Biological indicators of ecological quality in typical urban river-lake ecosystems: The planktonic rotifer community and its response to environmental factors

Ecological Indicators 112 (2020) 106127

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Original Articles

Biological indicators of ecological quality in typical urban river-lake ecosystems: The planktonic rotifer community and its response to environmental factors Diwen Lianga,b,d, Qing Wanga, , Nan Weia, Changkuan Tanga, Xian Suna,b,c, Yufeng Yanga,b, ⁎

T

⁎

a

Institute of Hydrobiology, Jinan University, 601 West Huangpu Avenue, Guangzhou, Guangdong, PR China Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, PR China c School of Marine Science, Sun Yat-Sen University, Guangzhou 510275, PR China d Department of Marine Sciences, University of Connecticut, Groton, CT 06340, USA b

ARTICLE INFO

ABSTRACT

Keywords: Rotifera Bioindicator Trophic state City river-lake ecosystem Sponge city

River-lake system is the framework of the “sponge city”, tackling urban water problems of flooding. Rotifers, an essential zooplankton component of water ecosystems, are sensitive to environmental changes. However, the utility of rotifers as indicators of water quality in urban river-lake ecosystems is still unclear. We investigated the response of rotifer community to environmental factors and evaluated the availability of traditional taxonomic indicators and individual rotifer indicators in the river-lake ecosystem of Changde, south central China. A total of 95 rotifer species were identified in 15 sampling sites during a year survey. Rotifer abundance ranged from 1 (the Yuan River) to 2628 ind.·L−1 (Lake Liuye). Temperature, water depth and trophic state were the key factors for spatial-temporal variation of rotifer community in the urban river-lake ecosystems. Individual rotifer indicators, biodiversity indices, the Brachionus: Trichocerca ratio and the Keratella-index were less useful in evaluating the trophic status in this study. However, we founded that the rotifer trophic state index (TSIROT) values and total rotifer abundance were most consistent with values of the comprehensive trophic level index (TLIc). Furthermore, TSIROT values showed stability and resistance to changes when the time horizon increased. This study suggests that the TSIROT index is a reliable indicator of water quality in river-lake ecosystems with high water depth variation. This is a pilot study for evaluating of reliable bioindicators for sponge city design. Longterm studies in more sponge cities should be taken to validate our findings.

1. Introduction Rotifers, small but essential zooplankton in freshwater ecosystems, are sensitive to environmental changes, acting as effective indicators of trophic conditions (Duggan et al., 2002; Devetter and Sed'A, 2003). As rotifers are r-selected organisms with relatively short generation times, they respond to changes in the environment and occupy open niches quickly. As such, they are widely distributed in the world and live in all kinds of water bodies (Segers, 2008). Because of eutrophication, smallbodied zooplankton has become the dominant zooplankton in Chinese lakes (Jiang et al., 2017a; Shao et al., 2010). Cyanobacterial blooms are correlated to small-sized zooplankton abundance, such as rotifers (Jiang et al., 2017a). Moreover, rotifer species are more useful as indicators of “bottom-up” processes than large-sized zooplankton because they are less affected by fish predation, and rotifer abundance is largely related to food type and quantity, which in turn vary with changes in ⁎

nutrient levels along the trophic gradient (Jurczak et al., 2018). Trophic state, including concentrations of phosphorus (May et al., 2014; Kuczyńska-Kippen and Nagengast, 2006), nitrogenous nutrients (Wen et al., 2011; Wang et al., 2010) and chlorophyll-a (BielańskaGrajner and Gładysz, 2010) have been considered as key factors in rotifer community succession in lakes and reservoirs. In downstream rivers and estuaries, salinity is the major factor affecting the variation of rotifer communities and it is negatively correlated with rotifer abundance and diversity (Wang et al., 2009; Wei and Xu, 2014). In lakes, depth and area plays an important role in controlling rotifers in temperate lake ecosystems (Jeppesen et al., 2010). Urban flooding caused by rainstorms have serious socioeconomic impacts. In response to mitigate flood risk while storing and purifying water to meet future use, the Chinese government has adopted a policy to build ‘‘sponge cities’’ designed to tackle urban water problems (Jiang et al., 2017b). River-lake systems are the framework and major

Corresponding authors at: Institute of Hydrobiology, Jinan University, 601 West Huangpu Avenue, Guangzhou, Guangdong, PR China (Y. Yang, Q. Wang). E-mail addresses: [email protected] (Q. Wang), [email protected] (Y. Yang).

https://doi.org/10.1016/j.ecolind.2020.106127 Received 20 April 2019; Received in revised form 6 January 2020; Accepted 20 January 2020 1470-160X/ © 2020 Elsevier Ltd. All rights reserved.

