The pattern of N/P/Si stoichiometry, although an important driver regulating river ecology, has received limited research attention for Ganga River. We investigated shifts in N/P/Si stoichiometry and ecological nutrient limitation as influenced by Varanasi urban core along a 37-km-long stretch of Ganga River. We also assessed the trophic status of the river in relation to shifting elemental stoichiometry. Together with point sources, atmospheric deposition coupled surface runoff appeared important factors leading to N/P/Si stoichiometric imbalances along the study stretch. The N/P and Si/P ratios declined downstream from 15.5 to 6.5 and 15.7 to 4.4, respectively, whereas N/Si increased from 1.01 to 1.6. Significant negative correlation of N/Si with biogenic silica to chlorophyll a (Chl a) ratios, and biogenic silica to phycocyanin ratios indicated increased growth of non-siliceous algae downstream signifying N and Si limitation with possible implications on food-web dynamics and feedback processes in the river in long run. Keywords Atmospheric deposition · N/P/Si stoichiometry · Ganga River · Phytoplankton · BSi · Autotrophic index Introduction toward Si limitation may shift phytoplankton from siliceous diatom to non-siliceous algal species (Harashim 2007). Anthropogenic-driven disproportionate nutrient loads have Thus, decreasing Si/N ratio and Si/P ratio may increase a fl g - led to shift the elemental stoichiometry in many aquatic ellated algae including harmful algal blooms with shift in ecosystems of the world (Gilbert 2012). A shift in stoichio- trophic cascade. Composition of phytoplankton assemblage metric ratio of critical nutrients may cause quantitative and regulates the feeding efficiency of zooplankton grazers, and qualitative change in composition of phytoplankton com- consequently, a shift from N to P limitation may lead to a munity with deviation in pattern of ecological processes major shift in zooplankton community as well (Elser et al. including nutrient limitation, biogeochemical cycling, car- 1998). Also, the composition of heterotrophs would change bon sequestration, trophic dynamics and biological diver- in response to changes in N/P/Si ratio and associated shift sity (Paerl 1997; Elser et al. 2009). Owing to optimal tem- in phytoplankton community. perature and light availability in tropical waters, nutrients During recent years, atmospheric deposition has become often become a key factor limiting phytoplankton growth. a dominant source of nutrient input to aquatic and terres- Elemental stoichiometry is the key node to explain resource trial ecosystems worldwide (Elser et al. 2009; Ellis et al. competition in phytoplankton (Tilman 1982) and associated 2015; Pandey et al. 2016a). Globally, fossil fuel burning shift in trophic cascades (Elser et al. 2009). Shift in N/P ratio adds ~ 25–33 Tg reactive N (Nr) and fertilizer application toward P, for instance, favors diazotrophic phytoplankton adds ~ 118 Tg Nr annually. On the other hand, major source over non-diazotrophs (Havens et al. 2003), while a change of atmospheric phosphorus is mineral aerosol adding ~ 3–4 Tg P per year (Carnicer et al. 2015). Biomass burning also is an important contributor of atmospheric P (Mahowald et al. * Jitendra Pandey 2005; Wang et al. 2015). Surface runoff, along with land use firstname.lastname@example.org influences, may be enriched by atmospherically added nutri - ents (Pandey et al. 2014a) stimulating blooms in receiving Ganga River Ecology Research Laboratory, Environmental Science Division, Centre of Advanced Study in Botany, waters (Beman et al. 2005). The Ganga River, together with Institute of Science, Banaras Hindu University, Brahmaputra and Meghna, is the second largest river system Varanasi 221005, India Vol.:(0123456789) 1 3 94 Page 2 of 12 Applied Water Science (2018) 8:94 of the world with respect to the amount of water discharge. more than tenfold than the average dry season discharge. The upper parts of the Ganges basin are mainly influenced The rainfall fluctuates between 780 mm in the upper part with glacier runoff, while middle and lower stretches are through 1040 mm in middle stretch, 1820 mm in the lower prominently influenced with land surface runoff emerging delta region reaching 2500 mm in north east. The soil of the from heterogeneous landscapes including large stretches basin is highly fertile alluvial fluvisol. of agricultural lands and staggered patches of densely This 3-year study was conducted during March populated metropolitan areas. Model studies show that the 2013–February 2016 (hereafter referred as 2013, 2014 Ganges basin alone contributes over 71% of India’s total and 2015) at three sites covering ~ 37 km of middle stretch gray water footprint (Mekonnen and Hoekstra 2015). The (25°18′ N lat.; 83°1′ E long.) of the Ganga River represent- basin receives ~ 3.32 Tg of Nr and ~ 173.2 Gg of P through ing up- and downstream Varanasi city (Fig. 1; Table 1). atmospheric deposition annually (Pandey et al. 2016a). Samples in triplicate were collected from three subsite of Atmospheric deposition coupled surface runoff causes dis- each site representing urban, midstream and offside. Selec- proportionate nutrient enrichment leading to nutrient imbal- tion of sites and subsites was based mainly on sources of ances and stoichiometric shift (Elser et al. 2009; Pandey nutrient input. et al. 2014b). Most of the studies so far on these issues have focused mainly on temperate lakes and oceans (Elser et al. 2009; Markaki et al. 2010; Lepori and Keck 2012). Data on Measurements changing N/P/Si stoichiometry, causal relationships, shift in nutrient limitation and implication in rivers of India, spe- Atmospheric deposition cially for the Ganga River, are very scarce (Pandey et al. 2014a, b, 2016b; Pandey and Yadav 2015). In this paper, Atmospheric deposition samples were collected at fort- we present data on causal relationship of shifting N/P/Si nightly interval using bulk samplers made up of a 5-L stoichiometry, with particular reference to the influence of high-density polyethylene bottle connected to a Teflon Varanasi urban core, and associated changes in ecological funnel. Thymol was used as biocide in collection buck- nutrient limitation along a 37-km stretch of Ganga River ets to avoid changes in nutrient concentration. After each representing up- and downstream Varanasi city. We hypoth- sampling, the funnels were rinsed with double distilled esized that, together with point sources emerging from the water to collect deposits from funnel walls. Analyses of − + 3− city, increased AD-coupled surface runoff has altered the NO, NH and PO in bulk samples were executed 3 4 4 N/P/Si stoichiometry and consequently the pattern of eco- spectrophotometrically. logical nutrient limitation in the river. To investigate this, we analyzed nutrient limitation pattern, phytoplankton biomass, Runoff chemistry autotroph–heterotroph proportions in the river over time and space in relation to nutrient input. For surface runoff sampling, stations were selected based primarily on land usage. The strategy was to collect storm water representing total washout characteristics of particu- Materials and methods lar site flushed at single outlet from the area marked for the purpose. Runoff samples in triplicate were collected manu- Study area ally from each site on event basis, initiated from the first flush, using pre-sterilized plastic bottles. NO in runoff The Ganga River basin is the 4th largest (1,086,000 km ) was quantified using brucin sulphanilic acid (Voghe 1971), trans-boundary river basin in the world and the largest NH was measured following Park et al. (2009), and total among river basins in India covering ~ 26.2% of total geo- dissolved nitrogen (TDN) following high-temperature per- graphical area of the country. Climate of the basin is tropi- sulfurate digestion. Dissolved reactive phosphorous (DRP, cal monsoonal to humid subtropical. The year shows dis- orthophosphate) was measured following ammonium molyb- tinct seasonal pattern; a humid monsoonal (July–October) date–stannous chloride method (APHA 1998). DON was − + with relative humidity reaching close to saturation; a cold calculated as TDN minus DIN (N O and NH ) (Perakis 3 4 winter (November–February) with minimum temperature and Hedin 2002). sometimes below 4 °C and, hot-dry summer (March–June) where temperature may exceed 46 °C. Southwest monsoon River water chemistry brings most of the rainfall with over 80% during monsoon season. The river is fed by the Himalayan snow from April River water samples in triplicate were collected from each to June and by rain-driven runoff from July to September subsite, from directly below the surface (15–25 cm depth) with recurrent floods. The average monsoonal discharge is in acid-rinsed 5-L plastic containers. Conductivity was 1 3 Applied Water Science (2018) 8:94 Page 3 of 12 94 Fig. 1 Map showing sampling locations Table 1 Sampling sites and characteristics of sub-catchments Nos. Name Code Location Characteristics 1 Adalpura Adpr Upstream and downstream Sheetla mata temple Relatively natural, agricultural and rural runoff 2 Assi Ghat Asht Downstream panton bridge Residential and urban runoff 3 Rajghat Rjht Upstream and downstream Malviya bridge Highly dense urban area, biomass burning urban emissions urban runoff measured on site using multiparameter tester (Oakton Biological variables 35425-10, USA). Biological oxygen demand (BOD) and dissolved oxygen (DO) were quantified following standard Biogenic silica (BSi) was