Variability in Macroinvertebrate Community Structure and Its

Ecological Engineering 140 (2019) 105595

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Ecological Engineering

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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,QiLia, 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

ARTICLE INFO ABSTRACT

Keywords: Macroinvertebrates are sensitive to changes in the river environment and ecological status. Ecological variables Macroinvertebrate over multi-spatial scales and macroinvertebrate community data were collected in June (normal flow season) Community structure and September (high flow season) of 2017 in the Weihe River Basin (WRB). A total of 14,377 individuals were Ecological factors identified, which were classified into 7 classes, 18 orders and 59 families. Macroinvertebrate community The Weihe River Basin 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 abundances and relatively long life cycle, and are easy to collect, but also are highly sensitive to deterioration or improvements in aquatic Macroinvertebrates are an important component of river ecosystems ecological conditions (Pan et al., 2015c; Calapez et al., 2017). Studies (Wallace and Webster, 1996; Cheng et al., 2018; Krajenbrink et al., based on benthic macroinvertebrates to evaluate river ecological health 2019). Mainly composed of Oligochaeta, Hirudinea, Gastropoda, In- have been published (Kerans and Karr, 1994; Meng et al., 2009; Shi secta and Malacostraca, they usually thrive in the stream bed sediments et al., 2017; Zhang et al., 2018b; Zhao et al., 2019). Macroinvertebrates of rivers, lakes, and oceans, feeding on algae, bacteria, and leaves, as form an important part of freshwater ecosystems since they play an well as other organic matter in water (Xu et al., 2012; Hauer and Resh, important role in the food webs (Grubh and Mitsch, 2004), and re- 2017). As good indicators for aquatic ecosystem assessments, macro- garded as the foundation of a stable ecosystem (Mehari et al., 2014; Luo invertebrates oﬀer feedbacks to changes in water condition (Schneid et al., 2018). Therefore, elucidating the eﬀects of human activities and et al., 2017; Silva et al., 2018; Slimani et al., 2019), impact the de- natural causes on stream ecological health by using benthic macro- composition of organic matter (Monroy et al., 2017; Raposeiro et al., invertebrates is important. 2017) and the migration and transformation of pollutant (Bian et al., Aquatic ecosystems are often subject to a variety of anthropogenic 2016). Compared with other aquatic organisms, benthic macro- activities stresses that interfere with the behavior of aquatic species invertebrates have important advantages. They not only have large (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. P. Su, et al. Ecological Engineering 140 (2019) 105595

Fig. 1. Sampling points of macroinvertebrates in WRB.

Calapez et al., 2017). One such example is river channel management, Ferreira et al., 2016; Mathers and Wood, 2016; Sterling et al., 2016; which influences the morphological processes in riverbeds and in- Fierro et al., 2017; Whitmore et al., 2017; Chessman, 2018; Davis et al., directly affects the habitat condition of benthic macroinvertebrate 2018). Rivers in different regions are subject to human disturbance and (Bylak et al., 2009; Wyżga et al., 2014; Bylak et al., 2017). Another natural habitat conditions, and the structure of the macroinvertebrates major problem is that urbanization has changed the predominant type communities is significantly different (Li et al., 2019). of land use from natural vegetation to constructed impervious surface As the “mother river ” of the Guanzhong region (Song et al., 2018), (Jiang, 2009; Li, 2015), resulting in increased impervious surface and Weihe River generated the Guanzhong Plain, which is an important increased surface runoff (Paul and Meyer, 2001; Luo et al., 2018). agricultural, industrial, and educational center in northwest China Agricultural activity can affect macroinvertebrate communities through (Chang et al., 2015; Zhang et al., 2018a). As the starting point of the multiple pathways and mechanisms (Maloney and Weller, 2011; Silk Road, the Weihe River Basin (WRB) has provided a solid founda- Gleason and Rooney, 2017). Industrial wastewater, when directly dis- tion for the development of the Guanzhong City Group, while playing charged into the river, could greatly increase the level of heavy metal an important role in national development strategies (Wang et al., pollution, causing heavy metal enrichment and deposition, which is 2018c). The region's production value can reach 900 billion yuan, destructive to benthic macroinvertebrates. (Roy et al., 2018; Pandey feeding nearly 24 million people (Dou et al., 2018). However, in recent et al., 2019). years, problems related to water resources (e.g., water demand rising, Besides human activities, natural factors can also result in changes annual average runoff decreasing, environmental pollution and in- in macroinvertebrate communities. Several studies have shown that creasing flood risks) have been exacerbated (Cai et al., 2016), which is during dry season, decreased water flow leads to decreased water sur- attributed to both population growth and climate change in the WRB face area and chain reactions in physicochemical variables affecting the (Chang et al., 2015). Therefore, the current water quality status in the survival of macroinvertebrates (Acuña et al., 2014; Kalogianni et al., WRB is not optimistic (Wang et al., 2018b). To explore the effect of 2017). Floods, one of the major natural disturbances to macro- human activities and natural factors on river ecology, an ecological invertebrates, are usually pulse disturbances (Rosser and Pearson, survey based on benthic macroinvertebrates was carried out in the 2018). In the flood stream, rapid velocity would redistribute substrate WRB. The specific objectives of this study were as follows: (1) describe materials (from sand to boulders), scour the streambed (Stitz et al., the characteristics of the ecological factors in the WRB; (2) investigate 2017), move detritus, snags, and change the channel itself (Scholl et al., the spatial and seasonal distribution of the macroinvertebrate assem- 2016), resulting in changes in the composition of benthic macro- blage structures; and (3) reveal the major ecological factors affecting invertebrate (Granzotti et al., 2018). the macroinvertebrate distribution. We incorporated different ecolo- Many studies have documented how macroinvertebrate assem- gical variables in our multivariate analysis to identify the key variables blages respond to ecological factors under the influence of anthro- that influence the distribution of macroinvertebrate assemblage. pogenic and natural properties (Liu et al., 2016; Cai et al., 2017a; Stitz et al., 2017; Lindholm et al., 2018). For example, water temperature, 2. Materials and methods dissolved oxygen (DO), substrate composition, stream flows and current velocity, total nitrogen (TN), total phosphorus (TP), chemical oxygen 2.1. Study area demand (COD), vegetation, urbanization and land use have been identified as the main factors affecting the distribution of macro- The WRB (33° 00′ N–37° 00′ N, 104° 00′ E–107° 00′ E) has a total invertebrates (Fausch et al., 2010; Chin et al., 2016; Ding et al., 2016; area of approximately 134,766 km2 (Fig. 1), with an average annual

