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Article

Phyto- and Zooplankton Diversity Under Land Use and Water Quality Dynamics in the Jialing River, China

by
Xiaopeng Tang
,
Yiling Huang
,
Chang Chen
,
Haoyun He
,
Qiang Qin
,
Fei Xu
and
Fubin Zhang
*
College of Environmental Science and Engineering, China West Normal University, Nanchong 637009, China
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(10), 707; https://doi.org/10.3390/d17100707
Submission received: 15 September 2025 / Revised: 10 October 2025 / Accepted: 12 October 2025 / Published: 13 October 2025
(This article belongs to the Section Freshwater Biodiversity)
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Abstract

Understanding the mechanisms that maintain biodiversity is crucial for effective conservation in riverine ecosystems. However, the direct and indirect mechanisms by which land use patterns and water quality parameters influence plankton α- and β-diversity remain poorly elucidated. Here, we undertook a comprehensive survey of plankton communities across the Jialing River basin. Our results showed that Bacillariophyta and Chlorophyta were the dominant phytoplankton groups, whereas Protozoa and Copepoda predominated among zooplankton. Redundancy analysis identified dissolved oxygen and total phosphorus as key environmental factors shaping plankton community structure. Additionally, random forest models indicated that anthropogenic stressors exerted consistent effects on both α- and β-diversity of phytoplankton. Importantly, the decomposition of β-diversity revealed that species turnover constituted the major component, underscoring the importance of basin-scale management approaches. Structural equation modeling further demonstrated that land use practices predominantly affected phytoplankton β-diversity indirectly via water quality alterations, with a relatively weak direct effect. In contrast, neither the direct nor indirect effects of land use were significant for zooplankton communities. These findings suggest that phytoplankton may serve as more reliable bioindicators of anthropogenic disturbance than zooplankton in this freshwater system. Moreover, our findings highlight the central role of water quality in regulating phytoplankton diversity responses to environmental change. Consequently, we recommend that conservation strategies in the Jialing River basin focus on water quality monitoring and the mitigation of its ecological effects.

1. Introduction

Rivers support diverse biological communities and provide critical ecological services essential for maintaining ecosystem stability [1,2]. However, increasing environmental pressures are seriously threatening aquatic biodiversity. Intensive anthropogenic disturbances, particularly rapid urbanization and expansion of agricultural activities, have significantly deteriorated water quality [3]. Consequently, ecosystem functioning is impaired and biodiversity loss is accelerated [4]. Among these anthropogenic pressures, land use changes represent a particularly pervasive stressor. These alterations exert profound impacts not only on terrestrial ecosystems but also extend to aquatic environments [5], serving as primary drivers for the degradation of river ecosystem functions [6]. The conversion of natural forested land to urbanized or agricultural landscapes induces substantial modifications in riverine ecosystem composition and structure [7], most notably affecting the dynamics of aquatic communities.
Relevant studies have demonstrated that land use patterns directly influence aquatic organisms [6]. Additionally, land use patterns indirectly regulate species distribution patterns and functional traits through modifications of habitat physicochemical characteristics [4]. Specifically, these impacts are primarily mediated through alterations in both physical and chemical parameters. Physically, watershed-scale land use changes affect hydrological characteristics and habitat conditions. Chemically, they lead to modifications in nutrient concentrations, such as total nitrogen (TN) and total phosphorus (TP). Such environmental changes significantly affect multiple aquatic biological communities, including planktonic assemblages [8], benthic macroinvertebrates [9], and fish communities [10,11]. These impacts ultimately disrupt the structure and function of aquatic environments. In this context, β-diversity analysis has emerged as a crucial tool for developing watershed conservation strategies [12].
Ecological assessments of regional biodiversity typically incorporate two complementary dimensions: α-diversity and β-diversity [13,14]. Of these, α-diversity quantifies species composition and its variability within local communities, with species richness representing its fundamental metric [15]. In contrast, β-diversity characterizes the variability in species composition among sites [16]. β-diversity serves as a crucial metric for evaluating habitat heterogeneity and landscape pattern dynamics, providing unique insights into biodiversity patterns and ecosystem processes [7]. Through decomposition into turnover and nestedness components, researchers can elucidate the underlying mechanisms driving β-diversity patterns [17]. Specifically, the turnover component quantifies spatial species replacement, whereas the nestedness component reflects situations where species-poor communities represent subsets of richer communities [18]. Notably, β-diversity is characterized independently of α-diversity metrics, enabling the identification of relative diversity change drivers [19]. This analytical framework, particularly the decomposition into turnover and nestedness, is therefore critical for pinpointing the mechanisms behind community changes in heterogeneous landscapes like the Jialing River watershed, thus providing scientific foundations for targeted conservation strategies.
Investigating β-diversity patterns across various biotic groups, including plankton communities, has emerged as a prominent research focus in recent years [16,20,21,22,23]. Planktonic organisms play dual critical roles in aquatic ecosystems: they are essential components in biogeochemical cycling and form the foundational trophic level of aquatic food webs [24,25]. These organisms exhibit high sensitivity to environmental changes and demonstrate remarkable adaptability to diverse ecological conditions [26]. These unique characteristics render them irreplaceable for maintaining aquatic ecosystem stability [27,28]. The ongoing conversion of natural habitats to anthropogenic landscapes necessitates a comprehensive understanding of land use impacts on planktonic communities. Elucidating both the direct effects on α- and β-diversity and the underlying indirect mechanisms carries substantial theoretical and practical significance for formulating effective ecological conservation and restoration strategies.
The middle Jialing River (JLR) was selected as the study area to systematically investigate the direct and indirect mechanisms through which land use practices influence planktonic α- and β-diversity. As the largest tributary system in the upper Yangtze River basin, the JLR maintains a diverse planktonic community [29]. The JLR originates in the northeastern Tibetan Plateau and flows through diverse geomorphological features and land use types, exhibiting high sensitivity to anthropogenic disturbances [30]. While this complexity means that the river is subject to multiple, cumulative anthropogenic pressures—making it challenging to isolate individual effects—it also makes the JLR a highly representative model system for large rivers undergoing complex human impacts. Therefore, it provides an ideal, real-world context to examine the integrated and combined effects of prevalent land use practices on phytoplankton diversity patterns. Current understanding of anthropogenic impacts on aquatic biodiversity patterns in the midstream JLR remains limited. To address these critical gaps, our study leverages the JLR as a model system to investigate the following questions: (1) What are the primary environmental drivers shaping the composition and diversity of plankton communities? (2) How can the relative contributions of turnover and nestedness components of β-diversity inform the identification of priority areas for ecological conservation within the watershed? (3) What are the relative effects of land use and water quality indicators on plankton α- and β-diversity? (4) To what extent do the observed patterns in plankton communities result from the direct effects of land use versus indirect effects mediated by alterations in water quality?

