Macroinvertebrate community in relation to water ...

Macroinvertebrate community in relation to water quality and riparian land use in a substropical mountain stream, China Xingzhong Wang & Xiang Tan

Environmental Science and Pollution Research ISSN 0944-1344 Environ Sci Pollut Res DOI 10.1007/s11356-017-9042-1

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Author's personal copy Environ Sci Pollut Res DOI 10.1007/s11356-017-9042-1

RESEARCH ARTICLE

Macroinvertebrate community in relation to water quality and riparian land use in a substropical mountain stream, China Xingzhong Wang 1 & Xiang Tan 2

Received: 4 January 2017 / Accepted: 13 April 2017 # Springer-Verlag Berlin Heidelberg 2017

Abstract Exploring how water quality and land use shape the benthic macroinvertebrate community composition is of widespread interest in biodiversity conservation and environmental management. In this study, we investigated the structures of benthic macroinvertebrate assemblages and their environmental controls in terms of water quality and riparian land use in the Jinshui River, China. We carried out three campaigns including wet season (August 2009), dry season (November 2009), and normal season (April 2010) based on the hydrological regime in Jinshui basin. The result showed that macroinvertebrate assemblage variations were better explained by water quality factors than land use based on variance partitioning procedure. The land use of 2 km upstream from the sampling sites had explained more variation than that of the whole riparian zone in upstream catchment on macroinvertebrate community, and land use of 2 km upstream also had more interactions with water quality. Canonical correspondence analysis (CCA) indicated that the elements or

nutrient of magnesium (Mn), selenium (Se), strontium (Sr), silicon (Si), dissolved inorganic nitrogen (DN), sulfur (S), total organic carbon (TOC), and total nitrogen (TN) in water exhibited a strong relationship with macroinvertebrate assemblages. However, the variance in water quality explained by land use was lower than that explained by water quality in rivers using redundancy analysis. Our study suggested that proximate factors (i.e., water quality) were more important to interpret the macroinvertebrate community compared to ultimate factors (i.e., land use) for macroinvertebrate assemblages in river system. Keywords Macroinvertebrate community . Mountain stream . Redundancy analysis . Variation partitioning analysis . Watershed management

Introduction Responsible editor: Thomas Hein Electronic supplementary material The online version of this article (doi:10.1007/s11356-017-9042-1) contains supplementary material, which is available to authorized users. * Xiang Tan [email protected] Xingzhong Wang [email protected] 1

Zhejiang Provincial Key Laboratory of Aquatic Resources Conservation and Development, College of life sciences, Huzhou University, Huzhou 313000, People’s Republic of China

2

Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, The Chinese Academy of Sciences, Wuhan 430074, People’s Republic of China

Water quality degradation caused by urban sewage and occasional pollution events from industries, as well as land use change including increased agriculture activities, have threatened aquatic ecosystems (Vörösmarty et al. 2010; Xu et al. 2014). Freshwater benthic macroinvertebrates are essential components in river system, and they are relatively easy to collect and identify. Moreover, most of them are sensitive with short life histories and can respond rapidly to the environment variation (Collier 2008). Therefore, benthic macroinvertebrates have been widely used as indicators of stream ecosystem health and are also the most commonly used biological indicators in running waters (Sandin 2003). However, understanding the complex interactions that governs macroinvertebrate assemblages in rivers faces a significant challenge (Bizzi et al. 2012). It is important for stream management and

