Influence of plant species on physical c

Eur J Forest Res (2010) 129:15–24
DOI 10.1007/s10342-008-0246-2

ORIGINAL PAPER

Influence of plant species on physical, chemical and biological soil
properties in a Mediterranean forest soil
A. Pe´rez-Bejarano Æ J. Mataix-Solera Æ
R. Zornoza Æ C. Guerrero Æ V. Arcenegui Æ
J. Mataix-Beneyto Æ S. Cano-Amat

Received: 30 December 2007 / Revised: 6 July 2008 / Accepted: 11 September 2008 / Published online: 20 December 2008
Ó Springer-Verlag 2008

Abstract In semiarid ecosystems plant cover plays an
important role in the improvement of physical, chemical and
biochemical soil properties. With the aim of studying the
influence of different plant species on soil properties, and
establishing the relationships between them, 160 soil samples from under four different plant species (Pinus
halepensis, Quercus coccifera, Juniperus oxycedrus and
Rosmarinus officinalis) were taken in a forest area of the

province of Alicante (SE Spain). The following soil properties were analyzed in all soil samples: organic carbon
content, microbial biomass, soluble organic carbon, aggregate stability, basal respiration, and some eco-physiological
ratios. In addition, the near infrared spectra (NIR) of all soil
samples were obtained to verify the similarities or differences between soil samples under the four species. Some
differences in parameters such as organic carbon content or
basal respiration were found mainly between the group of
P. halepensis and Q. coccifera with respect to J. oxycedrus
and R. officinalis. Despite this, the high organic carbon
content found under the four plant species showed an
influence on the rest of soil properties. Moreover, using a
discriminant analysis with factorial scores from NIR
absorbance data did not result in a good classification of

Communicated by A. Merino and A. Rubio.
This article belongs to the special issue ‘‘Plant–soil relationships in
Southern European forests’’.
A. Pe´rez-Bejarano
J. Mataix-Solera (&)
R. Zornoza

C. Guerrero
V. Arcenegui
J. Mataix-Beneyto
S. Cano-Amat
GEA—Grupo de Edafologı´a Ambiental—Environmental Soil
Science Group, Department of Agrochemistry and Environment,

University Miguel Herna´ndez, Avenida de la Universidad s/n,
03202 Elche, Alicante, Spain
e-mail: jorge.mataix@umh.es

samples in terms of the species, reflecting some similarities
between them. Our results show that the high contents
observed in some parameters under the four species, and the
lack of significant differences in most of them, prove the
important role of shrubland in semiarid conditions, it being
capable of promoting good soil conditions.
Keywords Soil organic carbon
Microbial biomass

Soluble organic carbon
Basal respiration

Aggregate stability
Eco-physiological indicators

Pinus halepensis
Quercus coccifera

Rosmarinus officinalis
Juniperus oxycedrus

Introduction
It is well known that vegetation is a key factor in soil genesis.
Furthermore, it provides soil protection and contributes to
enhance soil properties (Garcia et al. 1994), which are

influenced by the type of vegetation. In arid and semiarid
ecosystems, where variation in the spatial and temporal
availability of water and nutrients is extreme, dominant
plants cause changes in soil properties that lead to complex
local interactions between vegetation and soil (Wilson and
Agnew 1992). Vegetal debris contributes to soil organic
carbon which plays an important role in soil functions: it has
nutrient and pollutant retention capacity, it improves soil
structure and stability and it is source of nutrients and substrate for soil microbial community (Nambiar 1997; Vallejo
et al. 2005) and has influence over their distribution and
activity. Furthermore, biologically active fractions of soil
organic matter are important in understanding decomposition potential of organic materials, nutrient cycling
dynamics, and biophysical manipulation of soil structure
(Franzluebbers et al. 2001). In this way, soluble organic

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carbon is a useful tool as it represents the fraction easily

decomposable by microorganisms and is closely related
with microbial growth and activity (Jandl and Sollins 1997).
It can be used as a measure of the labile fraction of total soil
organic mater, which is actually available for soil microorganisms (West et al. 1992; Tanaka et al. 1998). Soil
microbial properties have proved to be powerful indicators
of soil quality. This has led to a great expansion of research
into the possibilities of using such parameters to assess
degradation processes and the feasibility of restoration
strategies (Goberna et al. 2006). A better understanding of
the ability of plants to promote soil microbial processes in
these conditions is necessary for successful soil reclamation
(Garcı´a et al. 2005). In this sense, soil microbial respiration
is a useful index for measuring soil microbial activity
(Nannipieri et al. 1990), being specifically the activity
related to the decomposition of the organic matter. Furthermore, the metabolic quotient (qCO2: ratio between
microbial respiration and microbial biomass), is a valid
indicator of the microbial efficiency in the use of energy and
the degree of substrate limitation for soil microbes (Dilly
and Munch 1998; Moscatelli et al. 2005).
In other way soil aggregate stability (AS) is one of the

most important properties controlling plant growth in semiarid Mediterranean environments (Hillel 1982; Letey
1985). Aggregates provide good conditions for plant
growth in soil, related to porosity, water movement, air
circulation and soil erosion resistance (Hillel 1982; Singer
et al. 1992). Plants also affect the composition of the soil
microbial community (Rolda´n et al. 1994), which can
influence soil AS (Lynch 1981; Caravaca et al. 2002a). The
agents responsible for AS are mainly organic, and hence
biological in origin (Rolda´n et al. 2006). A few studies
have demonstrated the important relationship between soil
organic carbon and soil microbial biomass, and the influence of microbial biomass over soil structure (Harris et al.
1964; Allison 1968; Insam and Domsch 1988). Moreover,
many authors have studied the positive relationship
between AS and organic matter (Pagliai et al. 1981; Clapp
et al. 1986; Oades 1993; Cerda´ 1998). There is a need for
studies that look for changes in soil AS in relation to
vegetation species.
An alternative method to measure soil characteristics is
using near infrared reflectance (NIR) spectroscopy. In the
near-infrared region, the radiation is absorbed by the different chemical bonds, such as C–H, N–H, S–H, C=O and

O–H of the compounds present in the sample. Moreover,
the radiation is absorbed in accordance with the concentration of these compounds. As a consequence, NIR spectra
contain information about the composition of a soil sample.
In this sense, many authors have observed that NIR spectra
contain information about physical (Moron and Cozzolino
2003), chemical (Reeves et al. 1999; Chang and Laird

