Environ Monit Assess (2014) 186:2375–2391 DOI 10.1007/s10661-013-3545-0

Spatial heterogeneity of stream environmental conditions and macroinvertebrates community in an agriculture dominated watershed and management implications for a large river (the Liao River, China) basin Xin Gao & Cuijuan Niu & Yushun Chen & Xuwang Yin Received: 23 June 2013 / Accepted: 12 November 2013 / Published online: 29 November 2013 # Springer Science+Business Media Dordrecht 2013

Abstract Understanding the effects of watershed land uses (e.g., agriculture, urban industry) on stream ecological conditions is important for the management of large river basins. A total of 41 and 56 stream sites (from first to fourth order) that were under a gradient of watershed land uses were monitored in 2009 and 2010, respectively, in the Liao River Basin, Northeast China. The monitoring results showed that a total of 192 taxa belonging to four phyla, seven classes, 21 orders and 91 families were identified. The composition of macroinvertebrate community in the Liao River Basin was dominated by aquatic insect taxa (Ephemeroptera and Diptera), Oligochaeta and Molluscs. The functional feeding group GC (Gatherer/Collector) was dominant in the whole basin. Statistical results showed that sites with less watershed impacts (lower order sites) were characterized by higher current velocity and habitat score, more sensitive taxa (e.g., Ephemeroptera), and the substrate was dominated by high percentage of cobble and X. Gao : C. Niu (*) Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Science, Beijing Normal University, Beijing 100875, China e-mail: [email protected] Y. Chen (*) Aquaculture and Fisheries Center, University of Arkansas, Pine Bluff, AR 71601, USA e-mail: [email protected] X. Yin College of Fisheries and Life Science, Dalian Ocean University, Dalian, Liaoning Province 116023, China

pebble. The sites with more impacts from agriculture and urban industry (higher order sites) were characterized by higher biochemical (BOD5) and chemical oxygen demand (COD), more tolerant taxa (e.g., Chironominae), and the substrate was dominated by silt and sand. Agriculture and urban-industry activities have reduced habitat condition, increased organic pollutants, reduced macroinvertebrate abundance, diversity, and sensitive taxa in streams of the lower Liao River Basin. Restoration of degraded habitat condition and control of watershed organic pollutants could be potential management priorities for the Basin. Keywords Watershed land uses . Liao River Basin . Agriculture . Urban industry . Environmental gradients . Benthic macroinvertebrate

Introduction Anthropogenic activities through changes in land use/ cover, degradations of habitat, and discharge of wastewater can have fundamental impacts on aquatic ecosystems (Allan and Flecker 1993; Dodds 2002; Dudgeon 2006). And understanding these watershed effects on stream conditions is important for developing watershed management strategies for large river basins. Environmental factors and macroinvertebrates are monitored as indicators for assessing stream health under watershed development (Allan 2004; Buss et al. 2004; Townsend et al. 2004; Chen et al. 2009). After benthic macroinvertebrate assemblages have been impacted by pollutants or other stressors, they may recover slowly

2376

even though the impairment remains undetected (Longing et al. 2010). Water quality and quantity issues are of great concern worldwide, with about 80 % of the global population are at risk of water security (Vörösmarty et al. 2010). Stream ecosystems are affected by non-point source pollution from agriculture (Sponseller et al. 2001; Richards et al. 1996) and urbanization (Paul and Meyer 2001; Roy et al. 2003), point source pollution from industrial and municipal discharges (Fu 2008), and hydrological alteration from dams and water abstraction (Poff et al. 1997). These stressors cause pollution by discharging nutrients, sediment, and chemical contaminants, modify channel geomorphology and riparian habitat, alter hydrological, light, and temperature regimes of stream systems, and have detrimental effects on biological communities and ecosystem processes (Allan 2004; Hotes et al. 2005; May 2006). These impacts have become increasingly severe, especially in China (e.g., Zhang et al. 2010; Li et al. 2012). Fu (2008) found about 40 % of untreated urban wastewater is discharged directly into streams, rivers, and lakes in China. Urban discharge and agricultural runoff have caused the deterioration of water quality in most Chinese rivers (Liu and Diamond 2005; Fu 2008). Although river health condition in China is currently classified based on physicochemical factors (Environmental quality standards for surface water, GB3838-2002), bioassessment with macroinvertebrates has become more common in recent years as a way to detect effects of human activities on aquatic ecosystem health in China and some other Asian countries (e.g., Wang 2002; Bae et al. 2005; Kawai and Tanida 2005; Morse et al. 2007; Jiang et al. 2010; Zhang et al. 2010). However, ecological information to support bioassessment and management in large, temperate rivers of East Asia is still limited. The Liao River Basin (hereafter, the Basin), the seventh largest watershed in China, is located in the northeast with a temperate, continental monsoon climate. Agriculture (row crops and animal production) is the predominant land use in the Basin, although a major industrial and urbanized zone encompassing the cities of Shenyang and Anshan is located in the downstream section of the Basin. In one study, Zhang et al. (2008) found that the Daliao River, a tributary of the Liao River, received more than 2 billion tons of industrial and domestic wastewater annually from neighboring areas within Liaoning Province. However, these agricultural,

