Effects of land use characteristics, physiochemical variables and river connectivity on fish assemblages in a lowland basin

doi:10.21203/rs.3.rs-2102552/v1

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Effects of land use characteristics, physiochemical variables and river connectivity on fish assemblages in a lowland basin

https://doi.org/10.21203/rs.3.rs-2102552/v1

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Human disturbances can have severe environmental impacts on freshwater ecosystems. The main aim of this study was to detect the influences of physiochemical variables, land use characteristics, and river connectivity on fish assemblages in Lake Chaohu Basin, China. A cluster analysis of the river connectivity variables identified four groups of sites that were characterized by significantly different river connectivity and longitudinal gradients in local scale. These four groups of sites showed increasing connectivity from the upper reaches to the lower reaches. Among the four groups, the values of the environmental variables generally increased from the upper reaches with less human activity towards the lower reaches with more human activities. In contrast, fish taxa richness and diversity indices were not significantly different among the four connectivity groups. However, fish assemblages showed significant variation among the connectivity groups. In addition, the study determined that upper riparian land uses, flow velocity, and elevation were environmental variables regulating the variance in the fish communities, and for the connectivity variables, only river order and number of branches along a path to the left of the main stem affected the variance in the fish communities. Therefore, new practices aimed at maintaining and even increasing the riparian canopy coverage and the flow velocity of rivers should be integrated into local conservation planning for freshwater ecosystems.

Fish assemblages

River connectivity

Land use

Physiochemical variables

Lowland basin

Spatial gradient

The species composition, abundance, and distribution of fish assemblages can be affected by both natural conditions and human activities along the spatial gradients of rivers (e.g., along gradients of upper reach-lower reaches) (He et al. 2017). Many natural factors caused by elevation gradients, such as temperature, precipitation, and flow velocity, co-determine species richness and species distribution patterns (Valenzuela-Aguayo et al. 2020). For instance, elevation and several physiochemical variables (e.g., water depth, river width, flow velocity, and conductivity) were the variables that most affected the fish distributions in Ivinhema River Basin. However, some studies have found that water temperature is one of the most important natural factors influencing fish growth and production (He et al. 2018), which also determine how many fish species can live and grow. In addition, elevation, catchment area, and river longitudinal gradients primarily determine the fish assemblages (Shao et al. 2019; Yu et al. 2022; Zhang et al. 2018).

Anthropogenic disturbances (e.g., land use changes, dam construction) can also influence fish assemblages (Mazur et al. 2022). Increased sediment and pollutant emissions and reduced flow velocity stability derived from land use changes (e.g., changes from woodland to cropland and/or built-up land) can reduce the species richness of fish communities (Shao et al. 2019). Dam construction is considered one of the major threats to the biodiversity of aquatic organisms because dams can alter the migratory patterns of species, influence the distribution of species, and block the ecological processes of natural rivers (He et al. 2017). For instance, the construction of Gezhou Dam led to significant losses of several endemic fish species in the Yangtze River because the dam prevented the upstream pathway of fish migration and substantially decreased their breeding grounds. Although a large number of studies have tried to determine the factor with the greatest influence on fish assemblages, the relative importance of isolated and combined effects of natural conditions and anthropogenic disturbances on freshwater fish still needs to be determined for different regions, as these factors are characterized by a range of spatial and temporal scales (Chen et al. 2022).

Recently, numerous studies have focused on the influences of loss of connectivity and fragmentation of habitats generally caused by dam construction and land use changes on fish assemblages (He et al. 2017; Valenzuela-Aguayo et al. 2020). One of the main reasons for focusing on these influences is that many connectivity-related variables are the most important factors affecting freshwater fish assemblages in rivers (He et al. 2017; Mazur et al. 2022; Yan et al. 2011). River connectivity (e.g., hydrologic connectivity) can be defined as the water-mediated transport of matter, energy, and/or organisms within or between elements of the hydrologic cycle (Mazur et al. 2022). Therefore, river connectivity has an important influence on the variance among freshwater fish species. For instance, the connectivity of lakes, which is correlated with lake size, depth, and distance from rivers, is crucial to structuring the fish assemblages in the fluvial lakes of the Mississippi River (Miranda 2005). Although very important to the contribution of connectivity-related variables to fish distribution, the combined influence of natural conditions and human activities have been proposed to be more important than the isolated influence of losses of connectivity on freshwater fish species distributions (Shao et al. 2019; Valenzuela-Aguayo et al. 2020).

