Catena 159 (2017) 1–8
Contents lists available at ScienceDirect
Catena
journal homepage: www.elsevier.com/locate/catena
Spatial relationship between soil macrofauna biodiversity and trees in
Zagros forests, Iran
MARK
Shaieste Gholami⁎, Bahareh Sheikhmohamadi, Ehsan Sayad
Natural Resources Department, Faculty of Agriculture, Razi University, Kermanshah, Iran
A R T I C L E I N F O
A B S T R A C T
Keywords:
Soil biodiversity
Spatial ecology
Trees
Cross -variogram
Microhabitat
One major factor in determining the spatial distribution pattern of soil macrofauna diversity in forest ecosystems
is the close relationship with tree species. Information about the spatial relationship between soil macrofauna
and tree diversity is scarce, especially at regional scales. To assess soil macrofauna distribution and evaluate the
spatial relationships between macrofauna diversity and tree cover properties at the regional scale, a study was
conducted in the Zagros forests of Iran. To investigate properties of tree cover, 83 plots (100 m2) in a
100 m × 100 m sampling grid were established. In each plot, soil macrofauna were extracted from
50 cm × 50 cm × 10 cm soil monoliths located in the center of the plots. We classified soil macrofauna into two
categories as total macrofauna (earthworm plus other taxa) and only earthworms. Data were analyzed using
geostatistics (variograms and cross-variograms) in order to describe the spatial dependence of macrofauna and
their relation to tree cover properties. Macrofauna abundance and diversity were distributed at geostatistical
large range (3110 and 2110 m), whereas, earthworm abundance had spatial autocorrelation at small range
(691 m). Macrofauna abundance and diversity were spatially related to tree cover density, diversity and evenness of tree species in addition to the density of Pyrus syriaca and Crataegus pontica at small ranges. Our results
showed that tree density and diversity could be the key drivers of the spatial patterning of soil macrofauna
diversity in this area. These results might be related to the conditions provided by the tree species in the surrounding environment. According to our results tree species diversity changed within a small distance (149 m)
creating patches that could lead to the heterogeneity of resources and microhabitats, promoting beneficial habitats for soil macrofauna. These microhabitats can provide the suitable conditions (light, moisture and litter
quality) for determining the macrofauna distribution.
1. Introduction
Soil biota is an integral part of a soil ecosystem and influences
biological processes that contribute to the provision of a wide range of
ecosystem services (Barrios, 2007). They participate in carbon and
nutrient cycles, soil structure modification and water filtration, which
are essential to the sustainable functions of the ecosystems (Decaëns
et al., 2006; Barrios, 2007; Dominati et al., 2010). Soil communities are
extremely complex and diverse (Lavelle et al., 2006) with millions of
species and billions of individual organisms (Bardgett and Van der
Putten, 2014), ranging from minute microorganisms to macroinvertebrates like earthworms, ants, and termites (Barrios, 2007;
Bardgett and Van der Putten, 2014).
Soil macrofauna are both actors and indicators of soil services and
functions (Marichal et al., 2014). Invertebrate soil macrofauna, mainly
earthworms, are known to affect the physical structure and function of
soils and modulate the habitat for other species (Lawton and Jones,
⁎
Corresponding author.
E-mail address: shaiestegholami@gmail.com (S. Gholami).
http://dx.doi.org/10.1016/j.catena.2017.07.021
Received 2 March 2017; Received in revised form 26 July 2017; Accepted 31 July 2017
0341-8162/ © 2017 Elsevier B.V. All rights reserved.
1995). Since macrofauna can be assessed by means of relatively simple
methodological approaches, such as hand sorting, soil macrofauna represents a good model for analysis of basic functional aspects of the
biodiversity present in soil. Thus, knowledge about macrofauna may
considerably improve the understanding of ecosystem functioning
(Barrios, 2007). Very few studies have focused on the diversity of soil
fauna in forests (Scheu, 2005; Wardle et al., 2006) despite the immense
diversity existing below-ground and the fundamental role that soil
fauna plays in forests ecosystem processes (Korboulewsky et al., 2016).
Specific among soil organisms, earthworms are recognized ecosystem
engineers (Lavelle et al., 1997). They are a major component of the
decomposer fauna in many forest ecosystems. Through soil bioturbation
(e.g. burrowing, casting, and mixing litter), they influence the physical
and chemical characteristics of soil. This has important consequences in
terms of plant productivity and plant community structure
(Korboulewsky et al., 2016).
Prerequisite of any estimation of functional effects of macrofauna is
Catena 159 (2017) 1–8
S. Gholami et al.
forests, Iran; and 2) to analyze the spatial relationships between macrofauna and tree cover properties in this area.
an assessment of spatial distribution patterns of these organisms
(Gholami et al., 2016). However, little information exists on spatial
distribution of soil biodiversity, and this makes it difficult to construct
appropriate models to explain the structure of soil communities (Ettema
and Wardel, 2002; Barrios, 2007; Sereda et al., 2012; Bardgett and Van
der Putten, 2014). On the other hand, one of the major challenges in
ecology is to determine how soil biodiversity is distributed across different scales (Bardgett and Van der Putten, 2014). At field scales, the
spatial variability of macrofauna has been the subject of several publications (Poier and Richter, 1992; Rossi et al.,1997; Cannavacciuolo
et al., 1998; Nuutinen et al., 1998; Decaëns and Rossi, 2001; Jimenez
et al., 2006; Joschko et al., 2006). From a number of field scale studies,
the spatial distribution pattern of soil macrofauna appears to be at the
regional scale (Rossi et al., 1997; Nuutinen et al., 1998). However, at
regional scales, knowledge about the macrofauna distribution is limited
making it a major challenge for soil biodiversity researchers (Ettema
and Wardel, 2002; Joschko et al., 2006). Such information is especially
needed for better understanding the ecology of macrofauna, for estimating macrofauna activity and for designing proper management
schemes at this dimension (Adams et al., 2006; Joschko et al., 2009;
Mueller et al., 2015).
Spatial distribution of soil biodiversity is controlled by a hierarchy
of environmental agents acting at different spatial scales (Jimenez
et al., 2001; Tsukamoto and Sabang, 2005; Decaëns, 2010; Bardgett and
Van der Putten, 2014; Korboulewsky et al., 2016). Little is known about
the factors that impact on spatial distribution patterns of soil macrofauna at different scales (Jimenez et al., 2001). Often, at the local scale,
spatial variability of soil organisms is related to variations in the physical and chemical properties of soil and the identity of vegetation
(Bardgett and Van der Putten, 2014). At greater scales, landscape dynamics and composition (Marichal et al., 2014), individual plant species, vegetation composition, plant species diversity and litter types
(Wardle, 2006) impact soil biodiversity.
