EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 55 (2019) Ann. Limnol. - Int. J. Lim., 55 (2019) 9 Full HTML
Free Access
Issue
Ann. Limnol. - Int. J. Lim.
Volume 55, 2019
Article Number 9
Number of page(s) 12
DOI https://doi.org/10.1051/limn/2019009
Published online 13 May 2019

© EDP Sciences, 2019

1 Introduction

Because of global industrialization, agriculture, and municipal organic pollution, there is an urgent need to assess and protect drinking water reservoirs to safeguard human and ecological health (Smith, 2003; Fontana et al., 2014). There is an enormous number of potential organic and inorganic contaminants in water, which together are very expensive to monitor (Hughes and Peck, 2008; USEPA, 2016). Therefore in recent decades, biological indicator species for assessing water pollution have been applied, but single indicators fail to account for natural differences in lake types (Launois et al., 2010). Also, nutrient and organic matter concentrations cannot distinguish the ecological effects of differing pollutants (Borics et al., 2012; Crossetti et al., 2013). Therefore, multiple indicators are increasingly being used for assessing the ecological status of lakes (e.g., Schaumburg et al., 2004; Nõges et al., 2010; USEPA, 2016).

The most commonly used water quality evaluation method for lakes and reservoirs is the Carlson (1977) trophic state index (TSI). As a biological–physical–chemical indicator, the TSI combines chlorophyll a (Chla), Secchi disc transparency, and total phosphorus (TP) and it has been used to evaluate trophic status of a wide variety of water bodies (e.g., Canfield Jr. et al., 1983; Kitaka et al., 2002; Prasad and Siddaraju, 2012; Bekteshi, 2015).

The Shannon diversity index (H) is also widely used for assessing ecological status and trends of many biological assemblages in many ecosystem types (e.g., Heip and Engels, 1974; Hughes and Gammon, 1987; Stoddard et al., 2008; Morris et al., 2014). It incorporates both taxa richness and taxa evenness and offers an ecological perspective of the water quality influences on the proportional abundances of the biota (Pongswat et al., 2001; Palleyi et al., 2011). Grover and Chrzanowski (2004) found that phytoplankton diversity was significantly correlated with the number of limiting nutrients in one Texas reservoir, but not a neighboring one. Berry et al. (2017) reported linkages between cyanobacterial harmful algal blooms and altered bacterial assemblage diversity in Western Lake Erie. Nonetheless, Stevenson (2014) cautioned the use of diversity indices for making ecological assessments because species richness may respond nonmonotonically with stressor changes. However, restricting diversity (or richness) to pollution-sensitive or pollution-tolerant taxa can reduce problems with total diversity indices (see below). Therefore, we sought to determine how well H related to the two other commonly used indicators of lake quality.

Recently, phytoplankton functional groups have been used to assess ecological conditions (Moreno-Ostos et al., 2008; Kruk and Segura, 2012; Crossetti et al., 2013). This approach is based on the morphological, physiological, and ecological similarities of phytoplankton species, versus only their taxonomic classifications (Reynolds et al., 2002; Reynolds, 2006; Padisák et al., 2009). Comparative analyses of phytoplankton assemblages are simplified and improved by using indices that incorporate assemblage structure and function (Sommer et al., 1993). One such index, the Q-index of phytoplankton (Q), weights functional group relative biomasses with an F factor for each functional group, which is related to limnological parameters of the water body. Those parameters include lake type (acidic, calcareous, alkaline), depth, surface area, and exchange rate (persistent, intermittent, run-of-river) (Padisák et al., 2006). Q scores range from 0 (poor quality) to 5 (excellent quality), and they can be applied without geographic limitation because they are calibrated for naturally varying limnological variables (Padisák et al., 2006). As primary producers in aquatic ecosystems, phytoplankton are small sized, reproduce rapidly, easily collected and identified, and extremely sensitive to environmental changes (Padisák, 1994). Also, most of the same phytoplankton species occur on all the ice-free continents. Therefore, Q scores can be compared between lakes in Europe (Éva and Padisák, 2008; Çelekli and Öztürk, 2014) and South America (Fonseca and Bicudo, 2011).

Each indicator has both strengths and weaknesses, meaning that for overall water quality assessments, any single abiotic parameter has limitations. Although it is a very useful indicator of trophic state, Chla is the only biological factor considered in the TSI (Carlson, 1977; Crossetti and Bicudo, 2008). Usually, H is calculated only from biological taxonomic variables without direct linkage to abiotic variables or functional variables; however, anthropogenic impacts affect both taxonomic and functional diversity (Abonyi et al., 2012). Q provides values based on relative phytoplankton biomass data and functional group contributions to define lake trophic status (Crossetti and Bicudo, 2008; Cellamare et al., 2012) and can be used without geographic limitations (Padisák et al., 2006; Crossetti and Bicudo, 2008; Pasztaleniec and Poniewozik, 2010). Moreover, functional groups highlight survival strategies, sensitivities, and tolerances in lakes having different trophic status and habitat features. However, the most difficult step in Q application is the determination of the F factors, which were based on European knowledge and experience; therefore, they require further evaluation on other continents and in multiple biomes and ecoregions (Crossetti and Bicudo, 2008). For example, Stevenson et al. (2013) developed a natural-variation adjusted lake diatom multimetric index that was applicable across the entire conterminous USA. Nonetheless, multiple methods considering both biotic and abiotic components offer a more comprehensive assessment of reservoir ecological status (e.g., USEPA, 2016).