2 P. Su, et al. Ecological Engineering 140 (2019) 105595 runoff is 7.57 billion m3. The WRB is characterized by an arid to sub- of differences between environmental variables and biological indices humid continental climate with a wet and hot summer, a dry and cold (density, biomass, H′, D, E and λ) in normal flow season and high flow winter and a comfortable spring and autumn (Zhao et al., 2016). The season were examined using student’s t test (t-test), and differences WRB spans three different geomorphic units, including the Loess Pla- among the WRS, JRS and BRS using a nested analysis of variance teau, the Guanzhong Basin and the Qinling Mountains. The entire river (nested ANOVA) with Bonferroni post hoc tests. To visualize the com- comprises three water systems, including the Weihe River system munity structure distribution characteristics of the study sites, we used (WRS), the Jinghe River system (JRS), the Beiluo River system (BRS), non-metric multi-dimensional scaling (NMDS) ordinations based on and their tributaries along with other small independent streams Bray-Curtis dissimilarity matrices. The differences of community (Fig. 1). structure were tested using analysis of similarities (ANOSIM). In order Water samples, sediments and macroinvertebrates were collected at to conduct a multivariate analysis of data, environmental variables and

49 river junctions in WRB (Fig. 1) during normal flow season (June) species variables should be converted to log10(x + 1) to be normally and high flow season (September) of 2017. The sampling sites were distributed. Ordination plot analysis was used to analyze the response further divided into three categories: 20 sampling sites (sites W1-W20) mechanism of the community structure to the ecological factors (Pan in the WRS, 18 sites (sites J1-J18) in the JRS, and 11 sites (sites B1-B11) et al., 2015b; Kalogianni et al., 2017). Only species with a frequency in the BRS (Fig. 1). The stream bed sediments upstream of the rivers are larger than 3 are retained for analysis to reduce the effects of rare composed of large rocks, gravel or cobble. The stream bed sediments species. Species data was analyzed using detrended correspondence downstream of the three water systems are mostly fine particles. 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 2.2. Ecological data collection and analysis axis was larger than 4. Therefore, canonical correspondence analysis (CCA) was used to analyze the response relationship between the spe- Water sample was collected using a water sampler and poured into a cies community structure and ecological factors. A Monte Carlo test was 250 mL bottle. Two parallel samples were collected from every site and used to select important environmental factors which explain the fixed with acid. Ecological factors were collected at the in-situ sampling abundance and distribution of macroinvertebrate assemblages under points and measured with laboratory testing. Water quality parameters the cut-off point of p < 0.05 (Pan et al., 2015b). 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 3. Results help of Trupulse 200; current velocity was obtained by using a portable flow meter (MGG/KL-DCB); water depth was acquired by using a ter- 3.1. Characteristics of the ecological factors rain probe; Global Positioning System (GPS Etrex 201X) was used to measure latitude, longitude and elevation information. Water samples At seasonal scale, differences (t-test, p < 0.05) were recorded for for TP was determined by ammonium molybdate spectrophotometry water temperature, water depth and proportion of cobble of WRB in (GB 11893-89); TN was measured by the gas phase molecular absorp- normal flow season and high flow season (Table 1). High water tem- tion spectrometer (GMA 3376). In the laboratory, TP and TN mea- perature, high proportion of cobble and low water depth were observed surements were conducted in accordance with the Chinese government in normal flow season. During normal flow season, differences were standard for Water and Wastewater Monitoring and Analysis (2002). detected in water temperature, river depth, water flux, DO, pH, EC and The sediment particle size was screened using a stand sieve at each TDS (ANOVA, p < 0.05) among WRS, JRS and BRS (Table 2). High sampling