From River to Reservoir: The Impact of Environmental Variables on Zooplankton Assemblages in Karst Ecosystems

Abstract

Zooplankton are ubiquitous in aquatic ecosystems and play crucial roles in material cycling and energy flow. However, the mechanisms governing zooplankton community assembly, particularly habitat-specific differences, remain poorly understood. In this two-year study, we monitored zooplankton communities across reservoir and river habitats within the Chayuan watershed, a representative karst region in southwest China. Our findings revealed significant spatial divergence in water-quality variables (including water temperature, pH, total nitrogen, total phosphorus, permanganate index, dissolved oxygen, chlorophyll-a, and ammonia nitrogen) between habitats. Twenty-nine dominant zooplankton species were identified in reservoir and river communities, with only eight shared between the two habitats. The mechanisms underlying the corresponding zooplankton community structures showed distinct segregation between habitats, with deterministic processes predominating in reservoir communities (explaining 25.1% of the variation) and stochastic processes predominating in river communities (3.4% of the variation explained). Environmental drivers differed substantially between habitats: reservoir communities were primarily influenced by total nitrogen, dissolved oxygen, and chlorophyll-a concentrations, whereas river communities responded predominantly to ammonia nitrogen levels. This study provides novel insights into the divergent mechanisms governing zooplankton community assembly in lentic versus lotic systems within a shared karst watershed, offering theoretical foundations for ecosystem-specific management strategies in fragile karst environments. Future research should focus on key climatic variables (e.g., extreme precipitation) and hydrological dynamics (such as flow velocity and water residence time) to further elucidate the mechanisms behind zooplankton community assembly, providing deeper insights to facilitate effective ecosystem management in karst environments.

1. Introduction

Zooplankton are abundant and widely distributed in aquatic ecosystems, playing a vital role in their ecological processes [1,2]. In addition, they facilitate material cycling and energy transfer within these ecosystems through processes such as grazing on phytoplankton. This trophic interaction promotes energy flow along the food chain, while their excretions and decomposed biomass contribute to nutrient recycling [3]. Research indicates that zooplankton not only directly participate in the decomposition and transformation of organic matter in water bodies through enzymatic digestion of detritus and acceleration of microbial mineralization via gut processes, but also serve as a vital link in the food web, connecting primary producers with higher-trophic-level consumers [2,4]. Furthermore, the structure and dynamics of zooplankton communities are highly sensitive to changes in water environments, making them important biological indicators for water-quality monitoring and ecological health assessment. Their dynamic fluctuations often reflect the overall health status of aquatic ecosystems [5,6]. In recent years, as the impacts of human activities and climate change on aquatic ecosystems has intensified, the spatiotemporal distribution patterns of zooplankton communities and their interactions with environmental factors have attracted increasing attention [7,8]. Therefore, studying the structural changes and distribution patterns of zooplankton communities is essential to gaining a deeper understanding of their ecological functions.

An understanding of the dynamic relationships between species and their environment is central to ecological research and plays a crucial role in maintaining ecological balance and predicting ecosystem evolution [9,10]. As a key approach to understanding interspecific relationships, the study of processes of community assembly utilizes methods such as statistical analysis, model inference, and network analysis to uncover community responses to environmental disturbances, providing valuable insights into the processes underpinning ecosystem maintenance [11,12]. Microbial community assembly is shaped by deterministic processes (e.g., environmental filtering, species interactions) and stochastic processes (e.g., ecological drift, dispersal limitation), with community structure regulated by species abundance, richness, and evenness [13]. Some studies have shown that deterministic and stochastic processes jointly affect the process of microbial community assembly, but the relative importance of these two processes to the process of community assembly varies across ecosystems [14,15]. These processes may interact in complex ways: when environmental filtering aligns with limited dispersal, they can act in synchrony, reinforcing each other’s influence and amplifying community structuring; in contrast, when decoupled, they may lead to more variable and unpredictable community patterns [16]. With the deepening of research on community assembly processes, this perspective has been widely used in phytoplankton research, with the study areas mostly found in lakes, reservoirs, and rivers [17,18,19,20]. Nevertheless, there has been little research on the process of zooplankton community assembly and maintenance of zooplankton communities in plateau reservoir ecosystems, particularly in karst aquatic environments.