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component of the “urban sponge”, solving the problems of rainstorm regulation, purification, utilization and water ecological restoration through the measures of regulating the water with sluice control (Ding et al., 2017). Most of the research focused on its capacity of mitigating stormwater with green infrastructure or grey infrastructure (Mei et al., 2018). However, there is little knowledge about the ecological quality in urban river–lake ecosystems, especially plankton communities and water quality assessment. Because urban rivers are more susceptible to anthropogenic pressure (Chau, 2005), it is highly recommended to use bioindicators that are sensitive to deteriorated trophic status, such as rotifers (Lodi et al., 2011). In general, traditional taxonomic indicators have their own limitations. Brachionus: Trichocerca ratio (BT) was proposed as an indicator of trophic state by Sládeček (1983), but is impossible to achieve when Trichocerca is absent. Keratella-index (KIN) has been proven to be an effective indicator in estuaries (Gopko and Telesh, 2013). Total abundance and diversity indices of rotifers were effective indicators of trophic state in subtropical and temperate lakes (May and O’Hare, 2005; Wen et al., 2011). However, total abundance failed in assessing the trophic status in lotic water bodies with salinity fluctuations, while BT and KIN indices were more reliable (Liang et al., 2019). It was reported that the rotifer trophic state index (TSIROT) can be a useful tool for assessing the ecological quality of shallow water bodies in a temperate zone (Jurczak et al., 2018). However, TSIROT has a limited application for assessing the water quality in brackish and oligotrophic water bodies. Some individual species have been proposed as indicators for particular environmental factors. For examples, the occurrence of particular rotifer species can be treated as indicators of the hydrological conditions in small water bodies (Kuczynska-Kippen and Pronin, 2018). Nevertheless, there is limited knowledge about the ability of individual rotifers to indicate water quality. Changes in water depth and water retention time can have pronounced implications for zooplankton distribution (Perbiche-Neves and Nogueira, 2013), and this creates difficulty for rotifer indicators in urban river–lake ecosystems. The aims of the investigation were: (1) to understand the key factors in shaping the heterogeneity of rotifer communities in urban river–lake ecosystems; (2) to quantify responses of major rotifers and traditional taxonomic indicators to trophic status in this ecosystem; and (3) to propose a reliable bioindicator for water quality assessment of urban river–lake ecosystems.

Liuye (L), including the upstream, middle reaches, downstream, lake shores and centers. Also, three sites including upstream, middle reaches and downstream were set up in both of the Chuanzi River (C) and the Changde segment of the Yuan River (Y). 2.2. Sampling and analytical procedure Quantitative samples of crustacea with volumes of 20 L were collected in triplicate from the surface layer (30–50 cm) and were concentrated on a plankton net (mesh size 64 μm). Five liters of surface water (30–50 cm) were collected in triplicate for rotifer quantitative samples and were concentrated on a plankton net with a mesh size of 30 μm. Qualitative samples for species identification were collected by towing a plankton net, with a mesh size of 20 μm, horizontally at surface and subsurface depths. Both quantitative and qualitative samples were immediately fixed with 5% formalin solution and preserved in a 50 mL polyethylene bottle. Physical factors, such as water temperature (Temp), dissolved oxygen (DO), pH, and salinity, were measured using a calibrated multiprobe (YSI-Plus, USA). Water depth (Dep) was measured using a fathometer (SM-5, USA). Water transparency (SD) was measured with a Secchi disc. Chlorophyll-a (Chl-a), chemical oxygen demand (COD), total dissolved phosphorus (TP), ammonium nitrogen (NH4-H) and total dissolved nitrogen (TN) were determined in the laboratory following standard analytical methods (GB3838-2002, MEE, China, 2002). Rotifer identification was based on the Koste (1978) and Dumont (2002), the latest and most authoritative rotifer taxonomy system. Trophi from the qualitative samples were examined by microscope for further identification and a list of rotifer species of different water bodies in the three areas was tabulated. Quantitative samples were concentrated to 10 mL after sedimentation. One milliliter of the concentrated sample was taken randomly after mixing and analyzed in a Sedgewick-Rafter chamber. The abundance counts were converted to ind.·L−1. 2.3. Statistical analysis Trophic level was calculated using the comprehensive trophic level index (TLIc) which was justified from 26 major lake investigations in China (Wang et al., 2019): m

TLIc =

2. Materials and methods

Wj TLI (j ) j =1

(1)

m

2.1. Study sites

Wj = rij2/

rij2 j=1

The city of Changde, located in Hunan province (south central China), is one of the 30 pilot cities for the construction of a national sponge city (Jiang et al., 2017b). The annual precipitation in the study area in the year we sampled was 1149 mm, with 86 mm in December 2016, 334 mm in March 2017, 899 mm in June and 346 mm in September (available at http://slt.hunan.gov.cn/hnsw/, accessed on 08/ 29/2019). Lake Liuye, a scenic spot located in the center of Changde, has a surface area of approximately 21.8 km2. The Yuan River is a tributary of the Yangtze River with an average annual runoff of 3.93 × 109 m3 and a flood period from May to July. The Chuanzi River is a river–lake waterway connecting Lake Liuye and the Yuan River with a total length of 17.3 km (Wei et al., 2010). This urban river–lake ecosystem is divided into three separate but connected water bodies (Lake Liuye, the Chuanzi River and the Yuan River), by sluice gates, as indicated at L9 and C3 in Fig. 1. The details of geographic information of the three water bodies are presented in Table A.1. Samples were taken on the boat at every site quarterly from December 2016 to September 2017. The present study was carried out at 15 sampling sites and the average interval of each sampling site was set to about three kilometers. Nine sampling sites were set up in Lake

