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Effect of complex hydraulic variables and physicochemical factors on freshwater mussel density in the largest floodplain lake, China

Ecological Processes volume 12, Article number: 15 (2023) Cite this article

Abstract

Background

Habitat degradation and flow regime alterations are two of the most prominent and common impact factors to freshwater mussel populations. Knowledge of the correlation between freshwater mussel distribution, density and habitat characteristics is important for maintaining and restoring their biodiversity and ecological functions. Information on predicting habitat suitability of freshwater mussels is lacking in China. Here, we aimed to analyze the correlation between freshwater mussel density and complex hydraulic and physicochemical variables to predict habitat suitability.

Results

The results showed that four complex hydraulic variables (boundary Reynolds number, critical shear stress, bed roughness and mean sediment particle size) and four physicochemical variables (water temperature, chlorophyll-a, transparency and pH) were key factors for predicting habitat suitability of freshwater mussels. Freshwater mussel density was significantly correlated with Froude number, water temperature and chlorophyll-a.

Conclusions

Our results confirmed that higher freshwater mussel density would be associated with areas that are stable in complex hydraulic and physicochemical variables. These results provide an important insight into the conservation of freshwater mussel diversity and their habitat restoration in China and globally.

Introduction

Freshwater mussels are among the most imperiled animal fauna globally (Bogan 2008; Lopes-Lima et al. 2017; Liu et al. 2022). Freshwater mussel population declines have been attributed to multiple anthropogenic pressures, including climate change, invasive species, and habitat degradation (e.g., water pollution, overexploitation, flow regime alterations, expansion of agricultural and urban landscapes) (Vörösmarty et al. 2010; Lopes-Lima et al. 2017; Böhm et al. 2021; Liu et al. 2022). Habitat degradation and flow regime alterations are two of the most prominent and common factors influencing freshwater mussel populations (Haag 2012). Freshwater mussels provide important ecosystem services including filtration, excretion of nutrients, and biodeposition (Geist 2010; Lummer et al. 2016; Vaughn 2018; Zieritz et al. 2019). Therefore, declines in their population will have negative impacts on the ecological function in freshwater ecosystems (Ilarri et al. 2018).

Freshwater mussels are benthic animals, and interact strongly with the water–sediment interface (Lummer et al. 2016; Vaughn 2018). As they live partly or wholly in the sediments of rivers and lakes, the substrate type has an impact on their assemblage structure (Strayer 2008). Some species can tolerate a wide range of sediments, while others can only occur in certain substrate types (Allen and Vaughn 2010; Colle and Callil 2012). Because they are sedentary, the dissolved oxygen-deprived habitat is particularly challenging for freshwater mussels. Dissolved oxygen for respiration may be the main habitat conditions for maintaining their populations, and habitats with low dissolved oxygen availability affect freshwater mussel survival (Strayer 2008). Flow is also an important driving factor of habitat heterogeneity and species distribution (Strayer 2008; Silva and Yalin 2017). At low flow, aquatic organisms can be exposed to lower dissolved oxygen concentrations, higher water temperatures, and dry environments, while increased flow and hydraulics can be equally harmful at high flow (Allen and Vaughn 2010; Stoeckl and Geist 2016). Distribution and density of freshwater mussels are also influenced by changes in flow conditions (Goodding et al. 2019; Stoeckl and Geist 2016). Therefore, knowledge of freshwater mussel habitat preferences is essential for their effective conservation and management (Ferreira-Rodrıguez et al. 2019).

Knowledge of the correlation between freshwater mussel distribution, density and habitat characteristics are important for maintaining and restoring their biodiversity and ecological functions (Haag and Williams 2014; Dobler et al. 2019; Smit and Kaeser 2016). Many studies have explored the response of freshwater mussel assemblage composition to human disturbances, such as flow alteration and habitat degradation (Cao et al. 2013; Daniel and Brown 2013; Johnson et al. 2014), which improved our knowledge of the effects of environmental change on freshwater mussel assemblage. Early studies have explored the correlation between freshwater mussel distribution, density and simple hydraulic variables (e.g., depth, current velocity, substrate type) to predict habitat suitability (Holland Bartels 1990; Strayer and Ralley 1993; Box et al. 2002). However, simple hydraulic variables did not reflect the effect of flow on the ecosystem for predicting habitat suitability (Allen and Vaughn 2010; Lopez and Vaughn 2021). Recently, some studies have provided evidence that freshwater mussel distribution and density was related to complex hydraulic variables (e.g., Froude and Reynolds numbers, shear stress and shear velocity) (Steuer et al. 2008; Allen and Vaughn 2010; Cao et al. 2015; Simeone et al. 2021). For example, low shear stress provides hydraulic refuges for freshwater mussels, while high shear stress may limit habitat suitability for them (Gangloff and Feminella 2007; Steuer et al. 2008; Stoeckl and Geist 2016). Complex hydraulic variables were related to flow conditions, which were more robust for predicting habitat suitability (Strayer 1999; Steuer et al. 2008; Simeone et al. 2021; Lopez and Vaughn 2021). Complex hydraulic variables have been used to predict habitat suitability in North America and Europe (Allen and Vaughn 2010; Steuer et al. 2008; Lopez and Vaughn 2021).

