Correlations of soil nutrients, microbial mass and enzyme activity in the hinterland of the Taklimakan

Desert, a case study

JIN Zhengzhong, LEI Jiaqiang, XU Xinwen, LI Shengyu

Xinjiang Institute of Ecology and Geography, CAS, Xinjiang, Urumqi, China 830011

jinzz@ms.xjb.ac.cn

ZHANG Zhongliang, PENG Huiqing, ZHONG Xianbin

Petro China Tarim Oilfield Company Korla, China 841000

Traim@126.com

Abstract—We explored the unique environmental conditions and management model of the Tarim Desert Highway shelter forest as well as the important roles of such shelter forest in development of the socio-economy of South Xinjiang. Experiments were conducted in the shelter forest lands drip-irrigated with underground saline water. Our results indicated that there are canonical correlations among soil nutrients, microbial amount and enzyme activity. The correlation between soil nutrients and soil microbial biomass was mainly attributed to total nitrogen, organic matter, total phosphate of nutrient factors, and amount of actinomycetes, carbon and phosphate content in microbe. The correlation between soil nutrients and soil enzyme activity was due to organic carbon, available potassium in soil and soil enzyme activities such as catalase activity and phosphatase activity. The correlation between soil microbial mass and activities of soil enzymes was due to phosphate and nitrogen contents in microbe and soil enzymes invertase and phosphatase activities. In addition to the correlations of soil nutrients and soil bio-activities, there is a vertical difference between these three factors in soil. We concluded that irrigation with saline groundwater had major effects on soil mineralization process. The release of soil nutrients in the process supported microbial mass colonization and soil enzyme activities in the Tarim Desert Highway shelter forest land. However, high level of salt in ground water adversely affected soil nutrient accumulation and microbe survival.

Keywords- sandy soil; soil nutrient; soil microbe; soil enzyme activity; canonical correlation

I. INTRODUCTION Almost all cycles of organic materials and bio-elements in a

forest ecosystem are catalyzed by the relevant microbial community and its enzymes. In a soil ecosystem, the type and amount of biomass and enzyme activity of the soil microbial community strongly influence nutrient cycling.

As a statistical method to analyze the correlation coefficient of two groups of variables, canonical correlation analysis can be used to calculate independent correlation coefficients between different variables and establish the corresponding linear equations to enclose linear relationships between the two groups of variables [1]. Accordingly, canonical correlation

analysis has been widely applied in studies of vegetation communities [2].

The Tarim Desert Highway, which is the longest highway through drift sand in the world, is located in the hinterland of the Taklimakan desert and crucial to oil and gas exploration, transportation and economic development in South Xinjiang [3]. However, the mobile sand environment induced by strong seasonal wind, flowing ground surface features and loose stratum structure along the Tarim Desert Highway, have threatened its operation [4]. Accordingly, to ensure smooth operation of the highway, a desert highway shelter-belt with artificial vegetations with drip irrigation was constructed. All the irrigation water was pumped out from wells on site. These wells provide unique situation as each well water has different amount of salt that created different patterns of plant growth, soil nutrient cycling and bioactivity. Such biodiversity in desert warrants further research in soil nutrient content, microbial biomass and enzyme activity under different saline water irrigation regime.

In this study, we investigated soils that have been drip-irrigated with different saline waters since 2003. Our aims are to determine if there is a relationship among three groups of variables, soil nutrient, microbial biomass and enzyme activity under saline water irrigation. Results herein present an example for shelter forest management and provide theoretical basis for ecological restoration and reconstruction in arid regions.