(2)

where TLI represents the weighted sum based on the correlations between Chl-a and other substances. Wj represents the weight of the environmental factor; rij is the correlation coefficients between Chl-a and each parameter j (Chl-a, 1; TP, 0.84; TN, 0.82; SD, −0.83; CODMn, 0.83). m represents the number of indicators. Each formula is established as following: TLI (Chl-a) = 10 [2.5 + 1.086ln (Chl-a)]

(3)

TLI (TP) = 10 [9.436 + 1.624ln (TP)]

(4)

TLI (TN) = 10 [5.453 + 1.694ln (TN)]

(5)

TLI (SD) = 10 [5.118 + 1.94ln (SD)]

(6)

TLI (COD) = 10 [0.109 + 2.661ln (COD)]

(7)

The dominant species was evaluated with the McNaughton dominance index (Y). Y = (Ni/N) × fi, where Ni is the abundance of species i in all samples, N is the total abundance of all species in all samples, fi is the frequency of occurrence of species i in all samples. When Y > 0.02, the dominant species is decided (Chi et al., 2017). The 2

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Fig. 1. Location of the study area and the sampling sites in the city of Changde (Sampling sites: L1-L9: Lake Liuye; C1-C3: the Chuanzi River; Y1-Y3: the Yuan River; Red arrows: sluice gates).

Brachionus: Trichocerca ratio (BT) (Sládeček, 1983), Keratella-index (KIN) (Gopko and Telesh, 2013) and the rotifer trophic state index (TSIROT) (Ejsmont-Karabin, 2012) were also used in the trophic state assessment. The TSIROT index consists of six formulas:

Dissimilarity analysis was performed to estimate the species contribution rate to the differences among the three water bodies. To assess the mean differences of physico-chemical parameters and rotifer taxonomic indices among three different water bodies, analysis of variance (ANOVA) with LSD test was conducted to determine significant differences (p < 0.05). The relationships between dependent variables for taxonomic indices (BT, KIN, TSIROT, and rotifer diversity) and independent variables for environmental factors were examined by linear regression. Also, we combined the latest data of 2018 into regression analysis (Ye et al., 2020). These statistical analyses were performed on SPSS 22, a widely used program for statistical analysis in ecology. The response of rotifer community structure to the environmental variables was analyzed using constrained ordination methods on CANOCO 4.5. The dataset was log transformed [log (n + 1)] and centered on species. Canonical Correlation Analysis (CCA) model or Redundancy analysis (RDA) model is determined based on the community composition by Detrended Correspondence Analysis (DCA). If the longest gradient is > 4, the unimodal method (CCA) will be applied. On the other hand, if that value is < 3, the linear method (RDA) is a better choice. In the range between 3 and 4, both methods can be applied. Only the environmental variables displaying significant (Monte Carlo permutation test with 999 permutations, α = 0.05) and varying inflation factors less than 10 (VIF < 10) were included in the analysis (ter Braak and Smilauer, 2002).

Abundance of rotifers (N, ind·L−1): TSIROT1 = 5.38 * Ln(N) + 19.28 (8) Total biomass of rotifer community (B, mg·L−1): TSIROT2 = 5.63 * Ln (B) + 64.47 (9) Percentage of bacterivores in the total number of rotifers (BAC, %): TSIROT3 = 0.23 * BAC + 44.30 (10) The proportion of K. tecta in the total abundance of rotifers K. cochlearis and K. tecta (TECTA, %): TSIROT4 = 0.187 * K. tecta + 50.38 (11) Ratio of rotifer biomass to TSIROT5 = 3.85 * (B: N) −0.318

abundance

(B:

N,

mg·L−1): (12)

Contribution of species that indicate high trophic states in the indicatory group's numbers (IHT, %): TSIROT6 = 0.203 * IHT + 40.0 (13) Rotifer community structure was analyzed with the community analysis software PRIMER 5 to obtain the α diversity indices: Margalef richness index, Brillouin diversity index, Shannon–Wiener diversity index, Simpson dominance index and Pielous evenness indices. Rotifer community β diversity analysis were processed on PRIMER 5 by BrayCurtis distance: Non-metric multidimensional scaling (NMDS) was used to reflect the rotifer community aggregated tendency among the river–lake ecosystem. The analysis of group dissimilarities (ANOSIM) was used to examine the significant differences and degrees among groups. In general, R > 0.75 means large difference; R > 0.5 means medium difference, R > 0.25 means small difference. In order to reduce the influence of outliers on the fitting effect of the model, the species abundance dataset was log transformed (log (n + 1)) and standardized.