Information on predicting habitat suitability of freshwater mussels is lacking in China, and the correlation between freshwater mussel density and physicochemical factors have sometimes been reported (Sun et al. 2019). Furthermore, there are no studies from China that combine complex hydraulic variables and physicochemical variables to analyze the effect of these changes on mussel density. This is of great concern because the habitats of Chinese basins have been modified by multiple anthropogenic pressures, including climate change, habitat loss and degradation, water pollution, flow regime alterations, expansion of agricultural and urban landscapes (Liu et al. 2020; Böhm et al. 2021; Liu et al. 2022). Many freshwater mussel populations have been extirpated or greatly reduced because of these negative impacts on habitats (Shu et al. 2009; Liu et al. 2020, 2022). In addition, information on the correlation between freshwater mussel distribution, density and habitat characteristic has been developed on streams or rivers (Allen and Vaughn 2010; Steuer et al. 2008; Simeone et al. 2021; Lopez and Vaughn 2021), whereas floodplain lakes have received much less attention. Floodplain lakes are environmental and fluvial systems with variable properties that create complex habitat, which contain unique biota, including rare and highly specialized species with high conservation value, and provide important ecosystem services (Ward et al. 1999; Amoros and Bornette 2002; Schindler et al. 2014). Hydraulic and physicochemical conditions available to biota in floodplain lakes may be more diverse than smaller systems. For example, change of water level and water flow in floodplain lakes was more diverse than smaller systems (Li et al. 2019). Some freshwater mussels may track water levels closely, moving shoreward during high water elevation and retreating to deeper water as water levels recede (Allen and Vaughn 2010; Gough et al. 2012). This movement behavior may reduce mortality because it can allow mussels to avoid emersion during times of receding water levels (Allen and Vaughn 2010). Therefore, floodplain lakes are good community systems to study the correlation between community composition and environmental variables. Historically, they are more likely hydraulically and physiochemically stable than smaller systems, but these conditions have been altered in China by multiple anthropogenic pressures (Liu et al. 2022). Poyang Lake is the largest floodplain lakes with ~ 50% of the endemic freshwater mussel species in China (Wu et al. 2000; Shu et al. 2009; Xiong et al. 2012; Liu et al. 2020). Poyang Lake is also a dynamic wetland system, covering an expansive area in the rainy season and a low water level in the dry season, and has diverse hydraulic and physicochemical conditions (Li et al. 2019). Here, we aimed to analyze the correlation between freshwater mussel density and complex hydraulic and physicochemical variables to predict habitat suitability. We hypothesized that the freshwater mussel density would exhibit significant spatial differences, where they were correlated with Froude number, water temperature and chlorophyll-a, and higher freshwater mussel density would be associated with complex hydraulic and physicochemical variables. This study provides an important reference for the conservation of freshwater mussel diversity and restoration of freshwater ecosystem in China and globally.

Methods

Study area

Poyang Lake (28° 22′–29° 45′ N, 115° 47′–116° 45′ E), the largest floodplain lake in China, is located in the middle reach of the Yangtze River and northern Jiangxi Province with a total area of 162,200 km2 and an average annual precipitation of 1350–2150 mm (Li et al. 2019; Fig. 1). Poyang Lake is interconnected river–lake–wetland system, fed by five rivers, including Gan River, Fu River, Xiu River, Xin River and Rao River. The fluctuations of water level in Poyang Lake exhibit significant seasonal change, which have the lowest surface area during dry season (146 km2) and the highest surface area during wet season (2993 km2; Wu et al. 2019; Li et al. 2019).

Fig. 1
figure 1

Map showing the study area in Poyang Lake, China

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Sampling areas were selected in the Poyang Lake based on considering different geomorphic units, environmental conditions (complex hydraulic and physicochemical variables), spatial distribution and sampling processes (for details, see Tables 1, 2; Li et al. 2019; Simeone et al. 2021; Wang et al. 2021). A total of 17 sampling sites were established across four different sampling areas with unique habitat characteristics. There was a significant difference in complex hydraulic and physicochemical variables among different habitats (ANOVA, p < 0.05). Sampling areas were established as follows: (a) M1 included the lake outflow into the Yangtze River (3 sampling sites); (b) M2 included the connected river channel of Poyang Lake (3 sampling sites); (c) M3 included the main lake area of Poyang Lake (7 sampling sites); (d) M4 included the mouths of the rivers meeting Poyang Lake (4 sampling sites) (Fig. 1).

Table 1 Habitat stability, sediment composition, particle size and water flow velocity in four habitats, Poyang Lake
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Table 2 Mean value and the one-way analysis of variance (ANOVA) of complex hydraulic variables and physicochemical factors in four habitats, Poyang Lake
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Sampling methods

Freshwater mussel surveys were mainly conducted in April, July, and October 2019 and January 2020 (Additional file 1: Table S1). Four replicate surveys were conducted of each site. We used homemade mussel rakes (60 cm wide, 20 mm mesh, rake tooth spacing 15 mm) to collect freshwater mussel samples across the survey site. The homemade mussel rakes were thrown into the water and dragged 50 m with slow uniform speed by a boat. The sampling area (30 m2) was obtained by multiplying the mussel rake mouth width (15 mm) by the dragging distance (50 m). Freshwater mussel samples were poured into a white porcelain container then sorted. Freshwater mussel specimens were then identified and counted. Taxonomic levels of freshwater mussel were mainly based on Liu et al. (1979), He and Zhuang (2013), Liu et al. (2022), MolluscaBase (https://molluscabase.org/index.php) and Graf and Cummings (2022).

Measurement of hydraulic variables and physicochemical factors

The data set of physicochemical factors were obtained from Lu et al. (2021). The hydraulic variables and physicochemical factors were measured on the same day mussel surveys took place in April, July, and October 2019 and January 2020 (Additional file 1: Table S1). We calculated a number of lake descriptors for each sampling areas, including: dissolved oxygen (DO; mg/L), hydrogen ions (pH), salinity (Sal; mg/L), turbidity (TURB; NTU), and water temperature (T; °C), chlorophyll-a (Chl-a; mg/L), water velocity (V; m/s), water depth (WD; m), Total nitrogen (TN; mg/L) and total phosphorus (TP; mg/L) (Table 3). The substrate samples were collected using a modified Petersen grab (area of 1/16 m2), which were then bagged and shipped to the laboratory. The substrate samples in the laboratory were first oven-dried at 105 °C for 24 h (Gordon et al. 2004). The substrate samples were sieved using mesh sieves and laser diffraction particle size analyzer (LS13320; for details see Table 1). The complex hydraulic variables were calculated according to the value of complex hydraulic variables in Table 2 and formula in Table 3.