Spatial patterns of soil biota in forest ecosystem often reflect the
domain of the effect and positioning of tree species (Ettema and Wardle,
2002). In mixed forests, litter quality differences among tree species can
lead to patchy distributions of soil organisms (Lavelle et al., 1997;
Saetre and Baath, 2000; Gonglanski et al., 2008; Schwarz et al., 2015;
Mueller et al., 2015). In semi-arid ecosystems, the spatial distribution
pattern of soil animals is related to isolated areas of plants that create
patches of fertility, which are beneficial for soil organisms (DelgadoBaquerizo et al., 2013). However, for forest ecosystems, it is known that
differences among tree species might be key drivers of the spatial patterning of soil biodiversity (Ettema and Wardle, 2002; Farska et al.,
2014) through both direct (litter quality and quantity) (Lavelle et al.,
1997; Sayad et al., 2012; Schwarz et al., 2015) and indirect effects
(microhabitats and environmental factors such as pH, radiation and soil
humidity) (Korboulewsky et al., 2016). Several studies indicated the
positive effect of tree diversity on soil animal biodiversity (Spehn et al.,
2000; Cesarz et al., 2007; Jacob et al., 2009). Indeed, plants and the
biodiversity that occurs beneath are strongly linked through a variety of
both direct and indirect interactions (Kardol and Wardle, 2010; Sylvain
and wall, 2011). Diversity of trees promotes the emergence of different
microhabitats that can shelter different species of soil biota (Lavelle
et al., 1997; Schwarz et al., 2015; Cavard et al., 2011).
Although, soil spatial analyses have gained increased recognition
during recent years (Rossi et al., 1997; Nuutinen et al., 1998; Ettema
and Wardle, 2002; Joschko et al., 2006, 2009; Mathieu et al., 2009), we
are not aware of any research about spatial relationships between soil
macrofauna and tree diversity. The first step for better understanding
ecological principle about the effects of tree cover properties on soil
animal communities (Ettema and Wardle, 2002) may be the spatial
analysis of relationships between macrofauna and tree cover properties.
The objectives of this study are: 1) to investigate the spatial distribution pattern of soil macrofauna community (abundance, evenness,
taxonomic richness and diversity) at the regional scale in Zagros Oak
2. Materials and methods
2.1. Study area
The study was carried out in Gahvareh forests (in Zagros forest region), western Iran (34° 26′ 30′′ - 34°27′ 00′′ N and 46o17′00′′46°17′39′ ′E). This area has a rugged topography with different slopes
(Henareh Khalyani and Mayer, 2013). The climate is semi-arid, with
precipitation concentrated in mid-autumn and winter and hot, dry
summers. Mean annual rainfall is 440 mm, and mean annual temperature is 11 °C. Forests in this area are characterized by Quercus
brantii Lindl, Pyrus syriaca Boiss, Crataegus pontica C·Koch and Ceracus
microcarpa (C.A.M) Boiss. Quercus brantii (covering more than 50% of
the Zagros forest region) is the most important tree species of the study
area (Sabeti, 2002).
2.2. Tree cover properties
For capturing field information about tree diversity and spatial
distribution, 83 plots (100 m2) in a 100 m × 100 m systematic sampling grid were established in Gahvareh forests. Tree species count in
each plot was done (Cesarz et al., 2007). In each plot, diversity of tree
cover (tree species diversity, tree species evenness and tree species
richness), density of tree cover (tree density), density of each tree
species (tree species density), and canopy cover were determined.
2.3. Soil macrofauna
Soil macrofauna were defined as invertebrates visible with the
naked eye (macroscopic organisms) (Warren and Zou, 2002). In this
study, both geobionts (large soil invertebrates that permanently inhabit
the soil), and geophiles (organisms that live in the soil only for some
phase of their life) (Maggenti et al., 2005) were assessed. In spring
(April 2016) when moisture and temperature are suitable and all
macrofauna species are active and representation of community structure is best (Spehn et al., 2000; Cesarz et al., 2007), soil macrofauna
were extracted by hand-sorting the samples in the field, from
50 cm × 50 cm × 10 cm soil monoliths located in the center of the plot
(83 plots). The organisms were placed in plastic bags and transported to
the lab for taxa determination. The number of individuals for each
sample was determined on the day of collection. We classified soil
macrofauna into two categories as total macrofauna (earthworm plus
other taxa) and earthworm (hereafter, macrofauna and earthworm,
respectively).
2.4. Diversity indices
In each plot, abundance (number of individuals), evenness (Sheldon
index), richness (Menhinick index), and diversity (Shannon H′ index) of
soil macrofauna (Gonglanski et al., 2005, 2008) and tree cover (Cesarz
et al., 2007) were calculated by using PAST version 1.39 (Palaentological Statistics Software Package, 2006).
2.5. Statistical approaches
2.5.1. Non-spatial statistics
The distribution of variables was analyzed with Q–Q plots (Timmer,
1998). All variables (except for macrofauna richness and diversity)
were log-normally distributed; therefore a log-transformation (log (x
+ 1)) was carried out before further analysis. For the initial analysis of
the relationship between soil macrofauna and tree cover, we calculated
the correlation among macrofauna and tree cover properties using the
rank correlation coefficient (rs) (SPSS 17). All results reported in the
2
Catena 159 (2017) 1–8
S. Gholami et al.
text are mean ( ± standard error).
Table 1
Summary statistics of soil macrofauna diversity indices and earthworm abundance.
2.5.2. Geostatistics
We applied geostatistical methods (semi-variogram and cross semivariogram) to investigate 1) the spatial pattern of soil macrofauna
(Rossi et al., 1995; Ettema and Wardle, 2002; Gonglanski et al., 2008;
Mathieu et al., 2009; Joschko et al., 2009) and 2) the spatial relationships with tree density and diversity. Geostatistics is a branch of statistics that explicitly incorporates the concept of spatial correlation in
the analysis of spatial data (Goovaerts, 1999).
