Ecology

Climate dataThermal profiles showed considerable diel variation at each field location (Fig. 3). Soil temperatures on the ground surface fluctuated the most, reaching maximum temperature (Mt Ginini, 45.5 °C; Piccadilly Circus first season, 46.5 °C; Piccadilly Circus second season, 46.0 °C; Cooma, 42.1 °C; Dartmouth, 47.5 °C) during the day (1330–1730 h) and dropping to low level (Mt Ginini, 4.0 °C; Piccadilly Circus first season, 8.5 °C; Piccadilly Circus second season, 6.5 °C; Cooma, 7 °C; Dartmouth 12.5 °C) at night (2330–0400 h) (ANOVA, F4, 6521 = 279.4, P  More

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Step-pools are natural geomorphologic forms developed under the action of extreme floods1 in mountain streams with bed slopes ranging from 3 to 20%2,3. The step-pools are characterized by poorly graded bed materials intricately packed to form a step and pool sequence, generating high energy tumbling and tranquil mountain flows. The typical bed morphology is irregular and results in spatially and temporally varied hydrodynamics over various functional units within the step-pools such as step, tread, base of step, and pool region (Fig. 1b). The step denotes the portion of bed comprising boulders or bedrock outcrops jam-packed across the width of the channel. The pool consists of finer bed materials and deeper cross-sections as a result of scour due to submerged or unsubmerged hydraulic jumps generated in the pool. The base of step is the section immediately downstream of step unit where the flow over the step impinges into the pool with high amounts of turbulence and self-aeration. The tread is the region extending from the downstream end of the scour pool up to the step unit. Step-pool systems can also exist without the presence of a tread region. In that case, the pool region directly ends in a step unit4.Figure 1Details of step-pool systems in the present study: (a) Longitudinal section of the field site, (b) Longitudinal section of the laboratory model, (c) Photograph of the field site, (d) Photograph of the experimental setup.Full size imageThe evaluation of flow parameters in step-pool streams does not follow the general criteria recommended for lowland rivers. The commonly used flow friction factors such as Manning’s n and Chezy’s C cannot be applied here due to the non-uniform nature of flow at meso-scale. In step-pool mountain streams, the rational frictional coefficient to define flow resistance is the non-dimensional Darcy Weisbach friction factor5,6,7. Dedicated field and laboratory investigations of the step-pools are necessary to create a sufficient database for the development of accurate hydraulic models.In addition, the design of step-pools is adopted for stream restorations8,9, storm water conveyance systems10, and for creating close-to-nature step-pool fish passes11,12,13. Primal research on step-pools has been largely limited to the analysis of bed morphology14,15,16,17, flow resistance18,19,20,21,22 and sediment transport23,24 by considering the step-pool reach as a single system. A detailed review on the hydrodynamics of step-pools in mountain streams is available in Kalathil and Chandra7.The variations in the flow characteristics imposed by the various morphological units within the step-pool system (SPS) were not studied until the 2000s. Adverse pressure gradients in the pools and upstream of steps lead to increased turbulence, while favourable pressure gradients on steps suppress turbulence. Accordingly, pools are dominated by wake turbulence and the steps, treads and runs are governed by form or bed-generated turbulence. The wake turbulence in pools is characterized by recirculation eddies and its strength diminishes with increase in distance from the impingement point25,26. Incidentally, the variations in hydrodynamics within step-pool systems do not furnish considerable differences in the sediment transport estimation since the measured and computed magnitudes differ up to an order of three because of the limited sediment availability in mountain streams27,28. Nevertheless, updated knowledge on the flow dynamics at different regions within the step-pool reach will aid in providing guidelines for designing close-to-nature fish passes to enable target species to pass through the fluvial system29,30. In recent times, with increased demands to implement and maintain environmental flow schemes, cost-effective and eco-friendly structures such as step-pools provide a promising tool to facilitate economic development together with ecological conservation.The presence of a wide range of substrates (fine sand to boulders) and varying flow conditions in step-pools facilitate the inhabitation, migration, and dispersal of diverse aquatic species31. The productive range of water depth and flow velocity for inhabitation lies between 0.16 m to 0.5 m and 0.3 m/s to 1.2 m/s, respectively11. In addition to the range of flow depth and velocity requirements, hydraulic shear stress and turbulence characteristics also affect fish behaviour and locomotion32. Depending on the turbulence scale and intensities, various damages on the fish body or disorientation of the species may occur. Therefore, it is important to consider fish behaviour, life stage, swimming ability, and hydraulic conditions including velocity and turbulence characteristics in step-pool structures prior to its ecological applications. Adequate design guidelines for the construction of close to nature fish passes are not available due to lack of studies31,33.Limited research addresses the influence of bed morphology on the turbulence characteristics in step-pools. Wohl and Thompson4 studied the variations in flow profiles at different locations in a step-pool with the use of an electromagnetic current meter of sampling frequency 0.5 Hz. The study was limited to the