Changes in Soil Properties before and after ... - Wiley Online Library

Clean – Soil, Air, Water 2012, 40 (10), 1125–1130 Chen Huang1 Junhong Bai1 Hongbo Shao2 Haifeng Gao1 Rong Xiao1 Laibin Huang1 Peipei Liu1 1

State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing, P. R. China 2 Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, China

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Research Article Changes in Soil Properties before and after Wetland Degradation in the Yellow River Delta, China The aim of this study is to determine whether wetland degradation in the Yellow River Delta has an effect on its soil properties. Twenty soil samples collected from degraded and undegraded wetlands were analyzed using hierarchical cluster analysis and principal correspondence analysis. The statistical analyses revealed that soil samples from both degraded and undegraded wetlands could be clearly distinguished according to their properties. These soil properties such as nitrate nitrogen, ammoniac nitrogen, and pH showed significant changes after wetland degradation than before. However, degraded wetland soils did not exhibit significant changes in electric conductivity (EC) and soil organic matter (SOM). Six soil properties excluding EC and SOM were transformed to establish a simplified chemical index of soil degradation (CDI) for evaluating wetland soil quality before and after wetland degradation. Lower CDI values of undegraded wetland soils were observed than those of degraded wetland soils. The methods used in this study can be further used in larger areas to evaluate and monitor the degradation status of wetlands, and can contribute to wetland management and restoration practices. Keywords: Cluster analysis; Degradation index; Ecology; Ordination analysis; Soil quality index Received: January 22, 2012; revised: April 9, 2012; accepted: April 27, 2012 DOI: 10.1002/clen.201200030

1 Introduction Wetlands perform vast ecological service functions in providing habitat, purifying water resources, decomposing pollutants, supplying groundwater, maintaining regional water balances, and adjusting climatic features. However, wetlands are undergoing severe ecological degradation worldwide due to intensified agricultural water deficiencies, climatic change, environmental pollution, and other anthropic interferences [1, 2]. It has been reported that coastal wetlands are being lost at the rate of 0.5–1.5% per year [3]. The Yellow River Delta of China, one of the most plenty biodiversity zone of the world, is also suffering from degradation due to water deficiencies and soil salinization [4]. Soil is an essential component of wetland ecosystem, which can support, hold and regulate water and nutrients, act as the growth medium of plants and attenuate the harmful effects of contaminants [5]. The changes in soil quality can significantly influence wetland structure and functions. Therefore, soil serves as one of the most important biogeochemical regulators influencing the quality of water, plants, animals, and agricultural products by means of physical, chemical, and biological processes [6]. However, a global assessment of soil degradation estimated about 13% of the land (including wetlands) in Asia and the Pacific region

has been degraded [7]. Recently, many studies have investigated the effects of soil properties on wetland plants and microorganisms in the Yellow River Delta. Zhang et al. [8] presented that soil salt content was an important factor that determined the halarch succession. Xiong et al. [9] observed that the distribution of different wetland vegetation communities was significantly correlated with soil salinity and pH, but not correlated with total phosphorus, total nitrogen (TN), and organic matter in the soil. And a study by Nie et al. [10] showed the abundances of total bacteria and hydrocarbon degraders in bulk soils were primarily determined by soil salinity and water content. Soil captures a cumulative record of nutrients and pollutants dynamics and soil chemistry values can serve as a better indicator of study site than those surface water chemistry values [11]. However, few studies have focused on the changes in soil properties and soil quality before and after wetland degradation [12]. This study presented a quantitative analysis on the effects of degradation on wetland soil properties. The primary objective is to determine whether wetland degradation has a significant effect on soil properties and to find a chemical degradation index of soil as an indicator of wetland degradation.