2. Materials and Methods

2.1. Study Area

As the largest tributary of the upper Yangtze River basin, the JLR spans 1120 km in length and covers a watershed area of 160,000 km2 [31]. Originating in the Qinling Mountains, it flows southward through the Sichuan Basin and converges with the Yangtze River in Chongqing. The midstream JLR, where the study area is situated, experiences a typical subtropical monsoon climate. The region receives an annual precipitation of approximately 1000 mm, with 80% concentrated during the rainy season from May to October [30,32]. Additionally, this region is characterized by hilly landforms with significant topographic relief, dominated by agricultural and forested land use [33,34]. However, rapid urbanization and agricultural expansion in recent years have severely threatened aquatic ecosystems in the region [35]. These changes have led to water quality deterioration and a decline in aquatic biodiversity.

2.2. Plankton Sample Collection and Processing

Phytoplankton samples were collected on 29 October 2024 from 13 sampling sections in the midstream JLR (Figure 1). This date falls within the autumn season in the study area. The autumn sampling was chosen because it represents a period of stable hydrological conditions after the summer wet season, which is ideal for investigating the spatial distribution of plankton communities. Quantitative sampling was conducted using a 2.5-L organic glass water sampler. Triplicate 1-L water samples were collected at each sampling section (n = 13), resulting in a total of 39 samples. All samples were immediately preserved with Lugol’s solution. The mean value of the triplicates from each section was used for subsequent statistical analysis. In the laboratory, the preserved samples were left to settle for at least 48 h in sedimentation columns. Thereafter, the supernatant was gently siphoned off, and the remaining sample was concentrated to a final volume of 30 mL. For qualitative analysis, samples were collected below the water surface using a plankton net with a mesh size of 64 μm (commonly referred to as 25# in China), transferred into sample bottles, and fixed with Lugol’s solution. After concentration via sedimentation to a final volume of 30 mL, phytoplankton samples were counted using a 0.1 mL counting chamber. The entire chamber was counted for each replicate. A minimum of 100 individuals of the species were enumerated to ensure statistical significance. Taxonomic classification was performed to the lowest possible level (genus or species) using specialized literature [36].
For qualitative zooplankton collection, a 25# plankton net was used to collect rotifers and protozoa, while a net with a larger mesh size of 112 μm (commonly referred to as 13#) was employed for copepoda and cladocera. Rotifers and protozoa were preserved using the same method as phytoplankton, whereas copepoda and cladocera were fixed in formaldehyde solution. For quantitative analysis, different methods were applied for different zooplankton groups. Triplicate samples were collected for each group at every sampling section. Specifically, for copepoda and cladocera, 50 L of water was filtered through a 13# plankton net per replicate. For the smaller rotifers and protozoa, 50 L of water was filtered through a finer 25# plankton net per replicate. All collected samples were immediately preserved in formaldehyde solution for subsequent microscopic identification and counting. Concentrated zooplankton samples were made up to a known volume (30 mL). Rotifers and protozoa were counted using a 1 mL counting chamber, while cladocera and copepoda were counted using a 5 mL counting chamber. The entire chamber was counted for each replicate. Zooplankton were identified to the lowest possible taxonomic level (genus or species) using relevant taxonomic literature [37,38].

