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Facilitative effects of shrubs in shifting sand on soil macro-faunal community in Horqin Sand Land of Inner Mongolia, Northern China
European Journal of Soil Biology 47 (2011) 316e321
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European Journal of Soil Biology journal homepage: http://www.elsevier.com/locate/ejsobi
Original article
Facilitative effects of shrubs in shifting sand on soil macro-faunal community in Horqin Sand Land of Inner Mongolia, Northern China Rentao Liu a, *, Halin Zhao b, Xueyong Zhao b, Sam Drake c a
Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwestern China of Ministry of Education, Ningxia University, 489 Helanshan West Road, Yinchuan, Ningxia 750021, China b Naiman Desertification Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 260 Donggang West Road, Lanzhou, Gansu 730000, China c Office of Arid Lands Studies, University of Arizona, 1955 E. 6th Street, Tucson, AZ 85719, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 February 2011 Received in revised form 30 June 2011 Accepted 4 July 2011 Available online 23 July 2011 Handling editor: Stefan Schrader
Shrubs can create “fertile islands” with improved soil and microclimatic conditions surrounded by a lownutrient matrix in arid and semi-arid areas. But the relationship of sandy-soil adapted shrubs (Caragana microphylla and Salix gordejevii) with the structure of soil macro-faunal assemblages is largely unknown in Horqin Sand Land, Northern China. The environmental variables and soil macro-faunal community were investigated under shrubs and in the open spaces between shrubs in this study. Environmental parameters (soil water, temperature, pH, EC, total organic carbon and nitrogen) indicated a significant alteration of the soil environment under shrubs in comparison with that in the open spaces. Significantly larger shrub canopy size and greater height were found for S. gordejevii than for C. microphylla. The microhabitats under shrubs maintained significantly higher abundance, group richness and diversity of soil macro-faunal communities in comparison with those in the open spaces, with a higher abundance and group richness but lower diversity under the legume C. microphylla than under S. gordejevii. Further, data for the different faunal groups characterized specific responses to varying microhabitats under shrubs. Results implied that soil microhabitats under shrubs, in addition to shrub characteristics, could facilitate macro-fauna assemblages and diversity in shifting sand lands, which by feedback is beneficial for recovery, conservation and sustainable management in a sandy ecosystem. Ó 2011 Elsevier Masson SAS. All rights reserved.
Keywords: Soil fauna Community structure Shrub microhabitat Soil ecology Horqin Sand Land
1. Introduction In arid and semi-arid areas, shrubland or shrub-hummock is commonly found, and has become one of the main vegetation types particularly in shifting sand land [6,35,36]. Some shrub species play crucial roles in preventing desertification processes [10,28] and in conservation and restoration of arid lands [4,15,37]. These shrubs with their root systems and shading canopies can create highnutrient patches in a low-nutrient matrix (i.e., fertile islands) and can modify the environment nearby, thus affecting arid land dynamics [4,35]. In addition, shrubs may provide species-specific shelter from temperature/drought extremes for macroarthropods, thus facilitating the recovery and reconstruction of terrestrial ecosystems [4,5,20,31]. A number of papers have shown the relationship of shrubs with soil fauna in arid and semi-arid regions [2,4,11,19,20,30,31], where
* Corresponding author. Tel./fax: þ80 (0) 751 2062838. E-mail address: [email protected] (R. Liu). 1164-5563/$ e see front matter Ó 2011 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejsobi.2011.07.006
soil fauna are ecologically important in many aspects: as pollinators, as important components of the food chain and nutrient cycles, and in altering soil structure and fertility [2,4,5]. One effect seen is that, the abiotic factors modulated by shrubs can dictate the spatial and temporal activity of soil animals in arid and semi-arid areas [30,31]. Shrub encroachment has considerable influence on the abundance and diversity of ground-dwelling arthropods at varying levels of shrub cover [2]. The spatio-temporal mosaic created by shrub microhabitat and seasonal variations in a strongly seasonal desert location can affect the distribution and dynamics of the macro-invertebrate community [4]. In addition, shrub litter has an impact on food sources and feeding habitats for macroarthropods, as shown in laboratory feeding experiments [27]. Overall the microhabitat under shrubs related to macroinvertebrate assemblages is a relevant issue to be considered in conservation, sustainable management and environmental restoration in heterogeneous arid systems to preserve their biodiversity and ecosystem characteristics [2,4,11,19]. In Horqin Sand Land, many researchers have investigated the effects of several shrub species on soil properties [28,35,38], seed
R. Liu et al. / European Journal of Soil Biology 47 (2011) 316e321
bank [35,36,39] and understory herbaceous community properties [35,36,39] to understand the restoration mechanisms of desertified lands. However, very little data exist on the community ecology of macro-fauna found in sandy shrublands in Horqin Sand Land of Inner Mongolia, Northern China. The relationship of soil faunal community structure with shrub types has been little studied. The main objectives of this study were: (1) to compare several abiotic variables in the open spaces between shrubs versus under shrub canopies, as well as two biotic variables (canopy size and height) among various shrub species; (2) to investigate relationships between the soil macro-faunal community and environmental factors mediated by shrubs in order to assess their effects on the macro-faunal community in shifting sand lands. 2. Materials and methods 2.1. Site description The study area, about 10 km away from the Naiman Desertification Research Station, is located in the south-western part (42 550 N and 120 420 E, 360 m elevation) of Horqin Sand Land, Inner Mongolia, Northern China. This area has a temperate continental semi-arid monsoonal climate. Annual precipitation is 366 mm, falling predominantly during the MayeSeptember period. Mean annual potential pan-evaporation is around 1935 mm, five times greater than annual precipitation. Mean annual temperature is around 6.4 C, and lowest and highest monthly mean temperatures are 13.1 C in January and 23.7 C in July. Mean annual wind velocity ranges from 3.2 to 4.1 m s1, and prevailing winds are northwest in winter and spring and southwest to south in summer and autumn. A wind erosion period often occurs from April to midJune before the rainy season arrives (Climate data from Naiman Desertification Research Station). The dominant soils are sandy Arenosols, light yellow in color with loose structure, and very susceptible to wind erosion. The topography is characterized by dunes and inter-dune lowlands [35,36]. Horqin Sand Land is one of the areas of most serious concern for land desertification in China, where desertified land comprises 57.8% of the total area [35e37]. However, since the mid-1970s some successful measures to combat desertification have been implemented, such as planting indigenous trees, shrubs and grasses adapted to sandy land, fencing grassland against grazing and using minimum tillage [28]. These have decreased the desertified area from 5163 km2 in 1987 to 4674 km2 in 2000 [34]. The vegetation consists largely of low, open shrubland dominated by Caragana microphylla (a leguminous shrub), Salix gordejevii and Artemisia halodendron, with the herbaceous stratum dominated by Pennisetum centrasiaticum, Corispermum macrocarpum and Setaria viridis. As is typical in areas of shifting sand, most of the ground surface, even under shrubs, is devoid of a herbaceous community, and thus lacking litter [34,35], which does occur under dwarf shrubs in fixed sand lands.