The basic tool of geostatistics is the (semi) variogram. The semivariance (γ(h)γ(h)) graphically describes the spatial variability of a
variable by plotting semivariance as a function of lag distance h (Eq
(1)):
γ (h) = 1 2N(h)
N(h)
∑i=1
[Z(Xi) − Z(Xi + h)]2
[Zu (Xi) − Zu (Xi + h)]. [Z v (Xi)‐Z v (Xi + h)]
i=1
Std. Error
Min
Max
Diversity
Richness
Evenness
Macrofauna abundance (individual/m2)
Earthworm abundance (individual/m2)
1.29
1.40
0.88
39.60
7.68
0.031
0.032
0.008
1.640
0.816
0.50
0.63
0.64
8.0
0.0
1.89
2.33
1.00
80.0
48.0
Table 2
Summary statistics of tree cover properties.
Variable
Mean
Std. error
Min
Max
Tree density (individual/plot)
Tree species diversity
Tree species richness
Tree species evenness
Quercus brantii (individual/plot)
Ceracus mocrocarpa(individual/plot)
Pyrus syriaca (individual/plot)
Crataegus pontica (individual/plot)
Canopy cover (percent/plot)
4.20
0.57
1.02
0.94
2.79
0.44
0.54
0.45
59.13
0.181
0.044
0.036
0.006
0.129
0.062
0.092
0.080
3.938
1
0.00
0.41
0.74
0.00
0
0
0
10.6
9
1.33
1.63
1.00
6.0
2
3
3
96.0
(1)
Density: Number of trees in plot; Evenness: Sheldon index; Richness: Menhinick index;
Diversity: Shannon H′ index. Plot: 100 m2.
3.2. Tree cover properties
The mean of tree cover density was 4.2 ( ± 0.18) individual per
plot. Canopy cover averaged at 59.13 ( ± 3.94) percent in the plot. The
mean values per plot of tree species diversity, evenness, and richness
were 0.57 ( ± 0.044), 0.94 ( ± 0.006), and 1.02 ( ± 0.036), respectively (Table 2).
3.3. Correlation among macrofauna diversity indices and tree cover
properties
Rank correlation analysis among soil macrofauna (abundance,
taxonomic richness, evenness, and diversity) and tree cover properties
showed there existed weak relationships (Table 3). Significant relationships were found between macrofauna diversity and density of
Crataegus pontica trees (positive) and tree species evenness (negative),
macrofauna abundance and tree cover density (positive), density of
Quercus brantii trees (positive) and tree species evenness (negative),
macrofauna richness and density of Crataegus pontica trees (positive),
macrofauna evenness and tree species evenness (negative); and, with
respect to earthworm abundance, weak negative relationships with tree
evenness (Table 3).
N(h)
∑
Mean
Macrofauna: earthworm plus other taxa; Abundance: Number of animals; Evenness:
Sheldon index; Richness: Menhinick index; Diversity: Shannon H′ index.
where z is the measured variable, xi is the coordinate of one sample,
xi + h is the coordinate of another sample at distance (lag) h and N (h)
is the number of pairs of samples z (xi) and z (xi + h) (Webster and
Oliver, 2009).
To describe spatial autocorrelation of a variable quantitatively, a
(theoretical) variogram model is fitted to the empirical variogram to
obtain the model parameters nugget, sill and range. Theoretically,
semivariance increases with increasing separation distance to a moreor-less constant value called the sill. The sill represents the maximum
sample variance, at a separation distance that the observations no
longer are spatially dependent. The lag distance at which the semivariance approaches the sill is called the range. The range represents
the maximum distance of spatial autocorrelation. At distances smaller
than the range we can observe spatial autocorrelation while observations with distances larger than the range are regarded as spatially independent (Atkinson and Tate, 2000; Gringarten and Deutsch, 2001;
Webster and Oliver, 2009). The semivariance at lag zero is called
nugget variance. The nugget represents random variability that is undetectable at the scale of sampling and is due to variation occurring at
scales below the minimum sampling distance.
Cross variogram analysis is a spatial analysis technique in which
two variables are used with the aim to examine the spatial co-structure
occurring between them. Variables that are well cross-correlated show
similar patterns of spatial variability (Goovaerts, 1999). Thus cross
variograms are adequate tools to study inter- relationship between
variables (Rossi et al., 1995). Empirical cross-variograms are plots of
the cross-semivariance against the lag distance. The cross-semivariance
for two variables is calculated as follows (Eq. (2)):
γ (h) = 1 2N(h)
Variable
(2)
where zu is the primary variable, zv is the covariate, xi is the coordinate
of the sample, N (h) is the number of pairs of samples z (xi) and z
(xi + h), separated by the separation distance (lag) h (Webster and
Oliver, 2009). Geostatistical analysis was performed using the software
GS + version 5.1.1 (Gamma design software, 1995).
3.4. Spatial pattern of macrofauna diversity indices and tree cover
properties
3. Results
3.4.1. Semi-variogram
The variograms of macrofauna abundance and diversity revealed
the presence of spatial autocorrelation. For macrofauna diversity and
abundance, spatial autocorrelation occurred at the large distances of 2
to 3 km; whereas, earthworm abundance showed for the variogram
model a spatial autocorrelation at 691 m. For all these variables,
bounded semi-variograms were found. The variograms of macrofauna
abundance and diversity were exponential while, earthworm abundance had the spherical model. The nugget-to-sill ratio showed a
moderate spatial dependence for earthworm and macrofauna (Table 4).
Tree diversity indices, tree species density and canopy cover were
spatially structured as well with ranges of several hundred meters and
3.1. Soil macrofauna
The soil macrofauna comprised the following groups: ants (25%, 9.9
( ± 0.92) individual/m2), beetles (Coleoptera, 12.5%, 5.0 ( ± 0.48)
individual/m2), millipedes (22.7%, 9.0 ( ± 0.88) individual/m2),
earthworms (19.2%, 7.7 ( ± 0.82) individual/m2), dipterans (4.1%, 1.6
( ± 0.32) individual/m2), spiders (5.9%, 2.3 ( ± 0.36) individual/m2),
isopods (3.5%, 1.4 ( ± 0.28) individual/m2), and insect larvae (6.2%,
2.5 ( ± 0.44) individual/m2). Mean macrofauna abundance was 39.6
( ± 1.64), individual/m2 (Table 1).
3
Catena 159 (2017) 1–8
S. Gholami et al.
Table 3
Rank correlation coefficients (rs) between macrofauna and tree cover properties.
