environmental gradients and community attributes underlying biodiversity patterns of semi-arid...
TRANSCRIPT
Environmental gradients and community attributesunderlying biodiversity patterns of semi-aridMediterranean grasslands
M. N. Alhamad Æ M. A. Alrababah ÆM. M. Bataineh Æ A. S. Al-Horani
Received: 3 April 2007 / Accepted: 22 August 2007 / Published online: 12 September 2007
� Springer Science+Business Media B.V. 2007
Abstract Protection and/or establishment of forest
plantation have been used as a management strategy
to conserve and stop the deterioration of semi-arid
Mediterranean grasslands ecosystems, producing a
mosaic of vegetation types. This study was intended
to investigate the changes in grassland community in
response to protection and forest tree plantation
practice as well as to explore the underlying
environmental gradients responsible for the observed
differences or similarities among these vegetation
types. Two multivariate analysis methods including
discriminate analysis and non-metric multi-dimen-
sional scaling were used to quantify changes in
community composition and attributes following
different management practices (free grazing, protec-
tion with open grassland, sparse and dense forest tree
plantations). This was investigated using species
frequency, species abundance, or habitat characteris-
tics. The study results showed that habitat types
differed significantly between each other and were
significantly separated using multivariate approaches.
Discrimination based on habitat characteristics and
species composition indicated that protection (or
grazing) and light (or shade) explained more than
90% of the observed variability in community
changes in response to the protection and forest tree
plantation. Also, results indicated that shade effect
can be attributed to tree canopy cover and/or litter
accumulation on the ground. It could be hypothesized
that protection from grazing and afforestation
resulted in complex environmental gradients of which
shade, litter accumulation as well as protection from
grazing disturbance are major constituents. A careful
manipulation of protection and afforestation can be
used to create a spatially different environmental
gradients leading to greater habitat diversity as well
as a greater species diversity, and better conservation
means of grassland in semi-arid areas.
Keywords Biodiversity � Protection �Grazing � Afforestation � Multivariate analysis �Semi-arid grassland
Introduction
Biodiversity and habitat protection have recently
received greater attention with a focus on developing
M. N. Alhamad (&) � M. A. Alrababah � M. M. Bataineh
Department of Natural Resources & Environment, Jordan
University of Science and Technology, P.O. Box 3030,
22110 Irbid, Jordan
e-mail: [email protected]
M. A. Alrababah
e-mail: [email protected]
M. M. Bataineh
e-mail: [email protected]
A. S. Al-Horani
Department of Plant Production, Jordan University of
Science and Technology, 22110 Irbid, Jordan
e-mail: [email protected]
123
Plant Ecol (2008) 196:289–299
DOI 10.1007/s11258-007-9354-1
new and better indicators. Comparing species diver-
sity of various habitats is a hot topic in community
ecology. The comparison might involve the investi-
gation of the relationship between variables
characterizing species and communities (Gittins
1985). Biodiversity of Mediterranean ecosystems is
of particular challenge because they tend to be more
complex than other systems and represent a hotspot
for both plant diversity and human population growth
(Cowling et al. 1996; Mooney et al. 2001). In such
complex ecosystem, no single factor can explain its
diversity patterns and or its habitat attributes. A
multitude of factors might play a role, therefore,
multivariate analysis seems to be more appropriate to
investigate diversity patterns and habitat attributes.
Habitat characteristics affecting diversity are
numerous. Multivariate analysis can be used to
quantify species-habitat relationships (Smith 1977;
Holmes et al. 1979) and to reveal specific habitat
characteristics that affect the species composition of
that habitat the most. Principal component analysis
was used to obtain ordinations of species along
vegetational gradients (James 1971; Gauch and
Whittaker 1972). A discriminant function analysis
was used to determine the vegetation variables that
are important in discriminating between species
habitat associations (James 1971). The multivariate
methodology has improved our understanding of the
interrelationships between species diversity pattern
and various ecosystem properties (Grace 1999), and
clarifying factors that affect species richness pattern.
Middle Eastern Mediterranean plant communities
including Jordan had evolved under livestock grazing
pressure (Perevolotsky and Seligman 1998). In Jor-
dan, an east Mediterranean country, semi-arid
grasslands are located in the North-eastern part of
the country, adjacent to the Eastern Badia (desert).
