Variability in macroinvertebrate community structure and its response to ecological factors of the Weihe River Basin, China

Ecological Engineering 140 (2019) 105595

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Variability in macroinvertebrate community structure and its response to ecological factors of the Weihe River Basin, China

T

⁎

Ping Sua, Xinxin Wanga, Qidong Lina, Jianglin Penga, Jinxi Songa,b, , Jiaxu Fua, Shaoqing Wanga, ⁎ Dandong Chengb,c, Haifeng Baia, Qi Lia, a

Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China b State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling 712100, China c University of Chinese Academy of Sciences, Beijing 100049, China

A R T I C LE I N FO

A B S T R A C T

Keywords: Macroinvertebrate Community structure Ecological factors The Weihe River Basin

Macroinvertebrates are sensitive to changes in the river environment and ecological status. Ecological variables over multi-spatial scales and macroinvertebrate community data were collected in June (normal flow season) and September (high flow season) of 2017 in the Weihe River Basin (WRB). A total of 14,377 individuals were identified, which were classified into 7 classes, 18 orders and 59 families. Macroinvertebrate community composition, density, biomass, the values of Pielou evenness index (E), Simpson diversity index (λ) were significantly different between normal flow season and high flow season. The dominant species (Tubificidae, Chironomidae and Baetidae) were the same in both seasons. The highest richness, abundance, density and biomass occurred at a stream bed depth of 0–10 cm. The results of canonical correspondence analyses (CCA) showed that ecological factors explained the major variation in macroinvertebrate community composition. Specifically, the increased nitrogen concentrations favored tolerant species, whereas high velocity and dissolved oxygen (DO) benefitted community taxa richness and biodiversity. The reduction of taxa richness, abundance, density and biomass in high flow season was related to the summer flood. Increased nutrient concentrations and macroinvertebrate habitat damage contributed to more tolerant, yet less diverse stream macroinvertebrate assemblages.

1. Introduction Macroinvertebrates are an important component of river ecosystems (Wallace and Webster, 1996; Cheng et al., 2018; Krajenbrink et al., 2019). Mainly composed of Oligochaeta, Hirudinea, Gastropoda, Insecta and Malacostraca, they usually thrive in the stream bed sediments of rivers, lakes, and oceans, feeding on algae, bacteria, and leaves, as well as other organic matter in water (Xu et al., 2012; Hauer and Resh, 2017). As good indicators for aquatic ecosystem assessments, macroinvertebrates offer feedbacks to changes in water condition (Schneid et al., 2017; Silva et al., 2018; Slimani et al., 2019), impact the decomposition of organic matter (Monroy et al., 2017; Raposeiro et al., 2017) and the migration and transformation of pollutant (Bian et al., 2016). Compared with other aquatic organisms, benthic macroinvertebrates have important advantages. They not only have large

abundances and relatively long life cycle, and are easy to collect, but also are highly sensitive to deterioration or improvements in aquatic ecological conditions (Pan et al., 2015c; Calapez et al., 2017). Studies based on benthic macroinvertebrates to evaluate river ecological health have been published (Kerans and Karr, 1994; Meng et al., 2009; Shi et al., 2017; Zhang et al., 2018b; Zhao et al., 2019). Macroinvertebrates form an important part of freshwater ecosystems since they play an important role in the food webs (Grubh and Mitsch, 2004), and regarded as the foundation of a stable ecosystem (Mehari et al., 2014; Luo et al., 2018). Therefore, elucidating the effects of human activities and natural causes on stream ecological health by using benthic macroinvertebrates is important. Aquatic ecosystems are often subject to a variety of anthropogenic activities stresses that interfere with the behavior of aquatic species (Fausch et al., 2010; Schinegger et al., 2012; Giorgio et al., 2016;

⁎ Corresponding authors at: Shaanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China (J. Song and Q. Li). E-mail addresses: [email protected] (J. Song), [email protected] (Q. Li).

https://doi.org/10.1016/j.ecoleng.2019.105595 Received 27 March 2019; Received in revised form 1 September 2019; Accepted 9 September 2019 0925-8574/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Sampling points of macroinvertebrates in WRB.

Ferreira et al., 2016; Mathers and Wood, 2016; Sterling et al., 2016; Fierro et al., 2017; Whitmore et al., 2017; Chessman, 2018; Davis et al., 2018). Rivers in different regions are subject to human disturbance and natural habitat conditions, and the structure of the macroinvertebrates communities is significantly different (Li et al., 2019). As the “mother river” of the Guanzhong region (Song et al., 2018), Weihe River generated the Guanzhong Plain, which is an important agricultural, industrial, and educational center in northwest China (Chang et al., 2015; Zhang et al., 2018a). As the starting point of the Silk Road, the Weihe River Basin (WRB) has provided a solid foundation for the development of the Guanzhong City Group, while playing an important role in national development strategies (Wang et al., 2018c). The region's production value can reach 900 billion yuan, feeding nearly 24 million people (Dou et al., 2018). However, in recent years, problems related to water resources (e.g., water demand rising, annual average runoff decreasing, environmental pollution and increasing flood risks) have been exacerbated (Cai et al., 2016), which is attributed to both population growth and climate change in the WRB (Chang et al., 2015). Therefore, the current water quality status in the WRB is not optimistic (Wang et al., 2018b). To explore the effect of human activities and natural factors on river ecology, an ecological survey based on benthic macroinvertebrates was carried out in the WRB. The specific objectives of this study were as follows: (1) describe the characteristics of the ecological factors in the WRB; (2) investigate the spatial and seasonal distribution of the macroinvertebrate assemblage structures; and (3) reveal the major ecological factors affecting the macroinvertebrate distribution. We incorporated different ecological variables in our multivariate analysis to identify the key variables that influence the distribution of macroinvertebrate assemblage.

