1 Introduction

Multitude human pressures, such as pollution (Oliveira et al., 2017; Hamid et al., 2021), nutrient enrichment (Ferreira et al., 2020), damming, or overexploitation (de Paula et al., 2018; Sofi et al., 2022), have severely damaged freshwater ecosystems worldwide, and this has been more intense over the past five decades (Bhat et al., 2021a, b; Santos & Ferreira, 2020). Anthropogenic activities such as river regulation (Zhang et al., 2020; Sofi et al., 2020) and pollution (Landrigan et al., 2018) have a potentially negative cumulative impact on rivers and streams water quantity and quality (Hoang et al., 2018; Sabha et al., 2020; Musonge et al., 2020). Stream ecosystem assessment has increased substantially in recent years, to evaluate the effects of urban development (Paul & Meyer, 2001) and impacts on aquatic biota and other ecosystem services (Cuffney et al., 2000; Pitt, 2002). Topography, geology, and soil conditions along with human-driven agriculture, urbanization, and forestry activities determine the abiotic and biotic characteristics of streams draining the landscapes (Allan, 2004). Catchments with high populations have more pressure on the stream water body which exhibited altered water quality and macroinvertebrate assemblages (Edegbene et al., 2021; Newall & Walsh, 2005; Taylor et al., 2004; Walsh et al., 2005). Anthropogenic pressure alters the physical and chemical environment of rivers and streams (Alig et al., 2004; Allan, 2004; Bhat et al., 2021a, b). The demand for water for consumption, agriculture, and navigation development has led nations to recommend action defensible over long periods (Bagla, 2014; Brito & Magalhães, 2017). Aquatic organisms such as fish, macroinvertebrates, and diatoms (Hering et al., 2006; Negi & Singh, 2021; Tampo et al., 2021) indicate quite well the characteristics and degradation level of the catchment area (Wenger et al., 2009; Walsh & Wepner, 2009) and are used as the biomonitoring tools. In Himalayan lotic ecosystems, biomonitoring has started gaining pace to report the anthropogenic pressure and to know the status of water bodies, such kind of studies were carried out on streams such as Vishav, Jhelum, Lidder, and Dagwan (Hamid et al., 2016; Rashid and Romshoo, 2013; Sabha et al., 2019; Khanday et al., 2020; Sabha et al., 2020; Hamid et al., 2021). In the Himalayan region, the Dachigam-Dara catchment in the northwestern Kashmir Himalayas is an important tributary to the Dal Lake. As the region lies in the periphery of Srinagar city, it is subjected to an increase in the human population with an increase in water demand and thus substantially pressure on the water resources present (Fazal & Amin, 2011). It is crucial to identify, monitor, and analyze the effects of such stressors to provide a proper management policy to preserve, manage, and restore freshwater ecosystems. The present study investigates the relationship between the macroinvertebrate assemblages and physico-chemical characteristics in order to find out the best environmental factors explaining the variations S in composition and distribution of macroinvertebrates in the Dara-Dachigam catchment. We hypothesized that along the longitudinal gradient, water quality deterioration in response to anthropogenic impact leads to a progressive shift of macroinvertebrate taxa from pollution sensitive to pollution tolerant taxa in response to changing environmental conditions. Furthermore, we suggest that benthic macroinvertebrate assemblages vary seasonally and are location-dependent as well.