These grassland ecosystems are in deteriorated con-
ditions as a result of uncontrolled grazing pressure
and inappropriate agricultural practices (HTS 1956,
Juneidi and Abu-Zanat 1993; Alhamad 2006). The
unwise land management of semi-arid Mediterranean
grassland in Jordan has lead to resource degradation,
including loss of biodiversity, gradual decline in
productivity, and vegetative cover (Al-Eisawi et al.
1996; NAP 2005).
Protection from grazing and/or establishment of
forest plantation have been suggested as a manage-
ment tool to stop the deterioration as well as to help
in ecological restoration of degraded semi-arid
grassland areas (Shaltout et al. 1996; Ayyad 2003).
The abundance of livestock grazing and the intro-
duction of forest trees into grassland has resulted in
the emergence of new habitat types and different
patterns of species diversity, i.e., different species
composition (Alrababah et al. 2007). Alrabbah et al.
(2007) suggested alternative management option for
the conservation of plant diversity in semi-arid
Mediterranean grasslands. However, the suggested
model warranted further investigation to measure the
responses of semi-arid plant communities to different
management options of protection from grazing and/
or afforestation in term of diversity pattern and cover
parameters as well as to reveal the environmental
gradient that underlined the observed changes in
plant communities. The generated knowledge should
have practical implication in the formulation of
sustainable management plans for semi-arid Medi-
terranean grassland communities.
Various studies investigated the important factors
affecting species richness pattern. These studies
showed different factors and attributes that affect
diversity, such as vegetation biomass (Grace and
Pugesek 1997), size of the species pool (Taylor et al.
1990; Gough et al. 1994), and dispersal history and
limitation (Hubbell 2001; Partel and Zobel 2007).
Other studies showed that soil properties (Fu et al.
2004; Weiher et al. 2004) and tree canopy cover were
among the factors that affect species richness pattern
(Casado et al. 2004; Weiher et al. 2004).
The effect of tree cover on diversity was found as a
result of its effect on biomass largely due to the
reduction of the amount of light that reaches the soil
surface. Light competition intensity was introduced
by Newman (1973) to explain the diversity pattern,
where competition for light is intensifying with
increasing productivity and thus reducing species
richness. With increasing plant biomass, plant cover
increases, thus, reducing the amount of light that
reach the soil surfaces (i.e. shade), and exerting an
indirect effect on species diversity pattern (Grace and
Pugesek 1997). Therefore, light competition is
expected to play a role under productive conditions,
while competition on soil resources is expected to be
the major factor under low productivity sites.
Grassland’s response to grazing is relatively well
studied in many parts of the world and several
hypotheses were suggested (Hobbs et al. 1985;
290 Plant Ecol (2008) 196:289–299
123
Milchunas and Lauenroth 1993; Briske and Richards
1995; Ferraro and Oesterheld 2002; del-Val and
Crawley 2004); however, limited information is
available in semi-arid Mediterranean grasslands
(Briske and Noy-Meir 1998). Noy-Meir et al.
(1989) indicated that plant growth forms significantly
determined plant responses to protection and grazing;
tall erect species increased following protection and
small prostrate species increased due to grazing.
The present study was intended to compare and
contrast four vegetation types, which reflected four
management practices(free grazing, protection from
grazing, sparse and dense tree plantation), in terms of
species composition and species abundance as well as
to explore the underlying environmental gradients
responsible for the observed differences or similar-
ities among these vegetation types. In specific the
study was aimed at the following objectives:
1. To investigate the underlying environmental
gradients responsible for grassland community
change under different management practices.
2. To identify the community attribute and plant
species that can be used as an indicator for
community changes in response to different
management practices.
Materials and methods
Site description
The study was located within the areas adjacent to the
campus of Jordan University of Science and Tech-
nology (32�340 N, 36�010 E; ca. 500 m a.s.l.), Irbid,
Jordan. A relatively long (31 years) history of
protection characterizes the campus whereas adjacent
areas are characterized by a long history of distur-
bance in the form of grazing. Certain locations within
the campus were planted with Pinus halepensis Mill.
trees since the establishment of the campus (1976).