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restoration, implementing biomonitoring programs, and predicting how human alterations will affect running water ecosystems. Traditionally, researchers are more concerned about the relationship between macroinvertebrate and water conditions. Obviously, numerous studies illustrated that macroinvertebrate assemblage structure was correlated with water quality (Heino et al. 2003; Soldner et al. 2004; Wang et al. 2012a). However, the public only knows that the relationship of macroinvertebrate and water quality cannot manage some environmental issues easily such as problem due to non-point source pollution. Land use has a relationship with non-point source pollution, and is relatively easy to manage. Thus, many studies have focused on land use information and indicated a relevance between land use and responses of benthic invertebrate communities (Theodoropoulos et al. 2015; Mwedzi et al. 2016). Land use can affect the macroinvertebrate assemblages by altering the food base (Parreira de Castro et al. 2016) and water quality (Li et al. 2008; Ye et al. 2009). There might be a complex impacted pathway between land use, water quality, and macroinvertebrate. Although the Habitat Templet Theory (Southwood 1977, 1988) regarded environmental factors as a filter early, determining the compositions of the macroinvertebrate community, it is difficult to quantitatively estimate the interactions of different filters for a taxon. Also, a single factor may not well explain the structure variations of species cocontrolled by multiple factors (Mantyka-Pringle et al. 2014). Fortunately, with the development of statistical techniques, there were some models such as structural equation model that can quantitatively impact the path of the filters. However, these models require big data input, and are not suitable for the small basin. Thus, it is vital to study the Bfilter mechanism^ of water quality and land use in structuring benthic macroinvertebrate community in our small mountain river. Riparian vegetation can control erosion and sedimentation, moderate stream temperature and light, and filter sediments and nutrients leached from different types of land use (Morase et al. 2014). However, if we intend to improve in-stream biodiversity by replanting riparian vegetation, we need more understanding of the relationship between the biodiversity and the factors that influence the status of macroinvertebrate community (Giling et al. 2015). And determining the suitable riparian buffers extent is important to guide local riparian restoration cost-effective. Currently, the riparian width-related studies are well discussed (see the review of Hansen et al. 2015). Yet, the relationship between riparian zone length and macroinvertebrate community is still poorly investigated. Therefore, it is necessary to explore the relationship between macroinvertebrate community and riparian land use within different lengths. In this study, we hypothesized that the river

water quality was mainly determined by the nearby riparian land use. Currently, the most riparian width-related studies are based on the width of less than 0.5 km (see the review of Hansen et al. 2015). Therefore, we chose the width of 0.5 km in this study to rule out the influences of riparian width on reaches. And there has been a study that showed that macroinvertebrates are closely related to riparian land use at 2.5 km upstream (Lorenz and Feld 2013). Thus, for each sampling site, we extracted the riparian land cover composition with the same width of 0.5 km but difference in length: (1) entire riparian zone upstream of sampling site and (2) 2 km along in the upstream. This study was located in the special area, the water source of the Middle Route of the South to North Water Transfer Project (MR-SNWTP), which drives water to the North China Plain including Tianjin and Beijing city for domestic, industrial, and irrigative purposes (Li et al. 2009). Previous studies have been conducted in this area, covering water quality and land use (Li et al. 2008), heavy metal (Li and Zhang 2010), and benthic diatom community (Tan et al. 2013). The objectives of this study were to determine how water quality and land use factor relate to macroinvertebrate assemblages structure and their relative importance in shaping macroinvertebrate structures. It is important for ecosystem research and management in this area.

Materials and methods Study area The Jinshui River is an upstream of the Han River, with a geographic extent spanning 33° 16′~33° 45′ N and 107° 40′~108° 10′ E (Fig. 1). It has a length of 87 km with a drainage area of 730 km2. Its watershed has an altitude difference of 2590 m from its river head to its estuary into the Han River. The Jinshui River originates from a Qinling mountain in Giant panda natural reserve and has a high coverage of vegetation (Table 1). The annual precipitation over the watershed is 950~1200 mm. According to the hydrologic regime, the high flow season is the period from July to October, the base flow season is the period from November to March of the next year, while the normal flow season is the period from April to June. Benthic macroinvertebrate We established three random sampling locations (30 × 30 cm2 quadrates) in each site for benthic macroinvertebrate measurements. The riverbed within the Surber sampler frame (mesh size = 420 μm) was sampled to a depth of about 10 cm in areas of


November in 2009 was dry season, and April in 2010 was normal season. Water sampling and analysis

Fig. 1 Location of the Jinshui River watershed in China and the distribution of sampling sites

