Planktonic indicators of trophic states for a shallow lake (Baiyangdian Lake, China)

Limnologica 78 (2019) 125712

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Planktonic indicators of trophic states for a shallow lake (Baiyangdian Lake, China)

T

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Caihong Tanga, Yujun Yia,b, , Zhifeng Yanga,b, Yang Zhoua, Teklit Zerizghia, Xuan Wanga,b, Xiuli Cuic, Pengyu Duanc a

State Key Labratory of Water Environmental Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China Ministry of Education Key Laboratory of Water and Sediment Science, School of Environment, Beijing Normal University, Beijing, 100875, China c Research Institute of Environmental Protection of Baoding City, Baoding, 071051, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Eutrophication Shallow lake Trophic state Phytoplankton Zooplankton

Eutrophication in lake ecosystems poses a serious threat to the water quality and function of aquatic ecosystems. The species composition and community structure of plankton change directly with variations in lake trophic states. Seasonal sample collections of phytoplankton, zooplankton and environmental variables were conducted in spring, summer, and fall from a large shallow lake, Baiyangdian Lake, China. The species richness, community composition and temporospatial variations of phytoplankton and zooplankton were analyzed. The lake trophic states were assessed using the comprehensive trophic state index (CTSI) and rotifer trophic state index (TSIROT). The results indicated that 69.1% of the lake area showed slight eutrophication, 29.3% showed mesotrophication, and 1.6% showed moderate eutrophication. The Shannon-Wiener diversity index and Pielou evenness index were employed to assess the community diversity of phytoplankton and zooplankton. The dominant taxa and most dominant species of phytoplankton and zooplankton were determined; Cyanophyta and Rotifera were the dominant phytoplankton and zooplankton taxa, respectively. Microcystis sp. and Polyarthra vulgaris were the most dominant species of phytoplankton and zooplankton, respectively. Redundancy analysis (RDA) was applied to identify the key environmental variables that influenced the species, diversity indices and species abundance of phytoplankton and zooplankton. The results showed that ammonia nitrogen (NH4+), total nitrogen (TN), total phosphorus (TP), and dissolved oxygen (DO) were the main environmental factors influencing the species abundance and diversity. The current lake plankton species number and diversity index were lower than those in past decades, as determined by comparing the community characteristics of phytoplankton and zooplankton with historical records. Comparisons of TN:TP ratios with those of other lakes suggested that nitrogen was the limiting nutrient for lake eutrophication. Baiyangdian Lake could have a high potential for eutrophication based on the environmental and ecological characteristics of the lake.

1. Introduction Shallow lakes are characterized by a shallow water depth, which causes water to mix with sediment containing nutrients throughout the whole depth profile under the effects of wind, allowing nutrients transported in the water to be used by aquatic organisms (Janssen et al., 2014; Tang et al., 2018). The lakes provide habitats for aquatic animals and macrophytes, which in turn contribute to lake ecosystem stability and rich water, fishing, and tourism resources for surrounding dense populations (Scheffer, 2004; Xu et al., 2010). In recent decades, many shallow lakes have become eutrophic, and some, such as Lake Taihu (China's third-largest lake) (Yue et al., 2014) and Lake Erie (United

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States of America) (Michalak et al., 2013), have even suffered cyanobacterial blooms. Algal blooms can last for long periods and make the lake water toxic because some cyanobacteria damage the human liver, intestines, and nervous system and threaten public health (Yue et al., 2014). Eutrophication has led to a decrease in the species abundance and diversity of vegetation, fish, and plankton; the lake ecosystem stability has also shifted from vegetation-dominated clear water to algae-dominated turbid water (Michalak et al., 2013; Scheffer, 2004). Phytoplankton is an essential primary producer that helps form the basis of many food webs and provides a wide range of lake ecosystem services (McCarthy et al., 2007). Zooplankton feeds on phytoplankton and is also prey for zooplanktivorous fish. Phytoplankton and

Corresponding author at: State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, 100875, China. E-mail address: [email protected] (Y. Yi).

https://doi.org/10.1016/j.limno.2019.125712 Received 3 December 2018; Received in revised form 10 June 2019; Accepted 8 July 2019 Available online 24 August 2019 0075-9511/ © 2019 Elsevier GmbH. All rights reserved.

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fall. Common reed (Phragmitescommunis L.) and Nelumbo nucifera are the main emergent and floating vegetation, respectively. All individual open waters are connected by thousands of shallow channels, and boats are the only transportation for local residents in the lake. Fishing and livestock breeding are other important sources of local income. This lake is currently facing extensive water pollution pressure and ecological damage due to natural climate changes and human pollution, such as from the tourist industry, breeding industry, agriculture and daily life (Liu et al., 2010; Yang and Yang, 2014).