In some ways, reservoirs are more complex and dynamic than most natural lakes. Frequently, major reservoir arms exist with different water quality and biota than those characteristic of the main bodies of the reservoirs (Sanches et al., 2016). Similarly, the waters of the upper reservoir zones are typically much shallower, more turbid, and with coarser bottom substrates than those in the lower reservoir zones near the dams, thereby supporting different biota (Thornton, 1990; Terra and Araujo, 2011; Sanches et al., 2016). River inflows and human demands for water consumption and hydropower markedly affect reservoir water exchange rates (Kolding and van Zwieten, 2012; Kaufmann et al., 2014). Finally, because reservoirs are filled by river inflows versus ground water, their water levels tend to fluctuate to a greater degree than most seepage and drainage lakes from seasonal and annual precipitation variability and drought regimes (Thornton, 1990).

Thus, in the current study, we used TSI, H, and Q to conduct a comprehensive assessment of the ecological status of Zhushuqiao Reservoir, based on physical, chemical, and biological parameters. Our objectives were to (1) conduct a comprehensive evaluation of the ecological status of the temporal and spatial dynamics of the reservoir by comparing the three indices and (2) explore relationships between those indices and environmental parameters. We hypothesized that the ecological status assessment results would differ among the three indices, and those values would vary with reservoir zone and season.

2 Materials and methods

2.1 Study area

Zhushuqiao Reservoir (113°–115° E, 27.5°–28.5° N) has a watershed area of 564 km2, was built in 1992, and is located about 80 km from Changsha, the capital of Hunan province in south-central China. With a volume of 2.78 × 109 m3, surface area of 10.7 km2, and mean depth of 20–30 m (maximum depth 65 m), it is the largest drinking water reservoir in Changsha. The local climate of the reservoir area is subtropical monsoon, with daily air temperature ranging from −4 to 39 °C, with a mean of 17.5 °C, and daily precipitation ranging from 0 to 511 mm, with annual precipitation of 1600 mm in 2016–2017.

2.2 Phytoplankton sample collection, processing, and identification

We established 5 sampling stations based on Zhushuqiao Reservoir topographic features ( Fig. 1), including the water inlet and outlet, and open water area. Vertical stratified sampling was conducted to understand the ecological status of different water layers. Vertical sampling intervals were set based on the depth of each sampling site, i.e., 3 m for sites 1 and 2 (5 samples each), and 5 m for the other sites (7 samples in sites 3 and 4, 9 samples in site 5). The samples were collected bimonthly from April 2016 to February 2017, with 1 L of water for phytoplankton analysis and at least 2 L of water for physical and chemical analyses, for a total of 198 samples. All phytoplankton samples were fixed with Lugol's solution and formalin in the field, transferred in a cooler to the laboratory, and then concentrated into 50 ml by the siphon method after standing for 24–48 h. After thorough mixing, a 0.1 ml sample of concentrated phytoplankton was counted in a counting chamber (20 × 20 mm) using the random field method (i.e., at least 400 units each). Phytoplankton taxa identification and counting were conducted with a Nikon Eclipse E100 direct light microscope (magnification: 10 × 40). Phytoplankton biomass was computed using cell surface area and specific biovolumes, which assumes a phytoplankton-specific density of 1 g cm−3 (Hillebrand et al., 1999). Phytoplankton taxa identifications followed Hu and Wei (2006) and phytoplankton functional group classification followed Reynolds et al. (2002) and Padisák et al. (2009).

thumbnail Fig. 1

Location of Zhushuqiao Reservoir and our sampling sites.

2.3 Physical and chemical parameters

Environmental parameters measured in the field included water temperature (WT, °C), pH, and dissolved oxygen (DO, mg L−1) with a YSI (Model 6600 v2) multimeter. Water transparency (m) was measured with a Secchi disk (SD). Water samples were collected at the same vertical intervals as the phytoplankton samples. The water samples were transported to our laboratory for analyzing total nitrogen (TN, mg L−1), TP (mg L−1), and chemical oxygen demand (COD, mg L−1) through use of Chinese standard methods for water quality analysis (SEPA, 2002). Chla was determined by the acetone method (Lin et al., 2005). The water level data were obtained from Department of Water Resources of Hunan Province, China (http://61.187.56.156/wap/index_sq.asp).

2.4 Ecological status

To quantify trophic state, we used a modified TSI following the approach of Carlson (1977) and Aizaki et al. (1981) as follows: Chl a * = 10× ( 2.46 + ln C h l a ln 2.5 ) SD* = 10× ( 2.46 + 3.69 − 1.5 ln S D ln 2.5 ) TP* = 10× ( 2.46 + 6.71 + 1.5 ln T P ln 2.5 ) T S I = 0.54×Chl a *+ 0.297×S D* + 0.163×T P * TSI values <37 were classified as oligotrophic, and TSI values of 37–53 and 54–65 were denoted as mesotrophic and eutrophic, respectively. TSI values >65 were considered hypereutrophic.

We used a modified H to assess species diversity: H = − ∑ ( n i / N ) × log ( n i / N ) , where ni is the biomass of the ith species, and N is total biomass (Graham et al., 2004). H values <1 indicated seriously polluted water; 1–2 indicated α-medium polluted; >2–3 indicated β-medium polluted; and >3 indicated clean (Gao et al., 2010).