2 Methodology

2.1 Study Area

Dachigam-Dara catchment of the world-famous Dal Lake is located in the vicinity of Srinagar city towards the east in the North-Western Kashmir Himalaya, India. The study area is situated between geographical coordinates of 34° 5′ 20″ to 34° 13′ 40″ N latitude and 74° 48′ 35″ to 74′ 08′ 32″ E longitude, with altitude ranging from 1592 to 4250 m above mean sea level. Therefore, the gradient between the most upper site (1828 m) and lower site (1592 m) is more than 230 m. Telbal-Dachigam in the northeast is the largest sub-watershed (230 km2) of Dal catchment comprising nearly 70% of the watershed and divided into the Telbal-Dara (87 km2) and Dachigam National Wildlife Reserve (143 km2) sub-watersheds. The average annual rainfall in Srinagar is 650 mm, whereas in Dachigam it is 870 mm. During the summer season, snow thawing in the upper reaches of the watershed leads to maximum discharge in Dachigam and Dara Nallah (Shah et al., 2014). The Dara catchment is subjected to several biotic interferences, resulting in loose soil, and it is via this nallah that the Telbal stream gets enormous amounts of eroded sediments throughout the year (Pandit, 1999). Water bodies in the study area have been altered biologically and hydrologically as a result of significant anthropogenic pressures as a result of changing socioeconomic conditions in the region (Khan, 1993a, b). Because of the rise in % impervious surface area due to developmental activities and human settlements in the Dal catchment, there is a quick peak flow in the streams (mostly Telbal Nallah) feeding the Dal Lake after a short period of precipitation, which enhances the rate of erosion (Amin & Romshoo, 2007). Furthermore, deforestation, grazing, and agricultural activities in the Dal watershed have resulted in large sediment loads, and nutrient loads into the lake have accelerated eutrophication (Badar & Romshoo, 2007). Twelve study sites were selected for the present investigation with sites 1, 2, 3, and 4 from the Dara catchment (North-Western side) are the left bank tributaries and are grouped as Dara zone (DARAZ); sites 5, 6, and 7 from the Dachigam catchment (North-Eastern side) being right bank tributaries were grouped as Dachigam zone (DACZ). Sampling locations 5 and 6 fall within the Dachigam National park which is home to endemic Kashmiri stag “Cervus Elaphus Hanglu” which is a critically endangered species in an IUCN list. The habitat and other environmental characteristics make it mandatory to keep it under observation. The streams from both catchments meet at a confluence point known as Wangund, and the sampling sites chosen were sites 8, 9, and 10 which were grouped in the Wangund zone (WANZ). Finally, all the streams and channels enter the Dal Lake at Shanpora, and two points were chosen, sites 11 and 12, which were named as Telbal zone (TELZ) (Supplementary Table S1, Fig. 1). The present work addresses the water quality using biotic and abiotic factors of the Dara-Dachigam catchment draining into the Dal Lake of Kashmir Himalayas India.

Fig. 1
figure 1

Map showing different study points taken for study in Dachigam-Telbal catchment of Dal Lake

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2.2 Material and Methods

2.2.1 Macroinvertebrate Collection

Macroinvertebrate samples were collected by kicking and displacing the bottom substrate from 12 sites from four zones of Dachigam-Dara catchment area on a seasonal basis from Spring 2016 to Winter 2018. A D-net (0.25 m × 0.25 m size) with pore size 0.5 mm was held in the stream with a lower frame placed close to the bottom of the stream. The bottom substrate upstream of the D-net was disturbed for at least 1 min to dislodge the organisms, which then float to the net with the natural flow (Cuffney et al., 1993; Barbour et al., 1999; Ligeiro et al., 2020). The procedure was repeated four times within a 100-m stream reach covering different hydrological regimes and substrate types to get an area of 1 m2 (Ilmonen & Paasivirta, 2005; Malmqvist & Hoffsten, 2000). Collection of macroinvertebrates at TELZ (soft bottomed downstream sites) was carried out using Ekmans Dredge made up of a 6-in. square brass box fitted with spring opened closing jaws (15.2 × 15.2). In the laboratory, collected macroinvertebrate samples were washed and cleaned, and were fixed with 70% ethanol and 4% formalin. Macroinvertebrates were identified to the genus level and enumerated using a dissecting microscope (× 6 magnification) according to the standard keys (Edmondson, 1959; Pennak, 1978; McCafferty and Provonsha, 1998; Borror et al., 1989; Ward, 1992; Engblom & Lingdell, 1999; Dar et al., 2002, Bouchard Jr, 2004; Merritt & Cummins, 2006; Subramanian & Sivaramakrishnan, 2007; Bhagat, 2013; APHA, 2012). Several ecological characteristics of macroinvertebrate assemblages were calculated including Shannon diversity (H) (Shannon and Weiner, 1949), Simpson diversity (1_D) (-Simpson, 1949), richness (Duan et al., 2008; Moore, 2013), Equitability_J (Magurran, 2003), and dominance (D) (Camargo, 1992). Macroinvertebrate samples were collected in four replicas, and the average values were used in the final claculations.