Calapez et al., 2017). One such example is river channel management, which influences the morphological processes in riverbeds and indirectly affects the habitat condition of benthic macroinvertebrate (Bylak et al., 2009; Wyżga et al., 2014; Bylak et al., 2017). Another major problem is that urbanization has changed the predominant type of land use from natural vegetation to constructed impervious surface (Jiang, 2009; Li, 2015), resulting in increased impervious surface and increased surface runoff (Paul and Meyer, 2001; Luo et al., 2018). Agricultural activity can affect macroinvertebrate communities through multiple pathways and mechanisms (Maloney and Weller, 2011; Gleason and Rooney, 2017). Industrial wastewater, when directly discharged into the river, could greatly increase the level of heavy metal pollution, causing heavy metal enrichment and deposition, which is destructive to benthic macroinvertebrates. (Roy et al., 2018; Pandey et al., 2019). Besides human activities, natural factors can also result in changes in macroinvertebrate communities. Several studies have shown that during dry season, decreased water flow leads to decreased water surface area and chain reactions in physicochemical variables affecting the survival of macroinvertebrates (Acuña et al., 2014; Kalogianni et al., 2017). Floods, one of the major natural disturbances to macroinvertebrates, are usually pulse disturbances (Rosser and Pearson, 2018). In the flood stream, rapid velocity would redistribute substrate materials (from sand to boulders), scour the streambed (Stitz et al., 2017), move detritus, snags, and change the channel itself (Scholl et al., 2016), resulting in changes in the composition of benthic macroinvertebrate (Granzotti et al., 2018). Many studies have documented how macroinvertebrate assemblages respond to ecological factors under the influence of anthropogenic and natural properties (Liu et al., 2016; Cai et al., 2017a; Stitz et al., 2017; Lindholm et al., 2018). For example, water temperature, dissolved oxygen (DO), substrate composition, stream flows and current velocity, total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), vegetation, urbanization and land use have been identified as the main factors affecting the distribution of macroinvertebrates (Fausch et al., 2010; Chin et al., 2016; Ding et al., 2016;

2. Materials and methods 2.1. Study area The WRB (33° 00′ N–37° 00′ N, 104° 00′ E–107° 00′ E) has a total area of approximately 134,766 km2 (Fig. 1), with an average annual 2

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runoff is 7.57 billion m3. The WRB is characterized by an arid to subhumid continental climate with a wet and hot summer, a dry and cold winter and a comfortable spring and autumn (Zhao et al., 2016). The WRB spans three different geomorphic units, including the Loess Plateau, the Guanzhong Basin and the Qinling Mountains. The entire river comprises three water systems, including the Weihe River system (WRS), the Jinghe River system (JRS), the Beiluo River system (BRS), and their tributaries along with other small independent streams (Fig. 1). Water samples, sediments and macroinvertebrates were collected at 49 river junctions in WRB (Fig. 1) during normal flow season (June) and high flow season (September) of 2017. The sampling sites were further divided into three categories: 20 sampling sites (sites W1-W20) in the WRS, 18 sites (sites J1-J18) in the JRS, and 11 sites (sites B1-B11) in the BRS (Fig. 1). The stream bed sediments upstream of the rivers are composed of large rocks, gravel or cobble. The stream bed sediments downstream of the three water systems are mostly fine particles.

of differences between environmental variables and biological indices (density, biomass, H′, D, E and λ) in normal flow season and high flow season were examined using student’s t test (t-test), and differences among the WRS, JRS and BRS using a nested analysis of variance (nested ANOVA) with Bonferroni post hoc tests. To visualize the community structure distribution characteristics of the study sites, we used non-metric multi-dimensional scaling (NMDS) ordinations based on Bray-Curtis dissimilarity matrices. The differences of community structure were tested using analysis of similarities (ANOSIM). In order to conduct a multivariate analysis of data, environmental variables and species variables should be converted to log10(x + 1) to be normally distributed. Ordination plot analysis was used to analyze the response mechanism of the community structure to the ecological factors (Pan et al., 2015b; Kalogianni et al., 2017). Only species with a frequency larger than 3 are retained for analysis to reduce the effects of rare species. Species data was analyzed using detrended correspondence analysis (DCA) to select an appropriate model (Pan et al., 2012; Luo et al., 2018). In this study, the gradient length of the first ordination axis was larger than 4. Therefore, canonical correspondence analysis (CCA) was used to analyze the response relationship between the species community structure and ecological factors. A Monte Carlo test was used to select important environmental factors which explain the abundance and distribution of macroinvertebrate assemblages under the cut-off point of p < 0.05 (Pan et al., 2015b).

