Clean – Soil, Air, Water 2011, 39 (2), 109–115
Wenzhi Liu Guihua Liu Quanfa Zhang Key Laboratory of Aquatic Botany and Watershed Ecology, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, P.R. China
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Research Article Influence of Vegetation Characteristics on Soil Denitrification in Shoreline Wetlands of the Danjiangkou Reservoir in China Soil denitrification in reservoir shoreline wetlands is an important process for removing excess inorganic nitrogen from upland runoff and controlling eutrophication in aquatic ecosystems. As yet, little is known about the influence of vegetation characteristics on the soil denitrification potential in reservoir shoreline wetlands, although vegetation can affect both denitrifying bacteria and soil properties. In this study, we measured the spatial variability of denitrification enzyme activity (DEA) using acetylene block method in shoreline wetlands of the Danjiangkou Reservoir, a water source of the South-to-North Water Transfer Project in China. Results indicated that DEA ranged from 0.001 to 2.449 mg N (N2O) g1 h1, with a mean of 0.384 mg N (N2O) g1 h1. DEA varied significantly among five representative plant communities and the highest DEA (0.248–2.449 mg N (N2O) g1 h1) was observed in the Polygonum hydropiper community. Plant biomass and vegetation cover were significantly and positively related to DEA and together explained 44.2% of the total variance. These results suggest that vegetation characteristics should also be considered in assessing soil denitrification capacity and restoring shoreline wetlands for nitrogen pollution removal in the Danjiangkou Reservoir after dam heightening. Keywords: Agricultural runoff; DEA; Eutrophication; Nitrogen; Restoration Received: September 29, 2009; revised: September 13, 2010; accepted: September 16, 2010 DOI: 10.1002/clen.200900212
1 Introduction In recent decades, many water bodies throughout the world have suffered serious nonpoint source pollution, which have resulted in excessive nitrogen loading, water quality deteriorated, and biodiversity loss in aquatic ecosystems [1, 2]. Nitrogen pollution originates from a variety of sources including agricultural runoff, animal feedlots, domestic sewage, and atmospheric deposition as a result of rapid agricultural development and urbanization [3, 4]. Increasingly, agricultural runoff has become the dominant source of nitrogen pollution in the majority of agricultural rivers of the world [2, 5]. Wetland has been recognized as an effective and economical manner for removing nitrogen pollution from agricultural runoff [6]. Nitrogen removal from runoff mainly depends on several processes including plant uptake, microbial immobilization, and soil denitrification [7]. Plant uptake and microbial immobilization result in temporary storages, and permanent nitrogen removal occurs only via soil denitrification, an anoxic process by which denitrifying bacteria convert NO 3 to nitrogenous gases that are released into the atmosphere [8]. Denitrification process involves the reduction of NO 3 to NO2 , and NO can be sequentially reduced to NO, N O, and N [9]. 2 2 2 Denitrification can be influenced by both soil and vegetation factors [9]. Soil characteristics such as dissolved oxygen, pH, temperature, nitrogen content, and carbon supply are significantly related to the
denitrification potential of wetlands [10–12]. However, there is relatively little information available on the effects of vegetation characteristics on soil denitrification, although vegetation can both influence denitrifying bacteria and soil properties. For instance, the soil organic carbon, as carbon and energy source for denitrifying bacteria, is supplied mainly by the vegetation detritus [13]. Previous studies indicated that community type, vegetation cover, biomass, and plant invasion are significantly correlated with soil denitrification [14–17]. In this study, soil denitrification potential was measured for five representative plant communities in shoreline wetlands of the Danjiangkou Reservoir in China. This reservoir serves as a water source of the China’s South-to-North Water Transfer Project and the dam will be heightened 14.6 m [18]. However, this reservoir is currently experiencing increasing nitrogen pollution and facing an eutrophication risk. Thus, primary purposes of this study were (i) to evaluate soil denitrification potential of the reservoir shoreline wetlands, (ii) to explore possible relationships between denitrification potential and vegetation characteristics. Ultimately, these findings will contribute to the vegetation restoration in the Danjiangkou Reservoir shorelines after dam heightening.