Planting densities and establishment success (affected
by precipitation and irrigation in the planting year) of
trees varied in locations, creating variable tree
densities (Alrababah et al. 2007). Climate of the
study area is characterized by mild rainy winters and
dry hot summers. Mean annual precipitation is
approximately 230 mm and mean annual temperature
is approximately 17�C.
Sampling and data collection
Twelve sampling sites, each 0.1 ha in area, that
reflected four vegetation types (communities) were
identified and chosen for sampling. Each vegetation
type was represented by three sampling sites and each
reflected one of the following four management
practices:
1. Free grazing (FG): represented by areas adjacent
to, but outside, the campus with a long history of
grazing pressure.
2. Protected with no tree cover (PN): represented by
areas within the campus and were not targeted in
afforestation programs.
3. Protected with sparse tree cover (PS): repre-
sented by areas within campus with 30–50% of
tree cover.
4. Protected with dense tree cover (PD): repre-
sented by areas within campus with about 80% of
tree cover.
Ten quadrats (each of area 0.25 m2) were nested
randomly within each sampling site. Within each
quadrat, all herbaceous vegetation was identified to
the species level, and number of plants per species
was recorded. Overall vegetation cover as well as
litter (herbaceous and trees), rocks, and bare soil
cover were estimated utilizing digital photography. In
this method, each quadrat was photographed (using a
Sony digital camera) from approximately 1.5 m
above ground ensuring that all sides of the quadrat
were photographed. The images were downloaded to
a personal computer, clipped to the boundary of the
quadrat, and resampled to a resolution of
1000 · 1000 pixels. Cover percentages were esti-
mated utilizing a digital dot-grid overlay in which
100 equally spaced dots were used.
Plant diversity analysis
Species frequency was calculated as percentage of
quadrats occupied by a species, while species density
was calculated as the number of individuals of those
species found per quadrat. Species richness (n) was
calculated as the number of species identified in each
quadrat, while evenness (E) was calculated by
dividing Shannon-Wiener index by the natural log-
arithm of the richness as follows:
Plant Ecol (2008) 196:289–299 291
123
E ¼
Pn
i¼1
pi lnðpiÞ
ln n
where, pi is the proportion of individuals of species i
to the total number of individuals in a quadrat and n is
the total number of species in a quadrat. Hereafter,
cover percentages, species richness, and evenness
will be referred to as attribute data.
Species abundance (i.e. number of plants per
species) and frequency data were arranged in Q
matrices in which quadrats served as rows and
species abundance/frequency served as columns. In
addition, cover percentages of vegetation, litter, rock,
and bare soil as well as species richness and evenness
were arranged in a Q matrix.
Discriminant analysis (DA)
Discriminant analysis was performed on each of the Q
matrices. The analysis proceeded utilizing vegetation
type (FG, PN, PS, PD) as the grouping variable and
species abundance/frequency as the predictor vari-
ables. The same grouping variable was utilized for
attribute data. Variables of the attribute data served as
the predictor variables. Although the data might not
have met the basic assumptions of DA (i.e. within-
groups multivariate normality, within-groups variance
homogeneity, and linearity among all pairs of vari-
ables), the use of DA was deemed to be appropriate to
explore the differences or similarities among vegeta-
tion types. In DA, the direct method (i.e. using all
predictors) was utilized and prior probabilities were
assumed to be equal among all vegetation types.
Non-metric multi-dimensional scaling (NMMDS)
The same Q matrices were subjected to NMMDS.
However, sampling plots rather than quadrats were
utilized in this analysis to minimize within-vegeta-
tion-type (FG, PN, PS, PD) variation and enhance the
similarities or differences among vegetation types. In
NMMDS, the slow and thorough option (i.e. maxi-
mum 400 iterations, 0.00001 instability criterion, six
starting axes, 40 real runs, and 50 randomized runs)
of the autopilot procedure and Sorensen distance
were utilized. To aid the interpretation of each axis,
joint plots were constructed utilizing each Q matrix
as both the main and the secondary matrix. Joint plots
were also constructed to explore the relationship
between attribute data (cover percentages of vegeta-
tion, litter, rock, and bare soil as well as richness and
evenness) and species abundance/frequency data. To
evaluate the quality of the data reduction technique
(NMMDS), an after-the-fact evaluation of the vari-
ance explained by each axis was performed by
calculating coefficient of determination values for
each axis (McCune and Mefford 1999). NMMDS was
performed using PC-ORD software (McCune and
Mefford 1999).
