Variation of Soil Nutrition in a Fagus engleriana Seem.- Cyclobalanopsis oxyodon Oerst. Community Over a Small Scale in the Shennongjia Area, China

Journal of Integrative Plant Biology 2006, 48 (7): 767−777

Received 13 Dec. 2005 Accepted 14 Apr. 2006

Supported by the State Council Three Gorges Construction Committee

Project (SX2004-011), the Knowledge Innovative Program of the Chinese

Academy of Sciences (KSCX2-SW-109), and the National Natural Science

Foundation of China (30070140).

*Author for correspondence. Tel: +86 (0)10 6283 6284; E-mail: <xie@ibcas.

ac.cn>.

Variation of Soil Nutrition in a Fagus engleriana Seem.-Cyclobalanopsis oxyodon Oerst. Community Over a

Small Scale in the Shennongjia Area, China

Mi Zhang1, 2, 3, Zong-Qiang Xie1*, Gao-Ming Xiong1 and Jin-Tun Zhang3

(1. Laboratory of Quantitative Vegetation Ecology, Institute of Botany, the Chinese Academy of Sciences, Beijing 100093, China;2. School of Graduate, the Chinese Academy of Sciences, Beijing 100039, China;

3. College of Life Sciences, Beijing Normal University, Beijing 100875, China)

Abstract

Soil nutrition is a key factor influencing species composition in a community, but it has clearly scale-dependent heterogeneity. In the present study, geostatistics methods and canonical correspondenceanalysis (CCA) were used to detect: (i) the variation range of soil spatial heterogeneity; (ii) the influence oftopographic factors on the distribution of soil nutrition; and (iii) the relationships between soil chemicalproperties and species in the community. In all, 23 soil variables were measured, including total N andorganic C, Al, Ba, Ca, Cr, Cu, Fe, Ga, Li, Mg, Mn, Na, NH4-N, Ni, NO3-N, Pb, pH, P, Sr, Ti, V, and Zn. Semi-variogramsof these variables were calculated and mapped. All indices showed autocorrelations, with ranges between29 and 200 m. When the sample method was larger than these distances, spatial autocorrelations wereavoided. The distribution patterns of Ca, Cr, Ga, K, Mg, organic C, P, Pb, and pH, and total N were related tothe microtopography and the distribution of these compounds was clumped in water catchments area. TheCCA method was used to investigate the relationship between plant species and soil properties in thiscommunity. Fagus engleriana Seem., Lindera obtusiloba Bl. Mus., and Acer griseum (Franch.) Pax werecorrelated with organic C, available N, and P.

Key words: biodiversity; beech; canonical correspondence analysis; soil chemical properties; topographic factors.

Zhang M, Xie ZQ, Xiong GM, Zhang JT (2006). Variation of soil nutrition in a Fagus engleriana Seem.-Cyclobalanopsisoxyodon Oerst. community over a small scale in the Shennongjia Area, China. J Integrat Plant Biol 48(7), 767−777.

www.blackwell-synergy.com; www.jipb.net

Soil nutrition is a key factor influencing the growth of plantindividuals, populations, and even community structure and di-versity (Janssens et al. 1998; Critchley et al. 2002; Wijesingheet al. 2005). Soil resource heterogeneity has been recognizedas a general phenomenon in natural ecosystems and is regu-larly invoked in competitive interactions between plants andresults in the formation of some nutrition patches (Chapin 1980;

Fitter 1982; Robertson et al. 1988). These types of resourcepatches are thought to be associated with both herbaceousand woody plants. High levels of resource heterogeneity couldarise either because woody plants are more successful onpatchy soils or because woody plants increase patchiness(Kleb and Wilson 1997). Herbaceous plants can exhibit plastic-ity in root growth in response to nutrient heterogeneity. Re-sponses to nutrient enrichment can be highly localized, withadditional root growth being concentrated within the nutrient-rich area (Farley and Fitter 1999). Topography is another im-portant factor causing soil resource heterogeneity and affect-ing the distribution of trees in mountainous forests, which cre-ates soil moisture and soil fertility gradients along a slope (Enokiet al. 1996; Tateno and Takeda 2003). Another important thesisof soil-based resources heterogeneity is to detect theautocorrelation scale using geostatistics to give a fitted

768 Journal of Integrative Plant Biology Vol. 48 No. 7 2006

research scale for other studies, such as sample design, spe-cies associations, and community structures (Overton 1996).