Environ Monit Assess (2014) 186:2375–2391

urban, and industrial stressors have not been comprehensively studied regard to their effects on ecological conditions within the Basin. In this paper, we assessed environmental and macroinvertebrate communities in the Basin in both 2009 and 2010 to determine the effect of human activities on river health. Thus, the objectives of the current study were to (1) assess effects of watershed land uses on environmental conditions and macroinvertebrate communities and (2) identify key environmental factors that impacted macroinvertebrates in the Basin. The research results will help decision makers to develop ecological restoration strategies and priorities for the Basin.

Methods Study area The Liao River Basin (40°30′–45°10′ N, 117°00′–125° 30′ E; Fig. 1) is located in northeastern China including the Liaoning province, part of Jilin, Hebei, and Inner Mongolia. The main stem and major tributaries flow a total of 1,430 km and the total drainage area is 229, 000 km2. The basin topography is comprised of 48.2 % mountains, 21.5 % hills, 24.3 % plains, and 6 % sand. The altitude decreases from the north to the south and from both the west and east to the middle subregion, which forms the Liao River Plain in the middle and lower reaches of the basin. The Liao River Plain has altitudes under 200 m while altitudes in the upper subbasins are more than 1,000 m, which causes the maximum altitude difference over 1200 m in the basin. The land use in the Liao River Basin is dominated by row crop agriculture (38.29 %), and followed by grassland (23.65 %), forestry (22.78 %), bare land (6.45 %), urban– rural residential land (4.43 %) and water body (4.4 %). Forestry and grassland dominate in the southeast, southwest, and northwest portions of the basin where most first order streams are located (i.e., areas with fewer impacts

Fig. 1 Location of the study sites in the Liao River Basin of„ China. Symbols with different shape and color indicate sites in different stream orders (i.e., empty circle: first order sites; empty triangle: second order sites; filled circle: third order sites; filled triangle: fourth order sites). Numbers represent the main streams (1 Xila' Mulun River, 2 Laoha River, 3 West Liao River, 4 East Liao River, 5 Liao River, 6 Hun River, 7 Taizi River, 8 Daliao River)

Environ Monit Assess (2014) 186:2375–2391

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2378

from agriculture and urban industry). The remaining area is dominated by agricultural land and scattered grass and urban-industrial land where higher order streams are located (i.e., areas with more impacts from agriculture and urban industry). This watershed land use gradient provided us a perfect opportunity to test effects of watershed land uses on stream health in the basin. The basin mainly experiences a temperate semi-humid and semi-arid monsoon climate. Annual precipitation decreases from the southeast to the northwest and ranges from 900 to 350 mm in the basin. There are three major tributaries (i.e., Xila' Mulun River, Laoha River, and West Liao River) that numbered 1, 2, and 3, respectively, in Fig. 1, have been in drought for years. Field sampling and laboratory analysis Field sampling was conducted within a 100-m stream reach at each of the 41 sites in August 2009 and 56 sites in June 2010, respectively (Fig. 1). In situ measurements include altitude (m), latitude and longitude (Explorist200; Magellan, San Dimas, CA, USA), water temperature (°C), pH, dissolved oxygen (DO, mg l−1), conductivity (μS cm−1), salinity (‰), seston (mg l−1), total dissolved solids (TDS) (YSI-80; YSI Inc., Yellow Springs, OH, USA), mean water depth (m), channel width (m), and current velocity (m s−1), measured by a flotation method at two transects 10 m apart from each other). Water samples were collected and transported with ice to the laboratory for analysis of hardness (mmol l −1 ), alkalinity (mmol l −1 ), chlorophyll a (μg l−1), biochemical oxygen demand (BOD5, mg l−1), chemical oxygen demand (CODCr or CODMn, mg l−1), nitrite (NO2−-N, mg l−1), nitrate (NO3−-N, mg l−1), ammonium (NH4+-N, mg l−1), total nitrogen (TN, mg l−1), PO43−-P (mg l−1), and total phosphorus (TP, mg l−1). Standard methods (Environmental quality standards for surface water, GB 3838–2002) were followed to conduct the above-mentioned analysis (SEPA and AQSIQ 2002). Dominant substrate were assessed and assigned into one of four types that modified by the criteria of Jiang et al. (2010): (1) silt plus clay, (2) sand, (3) pebble plus gravel, and (4) cobble plus boulder. The evaluation criteria of habitat quality was modified from Barbour et al. (1999), combined with the unique characteristics of the Basin to reflect the aquatic habitats with ten indices (i.e., substrate, habitat complexity, combination of current velocity and depth, bank stability, watercourse variability,