Karst watersheds are unique geological environments characterized by distinctive geomorphological features and complex hydrological conditions, making them critical areas for ecological research [21]. The dissolution of limestone leads to the formation of intricate groundwater systems, caves, and fracture-driven watercourses, which significantly influence the hydrological cycle, ecosystem structure, and functional dynamics within the catchment [22]. Water bodies in karst areas typically exhibit lower temperatures and higher alkalinity and facilitate enhanced solubility of minerals due to extensive groundwater input, dissolution of carbonate bedrock, and reduced surface runoff [23]. These physicochemical conditions shape aquatic biota by influencing species distributions, physiological adaptations, and nutrient dynamics. Elevated alkalinity promotes the dominance of calcifying organisms, whereas reduced water temperatures can constrain the metabolic performance and reproductive success of temperature-sensitive taxa. Compared to non-karst systems, these environmental features contribute to distinct community compositions and modified biogeochemical cycling [24]. In recent decades, karst catchments have become critical zones for integrated water-resource governance and biodiversity preservation, attracting substantial scientific attention due to their ecological vulnerability and unique hydrogeological characteristics [25,26]. Current research on zooplankton in karst regions primarily focuses on the community structure of zooplankton and the impact of unique environmental factors (high concentrations of calcium and magnesium ions and of carbonate) on zooplankton communities [27]. However, due to the complex hydrological and geological characteristics of these regions, the dynamics of zooplankton communities in karst waters and their interactions with environmental factors remain inadequately explored. In karst systems where carbonate weathering dominates biogeochemical cycles, zooplankton-mediated processes may produce unique feedbacks to hydrological pulses and ionic-composition gradients [28,29]. Therefore, investigating the community structures, functional roles, and environmental interactions of zooplankton in the reservoirs and rivers of karst catchments is essential for gaining a deeper understanding of the dynamic processes governing karst aquatic ecosystems and for informing sustainable water-resource management strategies.

This study focused on a Chayuan watershed in a typical karst region. In a departure from previous single-habitat investigations, this approach highlights the differences among habitats within the same watershed. Through long-term sampling of zooplankton and concurrent monitoring of environmental characteristics in two distinct aquatic habitats (rivers and reservoir), this research explored the assembly patterns of zooplankton communities under different hydrological conditions in karst landscapes. The objectives of this study were as follows: (i) investigate the differences in zooplankton community characteristics between river and reservoir habitats within the Chayuan watershed; (ii) explore the distinct processes underlying zooplankton community assembly in these two habitats; and (iii) analyze the influence of environmental variables on zooplankton communities in both river and reservoir environments. Thus, it can provide a basis and reference for zooplankton-related ecological research in karst aquatic environments and serve as a theoretical reference to support further understanding of the ecological processes underlying ecological functions in the karst ecosystem and the mechanisms by which they are maintained.

2. Materials and Methods

2.1. Study Area

The study was conducted in the Chayuan watershed, located in Duyun City, southern China, a region representative of subtropical karst terrain. This area is characterized by limestone-dominated geology, well-developed surface depression–doline systems, and a dual hydrological structure featuring seasonal sinking streams connected to subterranean conduits. The watershed includes the main Chayuan reservoir and its two epigenetic tributaries, the Yellow River and the Jianjiang River (Figure 1). As a typical subtropical karst basin, it exhibits the defining features outlined by the International Research Center on Karst, including shallow residual soils, moderate rocky desertification, and pronounced hydrological connectivity between surface and underground systems. The reservoir has a total storage capacity of 19.6 million m³. It serves as the sole source of drinking water for 300,000 urban residents. The watershed is characterized by a humid climate, with a mean annual precipitation of 1350 mm and a runoff depth of 740 mm (54.8% of precipitation), which yielded an average annual discharge of 105 million m³. The minimum recorded flow was 0.36 m³/s, highlighting seasonal water scarcity. Under normal water-storage conditions, the reservoir extends approximately 4.5 km upstream, with an average depth of 10 m and a maximum depth of 21 m. The flow velocities of the Yellow River (0.13 m/s) and Jianjiang River (0.11 m/s) represent annual averages derived from continuous hydrological monitoring over a complete hydrological year, reflecting the cumulative hydrodynamic conditions that influence sediment transport and deposition processes. These rivers and the reservoir are also subject to anthropogenic impacts, including nutrient and organic pollution from agricultural runoff and domestic wastewater discharge. The surrounding watershed is characterized by mixed land use, including urban settlements, farmland, and tea plantations, all of which contribute to varying degrees of ecological pressure on the aquatic environment.