2 Materials and methods 2.1 Site description
Correspondence: Professor Q. Zhang, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, P.R. China E-mail:
[email protected]
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The Danjiangkou Reservoir (328360 –338480 N, 1108590 –1118490 E) is one of the largest impoundment in the Yangtze River basin, located at the juncture of the Hubei and Henan provinces. This reservoir has a www.clean-journal.com
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watershed area of 6026 km2 with 19% used for agriculture [19]. Climate in this region is subtropical monsoon with annual mean temperature of 15.88C and annual precipitation of 804 mm, of which 80% concentrates in the time period from May to October [20]. The highest water level (157 m) in the reservoir occurs in winter for hydropower generation while the lowest water level (139 m) occurs in summer for flooding control. The shoreline wetlands of the Danjiangkou Reservoir have an area over 300 km2 and soil type in these wetlands was alluvial soil and composed of sandy loam. The Danjiangkou Reservoir stores water from both the Han River and the Dan River, the longest tributary of the Han River, and is composed of two small reservoirs, the Han Reservoir and the Dan Reservoir (Fig. 1). The Han Reservoir is mainly surrounded by hills with coniferous (e.g., Pinus massoniana, Cunninghamia lanceolata), broad-leaved mixed forest, shrub, and herb. The Dan Reservoir is surrounded by vast farmlands, which are dominated by rice (Oryza sativa) and corn (Zea mays). Major nonpoint source pollution in the Danjiangkou Reservoir comes from this region. The Danjiangkou Reservoir dam was constructed in the 1970s. In 2002, the Chinese government launched a gigantic project, the three-route South-to-North Water Transfer Project to channel 44.8 billion m3 of water from the Yangtze River and its tributaries to the drought-stricken North every year [18]. The Danjiangkou Reservoir is the water source of the project’s middle route. Accordingly, the Danjiangkou Reservoir dam will be increased from its present 162–176.6 m above mean sea level by 2010 [20]. Opposing to hydrological regime of river reaches, a lower water level is maintained for this reservoir in late spring and summer compared to autumn and winter, leading to an emergent and relatively dry shoreline wetlands around reservoir water body during the plant growing season [20]. Vegetation in these shoreline wetlands is mainly constituted by mesophyte and hydro-hygrophytes species such as Cynodon dactylon (L.) Pers., Polygonum hydropiper L., Alternanthera philoxeroides (Mart.) Griseb., Calystegia hederacea Wall., Abutilon theophrasti Medicus, and Xanthium sibiricum Patrin. [21].
2.2 Field sampling Five representative plant communities included P. hydropiper, Scirpus triqueter, Paspalum paspaloides, A. philoxeroides, and Echinochloa colonum
Clean – Soil, Air, Water 2011, 39 (2), 109–115
were chosen in the reservoir shoreline wetlands in August 2007 when plant growth was near maximum (Fig. 1). These five plant communities were classified into annual (P. hydropiper and E. colonum), perennial (S. triqueter, P. paspaloides, and A. philoxeroides), exotic (A. philoxeroides), and native (other four communities) classes. Five sampling plots (1 m 1 m) were selected for each plant community and 25 sampling plots were collected in total. Three intact soil cores (3 cm diameter 10 cm depth) were randomly collected in each plot and mixed to form a composite sample for denitrification and soil properties analyses. Vegetation cover was visually evaluated in each plot by a 1 m 1 m frame that was divided into 100 cells (0.1 m 0.1 m). Plant biomass was measured by clipping the plants for each plot and drying to constant weight at 808C before weighing. We described the plant diversity using three alpha diversity indices: species richness, Shannon–Wiener index, and Simpson index. Species richness was defined as the number of plant species per 1 m 1 m plot. Importance values were calculated for each plant species in a plot by averaging the species’ relative cover and relative frequency. The formula for the Shannon–Wiener index was calculated as: H0 ¼
X
pi ln pi
(1)
where pi is the proportion of importance value of the ith species in a plot. The Simpson index was calculated as: D ¼ 1
X
p2i
(2)
where pi is the proportion of importance value of the ith species in a plot.
2.3 Denitrification and soil properties measurements Denitrification enzyme activity (DEA) was determined with the acetylene block method [22, 23]. Twenty five gram of homogenized fresh soil from each sampling site was weighed into a 120-mL serum bottle with 25 mL of a solution containing 1 mM glucose, 1 mM KNO3, and 1 g L1 chloramphenicol. Each serum bottle was sealed and purged with N2 gas for 20 min to induce anaerobic conditions. About 10% of the serum bottle headspace was replaced with acetylene to block the
Figure 1. Location of the Danjiangkou Reservoir and 25 sampling plots. (&) S. triqueter; ( ) P. hydropiper; (~) P. paspaloides; (*) A. philoxeroides; (^) E. colonum.