Environ Monit Assess (2014) 186:2375–2391

flow condition, diversity of vegetation, water quality, intensity of human activity, pattern of land use around the riparian zone). Each index scored 0–20 points according to the quality of the habitat at a specific site, and a total of 0–200 points was scored for each site by summing up the points from all the ten indices. At each site, three quantitative macroinvertebrate samples were collected with a Surber net (30×30 cm in area, with 500 μm in mesh size). Samples were sorted from sediment on a white porcelain plate and preserved with 10 % buffered formalin solution. In the laboratory, samples were identified to the lowest feasible taxonomic level according to related references (Morse et al. 1994; Zhou et al. 2003). Each taxon was assigned to a functional feeding group according to Merritt and Cummins (1996) and Barbour et al. (1999). Surface of macroinvertebrate specimens was wiped dry by filter paper and weighed by an electronic balance (ISO 9001; Sartorius, Gottingen, Germany) to calculate biomass. Statistical analysis First, one-way analysis of variance (ANOVA) was used to determine whether environmental and biological parameters varied significantly among the stream orders/ watershed land use gradients. The Levene test and Kolmogorov–Smirnov test were pre-performed to confirm the homogeneity of variance and normality of distribution. Some parameters were transformed (log10(x+ 1)) to meet these two requirements. For parameters that did not meet the requirements after transformation, a nonparametric test (Kruskal–Wallis test; Yu and Xian 2009) was conducted. If a significance was detected, the LSD multiple comparison test was conducted to compare the group difference. All ANOVAs and Kruskal–Wallis tests were conducted by SPSS statistical software (version 16.0, SPSS Inc., Chicago, IL, USA). Then, principle component analysis (PCA) was performed to explore the main gradients and highlight similarities and differences of environmental parameters among different stream groups using the software CANOCO for Windows 4.5 version (Ter Braak and Šmilauer 2002). The Kolmogorov–Smirnov tests were conducted on the environmental parameters to test the normality assumption. The same transformation was conducted by the above-mentioned method to meet the normality. In order to make the means of all environmental parameters to be zero and standard deviations to be one, the centralization and standardization were performed

25.3±3.6

0.51±0.43a

37.98±36.30

0.32±0.24

Water temperature (°C)

Current velocity (m s−1)

Channel width (m)

Depth of water (m)

0.32±0.50ab

27.6±4.4

18.12±18.41b

468.8±372.1

0.18±0.19

9.22±2.90

3.01±1.51

Salinity (‰)

DO (mg l−1)

Hardness (mmol l−1)

0.29±0.18a

3.74±2.73b

27.87±26.43



BOD5 (mg l−1)

CODCr (mg l−1)

CODMn (mg l−1)