2 Materials and methods 2.1 Study area Correspondence: Dr. J. Bai, State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, P. R. China E-mail: [email protected] Abbreviations: CDI, chemical index of soil degradation; EC, electric conductivity; PCA, principal correspondence analysis; SOM, soil organic matter; SQI, soil quality index; TC, total carbon; TN, total nitrogen

ß 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

The Yellow River Delta is located in the northeast of Shandong Province, on the southern bank of the Bohai Sea (Fig. 1). The delta was formed as the Yellow River reentered the Bohai Sea in 1855. Now it spans from 1188070 E to 1198180 E, and from 368550 N to 388120 N, with an area of 6010 km2. The massive transportation of sediment from the Yellow River makes the delta expand by www.clean-journal.com

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Clean – Soil, Air, Water 2012, 40 (10), 1125–1130

Figure 1. Location map of the study area.

approximately 30 km2 per year [13]. This area has a warm temperate and semi-humid climate with a continental monsoon influence. The annual mean temperature ranges from 11.7 to 12.68C. The annual mean precipitation is 530–630 mm, 70% of which occurs in summer [14]. The delta is mainly covered by shallow sea and bog. The total area of wetlands is 4167 km2, accounting for 69.3% of the whole delta, and 75.1% of which (3131 km2) is natural wetland [15]. The five dominant species in the Yellow River Delta are reed (Phragmites australis (Cav.) Trin. ex Steud), Suaeda salsa (Suaeda heteroptera), cogon grass (Imperata cylindrical var. major), salt cedar (Tamarix chinensis Lout.), and willow (Salix matsudana Koidz) [16]. The study site was chosen in a typical estuary wetland with the dominant species of S. salsa, and it is nearby a crescent stagnant pool influenced by tidal ditch (378430 2100 N, 1198130 5500 E). The status of S. salsa community at this site was healthy when the first sampling took place in August of 2007. However, all the plants have died before the second sampling in October of 2007, whereas S. salsa around the study site still grew well. Therefore, this site is suffering wetland degradation.

2.2 Soil collection and analysis Eight sampling plots (group 1) were selected along the downstream tidal ditch of the stagnant pool with three paralleled sites for each plot during the first sampling. The second sampling selected 12 plots (group 2), eight plots were located in the downstream of the stagnant pool, and additional four plots were located in the upstream of the pool due to similar landscapes to the downstream. Topsoil samples (0–20 cm) with three replicates were collected and then mixed together for each sampling plot. All soil samples were placed in

polyethylene bags and brought to the laboratory at once. Some fresh soil samples were stored at 28C for the determination of nitrate þ nitrogen (NO 3 N) and ammoniac nitrogen (NH4 N). And all subsamples were air-dried for more than 3 wk. All air-dried soil samples were sieved through a 2-mm nylon sieve to remove coarse debris and stones and then ground with a pestle and mortar until all particles passed a 0.149-mm nylon sieve. Soil organic matter (SOM) was determined using the method presented by Walkley and Black [17]. Total carbon (TC) and TN was measured on the Elemental Analyzer (Vario El, Elementar, Germany). Samples were digested by acid and measured using ICP-AES for the determination of total phosphorous (TP). Soil pH and electric conductivity (EC) were measured by a pH meter and þ conductivity meter, respectively. NO 3 N and NH4 N were determined on Auto Analyzer 3 (AA3, Germany). Soil properties were listed in Tab. 1.

2.3 Data analysis The hierarchical cluster analysis (using SPSS 14.0) and principal correspondence analysis (PCA) for ordination (using CANOCO for Windows 4.5) were performed to analyze the variation of samples related to wetland degradation. Data indicated that PCA was more suitable than detrended correspondence analyses (DCA) since the longest gradient length was less than 3.0 [18]. The CANOCO program has been widely applied in vegetation ecology and had high effectiveness [19]. Soil quality index (SQI) is a useful indicator to investigate land deterioration or improvement. Numerous SQIs varying greatly in complexity have been developed [20, 21]. It was not the main object of this study to elaborate a comprehensive SQI, but to establish a

Table 1. Soil property data used in the experiment

Samples Group 1 Group 2

Avg. Max. Min. Avg. Max. Min.

pH

EC (ms/cm)

SOM (g/kg)

AP (mg/kg)

NO 3 N (mg/kg)

NHþ 4 N (mg/kg)

TN (g/kg)

TC (g/kg)

8.41 8.55 8.26 8.11 8.73 7.47

1.63 2.38 0.95 1.84 2.29 0.86

6.88 8.60 3.71 5.57 6.71 4.64

6.28 7.26 4.84 8.08 11.74 5.26

1.56 1.88 1.33 2.18 2.45 1.82

2.70 4.67 1.17 10.41 14.63 7.00

0.27 0.36 0.14 0.19 0.27 0.10

19.47 21.99 16.75 18.81 20.24 16.91

Average, maximum, and minimum values of soil samples in group 1 and group 2.