Table 1 The three extracted
Discriminant Analysis (DA)
Functions for density data,
frequency data and habitat
characteristics data and their
corresponding eigenvalues,
percentage of variance, and
canonical correlation
DA
functions
Eigenvalue Percentage
of variance
Cumulative
variance
Canonical
correlation
Abundance
1 9.142 70.7 70.7 0.949
2 2.556 19.8 90.5 0.848
3 1.224 9.5 100.0 0.742
Frequency
1 13.202 74.0 74.0 0.964
2 3.143 17.6 91.6 0.871
3 1.505 8.4 100.0 0.775
Habitat characteristics
1 6.072 88.9 88.9 0.927
2 0.712 10.4 99.3 0.645
3 0.045 0.7 100.0 0.207
292 Plant Ecol (2008) 196:289–299
123
Results
Discriminant analysis
Discriminant analysis (DA) was informative and
produced a good habitat ordination and separation.
DA screened out significant components; the first two
principle components explained high percentage of
the total variance. Quadrates were accurately classi-
fied into their respective habitat type (FG, PN, PS,
PD). There was a difference in the discrimination
power and classification accuracy depending on the
type of data used.
Discriminant function for the three data types;
species abundance, species frequency, and habitat
characteristics was significant (P \ 0.001) with a
Wilk’s Lambda (K) of 0.012, 0.007, and 0.079,
respectively. This indicates that vegetation types
differed significantly in their species abundance,
species frequencies, and habitat characteristics. The
first two discriminant functions accounted for
90.5%, 91.6%, and 99.3% of the total variation in
vegetation types using species abundance, species
frequency, and habitat characteristics, respectively
(Table 1). The third discriminant function
accounted for 9.5%, 8.4%, and 0.7% of the total
variation for the three data types, respectively
(Table 1).
Examination of the total canonical structure using
either species abundance or frequency revealed that
Filago palaestina, Lophochloa pumila, Schismus
arabicus, Herniaria hirsuta, Matricaria aurea, Ado-
nis dentata, Asphodelus aestivus, and Malva
sylvestris were highly correlated with the first discri-
minant function (Table 2) whereas Crepis aspera,
Malabaila secacul, Lactuca orientalis, Eremostachys
laciniata, Lagoecia cuminoides, and Achillea biber-
steinii were highly correlated with the second
discriminant function (Table 2). However, as fitted
by the discriminant function, correct classification of
quadrats into their corresponding vegetation types
reached 85.8% using species abundance and 92.5%
using species frequency. Within each vegetation type,
classification accuracy varied according to vegetation
type and data type used. Accuracy for FG, PN, PD,
and PS has reached 90%, 80%, 93.3%, and 80%,
respectively using species abundance data and
reached 96.7%, 90%, 96.7%, and 86.7%, respectively
using species frequency data (Table 3). As fitted by
the discriminant function, 76.7% of quadrats were
correctly classified into their corresponding vegeta-
tion types using habitat characteristic data. Within
Table 2 Within group correlations (loadings) of habitat attri-
butes, species density, and frequency as predictor variable with
the first and second discrimination function
Predictor variable Loadings
Function 1 Function 2
Habitat attributes
Litter cover –0.846 0.110
Bare soil cover 0.359 –0.336
Rock cover 0.289 –0.792
Vegetation cover 0.322 0.737
Richness 0.416 0.499
