全 文 ：第 26 卷第 10 期
2006 年 10 月
生 　　态 　　学 　　报
ACTA ECOLOGICA SINICA
Vol. 26 ,No. 10
Oct. ,2006
黄土丘陵区地形、土壤水分与草地的景观格局
胡相明1 ,3 ,程积民1 ,2 , 3 ,万惠娥1 ,2 ,赵艳云1 ,3
(1. 中国科学院水利部水土保持研究所 ,陕西 杨陵　712100 ; 21 西北农林科技大学水土保持研究所 ,陕西 杨陵　712100 ;
3. 中国科学院研究生院 ,北京　100039)
基金项目 :中国科学院水土保持研究所知识创新前沿领域资助项目 (SW04103) ;国家“十五”科技攻关计划资助项目 (2004BA508B16) ;国家 973 资
助项目 (2002CB1115) ;国家自然科学基金资助项目 (40371077) ;国家林业局荒漠化监测专项资助项目
收稿日期 :2005207215 ;修订日期 :2006204222
作者简介 :胡相明 (1981～) ,男 ,山东肥城人 ,硕士生 ,主要从事植物修复和草地生态学研究. 现在山东滨州学院城市与环境系任教. E2mail :
xiangming0727 @163. com3 通讯作者 Corresponding author. E-mail :gyzcjm @ms. iswc. ac. cn
Foundation item :The project was supported by Knowledge Innovation Project of Institute of Soil and Water Conservation of Chinese Academy of Science (No.
SW04103) ;National Science and Technology of China (No. 2004BA508B16) ;National Project of 973 ,China (No. 2002CB1115) ;National Nature Sceince Foundation
of China (No. 40371077) ;Special Fund in Desertation Supervision of National Forest State ,China
Received date :2005207215 ;Accepted date :2006204222
Biography :HU Xiang2Ming , Master candidate , mainly engaged in plant restoration and grassland ecology. E2mail : xiangming0727 @163. com
Acknowledgements 　The authors acknowledge Dr. Toby Ewing , Dr. Shang Guan Zhou2Ping and Dr. Ni Jian for critically reading the drafts of this manuscript
摘要 :在黄土丘陵区 ,地形因素和土壤水分是决定草地景观格局的主要因素 ,同时草地景观格局在不同尺度上影响着景观中的
流。地形因素、土壤水分和草地结构在不同尺度上有着密切的联系 ,研究它们之间的关系对于了解生态系统的过程十分重要。
针对黄土高原异质化的草地群落结构 ,选取黄土丘陵区经过 20 多年自然封育形成的天然草地 ,从坡面尺度对景观格局进行了
调查研究 ,在地形因素、土壤水分和草地结构中选取了有代表性的指标 14 个 ,用多元统计分析对选取的指标进行了主成分分析
和聚类分析。聚类分析将样方分成 3 种植被类型 ,不同植被类型的海拔、坡度、20～140cm土壤含水量以及物种丰富度和生物多
样性存在显著性差异。相关分析表明 :海拔对 0～300cm土壤含水量影响显著 ;海拔对草地群落盖度 ,坡位、坡向对草地群落的
物种丰富度和生物多样性有着重要影响 ;而草地群落的物种丰富度和生物多样性与 0～100cm土层的含水量关系密切。
关键词 :黄土丘陵区 ;地形因素 ;土壤水分 ;草地景观格局 ;多元统计分析
文章编号 :100020933(2006) 1023276210 　中图分类号 :Q149 ,Q948 ,S154 ,S812 　文献标识码 :A
Reciprocal relationships between topography , soil moisture , and native vegetation
patterns in the loess hilly region , China
HU Xiang2Ming1 ,3 , CHENG Ji2Min1 ,2 , 3 , WAN Hui2E1 ,2 , ZHAO Yan2Yun1 ,3 　 (1. Institute of Soil and Water Conservation , Chinese
Academy of Sciences and Ministry of Water Resources , Yangling , Shanxi 712100 , China ; 2. Northwestern Sci2Tech University of Agriculture and Forestry , Yangling ,
Shaanxi 712100 , China ; 3. Graduate School of Chinese Academy of Sciences , Beijing 100039 , China) . Acta Ecologica Sinica ,2006 ,26( 10) :3276～3285.