Generally, fish assemblages are likely to be affected by many factors; however, in this study, we focused on river connectivity, land use, and some common physicochemical factors in local scale. Therefore, the aims of this study were to test the hypothesis that connectivity variables regulate fish assemblages. Moreover, we tried to identify the key environmental variables that currently structure the fish assemblages in a lowland basin of China (Lake Chaohu Basin) and determine which factor was the most important.

2.1 Study area

The Lake Chaohu Basin is located in the lower reaches of the Yangtze River, with an area of 1.41×104 km² (Fig. 1). The average elevation within this area is approximately 65 m and ranges from 0 to 1498 m (higher in the west and lower in the east). More than 70% of the total area is lowland plain, with elevations ranging from 0–50 m (Gao et al. 2016). Woodland is the main land use type in the western mountains, and cropland is the main land use type in the eastern plains (Fig. 1). Generally, the most prevalent land use type in Lake Chaohu Basin was cropland (60.1% of total area), followed by woodland (15.2%), built-up land (11.8%), water bodies (8.7%) and grassland (4.2%) in 2015.

There is a high-density river network with more than 33 rivers centripetally distributed around Lake Chaohu (Gao et al. 2016). Most of the inflow comes from the Hangbu River, Baishitian River, and Nanfei River to Lake Chaohu (Zhang et al. 2018). However, only one river, the Yuxi River, links the lake to the Yangtze River. The Hangbu River has the highest drainage density, followed by the Baishitian River, Zhaoxi River (including the Zhao River and Yuxi River), Pai River, Nanfei River, and Zhegao River (Gao et al. 2016).

Fish sampling

The fish assemblages were surveyed by electrofishing along the studied reaches (~ 40-m long) in April 2013, and the sampling encompassed complete sets of the characteristic stream forms (e.g., pools and riffles) (Datry et al. 2016). At each site, the upstream and downstream ends of the sampled area were blocked by closing nets (1-mm mesh size). All captured fish were weighed (0.1 g), and their body lengths were measured (1 mm). Identifiable specimens were identified to the lowest practical level (usually species) and were released back into the stream in the field. Some unidentifiable specimens, which preserved in 10% formalin, would send to the laboratory and identify according to more characterizes (He et al. 2017).

2.2 Measured environmental variables

2.2.1 Local-scale physiochemical variables

A set of physiochemical variables was surveyed during the sampling period. The elevation at each site was recorded by GPS (Garmin GPSmap 629sc). The channel width at each site was recorded in the field by a laser rangefinder (SNDWAY SW-1500A). The water depth (Depth), river width (Width), water temperature (Temp), pH, and flow velocity (Flow) were recorded along cross-stream transects at 3- to 5-m intervals (depending on the stream size), with sampling points spaced 1-m apart (Datry et al. 2016). Flow velocity was recorded by a flow meter (LS300-A). An integrated water sample was collected using a 1-L organic glass water sampler. All the water samples were kept at 4°C prior to the analyses. Other physicochemical factors, including dissolved oxygen (DO), electrical conductivity (EC), total dissolved solid (TDS), alkalinity (Alka), turbidity (Turb), dissolved organic carbon (DOC), total nitrogen (TN), ammonium (NH₄⁺-N), nitrate (NO₃^--N), total phosphorus (TP), and phosphate (PO₄³⁺-P), were measured in the laboratory. After extraction in 90% hot ethanol of water samples, Chlorophyll a (Chl-a) concentrations was detected by Spectrophotometry (Pápista et al. 2002).