2.2.2 Physicochemical Variables

To assess the water quality of streams traversing the world-famous Dachigam National Park before and after their confluence, a detailed characterization of physical and chemical characteristics of 12 sites belonging to four zones was carried out on a seasonal basis for a period of 2 years from Spring 2016 to Winter 2018. Physico-chemical variables including discharge (Dis, m3s−1), velocity (Vel, ms−1), and water temperature (WT, °C) were measured while pH, dissolved oxygen (mgL−1), conductivity (µScm−1), and TDS (mgL−1) were measured using a multi-parameter probe (Eutech PCSTEST35-01 × 441,506/Oakton 35,425–10) calibrated with standard solutions in the laboratory. Total alkalinity (TA, mgL−1), total hardness (TH, mgL−1), calcium hardness (CH, mgL−1), calcium content (Ca2+,mgL−1), magnesium hardness (MH, mgL−1), magnesium content (Mg2+, mgL−1), sulfate (SO42−,mgL−1), sodium (Na+, mgL−1), and potassium (K+, mgL−1) were measured using titrimetric methods (APHA, 2012). Nitrate-nitrogen (NO3-N, µgL−1), ammonical-nitrogen (NH3-N, µgL−1), phosphate-phosphorus(PO4-P, µgL−1), total phosphorus (TP, µgL−1), and dissolved silica (Si, mgL−1) were measured by colorimetric assays using double-beam UV–Visible spectrophotometer following standard methods (APHA, 2012). Water quality variables were analyzed in four replicates, and the average values were used for the final calculations. The bottom substrate composition of the stream ecosystem was divided into one of the five categories: boulders (> 256 mm), pebble (256–64 mm), stone (64–20 mm), gravel (20–2.0 mm), sand (2.0–0.06 mm), and silt (< 0.06 mm) (Kaufmann et al., 1999; Sharma et al., 2004). At each sampling site, percentages of the substrate type were determined using a 1-m2 area.

2.2.3 Data Analysis

Multivariate statistical techniques were employed to understand the relationship between environmental factors and macroinvertebrates: non-metric multidimensional scaling (nMDS), a robust ordination technique that generates an ordination based on similarity or dissimilarity among samples. Stress value depicts the goodness of fit between the estimated matrix of dissimilarities and the associated distance within an nMDS plot (Clarke & Warwick, 2001). Analysis of similarity (ANOSIM), a non-parametric technique was used to test if there is a significant difference between macroinvertebrate assemblages among the two or more groups of samples as observed from the nMDS plot. ANOSIM uses a dissimilarity matrix and its values range from − 1 to + 1 at α = 0.05 to calculate the distinction between two or more groups. Similarity percentages (SIMPER) analysis based on the Bray–Curtis dissimilarity metric was used to measure the individual variable contribution to overall group similarity or dissimilarity (Clarke & Gorley, 2006). Before SIMPER analysis, abundance-based datasets were log-transformed log (x + 1) and the cut-off limit for small contributions was set at 90%. Principal component analysis, a data reduction procedure that allows interpreting data in a more meaningful way was employed to large datasets of physicochemical variables. PCA analysis reduced a large number of variables to a few pairwise and interpretable correlations among the variables as a linear combination to consider. The best subset of environmental variables (BIOENV) test was employed to evaluate the impact of environmental variable(s) on the macroinvertebrate assemblage structure and composition (Clarke & Ainsworth, 1993). BIOENV is a dissimilarity and exploratory technique for identifying the best subset of a set of environmental variables whose euclidean distance matches that of the Bray–Curtis matrix of biological data based on abundance data. BIOENV employs the weighted Spearman rank correlation coefficient (ρ) between the environmental factors and macroinvertebrate assemblages. Before the multivariate statistical analysis, biological and physico-chemical factors were transformed to fulfill the normality assumption and to allow comparison at the same scale. The group with the highest ρ is considered best for driving and structuring the macroinvertebrate assemblages. ANOSIM, SIMPER analysis, nMDS, PCA (principal component analysis), and BIOENV were performed using Primer v 7 (Clarke & Warwick, 2001). ANOVA and diversity indices were calculated using SPSS (Statistical Package for Social Science) and PAST 3.20 (Paleontological Statistics Software Package for Education and Data Analysis, Øyvind Hammer Natural History Museum University of Oslo), and remaining graphs were prepared in Origin 8 software. The Biological Working Monitoring Program (BWMP) and Average Score Per Taxon (ASPT) pollution indices were calculated to determine the pollution status variation between the zones (Armitage et al., 1983; AQEM, 2002; Mason, 2002).