Abstract : In loess hill landscapes , the pattern of vegetation affects movement of water and soil across the landscapes at multiple
scales ; likewise topography and soil moisture influence the structure of the plant community. At smaller scales , soil moisture is
heterogeneous. Small2scale heterogeneity has a large impact on the performance of individual plants , and therefore it influences the
structure and dynamics of plant populations and communities. These relationships must be studied in order to gain an
understanding of the ecosystem dynamics. We investigated at the slope scale the community structure of natural grassland on the
Loess Plateau. The study site had been fenced off for more than 20 years. We selected 14 topography , soil moisture , and
community structure metrics. Through the use of multivariate statistics (principle component analysis and cluster analysis) and
canonical correlation analysis , we explain the complex relationships between topography , soil moisture and community structure.
Three community types were identified by cluster analysis , distinguished by significant differences in elevation , slope , soil
moisture at the 20 140cm depth , species richness , and Shannon2Wiener index. Correlation analysis indicated that elevation
impacted community coverage , and slope position and slope aspect affected biodiversity of the plant community. Elevation and
slope position influenced soil moisture at 0 300cm depth , while the biodiversity of the plant community had a reciprocal
relationship with soil moisture at 0 100cm depth.
Key words :loess hill region ;topography ;soil moisture ;vegetation pattern ;multivariate statistical analysis
Understanding the fundamental mechanisms and spatial dynamics and variability of ecological flows of materials
(including organisms) , energy , and information across landscape mosaics is essential to landscape ecology. Soil moisture
is a major factor influencing fundamental ecosystem processes such as photosynthesis , respiration , and nutrient uptake[1 ] .
Moisture acts as a primary constraint on plant productivity[2 5 ] , and affects species composition[6 ] . It influences erosion[7 ] ,
pedogenesis[8 ] , geomorphology[9 ] , and infiltration2runoff partitioning in response to precipitation events[10 ] . Neave and
Norton , for instance , find a correlation between soil moisture and forest species distribution in southern Australia[11 ] , and
Stephenson argues that actual evapotranspiration is better correlate with vegetation distribution than temperature at a range
of scales[6 ] .
Vegetation pattern plays an important role in controlling spatial patterns of soil moisture by influencing the infiltration ,
runoff and evapotransipiration , particularly during the growth season[12 18 ] . Meanwhile , terrain indices aim to represent the
key hydrological processes controlling the spatial distribution of soil moisture in a simplified but realistic way[9 ,17 19 ] . The
relationships between soil moisture and topography attributes at this small catchment scale were found to be very variable by
Famiglietti [20 ] . In some cases there is a significant relation but in many other cases the relationship is insignificant[21 29 ] .
This may be due to differences in climate , topography , soil , vegetation , scale , time and depth of sampling methods[20 ] .
In the ecological community , the importance of topography and topography2related variation in local site conditions for
community structure , composition and successional pathways is well established[30 35 ] . Topography shapes pattern indirectly
through its influence on disturbance regimes and potential successional pathways , and directly , by creating permanent
natural breaks in vegetation pattern[36 ,37 ] .