determined in reach-scale sam- methods (APHA 1998). Nitrate was estimated using brucin ples of sediment (25–30 m reach; 0–10 cm depth). Air-dried sulphanilic acid method, NH following phenate method samples (< 2 mm) were digested in 0.1% N a CO , and BSi 2 3 (Park et al. 2009) and dissolved reactive phosphorous was determined following molybdate blue method (Michalo- (DRP) following ammonium molybdate–stannous chloride poulos and Aller 2004). Chlorophyll a (Chl a) was measured method. Dissolved silica (DSi) was quantified following following acetone extraction procedure (Maiti 2001) and molybdate blue method (Diatloff and Rengel 2001). gross primary productivity (GPP) by light and dark bottle 1 3 94 Page 4 of 12 Applied Water Science (2018) 8:94 method (APHA 1998). Phytoplankton biomass (ash free dry Results mass; AFDM) was determined by measuring the weight loss − + resulting from incineration at 525 °C for 30 min on glass The atmospheric deposition (AD) of N O, NH and 3 4 3− fiber filters. Chlorophyll a and AFDM were used to calculate PO increased overtime at all the study sites (Fig. 2). On autotrophic index (AI) (Wetzel and Likens 2000). Phycocya- spatial scale, the deposition showed a gradient of increas- nin was extracted in 100 mM phosphate buffer and analyzed ing order with over 2.6-fold increase in AD-NO , 2.01- + 3− spectrophotometrically (Boussiba and Richmond 1979). fold in AD-NH and 4.1-fold in AD-PO in 2015 from 4 4 Adpr to Rjht site. Seasonally, depositions were lowest in Statistical analysis monsoon and highest in winter (Fig. 2). Differences in AD- inputs were significant with respect to site, season and year Coefficient of variation (cv) was used as a measure of uncer - (Table 2). Similar to AD-input, the concentration of N and tainty. Significant effect of site and time was tested using P in surface runoff increased consistently over time, with analysis of variance (ANOVA). Correlation analyses and values being highest at Rjht and lowest at Adpr. Concen- regression models were employed to test linearity in rela- trations of dissolved inorganic nitrogen (DIN) in surface −1 tionships. Principal component analysis (PCA) was used to runoff ranged from 2.79 to 4.89 mg L . Dissolved organic ordinate sampling locations and environmental variables. nitrogen (DON) and dissolved reactive phosphorous (DRP) −1 SPSS package (version 16) was used for statistical analysis. in runoff ranged from 1.48 to 2.10 and 0.38 to 0.89 mg L , 14 6000 2015 2015 0 0 6 2500 1.6 1.4 1.2 1.0 0.8 600 0.6 0.4 0.2 0.0 0 W S RW S RW W SW R S RR S RS W RS W S R W S W R Adpr Asht Rjht Adpr Asht Rjht − + 3− Fig. 2 Atmospheric deposition (AD) of NO, NH and PO and (DRP) at study sites. Values are mean (n = 9) ± 1 SD; W winter, S 3 4 4 runoff concentrations of dissolved inorganic nitrogen (DIN), dis- summer, R rainy season solved organic nitrogen (DON) and dissolved reactive phosphorous 1 3 3- -1 -1 + -1 -1 - -1 -1 AD-PO ( Kg ha season ) AD-NH ( Kg ha season ) AD- NO ( Kg ha season ) -1 -1 Runoff DRP (µg L ) Runoff DON (µg L ) -1 µg L ) Runoff DIN ( Applied Water Science (2018) 8:94 Page 5 of 12 94 Table 2 F ratios obtained from analysis of variance (ANOVA) indicating significant effect of site, season, year and their interactions on AD, run- off and river water quality variables Variable Site Season Year Site * season Site * year Season * year Site * season * year 5 5 3 3 River-nitrate 2.31 × 10 *** 1.63 × 10 *** 6.05 × 10 *** 5.16 × 10 *** 470.22*** 2.4*** 129.32*** 4 4 River-ammonia 3.52 × 10 *** 7.06 × 10 *** 907.6*** 589.87*** 343.4*** 18.5*** 26.4*** 5 4 3 River-phosphate 2.17 × 10 *** 4.52 × 10 *** 1.43 × *** 7.78 × 10 *** 682.12*** 196.5*** 170.3*** 4 3 River DOC 3.41 × 10 *** 5.40 × 10 *** 71.7*** 82.1*** 8.83*** 2.7* 5.8*** River-DO 330.18*** 169.6*** 2.4* 30.1*** 2.41* 11.3*** 4.05** 4 3 River-BOD 1.26 × 10 *** 1.91 × 10 *** 507*** 176.14*** 96.01*** 43.7*** 33.5*** 4 4 3 3 River-conductivity 1.53 × 10 *** 4.71 × 10 *** 2.17 × 10 *** 1.35 × 10 *** 561.53*** 170.1*** 354.7*** 4 4 3 River-DSi 2.57 × 10 *** 3.8 × 10 *** 146.9*** 3.58 × 10 *** 72.0*** 167.8*** 165.15*** 4 4 3 River-BSi 3.83 × 10 *** 7.38 × 10 *** 209.8*** 1.6 × 10 *** 6.26*** 50.2*** 7.94*** 3 3 River-Chl a 1.01 × 10 *** 2.34 × 10 *** 48.21*** 168.9*** 3.09* 6.9*** 7.09*** 3 3 River-GPP 1.40 × 10 *** 5.81 × 10 *** 203.9*** 318.2*** 60.74*** 57.8*** 75.1*** 4 4 River-PC 1.48 × 10 *** 1.76 × 10 *** 314.5*** 732.07*** 63.06*** 27.9*** 56.09*** River-N/P 3.1 × 10 *** 58.5*** 2.54*** 82*** 2.31* 19.14*** 6.44*** 3 3 3 River-N/Si 1.56 × 10 *** 5.42 × 10 *** 100.43*** 36.64 × 10 *** 19.9*** 12.4*** 3.54** 5 4 River-Si/P 1.16 × 10 *** 2.66 × 10 *** 302.7*** 2.71*** 16.1*** 11.3*** 3.32** 4 5 3 3 River-AI 1.87 × 10 *** 1.67 × 10 *** 259.1*** 7.4 × 10 *** 567.6*** 461.6*** 1.42 × 10 *** 4 3 AD-nitrate 4.84 × 10 *** 8.13 × 10 *** 571.01*** 726.76*** 39.4*** 26.2*** 33.22*** 3 3 AD-ammonia 5.14 × 10 *** 2.81 × 10 *** 393.16*** 71.96*** 2.46* 13.6*** 8.68*** AD-phosphate 8.46 × 10 *** 292.9*** 132.39*** 21.71*** 10.98*** 10.9*** 3.26** 4 3 3 AD-organic carbon 2.49 × 10 *** 6.66 × 10 *** 451.96*** 1.007 × 10 *** 14.5*** 22.36*** 6.48*** 4 4 3 3 AD-N/P 7.32 × 10 *** 1.6 × 10 *** 3.28 × 10 *** 4.98 × 10 *** 837.53*** 1.56*** 513.93*** Runoff-DIN 8.52 × 10 *** – 225.49*** – 21.01*** – – Runoff-DON 2.13 × 10 *** – 40.31*** – 4.11* – – Runoff-DRP 9.95 × 10 *** – 55.05*** – 10.27*** – – Runoff-DOC 2.10 × 10 *** – 187.41*** – 9.11*** – – Values significant at *p < 0.1, **p < 0.01 and ***p < 0.0001 respectively. Differences in runoff variables were significant increased downstream and overtime with a marginally vari- (Table 2). able trend at Adpr. Spatiotemporal variations in these vari- Dissolved oxygen (DO) in river water declined down- ables were statistically significant (Table 2). N/P ratio was stream, being highest at Adpr (Fig. 3). As expected, dis- highest (15.5) at Adpr and lowest (6.5) at Rjht site (Fig. 5). solved oxygen was found to be lowest in summer and Similar trend was found in Si/P. For N/Si however, the ratio declined over time. Conductivity increased down the gradi- was lowest at Adpr (1.01) and highest at Rjht (1.6). Season- ent with only marginal difference on temporal scale. Con- ally, Si/P ratio was found highest in rainy season, while N/Si centration of N and P increased over time and were highest showed opposite trend being lowest in monsoon. Although in winter. Concentration of NO ranged from 185.5 (Adpr; deviation in N/P stoichiometric ratio did not show specific −1 + 2013) to 264.57 µg L (Rjht; 2015); NH from 17.9 (Adpr; pattern, on an average, the N/Si ratio increased, while Si/P −1 3− 2013) to 26.3 µg L (Rjht; 2015) and PO from 27.13 showed a declining trend at all sites. −1 (Adpr; 2013) to 96.8 µg L (Rjht; 2015). The biochemical The principal component analysis, based on water oxygen demand (BOD) showed a trend similar to nutrients, chemistry and biological variables, separated the least dis- but the values were highest in summer. Concentration of turbed site, Adpr from moderately and highly disturbed dissolved silica (DSi) declined marginally along the gradient sites Asht and Rjht (Fig. 6). The ratio of BSi/Chl a, PC/ with values being highest in monsoon. Biogenic silica (BSi), Chl a in low flow was lowest in 2015, with variable trend chlorophyll a (Chl a) biomass, gross primary productivity for Adpr (Fig. 7). River-NO showed significant positive − 2 (GPP) and phycocyanin (PC) all were found to be the highest correlation with AD-NO (R = 0.33; p < 0.001) and river- 3− 3− 2 in summer and lowest in monsoon (Fig. 4). Productivity var- PO with AD-PO (R = 0.63, p < 0.001). Similar rela- 4 4 3− iables followed a trend similar to the nutrient AD-input, sur- tions were found with runoff DIN and PO (Fig. 8). The face runoff and river water. Also, the autotrophic index (AI) 1 3 94 Page 6 of 12 Applied Water Science (2018) 8:94 7 10 0 0 350 500 0 0 35 600 0 0 S R WS R WS S RS W R WS R WS RS W R WS R WS RS W R WS R WS RS W R WS R WS RS W R WS R W R W Asht Rjht Adpr Adpr Asht Rjht Fig. 3 Physicochemical characteristics and concentration of nutrients at study sites in the Ganga River. Values are mean (n = 27) ± 1 SD. DO dis- solved oxygen, BOD biochemical oxygen demand, DSi dissolved silica, AI autotrophic index, S summer, R rainy season, W winter N/P stoichiometric ratio in river showed negative correla- Discussion tion with sediment BSi (R = 0.96, p < 0.001), river Chl a 2 2 (R = 0.92, p < 0.001), GPP (R = 0.84, p < 0.001) and PC Source partitioning (R = 0.61, p < 0.001). River Si/P and productivity vari- ables also were significantly negatively correlated. BSi/Chl Nutrient inputs to rivers and streams are regulated by a ratio was found significantly positively correlated with landscape features, land use, population density and stoichiometric ratio of N/P (R = 0.74, p < 0.001) and Si/P urban-industrial release. The nonpoint sources, which are (R = 0.78, p < 0.001) (Fig. 8) and negatively correlated with continuing to rise in developing countries such as India, N/Si (R = 0.72, p < 0.001). are complex, more widespread and relatively difficult to 1 3 3- -1 - -1 PO ( µg L ) + -1 4 NO ( µg L ) NH ( µg L ) 3 -1 DO ( mg L ) -1 -1 -1 Conductivity ( µS cm ) BOD ( mg L ) -1 -1 -1 DSi ( µg L ) AI (µg L AFDW µg L Chl a) Applied Water Science (2018) 8:94 Page 7 of 12 94 2014 16 Adpr Asht Rjht 2.2 2.0 1.8 1.6 1.4 10 1.2 1.0 0.8 0.6 SR WS RW SR W 20132014 2015 Fig. 5 Spatiotemporal variations in N/P/Si stoichiometry at study sites in the Ganga River; S summer, R rainy season, W winter fertilizers (NGRBA 2011). The region witnessed a tre- −1 mendous growth in chemical fertilizer use from 4 kg ha SR R W S R W SS R W R WS S R WW R W S R W S R W S −1 in 1962–1965 to 205 kg ha in 2003–2006 (GRB-EMP Adpr Asht Rjht 2011). Long-range transport from distant sources may also be an important source of NO (Ellis et al. 2015). Site I Fig. 4 Trends in productivity variables at study sites. Values are mean (Adalpura; population 2837, situated upstream) represents (n = 27, for BSi n = 9) ± SD. Chl a chlorophyll a, PC phycocyanin, GPP gross primary productivity, BSi biogenic silica, S summer, R rural to township type settlement with natural land patches rainy season, W winter and agricultural land. Other two sites are under direct urban influence of Varanasi, a city with over 1,500,000 control. Atmospheric deposition (AD), for instance, adds population ranks 33 among 225 metropolitan cities of the nutrients to river ecosystems directly on water surface and country. The city with high vehicular load per unit area via lateral transport through surface runoff. In this study, (about 274,331 in 2000 to over 588,000 in 2012) witnesses AD-nutrients increased with increasing urban-industrial inefficient public transport with about 85.05% vehicles −1 influence downstream. Additionally, agriculture is another r unning < 18 km h in peak traffic hours (Motor Trans - major factor for AD-Nr. Ganges basin consumes over 45% port Statistics of India 1999–2000). Streams in watersheds of the total chemical fertilizers used in the country out with higher road density have been shown to have high of which Uttar Pradesh alone consumes 38% of chemical levels of N and P (Moore et al. 2014). Seasonally, highest 1 3 -1 BSi (µg L ) -2 -1 -1 -1 GPP (mgC m h ) PC (µg L ) Chl a (µg L ) N: Si ( Molar) Si: P ( Molar) N: P ( Molar) 94 Page 8 of 12 Applied Water Science (2018) 8:94 Adpr Asht Rjht 0.8 Col 8 0.6 Col 9 Col 10 0.4 Fig. 6 Principal component analysis (PCA), based on water quality data and pollution input, separates sampling locations and other vari- ables at different ordinates. Rjht, the most polluted site, is separated − 3− + 0.2 with NO, PO, NH and conductivity, while Adpr, the least pol- 3 4 4 luted site, is separated opposite to Rjht 0.0 deposition in winter could be linked to low temperature, low wind, short range transport, low mixing height and concurrent formation of inversion especially in urban areas (Pandey et al. 2014a). Atmospheric deposition, although vary according to source density and distribution in the watershed, is the most important source of nutrient in some areas (Howarth et al. 2002; Pandey et al. 2016a). In agriculturally domi- nated Western Europe, N H constitutes the major share, whereas in USA, with prevalence of emission from fossils fuel combustion, N O constitutes the dominant component 0 2013 2014 2015 of AD-N (Paerl et al. 2002). AD contributes 13–19% of annual excess N input to North Atlantic Subtropical gyre Fig. 7 Spatiotemporal trends in ratio of productivity variables at (Zamara et al. 2010). The eastern US coast and eastern Gulf study sites in the Ganga River during low flow of Mexico receive 10–40% of new N as AD-input with influ- ence on biotic composition in receiving estuaries and coastal −2 −1 waters. Of the 40% of total N added as direct deposition in both dry and wet deposition, about 6.80 kg N ha year many coastal areas, ~ 30% comes from fossils fuel combus- in major rivers of northeastern USA, and it has been estab- −1 −1 tion and 10% from ammonia volatilization from agricultural lished that AD-N exceeding 6 kg ha year could signifi- sources. In this study, agriculture appeared the major fac- cantly enhance chlorophyll in surface waters (Elser et al. tor for remote areas, whereas biomass burning and auto- 2009). Previous studies have shown that AD-N and P could mobile emission seemed to be the principal determinant of account about 19.3–31.2 and 2.5–13.4%, respectively, of AD-input in urban and near-urban areas. Downstream sites GPP in Ganga River (Pandey et al. 2014a). receiving emission from burning of ~ 25,000 tons wood bio- Surface runoff adds large amount of nutrients to receiv - mass for crimination of over 36,000 dead bodies each year ing waters including rivers. Our data showed that dissolved − + show high AD-P. Biomass burning is an important source of inorganic-N (NO + NH ) contributed the major share in 3 4 atmospheric P (Mahowald et al. 2005). A shift in AD-N/P runoff N and, both DIN and DRP were high at anthropo- stoichiometry toward P observed in this study indicates more genically disturbed sites. Nutrient concentration in surface intense sources of atmospheric P relative to N. This has rele- runoff indicated the coupled effect of AD and land use. vance for river N/P as well (Pandey