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Eur J Forest Res (2010) 129:15–24

2002; Confalonieri et al. 2001; Zornoza et al. 2008) and
biological (Reeves et al. 2000; Zornoza et al. 2008) properties of soils. Such variables are well known to indirectly
influence soil microbial activity and plant growth (Odlare
et al. 2005). According to this, the NIR spectrum can be
considered as an integrative measure of many soil characteristics. With the aim of establish the influence of
vegetation on soil properties, samples under four different
plant species (Pinus halepensis, Quercus coccifera, Juniperus oxycedrus and Rosmarinus officinalis) from a forest
region in SE Spain, were taken. These selected species are
the most dominant in the study area and in Mediterranean

semiarid forests. We could expect some differences
because different inputs of organic debris would be
expected as we are comparing different species.
The main objectives of this study were: (1) to determinate the influence of different plant species on some soil
properties in a Mediterranean semiarid area, and (2) to
investigate the relationships among them.

Materials and methods
Study area
The study area is located in the ‘‘Sierra de la Taja’’
(38°230 N; 0°590 W) near Pinoso, in the province of Alicante
(southeast Spain). The region has a semiarid Mediterranean
climate with a mean annual precipitation of 260 mm and a
mean annual temperature of 15.8°C ranging from 7.8°C in
January to 24.1°C in August (average 1961–1990). The
whole area of the ‘‘Sierra de la Taja’’ is approximately
500 ha. The samples were taken within 3 ha, representative
of the whole area, with homogeneous conditions with
respect to soil type, geology, plant distribution and slope.
The soil is a Lithic Xerorthent (Soil Survey Staff 2006),

developed over Jurassic limestone, and has a loamy texture
consisting of 13% clay, 42% silt, and 45% sand. The mean
content of carbonates is 37% and the pH 8.2.
The tree stratus of the area is formed by P. halepensis
Miller of approximately 40 years old, and the shrub vegetation comprises mainly Q. coccifera L., R. officinalis
L. and J. oxycedrus L., which are the species where soil
samples were taken for this study. The range of height for
these species in the study area is: 6–12 m for P. halepensis,
1–2.5 m for Q. coccifera, 0.5–2 m for J. oxycedrus, and
less than 1 m for R. officinalis. Brachypodium retusum
Pers. (Beauv.), Stipa tenaccissima L., and Pistacia lentiscus L. are also present in the area. Tree and shrub species
are mixed in the study area, but as consequence of the
relatively low density of vegetation, it was possible to carry
out the sampling in microsites per stem of each species
avoiding interference between them.

Eur J Forest Res (2010) 129:15–24

Soil sampling
In October 2004, 40 soil samples were collected from the

first 5 cm of the mineral A horizon at micro-sites under
each of the four species (P. halepensis, Q. coccifera,
J. oxycedrus and R. officinalis). The sampling was done by
selecting stems randomly, and taking one sample per stem
(pooling soil material from ten different points around the
stem). The distance between the stems sampled was around
10 m. Each sample contained around 1.2–1.5 kg soil. All
soil samples were air- dried at room temperature (20–25°C)
to a constant weight, carefully sieved through a 2-mm
mesh, and the coarser material discarded and the remaining
fine-earth fraction gently mixed until it appeared to be
homogeneous. For AS measurements, aliquots of the
samples were sieved between 0.25 and 4 mm.
Soil parameters analysed
Soil organic carbon (Corg) was determined by wet oxidation with 1 N potassium dichromate in acidic medium and
back titration with 0.5 N ferrous ammonium sulphate, as
described by Walkley and Black (1934). Microbial biomass
carbon was determined using the fumigation-extraction
(Cmic–FE) procedure (Vance et al. 1987), and the substrateinduced respiration (SIR) method (Cmic–SIR) (Anderson
and Domsch 1978). For the SIR method, soil samples were

amended with glucose (optimum rate 4.5 g glucose g-1
soil) and were incubated at 25°C and 50% of water holding
capacity (WHC) during 4 h. Hourly rates of evolved CO2
(ll CO2 g-1 h-1) and consumed O2 (ll O2 g-1 h-1) were
measured using a respirometer (Micro-Oxymax, Columbus, USA). Two equations were used to calculate the Cmic–
SIR (mg C kg-1):


Cmic  SIR mg C kg1 ¼ 40:04  ðll CO2 g1 h1 Þ
þ 0:37
ð1Þ


Cmic  SIR mg C kg1 ¼ 19:6  ðll O2 g1 h1 Þ
ð2Þ
Results of Cmic–SIR using Eqs. 1 (Anderson and
Domsch 1978) and 2 (Sparling and West 1990) were
statistically similar (paired-samples t test P [ 0.05), and
closely related (r = 0.989). According to this, only the
Cmic-SIR obtained with the Eq. 2 was used. Soluble organic
carbon (Csol, extracted with K2SO4 0.5 M) was measured by
oxidation with K2Cr2O7 and measurement of absorbance at

590 nm (Sims and Haby 1971). Basal soil respiration
(CO2–C) was monitored over 4 days at 55% WHC and
25°C with a multiple sensor respirometer (Micro-Oxymax,
Columbus, OH, USA). AS was measured with the method
of Rolda´n et al. (1994). This method examines the
proportion of aggregates that remain stable after a soil
sample (sieved between 0.25 and 4 mm) is subjected to an