3. Results 3.1. Environmental characteristics The water temperature ranged between 11 and 28 °C with no significant differences among three water bodies (p > 0.05) (Fig. 2). pH (8.0 ± 0.4) and DO (8.7 ± 1.9 mg·L−1) were significantly higher in 3

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Fig. 2. The annual value of physicochemical factors in the three water bodies in the city of Changde. Letters indicate sample means that are similar (same letter) or significantly different (different letter) for each factor among: L, Lake Liuye (n = 9); C, the Chuanzi River (n = 3); Y, the Yuan River (n = 3); blank represents no significant difference.

Liuye Lake (L) than in the Yuan River (Y) and the Chuanzi River (C) (p < 0.05). The SD in the Yuan River (98 ± 55 cm) was the highest and reduced to 35 ± 18 cm during the flooding in June. At the same time, the highest concentration of TP (0.528 ± 0.091 mg·L−1) was recorded in the Yuan River. The highest concentration of TN (1.25 ± 1.25 mg·L−1) and NH4-N (0.19 ± 0.13 mg·L−1) was recorded in Chuanzi River. Moreover, the concentrations of TP and TN in the Chuanzi River were significantly (p < 0.01) higher than those in Lake Liuye in June 2017. There was no significant difference in NH4-N content among three water bodies (p > 0.05). In most seasons, Dep in the Yuan River was significantly higher than that in Lake Liuye and the Chuanzi River (p < 0.05), and the concentrations of Chl-a, COD and TLIc indices were significantly lower than that in Lake Liuye and the Chuanzi River (p < 0.05). However, there was no significant difference of most physicochemical parameters between Lake Liuye and the Chuanzi River (p > 0.05). This suggests that the characteristics of the aquatic environment between Lake Liuye and the Chuanzi River were more similar compared to the Yuan River. The mean value of TLIc of Lake Liuye and the Chuanzi River ranged from 38.5 ± 5.0 to 56.0 ± 3.2 and 41.9 ± 3.5 to 59.3 ± 0.1, respectively, indicating that Lake Liuye and the Chuanzi River were at the mesotrophic to light eutrophic level. Since TLIc of the Yuan River ranged from 26.0 ± 2.2 to 48.5 ± 1.4, this water body was at the oligotrophic to mesotrophic level (Fig. 2).

3.2. Species composition and abundance of rotifers A total of 95 rotifer species were identified in all sampling sites during the study period (Table A.2). The highest number of taxa occurred in Lake Liuye (87), followed by the Chuanzi River (58), and lowest (31) in the Yuan River. Moreover, 22 taxa were shared among the three water bodies (Fig. 3A). The dominant species, Polyarthra dolichoptera, Synchaeta stylata, Anuraeopsis fissa, Brachionus angularis, Keratella cochlearis, Filinia terminalis, Ascomorpha saltans, and A. ovalis were found in all water bodies. There were 31 unique species in Lake Liuye, and most of them were Brachionus and Trichocerca spp., which are considered to be common or eutrophic indicators. The species Trichocerca insignis, Euchlanis oropha and T. rousseleti were only found in the Chuanzi River, while Trichotria tetractis, Dissotrocha macrostyla were only found in the Yuan River (Table A.2). (A) Venn diagram showing the numbers of unique and shared species among three water bodies. (B) Non-metric multidimensional scaling (NMDS) plot of rotifer communities. Symbols: triangle, Lake Liuye; square, the Chuanzi River; circle, the Yuan River; blue, March; green, June; yellow, September; pink, December. (C) Main species and their contributions to the differences between Lake Liuye and the Chuanzi River. (D) Main species and their contribution to the differences between the Chuanzi River and the Yuan River. Legends: (L) Lake Liuye; (C) the Chuanzi River; (Y) the Yuan River Both the maximum and minimum abundance of rotifers occurred in 4

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Fig. 3. Analysis of differentiation of rotifer community structure among three water bodies.

June, the flooding period in the city of Changde. The highest average abundance of rotifers was found in Lake Liuye (1203 ± 874 ind.·L−1) in June and the Chuanzi River (1445 ± 501 ind.·L−1) in September (Fig. A. 1). The peak abundance appeared at L2 (2628 ind.·L−1) at the beach park for human activities, while the lowest appeared at Y2 (1 ind.·L−1). The genera Synchaeta and Ascomorpha dominated in December and March. Afterwards, the eutrophic genera Brachionus, Polyarthra and Anuraeopsis took their place in June and September (Fig. 4). In addition, the average copepod and cladoceran abundances decreased in the order: L > C > Y. The highest average abundances of copepods and cladocerans were found in Lake Liuye in June, only 15 ± 27 ind.·L−1 and 5 ± 3 ind.·L−1, respectively. The highest abundance of copepod naupii occurred in June (20 ± 11 ind.·L−1) and September (20 ± 12 ind.·L−1) (Fig. A. 1).

