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Article

Spatial–Temporal Pattern and Stability Analysis of Zooplankton Community Structure in the Lower Yellow River in China

by
Yaowei Wang
,
Shiyuan Zhang
,
Minfang Sun
,
Jiamin Han
,
Ziyue Wang
,
Xinlei Chen
,
Zengfei Chen
and
Haiming Qin
*
School of Life Sciences, Qufu Normal University, Qufu 273165, China
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(3), 162; https://doi.org/10.3390/d17030162
Submission received: 28 December 2024 / Revised: 19 February 2025 / Accepted: 24 February 2025 / Published: 25 February 2025
(This article belongs to the Special Issue Diversity and Ecology of Freshwater Plankton)
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Abstract

:
In March (spring), June (summer), October (autumn), and December (winter) 2022, zooplankton were quantitatively investigated in the lower reaches of the Yellow River in China. A total of 29 sampling points that were separated by about 20 km were set up in the survey area. The purpose of this study is to investigate the seasonal dynamics and spatial distribution characteristics of the zooplankton community in the Yellow River, which has a high sediment content. The main results are as follows: A total of 119 species of zooplankton were found during the survey, including 70 species of rotifers, 29 species of cladocerans, and 20 species of copepods. Because the temperate continental monsoon climate has four distinct seasons, the zooplankton community in the Yellow River showed typical seasonal dynamics. There were significant differences in the richness of zooplankton and dominant species across the four seasons (p < 0.05). There were 15 common species in each of the four seasons. The density and biomass of zooplankton were significantly higher in spring (16.76 ind./L; 0.049 mg/L) and summer (26.17 ind./L; 0.249 mg/L) compared to autumn (5.65 ind./L; 0.042 mg/L) and winter (1.56 ind./L; 0.006 mg/L) (p < 0.05). Additionally, the density and biomass of zooplankton were significantly lower in estuarine areas compared to other areas. The results of multidimensional non-metric ranking (NMDS) based on zooplankton abundance showed four distinct communities: a spring community, a summer community, an autumn community, and a winter community. The spatial heterogeneity of zooplankton communities in spring, summer, and autumn was significantly different (p < 0.05). However, only the estuarine area had a special zooplankton community in winter. Monte Carlo test results showed that pH, water temperature, electrical conductivity, dissolved oxygen, total nitrogen, and total phosphorus were the main environmental factors affecting the community structure of zooplankton (p < 0.05). The areas of the Yellow River affected by human disturbances have lower zooplankton community stability. Overall, the standing stock of zooplankton was very low (less than 15 ind./L), but the species richness was higher (119 species) in the river, which had a high sediment content and a fast flow.

1. Introduction

Rivers are the vital link between terrestrial ecosystems and marine ecosystems, and they play an important role in the transport of matter and energy [1]. The high heterogeneity of river habitats provides a complex living environment for aquatic organisms such as fish, zoobenthos, and zooplankton [2]. Under the dual influence of human activities and climate change, the structure and function of river ecosystems are facing serious challenges [3]. Studies about the restoration and protection of river ecosystems are one of the key areas of global ecological research [4].
The Yellow River, which is the second longest river in China, is an important corridor connecting the Qinghai–Tibet Plateau, the Loess Plateau, and the North China Plain [5]. The ecological protection and sustainable development of the Yellow River basin play a significant part in both social and economic development [6]. The Yellow River is affected by the continental monsoon climate in the north of China and has a glaciation period from October to March every year. Before and after the glaciation period, there is an ice flood in the Yellow River basin [7]. Due to the blockage of the river channel and the resistance of the water flow, the water level of the Yellow River rises significantly during this ice flood [8]. Additionally, as the middle reaches of the river flow through the Loess Plateau, a large amount of sediment enters the Yellow River, causing the river to have the largest sediment content in the world [9]. The water resource management measures in the Xiaolangdi Hydraulic Project have a significant impact on the dynamics of water and sediment in the downstream parts of the Yellow River [10], which, in turn, affects the aquatic biological community and ecosystem structure and function [11]. In addition, human activities in nearby cities, as well as the river mixing with sea water in estuarine areas, complicate the habitat characteristics in the downstream area of the Yellow River [12].
Zooplankton feed on bacteria and phytoplankton, which are important food sources for aquatic animals [13]. Zooplankton play an important role in the material circulation and energy flow of river ecosystems [14]. As the primary consumers of aquatic ecosystems, zooplankton can have significant impacts on both primary producers and secondary consumers through “top-down” and “bottom-up” approaches [15]. The stability of the zooplankton community refers to its ability to maintain the interaction between species and restore the original equilibrium state under the disturbance of water environmental changes [16]. Zooplankton is very sensitive to changes in the water environment; therefore, its species composition, standing stock, and community stability can be used as effective indicators for evaluating the health of river ecosystems [13]. This study has shown that the areas of the Yellow River with a high sediment content and a high velocity are not the best places for zooplankton, especially large crustaceans, to survive [17]. At present, there have been some reports on the zooplankton community in the downstream of the Yellow River, mostly concentrated in the tributaries or estuaries, and only few reports on the zooplankton community in the main stream of the Yellow River [18,19,20].
In the present study, the Shandong section of the Yellow River was selected as the research area to conduct quantitative surveys of zooplankton in spring (March), summer (June), autumn (October), and winter (December) in 2022. The purposes of this study are as follows: (1) to investigate the species composition, standing stock, and community structure of zooplankton in the Shandong section of the downstream part of the Yellow River; (2) to understand the seasonal dynamics and spatial heterogeneity of the zooplankton community in the downstream part of the Yellow River. Our research focuses on the frontiers, hot spots, and key issues in river ecology research. The results of this study can provide a reference for the ecological protection and high-quality development of the Yellow River basin.