17

artificial rainfall of known energy (270 J m-2). Five ecophysiological ratios were also calculated: the metabolic
quotient (qCO2: CO2–C/Cmic–FE), Cmic–FE/Corg, Csol/Corg,
CO2–C/Corg and Cmic–SIR/Cmic–FE.
NIR spectra of soil samples were obtained with the
following procedure: aliquots of around 50 g of soil samples were placed in glass Petri-dishes, and scanned on
reflectance mode from 12,000 to 3,800 cm-1. For these
measurements we used a Fourier-Transform near infrared
(FT-NIR) spectrophotometer (MPA, Bruker Optik GmbH,
Germany), equipped with quartz beamsplitter and PbS
detector. It is also equipped with an integrating macrosample sphere and rotating sample cup, allowing the
scanning of large areas of the samples. In each of the
reflectance measurements, 64 scans were averaged. Samples were measured in duplicate, increasing the surface of
soil sample scanned. After this, they were averaged again.
The time employed for the spectral measurement was
approximately 1 min per sample. The resolution used for
spectral analysis was 8 cm-1. Background corrections
were made before each sample scan. The x scale of each
NIR spectrum was transformed from wavenumber to
wavelength, obtaining a spectrum with 1,000 data of
absorbance comprising from 830 to 2,630 nm.
Statistical analyses
The fitting of the data to a normal distribution for all
properties measured was checked with the Kolmogorov–
Smirnov test. When necessary, analytical data were transformed using logarithms to assure normal distribution. A
one-way ANOVA was carried out with all properties and
eco-physiological ratios to assess the differences amongst
species. The separation of means was made according to
Tukey’s verified significant difference at P \ 0.05. Pearson’s correlation coefficients (r) were calculated to
quantify the linear relationship between parameters. A
discriminant analysis (DA) was performed using the factorial scores from NIR absorbance data of the soil samples
with the aim of evaluating if plant species have significantly influenced soil characteristics so that soil samples
can be classified by species. The samples were categorized
in four groups based on the different species: J. oxycedrus,
R. officinalis, Q. coccifera and P. halepensis. Previously, a
principal component analysis (PCA) was applied to the
NIR spectral matrix (160 samples 9 1,000 wavelengths),
extracting the first 20 principal components (PCs), that
explain the 99.9% of the variability. The scores of the first
20 PCs were used, reducing the size of the matrix (from
160 9 1,000 to 160 9 20). Thus, the DA was performed
on the 160 samples which represent 20 variables (the
scores of these 20 first PCs) in 4 groups or categories.

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Eur J Forest Res (2010) 129:15–24

Statistical analyses were performed with the software SPSS
for Windows, Version 14.0.

The NIR spectra of soil samples collected under four
plant species did not show great differences. The DA carried
out with the factorial scores of NIR absorbance data, that
explained 91.1% of variance (60.3% for the first discriminant function [DF1] and 30.8% for DF2) did not result in a
good classification of samples in terms of the species (Fig. 2,
Table 2). A 75% of soil samples taken under R. officinalis
and J. oxycedrus were correctly classified. However, classification was poorer for P. halepensis and Q. coccifera with
32.5 and 50% of soil samples, respectively, correctly classified, reflecting that soil samples beneath these two species
show similarities with the other species studied.

Results
Differences between species
Significant differences (P \ 0.01) between some of the
species were obtained in Corg, CO2–C, Cmic–FE/Corg,
qCO2, Cmic–SIR/Cmic–FE, whereas no differences were
found for the rest of soil parameters (Fig. 1, Table 1).
Mean values of Corg, Csol, AS and CO2–C, even though
being high in all samples, are relatively higher in samples
under P. halepensis and Q. coccifera. Samples under these
two species also showed higher mean values of Cmic–SIR/
Cmic–FE ratio but a clear statistical difference was only
observed between P. halepensis and R. officinalis.
Soil samples under Q. coccifera showed the highest
values in qCO2 (P \ 0.01). The ratio Cmic–FE/Corg was
significantly higher in samples under R. officinalis than
under Q. coccifera and P. halepensis. This species also
showed the highest mean values of the CO2–C/Corg ratio,
although with no statistically significant differences with
respect to the others.

The analysis of correlations (Table 3) showed that Corg and
Cmic–FE were closely related. Corg also showed positive
correlations with Csol and CO2–C, although the latter seems
to be indirectly influenced by the relationship between Corg
and Cmic–FE (as observed by analysis of partial correlations controlling the Cmic–FE effect). Furthermore Csol was
also positively correlated with Cmic–FE and AS. In addition, a strong correlation between soil microbial biomass
analysed by SIR and by fumigation-extraction method was
found (r = 0.735; P \ 0.001).
700

14
bc

ab

10

c

a

8
6
4

200

0
8
b

a
a

a

CO2-C (µg h-1 g-1)

a

7

1400
1200
1000
800
600

b
ab

6
5

a

4
3
2

400
200

1

0

0

96
a

94
a

a

a

J. oxycedrus
R. officinalis

AS (%)

Q. coccifera
90
88
86
84
82
80

123

a

300

0

92

a

400

2000
1800

Cmic -FE (mg kg-1)

500

a

100

2

1600

a

600

Csol (mg kg-1)

12

Corg (%)

Fig. 1 Mean values (±standard
deviation) of soil parameters
compared between species.
Different letters indicate
significant differences
(P \ 0.05) among values after
one-way ANOVA. Corg organic
carbon, Cmic–FE microbial
biomass carbon by fumigationextraction method, Csol soluble
carbon, CO2–C basal respiration
rate, AS aggregate stability

Relationships between soil parameters

P. halepensis

Eur J Forest Res (2010) 129:15–24

19

Table 1 Mean values (±standard deviation) of eco-physiological ratios compared between species
Soil parameter
Cmic–FE/Corg (%)
Csol/Corg (%)
qCO2 (mgC gCmic-1 h-1)
Cmic–SIR/Cmic–FE
-1
CO2–C/Corg (lgC gC-1
org h )

J. oxycedrus
1.6 ± 0.5 bc

R. officinalis

Q. coccifera

1.8 ± 0.3 c

1.3 ± 0.3 a

0.58 ± 17.5

0.64 ± 20.3

0.64 ± 16.9

2.9 ± 0.7 a

3.3 ± 1.6 a

0.83 ± 0.26 ab

0.80 ± 0.14 a

45 ± 14

61 ± 33

P. halepensis
1.5 ± 0.4 ab

Significance
***

0.58 ± 17.7

ns

4.5 ± 3.8 b

3.3 ± 1.0 a

**

0.91 ± 0.34 ab

0.97 ± 0.26 b

56 ± 44

47 ± 16

*
ns

Different letters indicate significant differences (P \ 0.05) among values after one-way ANOVA
Corg organic carbon, Cmic–FE microbial biomass carbon by fumigation-extraction method, Csol soluble carbon, CO2–C basal respiration rate,
qCO2 CO2–C/Cmic–FE, Cmic–SIR microbial biomass carbon by SIR method, ns non significant
Significant differences at *P \ 0.05; **P \ 0.01; ***P \ 0.001

(r = 0.648; P \ 0.001) and P. halepensis (r = 0.682;
P \ 0.001). Samples under P. halepensis showed the
strongest correlations between all parameters. In these
samples, all parameters had a positive correlation with Corg
except for eco-physiological ratios and AS. Moreover, Csol
was correlated with Cmic–FE (r = 0.682; P \ 0.001) and
CO2–C (r = 0.523; P \ 0.01).