Nutrient limitation is an important hypothesis that describeshow soil heterogeneity enhances the coexistence of differentspecies with different life forms that may differ in their ability tocompetite in different nutrients patches (Chapin 1980; Tilman1994; Koerselman and Meuleman 1996; Monokrousos et al.2004). There have also been some studies investigating therelationship between landform and vegetation patterns. Somestudies have demonstrated that soil N mineralization and nitrifi-cation rates vary along topographic gradients from ridge topsto valley floors, even within a single slope (Garten et al. 1994;Enoki et al. 1996; Hirobe et al. 1998). However, most previousstudies have dealt with grassland and few have examined theeffects of resource heterogeneity on woody plant communities.Fewer studies have quantitatively evaluated the effects of to-pography on soil nutrient distribution pattern.

In the present study, a field experiment was undertaken todescribe the spatial variability of 23 soil chemical properties ina subtropical mixed broad-leaved evergreen and deciduousforest. The community investigated was located in a large moun-tain area; thus, topographic factors influenced the distributionof soil nutrition and we used distribution maps of the soil prop-erties and compared them with contour lines to detect the ef-fects of topography on soil distribution. We also determined theextent of spatial variations in soil nutrient elements in a 9 600-m2 permanent plot and used this to analyze the relationshipbetween tree species and soil nutrition factors.

Results

Soil chemical properties

The average values of 23 soil chemical variables are listed inTable 1. The most abundant chemical resource in the perma-nent 9 600-m2 plot investigated in the present study wasNH4-N. However, NH4-N also varied considerably in the re-search area because its stand variation was found to be 6.81.The stand variation for organic-C, NO3-N, K, Fe, and Al wasfound to be 5.12, 2.25, 2.13, 9.33, and 12.34, respectively.Stand variations for other factors contributing to the chemicalproperties of the soil, such as Mg, Na, and Mn, were less than2 (Table 1; Figure 1).

The most abundant micro-element in the sampling area wasBa, at (703.95 ± 237.06) µg/g. Values of Sr, V, Cr, Zn werelarger than 100 µg/g, at 149.76, 156.48, 291.17, and 287.20 µg/g, respectively (Table 2; Figure 2).

Based on the 24 sample points, correlations were found be-tween soil properties (Table 3). Highly significant correlationswere found between some of the metal ions. The elements thatwere positively related included Fe-Al, Mg-P, Mg-Ba, P-Sr,

Mg-Cu, Ba-Cu, Cu-Zn, Pb-Ca, Pb-Mg, Ba-Pb, Ca-Mg, P-Ca, Ba-Ca, Cu-Ca, Al-Ga, Mg-Ga, Cu-Ga, Zn-Ga, Ca-Ga, and P-Li. Nega-tive relationships were found for the elements Ti-Ba, K-Sr, K-Sr, Cu-Al, Na-Cu, Pb-Ti, Fe-Ca, Ti-Ca, and Na-Ga. However,the relationships observed may reflect self-correlations (Roverand Kaiser 1999). Beyond this statistical problem, the resultsgiven in Table 3 show the co-occurrence of these metalelements.

Spatial variability

Spatial variability of the plot was estimated using geosta-tistics. In general, the soil properties investigated could be

Table 1. Mean (± SD) values of macro-element soil nutrients acrossthe sampling area

Sample Macro-element Mean (± SD)1 pH 5.80 ± 0.912 Total nitrogen (%) 0.23 ± 0.083 Organic C (%) 5.12 ± 2.844 NH4-N (mg/kg) 17.56 ± 6.815 NO3-N (mg/kg) 2.25 ± 1.506 Fe (%) 9.33 ± 1.077 Al (%) 12.34 ± 1.808 Mg (%) 1.59 ± 0.289 K (%) 2.13 ± 0.2510 Na (%) 1.10 ± 0.3111 Ti (%) 0.73 ± 0.0712 Mn (%) 0.71 ± 0.3713 P (mg/kg) 1.11 ± 0.1414 Ca (%) 1.24 ± 0.94

Figure 1. Histogram of Macro-element soil chemical properties.

Small-Scale Soil Mutrition Variation in a Fagus-Cyclobalanopsis Community 769

characterized according to the extent of their spatialheterogeneity. All 24 indices of soil properties were character-ized by reference to semi-variograms (Table 4). We chose totalN, P, K, Ca, and Mg as examples. Minimal spatial correlationwas found for these chosen elements (Figure 3). The spheri-cal model was fitted to the results of total N, Ca, and Mg, and R2

values of 0.995, 0.898, and 0.669, respectively, were obtained.An exponential rather than spherical model was fitted to P andthe R2 was very low (R2 = 0.331), indicating perhaps that thespatial dependence of P was not strong.