However , while some studies have integrated soil moisture dynamic and its interactions with topography and vegetation
pattern[38 42 ] , our understanding of how topography , soil moisture , and vegetation dynamics interact to form landscape
pattern is still limited. The relationships between topography , soil moisture , and vegetation have usually been studied in
terms of an ecosystem’s response to environmental extremes rather than as a response to a gradual transition in land cover
or patchiness. In this study , we use an alternative approach of examining the relationships between soil moistures ,
topography and vegetation pattern , by describing the full range of ecological variability at a typical slope scale in Loess
Plateau , China. In hilly areas of the Loess Plateau , geomorphology is complex and highly variable soil moistureis the main
determinant of plant growth[43 ,44 ] . Topography and soil moisture thus influence vegetation patterns across the landscape ,
while these patterns themselves have a strong effect on soil water use and movement . It is therefore necessary to study
relationships between topography , soil moisture and plant community structure in order to understand ecosystem functions
and processes in these areas. Due to specifics of the local geography and climate , combined with a long history of
agriculture in the region , the primal vegetation is so disrupted that most ecological research in the area has concentrated on
artificial forests and grasslands. Studies of the natural grassland are few. The natural grassland is a complex adaptive
system , with community type , species composition , and biodiversity evolving and interacting in response to particulars of
the local topography and soil moisture. The topography and soil moisture patterns therefore likely differ from those in
artificial grassland. In this research , we investigate at the slope scale the community structure of nature grassland , which
772310 期 胡相明 　等 :黄土丘陵区地形、土壤水分与草地的景观格局 　
has been fenced off from surrounding agricultural land in the loess hills area for more than 20 years. Field observations
were analyzed for quantitative relationships between topography , soil moisture and plant community structure.
Therefore , the objectives of this paper are (1) to understand the relative roles of community structure and topographic
attributes in controlling the observed spatial variability of the soil moisture ; (2) to analysis the influence of topographic
indices on the vegetation pattern at a slope scale in nature grassland ; (3) to explain the infection of soil moisture on the
community structure of nature grassland.
1 　Material and methods
1. 1 　Study area
The study was conducted at Wangwa town in the western part of Pengyang county in the Ning Xia Autonomous
Region , China. The study area is situated on the middle part of the Loess Plateau , and is located at 106°32′45′ 106°33′
15″E , 36°04′30″ 36°09′36″N. The region has an altitude of 1684 1890 m , soil slopes of 15° 40°, and has a
semiarid continental climate with an averaged annual temperature of 712 ℃, average annual evaporation of 1400mm , and a
frost2free growing season of 112 140 d and an average of 2110 h of sunshine each year. The mean annual precipitation is
450 mm with great inter2annual variability and 65 % of the rain falls in July , August and September. There is significant
topographic variability with typical loess hills and gully slope shapes within the study area. The soils , developing on wind2
accumulated loess parent material , are thick at an average of 50 80 m. The most common soil in the study area is
loessial with silt content ranging from 64 to 73 % and clay content varying from 17 to 20 %. The soil is weakly resistant to
erosion. The erosion rate is high at about 10000 12000 ton·km - 2 a - 1 . This research site , representative of the grassland
region , has been fenced off since 1984. The primary herbaceous plants in the study area are Stipa bungeana ,
Heteropappus altaicus , Arthraxon hispidus , Medicago lupulina , Stipa grandis , Androsace erecta , Artemisia sacrorum , A .
capillaries and A . f rigida .
1. 2 　Data collection
1. 2. 1 　Sampling methods
In order to better understand relationships between topography , soil moisture and plant community structure at the
slope scale in loess hilly region , four V2shaped transects were established with the interval of 50m ( Fig. 1) . Each“V”
had one sunny and one shaded slope. Transects extended from the top to the bottom of hills located adjacent to the Soil and
Water Conservation Station of in Wangwa , Pengyang County. Samples measuring 1m×1m were sited every 5m of altitude
change along each transect , giving 15 shaded and 20 sunny samples in each V for a total of 140 samples.
Fig. 1 　Spatial distribution of sampling transects
1. 2. 2 　Survey methods
(1) Plant data and Environmental attributes survey
In late August , the peak biomass time period , we
measured plant species composition , coverage , average height
and above2ground biomass for each sampling site. Average
height was weighted by species coverage. We then harvested
plants at the soil surface , bagged and weighed them , then
dried them for 20h at 80 ℃before measuring their dry weight .
Note that this measure of above2ground biomass includes the
standing crop , but not the litter or any standing dead plants.
Each sampling site was surveyed and its environmental
characteristics were recorded. The environ2mental attributes
assessed are : hillslope position , aspect , elevation (recorded
8723　 生 　态 　学 　报 26 卷
by elevation instrument ) and slope degree on each site.