Variable
Tree cover
density
Tree species
diversity
Tree species
richness
Tree species
evenness
Quercus brantii
density
Ceracus microcarpa
density
Pyrus syriaca
density
Crataegus pontica
density
Canopy
cover
Macrofauna
diversity
Macrofauna
evenness
Macrofauna richness
Macrofauna
abundance
Earthworm
abundance
0.208
0.214
0.087
− 0.247
0.108
− 0.111
0.13
0.278
0.171
− 0.255
− 0.186
− 0.133
0.119
− 0.134
− 0.163
− 0.131
−0.111
− 0.119
0.108
0.259
0.131
0.141
0.081
0.036
− 0.044
− 0.331
− 0.023
0.270
− 0.087
0.11
0.09
0.091
0.239
0.041
0.119
0.193
0.177
0.058
− 0.004
− 0.281
0.209
− 0.061
0.131
0.043
0.177
Macrofauna: earthworm plus other taxa; Evenness: Sheldon index; Richness: Menhinick index; Diversity: Shannon H′ index.
more. The parameters of the theoretical models fitted to the experimental variograms are given in Table 5. All variograms showed exponential model (except for variogram of tree cover density which had
the spherical model). Tree species richness and evenness had spatial
autocorrelation at 2110 m (similar to mucrofauna abundance and diversity) whereas, tree species diversity had spatial dependency at
149 m. Our results showed that density of tree species (Pyrus syriaca,
Crataegus pontica and Ceracus microcarpa) and canopy cover were autocorrelated at greater distances, 2110 to 3110 m, similar to the distribution ranges of macrofauna abundance and diversity. Parameters
obtained from the constructed variograms and the fitted models suggested moderate spatial dependence with small positive nugget values
(Table 5). Empirical semi-variograms and the fitted models of macrofauna abundance, macrofauna diversity, earthworm abundance, tree
cover density, tree species diversity and canopy cover are presented in
Fig. 1.
4. Discussion
Our results from geostatistical analyses indicated that macrofauna
abundance and diversity were spatially structured at large distances.
Similar to our observations, Gholami et al. (2016) found large autocorrelation distances of 500 m to 2 km for macrofauna abundance and
diversity in a riparian forest. This spatial structuring may be the outcome of large-scale heterogeneity, reflecting large-scale soil texture and
vegetative cover gradients. In forests, differences existing among plant
species might be key drivers of the spatial patterning of soil organisms
(Ettema and Wardle, 2002).
Based on our findings, earthworm abundance was spatially autocorrelated at the smaller range (distance 690 m) in contrast to macrofauna abundance at greater range (distance 3110 m). We could relate
the smaller distance result for earthworm distribution to individual
trees, tree characteristics such as species identity and to soil moisture
which indicated the importance of both food quality and habitat for
earthworm (Cesarz et al., 2007). Both biotic interactions and habitat
heterogeneity may lead to small-scale aggregations of soil macrofauna
(Birkhofer et al., 2010). On the other hand, with decreasing zones of
plant influence, patch sizes of soil organisms especially earthworms
decline correspondingly (Ettema and Wardle, 2002). For example, at
the forest site studied, we found a tree influence range of 690 m for
earthworm abundance, in contrast to Joschko et al. (2009) who reported for an agroecosystem, a range of 276 m for earthworm abundance and crop plants. This difference suggests that different scales of
influence exist for individual arable plants compared to trees (Ettema
and Wardle, 2002).
Although spatial distribution of tree species diversity appears to be
aggregated, tree cover density, tree species richness and evenness
showed large and smoothly continuous, probably as a function of large
scale gradients of soil texture, soil carbon and topography (Ettema and
Wardle, 2002). In forest communities, abiotic factors such as topography, moisture, and light conditions typically change at relatively
large scales, species-specific preferences to such heterogeneous environments and their related regeneration manner affect the organization of communities (Torimaru et al., 2013). The small scale pattern
(149 m) of tree diversity distribution found in our study, could be the
3.4.2. Cross-variogram
The spatial similarity was evaluated by cross-variograms for pairs of
soil macrofauna (macrofauna diversity indices and earthworm abundance) and tree cover properties (density of tree cover, tree diversity
indices, density of tree species and canopy cover). Referring to the
cross-variogram, soil macrofauna diversity was spatially related to tree
species diversity (range = 395 m), tree cover density (range = 431 m),
tree species evenness (range = 454 m, negatively) and density of Pyrus
syriaca (range = 368 m). Macrofauna abundance was spatially connected to tree cover density and tree species diversity, canopy cover
and density of Crataegus pontica at the distance of about 400 m, and to
tree species evenness at the small distance of 174 m (negatively)
(Table 6).
Tree species evenness at the large distance of 1426 m (negatively)
and canopy cover at the small distance of 439 m were spatially related
to earthworm abundance. The parameter values for the nugget, sill, and
range of the fitted models are shown in Table 6. The poor fit of the
models for the rest of the cross-variograms would suggest there exists
no spatial connection for the other variables at the sampling scale of
this study.
Table 4
Parameters of the fitted models to the experimental variograms of soil macrofauna.
Variable
Model
Nugget(Co)
Sill(Co + C)
Nugget to sill ratio
Range(m)
R2
R2 of cross-validation
Macrofauna diversity
Macrofuna abundance
Earthworms abundance
Exponential
Exponential
Spherical
0.075
0.159
0.618
0.150
0.319
1.701
0.50
0.49
0.36
2110
3110
691
0.17
0.13
0.89
0.03
0.06
0.04
Co: nugget variance: the variogram values at lag distance zero. Sill: the variance at which the variogram model reaches a maximum; C: structural variance; Range: the lag distance at
which the bounded variogram reaches the sill; Nugget to sill ratio: the indicator of the strength of the spatial autocorrelation, the variable is considered to have a strong spatial
dependence if the ratio is less than 25%, and has a moderate spatial dependence if the ratio is between 25% and 75%; otherwise, the variable has a weak spatial dependence. R2: goodness
of fit of theorical model fitted to the experimental variogram; R2 of cross-validation: regression coefficient. Macrofauna: earthworm plus other taxa; Abundance: Number of animals;
Diversity: Shannon H′ index.
4
Catena 159 (2017) 1–8
S. Gholami et al.
Table 5
Parameters of the fitted models to the experimental variograms of tree cover properties.