Evenness 0.371 0.234
Density
Filago palaestina 0.202 0.040
Lophochloa pumila 0.132 0.029
Schismus arabicus 0.117 0.026
Herniaria hirsuta 0.115 0.026
Matricaria aurea 0.114 0.025
Adonis dentata 0.110 0.024
Asphodelus aestivus 0.104 0.023
Malva sylvestris 0.088 0.020
Crepis aspera –0.084 0.266
Malabaila secacul –0.057 0.198
Lactuca orientalis –0.016 0.175
Eremostachys laciniata –0.071 0.136
Lagoecia cuminoides –0.033 0.129
Achillea bibersteinii –0.032 0.128
Frequency
Filago palaestina 0.205 –0.024
Lophochloa pumila 0.131 0.000
Schismus arabicus 0.131 0.000
Herniaria hirsuta 0.119 0.000
Matricaria aurea 0.119 0.000
Adonis dentata 0.107 0.000
Asphodelus aestivus 0.079 0.000
Malva sylvestris 0.205 –0.024
Crepis aspera –0.074 0.291
Malabaila secacul –0.045 0.207
Lactuca orientalis –0.057 0.136
Eremostachys laciniata 0.038 0.136
Lagoecia cuminoides –0.021 0.110
Achillea bibersteinii –0.021 0.110
Plant Ecol (2008) 196:289–299 293
123
each vegetation type, classification accuracy varied
with 76.7%, 60%, 100%, and 70% for FG, PN, PD,
and PS, respectively (Table 3). As for the habitat
characteristics, litter was highly correlated with the
first discriminant function whereas rock and vegeta-
tion cover were highly correlated with the second
discriminant function (Table 2).
The relationship among vegetation types based on
the three types of data is visualized in the low
dimensional (two-dimensional) space defined by the
first two discriminant functions (Fig. 1). Results of
the discriminant analysis based on species data
(abundance and frequency) showed that the first
discriminant function, which represents 70–74% of
the total variability is separating the two major
management regimes (i.e. grazing and protection)
(Fig. 1a and b). The second discriminant function,
which represents 18–20% of the total variability is
separating the three protected habitats according to
the degree of tree cover (i.e. PN, PS, and PD)
(Fig. 1a and b). It is noticed that PN is well
separated from the other two protected habitats and
that a degree of overlap was noticed between PS
and PD. Results of the discriminant function based
on habitat characteristics showed that the first
function, which represents 89% of the variability
is separating habitats according to their light con-
ditions. Protected habitat with dense (PD) tree cover
was separated from other vegetation types (Fig. 1c).
The second discriminant function which represents
10% of the total variability separated FG from
protected habitats (Fig. 1c).
Non-metric multi-dimensional scaling
For abundance and frequency data, a two-dimen-
sional solution was deemed appropriate (the highest
dimensionality that reduced the final stress by 5% or
more and better than random solution according to a
Monte Carlo test P = 0.02) (McCune and Mefford
1999). Final stress for the two-dimensional solution
was 10.11 for species abundance and 8.77 for species
frequency. According to an after-the-fact evaluation
of the quality of the ordination, the two-dimensional
solution explained 75% of total variation in the
species abundance data, with axis 1 accounting for
38% of total variation and axis 2 accounting for 37%
of total variation. The two-dimensional solution
explained 88% of total variation in the frequency
data, with axis 1 accounting for 46% of total variation
and axis 2 accounting for 42% of total variation.