2.2. Ecological data collection and analysis Water sample was collected using a water sampler and poured into a 250 mL bottle. Two parallel samples were collected from every site and fixed with acid. Ecological factors were collected at the in-situ sampling points and measured with laboratory testing. Water quality parameters including water temperature, DO, pH, electrical conductivity (EC) and total dissolved solids (TDS) was measured using a portable water quality meter (HACH HQ40d). River width data was measured with the help of Trupulse 200; current velocity was obtained by using a portable flow meter (MGG/KL-DCB); water depth was acquired by using a terrain probe; Global Positioning System (GPS Etrex 201X) was used to measure latitude, longitude and elevation information. Water samples for TP was determined by ammonium molybdate spectrophotometry (GB 11893-89); TN was measured by the gas phase molecular absorption spectrometer (GMA 3376). In the laboratory, TP and TN measurements were conducted in accordance with the Chinese government standard for Water and Wastewater Monitoring and Analysis (2002). The sediment particle size was screened using a stand sieve at each sampling point. Substrate composition was categorized according to size (D): boulders (D > 256 mm), cobble (16 mm ≤ D < 256 mm), gravel (2 mm ≤ D < 16 mm), fine particles (D < 2 mm) (Bae et al., 2014). Benthic macroinvertebrates samples were taken within 100 m at approximately 50 cm depth at each sampling site. A Hess sampler (S = 0.09 m2, 250 μm mesh) was used to collect the macroinvertebrates samples in the shallow areas. Hyporheic invertebrates were collected at the same location as macroinvertebrates, using a Bou-Rouch pump (1967) at different stream bed depths. Consisted of 6 L of water pumped at a constant rate of 4 L min−1. Samples collected were filtered through a 250 μm mesh sieve (Datry, 2012; Descloux et al., 2013). The samples were preserved in 75% alcohol and taken to the laboratory. In the laboratory, all macroinvertebrates were sorted and identified by hand on white porcelain pans. The macroinvertebrate specimens were identified at the family-level using microscopes (Nikon SMZ800) according to relevant references (Kalogianni et al., 2017; Yi et al., 2018). Compared to the identification of species-level or genus-level, the identification of family-level is more efficient (Luo et al., 2018).

3. Results 3.1. Characteristics of the ecological factors At seasonal scale, differences (t-test, p < 0.05) were recorded for water temperature, water depth and proportion of cobble of WRB in normal flow season and high flow season (Table 1). High water temperature, high proportion of cobble and low water depth were observed in normal flow season. During normal flow season, differences were detected in water temperature, river depth, water flux, DO, pH, EC and TDS (ANOVA, p < 0.05) among WRS, JRS and BRS (Table 2). High river width, water depth and water flux were distributed in WRS, whereas high water temperature, TN, TP, DO, pH, EC and TDS were mainly centralized in BRS. During high flow season, water quality parameters, including water temperature, river depth, TN, DO and pH were significantly different in WRS, JRS and BRS (ANOVA, p < 0.05) (Table 2). High velocity and pH, and low proportion of cobble were observed in JRS. According to the China National Quality Standards for Surface Waters (GB3838-2002), the average concentrations of TN in WRB sites exceeded the Class V guideline (≤2 mg/l) in both normal flow season and high flow season.

Table 1 Statistical descriptions (t-test) and water quality standard for physicochemical characteristics measured in normal flow season and high flow season of WRB.

2.3. Data analyses The application of biological indices for macroinvertebrate in China have been well-documented (Huang et al., 2015; Cai et al., 2017b; Chi et al., 2017; Wang et al., 2018a; Zhang et al., 2018c). We selected Shannon-Winer diversity index (H′) (Shannon, 1948), Margalef richness index (D) (Margalef, 1958), Pielou evenness index (E) (Pielou, 1966) and Simpson diversity index (λ) (Simpson, 1949) to describe the biodiversity of macroinvertebrates community. The statistical significance 3

Ecological factors

Normal flow season (n = 49)

High flow season (n = 49)

p

Water temperature (°C) River width (m) Water depth (m) Velocity (m/s) Water flux (m3/s) Proportion of cobble TN (mg/l) TP (mg/l) DO (mg/l) pH EC (μs/cm) TDS (mg/l)

23.060 ± 4.580

21.000 ± 3.650

0

57.500 ± 61.280 0.450 ± 0.190 0.510 ± 0.310 9.620 ± 9.390 0.500 ± 0.330 5.340 ± 2.390 0.150 ± 0.280 8.600 ± 1.800 9.250 ± 0.470 932.230 ± 475.910 615.960 ± 451.180

63.370 ± 57.300 0.579 ± 0.340 0.620 ± 0.490 13.400 ± 14.600 0.360 ± 0.320 5.730 ± 4.850 0.220 ± 0.300 8.080 ± 1.550 9.250 ± 0.390 859.580 ± 607.670 579.580 ± 424.710

0.227 0.020 0.184 0.088 0.001 0.517 0.172 0.076 0.903 0.393 0.622

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Table 2 Statistical descriptions (ANOVA) and water quality standard for physicochemical characteristics measured in WRS, JRS and BRS sites of two seasons. Values not sharing a common letter were significantly different at p < 0.05. Ecological factors

WRS (n = 20)

JRS (n = 18)

BRS (n = 11)

F

p

Seasons

Water temperature (°C)

23.240 ± 5.250 23.190 ± 2.380a

21.340 ± 3.900 18.510 ± 4.140b

25.550 ± 2.700 20.850 ± 1.290ab

3.384 10.686

0.029 0

normal flow high flow

River width (m)

93.000 ± 79.400a 98.880 ± 71.410a

38.610 ± 25.920b 42.100 ± 24.380b

24.050 ± 13.110b 32.320 ± 13.450b

7.212 8.956

0.002 0.001

normal flow high flow

Water depth (m)

0.460 ± 0.260 0.530 ± 0.360

0.450 ± 0.130 0.570 ± 0.260

0.420 ± 0.070 0.680 ± 0.420

0.221 0.688

0.803 0.508

normal flow high flow

Velocity (m/s)

0.470 ± 0.290 0.600 ± 0.500

0.540 ± 0.300 0.720 ± 0.550

0.540 ± 0.370 0.480 ± 0.290

0.331 0.823

0.720 0.446

normal flow high flow

Water flux (m3/s)