(R = 0.150, P < 0.01). These results were consistent with the NMDS statistical results. The differences in rotifer communities between Lake Liuye and the Chuanzi River mainly resulted from the abundance of the dominant species, while the differences between the Chuanzi River and the Yuan River were mainly caused by overall species composition (Fig. 3C, D). The average abundances of most main species in Lake Liuye were higher than those in the Chuanzi River. On the contrary, the abundances of A. fissa and B. angularis were higher in the Chuanzi River than in Lake Liuye (Fig. 3C). The abundances of the main species were the lowest in the Yuan River. In addition, A. saltans, T. pusilla and B. calyciflorus were never observed in the Yuan River (Fig. 3D).

3.3. Differentiation of rotifer community structure among the three water bodies

Rotifer average abundance in the Yuan River (10 ± 9 ind.·L−1) was significantly lower than that in Lake Liuye (652 ± 642 ind.·L−1) and the Chuanzi River (653 ± 613 ind.·L−1) (p < 0.05) (Fig. 5A). The highest TSIROT was recorded in Lake Liuye (48.3 ± 3.1; p < 0.05), followed by the Chuanzi River (48.1 ± 4.1), and the lowest (44.6 ± 3.4) in the Yuan River (Fig. 5B). The BT indices decreased in the order: L > C > Y, while the KIN indices decreased in the order: L > Y > C (Fig. 5C). The Margelef and Simpson indices ranged between 0 and 2.8 and 0–0.86, respectively, with no significant differences among the three water bodies. The Shannon-Weiner index in the Yuan River (1.53 ± 0.70) was significantly lower than that in Lake Liuye (2.24 ± 0.57) and the Chuanzi River (2.02 ± 0.57) (p < 0.01). A higher proportion of dominant species in Lake Liuye and the Chuanzi River resulted in a decrease of the values of Pielou’s evenness (0.65 ± 0.12; 0.64 ± 0.17, respectively), which were significantly

3.4. Rotifer taxonomic indices

Non-metric multidimensional scaling (NMDS) was used to reveal the spatial–temporal pattern of the rotifer community. The stress value of NMDS was 0.15 (< 0.2). The samples collected in June and September were mostly together and distributed on the left of the plot; the samples in March and December tended to be on the right (Fig. 3B). Furthermore, Lake Liuye and the Chuanzi River were mostly together and separated from the Yuan River (Fig. 3B). The ANOSIM (one-way) test demonstrated that rotifer communities were significantly different among the three water bodies in this urban lake–river ecosystem (Global R = 0.38, p < 0.01). Among them, the difference between Lake Liuye and the Yuan River was the highest (R = 0.617, P < 0.01), followed by the Chuanzi River and the Yuan River (R = 0.247, P < 0.01), and the lowest was Lake Liuye and the Chuanzi River 5

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Fig. 4. The proportions (Y axis on left) and the total rotifer abundances (Y axis on right) in 60 samples collected from three different water bodies in Changde city in the four seasons. The lines represent the total rotifer abundances and the columns represent the rotifer composition. L, Lake Liuye; C, the Chuanzi River; Y, the Yuan River.

lower than those in the Yuan River (0.86 ± 0.28; p < 0.01) (Fig. 5D).

Yuan River; blue, March; green, June; yellow, September; pink, December. (B) Relationship between species and environmental variables. Abbreviations: Abri, Asplanchna brightwellii; Aova, Ascomorpha ovalis; Asal, A. saltans; Afis, Anuraeopsis fissa; Bcal, Brachionus calyciflorus; Bang, B. angularis; Bcau, B. caudatus; Bdiv, B. diversicornis; Flon, Filinia longiseta; Fter, F. terminalis; Fcor, F. cornuta; Kcoc, Keratella cochlearis; Ktec, K. tecta; Ktro, K. tropica; Lham, Lecane hamata; Nlab, Notholca labis; Pdol, Polyarthra dolichoptera; Psub, Proalides subtilis; Ssty, Synchaeta stylata; Stru, S. tremula; Ttru, Trichotria truncate; Tpus, Trichocerca pusilla; Tsim, T. similis; Tsty, T. stylata; Tcyl, T. cylindrica; Tcap, T. capucina; Trat, T. rattus; Tgra, T. gracilis; The adjusted correlation coefficient (R2) of the linear regression between taxonomic indices (including main rotifers abundances) and the environmental factors in the present study are shown in Fig. 7A (Table A. 5). Chl-a was significantly positively correlated with all main species of rotifers. The genus Polyarthra was significantly correlated with seven environmental factors, showing stronger positive correlation than other species with COD (R2 = 0.58, p < 0.01), Chl-a (R2 = 0.58, p < 0.01) and TLIc (R2 = 0.34, p < 0.01). The genus Synchaeta was positively correlated with Chl-a (R2 = 0.30, p < 0.01) and TLIc (R2 = 0.14, p < 0.01). Ascomorpha was positively correlated with Chla (R2 = 0.27, p < 0.01) and was negatively correlated with Temp (R2 = 0.15, p < 0.01). Moreover, both A. fissa and B. angularis showed stronger positive correlation with COD (R2 = 0.33, p < 0.01; R2 = 0.32, p < 0.01; respectively) than with Chl-a (R2 = 0.15,