317
another four in the open spaces (Fig. 1). The distance was approximately 3 m between two sampling points with one under the shrub and another in the open space in the same direction. In each quadrat, all organisms were recovered by hand sorting, and soil samples were obtained for soil physico-chemical analyses. Soil temperature was measured by portable thermometer with conductivity wires (Sato Keiryoki MFG Co. Ltd., Japan). 2.3. Collection and identification of soil fauna Macro-fauna were preserved in 75% alcohol in the field and brought back to the laboratory for identification. The macro-fauna were identified at the level of the Order and Family according to “Pictorial Keys to Soil Faunas of China” [32], and classified into groups on the basis of morphological features under a binocular microscope (40 magnification). Larvae, nymphs and adults were separately counted. 2.4. Soil properties measurement Soil samples were taken to the laboratory and hand-sieved through a 2-mm screen to remove roots and other debris. Part of each sieved sample was oven-dried for determining soil water content (%). Part of each sieved sample was air-dried for determining selected chemical properties. Soil pH and electrical conductivity (EC, ms cm1) were determined in 1:1 soilewater slurry and in 1:5 soil water aqueous extract, respectively. Soil organic carbon (SOC, g kg1) was measured by the dichromate oxidation method of Walkey and Black [18] and soil total nitrogen (TN, g kg1) by the Kjeldahl procedure (UDK140 Automatic Steam Distilling Unit, Automatic Titroline 96, Italy) [1]. 2.5. Statistical analyses Faunal specimens from the four quadrats under a shrub were pooled as one sample, and likewise for the four quadrats outside a shrub. Faunal community indices were calculated for each sample: abundance (individuals m2), group richness, and the Shannon index, a diversity index based on faunal groups [13,33].
Shannon indexðHÞ : H ¼
S X i¼1
x Pi log2 Pi ; where Pi ¼ PS i
i ¼ 1 xi
where xi ¼ number of individuals in the faunal category ‘‘i’’, S ¼ number of faunal groups. Redundancy analysis (RDA) using CANOCO Software 4.5 (Microcomputer Power, Ithaca, USA) was chosen to examine the
2.2. Experimental design Two sites, one with C. microphylla and another with S. gordejevii, were selected in shifting sand lands in August, 2009. In each site three replicate 30 m 30 m plots were set with similar topography and soil properties. Four individual shrubs were chosen in each replicate plot, with plant height and the maximum crown diameter and the diameter orthogonal to this maximum one in the horizontal under measurement to estimate shrub size. Then at the spot of each individual shrub, eight regularly spaced quadrats (25 cm 25 cm 30 cm depth) at four different aspects (NE, NW, SW and SE) were excavated, with four under the shrub canopy and
Fig. 1. Layout of the plots for a sampling site. C, represent shrub individuals and soil sampling points respectively. Shade indicates shrub canopy.
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environmental variables (soil moisture, soil temperature, pH, EC, total organic C, total N) affecting the structure of the macro-faunal community [9,11,14]. The data were first analyzed by detrended correspondence analysis (DCA) which suggested RDA as an appropriate approach for further analysis (length of gradient < 3 for faunal communities). RDA correlated each faunal group to the environmental variables by selecting linear combinations of environmental variables that gave the smallest residual sum of squares. Descriptive statistics and significance tests on the raw data were conducted using SPSS 15.0 software. Multiple-comparison and oneway analysis of variance (ANOVA) procedures were used to compare the differences among the treatments. Least significant difference (LSD) tests were performed to determine the significance of treatment means at P < 0.05. 3. Results 3.1. Environmental variables A significant effect of “shrub” was found on soil temperature (P < 0.01) and organic carbon (P < 0.05) (Table 1), but not on soil water content, pH, EC or total nitrogen (P > 0.05). Soil temperature was significantly lower (P < 0.05) under S. gordejevii than that under C. microphylla or in the open spaces. Soil organic carbon was significantly higher under C. microphylla than in the open spaces, with an intermediate value under S. gordejevii. Soil water content was greater and soil pH was slightly lower in the open spaces compared to under C. microphylla and S. gordejevii, while soil electrical conductivity and total nitrogen was higher under C. microphylla compared to the open spaces and to locations under S. gordejevii, although differences in these four variables were not significant (P > 0.05) among the three treatments. Likewise, a significant effect of “shrub type” was observed on both biotic parameters (Fig. 2). The mean shrub canopy (P < 0.05) and height (P < 0.01) were both significantly greater under S. gordejevii than under C. microphylla. 3.2. Soil macro-faunal community structure Similar to the environmental parameters, a significant effect of “shrub” was also observed on total abundance (P < 0.01) and taxonomic richness (P < 0.05) as well as the Shannon index (P < 0.05) of the macro-faunal community (Fig. 3). This was reflected by the very high density and taxonomic richness under C. microphylla and very low density and richness in the open spaces, with intermediate values under S. gordejevii (Fig. 3A and B). The lowest Shannon index was found in the open spaces and the highest under S. gordejevii, with an intermediate value under C. microphylla (Fig. 3C). 3.3. Redundancy analysis Redundancy analysis (RDA) was carried out to determine the main factors affecting the faunal community structure in sandy
Fig. 2. Comparison of canopy size and height of two shrub species. C and S mean Caragana microphylla and Salix gordejevii respectively. The vertical bars indicate S.E. Significant differences between two shrub species are indicated: *P < 0.05, **P < 0.01.
shrubland soils (Table 2; Fig. 4). From Fig. 4 and Table 2, Axes 1 (P < 0.01) and 2 were found to explain 72.6% and 4.3% of the overall variance within the faunal group data respectively, accounting for a total of 76.9% of the total variance. The cumulative specieseenvironment relationship for Axes 1 and 2 was 96.5%, indicating that these axes accounted for the bulk of the variance in the faunal group data that could be attributed to environmental factors (soil physico-chemical properties). Specieseenvironment correlations for both axes were above 0.81, indicating that the faunal group data were strongly correlated with environmental parameters. Monte Carlo significance tests revealed that both the first axis, and all axes combined, explained a significant amount of the variation within the data. The plot in Fig. 4 can be interpreted quantitatively using the length of the arrow to indicate how much variance was explained by that factor; the direction of the arrows for individual environmental factors indicates an increasing concentration of that factor. From Fig. 4, canonical coefficients and intraset correlations for the environmental factors for each axis indicated that soil organic carbon (P < 0.01) had the greatest influence on the faunal community structure. Meanwhile, the faunal groups showed on the plot as arrows pointing in approximately the same direction as the environmental factor arrows indicated a high positive correlation (the longer the faunal group arrow, the stronger the relationship) [9]. The position of faunal groups, e.g. Salticidae, Lycosidae, Philodromidae, Pentatomidae, Cicadellidae, Tenebrionidae, Melolonthidae larvae, and Aslidae larvae, pointing in the same direction to the arrow for soil organic carbon, indicated that these faunal groups were positively correlated with soil organic carbon. Results from Pearson correlation also indicated which environmental factors significantly contributed to the variance in the faunal groups (Table 3). The overall trend for the faunal groups analyzed by Pearson correlation agreed with that of the multivariate analysis, with soil organic carbon appearing most frequently as a significant environmental factor. Fig. 4 also demonstrates that different populations within a community respond differently to specific environmental variables. For example, Myrmeleontidae larvae and Carabidae responded positively to soil water content, while Alydidae, Curculionidae, and Staphylinidae larvae responded negatively to soil temperature. The main effect of higher soil pH was a positive impact on Tenebrionidae larvae. Higher soil electrical conductivity (EC) had direct
Table 1 Environmental parameters in different treatments. Values with different letters in a column are significantly different (*P < 0.05, **P < 0.01; one-way ANOVA followed by posthoc LSD test). SW soil water content, ST soil temperature, EC soil electrical conductivity, SOC soil organic carbon, TN soil total nitrogen. CK in the open spaces between two shrubs, C under Caragana microphylla, S under Salix gordejevii.