13.700 ± 12.230a 15.510 ± 17.820

7.810 ± 5.010b 13.170 ± 12.110

5.170 ± 4.890b 10.880 ± 10.870

3.783 0.352

0.030 0.705

normal flow high flow

Proportion of cobble

0.470 ± 0.310 0.380 ± 0.300

0.550 ± 0.340 0.290 ± 0.300

0.470 ± 0.360 0.450 ± 0.370

0.268 0.934

0.766 0.400

normal flow high flow

TN (mg/l)

5.500 ± 1.870 7.220 ± 3.470a

4.850 ± 2.580 2.990 ± 1.730a

5.860 ± 2.750 7.500 ± 7.660b

0.660 5.231

0.522 0.009

normal flow high flow

TP (mg/l)

0.100 ± 0.060 0.310 ± 0.430

0.200 ± 0.440 0.190 ± 0.170

0.140 ± 0.160 0.130 ± 0.070

0.618 1.381

0.544 0.626

normal flow high flow

DO (mg/l)

7.900 ± 1.370a 6.970 ± 1.780a

8.660 ± 1.130ab 8.750 ± 0.440b

9.800 ± 2.560b 9.180 ± 0.630b

4.429 14.836

0.017 0

normal flow high flow

pH

8.930 ± 0.340a 9.070 ± 0.270a

9.330 ± 0.410b 9.390 ± 0.490b

9.690 ± 0.340c 9.300 ± 0.260c

15.020 15.020

0 0

normal flow high flow

EC (μs/cm)

848.500 ± 307.340a 797.400 ± 658.340

791.240 ± 426.720a 889.960 ± 664.840

1315.180 ± 589.320b 899.450 ± 360.990

5.403 0.140

0.008 0.870

normal flow high flow

TDS (mg/l)

388.210 ± 180.040a 440.580 ± 366.230

670.370 ± 526.210b 612.610 ± 412.970

941.000 ± 439.080b 770.910 ± 455.030

6.710 2.316

0.003 0.110

normal flow high flow

(p = 0.002) of the macroinvertebrates significantly differed between normal flow season and high flow season. In normal flow season, the density ranged from 22.22 ind/m2 to 13177.78 ind/m2, with an average density of 1754.85 ind/m2. The general trend decreased from upstream to downstream. In normal flow season, the density of species (Tubificidae, Chironomidae, Baetidae and Hydopsychidae) was higher than others families. The biomass ranged from 0.0022 g/m2 to 23.57 g/m2, with an average biomass of 2.92 ind/g2. Species with higher biomass mainly included Tubificidae, Lymnaeidae and Physidae. However, in high flow season, the density ranged from 0 ind/m2 to 2411.11 ind/m2, with an average density of 284.13 ind/m2. Chironomidae reached the highest density. The biomass ranged from 0 g/m2 to 0.93 g/m2, with an average biomass of 0.67 ind/g2. Herpobdellidae had the highest biomass (22.17%), followed by Planorbidae. The values of density and biomass in WRS, JRS and BRS were not significantly different between normal flow season and high flow season (ANOVA, p > 0.05). The values of H′ (1.59), D (2.41), E (0.64) and λ (0.68) in normal flow season were lower than those in high flow season (H′ = 1.67, D = 2.49, E = 0.79 and λ = 0.78). The values of E and λ were different between normal flow season and high flow season (t-test, p < 0.05). The values of H′, E and λ in WRS (H′ = 1.41, D = 2.04, E = 0.61 and λ = 0.62), JRS (H′ = 1.79, D = 3.01, E = 0.68 and λ = 0.73) and BRS (H′ = 1.57, D = 2.12, E = 0.64 and λ = 0.69) sites were not different in normal flow season (ANOVA, p > 0.05). But the values of H′, D, E and λ at JRS sites were higher than the values at WRS and BRS sites. In high flow season, the values of H′, D, E and λ were not different among WRS (H′ = 1.63, D = 2.47, E = 0.78 and λ = 0.77), JRS (H′ = 1.74, D = 2.62, E = 0.78 and λ = 0.77) and BRS (H′ = 1.56, D = 2.29, E = 0.70 and λ = 0.70) sites (ANOVA, p > 0.05).

3.2. Community composition of the macroinvertebrates and biological indices A total of 14,377 individuals belonging to 7 classes, 18 orders and 59 families were obtained at 49 sites in normal flow season and high flow season of WRB, mainly including Chironomidae, Perlidae, Heptageniidae, Tipulidae and Dytiscidae (Fig. 2). Chironomidae had the largest species richness, accounting for 38.1% of the total species richness. Chironomidae, Tubificidae and Baetidae, comprising 34.76%, 34.18% and 13.36% of total abundance, respectively. A total of 11,668 individuals were collected in normal flow season, belonging to 6 classes, 16 orders, and 50 families. Whereas, in high flow season, 2709 benthic macroinvertebrates belonging to 6 classes, 17 orders and 44 families were collected. A total of 32 common families were identified in two seasons. The WRS had the highest species richness, with 4939 individuals, belonging to 7 classes, 16 orders and 47 families. At JRS sites, Chironomidae, Baetidae and Tubificidae were dominant, accounting for 71.18% of JRS abundance. The BRS had the lowest species richness, but the highest abundance of species, accounting for 40% of the total abundance in the two seasons. Owing to the high degree of pollution-tolerant species of BRS in normal flow season, BRS was dominated by the Tubificidae species, accounting for 45.13% of the abundance of BRS in normal flow season. NMDS (Fig. 3) and ANOSIM analyses (Table 3) showed that the community structure of macroinvertebrates in the WRB was significantly different between normal flow season and high flow season (R = 0.117, P = 0.001). At catchment scale, the community compositions showed differences among WRS, JRS and BRS in normal flow season (R = 0.137, P = 0.002). However, ANOSIM analyses showed similar macroinvertebrate community compositions among three river systems in high flow season (R = 0.033, P = 0.184). Moreover, the dominant families (Chironomidae, Tubificidae and Baetidae) were the same in both seasons (Fig. 2). Both the density and biomass showed spatial and temporal variations (Fig. 4). T-test revealed that the density (p = 0) and biomass

