Next Article in Journal
Effects of Combined Upper and Lower Limb Plyometric Training Interventions on Physical Fitness in Athletes: A Systematic Review with Meta-Analysis
Previous Article in Journal
Association between Environmental Cadmium Exposure and Osteoporosis Risk in Postmenopausal Women: A Systematic Review and Meta-Analysis
Previous Article in Special Issue
Valuation of Ecosystem Services Based on EU Carbon Allowances—Optimal Recovery for a Coal Mining Area
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 20
Issue 1
10.3390/ijerph20010484
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Phytoplankton-Zooplankton Community Structure in Coal Mining Subsidence Lake

by
Tingyu Fan
1,2,*,
Hayat Amzil
1,2,
Wangkai Fang
1,2,
Liangji Xu
1,2,
Akang Lu
1,2,
Shun Wang
1,2,
Xingming Wang
1,2,
Yingxiang Chen
1,2,
Jinhong Pan
1,2 and
Xiangping Wei
3
1
School of Earth and Environment, Anhui University of Science and Technology, Huainan 232001, China
2
Anhui Engineering Laboratory for Comprehensive Utilization of Water and Soil Resources & Ecological Protection in Mining Area with High Groundwater Level, Huainan 232001, China
3
Huaibei Mining Group, Huaibei 235000, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(1), 484; https://doi.org/10.3390/ijerph20010484
Submission received: 6 November 2022 / Revised: 14 December 2022 / Accepted: 22 December 2022 / Published: 28 December 2022
(This article belongs to the Special Issue 2nd Edition of Mining and Environmental Health: Challenges and Opportunities)
Browse Figures
Versions Notes

Abstract

:
Land subsidence from coal mining has shaped new artificial aquatic ecosystems, these subsidence lakes are known for their restricted ecological system, water pollution, and extreme habitat conditions. However, knowledge concerning the community structure of plankton in these types of water bodies is still limited. Therefore, both phytoplankton and zooplankton communities’ abundance, distribution, and diversity, as well as relations of these communities to physicochemical water quality variables were analyzed, alongside the interaction between phytoplankton and zooplankton groups. The results indicate zooplankton abundance was 842.375 to 186,355.0 ind./L. Biomass ranged from 0.3408 to 10.0842 mg/L. Phytoplankton abundance varied between 0.541 × 106 cell/L and 52.340 × 106 cell/L while phytoplankton wet biomass ranged from 0.5123 to 5.6532 mg/L. Pearson correlation analysis revealed that both the zooplankton and phytoplankton total densities were significantly correlated with nutrients (TN, TP, PO43−) and CODcr; zooplankton abundance was significantly correlated with phytoplankton abundance. According to the biodiversity index of Shannon–Wiener, both phytoplankton and zooplankton revealed less biodiversity in the subsidence water region than in the Huihe river system and Xiangshun canal, with values ranging from 0.20 to 2.60 for phytoplankton and 1.18 to 2.45 for zooplankton; however, the phytoplankton community showed lower biodiversity index values compared to the zooplankton community. Overall, the knowledge gleaned from the study of plankton community structure and diversity represents a valuable approach for the evaluation of the ecological conditions within the subsidence lakes, which has significant repercussions for the management and protection of aquatic environments in mining areas.

1. Introduction

Coal is one of the most important energy sources in the world, and its exploitation and utilization are increasing year by year [1,2]. A large amount of coal mining has brought about a series of ecological and environmental problems [3]. Land subsidence due to coal mining operations is one of the critical factors affecting surface structures and water resources [4]. Excessive mining has led to ground displacement and deformation, and eventually the construction of a vast subsidence area, with low groundwater levels and rainfall. The original terrestrial environment then gradually evolves into an aquatic environment [5].
The quantities and qualities of these subsidence lakes were described, and recommendations for their ecological restoration were considered. Quantitatively, they are generally regulated as fishponds, wetland parks, and large plain reservoirs or ecological lakes based on the water bodies, scales, locations, and hydrology. However, the water qualities show different patterns and degrees of pollution [6]. As a result of their low topography, these water bodies may be polluted by mine water, leaching water from waste heaps, domestic sewage, and agricultural drainage [5,7].
The existence of coal mining activities, in addition to creating large ponds due to mining excavation, is also estimated to creature pressure on the surrounding aquatic ecosystem [8]. Certain subsidence water bodies affected by long-term rainfall and human activities continue to expand the scope of subsidence, and gradually connect with adjacent rivers, causing the enrichment of contamination sources in rivers [9].
In aquatic ecosystems, phytoplankton and zooplankton are important components, contributing to the ecosystem’s health and services [10]. Phytoplankton are the first bio-indicators of pollution in aquatic ecosystems [11]. Phytoplankton play a critical role in the structure and functioning of aquatic ecosystems. As primary producers, they are considered a significant component of water ecosystems [12]. Populations of phytoplankton are well known to be influenced by spatiotemporal variations in hydro-chemical and physical parameters [13,14], and they directly affect the water quality variables such as turbidity and dissolved oxygen, thus influencing many ecosystem processes [15]. The monitoring of phytoplankton and algae is of great significance because phytoplankton demonstrates the health of an aquatic ecosystem through changes in its community composition and distribution, and the proportion of sensitive species [16,17].
Zooplankton are a primary component of aquatic ecosystems that are included within the transformation of organic matter and the formation of matter and energy fluxes. The diversity and abundance of zooplankton species are sensitive indicators of anthropogenic changes in environmental conditions [18,19]. Zooplankton are characterized by short life cycles and fast adaptation to environmental changes [20]. They are sensitive to various substances in water such as nutrient enrichment and pollutants. Thus, they have often been used as indicators to assess the condition and change of the freshwater environment [21]. The relationships established by zooplankton with phytoplankton as the primary producers reflect the ecological conditions of the entire ecosystem [22].
Given the above information, the research of zooplankton and phytoplankton communities in relation to coal mining subsidence lake conditions were considered to be necessary. Abiotic and biotic factors of an ecosystem are interdependent, and the change of abiotic factors frequently affects the biotic factors, which will be reflected directly in their distribution, abundance, and diversity [23,24,25]. Therefore, it was assumed that the structure of zooplankton and phytoplankton communities is shaped under the influence of various abiotic and biotic factors [20,26,27]. Numerous related studies have been performed and stated that temperature, transparency (SD), and nutrient concentrations were important factors affecting the phytoplankton community [28,29], while nutrients, temperature, EC, TDS, and dissolved oxygen were major factors that restrict the abundance and diversity of the zooplankton communities [20,30]. However, these previous studies have mainly focused on natural freshwater ecosystems and there is still a lack of research on the community structure of phytoplankton and zooplankton in the coal mining subsidence lakes.
The Linhuan Mining Area is located in Suixi County, Huaibei City, Anhui Province of China; the Huihe river is a small to medium-sized seasonal river, which flows from west to east through this region. The Linhuan coal mining subsidence area is formed due to the coal mining subsidence. The accumulated water mainly comes from atmospheric precipitation and surface runoff, and the evaporation of surface water is the main excretion route. Part of the subsidence lake supplies water to the Linhuan Industrial Park, with the water surface covered with photovoltaic panels for solar power generation while the remaining part has been developed into a fish pond. The fly ash field and gangue hill are located to the east of the subsidence lake. To the southwest are the Linhuan Industrial Park and the small gangue hill. In the north, there is the Xiangshun canal (artificial diversion canal) connected to the Huihe River. The Huihe River is introduced into the subsidence lake as its water source [31]. With the progress of coal mining, the area of subsidence will be further increased. While this area’s ecological environment has shifted from a terrestrial to a water-land composite ecosystem, it is still impacted by acid wastewater from mines, agriculture, and domestic sewage. As a result, the ecosystem’s structure and function have been severely disrupted.
The present study, therefore, highlights the characteristics of both phytoplankton and zooplankton communities and their distribution, abundance, and diversity. It also assesses the trophic linkage between them and their relationships with the physicochemical parameters within the Linhuan subsidence lake in the Linhuan Mining Area, as well as the Huihe river area in Suixi County, Huaibei City, China. The outcome of this research will provide a better understanding of the Linhuan subsidence lake and the Huihe river as an important aspect of ecosystem management and environmental protection.