Discussion

Fig. 2 Discriminant analysis with factorial scores from NIR absorbance data of samples taken under four different species (n = 160).
Black circles Pinus halepensis, white circles Juniperus oxycedrus,
open triangles Rosmarinus officinalis, grey squares Quercus coccifera. Black stars denote the centroids for each group

Correlations among the studied parameters for each
species separately were also developed. Positive significant
correlations between Corg, Cmic–FE and Csol were observed
in all species. Samples under J. oxycedrus and R. officinalis
also showed positive correlations between Corg and AS
(r = 0.505; P \ 0.01 for J. oxycedrus, and r = 0.669;
P \ 0.001 for R. officinalis), while CO2–C was positively
correlated with Cmic-FE in samples under J. oxycedrus
Table 2 Percentage of soil
samples classified at each plant
species group using
discriminant analysis. Values in
bold indicate the percentage of
correctly classified. Leave-one
out validation method was
applied

Original

We have not found great differences in soil parameters
between species. P. halepensis, the only tree species
present in the study area, is not the only species which
guarantees the best conditions for the improvement of soil
properties. The lack of clear differences in most parameters
and the high microbial biomass and activity under shrub
species, suggest the importance of shrubs for soil functioning under semiarid conditions.
The property most clearly influenced by the different
species has been the Corg. We observed higher Corg contents under P. halepensis and Q. coccifera. This probably is
related to higher aboveground and belowground biomass
with increased C inputs in soil as litter and root exudates.
In this sense, a positive relation between vegetation size
and Corg content was observed. The influence of the vegetation type on soil properties due to different organic matter
dynamics has been shown by many authors (e.g.: Fyles and
Cote´ 1994; Dell’Abate et al. 1999). As reported very often,
plant cover has a considerable influence on the quantity of
exudates and plant debris the soil receives (Caravaca et al.

Predicted group
R. officinalis

P. halepensis

Q. coccifera

R. officinalis

75

P. halepensis

17.5

32.5

40

Q. coccifera

15

30

50

J. oxycedrus

7.5

7.5

5

12.5

12.5

J. oxycedrus
5
10
5
75

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Eur J Forest Res (2010) 129:15–24

Table 3 Correlation coefficients (r values) for relationships between the studied parameters for all soil samples (n = 160)
Soil parameter

Corg

log Cmic–FE

log Csol

log Cmic–FE

0.66*

–

log Csol

0.76*

0.60*

–

CO2–C

0.41*

0.34 ns

0.37 ns

AS
Cmic–FE/Corg

0.34 ns
-0.51*

0.32 ns

0.46*

0.26 ns

-0.29 ns

CO2–C

AS

Cmic–FE/Corg

0.19 ns

Cmic–SIR/Cmic–FE

–

-0.07 ns

-0.19 ns

0.69*

0.15 ns

0.26 ns

0.16 ns

–

0.07 ns

0.21 ns

log qCO2

0.05 ns

-0.26 ns

0.06 ns

0.76*

0.05 ns

-0.29 ns

Cmic–SIR/Cmic–FE

0.50*

0.09 ns

0.26 ns

0.28 ns

0.04 ns

-0.43*

-0.09 ns

-0.14 ns

-0.28 ns

log qCO2

–

Csol /Corg

CO2–C/Corg

Csol/Corg

0.75*

-0.06 ns

0.31 ns

–
0.05 ns
-0.12 ns
0.12 ns

–
0.21 ns
0.82*

–
-0.04 ns

Corg organic carbon, Cmic–FE microbial biomass carbon by fumigation-extraction method, Csol soluble carbon, CO2–C basal respiration rate,
qCO2 CO2–C/Cmic–FE, Cmic–SIR microbial biomass carbon by SIR method, AS aggregate stability, ns non significant
Significant differences at *P \ 0.001

2002b; Garcı´a et al. 2005). Vegetation exerts significant
influence on the accumulation and turnover of soil organic
matter (Quideau et al. 2001). Nonetheless, the organic
carbon content in soil under all species is high in comparison with other researches based on natural forests or
afforestations with similar environmental conditions
(Maestre et al. 2003; Maestre and Cortina 2004; Bastida
et al. 2008).
Associated to the high content of Corg, a high AS is
present in the soil under all species ([89%). Other studies on
forest soils of the region have also shown high values in AS
(Guerrero et al. 2001; Mataix-Solera and Doerr 2004). We
observed a positive correlation between Corg and AS under
J. oxycedrus and R. officinalis. This correlation was not
found for P. halepensis and Q. coccifera, nor by pooling all
soil samples from all species. These results suggest that an
increment of organic carbon in the samples with lowest
values cause an increment of AS. In any case the organic
content in soil is high enough to guarantee a correct aggregation, independently of the concrete species. Furthermore,
a positive correlation was found between AS and Csol, the
labile organic fraction, pointing to their action as being one
of the key cementing agents (Elliot and Lynch 1984). Garcı´a
et al. (2005) also found a positive correlation between labile
organic carbon fractions and the percentage of AS. This high
stability of aggregates indicates a good soil structure under
all species, and so increases resistance to erosive processes
(Rolda´n et al. 1994). It is also probable that the contribution
of other factors in the aggregation of particles in this area is
as important as organic matter content. The carbonates
content, clays, iron compounds, microbial activity and the
presence of divalent cations must play an important role in
the AS of these soils (Hillel 1982; Singer et al. 1992; Porta
et al. 2003; Lynch and Brugg 1985).
The positive correlation found between Corg and Csol has
also been observed in other studies (Garcı´a et al. 2005;