CK C S F
SW (%)
ST ( C)
pH
EC (ms cm1)
SOC (g kg1)
TN (g kg1)
1.48 0.07a 1.54 0.39a 1.53 0.19a 0.04
29.25 0.47a 29.07 0.20a 26.54 0.10b 9.98**
7.57 0.10a 7.47 0.07a 7.37 0.09a 1.07
27.20 8.28a 23.88 5.53a 13.73 2.04a 0.63
0.56 0.12b 1.63 0.47a 0.88 0.38ab 3.87*
0.17 0.02a 0.21 0.02a 0.16 0.01a 1.37
R. Liu et al. / European Journal of Soil Biology 47 (2011) 316e321
200
B
F=7.84, P<0.01
a
150 100 50
b b
Group richness
Abundance (Ind m-2)
A
16
319
F=5.69, P<0.05
a
12
a
8
b 4 0
0 CK
C
S
Shannon index (H)
C 2.50
F=6.27, P<0.05
C
S
a
ab
2.00 1.50
CK
b
1.00 0.50 0.00 CK
C
S
Fig. 3. (A) Abundance, (B) Group richness, and (C) Shannon diversity index of soil macro-faunal communities in different treatments. CK in the open spaces between two shrubs, C under Caragana microphylla shrubs, S under Salix gordejevii shrubs. The vertical bars indicate S.E. Values with different letters are significantly different at P < 0.05.
impacts on Carabidae, Coccinellidae, Elateridae Leach, Aphodiidae Leach, Carabidae larvae, and Aphodiidae Leach larvae (positive). More soil organic carbon had a positive influence on Salticidae, Lycosidae, Philodromidae, Pentatomidae, Cicadellidae, Tenebrionidae, Melolonthidae larvae, and Aslidae larvae. More soil total nitrogen did mediate a positive effect on Tenebrionidae and Melolonthidae larvae. These observations were supported by results from Pearson correlation for these eight faunal groups (Table 4), which revealed that soil organic carbon had a significant, positive effect on their abundances. 4. Discussion
Fig. 4. Redundancy analysis (RDA) ordination diagram of faunal group data, with environmental variables represented as red arrows and faunal groups underlined represented as black arrows. Environmental variables: SW soil water content, ST soil temperature, EC electrical conductivity, SOC soil organic carbon, TN total soil nitrogen. Macro-faunal taxa: 1 Araneidae, 2 Thomisidae, 3 Salticidae, 4 Lycosidae, 5 Philodromidae, 6 Gnapphosidae 7 Liocranidae, 8 Clubionidae, 9 Alydidae, 10 Coreidae, 11 Miridae, 12 Pentatomidae, 13 Gryllidae, 14 Cicadellidae, 15 Larval Myrmeleontidae, 16 Labiduridae, 17 Adult Carabidae, 18 Coccinellidae, 19 Adult Elateridae Leach, 20 Adult Aphodiidae Leach, 21 Adult Melolonthidae, 22 Curculionidae, 23 Adult Tenebrionidae, 24 unknown beetle 25 Larval Carabidae, 26 Larval Staphylinidae, 27 Larval Chrysomelidae, 28 Larval Meloidae, 29 Larval Elateridae Leach, 30 Larval Aphodiidae Leach, 31 Larval Rutelidae, 32 Larval Melolonthidae, 33 Larval Tenebrionidae, 34 Larval Aslidae, 35 Larval Tabanidae, 36 Larval Pyralididae, 37 Formicidae. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Shrubs have been identified as significant “fertile islands” having an important role in improving soil properties and
Table 2 Results of redundancy analysis (RDA). Values are for Axes 1 and 2 plotted in the RDA diagram in Fig. 4. The highest canonical coefficients and correlations are highlighted in red. SW soil water content, ST soil temperature, EC soil electrical conductivity, SOC soil organic carbon, TN soil total nitrogen. Axis Eigenvalues Cumulative percentage variance: of species data of specieseenvironment relation Summary of Monte Carlo test: F-ratio P-value Species-environment correlations Correlations (r): SW ST pH EC SOC TN Coefficients (c): SW ST pH EC SOC TN
1
2 0.73
72.6 91.2
0.04
23.80 0.02 0.92
76.8 96.5 For all axes: 5.85 0.01 0.81
0.24 0.15 0.33 0.10 0.84 0.52
0.09 0.29 0.61 0.56 0.23 0.30
0.26 0.16 0.36 0.11 0.91 0.57
0.11 0.36 0.76 0.69 0.28 0.37
facilitating vegetation recovery for controlling desertification processes [35,36,38]. The results from this study also support this point (Table 1). Shrubs reduced soil temperature and pH, and enhanced soil water content, electrical conductivity and total organic carbon and nitrogen in shifting sand lands. Meanwhile, two shrub species with significantly different canopy size and height (Fig. 2) showed varying impacts on the microhabitats under them (Table 1), which may be related to significant differences in their contribution to “fertile islands” in desert ecosystems [15,35,38]. A key question was whether the studied shrubs had an impact on the soil macro-faunal community due to their influence on environmental conditions in shifting sand lands.
Table 3 Correlation coefficients between macro-faunal community and environmental parameters. *Correlation was significant at 0.05 level (2-tailed). **Correlation was significant at 0.01 level (2-tailed). ***Correlation was significant at 0.001 level (2tailed) (red in the Table). SW soil water content, ST soil temperature, EC soil electrical conductivity, SOC soil organic carbon, TN soil total nitrogen.