analysis of mean velocity and coefficient of variation in velocity which is sometimes used synonymous to turbulence intensities. Although flow profiles showed variations in pattern, ANOVA and ANCOVA results were rather inconclusive regarding the dependence of flow parameters on the bed form types. Later on, Wilcox and Wohl34 and Wilcox et al.35 conducted three-dimensional velocity measurements using SonTek FlowTracker operating at 1 Hz sampling frequency to study the spatial variation of velocity and turbulence intensities in step-pools. The pools exhibit increased levels of turbulence intensities and less velocity reduction in cases where the upstream step-units do not span the entire width of the channel and effects as leaky or porous steps35. The turbulence characteristics in terms of the root mean square of the fluctuating velocities showed considerable differences between step, tread and pool. However, due to the low sampling frequency of the velocity measuring instrument, the accuracy of turbulence analysis is questionable. Considering the complex terrain in step-pool streams and practical difficulties in the use of high frequency instruments that require proper stationing and continuous power supply, it is arduous to produce good quality data of the fluctuating velocities. To bridge the gap in research on the fluctuating velocity components in step-pools, extensive laboratory studies are required.In this context, to shed light upon the variation of hydraulic parameters with the morphological units, we discuss the results from a physical model downscaled according to field measurements conducted in a step-pool stream in Erumakolli, Wayanad, India. Figure 1 shows the longitudinal sections and photographs of the field site and the laboratory experimental setup. The field investigation comprised the measurements of bed material size, bed topography and flow velocity measurements. The physical model study discusses the variation in the turbulence characteristics across steps, treads and pools. The analysis is limited to the vertical distribution of velocity magnitude and turbulence intensities across the morphological units, the propagation of velocity magnitude and Reynolds shear stress in the flow direction, relationship between the turbulent kinetic energy and velocity magnitude, and the evaluation of energy dissipation factor in the step-pools. The present study is the foremost attempt in the analysis and discussions of turbulence fluctuations, Reynolds shear stresses and energy dissipations in self-formed step-pool systems.Experimental setup validationThe laboratory experimental setup was created by establishing dynamic similarity between the field and the laboratory model through Froude’s Model Law. Model scales less than 10:1 can successfully simulate field conditions in the case of turbulent self-aerated flows36. A length scale ratio of 3.3: 1 was chosen for creating the physical model. The corresponding velocity scale and discharge scale are (3.3)1/2: 1 and (3.3)5/2: 1, respectively. The laboratory step-pool system is self-formed under a formative discharge and is not expected to generate the exact bed topography as observed in the field. However, effect of the influencing parameters such as D84, step-height, bed slope, and discharge on the velocity and turbulence characteristics would be adequately simulated. A comparison of the thalweg velocity and Froude number over step, tread and pool at d = 0.6 H between the field and laboratory data is presented in Table 1, where d is the depth of measurement and H is the total flow depth at that point. Since the measured data is location specific and due to the limitations in the number of data points available, only the reach scale average values of velocity and Froude number was used to estimate the error. An absolute error of 0.04 m/s (6.3%) and 0.02 (4.9%) was observed between the field and laboratory up scaled data for thalweg reach average velocity and Froude number, respectively.Table 1 Comparison of the thalweg velocity and Froude number for step, tread and pool at d = 0.6 H between the field and laboratory data.Full size tableVelocity and turbulence intensitiesThe velocity and turbulence characteristics pertinent to accurate design and model development of step-pools are velocity magnitude (VR), turbulence intensities (TI), normalized turbulent kinetic energy (K), and energy dissipation factor (EDF). We obtained velocity data in the physical model using Nortek Vectrino 3-D Acoustic Doppler Velocimeter. A total of 16 thalweg velocity data at d = 0.6H and 24 vertical velocity profiles at 1 cm intervals have been retained after velocity filtering and processing (see “Methods”), where d is the depth of measurement and H is the total flow depth at that point. The velocity measurements were confined in the range of 0.003 m/s to 0.796 m/s, bounding the productive range for aquatic species inhabitation in field scale (0.005 m/s to 1.446 m/s).The propagation of flow in a step-pool system is illustrated in Fig. 2. The x-axis shows the measurement sections along the longitudinal direction (X) for step-pool system 2 (see “Methods”). The variable on the y-axis z + H − d denotes the elevation of the measurement point above the datum which is set at the deepest scour point of X = 2.60 m. Where, z is the vertical distance from the datum to the bed surface, H is the total flow depth at the point, and d is the depth of measurement with respect to the free surface. The first vertical corresponding to X = 2.40 m is at a distance of 0.15 m downstream of a step unit. Any data collected closer to the steps were removed in data filtration. The average velocity at d = 0.6H is shown in the plot legend. The lowest velocity is observed at the deepest scour section of X = 2.60 m.Figure 2Variation of resultant velocity magnitude VR