The Q index of phytoplankton values was determined as proposed by Padisák et al. (2006): Q = ∑ i = 1 s ( p i × F i ) , where pi  = ni /N, ni  = biomass of the ith functional group, N = total biomass of all functional groups, and F is established for each functional group based on lake type ( Tab. 1; Padisák et al., 2006). We followed Padisák et al. (2006), who used Q for the European Union Water Framework Directive, for making our ecological status assessments: 0–1 = poor; >1–2 = tolerable; >2–3 = medium; <3–4 = good; and >4–5 = excellent.

Table 1

Phytoplankton functional groups (Padisák et al., 2009) and F values (Padisák et al., 2006) for Zhushuqiao Reservoir.

2.5 Statistical analyses

We employed three analyses to assess relationships among biological and environmental variables. (1) We developed an isoline map to detect spatial and temporal distribution characteristics of phytoplankton and environmental variables through use of Surfer 8.0 (Golden Software Inc., Golden, Colorado, US). Surfer 8.0 is a 3D data modeling and analysis software that uses isoline maps to visualize unevenly distributed data. (2) We used Pearson's correlation to analyze relationships between each environmental variable and each index one by one through use of SPSS 19.0 (International Business Machine, SPSS Inc., Armonk, New York). (3) Ordination analysis was performed in CANOCO 4.5 (Microcomputer Power, Ithaca, New York). A detrended correspondence analysis (DCA) was first run on the data, which directed us to use a redundancy analysis (RDA). RDA is a direct gradient analysis technique that was conducted to analyze relationships between all three indices and all environmental factors. For the RDA, environmental variables were transformed log (x + 1) (except for pH) to reduce skewness. The statistical significance of environmental variables for explaining the variance of indices in RDA was tested by a Monte Carlo permutation test.

3 Results

3.1 Environmental variables

As expected, water transparency and temperature showed seasonal patterns. Transparency peaked in summer and autumn (August and October), was the lowest in spring (April), and ranged from 1.6 to 2.6 m (average 2.0 m) ( Fig. 2a).

The normal water level is 165 m in Zhushuqiao Reservoir. The water level gradually rose from April (156.3 m) to August (163.5 m) in 2016. And then, it dropped as water was released downstream during winter (December 2016) and early spring (February 2017) (Fig. 2b).

As is common for stratified lakes, WTs were reduced from the surface to the bottom, highest surface WTs (>30 °C) were observed in August, and lowest surface WTs (16 °C) occurred in December and February (Fig. 2c).

Nutrient concentrations also showed spatio-temporal patterns of variation. The mean water column TN/TP values were lowest in August and highest in October. TN increased with depth in April, but was reduced while TP increased in June. In August, mean water column TN and TP concentrations were twice those of June, but in October average water column TP was reduced (Fig. 2d, Tab. 2). In February, TP concentrations were above 0.07 g/L, which indicated that its limitation was no longer strongly pronounced.

Mean water column concentrations of Chla and biomass varied widely, 1.8–65.9 μg L−1 and 0.14–16.89 mg L−1, respectively (Fig. 2e). The mean water column Chla values were lowest in April and highest in June and August (Chla >30 μg L−1). Mean water column biomasses were lowest in December and highest in August (Fig. 2f).

thumbnail Fig. 2

Water level and water quality in Zhushuqiao Reservoir. Darker shadings in (c), (d), (e), and (f) reflect higher values as indicated by the right-side scales.
thumbnail Fig. 2

(Continued)
Table 2

Mean monthly values of TN, TP, and TN/TP in Zhushuqiao Reservoir.

3.2 Spatial and temporal changes of ecological status

3.2.1 Trophic state index (TSI)

Mean water column TSI values ranged from 36.55 (oligotrophic) to 66.59 (eutrophic), with a mean of 48.42 (mesotrophic) for the entire reservoir ( Fig. 3a). The reservoir was eutrophic in June and mesotrophic in the other months. TSI values gradually decreased from the surface to the bottom at all sites. During all the sampling months, sites 1 and 2 had the highest TSI values in April, June, August, December, and February, and sites 4 and 5 had the highest TSI values in October.

thumbnail Fig. 3

Ecological status evaluations in Zhushuqiao Reservoir (a) TSI, (b) H,  and (c) Q. Darker shadings in (a), (b), and (c) reflect higher scores, as indicated by the right-side values.

3.2.2 Shannon diversity index (H)

H values ranged from 0.82 to 3.42 (mean = 2.23), indicating medium pollution levels for the entire reservoir (Fig. 3b). H clearly declined from upper to lower sites in June, August, and February, and gradually decreased with increased depth in June. H was <2 (α-medium pollution) in some water layers at sites 2, 3, 4, and 5 in April, June, and February; all other sites and visits indicated β-medium pollution.

3.2.3 Q Index of phytoplankton

Q scores ranged from 0.94 (poor) to 3.31 (good) with a mean score of 2.33, indicating medium ecological status for the entire reservoir (Fig. 3c). Mean water column Q scores were highest in December (medium to good), April (tolerable to medium), August (tolerable to medium), and February (bad to good), and lowest in June (tolerable to medium) and October (bad to medium). During those months, Q scores were the highest at site 5, and lowest at sites 2, 3, and 4 in February, April, and June and at sites 1 and 2 in August, October, and December. Q scores tended to be lowest at greater depths and highest at shallow and middle depths.