2. Study Area and Research Methods

2.1. Study Area Description

With a total length of 5464 km, the Yellow River originates from the northern foothills of the Bayan Har Mountains on the Tibetan Plateau [1]. The upper reaches flow through the provinces of Qinghai, Sichuan, Gansu, and Ningxia; the middle reaches pass through the provinces of Inner Mongolia, Shaanxi, and Shanxi; and the lower reaches flow through the provinces of Henan and Shandong [21]. The Shandong section of the Yellow River (34°26′~38°16′ N, 114°16′~119°16′ E) is about 628 km long [22]. It flows through the cities of Heze, Jining, Taian, Liaocheng, Dezhou, Jinan, Zibo, and Binzhou, and ultimately empties into the Bohai Sea at Dongying City [23]. Due to the influence of sediment inflow from the upper reaches of the Yellow River, a typical overland river has been formed, over many years, in the Shandong section of the lower reaches of the Yellow River [24]. This section of the river is significantly affected by coastal cities, particularly the human production and life in Jinan City, which has a significant impact on the water environment of the Yellow River [25]. In addition, the water input of Dawen River and Dongping Lake also has a significant impact on the water environment in the lower reaches of the Yellow River [26]. In particular, since the operation of the East Route of the South-to-North Water Diversion Project in 2013, Dongping Lake has had a more significant impact on the Yellow River [27]. After the Xiaolangdi Water Conservancy Project was completed and put into operation in 2000, the water and sediment dynamics in the lower reaches of the Yellow River have been significantly regulated, and the hydrological and water environments have undergone significant changes [28]. The Shandong section of the Yellow River is located in a temperate continental monsoon climate area, where the precipitation is mainly concentrated in the summer period [29]. The annual precipitation ranges from 640 to 700 mm, and the average annual temperature is approximately 13 °C [30].

2.2. Sample Designing

This study conducted quantitative zooplankton sampling surveys on the Shandong section of the Yellow River in March (spring), June (summer), October (autumn), and December (winter) of 2022. A total of 29 sampling sites (S1~S29) were set up along the main stream of the Yellow River within the survey area (Figure 1), where the distance between each sampling point was approximately 20 km. Based on the difference in environmental conditions and the different factors between different areas, the surveyed river section was divided into four reaches: XLDR, DPLR, JNR, and ER. XLDR is the area affected by the Xiaolangdi Hydraulic Project, which includes 8 sampling sites (S1~S8). DPLR is the area affected by water input from the Dawen River and the Dongping Lake, which includes 6 sampling sites (S9~S14). JNR is the reach affected by the urban area of Jinan, which includes 5 sampling sites (S15~S19). ER is affected by the tide in the estuary of the Yellow River, which includes 10 sampling sites (S20~S29).

2.3. Sample Collection and Identification

Zooplankton and water samples were collected in the shallow nearshore riverbed areas of the Yellow River during four seasons. In winter, sample collection activities in the frozen river were carried out after the ice was broken to create a man-made hole. A 5 L water sampler was used to collect 30 L of mixed water at a depth of 50 cm. A 25# plankton net (mesh size: 64 μm) was used to filter the water and obtain zooplankton. The zooplankton sample was transferred to 50 mL bottles with 4% formalin.
Zooplankton samples were taken back to the laboratory and stained with Sodium Acid Red 52 for 24 h. Species identification and counting were performed with 0.8–4.5 X magnification under a stereomicroscope (Olympus SZ61, Olympus Corporation, Tokyo, Japan). In the present study, copepod nauplii were considered as one taxon and were not included in the count of the dominant species. When there were more than 2000 individuals in one sample, the subsample method was used to estimate the actual number [31]. Three fauna identification guides were used for zooplankton identification [32,33,34].