Chlorophyll a (μg l−1) –



222.4±94.3



24.20±17.68

3.29±2.25b

1.88±1.14

0.90±1.14a



0.58±0.65

0.12±0.14b

0.09±0.14

1.95±1.22

3.83±1.34

9.02±2.98

0.17±0.10

482.4±218.6

7.51±0.7

b

31.27±25.63 8.5±0.6





208.1±100.5



36.96±17.58

2.83±2.16b

2.19±0.65

0.49±0.28ab



0.44±0.46

0.15±0.08ab

0.20±0.28

1.56±1.02

3.20±1.08

12.56±5.23

0.15±0.14

468.7±235.8

ac

18.27±18.99 8.8±0.5





251.5±72.1



41.60±8.36

6.93±1.98a

2.42±1.41

0.12±0.13b



0.64±0.53

0.03±0.06b

0.06±0.13

1.87±0.46

2.96±0.92

10.36±2.35

0.19±0.09

579.2±170.0

a

11.24±20.43

14.60±24.70b

43.48±46.51

30.68±47.88a

65.9±10.7

b

0.25±0.06





0.880



0.103

0.002

0.782

0.008



0.821

0.001

0.461

0.349

0.324

0.191

0.651

0.757

0.000

0.059

0.007

0.785

0.015

0.041

0.240 121.9±21.5 15.88±23.69

11.76±33.21a

9.08±8.91a

0.81±2.33



5.79±4.21b



4.67±2.29

1.90±1.07

0.64±2.09

0.58±2.06

2.31±5.50

0.33±0.14

0.06±0.08

2.76±1.38

3.61±1.31

7.94±3.87

0.22±0.16

445.7±316.4

8.0±0.5

41.46±26.32a 8.1±0.6

10.11±12.03a

0.51±0.50



8.93±5.86a



4.58±2.66

1.70±1.15

0.23±0.46

0.16±0.35

1.14±2.61

0.33±0.13

0.06±0.08

3.09±2.80

3.19±1.11

8.60±2.33

0.21±0.20

382.8±408.5

0.01±0.00b

23.7±2.5

Fourth 9 46.9±35.2b

0.002

0.583

0.000

P value

12.79±18.93c

58.20±42.13

14.29±37.80a

102.1±20.6

0.18±0.05

8.1±0.3

6.78±2.26ab

0.46±0.29



10.61±6.84a



6.78±2.26

1.91±2.06

0.17±0.20

0.13±0.19

1.04±1.67

0.34±0.14

0.07±0.10

3.47±1.62

3.75±1.60

6.33±2.55

0.27±0.21

568.1±422.3

5.21±1.88b

0.14±0.16



11.18±6.38a



5.21±1.88

1.29±0.68

0.06±0.08

0.03±0.04

0.40±0.27

0.35±0.09

0.07±0.07

2.49±1.57

2.91±0.68

9.25±3.17

0.22±0.10

438.6±209.6

8.5±0.5

17.42±23.08b

22.39±27.83bc

26.09±39.14

34.09±49.48a

116.7±19.9

0.20±0.09

0.011

0.319



0.025



0.180

0.617

0.394

0.150

0.969

0.968

0.592

0.630

0.369

0.302

0.690

0.319

0.081

0.033

0.020

0.226

0.045

0.162

0.459

150.96±82.83b 157.03±95.90b 0.000

0.38±0.43ab

23.3±4.9

Third 7 351.3±402.4ab

28.37±27.34ab 14.73±19.34b

36.86±19.44ab 43.98±29.20a

17.33±26.72

4.35±20.85b

120.9±19.7

0.16±0.09

112.9±117.0b

41.98±35.95a 0.16±0.10

0.26±0.32b

21.2±5.6

0.45±0.25a

22.5±4.6

Second 17 146.7±173.7b

Results were from one-way ANOVAs or Kruskal–Wallis tests and multiple comparison tests. Letters (a, b and c) are used to distinguish difference among different groups; P0.30 or4) would best fit the data, and hence canonical correspondence analysis (CCA) was

chosen to explore the relationship between environmental parameters and structure of macroinvertebrate community. Prior to the analysis, the fauna data were transformed (log10(x+1)). Species that occurred no less than three or more sites and the relative abundance was no less than 1 % in a site were removed from the analysis to reduce the influence of rare species. Environmental parameters were transformed using natural logarithms (Leps and Šmilauer 2003). The CCA was performed by the following steps.

Table 2 PCA loadings of each environmental parameter on the first four axes for the Liao River Basin in 2009 and 2010 2009

2010

PC 1

PC 2

PC 3

PC 4

PC 1

PC 2

PC 3

PC 4

Eigenvalue

0.259

0.174

0.101

0.097

0.30

0.13

0.101

0.08

% Var. explained

25.9

17.4

10.1

9.7

30.0

13.0

10.1

8.0

Altitude (m)

0.4691

0.051

−0.4491

0.2003

−0.1238

−0.5042

0.601

−0.0938

Water temperature (°C)

−0.6537

−0.2498

−0.1629

−0.0295

0.6369

0.2195

0.0716

0.429

Current velocity (m s−1)