in the longitudinal direction of the SPS 2.Full size imageTo examine the statistical differences in the distribution of velocity and turbulence intensities across the morphological units, the 24 vertical velocity measurements comprising longitudinal and cross-stream points have been subjected to Kruskal–Wallis ANOVA. The earlier studies that sought to distinguish the morphological units on the basis of velocity components performed one-way ANOVA on the datasets. Although the measurements on steps, treads and pools are independent of each other and randomly sampled, the available data fails to uniformly conform to normal distribution, which is a prerequisite for ANOVA test. Therefore, the present work revisits this analysis for velocity components, velocity magnitude and turbulence intensities using Kruskal–Wallis ANOVA which is a non-parametric test that does not assume a normally distributed dataset. The analysis is conducted on the ranks of the data values rather than the data values, and tests whether the median values are significantly different from each other. A resultant p value of 0 from the analysis indicates that there is significant difference between the groups, while a p value of 1 indicates vice-versa. The null hypothesis of Kruskal–Wallis ANOVA is that the sample groups come from the same population. Closeness of the p value to 0 is a measure of the confidence in rejecting the null hypothesis. In the present study, data points were categorized with respect to d/H values to normalize the effect of depth on the velocity variations, and the data points confined in the range d/H = 0.50–0.70 were considered for the test (Case I). The d/H is thus selected to obtain a wider range of data points pertaining to the average velocity which is typically at d/H = 0.6. The non-parametric test was also repeated for depth averaged values (Case II) to produce similar results. Except in the case of cross-stream velocity v, all other groupings showed significant difference between the median values for step, tread and pool data points. The p values of 0.24 and 0.41 were obtained for the hypothesis test on v for Case I and Case II, respectively. This shows that the variation in the cross-stream velocity is independent of the morphological type and is not a characteristic feature of step-pool system in a straight channel. The step-pool system that encounters bends within the reach may have an influence on the cross-stream velocity component. The results of the statistical analysis and box-plots of the velocity magnitude and turbulence intensities for both Cases I and II are given in Table 2 and Fig. 3, respectively. A negligible absolute error of 0.027 m/s and 0.031 m/s in velocity magnitude was obtained between the mean and median of Cases I and II, respectively. Whereas, a maximum absolute error of 0.134 and 0.087 was observed in the respective turbulence intensities. However, the differences in the methods are not substantial enough to alter the results of the hypothesis testing.Table 2 Comparison and analysis results of Kruskal–Wallis ANOVA for data points in the range of d/H = 0.50–0.70 and depth-averaged values at various verticals across the morphological units.Full size tableFigure 3Distribution of resultant velocity magnitude VR and turbulence intensities TI of the fluctuating velocities, u′, v′, and w′ for different morphological unit: (a) Case I: data points confined to d/H = 0.50–0.70. (b) Case II: depth-averaged values at each vertical.Full size imageThe average values of TIu′, TIv′, and TIw′ combining the 24 verticals (d/H ranging from 0.20 to 1.00) are 0.065,0.055 and 0.097 for steps, 0.146, 0.110, and 0.165 for treads, and 0.453, 0.265, and 0.523 for pools, respectively. The values show an increase of 55%, 50% and 41% for TIv′ with respect to TIu′ for step, tread and pool, respectively, while a sizeable increase of 597%, 382% and 439% for TIw′ with respect to TIu′, which evidently indicates the dominance of vertical fluctuations in the pools. The pattern of variation of turbulence intensities at step, tread and pools can be better understood with the help of vertical profiles. Figure 4 shows the vertical profiles of velocity magnitude and turbulence intensity profiles corresponding to step, tread and pool regions in different step-pool systems, namely, SPS 1, SPS 2, and SPS 3 (see “Methods”). Compared to the velocity profiles in step and tread, a visible mid-profile shear layer can be seen in the pool. Previous researchers have identified the presence of mid-profile shear in regions of wake turbulence. Thompson and Wohl4 illustrated the shear layer downstream of steps with the help of velocity profiles in step-pool systems. Baki et al.37 illustrated the presence of shear layer in the wake turbulence regions of a rock-ramp fish pass. A staggered arrangement of natural boulders of equivalent diameter 14 cm was used to prepare the rock-ramp bed. The wake area downstream of the boulders is similar to the downstream of steps in step-pool systems. Fang et al.38 illustrated the shift in the vertical profile of Reynolds shear stress due to near-bed and boulder-induced shear stresses. In the present study, the shear layer is prominent in SPS 3, milder in SPS 1 and fairly non-existent in SPS 2 which corresponds to the profile at X = 2.40 m in Fig. 2. The shear layer is generated due to the momentum exchange occurring in the pools consequent to flow impingement. The occurrence of the shear layer and the magnitude of velocity shift depend on the characteristics of the upstream step unit and spacing between the upstream step and point of interest. In the case of leaky step units where some portion of the step cross section is devoid of elevated step units, the flow passes through without causing consideration impingement to the downstream pool. Hence, the flow does