II. OVERVIEW OF STUDY AREA Natural environmental conditions along the desert highway

were characterized by extreme arid climate, scarce surface water resources, high saline groundwater, intense wind and sand activity and infertile soil [5]. According to the monitoring data, along the desert highway, multi-annual precipitation is less than 50 mm, potential evaporation reaches 3800 mm/y, the highest temperature is 43.2℃, the lowest temperature is -19.3℃, the highest wind speed is 24 m/s, and the duration of total annual sand-moving wind with a speed higher than 6.0 m/s is from 550 h to 800 h. Furthermore, dune morphology is complex and diverse; there are not only longitudinal composite dunes higher than 50 m, but also barchanic dunes lower than 1

Natural Science Foundation of China (No. 41101248, 41030530). Western Doctoral Program of Chinese Academy of Science (No. XBBS200905)

978-1-4673-0875-5/12/$31.00 ©2012 IEEE

m high. Coverage rate of secondary dune is over 60%, and annual moving distance of small dunes among big composite dunes is more than 15 m. Soil is mainly made up of aeolian soil with weak stability along the Desert Highway. The natural vegetation is rare, and vegetation coverage is quite low [6].

III. RESEARCH METHODS AND DATA ACQUISITION

A. Sampling procedures Based on similar conditions such as site condition, age of

the forest, peripheral tree species, distance from dripper and individual plant space, Soil samples were collected in August 2008 from four shelter-belt lands irrigated with different saline waters, in the Tarim Desert Highway ecological project of the Taklimakan Desert hinterland in China, as described in Table Ⅰ. The experimental design at each plot consisted of a random samplings with 5 replicates in the middle of each forest sites, with little disturbance from humans and herbaceous vegetation. Soil samples from each plot consisted of four composites of 2.5-cm-diameter cores taken from the soil layers of 0-5 cm, 5-15 cm, 15-30 cm and 30-50 cm, respectively. All soil samples were sieved through a 2-mm. Those soil samples of the same layer were fully mixed, immediately put into new sample bags, numbered, and stored at -20℃ until they were used.

TABLE I. INFORMATION OF THE WATER SOURCE WELLS FOR FOUR PLOTS

Plot number

Geographic coordinate

Plantation time

Mileage of the desert highway

Mineralization degree (g/L)

MI 41055/ N 85003/ E In 2003 176 km+000 m 2.58

MII 41053/ N 85001/ E In 2003 179 km+800 m 5.75

MIII 41032/ N 84058/ E In 2003 189 km+200 m 8.90

MIV 39007/ N 83042/ E In 2003 318 km+100 m 13.99

B. Soil physico-chemical analysis Contents of soil nutrient and salt were determined by the

conventional methods [7]. In detail, the contents of organic matter were determined by the potassium dichromate titration method; total N and available N were measured by kjeldahl method; total P and available P were determined by the Mo-Sb-Vc colorimetric; total K and available K were analyzed by flame photometry. The content of soluble salt was determined by the drying residue method, and pH was determined by potentiometry.

C. Analysis of soil microbe Soil microorganism number was determined by the

dilution-plate method [8].

D. Statistical analysis The statistical software STATISTICA for Windows was

used in this study. Analyses were conducted using the following linear combination functions for any two groups of variables by the canonical correlation analysis method among the soil nutrient, microbial biomass and enzyme activity [9]:

U=a1X1+a2X2+…+apXp (1)

V=b1Y1+b2Y2+…+bqYq (2)

W=c1Z1+c2Z2+…+crZr (3)

where, U, V and W represent three linear combination functions for the the soil nutrient, microbial biomass and enzyme activity; ap, bq and cr are three undetermined coefficients that are dependent on the maximum correlation coefficients between functions U, V and W, are regarded as the canonical correlation coefficients to describe the internal relationships between any two groups of variables.

In this study, the first group of variables consisted soil nutrient factors (X) organic carbon content (X1), organic matter content (X2), total nitrogen content (X3), total phosphorus content (X4), total potassium content (X5), available nitrogen content (X6), available phosphorus content (X7) and available potassium content (X8). Soil microbial factors comprised the second group of variables (Y) and included the bacterial amount (Y1), actinomycetes amount (Y2), fungi amount (Y3), microbial biomass carbon (Y4), microbial biomass nitrogen (Y5) and microbial biomass phosphorus (Y6). Soil enzyme activities are divided into a third group of variables consisting of catalase activity (Z1), phosphatase activity (Z2), urease activity (Z3), cellulose activity (Z4), invertase activity (Z5) and protease activity (Z6).

