. Conducted in Primer (Ver. 7.0), this ordination technique facilitated the visualization of seasonal and spatial patterns in species distribution along the river gradient (Clarke and Gorley 2015). Principal component analysis (PCA), another statistical technique for dimensional reduction and inter-variable data exploration, was conducted in R (Ver. 4.3.0) (R Development Core Team 2019). This approach transformed interrelated water quality variables into uncorrelated principal components, aiding in elucidating the key environmental gradients that influences species distribution across the sampling stations. To examine spatial variability in water quality parameters, one-way analysis of variance (ANOVA) was performed, with post-hoc comparisons using Duncan’s multiple range tests to distinguish significant difference among stations, using SPSS (Ver. 23.0) (Das et al. 2022). The BIO − ENV (Biota and/or Environment matching) method was executed using the vegan-package in R (R Development Core Team, 2019) to identify and quantify the environmental variables most strongly correlated with observed variations in fish community structure.

Table 1

Description and formulas used for the calculation of diversity indices
Sl no.	Diversity indices	Significance	Formula	Description of the formula
1	Shannon diversity index (H′)	provides information about community composition	H′=_i log_e P_i	H′= species diversity in bits of information per individual. n_i = proportion of the samples belonging to the i^th species
2	Margalef richness index (d)	weighs number of species in the community	d = (S-1) / log N	S = total number of species and N = total number of individuals in the collection.
3	Pielou’s evenness index (J’)	refers to the degree of species abundance in an ecosystem.	J′ = H'/ln(S)	H' is Shannon Weiner diversity and S is the total number of species in a sample
4	Simpson dominance index (λ)	measure of community diversity	D = Σp_i²	p_i is the proportion of individuals in the i^th species.

Species composition and diversity

This study documented 3399 individual fish, representing 69 species across 20 families and 46 genera from the Dhansiri River. A systematic checklist of fish and their IUCN conservation status are illustrated in Table 2. The Cyprinidae family was the most diverse group, represented by 22 species, followed by Danionidae (14 species), Sisoridae (6 species), Bagridae (5 species) and Channidae (5 species). Families such as Botiidae and Nemacheilidae were represented by 2 species each, while single species were identified in the families Siluridae, Erethistidae, Schilbeidae, Cobitidae, Gobiidae, Ambassidae, Osphronemidae, Mastacembelidae, Psilorhynchidae, Belonidae, Badidae, Balitoridae and Notopteridae. In terms of abundance, Cabdio morar, Salmostoma acinaces, Pethia ticto, P. conchonius, Puntius chola, Opsarius tileo and Acanthocobitis botia were dominant. Number of species was highest at S1 during monsoon season with 52 species, followed by 47 species in the post-monsoon and 37 species in the pre-monsoon season. At station S2, number of species was highest in the pre-monsoon (40), while both monsoon and post-monsoon recorded 38 species. For station S3, species numbers peaked during monsoon (39), followed by pre-monsoon (37) and post-monsoon (36). Station S4 showed similar trend, with 38 species recorded in the monsoon, 36 species in the pre-monsoon and 33 species in the post-monsoon. Overall, the highest number of species was recorded along the upper stretch of the river at S1 (54), followed by S2 (45), S3 (41 species) and lowest at S4 (37). Seasonal analysis showed that highest number of species was recorded in monsoon (64), followed by post-monsoon (59) and pre-monsoon (54). An assessment of the conservation status of fish species revealed that 57 species (82.61% of total) were classified as Least Concern (LC), 5 species (7.25%) as Near Threatened (NT), 3 species (4.35%) as Vulnerable (VU), 2 species (2.90%) as Data Deficient (DD). Schistura fasciata has not been evaluated and is grouped under Not Evaluated (NE). A single exotic species, Cyprinus carpio was also recorded from the river.

Biodiversity indices are depicted in Fig. 2. Shannon index (H') values ranged from 3.327 at station S4 (post-monsson) to 3.750 at station S1 (monsoon). Along the upper stretch (S1), value of H' peaked in the monsoon period, with slightly lower value observed during post-monsoon (3.664) and pre-monsoon (3.465) periods. In case of station S2 (middle stretch), the highest H' value was noted in pre-monsoon at 3.496, while post-monsoon at 3.431 and monsoon recorded the lowest at 3.385. Similarly, at station S3 (middle stretch), H' values were highest in pre-monsoon (3.414), followed closely by the monsoon (3.409) and post-monsoon (3.391). Along lower stretch (S4), H' reached its maximum during the pre-monsoon (3.427), with a slight decrease in the monsoon (3.361) and lowest in post-monsoon (3.327).