(2) Soil moisture measurement
Soil samples were taken in the center of each plot . All soil samples were weighed the same day that they were
collected , and all soil sampling was accomplished within a period of seven rain2free days. Sampling was done with a 5 cm
diameter screw auger , taking samples in 20cm increments down to 3m. Samples were weighed in moist condition , then
dried at 105 ℃to a constant mass. At each sample site two measurements were performed to measure moisture content at
fifty depths : 0 20 , 20 40 , 40 60 , 60 80 , 80 100 , 100 120 , 120 140 , 140 160 , 160 180 , 180
200 , 200 220 , 220 240 , 240 260 , 260 280 and 280 300 cm. The mean of the two measurements is the soil
moisture content at each depth on the sample site.
1. 3 　Initial data processing
The number of species was counted to indicate the species richness , and the Shannon2Wiener index ( H) was
calculated to show species diversity. The Shannon2Wiener index is given as
H = - 6 ( Pi ×ln Pi )
where Pi = N iΠN and N = 6 N i . N i denotes the coverage of species i within a plot , rather than the number of
individual plants of species i , because some herbaceous plants were so ramified that individual plants could not be
distinguished and counted.
1. 4 　Statistical analysis
All statistical analyses described below were performed using SPSS software.
1. 4. 1 　Principal component analysis
Principal Component Analysis ( PCA) reduces multiple variables to a small number of composite variables , while
minimizing loss of information. The composite variables summarize the majority of the information in the original data ,
decrease the number of variables , and encapsulate some internal relations among the original variables. We used PCA to
eliminate redundant factors , and enhance the accuracy of the subsequent Cluster Analysis.
A total of 14 metrics were recorded from all 140 plots , with the data describing plot topography , soil moisture , and
plant community structure. The topography variables were slope , slope aspect , elevation , and slope position. Soil moisture
variables were mass soil moisture at depths of 0 20cm , 20 60cm , 60 100cm , 100 140cm , 140 200cm and 200
300cm. The plant community structure indices were coverage , biomass , species richness and Shannon2Wiener index.
Non2numerical classification variables were assigned numerical values by empirical formulas. For example , a sunny slope
was assigned aspect 0. 3 , a partially shaded slope 0. 5 , a mostly shaded slope 0. 8 , and a slope in full shade 1. 0.
Similarly , an up2slope plot was assigned position 0. 4 , a mid2slope 1. 0 , and a down2slope 0. 8[45 ] . In order to have PCA
work with indices of similar magnitude , the initial data was standardized. Because the initial (raw) data was found to be
normally distributed , it could be standardized using the method of Z scores. At that point , PCA was used to calculate the
eigenvectors and eigenvalues.
1. 4. 2 　Cluster Analysis
Cluster Analysis is a multivariate statistical technique for classifying objects according to their characteristics. Given
the multiple characteristics of each sample , similarities among them are ascertained , and then samples with similar
characteristics are clustered together. Samples are multiply classified from large to small differences , resulting in a
similarity tree or dendrogram[45 ] . In this study , the 140 plots were classified by Ward’s method , which recognizes that
information can occur in different categories. We categorized input variables as relating to topography , soil moisture , or
plant community structure. Ward’s method was chosen because it minimizes intra2category discrepancies and maximizes
inter2category discrepancies.
972310 期 胡相明 　等 :黄土丘陵区地形、土壤水分与草地的景观格局 　
2 　Results
2. 1 　Data reduction
Four eigenvalues were greater than one ( Table 1) , having values of 5109 , 3101 , 2100 and 1154. Eigenvectors
corresponding to these eigenvalues respectively explained 37103 % , 25103 % , 15175 % and 9169 % of the data’s
variance , so they cumulatively explained 8715 % of the variance. In the first principal component , the loading capacities
of soil moistures of 20 60cm (0190) , 60 100cm (0196) and 100 140cm (0186) were the greatest , so this
component mainly encapsulates the role of soil water. The second principal component’s greatest loading capacities were
elevation (0174) and slope aspect (0173) , so it was primarily about topography. In the third principal component ,
loading capacities for biomass (0184) and coverage (0168) predominated , representing the productivity of the grassland.