In general, the four vegetation types differed in
species composition and abundance (Fig. 2). How-
ever, PS and PN showed similarities in species
composition and abundance represented by the prox-
imity of their plots in the NMMDS ordination space
(Fig. 2). FG and PD vegetation types occupied the
end points of axis one and differed greatly in their
species composition and abundance. PD was charac-
terized by the low abundance of Adonis dentata,
Filago palaestina, Herniaria hirsuta, Schismus ara-
bicus, and Crepis sancta, whereas FG was
characterized by high abundance of these annual
species (Fig. 2a). PD was also characterized by the
low frequency of Adonis dentata, Herniaria hirsuta,
Table 3 Percentages of
accurately and inaccurately
classified quadrats within
each vegetation type
Free grazing (FG), No tree
cover (PN), Dense (PD), and
Sparse (PS) as fitted by the
discriminant function using
density data
Vegetation type Data type Predicted vegetation type
FG % PN % PD % PS %
FG Abundance 90.0 0.0 10.0 0.0
Frequency 96.7 3.3 0.0 0.0
Habitat attributes 76.7 16.7 0.0 6.7
PN Abundance 0.0 80.0 6.7 13.3
Frequency 0.0 90.0 0.0 10.0
Habitat attributes 13.3 60.0 0.0 26.7
PD Abundance 0.0 0.0 93.3 6.7
Frequency 0.0 0.0 96.7 3.3
Habitat attributes 0.0 0.0 100.0 0.0
PS Abundance 0.0 3.3 16.7 80.0
Frequency 0.0 3.3 10.0 86.7
Habitat attributes 0.0 23.3 6.7 70.0
294 Plant Ecol (2008) 196:289–299
123
Schismus arabicus, Matricaria aurea, and Arthro-
cnemum spp., where these species were the most
frequent species for FG (Fig. 2b). PS and PN were
different from FG and PD and characterized by the
high abundance of Avena sterilis and Anthemis
palestina (Fig. 2a) and the high frequency of Avena
sterilis, Notobasis syriaca, and Crepis aspera as
compared to PD and PS vegetation types (Fig. 2b).
Species abundance-habitat attribute joint plots
indicated that FG vegetation type was opposite to
PD. FG was characterized by the high percentage
of rock and bare soil cover and the low percentage
of litter cover (Fig. 3a). In addition, FG and PD
vegetation types were generally characterized by
low overall vegetation cover compared to PS and
PN vegetation types, which had the highest vege-
tation cover (Fig. 3a). Species frequency-habitat
attribute joint plots indicated that PD was opposite
to FG and characterized mainly by the high litter
cover (Fig. 3b). PD was opposite to PN and PS
and characterized by the low vegetative cover
(Fig. 3b).
Discussion
A significant separation between habitat types was
noticed in the DA analysis using either species data
(species abundance or frequency) or using habitat
characteristics data. However, the separation based
on species data separated habitats according to their
degree of protection or disturbance level the most,
thereby first DA function can be termed as the
protection factor (Fig. 1 a and b) while the second
DA function separated protected habitats according to
their degree of shade or tree cover and thereby can be
termed as the shade factor.
Separation based on habitat characteristics gave
slightly different results (Fig. 1c) but leading to the
same conclusion that protection and shade are the two
major factors affecting community composition. The
first DA function based on habitat attributes separated
plots according to both their degree of protection and
percent shade by tree cover. FG occupied one end of
the first DA representing unprotected (grazed) hab-
itats with full sun exposure due to the lack of tree
cover and herbaceous litter as well. The other end is
occupied by PD representing protected (ungrazed)
habitats with full shade due to the dense tree cover
Function 1
6420-2-4
Func
tion
2Fu
nctio
n 2
Func
tion
2
6
4
2
0
-2
-4
Sparse (PS)Dense (PD)No Tree Cover (PN)Free Grazing (FG)
PS
PD
PN
FG
Function 1
1086420-2-4
6
4
2
0
-2
-4
Group Centroids
Sparse (PS)Dense (PD)No Tree Cover (PN)Free Grazing (FG)
Group Centroids
Sparse (PS)Dense (PD)No Tree Cover (PN)Free Grazing (FG)
Group Centroids
PS
PD
PN
FG
Function 1
420-2-4-6
4
2
0
-2
-4
-6
PS
PD
PN
FG
(a)
(b)
(c)
Fig. 1 The relationship between vegetation types as defined
by the first two discriminant functions (DA) using abun-
dance data (a), frequency data (b) and habitat characteristics
data (c)
Plant Ecol (2008) 196:289–299 295
123
and dense tree litter. PS and PN occupied interme-
diate level representing protected (ungrazed) habitats
but with an intermediate level of shade due to either
tree cover (PS) or due to shade imposed by the dense
herbaceous litter (PN).
Many studies have investigated the role of litter in
ecosystem functioning where spatially continuous
plant litter cover is available in high productive
environments. In semi-arid environments few studies
have addressed the role of litter accumulation in
shaping plant communities through exerting a
pronounced effect on resource distribution, plant
productivity and diversity, and animal activity (Bosy
and Reader 1995; Biondi and Manske 1996; Boeken
and Orenstein 2001; Alrababah et al. 2007).