zooplankton are positioned in the lower trophic levels in the lake food web and are vital components for maintaining the richness and stability of food webs (Yang, 2011; Wu and Wang, 2012). The increases in the phytoplankton cyanobacteria Microcystis sp. and Anabaena sp. from algal blooms produce the hepatotoxin microcystin and the neurotoxin anatoxin (Michalak et al., 2013). The characteristics of phytoplankton and zooplankton, such as the species richness, community structure, and biodiversity, are excellent indicators of the trophic status. Species richness and the abundance of phytoplankton are good biological indicators of water quality in Lake Chaohu (Jiang et al., 2014). EjsmontKarabin (2012) proved the usefulness of the rotifer species composition and abundance as indicators of trophic states by analyzing data from 41 dimictic and 33 polymictic lakes. Ochocka and Pasztaleniec (2016) also found that zooplankton indices were strong indicators for trophic states; they had the same sensitivity to trophic states as phytoplankton indices when comparing calculations of 16 plankton indices. Sewage discharge with rich nutrients, land use, agricultural practices, and meteorological conditions were regarded as reasons for water quality deterioration and eutrophication acceleration (Liu et al., 2010; Wu and Wang, 2012; Wang et al., 2011, 2013). Specifically, the concentration of total nitrogen (TN), chemical oxygen demand (COD), and Secchi disk transparency (SD) were the key factors (Michalak et al., 2013; Jiang et al., 2014). The water temperature (T), oxidation reduction potential (ORP), dissolved oxygen (DO) level, and orthophosphate level directly influenced the phytoplankton community structure (Jiang et al., 2014). Lake size, spatial heterogeneity and internal connectivity between lake compartments also changed the propagation and biomass of phytoplankton (Janssen et al., 2014). Previous studies revealed the relationships among the species composition, community structures, and environmental factors. The assessment of trophic states is vital for shallow lakes that suffer from serious eutrophication and even algal blooms. Therefore, comprehensively analyzing the influence of environmental factors on trophic states and the responses of the plankton community to trophic states would help improve the shortcomings of one-way analyses of the relationships between plankton characteristics and trophic states. For this purpose, we conducted seasonal analyses of environmental factors, phytoplankton and zooplankton in a large shallow lake. Lake trophic states were evaluated by assessing the comprehensive trophic state and rotifer trophic state. Analyses of the characteristics of phytoplankton and zooplankton, including the species abundance, biomass, community structures, diversity indices; the correlation between plankton characteristics and environmental factors; and the ratios of total nitrogen over total phosphorus were conducted to help determine the probability of lake eutrophication.

2.2. Sampling design Based on the above hydrological, ecological, and environmental characteristics, this lake area can be divided into five types of water areas, including port, tourist attraction, residential, aquaculture, and minimally distributed rural areas (Table 1). To represent the characteristics of different types of water areas, we established a total of 12 sampling sites in spring, summer and fall to collect environmental factors, phytoplankton and zooplankton samples in the lake (Fig. 1, Table 1). Site S1 is the main entrance of upstream rivers, domestic sewage, and agricultural pollution into the lake. It is also a port, where water is frequently disturbed by boats and polluted by oil. Sites S5 and S7 are away from human activity. S7 is also close to the only lake exit, controlled by gates. The gates are closed to store water and maintain the lake’s ecological water level most of the time. Sites S1, S3, S4 and S9 have severe pollution, near residential areas. S6 and S8 are aquaculture areas. Sites S2, S9, S10, S11, and S12 are tourist attraction areas. Baiyangdian Lake usually freezes from December to February. Therefore, all field samplings were conducted at the 12 sampling sites in the summer (July) and fall (November) of 2015 and spring (March) of 2016; no samples were collected during the winter when the lake was frozen. 2.3. Data collection Environmental variables were measured in the field using a YSI 6600 Multiparameter Probe (Fondriest Environmental, Inc.), including T, conductivity (Cond), total dissolved solids (TDS), DO, pH, ORP, ammonia nitrogen (NH4+), chloride (Cl−), turbidity (Tur), and chlorophyll-a (Chl-a). The transparency SD was measured using a standard Secchi disk with a diameter of 30 cm. TP, TN, and COD in water samples were measured in the laboratory under the guidance of CBEP (2002). In total, 12 water samples of 1 L for phytoplankton analyses were collected by mixing water from the surface, a depth of 0.5 m, and 0.5 m above the bottom in open waters. Samples were preserved with 1% Lugol's iodine solution and concentrated to 30 mL after sedimentation for 48 h. An Olympus CX31 optical microscope (Olympus, Tokyo, Japan) was used for plankton species identification. For each taxon, we measured a minimum of 20 cells and used the geometric shape closest to the cell shape to calculate the mean biovolume, which was then transformed to biomass (expressed by mg/L wet weight) based on an assumed density of 1 g/cm3 (Hillebrand et al., 1999). Ten liters of water for zooplankton was filtered using a zooplankton net with a mesh size of 64 μm. The filtered 10–15 mL water samples and animals were kept in 30 mL plastic bottles and preserved in 5% formalin for later species identification and biovolume calculation. Common zooplankton identification schemes consist of three types, rotifers, cladocerans, and copepods, excluding protozoa (Likens, 2010). In this study, only rotifers, cladocerans, and copepods were identified. Zooplankton biomass was equal to abundance multiplied by wet weight of individual zooplankton.