The Margalef richness index (d) varied across stations and seasons, with values ranging from 6.544 (S4, post-monsoon) to 9.875 (S1, monsoon). In case of station S1, the richness index (d) was highest in the monsoon, with slightly lower values observed during post-monsoon (8.626) and pre-monsoon (8.624). For the middle stretch at station S2, the richness index reached its maximum in pre-monsoon (8.544), while monsoon values were moderately lower (7.715) and the post-monsoon had the lowest index (7.601). At station S3, monsoon again recorded the highest richness index (7.924), followed closely by pre-monsoon (7.767) and then post-monsoon (7.135). At station S4, lowest value of ‘d’ was observed in post-monsoon, highest during monsoon (7.742) and intermediate in pre-monsoon (7.722). Generally, the value of ‘d’ was found to be higher during monsoon at all stations, except for S2, where highest species richness appeared in the pre-monsoon and the lowest in the post-monsoon. Pielou’s eveness index (J') ranged from 0.924 (S4, monsoon) to 0.9596 (S1, pre-monsoon). The J' values were lower during the monsoon across all stations and higher during pre-monsoon, except for station S3, where it was higher during post-monsoon (0.9463). The mean values of J' were highest at station S1 (0.9535), followed by S4 (0.9440) and lowest at S2 (0.9410). Simpson dominance index (D) ranged from 0.027 (S1, monsoon) to 0.045 (S4, monsoon). Values of D were found to be on the lower side at S1 (0.027–0.037) and increased downstream, with highest values at S4 (0.37–0.45). Values of D were intermediate in middle stretch, ranging from 0.037–0.042 at S2 and from 0.038–0.040 at S3.

The cumulative dominance curve (k-dominance) extracted for different stations showed that fish species dominance in S1, S2 and S3 did not differ significantly. However, station S4 exhibited a significantly different dominance pattern compared to other three stations. A higher curve in the cumulative dominance plot and more quickly the curve reaches 100% value, indicates lower fish species richness. S4 had the lowest species richness, while S1 showed the highest species richness, as evident in the k-dominance plot (Fig. 3).

Spatio-temporal variation in the relative abundance of different fish families is given in Fig. 4. Relative abundance of most families was higher at S1 and progressively decreased downstream. The proportion of fish families observed across the stations varied between 0% and 5.63%. Cyprinidae family was the most abundant, with its relative abundance varying between 1.81% (S1, pre-monsoon) and 5.63% (S1, post-monsoon), followed by Danionidae family, with values ranging from 0.87% (S1, pre-monsoon) to 3.02% (S1, post-monsoon). Relative abundance of all other fish families was found to be < 1%, except for Channidae family, which reached 1.27% at S1 during post-monsoon. Cobitidae followed by Botiidae were the least abundant families in terms of number of specimens reported during the study.

Non-metric multidimensional scaling reflected that samples from S4 were distinctively separated from those collected at S1, S2 and S3. Further, S1 post-monsoon sample also demonstrated distinct separation from the remaining samples. A stress value of 0.07 portrayed a good ordination pattern for the observed data (Fig. 5).