not produce a downstream wake region resulting in the absence of shear layers in the vertical profile. The same can be observed from Fig. 4e, where the vertical section in SPS 2 existed downstream of a leaky step unit, which also lead to lower levels of energy dissipation and less velocity reduction.Figure 4Variation of resultant velocity magnitude VR and turbulence intensities TI of the fluctuating velocities, u′, v′, and w′ along the depth: (a) VR at Step, (b) TI at Step, (c) VR at Tread, (d) TI at Tread, (e) VR at Pool, and (f) TI at Pool.Full size imageThe magnitude of turbulence intensities is lower on step and tread, and maximum in the pools as can be observed in Fig. 4b, d and f, respectively. The fluctuations are higher in the pools due to the varied velocity distribution and wake turbulence characteristics in the pools.Reynolds shear stressReynolds shear stress is the stress generated due to the momentum exchange between the fluctuating velocity components. The range of shear stress in the flow medium has implications in the suitability of a flow body to various aquatic lives since high levels of shear stress may even lead to major injuries or mortality to the species. The vertical profiles of the time averaged and normalized Reynolds shear stress in the x–z plane in the longitudinal direction is shown in Fig. 5. The x-axis shows the normalized Reynolds shear stress (- overline{{u^{{prime }} w^{{prime }} }} /V_{max }^{2}) for each section, where Vmax = 0.796 m/s is the maximum velocity measured during the experimental runs. The variable on the y-axis follows the same convention as described in Fig. 2. The fluctuations in the profile are more in the deeper locations in the pool (X = 2.40 m to 2.80 m) due to the increased turbulence at the bottom as a result of flow impingement. The error bar shown for X = 2.50, 2.90 and 3.05 is calculated from the additional 4 verticals measured in the cross-stream direction, for each of the sections. The maximum Reynolds shear stress variation is observed in x–z plane compared to x–y and y–z planes, with normalized values ranging from − 19.477 to 13.729. The absolute maximum value of 19.477 amounts to 12.34 N/m2 in model scale and 40.73 N/m2 in prototype scale. Reynolds shear stress as low as 30 N/m2 can cause reduction in startle response in some species39. Therefore, ensuring acceptable limits of turbulence fluctuations is essential in the design for artificial constructions of the step-pool morphology. While recreating the morphology for a fish pass design, control can be placed on the pool volume, characteristic grain size (equivalent to step height) or allowable discharge to reduce the turbulence levels in pools. However, this entails detailed study into the cause and effect of these parameters on the hydrodynamics.Figure 5Variation of time averaged and normalized Reynolds shear stress in the x–z plane (( – overline{{u^{{prime }} w^{{prime }} }} /V_{max }^{2} )) in the longitudinal direction of SPS 2 (Vmax = 0.796 m/s).Full size imageTurbulent kinetic energyAnother indicator of turbulence characteristic to the morphological units in the present study is Turbulent Kinetic Energy (TKE) which is a measure of kinetic energy per unit mass of the turbulent flow. It is an important parameter that determines the locomotive characteristics of various species40 and key to evaluating the energy loss to fishes41. In the present study, normalized form of turbulent kinetic energy (K) follows an inverse power relation to the velocity magnitude as shown in Fig. 6. The x-axis is normalized using Vmax and TKE is normalized by the transformation (K = sqrt {{text{TKE}}} /V_{R}). The data is inclusive of all the depth-wise data points measured over step, tread and pool regions along the thalweg. Larger values of K occurred in pools followed by tread and step regions. The pattern is comprehensible from visual observation of the flow field, where the flow occurs as high-velocity sheet with limited agitation over tread and step, resulting in plunging flow with recirculating eddies in the pools. A non-linear curve fitting of type Power-Allometric 1 was used to generate the empirical equation (K = aleft( {{{V_{R} } mathord{left/ {vphantom {{V_{R} } {V_{max } }}} right. kern-nulldelimiterspace} {V_{max } }}} right)^{b}), where a = 0.12398 and b = − 0.89947 with a standard error of ± 0.01018 and ± 0.03398, respectively. The coefficient of determination of the plot is 0.93.Figure 6Variation of normalized turbulent kinetic energy K with the time-averaged thalweg velocity ratio VR/Vmax (Vmax = 0.796 m/s).Full size imageEnergy dissipation factorThe energy dissipation factor (EDF) is an engineering design parameter that checks the turbulence level in the fish pathways. The flow energy must be sufficiently dissipated to ensure velocity levels less than 2 m/s. The EDF is also representative of the eddies and turbulence generated due to flow impingement into pools. Contrary to the conventional pool fish passes and slot fish passes, the pool cross-sections in natural step-pools are not uniform33. The pool dimensions vary along the reach and accordingly reflects in the EDF values. Hence, calculation of EDF using the equation (EDF=gamma QS/A) would result in considerable errors since A is not a constant within and across step-pool systems, where γ is the specific weight of water, Q is the discharge, S is the bed slope and A is the cross-sectional area of the pool. Here, the EDF calculations were based on the basic equation (EDF = gamma QDelta H/forall), where ΔH is the drop in water elevation level per pool and (forall) is the pool volume. The step-pool systems 1, 2 and 3 have been evaluated for EDF. The pool volume was calculated applying the trapezoidal rule to the wetted areas of cross-sections in pool spaced at 10 cm apart. The wetted area was