2. Materials and Methods

2.1. Study Area and Locations of Samples

The subsidence area is geographically located between 116°34′25″~116°44′27″ E and 33°36′50″~34°40′47″ N. The prevailing wind in the mining area is northeast in spring and autumn, mostly east to southeast in summer, and north to northwest in winter. The average wind speed, temperature and annual rainfall is 2.0 m/s, 14.1 °C, and 830.0 mm, respectively. The average water depth of the Linhuan Subsidence Lake is about 3.45 m, the maximum water depth is 9.0 m, and the accumulated water area is 5.5 km2. The water storage period exceeds 17 years [32].
In the present study, 12 sampling sites were set up in the coal mining subsidence in Linhuan Mining Area and Huihe River, along with hydrogeological conditions and on-site investigations. The coal mining subsidence lake (SL) includes five sampling points (S4, S5, S6, S7 and S8) and three sampling points (S1, S2, S3) in coal gangue hills (CGH). From upstream to downstream of the Huihe River (HR), three sampling points (S10, S11, and S12) were chosen, alongside one sampling point (S9) in Xiangshun canal. Figure 1 illustrates the distribution of the sampling points and Table S1 presents sampling point coordinates.

2.2. Samples Collection

The water samples were collected in April 2021 using pre-cleaned high-density polyethylene plastic bottles. For both the phytoplankton and zooplankton studies, Apstein plankton nets were used to collect samples along the water column by hauling vertically from the bottom to surface waters; due to the small depth, water samples for analysis were collected from 0.5 m below the water surface. The collected phytoplankton samples were preserved in 50 mL sterile polyethylene bottles and fixed with 10% of Ruger’s reagent, while the zooplankton samples were preserved in 50 mL polyethylene bottles and with 10% formalin. The samples were then transported to a bioinformatics company laboratory for identification and counting under a microscope.

2.3. Physicochemical Analysis

From each sampling point, basic parameters of water quality, such as water temperature (T), pH, total dissolved solids (TDS), electrical conductivity (EC), dissolved oxygen (DO), depth (H), and SD (Secchi depth) were determined in the field using a pH meter, Secchi disk, and multiparameter water quality sonde (YSI 6600 V2, Yellow Springs Instruments Inc., Yellow Spring, OH, USA).
Ions, Chemical Oxygen Demand (CODcr), and nutrients of the water samples were tested in a Bioinformatics company laboratory using analytical methods. The concentrations of anions (SO42−, NO3−, F−, Cl−) were estimated using Ion Chromatography, while the concentrations of Chemical Oxygen Demand (CODcr) were determined using the Potassium Dichromate Method. For nutrients concentrations (TN, NH4+, TP, PO43−), Total Nitrogen (TN) was determined using Alkaline potassium persulfate digestion UV spectrophotometry, ammonium ions (NH4+) were quantified using Nessler’s reagent spectrophotometry, and the concentrations of total phosphorus (TP) and orthophosphate (PO43−) were measured by Ammonium molybdate spectrophotometric method.

2.4. Biological Data Preparation and Analysis

Identification and counting, species name, species number, individual number of each species, total number, biomass, and density estimation as well as diversity index estimation of phytoplankton and zooplankton at each sampling point were completed and recorded by a bioinformatics company named Shandong Xiaochong Biotechnology Co., Ltd. and located in Shandong, China. Taxonomic identifications were carried out at the species level.
The phytoplankton samples were concentrated from 1 L to 50 mL. Phytoplankton cells were enumerated using a counting cell of a volume of 0.1 mL and an area of 400 mm2, under a microscope, and the number of fields of view counted was 100 view fields; the area of each field of view was 0.158962 mm2 and the counting area was 15.8962 mm2.
The phytoplankton community cell density of each taxon was calculated as follows [33]:
N = Cs × v × Pn/(Fs × Fn × V)
where N is the cell density, Cs is the area of the counting cell, v is the volume of the concentrated samples, Pn is the number of phytoplankton cells, Fs is the area of each view field, Fn is the number of view fields and V is the volume of the counting cell.
Zooplankton samples were concentrated from 1 L to 50 mL. Zooplankton were counted in 1 mL subsamples made with an automatic pipet after homogenization of the sample. Zooplankton densities were estimated based on the individuals found in the samples and the water samples’ volume, expressed in individual/L, according to the formula [34]:
  D = q f × V
where: D = zooplankton density (Ind./L), q = number of zooplankton found in the subsample (Ind), f = fraction taken (subsample volume per sample volume); V = volume of filtered water (L)
Plankton wet biomass was measured based on the dimension of cells and cell density for phytoplankton while for zooplankton wet biomass was estimated based on their wet weights and the species density.

2.5. Statistical Analysis

In assessing species diversity and water quality conditions, the biodiversity index of Shannon-Weiner (H′) [35] was used.
H ′ = − ∑ i = 1 s n i N × ln n i N
where: ni = number of individuals or amount (e.g., biomass or density) of each species (the ith species), N = total number of individuals (or amount) for the site, ln = the natural log of the number and, s refers to the species type number in a sampling site.
One-way ANOVA was conducted using IBM SPSS Statistics (Version 26.0) to statistically analyze the difference of abundance and biomass of zooplankton and phytoplankton communities between Coal gangue hills sites (S1, S2 and S3), subsidence lake (S4, S5, S6, S7, S8) and Huihe River along with Xiangshun canal (S9, S10, S11, S12).
Correlation coefficients were calculated with the use of Pearson correlation formed in R 4.1.0 and RStudio and RStudio based on the “tidyr” package to describe the degree of relationship between phytoplankton and zooplankton density and water quality parameters, as well as the interaction between phytoplankton and zooplankton taxonomic groups. The graphs of the abundance and biomass values of the plankton were generated with Origin Pro 2018 v 9.5.1 software (OriginLab, Northampton, MA, USA). Heatmaps were created by Prism 9.4.0 (GraphPad).