2.3. Environmental Characteristics

Environmental parameters were measured concurrently with plankton sampling. In situ measurements of water temperature (WT), dissolved oxygen (DO), electrical conductivity (Cond), and pH were conducted using a YSI portable water quality analyzer. River width (Wid) was measured using an SW-M500 rangefinder (SNDWAY Technology (Guangdong) Co., Ltd., Dongwan, China), and water turbidity (Tur) was assessed with a Hach 2100Q portable turbidimeter (Hach Company, Loveland, CO, USA). Additionally, mixed water samples were also collected at each site, cryopreserved, and transported to the laboratory for subsequent analysis. Laboratory analyses focused on nutrient concentrations, specifically total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (NH3-N).
In this study, land use data were obtained from the China Land Cover Data set (CLCD) [39]. Based on historical land use data in the middle reaches of the JLR, land use types were reclassified into five major categories using ArcGIS 10.7 software: Cropland, Forest, Grassland, Water, and Urban [34]. Sub-watersheds were delineated at each sampling point, and the area proportion of different land use types within each sub-watershed was quantified using ArcGIS 10.7 software. In this case, the sub-watershed extent was defined as the area upstream of each sampling point [10].

2.4. Data Analysis

In this study, dominant species in the plankton community were identified using the dominance index (Y), with a threshold value set at ≥0.02. The formula is as follows: Y = (ni/N) × fi, where ni is the number of the i species; N is the number of individuals of all species; and fi is the proportion of the number of individuals of the i species to the total number of individuals.
The α-diversity of the plankton community was characterized using several ecological indices, including species richness, Margalef richness index, Pielou evenness index, and Shannon–Wiener diversity index. These indices were calculated using the “vegan” package in R. The abundance data of plankton species are provided in File S1 (Supporting Information).
β-diversity was calculated following the method proposed by Baselga [18]. Using the Sørensen dissimilarity index, we partitioned β-diversity into three components: overall β-diversity (βsor), species turnover (βsim), and nestedness (βsne). These analyses were conducted using the “betapart” package [40].
Multivariate statistical analyses were conducted to explore the relationships between environmental parameters and plankton communities. First, detrended correspondence analysis (DCA) was applied to assess the gradient length of plankton community data. The maximum gradient value (<3) indicated that linear models were appropriate for analyzing community-environment relationships. Consequently, redundancy analysis (RDA) was selected as the primary analytical approach. Prior to analysis, environmental variables with correlation coefficients > 0.8 or variance inflation factors (VIF) > 5 were excluded [41]. All analyses were performed using the “vegan” package.
To assess the relationships between environmental stressors and planktonic biodiversity, Mantel and partial Mantel tests were performed. All computations were conducted with the “vegan” and “linkET” package in R.
Random forest (RF) modeling, an ensemble learning method based on decision trees, generates multiple classification and regression trees through random selection of variables and samples [42]. In this study, an RF regression model was developed to explore the relationships between plankton α/β-diversity (response variables) and environmental predictors (water quality parameters and land use). The contribution of each predictor to plankton diversity was evaluated using the variable importance index. Model implementation utilized the “randomForest” package [43]. Additionally, structural equation modeling (SEM) was conducted with the “lavaan” package [44], and parameters were estimated via maximum likelihood to elucidate direct and indirect pathways linking anthropogenic stressors to planktonic biodiversity.