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Clean – Soil, Air, Water 2011, 39 (2), 109–115
Relationship between Denitrification and Wetland Vegetation
bacterial conversion of N2O to N2 gas during denitrification. The bottles were then shaken on a reciprocating shaker at low speed and 258C. Five ml headspace gas samples were taken after 30 and 90 min incubation using a syringe and N2O was quantified using a Tremetrics 9001 gas chromatograph with an electron capture detector. DEA was calculated from the N2O increase during 60 min incubation per 1 g fresh soil. Percentage of sand was determined by a 50 mm soil sieve after digesting the sample with hydrogen peroxide to remove organic material [24]. Soil moisture was analyzed gravimetrically by drying for 24 h at 1058C. Soil pH was measured in a soil to water ratio of 1:5 v/v with a pH meter. After drying for 24 h at 1058C, organic matter content in soils was determined from the loss on ignition for 4 h at 4508C. Soil bulk density was measured by weighing three undisturbed soil cores (7.5 cm diameter 4 cm depth) after 48 h in an oven at 1058C. Twenty gram of fresh soil was extracted with 50 mL 2 M KCl for analyzing NH4-N and NO3-N concentrations using a Spectrumlab 752S spectrophotometer.
2.4 Statistical analyses Differences in DEA among the five plant communities were analyzed by one-way analysis of variance (ANOVA) with Tukey multiple comparisons. Prior to ANOVA, the normality of DEA data was assessed and logarithmic transformation was performed when necessary. We used t-test to explore the difference of DEA between the annual and perennial communities, and between exotic and native communities. Pearson correlation analysis was used to examine the corre-
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lation soil factors and vegetation characteristics. Linear regression and stepwise regression was performed to explore the relationships between DEA and vegetation characteristics. All these statistical analyses were performed using SPSS 13.0.
3 Results 3.1 Soil and vegetation characteristics Soil moisture, organic matter, NO3-N, NH4-N, plant biomass, vegetation cover, and Shannon diversity index were significantly different among the five communities (Tab. 1). Soil moisture, NO3-N, vegetation cover, and plant biomass in P. hydropiper community were relatively higher than those in other communities (Tab. 1). Plant biomass was positively associated with soil moisture, organic matter, soil NO3-N, and soil NH4-N content, but negatively related to soil pH (Tab. 2). No significant relationship was found between soil characteristics and vegetation cover.
3.2 Denitrification related to vegetation characteristics DEA in shoreline wetlands of the Danjiangkou Reservoir ranged from 0.001 to 2.449 mg N (N2O) g1 h1, averaging 0.384 mg N (N2O) g1 h1 (Tab. 3). These denitrification rates were similar to that obtained in natural wetlands in the United States, but obviously lower than that obtained in surface soils in constructed wetlands measured by acetylene inhibition method (Tab. 3).
Table 1. Soil and vegetation characteristics (mean SD) in different plant communities
S. triqueter (n ¼ 5) pH Soil moisture (%) Percentage of sand (%) Soil bulk density (g cm3) NH4-N (mg kg1) NO3-N (mg kg1) Soil organic matter (%) Vegetation cover (%) Plant biomass (g m2) Species richness Shannon–Wiener index Simpson index
P. hydropiper (n ¼ 5)
P. paspaloides (n ¼ 5)
A. philoxeroides (n ¼ 5)
6.36 0.17 28.23 3.11ab 60.74 15.66 1.07 0.08 17.72 5.03a 2.68 0.38a 15.12 3.53a
6.80 0.23 37.42 12.73a 63.23 18.93 1.04 0.09 17.36 5.69ab 2.83 0.89a 12.62 2.51ab
6.76 0.67 25.34 4.24b 55.13 15.69 1.05 0.12 12.78 5.95ab 1.06 0.84b 4.80 1.57b
6.92 0.36 27.35 2.51b 48.52 8.91 0.95 0.12 8.73 1.92b 0.96 0.56b 8.55 4.07ab
6.74 0.15 16.62 2.14b 54.87 2.94 1.01 0.07 9.33 3.59ab 0.83 0.32b 6.59 2.06b
6.72 0.39 26.99 8.91 56.50 13.55 1.02 0.10 13.19 5.79 1.67 1.08 9.93 3.38
67.00 2.74b 1358.71 529.71ab
94.00 4.18a 1801.88 419.17a
78.00 11.51b 757.92 390.21b
82.00 13.04ab 841.47 410.46b
82.60 4.88ab 491.97 64.68b
80.72 11.71 1050.39 599.62
3.00 1.22 0.85 0.33ab 0.64 0.05
2.40 1.52 0.63 0.35b 0.65 0.03
4.60 0.55 1.33 0.17a 0.62 0.02
3.60 2.07 0.87 0.43ab 0.66 0.02
E. colonum (n ¼ 5)
Overall (n ¼ 25)
3.60 0.55 1.06 0.17ab 0.62 0.01
3.44 1.42 0.95 0.37 0.58 0.03
Means with different superscript letters are significantly different at p < 0.05.