3.3. Spatial distribution characteristics of the macroinvertebrate community structure The biological values of richness, abundance, density and biomass were highest at a stream bed depth of 0–10 cm (Fig. 5). The distribution 4

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Fig. 2. Richness in normal flow season (a), abundance in normal flow season (b), richness in high flow season (c) and abundance in high flow season (d) of major macroinvertebrate families collected in WRB.

of macroinvertebrate richness, abundance, density and biomass varied in the different stream bed depths. The values of richness, abundance and density remained almost constant from 0 cm to 40 cm and gradually reduced at 40–50 cm. The biomass gradually increased at 15–50 cm, and a second peak appeared between 30 cm and 50 cm. The distributions of macroinvertebrate abundance, defined as the ratio of the abundance of each taxonomic group to the total abundance, varied in different stream bed depths (Fig. 6). The most abundant taxonomic groups were Tubificidae and Chironomidae, with a total relative abundance of 80%. In normal flow season, the most dominant taxonomic groups were Tubificidae, Chironomidae and Physidae. Tubificidae, Chironomidae and Baetidae were the most abundant taxonomic groups in high flow season. The density and biomass of macroinvertebrate in five layers varied in different stream bed depths (Fig. 7). The taxa were grouped into

Table 3 ANOSIM analysis in WRS, JRS and BRS of normal flow season and high flow season. Groups

R

p

Seasons

WRS, JRS and BRS

0.137 0.033

0.002 0.184

normal flow high flow

WRS and JRS

0.212 0.028

0.001 0.177

normal flow high flow

WRS and BRS

0.152 −0.026

0.039 0.633

normal flow high flow

BRS and JRS

−0.015 0.086

0.520 0.101

normal flow high flow

Fig. 3. Nonparametric multidimensional scaling (NMDS) ordination of sampling sites based on macroinvertebrates presence of WRB in normal flow season (a), in high flow season (b) and in both seasons (c). 5

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Fig. 4. Spatial distribution of macroinvertebrates density (a) and biomass (b). Both density and biomass data were log10(x + 1) transformed.

test revealed that six factors significantly influenced macroinvertebrate distribution in normal flow season (Table 4): velocity (F = 3.8, p = 0.01), TN (F = 3.1, p = 0.038), DO (F = 3, p = 0.04), pH (F = 2.8, p = 0.03), water temperature (F = 2.7, p = 0.012) and water depth (F = 2.3, p = 0.044). However, the main ecological factors that significantly affected macroinvertebrate distribution in high flow were DO (F = 4.1, p = 0.002), pH (F = 3.2, p = 0.004), TDS (F = 3, p = 0.002), water depth (F = 2.9, p = 0.004) and river width (F = 2.4, p = 0.016) (Table 4). In normal flow season, the eigenvalues of the CCA axis 1 and the CCA axis 2 were 0.6587 and 0.4235, and the total variation was 5.15840 (Table 5).The cumulative variation of the biological and environmental correlations was 74.33% Four environmental variables had significant correlations with the CCA axis 1, with DO (−0.6454) being the most important contributor among them, followed by velocity (0.5183), TN (−0.5033) and pH (0.4727). The ecological factors had a strong correlation with velocity and TN in the CCA axis 2, and the

Tubificidae, Chironomidae, Baetidae, Planorbidae, Physidae, Lymnaeidae, Hydopsychidae and other taxa. Among all the stream bed depths, the maximum density was observed at a stream bed depth of 0–10 cm. High density of Chironomidae and Tubificidae existed at a stream bed depth of 0–10 cm. The maximum biomass occurred at a stream bed depth of 0–10 cm. Lymnaeidae, Physidae, Hydopsychidae and Tubificidae occurred with high biomasses mainly at a stream bed depth of 0–10 cm. The biomass was also higher at the 40–50 cm depth than at the other stream bed depths. Planorbidae, Lymnaeidae and Physidae occurred mainly at a depth of 40–50 cm. The biomass of Herpobdellidae was high at bed depth of 20–40 cm. 3.4. Relationship between the macroinvertebrates and the ecological factors CCA was used to analyze the relationship between the benthic macroinvertebrate distribution and ecological factors (Fig. 8). The analysis of forward selection and Monte Carlo unrestricted permutation

Fig. 5. Distribution of average richness (a), average abundance (b), average density (c) and average biomass (d) along stream bed depths. 6

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Fig. 6. Distribution of macroinvertebrate relative abundance in normal flow season (a) and in high flow season (b) along stream bed depths.