2. Materials and methods 2.1. Study area Baiyangdian Lake is the largest shallow lake on the North China Plain, with an average water depth of 2–3 m and an approximate area of 366 km2 (Wang et al., 2014). The lake is located downstream of the Baiyangdian Basin and is the convergence point of the upstream tributaries (Fig. 1). Since the late 1950s, many reservoirs have been constructed upstream, which have restricted a large amount of water from flowing into the lake (Wang et al., 2014). Decreased rainfall caused by climate change has led to the shrinking of the water area (Wang and Xu, 2009). Wind and water supplements are the main driving forces that cause water circulation and pollutant movements in the lake (Tang et al., 2018). A total of ten towns, 134 villages and reed fields divide the lake into scattered water areas. Emergent, submerged, and floating vegetation are widely distributed in the lake areas. The collected submerged littoral vegetation is dominated by curled pondweed (Potamogeton crispus L.) in spring, and hornwort (Ceratophyllum demersum L.) in summer and

2.4. Physicochemical and plankton analysis The comprehensive trophic state index (CTSI) was used to describe 2

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Fig. 1. Map of Baiyangdian Lake in Baiyangdian Basin (a), and 12 sampling sites (b). (S1 is Dongguan; S2 is Yuanyangdao; S3 is Diantou; S4 is Duancun; S5 is Caiputai; S6 is Quantou; S7 is Zaolinzhuang; S8 is Guangdian; S9 is Wangjiazhai; S10 is Dakouzi; S11 is Shaochedian; S12 is Lvyoumatou). Table 1 Description of environmental and ecological parameters for 12 sampling sites in Baiyangdian Lake. Sites

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

Water depth (m) Environmental characteristics

4

1.5

3

5

5

2

2.9

3

2.7

2.5

1,3

2

3

3

5

4

5

4

2,3

2

S11

S12

Mean value ± SD

2

2.5

2

2

3 ± 1.1 –

Notes: Environmental characteristics of sampling sites included port1, tourist attractions2, residential area3, aquaculture area4, rural areas with low disturbance5. SD is the standard deviation.

TSI (Chl − a) = 10 × (2.5 + 1.086 × ln Chl − a)

(2)

0.82, −0.83, and 0.83 for Chl-a, TP, TN, SD, and COD, respectively, based on the investigation results of 26 Chinese lakes (Jin et al., 1995). The CTSI ranks on a numerical scale between 0 and 100. The lake trophic states were classified as oligotrophic (Oligo) (0 < CTSI ≤ 30), mesotrophic (Meso) (30 < CTSI ≤ 50), slight eutrophic (Slight-Eutro) (50 < nCTSI ≤ 60), moderate eutrophication (Mod-Eutro) (60 < CTSI ≤ 70), and hyper eutrophic (Hyper-Eutro) (70 < CTSI ≤ 100). The trophic state assessment results in different seasons were interpolated using the inverse distance weighted method in ArcGIS 10.0 ((ESRI): www.esri.com/software/arcgis). Dominant species was determined by the dominance index of species (Y), estimated by

TSI (TP ) = 10 × (9.436 + 1.624 × lnTP )

(3)

Y=

TSI (TN ) = 10 × (5.453 + 1.694 × lnTN )

(4)

TSI (SD) = 10 × (5.118 − 1.94 × lnSD)

(5)

TSI (COD) = 10 × (0.109 + 2.661 × lnCOD)

(6)

the trophic states for the study lake based on the individual trophic state index (TSI) of Chl-a, TP, TN, SD, and COD. The index was estimated by Eq. (1) (Wang et al., 2002). m

CTSI =

∑ [Wj × TSI (j)] j=1

(1)

where TSI(j) is the jth trophic state index among TSI(Chl-a), TSI(TN), TSI (TP), TSI(SD) and TSI(COD), calculated by Eqs. (2)–(6). m is the total number of trophic state indices, and m equals five here; Wj is the weight of TSI(j), based on the Chl-a content, and expressed by Eq. (7).

Wj =

rij 2 m ∑ j = 1 rij 2

nk f N k

(8)

in which, the dominance index Y represents the dominance of species. A dominant species was defined as Y ≥ 0.02 (Lin et al., 2011). N is the abundance of all species in all sampled sites, nk is the species abundance of the kth species in all locations, and fk is the occurrence frequency of the kth species in all study areas. The Shannon-Wiener diversity index (H) and the Pielou evenness index (J) were employed to reveal the community diversity and population distribution evenness of phytoplankton and zooplankton as follows (Hill, 1973; Keylock, 2005):

(7)

where rij is the correlation coefficient between the content of jth index and the concentration of Chl-a. The coefficient value can be 1, 0.84, 3

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H ′ = − ∑ Ps ln Ps s=1

spring and summer and two trophic states in fall (Fig. 3). The SlightEutro and Mod-Eutro areas were mainly located in the west, and Meso regions were mainly located in the middle and east of the lake. In summer, lake areas were ranked based on the CTSI results in the following order: Slight Eutro > Meso > Moderate Eutro; while in spring, the sequence was Meso > Slight Eutro > Moderate Eutro. The water area of Slight Eutro was larger than that of Meso in fall. Overall, the water trophic state in fall was better than in spring and summer in Baiyangdian Lake. From the perspective of lake space variation, the most severe eutrophication was at the entrance of the lake, and the CTSI values gradually decreased from west to the east over the three seasons (Fig. 3). Trophic states were ranked based on the CTSI assessment results from large to small in the following order: port > residential area > tourist attraction > aquaculture area > rural areas. The CTSI results for the three seasons showed the average trophic states for the whole lake; 69.1% of the lake area was in a slight eutrophic state, 29.3% was in a mesotrophic state, and 1.6% was in a moderate eutrophication state.