Table 3 Calculation of complex hydraulic variables for four habitats in Poyang Lake
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Data analysis

To predict habitat suitability of freshwater mussels, a Random Forest Model (RMRF) was used to assess the correlation between density and complex hydraulic and physicochemical variables in the randomForest package (Liaw and Wiener 2002) in GNU R 4.0.1 (R Core Team 2020). The repeated measures random forest (RMRF) was used to consider the possible independence and potential pseudo-duplication of randomly selected replicates within each sampling areas (Calhoun et al. 2021). The raw data of freshwater mussel density was log transformed by log (x + 1) transformation (Simeone et al. 2021). We overtrained the model by guiding the selection of sub-samples from the entire data set by bootstrapping (Simeone et al. 2021). We then ran the training model step-by-step to increase the number of predictors used for group splitting (mtry functions; Liaw and Wiener 2002; Simeone et al. 2021). The following settings were tested: mtry = 1 to 8 for physicochemical variables and mtry = 1 to 9 for hydraulic variables. These models were ran five times using different random seeds (ntree function = 200, 400, 600, 800 and 1000) at each level of mtry. Observations not included in the bootstrap subsample were defined as out-of-bag (oob) samples and used to create oob estimates for generalized errors in the model (Breiman 2001; Simeone et al. 2021). The optimal number of mtry was selected from the forest with the least generalized error for the final model (Breiman 2001; Liaw and Wiener 2002). We evaluated the relationship between the predictor and the response variable using the increase in mean standard error (MSE, Breiman 2001; Liaw and Wiener 2002).

One-way analysis of variance (ANOVA) performed by the SPSS 22.0 was used to test for significant differences (*: p < 0.05; **: p < 0.01; ***: p < 0.001) between density, complex hydraulic and physicochemical variables among each sampling site. The post hoc tests was used to make further comparisons, Tukey's honestly significant difference test was used to compare group means, but in cases of persistent heteroscedasticity we used Welch tests. Nonmetric multidimensional scaling (NMDS) ordination plots and the Bray–Curtis index were used to assess the variation in the freshwater mussel density among sampling sites. Permutational multivariate analysis of variance (PERMANOVA) was used to determine the significance of differences in the density of freshwater mussels among sampling sites. Heat map analysis was used to analyze the significance of the correlations between the freshwater mussel species density and complex hydraulic and physicochemical variables and t tests were used to analyze significant difference for these correlations. Redundancy analysis (RDA) was used to analyze the correlations between the freshwater mussel species density and complex hydraulic and physicochemical variables. The NMDS ordination plots, Bray–Curtis index, PERMANOVA and heat map analysis were performed in R 4.0.1 (R Core Team 2020) using the VEGAN (Oksanen et al. 2015). CANOCO Version 4.5 (ter Braak and Verdonschot 1995) were used to perform RDA.

Results

Freshwater mussel density in different habitats

There was a significant difference in freshwater mussel density among different sampling areas that contained unique habitat characteristics (ANOVA, Fdf1,df2 = 3.481, p = 0.016). The freshwater mussel density in M3 was the highest, followed by M2, the density in M4 was the lowest (Fig. 2a). The species with the highest density in M1 and M2 was Lanceolaria lanceolata (Fig. 2b). The species with the highest density in M3 and M4 was Nodularia douglasiae and Lamprotula caveata, respectively (Fig. 2b). The NMDS plot showed that there were two different groups of sites, and the structure of freshwater mussel community exhibited significantly spatial change based on the result of PERMANOVA test (F = 2.098, p = 0.014 < 0.05; Fig. 3).

Fig. 2
figure 2

Mean density of freshwater mussels (a) and species density (b) in different habitats, Poyang Lake

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Fig. 3
figure 3

Non-metric multidimensional scaling (NMDS) ordination for the community structure of freshwater mussels in Poyang Lake

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Habitat suitability for freshwater mussels in Poyang Lake based on hydraulic and physicochemical variables

The final RMRF hydraulic and physicochemical model to predict suitable habitats explained 84% and 90% of the total variance for freshwater mussels, and the oob generalized error was 0.21 and 0.19, respectively (Table 4), which indicated predictive ability for these models. The first four predictors ranked were both the key predictors for hydraulic and physicochemical model of freshwater mussels (Table 4). There was a significant difference in D, sorting index, bed roughness, Reynolds number, boundary Reynolds number, shear stress, critical shear stress, water depth, dissolved oxygen, hydrogen ions, water temperature and total nitrogen among different habitats (Table 2). The boundary Reynolds number (Re*), bed roughness (ks), water temperature, pH and transparency in different habitats ranged from 0.01 to 0.09, 0.1 to 0.5, 9 to 28, 5.2 to 8.0 and 20 to 100, respectively, and freshwater mussel density generally decreased for all of these variables (Fig. 4). The critical shear stress range (τc) ranged from 0.015 to 0.080, showing a bimodal trend, with the highest mussel density in 0.015–0.025 and 0.006–0.008 (Fig. 4). The mean sediment particle size (D) ranged from 0.025 to 0.25, showing a unimodal trend, with the highest density in 0.025–0.1 (Fig. 4). The chlorophyll-a ranged from 2.5 to 30.0, and freshwater mussel density generally increased for all of these variables (Fig. 4). Freshwater mussel species were absent in the habitats measured outside of these habitat measurement ranges.

Table 4 Random forest importance ranking of four habitat hydraulic variables and physicochemical factors on freshwater mussel density in Poyang Lake
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Fig. 4
figure 4

Dependence plots based on Random Forest regression, showing the relationship of the hydraulic (a) and physicochemical (b) predictors, with freshwater mussel density, in Poyang Lake, China. Dissolved oxygen (DO; mg/L), hydrogen ions (pH), salinity (Sal; mg/L), turbidity (TURB; NTU), and water temperature (T; °C), chlorophyll-a (Chl-a; mg/L), water velocity (V; m/s), water depth (WD; m), Total nitrogen (TN; mg/L) and total phosphorus (TP; mg/L), D (mm), Sorting index (So, unitless), Bed roughness (ks, mm), Froude number (Fr, unitless), Reynolds number (Re, unitless), Boundary Reynolds number (Re*, unitless), Shear velocity (V, m/s), Shear stress (τ, N/m2). Critical shear stress (τc, N/m2)

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The correlation between the density of each freshwater mussel species and complex hydraulic and physiochemical variables