Table 2

Systematic checklist of fishes recorded from Dhansiri River along with their IUCN conservation status
Family	Species	Conservation status	Site 1			Site 2			Site 3			Site 4
Family	Species	Conservation status	POM	PRM	MON	POM	PRM	MON	POM	PRM	MON	POM	PRM	MON
Notopteridae	Notopterus notopterus (Pallas, 1769)	LC	+	-	+	+	+	+	-	+	+	+	-	+
Cyprinidae	Bangana dero (Hamilton, 1822)	LC	+	+	+	+	+	+	-	-	-	-	-	-
	Chagunius chagunio (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	-	-	-
	C. nicholsi (Myers, 1924)	LC	+	+	+	+	+	+	+	+	+	-	-	-
	Cirrhinus reba (Hamilton, 1822)	LC	-	-	+	-	-	-	-	-	-	-	-	-
	C. cirrhosus (Bloch, 1795 )	VU	+	-	+	+	-	+	-	-	-	-	-	-
	Cyprinus carpio (Linnaeus, 1758)	Exotic	-	-	-	-	-	-	+	+	+	+	+	+
	Hypsibarbus wetmorei (Smith, 1931)	LC	+	-	+	-	-	-	-	-	-	-	-	-
	Labeo dyocheilus (McClelland, 1839)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	L. rohita (Hamilton, 1822)	LC	-	-	-	+	+	+	+	+	+	+	+	+
	L. pangusia (Hamilton, 1822)	NT	+	-	+	+	+	+	-	-	-	-	-	-
	L. gonius (Hamilton, 1822)	LC	-	-	-	+	+	+	+	+	+	+	+	+
	Neolissochilus hexagonolepis (McClelland, 1839)	NT	+	-	+	+	+	+	+	-	+	-	-	-
	Puntius chola (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	P. sophore (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	Pethia conchonius (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	P. ticto (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	Systomus sarana (Hamilton, 1822)	LC	+	-	+	-	-	-	-	-	-	-	-	-
	Tor tor (Hamilton, 1822)	DD	+	+	+	-	-	-	+	-	+	-	-	-
	Tariqilabeo latius (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	Garra gotyla (Gray, 1832)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	G. nasuta (McClelland, 1839 )	LC	-	+	+	+	+	+	-	+	+	-	-	-
	G. rupecula (McClelland, 1839)	NT	+	+	+	+	+	+	+	+	+	+	+	+
Danionidae	Salmostoma acinaces (Valenciennes, 1842)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	S. bacaila (Hamilton, 1822)	LC	-	-	+	-	-	+	-	-	-	-	-	-
	Amblypharyngodon mola (Hamilton, 1822)	LC	-	-	-	+	+	+	+	+	+	+	+	+
	Barilius barila (Hamilton,1822)	LC	+	+	+	+	+	+	+	+	+	-	-	-
	Opsarius bendelisis (Hamilton,1822)	LC	+	+	+	-	-	-	-	-	-	-	-	-
	O. dogarsinghi (Hora, 1921)	VU	+	+	+	+	+	-	-	+	+	-	-	-
	O. tileo (Hamilton,1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	O. barna (Hamilton,1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	Danio rerio (Hamilton, 1822)	LC	+	+	+	-	-	-	-	-	-	+	+	+
	Cabdio morar (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	Devario devario (Hamilton, 1822)	LC	+	+	+	-	-	-	+	+	+	-	+	+
	D. aequipinnatus (Hamilton, 1822)	LC	+	-	+	-	-	-	+	+	-	+	+	+
	Esomus danrica (Hamilton, 1822)	LC	-	-	-	-	-	-	+	+	+	+	+	+
	Rasbora rasbora (Hamilton, 1822)	LC	+	-	+	-	-	-	-	-	-	+	+	+
Psilorhynchidae	Psilorhynchus sucatio (Hamilton, 1822)	LC	+	+	+	+	+	+	-	-	-	-	+	-
Balitoridae	Balitora brucei (Gray, 1830)	NT	+	+	+	-	-	-	-	-	-	-	-	-
Nemacheilidae	Acanthocobitis botia (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
Nemacheilidae	Schistura fasciata (McClelland,1838)	NE	+	+	+	-	-	-	-	-	-	-	-	-
Cobitidae	Canthophrys gongota (Hamilton, 1822)	LC	-	-	-	-	+	-	-	-	-	-	-	-
Botiidae	Botia dario (Hamilton, 1822)	LC	-	-	-	-	-	-	-	-	-	-	-	+
Botiidae	B. rostrata (Grunther, 1868)	VU	+	-	-	-	-	-	-	-	-	-	-	-
Bagridae	Sperata seenghala (Hamilton,1822)	LC	-	-	-	-	-	-	-	-	-	+	-	+
	Olyra longicaudata (McClelland, 1842)	LC	-	-	-	-	+	-	-	-	-	-	+	+
	Mystus tengara (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	M. bleekeri (Day, 1877)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	M. dibrugarensis (Chaudhuri, 1913)	LC	-	-	-	-	-	-	+	+	+	-	+	+
Erethistidae	Hara jerdoni (Day, 1870)	LC	-	+	+	-	-	-	-	-	-	-	-	-
Siluridae	Ompok bimaculatus (Bloch, 1797)	NT	+	-	+	-	-	-	-	-	-	-	-	-
Schilbeidae	Clupisoma garua (Hamilton, 1822)	LC	+	-	+	+	-	+	+	-	+	-	-	-
Sisoridae	Bagarius lica (Volz, 1903)	LC	-	-	+	-	-	-	+	-	-	-	-	-
	Gagata cenia (Hamilton, 1822)	LC	-	-	+	-	-	-	-	-	-	-	-	-
	Glyptothorax telchitta (Hamilton, 1822)	LC	-	+	+	+	+	-	-	+	+	+	+	+
	G. trilineatus (Blyth, 1861)	LC	+	+	+	-	-	-	-	-	-	-	-	-
	G. platypogonoides (Bleeker, 1855)	LC	+	+	-	-	-	-	-	-	-	-	-	-
	Sisor rabdophorus (Hamilton, 1822)	LC	+	-	-	-	-	-	-	-	-	-	-	-
Belonidae	Xenentodon cancila (Hamilton, 1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
Ambassidae	Parambassis baculis (Hamilton, 1822)	LC	-	-	-	-	+	-	-	-	-	-	-	-
Badidae	Badis badis (Hamilton, 1822)	LC	-	-	-	+	+	+	+	+	+	+	+	+
Gobiidae	Glossogobius giuris (Hamilton,1822)	LC	+	+	+	+	+	+	+	+	+	+	+	+
Channidae	Channa barca (Hamilton, 1822	DD	-	-	+	-	-	-	-	-	-	-	-	-
	C. marulius (Hamilton, 1822)	LC	+	-	+	-	-	-	-	-	-	-	-	-
	C. gachua (Hamilton, 1822)	LC	-	-	-	+	+	+	-	-	-	+	+	+
	C. punctata (Bloch, 1793)	LC	+	+	+	+	+	+	+	+	+	+	+	+
	C. striata (Bloch, 1793)	LC	+	+	+	+	+	+	+	+	+	+	+	+
Osphronemidae	Trichogaster fasciata (Bloch and Schneider, 1801)	LC	-	-	-	-	+	+	-	+	+	0	+	+
Mastacembelidae	Mastacembelus armatus (Lacepede, 1800)	LC	+	+	+	+	+	+	+	+	+	+	+	+
+, Present; -,Absent