calculated from the measured bed and water elevation levels. The region starting immediately downstream of the steps up to the exit slope of the scour pool is considered for calculating the pool volume. Table 3 presents the pool dimensions and EDF values obtained in the present study. The EDF values obtained for step-pool systems 1, 2, and 3 were 321, 207, and 123 W/m3 in model scale, respectively. The results corresponds to 590, 380, and 226 W/m3 in prototype scale. However, a value of 150 W/m3 should not be exceeded to ensure acceptable levels of turbulence in the pools33. Specific EDF criteria for various fish species are available to design fish passes accommodating the requirements of the predominant fish population42.Table 3 Computation of energy dissipation factor in step-pool systems 1, 2 and 3.Full size tableConsidering the range of shear stresses and energy dissipation factors obtained in the present study, it can be inferred that construction of step-pool fish passes simulating the field parameters may not provide adequate flow conditions for a step-pool type fish pass. For the design of step-pool type fish passes, the pool volumes should be back calculated from the EDF equation for specific species. The translation of the pool volume in terms of the width of channel, pool length and pool depth will ensure lower levels of turbulence intensities and shear stresses in the passage. Since the interest towards close-to nature fish passes have been developed only in the recent years, specific guidelines for step-pool type fish passes are yet to be formulated. This calls for research in artificial step-pool constructions based on the pool and turbulence requirements of the dominant target species. Nevertheless, the nature of hydrodynamics in the self-formed and artificially constructed would coincide since the step-pool bed morphology has an inherent tendency to attain a state of maximum resistance. The concept of creating artificial structures is limited to providing and placing the bed materials into required bed slopes and approximate design dimensions. The ultimate bed morphology of the structure will be modelled over time by the hydraulic force of the flowing water. More

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Study areaThe important river Damodar (563 km) originates from Khamarpat hill under Palamau district of Jharkhand state (India). It flows toward east direction and ultimately it joins with river Bhagirathi-Hooghly in West Bengal. Upper and middle parts of the river basin have rich diversity of minerals and standard quality coal reserve of Gondwana formations. Abundant supply of fresh river water with high mineral and energy resources attracts many large, medium and small-scale industries since historical time. River Damodar is the principal supplier of water resource to drinking, industrial and domestic purpose in its catchment area. Therefore, such favourable environment attracts huge population along with industrial integration in this area. The present study area is bounded by 23° 28′ 28.7″ N to 23° 40′ 52.5″ N and 86° 49′ 26.8″ E to 87° 18′ 42.4″ E and 65.37 km river stretch has been selected for the study. In this section high, agglomeration of industries and allied human works intensively developed along the riverside. Many iron and steel plants, thermal power plant, sponge iron factory, chemical industries, coal mining fields and urban centres have been developed through the evolution of time. As a result, huge untreated waste (solid/ liquid), hot water, coal dust and urban effluents are being regularly discharged to the riverbed through various connecting channels which are locally called nallas (Fig. 1)10,11.Figure 1source QGIS 3.16 software (https://qgis.org/en/site/forusers/download.html).Location map of the study stretch of a tropical river Damodar (India). The diagram is prepared by openFull size imageSample collection and data analysisWater samples were collected from eleven discharged points of industrial effluents on main riverbed. First, samples were taken on December 2019 (pre-lockdown/ normal period), again second, samples were collected in June, 2020 (during lockdown) to assess the changes on river water quality due to temporarily closing of industries. Third, samples were obtained in November, 2020 (after unlock phase) to get clear idea about effects of industries on the river water quality. Samples were obtained from 0.5 m below the surface water level within 5 m influencing radius zone. Pre cleaned polyethylene bottles (500 ml) were used for the collection of five subsamples from each sampling site and mixed up to get a bulk contain (1 l). All samples were carried properly for further analysis in laboratory. Sample containers were labelled as S1, S2, S3… to S11 for properly identification (Fig. 1). Total 20 parameters were analysed from each sample of each period. Important parameters such as pH, electrical conductivity (EC), total dissolved solids (TDS), turbidity, magnesium (Mg2+), calcium (Ca2+), chloride (Cl-), sulphate (SO42–), nitrates (NO3−), Biological Oxygen Demand (BOD), Dissolved Oxygen (DO), zinc (Zn2+), cadmium (Cd2+), lead (Pb2+), nickel (Ni2+), chromium (Cr), iron (Fe2+), chlorophyll a (Chla), total phosphorus (TP), and Secchi disk depth (Sd) have been considered. Consequently, pH and EC were measured at the sampling sites using Thermo probe, Hanna HI9811-5 potable meters respectively. DO was determined through Winkler’s method at the sampling spot immediately28. EC denoted by microsiemens per centimetre. TDS was determined following the procedure given by Hem (1991). Turbidity was denoted by Nephelometric Turbidity Unit (NTU’s). All cation, anions, BOD and DO were expressed in mg/l while all heavy metals, TP and Chla denoted as microgram/l. All other physico-chemical parameters and heavy metals