2.2.2 Land use characteristics

Riparian land use characteristics in a 1-km buffer zone along a segment where the sampling site was located were determined using land use maps (Landsat ETM + images with 30-m resolution 2015) in Lake Chaohu Basin (Fig. 1), with five categories: cropland, woodland, grassland, water body and built-up land. The land use categories referred to Liu et al. (2005). Land use should not have changed significantly between 2013 and 2015. Therefore, the land uses in 2015 were used to detect the land use characteristics during the sampling period. Specifically, segments from the nearest upstream confluence site and the nearest downstream confluence site to the sampling site were also employed to create the 1-km buffer zones. These buffer zones were used to assess the influence of land use in each buffer zone on the fish assemblages according to our research (unpublished data). The percentage of each category of land use in each buffer zone was calculated using ArcGIS 10.1 (Environmental Systems Research Institute, Redlands, CA, U.S.A.).

2.2.3 River connectivity variables

In this study, the several common river connectivity variables were selected, which include river order, stream-link magnitude, branch link, confluence link, downstream link, upstream segment length, and downstream segment length. Specifically, river order (ROrder) was determined using the Strahler’s method. Stream-link magnitude (Link) is the number of unbranched source streams upstream of a given segment in a river network. The branch link (BLink) is the number of branches along a path to the right (BLink_R) or left (BLink_Lf) of the main stem (i.e., BLink = BLink_R–BLink_Lf). Confluence link (CLink) is the number of confluences downstream from each stream segment. Downstream link (DLink) is the link magnitude of the next downstream confluence (Osborne & Wiley 1992). Upstream-segment length (Up_L) is the distance from the nearest upstream confluence site to the sampling site in the same river segment, and downstream-segment length (Down_L) is the distance from the nearest downstream confluence site. Additionally, the location of the sampling site (LSS) in the segment is defined as the ratio of the upstream length to the total segment length (LSS = Up_L/T_L). A digital 1:50,000-scale hydrology map of Lake Chaohu Basin was used to create the river network (Fig. 1) and to calculate these river connectivity variables, which was performed in the software ArcGIS 10.1.

2.3 Statistical analysis

Hierarchical cluster analysis was used to group sites with similar connectivity (Yu et al. 2015), based on Euclidean distances as a measure of similarity obtained from four connectivity variables (Link, BLink, CLink, and DLink). Clusters with Ward’s linkage method are joined such that increases in within-group variance are minimized using PAST 3.21 software (Hammer et al. 2001).

Taxa richness and diversity indices, Shannon-Wiener, Simpson, Buzas and Gibson's evenness, Berger-Parker, Fisher's alpha, and Margalef, of the total number of fish captured were also calculated for each site based on species abundance data (Hammer et al. 2001). In addition, as variances in the local-scale physiochemical variables and diversity indices were not homogeneous, the nonparametric Kruskal-Wallis test was employed to detect the differences in the local-scale physiochemical variables and diversity indices among the connectivity groups using SPSS 20.0 (IBM Co. Ltd, USA) (Zhang et al. 2018).

Several statistical analysis methods were employed to test the hypothesis that connectivity variables regulate fish assemblages. Nonmetric multidimensional scaling (NMDS) was employed to assess the spatial variation in fish assemblages based on Bray-Curtis dissimilarity measures of fish abundance data. One-way analysis of similarity (ANOSIM) was also used to determine whether fish assemblages differed significantly among the connectivity groups (He et al. 2017; Zhang et al. 2018). Besides, similarity percentage (SIMPER) procedures were employed to identity the species that contributed the most to the dissimilarities among groups (Zhang et al. 2018). These analyses were performed using PAST 3.21 software (Hammer et al. 2001), accordingly.

Furthermore, to identify the key environmental variables that currently structure the fish assemblages and determine which factor was the most important, a canonical correspondence analysis (CCA) was performed based on abundance data (He et al. 2017). A manual forward selection procedure was employed to identify which environmental variables were significantly related to the fish assemblages. The environmental variables with a significance level of p < 0.1 were chosen based on 9,999 Monte Carlo permutation tests (Zhang et al. 2018). The CCAs were performed using CANOCO 4.5 software (Chen et al. 2022).