Environmental parameters

Mean values of environmental parameters for the Dhansiri River, Nagaland, across seasons and stations are given in Table 3. It was observed that water temperature was lower at S1 and increased downstream of river. Maximum value of DO was observed at S1 during post-monsoon (12 mg/l), while minimum value was observed at S4 during monsoon (8 mg/l). Water pH was observed to range from 7-8.5. NH₃ was not detected at S1, S2 and S3, while at S4 it was found to range from 1–3 mg/l. NO₂ and NO₃ were not detected in water samples at S1, while at S2 and S3, nitrate and nitrite respectively were found to range from 0.1–0.2 mg/l and 0.1–0.16 mg/l, and were observed during pre-monsoon and monsoon. Values of CO₂ were found to range from 1.7–3.2 mg/l, with higher values recorded at S4. TA was found to range from 95–160 mg/l, with a higher mean values observed during monsoon season at all stations except for S1, where higher mean value was observed in pre-monsoon. Spatial analysis showed that mean values of TA were reportedly higher at S1 and lowest at S2. TDS was observed to range from 76–125 mg/l, with S1 showing the highest mean values and S4 the lowest. Across seasons, higher mean values of TDS were observed in monsoon at all stations. Specific conductivity was observed to range from 116–174 µS/cm in the Dhansiri River. Higher mean values of specific conductivity were observed at S1 and lowest at S2. Specific conductivity did not show any particular trend across seasons in the river.