(9)

where H' is the species diversity index, S is the total number of plankton species, and Ps is the ratio of the species in sample s to the number of individuals. The Pielou evenness index (J), which reflects the uniformity of the distribution of individuals for different species, was calculated based on the species S and diversity index H' using the following equation (Hill, 1973):

J=

H′ ln S

(10)

Rotifers can be regarded as indicators of water quality and ecological quality assessment (Ejsmont-Karabin, 2012). The rotifer trophic state index (TSIROT) was used to assess the lake trophic state based on characteristics of the rotifer community, including the following (Ejsmont-Karabin, 2012): (1) rotifer number Nr (ind./L) TSIROT 1 = 5.38 ln (Nr ) + 19.28; (2) total biomass of rotifers B (mg ww/L) TSIROT2 = 5.63 ln (B ) + 64.47 ; (3) percentage of bacterivore species in total rotifer numbers (BAC, %) TSIROT3 = 0.23BAC + 44.30 ; (4) ratio of biomass to numbers B/Nr (mg ww/ind.) TSIROT 4 = 3.85(B / Nr )−0.318 ; (5) percentage of the tecta form in the population of Keratella cochlearis TSIROT5 = 0.144TECTA + 54.8; and (6) contribution of species that indicate high trophic state in the indicatory group’s numbers (IHT, %) TSIROT6 = 0.203IHT + 40.0 . The mean value of the above six rotifer indices of lake trophy (TSIROT ) were constructed on a scale of 0–100, including oligotrophy (TSIROT < 35), low mesotrophy (35 ≤ TSIROT < 40 ), high mesotrophy (40 ≤ TSIROT < 45), low mesoeutrophy (45 ≤ TSIROT < 50 ), high mesoeutrophy (50 ≤ TSIROT < 55), low eutrophy (55 ≤ TSIROT < 60 ), high eutrophy (60≤ TSIROT < 65), and hypertrophy (TSIROT ≥ 65). Ejsmont-Karabin (2012) suggested the genus Asplanchna was not considered due to the much larger body size than other rotifer species. Significant differences in 14 environmental factors, the ShannonWiener diversity index and the Pielou evenness index of phytoplankton and zooplankton in different seasons and at different spatial locations were determined using two-way analysis of variance (ANOVA). A post hoc test (Tukey) was used to analyze the significant interactions (p < 0.05) and determine the main effects. ANOVA and Pearson correlation analyses were conducted using SPSS 20. Ordinations were performed using CANOCO for Windows 4.5 (Lepš and Šmilauer, 2003). The detrended correspondence analysis (DCA) was first performed for phytoplankton and zooplankton species distribution to judge whether unimodal or linear models were more suitable. Relationships of environmental variables with community structures (species abundance), diversity index and evenness index of phytoplankton and zooplankton were identified using redundancy analysis (RDA).

3.2. Temporal-spatial variation of the plankton community structure and diversity 3.2.1. Community characteristics of phytoplankton A total of 116 phytoplankton species were collected, including eight phyla and 80 species in fall, seven phyla and 59 species in spring, and seven phyla and 63 species in summer (Table S1). Chlorophyta had the most species richness of total phytoplankton (53 species — 46%), followed by Bacillariophyta (28 species – 24.1%), Cyanophyta (18 species – 16%), Euglenophyta (6 species – 5%), Pyrrophyta (5 species – 4%), Cryptophyta (4 species – 3%), Chrysophyta (1 species – 1%), and Xanthophyta (1 species – 1%). The distribution of phytoplankton at the phylum level in different seasons is shown in Fig. 4. Chlorophyta was the richest phytoplankton taxa, and the average percentage of species richness at all locations was 41%, 56%, and 51%, respectively, in spring, summer, and fall. Bacillariophyta and Cyanophyta were the second and third most abundant taxa, respectively, in the lake over the three seasons. Similar proportions of Euglenophyta, Pyrrophyta, Chrysophyta, and Xanthophyta were found, and those proportions were generally less than those of Chlorophyta, Bacillariophyta, and Cyanophyta. However, obvious differences in community structures among different sampling sites were revealed in different seasons. For example, in fall, Xanthophyta was found at S6, S7, S8, and S11, but none were found in spring and summer. The species distribution of phytoplankton was significantly different among sampling sites. In fall, 34 species of phytoplankton were found at S6, while the fewest species (19) were identified at S1. The highest species richness was identified at S1 in spring, followed by S2, and the lowest species richness was found at S11. A total of 17 dominant species determined by Eq. (8) belonged to Cyanophyta (7 species), Chlorophyta (6 species), and Bacillariophyta (4 species). The dominant species varied with the season, but all the most dominant species in the three seasons belonged to Cyanophyta at the phylum level. Microcystis sp. was the most dominant species in spring, with Y = 0.23. The most dominant species was Merismopedia tenuissima in summer, with Y = 0.13, and Oscillatoria sp. was the most dominant in fall, with Y = 0.08. Temporally, the phytoplankton abundance and biomass in fall were notably higher than in spring and summer (Fig. 5). The mean cell density in the three seasons followed a sequence of fall (3.1 × 107 cells/ L) > summer (1.6 × 107 cells/L) > spring (1.5 × 107 cells/L) (Fig. 5a). The mean phytoplankton biomass in different seasons varied in the following order: fall (14.81 mg ww/L) > spring (2.85 mg ww/L) > summer (2.72 mg ww/L) (Fig. 5b). From the point of view of spatial variability, the 12 sampling areas showed notable differences in phytoplankton abundance and biomass. The highest cell density was 2.56 × 107 cells/L in the west (S2), 2.67 × 107 cells/L in the southwest (S4), and 8.97 × 107 cells/L in the southeast (S6), respectively, in