IV. RESULTS AND ANALYSIS

A. Canonical correlation of soil nutrients and microbial amount, biomass Evaluation of the simple correlation coefficient matrix of

soil nutrient and microbial factors (Table Ⅱ) revealed that the sandy soil fungi amount was closely related to the sandy soil organic matter content, with maximum positive correlation coefficient 0.93. In addition, the actinomycetes amount was positively correlated with the available nitrogen, available phosphorus and available potassium, with correlation coefficients of 0.90, 0.87 and 0.83, respectively. There were obvious positive relationships between microbial biomass carbon and organic matter content, microbial biomass nitrogen and total nitrogen content, and microbial biomass phosphorus and total phosphorus content. Among the sandy soil microbial factors, the sum of the absolute values for relationship coefficients to the nutrient factors were in the order Y2>Y4>Y6> Y3>Y5>Y1. For the sandy soil nutrient factors, the sum of the absolute values for relationship coefficients to the microbial factors were in the order X3>X6>X2>X4>X8>X7>X5>X1. These results indicate that soil nitrogen content was closely related to the soil microbial factors, and would have significant influences on microbial populations of the desert sandy soil. We also concluded from the highest r value in Table 2 that the development of actinomycetes in sandy soil was highly dependent on soil nutrient status.

TABLE II. CORRELATION BETWEEN NUTRIENT FACTORS AND MICROBIAL BIOMASS FACTORS.

Factors Ba Ac Fu MBC MBN MBP r OC 0.150 -0.155 0.540 0.180 0.317 -0.005 1.346 OM 0.498 0.571 0.930 0.833 0.825 0.732 4.389 TN 0.649 0.828 0.727 0.778 0.840 0.805 4.628 TP 0.538 0.794 0.613 0.801 0.690 0.848 4.285 TK 0.655 0.780 0.363 0.570 0.303 0.592 3.262 AN 0.686 0.897 0.631 0.777 0.595 0.861 4.447 AP 0.639 0.867 0.400 0.772 0.503 0.770 3.951 AK 0.683 0.720 0.607 0.670 0.696 0.645 4.020

r 4.498 5.611 4.811 5.380 4.769 5.257 Note: OC, OM, TN, TP, TK, AN, AP and AK represent soil contents of organic carbon, organic matter, total nitrogen, total phosphorus, total potassium, available nitrogen, available phosphorus and available potassium; Ba, Ac, Fu are respectively bacteria, actinomycete, fungi; MBC, MBN, MBP stand for microbial biomass carbon, nitrogen and phosphorus; r is sum of absolute value for the correlation coefficients in the same column or line. The same is in TABLE V, TABLE VIII.

TABLE III. CHI-SQUARE TEST OF CANONICAL CORRELATION COEFFICIENTS BETWEEN NUTRIENT FACTORS AND MICROBIAL FACTORS.

Typical vector λi λi

2 ∑λi2 DOF α λi/∑λi

2

1 1.000 1.000 126.518 48 0.000 0.789 2 0.996 0.992 60.052 35 0.005 0.178 3 0.947 0.896 24.047 24 0.459 0.029 4 0.806 0.649 9.487 15 0.851 0.004 5 0.710 0.504 3.711 8 0.882 0.001 6 0.549 0.302 0.897 3 0.826 0.000

Note: The critical value of the chi-square test is ∑λ0.052. λi-Canonical correlation coefficient, λi

2-Eigenvalue, ∑λi

2-Accumulative contribution ratio, DOF-Degrees of Freedom, α-Significance level. The same is in TABLE VI, TABLE IX.