Table 3

Mean values of physico-chemical parameters of Dhansiri River
STATION	S 1				S2				S3				S4
SEASON	POM	PRM	MON	AVG	POM	PRM	MON	AVG	POM	PRM	MON	AVG	POM	PRM	MON	AVG
Temperature (°C)	15.5 ± 0.5	21.75 ± 1.8	28 ± 0.5	21.88 ± 1.92	16.5 ± 0.5	23.25 ± 1.75	28.5 ± 0.5	22.88 ± 1.81	16.5 ± 0.71	23.5 ± 1.7	29.5 ± 0.5	23.25 ± 1.92	16.75 ± 0.3	24.25 ± 1.8	31.2 ± 1.2	24.11 ± 2.1
DO (mg/l)	11.75 ± 0.3	10.75 ± 2.5	9.5 ± 0.5	10.69 ± .34^a	11.5 ± 0.5	10 ± 0.41	8.85 ± 0.15	10.09 ± 0.41^ab	11.65 ± 0.49	9.75 ± 0.25	8.8 ± 0.2	9.99 ± 0.42^ab	11.35 ± 0.4	9.12 ± 0.31	8.15 ± 0.2	9.44 ± 0.47^b
pH	7.75 ± 0.25	8.25 ± 0.14	8.25 ± 0.25	8.12 ± 0.13^a	7.5 ± 0.5	7.87 ± 0.13	7.75 ± 0.25	7.75 ± 0.13^ab	7.6 ± 0.14	8.12 ± 0.12	8.25 ± 0.25	8.03 ± 0.12^a	7.1 ± 0.1	7.5 ± 0.2	7.75 ± 0.3	7.46 ± 0.14^b
TA (mg/l)	141 ± 1.0	151.25 ± 4.73	147.5 ± 2.5	147.83 ± 2.76^a	120.5 ± 0.5	133.7 ± 8.98	152.5 ± 2.5	135.13 ± 6.01^a	125 ± 7.07	141.2 ± 1.3	142.5 ± 2.5	137.5 ± 2.99^a	102.5 ± 7.5	126.25 ± 3.8	131 ± 1.0	121.5 ± 4.77^b
Nitrite (mg/l)	ND	ND	ND	0.0	ND	0.03 ± 0.025	0.15 ± 0.05	0.05 ± 0.03^a	ND	0.03 ± 0.02	0.1 ± 0.05	0.04 ± 0.02^a	0.045 ± 0.005	0.16 ± 0.11	0.45 ± 0.05	0.20 ± 0.08^b
Nitrate (mg/l)	ND	ND	ND	0.0	ND	0.03 ± 0.025	0.13 ± 0.025	0.04 ± 0.02^a	ND	0.03 ± 0.02	0.13 ± 0.03	0.04 ± 0.02^a	4.5 ± 0.5	4.75 ± 0.3	5.5 ± 0.5	4.88 ± 0.23^b
Ammonia (mg/l)	ND	ND	ND	0.0	ND	ND	ND	0.0	ND	ND	ND	0.0	1.1 ± 0.1	2.25 ± 0.3	2.75 ± 0.3	2.09 ± 0.26
CO₂ (mg/l)	1.85 ± 0.05	2.25 ± 0.16	1.9 ± 0.1	2.06 ± 0.11^a	1.75 ± 0.05	2.58 ± 0.09	2.45 ± 0.1	2.34 ± 0.13^a	2.65 ± 0.21	2.53 ± 0.17	2.05 ± 0.1	2.44 ± 0.12^a	2.25 ± 0.05	2.93 ± 0.15	3.2 ± 0.05	2.81 ± 0.15^b
Specific conductivity (uS/cm)	132.5 ± 12.5	160.5 ± 5.12	159 ± 1.0	153.13 ± 5.62^a	131 ± 1.0	129 ± 5.0	119 ± 1.0	127 ± 2.93^b	138.5 ± 30.41	128.7 ± 5.06	117 ± 1.0	128.25 ± 5.51^b	148 ± 12	140 ± 11.53	131 ± 1.0	139.75 ± 6.23^ab
TDS (mg/l)	109 ± 4.0	103 ± 3.14	117.5 ± 2.5	108.12 ± 2.82^a	82.5 ± 1.5	88.25 ± 1.18	106 ± 4.0	91.25 ± 3.48^b	88.5 ± 7.8	84.25 ± 3.8	120 ± 5.0	94.25 ± 6.08^ab	86 ± 4.0	94.25 ± 7.02	118 ± 2.0	98.12 ± 5.63^ab
Transparency (cm)	27.5 ± 2.5	20.75 ± 1.49	19.5 ± 0.5	22.13 ± 1.46^a	35.5 ± 1.0	28 ± 1.78	23.5 ± 1.5	28.75 ± 1.84^b	36 ± 1.41	31.75 ± 2.36	25.5 ± 0.5	31.25 ± 1.80^b	33 ± 1.0	30 ± 2.04	25.5 ± 0.5	29.63 ± 1.4^b
Depth (ft)	2.75 ± 0.3	3.5 ± 0.35	5.25 ± 0.25	3.75 ± 0.39	1.5 ± 0.5	2.25 ± 0.52	4.75 ± 0.3	2.69 ± 0.53	1.75 ± 0.0.36	2 ± 0.54	5.3 ± 0.25	2.75 ± 0.61	2.9 ± 0.1	3.25 ± 0.48	5.75 ± 0.3	3.78 ± 0.49
Values with different superscript are significantly different (p < 0.05); ND = Not detected

PCA plot of water quality parameters is shown in Fig. 6. The first principal component (Dim1), with an eigenvalue of 5.29 explains 44.13% of total variation. The second principal component (Dim2), with an eigenvalue of 3.86 explains an additional 32.23% of the variation. First principal component (Dim1) is mainly influenced by variables, such as pH (20.96%), total alkalinity (13.80%), specific conductivity (9.92%), NO₂ (12.19%), and TDS (6.22%). These variables contribute significantly to the observed variation in Dim1. On the other hand, second principal component (Dim2) is primarily influenced by NO₃ (53.43%) and water temperature (6.05%).

Influence of environmental parameters on fish community structure

The BIO-ENV analysis indicated significant correlations between fish abundance and composition and environmental variables, including NH₃, NO₃, CO₂, TDS, TA, pH, specific conductivity (Sp. Con.), DO and WT. The strongest correlation (ρ = 0.6203) was observed with the combination of NO₃, CO₂, TDS. This was followed by the combination of pH, NO₃, NH₃, CO₂, Sp. Con. and TDS (ρ = 0.6134) and by the combination of pH, NO₃, CO₂, Sp. Con., TDS (ρ = 0.6073). Details of correlation of environmental parameters with fish abundance are given in Table 4.