SW ST pH EC SOC TN
Abundance
Group richness
Shannon index
0.3711 0.0955 0.1799 0.1966 0.9102*** 0.5931*
0.4341 0.3127 0.2030 0.0391 0.6622** 0.3390
0.4204 0.6404** 0.2308 0.2149 0.3421 0.0541
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Table 4 Correlation coefficients between single macro-faunal groups and environmental parameters. *Correlation was significant at 0.05 level (2-tailed). **Correlation was significant at 0.01 level (2-tailed). ***Correlation was significant at 0.001 level (2-tailed) (red in the Table). Codes as in Fig. 4. Code
Faunal group
SW
ST
pH
EC
SOC
TN
3 4 5 9 12 14 15 17 18 19 20 22 23 25 26 30 32 33 34
Salticidae Lycosidae Philodromidae Alydidae Pentatomidae Cicadellidae Larval Myrmeleontidae Adult Carabidae Coccinellidae Adult Elateridae Leach Adult Aphodiidae Leach Curculionidae Adult Tenebrionidae Larval Carabidae Larval Staphylinidae Larval Aphodiidae Leach Larval Melolonthidae Larval Tenebrionidae Larval Aslidae
0.7695 0.7695 0.7695 0.1049 0.7695 0.2157 0.7823* 0.6359* 0.2680 0.2680 0.2680 0.1049 0.2294 0.2680 0.1049 0.2680 0.2682 0.1397 0.2168
0.4714 0.4714 0.4714 0.9428* 0.4714 0.3933 0.7082* 0.2911 0.0000 0.0000 0.0000 0.9428* 0.1538 0.0000 0.9428* 0.0000 0.2324 0.1865 0.0148
0.8893 0.8893 0.8893 0.3580 0.8893 0.3700 0.1777 0.0946 0.8673 0.8673 0.8673 0.3580 0.3297 0.8673 0.3580 0.8673 0.0417 0.6019* 0.1933
0.4947 0.4947 0.4947 0.6755 0.4947 0.1678 0.4613 0.6241* 0.9438* 0.9438* 0.9438* 0.6755 0.1037 0.9438* 0.6755 0.9438* 0.2454 0.4534 0.0030
0.9416* 0.9416* 0.9416* 0.1729 0.9416* 0.8579** 0.0714 0.5317 0.0709 0.0709 0.0709 0.1729 0.8395*** 0.0709 0.1729 0.0709 0.6802** 0.0612 0.8556***
0.7247 0.7247 0.7247 0.7725 0.7247 0.6601 0.0182 0.4686 0.7565 0.7565 0.7565 0.7725 0.5332* 0.7565 0.7725 0.7565 0.4998* 0.1718 0.3693
Focusing on the structure of the macro-faunal community, there was a significant increase in abundance, richness, and diversity of the macro-faunal community from shrub-free to locations under the shrubs (Fig. 3). This indicated that significant improvement of the soil microhabitats could provide suitable environmental conditions in addition to enough resources for soil fauna to dwell under shrubs [4,17,25]. Results are in accord with previous studies on microbial and nematode and macro-faunal communities of shrub patches [4,19,24] in arid regions, where there are the best conditions for soil bacterial, nematode and macro-faunal communities. Pearson correlation analysis demonstrated that soil temperature, total organic carbon and nitrogen were the important factors controlling soil macro-faunal communities (Table 3). RDA is a multivariate approach that is useful for revealing largescale trends [9,11,14]. The RDA relates each faunal group to linear combinations of observed environmental variables in order to reveal which environmental factors are correlated with changes in soil macro-faunal community. Results from redundancy analysis revealed that a large percentage (72.6%, first axis) of the variation in soil macro-faunal communities was attributable to one factor, soil organic carbon. The effects of soil pH and EC were very small in comparison, reinforcing the view that soil organic carbon content affects macro-faunal community structure far more. This showed that there were facilitative effects of shrubs in shifting sand on soil macro-faunal community [3,4,20] through their influences on the soil conditions, e.g. soil organic carbon [28,35,36], thus further beneficial for desert land restoration in Horqin Sand Land of Inner Mongolia, Northern China. RDA analysis was also used to find individual faunal groups responding to environmental variables due to their strong adaptation to, and selection of, specific habitats [13,22]. This approach could be used to identify faunal groups with particular abiotic conditions [12,13]. For example, if a specific fauna responds to a change in moisture conditions, this fauna may be used as a marker for moisture changes [7], irrespective of the cause of such changes. Where there are high numbers of Carabidae under shrubs with high soil water content. Namely, Carabidae tends to live in moist soil and can be regarded as a marker for moist conditions. Several other faunal groups have also been identified to be responsive to different physico-chemical properties across several sites (Fig. 4; Table 4). Curculionidae and Staphylinidae larvae tend to live in the soil under shrubs with lower temperature in comparison with the open spaces. Tenebrionidae larvae prefer
a lower soil alkalinity, e.g. under S. gordejevii. Melolonthidae larvae and Aslidae larvae may be regarded as markers for organic carbon changes, e.g. under C. microphylla with high soil organic carbon content. But a more extensive assessment is needed across several soil environments before such faunal markers can be used as indicators, due to comprehensive effects of various soil environments on soil faunal abundances [26,29]. Nevertheless, in the present study the different shrub species showed considerable differences in macro-faunal abundances, group richness and diversity (Fig. 3). Higher abundance and group richness but lower diversity of soil faunal community was found under C. microphylla than under S. gordejevii. This might be related to the shrubs’ characteristics [16,23] and the shelters they provide, in addition to soil conditions [20,21] mediated by the different shrub species. In our system, differences in soil nutrients among shrub species appeared as main factors explaining the observed differences in macro-invertebrate abundance and group richness [3]. High numbers of Cicadellidae, Teneberionidae, Melolonthidae larvae, and Araneae appeared to be related to high soil nutrient content (Table 4), resulting in significantly higher faunal abundance under the legume C. microphylla than under S. gordejevii (Fig. 3a and b). The lower temperature under S. gordejevii (Table 1), in addition to its greater canopy size (Fig. 2), could explain the higher diversity, together with greater numbers of Alydidae, Staphylinidae larvae and Staphylinidae [3,12]. The size of shrubs was not only correlated with the abundance of available resources, thus affecting the individual shrub’s carrying capacity [16], but also was related to the chance of attracting an active immigrant, likely due to the fact that the shrub “island” was more easily detected [23]. The shrub “island” can send much information of living conditions (soil temperature, water content and nutrients, etc.) to the soil fauna, beneficial for them to detect a suitable soil environment to inhabit in the shifting sand lands. Therefore, the higher diversity of soil macro-faunal community under S. gordejevii., was probably a consequence of what Gilpin and Diamond [8] described as the “target area effect” [23]. 5. Conclusions Shrubs in shifting sand not only mediated soil microhabitats, but also had a facilitative impact on the soil macro-faunal community in Horqin Sand Land of Northern China. Shrubs significantly raised the abundance, group richness and diversity of
R. Liu et al. / European Journal of Soil Biology 47 (2011) 316e321
soil faunal communities in comparison with the open spaces between shrubs, with higher abundance and group richness but lower diversity under the legume C. microphylla than under S. gordejevii. Soil faunal abundance and group richness were positively correlated with the amount of soil nutrients under shrubs, while soil faunal diversity was closely related to shrub characteristics. Further, there were specific faunal groups responding to different soil conditions due to the strong adaptation to, and selection of, specific habitats. Study results suggested that shrubs facilitated macro-fauna assemblies and could improve their biodiversity and ecosystem functioning by feedback for recovery and management in this semi-arid sandy area. Acknowledgments The authors are grateful to the anonymous reviewers for their critical review and comments on drafts of this manuscript. This study was financially supported by projects of the National Basic Research Program of China (No. 2009CB421303). We thank Prof. Xinmin Liu (Inner Mongolia Normal University, China) for the identification of faunal specimens. References [1] S.D. Bao, Soil and Agricultural Chemistry Analysis. Chinese Agriculture Press, Beijing, 2000. [2] N. Blaum, C. Seymour, E. Rossmanith, M. Schwager, F. Jeltsch, Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators, Biodiversity Conservation 18 (2009) 1187e1199. [3] E. Doblas-Miranda, F. Sánchez-Piñero, A. González-Megías, Soil macroinvertebrate fauna of a Mediterranean arid system: composition and temporal changes in the assemblage, Soil Biology and Biochemistry 39 (2007) 1916e1925. [4] E. Doblas-Miranda, F. Sánchez-Piñero, A. González-Megías, Different microhabitats affect soil macroinvertebrate assemblages in a Mediterranean arid ecosystem, Applied Soil Ecology 41 (2009) 329e335. [5] J.G. Ehrenfeld, B. Ravit, K. Elgersma, Feedback in the plantesoil system, Annual Review of Environment and Resources 30 (2005) 75e115. [6] J.M. Facelli, A.M. Temby, Multiple effects of shrubs on annual communities in arid lands of South Australia, Austral Ecology 27 (2000) 422e431. [7] J. Frouz, A. Ali, J. Frouzova, R.J. Lobinske, Horizontal and vertical distribution of soil macroarthropods along a spatio-temporal moisture gradient in Subtropical Central Florida, Environmental Entomology 33 (2004) 1282e1295. [8] M.E. Gilpin, J.M. Diamond, Calculations of immigration and extinction curves from the species-area distance relation, Proceedings of the National Academy of Sciences, USA 73 (1976) 4130e4134. [9] N. Kennedy, E. Brodie, J. Connolly, N. Clipson, Impact of lime, nitrogen and plant species on bacterial community structure in grassland microcosms, Environmental Microbiology 6 (2004) 1070e1080. [10] F.R. Li, L.Y. Zhao, X.Z. Wang, Effects of enclosure on soil seed bank and plant community structure in sandy grassland of Horqin Sand Land, Acta Pratadculturae Sinica 12 (2003) 32e40. [11] J.L. Liu, F.R. Li, Q.J. Liu, R.X. Niu, Relationship between ground beetle community distribution and microhabitats in an arid desert shrubland of the middle Heihe River Basin, Acta Ecologica Sinica 30 (2010a) 6389e6398. [12] J.L. Liu, F.R. Li, Q.J. Liu, R.X. Niu, Seasonal variation of ground dwelling arthropod communities in the middle of an arid desert of Heihe River Basin, Acta Prataculturae Sinica 19 (2010b) 161e169. [13] R.T. Liu, H.L. Zhao, X.Y. Zhao, S. Drake, Soil macrofaunal response to sand dune conversion from mobile dune to fixed dune in Horqin Sandy Land, Northern China, European Journal of Soil Biology 45 (2009) 417e422. [14] C.A. Macdonald, N. Thomas, L. Robinson, Physiological, biochemical and molecular responses of the soil microbial community after afforestation of pastures with Pinus radiata, Soil Biology and Biochemistry 41 (2009) 1642e1651.