TABLE IV. COMPOSITION OF THE FIRST TWO-PAIRS OF CANONICAL VARIABLES FOR NUTRIENT AND MICROBIAL BIOMASS

U1=0.081X1-0.360X2-0.540X3+0.398X4+0.034X5+0.522X6+0.356X7+0.112X8 V1=0.222Y1+0.399Y2+0.367Y3-0.643Y4-0.297Y5+0.395Y6

U2=0.270X1+0.578X2+0.484X3-0.534X4+0.149X5+0.215X6+0.040X7-0.062X8 V2=-0.262Y1+0.585Y2-0.474Y3+0.500Y4+0.334Y5+0.056Y6

Among the six canonical correlation coefficients of the nutrient factors and the microbial factors, the first two accounted for 96.64% of the correlation information of the total relative information for the two groups of variables (Table Ⅲ). Therefore, analyzing the coefficients in the equations of these two groups of variables may reflect the relative information of the whole system of variables. For the first pair of canonical variables, the coefficients for X3 and X6 were larger than the others in equation U1, while the coefficient for Y4 in equation V1 was higher than the others. These findings indicated that the correlation of the first pair of canonical variables could primarily reflect by the relationships from sandy soil total nitrogen, available nitrogen and the sandy soil microbial biomass carbon. For the component of the second pair of canonical variables, the coefficients for X2 and X4 were greater than the others in equation U2. Among the microbial factors, Y2 and Y6 had larger coefficients in equation V2. These results suggested that the sandy soil nutrient factors of organic matter and total phosphorus, as well as the soil microbial factors of actinomycetes and microbial biomass phosphorus are the main factors to determine the relative importance of canonical variables.

We found that the relationship of the sandy soil nutrient and microbial community in the Tarim Desert Highway was influenced primarily by nutrient factors such as total nitrogen, available nitrogen, organic matter, total phosphorus and microbial factors consisting of the actinomycetes amount, microbial biomass carbon, and microbial biomass phosphorus. These results were similar to the results obtained for the simple correlation analysis (Table Ⅱ).

Figure 1. Canonical variable ordination of soil nutrient and microbial factors (The ordinal numbers of MI, MII, MIII and MIV represent soils with mineralization values of irrigation water of forest land of 2.58, 5.75, 8.90 and 13.99 g•L-1, respectively. The numbers 1, 2, 3 and 4 express soil layers of 0-5, 5-15, 15-30 and 30-50cm. The same is in figure 2 and figure 3.)

The canonical variable ordination diagram (Fig.1) demonstrated that, the comprehensive factor (U) representing the sandy soil microbial population and the comprehensive factor (V) for the sandy soil nutrient, showed spatial variation among soil layers from areas irrigated with different groundwater. With increasing salt content in irrigation water, there was a decrease in U value, the V value, however, changed slightly among the soil layers.

B. Canonical correlation relationship of soil microbe and enzyme activity Table V shows the results from simple correlation

coefficient matrix of soil microbe and enzyme activity factors. The data revealed several close positive correlations between phosphatase activity and actinomycetes amount, phosphatase activity and microbial biomass carbon, and invertase activity and microbial biomass phosphorus, with correlation coefficients of 0.827, 0.823 and 0.829, respectively. Positive relationships were also observed between catalase activity and microbial biomass nitrogen, cellulose activity and microbial biomass nitrogen, cellulose activity and microbial biomass phosphorus, and invertase activity and microbial biomass nitrogen. Among the soil microbial factors, microbial biomasses of phosphorus and carbon were closely related to six types of soil enzyme activities, as reflected in the sum of the absolute value for r. For these enzyme activities, phosphatase activity was significantly related to the microbial factors and showed the largest correlation coefficients.