Table 4

BIO-ENV analysis of fish assemblage compared with environmental variables (pooled data)
No. of variables	Correlation selection	Spearman correlation ( p < 0.01)
1	NH₃	0.5093
2	NO₃, CO₂	0.5711
3	NO₃, CO₂,TDS	0.6203
4	TA,NO₃, CO₂,TDS	0.6066
5	pH,NO₃, CO₂, Sp. Con., TDS	0.6073
6	pH,NO₃, NH₃,CO₂, Sp. Con., TDS	0.6134
7	pH,TA,NO₃, NH₃,CO₂, Sp. Con., TDS	0.6031
8	DO, pH,TA,NO₃, NH₃,CO₂, Sp. Con., TDS	0.5964
9	WT, DO, pH, TA,NO₃, NH₃,CO₂, Sp. Con., TDS	0.5441
10	WT,DO, pH, TA,NO₃, NH₃,CO₂, Sp. Con., TDS, Depth	0.4910
11	WT,DO, pH, TA,NO₃, NH₃,CO₂, Sp. Con., TDS, Trans, Depth	0.4360
12	WT,DO, pH, TA,NO₂, NO₃, NH₃,CO₂, Sp. Con., TDS, Trans, Depth	0.3739

The present study investigated alterations in the structure, diversity and richness of fish species communities during three different seasons, while also comparing physico-chemical characteristics of the Dhansiri River, one of the largest tributaries of Brahmaputra. Spatio-temporal changes in environmental factors are key determinants of freshwater fish assemblages, influencing habitat structure and species composition (Fischer and Paukert 2008; Elías et al. 2020). This study offers valuable insights into ecological shifts within the river, enhancing the understanding of the intricate interrelationships between fish populations and their habitat. The Dhansiri River is characterized by high DO levels, high total alkalinity and an alkaline pH, which are comparable to other hilly rivers in the region (Gurumayum et al. 2014; Sarmah et al. 2020; Lkr et al. 2020). Spatial analysis of water quality parameters revealed that the upper stretch (S1) reflects a relatively pristine habitat with minimal human impact. On the contrary, a slight deterioration in water quality was noticed downstream, with comparatively poorest condition at S4 near Dimapur town. This stretch of the river is characterized by low DO levels, high CO₂ and presence of NH₃, NO₂ and NO₃. Stretch of the Dhansiri near Dimapur town has been listed as polluted and placed under Priority I category due to high BOD load (Anon 2019). Poor downstream environment quality may be attributed to increased human habitation, high anthropogenic discharge, agricultural runoff and changes in land use patterns (Premke et al. 2020; Ji et al. 2021). Pristine nature of water at S1 may be explained by its location within INP, where human interference is restricted. Distinct seasonal changes in current patterns, discharge rates, and temperature bring about unique alterations in the physico-chemical properties of riverine ecosystems (Pradhan et al. 2009). These seasonal changes affect the distribution of nutrients and dissolved gases, which in turn influence biological productivity and the species composition of the river.

PCA analysis indicated the importance of water chemistry, such as pH and total alkalinity in determining primary axis of water quality variation (Saalidong et al. 2022). The dominance of these parameters suggests that river’s buffering capacity and its ability to neutralize acidic inputs are critical to maintaining its ecological balance. Additionally, the significant presence of NO₂, NO₃ and TDS as influential contributors highlights the role of nitrogen compounds (Vorobyeva et al. 2021) and dissolved solids in shaping water quality profile and its biological productivity. Excess nitrogen in aquatic ecosystems can lead to eutrophication, which disrupts aquatic food webs and reduce biodiversity (Dodds and Smith 2016). Furthermore, water temperature also emerged as a critical abiotic factor influencing Dim2. Relatively smaller contributions of other variables to Dim2 indicate their secondary importance in explaining water quality variation.

The study revealed a total of 69 fish species, distributed across 20 families and 46 genera, indicating significant diversity of freshwater fishes comparable to other major river systems in India (Sarkar et al. 2010; Shukla and Bhat 2017). Highest number of species was recorded along the upper stretch of river at S1 (54), while the least was observed along the lower stretch at S4 (37). The upper stretch (S1) is located within the Intangki National Park, an authorized biodiversity conservation area. This site is restricted from anthropogenic interference and features a pristine ecosystem with high diversity, contributing to the observed richness and diversity of fish species. Conversely, the lower stretch (S4) near Dimapur town showed significant declines in fish diversity. This decline can be attributed to the deterioration of environmental variables, as recorded in the study (Table 3), along with the decline in structural habitat complexity, and the increased human habitation and activities observed during downstream sampling. Poor environmental parameters (lower DO, higher NH₃ and higher CO₂) (Anon 2019) along with increased anthropogenic pressure (e.g. pollution, over exploitation etc.) may have negatively impacted fish species richness, diversity, distribution and relative abundance. These findings are consistent with previous studies showing that degraded water quality and habitat deterioration adversely affect freshwater fish populations (Anon 2019; Dudgeon et al. 2006). Typically, in riverine systems species richness, diversity, and abundance increase from upper to lower stretches due to broad range of habitats and resources available (Weber and Peter 2007). However, the Dhansiri River exhibited an opposite trend, with species numbers declining from upstream to downstream. The observed spatial variation in diversity, number, and distribution of fish species is attributed to several factors, including natural topography, environmental influencers (Hashemi et al. 2015), pollution (Anon 2019), habitat complexity (Smokorowski and Pratt 2007), reduced water quality and increased anthropogenic pressures, which limit the suitability of downstream habitats for diverse fish communities (Table. 3) (Lakra et al. 2010; Pandey and Radhakrishnan 2022).