Variable
Model
Nugget(Co)
Sill(Co + C)
Nugget to sill ratio
Range(m)
R2
R2 of cross-validation
Tree cover density
Tree species richness
Tree species evenness
Tree species diversity
Ceracus microcarpa density
Pyrus syriaca density
Crataegus pontica density
Canopy cover
Spherical
Exponential
Exponential
Exponential
Exponential
Exponential
Exponential
Exponential
0.14
0.025
0.008
0.034
0.116
0.176
0.151
1097
0.42
0.05
0.001
0.090
0.234
0.354
0.304
2195
0.33
0.50
8.00
0.37
0.49
0.49
0.49
49.97
3110
2110
2110
149
3110
2110
3110
2110
0.64
0.13
0.54
0.96
0.50
0.58
0.50
0.15
0.15
0.08
0.12
0.15
0.02
0.02
0.07
0.04
Co: nugget variance: the variogram values at lag distance zero. Sill: the variance at which the variogram model reaches a maximum; C: structural variance; Range: the lag distance at
which the bounded variogram reaches the sill; Nugget to sill ratio: the indicator of the strength of the spatial autocorrelation, the variable is considered to have a strong spatial
dependence if the ratio is less than 25%, and has a moderate spatial dependence if the ratio is between 25% and 75%; otherwise, the variable has a weak spatial dependence. R2: goodness
of fit of theorical model fitted to the experimental variogram; R2 of cross-validation: regression coefficient. Evenness: Sheldon index; Richness: Menhinick index; Diversity: Shannon H′
index.
Fig. 1. Semi-variograms (dots) and the fitted models (lines) of a: Macrofauna abundance, b: Macrofauna diversity, c: earthworm abundance, d: tree diversity, e: tree density, f: canopy
cover, Macrofauna: earthworm plus other taxa; Abundance: Number of individuals; Evenness: Sheldon index; Richness: Menhinick index; Diversity: Shannon H′ index.
Table 6
Parameters of cross-semi-variograms for pairs of soil macrofauna and tree cover properties.
Variable
Model
Nugget(Co)
Sill(Co + C)
Nugget to sill ratio
Range(m)
R2
R2 of cross-validation
Macrofauna diversity- tree species diversity
Macrofauna diversity - tree species evenness
Macrofauna diversity – tree cover density
Macrofauna diversity - Pyrus syriaca
Macrofauna abund- tree cover density
Macrofauna abund - tree species evenness
Macrofauna abund - tree species diversity
Macrofauna abund- Crataegus pontica
Macrofauna abund - canopy cover
earthworm abund – tree species evenness
earthworm abund - canopy cover
Spherical
Spherical
Spherical
Spherical
Spherical
Spherical
Spherical
Spherical
Spherical
Spherical
Spherical
0.000
−0.000
0.001
0.000
0.017
−0.001
0.000
0.000
0.010
−0.003
1.720
0.016
− 0.002
0.050
0.017
0.080
− 0.005
0.030
0.027
3.580
− 0.01
9.290
0.000
0.000
0.020
0.000
0.212
0.200
0.000
0.000
0.003
0.300
0.185
395
454
431
368
459
174
430
459
480
1426
439
0.93
0.95
0.96
0.84
0.80
0.94
0.98
0.80
0.88
0.97
0.95
0.07
0.07
0.07
0.07
0.04
0.04
0.04
0.04
0.04
0.03
0.03
Co: nugget variance; C: structural variance; R2: goodness of fitness of theorical model fitted to the experimental variogram; R2 of cross-validation: regression coefficient. Macrofauna:
earthworm plus other taxa; abund = Abundance: Number of individuals; Evenness: Sheldon index; Diversity: Shannon H′ index. Pyrus syriaca, Crataegus pontica: density of Pyrus syriaca
and Crataegus pontica trees.
5
Catena 159 (2017) 1–8
S. Gholami et al.
result of the plant–plant interactions that generally occur at a finer
spatial scale, e.g., the extent to which individual crowns and/or root
systems develop (Torimaru et al., 2013).
We found that macrofauna diversity formed a large-scale distribution (2110 m) that closely matched that of tree species richness and
evenness, and density of Pyrus syriaca trees (2110 m). On the other
hand, macrofauna abundance spread at a larger scale (3110 m) that is
similar to the distribution ranges of tree cover density, Ceracus microcarpa density and Crataegus pontica density (3110 m). Rank correlation
analysis also showed the correlations between macrofauna diversity
and tree species evenness and macrofauna abundance and tree cover
density. These results suggest that tree species diversity and identity
might operate as determinants of macrofauna abundance and diversity
distribution. Spatial variability of trees is known to increase the diversity of macrofauna communities through modification in the structure of the habitat, determining the diversity of microhabitats and life
conditions for soil macrofauna (Decaëns et al., 1998).
Although previous studies (Saetre and Baath, 2000; Ettema and
Wardel, 2002; Wardle et al., 2006; Loranger-Merciris et al., 2007;
Gongalski et al. 2008; Cavard et al., 2011; Schwarz et al., 2015; Mueller
et al., 2015; Korboulewsky et al., 2016) pointed out the importance of
plant diversity on soil biota distribution, neither of them tested the
spatial relationship between macrofauna and tree properties. In our
study, according to the cross-variograms spatial relationships were
detected between macrofauna and tree properties. Because macrofauna
diversity and abundance were spatially related to density of tree cover,
tree species diversity, and evenness (at the distances of about 400 m),
we suspected that tree diversity and density were reflecting the distribution of macrofauna abundance and diversity. These spatial relationships indicate that tree species diversity, tree evenness, and tree
cover density might affect macrofauna diversity distribution at the
small distances. Since tree diversity may enhance resource and habitat,
it may increase macrofauna diversity by the mechanism of creating
small-scale microhabitat diversity (Hooper et al., 2000; Cesarz et al.,
2007). Microhabitat features that include both trees and the soil beneath can provide an attractive microenvironment for soil macrofauna
(Hooper et al., 2000; Pospiech and Skalski, 2006; Cesarz et al., 2007;
Mathieu et al., 2004; Mathieu et al., 2009). Indeed, tree species create a
gradient of specific physical and chemical properties, which are beneficial for different soil macrofauna (Korboulewsky et al., 2016).
In our case, the trees may be affecting the macrofauna in the underlying soil by improving the local soil microclimatic conditions, such
as modifying soil temperature and soil humidity, or by contributing
nutrients that have leached from the wood during rainfall. Zagros forests are open canopy, sparse oak forests which have experienced a long
history of disturbances and habitat fragmentation (Henareh Khalyani
and Mayer, 2013). This region is characterized by hot and dry summers
and low annual precipitation that influence soil macrofauna, by limiting resources and affecting available soil moisture. Thus, soil moisture
changes and reduction of soil temperature (De Smedt et al., 2016) due
to the presence of trees is likely to have important effects on soil
macrofauna in this area. These findings are in accordance with Mathieu
et al.'s (2009) suggestion that soil macrofauna is limited by high temperatures and that temperature is a strong determinant of many soil
macrofauna ecological niches.