In the fourth principal component , species richness (0189) and the Shannon2Wiener index (0159) had the greatest
loading capacities , representing the grassland’s biodiversity.
Table 1 　Factor loadings in four principal components. Eigenvalues , %
of variance and Cumulative % of four principal components in principal
component analysis
Factors
Principal components
Ⅰ Ⅱ Ⅲ Ⅳ
Shannon2Wiener index ( H) 0. 02 0. 12 0. 00 0. 59
Species richness 0. 15 0. 32 0. 13 0. 89
Elevation - 0. 54 0. 74 0. 06 - 0. 05
Slope - 0. 30 0. 32 - 0. 46 - 0. 23
Position 0. 27 0. 57 - 0. 08 0. 02
Aspect - 0. 13 0. 73 - 0. 08 - 0. 15
Coverage - 0. 06 0. 23 0. 68 - 0. 31
Biomass - 0. 17 - 0. 09 0. 84 0. 14
SW0220 0. 51 0. 21 0. 15 - 0. 09
SW20260 0. 90 0. 16 0. 10 - 0. 09
SW602100 0. 96 0. 05 0. 09 - 0. 07
SW1002140 0. 86 - 0. 06 - 0. 05 - 0. 06
SW1402200 0. 62 - 0. 11 - 0. 07 - 0. 06
SW2002300 0. 42 - 0. 21 - 0. 12 - 0. 08
Eigenvalue 5. 09 3. 01 2. 00 1. 54
% of variance 37. 03 25. 03 15. 75 9. 69
Cumulative ( %) 37. 03 62. 06 77. 81 87. 50
As the eigenvalues show , no single principal compo2
nent explains a large proportion of the variance. This
indicates that the heterogeneous landscape pattern is the
combined result of many factors in this topographically
complex region. In such a case we should expect extensive
reciprocity among the factors contributing to the landscape
pattern.
2. 2 　Plots Cluster
Following the Principal Component Analysis , we
selected those factors with the greatest information content
from each principal component . Three soil water variables
were selected based on the first principal component ,
representing depths of 20 60cm , 60 100cm and 100
140. Elevation and slope aspect were picked given the
loading in the second principal component . The productivity
indices biomass and coverage were chosen based on the
third principal component , and species richness and the
Shannon2Wiener diversity index were selected due to the fourth principal component . These nine factors were used in a
cluster analysis of the 140 plots. And the 140 plots were clustered into three types , which denoted Ⅰ: Heteropappus
altaicus , Ⅱ: Stipa bungeana and Ⅲ: Arthraxon hispidus communities respectively.
The three community types differ significantly in elevation and slope ( Table 2) . Type Ⅰ ( Heteropappus altaicus)
consists mostly of down2slope plots with relatively low elevation and slope. Type Ⅱ ( Stipa bungeana) Type Ⅲ ( Arthraxon
hispidus) was generally mid2 and upper2slope , with a higher average altitude.
Soil moistures also varied systematically among community types (Table 3) . Specifically , soil water content presented
as type Ⅲ< type Ⅱ< type Ⅰ. This difference in soil moisture is probably attributable to topography , because type I plots
were lowest in elevation. The coverage and biomass did not differ notably with community types , but species richness and
the Shannon2Wiener index differed significantly (Table 4) . There were a total of 40 species represented across all type Ⅰ
plots , with an average of 14 species per plot . Community type Ⅱcontained a total of 39 species , with an average of 11
appearing in each plot . Finally , type Ⅲ comprised 32 species , and community type Ⅲ averaged 10 species per plot .
Community type I clearly contained a greater average number of species , likely due to their greater soil water content .