3. Results

3.1. Water Characteristics and Physicochemical Parameters

The physicochemical parameters recorded at the sampled sites are summarized in Table S2. Water temperature obtained during the sampling period for all sampling sites ranged between 4.8 and 6 °C, with the highest temperature being observed in the Huihe river sites S10 and S12. The water samples were mainly weakly alkaline in all the sampling points ranging from 7.63 to 8.69. The DO concentrations were in general high in sampling sites ranging between 9.9 mg/L and 12.1 mg/L. The values of Secchi disc depth ranged from 0.8 m in S2 which had a water depth of 1.5 m, to 1.9 m in S4 with a water depth of 4.5 m. TDS, EC, CODcr alongside nutrients concentrations were higher in the coal gangue hill area compared to the Huihe river and subsidence lake. As the highest concentration of TP was marked in S1 with 0.268 mg/L, while the highest values of TN, PO43−, NH4+ were all shown in S3 with 2.71 mg/L, 0.1 mg/L, 0.28 mg/L, respectively.

3.2. Phytoplankton Community Characteristics

A total of 50 different phytoplankton species belonging to 7 phyla were identified from the collected samples, where Bacillariophyta, Chlorophyta and Cryptophyta were the most detected taxonomic groups. S2 showed the largest diversity of phytoplankton with a total of 26 species belonging to six phyla being identified.
The differences in total cell density of the phytoplankton community between the CGH, SL and HR were significant (p < 0.05). The highest total cell density was found at S3 with 52.340 × 106 cell/L followed by S1 with a total density of 36.650 × 106 cell/L where Cryptophyta displayed the highest cell density in both of the sampling points with 50.918 × 106 cell/L and 35.656 × 106 cell/L, respectively, belonging to Cryptophyta phylum. The abundant phytoplankton species in the study areas was Plagioselmis nannoplanctica. The lowest total density was found in S4, S6, and S5 with a total density of 0.541 × 106 cell/L, 0.767 × 106 cell/L, 1.485 × 106 cell/L, respectively (Table S3, Figure 2).
Similarly, the Total wet Biomass of the phytoplankton community showed significant variations among the sampling sites (CGH, SL and HR (p < 0.05)). It was found to be between 0.5123 mg/L and 5.6532 mg/L; the lowest wet biomass was recorded in S6 while the highest wet biomass value was found in S11 where the Chrysophyta occupied the largest biomass accounting for 4.6546 mg/L (Table S4, Figure 3).
In terms of the biodiversity of the phytoplankton community, the Shannon-Weiner (H’) index showed the highest value at S2 with 2.6, while the lowest values were recorded in S1 and S3 with 0.2 and 0.24 respectively, (Table S5).

3.3. Zooplankton Community Characteristics

A total of 41 zooplankton species belonging to 4 phyla (Protozoa, Rotifera, Cladocera, Copepoda) were identified from the collected samples. Protozoa and Rotifera were commonly found and were the most dominant phyla. While species of phyla Cladocera and Copepoda were rarely detected in sampling points, S9 showed the largest diversity of zooplankton with a total of 24 species belonging to the 4 phyla being detected.
Significant differences were observed in the taxa densities of the zooplankton community among the sampling sites (CGH, SL and HR (p < 0.05)). The total density ranged from 842.375 ind./L and 186,355.0 ind./L. The total density of the zooplankton community in S3 and S1 was significantly higher than in the other sampling points with 186,355.0 ind./L and 115,529.0 ind./L, respectively, where species of Protozoa phylum (Strobilidium sp., Ciliophora sp.) occupied the largest portion of the total density in both of the sampling points. The least total density was registered in S4 with a total density of 842.375 ind./L (Table S3, Figure 2).
Non-significant differences were observed in the total wet biomass of the zooplankton community among the sampling points (p > 0.05); the total wet biomass ranged between 0.3408 mg/L and 10.0842 mg/L. The highest biomass was observed in S3, while the lowest total biomass was registered in S4. (Table S4, Figure 3).
The biodiversity index of the Shannon-Weiner (H’) of the zooplankton community, varied between the lowest value of 1.18 registered in S1 and the highest value of 2.45 found in S9 (Table S5).

3.4. Correlation between Water Quality Parameters and Phytoplankton and Zooplankton

The Correlation coefficients show how strongly water quality variables are related to the plankton density. The correlation matrix (Table 1, Figure 4) showed a significant correlation of the phytoplankton total density with COD and nutrients in water; TP (correlation: 0.948), PO43− (correlation: 0.763), TN (correlation: 0.701) and with CODcr (correlation: 0.795). Similar to the results of the correlation between phytoplankton communities and water quality factors, the total zooplankton abundance also showed a significant positive correlation with nutrients; TP (correlation: 0.919), PO43− (correlation: 0.745), TN (correlation: 0.613) and with CODcr (correlation: 0.726).
The correlation between the total density of phytoplankton and zooplankton was estimated as a significant positive correlation of 0.987. Most of the zooplankton taxonomic groups showed a non-significant correlation with phytoplankton groups (Table 2, Figure 5), and the correlation matrix revealed the highest correlation was between Protozoa and Cryptophyta with a significant positive correlation of 0.998, followed by Copepoda and nauplii who shared a significant correlation with Cryptophyta 0.736 and 0.596, respectively.

4. Discussion

4.1. Phytoplankton Characteristics

Many aspects of ecology rely on spatial changes in phytoplankton communities, including the maintenance of species diversity and community stability [36,37]. An assortment of environmental factors and influences shape the community structure, spatial patterns, and abundance of dominant phytoplankton species [26].
Correlation analysis of the plankton communities with physical and chemical water quality factors revealed that nutrients TP, PO43−, TN and CODcr were the variables that had the greatest impact on this community. Nutrient availability within waters such as nitrogen or phosphorus are the factors driving the abundance of phytoplankton growth and/or primary productivity. This result is consistent with those of many studies that suggest phosphorus and nitrogen are the limiting nutrients for phytoplankton affecting the different characteristics of phytoplankton community in different water bodies [38,39,40,41], including subsidence lakes [42,43]. The differences in nutrient levels may potentially limit the absorption and utilization of nutrients by phytoplankton and regulate their population abundance and biodiversity [44]. Therefore, excess nutrients lead to excessive growth of phytoplankton, which was shown in the present study as the areas that revealed nutrient pollution, nitrogen, and phosphorus pollution for coal gangue hills area and nitrogen pollution for the Huihe River system; these were the areas that marked the highest total densities of phytoplankton. In addition of the excessive growth of phytoplankton, increased nutrient gradients conduct to decreased phytoplankton diversity [45]. However, even the lack of nutrients may affect the phytoplankton diversity, and it is known that the growth of some phytoplankton species could be restricted by the lack of certain nutrients. Other phytoplankton species with a relatively low demand for these nutrients can rapidly grow and become dominant, resulting in simplification of the species composition and a decrease in biodiversity [44]. This was reflected in the low diversity index in the Huihe River (S11, S10, S12) and Xiangshun canal (S9) as these sites noticed relatively low concentrations of TP.
Organic matter (OM) can play a critical role in advancing the wet biomass and abundance of certain phytoplankton taxa in aquatic environments [46,47], as it can be used by certain phytoplankton as a source of nitrogen and phosphorus [46], which will be reflected in their biodiversity and distribution. Previous studies have demonstrated that the change in phytoplankton structure has a strong correlation with chemical oxygen demand (CODcr) and OM [48]. Similarly, in the present study, the COD concentrations stated a significant positive correlation with phytoplankton total density (correlation: 0.795). The excessive concentrations of organic matter in the coal gangue hills (S1, S3), the Huihe River (S11, S10), and Xiangshun canal (S9) worked in favor of the abundance of cryptophytes and chrysophytes, as was the dominant taxa; however, it led to a decrease in the biodiversity in these areas, which was reflected in the uneven distribution of the phytoplankton groups. This suggests that the phytoplankton community in those areas is significantly disturbed.