123

Bastida et al. 2007; Zornoza et al. 2007a, b). Thus,
although differences in Csol are not significant, plant species with higher biomass show high values in this
parameter. Csol is the labile fraction of organic matter, and
root exudates and the amount of belowground biomass
fallen as litter can contribute to the increases in this organic
fraction.
The high content of organic matter under all species
contributes to high levels of microbial biomass. Soil
microbial biomass and activity are influenced by the
amounts of organic compounds (Goberna et al. 2006). A
positive correlation between Cmic–FE and Corg was
observed, indicating that organic matter content controls
microbial biomass (Zornoza et al. 2007b). Usually, this
correlation is found in soils at equilibrium status (Anderson
and Domsch 1993). Despite the fact that the soil under all
species showed similar levels in Cmic–FE, we observed that
Cmic–FE/Corg ratio was higher in samples under R. officinalis and J. oxycedrus. This ratio suggests that under these
species higher populations of microorganisms are maintained per unit of organic carbon. Thus, this brings to light
the great importance of shrubs under semiarid Mediterranean conditions, as a high proportion of microbial biomass
pool is established under these species. This ratio is influenced by climatic conditions (Insam 1990). In soils from
dry and arid zones, microorganisms can only metabolize
organic compounds during the brief periods when soil
moisture is increased after rainfall episodes. In these cases,
microbial populations can survive in starving situations
until climatic conditions are favourable, and as a result, the
ratio Cmic–FE/Corg is higher. In contrast, in humid zones,
microorganisms are more active most of the time and die
when easily mineralized compounds are exhausted. Thus,
this ratio could be indicating a more active soil microbial
community under Q. coccifera and P. halepensis. This
hypothesis was supported by the data of soil microbial

Eur J Forest Res (2010) 129:15–24

biomass measured by the SIR method (Cmic–SIR). With
this method, microbial biomass is estimated from active
heterotrophic microorganisms, while the fumigationextraction method (Cmic–FE) is based on the estimation of
microbial biomass from the extracted carbon constituent of
most microorganisms. Thus, the ratio Cmic–SIR/Cmic–FE
can inform about the proportion of active versus latent
microbial biomass. The Cmic–SIR must be lower or equal
to Cmic–FE. We observed that Q. coccifera and P. halepensis showed the highest values of the ratio Cmic–SIR/
Cmic–FE. These results may support the hypothesis that
microbial community under these two species is more
active. Furthermore, differences found in this ratio also
suggest differences in the microbial community structure
under each species. It has often been reported that plant
species condition microbial communities by the release of
radical exudates (Grayston et al. 1998; Yang and Crowley,
2000; Hackl et al. 2005).
Basal respiration was higher under P. halepensis and
Q. coccifera. High positive correlations between this
parameter and Corg have been found, as has also been
observed in other researches (Saviozii et al. 2001; Garcı´a
et al. 2005; Zornoza et al. 2007a). This suggests that total
organic carbon controls microbial activity. A high content
in organic carbon in soil provides more nutrients for
microbial communities development, and so, activity is
increased (Incla´n et al., this issue). This assertion is supported by the ratio CO2–C/Corg, which showed no
significant differences between species, reflecting an
equilibrium between microbial activity and organic matter.
Studied species have similar levels in qCO2 except for
Q. coccifera. It has been proved that soil microorganisms
divert more energy from growth into maintenance as stress
increases, and thus qCO2 can be a sensitive indicator of
stress (Killham and Firestone, 1984). However, in our case,
we do not think the increments of qCO2 under Q. coccifera
imply a stress for the soils. Metabolic quotient (qCO2) has
also been used as an indicator of efficiency in the use of
carbon (Insam and Haselwandter 1989). Soil under this
species has the highest content in Csol. It is well known that
Csol is the most easily mineralizable fraction of organic
matter, source of energy and nutrients, directly used by
microbial populations. Thus, the high content in Csol may
have led to increases in the qCO2.
All results shown here demonstrate the importance of
shrubs in semiarid environments, and question the traditionally believed statement that trees are more effective for
the improvement of soil conditions in comparison to shrub
species. Under a semiarid climate, the establishment of
trees is difficult owing to the high water deficit most of the
year, and the natural autochthonous vegetation normally
corresponds to shrub communities. Restoration policies
carried out during the 20th century in the semiarid areas of

21

the Mediterranean Basin were based on the reintroduction
of conifers, primarily P. halepensis (Maestre and Cortina
2004). Concretely, 87% of forests in the semiarid sector of
the province of Alicante are dominated by P. halepensis
(Bautista 1999). This species was chosen preferentially for
reforestation because of low-technical requirements for
nursery production, high-resistance to adverse climatic and
soil conditions, and because it was also considered a
pioneer species, favouring the establishment of late-successional species (Ruiz de la Torre 1973). Nonetheless,
these plantations have resulted in a low average survival as
well as low cover and productivity. Moreover, apart from
the fact that soil properties do not show great differences
between pines and shrub, P. halepensis plantations tend to
decrease species richness and overall plant cover in comparison to shrublands (Maestre and Cortina 2004).
The results of the DA carried out with NIR spectra of
soils also supported the idea that there are no great differences between the four species. The NIR spectra offer an
integrated vision of soil conditions, as well as synthesizing
information regarding mineralogy, soil chemistry, organic
matter and physical attributes (Cohen et al. 2005). Discriminant analyses have been successfully applied in NIR
analysis for the classification of soil texture (Mouazen et al.
2005), to differentiate soil spectra into different water
content groups (Mouazen et al. 2006), to differentiate soils
burned at different temperatures (Arcenegui et al. 2008)
and to discriminate soils under different land use (Zornoza
2007). The fact that soil samples taken under the four
studied species are not correctly classified by the DA
indicates that soil spectra are quite similar between species,
and so, organic compounds and mineral composition of soil
under all these species are also similar.
Although pines can facilitate the reactivation of nutrients
cycles and biological activities in degraded soils, native
shrubs have also proved to be effective, with overall positive effects not only in soil function, but also in the whole
ecosystem functionality. P. halepensis plantations often
homogenize the landscape and reduce habitat diversity, key
factors that negatively affect plants and animals diversity
(Lindernmayer and Hobbs 2004). Some studies have also
found lower soil organic matter content, AS or cation
exchange capacity in P. halepensis plantations than in
adjacent shrublands dominated by Q. coccifera, J. oxycedrus, Rhamnus lycioides or Pistacia lentiscus (Cortina et al.
2001; Castillo et al. 2002; Caravaca et al. 2002a). We have
found in our case that soils under shrub species have high
levels of organic carbon, microbial biomass and activity.
Thus, this work supports the importance of shrub species
under semiarid Mediterranean conditions, as being capable
of maintaining the biological status of soil, assuring the
biogeochemical cycles, as well as promoting high contents
of organic carbon and AS. The introduction of shrubs in

123

22

restoration practices in semiarid landscapes, where trees
development is limited due to climate constraints, is prone
to stimulate successional processes, increase ecosystem
resilience against disturbances (Maestre and Cortina 2004)
and improve soil conditions.