For complex hydraulic variables, the freshwater mussel density was significantly correlated with Froude number based on heat map analysis (t tests, p < 0.05; Fig. 5a). In addition, Sinosolenaia oleivora was significantly negatively correlated with Reynolds number. Lepidodesma languilati and Lanceolaria lanceolata were both significantly positively correlated with substrate index (Fig. 5a). Sinohyriopsis cumingii was significantly negatively correlated with boundary Reynolds number and Froude number (Fig. 5a). For physicochemical variables, the freshwater mussel density was significantly positively correlated with chlorophyll-a and negatively correlated with water temperature and based on heat map analysis (Fig. 5c). Lamprotula leaii was also significantly correlated with water depth and total nitrogen (Fig. 5c). Anemina arcaeformis and Sinohyriopsis cumingii were also significantly correlated with total nitrogen (Fig. 5c). Nodularia douglasiae and Lanceolaria lanceolata were also significantly correlated with water depth (Fig. 5c). Sinosolenaia oleivora was significantly correlated with transparency, total nitrogen and total phosphorus (Fig. 5c). The redundancy analysis (RDA) showed a similar pattern of correlation to the heat map analysis of complex hydraulic and physicochemical variables, with freshwater mussel density being significantly correlated with Froude number, sorting index, water temperature and chlorophyll-a (Fig. 5b, d).

Fig. 5
figure 5

Correlation between density of freshwater mussel species and hydraulic (a, b) and physicochemical variables (c, d) based on heat map analysis and redundancy analysis (RDA). The clustering dendrograms of heat maps including the distance metrics of density of freshwater mussel species, hydraulic and physicochemical variables used to produce it. Horizontal coordinates are hydraulic and physicochemical variables in heat map analysis, vertical coordinates are density of freshwater mussel species. Red shows positive correlation between density of freshwater mussel species and hydraulic and physicochemical variables. Blue shows negative correlation between density of freshwater mussel species and hydraulic and physicochemical variables. The color patch gradient is used to show the value of R2. The clustering dendrograms on the left and top are the result of clustering of species density (left), hydraulic and physicochemical variables (top), respectively. Significant results are in * (*p < 0.05; **p < 0.01; *** p < 0.001). Dissolved oxygen (DO; mg/L), hydrogen ions (pH), salinity (Sal; mg/L), turbidity (TURB; NTU), and water temperature (T; °C), chlorophyll-a (Chl-a; mg/L), water velocity (V; m/s), water depth (WD; m), Total nitrogen (TN; mg/L) and total phosphorus (TP; mg/L), D (mm), Sorting index (So, unitless), Bed roughness (ks, mm), Froude number (Fr, unitless), Reynolds number (Re, unitless), Boundary Reynolds number (Re*, unitless), Shear velocity (V, m/s), Shear stress (τ, N/m2), Critical shear stress (τc, N/m2). Code of freshwater mussel species see Additional file 2: Table S2

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Discussion

This study was one of the first to analyze the correlation between freshwater mussel density and complex hydraulic and physicochemical variables to predict habitat suitability in the floodplain lake, China. Our results clearly showed that freshwater mussel density would be associated with complex hydraulic and physicochemical variables.

Many previous studies estimated the relationship between hydraulic variables and freshwater mussel density, but the relationship between complex hydraulic variables and freshwater mussel density explained more community variability than did simple hydraulic variables (Steuer et al. 2008; Allen and Vaughn 2010; Simeone et al. 2021). Simple hydraulic variables, such as current velocity and substrate type, did not reflect the real impact on flow, so they produced weak predictions for the identification of these habitats (Hardison and Layzer 2001; Allen and Vaughn 2010). Complex hydraulic conditions can be more important factors for the habitat suitability of freshwater mussels (Allen and Vaughn 2010), due to the fact they may change with flow conditions (Morales et al. 2006; Newton et al. 2008; Drew et al. 2018). Some studies have showed the upper limit of near-bed complex hydraulic conditions as an important predictor of mussel distribution (Steuer et al. 2008; Allen and Vaughn 2010; Simeone et al. 2021). Our results showed that four hydraulic variables (boundary Reynolds number, critical shear stress, bed roughness and mean sediment particle size) were key factors for predicting suitable habitats of freshwater mussels, which indicated habitats with low hydrodynamic energy were more suitable for many freshwater mussels. Freshwater mussel density was usually the highest in the low flow period, while those habitats with low hydrodynamic energy in the high flow period were more likely to provide shelter habitats for mussels (Simeone et al. 2021). The hydrodynamic conditions in this study area were different (Li et al. 2019). For example, M2 was located in the inside shoreline bend of the lake and maintained a lower hydrodynamic condition and substrate stability in the high flow period, which provided shelter habitat for the survival of freshwater mussels (Hegeman et al. 2014; Simeone et al. 2018; Quinlan et al. 2015). M3 was located in the lake area and had low hydrodynamic energy and substrate stability, which may increase the survival of many freshwater mussels.

Habitat stability was positively correlated with freshwater mussel density, confirming similar studies in rivers or streams (Zigler et al. 2008; Randklev et al. 2019; Steuer et al. 2008; Simeone et al. 2021). Habitats with stable substrates are more likely to provide shelter for many freshwater mussels (Wilson et al. 2011; Mansur and Pereira 2006), and our studies confirmed that this view is an important factor for the survival of freshwater mussels in Poyang Lake. The shear stress is strongly correlated with the habitat stability. Low shear stress may provide a hydraulic shelter for freshwater mussels, while high shear stress may limit the habitat suitability of them (Gangloff and Feminella 2007; Steuer et al. 2008; Stoeckl and Geist 2016). In addition, habitat stability was associated with Froude number and Reynolds number (Simeone et al. 2021), because Froude number and Reynolds number are good predictors of habitat stability and describe flow conditions (Gordon et al. 2004). Our results also confirmed that boundary Reynolds number and critical shear stress were key factors for predicting suitable habitats of freshwater mussels. At the same time, the freshwater mussel density was significantly correlated with Froude number in this study.