A total of 171 taxa were identified during the study period; taxa from 18 functional groups contributed >2% to the total biomass. Dominant functional groups differed seasonally ( Fig. 4). In April, Chroomonas (X2, mesotrophic), Cryptophyta (Y, eurytrophic), and Oocystis (F, meso-eutrophic) were dominant. In June, Peridinium (Lo, eu-hypereutrophic) and Synedra (D, base-poor) had the greatest biomass. In August, Oocystis (F, meso-eutrophic) and Staurastrum (P, eutrophic) were dominant. In October, Peridinium (Lo, eu-hypereutrophic) accounted for most of the biomass in sites 1 and 2; Cryptophyta (Y, eurytrophic) and Oocystis (F, meso-eutrophic) were dominant in the other sites. In December, the dominant species were Melosira (C, eutrophic), Cryptophyta (Y, eurytrophic), Chroomonas (X2, mesotrophic), and Scenedesmus (J, eutrophic). In February, Cryptophyta (Y, eurytrophic), Chroomonas (X2, mesotrophic), and Eudorina (G, eutrophic) dominated in site 1; the other sites were dominated by Peridinium (Lo, eu-hypereutrophic), Chroomonas (X2, mesotrophic), and Cryptophyta (Y, eurytrophic).

thumbnail Fig. 4

The relative biomass (%) of functional groups in Zhushuqiao Reservoir. See Table 1 to relate codes to taxa.

3.3 Relationships between trophic indices and environmental variables

The RDA results for ecological index and environmental parameter relationships showed that the cumulative percentage variance of all analyzed axes accounted for a total of 99.2% of index variability ( Tab. 3). The first RDA axis explained 94.9% of the variation of the relationships between trophic indices and environmental variables, and it was statistically significant ( Fig. 5).

Individual correlations showed similar patterns as the RDA. H was positively associated with Secchi depth and TN/TP, and negatively with Chla (r = −0.20, P = 0.04, N = 198). Q was most strongly and positively associated with COD (r = 0.31, P = 0.00), and negatively associated with TN (r = −0.26, P = 0.00), Chla (r = −0.36, P = 0.00), WT (r = −0.33, P = 0.00), and TN/TP (r = −0.20, P = 0.00). TSI was most strongly and positively associated with Chla (r = 0.62, P = 0.00), TN (r = 0.62, P = 0.00), TP (r = 0.59, P = 0.00), and pH (r = 0.42, P = 0.00), and negatively associated with WT (r = −0.2, P = 0.006) and SD (r = −0.38, P = 0.00). Q and H were positively associated with each other but negatively associated with TSI ( Tab. 4).

Table 3

RDA results for ecological index and environmental parameter relationships in Zhushuqiao Reservoir. Note, because RDA is a constrained ordination analysis, it cannot provide loadings for single variables.

thumbnail Fig. 5

Redundancy analysis indicating relationships between ecological indices and environmental parameters in Zhushuqiao Reservoir.
Table 4

Pearson correlations between ecological indices and environmental parameters in Zhushuqiao Reservoir.

4 Discussion

4.1 Spatial and temporal variation of ecological status indicators

Ecological status, phytoplankton composition, and environmental variables differed seasonally, between upper- and lower-zone Zhushuqiao Reservoir sites, and with depth. This is because reservoirs change river topography, which leads to different biological adaptations to zones by the best suited biota (Wetzel, 1990; Straskraba et al., 1993; Terra and Araujo, 2011).

Taxa richness and H in the upper-zone sites were higher than in the lower-zone sites, but some Cyanophyta species had greater occurrence frequency and biomass and became dominant, which led to decreased Q scores and indicated greater eutrophication in the upper zones than in the lower zones. This was likely related to the higher concentrations of TN in the upper zone. Also, Q was dominated by only a few functional groups because they are the most sensitive to human impacts (Bonnet and Poulin, 2002; Latour et al., 2004). Lower hydrodynamic stability and greater turbidity were reflected by the functional group distribution in the upper sites as well. Therefore, flagellate taxa (such as Y, Lo, and X1) dominated because of their greater motility.

In the vertical dimension, all three indices indicated poor ecological status from the surface to the middle water layer, especially in June. This result is in accordance with Becker et al. (2010), who indicated long stratification periods and high light availability led to the development of high biomass in the epilimnion in summer. Seasonality also determines which species are able to maintain their populations and have the largest capability to dominate. Although weakly selective physical conditions may explain highly variable phytoplankton in the upper water (Reynolds and Descy, 1996), low taxa diversity may reflect severely selective environments in deeper waters.

4.2 Relationships between ecological indices and environmental variables

All three ecological indices indicated that the most serious eutrophication occurred in June and February. TSI indicated mesotrophic status in the other months. Q indicated tolerable status and H indicated medium pollution in June and February.

Three potential reasons could account for this.

First, TN and TP concentrations were similar from April to June. The increase of phytoplankton in June caused by increased temperature was possibly the main reason for the eutrophication of the water body. In Zhushuqiao Reservoir, there is a large fluctuation of temperature in spring, high temperature and rainfall in summer, high temperature in autumn, and mild temperature and little rainfall in winter. Without intense human disturbance, the eutrophication of water was greater in summer from June to October, especially the eutrophication period in June.