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Contents lists available at ScienceDirect
European Journal of Soil Biology journal homepage: http://www.elsevier.com/locate/ejsobi
Original article
Facilitative effects of shrubs in shifting sand on soil macro-faunal community in Horqin Sand Land of Inner Mongolia, Northern China Rentao Liu a, *, Halin Zhao b, Xueyong Zhao b, Sam Drake c a
Key Laboratory for Restoration and Reconstruction of Degraded Ecosystem in Northwestern China of Ministry of Education, Ningxia University, 489 Helanshan West Road, Yinchuan, Ningxia 750021, China b Naiman Desertification Research Station, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 260 Donggang West Road, Lanzhou, Gansu 730000, China c Office of Arid Lands Studies, University of Arizona, 1955 E. 6th Street, Tucson, AZ 85719, USA
a r t i c l e i n f o
a b s t r a c t
Article history: Received 27 February 2011 Received in revised form 30 June 2011 Accepted 4 July 2011 Available online 23 July 2011 Handling editor: Stefan Schrader
Shrubs can create “fertile islands” with improved soil and microclimatic conditions surrounded by a lownutrient matrix in arid and semi-arid areas. But the relationship of sandy-soil adapted shrubs (Caragana microphylla and Salix gordejevii) with the structure of soil macro-faunal assemblages is largely unknown in Horqin Sand Land, Northern China. The environmental variables and soil macro-faunal community were investigated under shrubs and in the open spaces between shrubs in this study. Environmental parameters (soil water, temperature, pH, EC, total organic carbon and nitrogen) indicated a significant alteration of the soil environment under shrubs in comparison with that in the open spaces. Significantly larger shrub canopy size and greater height were found for S. gordejevii than for C. microphylla. The microhabitats under shrubs maintained significantly higher abundance, group richness and diversity of soil macro-faunal communities in comparison with those in the open spaces, with a higher abundance and group richness but lower diversity under the legume C. microphylla than under S. gordejevii. Further, data for the different faunal groups characterized specific responses to varying microhabitats under shrubs. Results implied that soil microhabitats under shrubs, in addition to shrub characteristics, could facilitate macro-fauna assemblages and diversity in shifting sand lands, which by feedback is beneficial for recovery, conservation and sustainable management in a sandy ecosystem. Ó 2011 Elsevier Masson SAS. All rights reserved.
Keywords: Soil fauna Community structure Shrub microhabitat Soil ecology Horqin Sand Land
1. Introduction In arid and semi-arid areas, shrubland or shrub-hummock is commonly found, and has become one of the main vegetation types particularly in shifting sand land [6,35,36]. Some shrub species play crucial roles in preventing desertification processes [10,28] and in conservation and restoration of arid lands [4,15,37]. These shrubs with their root systems and shading canopies can create highnutrient patches in a low-nutrient matrix (i.e., fertile islands) and can modify the environment nearby, thus affecting arid land dynamics [4,35]. In addition, shrubs may provide species-specific shelter from temperature/drought extremes for macroarthropods, thus facilitating the recovery and reconstruction of terrestrial ecosystems [4,5,20,31]. A number of papers have shown the relationship of shrubs with soil fauna in arid and semi-arid regions [2,4,11,19,20,30,31], where
* Corresponding author. Tel./fax: þ80 (0) 751 2062838. E-mail address: [email protected] (R. Liu). 1164-5563/$ e see front matter Ó 2011 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejsobi.2011.07.006
soil fauna are ecologically important in many aspects: as pollinators, as important components of the food chain and nutrient cycles, and in altering soil structure and fertility [2,4,5]. One effect seen is that, the abiotic factors modulated by shrubs can dictate the spatial and temporal activity of soil animals in arid and semi-arid areas [30,31]. Shrub encroachment has considerable influence on the abundance and diversity of ground-dwelling arthropods at varying levels of shrub cover [2]. The spatio-temporal mosaic created by shrub microhabitat and seasonal variations in a strongly seasonal desert location can affect the distribution and dynamics of the macro-invertebrate community [4]. In addition, shrub litter has an impact on food sources and feeding habitats for macroarthropods, as shown in laboratory feeding experiments [27]. Overall the microhabitat under shrubs related to macroinvertebrate assemblages is a relevant issue to be considered in conservation, sustainable management and environmental restoration in heterogeneous arid systems to preserve their biodiversity and ecosystem characteristics [2,4,11,19]. In Horqin Sand Land, many researchers have investigated the effects of several shrub species on soil properties [28,35,38], seed
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bank [35,36,39] and understory herbaceous community properties [35,36,39] to understand the restoration mechanisms of desertified lands. However, very little data exist on the community ecology of macro-fauna found in sandy shrublands in Horqin Sand Land of Inner Mongolia, Northern China. The relationship of soil faunal community structure with shrub types has been little studied. The main objectives of this study were: (1) to compare several abiotic variables in the open spaces between shrubs versus under shrub canopies, as well as two biotic variables (canopy size and height) among various shrub species; (2) to investigate relationships between the soil macro-faunal community and environmental factors mediated by shrubs in order to assess their effects on the macro-faunal community in shifting sand lands. 2. Materials and methods 2.1. Site description The study area, about 10 km away from the Naiman Desertification Research Station, is located in the south-western part (42 550 N and 120 420 E, 360 m elevation) of Horqin Sand Land, Inner Mongolia, Northern China. This area has a temperate continental semi-arid monsoonal climate. Annual precipitation is 366 mm, falling predominantly during the MayeSeptember period. Mean annual potential pan-evaporation is around 1935 mm, five times greater than annual precipitation. Mean annual temperature is around 6.4 C, and lowest and highest monthly mean temperatures are 13.1 C in January and 23.7 C in July. Mean annual wind velocity ranges from 3.2 to 4.1 m s1, and prevailing winds are northwest in winter and spring and southwest to south in summer and autumn. A wind erosion period often occurs from April to midJune before the rainy season arrives (Climate data from Naiman Desertification Research Station). The dominant soils are sandy Arenosols, light yellow in color with loose structure, and very susceptible to wind erosion. The topography is characterized by dunes and inter-dune lowlands [35,36]. Horqin Sand Land is one of the areas of most serious concern for land desertification in China, where desertified land comprises 57.8% of the total area [35e37]. However, since the mid-1970s some successful measures to combat desertification have been implemented, such as planting indigenous trees, shrubs and grasses adapted to sandy land, fencing grassland against grazing and using minimum tillage [28]. These have decreased the desertified area from 5163 km2 in 1987 to 4674 km2 in 2000 [34]. The vegetation consists largely of low, open shrubland dominated by Caragana microphylla (a leguminous shrub), Salix gordejevii and Artemisia halodendron, with the herbaceous stratum dominated by Pennisetum centrasiaticum, Corispermum macrocarpum and Setaria viridis. As is typical in areas of shifting sand, most of the ground surface, even under shrubs, is devoid of a herbaceous community, and thus lacking litter [34,35], which does occur under dwarf shrubs in fixed sand lands.