3.5. Relationships between rotifer community composition and environmental variables As the longest gradient performed by DCA was 2.9, RDA model was used to estimate the relationship between rotifer species and environmental factors. The RDA summarized the relations between the rotifer species composition and environmental variables (Fig. 6). The first two ordinate axes explained 30% of the species-environment variability in the ordination of environmental variables (Tables A.3-A.4). After forward selection with Monte Carlo permutation tests, only Temp, COD and Chl-a were significant contributors to the variation of the rotifer community in the river–lake ecosystem and they all showed positive correlation with axis 1. The spatial–temporal pattern of rotifer communities varied along an increasing gradient of axis 1. Also, Fig. 6 A clearly showed that hot season (Jun and Sep) assemblages mostly stayed on top-right of the figure, while cold season (Dec and Mar) assemblages were mostly on bottom-left of the figure. Furthermore, coldwater species S. stylata, S. tremula and A. saltans were related to higher Chl-a content. Thermophilous species such as A. fissa, B. forficula, B. angularis, F. terminalis and T. pusilla were associated with both COD and Chl-a (Fig. 6B). (A) Relationship between samples and environmental variables. Symbols: triangle, Lake Liuye; square, the Chuanzi River; circle, the 6

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Fig. 5. Rotifer taxonomic indices among the three water bodies. (A) Rotifer total abundance; (B) Ejsmont-Karabin’s rotifer trophic state index; (C) Brachionus: Trichocerca ratio (BT) and Keratella-index (KIN) values; (D) α biodiversity indices. Letters indicate sample means that are similar (same letter) or significantly different (different letter) among different water bodies; blank represents no significant difference. Legends: (L) Lake Liuye (n = 36); (C) the Chuanzi River (n = 12); (Y) the Yuan River (n = 12).

Fig. 6. Redundancy analysis (RDA) of the rotifer community with environmental variables. 7

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Fig. 7. Adjusted correlation coefficient (R2) of the linear regression model between the taxonomic indices and environmental factors. (A) 2016–2017 (B) 2016–2018, combined with Ye et al. (2020) data; Abbreviation: TP, Total phosphorus; TN, Total nitrogen; TLIc, trophic level index; Temp, Temperature; SD, Transparency; NH4N, Ammonium nitrogen; DO, Dissolved oxygen, Dep, Depth; COD, chemical oxygen demand; Chl. a, chlorophyll-a. BT, Brachionus: Trichocerca ratio; KIN, Keratellaindex; TSIROT, Ejsmont-Karabin’s rotifer trophic state index.

p < 0.01; R2 = 0.08, p < 0.05; respectively). The total abundance showed the strongest correlation with Chl-a (R2 = 0.57, p < 0.01), followed by TLIc (R2 = 0.36, p < 0.01) and COD (R2 = 0.34, p < 0.01). Also, TSIROT index was significantly correlated with seven environmental factors, showing positive correlation with COD (R2 = 0.49, p < 0.01), TLIc (R2 = 0.29, p < 0.01) and Chl-a (R2 = 0.22, p < 0.01) (Fig. 7A). However, most of the R2 values of the linear regressions between main rotifers abundances and the chemical factors decreased when combined with the latest data from 2018 (Fig. 7B, Table A. 6). For example, the R2 of A. fissa-COD and B. angularis-COD decreased to 0.22 and 0.28 respectively; the R2 of Polyarthra-COD, Polyarthra-Chl-a and Polyarthra-TLIc decreased to 0.30, 0.35 and 0.29 respectively. Also, the R2 of total abundance-COD, total abundance-Chl-a and total abundance-TLIc decreased to 0.23, 0.39 and 0.3. Nevertheless, the R2 of TSIROT-Chl-a (0.21) and TSIROT-TLIc (0.28) were stable. The numbers of significant correlations between taxonomic indices and environmental factors have increased with the enlarged sample size.