point. Substrate composition was categorized according to river width, water depth and water flux were distributed in WRS, size (D): boulders (D > 256 mm), cobble (16 mm ≤ D < 256 mm), whereas high water temperature, TN, TP, DO, pH, EC and TDS were gravel (2 mm ≤ D < 16 mm), fine particles (D < 2 mm) (Bae et al., mainly centralized in BRS. During high flow season, water quality 2014). parameters, including water temperature, river depth, TN, DO and pH Benthic macroinvertebrates samples were taken within 100 m at were significantly different in WRS, JRS and BRS (ANOVA, p < 0.05) approximately 50 cm depth at each sampling site. A Hess sampler (Table 2). High velocity and pH, and low proportion of cobble were (S = 0.09 m2, 250 μm mesh) was used to collect the macroinvertebrates observed in JRS. According to the China National Quality Standards for samples in the shallow areas. Hyporheic invertebrates were collected at Surface Waters (GB3838-2002), the average concentrations of TN in the same location as macroinvertebrates, using a Bou-Rouch pump WRB sites exceeded the Class V guideline (≤2 mg/l) in both normal (1967) at different stream bed depths. Consisted of 6 L of water pumped flow season and high flow season. − 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 la- Table 1 boratory, all macroinvertebrates were sorted and identified by hand on Statistical descriptions (t-test) and water quality standard for physicochemical fl fl white porcelain pans. The macroinvertebrate specimens were identified characteristics measured in normal ow season and high ow season of WRB. at the family-level using microscopes (Nikon SMZ800) according to Ecological factors Normal flow season High flow season p relevant references (Kalogianni et al., 2017; Yi et al., 2018). Compared (n = 49) (n = 49) to the identification of species-level or genus-level, the identification of Water temperature 23.060 ± 4.580 21.000 ± 3.650 0 ffi family-level is more e cient (Luo et al., 2018). (°C) River width (m) 57.500 ± 61.280 63.370 ± 57.300 0.227 2.3. Data analyses Water depth (m) 0.450 ± 0.190 0.579 ± 0.340 0.020 Velocity (m/s) 0.510 ± 0.310 0.620 ± 0.490 0.184 Water flux (m3/s) 9.620 ± 9.390 13.400 ± 14.600 0.088 The application of biological indices for macroinvertebrate in China Proportion of cobble 0.500 ± 0.330 0.360 ± 0.320 0.001 have been well-documented (Huang et al., 2015; Cai et al., 2017b; Chi TN (mg/l) 5.340 ± 2.390 5.730 ± 4.850 0.517 et al., 2017; Wang et al., 2018a; Zhang et al., 2018c). We selected TP (mg/l) 0.150 ± 0.280 0.220 ± 0.300 0.172 Shannon-Winer diversity index (H′)(Shannon, 1948), Margalef richness DO (mg/l) 8.600 ± 1.800 8.080 ± 1.550 0.076 index (D) (Margalef, 1958), Pielou evenness index (E) (Pielou, 1966) pH 9.250 ± 0.470 9.250 ± 0.390 0.903 EC (μs/cm) 932.230 ± 475.910 859.580 ± 607.670 0.393 λ and Simpson diversity index ( )(Simpson, 1949) to describe the bio- TDS (mg/l) 615.960 ± 451.180 579.580 ± 424.710 0.622 diversity of macroinvertebrates community. The statistical significance

3 P. Su, et al. Ecological Engineering 140 (2019) 105595

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 21.340 ± 3.900 25.550 ± 2.700 3.384 0.029 normal ﬂow 23.190 ± 2.380a 18.510 ± 4.140b 20.850 ± 1.290ab 10.686 0 high ﬂow

River width (m) 93.000 ± 79.400a 38.610 ± 25.920b 24.050 ± 13.110b 7.212 0.002 normal ﬂow 98.880 ± 71.410a 42.100 ± 24.380b 32.320 ± 13.450b 8.956 0.001 high ﬂow

Water depth (m) 0.460 ± 0.260 0.450 ± 0.130 0.420 ± 0.070 0.221 0.803 normal ﬂow 0.530 ± 0.360 0.570 ± 0.260 0.680 ± 0.420 0.688 0.508 high ﬂow

Velocity (m/s) 0.470 ± 0.290 0.540 ± 0.300 0.540 ± 0.370 0.331 0.720 normal ﬂow 0.600 ± 0.500 0.720 ± 0.550 0.480 ± 0.290 0.823 0.446 high ﬂow

Water flux (m3/s) 13.700 ± 12.230a 7.810 ± 5.010b 5.170 ± 4.890b 3.783 0.030 normal flow 15.510 ± 17.820 13.170 ± 12.110 10.880 ± 10.870 0.352 0.705 high flow

Proportion of cobble 0.470 ± 0.310 0.550 ± 0.340 0.470 ± 0.360 0.268 0.766 normal ﬂow 0.380 ± 0.300 0.290 ± 0.300 0.450 ± 0.370 0.934 0.400 high ﬂow

TN (mg/l) 5.500 ± 1.870 4.850 ± 2.580 5.860 ± 2.750 0.660 0.522 normal ﬂow 7.220 ± 3.470a 2.990 ± 1.730a 7.500 ± 7.660b 5.231 0.009 high ﬂow

TP (mg/l) 0.100 ± 0.060 0.200 ± 0.440 0.140 ± 0.160 0.618 0.544 normal ﬂow 0.310 ± 0.430 0.190 ± 0.170 0.130 ± 0.070 1.381 0.626 high ﬂow