Seasonally, a higher number of fish species was observed in the Dhansiri River during monsoon at all stations, with the exception of S2 (pre-monsoon). Water discharge has been identified as the single most important factor driving fish species richness in Himalayan rivers (Bhat et al. 2012). Increased water discharge during monsoon creates new habitats and improves connectivity between different habitats and sections of the river, facilitating fish migration and colonization of new areas, thereby increasing likelihood of encountering higher number of species. Higher number of fish species observed at S2 during pre-monsoon may be attributed to increased natural food availability (Acharjee and Barat 2014). Previous studies across rivers in south and Southeast Asia have consistently reported cyprinids as most dominant group of fishes (Bhat 2003). The results of this study, which observed that cyprinids exhibit the highest relative abundance compared to other groups is a testimony to this statement. Cyprinids dominate fish community structure in tropical Indian rivers owing to their high adaptive variability and ability to occupy heterogeneous habitats (Johnson and Arunachalam 2009).

Diversity indices in general are functions of different species available in an ecosystem and relative abundance of each species. Shannon diversity index is widely used to quantify diversity or richness of species in a specific ecological community and is one of the most commonly employed indices for comparing species diversity across habitats (Clarke and Warwick 2001). Margalef richness index is a biodiversity metric used to assess species richness in an ecological community (Margalef 1958). Simpson dominance index assesses the dominance or evenness of species abundances within an ecological community (Simpson 1949). On a spatial scale, values of ‘H′’ and ‘d’ were highest in pristine upper stretch (S1) with a gradual decline downstream. Conversely, values of ‘D’ increased from upstream to downstream, indicating a higher dominance of fewer species. Highest values of ‘H′’ (3.750) and ‘d’ (9.875) and lowest value of ‘D’ were observed during monsoon at S1, highlighting a high level of species richness and uneven distribution. This aligns with established ecological principles, as fish typically migrate, spawn and recruit during monsoon, leading to increased diversity and abundance in the ecosystem (Sreekanth et al. 2016). Restricted human interference by virtue of location and congenial habitat might be major factors for migration and breeding of fish during monsoon, leading to higher fish diversity and low dominance at S1. Pielou's evenness index quantifies the distribution of species within a community, providing a measure of how evenly individuals are spread across species. The index ranges from 0 to 1, where 0 represents maximum species dominance (only one species present), and 1 represents maximum evenness (all species are equally abundant) (Pielou 1966). In this study, it was observed that upper stretch (S1) has an even distribution of fish species, followed by S4. It should be noted that instances of high abundance in certain species may result in a deviation from evenness in overall species diversity (Saha et al. 2022), which might be one of the possible reasons for lower evenness in S2 and S3.

Cumulative dominance curve (k-dominance), which ranks fish species based on their abundance and the proportion of individuals per species, plotted against the logarithmic rank of species (Clarke 1990). The curved showed S4 as least diverse site, with a steep dominance curve indicating a few species dominating the community. Concept of species richness is commonly utilized as an indicator of environmental quality, as it tends to rise in presence of healthy ecosystems and minimal anthropogenic impairment (Roth et al. 2000; Kirk and Rahel 2022). Conversely, a decrease in species richness accompanied by proliferation of a few dominant species indicates a progressively deteriorating environment (Jacinto et al. 2023). It is important to note that freshwater assemblages are not solely affected by species zonation or spatial arrangement, but also shaped by physical disturbances, and habitat modifications (Daufresne and Boet 2007; Perkin et al. 2015). NMDS is an ordination technique used to visualize the similarity between samples based on ecological data. In the present study NMDS analysis revealed a clear separation of S4 from the other three stations. As per guidelines forwarded by Clarke (1993) stress values < 0.05 are considered excellent; < 0.10, good; < 0.20, useable; and > 0.20, not reliable. Lower the stress value, better the fitting in ordination plot. In the present study, a stress value of 0.07 indicates a good fit for the distances/dissimilarities in the spatial ordination plot.