317
another four in the open spaces (Fig. 1). The distance was approximately 3 m between two sampling points with one under the shrub and another in the open space in the same direction. In each quadrat, all organisms were recovered by hand sorting, and soil samples were obtained for soil physico-chemical analyses. Soil temperature was measured by portable thermometer with conductivity wires (Sato Keiryoki MFG Co. Ltd., Japan). 2.3. Collection and identification of soil fauna Macro-fauna were preserved in 75% alcohol in the field and brought back to the laboratory for identification. The macro-fauna were identified at the level of the Order and Family according to “Pictorial Keys to Soil Faunas of China” [32], and classified into groups on the basis of morphological features under a binocular microscope (40 magnification). Larvae, nymphs and adults were separately counted. 2.4. Soil properties measurement Soil samples were taken to the laboratory and hand-sieved through a 2-mm screen to remove roots and other debris. Part of each sieved sample was oven-dried for determining soil water content (%). Part of each sieved sample was air-dried for determining selected chemical properties. Soil pH and electrical conductivity (EC, ms cm1) were determined in 1:1 soilewater slurry and in 1:5 soil water aqueous extract, respectively. Soil organic carbon (SOC, g kg1) was measured by the dichromate oxidation method of Walkey and Black [18] and soil total nitrogen (TN, g kg1) by the Kjeldahl procedure (UDK140 Automatic Steam Distilling Unit, Automatic Titroline 96, Italy) [1]. 2.5. Statistical analyses Faunal specimens from the four quadrats under a shrub were pooled as one sample, and likewise for the four quadrats outside a shrub. Faunal community indices were calculated for each sample: abundance (individuals m2), group richness, and the Shannon index, a diversity index based on faunal groups [13,33].
Shannon indexðHÞ : H ¼
S X i¼1
x Pi log2 Pi ; where Pi ¼ PS i
i ¼ 1 xi
where xi ¼ number of individuals in the faunal category ‘‘i’’, S ¼ number of faunal groups. Redundancy analysis (RDA) using CANOCO Software 4.5 (Microcomputer Power, Ithaca, USA) was chosen to examine the
2.2. Experimental design Two sites, one with C. microphylla and another with S. gordejevii, were selected in shifting sand lands in August, 2009. In each site three replicate 30 m 30 m plots were set with similar topography and soil properties. Four individual shrubs were chosen in each replicate plot, with plant height and the maximum crown diameter and the diameter orthogonal to this maximum one in the horizontal under measurement to estimate shrub size. Then at the spot of each individual shrub, eight regularly spaced quadrats (25 cm 25 cm 30 cm depth) at four different aspects (NE, NW, SW and SE) were excavated, with four under the shrub canopy and
Fig. 1. Layout of the plots for a sampling site. C, represent shrub individuals and soil sampling points respectively. Shade indicates shrub canopy.
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environmental variables (soil moisture, soil temperature, pH, EC, total organic C, total N) affecting the structure of the macro-faunal community [9,11,14]. The data were first analyzed by detrended correspondence analysis (DCA) which suggested RDA as an appropriate approach for further analysis (length of gradient < 3 for faunal communities). RDA correlated each faunal group to the environmental variables by selecting linear combinations of environmental variables that gave the smallest residual sum of squares. Descriptive statistics and significance tests on the raw data were conducted using SPSS 15.0 software. Multiple-comparison and oneway analysis of variance (ANOVA) procedures were used to compare the differences among the treatments. Least significant difference (LSD) tests were performed to determine the significance of treatment means at P < 0.05. 3. Results 3.1. Environmental variables A significant effect of “shrub” was found on soil temperature (P < 0.01) and organic carbon (P < 0.05) (Table 1), but not on soil water content, pH, EC or total nitrogen (P > 0.05). Soil temperature was significantly lower (P < 0.05) under S. gordejevii than that under C. microphylla or in the open spaces. Soil organic carbon was significantly higher under C. microphylla than in the open spaces, with an intermediate value under S. gordejevii. Soil water content was greater and soil pH was slightly lower in the open spaces compared to under C. microphylla and S. gordejevii, while soil electrical conductivity and total nitrogen was higher under C. microphylla compared to the open spaces and to locations under S. gordejevii, although differences in these four variables were not significant (P > 0.05) among the three treatments. Likewise, a significant effect of “shrub type” was observed on both biotic parameters (Fig. 2). The mean shrub canopy (P < 0.05) and height (P < 0.01) were both significantly greater under S. gordejevii than under C. microphylla. 3.2. Soil macro-faunal community structure Similar to the environmental parameters, a significant effect of “shrub” was also observed on total abundance (P < 0.01) and taxonomic richness (P < 0.05) as well as the Shannon index (P < 0.05) of the macro-faunal community (Fig. 3). This was reflected by the very high density and taxonomic richness under C. microphylla and very low density and richness in the open spaces, with intermediate values under S. gordejevii (Fig. 3A and B). The lowest Shannon index was found in the open spaces and the highest under S. gordejevii, with an intermediate value under C. microphylla (Fig. 3C). 3.3. Redundancy analysis Redundancy analysis (RDA) was carried out to determine the main factors affecting the faunal community structure in sandy
Fig. 2. Comparison of canopy size and height of two shrub species. C and S mean Caragana microphylla and Salix gordejevii respectively. The vertical bars indicate S.E. Significant differences between two shrub species are indicated: *P < 0.05, **P < 0.01.