were analysed by standard procedure which was prescribed by American Public Health Association (APHA)29. Chla and total phosphorus were estimated following APHA29 standard procedures. Secchi disk (Sd) with 8 in. diameter and attached cord in disk centre was used for depth measurement and expressed in meters at the maximum limit of depth where disk was seen from the above into the water.Modified water quality index (MWQI)MWQI of the 33-sample water was conducted for 11 sample sites by important water quality parameters namely pH, TDS, EC, turbidity, Mg2+, Ca2+, Cl-, SO42–, NO3–, BOD and DO. We considered 11 variables per sample in the index. The calculation of MWQI was conducted following the method of Vasistha and Ganguly30.At first, pre defined weightage was assigned for each selected parameter. The weightage of each parameter was obtained from previous literatures. After that, relative weight of each parameter was derived by the formula.$$ RW = AW/sumlimits_{i = 1}^{n} {AW} $$
(1)
where RW is relative weight of each parameter, AW is assigned weight obtained from past literature (AW of pH = 1, TDS = 1.79, EC = 1.78, turbidity = 1.09, Ca2+  = 0.8, Mg2+  = 0.72, Cl– = 1.28, SO42– = 1.60, NO3– = 2.32, BOD = 1.72, DO = 2.85) and n is total number of parameters considered for analysis.Second, quality assessment (Qi) of each parameter was obtained following the formula.$$ Q_{i} = (C_{i} times S_{i} ) times 100 $$
(2)
where Ci is concentration of particular parameter in sample water, Si is standard permissible limit of each parameter as suggested by BIS31 and WHO31 (Table 1).Table 1 Descriptive statistics of twenty variables of physio chemical, heavy metals and biological parameters in three period.Full size tableQi for pH and DO was obtained through some modification of Eq. (1.2) because optimum concentration of these two parameters are little different from others. The optimum value of pH and DO is considered as 7.0 and 14.6 mg/l (100% saturation at 23 °C), respectively32. Thus, Qi for these two parameters were performed using the formula.$$ Q_{i} = (frac{{C_{i} – V_{i} }}{{S_{i} – V_{i} }}) times 100 $$
(3)
where Vi denotes optimum values of pH and DO.Third, in this step sub index (SIi) was calculated for each considered parameter by multiplication of relative weight (RW) with quality assessment (Qi) value of each parameter using formula below.$$ SI_{i} = RW times Q_{i} $$
(4)
At last, MWQI was obtained for each sample site by summation of SIi of each parameter as below:$$ MWQI = sumlimits_{i = 1}^{n} {SI_{i} } $$
(5)
Water quality (based on MWQI values) has been categorised into 5 classes such as excellent (≤ 50), good (50–100), poor (100–200), very poor (200–300) and unfit for drinking (≥ 300) as suggested by BIS31 (IS:10500).Heavy metal index (HMI)Analysis of heavy metal index was done using 6 parameters as Cd2+, Zn2+, Cr, Pb2+, Ni2+, and Fe2+. Calculation was conducted through this formula33.$$ Wi = K/Si $$
(6)
where Wi suggests weightage of ith parameter, K means constant value (1), Si means standard value of ith parameter as per BIS31, and WHO32. In the next step, sub index calculation (Qi) was done through this formula.$$ Qi = sumlimits_{i = 1}^{n} {frac{Mi}{{Si}}} times 100 $$
(7)
where Mi is the value of heavy metal concentration in sample water, Si is maximum limit of permissible of ith parameter in µg/l according to BIS31 and WHO32 (Table 1). At last, HPI was calculated using this formula which is given below.$$ HPI = frac{{sumlimits_{i = 1}^{n} {WiQi} }}{{sumlimits_{i = 1}^{n} {Wi} }} $$
(8)
where n indicates total number of parameters used for calculation of HPI. HPI can be classified into five categories such as excellent (0–25), good (26–50), poor (51–75), very poor (75–100) and unfit for drinking ( > 100).Potential ecological risk (RI)To assess the environmental response of heavy metal contamination, a new index was applied from sedimentological perspective and it was proposed by Hakanson33. In this method, effects of heavy metals on environment and possibilities to ecological risk can be determined by a single contamination coefficient, toxic response coefficient of heavy metals and comprehensive contamination of metals for any aquatic or soil environment using this formula34.$$ C_{f}^{i} = C_{s}^{i} /C_{n}^{i} ,;c = sumlimits_{i = 1}^{n} {C_{f}^{i} } $$
(9)
$$ E_{r}^{i} = T_{r}^{i} times C_{f}^{i} ,;RI = sumlimits_{i = 1}^{m} {E_{r}^{i} } $$
(10)
where Csi specifies heavy metal contamination value, Cni indicates reference value of heavy metals, C stands for degree of contamination by toxic heavy metals, Eri represents ecological risk factor of any single substance, Tri indicates ‘Toxic- response’ of any particular metal and RI denotes potential ecological risk index of all measured toxic metals. In this study, reference value of heavy metals was taken from standard preindustrial values of heavy metals as Cd = 1.0, Pb = 70, Cr = 90 and Zn = 175. Toxic response of heavy metals was used as follows: Cd = 30, Pb = 5, Cr = 2 and Zn = 1 (Hakanson33). Values of RI can be classified into four categories such as Practically uncontaminated ( 600).Trophic State Index (TSI)Trophic status of river was identified by Trophic State Index (TSI) considering three parameters such as Secchi disk depth (Sd), Chlorophyll-a (Chla), Total phosphorus (TP). Trophic State Index (TSI) was calculated by Carlson method35.$$ TS(Sd) = 60.0 – 14.41 times Ln(Sd) $$
(11)
$$ TS(TP) = 14.42 times Ln(TP) + 4.15 $$
(12)
$$ TS(Chla) = 30.6 + 9.81 times Ln(Chla) $$
(13)
$$ {text{TSI }}left( {text{Trophic State Index}} right) = left[ {TS(Sd) + TS(TP) + TS(Chla)} right]/3 $$
(14)