2.4. Water Environmental Factor Determination

Water temperature (WT), pH, dissolved oxygen (DO), and conductivity (Cond) were measured on-site using a multi-parameter water quality analyzer (YSI EXO2). Total phosphorus (TP) was determined using the continuous flow ammonium molybdate spectrophotometric method [35]; total nitrogen (TN) was measured using the gas-phase molecular absorption spectrometry [36]; nitrate nitrogen (NO3−-N) was determined using ultraviolet spectrophotometry [37]; ammonia nitrogen (NH4+-N) was measured using the gas-phase molecular absorption spectrometry method [38]; and total organic carbon (TOC) was determined via chemical oxidation [39].

2.5. Data Statistical Analysis

The dominance index (Y) was calculated using the following formula [40]:
Y = n i N × f i
When calculating the dominance index of each species in different seasons and reaches, ni is the total number of the i-th species in the four river reaches/seasons, N is the total number of zooplankton in the four river reaches/seasons, and fi is the occurrence frequency of the i-th species in the four river reaches/seasons. When the dominance index Y ≥ 0.02, the species is considered to be dominant.
The density of zooplankton was calculated by dividing the number of individual zooplankton by the sampling volume; this was expressed in ind./L. The biomass of rotifers was calculated using the volumetric method with a specific gravity of 1; the biomass of cladocerans and copepods was calculated using the body length–weight regression equation [41]. The weight of each nauplii was estimated to be approximately 0.003 mg [42].
The Venn diagram package in R v 4.1.2 was used to generate a diagram of zooplankton species to compare the seasonal differences and spatial heterogeneity of the species composition. One-way ANOVA was used to analyze the density and biomass of zooplankton and to determine the differences between the four seasons and four river reaches through the software SPSS 26.0. When the significance level p value was less than 0.05, there was a significant difference between two samples. In the statistical analysis software Primer 5.0, zooplankton density data were analyzed using a sorted similarity matrix based on the Bray–Curtis similarity measure. A similarity analysis (ANOSIM) of zooplankton communities was conducted, and a multidimensional non-metric (NMDS) ranking map was made to reveal the characteristics of zooplankton communities. The software Canoco V4.5 was used to perform redundancy analysis (RDA) for zooplankton and water physicochemical factors; Monte Carlo tests were used to identify environmental factors that had significant effects on zooplankton.
The collinear network maps of zooplankton in different seasons and different river reaches were generated through the software Gephi 0.9.2. When the zooplankton collinear network analysis diagram was made, the zooplankton species were taken as the image nodes, and the correlation coefficients between the nodes were calculated using the “psych package” in R v 4.1.2 software. At the same time, the density (D) and average clustering coefficient (T) were calculated in the software Gephi 0.9.2. The D value was the ratio between the number of edges that actually exist in a network and the maximum number of edges that can exist. A high-density network meant that interactions between species were dense. Conversely, a low-density network reflected sparse interactions. The T value was used to measure the closeness of the connection between a node in a graph and its neighbors. The stability of the zooplankton community structure in different seasons and river reaches was evaluated by comparing the D/T ratio. The smaller the value of D/T, the more stable the community structure, while the larger the value of D/T, the more unstable the community structure.

3. Results

3.1. Species Composition of Zooplankton

3.1.1. Species Richness

In the present study, a total of 119 species of zooplankton were identified and recorded. There were 70 species of Rotifera (58.82%), 29 species of Cladocera, and 20 species of Copepoda. These three main taxa made up 58.82%, 24.37%, and 16.81% of the total species number, respectively. One-way ANOVA revealed that the number of zooplankton species had significant seasonal differences (p < 0.05). However, there was no significant spatial difference in species richness.
In terms of the four seasons (Figure 2A), the species richness of zooplankton was the highest in summer (81 species), followed by spring and autumn with 66 and 56 species, respectively. There were only 36 species in winter. The Venn diagram of zooplankton species showed that there were 15 common species in the four seasons, including 7 rotifers, 2 cladocerans, and 6 copepods. In summer, the most unique species (25 species) were found, followed by spring and autumn with 13 and 10 species, respectively, and only 6 unique species were found in winter.
In terms of the four reaches (Figure 2B), the species richness of zooplankton was the highest in XLDR (87 species), followed by ER, JNR, and DPLR with 83, 78, and 72 species, respectively. The Venn diagram of zooplankton species showed that there were 48 common species in the four reaches, including 23 rotifers, 9 cladocerans, and 16 copepods. In XLDR, the most unique species (11 species) were found, followed by ER and JNR with 9 and 7 species, respectively; only 3 unique species were found in DPLR.