Based on our results, tree species diversity changes at the small
range (149 m) could promote heterogeneity of resources and microhabitats for macrofauna, and therefore increase their diversity. Spehn
et al. (2000) showed also that soil macrofauna abundance increased
with tree diversity. Mathieu et al. (2009) highlighted the positive effect
of increasing vegetation cover on soil macrofauna biodiversity. In
contrast to these observations, there are several other studies which
have found only weak linkages between plant species diversity and
macrofauna diversity (Hooper et al., 2000; Wardle, 2006; Wardle et al.,
2006). Aubert et al. (2003) found no evidence of any impact of tree
diversity effect on macrofaunal biodiversity. Cavard et al. (2011) and
Schwarz et al. (2015) found no relationship between earthworm and
tree richness. These different results may be the consequence of unique
differences in studied ecosystems. The effect of tree species on the
spatial distribution of soil biota may prevail in the situations, like our
study area, in which trees are spaced at a sufficient distance to have a
dominating influence on the resources that they accumulate under them
(Aubert et al., 2003). Hence, macrofauna increase observed with tree
diversity and density in our case, suggests the trees can create microhabitats that provide suitable conditions (light, moisture, litter quality)
for soil biota.
We also found that macrofauna abundance spatially related to
Crataegus pontica density and macrofauna diversity to Pyrus syriaca
density at the small distances of 459 m and 368 m, respectively. These
results could highlight the importance of tree species identity, as an
agent for specific litter attributes that affect the spatial distribution of
soil macrofauna (Korboulewsky et al., 2016). The identity of tree species is often a main determinant of soil organism distribution because
tree species differ enormously in the quality of litter produced (Wardle
et al., 2006).
Our results indicated that earthworm abundance was spatially related to tree species evenness at a large distance (1429 m). The cross
semi-variance values of macrofauna abundance, diversity and earthworm abundance and tree evenness and the slope of the cross-variogram were negative, hence indicating a negative spatial co-structure
(Rossi et al., 1995). These results may confirm the positive correlation
between earthworm and tree species diversity that indicate the importance of diverse food qualities and microhabitat conditions for the
decomposer fauna (Cesarz et al., 2007). The spatial relationship between earthworm abundance and canopy cover was observed at distance of 439 m, suggesting variations in microclimatic conditions (soil
temperature and moisture), which might facilitate species coexistence
by creating microhabitats for earthworms (Mueller et al., 2015).
Moreover, the cross semi-variogram of earthworm abundance and
tree species evenness represented a range of influence more than that
for the semi-variogram of each of them, but equal to the range of tree
species evenness. Whereas cross semi-variograms of macrofauna diversity and abundance and tree cover properties showed ranges smaller
than their semi-variograms but relatively, equal to the ranges of tree
species diversity and canopy cover. Thus, making use of cross semivariogram proved the importance of identifying the spatial distribution
pattern of soil macrofauna and earthworm by features of tree species
diversity and canopy cover in this area. The main factor constraining
soil biodiversity is the heterogeneity of resources and habitat that allow
high levels of biodiversity. This heterogeneity is increased by the impact of trees that generate the resources patchiness at a range of spatial
scales (Decaëns, 2010).
5. Conclusion
Our findings showed from geostatistical analyses that spatial distribution of soil macrofauna abundance and diversity occurred at the
larger distances than earthworm abundance. Macrofauna diversity and
abundance were spatially correlated to tree species density, diversity,
and evenness at the small ranges, whereas earthworm distribution had
spatial relationship to the tree species evenness at a larger range. These
results highlight that tree density and diversity are key driver factors for
macrofauna diversity distribution suggesting relationships to microhabitats and complex gradients of soil properties, which trees provide
(Vivanco and Austin, 2008). However to fully understand soil macrofauna distribution in Zagros Oak forests, a careful study of the vegetation cover (e.g. litter quantity and quality) around the macrofauna
samples is still required. We put forward that these types of patterns are
not unique to Zagros forests, but are also likely to occur in many other
systems and need to be accounted for in soil macrofauna biodiversity
studies. Further studies about the soil macrofauna biodiversity in microhabitat types may help reveal factors determining the microhabitat
6
Catena 159 (2017) 1–8
S. Gholami et al.
Jacob, M., Weland, N., Platner, C., Schaefer, M., Leuschner, C., Thomas, F.M., 2009.
Nutrient release from decomposing leaf litter of temperate deciduous forest trees
along a gradient of increasing tree species diversity. Soil Biol. Biochem. 41,
2122–2130.
Jimenez, J.J., Rossi, J.P., Lavelle, P., 2001. Spatial distribution of earthworm in acid-soil
savannas of the eastern plains of Colombia. Appl. Soil Ecol. 17, 267–278.
Jimenez, J.J., Decaëns, T., Rossi, J.P., 2006. Stability of the spatio-temporal distribution
and niche overlap in neotropical earthworm assemblages. Acta Oecol. 30, 299–311.
Joschko, M., Fox, C.A., Lentzsch, P., Kiesel, J., Hierold, W., Kruck, S., Timmer, J., 2006.
Spatial analysis of earthworm biodiversity at the regional scale. Agric. Ecosyst.
Environ. 112, 367–380.
Joschko, M., Gebbers, R., Barkusky, D., Rogasik, J., Hohn, W., Hierold, W., Fox, C.A.,
Timmer, J., 2009. Location-dependency of earthworm response to reduced tillage on
sand soil. Soil Tillage Res. 102, 55–66.
Kardol, P., Wardle, D.A., 2010. How understanding aboveground–belowground linkages
can assist restoration ecology. Trends Ecol. Evol. 25, 670–679.
Korboulewsky, N., Perez, G., Chauvat, M., 2016. How tree diversity affects soil fauna
diversity: a review. Soil Biol. Biochem. 94, 94–106.
Lavelle, P., Bignell, D., Lepage, M., Wolters, V., Roger, P., Ineson, P., Heal, O.W., Dhillion,
S., 1997. Soil function in a changing world: the role of invertebrate ecosystem engineers. Eur. J. Soil Biol. 33, 159–193.
Lavelle, P., Decaëns, T., Aubert, M., Barot, S., Blouin, M., Bureau, F., Margerie, P., Mora,
P., Rossi, J.P., 2006. Soil invertebrates and ecosystem services. Eur. J. Soil Biol. 42,
S3–S15.
Lawton, J.H., Jones, C.G., 1995. Linking species and ecosystems: organisms as ecosystem
engineers. In: Jones, C.G., Lawton, J.H. (Eds.), Linking Species and Ecosystems.
Chapman and Hall, London, UK, pp. 141–150.