The spatial difference and the complexity of habitat characteristics affected the community structure of freshwater mussels (Haag 2012). Many studies showed that the complex habitat heterogeneity had higher diversity of freshwater mussels (Daniel and Brown 2014; Sun et al. 2019). This study showed that the community structure of freshwater mussels in Poyang Lake showed spatial differences. Freshwater mussels are sensitive to environmental change, and species with stronger adaptability to environmental change may become dominant (Bogan 2008; Vaughn 2018). Information on the correlation between freshwater mussels and habitat characteristics is considered as the key way to protect endangered freshwater mussels (Williams et al. 1993; Vaughn 2018; Lopez and Vaughn 2021), and we may apply that information to make decisions involving habitat management. The habitat characteristics of freshwater mussels are different (Vaughn 2018). For example, species within Nodularia and Sinanodonta have extensive habitats in lakes, rivers, reservoirs and ponds with mud substrates, while species within Aculamprotula and Sinosolenaia prefer habitats with rapid water flow, clear water and hard mud substrate (Liu et al. 2022). Some studies have also shown that habitat characteristics, such as physicochemical factors affected the survival of freshwater mussels, such as water depth, current velocity, pH, transparency, water temperature and chlorophyll-a (Strayer and Ralley 1993; Vaughn et al. 2004; Nakano et al. 2007; Zieritz et al. 2016; Simeone et al. 2018; Sun et al. 2019). For example, some studies showed that the survival and growth of freshwater mussels was significantly correlated with turbidity (Osterling et al. 2008; Sun et al. 2019). Water temperature is an important physical factor driving the change of freshwater mussel community structure, which affects growth and reproduction of mussels (Clarke 2010; Xiong et al. 2012; Su et al. 2014; Yang et al. 2011). In addition, some studies showed higher freshwater mussel density occurred in environments with lower pH (Simeone et al. 2018). Our results showed that four physicochemical variables (water temperature, chlorophyll-a, transparency and pH) were key factors for predicting suitable habitats of freshwater mussels. Freshwater mussel density was significantly correlated with water temperature and chlorophyll-a.

Habitat loss and fragmentation are usually considered one of the most important threats to freshwater mussels (Lopes-Lima et al. 2017; Böhm et al. 2021; Liu et al. 2022). When attempting to preserve declining freshwater mussel populations, habitat restoration and suitability is suggested as one of the most effective methods for increasing freshwater mussel diversity (Cope and Waller 2010). Direct physical habitat disturbance, such as dams and sand mining, resulting in alteration of flow regimes and habitat fragmentation of rivers or lakes, usually leads to mussel diversity and abundance decline (Mueller et al. 2011). Knowledge of the correlation between freshwater mussel distribution, density and hydraulic and physicochemical variables for predicting suitable habitats is important for maintaining and restoring their biodiversity and ecological functions (Haag and Williams 2014; Dobler et al. 2019; Smit and Kaeser 2016). Our results provide insights into habitat suitability of freshwater mussels for Poyang Lake correlated with four key hydraulic variables (boundary Reynolds number, critical shear stress, bed roughness and mean sediment particle size) and four key physicochemical variables (water temperature, chlorophyll-a, transparency and pH). These results showed that hydrodynamics habitat was important for maintaining freshwater mussel populations. Therefore, management plans should consider habitat diversity in terms of hydrodynamics types, which are important for freshwater mussels and other aquatic diversity. In addition, landscape design should give priority to the protection of shoreline buffer habitats to maintain the freshwater mussel populations and habitat quality.

Availability of data and materials

All data will be available in the data center of Nanchang University (http://www.ncu.edu.cn/) after publication.

References

  • Allen DC, Vaughn CC (2010) Complex hydraulic and substrate variables limit freshwater mussel species richness and abundance. J N Am Benthol Soc 29:383–394

    Article  Google Scholar 

  • Amoros C, Bornette G (2002) Connectivity and biocomplexity in waterbodies of riverine floodplains. Freshw Biol 47:761–776

    Article  Google Scholar 

  • Bogan AE (2008) Global diversity of freshwater mussels (Mollusca, Bivalvia) in freshwater. Hydrobiologia 595(1):139–147

    Article  Google Scholar 

  • Bohm M, Dewhurst-Richman NI, Seddon M, Ledger SEH, Albrecht C, Allen D, Bogan A, Cordeiro J, Cummings KS, Cuttelod A, Darrigran G, Darwall W, Fehér Z, Gibson C, Graf DL, Köhler F, Lopes-Lima M, Pastorino G, Perez K, Smith K, Damme DV, Vinarski M, Proschwitz T, Rintelen T, Aldridge DC, Aravind NA, Budha P, Clavijo C, Do VT, Olivier G, Ghamizi M, Haase M, Hilton-Taylor C, Johnson PD, Kebapçı U, Lajtner J, Lange CN, Lepitzki D, Martínez-Ortí A, Moorkens E, Neubert E, Pollock CM, Prié V, Radea C, Ramírez R, Ramos MA, Santos SB, Slapnik R, Son M, Stensgaard A, Collen B (2021) The conservation status of the world’s freshwater molluscs. Hydrobiologia 848(12):3231–3254

    Article  Google Scholar 

  • Box JB, Dorazio RM, Liddell WD (2002) Relationships between streambed substrate characteristics and freshwater mussels (Bivalvia: Unionidae) in Coastal Plain streams. J N Am Benthol Soc 21(2):253–260

    Article  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  • Calhoun P, Levine RA, Fan J (2021) Repeated measures random forests (RMRF): Identifying factors associated with nocturnal hypoglycemia. Biometrics 77:343–351

    Article  Google Scholar 

  • Cao Y, Huang J, Cummings KS, Holtrop A (2013) Modeling changes in freshwater mussel diversity in an agriculturally dominated landscape. Freshw Sci 32:1205–1218

    Article  Google Scholar 

  • Cao Y, Stodola A, Douglass S, Shasteen D, Cummings K, Holtrop A (2015) Modelling and mapping the distribution, diversity and abundance of freshwater mussels (Family Unionidae) in wadeable streams of Illinois, U.S.A. Freshw Biol 60:1379–1397