A spherical model was fitted for the distribution of organic-C,Al, Cr, Fe, Ga, Li, Mn, Na, Pb, pH, Ti, and V, whereas an expo-nential model was fitted for Ba, NH4-N, P, and Sr. A linear modelwas used for Cu, Ni, NO3-N, and Zn. Sample “a0” was a coef-ficient known as the “range”, which could indicate samplemethods. In this case, the ranges of those elements were from29 to 200 m. This means that when the interval sampled was

larger than 200 m, the effects of spatial auto-correlations wouldbe avoided. The model coefficient C0 is known as the “nugget”and represents unexplained or “random variance”, often causedby measurement errors or variability of the measured propertyat a spatial scale smaller than the one used for sampling. In thepresent study, Ba, Cr, Ni, P, and Zn exhibited a larger C0, indi-cating that their distribution may be influenced by random events.

Interpolating from calculated values of the semi-variogram,we produced distribution maps based on spatial patterns for allthe elements and present those that are distributed regularly.Kriging interpolation maps of soil properties are shown in Fig-ures 4 and 5. All the high values of the elements are distributedin the bottom right-hand corner of Figure 4. Elements with highvalue in Figure 5 appear in the upper area of the plot.

Ordination of species and soil chemical characteristics

Canonical correspondence analysis (CCA) was used to inves-tigate the relationship between plant species in this communityand soil properties. This type of analysis allows environmentaldata to be incorporated into the analysis. The dominant specieswere Fagus engleriana Seem. (a multi-stemmed deciduouscanopy tree that is found only in China) and Cyclobalanopsisoxyodon Oerst. (a second-layer evergreen species). Otherimportant species included Rhododendron hypoglaucum Hemsl.(second-layer evergreen species), Lithocarpus glaber (Thunb.)Nakai., Cornus kousa Hance. var. chinensis Osborn., Toonasinensis (A. Juss.) Roem., Acer cappadocicum Gled., Acergriseum (Franch.) Pax, and Lindera obtusiloba Bl. Mus.(Zhanget al. 2003). These nine species were selected for analysisbecause there were sufficient numbers of individuals of eachspecies (more or near than 50) in the area being investigated.The results of CCA are shown in Figure 6. It seems that F.engleriana, L. obtusiloba, and A. griseum were correlatedwith organic-C, available N, and P.

Discussion

Relationship between plants and soil

The relationship between the environment and plants is veryimportant to ecosystems (Jafari et al. 2004). The factors influ-encing soil properties include climate, plants, and topography.In the present study, F. engleriana, L. obtusiloba, and A.griseum were correlated with organic-C, available N, and P.This may be an indication that these three species need morefertilizer and may also reflect the results of species competition.The results support the notion that plant distribution is corre-lated with soil type, even over a small scale (1 hm2). Fagusengleriana, L. obtusiloba, and A. griseum are all deciduoushigh woody plants and F. engleriana was the dominant

Table 2. Mean (± SD) values of micro-element soil nutrients acrossthe sampling area

Sample Micro-element (µg/g) Mean (± SD)1 Ba 703.95 ± 237.062 Sr 149.76 ± 8.963 V 156.48 ± 37.584 Ni 89.68 ± 10.335 Cu 92.26 ± 41.666 Cr 291.17 ± 52.177 Zn 287.20 ± 194.638 Ga 38.11 ± 12.669 Li 39.63 ± 18.2310 Pb 39.53 ± 11.87

Figure 2. Histogram of Micro-element soil chemical properties.


Table 3. Correlationship between soil chemical elementAl Mg K Na T i Mn P Ba Sr V Ni Cu Cr Zn Pb Ca Ga Li

Fe 0.61** –0.40* 0.22 0.20 0.19 0.41* –0.34* –0.23 –0.10 0.22 0.19 –0.44* –0.24 0.13 –0.17 –0.53** –0.38* 0.19

Al –0.27 0.12 0.35* 0.13 0.37* –0.25 –0.12 –0.04 –0.14 –0.21 –0.49** –0.39* –0.04 –0.21 –0.43* –0.58** 0.09

Mg 0.18 –0.24 –0.74** –0.01 0.64** 0.63** –0.04 –0.19 –0.16 0.56** 0.31 0.06 0.52** 0.90** 0.46** –0.05