3 Results and Discussion

3.1 Water Quality

The water quality variables measured from 12 sites belonging to four zones of Dachigam-Dara catchment indicated good status except at TELZ where a higher concentration of electrical conductivity and other key nutrients in comparison with other zones was recorded. The concentration of key plant nutrients (nitrogen and phosphorus) responsible for eutrophication at downstream zone was found crossing suggested trophic boundaries for streams (Dodds et al., 1998). The water quality index (WQI) calculated using BIS requirements rendered DARZ, DACZ, and WANZ as “clean water zones” and TELZ as “moderately polluted zone” (Table 1).

Table 1 Descriptive statistics of physico-chemical variables for the studied period compared with WHO/BIS standards
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Repeated measure analysis of variance (RM-ANOVA) test was performed on BMWP and ASPT scores at four zones in the Dachigam-Dara catchment. BMWP (F3,7 = 15.009, p = 0.001) and ASPT (F3,7 = 8.04, p = 0.008) showed a significant difference between zones. The BMWP and ASPT findings show clean water conditions in the DARZ, DACZ, and WANZ zones, which were characterized by a higher number of taxa, pollution sensitive taxa, high dissolved oxygen, low electrical conductivity, and nutrients while TELZ was classified as moderately polluted with the lowest number of taxa, pollution tolerant taxa, low dissolved oxygen, high concentration of electrical conductivity, and nutrients. Therefore, based on calculated WQI and BMWP/ASPT scores, TELZ is not meeting the “Good Ecological Status” requirements expected in freshwater water ecosystems (Table 1 and Fig. 2).

Fig. 2
figure 2

Box and whisker plots showing BWMP and ASPT scores at four zones in Dachigam-Telbal catchment dissimilar letters (a, b, and c) indicate significant difference (Tukey’s HSD test)

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3.2 Benthic Macroinvertebrate Assemblages

Sampling each of the 12 sites belonging to four different zones from Dachigam-Dara catchment of the Dal Lake resulted in a total of 5737 individuals representing 73 taxa belonging to 7 classes, 12 orders, and 41 families and spread over 3 phyla Mollusca (Gastropoda and Bivalvia), Annelida (Oligochaeta and Hirudinea), and Arthropoda (Insecta, Crustacea, and Arachnida). Class Insecta, was the most diverse group contributing 61 taxonomic forms belonging to 7 different orders (Hemiptera-1, Odonata-2, Plecoptera-6, Coleoptera-8, Trichoptera-14, Ephemeroptera-11, and Diptera-19) (Supplementary Table S2). The Arthropoda was the most dominant contributing 37 invertebrate families (constituting 90% of the total macroinvertebrate community including Crustacea and Arachnida) followed by Mollusca and Annelida constituting 5% each. Surprisingly, rivers and streams are found to support a large fraction of global biodiversity relative to their volume and aerial extent (McAllister et al., 1997). The presence of adequate benthic macroinvertebrate assemblages in the Dachigam-Dara catchment seems to be driven by favorable environmental conditions including water temperature, abundant food availability, habitat heterogeneity and stability, and hydraulic conditions (Brittain & Milner, 2001; Nicacio et al., 2020; Stanford et al., 2005).