    Article  Google Scholar 

  • Clarke LR (2010) Population density and growth of the freshwater mussel Anodonta californiensis in a flow-fragmented stream. J Freshwater Ecol 25(2):179–192

    Article  Google Scholar 

  • Colle AC, Callil CT (2012) Environmental influences on the composition and structure of the freshwater mussels in shallow lakes in the Cuiaba River floodplain. Braz J Biol 72:249–256

    Article  CAS  Google Scholar 

  • Cope WG, Waller DL (2010) Evaluation of freshwater mussel relocation as a conservation and management strategy. River Res Appl 11(2):147–155

    Google Scholar 

  • Daniel WM, Brown KM (2013) Multifactorial model of habitat, host fish, and landscape effects on Louisiana freshwater mussels. Freshw Sci 32:193–203

    Article  Google Scholar 

  • Dobler AH, Geist J, Stoeckl K, Inoue K (2019) A spatially explicit approach to prioritize protection areas for endangered freshwater mussels. Aquat Conserv 29:12–23

    Article  Google Scholar 

  • Drew CA, Eddy M, Kwak TJ, Cope WG, Augspurger T (2018) Hydrologic characteristics of freshwater mussel habitat: Novel insights from modeled flows. Freshw Sci 37:343–356

    Article  Google Scholar 

  • Ferreira-Rodrıguez N, AkiyamaYB AOV, Araujo R, Christopher Barnhart M, Bespalaya YV, Bogan AE, Bolotov IN, Budha PB, Clavijo C, Clearwater SJ, Darrigran G, Do VT, Douda K, Froufe E, Gumpinger C, Henrikson L, Humphrey CL, Johnson NA, Klishko O, Klunzinger MW, Kovitvadhi S, Kovitvadhi U, Lajtner J, Lopes-Lima M, Moorkens EA, Nagayama S, Nagel KO, Nakano M, Negishi JN, Ondina P, Oulasvirta P, Prie V, Riccardi N, Rudzıte M, Sheldon F, Sousa R, Strayer DL, Takeuchi M, Taskinen J, Teixeira A, Tiemann JS, Urbanska M, Varandas S, Vinarski MV, Wicklow BJ, Zajac T, Vaughn CC (2019) Research priorities for freshwater mussel conservation assessment. Biol Conserv 231:77–87

    Article  Google Scholar 

  • Gangloff MM, Feminella JW (2007) Stream channel geomorphology influences mussel abundance in southern Appalachian streams, U.S.A. Freshw Biol 52:64–74

    Article  Google Scholar 

  • Geist J (2010) Strategies for the conservation of endangered freshwater pearl mussels (Margaritifera margaritifera L.): a synthesis of conservation genetics and ecology. Hydrobiologia 644(1):69–88

    Article  Google Scholar 

  • Goodding DD, Williams MG, Ford DF, Williams LR, Ford NB (2019) Associations between substrate and hydraulic variables and the distributions of a sculptured and an unsculptured unionid mussel. Freshw Sci 38:543–553

    Article  Google Scholar 

  • Gordon ND, McMahon TM, Finlayson BL, Gippel CJ, Nathan RJ (2004) Stream hydrology: an introduction for ecologists. Wiley, Hoboken

    Google Scholar 

  • Gough HM, Landis AMG, Stoeckel JA (2012) Behavior and physiology are linked in the responses of freshwater mussels to drought. Freshwater Biol 57(11):2356–2366

    Article  Google Scholar 

  • Graf DL, Cummings KS (2022) The MUSSEL project database. http://musselproject.uwsp.edu/db. Accessed 4 Jan 2022.

  • Haag WR (2012) North American freshwater mussels: Natural history, ecology, and conservation. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Haag WR, Williams JD (2014) Biodiversity on the brink: an assessment of conservation strategies for North American freshwater mussels. Hydrobiologia 735:45–60

    Article  Google Scholar 

  • He J, Zhuang Z (2013) The Freshwater Bivalves of China. ConchBooks, Harxheim

    Google Scholar 

  • Hegeman EE, Miller SW, Mock KE (2014) Modeling freshwater mussel distribution in relation to biotic and abiotic habitat variables at multiple spatial scales. Can J Fish Aquat Sci 71:1483–1497

    Article  Google Scholar 

  • Holland-Bartels LE (1990) Physical factors and their influence on the mussel fauna of a main channel border habitat of the Upper Mississippi River. J N Am Benthol Soc 9(4):327–335

    Article  Google Scholar 

  • Ilarri MI, Amorim L, Souza AT, Sousa R (2018) Physical legacy of freshwater bivalves: effects of habitat complexity on the taxonomical and functional diversity of invertebrates. Sci Total Environ 634:1398–1405

    Article  CAS  Google Scholar 

  • Johnson GC, Krstolic JL, Ostby BJK (2014) Influences of water and sediment quality and hydrologic processes on mussels in the Clinch River. J Am Water Resour Assoc 50(4):878–897

    Article  Google Scholar 

  • Li K, Liu XJ, Zhou Y, Xu Y, Lv Q, Ouyang S, Wu XP (2019) Temporal and spatial changes in macrozoobenthos diversity in Poyang Lake Basin, China. Ecol Evol 9(11):6353–6365

    Article  Google Scholar 

  • Liaw A, Wiener M (2002) Classification and regression by random-Forest. R News 2:18–22

    Google Scholar 

  • Liu YY, Zhang WZ, Wang YX (1979) Economic Fauna of China: Freshwater Mollusks. Science Press, Beijing

    Google Scholar 

  • Liu XJ, Wu RW, Lopes-Lima M, Xue TT, Zhou Y, Li K, Xu Y, Qin JJ, Ouyang S, Wu XP (2020) Changes and drivers of freshwater mussel diversity patterns in the middle and lower Yangtze River Basin, China. Global Ecol Conserv 22:e00998

    Article  Google Scholar 

  • Liu XJ, Liu YY, Wu RW, Zanatta DT, Lopes-Lima M, Gonçalves DV, Bogan A, Ouyang S, Wu XP (2022) Systematics, distribution, biology, and conservation of freshwater mussels (Bivalvia: Unionida) in China. Aquat Conserv 32(5):859–895