4.2. Zooplankton Characteristics

Zooplankton abundance, distribution, and diversity within aquatic ecosystems are influenced by plenty of factors; biotic such as predation by other aquatic organisms including other zooplankton as well as changes in the phytoplankton communities within the aquatic environment, or abiotic factors which are mainly physicochemical parameters within the aquatic environment [49,50,51].
Similar to [52], Rotifera were dominant as well in the present study, however, Protozoa was the dominant taxa followed by Rotifers as the second dominant taxa. Feeding habits helped to shape the abundance of zooplankton groups in the studied sites. All protozoa require organic materials, whether they be particulate or in solution, as their nutrition is holozoic [53]. Thus, the high concentrations of organic matter favored their growth in all of the investigated sites, especially in coal gangue hills area (S1, S3), which also explains the significant positive correlation between the zooplankton density and CODcr. Rotifers play a pivotal role in many freshwater ecosystems. They are ubiquitous, occurring in almost all types of freshwater habitats [54], which explains their presence in all of the sampling points. The high number of rotifers in a freshwater ecosystem is due to their wide variety of feeding habits, high fecundity, and rapid turnover rates [55]. Rotifers feed on protozoa, phytoplankton, detritus, or other organic matter, and they are considered as and are important filter-feeders on algae and bacteria [54,56].
Crustaceans (Cladocera, copepods, and nauplii), as opposed to protozoa and rotifers, were rarely found in all of the sampling points and that is because crustaceans have a longer development cycle, and they are heavier and larger than rotifers [57]. This makes it difficult to maintain crustacean populations in small rivers and even in small reservoirs. Crustaceans occur in rivers with suitable conditions, such as long water retention time, high open water zones, and the presence of macrophytes [58,59,60,61,62,63].
The change in biodiversity is usually related to species dominance [64]. In the present study, the dominance of protozoa and rotifers and the rarity of the crustaceans community reflected the relatively low to moderate biodiversity of the investigated areas.
The decomposition of the organic matter by the dominant phyla Protozoa and Rotifera may potentially explain both the decrease in CODcr and the increase of biodiversity index values of some sampling points in both the waters in the coal gangue hills area (S2) and the Huihe river (S12). However, the subsidence region (subsidence lake and the Coal Gangue Hills) showed less biodiversity than the Huihe River and Xiangshun canal which can provide potential indications of environmental changes or disturbances in those areas, especially in the subsidence water region.

4.3. Interactions between Phytoplankton and Zooplankton

Phytoplankton and zooplankton share a predator-prey relationship. Thus, a negative relationship between these two organisms is expected [65]. However, in this study, the total densities of phytoplankton and zooplankton showed a significant positive (very strong) correlation (correlation: 0.987).
Although phytoplankton bloom is closely related to nutrient enrichment, it can be drastically reduced by intensive grazing [27,66,67]. The linkage between phytoplankton and zooplankton is a dynamic process controlled by several factors. Grazing is one of the most important factors controlling the relationship between the two communities as well as zooplankton composition, as the grazing pressure depends on the zooplankton composition since the nature of food selection varies among herbivore taxa [27,68]. Although the grazing causes serious changes in the phytoplankton structure, the bulk of these changes occurs in the dominant species, which are usually grazed more than those that exist in low density or as rarely [27]. In the present study, Protozoa who were the dominant phyla of the zooplankton community, did not show any significant negative correlation with any of the dominant groups of the phytoplankton community (Cryptophyta and Chrysophyta). Similarly, Rotifers who are the second dominant taxa, (although many rotifer species are described as herbivores and microalgae) are the main natural food for rotifers and provide the highest growth and fertility [69]; however, they shared a non-significant correlation with Cryptophyta (correlation: −0.477). Generally, grazing by rotifer-dominated communities is known to be weak, impacting only small cells [70]. This signifies that both Cryptophyta and Chrysophyta seemed to be not preferable by zooplankters during the present study, which lessened the grazing pressure on these phytoplankton phyla and thus favored their growth. This may explain the significant positive correlation between the total density of phytoplankton and zooplankton communities.
Zooplankton appear to have particular impacts on the phytoplankton community, directly through herbivory and indirectly through the recycling of nutrients [71]. Correlation analysis of the zooplankton communities with water quality variables revealed that nutrients TP, PO43− and TN were the variables that were strongly related to zooplankton communities. So, this positive relationship between zooplankton and phytoplankton communities may probably be connected with nutrient release by zooplankton, which stimulates algal growth [72]. Zooplankton communities are important contributors to the nitrogen and phosphorus required by phytoplankton for primary production [73,74], which serves as an additional source of nutrients for phytoplankton [75]. This indicates that phytoplankton growth can be stimulated by zooplankton [64].