3.1.2. Dominant Species

The dominant species of zooplankton showed significant temporal and spatial heterogeneity between the four seasons and the four river reaches (Table 1; Table 2).
Concerning the four seasons (Table 1), the number of dominant species of zooplankton in autumn was the largest (six species), and the number of dominant species for the other three seasons was the same (four species). Brachionus calyciflorus (Pallas, 1766) was a common and dominant species in all four seasons. Brachionus angularis (Gosse, 1851), Keratella quadrala (Müller, 1786), and Filinia maior (Colditz, 1924) only dominated in spring; Brachionus diversicornis (Daday, 1883) and Diaphanosoma dubium (Manujlova, 1964) only dominated in summer; Schmackeria forbesi (Poppe et Richard, 1890), Microcyclops varicans (Sars, 1963), and Mesocyclops leuckarti (Claus, 1857) only dominated in autumn; and Notholca labis (Gosse, 1887) and Polyarthra dolichoptera (Koste, 1978) only dominated in winter.
Concerning the four reaches (Table 2), JNR and ER had four dominant species, while XLDR and DPLR had three dominant species. B. calyciflorus and Bosmina longirostris (O. F. Müller, 1785) were the common and dominant species across the four areas. D. dubium was a unique and dominant species in XLDR; Sinocalanus dorrii (Brehm, 1909) was a unique and dominant species in DPLR; however, JNR and ER had no unique and dominant species.

3.2. Standing Stock of Zooplankton

3.2.1. Density

The average density of zooplankton in the Shandong section of the Yellow River during the four seasons was 12.53 ± 5.57 ind./L. Rotifers contributed the most to the total density, accounting for 52.74%, while the density of cladocerans (18.83%) and copepods (28.43%) was smaller.
There were significant seasonal differences in the density of zooplankton (p < 0.05). The density of zooplankton in summer (26.17 ± 2.53 ind./L) and spring (16.78 ± 1.13 ind./L) was significantly higher than that in autumn (5.65 ± 0.96 ind./L) and winter (1.56 ± 0.14 ind./L) (Figure 3A). The seasonal variation in the density of rotifers (Figure 3B) and copepods (Figure 3C) was consistent with the total density of zooplankton. However, the density of cladocerans (Figure 3D) in summer (5.69 ± 1.23 ind./L) and autumn (3.39 ± 0.77 ind./L) was significantly higher than that in spring (0.31 ± 0.06 ind./L) and winter (0.03 ± 0.01 ind./L).
There were significant differences among the four reaches in terms of the density of zooplankton (p < 0.05). The density of zooplankton in DPLR (18.41 ± 3.59 ind./L) and XLDR (14.19 ± 2.08 ind./L) was significantly higher than that in JNR (9.52 ± 1.75 ind./L) and ER (8.84 ± 1.49 ind./L). The spatial distribution characteristics of the density of cladocerans and copepods was consistent with the density of zooplankton. However, the density of rotifers in DPLR was significantly higher than that in the other three reaches.

3.2.2. Biomass

The average biomass of zooplankton in the Shandong section of the Yellow River during the four seasons was 0.087 ± 0.055 mg/L. Cladocerans contributed the most to the total biomass, accounting for 56.94%, while the biomass of rotifers (14.43%) and copepods (28.63%) was smaller.
There were significant seasonal differences in the biomass of zooplankton (p < 0.05). The highest biomass of zooplankton was observed in summer (0.249 ± 0.047 mg/L), followed by spring (0.049 ± 0.004 mg/L), autumn (0.042 ± 0.006 mg/L), and winter (0.006 ± 0.001 mg/L) (Figure 3E). The seasonal variation in the biomass of rotifers (Figure 3F) and copepods (Figure 3H) was consistent with the total biomass of zooplankton. However, the biomass of cladocerans in summer (0.173 ± 0.048 mg/L) was significantly higher than that in the other three seasons (Figure 3G).
There were significant differences among the four areas in terms of the biomass of zooplankton (p < 0.05). The biomass of zooplankton in the upper reaches was significantly higher than that in the lower reaches. The highest biomass of zooplankton was in XLDR (0.189 ± 0.048 mg/L), followed by DPLR (0.096 ± 0.021 mg/L), JNR (0.037 ± 0.004 mg/L), and ER (0.035 ± 0.006 mg/L). The spatial distribution characteristics of the biomass of cladocerans and copepods were consistent with the biomass of zooplankton. However, the biomass of the rotifers in DPLR (0.024 ± 0.007 mg/L) was significantly higher than that in the other three reaches.