Disturbance in the form of grazing has been
recognized by most researchers as a major factor
shaping grassland communities worldwide and Med-
iterranean semi-arid grasslands specifically (Noy-
Meir and Seligman1979; McNaughton 1984; Mack
and Thompson 1984; Perevolotsky and Seligman
1998; Alhamad and Alrababah 2007; Alrababah et al.
Fig. 2 Ordination
(NMMDS) of sampling
plots within four vegetation
types with the most
abundant species (a) and the
most frequent species (b)
represented as vectors, the
length and angle of which
represents their relationship
with each sampling plot.
FG = Free grazing;
PS = Sparse; PD = Dense;
PN = No tree cover
296 Plant Ecol (2008) 196:289–299
123
2007). Shade of tree canopy or litter accumulation
appeared to play a significant role in these grasslands
(Casado et al. 2004; Alrababah et al. 2007). How-
ever, it was quite interesting to find that the light
factor was important in shaping semi-arid Mediter-
ranean communities in addition to grazing. This
finding was supported by looking to the second axis
of DA or NMMDS analysis where PD and FG
habitats were close to each other indicating that the
effect of grazing disturbance is equal to high shading
stress exerted by dense tree plantation and vise versa.
Although no environmental data were collected
from each sampling plot, NMMDS ordination
allowed indirectly for the exploration of underlying
environmental gradients responsible for the observed
vegetation patterns. It is apparent that axis one
reflected an environmental gradient along which FG
and PD vegetation types occupied the two opposing
ends and PS and PN occupied the midpoint of this
gradient (Fig. 2 and 3). It is also apparent that axis
two (37% of total variation) reflected an environ-
mental gradient along which FG and PD vegetation
Fig. 3 Non-metric Multi-
Dimensional Scaling joint
plot showing the
relationship between cover
percentages (vegetation,
litter, rock, and bare soil
cover) and species richness
and evenness on one hand
and species abundance (a)
and species frequency (b)
on the other hand.
FG = Free grazing;
PS = Sparse; PD = Dense;
PN = No tree cover
Plant Ecol (2008) 196:289–299 297
123
types occupied one end whereas PS and PN vegeta-
tion types occupied the other end of this gradient.
Despite its reflection of somewhat redundant infor-
mation, the second axis reflected an environmental
gradient along which grazing effect on species
abundance was similar to that of dense tree cover.
Grazing was previously identified as a major
player in arid Mediterranean grasslands; however,
shade of dense tree cover and the subsequent litter
accumulation was found to be a major player as well.
High shade negatively affected herbaceous cover and
diversity in accordance with other studies (Casado
et al. 2004). Further, allelopathic effects of Piuns leaf
leachates or root exudates might also be detrimental
to companion plant species (Fernandez et al. 2006).
Preliminary results of the effect of Pinus halepensis
leaves on other plants showed that allelopathy might
affect species abundance but not frequency (unpub-
lished data). The dense tree canopy cover and
accumulation of litter will reduce the amount of
solar radiation that reach the ground and reduce the
herbaceous species biomass and richness (Facelli and
Pickett 1991; ter Heerdt et al. 1991; Huston 1994;
Gillet et al. 1999). Light intensity was suggested as
an alternative predictor of diversity in addition to the
other classical parameters, such as grazing (Grace
1999).
Conclusion
Multivariate techniques proved to be a powerful
technique to investigate and understand factors
affecting the diversity patterns of complex ecosys-
tems. The results indicated that grazing is a major
factor affecting biodiversity of semi-arid Mediterra-
nean grasslands; however, the shade imposed by tree
cover showed that light is a second major factor. This
study showed that even under conditions with low
productivity, light still is a major player contributing
to the observed diversity patterns. It could be
hypothesized that protection from grazing and tree
cover plantation resulted in complex environmental
gradients of which shade and litter accumulation as
well as protection from grazing disturbance are major
constituents. Results of this study indicate that a
careful manipulation of protection and afforestation
could lead to the creation of a multitude of different
environmental gradients leading to the creation of a
greater habitat diversity leading to a greater species
diversity and better conservation mean.
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