5. Conclusions

Zooplankton and phytoplankton characteristics and composition may reflect the health of an aquatic ecosystem, both from the point of view of their structural parameters and also of their interrelations with one another. In this context, the present study was undertaken to assess zooplankton and phytoplankton abundance, distribution, and diversity in Linhuan coal mining subsidence area and its surroundings (Huihe River and Xiangshun canal) and the influence of water quality parameters on these communities. The results revealed that both zooplankton and phytoplankton abundance was much higher in the waters of the mining area than in the Huihe River system, while phytoplankton biomass was higher in the Huihe River. Moreover, plankton abundance has been seen to be enhanced by organic and nutrient pollution, giving an additional source of nutrients; however, the ability to use organic nutrients from OM played a role in the distribution in the present study and favored the growth of certain groups (Protozoa and Rotifera for zooplankton and Cryptophyta and Chrysophyta for the phytoplankton community), which is what weakens the diversity of these communities. The interaction between phytoplankton and zooplankton highlighted the selective grazing by zooplankton, as it was among the factors affecting the structure of phytoplankton communities alongside nutrients-release by zooplankton, reflecting a significant positive relationship between these two communities. Both phytoplankton and zooplankton showed less biodiversity in the subsidence water area compared to the Huihe River and Xiangshun canal; however, all these areas revealed disturbed phytoplankton community with a low biodiversity index, while the zooplankton community was more stable in the Huihe River and Xiangshun canal, showing a moderate diversity index. Overall, the findings of the present study provide useful knowledge on the plankton community within the Linhuan subsidence lake as well as the Huihe river area, as it can be used in management strategies to protect the aquatic biodiversity in the subsidence lakes in coal mining areas. Many of the conclusions in this study corroborate the patterns obtained by previous researches. However, some of the different conclusions call for further studies. Therefore, long-term plankton investigations on a spatiotemporal scale are needed to better comprehend the combined effects of physicochemical and biological factors caused by anthropogenic activities, specifically mining activities on both phytoplankton and zooplankton communities in mining areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph20010484/s1, Table S1. Longitude and latitude coordinates of sampling points; Table S2: Physical and chemical indices of water samples in different sampling points; Table S3: Spatial distribution of plankton abundance in the sampling points; Table S4: Biomass of plankton community in the sampling points; Table S5: Bio-diversity index of plankton community in sampling points.

Author Contributions

Conceptualization, Resources, Funding acquisition, T.F.; Data Curation, Writing—Original Draft, Supervision, H.A.; Formal analysis, Supervision, W.F.; Supervision, Validation, L.X.; Investigation, Resources, Project administration, A.L.; Validation, Visualization, S.W.; Software, Investigation, X.W. (Xingming Wang); Investigation, Software, Investigation, Y.C.; Resources, J.P.; Resources, X.W. (Xiangping Wei). All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Research Foundation of Huaibei Mining Group in 2022, Research Foundation of the Institute of Environment-friendly Materials and Occupational Health (Wuhu), Anhui University of Science and Technology (ALW2020YF08), and National Key Research and Development Program (2022YFF1303303).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to thank the anonymous reviewers for their instructive reviews and constructive feedback.

Conflicts of Interest

The authors declare no conflict of interest. Author Xiangping Wei was employed by the company Huaibei Mining Group. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