et al. 2014b). Boyer et al. Earlier studies have shown that AD enhances soil nutri- (2002) estimated average oxidized-N deposition, including ent release and consequently their concentration in surface 1 3 PC: Chl a BSi: PC BSi: Chl a Applied Water Science (2018) 8:94 Page 9 of 12 94 R = 0.33 (p< 0.001) - - Runoff- DIN vs River- NO AD- NO vs River- NO 3 3 y= 172.06 x + 7.4 350 + + AD- NH vs River- NH Runoff- DIN vs River- NH 4 4 4 3- 3- 3- R = 0.95 (p< 0.001) AD- PO vs River- PO Runoff- DRP vs River- PO 300 4 4 y= 83.66 x + 0.036 NS R = 0.656 R = 0.63 (p< 0.001) 150 R =0.96 (p< 0.003) y= 7.8 x + 0.003 y= 15.8 x + 61.59 y= -24.77 x + 0.1349 NS R = 0.0042 y= 80.02 x + 1.2 ab 0 246 81012140100020003000400050006000 -1 -1 -1 Runoff (µg L ) Atmospheric deposition (Kg ha season ) 4.4 4.4 R = 0.72 (p< 0.001) R = 0.74 (p< 0.001) 4.2 4.2 y= 5.27 x - 1.22 y= 2.7 x + 0.08 4.0 4.0 3.8 3.8 3.6 3.6 3.4 3.4 3.2 3.2 3.0 3.0 cd 2.8 2.8 46 81012141618 1.21.4 1.61.8 N: P (Molar) N: Si ( Molar) 4.4 7.5 R = 0.78 (p< 0.001) R = 0.73 (p< 0.001) 4.2 y= 2.91 x + 0.08 y= 10.7 x - 3.33 7.0 4.0 6.5 3.8 6.0 3.6 5.5 3.4 5.0 3.2 4.5 3.0 ef 2.8 4.0 2468 10 12 14 16 1.21.4 1.61.8 Si: P ( Molar) N: Si ( Molar) Fig. 8 Correlation between a atmospheric N, P and river N, P; b runoff N, P and river N, P; c river N/P ratio and BSi/Chl a; d river N/Si ratio and BSi/Chl a; e river Si/P ratio and BSi/Chl a; and f river N/Si ratio and BSi/PC −1 −1 runoff (Pandey et al. 2014a). Site-wise differences indicated 10 kg ha year of N leaching (He et al. 2011), the river in major influence of agriculture and urban factors regulat- the study sub-watershed receives approximately 738 tons of ing N and P fluxes through surface runoff. In addition to DIN annually through this process. Thus, the Ganga River, surface runoff, leaching from agricultural land also con- with highest population density and 73.44% agricultural land tributes substantially to nutrient enrichment. Assuming a in its vast drainage basin, is more prone to such influences. 1 3 -1 River nutrient (µg L ) BSi: Chl a BSi: Chl a BSi: Chl a BSi: PC -1 River nutrient (µg L ) 94 Page 10 of 12 Applied Water Science (2018) 8:94 Our study shows, although the study river receives massive Correlative evidence indicated strong links between AD- input through point sources, nonpoint sources such as AD, coupled runoff nutrients and river N, P and productivity. leaching and surface runoff are important contributors for Relatively low Si results possibly from low input through shifting elemental stoichiometry. urban landscapes. At Asht site, N/Si ratio was 1.1–1.2 The variations in river nutrients and productivity were (N/Si ~ 1) and BSi was highest indicating highest diatom found to be statistically significant (p < 0.001). The PCA growth. The freshwater diatom species contain high silica bi-plot displayed 95.7% variations on 1st axis and 4.2% on content per unit biovolume than the marine species (Conley 2nd axis and identified three different groups of sampling et al. 1989). Relative DSi availability determines the propor- locations based on water quality and pollution input. Adpr, tion of siliceous diatoms (Baines et al. 2010), and freshwater the least polluted site, appeared almost opposite to polluted pinnate diatoms are heavily silicified compared to freshwater and most polluted sites Asht and Rjht, respectively. Rjht centrics (Conley et al. 1989). Thus, downward increase in site under strong urban control was influenced by NO and BSi and decrease in DSi may reflect abundance of highly 3− PO . Also, this site showed influence of BSi, conductiv - silicified diatom species (Pandey et al. 2015). Centric diatom ity and N H separating it from Asht and Adpr. Adpr site blooms have been a growing concern for eutrophied rivers. positioned toward opposite axis representing least influence Among centric species, Cyclotella, a highly silicified dia- of the nutrients and other variables. The declining N/P and tom, has low grazing pressure and high sedimentation rate 6 −2 −1 Si/P stoichiometric ratio at highly disturbed sites indicates (3–304 × 10 cells m day ) (Ardiles et al. 2012). As also large P sources relative to N and Si. The river receives dis- reported in our previous studies (Pandey et al. 2015, 2017), proportionately high amount of N and P from anthropogenic dominance of such species can enhance export of biogenic sources. In addition to AD and surface runoff derived nutri- silica and C to river sediment (Ardiles et al. 2012). Thus, a ents, the study stretch receives 155 MLD treated sewage, shift in nutrient limitation and associated resource competi- 59.6 MLD untreated sewage and 558.6 MLD wastewater tion among algal groups may lower the proportion of less added to the river directly (CPCB 2013). Sub-watershed