0.7818

0.3528

0.0514

0.1169

−0.3181

−0.6228

0.2687

0.231

Channel width (m)

−0.2499

−0.4311

−0.1315

−0.441

0.0268

0.2489

0.0431

−0.547

Depth of water (m)

−0.0323

0.5139

0.0145

0.0503

0.1135

−0.0451

0.2911

0.0571

Habitat score

0.6177

0.3195

0.3469

−0.2052

−0.7406

−0.098

−0.4747

−0.0152

Silt plus clay (%)

−0.3186

−0.3343

0.6723

0.1941

0.2795

0.5119

−0.2844

−0.0707

Sand (%)

−0.221

−0.1487

−0.7716

0.2947

0.3543

0.0225

0.6661

−0.3282

Pebble plus gravel (%)

0.2115

0.108

−0.2896

−0.6519

−0.3826

−0.3047

−0.3135

−0.1684

Cobble plus boulder (%)

0.5965

0.427

0.3097

−0.2619

−0.4117

−0.3737

−0.1519

0.6528

pH

−0.4884

−0.664

0.1727

0.1341

0.0827

0.5871

0.411

0.4813

Conductivity (μS cm−1)

−0.8006

0.5139

−0.095

0.0452

0.8356

−0.2481

−0.0959

0.0418

Salinity (‰)

−0.6931

0.6239

−0.03

−0.0171

0.8272

−0.2886

−0.1627

0.1837

DO (mg l−1)

−0.4139

−0.4587

0.1239

−0.4147

−0.5271

0.3601

0.0714

0.4896

Hardness (mmol l−1)

−0.6363

0.4102

−0.2007

−0.2063

0.7662

−0.3842

−0.1964

0.2324

Alkalinity (mmol l−1)

−0.2545

−0.4178

−0.1768

−0.6683

0.8085

−0.2872

0.1483

0.141

NO2−-N (mg l−1)

−0.3923

0.2634

−0.0001

−0.5841

0.4234

−0.1446

−0.2075

−0.2549

NO3−-N (mg l−1)

0.2535

0.1489

−0.3034

−0.4227

−0.7595

−0.0198

0.1073

0.0722

NH4+-N (mg l−1)

−0.4521

0.5543

0.3343

−0.0505

0.5485

−0.1557

−0.308

0.0474

−1









0.4319

0.0166

−0.4308

−0.1144

TP (mg l−1)

0.0225

0.7717

−0.2101

0.1507

0.4434

0.008

−0.4334

−0.1066

TN (mg l−1)

−0.2276

0.3372

0.5702

−0.2447

0.5085

−0.1658

−0.6027

−0.0155

BOD5 (mg l−1)

−0.5744

−0.2026

0.3264

0.1458

0.7364

−0.0679

0.1831

0.0744

CODCr (mg l−1)

−0.7596

−0.0068

−0.0972

0.3249







– −0.0914

PO43−-P

(mg l )

−1

CODMn (mg l )









0.6951

0.3733

0.1794

TDS (mg l−1)

−0.7611

0.565

−0.1047

0.0285









Seston (mg l−1)









0.4785

−0.1802

0.2434

0.0515

Chlorophyll a (μg l−1)









0.2482

0.7854

−0.2227

0.1463

Dashes (−) represent the unmeasured environmental parameters; significant principal component loadings were highlighted in bold; % Var. explained = percentage of variance explained

Environ Monit Assess (2014) 186:2375–2391

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Fig. 2 PCA plots (axes 1 and 2) of environmental parameters and sampling sites in 2009 (a) and 2010 (b). Sampling sites were presented in different shape and color (i.e., empty circle: first order sites; empty triangle: second order sites; filled circle: third order

sites; filled triangle: fourth order sites) and environmental parameters were presented by solid arrows. The length of the arrow is a measure of the importance of the parameter and the arrowhead points in the direction of increasing influence

First, to avoiding high co-linearity, the preliminary CCA was performed with the data sets that contained all the environmental parameters. And then some parameters were selectively removed that had variation inflation factors >20 (McCune and Grace 2002). The analysis was then repeated until there were no redundant parameters. To assess the contribution and significance of each parameter, we used the inter-set correlations between the CCA axes and environmental parameters, as well as Monte Carlo test with 999 permutations and the forward selection option within CANOCO (Yu and Xian 2009).