3.3. Characteristics of Zooplankton Community Structure

Temporally, four communities could be clearly distinguished—a spring community, a summer community, an autumn community, and a winter community (Figure 4). Similarity analysis (ANOSIM) revealed significant differences in zooplankton communities between the four seasons (Global test: R = 0.844; p = 0.001).
Spatially, zooplankton could be clearly divided into four communities in spring, summer, and autumn; these were the XLDR community, the DPLR community, the JNR community, and the ER community (Figure 5A–C). Similarity analysis (ANOSIM) revealed that there were significant differences in the zooplankton communities among the four reaches. The results of the Global test were as follows: spring (R = 0.876; p = 0.001), summer (R = 0.912; p = 0.001), and autumn (R = 0.823; p = 0.001). In winter, only the zooplankton in the ER community gathered into a single community, and the zooplankton community structure differentiation in the other three reaches was low (Global test: R = 0.44; p = 0.001) (Figure 5D).

3.4. Relationship Between Zooplankton and Water Environmental Factors

Firstly, the abundance of zooplankton was tested by DCA, and the results showed that the maximum eigenvalue of the first axis was 2.497 (less than 3). Therefore, redundancy analysis (RDA) was selected to analyze the correlation between zooplankton and water environmental factors. Based on the redundancy analysis (RDA) of zooplankton abundance and water physicochemical factors, the contribution rate of pH, water temperature (WT), electrical conductivity (Cond), and dissolved oxygen (DO) to the seasonal variance in the zooplankton communities was 91.2%. Among them, the variance contribution rate of the first characteristic axis was 48.4%, while that of the second characteristic axis was 42.8% (Figure 6A). The Monte Carlo test showed that pH, WT, Cond, and DO had significant effects on the community structure of zooplankton (p = 0.002). There was a significant positive correlation between the zooplankton community and Cond in spring. However, the zooplankton community was significantly positively correlated with WT and pH in summer. In addition, the Monte Carlo test results revealed that most zooplankton were positively correlated with WT, pH, and electrical Cond.
The results of the redundancy analysis (RDA) based on the zooplankton abundance and nutrient salts showed that the contribution rate of five nutrients (NO3−-N, NH4+-N, TN, TOC, and TP) to the seasonal variance in zooplankton communities was 91.0%. The variance contribution of the first characteristic axis was 61.2%, while that of the second characteristic axis was 29.8% (Figure 6B). The results of the Monte Carlo test showed that TN, NO3−-N, TP, and NH4+-N had significant effects on the community structure of zooplankton (TN: p = 0.002; NO3−-N: p = 0.002; TP: p = 0.002; NH4+-N: p = 0.008). There were no significant effects of TOC on the zooplankton community (p = 0.314). RDA results also showed that the zooplankton community in spring had a significant positive correlation with NO3−-N, NH4+-N, and TN, as well as a significant negative correlation with TP (Figure 6B).

3.5. Stability of Zooplankton Community

The results of the collinear network analysis showed that there were differences in the stability of zooplankton communities in four seasons and four reaches. Temporally, the D/T (value = 0.060) of the zooplankton community in summer was the lowest, which indicated that the zooplankton community structure in summer was the most stable (Figure 7J). However, the D/T (value = 0.158) of the zooplankton community in winter was the highest, indicating that the zooplankton community structure in winter was the most unstable. Spatially, the zooplankton community of XLDR had the lowest D/T (value = 0.086), which indicated that the zooplankton community structure of XLDR was the most stable. However, the zooplankton community of JNR had the highest D/T (0.015), indicating that the zooplankton community structure of JNR was the most unstable.