Second, Synedra (D) became dominant during this period, which has been reported in highly polluted and eutrophic waters (Leitão and Léglize, 2000).

Finally, hydrologic connections are often emphasized in water quality assessments, because nutrients and biota are affected by changes in flow regime and water levels (Pringle, 2001; Launois et al., 2010). In this case, the water level was lowered to facilitate seining the whole reservoir for fish. The fish harvesting also likely re-suspended sediment nutrients, which increased TP and TN to the maximum values observed in the whole reservoir for the entire year and 2–3 times higher than those in December. This was likely the main reason for inducing eutrophication in February, which differed from the results of Yang (2008).

August is the rainy season in the study area, resulting in increased water level, increased nutrients and phytoplankton biomass in the reservoir, so the water ecological status was also poor. As TP and WT decreased in October, the number of Peridinium (Lo) and Oocystis (F), which can adapt to the lower nutrient concentrations (Padisák et al., 2009), increased. In December, the highest ecological status period, the reason for the increase of Q value was that the growth of most Cyanophyta and Chlorophyta was inhibited by low temperature while the diatom Melosira granulata var.angustissima mull (C) became dominant, which has a higher F factor and a preference for cold water (Lopes et al., 2005; Wang et al., 2011; Stević et al., 2013). Indeed, Cellamare et al. (2012) and USEPA (2016) report that winter is an inappropriate period to assess temperate lake ecological status; rather, lakes should be monitored when they are likely to be in their worst condition. But this may not be the case in tropical ecoregions where winters are relatively warm and precipitation and water levels are low (Callisto et al., 2014).

In our study reservoir, TP was the limiting factor for phytoplankton. When TP increased but TN did not, phytoplankton increased little; but biomass increased greatly when both TP and TN concentrations increased. This indicates that when water TP and TN increase simultaneously phytoplankton production will be promoted. This result has been reported in many other lakes and reservoirs (e.g., Fourqurean et al., 1993; Yoshimura and Kudo, 2001). In future studies, greater attention should be paid to ecological status impacts from weather shifts and human activities. Also, in addition to TP and TN, other nutrients, such as iron and silica, should be monitored because they are affected by those same changes, and they in turn affect phytoplankton production.

5 Conclusions

  • (1)

    The three evaluation methods indicated that water quality was worse in the upper reservoir than in the lower reservoir. Dominant species in the upper reservoir differed from those in the lower reservoir.

  • (2)

    The degree of eutrophication was most apparent in June. Other periods indicated mesotrophic status, with the best water quality observed in winter.

  • (3)

    Changes in WT were the main drivers of seasonal changes in both biotic and abiotic indicators, but hydrological changes were important factors affecting water quality as well.

  • (4)

    TP was the limiting factor for phytoplankton, but phytoplankton increased greatly when both TP and TN concentration increased.

  • (5)

    Each of the three indicators in this study had its own strengths and weaknesses, indicating the need for comprehensive evaluation by employing multiple indicators.

Acknowledgments

This study was supported by the Chinese Academy of Sciences (grant numbers Y623021201, Y45Z041201, and ZDRW-ZS-2017-3-2), State Key Laboratory of Freshwater Ecology and Biotechnology (Grant numbers 2016FBZ10 and 2019FBZ02), and the Hunan Provincial Innovation Foundation for Postgraduates (grant numbers CX2017B349).