shrubland soils (Table 2; Fig. 4). From Fig. 4 and Table 2, Axes 1 (P < 0.01) and 2 were found to explain 72.6% and 4.3% of the overall variance within the faunal group data respectively, accounting for a total of 76.9% of the total variance. The cumulative specieseenvironment relationship for Axes 1 and 2 was 96.5%, indicating that these axes accounted for the bulk of the variance in the faunal group data that could be attributed to environmental factors (soil physico-chemical properties). Specieseenvironment correlations for both axes were above 0.81, indicating that the faunal group data were strongly correlated with environmental parameters. Monte Carlo significance tests revealed that both the first axis, and all axes combined, explained a significant amount of the variation within the data. The plot in Fig. 4 can be interpreted quantitatively using the length of the arrow to indicate how much variance was explained by that factor; the direction of the arrows for individual environmental factors indicates an increasing concentration of that factor. From Fig. 4, canonical coefficients and intraset correlations for the environmental factors for each axis indicated that soil organic carbon (P < 0.01) had the greatest influence on the faunal community structure. Meanwhile, the faunal groups showed on the plot as arrows pointing in approximately the same direction as the environmental factor arrows indicated a high positive correlation (the longer the faunal group arrow, the stronger the relationship) [9]. The position of faunal groups, e.g. Salticidae, Lycosidae, Philodromidae, Pentatomidae, Cicadellidae, Tenebrionidae, Melolonthidae larvae, and Aslidae larvae, pointing in the same direction to the arrow for soil organic carbon, indicated that these faunal groups were positively correlated with soil organic carbon. Results from Pearson correlation also indicated which environmental factors significantly contributed to the variance in the faunal groups (Table 3). The overall trend for the faunal groups analyzed by Pearson correlation agreed with that of the multivariate analysis, with soil organic carbon appearing most frequently as a significant environmental factor. Fig. 4 also demonstrates that different populations within a community respond differently to specific environmental variables. For example, Myrmeleontidae larvae and Carabidae responded positively to soil water content, while Alydidae, Curculionidae, and Staphylinidae larvae responded negatively to soil temperature. The main effect of higher soil pH was a positive impact on Tenebrionidae larvae. Higher soil electrical conductivity (EC) had direct
Table 1 Environmental parameters in different treatments. Values with different letters in a column are significantly different (*P < 0.05, **P < 0.01; one-way ANOVA followed by posthoc LSD test). SW soil water content, ST soil temperature, EC soil electrical conductivity, SOC soil organic carbon, TN soil total nitrogen. CK in the open spaces between two shrubs, C under Caragana microphylla, S under Salix gordejevii.
CK C S F
SW (%)
ST ( C)
pH
EC (ms cm1)
SOC (g kg1)
TN (g kg1)
1.48 0.07a 1.54 0.39a 1.53 0.19a 0.04
29.25 0.47a 29.07 0.20a 26.54 0.10b 9.98**
7.57 0.10a 7.47 0.07a 7.37 0.09a 1.07
27.20 8.28a 23.88 5.53a 13.73 2.04a 0.63
0.56 0.12b 1.63 0.47a 0.88 0.38ab 3.87*
0.17 0.02a 0.21 0.02a 0.16 0.01a 1.37
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200
B
F=7.84, P<0.01
a
150 100 50
b b
Group richness
Abundance (Ind m-2)
A
16
319
F=5.69, P<0.05
a
12
a
8
b 4 0
0 CK
C
S
Shannon index (H)
C 2.50
F=6.27, P<0.05
C
S
a
ab
2.00 1.50
CK
b
1.00 0.50 0.00 CK
C
S
Fig. 3. (A) Abundance, (B) Group richness, and (C) Shannon diversity index of soil macro-faunal communities in different treatments. CK in the open spaces between two shrubs, C under Caragana microphylla shrubs, S under Salix gordejevii shrubs. The vertical bars indicate S.E. Values with different letters are significantly different at P < 0.05.
impacts on Carabidae, Coccinellidae, Elateridae Leach, Aphodiidae Leach, Carabidae larvae, and Aphodiidae Leach larvae (positive). More soil organic carbon had a positive influence on Salticidae, Lycosidae, Philodromidae, Pentatomidae, Cicadellidae, Tenebrionidae, Melolonthidae larvae, and Aslidae larvae. More soil total nitrogen did mediate a positive effect on Tenebrionidae and Melolonthidae larvae. These observations were supported by results from Pearson correlation for these eight faunal groups (Table 4), which revealed that soil organic carbon had a significant, positive effect on their abundances. 4. Discussion
Fig. 4. Redundancy analysis (RDA) ordination diagram of faunal group data, with environmental variables represented as red arrows and faunal groups underlined represented as black arrows. Environmental variables: SW soil water content, ST soil temperature, EC electrical conductivity, SOC soil organic carbon, TN total soil nitrogen. Macro-faunal taxa: 1 Araneidae, 2 Thomisidae, 3 Salticidae, 4 Lycosidae, 5 Philodromidae, 6 Gnapphosidae 7 Liocranidae, 8 Clubionidae, 9 Alydidae, 10 Coreidae, 11 Miridae, 12 Pentatomidae, 13 Gryllidae, 14 Cicadellidae, 15 Larval Myrmeleontidae, 16 Labiduridae, 17 Adult Carabidae, 18 Coccinellidae, 19 Adult Elateridae Leach, 20 Adult Aphodiidae Leach, 21 Adult Melolonthidae, 22 Curculionidae, 23 Adult Tenebrionidae, 24 unknown beetle 25 Larval Carabidae, 26 Larval Staphylinidae, 27 Larval Chrysomelidae, 28 Larval Meloidae, 29 Larval Elateridae Leach, 30 Larval Aphodiidae Leach, 31 Larval Rutelidae, 32 Larval Melolonthidae, 33 Larval Tenebrionidae, 34 Larval Aslidae, 35 Larval Tabanidae, 36 Larval Pyralididae, 37 Formicidae. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
Shrubs have been identified as significant “fertile islands” having an important role in improving soil properties and
Table 2 Results of redundancy analysis (RDA). Values are for Axes 1 and 2 plotted in the RDA diagram in Fig. 4. The highest canonical coefficients and correlations are highlighted in red. SW soil water content, ST soil temperature, EC soil electrical conductivity, SOC soil organic carbon, TN soil total nitrogen. Axis Eigenvalues Cumulative percentage variance: of species data of specieseenvironment relation Summary of Monte Carlo test: F-ratio P-value Species-environment correlations Correlations (r): SW ST pH EC SOC TN Coefficients (c): SW ST pH EC SOC TN
1
2 0.73
72.6 91.2
0.04
23.80 0.02 0.92
76.8 96.5 For all axes: 5.85 0.01 0.81
0.24 0.15 0.33 0.10 0.84 0.52
0.09 0.29 0.61 0.56 0.23 0.30
0.26 0.16 0.36 0.11 0.91 0.57
0.11 0.36 0.76 0.69 0.28 0.37
facilitating vegetation recovery for controlling desertification processes [35,36,38]. The results from this study also support this point (Table 1). Shrubs reduced soil temperature and pH, and enhanced soil water content, electrical conductivity and total organic carbon and nitrogen in shifting sand lands. Meanwhile, two shrub species with significantly different canopy size and height (Fig. 2) showed varying impacts on the microhabitats under them (Table 1), which may be related to significant differences in their contribution to “fertile islands” in desert ecosystems [15,35,38]. A key question was whether the studied shrubs had an impact on the soil macro-faunal community due to their influence on environmental conditions in shifting sand lands.