3. Results 3.1. Assessment of lake trophic states Values of each environmental factor in spring, summer, and fall are shown in Fig. 2. TN, TP, Chl-a, COD, T, NTU, and Cond were highest in summer; NH4+, Cl−, and ORP were the highest in fall. The measurement results showed that T (p = 0.000), NH4+ (p = 0.000), and Cl− (p = 0.000) changed significantly in different seasons (p < 0.05) based on the two-way ANOVA results. From the perspective of spatial changes, Chl-a, TDS, Cond and Cl− showed significant differences (p < 0.05), and the remaining environmental factors showed no significant differences (p > 0.05) at all sampling sites. On the basis of TN and TP, shown in Fig. 2, the TN:TP ratio ranged from 0.75:1 to 69:1, 0.5:1 to 27:1 and 0.1:1 to 6:1, respectively in spring, summer, and fall. The trophic state assessment results showed three trophic states in 4

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Fig. 2. Environmental variables of Baiyangdian Lake in three seasons.

Fig. 3. Spatial distribution of the comprehensive trophic state index (CTSI) in Baiyangdian Lake.

followed by spring and then fall (Fig. 7a). The average zooplankton abundance was 4.2 × 103 ind./L, ranging from 1.8 × 102 ind./L at S7 to 1.2 × 104 ind./L at S1 in summer. In spring, the average zooplankton abundance was 4.0 × 103 ind./L, ranging from 4.2 × 102 ind./L at S8 to 1.0 × 104 ind./L at S2. In fall, the mean abundance in Baiyangdian Lake was 3.0 × 103 ind./L, ranging from 3.1 × 102 ind./L at S12 to 6.3 × 103 ind./L at S4. Community abundance was the highest in the western areas of the lake at sites S1–S4, and decreased to the east. This trend was consistent with abundance changes in spring, summer, and fall. The spatial distribution of zooplankton density conformed well to water quality and eutrophication distribution in the whole lake. The biomass of zooplankton showed a consistent spatial variation with zooplankton abundance (Fig. 7b). The biomass was higher overall in the west than in the middle and the east and was mainly concentrated at S1–S4. The highest biomass value was sequenced as spring (8.89 mg ww/L) > fall (6.51 mg ww/L) > summer (5.85 mg ww/L). The mean

spring, summer, and fall. The highest biomass was also concentrated in the west (S2) in spring and summer, and S6 in fall. The contribution of blue algae (Cyanophyta) to the total cell density (41%–56%) was significant in the three seasons, followed by Chlorophyta (14%–33%) and Bacillariophyta (13%–21%).

3.2.2. Community characteristics of zooplankton A total of 65 zooplankton species were found in Baiyangdian Lake in this study, as listed in Table S2. Rotifers, cladocerans, and copepods constituted 68%, 16%, and 16% of the total species richness, respectively. Zooplankton richness was sequenced as summer (42 species) > spring (36 species) > fall (20 species). Rotifers were the dominant zooplankton taxa, with an average species richness of 61.5% in all three seasons, followed by copepods (20%) and cladocerans (18.5%) (Fig. 6 and Table S2). In general, the abundance of zooplankton was highest in summer, 5

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Fig. 4. Community structure of phytoplankton in different seasons and locations.

biomass was ranked as spring (2.07 mg ww/L) ≥ summer (2.07 mg ww/L) > fall (1.74 mg ww/L). Rotifers contributed 99.8% of the total abundance in fall, 99.5% in spring, and 99.3% in summer. A total of 11 dominant species of zooplankton existed in the rotifer taxon. Polyarthra vulgaris was the most dominant of the four dominant species in spring, with a dominance

index of 0.22. Trichicerca pusilla was the most dominant species among nine dominant species in summer, with Y = 0.17, and Polyarthra trigla was the most among the three dominant species in fall, with Y = 0.15.

Fig. 5. Cell density (a) and biomass (b) of phytoplankton in Baiyangdian Lake. Biomass was described by mg wet weight/L, with abbreviation of mg ww/L. 6

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Fig. 6. Community structure and distribution of zooplankton in different seasons and sites.

Fig. 7. Community abundance (a) and biomass (b) of zooplankton of Baiyangdian Lake.

Table 2 Diversity and trophic indices of plankton.

Spring Summer Fall

H' of phytoplankton

J of phytoplankton

H' of zooplankton

J of zooplankton

CTSI

TSIROT

2.24 ± 0.29 2.14 ± 0.25 2.14 ± 0.26

0.74 ± 0.10 0.69 ± 0.06 0.67 ± 0.09

1.39 ± 0.28 1.89 ± 0.48 1.07 ± 0.55

0.58 ± 0.12 0.70 ± 0.12 0.55 ± 0.25

50.75 ± 5.91 54.87 ± 5.73 51.35 ± 3.98

56.84 ± 7.09 57.31 ± 6.45 56.65 ± 4..35

7

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concentrations were most correlated with species of copepods and cladocera. Cyanophyta, Chlorophyta and Euglenophyta were mainly positively correlated with TN, TP, SD, pH, and DO and negatively correlated with NH4+ and ORP. The indices of H' and J of phytoplankton were also positively impacted by TN, TP, SD, pH, and DO (Fig. 8). Concentrations of TN affected the species abundance of Cyanophyta and Chlorophyta the most. The correlation of Pyrrophyta, Cryptophyta, Xanthophyta and Bacillariophyta with environmental variables was the opposite to that of Cyanophyta, Chlorophyta, and Euglenophyta. Overall, the species richness and diversity of phytoplankton and zooplankton were mainly affected by the contents of NH4+, TN, TP, and DO. The species richness, diversity and evenness of zooplankton were consistent with the temporospatial changes of phytoplankton. Variations in abundance, diversity, and evenness of the three zooplankton taxa (cladocerans, copepods, rotifers) were positively correlated with Cyanophyta, Chlorophyta, and Euglenophyta, and negatively correlated with Pyrrophyta, Bacillariophyta, Cryptophyta, Chrysophyta, and Xanthophyta.