References

  • Abonyi A, Leitão M, Lançon AM, Padisák J. 2012. Phytoplankton functional groups as indicators of human impacts along the River Loire (France). Hydrobiologia 698: 233–249. [CrossRef] [Google Scholar]
  • Aizaki M, Otsuki A, Fukishim T, Kawai T, Hosomi M, Muraoka K. 1981. Application of modified Carlson's trophic status index to Japanese lakes and its relationships to other parameters related to trophic status (in Japanese with English summary). Res Rep Natl Inst Environ Stud Jpn 23: 13–31. [Google Scholar]
  • Becker V, Caputo L, Ordóñez J, Marcé R, Armengol J, Crossetti LO, Huszar VL. 2010. Driving factors of the phytoplankton functional groups in a deep Mediterranean reservoir. Water Res. 44: 3345–3354. [CrossRef] [PubMed] [Google Scholar]
  • Bekteshi A. 2015. Use of trophic status index (Carlson, 1977) for assessment of trophic status of the Shkodra Lake. J Environ Prot Ecol 15: 359–365. [Google Scholar]
  • Berry MA, Davis TW, Cory RM, Duhaime MB, Johengen TH, Kling GW, Marino JA, Den Uyl PA, Gossiaux D, Dick GJ, Denef VJ. 2016. Cyanobacterial harmful algal blooms are a biological disturbance to Western Lake Erie bacterial communities. Environ Microbiol 19: 1149–1162. [CrossRef] [Google Scholar]
  • Bonnet MP, Poulin M. 2002. Numerical modelling of the planktonic succession in a nutrient-rich reservoir: environmental and physiological factors leading to Microcystis aeruginosa, dominance. Ecol Model 156: 93–112. [CrossRef] [Google Scholar]
  • Borics G, Tóthmérész B, Lukács BA, Várbíró G. 2012. Functional groups of phytoplankton shaping diversity of shallow lake ecosystems. Hydrobiologia 698: 251–262. [CrossRef] [Google Scholar]
  • Callisto M, Hughes RM, Lopes JM, Castro MA (eds.). 2014. Ecological Conditions in Hydropower Basins, Peixe Vivo Series 2. Belo Horizonte, Brazil: Companhia Energética de Minas Gerais. [Google Scholar]
  • Canfield, Jr. DEC, Langeland KA, Maceina MJ, Haller WT, Shireman JV, Jones J. 1983. Trophic status classification of lakes with aquatic macrophytes. Can J Fish Aquat Sci 40: 1713–1718. [CrossRef] [Google Scholar]
  • Carlson RE. 1977. A trophic status index for lakes. Limnol Oceanogr 22: 361–369. [Google Scholar]
  • Çelekli A, Öztürk B. 2014. Determination of ecological status and ecological preferences of phytoplankton using multivariate approach in a Mediterranean reservoir. Hydrobiologia 740: 115–135. [CrossRef] [Google Scholar]
  • Cellamare M, Morin S, Coste M, Haury J. 2012. Ecological assessment of French Atlantic lakes based on phytoplankton, phytobenthos and macrophytes. Environ Monit Assess 184: 4685–4708. [Google Scholar]
  • Crossetti LO, Bicudo CEDM. 2008. Phytoplankton as a monitoring tool in a tropical urban shallow reservoir (Garças Pond): the assemblage index application. Hydrobiologia 610: 161–173. [CrossRef] [Google Scholar]
  • Crossetti LO, Stenger-Kovács C, Padisák J. 2013. Coherence of phytoplankton and attached diatom-based ecological status assessment in Lake Balaton. Hydrobiologia 716: 87–101. [CrossRef] [Google Scholar]
  • Éva H, Padisák J. 2008. Analysis of long-term ecological status of Lake Balaton based on the ALMOBAL phytoplankton database. Hydrobiologia 599: 227–237. [CrossRef] [Google Scholar]
  • Fonseca BM, Bicudo CEDM. 2011 Phytoplankton seasonal and vertical variations in a tropical shallow reservoir with abundant macrophytes (Ninféias Pond, Brazil). Hydrobiologia 665: 229–245. [CrossRef] [Google Scholar]
  • Fontana L, Albuquerque ALS, Brenner M, Bonotto DM, Sabaris TPP, Pires MAF, Cotrim MEB, Bicudo DC. 2014. The eutrophication history of a tropical water supply reservoir in Brazil. J Paleolimnol 51: 29–43. [CrossRef] [Google Scholar]
  • Fourqurean JW, Jones RD, Zieman JC. 1993. Process influencing water column nutrient characteristics and phosphorus limitation of phytoplankton biomass in Florida Bay, FL, USA: inferences from spatial distributions. Estuar Coast Shelf Sci 36: 295–314. [CrossRef] [Google Scholar]
  • Gao Y, Qi SC, Su YX, Ci HX. 2010. Seasonal changes of phyptoplankton diversity and water quality in Yi River and Beng River. Trans Oceanol Limnol 22: 109–113 (in Chinese). [Google Scholar]
  • Graham JM, Kent AD, Lauster GH, Triplett E. 2004. Seasonal dynamics of phytoplankton and planktonic protozoan communities in a northern temperate humic lake: diversity in a dinoflagellate dominated system. Microb Ecol 48: 528–540. [CrossRef] [PubMed] [Google Scholar]
  • Grover JP, Chrzanowski TH. 2004. Limiting resources, disturbance, and diversity in phytoplankton communities. Ecol Monogr 74: 533–551. [CrossRef] [Google Scholar]
  • Heip C, Engels P. 1974. Comparing species diversity and evenness. J Mar Biol Assoc UK 54: 559–563. [CrossRef] [Google Scholar]
  • Hillebrand H, Dürselen CD, Kirschtel U, Pollingher D, Zohary T. 1999. Biovolume calculation for pelagic and benthic microalgae. J Phycol 35: 403–424. [CrossRef] [Google Scholar]
  • Hu HJ, Wei YX. 2006. The Freshwater Algae of China Systematics, Taxonomy and Ecology. Beijing, China: Science Press (in Chinese). [Google Scholar]
  • Hughes RM, Gammon JR. 1987. Longitudinal changes in fish assemblages and water quality in the Willamette River, Oregon. Trans Am Fish Soc 116: 196–209. [Google Scholar]
  • Hughes RM, Peck DV. 2008. Acquiring data for large aquatic resource surveys: the art of compromise among science, logistics, and reality. J N Am Benth Soc 27: 837–859. [Google Scholar]
  • Kaufmann PR, Hughes RM, Van Sickle J, Whittier TR, Seeliger CW, Paulsen SG. 2014. Lakeshore and littoral habitat structure: a field survey method and its precision. Lake Reserv Manage 30: 157–176. [CrossRef] [Google Scholar]
  • Kitaka N, Harper DM, Mavuti KM. 2002. Phosphorus inputs to Lake Naivasha, Kenya, from its catchment and the trophic status of the lake. Hydrobiologia 488: 73–80. [CrossRef] [Google Scholar]
  • Kolding J, van Zwieten PAM. 2012. Relative lake level fluctuations and their influence on productivity and resilience in tropical lakes and reservoirs. Fish Res 115: 99–109. [CrossRef] [Google Scholar]
  • Kruk C, Segura AM. 2012. The habitat template of phytoplankton morphology-based functional groups. Hydrobiologia 698: 191–202. [Google Scholar]
  • Latour D, Sabido O, Salençon MJ, Giraudet H. 2004. Dynamics and metabolic activity of the benthic cyanobacterium Microcystis aeruginosa in the Grangent reservoir (France). J Plankton Res 26: 719–726. [CrossRef] [Google Scholar]
  • Launois L, Veslot J, Irz P, Argillier C. 2010. Selecting fish based metrics responding to human pressures in French natural lakes and reservoirs: towards the development of a fish‐based index (FBI) for French lakes. Ecol Freshw Fish 20: 120–132. [CrossRef] [Google Scholar]
  • Leitão M, Léglize L. 2000. Long-term variations of epilimnetic phytoplankton in an artificial reservoir during a 10-year survey. Hydrobiologia 424: 39–49. [CrossRef] [Google Scholar]