Gruberts et al., 2007). Our results agree with the above findings. The main rotifers and total abundance showed significantly negative correlation with water depth in this river–lake ecosystem (Fig. 7). In rivers, runoff is one of the impact factors for rotifer abundance (Pace et al., 1992). Because rotifers are less mobile, they exhibit no migratory behavior (Mendonça et al., 2015). The water depth rises due to the increase of runoff, which dilutes the rotifers abundance and increases the evenness of community. Our study indicated that temperature is a driving factor of rotifer community succession in the urban lake–river ecosystem, consistent with the studies on rotifer community structure in other subtropical lakes (Wen et al., 2011; Ji et al., 2013; Chen et al., 2012). The results suggest that temperature is the key variable affecting rotifer communities in subtropical waters in China. ‘Top-down’ forces have been considered as biotic factors in controlling the variation of rotifer abundance (Yoshida et al., 2003). Small rotifers suffer from predation and competition by large-sized zooplankton such as copepods and cladocerans (Yoshida et al., 2003), but are less vulnerable to planktivorous fishes than large-sized zooplankton (Shao et al., 2010). The abundances of copepods, cladocerans and naupii in this study were relatively lower when compared with other studies in subtropical waters (Wen et al., 2017; Shao et al., 2010). Also, they varied in the same trend as rotifer abundance. This implies that the rotifer community in the river–lake ecosystem was mainly regulated by ‘bottom-up’ forces. Trophic status plays an important role in regulating the variation of rotifer communities. Lake Liuye and the adjacent waters were at light eutrophic level during 2007 to 2009 (Wei et al., 2010) and they were at mesotrophic to light eutrophic level during 2016 to 2017. This suggests that the trophic state of Lake Liuye and adjacent waters varies smoothly and the community structure tends to be stable. Our studies showed that the difference between Lake Liuye and the Chuanzi River was mainly caused by the abundances of their shared dominant species, while the difference between the Chuanzi and Yuan Rivers resulted from overall species composition (presence or absence). Abundances of the most dominant species tended to be higher in Lake Liuye. On the contrary, the abundances of A. fissa and B. angularis were higher in the

4. Discussion 4.1. The effects of key environmental factors on rotifer community shifts Water depth plays an important role in affecting the rotifer community. The Yuan River was significantly deeper than Lake Liuye and the Chuanzi River with the mean value of 16 m. Lake Liuye and Chuanzi River can be considered as shallow water bodies with the mean depth of 4.5 and 3.0, respectively. Also, ANOSIM analysis showed that rotifer communities of Lake Liuye and the Chuanzi River were similar but distinct from that of the Yuan River. This implies that the spatial pattern of rotifer communities in the urban river–lake ecosystem was regulated partly by water depth. Jeppesen et al. (2010) reported that zooplankton abundance and diversity were positively correlated with depth in shallow lakes, which might be explained by a greater number of available niches in deep lakes. However, studies carried out in rivers showed an inverse and statistically significant correlation between water depth and zooplankton abundance (Goździejewska et al., 2016; 8

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Chuanzi River. This could be attributed to rotifer food collection and selection. On one hand, it was reported that the phytophagous rotifers, such as Synchaeta and Polyarthra, feed mainly on edible algae, while microfilter-feeders (A. fissa) and fine particle sedimentators (B. angularis) are inclined to consume bacteria, detritus particles and nanoplankton (Virro et al., 2009; Špoljar et al., 2005). This implies that the Chuanzi River, with higher nutrient content but lower phytoplankton content and transparency in summer, provided the optimum habitat for A. fissa and B. angularis. On the contrary, Lake Liuye was dominated by phytophagous rotifers. On the other hand, microalgae is the best food for zooplankton (Bielańska-Grajner and Gładysz, 2010). The RDA results corresponded with the linear regression between rotifer species and environmental factors. A. fissa and B. angularis showed a stronger positive correlation with COD rather than Chl-a. Our results were in accord with the idea that microphagous rotifers with a relatively small body mass would be more likely to thrive in conditions of poor food quality than would raptorial species (Wen et al., 2017). Thus, the structural dynamics of rotifer community were primarily controlled by trophic status (food source).

TSIROT index of the three water bodies decreased in the order: Lake Liuye > the Chuanzi River > the Yuan River. By these metric, Lake Liuye and the Chuanzi River can be considered as slightly eutrophic, while the Yuan River can be considered as mesotrophic. The results of values were similar to those of the TLIc. 4.3. Feasibility of using TSIROT index in water quality assessment It is very difficult to establish one-to-one causal relationships between rotifer species and trophic conditions, because temperature has a predominant influence on rotifers compared to that of trophic conditions in subtropical lakes (Ji et al., 2013). We observed that Polyarthra showed the strongest correlation with trophic status and was more tolerant to seasonal changes than other rotifers during 2016–2017. However, the R2 between Polyarthra and trophic state decreased when the latest data of 2018 was combined. This is very common in model prediction. As the time horizon increases, the reliability and accuracy of the models decrease (Shamshirband et al., 2019; Chen and Chau, 2019). In general, multiple individual models result in superior performance over the best single forecast models (Alizadeh et al., 2018; Shamshirband et al., 2019). Total abundance contains of different species abundance and it is more stable than individual rotifer species indicators, though it also decreases as the time horizon increases. TSIROT index is the most stable and reliable indicator as the R2 changed little after we combined the data of 2018. Some rotifers, even the widespread Polyarthra may be absent in some oligatrophic waters. The TSIROT index can overcome the weakness of using individual rotifer indicator, because it covers six formulas of different eutrophic species proportion, biomass and different rotifers abundances, reducing errors caused by the absence of individual rotifers. In addition, compared to using only rotifer abundance, the comprehensive index shows greater reliability and resistance to the effects of hydrological changes in lotic waters. Our results suggested that the TSIROT index is an ideal indicator of water quality not only in shallow water bodies in the temperate zone (Jurczak et al., 2018), but also the river–lake ecosystems with high water depth variation. However, despite the promising stability, the TSIROT index has a limited application for assessing the water quality in oligatrophic waters, which is consist with the previous studies (Ejsmont-Karabin, 2012; Jurczak et al., 2018). Dissotrocha macrostyla which is usually found in ultraoligotrophic lakes, stream bottoms, and macrophyte communities (Jenny and Schmid‐Araya, 2007; Sládeček, 1983) was a unique species recorded in the Yuan River. Adding some oligotrophic indicators such as Dissotrocha macrostyla into the future TSIROT index may enhance its ability for evaluating water quality in oligatrophic waters. A one-year study is limited in its ability to prove the utility of taxonomic indices to indicate water quality, but this is a pilot study of reliable bioindicators for sponge city construction. Also, long-term studies in other sponge cities should be taken to validate the findings we reported here.