3. Results

3.1. Environmental Characteristics

The study area, located in the midstream JLR, exhibited pronounced spatial heterogeneity in environmental characteristics across sampling sites (Table 1). Regarding physical parameters, substantial variations in river width were observed among sampling locations. Water temperature ranged from 20.5 °C to 23.7 °C across stations, with S1 recording the lowest temperature (20.5 °C) and S11 showing the highest temperature (23.7 °C). The mean water temperature across all sampling stations was 22.0 °C.
Water chemistry analysis revealed distinct spatial patterns in measured parameters. Electrical conductivity demonstrated significant spatial variation, ranging from 293 to 329 μs·cm−1, with maximum values recorded at S5 and minimum values observed at S2. The pH values remained within a narrow weakly alkaline range (8.0–8.6), with a mean pH of 8.4 across all sampling stations. Turbidity measurements showed moderate variation, ranging from 4.1 to 5.4 NTU.
Nutrient analysis revealed distinct concentration patterns among different parameters. Ammonia nitrogen concentrations ranged from 0.066 to 0.282 mg·L−1, showing moderate spatial variation. Total nitrogen concentrations remained relatively stable across sampling sites, with a mean concentration of 0.733 mg·L−1. In contrast, total phosphorus concentrations exhibited substantial spatial variability, ranging from 0.001 to 0.038 mg·L−1. The proportion of agricultural and construction land in the study area exhibited significant variation among sampling sites. This spatial heterogeneity may importantly influence the physicochemical characteristics of water bodies. The pronounced gradients in key nutrients (e.g., TP) and land use provided an ideal natural experiment to test their effects on plankton communities. The complete dataset of these environmental variables is available in Supplementary File S2.

3.2. Plankton Community Structure

Phytoplankton community analysis during the October 2024 survey identified 264 species across 8 phyla, 22 orders, 39 families, and 86 genera. Bacillariophyta (138 species, 52.27%) and Chlorophyta (69 species, 26.14%) constituted the dominant taxa (Figure 2a). Subsequent taxonomic distribution revealed Cyanobacteria (30 species, 11.36%), Euglenozoa (18 species, 6.82%), Cryptophyta (3 species), Xanthophyta (2 species), Dinophyta (2 species), and Chrysophyta (1 species). Total phytoplankton abundance reached 7.24 × 106 ind L−1, with a mean abundance of 5.57 × 105 ind L−1 (Figure 2c). In addition, Bacillariophyta exhibited absolute dominance in relative abundance (Figure 2a), with a mean abundance of 1.93 × 105 ind L−1 and a maximum abundance of 9.02 × 105 ind L−1, followed by Chlorophyta. The most relatively abundant phytoplankton species (dominance degree Y ≥ 0.02) are listed in Table 2. The low dominance values across species highlight the high diversity and the absence of a single strong dominant taxon in the community.
In the zooplankton survey, a total of 79 species were identified, spanning four phyla. Among these, Protozoa accounted for the highest proportion (32.91%), followed by Rotifers (31.65%). Cladocera and Copepoda accounted for 20.25% and 15.19%, respectively. The total zooplankton abundance across all samples was 187.6 ind L−1, with a mean abundance of 14.4 ind L−1 (Figure 2c. The relative abundance was dominated by Protozoa and Copepoda (Figure 2b). The dominant species included five species such as Cyclopyxis arcelloides, Difflugia corona, Difflugia globulosa, Sinocalanus dorrii, and Nauplii.

3.3. Drivers of Communities

Community-environment relationships were analyzed using constrained ordination. Detrended correspondence analysis (DCA) of preliminary data indicated redundancy analysis (RDA) as the appropriate analytical approach (axis lengths < 3.0). Before RDA, environmental variables were screened for multicollinearity using Pearson correlation coefficients (r > 0.80) and variance inflation factors (VIF > 5), with collinear variables systematically removed to ensure analytical robustness.
Redundancy analysis revealed significant influences of total phosphorus (p = 0.023) and dissolved oxygen (p = 0.026) on phytoplankton community structure (Figure 3a). The first two RDA axes explained 74.90% of the total variance, with RDA1 accounting for 56.66% and RDA2 contributing 18.24% of the explained variation.
Zooplankton community structure showed similar environmental dependencies, with total phosphorus (p = 0.017) and dissolved oxygen (p = 0.018) emerging as significant explanatory variables (Figure 3b). The first two RDA axes explained 62.49% of the total variance (RDA1: 45.44%; RDA2: 17.05%). These findings, consistent with phytoplankton community patterns, highlight total phosphorus and dissolved oxygen as key determinants of plankton community assembly in the study area. This suggests that nutrient enrichment and oxygen availability are primary environmental filters sorting plankton species across the Jialing River landscape.

3.4. Biodiversity Indices

α-diversity analysis revealed that phytoplankton communities maintained significantly greater species diversity and structural complexity than zooplankton communities (Figure 4a–d). However, to understand how these communities change across space, we turned to β-diversity. Our analysis demonstrated that species turnover (βsim), rather than nestedness (βsne), was the overwhelming mechanism structuring both phytoplankton and zooplankton communities (Figure 4e). This key finding indicates that environmental filtering or spatial processes are causing distinct species assemblages to replace each other along the river, rather than simply adding or subtracting species from a common pool.