Values of TSI were classified into seven categories such as low oligotrophic ( 80).Statistical and spatial analysisA meta analysis such as descriptive statistics, Pearson correlation coefficient, analysis of variance (ANOVA test), principal component analysis (PCA) of all physico-chemical parameters, biological and heavy metals were applied to quantify the significant changes in three phases using least significant difference (LSD) at 0.05 level. All statistical analysis has been performed using SPSS 20 and MS-excel software while R programming language v. R 4.1.1 is used only for diagrammatic presentation. Inverse Distance Weightage (IDW) technique was performed on QGIS v.3.16 software for revealing spatial variation of water quality in three periods on the basis of different indexing method. More

Predicting community responses to ecosystem changes is essential for improving ecosystem management. From an industrial perspective, we are dependent on stable microbial communities that perform well. Moreover, we live in a time where humans create disturbances at various levels in natural ecosystems. It is therefore important to comprehend the consequences of our activity. To predict the community response to external forces, we need to understand how different ecosystems affect the community assembly processes.We aimed to fill the knowledge gap on how carrying capacity and periodical disturbances affect the community assembly. It has previously been shown that the carrying capacity affects the community composition [46]. However, its effect on the assembly processes has remained unclear. Ecosystems with a lower carrying capacity support lower community size. Because the outcome of drift is density-dependent [6], communities with a low carrying capacity should have more populations vulnerable to drifting to extinction. However, our five-times difference in carrying capacity between cultivation regimes did not result in apparent differences in community assembly. The only exception was for the disturbed communities in Period 2, where the low carrying capacity regime (UDL) indicated a stronger influence of selection than the high (UDH; Fig. 4b). This observation was surprising as we hypothesised that drift might be more pronounced in systems with lower carrying capacity. In conclusion, the minor effects of carrying capacity observed for the replicate similarity rate for the undisturbed communities suggest that the effect of carrying capacity should be investigated further, including larger differences in carrying capacity.The effect of the disturbance regime on the microbial community assembly was more evident. The disturbance we investigated was a substantial dilution of the microcosm’s inoculum. The dilution has two significant effects: the community size is reduced, and the concentration of resources increases strongly for the remaining individuals. These two changes are relevant in natural and human-created ecosystems, where resource supply vary due to natural processes (e.g. patchiness and floods) and human activity (e.g. eutrophication and saprobiation).Investigating the temporal community composition through ordinations can reveal overall successional trajectories [47]. We found that whereas the PCoA ordinations indicated an overall deterministic trajectory for the undisturbed communities, the replicate similarity rate indicated that drift dominated the community assembly. This was evident for the microcosms starting with undisturbed culture conditions (UD Δµ  > 0; Fig. 5). However, the results were less evident for the communities going from disturbed to undisturbed conditions (DU) as the replicate similarity rate was around zero. Nonetheless, there was an apparent decrease in the replicate similarity rate when going from disturbed (Δµ 1.1 × 10−2) to undisturbed conditions (Δµ 5 × 10–4).The strength and unique feature of our experiment is the crossed design of the disturbance regimes. This crossing considerably increases the robustness of the conclusions drawn from the data. First, during the first period, all microcosms were inoculated with the same community, but in the second period, the twelve communities had assembled individually for 28 days. We could therefore investigate the effects of our experimental variables on drift and selection with different starting conditions. The temporal trends in the data were found to be independent of the starting condition, substantially increasing the strength of our conclusion.Second, subjecting the communities to the opposite disturbance regime in Period 2 supports that we had stable attractors in our systems. An attractor is a point or a trajectory in the state space of a dynamical system. If the attractor is locally stable, the system will tend to evolve toward it from a wide range of starting conditions and stay close to it even if slightly disturbed [48]. We observed locally stable attractors based on the disturbance regime and thus one stationary phase for each disturbance regime. Some ecological systems show dramatic regime shifts between alternative stationary states in response to changes in an external driver [49]. Such systems typically exhibit hysteresis in the sense that they will not return directly to the original state by an opposite change in the driver. We found that community composition was reversible and dependent on the disturbance regime, as highlighted by the Bray–Curtis ordinations (Fig. 4). This reversibility indicates that the community changes we observed were not catastrophic bifurcations or regime shifts and that it is unlikely that the systems contain multiple stationary states within the same disturbance regime. We think this gives strong support for assuming that drift is the main driver for divergence in the community composition and that selection towards alternative attractors probably plays a minor role. Thus, we can conclude that shifting from a disturbed to an undisturbed ecosystem increased the contribution of drift. Our observations corroborate other investigations of bioreactors [15, 