The fish abundance data were log(x + 1) transformed, before the analyses, to reduce the influence of non-normal distribution. Local-scale physiochemical variables (except TDS, TN, NH₄⁺-N, PO₄³⁺-P) and land use characteristics (except proportion of cropland) were included in CCA analyses as there are multiple collinearities between the removed variables and other variables. Species that occurred at fewer than five sites were excluded from NMDS, ANOSIM, and CCA analyses (He et al. 2017).

3.1 Clustering river connectivity variables

The cluster analysis resulted in four groups of sites with a Euclidean similarity of 200 among sites within a cluster. The sampling sites of the four groups showed an upper-lower reach distribution pattern across the basin (Fig. 2). The four groups formed in the cluster analysis had significantly different connectivity according to the Kruskal-Wallis test (Fig. 3 and Table S1). For example, sites in Group 1 had the highest Link, BLink, and DLink, followed by the sites in Groups 2, 3 and 4. However, sites in Group 4 had the highest values of CLink, followed by the sites in groups 2, 3 and 1. Generally, these results indicated that sites in Group 1 usually had the highest connectivity from the lower to upper reaches, followed by sites in Groups 2, 3 and 4 (Figs. 2 and 3).

Therefore, sites from Group 1 were mainly located in the lower reaches with high-connectivity segments (e.g., Hangbu River and Zhaoxi River). Sites from Group 2 were clustered in the upper reaches with moderate-connectivity segments (e.g., Hangbu River and Zhaoxi River). Sites from Group 3 were mainly distributed in the middle reaches with low-connectivity segments (e.g., Nanfei River, Pai River, Zhegao River, and Baishitian River). Sites from Group 4 were situated in the upper reaches with the lowest connectivity segments (e.g., Nanfei River, Pai River, Zhegai River, Baishitian River, Hangbu River, and Zhaoxi River).

Generally, river orders in Lake Chaohu Basin ranged from 1st to 5th (Table S1 in Supplementary Materials). Link, BLink_R, BLink_Lf, BLink, CLink, and DLink ranged from 1 to 567, 0 to 970, 0 to 537, 0 to 1423, 1 to 58, and 1 to 499, respectively. Upstream segment length (Up_L), downstream segment length (Down_L) ranged from 0.01 to 5.23 km and 0.01 to 16.50 km, respectively. The location of sampling site (LSS) ranged from 0.01 to 0.99.

3.2 Spatial gradients of physiochemical variables among connectivity groups

Although many physiochemical variables were not significantly different among the four connectivity groups, several variables (e.g., Width, Depth, NO₃^--N, TP, DOC, and PO₄³⁺-P) were significantly different among the four groups according to the Kruskal-Wallis test (Table S2 and Fig. 4). Specifically, in comparison to the other sites and Groups, major sites in Group 3 had the highest pollution (e.g., EC and Turb) and nutrient levels (e.g., TN, NO₃^--N, TP, PO₄³⁺-P, and DOC) and the lowest values for DO, Flow and NH₄⁺-N, whereas sites in Groups 4 and 2 had low nutrient and pollution levels and higher flow velocity. Sites in Group 1 had the highest Width, Depth, and DO and even the highest pH values.

There was a significant spatial gradient for the land use characteristics along the river segments (Table S3 and Figure S1). The percentage of grassland in the 1-km buffer along the upstream segments (U_Grass) decreased from the upper reach sites (Group 4) to lower reach sites (Group 1). On the other hand, the percentage of water body in the 1-km buffer along the upstream segments (U_Water) showed the opposite trend, increasing from the upper reach sites (Group 4) to lower reach sites (Group 1). The percentage of woodland in the 1-km buffer along the upstream segments (U_Wood) showed a complex trend along the segments, with the highest values in the upstream segments of sites in Group 4. The next highest U_Wood values were in the upstream segments of sites in Group 2, followed by Group 3 and Group 1. Although the percentage of cropland in the 1-km buffer along the upstream segments (U_Crop) and the percentage of built-up land in the 1-km buffer along the upstream segments (U_Built) were not significantly different among the four connectivity groups, U_Crop increased from the upper reach sites (Group 4) to lower reach sites (Group 1), and the highest U_Built was clustered in Group 3. The same results were found downstream and for all segments of the sites (Table S3 and Figure S1).