Conclusions
Despite the fact that there are some differences in soil
properties between P. halepensis and Q. coccifera with
respect to J. oxycedrus and R. officinalis, the results of this
study demonstrated that the soil beneath the four species
has very good qualities. The high organic carbon contents
found under all species control most soil properties, providing an adequate microbial biomass and activity, as well
as a high stability of aggregates. These results prove the
important role of shrubland in semiarid conditions, being
capable of maintaining similar soil conditions to those
under a tree species such as P. halepensis.
Acknowledgments This research was supported by the CICYT
co-financed FEDER program (Reference CGL2004-01335/BOS).
A. Pe´rez-Bejarano and V. Arcenegui acknowledge the grants from
‘‘Caja de Ahorros del Mediterra´neo’’. The authors also acknowledge
the ‘‘Aula de la Naturaleza’’ of Pinoso and the cerdocarpa team for
their collaboration and Frances Young for improving the English.

References
Allison FE (1968) Soil aggregation—some facts and fallacies as
seen by a microbiologist. Soil Sci 106:136–143. doi:10.1097/
00010694-196808000-00010
Anderson TH (2003) Microbial eco-physiological indicators to asses
soil quality. Agr Ecosyst Environ 98:285–293. doi:101016/
S0167-8809(03)00088-4
Anderson JPE, Domsch KH (1978) A physiological method for
the quantitative measurement of microbial biomass in soils.
Soil Biol Biochem 10:215–221. doi:10.1016/0038-0717(78)
90099-8
Anderson JPE, Domsch KH (1980) Quantities of plant nutrients in the
microbial biomass of selected soils. Soil Sci 130:211–216. doi:
10.1097/00010694-198010000-00008
Anderson TH, Domsch KH (1993) The metabolic quotient for CO2
(qCO2) as a specific activity parameters to assess the effects of
environmental conditions, such as pH, on the microbial biomass
of the soil. Soil Biol Biochem 25:393–395. doi:10.1016/00380717(93)90140-7
Arcenegui V, Guerrero C, Mataix-Solera J, Mataix-Beneyto J,
Zornoza R, Morales J et al (2008) The presence of ash as an
interference factor in the estimation of the maximum temperature reached in burned soils using near-infrared spectroscopy
(NIR). Catena 74(3):177–184
Bastida F, Moreno JL, Herna´ndez T, Garcı´a C (2007) The long-term
effects of the management of a forest soil on its carbon content,
microbial biomass and activity under a semi-arid climate. Appl
Soil Ecol 37:53–62. doi:10.1016/j.apsoil.2007.03.010
Bastida F, Barbera´ GG, Garcı´a C, Herna´ndez T (2008) Influence
of orientation, vegetation and season on soil microbial and

123

Eur J Forest Res (2010) 129:15–24
biochemical characteristics under semiarid conditions. Appl Soil
Ecol 68:62–70. doi:10.1016/j.apsoil.2007.09.002
Bautista S (1999) Regeneracio´n post-incendio de un pinar (Pinus
halepensis Miller) en ambiente semia´rido. Erosio´n del suelo y
medidas de conservacio´n a corto plazo. PhD Thesis, University
of Alicante, Spain
Caravaca F, Herna´ndez T, Garcı´a C, Rolda´n A (2002a) Aggregate
stability changes after organic amendment and mycorrhizal
inoculation in the afforestation of a semiarid site with Pinus
halepensis. Appl Soil Ecol 19:199–208. doi:10.1016/S09291393(01)00189-5
Caravaca F, Herna´ndez T, Garcı´a C, Rolda´n A (2002b) Improvement of
rhizosphere aggregate stability of afforested semiarid plant
species subjects to mycorrhizal inoculation and compost addition.
Geoderma 108:133–144. doi:10.1016/S0016-7061(02)00130-1
Castillo V, Gonza´lez-Barbera´ G, Mosch W, Navarro-Cano JA,
Consesa C, Lo´pez-Bermu´dez F (2002) Seguimiento y Evaluacio´n de los trabajos de restauracio´n hidrolo´gico-forestal, III. In:
Bermu´dez Lo´pez (ed) Seguimiento y Evaluacio´n de los efectos
sobre el medio natural de la sequı´a y los procesos erosivos en la
Regio´n de Murcia. Consejerı´a de Agricultura, Agua y Medio
Ambiente de la Regio´n de Murcia, Murcia, pp 166–233
Cerda´ A (1998) Soil aggregate stability under different Mediterranean
vegetation types. Catena 32:73–86. doi:10.1016/S0341-8162(98)
00041-1
Chang CW, Laird DA (2002) Near-infrared reflectance spectroscopic
analysis of soil C and N. Soil Sci 167:110–116
Chaparro J (1994) Consecuencias ambientales de repoblaciones
forestales mediante aterrazamientos en ambientes semia´ridos.
MSc Thesis, University of Murcia, Spain
Clapp CE, Stark SA, Clay De, Larson WE (1986) Sewage sludge
organic matter and soil properties. In: Avnimilech Y, Chen Y
(eds) The role of organic matter in modern agriculture. Martinus
Nijhoff, Dordrecht
Cohen MJ, Prenger JP, DeBusk WF (2005) Visible-near infrared
reflectance spectroscopy for rapid, non-destructive assessment of
wetland soil quality. J Environ Qual 34:1422–1434. doi:10.
2134/jeq2004.0353
Confalonieri M, Fornasier F, Ursino A, Boccardi F, Pintus B, Odoardi
M (2001) The potential of near infrared reflectance spectroscopy
as a tool for the chemical characterisation of agricultural soils.
J Near Infrared Spectrosc 9:123–131
Cortina J, Valdecantos A, Fuentes D, Casanova G, Vallejo VR, Dı´az
Bertrana JM et al (2001) El uso de bioso´lidos en el sector forestal
valenciano. Foresta 13:64–69
Dell’Abate MT, Pinzari F, Benedetti A, Dais C (1999) Soil organic
matter evolution in Mollisols of two reafforested sites in Sicily
(Italy). In: Bech J (ed) Extended abstracts of the sixth
international meeting on soils with Mediterranean type of
climate. University of Barcelona, Barcelona, p 1071
Dilly O, Munch JC (1998) Ratios between estimates of microbial
biomass content and microbial activity in soils. Biol Fertil Soils
27:374–379. doi:10.1007/s003740050446
Elliot LF, Lynch JM (1984) The effect of available carbon and
nitrogen in straw on soil and ash aggregation and acetic acid
production. Plant Soil 78:335–343. doi:10.1007/BF02450367
Franzluebbers AJ, Haney RL, Honeycutt CW, Arshad MA, Schomberg HH, Hons FM (2001) Climatic influences on active
fractions of soil organic matter. Soil Biol Biochem 33:1103–
1111. doi:10.1016/S0038-0717(01)00016-5
Fyles JW, Cote´ B (1994) Forest floor and soil nutrient status under
Norway spruce and red pine in a plantation in southern Quebec.
Can J Soil Sci 74:211–217
Garcia C, Herna´ndez T, Costa F (1994) Microbial activity in soils
under Mediterranean environmental conditions. Soil Biol Biochem 26:1185–1191. doi:10.1016/0038-0717(94)90142-2