TABLE V. SIMPLE CORRELATION BETWEEN MICROBIAL QUANTITY FACTORS AND THE ACTIVITY OF ENZYME FACTORS

Factor Ca Ph Ur Ce In Pr r

Ba 0.335 0.523 -0.102 0.247 0.446 0.433 2.086

Ac 0.552 0.827 -0.255 0.563 0.651 0.104 2.953

Fu 0.609 0.702 -0.213 0.541 0.554 0.119 2.738

MBC 0.378 0.823 -0.363 0.485 0.588 0.001 2.637

MNN 0.744 0.667 -0.196 0.742 0.796 0.217 3.362

MBP 0.708 0.776 -0.278 0.799 0.829 0.036 3.426

r 3.328 4.318 1.407 3.376 3.863 0.910

Note: Ca- Catalase, Ph- Phosphatase, Ur- Urease, Ce- Cellulase, In- Invertase, Pr- Protease. The same is in TABLE VIII.

TABLE VI. CHI-SQUARE TEST OF CANONICAL CORRELATION COEFFICIENT BETWEEN MICROBIAL BIOMASS AND ENZYME ACTIVITY

Typical vector λi λi

2 ∑λi2 DOF α λi/∑λi

2

1 0.994 0.988 66.584 36 0.001 0.863 2 0.940 0.883 25.270 25 0.447 0.124 3 0.760 0.578 7.974 16 0.950 0.012 4 0.531 0.282 2.007 9 0.991 0.001 5 0.182 0.033 0.153 4 0.997 0.000 6 0.023 0.001 0.002 1 0.966 0.000

TABLE VII. COMPOSITION OF THE FIRST PAIR OF CANONICAL VARIABLES FOR MICROBIAL BIOMASS AND ENZYME ACTIVITY

V=-0.238Y1-0.187Y2+0.066Y3-0.398Y4+0.505Y5+0.701Y6 W=-0.338Z1+0.745Z2-0.067Z3-0.103Z4+0.556Z5-0.083Z6

MI-1

MI-2

MI-3

MI-4

MII-1

MII-2

MII-3

MII-4MIII-1

MIII-2

MIII-3

MIII-4MIV-1

MIV-2MIV-3

MIV-4

75 95 115 135 155 175 195 215

V

3.5

4.0

4.5

5.0

W

Figure 2. Canonical variable ordination of microbial biomass and soil enzyme activity

As shown in Table Ⅵ , the first canonical correlation coefficient among all coefficients between the soil microbial factors and soil enzyme activities was significant by the Chi-square test, and accounted for 86.26% of all the components analyzed. Accordingly, analysis of the coefficient for the first pair of canonical variables could reflect the main relative information of two groups of the variables. Analysis of the components of the first pair of canonical variables (Table Ⅶ)

revealed that the coefficients of Y6 and Y5 were larger than the other microbial factors in equation V. The coefficients of Z2 and Z5 were higher than the other enzyme activity factors, indicating that the relationship between the first pair of variables was primarily related to microbial biomass phosphorus, microbial biomass carbon, invertase and phosphatase. These results are in consistent with the simple correlation analysis as shown in Table Ⅴ.

The canonical variable ordination diagram for the microbial biomass and soil enzyme activity (Fig.3) shows the comprehensive factor (V) representing the sandy soil microbial population and the comprehensive factor (W) for the sandy soil enzyme activity. The results demonstrated that the value of the U factor increased as the mineralization of the drip-irrigation water decreased, but the value of the W factor varied only slightly with different irrigation water. Overall, the comprehensive factors of U and W representing the sandy soil microbial population and enzyme activity ranked as follows: 5-15cm>15-30cm>0-5cm.