0823　 生 　态 　学 　报 26 卷
Table 2 　The difference of topography features in three community types
Community type
Elevation (m)
(μ±σ)
Number of plots at slope position Number of plots with aspect :
Up Middle Down Shady Sunny
Slope (°)
(μ±σ)
Ⅰ ( Heteropappus altaicus) 1704 ±14. 6 0 8 44 40 12 19. 9 ±11. 7
Ⅱ ( Stipa bungeana) 1745 ±20. 5 14 10 6 8 22 30. 0 ±14. 5
Ⅲ ( Arthraxon hispidus) 1783 ±10. 5 26 22 10 12 46 27. 3 ±10. 7
P value 0. 00 < 0. 01
　　μ±σdenotes average ±standards deviation ; P value suggests significance level
Table 3 　The difference of soil moisture of different soil layers within the three community types
Community types Number of plots SW0220 SW20260 SW602100 SW1002140 SW1402200 SW2002300
Ⅰ ( Heteropappus altaicus) 15 9. 3 ±1. 4 9. 0 ±1. 4 8. 6 ±1. 2 8. 5 ±1. 0 9. 3 ±1. 1 9. 3 ±1. 3
Ⅱ ( Stipa bungeana) 35 9. 1 ±0. 6 8. 3 ±0. 3 8. 3 ±0. 5 8. 1 ±0. 8 9. 0 ±0. 4 8. 9 ±0. 6
Ⅲ ( Arthraxon hispidus) 20 8. 9 ±0. 5 7. 1 ±0. 5 7. 0 ±0. 4 7. 7 ±0. 4 8. 7 ±0. 4 8. 7 ±0. 5
P value 0. 10 0. 01 0. 01 0. 03 0. 07 0. 08
　　μ±σdenotes average ±standards deviation ; P value suggests significance level
Table 4 　The difference of community structure within the three community types
Plot types Dry biomass
(kgΠm2)
(μ±σ)
Coverage ( %)
(μ±σ)
Shannon2Wiener index
(μ±σ)
Species richness
(μ±σ)
Ⅰ ( Heteropappus altaicus) 0. 11 ±0. 03 66. 3 ±10. 1 2. 13 ±0. 42 14 ±3
Ⅱ ( Stipa bungeana) 0. 09 ±0. 10 59. 9 ±15. 4 1. 98 ±0. 20 11 ±1
Ⅲ ( Arthraxon hispidus) 0. 11 ±0. 09 60. 3 ±13. 1 1. 90 ±0. 27 10 ±2
P value 0. 16 0. 26 0. 02 0. 00
　　μ±σdenotes average ±standards error ; P value suggests significance level
Table 5 　Correlation coefficients between topography and soil water , topography and community structure
Topography Coverage Biomass Richness Isw SW0220 SW20260 SW602100 SW1002140 SW1402200 SW2002300
Slope - 0. 20 - 0. 45 3 0. 02 0. 04 0. 04 0. 03 - 0. 07 - 0. 20 - 0. 20 - 0. 26
Elevation - 0. 25 3 0. 09 - 0. 17 0. 05 - 0. 56 3 3 - 0. 59 3 3 - 0. 82 3 3 - 0. 85 3 3 - 0. 85 3 3 - 0. 85 3 3
　　3 3 Correlation is significant at the 0. 01 level (22tailed) ; 3 Correlation is significant at the 0. 05 level (22tailed)
Table 6 　The difference of community structure and soil water within the two aspect types
Aspect Coverage Biomass Richness Isw SW0220 SW20260 SW602100 SW1002140 SW1402200 SW2002300
Shady 63. 9 ±14. 0 0. 3 ±0. 1 11 ±2 2. 0 ±0. 2 9. 4 ±1. 3 8. 7 ±1. 1 8. 1 ±1. 1 7. 6 ±1. 3 7. 5 ±1. 2 7. 3 ±0. 6
Sunny 75. 5 ±9. 0 0. 3 ±0. 1 13 ±2 2. 2 ±0. 2 11. 1 ±0. 5 10. 3 ±0. 4 9. 7 ±0. 2 8. 6 ±0. 6 8. 0 ±1. 0 7. 4 ±0. 4
P value 0. 00 0. 77 0. 00 0. 16 0. 00 0. 00 0. 00 0. 14 0. 48 0. 79
Table 7 　The difference of community structure and soil water within the three slope positions
Position Coverage Biomass Richness Isw SW0220 SW20260 SW602100 SW1002140 SW1402200 SW2002300
Up 68. 8 ±13. 3 0. 4 ±0. 1 11 ±2 2. 0 ±0. 2 8. 9 ±0. 6 7. 8 ±0. 7 7. 1 ±0. 4 6. 0 ±0. 3 6. 0 ±0. 5 6. 6 ±0. 3
Middle 69. 0 ±18. 1 0. 3 ±0. 1 12 ±3 1. 9 ±0. 4 10. 2 ±1. 5 9. 3 ±1. 1 9. 4 ±0. 5 9. 3 ±0. 3 8. 7 ±0. 3 7. 9 ±0. 2