Eur J Forest Res (2010) 129:15–24
Garcı´a C, Rolda´n A, Herna´ndez T (2005) Ability of different plant
species to promote microbiological processes in semiarid soil.
Geoderma 124:193–202. doi:10.1016/j.geoderma.2004.04.013
Goberna M, Sa´nchez J, Pascual AJ, Garcı´a C (2006) Surface and
subsurface organic carbon, microbial biomass and activity in a
forest soil sequence. Soil Biol Biochem 38:2233–2243. doi:
10.1016/j.soilbio.2006.02.003
Grayston SJ, Wang S, Campbell RD, Edwards AC (1998) Selective
influence of plant species on microbial diversity in the
rhizosphere. Soil Biol Biochem 30:369–378. doi:10.1016/S00380717(97)00124-7
Guerrero C, Mataix-Solera J, Garcı´a-Orenes F, Go´mez I, NavarroPedren˜o J (2001) Different patterns of aggregate stability in
burned and restored soils. Arid Land Res Manage 15:163–171.
doi:10.1080/15324980151062823
Hackl E, Pfeffer M, Donat C, Bachmann G, Zechmeister-Boltenstern
S (2005) Composition of the microbial communities in the
mineral soil under different types of natural forest. Soil Biol
Biochem 37:661–671. doi:10.1016/j.soilbio.2004.08.023
Harris RF, Chesters G, Allen ON, Attoe OJ (1964) Mechanisms
involved in soil aggregate stabilization by fungi and bacteria.
Soil Sci Soc A Proc 28:529–532
Hillel D (1982) Introduction to soil physics. Academic Press, New
York
Incla´n R et al (this issue) Carbon dioxide fluxes across the Sierra de
Guadarrama, Spain. Eur J Forest Res. doi:10.1007/s10342-0080247-1
Insam H (1990) Are the soil microbial biomass and basal respiration
governed by the climatic regime? Soil Biol Biochem 22:525–
532. doi:10.1016/0038-0717(90)90189-7
Insam H, Domsch KH (1988) Relationship between soil organic
carbon and microbial biomass on chronosequences of reclamation sites. Microb Ecol 15:177–188. doi:10.1007/BF02011711
Insam H, Haselwandter K (1989) Metabolic quotient of the soil
microflora in relation to plant succession. Oecologia 79:174–
178. doi:10.1007/BF00388474
Jandl R, Sollins P (1997) Water extractable soil carbon in relation to
the belowground carbon cycle. Biol Fertil Soils 25:196–201. doi:
10.1007/s003740050303
Killham K, Firestone MK (1984) Salt stress control of intracellular
solutes in streptomycetes indigenous to saline soils. Appl
Environ Microbiol 47:301–306
Letey J (1985) Relationships between soil physical properties and
crop production. Adv Soil Sci 1:277–294
Lindernmayer DB, Hobbs RJ (2004) Fauna conservation in Australian
plantation forests—a review. Biol Conserv 119:151–168. doi:
10.1016/j.biocon.2003.10.028
Lynch JM (1981) Promotion and inhibition of aggregate stabilization
by soil microorganisms. J Gen Microbiol 126:371–375
Lynch JM, Brugg E (1985) Microorganisms and soil aggregate
stability. Adv Soil Sci 2:134–170
Maestre FT, Cortina J (2004) Are Pinus halepensis plantations useful
as a restoration tool in semiarid Mediterranean areas? For Ecol
Mange 198:303–317. doi:10.1016/j.foreco.2004.05.040
Maestre FT, Cortina J, Bautista S, Bellot J (2003) Does Pinus
halepensis facilitate the establishment of shrubs in Mediterranean semi-arid afforestations? For Ecol Manage 176:157–160
Mataix-Solera J, Doerr SH (2004) Hydrophobicity and aggregate
stability in calcareous topsoil from fire affected pine forests in
southeastern Spain. Geoderma 118:77–88. doi:10.1016/S00167061(03)00185-X
Moron A, Cozzolino D (2003) Exploring the use of near infrared
reflectance spectroscopy to study physical properties and microelements in soils. J Near Infrared Spectrosc 11:145–154
Moscatelli MC, Lagomarsino A, Marinari S, De Angelis P, Grego S
(2005) Soil microbial indices as bioindicators of environmental