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Soil Degradation in the Yellow River Delta

simplified chemical index of soil degradation (CDI) to evaluate the changes in wetland soil quality in the Yellow River Delta. The method for developing CDI is given by Fu et al. [22]. The CDI was calculated by selected soil factor membership values and their weights as in the following equation: CDI ¼

X

Wi Q ðxi Þ

(1)

where Wi is the weight vector of i soil quality factor and Q(xi) is the membership value of each soil quality factor. The weight vector Wi was determined by the results of PCA. The membership values Q (xi) were calculated by the ascending and descending functions as follows: Q ðxi Þ ¼

xij xi min xi max xi min

(2)

Q ðxi Þ ¼

xi max xi xi max xi min

(3)

where xij is the value of soil properties selected for soil quality; xi max and xi min are the maximum and minimum value of soil property i, respectively. Membership values were calculated in ascending or descending functions depending on whether a higher value was found in undegraded or degraded wetlands. For higher soil properties in undegraded wetlands, membership values were calculated using Eq. (2), and for higher soil properties in degraded wetlands, membership values were calculated using Eq. (3). The soil property data were subjected to one-way ANOVA using SPSS 14.0 software package. The significances between groups together with PCA ordination results contributed to selecting proper soil properties to build the CDI.

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3.2 Hierarchical cluster analysis Hierarchical cluster analysis of all functional genes was performed to generate a dendrogram to examine the relationship between soil samples. As shown in Fig. 2, soil samples fell into two clusters: cluster 1 including eight samples collected in August 2007, and cluster 2 including 12 samples collected in October 2007. Both clusters were consistent with group 1 and group 2. This suggested that soil properties greatly changed before and after wetland degradation. The dendrogram shows no gradient of soil samples within the two groups.

3.3 PCA ordination analysis The PCA ordination diagram (Fig. 3) showed that the samples belonging to group 1 distributed on the right side of the first PCA axis and samples belonging to group 2 distributed on the left side. The two groups were clearly separated. This result was similar to that of hierarchical cluster analysis. As shown in Tab. 3, the first PCA axis which explained 44.5% of variation in soil properties was related to pH, SOM, TN, NO 3 N, and NHþ N. The second PCA axis which explained 23.3% of variation in soil 4 properties was correlated with EC, available phosphorus, and TC. The third and fourth PCA axes were related to EC and available phosphorus (Tab. 1). However, samples were not regularly distributed along the second, third or fourth axes, suggesting these axes were not suitable for representing degradation process.

3.4 Soil degradation assessment One-way ANOVA results showed no significant differences in EC and TC ( p > 0.05) between two groups. The result was corresponded to the distribution of samples along the first PCA axis. These two properties

3 Results 3.1 Soil properties before and after wetland degradation Table 1 demonstrates soil properties of group 1 (before degradation) and group 2 (after degradation) and Tab. 2 demonstrates F-value and significance of soil properties between group 1 and group 2. After wetland degradation, the average contents of NHþ 4 N, NO3 N, and AP in wetland soils showed a significant increase ( p < 0.05) while soil pH value and SOM and TN contents decreased significantly ( p < 0.05). Although the average contents of TC showed a slight decrease and average EC value showed a slight increase after wetland degradation, no significant difference could be observed ( p > 0.05).

Table 2. F-value and significance of soil properties between group 1 and group 2

Name pH EC SOM AP NO 3 N NHþ 4 N TC TN

F-value

Significance

11.25 2.93 5.04 8.52 41.22 55.92 0.47 5.96

0.00 0.10 0.04 0.01 0.00 0.00 0.50 0.03


Figure 2. Hierarchical cluster analysis of 20 soil samples. The samples were divided into cluster 1 and cluster 2.