Down 68. 0 ±12. 4 0. 3 ±0. 1 12 ±2 2. 1 ±0. 3 10. 0 ±1. 2 9. 6 ±0. 9 8. 9 ±0. 9 8. 0 ±0. 6 7. 7 ±0. 7 7. 4 ±0. 3
P value 0. 97 0. 20 0. 24 0. 05 0. 00 0. 00 0. 00 0. 00 0. 00 0. 00
Table 8 　Correlation coefficients between Soil water and Community structure
Community structure SW0220 SW20260 SW602100 SW1002140 SW1402200 SW2002300
Coverage 0. 05 0 0. 06 0. 11 0. 11 0. 17
Biomass - 0. 08 - 0. 08 - 0. 13 - 0. 12 - 0. 03 0. 06
Richness 0. 33 3 3 0. 34 3 3 0. 34 3 3 0. 16 0. 12 0. 12
Isw 0. 27 3 0. 25 3 0. 14 - 0. 07 - 0. 08 - 0. 08
　　3 , 3 3 denotation is same to table 5
182310 期 胡相明 　等 :黄土丘陵区地形、土壤水分与草地的景观格局 　
2. 3 　Relationships between topography attributes , vegetation pattern and soil moisture content
　　From the correlation coefficients between topography and soil water , topography and community structure (Table 5) ,
we could see that slope had a significant correlation with grassland biomass , and elevation had very important effects on
grassland community coverage and soil moisture of 0 300 cm. The difference of community structure and soil water within
two aspects suggested that slope aspect had great influence on community coverage , species richness of community and soil
moisture of 0 100cm (Table 6) . Besides , Table 7 suggested that soil water of 0 300cm were effected significantly by
slope position.
According to the correlation coefficients between soil water and community structure ( Table 8) , we knew that the
species richness and biodiversity of the grassland community was closely related to the soil moisture at 0 100 cm depth ,
which might attribute that the soil at 0 100 cm depth was the main distribution range of root systems of herbaceous
species. It suggested that there were a reciprocal interaction between the soil moisture at 0 100 cm depth and the
biodiversity of the grassland community.
3 　Discussion
In this research , 14 metrics about topography , soil water and community structure were chosen , and 140 plots
investigated were classified into three community types by using PCA and Cluster Analysis. Discrepancies in elevation ,
slope gradient , soil moisture of 20 140cm , species richness , Shannon2Wiener index were significant in three community
types. Type Ⅰwas dominated by Heteropappus altaicus community , and the species richness and biodiversity were the
biggest . It attributed that most plots of this type were located in down2slope with low altitude and slope gradient , and more
soil water , which played controlling roles in the species survival and reproduction. Type Шwas Arthraxon hispidus
community , in which the species richness and biodiversity was the lowest in the three types. The reason was that the
majority of plots located in up2slope with high elevation , steep slope and low soil moisture. TypeΠwas Stipa bungeana
community , and most of plots distributed in the middle of sunny slope , and the soil moisture and biodiversity were
intervenient in those three types.
Numerous studies suggested that topography played an important role in the forming of vegetation landscape pattern.
