Benthic Invertebrates Monitoring of the Muling River basin in Northeast China Xu Sun ( [email protected] ) Northeast Forestry University https://orcid.org/0000-0002-7811-1662 Hongxian Yu Northeast Forestry University Research Keywords: Muling River basin, macroinvertebrates, functional feeding groups, environmental variables Posted Date: June 26th, 2020 DOI: https://doi.org/10.21203/rs.3.rs-37350/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Page 1/14 Abstract Background: Muling River is the fifth-largest river in Heilongjiang Province, and it is also the main feeding river to the Ussuri River which is the boundary river of China and Russia in Heilongjiang Province northeast of China. Muling River basin located in the south of Sanjiang Plain. Macroinvertebrate samples were collected using a D-frame net and Shannon-Wiener index were calculated in terms of abundance. Results: A total of 158 genera or species macroinvertebrate were collected from the 28 sampling sites and classied into six functional feeding groups including 61 gatherers/collectors, 42 predators, 22 scrapers, 14 shredders, 11 lterers/collectors and 8 omnivores. The correlation and relationship between environmental variables and macroinvertebrate functional feeding groups was explored using Pearson analysis and redundancy analysis. The analysis results displayed that macroinvertebrate functional feeding groups had strong relationships with the environmental variables in the Muling River basin. Conclusions: All FFGs, total abundance and Shannon-Wiener index were not signicantly different. Total abundance of macroinvertebrates was higher in summer and biodiversity index was higher in autumn. Environmental factors of natural gradients and nutrition indicator were not signicantly different, while others were signicantly different. Backgroud Muling River basin located in the south of Sanjiang Plain with an area of 18427 km2, and it is the fth-largest river in Heilongjiang Province northeast of China, which is the main feeding river to the Ussuri River the boundary river of China and Russia [1]. The approximately length of the Muling River is 834 km with annual water ows of 2.35 billion m3. The river ows through ve counties or cities of Muling, Jixi, Jidong, Mishan and Hulin from the south to the northeast of Heilongjiang Province [2]. Upstream of the river is characterized by temperate continental climate with a hot summer rainy and long cold winter. The annual average precipitation in the upstream is 530 mm and mainly occurs from July to September. In the midstream, the climate is temperate and semi-humid monsoonal with annual average temperature of 3.1 °C (-18 °C ~ 21 °C). The annual precipitation is 522 mm and the frost-free period is 149 days. At downstream area, the climate is characterized by temperate continental monsoonal. In recent years, with the aggravation of agricultural non-point source pollution, industrial discharge pollution and urban living pollution in Muling River Basin, the water quality of Muling River is deteriorating, which has had a negative impact on the local people's production and life. Functions of river ecosystem research mostly carried out based on the traditional classication of species. However, recent studies have shown that ecosystem functions are mainly subject to the diversity of functional traits, i.e. the distribution of functional traits and the spatial-temporal pattern of abundance [3]. Functional traits are sensitive to environmental changes and play a key role in the study of the relationship between biodiversity and ecosystem functions. Functional diversity based on biological traits is closely related to ecosystem processes and is the key to understand ecosystem and community functions [4]. Macroinvertebrates are widely used to monitor the damage of aquatic ecosystem, and they are also an important part of aquatic food web, which is the basis of nutrient cycle and ecological balance of ecosystem [5]. There are so many damages to the aquatic ecosystem in the Muling River basin, and the importance of monitoring water quality in the Muling River basin through macroinvertebrates is self-evident. The species characteristics of functional groups are more closely related to the environment, which can more directly reect the ecological process of the ecological environment affecting aquatic communities, and better understand the water ecosystem and its biodiversity [6, 7]. In the river ecosystem, the functional diversity of macroinvertebrates can better reect the function of ecosystem than community structure. Many studies showing that substrate type and aquatic vascular plants which affecting the growth and functional group distribution of macroinvertebrate [8–10]. This study