4.2. Response of rotifer taxonomic indices to water quality It is more efficient to combine the rotifer taxonomic indices and the TLIc index in water quality assessment. The TLIc index indicated that the Yuan River was at the oligotrophic to mesotrophic level. According to the rotifer abundance in relation to trophic state, 200–1000 ind.·L−1 is considered characteristic of mesotrophic lakes, 1000–2500 ind.·L−1 slightly eutrophic, and 3000–4000 ind.·L−1 moderately eutrophic (Ji et al., 2013; Wen et al., 2011). By these criteria, the Yuan River can be considered as oligotrophic due to its low abundances (10 ± 9 ind.·L−1). Because of the rotifer abundance of around 200–2500 ind.·L1, the Chuanzi River and Lake Liuye can be considered as mesotrophic to slightly eutrophic. It was reported that total abundance was less useful as an indicator in rivers (Liang et al., 2019). As the rotifer abundance is influenced by river runoff, fluctuations in water depth reduced the accuracy of total abundance as an indicator of trophic status assessment in urban river–lake ecosystems. Biodiversity is one of the important characteristics for ecosystem monitoring. It was reported that the diversity is higher in lakes at a medium trophic level, while the community structure is simple and the species diversity is lower under extreme oligotrophic or hyper-eutrophic levels (Qian et al., 2007). The Shannon-Weiner index of the Yuan River was the lowest, while those in Lake Liuye and the Chuanzi River were similar. The Yuan River can be considered to be extreme oligotrophic by these metric. However, biodiversity is hard to evaluate trophic state from mesotrophic to slightly eutrophic level. According to Brachionus: Trichocerca ratio (BT), values < 1.0 mean oligotrophic condition (Sládeček, 1983). The present three water bodies can be considered as oligotrophic. KIN values < 0.2 means oligotrophic condition (Gopko and Telesh, 2013). Under these definitions, the Chuanzi River can be considered as oligotrophic. Obviously, the conclusion is incorrect. BT and KIN indices are useful indicators of brackish water in south China with the salinity ranged from 0 to 34‰(Liang et al., 2019), but it seems that they are less useful in this river–lake ecosystem in south central China with no salinity. As temperature was the driving factor of rotifer community succession in this study, some eutrophication-tolerant species such as Brachionus spp. and Keratella tecta disappeared in winter. Moreover, since BT index was developed in temperate zone (Sládeček, 1983), it may be reliable for summer or areas with small interannual temperature differences. It has been reported that the TSIROT index could be a useful tool for assessing the ecological quality of shallow water bodies in the temperate zone (Jurczak et al., 2018). Based on TSIROT definitions in lakes, the value under 45 was mesotrophic, 45–55 was mesoeutrophic, 55–65 was eutrophic and > 65 was hypereutrophic (Ejsmont-Karabin, 2012).

5. Conclusion The temporal heterogeneity of the rotifer community in the urban river–lake ecosystem was controlled by temperature, while the spatial heterogeneity was regulated by water depth and trophic state. The TLIc index indicated that the Chuanzi River and Lake Liuye were mesotrophic to slightly eutrophic, while the Yuan River was oligotrophic to mesotrophic. Individual rotifer indicators, biodiversity indices, the Brachionus: Trichocerca ratio and the Keratella-index were less useful in assessing the trophic status in this study. The results of the TSIROT values and total abundance were most consistent with those of the TLIc. Furthermore, TSIROT values showed stability and resistance to changes when the time horizon increased. Our results suggest that the TSIROT index is a reliable indicator of water quality in river–lake ecosystems with high water depth variation. Adding some oligotrophic rotifers into 9

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the future TSIROT index may enhance its utility in oligotrophic waters. The present one-year case study is limited and long-term studies in river–lake ecosystems of other sponge cities should be taken to confirm our findings.

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