50] and simulations [51] that report that stochasticity is fundamental for the assembly of communities. However, the finding that drift was important for structuring the undisturbed microcosms was unexpected.In dispersal-limited communities where resources are supplied continuously, such as in the undisturbed communities examined here, the selective process competition has been hypothesised to be high [7]. However, our experimental environment offered little variation in the resources provided, as the medium provided was the same throughout the experiment. This may have led to populations becoming “ecologically equivalent”, meaning that their fitness difference was too small to result in competitive exclusion on the time scale of our experiment [5, 52]. Under these assumptions, community assembly is similar to the neutral model in which the growth rates of the community members are comparable [53].During disturbances, we found that selection dominated community assembly. Our results support Zhou et al. hypothesis stating that determinism should increase due to biomass loss in dispersal-limited communities [24]. However, they oppose their other hypothesis stating that nutrient inputs should increase stochasticity [24], making low abundant populations vulnerable to local extinction [6, 7]. During the disturbances, the Sørensen similarity between replicates was stable or increasing, indicating that the periodical disturbance did not result in the extinction of low abundant populations. Instead, it appears that the dilution removed competition for some time, resulting in a phase where all populations got “a piece of the cake”. Several studies have observed increased stochasticity as a result of increased resource availability [7, 11, 24, 26]. However, we found that disturbances resulting in periods with exponential growth due to density-independent loss of individuals and high resource input suppressed the effect of stochastic processes. This exponential growth period without competition would enable more populations to stay above the detection limits of the 16S-rDNA-sequencing method.More OTUs were enriched under the disturbed regime than under the undisturbed. During the disturbance, the microcosms were diluted ~2 day−1, whereas the dilution factor was 1 day−1 during the undisturbed regime. We cannot assume steady-state in the disturbed microcosms, but it was interesting to see a substantial increase in the abundance of OTUs classified as Gammaproteobacteria. Gammaproteobacteria include many opportunists [54] that appeared to exploit the resource surplus following the disturbance. This opportunistic lifestyle fits within the r- and K-strategist framework [55].Organisms with high maximum growth rates but low competitive abilities are classified as r-strategists. These r-strategists are superior in environments where the biomass is below the carrying capacity. On the other hand, K-strategists are successful in competitive environments due to their high substrate affinity and resource specialisation [56]. Based on the taxonomic responses, it appears as disturbances in the form of dilutions selected for r-strategists, whereas the undisturbed regime selected for K-strategists. The r-strategists selected for during the disturbance periods included genera such as Vibrio and Colwellia [57], and the genus Vibrio includes many pathogenic strains [58]. Thus, our findings may have implications for land-based aquaculture systems where conditions favouring r-strategists is linked to high mortality and reduced viability of fish [56].The DeSeq2 results pose some new questions regarding the link between phylogeny and niche fitness. Generally, ecologists assume that closely related taxa have similar niches, as they have a common evolutionary history and, thus, similar physiology [59, 60]. For example, here, OTUs belonging to Gammaproteobacteria co-occurred when the environment was disturbed. However, for other classes such as Alphaproteobacteria and Flavobacteria, the OTUs responded differently to the disturbance regimes, despite belonging to the same class. This lack of phylogenetically coherent response indicates that the paradigm of correlation between phylogeny and niche requires further studies.This study was performed on complex marine microbial communities cultivated under controlled experimental conditions. We found that undisturbed environments enhanced the contribution of drift on community assembly and that disturbances increased the effect of selection. These observations might be different in more diverse ecosystems such as soils or the human gut. In such ecosystems, the microbes are more closely associated with, for example, soil particles or attached to the gut lining. It has been shown that the biofilm-associated and planktonic microbial communities have different community compositions [61]. Consequently, the community assembly processes may be affected differently by environmental fluctuations. Our experimental variables should therefore be tested in other ecosystem settings to verify our conclusions.To our knowledge, this study is the first to experimentally estimate the effect of periodical disturbances and carrying capacity on community assembly in dispersal-limited ecosystems. We observed that carrying capacity had little effect on community assembly and that undisturbed communities were structured more by drift than disturbed systems dominated by selection. Using an experimental crossover design for the disturbance regime, we showed that these observations were independent of the initial community composition. Our experiment illustrates that cultivating complex natural microbial communities under lab conditions allowed us to test ecologically relevant system variables and draw robust conclusions. More

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