Environmental factors play a crucial role in maintaining fundamental habitat characteristics and ecosystem functions, and they are known to significantly influence fish community structure in aquatic systems (Mondal and Bhat 2020; Debnath et al. 2022). Understanding the impact of environmental influencers on fish communities is essential for elucidating fish-habitat relationships, which can contribute to more effective ecosystem management and preservation (Gillooly et al. 2001; Wang and Lyons 2003; Li et al. 2018). BIO-ENV analysis found strong correlations between environmental parameters, fish abundance and composition, with NH₃, NO₃, CO₂, TDS, TA, pH, specific conductivity, DO and WT being the most influential factors. Highest degree of correlation with fish abundance was observed in the combinations of NO₃, CO₂ and TDS. Environmental variables affects fish abundance and composition both directly or indirectly by influencing dynamics of fish food organisms such as plankton (Gogoi et al. 2019) and aquatic invertebrates (Das et al. 2022). WT is one of the critical influencers that affect distribution of fishes, both directly by influencing their metabolic and physiological processes (Gillooly et al. 2001) and indirectly by affecting biological productivity and food matter availability (Sarkar et al. 2021). Additionally, TDS, TA and pH have been shown to reportedly influence chlorophyll contents in rivers (Sarkar et al. 2021), which are closely associated with productivity and the availability of food sources, thereby affects fish distribution. Similarly, NH₃, NO₃, CO₂ are also associated with biological productivity of aquatic ecosystems (Galloway et al. 1994; Sigman and Hain 2012) and can directly influence fish community structure. Parameters like pH, WT, TDS, NO₃, DO are known to influence dynamics of fish food organisms (Gogoi et al. 2019; Das e al. 2022), which in turn affects distribution of fishes. Furthermore, DO, pH, NH₃, NO₃, CO₂, TA have direct effects on fish metabolism, which may influence their distribution and abundance (Boyd and Tucker 1998). It is to be noted that, DO is the single most critical parameter for the survival of all forms of aquatic life (Keke et al. 2016) and it therefore exerts influence on fish communities. Managing adequate DO levels is essential for sustaining healthy fish populations and preserving the overall balance of aquatic ecosystems.

The present study highlighted the impact of human activities, particularly at S4 near Dimapur town, as evidenced by reduced species richness, higher dominance of fewer species, and poorer water quality parameters. The presence of higher concentration of NH₃, NO₃ and CO₂ at S4 reflects inputs from agricultural runoff, urban wastewater and industrial discharges, all of which contribute to the degradation of Dhansiri River ecosystem. The finding of this study underscore the need for implementation of effective regulation and management measures to protect this Dhansiri River’s biodiversity, particularly in downstream areas. Establishing buffer zones, improving wastewater treatment and controlling agricultural runoff are critical steps towards mitigating the negative impact of anthropogenic activities (Hughes et al. 2018). Additionally, the presence of several species listed as Near Threatened and Vulnerable highlights the importance of targeted conservation efforts. Protecting critical habitats, especially in the upper stretches of the river, is essential for maintaining the ecological integrity of the entire system. Further research on the population dynamics of these threatened species is necessary to inform conservation strategies and ensure their long-term survival. As previously mentioned, environmental aspects participate crucially in shaping fish communities. Given the current state of rapid urbanization and climate change, it is imperative that we gain critical insights into ecological mechanisms affecting fish communities in order to develop effective protection and conservation strategies. The present study, conducted in a least-explored tropical river from a globally significant biodiversity hotspot, contributes to our knowledge and understanding, providing cues for sustainable riverine fisheries management.

Ethics approval

Procedures and activities performed during the study period involving animals were in agreement with ethical standards of the institution. Sampling was performed after due approval from Institute Research Committee (IRC) of ICAR-Central Institute of Fisheries Education.

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

SO, major contribution in writing, data collection; ATL, overall guidance, supervision, manuscript writing and revision and facilitation; KR, data analysis, manuscript writing and methodology; SB, conceptualization, major contribution in writing, data analysis and methodology; JB, data collection, taxonomy; SKM, manuscript correction and facilitation; NC, Data analysis and graphs, contribution in manuscript writing and revising; BKD, overall guidance, manuscript correction and facilitation.

Funding

The authors are thankful to the Indian Council of Agricultural Research, New Delhi for financial support to carry out the research work.

Declaration of 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.

Availability of data and materials

The data generated in the present study have been submitted to ICAR-CIFE 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.

Acknowledgement

Authors are grateful to the Director, ICAR-CIFE, Mumbai for providing necessary facilities to carry out the research work. The first author thankful to the Director, ICAR-CIFRI, Barrackpore, Kolkata for providing necessary laboratory facilities and valuable guidance for completing the research work. The authors also thankful to the fisher community of river Dhansiri River, Nagaland, India.

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.

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Fish community structure in accordance with environmental signatures in tropical river ecosystem, Eastern Himalayan eco-region

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Abstract

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Introduction

Materials and methods

Study area and sampling station description

Fish diversity

Environmental parameters

Statistical analysis

Results

Species composition and diversity

Environmental parameters

Influence of environmental parameters on fish community structure

Discussion

Declarations

References

Additional Declarations

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Journal Publication

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