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Figure 3. The ordination diagram from principal correspondence analysis of eight soil properties and twenty soil samples in the Yellow River Delta. Shown are biplots of the first and second PCA axis (a), and the third and fourth PCA axis (b). The solid circles represent soil samples collected in August 2007 and hollow circles represent soil samples collected in October 2007. The arrows indicate the direction of increasing values for these soil properties. Longer vectors denote a greater range of variation in the observed values of these variables. More acute angles between vectors and axes indicate a stronger correlation.

were excluded from SQI as they varied little before and after wetland degradation. The other six properties were considered as suitable factors to generate the CDI. Soil pH, SOM, and TN were positively correlated with the first axis (Fig. 3), so their membership values were calculated using Eq. (3) while those of the other three properties were calculated using Eq. (2). The weight vector W of each property was absolute value of its eigenvalue of PCA axis 1, and the values were listed as follows: 0.954 (pH); 0.864 (SOM); 0.547 (AP); 0.966 (NO 3 N); N); and 1.016 (TN). The membership and CDI values of 0.859 (NHþ 4 each soil quality factor were shown in Tab. 4. The CDI values for undegraded wetland soils ranged from 0.49 to 1.92, whereas the values for degraded wetland soils varied from 2.80 to 4.36. The threshold of CDI for degraded wetlands was between 1.9 and 2.8.

4 Discussion 4.1 Changes in soil properties of degraded and undegraded wetlands The most obvious change during the process of wetland degradation was the increase in NHþ 4 N and NO3 N contents. The death of S. salsa due to wetland degradation might be the main reason for

the changes. The nitrogen absorbed by plants was released to the surface soil after decomposition [23]. It was notable that there were seasonal dynamics for NHþ 4 N and NO3 N contents in wetland soils due to plant uptake and return. Luo et al. [24] presented that both NHþ 4 N and NO3 N contents in the Yellow River Delta wetland were higher in October than in August. Currently, we could consider that wetland degradation resulted in the change of NHþ 4 N and NO 3 N contents as the reported seasonal dynamics were not so significant. And more analysis on both undegraded and degraded wetland soils in the same season would contribute to confirming the assumption. The AP content was also higher in degraded wetland soils, but not as significant as for NHþ 4 N and NO3 N. A probably reason was that phosphorus uptake was lower than nitrogen uptake by S. salsa [25], and thus the impact of the death of S. salsa for AP was less notable. Cui et al. [26] presented that soil salinity was a key factor limiting the growth and distribution of S. salsa. Initially we suspected that soil salinization, as reported by Cui et al. [27], was the dominant driving factor of wetland degradation. However, the results of PCA ordination diagram and ANOVA showed that soil pH was significantly lower in degraded wetland soils than those in undegraded wetland soils. Although an increasing trend of EC after wetland degradation could be observed in the PCA ordination diagram, the ANOVA result suggested that the change was not significant.

Table 3. Principal correspondence analysis axes and species scores of soil properties

Property AX1 AX2 AX3 AX4

Eigenvalue

pH

EC

SOM

AP

NO 3 N

NHþ 4 N

TN

TC

0.4454 0.2329 0.1293 0.0797

0.9541 0.3271 0.4614 0.5741

0.4874 0.5076 1.3312 0.1364

0.8642 0.5174 0.358 0.6574

0.547 0.9178 0.2186 1.0167

0.9658 0.1584 0.6802 0.6046

0.8588 0.8147 0.3407 0.2449

1.0164 0.7442 0.0448 0.0723

0.6645 1.0695 0.367 0.1171




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Table 4. Membership value and CDI of each selected soil quality factor

Sample Group 1

Group 2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Q (pH)

Q (SOM)

Q (AP)

Q (NO 3 N)

Q (NHþ 4 N)

Q (TN)

CDI

0.14 0.26 0.25 0.37 0.33 0.15 0.31 0.20 0.00 0.41 0.50 0.62 0.57 0.52 0.48 0.50 0.14 1.00 0.58 0.56

1.00 0.35 0.21 0.34 0.30 0.18 0.44 0.00 0.53 0.68 0.55 0.63 0.80 0.63 0.39 0.81 0.74 0.75 0.48 0.46

0.35 0.00 0.18 0.27 0.29 0.27 0.10 0.21 0.48 0.70 0.06 0.91 0.35 0.45 0.42 0.21 0.19 0.34 0.54 1.00