correlation coefficients was −0.6560 and 0.4607. The results of the CCA analysis in high flow season were different from in normal flow season (Table 5). The CCA axis 1 accounted for 8.76% of the variance in the species distribution and of the relationship between the species distribution and the ecological factors. The CCA axis 2 accounted for 7.33% of the variance in the species distribution and 20.32% of the relationship between the species distribution and the ecological factors. The CCA axis 1 was positively correlated with water temperature (r = 0.5285) and TN (r = 0.4516), but negatively correlated with water depth (r = −0.6281) and TDS (r = −0.6379). The CCA axis 2 was correlated positively with river width (r = 0.5372) and water flux (r = 0.6286) and negatively with DO (r = −0.8494) and pH (r = −0.594). The macroinvertebrate distribution had different responses to the ecological factors (Fig. 8). During normal flow season, Chironomidae, Corixidae and Psychodidae were positively related to velocity. Tubificidae, Lymnaeidae, Physidae and Tipulidae were greatly affected by DO. Dytiscidae, Hydopsychidae, Herpobdellidae, Gammaridae and Dytiscidae were mainly influenced by river width. Tipulidae, Corixidae, Physidae and Planorbidae were negatively correlated with pH. In high flow season, Viviparidae, Chironomidae, Corixidae, Planorbidae and Psychodidae were negatively related to TN. Tubificidae and Herpobdellidae strongly responded to DO. There was a positive correlation between Staphylinidae and proportion of cobble. However, Chironomidae, Perlidae, Hydopsychidae and Dytiscidae were negatively correlated with TN. Psychodidae, Viviparidae and Dytiscidae were positively related to river width. Tipulidae, Corixidae, Physidae and Planorbidae were positively related with pH. Hydropsychidae, Baetidae, Staphylinidae and Herpobdellidae showed a preference for water environments with high flow velocity.

4. Discussion 4.1. Characteristics of macroinvertebrate composition The macroinvertebrate community structure of the WRB resembles that in Chishui River of southwestern China (Jiang et al., 2017). A total of 14,377 individuals, 59 families, were identified in the investigation. The dominant species were Chironomidae, Tubificidae and Baetidae, with a wide range of distribution and a high frequency of occurrence in WRB. These species were also found to be significant indicators of mesotrophic or polytrophic streams in Korean streams nationwide (Jun et al., 2016). In WRB, a dramatic reduction and even elimination of sensitive species, such as EPT taxa (Heptageniidae, Siphlonuridae, Perlidae, Hydroptilidae and Rhycophilidae) were detected. Meanwhile, increased dominance of a few tolerant taxa (particularly Chironomidae and Tubificidae) were identified, along with a serious decline in biodiversity measured as diversity index and increased dominance of collector-gatherers. These results were consistent with those of previous studies in the Evrotas River, rivers of central Manaus and Chaohu Lake (Kalogianni et al., 2017; Martins et al., 2017; Zhang et al., 2018c). 4.2. Seasonal and spatial variability in macroinvertebrate community structure Our study revealed seasonal and spatial variability in macroinvertebrate communities and related biological indices in a northwestern river system which keep a rare situation uncommon in rivers of China. The comparison of the seasonal variation in the aquatic macroinvertebrate communities presented the highest species richness, abundance, density and biomass in normal flow season of WRB (Figs. 2 and 4), similar results given by Ferreira (2010), Klerk and Wepener (2013) and Burger et al. (2018). The WRB had a relatively low species richness, abundance, density and biomass in high flow season, following summer rainfall. The reduction of macroinvertebrate richness, 7

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Fig. 7. Density in normal flow season (a) and high flow season (b), biomass in normal flow season (c) and high flow season (d) of macroinvertebrate in five layers at different stream bed depths.

abundance, density and biomass in high flow season was related to the summer flood because of the association concentrated rainfall with temperate monsoon. The disturbance associated with increased flows

would induce habitat washout and increased mortality in most benthic macroinvertebrate taxa (Leung and Dudgeon, 2011). High flow would affect the intensity of abiotic and biotic influences on benthic

Fig. 8. Ordination plot of CCA on macroinvertebrates and ecological factors in normal flow season (a) and high flow season (b). 8

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(Calderon et al., 2017; Jiang et al., 2017; Robinson et al., 2018). Variability in composition of benthic macroinvertebrate communities were observed among WRS, JRS and BRS. In this study, BRS had the lowest taxon richness and highest taxa abundance, and Tubificidae and Chironomidae became abundant in both two seasons. In BRS, catchment gradient was related to changes in a series of environmental variables, e.g., TN, EC, TDS, and substrate size. For example, finer substrate and simpler microhabitats in BRS can directly lead to decreased taxon richness and abundance of EPT taxa and increased density of Tubificidae (Jiang et al., 2017). Furthermore, land use intensity commonly varies in different region of river basins: BRS usually have a greater percentage of industry land use (oil and coal) than WRS and JRS. The industrial stressors (e.g. deposited heavy metal and dissolved nutrient concentrations) would also partly induce the paucity of EPT and prosperity of Tubificidae and Chironomidae in BRS (Sharifinia et al., 2016). In WRS sites, owing to interference of human activities, the benthic macroinvertebrates showed a decreasing trend in richness, abundance and biological diversity. For example, at study points W19 and W20 of the WRS, owing to large-scale sand mining activities seriously influenced the macroinvertebrate (Aazami et al., 2015), the community structure and diversity was poor. ANOSIM analyses and NMDS showed different structures of macroinvertebrate communities between two seasons. Meanwhile, analyses also showed that there was a spatial difference among the WRS, JRS and BRS regions during the normal flow season. Possible reasons for this seasonal difference are that: the occurring floods in WRB caused changes in macroinvertebrate communities (Jiang et al., 2017). Flood scoured large amount of clay and sand into the rivers, leading to the formation of sandy sediments. The scoured sand substrata could cause significant changes in macroinvertebrate community structure, with declines both in taxa richness and abundance (Bae et al., 2014; Jiang et al., 2017). During normal flow season, the difference of macroinvertebrate community structure among WRS, JRS and BRS could be resulted from changes in macroinvertebrate community structure affected by environmental factors including DO, river width, flow, EC and TDS. The values of H′, D, E and λ in high flow season were higher than those in normal flow season. One possible explanation is that seasonally occurring floods are predictable in evolutionary time, therefore, relative stable responses to such disturbance are possible in macroinvertebrate taxa (Brewin et al., 2010; Jacobsen et al., 2008; Jiang et al., 2017). In normal flow season, the values of H′, D, E and λ in JRS are higher than those in WRS and BRS. This is related to the high proportion of cobble and less human activities in the JRS sites leading to relatively stable macroinvertebrate communities.