2.2. Sampling

During July 2022 and June 2024, monthly sampling was conducted at 12 fixed sites: sites S1–S7 (river region, spanning the upstream reaches of the Yellow River and Jianjiang river) and S8–S12 (reservoir region, distributed along the primary axis of the Chayuan reservoir, Table S1). To evaluate the impact of water-quality variables on the zooplankton community, the study focused on water temperature (WT), pH, dissolved oxygen (DO), electrical conductivity (EC), chlorophyll-a (Chl a), permanganate index (COD_Mn), ammonia nitrogen (NH₃–N), total phosphorus (TP), and total nitrogen (TN). For each sampling site, WT, pH, DO, and EC levels were measured in situ using a portable multi-parameter water-quality analyzer (Model HI 98194; Harderwood, Italy). Water samples were obtained 0.5 m below the surface using a 5 L plexiglass water sampler and stored in a sterile plastic bottle for laboratory analysis of the remaining variables.

At each sampling site, zooplankton samples were collected by filtering 30 L of water through a 64 μm plankton net. In river regions, 30 L of water was collected individually at each site from 0.5 m below the surface as a single integrated sample representing that location. For reservoirs with an average depth of 10 m, a stratified sampling protocol was applied, as follows. Using a 5 L plexiglass water sampler, 10 L of water was collected from each of three vertical layers: surface (0~2 m below the water surface), middle (mid-depth, 2~8 m), and bottom (8~10 m). The 30 L total volume was pooled and filtered as a single composite sample. This approach ensured consistent total sampling volumes across river and reservoir sites while accounting for vertical heterogeneity in zooplankton distribution within stratified water bodies. Samples were fixed with 4% formaldehyde and concentrated to 20 to 40 mL, with the final volume chosen based on the suspended-matter content to facilitate the identification of zooplankton species and analysis of their abundance. The species composition and abundance of zooplankton were determined and quantified under a light microscope using a 1 mL sample placed in a Sedgwick–Rafter counting chamber. Each sample was counted three times to reduce observational error, and the mean value was used for abundance estimation. Zooplankton abundance was calculated using the following formula:

N = V_S.V_t/V.V_a

(1)

where N was the abundance (ind./L); V_S was the average number of individuals counted in 1 mL; V_t was the total volume of the concentrated sample (mL); V was the volume of the counted subsample (1 mL); and Va was the volume of the original water sample (L).

2.3. Statistical Analysis

ArcGIS 10.8 was utilized to construct the sampling maps, which were constructed with the aim of presenting the sampling locations and associated details in a visual manner.

The zooplankton community structure within the Chayuan watershed was analyzed by employing measures of the α diversity index (Margalef’s Richness index, Pielou’s evenness index, Shannon–Wiener diversity index, and Simpson’s diversity index).

Margalef’s Richness index (D) = S − 1/lnN

(2)

Pielou’s evenness index (J) = H′/lnS

(3)

Shannon–Wiener diversity index (H′) = −∑P_ilnP_i

(4)

Simpson’s diversity index (D) = 1 − ∑P_i²

(5)

The variables in the diversity indices are defined as follows: P_i represents the relative abundance of the i-th species, calculated as its individual count divided by the total abundance of all species; S denotes the species richness (total number of distinct taxa), and N corresponds to the summed abundance of all organisms within the sampled community.