4. Discussion

4.1. The Spatial–Temporal Pattern of Zooplankton Species Composition

In this present study, a total of 119 zooplankton species were found in the Shandong section of the Yellow River, which was significantly higher than the number of zooplankton species reported in recent studies [10,42]. This may be related to the comprehensive duration of the study (four seasons) and the density of the setting location (29 sampling points). For example, in a study conducted by Leng et al., in the same river section, only 10 sampling points were set up over a period of five months, and the survey recorded only 28 zooplankton species. In addition, the increase in the number of zooplankton species may be closely related to the ecological protection efforts in the Yellow River basin over the past decade [43]. Other studies had found that there were significant seasonal differences in the species composition of zooplankton in the Yellow River, with a notably higher number of species in summer compared to winter [44]. In this study, the seasonal variation in zooplankton species was consistent with previous findings. This strongly indicates that seasonal climate change directly drives variations in physicochemical factors, which has a significant impact on the species composition of zooplankton [45].
The spatial differences in zooplankton species composition between the four river reaches were not significant, which was related to the connectivity of the river and to the high similarity in habitats among the studied river reaches [46]. In total, in the present study, 29 sampling points were located in the lower region of the Yellow River, which exhibited typical hydrological features of downstream reaches, such as wide riverbeds and high sediment content [47]. Previous studies have pointed out that rotifers were highly adaptable to environmental changes and had a wide distribution range [42]. The results of this study were consistent with this, as the common species across all four seasons and all four river reaches were primarily rotifers. This study also found that Brachionus calyciflorus was the common and dominant species across the four seasons and river reaches. This suggested that Brachionus calyciflorus was a generalist species with a wide ecological niche, which was well adapted to the varying physicochemical environments of the lower Yellow River throughout the different seasons [48]. Another result of this study indicated that the JNR and ER communities had a higher number of unique zooplankton species, but these unique species were not dominant. This suggested that urban wastewater discharge and the confluence of fresh and saline waters had created a unique aquatic environment, providing favorable living conditions for the unique zooplankton species [49].

4.2. The Spatial–Temporal Pattern of Zooplankton Standing Stock

The results of this study indicated that there was a seasonal synchrony in the standing stock of zooplankton, with the density and biomass of zooplankton adhering to the following seasonal pattern: summer > spring > autumn > winter. This pattern was consistent with the findings of many other studies on the standing stock of zooplankton in rivers [50,51]. Previous research has pointed out that water temperature is a key factor influencing the growth, development, and reproduction of zooplankton [52], and the findings of this study supported this conclusion. The main reason for this pattern was that as the temperature increases, the growth rate of zooplankton accelerates and the reproductive cycle of zooplankton shortens, leading to a rapid increase in their density and biomass during the summer [53]. The low biomass of zooplankton in winter is due to the fact that many species cope with the cold winter conditions by producing resting eggs or cysts [54]. The correlation analysis between zooplankton and environmental factors in this study indicated that the zooplankton community in the lower part of the Yellow River was significantly positively correlated with WT; however, in winter, the community structure showed a significant negative correlation with WT. This also revealed that the standing stock of zooplankton was significantly affected by WT.
The spatial distribution of the standing stock of zooplankton in the four studied reaches followed a pattern of decreasing from upstream to downstream, which was not consistent with the findings of other studies on zooplankton in domestic rivers [55,56]. This discrepancy may have been due to the influence of hydraulic engineering projects, lake water input, urban human activities, and tidal effects on the hydrological environment and nutrient distribution in the studied reaches, which drove the heterogeneous distribution of the standing stock of zooplankton along the river [11,57]. This study found that the standing stock of zooplankton in autumn was significantly lower than that in summer; in particular, the density and biomass of Cladocera were significantly lower in autumn than in summer. This was not only influenced by seasonal changes in water conditions [45] but was also directly related to the water diversion and sediment regulation activities in the upstream hydraulic projects during the summer and autumn zooplankton surveys [10]. During the summer zooplankton sampling, the upstream water management projects had not carried out water diversion or sediment regulation, while in autumn, water diversion and sediment regulation activities increased the water flow speed and sediment content in XLDR. This kind of water environment was unsuitable for the survival of Cladocera [58]. This study found that the zooplankton standing stock in DPLR during summer was significantly higher than in the other three reaches, with rotifers contributing 74.88% to the total standing stock. This was primarily due to the rising water levels of the Yellow River′s tributaries, such as the Dawen River and Dongping Lake. It could carry large amounts of organic debris and nutrients into the nearby DPLR [59]. The abundant food resources led to the massive proliferation of rotifers [60]. This study found that the density of rotifers in JNR during spring was significantly higher than in the other reaches. This is due to the input of urban domestic sewage from the nearby city of Jinan, which increased the nutrient levels and elevated the water conductivity [22], thereby altering the original oligotrophic condition of the JNR and promoting the growth and reproduction of rotifers [60].
In this present study, we found that the average density of zooplankton in the lower reaches of the Yellow River was below 15 ind./L, and the average biomass was below 0.1 mg/L, which is significantly lower than the standing stock of zooplankton in lakes [61,62,63]. It indicates that rivers with a high sediment content and oligotrophic conditions are not ideal habitats for zooplankton, which is consistent with previous studies [13].