K 0.16 –0.35* 0.14 –0.21 0.38* –0.45** –0.20 –0.17 0.38* –0.20 0.17 –0.06 0.17 0.33* 0.22

Na 0.14 0.44* –0.15 –0.12 0.09 –0.21 –0.02 –0.50** –0.06 –0.29 –0.40* –0.21 –0.47** –0.08

Ti –0.20 –0.35* –0.68** 0.25 0.28 –0.02 –0.43* –0.10 –0.19 –0.55** –0.80** –0.41 0.06

Mn 0.09 0.27 0.10 –0.35* 0.04 –0.27 –0.16 –0.14 0.21 0.05 –0.23 –0.08

P 0.16 0.57** –0.20 –0.16 0.19 0.15 –0.13 0.28 0.67** 0.15 –0.49**

Ba –0.24 –0.19 –0.11 0.60** 0.19 0.26 0.52** 0.68** 0.40 0.16

Sr –0.09 0.00 –0.28 0.19 –0.26 –0.08 0.05 –0.23 –0.24

V 0.41* –0.14 0.02 0.19 –0.15 –0.22 0.07 0.38*

Ni –0.29 0.03 –0.11 0.21 –0.12 –0.06 0.27

Cu 0.41* 0.45** 0.40* 0.57** 0.83** 0.05

Cr 0.27 0.28 0.24 0.33* 0.08

Zn 0.08 0.08 0.50** 0.15

Pb 0.55** 0.38* 0.06

Ca 0.55** –0.11

Ga 0.16

*P < 0.05, **P < 0.01.

Table 4. Semi-variance of soil chemical propertiesSample Fitted model C0 a0 R2

Total nitrogen Spherical model 0 129.90 0.995

Organic C Spherical model 2.56 75.60 1.000

Al Spherical model 1.80 131.60 0.992

Ba Exponential model 12 000.00 22.10 0.967

Ca Spherical model 0.24 209.60 0.898

Cr Spherical model 1 040.00 175.60 0.958

Cu Linear 1.00 82.50 0.953

Fe Spherical model 0.47 107.80 0.999

Ga Spherical model 0.10 65.50 0.956

Li Spherical model 4.00 29.60 0.223

Mg Spherical model 0.03 200.00 0.669

K Exponential model 0 15.50 0.870

Mn Spherical model 0.01 102.80 0.990

Na Spherical model 0.01 59.40 0.996

NH4-N Exponential model 6.60 14.40 0.998

Ni Liner model 100.41 65.04 0.422

NO3-N Liner model 2.53 65.04 0.956

Pb Spherical model 59.50 99.40 0.999

pH Spherical model 0.17 151.60 0.989

P Exponential model 15 670.00 210.90 0.331

Sr Exponential model 55.30 60.40 0.838

Ti02 Spherical model 0.00 181.90 0.939

V Spherical model 61.00 36.90 0.939

Zn Liner 35 717.87 65.04 0.890


species in the community. These three species had some ad-vantage in the competition for nutritions.

Another important issue is the concept of nutrition patches.Soil nutrition is not distributed evenly and shows significantheterogeneity at different scales. Soil heterogeneity is causedby different things. Plants absorb soil nutrition and also returnas a result of litterfall. Fagus engleriana, L. obtusiloba and A.griseum may return more to the soil as a result of litterfalls,resulting in a soil nutrition patch. Or it may be that they have agreater ability to absorb more nutrition around them, whichcauses the patch. The experiments of Kleb and Wilson (1997)suggest that plant uptake contributes to greater heterogeneityin forests. Weiner et al. (1997) suggested that soil heterogene-ity could increase competition asymmetry. In summary, the re-lationship between soil and plants is one of an interaction.Although there was some relationship between species distri-bution and nutrient spatial pattern, it was still difficult to inter-pret the relationship between soil nutrient and communitycomposition, perhaps because nutrient patches influencedcommunity composition and plant species composition affectednutrient mineralization.

There was no significant relationship between other spe-cies and soil nutrition, which may be because those specieshad no special requirements in terms of soil nutrition. Such

species were mostly second-layer evergreen species, suchas C. oxyodon, R. hypoglaucum, and L. glaber, which maysuggest that the distribution of the second-layer trees is notinfluenced by soil nutrition.

Soil patches are also variable over time (Farley and Fitter1999). Taylor et al. (1982) found seasonal differences in ni-trate concentration in both acidic and calcareous soils, with aseasonal peak in early spring that decreased rapidly in summer.In arable and grassland soils in Denmark, inorganic phosphatedecreased in autumn and winter, before staring to rise again inspring (Magid and Nielsen 1992). In the present study, we de-tected seasonal variation in just one season, so there was nosignificant corelationship between soil patch and plant species.