3.3. Plankton diversity and trophic indices The diversity index H’ and evenness index J of plankton, CTSI and TSIROT in different sampling locations are listed in Table 2. The mean values of H’ and J of phytoplankton varied as: spring > summer ≥ fall. The standard deviations of H’ and J of phytoplankton accounted for less than 15% of the mean values for each season, which meant spatial variations in the phytoplankton diversity and evenness were low. The results of two-way ANOVA also supported that H’ (p = 0.672, F = 0.762) and J (p = 0.489) varied nonsignificantly by location. H’ and J for zooplankton were highest in summer, and ranked as summer > spring > fall, as influenced by seasonal variations in zooplankton species and abundance. Standard deviations of H’ and J of zooplankton in summer reached 51% and 45% of the mean values, which represented a large difference in diversity and evenness in the different lake areas. The diversity index significantly differed (p < 0.05) over the three seasons, but the spatial differences in the diversity indices and evenness indices were not statistically significant (p = 0.32). The H’ values of phytoplankton listed in Table 2 were lower than the evenness values, ranging from 3.19 to 4.5 in 1992 (Zhang et al., 1995) and from 3.43 to 4.33 in 2005 (Shen and Liu, 2008). The Pielou evenness index J of phytoplankton in Table 2 indicated a large difference in different regions and had a larger range than that of 0.62–0.81 in 2005 (Shen and Liu, 2008). The current biodiversity of plankton is lower, and phytoplankton distribution in different locations was more different than that in the past. The comprehensive trophic states (CTSI) were slightly eutrophic based on environmental factors (Fig. 3 and Table 2). All rotifer trophic states (TSIROT) over three seasons ranged from 55 to 60 and indicated a low eutrophic state. This result was consistent with CTSI results, which suggested rotifer trophic state could be a good indicator to reflect water trophic states from biological communities.

3.5. Comparison with historical data The historical taxa richness of phytoplankton and zooplankton at species and genius levels from 1958 to 2016 are presented in Table 3. Collected phytoplankton was identified at the genus level from 1958 to 1996, and a 53% decreasing rate of genus richness of phytoplankton was observed from Table 3. The highest three taxa richness measurements appeared at the genus level in 1958, 1988, and 1992. Phytoplankton has been identified at the species level since 1992. The species richness drastically decreased from 398 species in 1992 to 59 species in 2016 with a decreasing rate of 85%. Zooplankton richness continuously decreased by 62% from 95 species in 1958 to 36 species in 2016. Overall, phytoplankton and zooplankton richness dramatically decreased with a rate of more than 50%, and up to 85% over the years.

3.4. Correlation between plankton and environmental factors

4. Discussion

The ordination of sampling sites reflected differences in the characteristics of the sampling sites over the three seasons. The distributions of species and variations in environmental characteristics are shown in Fig. 8. The largest length of the gradient of DCA results was 1.021 (less than three), which indicated that a linear model (RDA) was preferred to determine the relationship between species and environmental variables. The first axis of RDA accounted for 53.6% of the species-environment relations, and the second axis accounted for 33.3% of the species environment relations. The explanations of the first axis and all axes were significant based on the Monte Carlo test results (p = 0.018, p = 0.010, respectively). Phytoplankton and zooplankton were classified into phyla and class level, respectively, to conduct the RDA analysis. Fig. 8 shows that copepod, cladoceran, and rotifer species and H’ and J of zooplankton were positively correlated with TN, TP, SD, pH, and DO and negatively correlated with NH4+, ORP and Cl−. DO, pH, and SD gave the best positive correlations with Rotifera abundance. TN

4.1. Influence of environmental factors on trophic states Temperature strongly influences water quality parameters in different seasons. Increased temperature can lead to a reduction in DO (Jiang et al., 2014). In spring, emergent and submerged vegetation absorbs nutrients from the humus of dead plants and animals. The decomposition of microorganisms accelerates the growth of vegetation, and the metabolic activity of microorganisms increases the pH in spring and fall. High concentrations of Chl-a mainly appear in tourist and residential areas. Overall, the median Chl-a concentration in summer is greater than that in fall or spring. The variations of environmental factors in different seasons, e.g., high Chl-a concentration, can lead to a predicted increase in the rate of water eutrophication in summer (Jöhnk et al., 2008). Mesotrophication was concentrated in the eastern and northern lake regions. Aquaculture produced excessive P for feeding fish and crayfish in Baiyangdian Lake, which caused water pollution and eutrophication (Bian et al., 2012). An abundance of Fig. 8. RDA ordination of community structures of phytoplankton and zooplankton taxa and environmental variables for Baiyangdian Lake. Blue circles, purple squares, and green diamonds are sites in fall, spring and summer, respectively. Cyan, Pyrr, Eugl, Baci, Cryp, Chlo, Chry and Xant are abbreviations of Cyanophyta, Pyrrophyta, Euglenophyta, Bacillariophyta, Cryptophyta, Chlorophyta, Chrysophyta, and Xanthophyta, respectively. Spec-Phy, J-Phy, and H'-Phy are species richness, evenness and diversity indices of phytoplankton, respectively. Spec-Zoo, J-Zoo, and H'-Zoo are the zooplankton species richness, evenness and diversity indices, respectively.).