References

  1. Mirchi, A.; Hadian, S.; Madani, K.; Rouhani, O.M.; Rouhani, A.M. World Energy Balance Outlook and OPEC Production Capacity: Implications for Global Oil Security. Energies 2012, 5, 2626–2651. [Google Scholar] [CrossRef] [Green Version]
  2. Price, J.G. The world is changing. GSA Today 2016, 26, 4–10. [Google Scholar] [CrossRef]
  3. Yang, Z.; Li, W.; Pei, Y.; Qiao, W.; Wu, Y. Classification of the type of eco-geological environment of a coal mine district: A case study of an ecologically fragile region in Western China. J. Clean. Prod. 2018, 174, 1513–1526. [Google Scholar] [CrossRef]
  4. Masood, N.; Hudson-Edwards, K.; Farooqi, A. True cost of coal: Coal mining industry and its associated environmental impacts on water resource development. J. Sustain. Min. 2020, 19, 135–149. [Google Scholar] [CrossRef]
  5. Ouyang, Z.; Gao, L.; Yang, C. Distribution, sources and influence factors of polycyclic aromatic hydrocarbon at different depths of the soil and sediments of two typical coal mining subsidence areas in Huainan, China. Ecotoxicol. Environ. Saf. 2018, 163, 255–265. [Google Scholar] [CrossRef]
  6. Kai, X.; Yanqiu, Z.; Qitao, Y.; Jiaping, Y. Optimal resource utilization and ecological restoration of aquatic zones in the coal mining subsidence areas of the Huaibei Plain in Anhui Province, China. Desalination Water Treat. 2013, 51, 19–21. [Google Scholar]
  7. Stojiljkovic, E.; Grozdanovic, M.; Marjanovic, D. Impact of the underground coal mining on the environment. Acta Montan. Slovaca 2014, 19, 6–14. [Google Scholar]
  8. Fikri, H.M.; Erna, R.; Abdul, R.Z.; Yosep, R.; Wawan, K.; Marlon, I.A. A study of River quality and pollution index in the water around Coal Mining area. J. Biodivers. Environ. Sci. 2019, 15, 66–76. [Google Scholar]
  9. Hu, J.; Chen, X.; Chen, Y.; Li, C.; Ren, M.; Jiang, C.; Chen, Y.; An, S.; Xu, Y.; Zheng, L. Nitrate sources and transformations in surface water of a mining area due to intensive mining activities: Emphasis on effects on distinct subsidence waters. J. Environ. Manag. 2021, 298, 113451. [Google Scholar] [CrossRef]
  10. Xue, X.; Wang, L.; Xing, H.; Zhao, Y.; Li, X.; Wang, G.; Wang, Z. Characteristics of phytoplankton-zooplankton communities and the roles in the transmission of antibiotic resistance genes under the pressure of river contamination. Sci. Total Environ. 2021, 780, 146452. [Google Scholar] [CrossRef]
  11. Pourafrasyabi, M.; Ramezanpour, Z. Phytoplankton as bio-indicator of water quality in Sefid Rud River—Iran (South Caspian Sea). Caspian J. Environ. Sci. 2014, 12, 31–40. [Google Scholar]
  12. Ligez, S.; Wilk-Woźniak, E. The occurrence of a Euglena pascheri and Lepocinclis ovum bloom in an oxbow lake in southern Poland under extreme environmental conditions. J. Ecol. Indic. 2011, 11, 925–929. [Google Scholar] [CrossRef]
  13. UNESCO. Coastal lagoon research, present and future. UNESCO Technol. Pap. Mar. Sci. 1981, 32, 51–79. [Google Scholar]
  14. Cloern, J.E.; Powell, T.M.; Huzzley, L.M. Spatial and temporal variability in South Francisco Bay (USA). II. Temporal changes in salinity, suspended sediments, phytoplankton biomass and productivity over tidal time scales. Estuary Coast. J. Shelf Sci. 1989, 28, 599–613. [Google Scholar] [CrossRef]
  15. Garmendia, M.; Borja, Á.; Franco, J.; Revilla, M. Phytoplankton composition indicators for the assessment of eutrophication in marine waters: Present state and challenges within the European directives. Mar. Pollut. Bull. 2013, 66, 7–16. [Google Scholar] [CrossRef] [PubMed]
  16. Gharib, S.M.; El-Sherif, A.Z.M.; Abdelhalim, M.; Radwan, A.A. Phytoplankton and environmental variables as a water quality indicator for the beaches at Matrouh, southeastern Mediterranean Sea, Egypt: An assessment. Oceanologia 2011, 53, 819–836. [Google Scholar] [CrossRef] [Green Version]
  17. Yusuf, Z.H. Phytoplankton as bioindicators of water quality in Nasarawa reservoir, Katsina State Nigeria. Acta Limnol. Bras. 2020, 32, e4. [Google Scholar] [CrossRef] [Green Version]
  18. Mukhortova, O.V.; Bolotov, S.E.; Tarasova, N.G. Zooplankton of an urbanized water body and factors determining its development (a case study of Bol’shoe Vasil’evskoe Lake, Tolyatti, Samara oblast). Povolzh. Ekol. Zh. 2015, 4, 409–421. [Google Scholar]
  19. Imant, E.N.; Novoselov, A.P. Dynamics of Zooplankton Composition in the Lower Northern Dvina River and Some Factors Determining Zooplankton Abundance. Russ. J. Ecol. 2021, 52, 59–69. [Google Scholar] [CrossRef]
  20. Goździejewska, A.M.; Skrzypczak, A.R.; Paturej, E.; Koszałka, J. Zooplankton diversity of drainage system reservoirs at an opencast mine. Knowl. Manag. Aquat. Ecosyst. 2018, 419, 33. [Google Scholar] [CrossRef]
  21. Rissik, D.; Senden, D.V.; Doherty, M.; Ingleton, T.; Ajani, P.; Bowling, L.; Gibbs, M.; Gladstone, M.; Kobayashi, T.; Suthers, I.; et al. Plankton-related environmental and water-quality issues. In Plankton—A Guide to Their Ecology and Monitoring for Water Quality, 1st ed.; Suthers, I.M., Rissik, D., Eds.; CSIRO Publishing: Melbourne, Australia, 2009; pp. 39–72. [Google Scholar]
  22. Florescu, L.I.; Moldoveanu, M.M.; Catană, R.D.; Păceșilă, I.; Dumitrache, A.; Gavrilidis, A.A.; Iojă, C.I. Assessing the Effects of Phytoplankton Structure on Zooplankton Communities in Different Types of Urban Lakes. Diversity 2022, 14, 231. [Google Scholar] [CrossRef]
  23. Pant, B.; Lohani, V.; Trakroo, M.D.; Tewari, H. Study of water quality by physicochemical analysis of a Himalayan lake of Uttarakhand, India. Econ. Environ. Conserv. 2017, 23, 1128–1134. [Google Scholar]
  24. Ghidini, A.R.; Serafim-Ghidini, A.; Serafim, M., Jr.; Perbiche-Neves, G.; Brito, L. Distribution of planktonic cladocerans (Crustacea: Branchiopoda) of a shallow eutrophic reservoir (Paraná State, Brazil). Pan-Am. J. Aquat. Sci. 2009, 4, 294–305. [Google Scholar]
  25. El Ghizi, S.; El Aadel, N.; Sadik, M.; El Bouch, M.; Hasnaoui, M. The physicochemical characteristics and the pollution level of Dayet Er-Roumi Lake in Morocco. E3S Web Conf. 2021, 234, 00036. [Google Scholar] [CrossRef]
  26. Brogueira, M.J.; Oliveira, M.D.R.; Cabeçadas, G. Phytoplankton community structure defined by key environmental variables in Tagus estuary, Portugal. Mar. Environ. Res. 2007, 64, 616–628. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Abdel-Aziz, N.E.; Gharib, S. The interaction between phytoplankton and zooplankton in a Lake-Sea connection, Alexandria, Egypt. Int. J. Ocean. Oceanogr. 2006, 1, 151–165. [Google Scholar]
  28. Wang, L.; Wang, C.; Deng, D.; Zhao, X.; Zhou, Z. Temporal and spatial variations in phytoplankton: Correlations with environmental factors in Shengjin Lake, China. Environ. Sci. Pollut. Res. 2015, 22, 14144–14156. [Google Scholar] [CrossRef] [PubMed]
  29. Nowrouzi, S.; Valavi, H. Effects of Environmental Factors on Phytoplankton Abundance and Diversity in Kaftar Lake. J. Fish. Aquat. Sci. 2011, 6, 130–140. [Google Scholar] [CrossRef] [Green Version]