Table 4 Monte Carlo unrestricted permutation test on macroinvertebrate distribution and environmental factors. Ecological factors

F

p

Seasons

Velocity

3.800 1.600 3.100 2.300 3.000 4.100 2.800 3.200 2.700 1.700 2.700 2.000 2.300 2.900 2.200 0.800 1.500 2.100 1.400 2.400 1.200 1.300 1.100 3.000

0.010 0.142 0.038 0.056 0.004 0.002 0.030 0.004 0.114 0.086 0.012 0.070 0.044 0.004 0.066 0.538 0.174 0.052 0.172 0.016 0.272 0.188 0.278 0.002

normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow

TN DO pH TP Water temperature Water depth EC Water flux River width Proportion of cobble TDS

Table 5 CCA summary for macroinvertebrate taxa in two seasons of WRB. CCA axes

1

2

3

4

Seasons

Eigenvalues

0.6587 0.6719 12.7700 9.7200 0.9159 0.9346 28.1400 22.5600

0.4235 0.6054 20.9800 18.4800 0.8827 0.9315 46.2300 42.8800

0.3603 0.4846 27.9600 25.5000 0.7363 0.9197 61.6200 59.1500

0.2977 0.3889 33.7300 31.1300 0.6792 0.7949 74.3300 72.2100

normal flow high flow normal flow high flow normal flow high flow normal flow high flow

0.4607 0.0189 −0.0787 −0.8494 0.2023 −0.5940 0.0158 −0.0514 0.1680 −0.1550 0.0029 −0.0452 0.4121 0.5372 0.0185 0.3212 −0.1126 0.6286 0.0553 0.3147 −0.6560 −0.3546 −0.1558 −0.4302

0.2773 −0.5088 0.1921 −0.2227 0.0047 0.4532 −0.3767 0.2823 −0.4268 0.1522 0.0392 0.2329 0.2114 −0.2126 0.4987 −0.2188 0.2303 −0.2548 −0.1500 −0.0047 −0.0348 0.2954 −0.2283 −0.0996

−0.2238 0.2830 0.1736 0.0963 0.6223 −0.1537 0.4231 −0.4285 −0.3202 −0.0637 −0.1293 0.5598 −0.2986 0.2251 0.2838 −0.0866 0.0597 −0.1597 −0.5283 0.2481 0.1507 −0.2574 −0.2529 0.1644

normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow normal flow high flow

Explained variation (cumulative) Pseudo-canonical correlation Explained fitted variation (cumulative)

Inter-set correlation with axes: TN −0.5033 0.4516 DO −0.6454 −0.4339 pH 0.4727 −0.3728 TP −0.4297 −0.2047 EC 0.4022 −0.0442 0.2847 TDS −0.6379 River width 0.0071 0.3484 water depth 0.2468 −0.6281 water flux 0.2588 0.1049 water temperature 0.4373 0.5285 velocity 0.5183 0.1358 proportion of cobble −0.0283 0.1138

4.3. Effects of ecological factors on the community structure of macroinvertebrates The ecological factors affecting the benthic macroinvertebrate community changed seasonally, which were velocity, TN, DO, pH, water temperature and water depth in normal flow season, and DO, pH, TDS and water depth in high flow season. Velocity was the most important factor in normal flow season and DO in high flow season. This result is consistent with the study on the relationship between macroinvertebrate community distribution and DO, current velocity and temperature in southeast Australia (Chessman, 2018). DO, pH and water depth were also the common factors influencing the macroinvertebrate community distribution in two seasons. Related studies have shown that optimal flow velocity for macroinvertebrates ranges from 0.3 m/s to 0.7 m/s (Theodoropoulos et al., 2017). The values of flow velocity was suitable for benthic macroinvertebrate in this study. Velocity showed significant relevance for macroinvertebrate community compositions in normal flow season. High flow velocity could facilitate water oxygenation and transport of organic matter, which minimize the effects of river pollution on

macroinvertebrate community structure (Kregting et al., 2016; Growns et al., 2017), as indicated by the significantly higher TP and TN, percentage of fine particles and low DO in this study (Table 1-a). Therefore, floods would be the most important factor contributing to seasonal differences in macroinvertebrate richness and abundance in rivers 9

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while cobbles have large interstices allowing water and nutrients to flow through (Wang et al., 2009). The vertical distributions of macroinvertebrate in freshwater rivers have been reported in this study of WRB. Results indicated substrate porosity, organic matter and oxygen were main ecological factors affecting the vertical distribution of macroinvertebrates in WRB, similar results in Taihu Lake observed by Chen et al. (2018). Macroinvertebrate taxa richness, abundance, density and biomass varied in the different stream bed depths. The highest richness, abundance, density and biomass occurred at a stream bed depth of 0–10 cm, similar with the findings of study in the Loire River and Galaure River of France (Maridet et al., 1992). At shallow stream bed depths (0–10 cm), substrate porosity is the primary physical factor that affects macroinvertebrate richness, abundance, density and biomass. Nevertheless, in deep bed depths, organic matter and oxygen availability causes decrease of the richness, abundance, density and biomass (Xu et al., 2012).