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Table 3 Historical records of phytoplankton and zooplankton richness at species and genus levels from 1958 to 2016. Investigation time

Genus richness of phytoplankton

Species richness of phytoplankton

References

Investigation time

Species richness of zooplankton

References

1958 1975 1980 1988

129 92 63 135

– – – –

Zhang et al., Zhong et al., Zhang et al., Zhang et al.,

1995 2005 2007 1995

1958 1975 1980 1993

95 79 49 74

Zhang et al., 1995 Gao and Xu, 1995 Feng, 1999 Liu et al., 2010 Shen and Liu, 2008 Wang et al., 2011 Yang, 2011 This study This study

2007 2010 2015 2016

72 42 25 36

Huang et al., 1959 Xu et al., 1995 Yang, 2011 Xu et al., 1995; Yang, 2011 Zhang et al., 2008 Yang, 2011 This study This study

1992 1993 1996 2005 2006

135 109 86 – –

398 208 170 152 155

2009 2010 2015 2016

– – –

133 86 80 59

state (Yuan, 2011). The plankton composition is easily affected by large disturbances, such as heavy storms and sudden water pollution events (Scheffer, 2004). Cyanophyta (49.4%), Chlorophyta (24.2%), and Bacillariophyta (17.1%) were the dominant phytoplankton taxa of Baiyangdian Lake based on the mean abundance over the three seasons in this study, and these groups were consistent with the dominant groups noted by Gao and Xu (1995) and Liu et al. (2010) based on investigations in 1993 and 2005, respectively. This proportion of dominant taxa to total phytoplankton abundance was similar to that of Lake Taihu during a summer cyanobacterial bloom, Cyanophyta (59.7%), Chlorophyta (33.5%), and Bacillariophyta (5.87%) (McCarthy et al., 2007). Although no algal blooms were recorded before for the study lake, the high proportion of Cyanophyta (49.4%) was also close to that of another eutrophic lake, Lake Erie, which suffered algal blooms with 60% cyanobacteria (Michalak et al., 2013). Wang et al. (2013) proved that having dominant taxa of Chlorophyta and Cyanophyta means that a lake is eutrophic to some extent. Therefore, we could infer that only the population of phytoplankton has been reduced, the community structure remains consistent with the past, and a high proportion of Cyanophyta would greatly increase the possibility of lake eutrophication. Cladocerans are relatively selective based on feeding characteristics, such as the particle size, taste, and presence of flagella (Sterner, 1989). They are capable of consuming the smallest filaments of cyanobacteria (Kâ et al., 2012) and developing tolerance against toxic cyanobacteria (Hairston et al., 2001). Copepods have a strong ability to actively select ingested phytoplankton species and can effectively avoid toxic cyanobacteria (Kâ et al., 2012). Feeding on Microcystis by rotifers would reduce their population growth rate (Alva-Martínez et al., 2009), and the abundance of rotifers is also suppressed by Microcystis blooms (Soares et al., 2010). Cladocerans (Moina micrura) and rotifers (Brachionus calyciflorus) have the potential to limit colonial chlorophytes (Pagano, 2008). These studies suggested that cladocerans, rotifers, and copepods fed on planktonic Chlorophyta and Euglenophyta and selectively ingested Cyanophyta to effectively avoid damage by toxic cyanobacteria. Predator-Prey relationships can lead to a decrease in the abundance of prey species, but Chlorophyta and Cyanophyta were the dominant taxa, with a large population in Baiyangdian Lake. Overall, the predation relationships promoted the growth of zooplankton without affecting the population increases of Chlorophyta, Cyanophyta, and Euglenophyta. Furthermore, there were interspecific competitions between Chlorophyta, Cyanophyta, Euglenophyta and the rest taxa of phytoplankton, resulting in a negative correlation. Lake trophic states change phytoplankton populations and community structure, which affects on zooplankton by prey-predation relationships. The successful management strategies of aquacultural areas limitations, centralized sewages processing, and ecological water supplement and restoration projects are strongly