We also collected surface water at each site where benthic macroinvertebrates were captured. Water quality measurements including water temperature (T), pH, total dissolved solids (TDS), turbidity, electrical conductivity (EC), dissolved oxygen (DO), total nitrogen (TN), ammonium (NH4-N), nitrate (NO3-N), total phosphorus (TP), orthophosphate (PO4P), soluble reactive phosphorus (SRP), chemical oxygen demand (COD), total organic carbon (TOC), dissolved organic carbon (DOC), ions (e.g., potassium (K), calcium (Ca), sodium (Na), sulfur (S), magnesium (Mg), bicarbonate ion (Mg), bicarbonate ion (HCO3-), fluorine ion (F-), chloridion ion (Cl-) and sulfate ion (SO42-), and heavy metals (e.g., aluminum (Al), arsenic (As), barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), manganese (Mn), nickel (Ni), plumbum (Pb), antimony (Sb), selenium (Se), silicon (Si), strontium (Sr), and vanadium (V)) were measured. Related water sampling proceedings and analysis were referenced from Tan et al. (2014) and Li and Zhang (2010). Dissolved inorganic nitrogen (DN) was calculated as the sum of NH4-N and NO3-N. Land use data

unconsolidated substrata. All stones in the sample area were scrubbed with a soft bush to remove attached organisms. Macroinvertebrates were separated from the sand and mud by hand in laboratory, and preserved in 10% formalin. The biological samples were identified according to taxonomic references (Epler 2001; Morse et al. 1994; Merritt et al. 2008). The samples were collected in seven sites throughout the Jinshui River (Fig. 1) in three field surveys, carried out in August 2009, November 2009, and April 2010, respectively. Among them, August in 2009 was rainy season, Table 1 Site

S1 S2 S3 S4 S5 S6 S7

Land use data used in this study was derived from Landsat Thematic Mapper imagery using supervised classification (Shen et al. 2006). We selected four land cover categories, including (1) natural vegetation, including forests (coniferous, deciduous, mixed coniferous, and broad-leaved), bushes, and herb; (2) cropland, including paddy field, dry land, and orchard; (3) bare lands, including small gravels, bare ground, and rocks; and (4) urban, including industrial and residential area. ArcGIS10.0, ENVI 4.8, and Excel were used to achieve the composition of land use.

General land use characteristics at the sampling sties (whole riparian zone upstream and 2 km riparian zone upstream of the sites) Whole riparian zone upstream

2 km riparian zone upstream

Urban (%)

Cropland (%)

Vegetation (%)

Bare lands (%)

Urban (%)

Cropland (%)

Vegetation (%)

Bare lands (%)

0.00 0.12 0.08 0.00 0.09 0.09 0.00

0.23 5.08 0.41 2.03 1.95 2.37 0.02

99.77 94.27 99.51 97.91 97.94 97.45 99.98

0.00 0.47 0.00 0.06 0.02 0.08 0.00

0.00 0.00 0.19 0.04 0.08 0.79 0.00

1.82 28.80 0.61 3.12 4.01 11.07 0.04

98.18 67.05 99.20 96.71 95.60 87.06 99.96

0.00 4.14 0.00 0.14 0.31 1.08 0.00


Data analysis Macroinvertebrate species data were log10 (x + 1) transformed to approach the assumed conditions of normality and homoscedasticity of the data before analysis. Variance partitioning analysis was used to determine the relative importance of water quality and land use, i.e., how much of the overall variance in macroinvertebrate community structure can be uniquely ascribed to water quality and land use and their combination (Legendre and Legendre 1998; White and Hurlbert 2010). The relationship between measured water quality variables and benthic macroinvertebrate assemblages was explored using canonical correspondence analysis (CCA). Forward selection and Monte Carlo permutations were used to select a minimum set of variable that significant and independent effects on benthic macroinvertebrate distribution. Redundancy analysis (RDA) was employed to assess the effects of land use on river water quality. RDA is form of direct gradient analysis to explore the relationship between two matrixes (Leps and Smilauer 2003). Specifically, a matrix of predictor variables (e.g., land use) was used to quantify variation in a matrix of response variables (e.g., river water quality) (Ye et al. 2009). The partial RDA was used to determine the independent influences of land use on water quality. CCA, variance partitioning analysis, and RDA were carried out by CANOCO (version 4.5).