Loranger-Merciris, G., Imbert, D., Bernhard-Reversat, F., Ponge, J.F., Lavelle, P., 2007.
Soil fauna abundance and diversity in a secondary semi-evergreen forest in
Guadeloupe (Lesser Antilles): influence of soil type and dominant tree species. Biol.
Fertil. Soils 44, 269–276.
Maggenti, M.A., Maggenti, A.R., Gardner, S.L., 2005. Online Dictionary of Invertebrate
Zoology. http://digitalcommons.unl.edu/onlinedictinvertzoology (web archive link,
10 October 2015) (accessed 10.10.15.).
Marichal, R., Grimaldi, M., Feijoo, M.A., Oszwald, J., Praxedes, C., Ruiz Cobo, D.H.,
Hurtado, M.D.P., Desjardins, T., Lopes da Silva Junior, M., Silva, L.G. Souza Miranda,
Delgado, I., Oliveira, M.N., Brown, G.G., Tselouiko, S., Martins, M.B., Decaëns, T.,
Velasquez, E., Lavelle, P., 2014. Soil macroinvertebrate communities and ecosystem
services in deforested landscapes of Amazonia. Appl. Soil Ecol. 83, 177–185.
Mathieu, J., Rossi, J.P., Grimaldi, M., Mora, P., Lavelle, P., Rouland, C., 2004. A multiscale study of soil macrofauna biodiversity in Amazonian pastures. Biol. Fertil. Soils
40, 300–305.
Mathieu, J., Grimaldi, M., Jouquet, P., Rouland, C., Lavelle, P., Desjardins, T., Rossi, J.P.,
2009. Spatial pattern of grasses influence soil macrofauna biodiversity in Amazonian
pastures. Soil Biol. Biochem. 41, 586–593.
Mueller, K.E., Eisenhauer, N., Reich, P.B., Hobbie, S.E., Chadwick, O.A., Chorover, J.,
Dobies, T., Hale, C.M., Jagodzinski, A.M., Kalucka, I., Kasprowicz, M., KieliszewskaRokicka, B., Modrzynski, J., Rozen, A., Skorupski, M., Sobczyk, L., Stasinska, M.,
Trocha, L.K., Weiner, J., Wierzbicka, A., Oleksyn, J., 2015. Light, earthworms, and
soil resources as predictors of diversity of 10 soil invertebrate groups across monocultures of 14 tree species. Soil Biol. Biochem. 92, 184–198.
Nuutinen, V., Pitkanen, J., Kuusela, E., Widbon, T., Lohilahti, H., 1998. Spatial variation
of an earthworm community related to soil properties and yield in a grass-clover
field. Appl. Soil Ecol. 8, 85–94.
Poier, K.R., Richter, J., 1992. Spatial distribution of earthworms and soil properties in an
arable loess soil. Soil Biol. Biochem. 24, 1601–1608.
Pospiech, N., Skalski, T., 2006. Factors influencing earthworm communities in post-industrial area of Krakow Soda Works. Eur. J. Soil Biol. 42, S278–S283.
Rossi, J.P., Lavelle, P., Tondoh, J.E., 1995. Statistical tool for soil biology X.geostatistical
analysis. Eur. J. Soil Biol. 31, 173–181.
Rossi, J.P., Lavelle, P., Albrecht, A., 1997. Relationships between spatial pattern of the
endogeic earthworm Polypheretima elongata and soil heterogeneity. Soil Biol.
Biochem. 29, 485–488.
Sabeti, H., 2002. Trees and Shrubs of Iran. Yazd University, Yazd.
Saetre, P., Baath, E., 2000. Spatial variation and patterns of soil microbial community
structure in a mixed spruce–birch stand. Soil Biol. Biochem. 32, 909–917.
Sayad, E., Hosseini, S.M., Hosseini, V., Salehe-Shooshtari, M.H., 2012. Soil macrofauna in
relation to soil and leaf litter properties in tree plantations. J. For. Sci. 58, 170–180.
Scheu, S., 2005. Linkages between tree diversity, soil fauna and ecosystem processes. In:
Forest Diversity and Function: Temperate and Boreal Systems. 176. pp. 211–233.
Schwarz, B., Dietrich, C., Cesarz, S., Scherer-Lorenzen, M., Auge, H., Schulz, E.,
Eisenhauer, N., 2015. Non-significant tree diversity but significant identity effects on
earthworm communities in three tree diversity experiments. Eur. J. Soil Biol. 67,
17–26.
Sereda, E., Blick, T., Dorow, W., Wolters, V., Birkhofer, K., 2012. Spatial distribution of
spiders and epedaphic Collembola in an environmentally heterogeneous forest floor
habitat. Pedobiology 55, 241–245.
Spehn, E.M., Joshi, J., Schmid, B., Alphie, J., Komer, C., 2000. Plant diversity effects on
soil heterotrophic activity in experimental grassland ecosystems. Plant Soil 224,
217–230.
Sylvain, Z.A., Wall, D.H., 2011. Linking soil biodiversity and vegetation: implications for
a changing planet. Am. J. Bot. 98, 517–527.
Timmer, J., 1998. Modeling noisy time series: physiological tremor. Int. J. Bifurcation
Chaos 8, 1505–1516.
Torimaru, T., Akada, Sh., Ishida, K., Matsuda, Sh., Narita, M., 2013. Spatial associations
among major tree species in a cool-temperate forest community under heterogeneous
preference of macrofauna.
Acknowledgment
We should thank Razi University (12390) for funding this research.
Two anonymous reviewers are thanked for their constructive critique
on the manuscript.
Appendix A. Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.catena.2017.07.021.
References
Adams, B.J., Bardgett, R.D., Ayres, E., Wall, D.H., Aislabie, J., Bamforth, S., Bargagli, R.,
Cary, C., Cavacini, P., Connell, L., Convey, P., Fell, J.W., Frati, F., Hogg, I.D.,
Newshman, K.K., O'Donnell, A., Russell, N., Seppelt, R.D., Stevens, M.I., 2006.
Diversity and distribution of Victoria land biota. Soil Biol. Biochem. 38, 3003–3018.
Atkinson, P.M., Tate, N.J., 2000. Spatial scale problems and geostatistical solutions: a
review. Prof. Geogr. 52, 607–623.
Aubert, M., Hedde, M., Decaëns, T., Bureau, F., Margerie, P., Alard, D., 2003. Effects of
tree canopy composition on earthworms and other macro-invertebrates in beech
forests of Upper-Normandy (France). Pedobiologia 47, 904–912.