4.3. The Spatial–Temporal Pattern of Zooplankton Community Structure

Many ecological factors such as climate and habitat influenced the spatiotemporal dynamics of zooplankton communities [64]. Among these, climate conditions were the primary drivers of the seasonal succession of zooplankton communities [65]. In this study, the seasonal succession characteristics of the zooplankton community also supported this conclusion. The zooplankton community in the Shandong section of the Yellow River could be clearly divided into four seasonal communities—the spring community, the summer community, the autumn community, and the winter community. The results of the Monte Carlo test revealed that pH, water temperature (WT), conductivity (Cond), dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP) are important water environmental factors causing significant seasonal differences in the zooplankton community. This is consistent with findings reported in previous studies [66].
The differences in hydrological characteristics among different river reaches led to distinct spatial patterns in the zooplankton community [67]. In spring, summer, and autumn, four spatial communities of zooplankton could be clearly distinguished, i.e., the XLDR community, the DPLR community, the JNR community, and the ER community. The XLDR community was mainly influenced by the upstream hydraulic engineering projects, which was consistent with previous findings [68]. The DPLR community was mainly influenced by the input of nutrients and organic matter from the lake water [69]. The JNR community was mainly influenced by the discharge of urban domestic sewage [47]. The ER community was primarily affected by the unique brackish water estuarine environment [70]. In winter, the changes caused by spatial differences were overshadowed by climatic factors [18]. WT became the dominant factor influencing the zooplankton community structure [62]. There were no significant differences in the zooplankton communities among XLDR, DPLR, and JNR. However, the zooplankton community in ER remains unique due to the influence of brackish water convergence, allowing it to form a distinct community on its own.

4.4. Zooplankton Community Stability

The balance theory of community stability suggests that species interact with each other through mutual restraint, leading to stability characteristics within the community. In a stable state, the species composition of the community remains relatively unchanged. Previous studies pointed out that the richness and diversity of zooplankton were essential conditions directly influencing the stability of zooplankton communities [71]. The results of this study were consistent with this. In summer, the species richness of zooplankton was highest, and the interactions among species were more complex, resulting in the highest stability of zooplankton communities [72]. In contrast, the stability of zooplankton communities was lowest in winter. Due to the influence of urban wastewater discharge, the zooplankton community structure tended to become simplified [73]. In addition, the overuse of antibiotics, which entered rivers through urban wastewater, harmed zooplankton growth, development, and reproduction by inhibiting their feeding efficiency and digestive enzyme activity [74,75]. The results of this study were consistent with this, as the stability of the zooplankton community in JNR was significantly lower than in the other reaches. The stability of the zooplankton community in XLDR was significantly higher than in the other three reaches. This was due to the more than 20 years of operation of the Xiaolangdi Hydraulic Project, during which researchers effectively utilized the self-regulating capacity of the river ecosystem, achieving continuous environmental improvement and the goal of sustainable development in XLDR [76].

5. Conclusions

In this study, a seasonal quantitative survey of the zooplankton community in the Shandong section of the lower Yellow River was carried out. The characteristics and the stability of the zooplankton community were analyzed. It was found that the community succession of zooplankton in the lower reaches of the Yellow River has obvious seasonal characteristics. The species richness of zooplankton in the lower Yellow River was higher, but the standing stock was lower. Human activities and environmental factors jointly drove the typical spatial heterogeneity of zooplankton communities in the lower Yellow River. The stability of the zooplankton community was lower in conjunction with long periods of human disturbance. On the whole, rivers with a high sediment content and a fast flow are not suitable for the survival of zooplankton.

Author Contributions

Conceptualization, H.Q. and S.Z.; methodology, H.Q.; software, S.Z.; validation, Y.W., H.Q. and Z.C.; formal analysis, M.S.; investigation, Y.W., Z.W. and Z.C.; resources, H.Q.; data curation, X.C. and M.S.; writing—original draft preparation, Y.W. and S.Z.; writing—review and editing, H.Q.; visualization, S.Z.; supervision, H.Q.; project administration, J.H.; funding acquisition, H.Q. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the project of National Natural Science Foundation of China (31560133). This work was supported by the project of Ecology and Environment Department of Shandong Province (37000000040200120210063-007).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.