macroinvertebrate communities (Cabria et al., 2011; Morris and Hondzo, 2013). In addition, high current velocity could wash away the food resources for benthic macroinvertebrate and alter the composition of sediments (Chen et al., 2013; Pan et al., 2015a; Kangeri et al., 2016). Flow velocity played an important role in macroinvertebrate distribution. For example, Chironomidae, Corixidae and Psychodidae were positively related to velocity in normal flow season (Fig. 8a), while a slow flow velocity is considered a stressor to rheophilic taxa, which can be expected to enhance the habitat suitability (Calapez et al., 2017; Juras et al., 2018). Chironomidae, Corixidae and Psychodidae showed a negative correlation with the high flow rate during high flow season. Hydropsychidae, Baetidae, Staphylinidae and Herpobdellidae showed a preference for water environments with high flow velocity (Fig. 8b), possibly because the high flow velocity enhanced oxygen uptake (Lancaster and Belyea, 2006). Essential for the survival of aquatic life, DO have been reported as main factor affecting macroinvertebrates community structure and function (Boix et al., 2010; Effendi et al., 2015; Ding et al., 2016; Chessman, 2018; Karaouzas et al., 2019). In WRB, DO was the most important factor in high flow season, as well as contributing to the species distribution during normal flow season. Physidae, Lymnaeidae, Planorbidae and Tubificidae were positively related to DO in normal season (Fig. 8a), we believe that sufficiently oxygen can bring enough food to facilitate reproduction. Tubificidae, Baetidae, Corixidae, Dolichopodidae and Herpobdellidae showed a preference for water environments with high DO in high flow season (Fig. 8b). By referring to the species of tolerant Chironomidae and Tubificidae in DO limited habitats become more abundant while the abundances of sensitive species, such as the EPT decline (Zhang et al., 2018c). In this study, we found that TN was an important factor in regulating the distribution of macroinvertebrates in normal flow season. (Fig. 8a). With increasing nitrogen concentration, the EPT taxa gradually decreased, while the abundance of Tubificidae, Chironomidae, Lymnaeidae, Physidae and Herpobdellidae increased (Luo et al., 2018; Zhang et al., 2018c). Macroinvertebrate taxa responded differently to the TN owing to their different tolerance levels. Lymnaeidae, Tubificidae, Physidae and Herpobdellidae were greatly affected by TN (Fig. 8). In WRB, the increased nitrogen concentrations were mainly due to land runoff and industrial sewage. The relationship between the macroinvertebrate assemblages and TN suggested that human activities affected the benthic macroinvertebrate habitats in streams and rivers through transporting nutrient towards the stream sites. Related studies showed that the pH can manipulate the chemical and biological processes and is important in the rivers (Tekile et al., 2015; Chen et al., 2018). In this study, pH in the rivers is a main factor affecting macroinvertebrate community distribution in both seasons (Fig. 8). Tipulidae, Corixidae, Physidae and Planorbidae were negatively correlated with pH in normal flow season, while Tipulidae, Corixidae, Physidae and Planorbidae were positively related with pH in high flow season. Water depth was also an important factor affecting macroinvertebrate community distribution in both seasons. Studies have shown that the diversity of macroinvertebrates is negatively correlated with water depth (Martínez et al., 2016). In this study, we have found that multiple species were negatively correlated with water depth (Fig. 8), such as the Tipulidae, Physidae, Corixidae in normal flow season, and Hydophilidae, Lymnaeidae, Tabanidae and Staphylinidae in high flow season. The type of substrate was an important factor affecting the structural components of the macroinvertebrate community. Substrate could provide a stable habitat for benthic macroinvertebrates to avoid adverse environments. Related studies have shown that the size of the substrate, heterogeneity, compactness, voids and surface structures on the stream bed impact the composition of benthic macroinvertebrates. Macroinvertebrate richness, abundance, density and biomass in cobble stream beds are much higher than in coarse sand and fine or silt sand. Sand, silt and clay stream beds are not suitable for macroinvertebrates,

4.4. Implications One of the central topics in ecological research and conservation is the analysis of the characteristics of species community structure (Cai et al., 2017b). Species community structure in freshwater ecosystems is regulated by different driving environment factors (Cardinale et al., 2012). East Asia is a global biodiversity hotspot suffering from increasing anthropogenic disturbances, but the aquatic biodiversity and ecosystem integrity remain poorly explored (Jun et al., 2016). Rivers in northwest China are facing many challenges, particularly the impacts of social development. Description and prediction of the spatio-temporal trends and ecological factors of macroinvertebrate communities can provide useful information towards sustainable management of river ecosystems (Jiang et al., 2017). This study offers insights into river health assessment based on benthic macroinvertebrate. Meanwhile, this study can be used for building water quality assessment models and providing a reliable biological approach to the sustainable development of river ecosystems (Chen et al., 2013). There is a need to assess the impact of land use on the variability in macroinvertebrate communities of the WRB in future studies. 5. Conclusions Increasing attention has been paid to the impact of benthic macroinvertebrate community structure variation on river ecosystems. Shifts in macroinvertebrate communities are generally one of the first signals of changes in water quality and habitat quality within streams (Pan et al., 2015b; Li et al., 2018; Yi et al., 2018). Our study demonstrated the seasonal and temporal variability in macroinvertebrate community compositions. Community taxa richness, abundance and EPT richness were found to decrease and Tubificidae and Chironomidae were shown to increase with increased human activities. The reduction of taxa richness, abundance, density and biomass in high flow season was related to the summer flood. Environmental variables were more predictive of macroinvertebrate community distribution. The ecological factors significantly affecting the benthic macroinvertebrate community changed seasonally, which were velocity, TN, DO, pH, water temperature and water depth in normal flow season, DO, pH, TDS and water depth in high flow season. Velocity and DO were the most important influencing factors in normal flow season and high flow season, respectively. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 10

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Acknowledgments

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