The proportion of EPT (Ephemeroptera, Plecoptera, and Trichoptera) characterized mostly by pollution-sensitive taxa was significantly higher in DARZ, DARZ, and WANZ zones and became negligible in TELZ (Fig. 3). The higher proportion of EPT in DARZ, DARZ, and WANZ zones indicates their preference for stress-free environments (Mourier et al., 2019). EPT is considered pollution sensitive and prefers cold, oxygen-rich environs, and coarser substrate (help in retaining coarse particulate organic matter) (Jacobsen and Marín, 2008; Mourier et al., 2019; Pandiarajan et al., 2019). Furthermore, the occasional occurrence of Bibliocephala sp., Polycentropus sp. Corixidae sp., Agabus sp., and Cheumatopsyche sp. typically at the upper reaches belonging to DARZ and DACZ indicates a “healthy and natural” stream ecosystem condition (Ivol-Rigaut et al., 1997; Kaboré et al., 2016). There was a progressive shift in the benthic assemblages while moving down to the TELZ, marked by the higher proportion of Diptera, Opisthopora, Pharyngobdellida, Venerida, and Gastropoda which are mostly pollution tolerant (Bertaso et al., 2015). Increasing anthropogenic activities along the river continuum lead to water quality deterioration, subsequently leading to the progressive shift in benthic taxonomic composition from pollution-sensitive EPT to tolerant taxa (Harding et al., 1999). The TELZ zone characterized by deteriorated water quality as depicted by water quality assessment, BMWP/ASPT scores, and presence of fine-grained substrate (sand and silt) had less EPT and instead was dominated by Diptera, Opisthopora, Pharyngobdellida, Venerida, and Gastropoda (Bertaso et al., 2015). Slow-moving waters, with poor water quality (high electrical conductivity, nutrients), are unsuitable for rhithronic, rheophilus, and other sensitive species (Vander Laan et al., 2013, Jun et al., 2016a, b). Macroinvertebrate communities are highly adaptable to a variety of ecological factors, and they actively prefer suitable aquatic environments (Batzer et al., 2004).

Fig. 3
figure 3

Box plots showing various diversity indices at four zones. Dissimilar letters (a, b, c, and d) indicate significant difference (Tukey’s HSD test)

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The lower proportion of Diptera (comprising of pollution sensitive taxa and extremely pollution-tolerant taxa) at DARZ, DACZ, and WANZ zones and high proportion at TELZ zones (Fig. S1) display their potential to adapt in a variety of habitats (Bouchard Jr, 2004). In DARZ, DACZ, and WANZ zones, pollution-sensitive Dipterans representing natural conditions (Blephaceridae sp., Bibliocephala sp., Diamesinae sp., Atherix sp., Limonia sp., Antocha sp., Tipula sp., Simulium sp.) were progressively replaced with moderately to extremely pollution-tolerant taxa (Chrysops sp., Hexatoma sp., Tabanus sp., Chironomus sp., Psychodidae, Tanypodinae sp., Culex sp., and Procladius sp.) at TELZ zone indicating water quality deterioration (Barbour et al., 1999; Bouchard & Ferrington, 2011).