Subsequently, principal coordinate analysis (PCoA) was carried out based on the Bray–Curtis distance, enabling the visualization of zooplankton community differences. The dominance index of species was calculated to determine the dominant zooplankton species in the Chayuan watershed throughout the year. The formula was as follows:

Dominance Index (Y) = (n_i.f_i)/N

(6)

where n_i represents the number of individuals of the i-th species, N is the total number of individuals of all species, and f_i denotes the frequency of occurrence of the i-th species. Species with a dominance index greater than 0.02 were identified as dominant species.

To assess the relative roles of deterministic and stochastic processes in shaping zooplankton community assembly within the Chayuan watershed, we applied the Neutral Community Model (NCM), null model analysis, and the Normalized Stochasticity Ratio (NST) across spatial gradients from upstream riverine areas to reservoir regions. All three approaches were based on species-abundance data derived from zooplankton community surveys. The NCM was fitted using the Sloan neutral model framework; the null model was constructed using a fixed–fixed algorithm to calculate beta deviation; and NST was calculated based on Bray–Curtis dissimilarity matrices to quantify the proportion of processes driving community assembly that could be characterized as stochastic. Additionally, the environmental distance was computed for distance-attenuation analysis. To ensure the absence of significant multicollinearities among environmental variables in the Chayuan watershed, we conducted a variance inflation factor (VIF) analysis on the selected environmental variables. The variance partitioning analysis (VPA) was utilized to elucidate the explanatory power of environmental and geographical variables regarding the variation in the zooplankton community structure. The relative importance of changes in environmental parameters to the composition of zooplankton communities was analyzed using the Aggregated Boosted Trees (ABT) model. All of the above analyses were conducted in R 4.3.0.

With the exception of the sampling maps, all of the graphs mentioned above were created using R 4.3.0 and Origin 2021. The Mann–Whitney Wilcoxon rank-sum test in SPSS 27.0 was employed to conduct variance analysis within the watersheds.

3. Results

3.1. Characteristics of Aquatic Environmental Variables

To investigate the changes in aquatic environmental variables within the Chayuan watershed, we sampled the reservoir and river regions (Figure 2 and Figure S1). The research found that the WT ranges of the reservoir and the river areas were 9.1~30.2 °C and 6.9~26.3 °C, respectively. The DO ranges were 4.1~14.1 mg/L and 5.1~13.4 mg/L, respectively. The pH ranges were 6.73~9.12 and 6.44~10.01, respectively. The Chl a value ranges were 0.658~13.237 mg/L and 0.01~6.88 mg/L, respectively. The TN value ranges were 0.18~2.75 mg/L and 0.038~3.369 mg/L, respectively. The EC value ranges were 49~318 μS/cm and 34~459 μS/cm, respectively. The NH₃–N value ranges were 0.002~0.468 mg/L and 0.002~0.739 mg/L, respectively. The COD_Mn value ranges were 0.67~3.32 mg/L and 0.49~6.52 mg/L, respectively. The TP value ranges were 0.003~0.055 mg/L and 0.001~0.027 mg/L, respectively. Compared to those for the reservoir, the values of the environmental variables for the river had wider distributions, indicating that the variability of environmental variables in river areas was greater. The median values of environmental variables in the reservoir area exceeded those in the rivers, signifying that the average levels of these factors in the reservoir were comparatively elevated. Therefore, the environmental variables of the reservoir showed relative stability, while those of the rivers showed greater dynamism and complexity. Further comparative analysis of the environmental variables in the reservoir and the rivers was conducted using the Mann–Whitney U test. The results revealed significant differences in the concentrations of pH, DO, TN, COD_Mn, and NH₃–N between the reservoir and the rivers (n = 288, p < 0.05). Notably, extremely significant differences were observed in the concentrations of WT, Chl a, and TP (n = 288, p < 0.01). The environmental variables in the Chayuan watershed exhibited pronounced seasonal fluctuations, particularly for WT, DO, Chl a, NH₃–N, COD_Mn, and TP, which displayed typical characteristics of higher values in spring and summer and lower values in autumn and winter. In contrast, the annual variations in environmental variables in the reservoir were relatively moderate, demonstrating greater stability compared to the rivers. This difference is likely associated with the longer water-retention time in the reservoir.

3.2. Community Structure and Diversity of the Zooplankton

The community structures of zooplankton in the reservoir and river of the Chayuan watershed were analyze (Figure 3). The results indicated that the relative abundances of all species fluctuated more significantly in the reservoir compared to the rivers. In reservoir community structures, rotifers and Copepoda were predominant. The relative abundance of rotifers was relatively high in spring and summer, while it was relatively low in autumn and winter. In contrast, the zooplankton communities in the river were mainly composed of rotifers throughout the year. Regarding the total number of species, the trends in seasonal variation in the total species number in the reservoir and the river were similar, with lower values in autumn and winter and higher values in spring and summer. The study on the absolute abundance of zooplankton in the Chayuan watershed revealed that the abundance of rotifers in the reservoir was significantly higher than that in the rivers and exhibited pronounced seasonal fluctuations, with peaks observed from August 2022 to January 2023 and in March 2023. In contrast, rotifer abundance in the rivers displayed smaller fluctuations and lower peak values. The abundance of copepods remained relatively stable in both types of water body, though they were slightly more abundant in the reservoir. Cladocerans showed extremely low abundance in both systems, with almost no significant variations. These results indicate that the water-body type (reservoir vs. rivers) significantly influenced the zooplankton community structure: rotifers dominated in the reservoir, while cladoceran population sizes were constrained in both water bodies (Figure S2).

In this study, we observed that Synchaeta oblonga, a rotifer, exhibited extremely high dominance (0.891) in the reservoir environment, whereas Trichocerca cylindrica and Keratella valga showed relatively high dominance in both the reservoir and the rivers, with values of 0.247 and 0.182, respectively. The copepod Tropocyclops longiabdominalis demonstrated high dominance in the reservoir (0.128), while the cladoceran Ceriodaphnia pulchella also displayed significant dominance in the reservoir (0.074) and nauplii exhibited dominance in the reservoir (0.235) and the rivers (0.087). These results revealed differences in the distribution of various zooplankton groups between reservoir and river ecosystems, with some species showing clear ecological advantages in specific environments; Synchaeta oblonga, for example, dominated in the reservoir, likely due to its adaptation to lentic, nutrient-rich conditions (

Table 1. Dominant zooplankton species and their dominance in the Chayuan watershed.

	Species	Reservoir	River
Rotifer	Keratella cochlearis	0.054	0.154
	Keratella valga	0.182	0.138
	Trichocerca cylindrica	0.247	0.029
	Trichocerca longiseta	0.156	0.062
	Trichocerca pusilla	0.034	0.025
	Trichocerca similis	0.064
	Polyarthra remata	0.034
	Polyarthra dolichoptera	0.208
	Asplanchna priodonta	0.101
	Synchaeta oblonga	0.891
	Conochilloides dossuarius	0.102
	Conochilus unicornis	0.073
	Ascomorpha ecaudis	0.087
	Lepadella patella		0.066
	Colurella adriatica		0.079
	Philodina Ehrenberg		0.080
	Notholon labis		0.023
	Cephalodella gibba		0.031
	Rotaria rotatoria		0.068
	Euchlanis dilatata		0.031
Copepoda	Phyllodiaptomus tunguidus	0.076	0.029
	Mesocyclops leuckarti	0.048	0.023
	nauplii	0.235	0.087
	Tropocyclops longiabdominalis	0.128
	Thermocyclops hyalinus	0.024
	Thermocyclops brevifurcatus	0.040
	Tropocyclops prasinus	0.033
Cladocera	Bosmina coregoni	0.052
	Ceriodaphnia pulchella	0.074

).

The α-diversity of zooplankton in the Chayuan watershed was evaluated using four parameters, namely Margalef’s Richness index, Pielou’s evenness index, Shannon–Wiener diversity index, and Simpson’s diversity index (Figure 4). In terms of the Margalef richness index, the values of the reservoir ranged from 0.5~3.9 and those of the river areas ranged from 0.1~5.9. Regarding the Shannon–Wiener diversity index, the values of the reservoir ranged from 0.2~2.6 and those of the river areas ranged from 0.1~2.7. For the Pielou’s evenness index, the values of the reservoir ranged from 0.1~0.8 and those of the river areas ranged from 0.5~0.9. In the case of the Simpson’s diversity index, the values of the reservoir ranged from 0.1~0.7 and those of the river areas ranged from 0.05~0.85. The results showed that although the Shannon–Wiener diversity index showed no significant difference between the reservoir area and the rivers, the other three indices were all significantly higher in the river area than in the reservoir area (n = 288, p < 0.05). PCoA analysis was performed on zooplankton using the Bray–Curtis distance (Figure 5). The results showed that the first and second axes of the principal coordinates explained 15.01% and 9.53% of the variation, respectively. Meanwhile, the zooplankton communities in the Chayuan watershed showed significant differences between the reservoir area and the river areas (R = 0.115; p ≤ 0.001).

3.3. Changes in Community Assembly Process

The NCM was used to analyze the reservoir area and the rivers in the Chayuan watershed (Figure 6a,b). The results indicated that the rates of random explanation of community structure variation in the reservoir and the rivers were 48.8% and 58.2%, respectively. Consequently, the variation in the community structure of zooplankton in the rivers was more significantly affected by random factors compared to that in the reservoir. N_m represented the species migration rate in the environment. The findings showed that the N_m values of zooplankton in the reservoir and rivers were two and five, respectively, suggesting that the migration of zooplankton in the reservoir was more restricted.

Further analysis of the community structure using the null model revealed that in the reservoir, all the observed C-score values (C-score_obs) were higher than the simulated C-score values (C-score_sim), whereas in the rivers, these two values were nearly the same (Figure 6c). This indicated that the community structure of zooplankton in the reservoir was more strongly influenced by non-random factors. Standardized effect sizes less than −2 and greater than 2 represented the aggregation and dispersion of species, respectively. In this study, the distribution patterns of zooplankton differed between the reservoir and river areas, with the abundance of zooplankton being significantly higher in the reservoir than in the rivers.

The NST could be used to evaluate the significance of deterministic and random processes in the formation of community structure. In this study, NST values greater than 0.5 in the reservoir were less than 50% of the total community, while the opposite was true for the rivers (Figure 6d). These results suggest that the zooplankton community in the reservoir area of the Chayuan watershed was more subject to deterministic processes, while in the rivers, it was more influenced by stochastic processes.

The VIF of environmental variables in the reservoir area ranged from 1.170 to 4.272, while those in the riverine area ranged from 1.042 to 1.798. All VIF values were below 5, indicating no significant multicollinearity among the environmental variables (Table S2). Distance decay curves were used to analyze the relationship between the similarity of zooplankton community structure and environmental distance in the reservoir and rivers, respectively (Figure 7a,b). The results showed that there was a significant negative correlation between zooplankton similarity and environmental distance in the rivers (n = 288, p < 0.001), but no such relationship was found in the reservoir (n = 288, p = 0.075). In addition, the variance decomposition analysis indicated that in the reservoir, environmental variables and geographical variables jointly accounted for 31.7% of the differences in community structure. Specifically, the portion jointly accounted for by the two variables was 2.4%, with environmental variables alone accounting for 25.1% and geographical variables alone accounting for 4.2%. In the rivers, environmental variables and geographical variables jointly accounted for 5.5% of the differences in community structure. Specifically, the two variables jointly explained 0.2%, while environmental variables alone accounted for 3.4% and geographical variables alone accounted for 1.9% (Figure 7c,d).

3.4. Responses of Zooplankton Community Assembly to Environmental Variables

The results revealed that in the reservoir, TN, DO, and Chl a were the principal variables associated with changes in the zooplankton community (n = 288, p < 0.05). In contrast, in the rivers, ammonia nitrogen was found to be the main factor relevant to changes in the zooplankton community (n = 288, p < 0.05). The significance of the environmental variables influencing the reservoir area and the river area of the Chayuan watershed was evident (Figure 8).

4. Discussion

4.1. Composition and Diversity of Zooplankton Communities

There were significant regional differences in the community structures of zooplankton in the Chayuan watershed. Previous studies have revealed that in water-source-protection areas, the zooplankton community structure is predominantly characterized by a higher abundance of rotifer species, which are small zooplankton, while copepods and cladocerans, categorized as large zooplankton, were relatively scarce [30]. A similar phenomenon was also found in this study. The relative abundance of rotifers throughout the year was greater than 80% in the river area and reached 51% in the reservoir. However, the zooplankton community structure in the reservoir area had typical seasonal variation characteristics, while this seasonal variation was relatively weak in the rivers. Some research found in their study that compared with rotifers, copepods and cladocerans could adapt to lower water temperatures [31,32]. Therefore, the relative abundance of rotifers would decrease when the water temperature dropped. However, compared with the reservoir, the river habitat had the characteristics of relatively stable water flow, relatively constant water temperature, relatively stable food resources and stable biological interactions [33,34]. These characteristics were the main reasons for the relatively weak seasonal variation in the zooplankton community structure in the river habitat. It has been found that although lakes exhibit faster species formation and net diversification rates, the majority of freshwater biodiversity is generated in the rivers. This highlights the importance of rivers in terms of species differentiation and indicates that species diversity in rivers tends to be relatively high [35,36]. In this study, it was found that the Margalef’s Richness index, Pielou’s evenness index and Simpson’s diversity index of zooplankton in the rivers were significantly higher than the corresponding values in the reservoir area, reflecting greater species diversity.

4.2. Dynamics of Zooplankton Community Assembly Processes

We found that in the Chayuan watershed, the zooplankton community assembly process was more dominated by stochasticity than by determinacy in the river area, whereas in the reservoir area, the deterministic process was more dominant than the stochastic one. Previous studies demonstrated that during the construction of biological communities, both deterministic and stochastic processes occurred simultaneously [37,38]. Quantifying the importance of stochastic and deterministic processes in biological community assembly had long been a challenge in ecological research [39,40]. The neutral community model (NCM) served as a process model based on neutral theory. By providing a reference model completely dominated by stochastic processes, it helped clarify the stochastic components of the process of community assembly [41,42]. The null model, a theoretical framework assuming the absence of specific ecological effects, removed the influence of ecological processes, thereby distinguishing between the roles of random factors and deterministic processes in shaping community structures and ecological functions [43,44]. Additionally, the normalized stochasticity ratio (NST) quantified stochasticity and determinism in species turnover among communities, distinguishing between stochastic and deterministic processes shaping community assembly [45].

Methodologically, this conclusion is supported by the results of the NCM, null model, and NST, all of which consistently showed a dominance of stochastic processes in rivers and of deterministic processes in reservoirs. In particular, the significant distance-decay relationship observed in the reservoir but not in the rivers offers further evidence for stronger spatial structuring driven by deterministic mechanisms in the reservoir. Stratification, hydrological retention, and consistent food resources likely act as strong filters in the reservoir, while in the rivers, frequent hydrological disturbances and short residence times reduce the strength of environmental filtering and increase the influence of stochastic dispersal. Earlier research has demonstrated that in reservoir management, especially in karst reservoirs, the upstream river significantly impacts water quality and microbial activity in the downstream reservoir through nutrient transport and hydrological regulation [46,47]. This study found significant differences in the levels of most environmental factor between the rivers and the reservoir, which likely drove the distinct zooplankton community assembly processes in these two systems. Additionally, variance partitioning analysis revealed that environmental variables explained a greater proportion of community variation in the reservoir than in the rivers, reinforcing the conclusion that deterministic forces were more pronounced in the lentic habitat.

Previous studies demonstrated that in subtropical reservoir, the community assembly process of testate amoebae was predominantly driven by stochastic processes [15]. This contrasted with the findings of this study on zooplankton community assembly. Potential reasons for this discrepancy were considered, as follows. Firstly, testate amoebae, as protozoa, exhibited significantly more ecological drift compared to zooplankton. As a result, their community assembly process was more likely to have been influenced by stochastic processes. Secondly, environmental heterogeneity and resource differences also played a role. Previous studies have demonstrated that pronounced environmental gradients and spatially heterogeneous niche partitioning can drive interspecific competition or adaptive selection, which may override stochastic processes (e.g., ecological drift) and ultimately govern community assembly patterns [48,49]. Furthermore, this study focused on a typical karst reservoir. The unique environmental characteristics of karst reservoirs, such as heterogeneity in water quality, hydrology, and groundwater flow, likely caused the zooplankton community-assembly process to differ from those in other types of water bodies. Consequently, the community assembly of zooplankton was more dominated by deterministic processes. These findings suggest that strategies different from those used in the management of conventional reservoirs—such as adaptive water-level regulation to mitigate rapid groundwater exchange through karst conduits—should be developed for karst reservoirs to ensure the stability and functional sustainability of their ecosystems.

4.3. Environmental Drivers of Zooplankton Community Dynamics

There was significant spatial heterogeneity in the environmental factor variables between the reservoir area and the river area in the Chayuan watershed. This heterogeneity may have contributed to the differences in the environmental variables that affected the zooplankton communities. Environmental variables could influence the structure, function, and diversity of zooplankton communities through both direct and indirect pathways [50,51]. In particular, in the karst reservoir, due to the unique hydrological characteristics of karst regions (such as slow water exchange and uneven nutrient distribution), the response of zooplankton communities to environmental variables was more pronounced. In this study, TN, DO, and Chl a were identified as key variables significantly shaping zooplankton communities in the reservoir habitat, whereas NH₃–N emerged as the dominant factor structuring communities in the river area. Nitrogen, as a vital nutrient, plays a pivotal role in controlling phytoplankton biomass and species composition, which in turn affects the availability and quality of food for zooplankton. In the reservoir, elevated TN levels may promote algal blooms, indirectly influencing zooplankton by altering trophic conditions. DO is a fundamental determinant of zooplankton survival and distribution, affecting their respiration and vertical migration behaviors. Chl a, as a proxy for phytoplankton biomass, directly reflects food supply and thus exerts a strong bottom-up effect on zooplankton dynamics. In contrast, NH₃–N, often associated with anthropogenic inputs, may influence riverine zooplankton communities both directly, through toxicity at high concentrations, and indirectly, by altering microbial and algal assemblages. These interpretations are supported by our VPA results, which show that environmental variables had greater explanatory power in the reservoir. However, 69.3% of the community variation in the river area and 94.5% of the community variation in the reservoir area remain unexplained, indicating the potential influence of unmeasured variables such as hydrodynamic disturbance, microhabitat heterogeneity, or biotic interactions.

5. Conclusions

In this study, we highlight, for the first time, the differences in the processes of zooplankton community assembly across different habitats within the same watershed. Our research provides a novel perspective on the long-term dynamics of zooplankton communities and the underlying processes in karst reservoirs and rivers. We found significant differences in water-quality variables and zooplankton community structures between the reservoir and river areas within the same river area, with distinct community assembly processes observed in each habitat. Specifically, the community assembly process in the reservoir area was predominantly deterministic, while in the rivers, it was primarily stochastic. Furthermore, we observed that the environmental variables driving community assembly differed between the reservoir and river areas. TN, DO, and Chl a were the primary variables in the reservoir, while NH₃–N was the main factor in the rivers. However, the power of environmental factors to explain zooplankton community variation in both the reservoir and the river areas was relatively low. This suggests the influence of unmeasured variables and stochastic processes. Potential missing factors include climatic drivers such as temperature and precipitation variability, as well as hydrological dynamics like water-level fluctuations, flow velocity, and hydraulic retention time. Incorporating these additional variables in future studies may enhance their explanatory power and contribute to a more comprehensive understanding of the mechanisms driving zooplankton community dynamics in karst aquatic systems.