    Article  Google Scholar 

  • Lopes-Lima M, Sousa R, Geist J, Aldridge DC, Araujo R, Bergengren J, Bespalaya Y, Bódis E, Burlakova L, Damme DV, Douda K, Froufe E, Georgiev D, Gumpinger C, Karatayev A, Kebapci U, Killeen I, Lajtner J, Larsen BM, Lauceri R, Legakis A, Lois S, Lundberg S, Moorkens E, Motte G, Nagel KO, Ondina P, Outeiro A, Paunovic M, PriéV PTV, Riccardi N, Rudzite M, Rudzitis M, Scheder C, Seddon M, Sereflisan H, Simic V, Sokolova S, Stoeckl K, Taskinen J, Teixeira A, Thielen F, Trichkova T, Varandas S, Vicentini H, Zajac K, Zajac T, Zogaris S (2017) Conservation status of freshwater mussels in Europe: state of the art and future challenges. Biol Rev 92(1):572–607

    Article  Google Scholar 

  • Lopez JW, Vaughn CC (2021) A review and evaluation of the effects of hydrodynamic variables on freshwater mussel communities. Freshw Biol 66:1665–1679

    Article  Google Scholar 

  • Lu QF, Liu XJ, Qiu XM, Liao T, Chen JP, Zhao S, Ouyang S, Jin BS, Wu XP (2021) Changes and drivers of zooplankton diversity patterns in the middle reach of Yangtze River floodplain lakes, China. Ecol Evol 11:17885–17900

    Article  Google Scholar 

  • Lummer EM, Auerswald K, Geist J (2016) Fine sediment as environmental stressor affecting freshwater mussel behavior and ecosystem services. Sci Total Environ 571:1340–1348

    Article  CAS  Google Scholar 

  • Mansur MCD, Pereira D (2006) Bivalves límnicos da bacia do rio dos Sinos, Rio Grande do Sul, Brasil (Bivalvia, Unionoida, Veneroida e Mytiloida). Revista Brasileira De Zoologia 23:1123–1147

    Article  Google Scholar 

  • Morales Y, Weber LJ, Mynett AE, Newton TJ (2006) Effects of substrate and hydrodynamic conditions on the formation of mussel beds in a large river. J N Am Benthol Soc 25:664–676

    Article  Google Scholar 

  • Mueller M, Pander J, Geist J (2011) The effects of weirs on structural stream habitat and biological communities. J Appl Ecol 48(6):1450–1461

    Article  Google Scholar 

  • Nakano M, Yamamoto T, Takakura KI, Urabe M (2007) Unionid freshwater mussels in irrigation ditches are affected by physical environmental factors and proximity to paddy fields. Zool Sci 32(4):378–382

    Google Scholar 

  • Newton TJ, Woolnough DA, Strayer DL (2008) Using landscape ecology to understand and manage freshwater mussel populations. J N Am Benthol Soc 27:424–439

    Article  Google Scholar 

  • Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, OHara R, Simpson GL, Solymos P, Stevens H, Wagner H (2015) vegan: Community Ecology Package. R package version 2.3–2. https://cran.r-project.org Accessed 8 June 2015

  • Österling M, Greenberg LA, Arvidsson B (2008) Relationship of biotic and abiotic factors to recruitment patterns in Margaritifera margaritifera. Biol Conserv 141:1365–1370

    Article  Google Scholar 

  • Quinlan E, Gibbins C, Malcolm I, Batalla R, Vericat D, Hastie L (2015) A review of the physical habitat requirements and research priorities needed to underpin conservation of the endangered freshwater pearl mussel Margaritifera margaritifera. Aquat Conserv 25:107–124

    Article  Google Scholar 

  • R Core Team (2020) R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.r-project.org/.

  • Randklev CR, Hart MA, Khan JM, Tsakiris ET, Robertson CR (2019) Hydraulic requirements of freshwater mussels (Unionidae) and a conceptual framework for how they respond to high flows. Ecosphere 10:e02975

    Article  Google Scholar 

  • Schindler S, Sebesvari Z, Damm C, Euller K, Mauerhofer V, Schneidergruber A, Biró M, Essl F, Kanka P, Lauwaars SG, Schulz-Zunkel C, Sluis T, Kropik M, Gasso V, Krug A, Pusch M, Zulka KP, Lazowski W, Hainz-Renetzeder C, Henle K, Wrbka T (2014) Multifunctionality of floodplain landscapes: Relating management options to ecosystem services. Landscape Ecol 29:229–244

    Article  Google Scholar 

  • Shu FY, Wang HJ, Pan BZ, Liu XQ, Wang HZ (2009) Assessment of species status of mollusca in the mid lower Yangtze lakes. Acta Hydrobiol Sin 33(6):1051–1058

    Article  Google Scholar 

  • Silva AMF, Yalin MS (2017) Fluvial processes. Taylor & Francis Group, Milton Park

    Book  Google Scholar 

  • Simeone D, Santos C, Gisane F, Tagliaro CH, Beasley CR (2018) Greater macroinvertebrate diversity and freshwater mussel density in meander margins of an Amazon river. Freshw Biol 63:1118–1129

    Article  CAS  Google Scholar 

  • Simeone D, Tagliaro CH, Beasley CR (2021) Novel insights into habitat suitability for Amazonian freshwater mussels linked with hydraulic and landscape drivers. Ecol Evol 11(17):11786–11798

    Article  Google Scholar 

  • Smit R, Kaeser A (2016) Defining freshwater mussel mesohabitat associations in an alluvial, coastal plain river. Freshw Sci 35:1276–1290

    Article  Google Scholar 

  • Steuer JJ, Newton TJ, Zigler SJ (2008) Use of complex hydraulic variables to predict the distribution and density of unionids in a side channel of the Upper Mississippi River. Hydrobiologia 610:67–82

    Article  Google Scholar 

  • Stoeckl K, Geist J (2016) Hydrological and substrate requirements of the thick-shelled river mussel Unio crassus (Philipsson 1788). Aquat Conserv 26:456–469

    Article  Google Scholar 

  • Strayer DL (1999) Use of flow refuges by unionid mussels in rivers. J N Am Benthol Soc 18:468–476