23
changes in a poplar plantation. Ecol Indic 5:171–179. doi:
10.1016/j.ecolind.2005.03.002
Mouazen AM, Karoui R, De Baerdemaeker J, Ramon H (2005)
Classification of soil texture classes by using soil visual near
infrared spectroscopy and factorial discriminant analysis techniques. J Near Infrared 13(Spec):231–240
Mouazen AM, De Baerdemaeker J, Ramon H (2006) Effect of
wavelength range on the measurement accuracy of some selected
soil constituents using visual-near infrared spectroscopy. J Near
Infrared 14(Spec):189–199
Nambiar EKS (1997) Sustained productivity of forests as a continuing
challenge to soil science. SSSAJ 60:1629–1642
Nannipieri P, Greco S, Ceccanti B (1990) Ecological significance of
the biological activity in soil. In: Bollag JM, Strozky G (eds) Soil
Biochem, vol 6. Marcel Dekker, New York, pp 293–354
Oades JM (1993) The role of biology in the formation, stabilization
and degradation of soil structure. Geoderma 56:377–400. doi:
10.1016/0016-7061(93)90123-3
Odlare M, Svensson K, Pell M (2005) Near infrared reflectance
spectroscopy for assessment of spatial soil variation in an
agricultural field. Geoderma 126:193–202. doi:10.1016/j.
geoderma.2004.09.013
Pagliai M, Guidi G, La Marca M, Giachetti M, Lucamante G (1981)
Effects of sewage sludge and compost on soil porosity and
aggregation. J Environ Qual 10:556–561
Porta J, Lopez-Acevedo M, Roquero R (2003) Edafologı´a para la
agricultura y el medio ambiente. Mundi-Prensa, Madrid
Quideau SA, Chadwick OA, Bensi A, Graham RC, Anderson MA
(2001) A direct link between forest vegetation type and soil
organic matter composition. Geoderma 104:41–60. doi:10.1016/
S0016-7061(01)00055-6
Reeves JBIII, McCarty GW, Meisinger JJ (1999) Near infrared
reflectance spectroscopy for the analysis of agricultural soils.
J Near Infrared Spectrosc 7:179–193
Reeves JBIII, McCarty GW, Meisinger JJ (2000) Near infrared
reflectance spectroscopy for the determination of biological
activity in agricultural soils. J Near Infrared Spectrosc 8:161–170
Rolda´n A, Garcı´a-Orenes F, Lax A (1994) An incubation experiment
to determinate factors involving aggregation changes in an arid
soil receiving urban refuse. Soil Biol Biochem 26:1699–1707.
doi:10.1016/0038-0717(94)90323-9
Rolda´n A, Carrasco L, Caravava F (2006) Stability of desiccated
rhizosphere soil aggregates of mycorrhizal Juniperus oxycedrus
grown in a desertified soil amended with a composted organic
residue. Soil Biol Biochem 38:2722–2730. doi:10.1016/j.soilbio.
2006.04.024
Ruiz de la Torre J (1973) Significacio´n de los pinares xero´filos. Vida
silvestre 6:108–113
Saviozii A, Levi-Minzi R, Cardelli R, Riffaldi R (2001) A comparison
of soil quality in adjacent cultivated forest and native grassland
soils. Plant Soil 233:251–259. doi:10.1023/A:1010526209076
Sims JR, Haby VA (1971) Simplified colorimetric determination of
soil organic matter. Soil Sci 112:137–141
Singer MJ, Southard RJ, Warrington DN, Janitzk YP (1992) Stability
of synthetic sand-clay aggregates after wetting and drying
cycles. Soil Sci Soc Am J 56:1843–1848
Soil Survey Staff (2006) Keys to soil taxonomy, 10th edn. NRCS,
Washington DC
Sparling GP, West AW (1990) A comparison of gas chromatography
and differential respirometer methods to measure soil respiration
and to estimate the soil microbial biomass. Pedobiologia (Jena)
34:103–112
Tanaka S, Funakawa S, Kaewkhongkha T, YonebaYashi D (1998)
Labile pools of organic matter and microbial biomass in the
surface soil under shifting cultivation in northern Thailand. Soil
Sci Plant Nutr 44:527–537

123

24
Vallejo VR, Dı´az-Fierros F, de la Rosa D (2005) Impactos sobre los
recursos eda´ficos. In: Evaluacio´n Preliminar de los Impactos en
Espan˜a por Efecto del Cambio Clima´tico. Ministerio de Medio
Ambiente, pp 355–398
Vance ED, Brookes PC, Jenkinson DS (1987) An extraction method
for measuring soil microbial biomass C. Soil Biol Biochem
19:703–707. doi:10.1016/0038-0717(87)90052-6
Walkley A, Black IA (1934) An examination of the Degtjareff
method for determining soil O. M. and a proposed modification
of the chromic acid titration method. Soil Sci 37:29–38. doi:10.
1097/00010694-193401000-00003
West AW, Sparling GP, Feltham CW, Reynolds J (1992) Microbial
activity and survival in soils dried at different rates. Aust J Soil
Res 30:209–222. doi:10.1071/SR9920209
Wilson JW, Agnew ODQ (1992) Positive-feedback switches in plant
communities. Adv Ecol Res 20:265–336
Yang C, Crowley DE (2000) Rhizosphere microbial community
structure in relation to root location and plant iron nutritional
status. Appl Environ Microbiol 66:345–351

123

Eur J Forest Res (2010) 129:15–24
Zornoza R (2007) Evaluacio´n de la calidad ambiental en suelos de la
provincia de Alicante: desarrollo y aplicacio´n de diferentes
te´cnicas. Ph.D. Thesis. Departamento de Agroquı´mica y Medio
Ambiente. Universidad Miguel Herna´ndez de Elche
Zornoza R, Guerrero C, Mataix-Solera J, Arcenegui V, Garcı´a-Orenes
F, Mataix-Beneyto J (2007a) Assessing the effects of air-drying
and rewetting pre-treatment on soil microbial biomass, basal
respiration, metabolic quotient and soluble carbon under Mediterranean conditions. Eur J Soil Biol 43:120–129. doi:
10.1016/j.ejsobi.2006.11.004
Zornoza R, Mataix-Solera J, Guerrero C, Arcenegui V, Mayoral AM,
Morales J et al (2007b) Soil properties under natural forest soil in
the Alicante Province of Spain. Geoderma 142:334–341. doi:
10.1016/j.geoderma.2007.09.002
Zornoza R, Guerrero C, Mataix-Solera J, Scow KM, Arcenegui V,
Mataix-Beneyto J (2008) Near infrared spectroscopy for determination of various physical, chemical and biochemical
properties in Mediterranean soils. Soil Biol Biochem 40:1923–
1930. doi:10.1016/j.soilbio.2008.04.003