3.3 Influence of river connectivity on fish assemblages

A total of 2166 individuals were collected throughout Lake Chaohu Basin at 57 sites, representing 38 species in 35 genera and 13 families. Species richness and number of individuals caught varied from 1 to 14 and 1 to 445, respectively, across sites. Based on the Kruskal-Wallis test, fish taxa richness and diversity indices were not significantly different among the connectivity groups (Table S4).

However, one-way ANOSIM results showed that fish assemblages significantly varied by connectivity groups (global R = 0.089, p = 0.026). Specifically, fish assemblages could be distinguished between Groups 3 and 4 (R = 0.160, p = 0.006) and Groups 2 and 4 (R = 0.127, p = 0.048) (Table 1). The SIMPER analysis revealed that the species that primarily contributed to the dissimilarity between Groups 3 and 4 were Carassius auratus (Linnaeus) (16.72% of contribution), Ctenogobius sp. (13.14%), Hemiculter leucisculus (Basilewsky) (11.45%), and Misgurnus anguillicaudatus (Cantor) (8.53%), while Ctenogobius auratus (18.10%), Ctenogobius sp. (14.24%), H. leucisculus (11.41%), and Acheilognathus barbatulus (Günther) (6.10%) mostly contributed to the difference between Groups 2 and 4. In addition, C. auratus, Ctenogobius sp., and H. leucisculus were the dominant species and occurred at most of the sites throughout Lake Chaohu Basin. Furthermore, the minimum stress value was 0.14 in the NMDS ordination solution for the river connectivity groups (Fig. 5a). The NMDS analysis revealed that sampling sites in Group 3 were mainly located on the right of the graph, while the sites in Group 4 were gathered to the left. At the same time, sampling sites in Groups 1 and 2 were mainly clustered to the top of the plot.

Table 1

One-way ANOSIM showing significance levels of fish community structure among the four groups. The upper triangular matrix showed the p values, and the lower triangular matrix showed the global R statistic.
	Group 1	Group 2	Group 3	Group 4
Group 1		0.696	0.320	0.104
Group 2	-0.042		0.390	0.048*
Group 3	0.029	0.004		0.006**
Group 4	0.100	0.127	0.160
* p < 0.05, ** p < 0.01

Based on the Kruskal-Wallis test, fish taxa richness and diversity indices were not significantly different among river order groups (Table S5). Similarly, there was also no significant difference in fish assemblages among river orders (global R = 0.004, p = 0.424). Therefore, river order cannot influence fish assemblages in Lake Chaohu Basin. However, the minimum stress value was 0.14 in the NMDS ordination solution for river orders (Fig. 5b). The NMDS analysis revealed that the sampling sites in the 2nd- and 3rd-order streams were mainly located on the right of the graph, while the sampling sites in the 5th-order streams were gathered to the left. At the same time, the sampling sites in the 1st-order streams were mainly clustered to the bottom right, and the sampling sites in the 4th-order streams were mainly located at the top.