    Article  Google Scholar 

  • Strayer DL (2008) Freshwater mussel ecology: a multifactor approach to distribution and abundance. University of California Press, London

    Book  Google Scholar 

  • Strayer DL, Ralley J (1993) Microhabitat use by an assemblage of stream-dwelling Unionaceans (Bivalvia), including two rare species of Alasmidonta. J N Am Benthol Soc 12(3):247–258

    Article  Google Scholar 

  • Su YP, Chen XJ, Liu H, Wu XG, Yang J (2014) The dietary algae at different juvenile stages of Anodonta woodiana. J Fish Sci China 21(4):736–746

    Google Scholar 

  • Sun WW, Liu XJ, Wu RW, Wang WK, Wu YL, Ouyang S, Wu XP (2019) Declining freshwater mussel diversity in the middle and lower reaches of the Xin River Basin: threat and conservation. Ecol Evol 9(24):14142–14153

    Article  Google Scholar 

  • ter Braak CJF, Verdonschot PFM (1995) Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquat Sci 57:255–289

    Article  Google Scholar 

  • Vaughn CC (2018) Ecosystem services provided by freshwater mussels. Hydrobiologia 810(3):15–27

    Article  Google Scholar 

  • Vaughn CC, Gido KB, Spooner DE (2004) Ecosystem processes performed by unionid mussels in stream mesocosms: Species roles and effects of abundance. Hydrobiologia 35:35–47

    Article  Google Scholar 

  • Vörösmarty CJ, Mcintyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, Glidden S, Bunn SE, Sullivan C, Liermann SR, Davies PM (2010) Global threats to human water security and river biodiversity. Nature 467(11):555–561

    Article  Google Scholar 

  • Wang SY, Gao Y, Jia JJ, Kun S, Lyu SD, Li ZX, Lu Y, Wen XF (2021) Water level as the key controlling regulator associated with nutrient and gross primary productivity changes in a large floodplain-lake system (Lake Poyang), China. J Hydrol 599:126414

    Article  CAS  Google Scholar 

  • Ward JV, Tockner K, Schiemer F (1999) Biodiversity of floodplain river ecosystems: ecotones and connectivity. River Res Appl 15:125–139

    Google Scholar 

  • Williams JD, Warren ML, Cummings KS, Harris JL, Neves RJ (1993) Conservation status of freshwater mussels of the United States and Canada. Fisheries 18:6–22

    Article  Google Scholar 

  • Wilson CD, Roberts D, Reid N (2011) Applying species distribution modelling to identify areas of high conservation value for endangered species: a case study using Margaritifera margaritifera (L.). Biol Conserv 144:821–829

    Article  Google Scholar 

  • Wu XP, Liang YL, Wang HZ, Xie ZC, Ouyang S (2000) Distribution and species diversity of freshwater Mollosca of lakes along mid-lower reaches of the Yangtze river. J Lake Sci 12:111–118

    Article  Google Scholar 

  • Wu ZS, Liu JT, Huang JC, Cai YJ, Chen YW, Li KY (2019) Do the key factors determining phytoplankton growth change with water level in China’s largest freshwater lake? Ecol Indic 107:105675

    Article  Google Scholar 

  • Xiong LF, Ouyang S, Wu XP (2012) Fauna and standing crop of freshwater mussels in Poyang Lake, China. J Oceanol Limnol 30:124–135

    Article  Google Scholar 

  • Yang XL, Li HC, Song L (2011) Gonadal develpment and growth of freshwater mussel Solenaia oleivora. Fish Sci 30(9):580–582

    Google Scholar 

  • Zieritz A, Lopes-Lima M, Bogan AE, Sousa R, Walton S, Adha K, Wilson JJ, Ng PY, Froufe E, McGowan S (2016) Factors driving changes in freshwater mussel (Bivalvia, Unionida) diversity and distribution in Peninsular Malaysia. Sci Total Environ 571:1069–1078

    Article  CAS  Google Scholar 

  • Zieritz A, Mahadzir FN, Chan WN, McGowan S (2019) Effects of mussels on nutrient cycling and bioseston in two contrasting tropical freshwater habitats. Hydrobiologia 835:179–191

    Article  CAS  Google Scholar 

  • Zigler SJ, Newton TJ, Steuer JJ, Bartsch MR, Sauer JS (2008) Importance of physical and hydraulic characteristics to unionid mussels: a retrospective analysis in a reach of large river. Hydrobiologia 598:343–360

    Article  Google Scholar 

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Acknowledgements

This study was supported from the National Key R&D Program of China (2018YFD0900801) and Educational Commission of Guangdong Province of China (2021ZDZX4054). The authors report no conflict of interest. The authors alone are responsible for the content and writing of this article.

Funding

This work was supported by the National Key R&D Program of China (2018YFD0900801) and Educational Commission of Guangdong Province of China (2021ZDZX4054).

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Authors and Affiliations

  1. School of Life Sciences, Nanchang University, Nanchang, 330031, China

    Chenchen Jia, Chenhuizi Wu, Xiaochen Huang, Chunhua Zhou, Shan Ouyang & Xiaoping Wu

  2. Guangdong Provincial Key Laboratory of Conservation and Precision Utilization of Characteristic Agricultural Resources in Mountainous Areas, School of Life Sciences, Jiaying University, Meizhou, 514015, China

    Xiongjun Liu

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Contributions

JCC, LXJ, OYS and WXP conceived the study. All authors contributed to the study design and data collection. JCC and LXJ analyzed the data. JCC, LXJ, OYS and WXP led the writing of the manuscript. All authors read and approved the final manuscript.

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Supplementary Information

Additional file 1: Table S1.

Surveys of freshwater mussel density and measurement of hydraulic variables and physicochemical factors in Poyang Lake.

Additional file 2: Table S2.

Code of freshwater mussel species.

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Jia, C., Wu, C., Huang, X. et al. Effect of complex hydraulic variables and physicochemical factors on freshwater mussel density in the largest floodplain lake, China. Ecol Process 12, 15 (2023). https://doi.org/10.1186/s13717-023-00427-y

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