Acknowledgments

We thank Yi Li and Jinhui Liang of the Shandong Academy for Environmental Planning for their assistance in this work. We are particularly grateful to president Yanbo Peng of Shandong Academy for Environmental Planning for his guidance on this work. We would like to thank professor Yuewei Yang and Fengyue Shu of Qufu Normal University for their guidance and assistance in this work. We are grateful to the anonymous reviewers for their valuable comments. We are grateful to the editors for their review and revision of the manuscript. The authors declare no conflicts of interest.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the Yellow River and the zooplankton sampling points (different colored dots represent the sampling points of different river reaches).
Figure 1. Location of the Yellow River and the zooplankton sampling points (different colored dots represent the sampling points of different river reaches).
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Figure 2. Venn diagram of zooplankton species in the Shandong section of the Yellow River ((A): four seasons; (B): four reaches).
Figure 2. Venn diagram of zooplankton species in the Shandong section of the Yellow River ((A): four seasons; (B): four reaches).
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Figure 3. Temporal and spatial changes in zooplankton standing stocks. (A–D) showed the density of zooplankton. (E–H) showed the biomass of zooplankton. a, b, c and d indicate that there are significant differences in zooplankton standing stocks of four seasons. In each season, the sample size was 29 (n = 29).
Figure 3. Temporal and spatial changes in zooplankton standing stocks. (A–D) showed the density of zooplankton. (E–H) showed the biomass of zooplankton. a, b, c and d indicate that there are significant differences in zooplankton standing stocks of four seasons. In each season, the sample size was 29 (n = 29).
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Figure 4. Non-metric multidimensional scaling ordination of the seasonal characteristics of communities based on zooplankton abundance.
Figure 4. Non-metric multidimensional scaling ordination of the seasonal characteristics of communities based on zooplankton abundance.
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Figure 5. Non-metric multidimensional scaling ordination of the spatial characteristics of communities based on zooplankton abundance ((A): spring; (B): summer; (C): autumn; (D): winter).
Figure 5. Non-metric multidimensional scaling ordination of the spatial characteristics of communities based on zooplankton abundance ((A): spring; (B): summer; (C): autumn; (D): winter).
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Figure 6. Redundancy analysis (RDA) based on zooplankton abundance and water physicochemical factors (the black triangles represent the species of zooplankton, and the dots represent samples from each season). (A): Physicochemical factors. (B): Nutrient salt.
Figure 6. Redundancy analysis (RDA) based on zooplankton abundance and water physicochemical factors (the black triangles represent the species of zooplankton, and the dots represent samples from each season). (A): Physicochemical factors. (B): Nutrient salt.
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Figure 7. Collinear network analysis based on zooplankton abundance.
Figure 7. Collinear network analysis based on zooplankton abundance.
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Table 1. Dominant species of zooplankton in four seasons.
Table 1. Dominant species of zooplankton in four seasons.
Dominant SpeciesDominance (Y)
SpringSummerAutumnWinter
Brachionus angularis0.094
Brachionus calyciflorus0.2570.4760.0830.115
Brachionus diversicornis 0.034
Keratella quadrala0.094
Notholca labis 0.219
Polyarthra dolichoptera 0.146
Filinia maior0.066
Diaphanosoma dubium 0.116
Bosmina longirostris 0.0490.582
Sinocalanus dorrii 0.0210.049
Schmackeria forbesi 0.031
Microcyclops varicans 0.037
Mesocyclops leuckarti 0.066
Table 2. Dominant species of zooplankton in four reaches.
Table 2. Dominant species of zooplankton in four reaches.
Dominant SpeciesDominance (Y)
XLDRDPLRJNRER
Brachionus angularis 0.0280.029
Brachionus calyciflorus0.1510.4990.3980.350
Keratella quadrata 0.0350.020
Diaphanosoma dubium0.077
Bosmina longirostris0.0860.1190.0310.021
Sinocalanus dorrii 0.022
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Wang, Y.; Zhang, S.; Sun, M.; Han, J.; Wang, Z.; Chen, X.; Chen, Z.; Qin, H. Spatial–Temporal Pattern and Stability Analysis of Zooplankton Community Structure in the Lower Yellow River in China. Diversity 2025, 17, 162. https://doi.org/10.3390/d17030162

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Wang Y, Zhang S, Sun M, Han J, Wang Z, Chen X, Chen Z, Qin H. Spatial–Temporal Pattern and Stability Analysis of Zooplankton Community Structure in the Lower Yellow River in China. Diversity. 2025; 17(3):162. https://doi.org/10.3390/d17030162

Chicago/Turabian Style

Wang, Yaowei, Shiyuan Zhang, Minfang Sun, Jiamin Han, Ziyue Wang, Xinlei Chen, Zengfei Chen, and Haiming Qin. 2025. "Spatial–Temporal Pattern and Stability Analysis of Zooplankton Community Structure in the Lower Yellow River in China" Diversity 17, no. 3: 162. https://doi.org/10.3390/d17030162

APA Style

Wang, Y., Zhang, S., Sun, M., Han, J., Wang, Z., Chen, X., Chen, Z., & Qin, H. (2025). Spatial–Temporal Pattern and Stability Analysis of Zooplankton Community Structure in the Lower Yellow River in China. Diversity, 17(3), 162. https://doi.org/10.3390/d17030162

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