3.5. Multiple Factors Affecting Diversity

To untangle the complex web of direct and indirect effects, we first identified key associations using Mantel tests and random forest models (Figure 3c–f and Figure 5a–d). These analyses pointed to forest cover, urban land use, dissolved oxygen (DO), and total phosphorus (TP) as critical factors. We subsequently integrated these insights into a structural equation model (SEM) to test the hypothesized pathways through which land use influences plankton diversity.
The structural equation model (SEM) revealed a contrasting narrative for phytoplankton and zooplankton (Figure 5e,f). For phytoplankton, land use acted primarily as an indirect driver. Forest cover promoted phytoplankton α-diversity directly (path coefficient = 0.79), but also indirectly shaped β-diversity by regulating water chemistry: increasing forest cover was associated with TP levels (path = 1.16), which in turn influenced community turnover. Urban land use showed a similar indirect effect. In stark contrast, the model found no significant pathways from land use or water quality to zooplankton diversity, suggesting that other unmeasured factors (e.g., biotic interactions and finer-scale habitat variables) may be more important drivers for this group.

4. Discussion

4.1. Analysis of Plankton Community Structure

We conducted a systematic analysis of phytoplankton communities in the midstream JLR. This investigation identified 264 species across eight phyla, with Bacillariophyta and Chlorophyta emerging as the dominant taxonomic groups. Comparative analysis with historical records [29,42] revealed significant increases in phytoplankton richness at the phylum level. These observed differences may be attributed to the spatial heterogeneity of sampling sites in community composition.
The phytoplankton community was dominated by diatom and green algal species. Previous studies have identified current velocity and cellular buoyancy regulation as key determinants of phytoplankton community structure [45]. In the mainstem of the Jialing River’s middle reaches, the preserved natural flow regime and relatively high flow velocities create optimal conditions for diatom growth. Furthermore, Diatoms exhibit remarkable physiological adaptations to low-temperature environments [46]. These adaptations enable them to maintain elevated population densities even when water temperature decreases in the autumn [47]. The sampling period was in late autumn (October 2024). The lower water temperature provided a suitable environment for the growth of diatoms.
Notably, green algal taxa also show some low-temperature tolerance. Previous studies have demonstrated the ecological dominance of green algal assemblages in low-temperature aquatic environments [48]. These findings align with our observational results. The findings further elucidate the ecological mechanisms underlying the persistent dominance of green algal assemblages in the phytoplankton communities of the Jialing River’s middle reaches.
Our investigation identified 79 zooplankton species spanning four taxonomic groups in the midstream JLR. The observed zooplankton species richness was significantly lower than that of phytoplankton across all sampling stations, a pattern that is consistent with previous findings in the midstream JLR [29].
Zooplankton species richness demonstrated a significant reduction relative to phytoplankton communities across all sampling stations. These observed differences may be attributed to habitat modification and the sensitivity of the phytoplankton community to environmental gradients [49]. In terms of species composition, the zooplankton community was dominated by Protozoa and Rotifers, consistent with findings from prior studies [29].
The community structure of zooplankton in riverine ecosystems is fundamentally constrained by the habitat’s inherent nature, particularly the erosive currents, which limit the development of stable and diverse communities [50]. Given these natural physical limitations, the biological traits of specific zooplankton taxa become critical in determining community composition. Protozoa and Rotifers, with their small size, rapid reproduction, and high adaptability [16], are pre-adapted to thrive in such fluctuating environments. These traits enable them to quickly recover from displacement and capitalize on brief periods of favorable conditions. Furthermore, within the simplified food webs of these physically demanding habitats, large zooplankton (e.g., Cladocera and Copepoda) are more vulnerable to predation by omnivorous fish [51]. In contrast, the small body size of Protozoa and Rotifers confers a survival advantage against visual predators [16]. Therefore, the dominance of Protozoa and Rotifers in the midstream JLR is best explained by the combination of these limiting natural riverine conditions and the ecological traits that make these groups uniquely suited to persist under such constraints.
The plankton communities in the Middle JLR were characterized by high species diversity and low dominance, as indicated by the low dominance degrees of the dominant species (Table 2). This pattern is often associated with stable environmental conditions and high habitat heterogeneity, which prevent any single species from outcompeting others and monopolizing resources [52].
While our microscopy-based approach successfully identified the species and established clear links to environmental drivers, it is important to acknowledge its limitations in capturing the full extent of microbial diversity. Molecular techniques offer a powerful complementary approach by providing higher taxonomic resolution and detecting rare or non-cultivable taxa. However, microscopic enumeration remains indispensable for generating quantitative abundance data and deriving functional insights from morphological traits. Therefore, we posit that our study provides a critical ecological baseline of the functionally active plankton fraction. Future research integrating morphological and molecular methods will be invaluable for achieving a more holistic understanding of biodiversity and ecosystem function in this region.
However, as this study was based on a single sampling event of plankton in the middle JLR, the findings may not fully capture the dynamic characteristics of the local plankton community. Future studies should incorporate multi-seasonal and multi-annual sampling to better elucidate the spatiotemporal patterns and underlying driving mechanisms of plankton assemblage variations.