By redistributing resources , such as light , heat , water and so on , topography impacted on matter flows across landscape
elements , and dominated many of the biotic and abiotic processes along topography gradient , thereby influenced on the
forming of landscape pattern[39 ,46 50 ] . Our research showed that elevation had a great affect on soil moisture at the depth of
0 300cm , and topography factors such as elevation , slope gradient and slope aspect had a close relationship with
coverage , species richness and biodiversity of community at the slope scale , which mainly attributed that altitude , slope
and slope aspect had an important influence on the matter flows across landscape elements. Generally , the position with
high elevation and precipitous slope would receive less surface runoff and stream in soil from higher place , and contrarily
the position with low elevation and slope would obtain more water. In addition , nutrient elements such as soluble nitrogen ,
soluble phosphorus and soluble potassium , generally flowed following the water , and gave arise to nutrient migration ,
thereby formed different habitat conditions along topography gradient . During the restoration process of vegetation , an
increase of species richness and biodiversity not only depended on the habitat condition , but also lay on the seed
availability. Usually , the surface runoff could give arise to migration of seeds and propagulums in a certain habitat , and
became the new species in the habitat , which was another important reason for discrepancy of species richness and
biodiversity caused by topography.
Specifically , soil moisture had been considered the most limiting factor for the vegetation landscape pattern in the
semiarid area , which had an important influence on the species distribution , vegetation formation , and vegetation
productivity[2 ,3 , 51 53 ] . Likewise vegetation changes exerted a control in the vertical water fluxes between the atmosphere ,
2823　 生 　态 　学 　报 26 卷
land surface and subsurface[54 57 ] . As early as 1947 , Watt had found that vegetation pattern could control microclimate
condition[55 ] . Large number of researches indicated that plant cover structure , including surface roughness of the canopy
and the root systems , could modify the water cycling in a landscape influencing the partitioning of its flux into
evapotranspiration , surface runoff or soil percolation[56 ,57 ] . Our study showed that the species richness and biodiversity of
the grassland community had a reciprocal relationship with the soil moisture at 0 100cm depth. It attributed that the soil
layer of 0 100cm depth was the primary distribution range of roots in the native grassland , and then the soil moisture
content of it reflected the habitat condition of the native grassland in the loess hilly region on the whole , thereby affecting
the plants distribution and species richness. In the same way , the species richness and biodiversity of the native grassland
impacted greatly the soil moisture at the depth of 0 100cm , which attributed that in a certain extent the species richness
reflected plants cover structure and roots systems influencing the water fluxes between the atmosphere , land surface and
subsurface. In the grassland with higher biodiversity , the structure of plant cover and root systems might be complex , and
be able to slow down or stop surface runoff greatly , and then increase soil percolation ability and promote precipitation
infiltration on the spot . In contrary , in the grassland with lower biodiversity , the structure of plant cover and root systems
might be simple , and have a little influence on slowing down surface runoff , and then the soil percolation ability might be
weaker and the soil moisture content was lower.
The vegetation distributions across landscapes in semiarid terrain are driven by soil moisture variation , which in turn
are closely associated with the changes in topography. These vegetation changes exert a control in the vertical water fluxes.
Reasonable landscape structure could be in favor of the water cycling , and enhances vegetation productivity and improves
regional environment . However , unfavorable landscape structure could cause maladjustment of the water cycling , and then
brings some adverse problems of environment . Our research illuminated that landscape pattern of the native grassland
represented a large heterogeneity due to the influence of topography in loess hilly region at the slope scale. To this
question , we should ascertain some sections in the light of topography , soil moisture and vegetation type in this area , and
choose approximating nature species patterns corresponding with the sections to enhance resistance against disturbance and
promote the healthy cycling of ecosystem.
However , this empirical study was only concerned with the spatial relationships between topography , soil moisture and
vegetation. Of course , the temporal relationships are equally important to ecosystem restoration , and further study on
spatiotemporal relationships between them , while much more intensive , may be needed to supply a better answer to the
question of ecological restoration in the loess hilly region.
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