DO (mg/l) 7.900 ± 1.370a 8.660 ± 1.130ab 9.800 ± 2.560b 4.429 0.017 normal ﬂow 6.970 ± 1.780a 8.750 ± 0.440b 9.180 ± 0.630b 14.836 0 high ﬂow

pH 8.930 ± 0.340a 9.330 ± 0.410b 9.690 ± 0.340c 15.020 0 normal ﬂow 9.070 ± 0.270a 9.390 ± 0.490b 9.300 ± 0.260c 15.020 0 high ﬂow

EC (μs/cm) 848.500 ± 307.340a 791.240 ± 426.720a 1315.180 ± 589.320b 5.403 0.008 normal ﬂow 797.400 ± 658.340 889.960 ± 664.840 899.450 ± 360.990 0.140 0.870 high ﬂow

TDS (mg/l) 388.210 ± 180.040a 670.370 ± 526.210b 941.000 ± 439.080b 6.710 0.003 normal ﬂow 440.580 ± 366.230 612.610 ± 412.970 770.910 ± 455.030 2.316 0.110 high ﬂow

3.2. Community composition of the macroinvertebrates and biological (p = 0.002) of the macroinvertebrates significantly differed between indices 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 A total of 14,377 individuals belonging to 7 classes, 18 orders and density of 1754.85 ind/m2. The general trend decreased from upstream 59 families were obtained at 49 sites in normal flow season and high to downstream. In normal flow season, the density of species (Tubifi- flow season of WRB, mainly including Chironomidae, Perlidae, cidae, Chironomidae, Baetidae and Hydopsychidae) was higher than Heptageniidae, Tipulidae and Dytiscidae (Fig. 2). Chironomidae had others families. The biomass ranged from 0.0022 g/m2 to 23.57 g/m2, the largest species richness, accounting for 38.1% of the total species with an average biomass of 2.92 ind/g2. Species with higher biomass richness. Chironomidae, Tubificidae and Baetidae, comprising 34.76%, mainly included Tubificidae, Lymnaeidae and Physidae. However, in 34.18% and 13.36% of total abundance, respectively. A total of 11,668 high flow season, the density ranged from 0 ind/m2 to 2411.11 ind/m2, individuals were collected in normal flow season, belonging to 6 with an average density of 284.13 ind/m2. Chironomidae reached the classes, 16 orders, and 50 families. Whereas, in high flow season, 2709 highest density. The biomass ranged from 0 g/m2 to 0.93 g/m2, with an benthic macroinvertebrates belonging to 6 classes, 17 orders and 44 average biomass of 0.67 ind/g2. Herpobdellidae had the highest bio- families were collected. A total of 32 common families were identified mass (22.17%), followed by Planorbidae. The values of density and in two seasons. The WRS had the highest species richness, with 4939 biomass in WRS, JRS and BRS were not significantly different between individuals, belonging to 7 classes, 16 orders and 47 families. At JRS normal flow season and high flow season (ANOVA, p > 0.05). sites, Chironomidae, Baetidae and Tubificidae were dominant, ac- The values of H′ (1.59), D (2.41), E (0.64) and λ (0.68) in normal counting for 71.18% of JRS abundance. The BRS had the lowest species flow season were lower than those in high flow season (H′ = 1.67, richness, but the highest abundance of species, accounting for 40% of D = 2.49, E = 0.79 and λ = 0.78). The values of E and λ were different the total abundance in the two seasons. Owing to the high degree of between normal flow season and high flow season (t-test, p < 0.05). pollution-tolerant species of BRS in normal flow season, BRS was The values of H′, E and λ in WRS (H′ = 1.41, D = 2.04, E = 0.61 and dominated by the Tubificidae species, accounting for 45.13% of the λ = 0.62), JRS (H′ = 1.79, D = 3.01, E = 0.68 and λ = 0.73) and BRS abundance of BRS in normal flow season. (H′ = 1.57, D = 2.12, E = 0.64 and λ = 0.69) sites were not different NMDS (Fig. 3) and ANOSIM analyses (Table 3) showed that the in normal flow season (ANOVA, p > 0.05). But the values of H′,D,E community structure of macroinvertebrates in the WRB was sig- and λ at JRS sites were higher than the values at WRS and BRS sites. In nificantly different between normal flow season and high flow season high flow season, the values of H′, D, E and λ were not different among (R = 0.117, P = 0.001). At catchment scale, the community composi- WRS (H′ = 1.63, D = 2.47, E = 0.78 and λ = 0.77), JRS (H′ = 1.74, tions showed differences among WRS, JRS and BRS in normal flow D = 2.62, E = 0.78 and λ = 0.77) and BRS (H′ = 1.56, D = 2.29, season (R = 0.137, P = 0.002). However, ANOSIM analyses showed E = 0.70 and λ = 0.70) sites (ANOVA, p > 0.05). similar macroinvertebrate community compositions among three river systems in high flow season (R = 0.033, P = 0.184). Moreover, the 3.3. Spatial distribution characteristics of the macroinvertebrate community dominant families (Chironomidae, Tubificidae and Baetidae) were the structure same in both seasons (Fig. 2). Both the density and biomass showed spatial and temporal varia- The biological values of richness, abundance, density and biomass tions (Fig. 4). T-test revealed that the density (p = 0) and biomass were highest at a stream bed depth of 0–10 cm (Fig. 5). The distribution