4.2. Drivers of Plankton Community Dynamics

Environmental factors are critical drivers of material cycling and energy transfer within aquatic environments [53]. These processes are crucial for maintaining plankton diversity. Plankton community structure is regulated by a variety of environmental factors. In addition, significant differences in the influence of physicochemical parameters on community assembly have also been observed across different water bodies [54].
In this study, we found that the spatial distribution pattern of phytoplankton in the study area was significantly correlated with dissolved oxygen and total phosphorus. Dissolved oxygen, an important indicator of aquatic ecosystem health, decisively influences the distribution and growth of aquatic organisms [55]. It is essential for phytoplankton photosynthesis and regulates their metabolic processes [56]. Redundancy analysis further confirmed dissolved oxygen as a major driver of phytoplankton community structure in this study (Figure 3a).
Nutrient salt concentrations, particularly total phosphorus, are key factors limiting phytoplankton growth and reproduction [8]. As an essential nutrient for phytoplankton, elevated phosphorus levels significantly promote the proliferation of taxa such as diatoms and green algae. Redundancy analysis results from this study further support the critical role of total phosphorus in shaping phytoplankton community structure. The study area, characterized predominantly by agricultural and urban land uses, has experienced long-term anthropogenic impacts. Changes in vegetation cover and land use practices have facilitated the transport of phosphorus into rivers via surface runoff and infiltration [57]. These processes profoundly influence phytoplankton community dynamics.
In this study, dissolved oxygen and total phosphorus were identified as key environmental variables regulating the spatial distribution of zooplankton communities. Dissolved oxygen, a critical parameter in aquatic ecosystems, significantly influences zooplankton population density and biomass [58]. High dissolved oxygen concentrations promote the rapid reproduction of Rotifers and Protozoa. In contrast, low-oxygen conditions inhibit the growth of Cladocera and reduce the spawning rate, survival rate, and feeding efficiency of Copepoda [59]. These findings align with the results of the RDA conducted in this study. The analysis further confirms the significant correlation between dissolved oxygen and zooplankton community dynamics.
The study revealed a significant association between total phosphorus concentration and zooplankton community variability (Figure 3b). This finding is consistent with previous studies in the JLR [29]. Zooplankton population dynamics are regulated by the physicochemical properties of the water column and are closely linked to the growth cycle of phytoplankton [47]. As the primary food source for zooplankton, phytoplankton abundance directly influences their feeding behavior [16]. At the same time, the grazing pressure of zooplankton also exerts feedback effects on the phytoplankton community [60]. consequently, phosphorus levels in the water column indirectly regulate zooplankton feeding and reproduction by directly influencing algal growth.
Notably, redundancy analysis did not fully explain the variability in the plankton community. This suggests the potential influence of unmeasured key drivers. Additionally, this study relied solely on fall sampling data, while seasonal dynamics of environmental factors are known to significantly impact plankton diversity [7]. Therefore, integrating long-term environmental monitoring data in future studies will enhance our understanding of the mechanisms underlying plankton community assembly.