C. Canonical correlation of soil nutrient and enzyme activity

TABLE VIII. SIMPLE CORRELATION BETWEEN NUTRIENT FACTORS AND ACTIVITY OF ENZYME FACTORS

Factor Ca Ph Ur Ce In Pr r

OC 0.678 0.722 0.236 0.745 0.649 0.063 3.092 OM 0.358 0.554 0.342 0.847 0.564 0.047 2.712 TN 0.809 0.590 0.226 0.608 0.472 0.035 2.740 TP 0.571 0.562 0.242 0.342 0.525 0.540 2.784 TK 0.438 0.352 0.061 0.297 0.471 0.408 2.027 AN 0.738 0.605 0.005 0.482 0.576 0.323 2.730 AP 0.391 0.384 0.472 0.339 0.142 0.440 2.167 AK 0.682 0.623 0.391 0.682 0.609 0.012 3.000

r 4.665 4.392 1.975 4.342 4.008 1.869

The simple correlation coefficient matrix of soil microbe and enzyme activity factors (Table Ⅷ ) revealed that the catalase activity and total nitrogen content had the largest positive correlation coefficient (0.81), and that catalase activity was also positively correlated with the available nitrogen content (0.74). Furthermore, we observed significant relationships between phosphatase activity and organic matter content, cellulose activity and organic carbon content, invertase activity and organic carbon content. There were positive relationships between the different nutrient and microbial factors. Among the enzyme factors, the correlation coefficients of the nutrient factors ranked as follows: catalase > phosphatase > celluloase > invertase > urease > protease, with the former four enzyme activities differing slightly, but higher than the latter two enzyme activities. Investigation of the nutrient factors revealed that the organic carbon and nitrogen contents were closely related to the enzyme activities. We suggest that increase in organic carbon content may promote soil enzyme activity, and that the nitrogen content obviously influenced the catalase activity.

TABLE IX. CHI-SQUARE TEST OF CANONICAL CORRELATION COEFFICIENT BETWEEN SOIL NUTRIENTS AND ACTIVITIES OF SOIL ENZYMES

Typical vector λi λi

2 ∑λi2 DOF α λi/∑λi

2

1 0.997 0.994 95.257 48 0.000 0.748 2 0.977 0.954 48.858 35 0.060 0.197 3 0.962 0.925 24.384 24 0.440 0.049 4 0.854 0.730 8.297 15 0.911 0.006 5 0.576 0.332 1.872 8 0.985 0.000 6 0.351 0.123 0.328 3 0.955 0.000

TABLE X. COMPOSITION OF THE FIRST PAIR OF CANONICAL VARIABLES FOR SOIL NUTRIENT AND ENZYME ACTIVITY

U=0.617X1+0.105X2-0.209X3+0.265X4+0.0445X5+0.394X6-0.201X7-0.546X8 W=0.624Z1-0.556Z2-0.189Z3-0.420Z4+0.045Z5-0.296Z6

Among the six canonical correlation coefficients shown in

Table Ⅸ , the first accounted for 74.82% of the total components of the two groups of variables. Therefore, analyzing the coefficient for the first pair of canonical variables might reflect most information for the two groups of variables. Investigation of the constituents of the first pair of canonical variables (Table Ⅹ) revealed that the coefficient components of X1 and X8 were larger than the other nutrient factors in equation U. The coefficient components of Z1 and Z2 had higher enzyme activities than those of the other factors. These results suggested that organic carbon, available phosphorus, catalase, and phosphatase were the primarily factors influencing the soil ecosystem on the two sides of Tarim desert highway (Table Ⅷ).