adapted taxa and consequently C sequestration. scale calculations show that Assi drain adds ~ 485 tons of The elemental stoichiometry regulates resource competi- DIN and 121 tons of DRP in the Ganga River annually. tion and, in turn, the consumer-driven nutrient cycling and Between Assi Ghat and Rajghat, AD contributes approxi- food chain efficiency (Elser et al. 1998 ; Piehler et al. 2004). mately 14 tons of DIN and 1 tons of DRP, while surface High P loading, for instance, decreases species richness of runoff adds 263 tons of DIN and 42 tons of DRP annually. phytoplankton and the biomass ratio of piscivores to plank- These inputs could generate strong imbalances in elemental tivores and zooplankton to phytoplanktons (Jappesen et al. stoichiometry in the river. 2000). In this study, sediment BSi, an indicator of C turno- ver, was high in summer and showed high correspondence Nutrient limitation with Chl a and GPP, indicating that a major proportion of sediment BSi was of diatom origin. Since these observations Riverine silica originates from lithogenic sources, and its are based on low flow season records, BSi of terrigenous concentration is highly sensitive to human activities such origin had been assumed to have no effect. BSi and PC are as damming and agriculture as witnessed in the Ganges the main indicators of diatom and diazotrophic population, basin. DSi drives the growth of diatoms that form a major respectively. Here, increased phytoplankton growth links part of phytoplankton autochthonous C in rivers (Bernard low BSi/Chl a and PC/Chl a overtime. Spatially, Rjht site et al. 2011). The southeast Asia with largest population and with lowest BSi/Chl a, BSi/PC ratios and highest PC/Chl urbanization is witnessing deviation in nutrient stoichiom- a ratio indicates relatively reduced share of diatom and etry and phytoplankton growth (Bernard et al. 2011; Pandey enhanced proportion of non-siliceous phytoplankton. A et al. 2016b). In this study, the ratios appeared deviated from shift toward N and Si limitation could possibly account for canonical Redfield ratio with a decline in N/P and Si/P indi- shift in phytoplankton composition from siliceous to non- cating a shift toward N and Si limitation. Low N/Si ratio siliceous phytoplankton at Rjht site. Relatively less disturbed in monsoon indicates high Si addition from the catchment Adpr site was found with lowest phytoplankton growth. As during monsoon. Summer season increase in nutrients and nutrient concentration increased from Adpr to Asht, high productivity is fueled by supply of nutrients through sewage phytoplankton biomass including those of diatom origin and atmospheric deposition. In Ganga River, after construc- could account highest BSi/PC ratio at this site. A decrease tion of Tehri dam, there has been a massive decline in river in BSi/Chl a ratio further downstream indicates declining discharge in dry season. The productivity variables were contribution of diatom to overall productivity. The BSi/Chl a significantly (p < 0.001) negatively correlated with river ratios showed significant positive correlation with river N/P 2 2 flow. High flow season witnesses surplus of nutrients but (R = 0.74; p < 0.001) and Si/P (R = 0.78; p < 0.001). Fur- the dilution effect reduces concentration in per unit volume. ther, significant negative correlation between BSi/Chl a and 1 3 Applied Water Science (2018) 8:94 Page 11 of 12 94 N/Si ratio (R = 0.72, p < 0.001) and BSi/PC and N/Si ratio References (R = 0.73; p < 0.001) indicated production of phytoplankton American Public Health Association (APHA) (1998) Standard methods biomass with increasing non-siliceous species downstream for the examination of water and wastewater. APHA, Washington, signifying N and Si limitation. It seems that rising N, P and DC declining Si level would lead less silicified diatom and non- Ardiles V, Alcocer J, Vilaclara G, Oseguera LA, Velasco L (2012) siliceous phytoplankton to prevail in near future. Low N/P Diatom fluxes in a tropical, oligotrophic lake dominated by large- sized phytoplankton. Hydrobiologia 679:77–90 and Si/P ratios in relation to BSi/Chl a ratio depict high P Baines SB, Twining BS, Brzezinski MA, Nelson DM, Fisher NS (2010) input to be a major factor for phytoplankton growth down Cause and biogeochemical implications of regional differences in the river. Autotrophic index, a ratio of AFDW/Chl a, is used silicification of marine diatoms. Glob Biogeochem Cycl 24:1–15 as an indicator of heterotrophic and autotrophic population Beman JM, Arrigo KR, Matson PA (2005) Agricultural runoff fuels large phytoplankton blooms in vulnerable areas of the ocean. in streams. In this study, autotrophic index increased down- Nature 434:211–214 stream, being highest at Rjht indicating high relative