4.3. Response of Plankton Community Diversity to Land Use Patterns

β-diversity, an important indicator of biodiversity, is valuable for revealing ecological patterns and developing conservation strategies [16]. To better understand these patterns and their underlying mechanisms, it is essential to resolve the relative contributions of turnover and nestedness to β-diversity [17]. This decomposition approach helps identify the ecological processes driving β-diversity patterns [7]. In the present study, we found that the β-diversity of phytoplankton and zooplankton in this area was primarily driven by the turnover component (Figure 4e). This result suggests that species turnover, rather than species loss, is the main factor contributing to the variability of the plankton community [20]. When turnover components dominate, basin-wide conservation strategies are particularly important [18].
Random forest modeling analysis identified forest cover and dissolved oxygen as key determinants of phytoplankton α-diversity (Figure 5a). In contrast, dissolved oxygen, total phosphorus, and urban land use were the main environmental variables affecting phytoplankton β-diversity (Figure 5b). Notably, urban land use significantly influenced zooplankton α-diversity (Figure 5c), whereas zooplankton β-diversity showed no significant correlation with land use or water quality parameters (Figure 5d). Interestingly, SEM revealed significant direct and indirect effects of land use on phytoplankton, whereas no significant effects were observed on zooplankton. Phytoplankton are often used as important bioindicators for monitoring water quality, owing to their high sensitivity to environmental fluctuations [61]. This sensitivity also explains why phytoplankton are more susceptible to human activities than zooplankton.
The mechanisms underlying the effects of environmental stressors on phytoplankton α- and β-diversity share similarities. Previous studies have demonstrated that hydrological characteristics and water quality indicators are the primary drivers of biodiversity, whereas the influence of land use practices is relatively minor [4]. However, in the context of increasing human activities, landscape patterns have been altered. These alterations have led to anthropogenic disturbances that may have a greater impact on biodiversity than other natural factors [62]. Overall, land use can exert both direct effects on biological communities by modifying habitat conditions and indirect effects through changes in water quality parameters [6,63].
In this study, an interesting phenomenon was observed: although the study area encompassed multiple land use types, their direct influence on community changes was relatively minor (Figure 5e). Instead, land use types such as forest cover and urban construction indirectly influenced phytoplankton α- and β-diversity primarily by altering environmental variables, including total phosphorus and dissolved oxygen. Specifically, nutrients and pollutants may enter the river system via surface runoff or subsurface infiltration, thereby significantly affecting the phytoplankton community structure [64]. This finding highlights that water quality parameters are the most direct drivers of phytoplankton diversity changes in the middle JLR. It also emphasizes the necessity of prioritizing water quality improvement in future watershed protection and management strategies.
A key characteristic of our study system, the Jialing River, is that it is impacted by a mixture of anthropogenic activities (e.g., agriculture, urban development) along its course. This is representative of the reality for many large river basins globally but presents a challenge in attributing observed ecological changes to any single land use type, as their effects are cumulative and potentially synergistic or antagonistic [6,8,65]. While our models reveal a strong overall relationship between land use patterns and plankton diversity, future studies in watersheds dominated by a single primary stressor (e.g., intensive agriculture versus pure urbanization) would be invaluable for teasing apart the specific mechanisms driven by each land use type. Nonetheless, our findings provide critical insights into the net ecological outcome of the combined land use pressures that are prevalent in China.

5. Conclusions

This study systematically explored the mechanisms through which land use practices and water quality characteristics influence plankton biodiversity in the JLR. Our results identified dissolved oxygen and total phosphorus as the key environmental drivers regulating plankton community structure. Land use exerted minimal direct effects on phytoplankton β-diversity but indirectly influenced the diversity by altering water quality parameters. In contrast, land use showed no significant direct or indirect effects on zooplankton communities. These findings confirm that within the studied riverine system, water quality characteristics, rather than land use, are the dominant regulators of phytoplankton dynamics. Therefore, we recommend prioritizing water quality monitoring and improvement in future conservation strategies for the JLR basin.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d17100707/s1. File S1: Abundance matrix of plankton species identified in the Jialing River; File S2: Spatial dynamics of environmental characteristics along the river stations.

Author Contributions

X.T.: Conceptualization, data curation, software, methodology, and writing—original draft. Y.H.: Investigation and validation. C.C.: Investigation. H.H. Investigation. Q.Q.: Data curation, software, and writing—review and editing. F.X.: Methodology and writing—review and editing. F.Z.: Writing—review and editing and funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (NSFC) (31901219), the Natural Science Foundation of Sichuan (2022NSFSC1646).