Table 3 Correlation coefficients between macro-faunal community and environmental parameters. *Correlation was significant at 0.05 level (2-tailed). **Correlation was significant at 0.01 level (2-tailed). ***Correlation was significant at 0.001 level (2tailed) (red in the Table). SW soil water content, ST soil temperature, EC soil electrical conductivity, SOC soil organic carbon, TN soil total nitrogen.
SW ST pH EC SOC TN
Abundance
Group richness
Shannon index
0.3711 0.0955 0.1799 0.1966 0.9102*** 0.5931*
0.4341 0.3127 0.2030 0.0391 0.6622** 0.3390
0.4204 0.6404** 0.2308 0.2149 0.3421 0.0541
320
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Table 4 Correlation coefficients between single macro-faunal groups and environmental parameters. *Correlation was significant at 0.05 level (2-tailed). **Correlation was significant at 0.01 level (2-tailed). ***Correlation was significant at 0.001 level (2-tailed) (red in the Table). Codes as in Fig. 4. Code
Faunal group
SW
ST
pH
EC
SOC
TN
3 4 5 9 12 14 15 17 18 19 20 22 23 25 26 30 32 33 34
Salticidae Lycosidae Philodromidae Alydidae Pentatomidae Cicadellidae Larval Myrmeleontidae Adult Carabidae Coccinellidae Adult Elateridae Leach Adult Aphodiidae Leach Curculionidae Adult Tenebrionidae Larval Carabidae Larval Staphylinidae Larval Aphodiidae Leach Larval Melolonthidae Larval Tenebrionidae Larval Aslidae
0.7695 0.7695 0.7695 0.1049 0.7695 0.2157 0.7823* 0.6359* 0.2680 0.2680 0.2680 0.1049 0.2294 0.2680 0.1049 0.2680 0.2682 0.1397 0.2168
0.4714 0.4714 0.4714 0.9428* 0.4714 0.3933 0.7082* 0.2911 0.0000 0.0000 0.0000 0.9428* 0.1538 0.0000 0.9428* 0.0000 0.2324 0.1865 0.0148
0.8893 0.8893 0.8893 0.3580 0.8893 0.3700 0.1777 0.0946 0.8673 0.8673 0.8673 0.3580 0.3297 0.8673 0.3580 0.8673 0.0417 0.6019* 0.1933
0.4947 0.4947 0.4947 0.6755 0.4947 0.1678 0.4613 0.6241* 0.9438* 0.9438* 0.9438* 0.6755 0.1037 0.9438* 0.6755 0.9438* 0.2454 0.4534 0.0030
0.9416* 0.9416* 0.9416* 0.1729 0.9416* 0.8579** 0.0714 0.5317 0.0709 0.0709 0.0709 0.1729 0.8395*** 0.0709 0.1729 0.0709 0.6802** 0.0612 0.8556***
0.7247 0.7247 0.7247 0.7725 0.7247 0.6601 0.0182 0.4686 0.7565 0.7565 0.7565 0.7725 0.5332* 0.7565 0.7725 0.7565 0.4998* 0.1718 0.3693
Focusing on the structure of the macro-faunal community, there was a significant increase in abundance, richness, and diversity of the macro-faunal community from shrub-free to locations under the shrubs (Fig. 3). This indicated that significant improvement of the soil microhabitats could provide suitable environmental conditions in addition to enough resources for soil fauna to dwell under shrubs [4,17,25]. Results are in accord with previous studies on microbial and nematode and macro-faunal communities of shrub patches [4,19,24] in arid regions, where there are the best conditions for soil bacterial, nematode and macro-faunal communities. Pearson correlation analysis demonstrated that soil temperature, total organic carbon and nitrogen were the important factors controlling soil macro-faunal communities (Table 3). RDA is a multivariate approach that is useful for revealing largescale trends [9,11,14]. The RDA relates each faunal group to linear combinations of observed environmental variables in order to reveal which environmental factors are correlated with changes in soil macro-faunal community. Results from redundancy analysis revealed that a large percentage (72.6%, first axis) of the variation in soil macro-faunal communities was attributable to one factor, soil organic carbon. The effects of soil pH and EC were very small in comparison, reinforcing the view that soil organic carbon content affects macro-faunal community structure far more. This showed that there were facilitative effects of shrubs in shifting sand on soil macro-faunal community [3,4,20] through their influences on the soil conditions, e.g. soil organic carbon [28,35,36], thus further beneficial for desert land restoration in Horqin Sand Land of Inner Mongolia, Northern China. RDA analysis was also used to find individual faunal groups responding to environmental variables due to their strong adaptation to, and selection of, specific habitats [13,22]. This approach could be used to identify faunal groups with particular abiotic conditions [12,13]. For example, if a specific fauna responds to a change in moisture conditions, this fauna may be used as a marker for moisture changes [7], irrespective of the cause of such changes. Where there are high numbers of Carabidae under shrubs with high soil water content. Namely, Carabidae tends to live in moist soil and can be regarded as a marker for moist conditions. Several other faunal groups have also been identified to be responsive to different physico-chemical properties across several sites (Fig. 4; Table 4). Curculionidae and Staphylinidae larvae tend to live in the soil under shrubs with lower temperature in comparison with the open spaces. Tenebrionidae larvae prefer
a lower soil alkalinity, e.g. under S. gordejevii. Melolonthidae larvae and Aslidae larvae may be regarded as markers for organic carbon changes, e.g. under C. microphylla with high soil organic carbon content. But a more extensive assessment is needed across several soil environments before such faunal markers can be used as indicators, due to comprehensive effects of various soil environments on soil faunal abundances [26,29]. Nevertheless, in the present study the different shrub species showed considerable differences in macro-faunal abundances, group richness and diversity (Fig. 3). Higher abundance and group richness but lower diversity of soil faunal community was found under C. microphylla than under S. gordejevii. This might be related to the shrubs’ characteristics [16,23] and the shelters they provide, in addition to soil conditions [20,21] mediated by the different shrub species. In our system, differences in soil nutrients among shrub species appeared as main factors explaining the observed differences in macro-invertebrate abundance and group richness [3]. High numbers of Cicadellidae, Teneberionidae, Melolonthidae larvae, and Araneae appeared to be related to high soil nutrient content (Table 4), resulting in significantly higher faunal abundance under the legume C. microphylla than under S. gordejevii (Fig. 3a and b). The lower temperature under S. gordejevii (Table 1), in addition to its greater canopy size (Fig. 2), could explain the higher diversity, together with greater numbers of Alydidae, Staphylinidae larvae and Staphylinidae [3,12]. The size of shrubs was not only correlated with the abundance of available resources, thus affecting the individual shrub’s carrying capacity [16], but also was related to the chance of attracting an active immigrant, likely due to the fact that the shrub “island” was more easily detected [23]. The shrub “island” can send much information of living conditions (soil temperature, water content and nutrients, etc.) to the soil fauna, beneficial for them to detect a suitable soil environment to inhabit in the shifting sand lands. Therefore, the higher diversity of soil macro-faunal community under S. gordejevii., was probably a consequence of what Gilpin and Diamond [8] described as the “target area effect” [23]. 5. Conclusions Shrubs in shifting sand not only mediated soil microhabitats, but also had a facilitative impact on the soil macro-faunal community in Horqin Sand Land of Northern China. Shrubs significantly raised the abundance, group richness and diversity of
R. Liu et al. / European Journal of Soil Biology 47 (2011) 316e321
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