  30. Abdulwahab, A.; Rabee, A.M. Ecological factors affecting the distribution of the zooplankton community in the Tigris River at Baghdad region, Iraq. Egypt. J. Aquat. Res. 2015, 41, 187–196. [Google Scholar] [CrossRef]
  31. Kong, L.; Jiang, C.; Zheng, L.; Cheng, H.; Ren, M.; Min, F.; Fang, L. Characters of Hydrochemistry and Their Influenced Factors of Different Waters in the Linhuan Coal Mining Subsidence Area of Huaibei City. J. Lake Sci. 2017, 29, 10. [Google Scholar]
  32. Chen, X.; Zheng, L.; Jiang, C.; Huang, W.; Dong, X. Characteristics of Surface Water Chemistry and Sulfur Hydrogen Oxygen Isotope Composition in Linhuan Mining Area of Huaibei City, Anhui Province. Earth Environ. 2019, 47, 9. [Google Scholar]
  33. Reynolds, C. The Ecology of Phytoplankton; Cambridge University Press: Cambridge, UK, 2006. [Google Scholar]
  34. Wickstead, J.H. An Introduction to Study of Tropical Plankton; Hutchinson and Co: London, UK, 1965. [Google Scholar]
  35. Shannon, C.E.; Wiener, W. The Mathematical Theory of Communication; University Illinois Press: Urbana, IL, USA, 1963; 360p. [Google Scholar]
  36. Garibotti, I.A.; Vernet, M.; Ferrario, M.E.; Smith, R.C.; Ross, R.M.; Quetin, L.B. Phytoplankton spatial distribution patterns along the western Antarctic Peninsula (Southern Ocean). Mar. Ecol. Prog. Ser. 2003, 261, 21–39. [Google Scholar] [CrossRef] [Green Version]
  37. Fukai, Y.; Abe, Y.; Matsuno, K.; Yamaguchi, A. Spatial changes in the summer diatom community of the northern Bering Sea in 2017 and 2018. Deep Sea Res. II Top. Stud. Oceanogr. 2020, 181–182, 104903. [Google Scholar] [CrossRef]
  38. Becker, V.; Caputo, L.; Ordóñez, J.; Marcé, R.; Armengol, J.; Crossetti, L.O.; Huszar, V.L. Driving factors of the phytoplankton functional groups in a deep Mediterranean reservoir. Water Res. 2010, 44, 3345–3354. [Google Scholar] [CrossRef] [PubMed]
  39. Cao, J.; Hou, Z.; Li, Z.; Chu, Z.; Yang, P.; Zheng, B. Succession of phytoplankton functional groups and their driving factors in a subtropical plateau lake. Sci. Total Environ. 2018, 631–632, 1127–1137. [Google Scholar] [CrossRef]
  40. Søndergaard, M.; Jensen, J.P.; Jeppesen, E. Internal phosphorus loading in shallow Danish lakes. Hydrobiologia 1999, 191, 139–148. [Google Scholar] [CrossRef]
  41. Ding, Y.; Pan, B.; Zhao, X.; Zhao, G.; Han, X.; Li, M. Will a heavy sediment load affect responses of phytoplankton functional groups to aquatic environmental changes in different water body types? Sci. Total. Environ. 2022, 837, 155863. [Google Scholar] [CrossRef]
  42. Wan, Y.; Zhou, Z.; Wang, C.; Lin, Z.; Wu, Q. Community structure characteristics of phytoplankton in Digou coal mining subsidence lake. J. Biol. 2018, 35, 75. [Google Scholar]
  43. Xu, X.; Yi, Q.T.; Wang, X.M.; Zhang, L.; Xie, K. Phytoplankton community in the small subsided lakes around Huainan coal mine subsidence areas. Acta Hydrobiol. Sin. 2015, 39, 740–750. [Google Scholar]
  44. He, R.; Luo, H.; He, N.; Chen, W.; Yang, F.; Huang, W.; Li, N.; Sun, L.; Peng, S. Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau. Int. J. Environ. Res. Public Health 2022, 19, 7788. [Google Scholar] [CrossRef]
  45. Tian, W.; Zhang, H.; Zhao, L.; Zhang, F.; Huang, H. Phytoplankton Diversity Effects on Community Biomass and Stability along Nutrient Gradients in a Eutrophic Lake. Int. J. Environ. Res. Public Health 2017, 14, 95. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Burpee, B.; Saros, J.E.; Northington, R.M.; Simon, K.S. Microbial nutrient limitation in Arctic lakes in a permafrost landscape of southwest Greenland. Biogeosciences 2016, 13, 365–374. [Google Scholar] [CrossRef] [Green Version]
  47. Kissman, C.E.H.; Williamson, C.E.; Rose, K.C.; Saros, J.E. Nutrients associated with terrestrial dissolved organic matter drive changes in zooplankton:phytoplankton biomass ratios in an alpine lake. Freshw. Biol. 2016, 62, 40–51. [Google Scholar] [CrossRef]
  48. Lee, Y.S.; Kang, C.-K. Causes of COD increases in Gwangyang Bay, South Korea. J. Environ. Monit. 2010, 12, 1537–1546. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  49. Yongo, E.; Outa, N. Spatial distribution and abundance of zooplankton communities in Lake Victoria, Kenya. Int. J. Fish. Aquat. Res. 2017, 2, 33–35. [Google Scholar]
  50. Nowicki, C.J.; Bunnell, D.B.; Armenio, P.M.; Warner, D.M.; Vanderploeg, H.A.; Cavaletto, J.F.; Mayer, C.M.; Adams, J.V. Biotic and abiotic factors influencing zooplankton vertical distribution in Lake Huron. J. Great Lakes Res. 2017, 43, 1044–1054. [Google Scholar] [CrossRef]
  51. Sousa, W.; Attayde, J.L.; Rocha, E.D.S.; Eskinazi-Sant’Anna, E.M. The response of zooplankton assemblages to variations in the water quality of four man-made lakes in semi-arid northeastern Brazil. J. Plankton Res. 2008, 30, 699–708. [Google Scholar] [CrossRef] [Green Version]
  52. Lin, Z.; Wan, Y.; Xu, M.; Wang, C.; Wu, Q.; Zhou, Z. Metazoan zooplankton community structure and environmental drivers of Digou coal mining subsided lakes in Huainan. J. Lake Sci. 2018, 30, 171–182. [Google Scholar] [CrossRef] [Green Version]
  53. Yaeger, R.G. Protozoa: Structure, Classification, Growth, and Development. In Medical Microbiology, 4th ed.; Chapter, 77; Baron, S., Ed.; University of Texas Medical Branch at Galveston: Galveston, TX, USA, 1996. [Google Scholar]
  54. Segers, H. Global diversity of rotifers (Rotifera) in freshwater. Hydrobiologia 2008, 595, 49–59. [Google Scholar] [CrossRef]
  55. Sharma, B.K. Biological monitoring of freshwaters with reference to role of freshwater Rotifera as biomonitors. In Water Quality Assessment Biomonitoring and Zooplanktonic Diversity; Ministry of Environment and Forests, Government of India: New Delhi, India, 2001; pp. 83–97. [Google Scholar]
  56. Gilbert, J.J. Food niches of planktonic rotifers: Diversification and implications. Limnol. Oceanogr. 2022, 67, 2218–2251. [Google Scholar] [CrossRef]
  57. Bledzki, L.A.; Rybak, J.I. Freshwater Crustacean Zooplankton of Europe: Cladocera & Copepoda (Calanoida, Cyclopoida) Key to Species Identification, with Notes on Ecology, Distribution, Methods and Introduction to Data Analysis; Springer: Berlin/Heidelberg, Germany, 2016. [Google Scholar] [CrossRef]
  58. Sługocki, Ł.; Czerniawski, R.; Kowalska-Góralska, M.; Senze, M.; Reis, A.; Carrola, J.S.; Teixeira, C.A. The Impact of Land Use Transformations on Zooplankton Communities in a Small Mountain River (The Corgo River, Northern Portugal). Int. J. Environ. Res. Public Health 2019, 16, 20. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Grabowska, M.; Ejsmont-Karabin, J.; Karpowicz, M. Reservoir-river relationships in lowland, shallow, eutrophic systems: An impact of zooplankton from hypertrophic reservoir on river zooplankton. Pol. J. Ecol. 2013, 61, 759–768. [Google Scholar]
  60. Richardson, W.B. Microcrustacea in flowing warer—Experimantal analysis of washout times and a field-test. Freshwater Biol. 1992, 28, 217–230. [Google Scholar] [CrossRef]
  61. De Bie, T.; Declerck, S.; Martens, K.; De Meester, L.; Brendonck, L. A comparative analysis of cladoceran communities from different water body types: Patterns in community composition and diversity. Hydrobiologia 2008, 597, 19–27. [Google Scholar] [CrossRef]