Data Availability Statement

Data are contained within the article or Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution of sampling sites and land use types in the midstream Jialing River.
Figure 1. Distribution of sampling sites and land use types in the midstream Jialing River.
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Figure 2. Spatial distribution of plankton communities across the sampling sites. (a) Chord diagram showing the relative abundance of major phytoplankton phyla at each site. The nodes at the top semicircle represent the sampling sites, and the nodes at the bottom semicircle represent the phytoplankton phyla. The width of each connecting band (link) is proportional to the relative abundance of a given phylum at a specific site; (b) Chord diagram showing the relative abundance of major zooplankton groups at each site. The layout is identical to (a), with sampling sites at the top and zooplankton groups (Rotifers, Cladocera, Copepoda, and Protozoa) at the bottom. The band widths illustrate the contribution of each group to the community at different sites; (c) Spatial variation in phytoplankton and zooplankton abundance across sampling sites.
Figure 2. Spatial distribution of plankton communities across the sampling sites. (a) Chord diagram showing the relative abundance of major phytoplankton phyla at each site. The nodes at the top semicircle represent the sampling sites, and the nodes at the bottom semicircle represent the phytoplankton phyla. The width of each connecting band (link) is proportional to the relative abundance of a given phylum at a specific site; (b) Chord diagram showing the relative abundance of major zooplankton groups at each site. The layout is identical to (a), with sampling sites at the top and zooplankton groups (Rotifers, Cladocera, Copepoda, and Protozoa) at the bottom. The band widths illustrate the contribution of each group to the community at different sites; (c) Spatial variation in phytoplankton and zooplankton abundance across sampling sites.
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Figure 3. Multivariate analysis of plankton community-environment relationships. (a) Redundancy analysis (RDA) of phytoplankton community structure; (b) RDA of zooplankton community composition; (c) Mantel test results for phytoplankton α-diversity; (d) Mantel test results for phytoplankton β-diversity; (e) Mantel test results for zooplankton α-diversity; (f) Mantel test results for zooplankton β-diversity.
Figure 3. Multivariate analysis of plankton community-environment relationships. (a) Redundancy analysis (RDA) of phytoplankton community structure; (b) RDA of zooplankton community composition; (c) Mantel test results for phytoplankton α-diversity; (d) Mantel test results for phytoplankton β-diversity; (e) Mantel test results for zooplankton α-diversity; (f) Mantel test results for zooplankton β-diversity.
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Figure 4. Spatial dynamics of biodiversity in the plankton community of the Jialing River. (a) Species richness; (b) Margalef richness index; (c) Pielou evenness index; (d) Shannon diversity index; (e) β-diversity index.
Figure 4. Spatial dynamics of biodiversity in the plankton community of the Jialing River. (a) Species richness; (b) Margalef richness index; (c) Pielou evenness index; (d) Shannon diversity index; (e) β-diversity index.
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Figure 5. Multivariate modeling of biodiversity-environment relationships in the Jialing River’s middle reaches. (a) Random forest model for phytoplankton α-diversity; (b) random forest model for phytoplankton β-diversity; (c) random forest model for zooplankton α-diversity; (d) random forest model for zooplankton β-diversity; (e) structural equation model (SEM) analyzing phytoplankton diversity responses to land use and water quality indicators; (f) structural equation model (SEM) analyzing zooplankton diversity responses to land use and water quality indicators. * p < 0.05.
Figure 5. Multivariate modeling of biodiversity-environment relationships in the Jialing River’s middle reaches. (a) Random forest model for phytoplankton α-diversity; (b) random forest model for phytoplankton β-diversity; (c) random forest model for zooplankton α-diversity; (d) random forest model for zooplankton β-diversity; (e) structural equation model (SEM) analyzing phytoplankton diversity responses to land use and water quality indicators; (f) structural equation model (SEM) analyzing zooplankton diversity responses to land use and water quality indicators. * p < 0.05.
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Table 1. Environmental variables in the midstream JLR.
Table 1. Environmental variables in the midstream JLR.
UnitMinMaxMediumMeanSD
Widm345.2794.1478.8513.83148.66
TurNTU4.15.44.84.850.36
WT°C20.523.722.021.980.79
DOmg·L−16.529.097.387.600.77
Condμs·cm−1293329299302.549.52
pH—8.08.68.48.330.19
NH3-Nmg·L−10.0660.2820.1380.1640.076
TNmg·L−10.7310.7430.7330.7350.004
TPmg·L−10.0010.0380.0120.0150.009
Cropland%31.0180.4566.7861.6014.43
Forest%04.980.671.461.58
Grassland%00.0800.010.02
Water%17.4044.8228.7228.678.68
Urban%045.632.238.2612.85
Table 2. Dominant species of the plankton community.
Table 2. Dominant species of the plankton community.
PhylaDominant SpeciesDominance Index
Phytoplankton
BacillariophytaCyclotella bodanica0.02
Cyclotella sp.0.02
ChlorophytaChlorella vulgaris0.04
Platymonas incisa0.24
Zooplankton
ProtozoaCyclopyxis arcelloides0.06
Difflugia corona0.05
Difflugia globulosa0.06
CopepodaSinocalanus dorrii0.03
Nauplii0.22
Note: Nauplii are considered a separate species in this study.
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Tang, X.; Huang, Y.; Chen, C.; He, H.; Qin, Q.; Xu, F.; Zhang, F. Phyto- and Zooplankton Diversity Under Land Use and Water Quality Dynamics in the Jialing River, China. Diversity 2025, 17, 707. https://doi.org/10.3390/d17100707

AMA Style

Tang X, Huang Y, Chen C, He H, Qin Q, Xu F, Zhang F. Phyto- and Zooplankton Diversity Under Land Use and Water Quality Dynamics in the Jialing River, China. Diversity. 2025; 17(10):707. https://doi.org/10.3390/d17100707

Chicago/Turabian Style

Tang, Xiaopeng, Yiling Huang, Chang Chen, Haoyun He, Qiang Qin, Fei Xu, and Fubin Zhang. 2025. "Phyto- and Zooplankton Diversity Under Land Use and Water Quality Dynamics in the Jialing River, China" Diversity 17, no. 10: 707. https://doi.org/10.3390/d17100707

APA Style

Tang, X., Huang, Y., Chen, C., He, H., Qin, Q., Xu, F., & Zhang, F. (2025). Phyto- and Zooplankton Diversity Under Land Use and Water Quality Dynamics in the Jialing River, China. Diversity, 17(10), 707. https://doi.org/10.3390/d17100707

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