propor- Bernard CY, Durr HH, Heinze C, Segschneider J, Maier-Reimer E tion of heterotrophic community influenced by high organic (2011) Contribution of riverine nutrients to the silicon biogeo- load driven by autotrophic and allochthonous-C input. Auto- chemistry of the global ocean—a model study. Biogeosciences 8:551–564 trophic index (AI) ~ 250 for periphyton is considered an indi- Boussiba S, Richmond AE (1979) Isolation and characterization of cator of nutrient-enriched eutrophied condition. The AI in phycocyanin from the blue green algal Spirulina platensis. Arch marine phytoplanktons and pond ranges from 76–200 and Microbiol 120:155–159 44–221, respectively (Weber 1973). In this study, average Boyer TP, Stephens C, Antonov JI, Conkright ME, Locarnini RA, O’Brian TD, Garcia HE (2002) World Ocean Atlas 2001. Salin- autotrophic index ranging from 81.3 to 103.5 indicates an ity 2:165 oligotrophic condition of the river. Carnicer J, Sardans J, Stefanescub C, Ubach A, Bartrons M, Asensio D, Nuelas JP (2015) Global biodiversity, stoichiometry and eco- system function responses to human-induced C–N–P imbalances? J Plant Physiol 172:82–91 Conclusions Central Pollution Control Board (2013) Pollution assessment: River Ganga. Ministry of Environment and Forests, Govt. of India, Pari- vesh Bhawan, East Arjun Nagar, 110032,Delhi Nutrient-enriched conditions, increased autotrophic produc- Conley DJ, Kilham SS, Theriot E (1989) Differences in silica con- tion and associated growth of heterotrophy in the river indi- tent between marine and freshwater diatoms. Limnol Oceanogr cate a likely shift toward eutrophy. Based on the autotrophic 34:205–213 Diatloff E, Rengel Z (2001) Compilation of simple spectrophotometric index (AI), the river appears to be in oligotrophic condi- techniques for the determination of elements in nutrient solutions. tion. Together with point sources, especially urban sewage, J Plant Nutr 24:75–86 increased AD-coupled surface runoff has altered the N/P/Si Ellis BK, Craft JA, Flathead JAS (2015) Long-term atmospheric depo- stoichiometry of the river with a shift toward N and Si limi- sition of nitrogen, phosphorus and sulfate in a large oligotrophic lake. Peer J 3:1–20 tation over P limitation, which in turn, could promote growth Elser JJ, Chrzanowski TH, Sterner RW, Mills KH (1998) Stoichiomet- of less silicified diatom and non-siliceous algae. Further, a ric constraints on food-web dynamics: a whole-lake experiment shift in N/P/Si stoichiometry toward Si limitation would trig- on the Canadian Shield. Ecosystems 1:120–136 ger shifting diatom associated C sequestration and trophic Elser JJ, Andersen T, Baron JS, Bergstrom AK, Jansson M, Kyle M, Nydick KR, Steger L, Hessen DO (2009) Lake stoichiometry and cascades with long-term implications on food-web dynamics nutrient limitation driven by atmospheric nitrogen deposition. Sci- and ecological feedbacks. Our study highlights the need for ence 326:835–837 large-scale inter regional time series data on human-driven Ganga river basin: Environment management plan by Indian Institute shift in elemental stoichiometry for predicting long-term of Technology (2011) Trends in agriculture and agricultural prac- tices in Ganga basin. An overview. Report code 015 GBP IIT SEC changes in ecosystem attributes and associated feedbacks ANL 01 VER 1 in river ecosystems. Gilbert PM (2012) Ecological stoichiometry and its implications for aquatic ecosystem sustainability. Curr Opin Environ Sustain Acknowledgements The authors are thankful to the Coordinator, Cen- 4:272–277 tre of Advanced Study in Botany, Banaras Hindu University for facili- Harashim A (2007) Evaluating the effects of change in input ratio of ties and to Banaras Hindu University (Grand No. 46233) for funding N: P: Si to coastal marine ecosystem. J Environ Sci Sustain Soc support in the form of fellowship to AY. 1:33–38 Havens KE, James RT, East TL, Smith VH (2003) N: P ratios, light Open Access This article is distributed under the terms of the Crea- limitation, and cyanobacterial dominance in a subtropical lake tive Commons Attribution 4.0 International License (http://creat iveco impacted by non-point source nutrient pollution. Environ Pollut mmons.or g/licenses/b y/4.0/), which permits unrestricted use, distribu- 122:379–390 tion, and reproduction in any medium, provided you give appropriate He B, Kanae S, Oki T, Hirabayashi Y, Yamashiki K (2011) Assess- credit to the original author(s) and the source, provide a link to the ment of global nitrogen pollution in rivers using an integrated Creative Commons license, and indicate if changes were made. biochemical modeling framework. 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Published: Jun 2, 2018
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