3.3 Diversity Indices

The overall number of benthic macroinvertebrate taxa, as well as their richness and diversity indices (Shannon and Simpson) and equitability were substantially higher (p < 0.05) in DACZ followed by DARZ and lowest in TELZ (Fig. 3). However, dominance indices were significantly higher (p < 0.05) in TELZ. According to our findings, the DACZ and DARZ zones have significantly higher diversity and equitability values than the TELZ, indicating that the upper zones not only have a greater number of taxa, but also that the individuals in the macroinvertebrate community are distributed equally among the species (Begon et al., 1996; Rosenzweig, 1995). In the TELZ zone, there are fewer species, and individuals in the community are not distributed equally resulting in higher dominance values. Furthermore, the higher the dominance index, the lower is the diversity and vice versa (Ludwig & Reynolds, 1988). Therefore, diversity indices not only provide information about the species richness, but they also account for the rarity and commonality of species and levels of disturbance in the ecosystem (Begon et al., 1996; Rosenzweig, 1995). Upstream sites characterized by clean water conditions and habitat heterogeneity support a higher diversity of benthic macroinvertebrates (Azrina et al., 2006; Abhijna et al., 2013; Maneechan & Prommi, 2015) holds in the present study. Diversity indices based on richness and abundance indicates that the disturbance in the aquatic ecosystems under stress leads to the reduction in the diversity (De Pauw et al., 2006). Biodiversity indices clearly reflected the habitat degradation effectively with increase pollution taxa at the TELZ zone.

nMDS based on abundance data (Fig. 4) resulted in a clear distinction among the locations of the sites (stress value of 0.117) in two-dimensional space. The zonation between the sites showed that the Wangund zone was found to be scattered within the biplot whereas, the Telbal zone resulted in a separate cluster at the right bottom of the biplot. The Dara zone and the Dachigam zone are seen together in the extreme right. At each of the four zones, one-way nested ANOSIM revealed a substantial variation in macroinvertebrate assemblages (global test R = 0.372, p = 0.001). Pairwise ANOSIM test specified that the following zones were significantly different: DARZ and DACZ (global test R = 0.086, p = 0.064); DARZ and WANZ (global test R = 0.228, p = 0.001); DARZ and TELZ (global test R = 0.936, p = 0.001); DARZ and WANZ (global test R = 0.175, p = 0.001), DACZ and TELZ (global test R = 0.961, p = 0.001), and WANZ and TELZ (global test R = 0.277, p = 0.007). The results showed that the Telbal zone was significantly different from other zones.

Fig. 4
figure 4

nMDS ordination plot of benthic macroinvertebrate assemblages between various zones

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To provide further insight, the SIMPER test was used to examine the macroinvertebrate assemblage, and the obtained results were compared with nMDS results. Within each zone, the SIMPER test revealed the highest average similarity (54.75%) in DARZ with the major contributing families Baetidae (11.75%), Erpobdellidae (11.32%), and Limnephillidae (6.85%). DACZ exhibited the second-highest average similarity (55.34%); the key contributing families were Baetidae (9.88%), Heptagenidae (8.45%), and Glossosomatidae (6.32%). The presence of Heptagenidae and Glossosomatidae in the upstream sites shows clean water conditions have been recorded by several other workers (Edema et al., 2002; Ikomi et al, 2005; Walsh et al, 2002). The average similarity of 22.78% was found at WANZ, and the contributing families were Batidae (17.80%), Gammaridae (15.90%), and Tipulidae (9.69%). The Telbal zone (TELZ) exhibited an average similarity score of 46.95% with major families contributing are Chironomidae (46.33%), Erpobdellidae (30.91%), and Gammaridae (9.22%). The higher proportion of Chironomidae and Erpobdellidae at downstream zone indicates the poor water quality in response to increasing anthropogenic conditions (Langdon et al., 2006) and possesses the ability to thrive in areas of low competition (Arimoro, 2009). A substantial proportion of Gammaridae downstream zone is in response to the considerable load of organic particles and seasonal improvement in water quality (Medupin, 2019; Miyake & Nakano, 2002). The present study depicted that Ephemeroptera dominated the macroinvertebrate assemblage and composition mainly by Baetidae at first three zones and Chironomidae at downstream zone, a characteristic feature of Asian streams (Jun et al., 2016a, 2016b). The SIMPER results showed the highest average dissimilarity between WANZ and TELZ (77.89%) followed by DACZ and TELZ (76.31%), DARZ and TELZ (71.08%), DACZ and WANZ (67.46%), DARZ and WANZ (64.87%); and least dissimilarity was exhibited in between DARZ and DACZ (46.95%).

3.4 Principal Component Analysis

Principal component 1 (PC1) accounted for 62.3% of the total variation and was dominated by altitude, boulders, pebble, stone, and gravel, whereas principal component 2 (PC2) accounted for 16.0% of the total variance and was dominated by velocity, pH, dissolved oxygen, and calcium hardness. To determine the physicochemical variables directly affecting the water quality of the Dachigam-Dara catchment under investigation, a backward elimination of highly weighted variables altitude, boulders, pebble, velocity, pH, and dissolved oxygen was executed. The new PC1 showed high values on discharge, ammonical-nitrogen, iron, sulfate, nitrate-nitrogen, nitrite-nitrogen, and stones which accounted for 67.3% overall variance, while PC2 accounted for about 10.1% of the overall variance. The results explain the difference between the different zones, i.e., Telbal zone forming the separate cluster in both the plots with higher water temperature, ammonical-nitrogen, electrical conductivity, sand, and silt (Fig. 5). The water quality variables measured in the DARZ, DACZ, and WANZ (upstream zones) had good water quality status while downstream zone (TELZ) moderately polluted status. Several studies have demonstrated the applicability of PCA to interpret and evaluate the relationship between benthic macroinvertebrates and water quality variables (Serpa et al., 2014; Tan and Beh, 2016). The outcome of the principal component analysis demonstrates that there are several potential macroinvertebrate taxa that could be used as bioindicators.

Fig. 5
figure 5

Principal components of physicochemical variables at different zones. (a) Variables included are Dis, Vel, WT, pH, EC, TDS, DO, FC, TA, Chl, TH, CH, Ca2+, MH, Mg2+, SO42−, NH3-N, NO2-N, NO3-N, PO4-P, TP, SI, Fe, Na+, K+, alt, boulder, pebble, gravel, stones, sand, and silt. (b) WT, DIS, FC, Mg2+, SO42−, NH3-N, NO2-N, NO3-N, TP, SI, and stones

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3.5 Relation Between Physicochemical Variables and Macroinvertebrate Assemblages

The environmental parameters that significantly correlate with the macroinvertebrate assemblages were pH, electrical conductivity, dissolved oxygen, and phosphate-phosphorous with correlation coefficient (ρ) = 0.694 in BIOENV analysis (Table 2). Other physicochemical parameters were not recognized as having a significant impact on macroinvertebrate assemblages; the results suggest key factors that would influence macroinvertebrate assemblage patterns which are pH, electrical conductivity, dissolved oxygen, and phosphate-phosphorous. The BIOENV supported the results obtained from nMDS, thereby indicating that the downstream zone (TELZ) is characterized by higher values of EC, nutrients, and low dissolved oxygen and therefore suggests the impact of anthropogenic activities at these sites (WFD Uktag, 2013).

Table 2 Weighted Spearman’s rank correlation various biotic and abiotic variables using the BIOENV procedure
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4 Conclusion

The influence of anthropogenic activities on water quality, distribution, and diversity of benthic invertebrates has steadily increased. Pollution-tolerant taxa were noticed in the downstream zone of Dachigam-Dara catchment, which had poor and low water quality index, BMWP, and ASPT scores, compared to upstream zones. The higher abundance of Chironomidae and Erpobdellidae at TELZ zone indicates the poor water quality in response to increasing anthropogenic conditions, while sizeable presence of Gammaridae TELZ zone is in response to the considerable load of organic particles and seasonal improvement in water quality. The standardized statistical tests tested for the Dachigam-Dara catchment indicated that the macroinvertebrate metrics and diversity indices varied significantly across the four zones. Moreover, our results indicate that the TELZ zone stands out from other zones as revealed from nMDS, SIMPER, and ANOSIM in response to water quality and macroinvertebrate assemblage. The BIOENV technique was shown to be useful in explaining the key water quality parameters responsible for the observed macroinvertebrate assemblage differences between zones. The results obtained from this study support the use of certain macroinvertebrate taxa as potential bioindicators for evaluating water quality. The findings of this study could be used as a baseline for future research and to inform decision-makers and policymakers on how bioindicators can aid in-stream monitoring, management, and stream conservation in the area.