concentrations in the head water sites. Both BOD5 and COD showed an increasing pattern from the head to the mouth in the basin in both years (Table 1). The PCA loadings of each environmental parameter on the first four PCs were presented in Table 2. In 2009, PC 1 was mainly characterized by increasing current velocity, habitat score, cobble plus boulder, and altitude, and decreasing conductivity, TDS, COD, salinity, water temperature, hardness, BOD5, pH, NH4+-N, DO, NO2−N, and silt plus clay (Table 2). In 2010, PC 1 was characterized by increasing water temperature, conductivity, salinity, NO2−-N, NH4+-N, phosphate, TP, TN, hardness, alkalinity, BOD5, CODMn, seston, percentage of sand, percentage of silt plus clay, and decreasing current velocity, habitat score, DO, NO3−-N, percent pebble plus gravel, and percent cobble plus boulder (Table 1). In 2009, the first two order sites were characterized by high current velocity, habitat score and the substrate was dominated by cobble and boulder. The third and fourth order sites were characterized by the high pH, DO, water temperature, BOD5, alkalinity and the substrate was dominated by silt and sand (Fig. 2). In 2010, the first two order sites were characterized by high current velocity, habitat score, altitude and the cobble substrate (Fig. 2). The last two order sites were

Results Spatial heterogeneity of environmental conditions There were nine and eight environmental parameters showing significant difference among stream orders in 2009 and 2010, respectively (Table 1). And four of these parameters (i.e., altitude, current velocity, silt plus clay, and pebble plus gravel) had significant difference among the stream orders in both years (Table 1). Habitat score generally decreased from the head water to the lower order sites in both years. NO2−-N, NO3−-N, NH4+-N, PO43−-P, TP, and chlorophyll a showed higher

27.9±35.6

0.01±0.02

0.6±1.8

2.9±4.0

1.2±3.0

0.8±2.6

0.2±0.6

0.01±0.03

0.01±0.21

Oligochaeta

Limnodrilus hoffmeisteri

L. claparedianus

Branchiura sowerbyi

Molluscs

Radix ovata

Polypylis hemisphaerula

Crustacea

Gammarus sp.

Leeches

78.9±19.0

7.4±8.9

GC

PR

FFGs (%)

0.5±0.7

28.7±36.5

Coleoptera

2.6±4.4

0.04±0.08

Hemiptera

4.7±8.1

Trichoptera

Hydropsyche sp.

2.6±4.9

Serratella sp.

7.9±12.0

60.0±28.7

1.03±2.34

3.0±8.6

3.1±8.6

0.2±0.4

4.7±7.4

13.0±15.1

0.6±1.1

0.8±2.7

22.1±27.0

24.2±27.0

0.5±0.9

3.2±10.8

9.2±16.6

10.3±17.0

0.04±0.1

0.8±0.8a

5.3±7.2a

Baetis sp.

0.02±0.05 7.1±10.8

0.5±0.9

15.4±16.3

Ephemeroptera

0.8±1.8

2.4±3.1

8.6±11.1

21.5±19.6

37.0±22.7

58.5±26.0

Simulium sp.

3.5±7.4

16.1±18.3

Orthocladiinae

1.9±2.8

23.5±19.1

Chironominae

Antocha sp.

46.5±30.0

Diptera

Tanypodinae

68.0±36.5

Aquatic insect

Relative abundance (%)

2.50±0.95a

8.0±13.8

91.3±14.3

0.06±0.14

0

0

0.03±0.1

0.1±0.2

0.4±0.5

4.4±8.3

9.2±21.4

27.3±32.2

49.3±35.3

0.05±0.12

0.1±0.2

0.2±0.4

0.2±0.4

0

1.3±3.2ab

1.4±3.2

0.04±0.10

0.01±0.03

7.7±13.8

15.4±37.4

33.4±37.0

56.7±36.1

58.4±37.6

1.21±0.55c

5.1±6.7

46.6±62.0

20.4±32.9

2.23±1.01ab

Shannon–Wiener diversity

Third 6 7.5±4.6c 3,205.6±2,405.3

5,870.4±8,604.6

Abundance (ind m−2) Biomass (g m−2)