A

MI-1

MI-2

MI-3

MI-4

MII-1

MII-2

MII-3

MII-4

MIII-1

MIII-2

MIII-3MIII-4

MIV-1

MIV-2MIV-3MIV-4

150 200 250 300 350 400 450 500 550 600

U 1

10

20

30

40

50

60

W1

B

MI-1

MI-2

MI-3

MI-4

MII-1

MII-2

MII-3

MII-4

MIII-1

MIII-2

MIII-3

MIII-4

MIV-1

MIV-2MIV-3MIV-4

-500 -450 -400 -350 -300 -250 -200 -150 -100

U2

-22

-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

W2

Figure 3. Canonical variable ordination of soil nutrients and enzyme activity

The canonical variable ordination diagram of the soil nutrient and soil enzyme activity (Fig.4) revealed that the comprehensive factor (U) representing the sandy soil nutrients and the comprehensive factor (W) for the sandy soil enzyme activity varied regularly under different saline water drip-irrigation regimes. Considering attributes in the two figures, there were opposite tendencies between the comprehensive nutrient factors (U1, U2) and the enzyme activity factors (W1, W2). Among the different soil layers, the U values occurred in the order of 5-15cm>0-5cm>15-30cm, but the W values changed little. These results are similar to those presented in the ordination diagrams.

V. CONCLUSION AND DISCUSSION Among various mineral elements, plants have the largest

demands for soil nitrogen, phosphorus, and potassium. Soil nitrogen and phosphorus of forest land are also considered to be the elements limiplant growth [10]. Organic matter is one of the most important components determining soil fertility [11]. Nutrients in soil are the material basis for soil microbial survival, maintaining soil microbial species, as well as microbial abundance, biomass and enzyme activity. The microbial abundance in turn can activate soil nutrients and help to increase soil fertility for plant growth. In this regards, soil microbial biomasses (carbon, phosphorus, and potassium) are viewed as the sources of active nutrients for plant growth [12]. Soil enzymes promote decomposition, and cycling of the soil organic matter, while the enzymatic reaction intensity reflects the soil enzyme activity [13]. Therefore, soil biological factors including microbial biomass, microbial amount and enzyme activity are crucial components for conversion of soil nutrient [14]. In the Tarim Desert Highway shelter-forest land, we found that the soil nutrients and soil microbes were influenced by several nutrient factors including total nitrogen, available nitrogen, organic matter, total phosphorus and some microbial factors including actinomycete amount, microbial carbon and microbial phosphorus. Additionally, the relationship between soil nutrients and soil enzyme activity primarily depended on the relationship between the contents of organic matter and available potassium and the activities of catalase and phosphatase. We confirmed that there was a close relationship

between soil nitrogen content and soil microbial biomass. In the shelter-forest land, soil organic carbon content obviously influenced the six kinds of enzyme activities investigated herein and that the level of the soil catalase activity depended on the soil nitrogen content. It has been reported that soil catalase activity could accelerate oxidation of soil compounds and enhance intensity of soil microbial activity. Our results suggested that soil phosphatase may also promote dephosphorization of soil organic phosphorus. We previous observed that actinomycetes were widely distributed in slightly-alkaline soils with good ventilation [15]. Actinomycetes are thermophilic and dry-tolerant, and can depose refractory materials [16].

Understanding the relationship between soil microbes and soil enzymes can improve our management of soil biological activity [16]. Soil enzymes are primarily produced by soil microbes and the secretion of plant roots. Soil bacteria, fungi and actinomycetes have always been considered important sources of soil enzyme activity [16]. These enzymes are sensitive to changes in soil environmental conditions, and are viewed as indictors of soil biological activity [17]. In this study, we analyzed that the relationship between soil microbes and soil enzymes in the Tarim Desert Highway shelter-forest land and foundthe enzymes were primarily induced by interaction from microbial biomasses of phosphorus, nitrogen and activities of invertase and phosphatase. Enhancing the activities of invertase and phosphatase could elevate the contents of available nitrogen and phosphorus, resulting in accumulation of the soil microbial biomasses of nitrogen and phosphorus and help plant growth.

As a conventional statistical method to analyze the relationship between two groups of variables, canonical correlation analysis was introduced in this study to identify correlations between sandy soil nutrient levels and biological activity of the soil. We analyzed the possible mechanism by which drip-irrigation water and fertilization influenced shelter-forest land. These results provided evidences in soil microbes in extreme arid lands under salt water irrigation. Our results provided useful information that could be applied for ecological restoration and reconstruction in arid regions with availability of salt water.