0.23 0.49 0.17 0.43 0.07 0.14 0.16 0.00 0.94 0.43 0.90 0.81 0.89 0.60 0.95 0.59 0.70 1.00 0.69 0.66

0.09 0.13 0.00 0.04 0.26 0.22 0.04 0.13 0.74 0.69 0.52 0.91 0.43 0.56 0.78 0.56 0.82 0.78 1.00 0.43

0.43 0.56 0.21 0.56 0.21 0.00 0.84 0.08 0.62 0.68 0.49 0.70 0.73 0.58 0.37 1.00 0.64 0.92 0.61 0.48

1.92 1.72 0.91 1.81 1.22 0.76 1.77 0.49 2.89 3.07 2.80 3.91 3.39 2.94 2.99 3.37 2.91 4.36 3.41 2.97

Therefore, salinization was unlikely to be the key reason for soil degradation in the study area, and further studies are needed to find the true reason. Soil organic matter and TC were sensitive indicators of soil quality and served as legitimate indicators of soil quality in previous documents [28, 29]. However, in this study, only a little (SOM) or no (TC) changes were observed between undegraded and degraded wetland soils.

4.2 Chemical index of soil degradation The generated CDI successfully distinguished degraded soil samples from undegraded soil samples and much higher CDI values were observed for degraded wetland soils. The result preliminarily supported that the index could be used to evaluate wetland soil degradation in the Yellow River Delta. The CDI in this study is a simple type of SQI. Choosing appropriate indicators for a minimum data set is the first step for soil quality indexing. The choice of the minimum data set usually depend on expert opinions about the functions of indicators or, more often, by statistical methods including principle component analysis, multiple correlation, factor analysis, and cluster analysis [30]. Some indicators that were different before and after wetland degradation could be chosen in order to generate CDI because available soil properties were not enough for statistical analysis. Although indexing practices that simply chose indicators differentiate among systems are common [31], it is better to improve this simplified method when more indicators are available. Indicator transformation techniques involve linear scoring and nonlinear scoring as well. Although the nonlinear scores could better represent system function than the linear scores, they were not selected as there were not enough data to determine the parameters of the curvilinear scoring function. Moreover, the simplicity of a linear scoring technique required little prior knowledge of the system [32]. There is some criticism of using integrated indices as definitive ecological assessment criteria [11, 33]. A general criticism is that it is difficult to determine the ‘‘reference condition’’ as few study area can remain untouched nowadays. To avoid this pitfall, we chose an


area that obviously underwent wetland degradation during three months as study site. But on the other hand, the single study site made it more difficult to determine the exact CDI threshold. We only could assume that if the CDI of soil in a wetland ecosystem exceed 2.0, the wetland might have undergone degradation. Further study is needed to testify the assumption.

5 Concluding remarks In this study, we attempted to use different methods to analyze soil properties of degraded and undegraded wetland soils. The results showed the changes of soil properties during degradation and provided a preliminary verification of the methods. However, as all the soil samples were taken from a small study area and the samples in ‘‘before degradation’’ group and ‘‘after degradation’’ group were homogeneous, more samples should be taken and analyzed to further confirm the rules of soil property changes before and after wetland degradation. The integration of more soil chemical properties, or even physical or biological properties could improve the current CDI. But the expansion should be careful because combining too many soil properties simultaneously may introduce ‘‘noise’’ that obscure the true wetland soil degradation status [34]. Additionally, in order to understand the reason of current wetland degradation, the distinction of changes in soil properties was necessary as well since they led to wetland degradation. As one of the largest and most important wetlands in China, the study on the soil quality degradation in the Yellow River Delta is of scientific and practical significance for the protection, management, and restoration of this wetland in the future. If further verified, the methodology used in this study could serve as a tool to evaluate and monitor the wetland ecosystem health and contribute to sustainable management practices of wetlands.

Acknowledgments This work was financially supported by the National Basic Research Program of China (no. 2010CB951102), the National Natural Science Found (no. 51179006), the Program for New Century Excellent www.clean-journal.com

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Talents in University (NECT-10-0235), the Fok Ying-Tong Education Foundation (132009), and the Fundamental Research Funds for the Central Universities (no. 2009SD-24). The authors acknowledge all colleagues for their contribution in the field work. The authors have declared no conflict of interest.

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