3.4 Linking environmental variables to fish assemblages

The forward selection procedure for the CCA identified eight environmental variables (U_Wood, U_Grass, Flow, ROrder, Alka, Elevation, BLink_Lf, and DO) that were highly correlated with the fish communities (Fig. 6). U_Wood explained the most variance (15.4%), followed by U_Grass (5.4%), Flow (5.1%), ROrder (3.0%), Alka (2.8%), Elevation (2.7%), BLink_Lf (2.6%), and DO (2.5%). The first and second axes accounted for 16.5 and 6.8%, respectively, of the total variation in fish species abundances. The first axis was highly related to the variables U_Wood (canonical coefficient, r = 0.75), Flow (r = 0.75), and Elevation (r = 0.58), while the second axis was corrected to U_Grass (r = 0.42). In addition, the first axis was negatively related to ROrder (r=-0.50). Based on the CCA plot, many sites in the lower reaches of the high-connectivity rivers (Group 1) were clustered around ROrder, while some sites in the upper reaches of the lower-connectivity rivers (Group 4) were gathered around BLink_Lf and Alka. Sites in the lower reaches of high-connectivity rivers (Group 1) were located around BLink_Lf and DO. In addition, sites in the middle reaches of low-connectivity rivers (Group 3) were plotted along Flow, U_Wood, U_Grass, and Elevation.

The CCA results also showed the relationships between some common species and the eight environmental variables (Fig. 6b). Two dominant species, C. auratus and H. leucisculus, were positively related to BLink_Lf and negatively related to U_Wood, Flow, and Elevation. This result means that these two species would prefer to live in the lower reaches with high BLink_Lf and low Elevation, U_Wood, and Flow. The other dominant species, Ctenogobius sp., were positively correlated with Flow, U_Wood, and Elevation. Similarly, five species belonging to Cypriniformes (Zacco platypus (Temminck & Schlegel) and Misgurnus anguillicaudatus (Cantor)), Perciformes (Ctenogobius sp. and Odontobutis sinensis (Wu, Chen & Chong)), and Siluriformes (Pseudobagrus truncates (Regan)) were positively correlated to Elevation, U_Wood, and Flow. In other words, these six species generally occurred in the upper reaches with high Elevation, U_Wood, and Flow. Moreover, two species of Cypriniformes (Abbottina rivularis (Basilewsky) and Rhodeus lighti (Wu)) and one species of Perciformes (Hypseleotris swinhonis (Günther)) were most frequently found in the upper reaches of high U_Grass and low ROrder. Cobitis sinensis (Sauvage & Dabry de Thiersant) and Sarcocheilichthys nigripinnis (Günther) were found in the upper and middle reaches with high DO.

4.1 Spatial heterogeneity of environmental variables

Many environmental variables showed significant variation along the longitudinal gradients of the rivers throughout Lake Chaohu Basin. For example, sites in Group 1 had the highest values of the connectivity variables (ROrder, Link, BLink, and DLink), local-size variables (river width, water depth, and DO), and U_Crop. The sites in Group 4 had the highest Elevation, U_Wood, and U_Grass; the lowest river width, water depth and U_Crop; and the lowest nutrient levels (e.g., TP, NO₃^--N, PO₄³⁺-P). Specifically, sites in Group 3 had the highest nutrient levels (e.g., TN, NO₃^--N, TP, and PO₄³⁺-P), which was likely because this area has the highest area of built-up land along the segments of these sites (Zhang et al. 2018). Moreover, the nutrient levels showed an increasing trend from sites in the upper reaches to the lower reaches (Table S2). However, due to anthropogenic disturbances, such as increasing the area of built-up land and cropland, this increasing trend of nutrient variation can be transformed suddenly (Shao et al. 2019; Zhang et al. 2018). For example, sites in Group 3, which were located in the middle and lower reaches, had the highest nutrient levels. This scenario means that human activities (land use change from woodland and water body to the built-up land and cropland) can strongly influence the water quality in this region (Chen et al., 2022; Yu et al. 2022).

Moreover, in this study, a cluster analysis of the river connectivity variables identified four groups of sampling sites that were characterized by significantly different river connectivity and longitudinal gradients. This cluster is significantly different from the results in Yu et al. (2015), who simply gathered the sampling sites into two groups, isolated and connected groups. Their study focused on the relationships between the water quality of the isolated and connected wetlands and their surrounding watershed, documenting that the DOC was significantly lower in connected wetlands than in isolated wetlands when the land use did not change significantly, while this difference can be shift completely due to changes in land use. Furthermore, according to varying flood intensities, the sampling sites were grouped into five categories: drought-connected, low flood-connected, moderate flood-connected, high flood-connected, and extreme flood-connected (Mazur et al. 2022). Research has determined the effect of connectivity on water chemistry and shown that lakes with greater connectivity usually have higher DO and NO₃^--N and lower TN and TP. These different results may vary from those of our study due to the different ways to cluster connectivity and the different subjects, wetlands/lakes vs rivers.