  • Lin SJ, He LJ, Huang PS, Han BP. 2005. Comparison and improvement on the extraction method for chlorophyll a in phytoplankton. Ecol Sci 24: 9–11 (in Chinese). [Google Scholar]
  • Lopes MRM, Bicudo CEDM, Ferragut MC. 2005. Short term spatial and temporal variation of phytoplankton in a shallow tropical oligotrophic reservoir, southeast Brazil. Hydrobiologia 542: 235–247. [CrossRef] [Google Scholar]
  • Moreno-Ostos E, Cruz-Pizarro L, Basanta A, George DG. 2008. The spatial distribution of different phytoplankton functional groups in a Mediterranean reservoir. Aquat Ecol 42: 115–128. [CrossRef] [Google Scholar]
  • Morris EK, Caruso F, Buscot F, Fischer M, Hancock C, Maier TS, Meiners T, Müller C, Obermaier E, Prati D, Socher SA, Sonnemann I, Wäschke N, Wubet T, 2014 Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4: 3514–3524. [CrossRef] [PubMed] [Google Scholar]
  • Nõges P, Mischke U, Laugaste R, Solimini AG. 2010. Analysis of changes over 44 years in the phytoplankton of Lake Võrtsjärv (Estonia): the effect of nutrients, climate and the investigator on phytoplankton-based water quality indices. Hydrobiologia 646: 33–48. [CrossRef] [Google Scholar]
  • Padisák J. 1994. Identification of relevant time-scales in non-equilibrium community dynamics: conclusions from phytoplankton surveys. New Zeal J Ecol 18: 169–176. [Google Scholar]
  • Padisák J, Borics G, Grigorszky I, Soroczki-Pinter E. 2006. Use of phytoplankton assemblages for monitoring ecological status of lakes within the water framework directive: the assemblage index. Hydrobiologia 553: 1–14. [CrossRef] [Google Scholar]
  • Padisák J, Crossetti L, Naselli-Flores L. 2009. Use and misuse in the application of the phytoplankton functional classification: a critical review with updates. Hydrobiologia 621: 1–19. [CrossRef] [Google Scholar]
  • Palleyi S, Kar RN, Panda CR. 2011. Influence of water quality on the biodiversity of phytoplankton in Dhamra River Estuary of Odisha Coast, Bay of Bengal. J Appl Sci Environ Manage 15: 69–74. [Google Scholar]
  • Pasztaleniec A, Poniewozik M. 2010. Phytoplankton based assessment of the ecological status of four shallow lakes (Eastern Poland) according to Water Framework Directive a comparison of approaches. Limnologica 40: 251–259. [CrossRef] [Google Scholar]
  • Pongswat S, Thammathaworn S, Peerapornpisal Y, Thanee N. 2001. Use of phytoplankton biodiversity for monitoring water quality in Rama IX Lake, Pathumthani province, in 5th BRT Annual Conference, Udon Thani, Thailand, 8–11 October 2001. [Google Scholar]
  • Prasad AGD, Siddaraju. 2012. Carlson's Trophic State Index for the assessment of trophic status of two Lakes in Mandya district. Adv Appl Sci Res 3: 2992–2996. [Google Scholar]
  • Pringle CM. 2001. Hydrologic connectivity and the management of biological reserves: a global perspective. Ecol Appl 11: 981–998. [CrossRef] [Google Scholar]
  • Reynolds CS. 2006. Ecology of Phytoplankton. Cambridge, UK: Cambridge University Press. [Google Scholar]
  • Reynolds CS, Descy JP. 1996. The production, biomass and structure of phytoplankton in large rivers. Archives of Hydrobiology, Supplement 113. Large Rivers 10: 161–187. [Google Scholar]
  • Reynolds CS, Huszar VLM, Kruk C, Naselli-Flores L, Melo S. 2002. Towards a functional classification of the freshwater phytoplankton. J Plankton Res 24: 417–428. [Google Scholar]
  • Sanches BO, Hughes RM, Macedo DR, Callisto M, Santos GB. 2016. Spatial variations in fish assemblage structure in a southeastern Brazilian reservoir. Braz J Biol 76: 185–193. [CrossRef] [Google Scholar]
  • Schaumburg J, Schranz C, Hofmann G, Stelzer D, Schneider SC, Schmedtje U. 2004. Macrophytes and phytobenthos as indicators of ecological status in German lakes-a contribution to the implementation of the Water Framework Directive. Limnologica 34: 302–314. [CrossRef] [Google Scholar]
  • SEPA (State Environmental Protection Administration of China). 2002. Environmental Quality Standards for Surface Water (GB3838-2002). Beijing: China Standards Press (in Chinese). [Google Scholar]
  • Smith VH. 2003. Eutrophication of freshwater and coastal marine ecosystems: a global problem. Sci Pollut Res Int 10: 126–39. [Google Scholar]
  • Sommer U, Padisák J, Reynolds CS, Juhász-Nagy P. 1993. Hutchinson's heritage: the diversity-disturbance relationship in phytoplankton. Hydrobiologia 249: 1–7. [CrossRef] [Google Scholar]
  • Stevenson J. 2014. Ecological assessments with algae: a review and synthesis. J Phycol 50: 437–461. [CrossRef] [PubMed] [Google Scholar]
  • Stevenson RJ, Zalack JT, Wolin J. 2013. A multimetric index of lake diatom condition based on surface-sediment assemblages. Freshw Sci 32: 1005–1025. [CrossRef] [Google Scholar]
  • Stević F, Mihaljević M, Špoljarić D. 2013. Changes of phytoplankton functional groups in a floodplain lake associated with hydrological perturbations. Hydrobiologia 709: 143–158. [CrossRef] [Google Scholar]
  • Stoddard JL, Herlihy AT, Peck DV, Hughes RM, Whittier TR, Tarquinio E. 2008. A process for creating multi-metric indices for large-scale aquatic surveys. J N Am Benthol Soc 27: 878–891. [Google Scholar]
  • Straskraba M, Tundisi JG, Duncan A. 1993. State-of-the-art of reservoir limnology and water quality management. in Straskraba M., Tundisi J.G. and Duncan A. (eds.), Comparative Reservoir Limnology and Water Quality Management. Heidelberg, Germany: Springer, pp. 213–288. [CrossRef] [Google Scholar]
  • Terra BF, Araujo FG. 2011. A preliminary fish assemblage index for a transitional river-reservoir system in southeastern Brazil. Ecol Indic 11: 874–881. [CrossRef] [Google Scholar]
  • Thornton KW. 1990. Perspectives on reservoir limnology. in Thornton K.W., Kimmel B.L. and Payne F.E. (eds.), Reservoir Limnology: Ecological Perspectives. New York: John Wiley and Sons, pp. 1–15. [Google Scholar]
  • USEPA (U.S. Environmental Protection Agency). 2016. National Lakes Sssessment 2012: A Collaborative Survey of Lakes in the United States. EPA 841-R-16-113. Washington, DC: Office of Water/Office of Research and Development. [Google Scholar]
  • Wang L, Cai Q, Xu Y, Kong LH, Tan L, Zhang M. 2011. Weekly dynamics of phytoplankton functional groups under high water level fluctuations in a subtropical reservoir-bay. Aquat Ecol 45: 197–212. [CrossRef] [Google Scholar]
  • Wetzel RG. 1990. Reservoir ecosystems: conclusions and speculations. in Thornton K.W., Kimmel B.L. and Payne F.E. (eds.), Reservoir Limnology: Ecological Perspectives. New York: John Wiley and Sons, pp. 227–238. [Google Scholar]
  • Yang XL. 2008. Study on the community characteristics of zooplankton in Zhushuqiao Reservoir and biological evaluation of water quality. M.Sc. thesis, Central South University of Forestry and Technology, Hunan, China. [Google Scholar]
  • Yoshimura T, Kudo I. 2001. Seasonal variations in nutrients and a factor limiting phytoplankton growth in Lake Ohnuma. Jpn J Limnol 62: 205–217. [CrossRef] [Google Scholar]