Fig. 2 The proportions of variation in macroinvertebrate community portioned into fractions explained purely by water quality variables and purely by land use of whole riparian zone upstream (a) and 2 km riparian zone upstream of the sites (b). Also, shared fractions are shown

sampling sites was more influential than that of whole riparian zone upstream on macroinvertebrate community, and had a little more interaction with water quality.

Relationship between macroinvertebrate community and water quality variables

Results Relative importance of water quality and land use for macroinvertebrate community The river had high vegetation coverage in two reaches (whole riparian zone upstream and 2 km riparian zone upstream of the sampling sites) in background (Table 1). In the case of whole riparian zone upstream of sampling sites, water quality alone accounted for 54.8% of the total variation of macroinvertebrate community structures. Land use represented 25.8% of the total explained variation. And the 5.4% of the macroinvertebrate data variation was shared between water quality and land use (Fig. 2a). While in the case of the 2 km riparian zone upstream of the sampling sites, water quality alone accounted for 53.9% of the total macroinvertebrate data variation. Land use represented 29% of the total explained variation. And the 6.3% of the macroinvertebrate data variation was shared between water quality and land use (Fig. 2b). Thus, the land use of 2 km upstream of the

CCA on the relative importance of water quality revealed that the variation of macroinvertebrate community structure has strong relationship with the water quality parameters Si, Mn, Se, S, Sr, TOC, TN, and DN (Fig. 3). The condition of these water quality parameters were showed in Table 2. The most important factor explained on the first axis of the CCA diagram was Mn, while the second axis displayed a gradient of Si. There was a strong correlation between the TOC and the third axis. However, all eigenvalues of axis were low, indicating that the total variation in taxon composition was low and the environmental gradients extracted were short. The CCA ordination explained 37.1% of the total variance on the first three axes. For macroinvertebrate, Caenis sp. and Ambrysus sp. have a good relationship with Mn. Heptagenia sp., Parachauliodes continentalis, C e r a t o p s y c h e s p. , C h im a rr a s p . , a n d G y r a u l u s convexiusculus showed strong relationship with Si. And Onychogomphus sp. indicated a fine relationship with TOC (Fig. 3).

Author's personal copy Environ Sci Pollut Res Fig. 3 CCA ordination diagrams of macroinvertebrates: species– water quality variables (the small letters are the codes of the Latin name of the species and are listed in the Appendix 1)

The relationship of land use and water quality variable which were most strongly related to macroinvertebrate assemblage structure The partial RDA demonstrated that riparian land use along whole upstream explained 13.2% and that of 2 km length explained 18.7% of the variations. The variations (44.8%) in Si accounted for land use of catchment upstream from the sites (Fig. 4a). And 21.9% variations of TOC were explained by land use of whole riparian zone upstream (Fig. 4a). Otherwise, water quality variables were poorly correlated with land use of whole riparian zone upstream. Similarly, 41.6% variations of Table 2

Si were explained by land use of 2 km upstream of the sites (Fig. 4b). And 25.3% of the variations in DN were accounted for land use of 2 km riparian zone upstream (Fig. 4b), while water quality variables were poorly correlated with land use of 2 km riparian zone upstream.

Discussion The current study found that the water quality and riparian land use determined the macroinvertebrate community structure within two reaches (0.86 and 0.89 explain rates; Fig. 2).

The water quality parameters which have a good relationship with macroinvertebrate during the sampling period (mean ± SD)

Site

TOC (mg/L)

TN (mg/L)

DN (mg/L)

Mn (mg/L)

S (mg/L)

Se (mg/L)

Si (mg/L)

Sr mg/L

S1 S2 S3 S4 S5 S6 S7

1.65 ± 2.27 1.10 ± 1.18 1.02 ± 1.40 4.63 ± 5.50 0.77 ± 0.30 0.14 ± 0.19 0.41 ± 0.57

0.80 ± 0.23 2.68 ± 2.49 1.66 ± 0.82 2.10 ± 0.18 1.97 ± 0.29 0.97 ± 0.27 0.57 ± 0.80