REFERENCES [1] Q. Y. Tang, M. G. Feng. Practical Statistics Analysis and DPS Data

Processing System. Beijing: Science Press, 2002.

[2] Y. L. Li, Z. H. Li, S. L. Peng, H. Ren, R. G. Zhu, Z. A. Li, Application of canonical correlation analysis in the relation between soil enzyme activities and nutrient elements in eucalyptus plantation soils. Chinese Journal of Applied & Environmental Biology, 2002, 8(5):544-549.

[3] X. Q. Wang, J. Q. Lei. Assessment indicators of drifting sand hazard along Tarim Desert Highway, Xinjiang. Arid Land Geography, 1999, 22(1):81-87.

[4] X. W. Xu, B. W. Li, X. J. Wang. Progress in study on irrigation practice with saline groundwater on sand lands of Taklimakan Desert hinterland. Chinese Science Bulletin, 2006, 51(Suppl.):133-136.

[5] S. Y. Zhang, X. W. Xu, Q. K. Wen, B. Chen, Study on physical and chemical properties of aeolian sandy soil under the different local conditions along the Tarim Desert Highway, Arid Land Geography, 28 (2005) 627-631.

[6] S. Y. Li, H. Z. Li, J. Q. Lei, B.W. Li, X. W. Xu, H. W. Zhou, Analysis of growth differences of seedlings irrigated with high degree of mineralization water, Journal of Soil Water Conservation, 18 (2004) 118-122.

[7] S. D. Bao, Analysis of Agricultural Chemistry of Soil. China Agriculture Press, Beijing, 2000.

[8] S. T. Bagley, R. J. Seidler, Primary klebsiella identification with Macconkey-inositol carbenicillin agar, Applied and Environmental Microbiology, 36 (1978) 536-538.

[9] Y. Teng, C. Y. Huang, Y. M. Luo, Z. G. Li, Changes in microbial activities and its community structure of red earths polluted with mixed heavy metals. Acta Pedologica Sinica, 2005, 42(5):819-828.

[10] L. E. Kellogg, S. D. Bridgham. Phosphorus retention and movement across an ombrotrophic minerotrophic peatland gradient. Biogeochemistry, 2003, 63:299-315.

[11] G. Schroth, F. L. Sinclair. Trees, Crops and Soil Fertility. Oxford: Oxford University Press, 2003, 77-91.

[12] S. Xue, G. B. Liu, Q. H. Dai, X.H. Dang, P. Zhou, Effect of different vegetation restoration models on soil microbial biomass in eroded hilly loess plateau. Journal of Natural Resource, 2007, 22(1):20-27.

[13] H. X. Yu, W. Dai, L. F. Xia, Z. F. Deng, X. Q. Wang, L. S. Nie, Effects of different plantations on the activity of soil enzymes and their kinetic characteristics in subtropical zone. Journal of Beijing Forestry University, 2007, 29(1):114-118.

[14] T. Yao, R. J. Long, S. L. Shi, D. G. Zhang, Populations of soil nitrogen bacteria groups in Alpine steppe of different disturbed habitats in Tianzhu. Acta Pedologica Sinica, 2007, 44(1):122-129.

[15] M. Wang, J. K. Chen, B. Li, Characterization of bacterial community structure and diversity in rhizosphere soils of three plants in rapidly changing salt marshes using 16S rDNA. Pedosphere, 2007, 17(5):545-556.

[16] Z. J. Tan, Q. M. Xiao, Z. B. Zu, Effect of recreation activities on soil microflora and activities in Zhangjiajie national park. Acta Pedologica Sinica, 2007, 44(1):184-187.

[17] Y. Q. Geng, C. X. Bai, T. R. Zhao, S. S. Wang, J. Q. Chen, Soil enzyme activity and its relationship with the soil fertility in Badaling Mountain Area of Beijing. Journal of Beijing Forestry University, 2006, 28(5):7-11.