Relationship between soil space patterns andmicrotopography

Many studies have shown that topography plays an importantrole in determining the spatial distribution of nutrient pools andfluxes (Itoh et al. 2003). A number of environmental factorspotentially influenced tree species composition in the presentresearch plot, the most important of these being topography(including specific effects of soil structure and drainage), soiltype and parent material, and small-scale disturbance

Figure 3. Semi-variances (……) and fitted models (____) of (A) total N, (B) P, (C) K, (D) Ca, and (E) Mg.


Figure 4. Contour plots of Ca, Cr, Ga, K, Mg, Organic-C, P, Pb, pH, and total N.


(Lieberman et al. 1985). Altitude within the plots has a range of80 m, distributed over a moderately complex topographicsurface. The factors varying with altitude, particularly drainage,are of floristic importance and the available soil maps are basedon a few samples. Moody and Meentemeyer (2001) also con-sidered the factors of slope, elevation, photosynthetically ac-tive radiation, topographic moisture index, and local topographicvariability.

In the present study, we measured indices of soil nutritionand investigated whether the distribution of these indices wasaffected by topographic factors. From the distribution map, wecan generally say that the spatial patterns of Ca, Cr, Ga, K, Mg,organic-C, P, Pb, pH, and total N tend to distribute withmicrotopography (Figure 4). It is clear that the high values ofthese elements appear in the bottom righthand area of this plot,which represents the steepest area. All these elements exhib-

ited the same type of distribution pattern, which was signifi-cantly affected by topography and was related to the watercatchment areas. Other elements, such as Al, Li, Mn, Ti, Na,and Fe, were distributed in the upper area in the plot, whichwas near the top of the hill. These elements were not influ-enced by the microtopography (Figure 5). These results areconsist with those reported by Su et al.(2002) and Li et at.(2003). These authors proved that the concentration of K, Ca,Mg, organic-C, and N is high at the foot of the hill rather than atthe top.

Autocorrelation

Geostatistic methods can quantify the scale and degree of soilvariability (Jackson and Caldwell 1993; Chappell 1998). Thesemi-variogram is the central tool of geostatisitcs for

Figure 5. Contour plots of Al, Li, Mn, Ti, Na, and Fe.


identifying both the structure of the variability of dimension andquantifying the scale of autocorrelation (Rossi et al. 1992;Goovaerts 1998). The semi-variograms calculated can then beused to produce maps of the property being investigated by“Kriging”, an interpolat ion method. The property ofautocorrelation is convenient to estimate unsampled points, butthe existence of spatial heterogeneity is a substantial problemin ecological research. Assessing the spatial structure of a sitewill help develop better nutrient management strategies and canserve as a means for designing suitable sampling patterns (Stark etal. 2004). It is well known that soils vary spatially, even over shortdistances. In the present study, autocorrelation occurred over therange 29–200 m and we can also identify the autocorrelationfrom the data present in Table 3. Some soil prosperities tendedtowards co-occurrence. The present results gave a fitted samplemethod for this type of community in this area.

Soil spatial structure has been investigated at different scales(cm, m, and km) and with different objectives. A number ofstudies have started to examine the heterogeneity of soil prop-erties (Webster and Oliver 1985). The spatial variability of thesoil is the result of intrinsic and extrinsic variability, which isinfluenced by land use, soil carbon content, topography, plant

cover, soil aggregates, and fine roots (Stark et al. 2004). In thestudy of Jackson and Caldwell (1993), the autocorrelation scaleof soil fertility (including ammonium, nitrate, phosphate, andpotassium) was less than 1 m. In the present study, theautocorrelation was in the range 29–200 m, which was relatedto the sample method, because the smallest distance we ex-amined was 20 m, so it was imposable to detect autocorrelationat this distance. In addition, the nutrition situation affected thedistribution of plants.

Comparing Table 3 and Figures 4 and 5, we can concludethat Ca-Mg, Ca-Ga, Ca-P, Ca-Pb, Ga-Mg, Mg-P, Mg-Pb, and Fe-Al all co-occurred, because these elements have positive rela-tionships and the same distribution patterns.