aimed to collect macroinvertebrate fauna, and explore the relationships between macroinvertebrate functional feeding groups and environmental variables in the wetland environments of Muling River basin. Materials And Methods Study area Muling River is the fth-largest river in Heilongjiang Province, and it is also the main feeding river to the Ussuri River which is the boundary river of China and Russia (Fig. 1). According to the inland waters shery natural resources investigation [11], and principles to Page 2/14 the requirement of sampling sites, in combination with natural form of Muling River basin, 28 sampling sites were selected (Fig. 1). Remote Sensing Image Of Land-use Digital elevation model (DEM) data and Landsat Thematic Mapper imagery were used to delineate catchment boundaries and map the land use composition in Muling River basin (Fig. 2). The original and detailed land-use classes were organized into the following groups by combining the similar land-use types into one broad category: (1) Khanka Lake, including Xiaoxingkai Lake and Daxingkai Lake which in Chinese side; (2) forests, including wooded regions and mixed forest regions; (3) wetlands, including riverine marsh, bogs, fens, meadows, pond and bayou; (4) cities, including counties, towns and villages; (5) grassland, including meadow and lea; (6) farmland, including dry farmland and paddy eld. Environmental Variables Data Sampling Samples were collected 3 times from 28 sampling sites of Muling River basin in May, July and September periods in 2015. Water transparency (SD) and water depth (WD) were measured in the eld using a Secchi disk and graduated portable staff gauge, respectively. Electric conductivity (EC), dissolved oxygen (DO), pH and water temperature (T) also measured in the eld using a portable multi-probe (YSI 6600, YSI Inc., USA). We used the Chinese standard methods proposed by Ministry of Environmental Protection of People’s Republic of China [12] to determine the concentration of total nitrogen (TN), total phosphorus (TP), N:P ratio (N:P), ammonium + − nitrogen (NH4 -N), nitrate nitrogen (NO3 -N), chemical oxygen demand (CODMn). Macroinvertebrates Data Sampling Three random subsamples were collected at locations of 1 m2 at each sampling site by using a D-frame net (30-cm diameter, 500-mm mesh). All macroinvertebrates samples were composited into a single sample, preserved in 75% ethanol and transported to the laboratory for identication. In the laboratory, all samples were sorted on white porcelain pans, identied, and counted with a light stereomicroscope. All individuals were identied to genus or species using appropriate identication guides [13, 14]. Taxa were divided into six functional feeding groups according to Cummins et al. (1974) and Duan et al. (2010): predators (PR), omnivores (OM), gatherers/collectors (GC), lterers/collectors (FC), scrapers (SC) and shredders (SH) [15, 16]. Statistical Analyses Variation of environmental variables and abundance of functional groups in different sampling periods were analyzed using One-way ANOVA in SPSS 19.0 software. Before analysis, the data was log10(x + 1) transformed to manage variance heterogeneity and ensure the data is normally distributed. In this study, the gradient length of the rst ordination axis was 0.404 in the detrended correspondence analysis (DCA). Therefore, redundancy analysis (RDA) with Monte Carlo simulations (499 permutations) ordination based on unimodal method was selected to analyze the relation by using CANOCO for Windows 4.5 software (Microcomputer Power, New York, USA). Pearson correlation analysis was carried out to conrm the signicant relationships between environmental variables and the abundance of functional feeding group. Cluster and NMDS analyses were conducted using the PRIMER 7 software package [17]. Diversity of macroinvertebrate FFGs was represented by Shannon-Wiener index [18] as follows: Where, S is the number of FFGs within the given sample; and Pi is the percentage of FFGs i in the total number of individuals. Results Environmental variables Page 3/14 Among all sampling sties, natural gradients (e.g., dissolved oxygen and temperature) and nutrient indicators (e.g., total phosphorus, N:P ratio, ammonium nitrogen and nitrate nitrogen) were not signicantly different (p > 0.05), but water transparency, water depth, electric conductivity, total nitrogen and chemical oxygen demand were signicantly different (p < 0.01) (Table 1). Table 1 One-Way ANOVA of environmental variables and macroinvertebrate FFGs abundance. Data are average values (with SE). 2015May 2015Jul. 2015Sep. F p-value Environmental variables SD (m) 0.35(0.05) 0.32(0.07) 0.48(0.07) 10.418 0.000** WD (m) 2.72(0.76) 3.13(1.04) 3.02(1.04) 75.232 0.000**