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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 Table 3 in the different stream bed depths. The values of richness, abundance ANOSIM analysis in WRS, JRS and BRS of normal flow season and high flow and density remained almost constant from 0 cm to 40 cm and gradu- season. – ally reduced at 40 50 cm. The biomass gradually increased at Groups R p Seasons 15–50 cm, and a second peak appeared between 30 cm and 50 cm. The distributions of macroinvertebrate abundance, defined as the WRS, JRS and BRS 0.137 0.002 normal flow fl ratio of the abundance of each taxonomic group to the total abundance, 0.033 0.184 high ow varied in different stream bed depths (Fig. 6). The most abundant WRS and JRS 0.212 0.001 normal flow fl taxonomic groups were Tubificidae and Chironomidae, with a total 0.028 0.177 high ow relative abundance of 80%. In normal flow season, the most dominant WRS and BRS 0.152 0.039 normal flow taxonomic groups were Tubificidae, Chironomidae and Physidae. Tu- −0.026 0.633 high flow bificidae, Chironomidae and Baetidae were the most abundant taxo- BRS and JRS −0.015 0.520 normal flow nomic groups in high flow season. 0.086 0.101 high flow The density and biomass of macroinvertebrate in five layers varied in different stream bed depths (Fig. 7). The taxa were grouped into

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).

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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.

Tubificidae, Chironomidae, Baetidae, Planorbidae, Physidae, Lym- test revealed that six factors significantly influenced macroinvertebrate naeidae, Hydopsychidae and other taxa. Among all the stream bed distribution in normal flow season (Table 4): velocity (F = 3.8, depths, the maximum density was observed at a stream bed depth of p = 0.01), TN (F = 3.1, p = 0.038), DO (F = 3, p = 0.04), pH (F = 2.8, 0–10 cm. High density of Chironomidae and Tubificidae existed at a p = 0.03), water temperature (F = 2.7, p = 0.012) and water depth stream bed depth of 0–10 cm. The maximum biomass occurred at a (F = 2.3, p = 0.044). However, the main ecological factors that sig- stream bed depth of 0–10 cm. Lymnaeidae, Physidae, Hydopsychidae nificantly affected macroinvertebrate distribution in high flow were DO and Tubificidae occurred with high biomasses mainly at a stream bed (F = 4.1, p = 0.002), pH (F = 3.2, p = 0.004), TDS (F = 3, p = 0.002), depth of 0–10 cm. The biomass was also higher at the 40–50 cm depth water depth (F = 2.9, p = 0.004) and river width (F = 2.4, p = 0.016) than at the other stream bed depths. Planorbidae, Lymnaeidae and (Table 4). Physidae occurred mainly at a depth of 40–50 cm. The biomass of In normal flow season, the eigenvalues of the CCA axis 1 and the Herpobdellidae was high at bed depth of 20–40 cm. 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 en- 3.4. Relationship between the macroinvertebrates and the ecological factors vironmental correlations was 74.33% Four environmental variables had significant correlations with the CCA axis 1, with DO (−0.6454) being CCA was used to analyze the relationship between the benthic the most important contributor among them, followed by velocity − macroinvertebrate distribution and ecological factors (Fig. 8). The (0.5183), TN ( 0.5033) and pH (0.4727). The ecological factors had a analysis of forward selection and Monte Carlo unrestricted permutation strong correlation with velocity and TN in the CCA axis 2, and the

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

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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. 4. Discussion 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 4.1. Characteristics of macroinvertebrate composition 8.76% of the variance in the species distribution and of the relationship between the species distribution and the ecological factors. The CCA The macroinvertebrate community structure of the WRB resembles axis 2 accounted for 7.33% of the variance in the species distribution that in Chishui River of southwestern China (Jiang et al., 2017). A total and 20.32% of the relationship between the species distribution and the of 14,377 individuals, 59 families, were identified in the investigation. ecological factors. The CCA axis 1 was positively correlated with water The dominant species were Chironomidae, Tubificidae and Baetidae, temperature (r = 0.5285) and TN (r = 0.4516), but negatively corre- with a wide range of distribution and a high frequency of occurrence in lated with water depth (r = −0.6281) and TDS (r = −0.6379). The WRB. These species were also found to be significant indicators of CCA axis 2 was correlated positively with river width (r = 0.5372) and mesotrophic or polytrophic streams in Korean streams nationwide (Jun water flux (r = 0.6286) and negatively with DO (r = −0.8494) and pH et al., 2016). In WRB, a dramatic reduction and even elimination of (r = −0.594). sensitive species, such as EPT taxa (Heptageniidae, Siphlonuridae, The macroinvertebrate distribution had different responses to the Perlidae, Hydroptilidae and Rhycophilidae) were detected. Meanwhile, ecological factors (Fig. 8). During normal flow season, Chironomidae, increased dominance of a few tolerant taxa (particularly Chironomidae Corixidae and Psychodidae were positively related to velocity. Tubifi- and Tubificidae) were identified, along with a serious decline in bio- cidae, Lymnaeidae, Physidae and Tipulidae were greatly affected by diversity measured as diversity index and increased dominance of col- DO. Dytiscidae, Hydopsychidae, Herpobdellidae, Gammaridae and lector-gatherers. These results were consistent with those of previous Dytiscidae were mainly influenced by river width. Tipulidae, Corixidae, studies in the Evrotas River, rivers of central Manaus and Chaohu Lake Physidae and Planorbidae were negatively correlated with pH. In high (Kalogianni et al., 2017; Martins et al., 2017; Zhang et al., 2018c). flow season, Viviparidae, Chironomidae, Corixidae, Planorbidae and fi Psychodidae were negatively related to TN. Tubi cidae and Herpob- 4.2. Seasonal and spatial variability in macroinvertebrate community dellidae strongly responded to DO. There was a positive correlation structure between Staphylinidae and proportion of cobble. However, Chir- onomidae, Perlidae, Hydopsychidae and Dytiscidae were negatively Our study revealed seasonal and spatial variability in macro- correlated with TN. Psychodidae, Viviparidae and Dytiscidae were po- invertebrate communities and related biological indices in a north- sitively related to river width. Tipulidae, Corixidae, Physidae and Pla- western river system which keep a rare situation uncommon in rivers of norbidae were positively related with pH. Hydropsychidae, Baetidae, China. The comparison of the seasonal variation in the aquatic mac- Staphylinidae and Herpobdellidae showed a preference for water en- roinvertebrate communities presented the highest species richness, fl vironments with high ow velocity. 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, fol- lowing summer rainfall. The reduction of macroinvertebrate richness,