Bardgett, R.D., Van der Putten, W.H., 2014. Belowground biodiversity and ecosystem
functioning. Nature 515, 505–511.
Barrios, E., 2007. Soil biota, ecosystem services and land productivity. Ecol. Econ. 24,
269–285.
Birkhofer, K., Scheu, S., Wiegand, T., 2010. Assessing spatiotemporal predator–prey
patterns in heterogeneous habitats. Basic Appl. Soil Ecol. 11, 486–494.
Cannavacciuolo, M., Bellido, A., Cluzeau, D., Gascuel, C., Trehen, P., 1998. A geostatistical approach to the study of earthworm distribution in grassland. Appl. Soil Ecol. 9,
345–349.
Cavard, X., Macdonald, S.E., Bergeron, Y., Chen, H.Y.H., 2011. Importance of mixedwoods for biodiversity conservation: evidence for understory plants, songbirds, soil
fauna, and ectomycorrhizae in northern forests. Environ. Rev. 19, 142–161.
Cesarz, S., Fahrenholz, N., Migge-Kleian, S., Platner, C., Schaefer, M., 2007. Earthworm
communities in relation to tree diversity in a deciduous forest. Eur. J. Soil Biol. 43,
S61–S67.
De Smedt, P., Wuyts, K., BAETEN, L., De Schrijver, A., Proesmans, W., De Frenne, P.,
Ampoorter, E., Remy, E., Gijbel, M., Hermy, M., Bonte, D., Verheyen, K., 2016.
Complementary distribution patterns of arthropod detritivores (woodlice and millipedes) along forest edge-to-interior gradients. Insect Conserv. Divers. 9, 456–469.
Decaëns, T., 2010. Macroecological patterns in soil communities. Glob. Ecol. Biogeogr.
19, 287–302.
Decaëns, T., Rossi, J.P., 2001. Spatio-temporal structure of earthworm community and
soil heterogeneity in a tropical pasture. Ecography 24, 671–682.
Decaëns, T., Dutoit, T., Alard, D., Lavelle, P., 1998. Factors influencing soil macrofaunal
communities in post-pastoral successions of western France. Appl. Soil Ecol. 9,
361–367.
Decaëns, T., Jimenez, J.J., Gioia, C., Measey, G.J., Lavelle, P., 2006. The values of soil
animals for conservation biology. Eur. J. Soil Biol. 42, S23–S38.
Delgado-Baquerizo, M., Covelo, F., Maestre, F.T., Gallardo, A., 2013. Biological soil crusts
affect small-scale spatial patterns of inorganic N in a semiarid Mediterranean grassland. J. Arid Environ. 91, 147–150.
Dominati, E., Patterson, M., Mackay, A., 2010. A framework for classifying and quantifying the natural capital and ecosystem services of soils. Ecol. Econ. 69, 1858–1868.
Ettema, C.H., Wardle, D.A., 2002. Spatial soil ecology. Trends Ecol. Evol. 17, 177–183.
Farska, J., Prejzkova, K., Rusek, J., 2014. Management intensity affects traits of soil
microarthropod community in montane spruce forest. Appl. Soil Ecol. 75, 71–79.
Gamma Design Software, 1995. GS +: Geostatistics for the Environmental Sciences,
Version 5.1.1. Gamma Design Software, Plainwell, Michigan.
Gholami, Sh., Sayad, E., Gebbers, R., Schirrmann, M., Joschko, M., Timmer, J., 2016.
Spatial analysis of riparian forest soil macrofauna and its relation to abiotic soil
properties. Pedobiologia 59, 27–36.
Gonglanski, K.B., Savin, F.A., Pokarzhevskii, A.D., Filimonova, Z.V., 2005. Spatial distribution of isopods in an oak-beech forest. Eur. J. Soil Biol. 41, 117–122.
Gonglanski, K.B., Gorshkova, I.A., Karpov, A.I., Pokarzhevskii, A.D., 2008. Do boundaries
of soil animal and plant communities coincide? A case study of a Mediterranean
forest in Russia. Eur. J. Soil Biol. 44, 355–363.
Goovaerts, P., 1999. Geostatistics in soil science: state-of-the-art and perspectives.
Geoderma 89, 1–45.
Gringarten, E., Deutsch, C.V., 2001. Variogram interpretation and modelling. Math. Geol.
33, 507–534.
Henareh Khalyani, A., Mayer, A.L., 2013. Spatial and temporal deforestation dynamics of
Zagros forests (Iran) from 1972 to 2009. Landsc. Urban Plan. 117, 1–12.
Hooper, D.U., Bignell, D.E., Brown, V.K., Brussaard, L., Dangerfield, J.M., Wall, D.H.,
Wardle, D.A., Coleman, D.C., Giller, K.E., Lavelle, P., Van der Van der Putten, W.H.,
De Ruiter, P.C., Rusek, J., Silver, W.L., Tiedje, J.M., Wolters, V., 2000. Interactions
between aboveground and belowground biodiversity in terrestrial ecosystems: patterns, mechanisms, and feedbacks. Bioscience 50, 1049–1061.
7
Catena 159 (2017) 1–8
S. Gholami et al.
Warren, M.W., Zou, X., 2002. Soil macrofauna and litter nutrients in three tropical tree
plantations on a disturbed site in Puerto Rico. For. Ecol. Manag. 170, 161–171.
Webster, R., Oliver, M.A., 2009. Geostatistics for Environmental Scientists, 2nd ed. Wiley,
Chichester, England.
topography and canopy conditions. Popul. Ecol. 55, 261–275.
Tsukamoto, J., Sabang, J., 2005. Soil macro-fauna in an Acacia mangium plantation in
comparison to that in a primary mixed dipterocarp forest in the lowlands of Sarawak,
Malaysia. Pedobiologia 40, 69–80.
Vivanco, L., Austin, A.T., 2008. Tree species identity alters forest litter decomposition
through long-term plant and soil interactions in Patagonia, Argentina. J. Ecol. 96,
727–736.
Wardle, D.A., 2006. The influence of biotic interactions on soil biodiversity. Ecol. Lett. 9,
870–886.
Wardle, D.A., Yeates, G.W., Barker, G.M., Bonner, K.I., 2006. The influence of plant litter
diversity on decomposer abundance and diversity. Soil Biol. Biochem. 38,
1052–1062.
Web references
PAST, 2006. Palaeontological Statistics Software Package for Education and Data
Analysis. http://Fulk.uio.no/oammer/past (web archive link, 12 October 2006) (Site
visited: 10.12.2006).
8