1.16 ± 0.14 0.92 ± 0.43 1.72 ± 0.11 8.01 ± 7.53 6.66 ± 6.79 1.61 ± 0.52 1.06 ± 0.85

0.001 ± 0.0008 0.002 ± 0.002 0.001 ± 0.001 0.002 ± 0.0004 0.001 ± 0.002 0.0005 ± 0.0007 0.0009 ± 0.001

4.91 ± 0.75 64.75 ± 105.36 4.01 ± 0.27 4.31 ± 0.16 4.08 ± 0.26 4.36 ± 1.24 98.34 ± 135.14

0.04 ± 0.06 0.04 ± 0.07 0.002 ± 0.003 0.0004 ± 0.0005 0±0 0.06 ± 0.08 0.0007 ± 0.0009

3.46 ± 0.66 3.10 ± 0.68 2.74 ± 0.47 3.06 ± 0.45 2.8 ± 0.37 3.79 ± 0.64 3.99 ± 0.19

0.14 ± 0.02 0.10 ± 0.02 0.10 ± 0.02 0.07 ± 0.02 0.06 ± 0.02 0.10 ± 0.003 0.04 ± 0.008


Fig. 4 Biplots of the river water quality and land use whole riparian zone upstream (a) and 2 km of riparian zone upstream (b) up from sites by the redundancy analysis

Similarly, most parallel studies showed that both water quality in-stream and land use influences were important in structuring macroinvertebrate assemblages (Theodoropoulos et al. 2015). However, the isolate water quality had very high explain rate (0.55 and 0.54 explain rates; Fig. 2) on macroinvertebrate community in this study. In contrast, Stoll et al. (2016) found that the maximum explain rate of regional hydromorphological habitat effects on the ecological quality class of benthic invertebrate was 0.16. There are two reasons to support our high explain rates. First, our study was taken at the small spatial scale, a small river (87 km long and has a drainage area of 730 km2) originated from high mountains. Mykrä et al. (2007) illustrated that the importance of local environmental factors explaining macroinvertebrate assemblage structure increases with decreasing spatial extent. Second, some great influencing factors on macroinvertebrate community may develop the high explain rates. The parallel

study found that the high explain rate (0.58) of metals impacts on macroinvertebrate assemblages, because of the heavy metal pollution in that watershed (Bere et al. 2016). However, the river we studied was in water source area, and had high vegetation coverage (Table 1) and no obvious pollution events. The reason of our high explain rate might be that our water quality parameters not only contain nutrient but also include ions, which may have great relationship with macroinvertebrate community. Therefore, the more water quality parameters may provide the more explain rate of the macroinvertebrate community. In this study, water quality had more important than land use on structuring macroinvertebrate community. The water quality often has good relationship with macroinvertebrate compared with other environment factors in other studies (e.g., Bere et al. 2016; Rico et al. 2016). The result may be important for developing stream water quality bioindicators. Numerous studies have demonstrated the manifold complexities of land-water interface influences on stream communities (Hale et al. 2015). We also found this interface influence, but it was not the key factor explaining the macroinvertebrate community variation. Although the quantitative relationship between water quality, land use, and benthic macroinvertebrate was understood in this study, their quantitative interaction paths were absent. We think that the mechanism of interaction based on the quantitative relationship is more suitable for management. We have also compared the two lengths of land use (whole riparian zone upstream and 2 km riparian zone upstream of the sampling sites) in relation to macroinvertebrates and water quality. We found that the land use of 2 km riparian zone upstream of the sites was more related to macroinvertebrate community, and had a little more interaction with water quality than the land use of whole riparian zone upstream. Our finding corroborated the study of Lorenz and Feld (2013), which showed that the land use of 5 km upstream riparian had significant effects on the ecological quality at restored stream sites. Although the macroinvertebrate community structure was related to the land use of upstream far away because of the continuity of the river, it suggested a stronger linkage of 2 km upstream riparian land use to macroinvertebrate than the whole riparian zone upstream. However, some studies showed that the strongest influence on stream macroinvertebrate community structure occurred within small to medium widths (