  62. Czerniawski, R. Spatial pattern of potamozooplankton community of the slowly flowing fishless stream in relation to abiotic and biotic factors. Pol. J. Ecol. 2012, 60, 323–338. [Google Scholar]
  63. Czerniawski, R.; Sługocki, Ł. Analysis of zooplankton assemblages from man-made ditches in relation to current velocity. Oceanol. Hydrobiol. Stud. 2017, 46, 199–211. [Google Scholar] [CrossRef]
  64. Hillebrand, H.; Bennett, D.M.; Cadotte, M.W. Consequences of Dominance: A Review of Evenness Effects on Local and Regional Ecosystem Processes. Ecology 2008, 89, 1510–1520. [Google Scholar] [CrossRef]
  65. Ryszard, G.; Kowalczewska-Madura, K. Interactions between phytoplankton and zooplankton in the hypertrophic Swarzędzkie Lake in western Poland. J. Plankton Res. 2008, 30, 33–42. [Google Scholar]
  66. Mellina, E.; Rasmussen, J.B.; Mills, E.L. Impact of Zebra mussels (Dressena polymorpha) on phytoplankton cycling and chlorophyll in lakes. Can. J. Fish. Aquat. Sci. 1995, 52, 2553–2579. [Google Scholar] [CrossRef]
  67. Prins, T.C.; Escaravage, V.; Smaal, A.C.; Peters, J.C.H. Nutrient cycling and phytoplankton dynamics in relation to mussel grazing in a mesocosm experiment. Ophelia 1995, 41, 289–315. [Google Scholar] [CrossRef]
  68. Havens, K.E. The importance of rotiferan and crustacean zooplankton as grazers of algal productivity in a freshwater estuary. Arch. Hydrobiol. 1991, 122, 1–22. [Google Scholar] [CrossRef]
  69. Sanders, R.W.; Wickham, S.A. Planktonic protozoa and metazoa: Predation, food quality and population control. Mar. Microb. Food Webs 1993, 7, 197–223. [Google Scholar]
  70. Sterner, R.W. The role of grazers in phytoplankton succession. In Plankton Ecology: Seasonal Succession in Plankton Communities; Sommer, U., Ed.; Springer: Berlin/Heidelberg, Germany, 1989; pp. 107–170. [Google Scholar]
  71. Bergquist, A.M.; Carpenter, S.R. Limnetic Herbivory: Effects on Phytoplankton Populations and Primary Production. Ecology 1986, 67, 1351–1360. [Google Scholar] [CrossRef]
  72. Kawecka, B.; Eloranta, P.V. The Outline of Algae Ecology in Freshwater and Terrestrial Environments; PWN Scientific Publishers: Warsaw, Poland, 1994; pp. 1–252. [Google Scholar]
  73. Alcaraz, M.; Almeda, R.; Calbet, A.; Saiz, E.; Duarte, C.M.; Lasternas, S.; Agusti, S.; Santiago, R.; Movilla, J.; Alonso, A. The role of arctic zooplankton in biogeochemical cycles: Respiration and excretion of ammonia and phosphate during summer. Polar Biol. 2010, 33, 1719–1731. [Google Scholar] [CrossRef]
  74. Coello-Camba, A.; Llabrés, M.; Duarte, C.M.; Agusti, S. Zooplankton excretion metabolites stimulate Southern Ocean phytoplankton growth. Polar Biol. 2017, 40, 2035–2045. [Google Scholar] [CrossRef] [Green Version]
  75. Sterner, R.W. Herbivores’ Direct and Indirect Effects on Algal Populations. Science 1986, 231, 605–607. [Google Scholar] [CrossRef]
Ijerph 20 00484 g001 550
Figure 1. Study area and sampling points. (A) map of China, and (B) sampling sites in the coal mining subsidence lake and in the Huihe river.
Figure 1. Study area and sampling points. (A) map of China, and (B) sampling sites in the coal mining subsidence lake and in the Huihe river.
Ijerph 20 00484 g001
Ijerph 20 00484 g002 550
Figure 2. Spatial distribution of plankton abundance in the sampling points. Total density of (a) Phytoplankton, (b) Zooplankton.
Figure 2. Spatial distribution of plankton abundance in the sampling points. Total density of (a) Phytoplankton, (b) Zooplankton.
Ijerph 20 00484 g002
Ijerph 20 00484 g003 550
Figure 3. Biomass of plankton community in the sampling points. Total biomass of (a) phytoplankton, (b) Zooplankton.
Figure 3. Biomass of plankton community in the sampling points. Total biomass of (a) phytoplankton, (b) Zooplankton.
Ijerph 20 00484 g003
Ijerph 20 00484 g004 550
Figure 4. Pearson correlation analysis of the abundance of phytoplankton and zooplankton and water quality variables. The color represents the values of Pearson correlation coefficient.
Figure 4. Pearson correlation analysis of the abundance of phytoplankton and zooplankton and water quality variables. The color represents the values of Pearson correlation coefficient.
Ijerph 20 00484 g004
Ijerph 20 00484 g005 550
Figure 5. Pearson correlation analysis of the abundance of phytoplankton and zooplankton groups. The color represents the values of Pearson correlation coefficient.
Figure 5. Pearson correlation analysis of the abundance of phytoplankton and zooplankton groups. The color represents the values of Pearson correlation coefficient.
Ijerph 20 00484 g005
Table
Table 1. Correlation coefficient of Pearson correlation of water quality variables, phytoplankton and zooplankton density.
Table 1. Correlation coefficient of Pearson correlation of water quality variables, phytoplankton and zooplankton density.
Water Quality VariablesPhytoplankton DensityZooplankton Density
SD−0.559−0.479
CODcr0.795 **0.726 **
TP0.948 ***0.919 ***
PO43−0.763 **0.745 **
TN0.701 *0.613 *
NH4+0.4240.434
SO42−0.1790.162
NO3−−0.135−0.100
F−0.0780.083
DO−0.184−0.187
T°−0.336−0.355
TDS0.3770.280
EC0.4230.326
pH−0.375−0.391
CL−0.2640.213
Phytoplankton density1.0000.987 ***
Zooplankton density0.987 ***1.000
2-tailed test of significance is used; Correlation is significant at p < 0.05; * p < 0.05, ** p < 0.01, *** p < 0.001.
Table
Table 2. Correlation coefficient of Pearson correlation of phytoplankton and zooplankton groups.
Table 2. Correlation coefficient of Pearson correlation of phytoplankton and zooplankton groups.
CyanophytaBacillariophytaChlorophytaCryptophytaEuglenophytaPyrrophytaChrysophyta
Protozoa−0.0310.364−0.0310.998 ***0.221−0.238−0.309
Rotifera−0.268−0.338−0.144−0.477−0.2820.3050.255
Cladocera0.4050.604 *0.2080.1590.095−0.0170.351
Copepoda0.2970.3570.0860.736 **0.309−0.178−0.166
Nauplii−0.1240.128−0.0360.596 *−0.264−0.305−0.164
The 2-tailed test of significance is used; Correlation is significant at p < 0.05; * p < 0.05, ** p < 0.01, *** p < 0.001.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fan, T.; Amzil, H.; Fang, W.; Xu, L.; Lu, A.; Wang, S.; Wang, X.; Chen, Y.; Pan, J.; Wei, X. Phytoplankton-Zooplankton Community Structure in Coal Mining Subsidence Lake. Int. J. Environ. Res. Public Health 2023, 20, 484. https://doi.org/10.3390/ijerph20010484

AMA Style

Fan T, Amzil H, Fang W, Xu L, Lu A, Wang S, Wang X, Chen Y, Pan J, Wei X. Phytoplankton-Zooplankton Community Structure in Coal Mining Subsidence Lake. International Journal of Environmental Research and Public Health. 2023; 20(1):484. https://doi.org/10.3390/ijerph20010484

Chicago/Turabian Style

Fan, Tingyu, Hayat Amzil, Wangkai Fang, Liangji Xu, Akang Lu, Shun Wang, Xingming Wang, Yingxiang Chen, Jinhong Pan, and Xiangping Wei. 2023. "Phytoplankton-Zooplankton Community Structure in Coal Mining Subsidence Lake" International Journal of Environmental Research and Public Health 20, no. 1: 484. https://doi.org/10.3390/ijerph20010484

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop