Next Article in Journal
A New Holoplanktonic Nudibranch (Nudibranchia: Phylliroidae) from the Central Mexican Pacific
Previous Article in Journal
The Influence of Infrastructure on the Breeding Distribution of a Threatened Top Predator
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Diversity
Volume 17
Issue 7
10.3390/d17070478
diversity-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
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Impacts of Continuous Damming on Zooplankton Functional Diversity in Karst Rivers of Southwest China: Different Hydrological Periods and Implications for Karst Reservoir Management

by
Xiaochuan Song
1,2,
Qiuhua Li
2,3,*,
Yue Long
4,
Jingze Zhang
1,5,6,
Heng Wang
2,7,
Bo Yang
1,5 and
Jing Xiao
1,5
1
Key Laboratory for Information System of Mountainous Area and Protection of Ecological Environment of Guizhou Province, Guizhou Normal University, Guiyang 550001, China
2
Guizhou Key Laboratory of Advanced Computing, Guizhou Normal University, Guiyang 550001, China
3
Guizhou International Cooperative Research Base-International Joint Research Center for Water Ecology, Guizhou Normal University, Guiyang 550001, China
4
Guizhou Provincial Ecological Environment Monitoring Center, Guiyang 550001, China
5
Guizhou Province Field Scientific Observation and Research Station of Hongfeng Lake Reservoir Ecosystem, Guiyang 550001, China
6
Institute of Environment, Hefei Comprehensive National Science Center, Hefei 230031, China
7
School of Cyber Science and Technology, Guizhou Normal University, Guiyang 550001, China
*
Author to whom correspondence should be addressed.
Diversity 2025, 17(7), 478; https://doi.org/10.3390/d17070478
Submission received: 5 June 2025 / Revised: 3 July 2025 / Accepted: 7 July 2025 / Published: 10 July 2025
Browse Figures
Versions Notes

Abstract

Continuous damming in karst rivers fragmented the longitudinal structure of river systems, disrupting plankton habitats, limiting dispersal, and reducing biodiversity. This study examined variations in zooplankton functional diversity in a dammed river system during dry and wet seasons. Sampling across both seasons yielded 44 samples, with 64 zooplankton taxa categorized into seven functional groups based on their traits. Functional diversity indices were calculated. Results revealed significant differences in nutrient concentrations between upstream and downstream sections, particularly during the dry season (R2 = 0.11, p < 0.01). Zooplankton functional diversity decreased from upstream to downstream, with more pronounced differences in the dry season (R2 = 0.94, p < 0.05), driven by reduced dispersal stochasticity (βBC close to −1). Continuous damming primarily affected smaller zooplankton, such as rotifers, while dissolved oxygen, water temperature, and pH influenced distribution patterns related to habitat depth, breeding season, life span, and reproduction. These findings underscored the impact of damming on zooplankton functional diversity and informed dam management strategies for biodiversity conservation.

1. Introduction

Rivers sustain environmental health, drive economic prosperity, and enhance human well-being [1]. For millennia, rivers have supplied food, water, transportation routes, and energy, while forming the foundation for industrial production [2]. To meet human demands, nearly 2.8 million dams (with reservoir areas >103 m2) have been constructed globally, with plans to build an additional 3700 hydropower dams (each ≥ 1 MW capacity) [3]. Globally, 63% of the longest rivers (≥1000 km) have lost their free-flowing status, accounting for 41% of global river discharge; dams, reservoirs, and their impacts on fragmentation and flow regulation are the primary drivers of widespread connectivity loss in river systems [1]. Dams fragment river connectivity, regulate water flow, and impede the transport of essential nutrients (carbon, phosphorus, nitrogen, and silicon), disrupting fundamental river processes and functions and accelerating biodiversity loss as well as degrading critical ecosystem services [4,5,6].
Unlike single dams, the construction of multiple dams fragments rivers, disrupting longitudinal species connectivity and upstream-downstream movements. Reservoir water volume management across hydrological regimes interferes with organisms’ habitats. Additionally, cascading dam systems produce cumulative watershed effects, leading to sediment accumulation and exacerbated pressure on river ecosystems [7]. Studies show that cascading dam operations modify river hydrology and disrupt floodplain ecosystems, leading to reduced habitat diversity and heightened species homogenization [8]. Damming-induced hydrodynamic changes impact zooplankton more in dry seasons than wet seasons: flood releases during wet seasons improve aquatic habitat connectivity, reducing environmental heterogeneity and zooplankton β-diversity, whereas reservoir storage in dry seasons intensifies environmental and biological heterogeneity [9,10]. Zooplankton is highly sensitive to environmental changes, and dam presence/operation may alter their populations. Dams affect thermal conditions through water retention, release patterns, and discharge heights. Given species’ temperature sensitivity, these alterations may impact zooplankton community structure. During rainy seasons, large-volume cold water discharges for flood control significantly affect downstream organisms [11,12]. Under seasonal reservoir management modes, water storage and release modify dissolved oxygen (DO) distribution. Zooplankton communities—dependent on DO for respiration and metabolism—are particularly vulnerable to hypoxia, as thermally stratified reservoirs are susceptible to oxygen depletion [13]. Zooplankton surveys provide valuable insights into upstream-downstream biological differences. However, management practices often prioritize water quality parameters over biological indicators, and assessment frameworks incorporating zooplankton remain underutilized.
Karst landscapes, covering 15% of the global land area, form unique river ecosystems with distinct environmental and biological characteristics due to specific geological conditions. Although many studies have analyzed species composition changes in dam habitats based on environmental factors and spatial processes, this approach often overlooks ecological similarities and differences among species [14,15]. Functional approaches to studying aquatic organisms are increasingly used as alternatives to taxonomy-based methods for assessing aquatic ecosystems [16]. Functional diversity—a critical predictor of ecosystem functioning—has emerged as a vital component of biodiversity research, complementing species and genetic diversity [17,18]. Traits enable quantitative comparisons, and changes in multidimensional trait space can be ecologically interpreted (e.g., responses to environmental shifts). The environmental drivers of functional diversity have been widely validated [19,20,21,22].
This study uses zooplankton communities as a biological model to explore the impact of continuous damming on aquatic ecosystems in karst rivers through zooplankton functional diversity. The objectives are the following: (1) evaluate consecutive dam constructions’ effects on water quality across hydrological seasons; (2) determine whether dams induce upstream-downstream differences in zooplankton communities and functional diversity; and (3) investigate environmental and spatial factors influencing zooplankton functional traits. Finally, it aims to offer ecological perspectives for dam management.

2. Materials and Methods

2.1. Study Area

The Nanpanjiang River, the source tributary of the Xijiang River, forms the main channel of China’s second-largest river, the Pearl River. The Guizhou section of the Nanpanjiang River, located in a typical karst region, serves as the study area. Characterized by a subtropical monsoon climate with four distinct seasons, it has an average annual temperature of 13.6–19.1 °C and an annual rainfall of approximately 1225 mm. Within the study area, there are three dams corresponding to three reservoirs, including Wanfeng Lake (Tianshengqiao Reservoir), Tianshengqiao Secondary Reservoir, and Pingban Hydropower Station Reservoir. Wanfeng Lake is located in the combined area of Yunnan, Guizhou, and Guangxi provinces and districts, with a surface area of 176.00 km2 and a storage capacity of 10,200 million m3. Tianshengqiao Secondary Reservoir has a crest elevation of 658.70 m, a maximum height of 60.70 m, and a total length of 470.97 m. The design storage level of the reservoir of the power station and the flood limit level is 645.00 m, with the corresponding capacity of 14.06 million m3. The control basin area of the reservoir of Pingban Hydropower Station is 5.60 km2, with the total capacity of 278 million m3, and the regulating capacity of 0.026 billion m3. Land use types in the study area are shown in Figure 1. (Data source: http://www.prwri.com.cn/).
In December 2021 and July 2022, 22 sampling sites were established in the study area to assess the effects of successive damming on zooplankton functional diversity in the river network. To better explore the effects of damming, the Nanpan River was divided into four separate groups (Figure 2), and at each sampling site, large quantities of zooplankton were collected by making multiple tows from 0.5 m above the bottom to the surface of the water, with each tow using a 0.064 mm nylon net. Each bundle of zooplankton samples was transferred to a 100 mL sample bottle and fixed by adding 4–5% formaldehyde solution by volume. After fixation, the samples were allowed to stand indoors for 48 h, and then the supernatant was removed and the samples were concentrated to 30 mL. During identification, precipitated samples were mixed, and zooplankton species were identified using a 1 mL counting plate under an optical microscope at 100–400× magnification, while zooplankton trait data were also collected.

2.2. Methods of Analysis

For the functional classification of zooplankton species, we used seven functional traits: average body length (mm), habitat depth, feeding type, life span, breeding method, predatory escape response, and breeding season [23,24,25,26] (Table 1). Determination of dissolved oxygen (DO), pH, water temperature (WT), and electrical conductivity (EC) was performed using a portable water quality analyzer (HORIBA, U-52, Japan). Geographic coordinates and elevation of sampling sections were recorded using the Global Positioning System (GPS) (USA). We also collected mixed water samples (1 L) for measurement of nutrients, including total nitrogen (TN), total phosphorus (TP), ammonia (NH4+), nitrate (NO3−), nitrite (NO2−), permanganate index (CODMn), and orthophosphate (PO43−).

2.3. Statistical Analysis

To quantify the functional diversity of zooplankton at each sampling site, we employed three indices: functional richness (FRic), functional evenness (FEve), and Rao’s quadratic entropy (Rao’s Q) [27]. FRic represents the extent of ecological space utilized by the community, with higher values indicating greater utilization [28]. FEve measures the evenness of functional trait distribution within the community, reflecting a more comprehensive and efficient use of available resources at higher values [28]. Rao’s Q, a generalization of Simpson’s diversity index, integrates species richness with the functional differences between species, capturing both the number of species and their functional distinctiveness [29]. This index effectively combines aspects of functional richness and differentiation, providing a comprehensive measure of functional diversity [30]. The three functional diversity indices were calculated using the ‘FD’ package in R (version 4.4.1) [31].
To model the response of zooplankton functional diversity to environmental variables and spatial factors, we employed Random Forest regression. This approach allowed us to identify the most significant predictor variables. The analysis was conducted using the “randomForest” package in R (version 4.4.1), with the importance() function providing relative importance scores for the predictor variables. Subsequently, variance partitioning analysis was performed on the selected environmental variables and spatial factors to determine the primary influencing factors and their explanatory power. The results were visualized using an UpSet matrix plot, facilitating the interpretation of the complex interactions.
We used NMDS with Raup–Crick dissimilarity (βRC) and null models to assess disturbance effects on β-diversity [32]. βRC ranges from −1 (high similarity) to 1 (high dissimilarity). We compared observed βRC to null expectations to test significance of compositional differences: βRC near 1 indicates fewer shared species than expected; βRC near −1 indicates more shared species. Under random community assembly, mean βRC between groups approaches 0. Environmental filters producing similar communities drive βRC toward −1, while filters producing dissimilar communities drive βRC toward 1 [33]. Analyses were conducted in R version 4.1.1 using the ‘vegan’ package.
Functional β-diversity reflects the variation in functional trait composition among different communities. In this study, lower values of functional β-diversity indicate greater similarity in the functional composition of communities, and reductions in functional β-diversity are associated with the loss of unique ecosystem traits. Decomposing overall functional β-diversity (βsor) into functional divergence (βsim, associated with species loss) and nestedness (βsne, associated with species replacement) provides insights into the processes underlying spatial and temporal changes in community composition. Functional β-diversity was calculated using the ‘functional.beta.multi’ and ‘functional.beta.pair’ functions from the ‘betapart’ package in R(version 4.4.1), which compute multi-site and pairwise functional differences, respectively.
RLQ (R-mode linked to Q-mode) analysis and fourth-corner analysis are integrated approaches for examining the relationships between species traits and environmental variables. RLQ analysis offers an ordination method that assigns scores to plots, species, traits, and environmental variables along orthogonal axes, thereby providing a low-dimensional graphical summary of the primary structures within the dataset. Conversely, fourth-corner analysis evaluates and tests the associations between species traits and environmental variables on a pairwise basis. These methods are frequently employed together in trait-environment studies, complementing each other in the description of multivariate patterns and the assessment of significant binary associations. The analyses were performed using the ‘ade4′ package in R, version 4.4.1.
Spatiotemporal variations in water quality across dry and rainy seasons were evaluated within the study area using inverse distance weighting (IDW) interpolation. Environmental dissimilarities among sampling sites were quantified via Bray–Curtis indices. Regression models integrating dam density and water quality parameters were developed to assess cascade dam system impacts. Relationships among environmental variables, spatial factors, and functional diversity indices (Feve, FRic, Rao’s Q) were examined using Mantel tests.

3. Results and Analysis

3.1. The Impact of Continuous Damming on Water Quality Across Different Hydrological Seasons

The study utilized inverse distance weighting interpolation to analyze water quality variations between dry and wet seasons, revealing significant spatial differences in nutrient concentrations (Figure 3a–d). During the dry season, TN concentrations were significantly elevated in dammed areas compared to downstream regions, while TP concentrations peaked primarily in pre-dam and downstream dammed zones. In contrast, the wet season demonstrated more homogeneous nutrient distributions, with higher TN and TP levels observed in upstream dammed areas than downstream. To evaluate the influence of cascading dams on water quality gradients, a curve was generated based on dam counts and upstream-downstream water quality variations. The results demonstrated that while no significant water quality differences were associated with increasing dam counts during the wet season, dry season conditions showed pronounced water quality variations correlated with dam numbers (Figure 3e,f). The UpSet matrix analysis (Figure 3g,h) revealed that spatial factors accounted for 36.7% of total contributions in the dry season, with distance-based dam metrics (Dis Dam) contributing substantially (35.42%)—a markedly higher proportion than the 6.8% spatial contribution observed during the wet season.

3.2. The Influence of Environmental and Spatial Factors on Functional Diversity Indices

A total of 64 zooplankton taxa were identified across 22 sampling sites, comprising 49 rotifer species, 6 copepod species, and 9 cladoceran species. Scatter plots with fitted curves of the functional diversity indices (FEve, FRic, and Rao’s Q) against the distance from the river source were constructed to assess their trends from upstream to downstream. FRic exhibited a consistent pattern in both dry and wet seasons, with higher values observed upstream. In contrast, FEve and Rao’s Q showed similar trends in the dry season, characterized by higher values upstream, while no clear trend was evident in the wet season (Figure 4a–c).
The analysis revealed distinct seasonal patterns in the relationships between environmental variables and functional diversity indices. During the dry season, PO43− emerged as a significant predictor, demonstrating a strong positive correlation with FRic (n = 22, p < 0.01) and a moderate positive correlation with FEve (n = 22, p < 0.05). Additionally, elevation showed a highly significant positive association with FEve (n = 22, p < 0.01). In contrast, the wet season exhibited more complex interactions, with FRic and Rao’s Q being influenced by multiple factors. FRic displayed a significant negative correlation with NO2−(n = 22, p < 0.05) and a strong positive correlation with WT (n = 22, p < 0.01), while Rao’s Q showed significant negative associations with DO (n = 22, p < 0.05) and PO43− (n = 22, p < 0.01) (Figure 4d,e).
A RF model was employed to assess the relative importance of environmental and spatial factors on zooplankton functional diversity. During the dry season, FEve was primarily influenced by dam counts and elevation, while Rao’s Q was significantly predicted by TN and NH4+ concentrations. FRic showed strong dependencies on PO43−, EC, and elevation. In contrast, wet season analyses revealed distinct patterns: FEve exhibited no significant environmental or spatial dependencies, whereas Rao’s Q was predominantly affected by PO43− and pH levels, and FRic was mainly determined by NO2− and NO3− concentrations (Figure 5a).
Dry season analysis revealed TN as the dominant factor, accounting for 46.36% of variation independently and contributing an additional 25.30% through interactions with other variables. Dam Number emerged as the secondary factor, explaining 16.30% independently and 24.46% in combination with Elevation. During the wet season, WT served as the primary driver, independently explaining 3.70% of variation and contributing 15.28% through interactions, particularly through its combined effects with NO3− (13.16%) and with DO and pH (6.59%) (Figure 5b).

3.3. Seasonal Regulation by Continuous Dams Constrains Zooplankton Dispersal Between Upstream and Downstream River Reaches

Longitudinal functional divergence in zooplankton communities along the upstream-downstream continuum was analyzed in relation to dam numbers. The results demonstrated a positive correlation between functional differences and cumulative dam counts across both hydrological seasons, with more significant divergence observed during the dry season (p < 0.005) (Figure 6a,b).
Functional β-diversity analysis (Figure 6c,d) revealed distinct seasonal patterns: during the dry season, functional turnover (βsim) significantly exceeded functional nestedness (βsne) between Group4 and both Group2 and Group3, while Group1 exhibited the inverse pattern relative to other groups. In contrast, wet season analyses demonstrated predominant functional nestedness (βsne > βsim) across most group comparisons, except between Group2 and Group4, indicating limited dam barrier effects on zooplankton community functional diversity.
The study employs the Raup–Crick index in null model analyses to assess the relative importance of deterministic and random processes in the assembly of zooplankton communities. This index helps determine whether observed differences in community composition deviate significantly from those expected by chance, thereby evaluating the roles of deterministic and random processes in shaping these communities. Non-metric multidimensional scaling (NMDS) analysis, based on the Raup–Crick index, reveals that zooplankton communities during the dry and wet seasons are distinct, with minimal deviation from null expectations (Figure 6e). This suggests that random processes may play a more significant role, with community differences influenced by river flow volume. Further analysis of the Raup–Crick index (βRC) between groups 1, 2, 3, and 4 indicates that dam barriers have fragmented zooplankton communities (Figure 6f). In the dry season, average βRC values range from −0.63 to −0.94, suggesting communities are more similar than expected by chance, indicating deterministic processes dominate. In contrast, the wet season shows varied patterns: groups 1 and 2 exhibit βRC values close to −1, indicating deterministic assembly, while groups 3 and 4 have values around −0.5, suggesting increased randomness in community assembly. Thus, in the dry season, dams impose strict constraints on zooplankton communities, with deterministic processes being predominant. In the wet season, upstream communities remain deterministic, whereas downstream communities exhibit more random assembly. This highlights the nuanced influence of environmental and stochastic factors across different hydrological conditions and dam-impacted sections.

3.4. Relationship Between Environmental and Spatial Factors and Functional Characteristics

RLQ analysis was employed to simultaneously ordinate species, traits, and environmental data. Each data matrix was subjected to an ordination method suitable for its data type, and the results were combined using species data as a mediator to elucidate the relationships between environmental factors and traits. RLQ analysis results (Figure 7a,c) show that the left (negative) part of the Q-axis represents species such as Chromogaster oualis, Trichocerca longiseta, Euchlanis dilatata, Colurella adriatica, Trichotria tetractis, etc., which have deeper habitat depths, broader breeding seasons, and are found in environments with lower pH, NH4+, and NO3-, and are primarily influenced by dam count. The lower (negative) part of the Q-axis represents species such as Daphnia cucullata, Alona quadrangularis, Mesocyclops leuckarti, Neodiaptomus yangtsekiangensis, Sinodiapiomus sarsi, etc., which have larger body sizes, stronger predatory escape responses, longer lifespans, and are positively correlated with PO43- and NO3-, and inhabit environments with lower EC, DO, and WT. The bar chart of RLQ eigenvalues in the lower right shows that the first two axes of RLQ account for most of the covariance inertia, indicating that the first two RLQ axes chosen for display are highly representative. The fourth-corner analysis results (Figure 7b) show that DO, WT, and pH are the main factors determining the distribution patterns of zooplankton traits such as depth of habitat, breeding season, lifespan, and mode of reproduction.
Diversity 17 00478 g006
Figure 6. (a,b) Longitudinal functional divergence patterns across hydrological seasons, showing the relationship between cumulative dam numbers and inter-site functional differences, with shaded areas representing 95% confidence intervals. (c,d) Functional β-diversity partitioning among zooplankton groups, where sor denotes total functional β-diversity, sim indicates functional turnover, and sne represents functional nestedness. (e) NMDS ordination of zooplankton community β-diversity based on modified Raup–Crick dissimilarity indices. (f) Null model analysis of βRC values across distinct zooplankton functional groups.
Figure 6. (a,b) Longitudinal functional divergence patterns across hydrological seasons, showing the relationship between cumulative dam numbers and inter-site functional differences, with shaded areas representing 95% confidence intervals. (c,d) Functional β-diversity partitioning among zooplankton groups, where sor denotes total functional β-diversity, sim indicates functional turnover, and sne represents functional nestedness. (e) NMDS ordination of zooplankton community β-diversity based on modified Raup–Crick dissimilarity indices. (f) Null model analysis of βRC values across distinct zooplankton functional groups.
Diversity 17 00478 g006

4. Discussion

4.1. Seasonal Impacts of Continuous Damming on Water Quality and Zooplankton Functional Diversity Indices

The study revealed significant variations in nutrient distribution across the study area, with notable concentrations upstream of the first dam (Figure 3a–d), particularly during the dry season. Additionally, an increase in the number of dams between sampling sites was associated with greater water quality differences (Figure 3e,f). The construction of cascading dams altered the river’s hydraulic conditions, leading to sediment accumulation in reservoirs and, consequently, variations in nutrient distribution [34]. Modulation of reservoir water retention time during the dry and wet seasons is a key factor influencing nutrient transport and retention [35]. The downstream river area, characterized by extensive agricultural land and limited urban development, experiences pollutant and nutrient inflows via discharge, infiltration, or surface runoff. Notably, large-scale agricultural practices, including fertilization and irrigation during the dry season, are probable primary drivers of elevated TP levels in downstream waters [36]. In the wet season, due to increased flood discharge frequency, water retention time decreases, water flow increases, and nutrient salts are diluted due to higher flow velocities. Under the influence of water potential, both in the dry and wet seasons, zooplankton functional richness decreases from upstream to downstream, with a greater decrease observed in the wet season compared to the dry season. Furthermore, dam blockage and changes in water flow result in a declining trend in FRic (functional richness), which quantifies the extent of ecological niche space occupied by species traits. Additionally, zooplankton exhibit slower reproduction in winter, utilizing fewer resources [37,38]. FEve and Rao’s Q exhibit similar trends in both the dry and wet seasons (Figure 4a–c). FEve quantifies the evenness of functional traits in the ecological space, while Rao’s Q measures the variation in functional dissimilarities between species. In the wet season, the opening of flood control channels increases river flow, resulting in smaller differences in zooplankton communities between upstream and downstream areas. In the dry season, the dam blockage divides low-flow rivers into interconnected reservoirs, disrupting the river continuum, increasing water retention time, and leading to larger community differences as more adaptive species are retained in the reservoirs due to changes in habitat and water quality [36]. Levels of N and P in the system are critical factors influencing zooplankton functional diversity, irrespective of season, suggesting that zooplankton may be sensitive to environmental alterations. The construction of dams directly affects zooplankton functional diversity in both dry and wet seasons, with more pronounced restrictions during the dry season, disrupting connectivity between upstream and downstream areas and limiting zooplankton migration.

4.2. Seasonally Stepped Damming Alters Upstream and Downstream Zooplankton Community Differences and Functional β-Diversity

In both the dry and wet seasons, an increase in the number of dams between sampling sites was associated with a concomitant rise in functional beta-diversity (βsor), with a more pronounced effect observed during the dry season (Figure 6a,b). This pattern may be attributed to the reduced connectivity of the river system due to dam construction, which enhances functional differences between upstream and downstream sampling sites, particularly in the dry season. During the dry season, downstream Group4 exhibited higher functional turnover (βsim > βsne) compared to functional nesting when compared to the other groups, whereas upstream Group1 displayed the opposite trend, a pattern consistent with that observed in the wet season (Figure 6c,d). Functional beta-diversity reflects two distinct ecological processes: species replacement, which drives functional turnover (βsim), and species loss, which influences functional nesting (βsne) [39]. Marked differences in the functional β-diversity of zooplankton between upstream and downstream areas during the dry and wet seasons may be attributed to variations in river flow. In the dry season, lower flow rates and differing reservoir management practices between upstream and downstream regions lead to imbalances in the distribution of zooplankton functional traits. In contrast, during the wet season, high-frequency flood control allows zooplankton to rapidly traverse barriers and reach downstream areas, resulting in smaller differences in functional characteristics between upstream and downstream [14]. The analysis of the relative importance of deterministic and stochastic processes in the assembly of zooplankton communities reveals that communities during the dry and wet seasons are more similar, deviating from the null model. This indicates that deterministic processes play a significant role in the assembly and succession of zooplankton communities in both seasons, with their relative importance depending on flow rates. Comparing the βRC values of Group1, Group2, Group3, and Group4 against the null expectation, we find that during the dry season, the βRC values are close to −1, suggesting the presence of strong environmental filters that strictly constrain zooplankton communities, resulting in highly similar communities upstream and downstream. In contrast, during the wet season, βRC values are more dispersed. Group1 and Group2 exhibit values close to −1, while Group3 and Group4 range between −0.2 and −0.5. This discrepancy is attributed to the upstream area being blocked by the Tianshengqiao Level 1 dam, where a large elevation drop and flow velocity interfere with community assembly. In the relatively gentler downstream areas, increased flow leads to more random community assembly and higher dispersion among communities. Finally, the construction of dams leads to significant differences in β-diversity between adjacent sites, thereby limiting the dispersal of zooplankton [9].

4.3. Relationship Between Species, Environmental Factors, and Functional Characteristics

The results from RLQ analysis and fourth-corner analysis indicate that species with larger body sizes and longer lifespans are less affected by dams, while the environment altered by damming primarily impacts smaller-bodied species, mainly rotifers, in terms of habitat depth and breeding season (Figure 7). The disruption of the river continuum, leading to the formation of rapids, transition zones, and still water areas, results in habitat heterogeneity for zooplankton [9]. Studies have shown that selective water releases from large reservoirs can alter the temperature of downstream rivers [4]: in summer, it is predicted that 73% of future dams may cause downstream rivers to cool by up to 6.6 °C. Winter operations are expected to continue warming downstream river temperatures by up to 2 °C, with strongly stratified reservoirs most likely to impact the thermal conditions immediately downstream of dams. Such fluctuating thermal regimes could disrupt the growth stages of downstream zooplankton communities. Larger-bodied species, which generally possess stronger dispersal abilities, may be more influenced by spatial factors such as river connectivity, flow direction, and the presence of man-made dams, due to their larger body sizes and relatively lower mobility. In contrast, local environmental factors, such as physicochemical habitat characteristics, may play a more significant role in shaping crustacean metacommunity structures, given their larger body sizes and greater mobility [14]. Zooplankton are highly sensitive to temperature fluctuations. In warmer conditions, some species migrate towards polar or deeper waters; however, this response varies significantly among different species, necessitating further research [40]. Oxygen serves as the foundation of the aquatic food web, with all complex life forms dependent on DO. Globally, temperate lakes are experiencing a decline in dissolved oxygen levels in both surface and deep waters over time, a trend that may also impact zooplankton communities [41]. pH levels influence the trophic interactions of zooplankton, thereby affecting their roles in aquatic ecosystems [42].

5. Conclusions

In karst regions with cascading dams in river ecosystems, dam barriers lead to differences in nutrient salts upstream and downstream, which are regulated by river flow, with significant differences in the dry season (R2 = 0.11, p < 0.05). Spatial factors explained the 37.6% water quality variation. Dry season analyses revealed significant impacts of environmental variables and dam numbers on zooplankton functional diversity, showing a distinct upstream-downstream decreasing gradient. Across both hydrological seasons, dam numbers significantly influenced upstream-downstream functional divergence in zooplankton communities (R2 = 0.94, p < 0.05), with enhanced effects during the dry season attributable to reduced stochasticity in zooplankton dispersal patterns (βRC approaching −1). Dams primarily affect smaller zooplankton groups, such as Rotifers, while DO, WT, and pH determine the main distribution patterns of zooplankton traits, including depth of habitat, breeding season, lifespan, and mode of reproduction.

6. Recommendations for the Management of Terraced Karst Reservoirs in Different Seasons

Zooplankton communities are a vital component of aquatic ecosystems, acting as a key pathway for energy transfer from primary producers to higher trophic levels and influencing aquatic biogeochemical cycles through direct or indirect feedback mechanisms [43]. Multi-criteria decision-making (MCDM) techniques are well-suited for dam management [44], but this approach rarely incorporates biodiversity indicators into the evaluation framework. This study demonstrated that zooplankton functional diversity serves as a sensitive indicator of habitat alterations, highlighting its potential inclusion in ecological assessment frameworks. Key environmental drivers, particularly nutrient concentrations and WT, significantly influenced zooplankton functional diversity indices. These findings underscore the importance of implementing watershed-wide pollution control measures and optimizing dry-season water retention management for zooplankton conservation. Furthermore, thermal stratification management through discharge strategies was identified as crucial, with surface water discharge better approximating natural thermal regimes than bottom water release. The study recommends incorporating multi-level discharge outlets in dam design to minimize thermal impacts on aquatic ecosystems through strategic water release management [45]. During dry seasons, decreased river discharge coupled with sluice gate closure often results in extended pollutant retention within reservoirs. To address these challenges, implementing appropriate ecological flow regimes, strengthening longitudinal ecological compensation mechanisms, and creating specialized biological corridors are recommended as effective strategies to mitigate dam-induced impacts on riverine ecosystems.

Author Contributions

X.S.: Conceptualization, Methodology, Investigation, Writing—Original Draft; Q.L.: Software, Investigation, Data Curation, Writing—Original Draft, Supervision; H.W.: Data Curation, Writing—Review and Editing; Y.L.: Data Curation, Writing—Review and Editing; J.Z.: Data Curation; B.Y.: Data Curation; J.X.: Investigation, Formal Analysis, Data Curation. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key Research and Development Program of China (No. 2022YFC3705005), and the Guizhou Provincial Science and Technology Program (ZSYS[2025]004, ZC[2023]213, RC[2020]6009-2, FQ[2024]016, FQ[2023]010).

Institutional Review Board Statement

Not applicable.

Data Availability Statement

All data were included in the paper.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Grill, G.; Lehner, B.; Thieme, M.; Geenen, B.; Tickner, D.; Antonelli, F.; Babu, S.; Borrelli, P.; Cheng, L.; Crochetiere, H.; et al. Mapping the world’s free-flowing rivers. Nature 2019, 569, 215–221. [Google Scholar] [CrossRef]
  2. Ripl, W. Water: The bloodstream of the biosphere. Philos. Trans. R. Soc. B Biol. Sci. 2003, 358, 1921–1934. [Google Scholar] [CrossRef] [PubMed]
  3. Zarfl, C.; Lumsdon, A.E.; Berlekamp, J.; Tydecks, L.; Tockner, K. A global boom in hydropower dam construction. Aquat. Sci. 2015, 77, 161–170. [Google Scholar] [CrossRef]
  4. Ahmad, S.K.; Hossain, F.; Holtgrieve, G.W.; Pavelsky, T.; Galelli, S. Predicting the Likely Thermal Impact of Current and Future Dams Around the World. Earth’s Future 2021, 9, e2020EF001916. [Google Scholar] [CrossRef]
  5. Maavara, T.; Chen, Q.; Van Meter, K.; Brown, L.E.; Zhang, J.; Ni, J.; Zarfl, C. River dam impacts on biogeochemical cycling. Nat. Rev. Earth Environ. 2020, 1, 103–116. [Google Scholar] [CrossRef]
  6. Dudgeon, D.; Arthington, A.H.; Gessner, M.O.; Kawabata, Z.-I.; Knowler, D.J.; Lévêque, C.; Naiman, R.J.; Prieur-Richard, A.-H.; Soto, D.; Stiassny, M.L.J.; et al. Freshwater biodiversity: Importance, threats, status and conservation challenges. Biol. Rev. 2006, 81, 163–182. [Google Scholar] [CrossRef]
  7. Zhang, Z.; Liu, J.; Huang, J. Hydrologic impacts of cascade dams in a small headwater watershed under climate variability. J. Hydrol. 2020, 590, 125426. [Google Scholar] [CrossRef]
  8. Bozelli, R.L.; Thomaz, S.M.; Padial, A.A.; Lopes, P.M.; Bini, L.M. Floods decrease zooplankton beta diversity and environmental heterogeneity in an Amazonian floodplain system. Hydrobiologia 2015, 753, 233–241. [Google Scholar] [CrossRef]
  9. Wu, H.; Chen, J.; Xu, J.; Zeng, G.; Sang, L.; Liu, Q.; Yin, Z.; Dai, J.; Yin, D.; Liang, J.; et al. Effects of dam construction on biodiversity: A review. J. Clean. Prod. 2019, 221, 480–489. [Google Scholar] [CrossRef]
  10. de Souza, C.A.; Beisner, B.E.; Velho, L.F.M.; de Carvalho, P.; Pineda, A.; Vieira, L.C.G. Impoundment, environmental variables and temporal scale predict zooplankton beta diversity patterns in an Amazonian river basin. Sci. Total Environ. 2021, 776, 145948. [Google Scholar] [CrossRef]
  11. Rose, V.; Rollwagen-Bollens, G.; Bollens, S.M.; Zimmerman, J. The effects of run-of-river dam spill on Columbia River microplankton. River Res. Appl. 2019, 35, 1478–1488. [Google Scholar] [CrossRef]
  12. Sabo, J.L.; Ruhi, A.; Holtgrieve, G.W.; Elliott, V.; Arias, M.E.; Ngor, P.B.; Räsänen, T.A.; Nam, S. Designing river flows to improve food security futures in the Lower Mekong Basin. Science 2017, 358, eaao1053. [Google Scholar] [CrossRef] [PubMed]
  13. Zhang, Y.; Zhang, L.; Mitsch, W.J. Predicting river aquatic productivity and dissolved oxygen before and after dam removal. Ecol. Eng. 2014, 72, 125–137. [Google Scholar] [CrossRef]
  14. Zhao, K.; Song, K.; Pan, Y.; Wang, L.; Da, L.; Wang, Q. Metacommunity structure of zooplankton in river networks: Roles of environmental and spatial factors. Ecol. Indic. 2017, 73, 96–104. [Google Scholar] [CrossRef]
  15. Wan, Y.; Yang, J.Q.; Zou, D.W.; Li, J.J.; Qiao, Y.J.; An, S.Q.; Leng, X. Effects of multiple dams on the metacommunity structure of stream macroinvertebrates. Mar. Freshw. Res. 2018, 69, 721. [Google Scholar] [CrossRef]
  16. Visconti, A.; Caroni, R.; Rawcliffe, R.; Fadda, A.; Piscia, R.; Manca, M. Defining Seasonal Functional Traits of a Freshwater Zooplankton Community Using δ13C and δ15N Stable Isotope Analysis. Water 2018, 10, 108. [Google Scholar] [CrossRef]
  17. Griffin, J.N.; Méndez, V.; Johnson, A.F.; Jenkins, S.R.; Foggo, A. Functional diversity predicts overyielding effect of species combination on primary productivity. Oikos 2009, 118, 37–44. [Google Scholar] [CrossRef]
  18. Cardoso, S.J.; Nabout, J.C.; Farjalla, V.F.; Lopes, P.M.; Bozelli, R.L.; Huszar, V.L.M.; Roland, F. Environmental factors driving phytoplankton taxonomic and functional diversity in Amazonian floodplain lakes. Hydrobiologia 2017, 802, 115–130. [Google Scholar] [CrossRef]
  19. Dunck, B.; Algarte, V.M.; Cianciaruso, M.V.; Rodrigues, L. Functional diversity and trait–environment relationships of periphytic algae in subtropical floodplain lakes. Ecol. Indic. 2016, 67, 257–266. [Google Scholar] [CrossRef]
  20. Pease, A.A.; González-Díaz, A.A.; Rodiles-Hernández, R.; Winemiller, K.O. Functional diversity and trait–environment relationships of stream fish assemblages in a large tropical catchment. Freshw. Biol. 2012, 57, 1060–1075. [Google Scholar] [CrossRef]
  21. Hong, P.; Schmid, B.; De Laender, F.; Eisenhauer, N.; Zhang, X.; Chen, H.; Craven, D.; De Boeck, H.J.; Hautier, Y.; Petchey, O.L.; et al. Biodiversity promotes ecosystem functioning despite environmental change. Ecol. Lett. 2022, 25, 555–569. [Google Scholar] [CrossRef] [PubMed]
  22. Peng, Z.; Qian, X.; Liu, Y.; Li, X.; Gao, H.; An, Y.; Qi, J.; Jiang, L.; Zhang, Y.; Chen, S.; et al. Land conversion to agriculture induces taxonomic homogenization of soil microbial communities globally. Nat. Commun. 2024, 15, 1–13. [Google Scholar] [CrossRef] [PubMed]
  23. Obertegger, U.; Flaim, G. Taxonomic and functional diversity of rotifers, what do they tell us about community assembly? Hydrobiologia 2018, 823, 79–91. [Google Scholar] [CrossRef]
  24. Lokko, K.; Virro, T.; Kotta, J. Seasonal variability in the structure and functional diversity of psammic rotifer communities: Role of environmental parameters. Hydrobiologia 2017, 796, 287–307. [Google Scholar] [CrossRef]
  25. Obertegger, U.; Flaim, G. Community assembly of rotifers based on morphological traits. Hydrobiologia 2015, 753, 31–45. [Google Scholar] [CrossRef]
  26. Kuczyńska-Kippen, N.; Špoljar, M.; Zhang, C.; Pronin, M. Zooplankton functional traits as a tool to assess latitudinal variation in the northern-southern temperate European regions during spring and autumn seasons. Ecol. Indic. 2020, 117, 106629. [Google Scholar] [CrossRef]
  27. Mammola, S.; Carmona, C.P.; Guillerme, T.; Cardoso, P. Concepts and applications in functional diversity. Funct. Ecol. 2021, 35, 1869–1885. [Google Scholar] [CrossRef]
  28. Petchey, O.L.; Gaston, K.J. Functional diversity (FD), species richness and community composition. Ecol. Lett. 2002, 5, 402–411. [Google Scholar] [CrossRef]
  29. Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 2005, 16, 533–540. [Google Scholar] [CrossRef]
  30. Villéger, S.; Mason, N.W.H.; Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 2008, 89, 2290–2301. [Google Scholar] [CrossRef]
  31. Grenié, M.; Gruson, H. fundiversity: A modular R package to compute functional diversity indices. Ecography 2023, 2023, e06585. [Google Scholar] [CrossRef]
  32. Anderson, M.J.; Crist, T.O.; Chase, J.M.; Vellend, M.; Inouye, B.D.; Freestone, A.L.; Sanders, N.J.; Cornell, H.V.; Comita, L.S.; Davies, K.F.; et al. Navigating the multiple meanings of β diversity: A roadmap for the practicing ecologist. Ecol. Lett. 2011, 14, 19–28. [Google Scholar] [CrossRef] [PubMed]
  33. Zhou, J.; Deng, Y.; Zhang, P.; Xue, K.; Liang, Y.; Van Nostrand, J.D.; Yang, Y.; He, Z.; Wu, L.; Stahl, D.A.; et al. Stochasticity, succession, and environmental perturbations in a fluidic ecosystem. Proc. Natl. Acad. Sci. USA 2014, 111, E836–E845. [Google Scholar] [CrossRef]
  34. Best, J. Anthropogenic stresses on the world’s big rivers. Nat. Geosci. 2019, 12, 7–21. [Google Scholar] [CrossRef]
  35. Zhao, B.; Zeng, Q.; Wang, J.; Jiang, Y.; Liu, H.; Yan, L.; Yang, Z.; Yang, Q.; Zhang, F.; Tang, J.; et al. Impact of cascade reservoirs on nutrients transported downstream and regulation method based on hydraulic retention time. Water Res. 2024, 252, 121187. [Google Scholar] [CrossRef]
  36. Haase, P.; Bowler, D.E.; Baker, N.J.; Bonada, N.; Domisch, S.; Marquez, J.R.G.; Heino, J.; Hering, D.; Jähnig, S.C.; Schmidt-Kloiber, A.; et al. The recovery of European freshwater biodiversity has come to a halt. Nature 2023, 620, 582–588. [Google Scholar] [CrossRef]
  37. Li, Y.; Hou, J.; Xu, L.; Li, M.; Chen, Z.; Zhang, Z.; He, N. Variation in functional trait diversity from tropical to cold-temperate forests and linkage to productivity. Ecol. Indic. 2022, 138, 108864. [Google Scholar] [CrossRef]
  38. Giovannoni, S.J.; Vergin, K.L. Seasonality in Ocean Microbial Communities. Science 2012, 335, 671–676. [Google Scholar] [CrossRef] [PubMed]
  39. Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 2010, 19, 134–143. [Google Scholar] [CrossRef]
  40. Ratnarajah, L.; Abu-Alhaija, R.; Atkinson, A.; Batten, S.; Bax, N.J.; Bernard, K.S.; Canonico, G.; Cornils, A.; Everett, J.D.; Grigoratou, M.; et al. Monitoring and modelling marine zooplankton in a changing climate. Nat. Commun. 2023, 14, 1–17. [Google Scholar] [CrossRef]
  41. Jane, S.F.; Hansen, G.J.; Kraemer, B.M.; Leavitt, P.R.; Mincer, J.L.; North, R.L.; Pilla, R.M.; Stetler, J.T.; Williamson, C.E.; Woolway, R.I.; et al. Widespread deoxygenation of temperate lakes. Nature 2021, 594, 66–70. [Google Scholar] [CrossRef] [PubMed]
  42. Xu, X.; Wu, X.; Xu, W.; Sun, Y.; Zhang, L.; Yang, Z. Water acidification weakens the carbon sink capacity of mixotrophic organisms. Sci. Total. Environ. 2023, 865, 161120. [Google Scholar] [CrossRef] [PubMed]
  43. Heneghan, R.F.; Everett, J.D.; Blanchard, J.L.; Richardson, A.J. Zooplankton Are Not Fish: Improving Zooplankton Realism in Size-Spectrum Models Mediates Energy Transfer in Food Webs. Front. Mar. Sci. 2016, 3, 201. [Google Scholar] [CrossRef]
  44. Zamarrón-Mieza, I.; Yepes, V.; Moreno-Jiménez, J.M. A systematic review of application of multi-criteria decision analysis for aging-dam management. J. Clean. Prod. 2017, 147, 217–230. [Google Scholar] [CrossRef]
  45. Michie, L.E.; Hitchcock, J.N.; Thiem, J.D.; Boys, C.A.; Mitrovic, S.M. The effect of varied dam release mechanisms and storage volume on downstream river thermal regimes. Limnologica 2020, 81, 125760. [Google Scholar] [CrossRef]
Diversity 17 00478 g001
Figure 1. Land use in the study area.
Figure 1. Land use in the study area.
Diversity 17 00478 g001
Diversity 17 00478 g002
Figure 2. Study area and sampling locations.
Figure 2. Study area and sampling locations.
Diversity 17 00478 g002
Diversity 17 00478 g003
Figure 3. Spatiotemporal patterns of water quality under continuous damming across hydrological seasons and their influencing factors. (a–d) Spatial distribution of nutrient concentrations; (e,f) relationship between dam counts and water quality variations, with shaded areas representing 95% confidence intervals (e: dry season, f: wet season); panels (g,h) present the impact of spatial factors (Dis Dam: distance based on dams, Number of dams: dam counts between sites, Elevation: site elevation) on nutrient levels, where green signifies the dry season and yellow the wet season.
Figure 3. Spatiotemporal patterns of water quality under continuous damming across hydrological seasons and their influencing factors. (a–d) Spatial distribution of nutrient concentrations; (e,f) relationship between dam counts and water quality variations, with shaded areas representing 95% confidence intervals (e: dry season, f: wet season); panels (g,h) present the impact of spatial factors (Dis Dam: distance based on dams, Number of dams: dam counts between sites, Elevation: site elevation) on nutrient levels, where green signifies the dry season and yellow the wet season.
Diversity 17 00478 g003
Diversity 17 00478 g004
Figure 4. (a–c) Longitudinal variations in zooplankton functional diversity indices (FRic, FEve, Rao’s Q) across upstream-downstream gradients during dry and wet seasons; (d,e) Mantel test results demonstrating correlations between environmental/spatial factors and zooplankton community functional diversity indices (FRic, FEve, Rao’s Q).
Figure 4. (a–c) Longitudinal variations in zooplankton functional diversity indices (FRic, FEve, Rao’s Q) across upstream-downstream gradients during dry and wet seasons; (d,e) Mantel test results demonstrating correlations between environmental/spatial factors and zooplankton community functional diversity indices (FRic, FEve, Rao’s Q).
Diversity 17 00478 g004
Diversity 17 00478 g005
Figure 5. (a) The relative importance of environmental and spatial factors was evaluated using a random forest (RF) model, quantified through percentage increase in mean squared error (MSE) upon variable permutation, where higher MSE% values indicate greater predictive importance (significance levels: * p < 0.05, ** p < 0.01). (b) Variance partitioning results were visualized through an UpSet matrix plot, with vertical bars representing combined explanatory power of factors on zooplankton functional diversity and horizontal bars depicting individual contributions of environmental and spatial variables.
Figure 5. (a) The relative importance of environmental and spatial factors was evaluated using a random forest (RF) model, quantified through percentage increase in mean squared error (MSE) upon variable permutation, where higher MSE% values indicate greater predictive importance (significance levels: * p < 0.05, ** p < 0.01). (b) Variance partitioning results were visualized through an UpSet matrix plot, with vertical bars representing combined explanatory power of factors on zooplankton functional diversity and horizontal bars depicting individual contributions of environmental and spatial variables.
Diversity 17 00478 g005
Diversity 17 00478 g007
Figure 7. (a,c,d) RLQ analysis results on the first two axes, illustrating the co-variation among species, environmental variables, and trait patterns. Displayed are the correlations between the axes and the RLQ axes, along with eigenvalues, where the first two axes are highlighted in yellow. (b) Fourth-corner analysis depicting relationships between environmental variables and zooplankton traits, with blue indicating significant negative correlations (p < 0.05).
Figure 7. (a,c,d) RLQ analysis results on the first two axes, illustrating the co-variation among species, environmental variables, and trait patterns. Displayed are the correlations between the axes and the RLQ axes, along with eigenvalues, where the first two axes are highlighted in yellow. (b) Fourth-corner analysis depicting relationships between environmental variables and zooplankton traits, with blue indicating significant negative correlations (p < 0.05).
Diversity 17 00478 g007
Table 1. Classification of zooplankton functional traits.
Table 1. Classification of zooplankton functional traits.
TraitTrait State (Modality)
Average body lengthSize of body length
Habitat depthShallow, Intermediate, Deep
Feeding typeFiltration-R, Sugador-R, Predator-R, Raptorial-Cop, Filtration-Cop, Filtration-Clad and Scraper-Clad
Life spanShort, Long
Breeding methodAsexual, Sexual
Predatory escape responseLow, Big, Maximum, Medium
Breeding seasonWarm Season, Cold Season, Extensive
Note: R, Rotifer; Cop, Copepods; Clad, Cladocera.
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

Song, X.; Li, Q.; Long, Y.; Zhang, J.; Wang, H.; Yang, B.; Xiao, J. Impacts of Continuous Damming on Zooplankton Functional Diversity in Karst Rivers of Southwest China: Different Hydrological Periods and Implications for Karst Reservoir Management. Diversity 2025, 17, 478. https://doi.org/10.3390/d17070478

AMA Style

Song X, Li Q, Long Y, Zhang J, Wang H, Yang B, Xiao J. Impacts of Continuous Damming on Zooplankton Functional Diversity in Karst Rivers of Southwest China: Different Hydrological Periods and Implications for Karst Reservoir Management. Diversity. 2025; 17(7):478. https://doi.org/10.3390/d17070478

Chicago/Turabian Style

Song, Xiaochuan, Qiuhua Li, Yue Long, Jingze Zhang, Heng Wang, Bo Yang, and Jing Xiao. 2025. "Impacts of Continuous Damming on Zooplankton Functional Diversity in Karst Rivers of Southwest China: Different Hydrological Periods and Implications for Karst Reservoir Management" Diversity 17, no. 7: 478. https://doi.org/10.3390/d17070478

APA Style

Song, X., Li, Q., Long, Y., Zhang, J., Wang, H., Yang, B., & Xiao, J. (2025). Impacts of Continuous Damming on Zooplankton Functional Diversity in Karst Rivers of Southwest China: Different Hydrological Periods and Implications for Karst Reservoir Management. Diversity, 17(7), 478. https://doi.org/10.3390/d17070478

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