Cite this article as: Huang G, Chen Y, Wang X, Hughes RM, Xu L. 2019. Using multiple indicators to assess spatial and temporal changes in ecological condition of a drinking water reservoir in central China. Ann. Limnol. - Int. J. Lim. 55, 9

All Tables

Table 1

Phytoplankton functional groups (Padisák et al., 2009) and F values (Padisák et al., 2006) for Zhushuqiao Reservoir.

In the text
Table 2

Mean monthly values of TN, TP, and TN/TP in Zhushuqiao Reservoir.

In the text
Table 3

RDA results for ecological index and environmental parameter relationships in Zhushuqiao Reservoir. Note, because RDA is a constrained ordination analysis, it cannot provide loadings for single variables.

In the text
Table 4

Pearson correlations between ecological indices and environmental parameters in Zhushuqiao Reservoir.

In the text

All Figures

thumbnail Fig. 1

Location of Zhushuqiao Reservoir and our sampling sites.
In the text
thumbnail Fig. 2

Water level and water quality in Zhushuqiao Reservoir. Darker shadings in (c), (d), (e), and (f) reflect higher values as indicated by the right-side scales.
In the text
thumbnail Fig. 2

(Continued)
In the text
thumbnail Fig. 3

Ecological status evaluations in Zhushuqiao Reservoir (a) TSI, (b) H,  and (c) Q. Darker shadings in (a), (b), and (c) reflect higher scores, as indicated by the right-side values.
In the text
thumbnail Fig. 4

The relative biomass (%) of functional groups in Zhushuqiao Reservoir. See Table 1 to relate codes to taxa.
In the text
thumbnail Fig. 5

Redundancy analysis indicating relationships between ecological indices and environmental parameters in Zhushuqiao Reservoir.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.