Similarly, some environmental variables did not show significant variation along the rivers, but several main nutrients had remarkably different concentrations among the connectivity groups. This result is also slightly different from the results of Zhang et al. (2018). Their research showed that all the environmental variables had significant differences among their cluster groups. They may have used species abundance data to cluster the sampling sites in their research, and we used the four connectivity variables to cluster the sampling sites in this study due to the aims of our research.

4.2 Connectivity variables slightly influence the variation in fish assemblages

Due to the lack of available data on fish assemblages in Lake Chaohu Basin, temporal variability was not detected in this study. The most significant way in which our study differed from most others is that the sampling sites were grouped according to four river connectivity variables. Thus, we focused on the spatial variance in fish assemblages and tested the hypothesis that fish assemblages are regulated by river connectivity variables in the lowland basin (Lake Chaohu Basin, China). The results of this study cannot support our hypothesis: river connectivity variables regulate fish assemblages. Fish taxa richness and diversity indices were not significantly related to all river connectivity variables (not shown in the text) and no significant difference existed among the connectivity groups (p > 0.099, Table S4), fish assemblages significantly varied by connectivity groups (global R = 0.089, p = 0.026), particularly between Groups 3 and 4 and Groups 2 and 4. In addition, according to the results of the CCA, ROrder and BLink_Lf explain a fraction of the variance (3.0% and 2.6%, respectively). Therefore, the results indicated that the connectivity variables could explain a small part of the variance and slightly influence the variation in fish assemblage in the lowland basin, China. In contrast, the LSS was not significantly different among river order groups (Table S1) and did not explain any variance in fish assemblages (Fig. 6), which means that the location of the sampling site in the same segment does not affect the fish assemblages in a river segment; thus, there is no significant difference in fish communities in the same segment. The most likely reason for this result is the high migration ability of fish (Yan et al. 2011).

Our results are slightly different from those of He et al. (2017), Osborne and Wiley (1992), Smith and Kraft (2005), and Yan et al. (2011). These studies demonstrated that CLink, Link, ROrder, or DLink could explain the highest proportion of fish assemblage variance. For instance, Osborne and Wiley (1992) found that DLink explained the greatest influence on fish assemblages, while Smith and Kraft (2005) showed that CLink and ROrder could explain the highest portion of fish assemblage variance. He et al. (2017) found that the downstream, mainstream confluence site and DLink together can explain more than 50% of the total variance in fish assemblages. However, based on our results, the most important environmental variable influencing the variance in fish assemblage was upstream land use, followed by flow velocity, and river connectivity variables were less important. Therefore, our results support the idea that freshwater fish communities are generally determined by environmental varibales in local scale, although river connectivity seems very important to fish assemblages (Lansac-Tôha et al. 2016; Shao et al. 2019; Valenzuela-Aguayo et al. 2020; Yan et al. 2011).

4.3 Upstream land use and flow velocity play more important roles in the variation in fish assemblages

Another aim of this study was to test the relative importance of physiochemical variables, land use characteristics, and river connectivity variables on fish assemblage structure in the lowland basin. This study found that riparian vegetation, such as woodland and grassland areas, could be the most important factor affecting the fish assemblages, followed by the flow velocity of rivers. These two factors can explain 25.9% of the total variation among fish species abundances in Lake Chaohu Basin, although their explanatory power is lower than the factors detected in previous studies (He et al. 2017; Osborne & Wiley 1992; Smith & Kraft 2005; Yan et al. 2011). Thus, U_Wood, U_Grass and Flow play a more important role in the variation among fish assemblages than connectivity variables (e.g., ROrder and BLink_Lf, which together accounted for 5.6% of the total variation). These results were highly consistent with results from previous studies, which documented that both local-scale variables (e.g., habitant characteristics) and spatial structure (e.g., connectivity) indices can influence fish assemblages in river networks (Shao et al. 2019; Smith & Kraft 2005; Valenzuela-Aguayo et al. 2020).