submerged vegetation appears in eastern areas (Yuan, 2011), which could efficiently suppress the deterioration of water quality, and decrease the risk of eutrophication in large water bodies (Zeng et al., 2017; Zhang et al., 2015). Trophic states in different areas varied, sequenced as port > residential area > tourist attraction > aquaculture area > rural areas, which indicated trophic states in areas with dense human activates were worse than areas with less disturbances. For the whole lake, the trophic states ranged from slight eutrophication to moderate eutrophication, which is consistent with the lake state ranging from grassy clean-water to algal turbid-water (Yuan, 2011). The TN/TP values in summer and fall were less than 27:1 in Baiyangdian Lake, which was similar to Lake Taihu, but the TN:TP ratios in spring (0.75:1-69:1) in Baiyangdian Lake were far lower than those in Lake Taihu (52:1-202:1) (Xu et al., 2010). Excessive nutrients are still the major causes of lake eutrophication (Michalak et al., 2013). This suggests that N might be the limiting nutrient rather than P for lake eutrophication in this study, which was supported by the P limitations in the input rivers of Lake Taihu with TN/TP = 69, as reported by McCarthy et al. (2007). 4.2. Response of the plankton community to trophic states The community structures of plankton varied with trophic states (Ejsmont-Karabin, 2012; Almanza et al., 2019). Long-term investigations of phytoplankton and zooplankton are capable of effectively observing to variations in trophic states. Historical investigation shows that more phytoplankton and zooplankton taxa were observed in the past than in this study period. Dam construction on upstream rivers that reduced water inputs, untreated sewage discharges from upstream cities, and climate change led to water pollution in Baiyangdian Lake, which also resulted in reductions in the water level, plankton taxa, fish and other aquatic flora and fauna (Wang and Xu, 2009; Zhang et al., 1995). However, rapid industrial and agricultural development led to extensive pollutants and nutrients retained in the water and sediment, which accelerated water eutrophication and biological resource reduction (Yuan, 2011). Beginning in 2009, the water pollution and ecological deterioration of the lake attracted the public's attention. Water supplementation and pollution source control began, and environmental flows were released from reservoirs and rivers to supplement the water supply for Baiyangdian Lake (Tang et al., 2018). Hoverer, the phytoplankton species richness did not recover to the numbers in the 1950s, which means a long time is required for the lake ecology to recover because the lake water quality and trophic states influence the plankton species number and community structure (Michalak et al., 2013). Baiyangdian Lake is in a transition state between a vegetation-dominated clear state and a turbid nonvegetated 9

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recommended to improve lake trophic states and enhance biodiversity in Baiyangdian Lake.

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5. Conclusion This study analyzed the trophic states, species numbers, community structures, biodiversity of phytoplankton and zooplankton in Baiyangdian Lake. The lake trophic states gradually improved from east to west in the lake, and the highest degree of eutrophication was at station S1. Seasonal eutrophication was ranked as summer > spring > fall. The whole lake trophic states were 69.1% slight eutrophic, 29.3% mesotrophic, and 1.6% moderate eutrophic in the three seasons based on the CTSI. The trophic state results based on the rotifer index agreed with a slight eutrophic state for Baiyangdian Lake. The species richness, abundance, diversity index, evenness index of phytoplankton and zooplankton showed significant seasonal differences. These differences were attributed to the influence of seasonal variations on the plankton diversity. The phytoplankton abundance was highest in fall, and the zooplankton abundance was highest in summer. Cyanophyta (with an average percent of 49.4%) and small Rotifera (99.5%) were the dominant taxa of phytoplankton and zooplankton. The dominant species of phytoplankton and zooplankton were Microcystis sp. and Polyarthra vulgaris, respectively. The current plankton species number and diversity were lower than historical records. NH4+, TN, TP, and DO were the main environmental factors influencing the species number and diversity of phytoplankton and zooplankton based on the redundancy analysis (RDA) results. Nitrogen was the limiting nutrient for lake eutrophication. The dominant taxa, dominant species, trophic state results based on CTSI and TSIROT, and nutrient limitation suggested that Baiyangdian Lake had a high potential to suffer from lake eutrophication. Acknowledgements This study was supported by the National Natural Science Foundation of China (No. 51439001 and No. 51722901) and National Key R&D Program of China (2018YFC0407403). All data necessary to evaluate and build upon the research in this paper are provided in the cited references, tables, and figures. Additional data could be obtained upon request from authors. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.limno.2019.125712. References Almanza, V., Pedreros, P., Dail Laughinghouse, H., Félez, J., Parra, O., Azócar, M., Urrutia, R., 2019. Association between trophic state, watershed use, and blooms of cyanobacteria in south-central Chile. Limnologica 75, 30–41. Alva-Martínez, A.F., Fernández, R., Sarma, S.S.S., Nandini, S., 2009. Effect of mixed toxic diets (Microcystis and Chlorella) on the rotifers Brachionus calyciflorus and Brachionus havanaensis cultured alone and together. Limnologica 39, 302–305. Bian, W., Wang, L.G., Zhang, H.Z., Wang, J.F., Tian, Z.F., Chen, X.Y., 2012. Study on phosphorus pollution load of aquaculture in Baiyangdian Lake. Adv. Mater. Res. 518–523, 1406–1411. China Bureau of Environmental Protection (CBEP), 2002. Methods for Monitoring and Analysis of Water and Wastewater. China Environmental Science Press, Beijing (In Chinese). Ejsmont-Karabin, J., 2012. The usefulness of zooplankton as lake ecosystem indicators: rotifer trophic state index. Polish J. Ecol. 60, 339–350. Feng, J.S., 1999. Phytoplankton and water quality assessment for the Baiyangdain Lake. Jiangsu Environ. Sci. Technol. 02, 27–29 (In Chinese with English abstract). Gao, Y.R., Xu, M.Q., 1995. Eutrophication of Baiyangdian Lake Based on the Investigation of Phytoplankton Community. Science Press, Beijing, pp. 154–165 (In Chinese with English abstract). Hairston, N.G., Holtmeier, C.L., Lampert, W., Weider, L.J., Post, D.M., Fischer, J.M., Cáceres, C.E., Fox, J.A., Gaedke, U., 2001. Natural selection for grazer resistance to toxic cyanobacteria: Evolution of phenotypic plasticity? Evolution 55, 2203–2214. Hill, M.O., 1973. Diversity and evenness: a unifying notation and its consequences.

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