Table 2. Pearson correlation matrix between soil and vegetation characteristics
Species richness pH Soil moisture Percentage of sand Soil organic matter Soil bulk density NO3-N NH4-N a) b)
a)
0.453 0.302 0.265 0.192 0.373 0.150 0.134
Plant biomass a)
–0.398 0.500a) 0.108 0.523b) 0.313 0.699b) 0.538b)
Vegetation cover 0.177 0.324 0.003 0.312 0.186 0.157 0.019
Shannon–Wiener index a)
0.464 0.379 0.236 0.130 0.364 0.097 0.107
Simpson index 0.056 0.267 0.272 0.084 0.278 0.029 0.071
Correlation is significant at the 0.05 level (2-tailed). Correlation is significant at the 0.01 level (2-tailed).
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Clean – Soil, Air, Water 2011, 39 (2), 109–115
Table 3. Examples of a few DEAs (mg N (N2O) g1 h1) measured by acetylene inhibition technique in wetland or riparian habitats in the United States
Citation
Habitat
Location
DEA range
DEA mean
This study [42] Gift et al. (2010) [12] Groffman et al. (2003) [43] Hopfensperger et al. (2009) [44] Hunt et al. (2004)
Reservoir shoreline Restored riparian Riparian Riparian wetland Riparian
China Maryland, USA Maryland, USA New York, USA North Carolina, USA
0.001–2.449 No data 0.23–7.58 No data 0.003–1.6
0.384 0.5265 No data 0.4196 0.457
10 10 10 10 15.2
[45] Hunter and Faulkner (2001)
Natural hardwood wetland Restored hardwood wetland Floodplain
Louisiana, USA
No data
0.657
15
Louisiana, USA
No data
0.167
15
Wisconsin, USA
0.00–0.015
0.00141
10
Oregon, USA Oregon, USA Florida, USA Florida, USA North Carolina, USA
No data No data No data No data No data
1.15 0.8 2.69 1.08 1.925
10 10 10 10 2.5
North Carolina, USA
0.6–6.56
2.6998
2.5
North Carolina, USA
No data
1.7
2.5
[45] Hunter and Faulkner (2001) [46] Orr et al. (2007) [47] Rich and Myrold (2004) [47] Rich and Myrold (2004) [37] White and Reddy (1999) [37] White and Reddy (1999) [48] Hunt et al. (2002) [49] Hunt et al. (2006) [50] Hunt et al. (2009)
Riparian Creek Everglade Everglade Constructed wetland Constructed wetland Constructed wetland
The ANOVA results showed that DEA varied significantly among the five plant communities (Fig. 2). P. hydropiper community showed the highest DEA (1.172 0.968 mg N (N2O) g1 h1) while E. colonum community showed the lowest (0.023 0.019 mg N (N2O) g1 h1). S. triqueter community was three times greater in DEA (0.511 0.164 mg N (N2O) g1 h1) than the P. paspaloides community (0.161 0.341 mg N (N2O) g1 h1). No significant difference in DEA was observed between annual and perennial communities or between exotic and native communities (Fig. 3).
DEA (µg N (N2O) g-1 h-1)
2.1 1.8 1.5 1.2 0.9
ab
0.6
b b
0.3
b
0
S. triqueter
Sampling season August No data June July Seasonally sampled Seasonally sampled Seasonally sampled June, July, August April April August February Seasonally sampled Seasonally sampled July, August
The linear regression showed that DEA was positively related to plant biomass (r2 ¼ 0.24, p < 0.01) and vegetation cover (r2 ¼ 0.17, p < 0.01). There was no significant relationship between DEA and three diversity indices (Fig. 4). Multiple stepwise regression analysis revealed that vegetation cover and plant biomass together explained 39.1% of the total variance of the DEA (Tab. 4).
4 Discussion 4.1 Effect of vegetation factors on denitrification potential
a
2.4
Soil depth (cm)
P. hydropiper P. paspaloides
A. philoxeroides
E. colonum
Figure 2. DEA (mean SD) in soils dominated by P. hydropiper, S. triqueter, P. paspaloides, A. philoxeroides, and E. colonum, respectively. The different letters (a, b) indicated significant difference among plant communities at 0.05 level.
Vegetation influences soil denitrification both in physical and biochemical approaches [25]. Organic carbon acts as an electron supply in soil denitrification process. Higher correlations between plant litter and soil carbon quality and quantity suggest that plant species can impact soil denitrification indirectly [26]. Wetland plant transports oxygen to the root area to enable aerobic microbes to oxidize nitrite to nitrate and plant root provides suitable surfaces for denitrifying bacteria growth [25, 27, 28]. Differences of microbial 2.5
2
y = 0.02x - 1.4 r = 0.17 p