7 P. Su, et al. Ecological Engineering 140 (2019) 105595

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 would induce habitat washout and increased mortality in most benthic summer flood because of the association concentrated rainfall with macroinvertebrate taxa (Leung and Dudgeon, 2011). High flow would temperate monsoon. The disturbance associated with increased flows 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 P. Su, et al. Ecological Engineering 140 (2019) 105595

Table 4 (Calderon et al., 2017; Jiang et al., 2017; Robinson et al., 2018). Monte Carlo unrestricted permutation test on macroinvertebrate distribution Variability in composition of benthic macroinvertebrate commu- and environmental factors. nities were observed among WRS, JRS and BRS. In this study, BRS had fi Ecological factors F p Seasons the lowest taxon richness and highest taxa abundance, and Tubi cidae and Chironomidae became abundant in both two seasons. In BRS, Velocity 3.800 0.010 normal flow catchment gradient was related to changes in a series of environmental fl 1.600 0.142 high ow variables, e.g., TN, EC, TDS, and substrate size. For example, finer TN 3.100 0.038 normal flow 2.300 0.056 high flow substrate and simpler microhabitats in BRS can directly lead to de- DO 3.000 0.004 normal flow creased taxon richness and abundance of EPT taxa and increased den- 4.100 0.002 high flow sity of Tubificidae (Jiang et al., 2017). Furthermore, land use intensity fl pH 2.800 0.030 normal ow commonly varies in different region of river basins: BRS usually have a 3.200 0.004 high flow TP 2.700 0.114 normal flow greater percentage of industry land use (oil and coal) than WRS and 1.700 0.086 high flow JRS. The industrial stressors (e.g. deposited heavy metal and dissolved Water temperature 2.700 0.012 normal flow nutrient concentrations) would also partly induce the paucity of EPT 2.000 0.070 high flow and prosperity of Tubificidae and Chironomidae in BRS (Sharifinia fl Water depth 2.300 0.044 normal ow et al., 2016). In WRS sites, owing to interference of human activities, 2.900 0.004 high flow EC 2.200 0.066 normal flow the benthic macroinvertebrates showed a decreasing trend in richness, 0.800 0.538 high flow abundance and biological diversity. For example, at study points W19 Water flux 1.500 0.174 normal flow and W20 of the WRS, owing to large-scale sand mining activities ser- fl 2.100 0.052 high ow iously influenced the macroinvertebrate (Aazami et al., 2015), the River width 1.400 0.172 normal flow 2.400 0.016 high flow community structure and diversity was poor. Proportion of cobble 1.200 0.272 normal flow ANOSIM analyses and NMDS showed different structures of mac- 1.300 0.188 high flow roinvertebrate communities between two seasons. Meanwhile, analyses TDS 1.100 0.278 normal flow also showed that there was a spatial difference among the WRS, JRS fl 3.000 0.002 high ow 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 Table 5 scoured large amount of clay and sand into the rivers, leading to the CCA summary for macroinvertebrate taxa in two seasons of WRB. formation of sandy sediments. The scoured sand substrata could cause CCA axes 1234Seasons significant changes in macroinvertebrate community structure, with