Second 13 19.8±8.0a 4,123.4±3,065.2

First 12 17.6±10.2ab

Stream order Number of sites Richness per site

2009

13.8±27.9

71.2±32.4

0.05±0.14

0.06±1.92

0.1±0.2

0.3±0.6

0.03±0.1

4.2±9.9

0.5±1.3

0.7±2.3

18.9±27.5

20.2±29.0

0.3±0.8

12.1±27.6

9.0±21.6

9.0±21.6

0

0.02±0.06b

1.6±2.3

0.004±0.10

0

0.8±1.1

2.6±4.7

48.0±34.8

52.4±36.3

75.5±32.7

1.46±0.83bc

21.4±40.7

2,799.7±3,272.2

Fourth 10 12.1±7.0bc

0.052

0.087

0.062

0.551

0.107

0.183

0.054

0.056

0.562

0.421

0.988

0.394

0.437

0.166

0.330

0.303

0.227

0.033

0.205

0.138

0.107

0.522

0.221

0.385

0.517

0.600

0.010

0.063

0.364

0.015

P value

2010

18.6±19.2

73.4±23.0

0.7±2.1

5.4±5.6

60.2±38.4

0.6±1.1

3.3±13.0

6.0±16.0a

0.1±0.5b 0.1±0.5

0.2±0.4

0.8±2.6

1.0±3.4

24.1±36.0a

2.5±5.3b 0.4±0.7

0.2±0.9

3.7±7.5

1.7±3.9

6.0±7.6

0.1±0.2

2.0±4.0

5.6±20.7

5.8±20.8

1.3±2.4

b

1.3±4.1b

1.5±3.7

6.1±16.0

4.4±17.0

15.9±22.9

0.7±2.1

0.8±3.1

2.3±3.7

2.5±4.0

12.7±18.6

a

6.2±14.8b

6.5±8.0a

0.1±0.4

0.1±0.2 22.6±25.6a

0.7±1.8 0.1±0.4b

1.6±2.7

23.6±28.8

22.7±28.9

48.8±34.4

62.8±35.1

1.65±0.82

10.4±20.1

2,556.6±3,867.6

Second 17 11.4±6.6

1.4±3.6a

25.6±23.2

21.0±19.3

54.4±25.1

80.9±22.2

2.10±0.63

4.2±3.5

2,967.5±3,208.8

First 23 12.7±5.3

8.0±11.0

82.9±19.6

0.2±0.6

0

0b

0.03±0.1

0

0.6±1.3b

1.5±4.0

0.6±1.6

25.4±43.4

30.6±43.7

3.4±6.1

0.6±1.6

0.1±0.2

0.1±0.2

0.6±1.6

b

0.6±1.6ab

1.5±3.9b

3.0±8.0

0.2±0.4ab

2.4±6.3

34.3±37.2

16.7±21.9

62.3±40.1

67.9±43.0

1.32±0.77

27.7±68.3

1,303.2±1,634.6

Third 7 7.0±4.4

12.3±15.1

63.5±34.4

1.0±2.5

0.5±1.4

6.3±16.4a

0.2±0.5

0.03±0.1

1.5±1.4b

1.0±2.0

3.7±7.5

0.8±2.3

12.0±19.2

0.3±0.6

5.0±14.0

1.5±3.2

1.8±3.8

0.4±0.9b

0.8±1.1b

19.0±32.1ab

4.1±12.2

0b

4.6±8.9

6.2±7.8

36.9±32.0

52.4±36.5

78.5±22.6

1.75±0.87

58.7±135.6

1,415.8±955.1

Fourth 9 13.1±9.3

0.055

0.415

0.892

0.060

0.003

0.721

0.194

0.009

0.336

0.695

0.427

0.604

0.343

0.149

0.180

0.264

0.007

0.031

0.016

0.650

0.009

0.117

0.188

0.237

0.818

0.314

0.078

0.686

0.174

0.198

P value

Table 3 Comparisons of macroinvertebrate richness, abundance, Shannon–Wiener diversity, relative abundance of dominant taxa and FFGs (means ± SD) among different stream orders in the Liao River Basin in 2009 and 2010

2382 Environ Monit Assess (2014) 186:2375–2391

2383

characterized by high conductivity, salinity, alkalinity, hardness, CODMn and chlorophyll a and the substrate was dominated by silt plus clay (Fig. 2).

GC Gather/Collector, PR Predator, SC Scraper, FC Filter/Collector, SH Shredder, OM Omnivore

Spatial heterogeneity of macroinvertebrate community

P

Spatial heterogeneity of stream environmental conditions and macroinvertebrates community in an agriculture dominated watershed and management implications for a large river (the Liao River, China) basin.

Understanding the effects of watershed land uses (e.g., agriculture, urban industry) on stream ecological conditions is important for the management o...
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