In addition, some studies have emphasized the role of physiochemical variables (Yan et al. 2011) and land use characteristics (Matono et al. 2013) in the variance among fish communities. Land use has been considered a primary driver of environmental impacts on rivers (Leitão et al. 2018; Matono et al. 2013). For instance, increasing cropland could lead to high levels of nutrients and sediments and decreased flow stability of rivers (Leitão et al. 2018). Moreover, loss of riparian canopy coverage (e.g., deforestation) could alter river conditions (e.g., river morphology and substrate) and the structure of fish assemblages (e.g., alter the distribution of fish taxa) (Leitão et al. 2018). Similar results were found in this study, which showed a shift from herbivore (R. lighti), omnivore (Ctenogobius sp. and Z. platypus) and primary carnivore (A. rivularis, H. swinhonis, and O. sinensis) fish assemblages in segments with the highest amount of woodland and grassland along the upper streams to omnivore (H. leucisculus and P. parva) and secondary carnivore (C. argus and C. erythropterus) fish assemblages in segments with the highest cropland and built-up land along the upper streams.

Moreover, other main factors of local-scale physiochemical variables, which can influence the fish assemblages, are flow velocity and elevation (Fig. 6). Due to the highest elevations occurring in the southwestern basin, where the mountain area is located, the flow velocity is usually higher in this area than in other regions. Indeed, elevation is unable to affect the fish assemblages directly; it can alter many physiochemical variables, for example flow velocity, temperature, and DO, and then influence the species distribution. Therefore, our findings indicate that the factors that most affected the fish assemblages in Lake Chaohu Basin were riparian land use characteristics (U_Wood and U_Grass), followed by local-scale physiochemical variables (Flow and Elevation) and river connectivity variables (ROrder and BLink_Lf).

Our results indicated how the fish communities in Lake Chaohu Basin were structured by physicochemical variables, land use, and river connectivity. Sampling sites were clustered into four groups according to river connectivity. Therefore, the influences of the environmental variables on fish assemblages can be detected among the connectivity groups. The results showed that several nutrients (NO₃^--N, TP, and PO₄³⁺-P) were significantly different among the connectivity groups. More importantly, although this study cannot support our hypothesis: river connectivity variables regulate fish assemblages, the results shown that the most important environmental variable influencing the variance in fish assemblages was upstream land use, followed by flow velocity and Elevation, and river connectivity variables were less important, although fish assemblages showed significant variation among the connectivity groups. Therefore, particular attention should be paid to maintaining the riparian canopy coverage upstream areas and the flow velocity of rivers in the upper reaches of the basin because these factors could improve the biodiversity and conservation status of fishes.

ACKNOWLEDGMENTS

This work was supported by the National Natural Science Foundation of China, China (NO. 41977194). We would like to send a great thanks to H.B. Yin, T. Xia, and K. Liu for their assistance with field work.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.

CONFLICT OF INTEREST

The authors declare there is no conflict.

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No competing interests reported.

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Effects of land use characteristics, physiochemical variables and river connectivity on fish assemblages in a lowland basin

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Abstract

Figures

1 Introduction

2 Methods

2.1 Study area

2.2 Measured environmental variables

2.2.1 Local-scale physiochemical variables

2.2.2 Land use characteristics

2.2.3 River connectivity variables

2.3 Statistical analysis

3 Results

3.1 Clustering river connectivity variables

3.2 Spatial gradients of physiochemical variables among connectivity groups

3.3 Influence of river connectivity on fish assemblages

3.4 Linking environmental variables to fish assemblages

4 Discussion

4.1 Spatial heterogeneity of environmental variables

4.2 Connectivity variables slightly influence the variation in fish assemblages

5 Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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