Eigenvalues 0.6587 0.4235 0.3603 0.2977 normal flow declines both in taxa richness and abundance (Bae et al., 2014; Jiang 0.6719 0.6054 0.4846 0.3889 high flow et al., 2017). During normal flow season, the difference of macro- Explained variation 12.7700 20.9800 27.9600 33.7300 normal flow invertebrate community structure among WRS, JRS and BRS could be (cumulative) 9.7200 18.4800 25.5000 31.1300 high flow resulted from changes in macroinvertebrate community structure af- fl Pseudo-canonical 0.9159 0.8827 0.7363 0.6792 normal ow fected by environmental factors including DO, river width, flow, EC and correlation 0.9346 0.9315 0.9197 0.7949 high flow Explained fitted 28.1400 46.2300 61.6200 74.3300 normal flow TDS. variation 22.5600 42.8800 59.1500 72.2100 high flow The values of H′, D, E and λ in high fl ow season were higher than (cumulative) those in normal flow season. One possible explanation is that seasonally Inter-set correlation with axes: occurring floods are predictable in evolutionary time, therefore, re- TN −0.5033 0.4607 0.2773 −0.2238 normal flow lative stable responses to such disturbance are possible in macro- 0.4516 0.0189 −0.5088 0.2830 high flow invertebrate taxa (Brewin et al., 2010; Jacobsen et al., 2008; Jiang DO −0.6454 −0.0787 0.1921 0.1736 normal flow et al., 2017). In normal flow season, the values of H′, D, E and λ in JRS −0.4339 −0.8494 −0.2227 0.0963 high flow pH 0.4727 0.2023 0.0047 0.6223 normal flow are higher than those in WRS and BRS. This is related to the high −0.3728 −0.5940 0.4532 −0.1537 high flow proportion of cobble and less human activities in the JRS sites leading TP −0.4297 0.0158 −0.3767 0.4231 normal flow to relatively stable macroinvertebrate communities. −0.2047 −0.0514 0.2823 −0.4285 high flow − − fl EC 0.4022 0.1680 0.4268 0.3202 normal ow ff −0.0442 −0.1550 0.1522 −0.0637 high flow 4.3. E ects of ecological factors on the community structure of TDS 0.2847 0.0029 0.0392 −0.1293 normal flow macroinvertebrates −0.6379 −0.0452 0.2329 0.5598 high flow River width 0.0071 0.4121 0.2114 −0.2986 normal flow The ecological factors affecting the benthic macroinvertebrate − fl 0.3484 0.5372 0.2126 0.2251 high ow community changed seasonally, which were velocity, TN, DO, pH, water depth 0.2468 0.0185 0.4987 0.2838 normal flow fl −0.6281 0.3212 −0.2188 −0.0866 high flow water temperature and water depth in normal ow season, and DO, pH, water flux 0.2588 −0.1126 0.2303 0.0597 normal flow TDS and water depth in high flow season. Velocity was the most im- 0.1049 0.6286 −0.2548 −0.1597 high flow portant factor in normal flow season and DO in high flow season. This − − fl water temperature 0.4373 0.0553 0.1500 0.5283 normal ow result is consistent with the study on the relationship between macro- 0.5285 0.3147 −0.0047 0.2481 high flow velocity 0.5183 −0.6560 −0.0348 0.1507 normal flow invertebrate community distribution and DO, current velocity and 0.1358 −0.3546 0.2954 −0.2574 high flow temperature in southeast Australia (Chessman, 2018). DO, pH and proportion of cobble −0.0283 −0.1558 −0.2283 −0.2529 normal flow water depth were also the common factors influencing the macro- 0.1138 −0.4302 −0.0996 0.1644 high flow invertebrate 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., macroinvertebrate community structure (Kregting et al., 2016; Growns 2017). The values of flow velocity was suitable for benthic macro- et al., 2017), as indicated by the significantly higher TP and TN, per- invertebrate in this study. Velocity showed significant relevance for centage of fine particles and low DO in this study (Table 1-a). There- macroinvertebrate community compositions in normal flow season. fore, floods would be the most important factor contributing to seasonal High flow velocity could facilitate water oxygenation and transport of differences in macroinvertebrate richness and abundance in rivers organic matter, which minimize the effects of river pollution on