Materials and Methods

Study area

The study area is in the Shennongjia region (31°19'4'' N,110°29'44'' E), west of Hubei Province, central China. This re-gion is an important biodiversity conservation area in both China

Figure 6. Ordination of the species samples and soil chemical variables in the canonical correspondence analysis (CCA) biplot.


and the world (Zhu and Song 1999). The region is in the transi-tion zone between the north subtropical and warm temperateclimates. The landform consists of relatively gentle, broad ridgesand steep side slopes. Vegetation varies along an elevationgradient, from evergreen broad-leaved forests at low eleva-tions to mixed evergreen deciduous broad-leaved forests, mixedconiferous and broad-leaved forests, coniferous forests, anda Rhododendron-Sinarundinaria nitida shrub community. Themean annual precipitation of this area is 1 306.2–1 722.0 mm,the mean annual temperature is 12.1 °C, and the mean tempera-tures in January and July are –4.3 °C and 26.5 °C, respectively.The soil type is yellow-brown soil (Chen and Wang 1999; Zhuand Song 1999).

Sampling

In 2001, a 120 m × 80 m permanent plot was established on thesouth side of Shennongjia Mountain at an elevation of 1 750 m.The plot was divided into 20 m × 20 m subplots (horizontaldistance), which were further divided into 5 m × 5 m quadratsand a post was placed in each corner of each quadrat. Thecommunity was a mixed evergreen deciduous broad-leavedforest, which is an important forest type at this elevationgradient. We identified and mapped all individual stems ofcanopy and sub-canopy species that were greater than 4 cmdiameter at breast height (DBH). All stems were marked by analuminum plate with a running number. If a tree had multiple

trunks, each trunk was measured and tagged, but stems withtheir own roots in soil were defined as individuals. There werea total of 3 000 trees and large shrubs in the plot, comprising 22different families, 27 genera, and 46 species in the community.

Twenty-four surface soil samples (0–20 cm) were collectedwithin the sub-plots (Figure 7). In each sub-plot, surface soil,to a depth of 20 cm, was obtained from five sample pointsusing an iron jimmy. The five soil samples were from each ofthe four corners and from the center of each sub-plot. Sampleswere taken back to the laboratory, where they were mixedtogether as one sample. Twenty-three variables weremeasured, including soil pH, total nitrogen, organic-C, Al, Ba,Ca, Cr, Cu, Fe, Ga, Li, Mg, Mn, Na, NH4-N, Ni, NO3-N, Pb, P, Sr, Ti,V, and Zn. The macro-elements were N, P, K, Ca, and Mg,whereas the micro-elements were Cr, Cu, Mn, Pb, and Zn.

Soil chemical analysis

Samples were passed through a 2-mm sieve, dried, ground,and analyzed for C and N. For the determination of total carbon,Ct, and total nitrogen, Nt, samples were air-dried, passed througha 2-mm sieve, f inely ground, and analyzed by gaschromatography. Soil pH, extractable K and Mg, total N, andorganic matter were measured using standard soil analysistechniques. The pH was measured in a soil-to-solution (1 mol/LKCl) ratio of 1 : 2 without replicates. The NH4

+ and NO3– were

analyzed by automated colorimetry. The cations and some trace

Figure 7. Soil sample site. ( ), all trees in that plot; ( ), sample points.


elements (K, Na, Ca, Mg, Fe, Al, Li, Sr, Ba, Ga, Mn, Cr, V, Zn,Cu, and Ti) were determined in a saturation extract.

Data analysis

Soil special variability analysisSpatial variability was analyzed using the geostatistic method.The equation of semi-variance was as follows:

r(h) = [Z(xi)-Z(xi +h)]2

where r(h) is the semi-variograms at a given distance h, N(h) isthe number of pairs of observations separated by h, Z(xi) is thevalue of a particular soil property at sampling point xi, and Z(xi+h) is the value of the soil property at a sampling point at adistance h from xi. In the present study, twenty-four 20 m × 20m sub-plots were analyzed. For all soil chemical propertiesmeasured, autocorrelations were calculated and estimates ofsemi-variograms were obtained using GS+ software (version5.3 by Gamma Design Software, www. Gammadesing.com,Plainwell, Michigan 49080 USA).

Canonical correspondence analysisThe relationship between plant community type and soil prop-erties was investigated using CCA. The CCA procedure is amultivariate direct gradient analysis method that is widely usedin ecology (Zhang 1995). Canonical correspondence analysiswas performed using MVSP software (Multi-variate StatisticalPackage; http://www.kovcomp.com).

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12N(h)

n

i =1


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Variation of Soil Nutrition in a Fagus engleriana Seem.- Cyclobalanopsis oxyodon Oerst. Community Over a Small Scale in the Shennongjia Area, China