9 P. Su, et al. Ecological Engineering 140 (2019) 105595 macroinvertebrate communities (Cabria et al., 2011; Morris and while cobbles have large interstices allowing water and nutrients to Hondzo, 2013). In addition, high current velocity could wash away the flow through (Wang et al., 2009). food resources for benthic macroinvertebrate and alter the composition The vertical distributions of macroinvertebrate in freshwater rivers of sediments (Chen et al., 2013; Pan et al., 2015a; Kangeri et al., 2016). have been reported in this study of WRB. Results indicated substrate Flow velocity played an important role in macroinvertebrate distribu- porosity, organic matter and oxygen were main ecological factors af- tion. For example, Chironomidae, Corixidae and Psychodidae were fecting the vertical distribution of macroinvertebrates in WRB, similar positively related to velocity in normal flow season (Fig. 8a), while a results in Taihu Lake observed by Chen et al. (2018). Macroinvertebrate slow flow velocity is considered a stressor to rheophilic taxa, which can taxa richness, abundance, density and biomass varied in the different be expected to enhance the habitat suitability (Calapez et al., 2017; stream bed depths. The highest richness, abundance, density and bio- Juras et al., 2018). Chironomidae, Corixidae and Psychodidae showed a mass occurred at a stream bed depth of 0–10 cm, similar with the negative correlation with the high flow rate during high flow season. findings of study in the Loire River and Galaure River of France Hydropsychidae, Baetidae, Staphylinidae and Herpobdellidae showed a (Maridet et al., 1992). At shallow stream bed depths (0–10 cm), sub- preference for water environments with high flow velocity (Fig. 8b), strate porosity is the primary physical factor that affects macro- possibly because the high flow velocity enhanced oxygen uptake invertebrate richness, abundance, density and biomass. Nevertheless, in (Lancaster and Belyea, 2006). deep bed depths, organic matter and oxygen availability causes de- Essential for the survival of aquatic life, DO have been reported as crease of the richness, abundance, density and biomass (Xu et al., main factor affecting macroinvertebrates community structure and 2012). 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 4.4. Implications important factor in high flow season, as well as contributing to the fl species distribution during normal ow season. Physidae, Lymnaeidae, One of the central topics in ecological research and conservation is fi Planorbidae and Tubi cidae were positively related to DO in normal the analysis of the characteristics of species community structure (Cai ffi season (Fig. 8a), we believe that su ciently oxygen can bring enough et al., 2017b). Species community structure in freshwater ecosystems is fi food to facilitate reproduction. Tubi cidae, Baetidae, Corixidae, Do- regulated by different driving environment factors (Cardinale et al., lichopodidae and Herpobdellidae showed a preference for water en- 2012). East Asia is a global biodiversity hotspot suffering from in- fl vironments with high DO in high ow season (Fig. 8b). By referring to creasing anthropogenic disturbances, but the aquatic biodiversity and fi the species of tolerant Chironomidae and Tubi cidae in DO limited ecosystem integrity remain poorly explored (Jun et al., 2016). Rivers in habitats become more abundant while the abundances of sensitive northwest China are facing many challenges, particularly the impacts of species, such as the EPT decline (Zhang et al., 2018c). social development. Description and prediction of the spatio-temporal In this study, we found that TN was an important factor in reg- trends and ecological factors of macroinvertebrate communities can fl ulating the distribution of macroinvertebrates in normal ow season. provide useful information towards sustainable management of river (Fig. 8a). With increasing nitrogen concentration, the EPT taxa gradu- ecosystems (Jiang et al., 2017). This study offers insights into river fi ally decreased, while the abundance of Tubi cidae, Chironomidae, health assessment based on benthic macroinvertebrate. Meanwhile, this Lymnaeidae, Physidae and Herpobdellidae increased (Luo et al., 2018; study can be used for building water quality assessment models and ff Zhang et al., 2018c). Macroinvertebrate taxa responded di erently to providing a reliable biological approach to the sustainable development ff fi the TN owing to their di erent tolerance levels. Lymnaeidae, Tubi - of river ecosystems (Chen et al., 2013). There is a need to assess the ff cidae, Physidae and Herpobdellidae were greatly a ected by TN impact of land use on the variability in macroinvertebrate communities (Fig. 8). In WRB, the increased nitrogen concentrations were mainly of the WRB in future studies. 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 5. Conclusions through transporting nutrient towards the stream sites. Related studies showed that the pH can manipulate the chemical Increasing attention has been paid to the impact of benthic mac- and biological processes and is important in the rivers (Tekile et al., roinvertebrate community structure variation on river ecosystems. fi 2015; Chen et al., 2018). In this study, pH in the rivers is a main factor Shifts in macroinvertebrate communities are generally one of the rst affecting macroinvertebrate community distribution in both seasons signals of changes in water quality and habitat quality within streams (Fig. 8). Tipulidae, Corixidae, Physidae and Planorbidae were nega- (Pan et al., 2015b; Li et al., 2018; Yi et al., 2018). Our study demon- tively correlated with pH in normal flow season, while Tipulidae, strated the seasonal and temporal variability in macroinvertebrate Corixidae, Physidae and Planorbidae were positively related with pH in community compositions. Community taxa richness, abundance and fi high flow season. Water depth was also an important factor affecting EPT richness were found to decrease and Tubi cidae and Chironomidae macroinvertebrate community distribution in both seasons. Studies were shown to increase with increased human activities. The reduction fl have shown that the diversity of macroinvertebrates is negatively cor- of taxa richness, abundance, density and biomass in high ow season fl related with water depth (Martínez et al., 2016). In this study, we have was related to the summer ood. Environmental variables were more found that multiple species were negatively correlated with water depth predictive of macroinvertebrate community distribution. The ecological fi ff (Fig. 8), such as the Tipulidae, Physidae, Corixidae in normal flow factors signi cantly a ecting the benthic macroinvertebrate commu- season, and Hydophilidae, Lymnaeidae, Tabanidae and Staphylinidae nity changed seasonally, which were velocity, TN, DO, pH, water fl in high flow season. temperature and water depth in normal ow season, DO, pH, TDS and fl The type of substrate was an important factor affecting the struc- water depth in high ow season. Velocity and DO were the most im- fl fl fl tural components of the macroinvertebrate community. Substrate could portant in uencing factors in normal ow season and high ow season, provide a stable habitat for benthic macroinvertebrates to avoid ad- respectively. verse environments. Related studies have shown that the size of the substrate, heterogeneity, compactness, voids and surface structures on Declaration of Competing Interest the stream bed impact the composition of benthic macroinvertebrates. Macroinvertebrate richness, abundance, density and biomass in cobble The authors declare that they have no known competing financial stream beds are much higher than in coarse sand and fine or silt sand. interests or personal relationships that could have appeared to influ- Sand, silt and clay stream beds are not suitable for macroinvertebrates, ence the work reported in this paper.

10 P. Su, et al. Ecological Engineering 140 (2019) 105595

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