a new approach to diversity indices u2013 modeling and mapping plant biodiversity of nallihan.pdf
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A new approach to diversity indices – modeling andmapping plant biodiversity of Nallihan(A3-Ankara/Turkey) forest ecosystem in frame of geographic information systems
HAKAN METE DOGAN1,* and MUSA DOGAN21GIS and RS Department, Central Research Institute for Field Crops (CRIFC), Gulderen Sok.
Ziraat Loj. No: 3, Yenimahalle, 06070 Ankara, Turkey; 2Biology Department, Middle East Technical
University, Ankara, Turkey; *Author for correspondence (e-mail: [email protected]; phone:
+90-312-327-01-50; fax; +90-312-315-14-66)
Received 10 February 2004; accepted in revised form 4 August 2004
Key words: Flora, Geographic information systems, Mapping, Modeling, Plant biodiversity, Plant
ecology, Remote sensing, Spatial analysis
Abstract. Modeling and mapping possibilities of Shannon–Wiener, Simpson, and number of
species (NS) indices were researched using geographic information systems (GIS) and remote
sensing (RS) tools in Nallihan forest ecosystem of Turkey. The relationships between the indices
and a number of independent variables such as topography, geology, soil, climate, normalized
difference vegetation index (NDVI), and land cover were investigated to understand relationships
between plant diversity and ecosystem. Georeferenced field data from the established 56 quadrats
(50 · 20 m) were used to calculate the indices. Principle component analysis (PCA) and multipleregression were employed for data reduction and model development, respectively. Three diversity
maps were produced using the developed models. Residual maps and logical interpretations in
ecological point of view were used to test the validity of the models. Elevation and climatic factors
formed the most important components that are effective determinants of plant species diversity,
but geological formations, soil, land cover and land-use characteristics also influenced plant
diversity. Considering the different responses of the models, Shannon–Wiener (SWI) and NS
models were found suitable for rare cover types, while Simpson (SIMP) model might be appro-
priate for single dominant land covers in the study area.
Introduction
Conservation Biology is an emerging discipline dedicated to the preservation of
endangered species and habitats. To develop effective protection strategies,experts need to understand the relationship between species and ecosystem.
Most importantly, they need to decide which areas are the most important to
protect. Consequently, mapping the areas with high plant biodiversity has a
priority for decision-makers. Effective management plans and actions can only
be achieved with this valuable spatial information. Ecologists define species
diversity on the basis of two factors: species richness and species evenness. The
number of species (NS) in the community is called species richness, while the
relative abundance of species is described as species evenness (Molles 1999).
Biodiversity and Conservation (2006) 15:855–878 Springer 2006
DOI 10.1007/s10531-004-2937-4
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How environmental structure affect species diversity is one of the most fun-
damental subject of investigation about communities (Barbour et al. 1987;
Molles 1999). Although having been much criticized for the imperfect defini-
tion of the concept of diversity and sampling difficulties, diversity indices are
still widely used to evaluate, survey, and conserve ecosystems (Pielou 1966;
Barbour et al. 1987; Riitters et al. 1995; Mouillot and Leprêtre 1999). The most
popular indices that have been used to quantify landscape composition are
Shannon’s index, believed to emphasize the richness component of diversity,
and Simpson’s index, emphasizing the evenness component (Magurran 1988;
Nagendra 2002). Choosing appropriate methods and tools, it is believed that
these indices have a potential to map diversity. At this point, geographic
information systems (GIS) that has been recently recognized by conservationbiologists can be an appropriate and powerful tool in the spatial analyzes
performed in conservation biology (Kadmon 1997; Dogan 1998; Kress et al.
1998; Lenton et al. 2000; Dogan 2001). The spatial nature of the biological data
lets GIS to develop spatial models of which they might also be used as a
solution for predictive mapping (Franklin 1998; Gottfried et al. 1998). Where
as the monitoring results and mapping of earlier periods are considered as vital
information for such kind of GIS databases. This need is fulfilled generally by
aero-space remotely sensed data (Fjeldsa et al. 1997) in which at some regions
of the globe the data set can go back to early 1950s via aerial photographs to
recent via high resolution multispectral global coverages for the diversity
studies. Within this frame, the aim of this study is to create a new approach to
the conventional diversity (Shannon-Wiener, Simpson, NS) indices using GISand remote sensing (RS) tools. Consequently, in order to reach this goal plant
biodiversity of Nallihan forest ecosystem was modeled and mapped within the
frame of this new approach between the years 2001 and 2002.
Materials and methods
Study area
This study was conducted in Nallihan administrative district of Ankara
province in Turkey. According to the grid system based on two degrees of
latitude and longitude (Davis 1965–1988); the study area is located in the A3
grid square of Central Anatolia (Figure 1a). This location is within the Irano-Turanian phytogeographical region with some Mediterranean penetrations
(Davis 1971), and the records of Turkey’s Plant Database (TUBIVES 2003)
pointed out 119 family, 553 genera, and 1350 plant species in this square.
Recently, a new Acantholimon (Plumbaginaceae) species was published from
the area (Dogan and Akaydin 2002). The study area is specifically called
Erenler forest region, and covers 327.31 km2 (32731.29 ha) area. The general
topography of the study area is mountainous (Figure 1b). Generally, agricul-
tural lands are concentrated along the river basins, while forests dominate the
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higher elevations. There are 28 settlements in the study area, and majority of
them are small villages. Major human effects on the forest can be seen in the
area like agriculture and urbanization. Moreover, a considerable part of this
area faces erosion. About 5.6% forest was degraded by natural or anthropo-
genic causes in the area.
According to Emberger classification system, the climate of the study area
showed ‘Semi-arid Upper Mediterranean Bioclimate’ characteristics with cold
winters (Akman and Daget 1971; Akman 1999). Basically, four climatic sea-
sons are recognized in the study area. Precipitation is mostly in the form of rain
Figure 1. Physiographic setting (a) and physical geography (b) of the study area (projection
systems of physiographic setting and physical geography maps were defined as geographic with
European datum (spheroid international 1909) and UTM with European datum (zone: 36, spheroid
international 1909), respectively).
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throughout the year except winters, and total number of snowy days does not
exceed 20 days. The main tree species of the study area are black pine (Pinus
nigra), juniper (Juniperus spp.), red pine (Pinus brutia), and oak (Quercus spp.).
According to the digital forest stand map, the study area can be generalized in
six categories as non forest (28.39%), oak forest (0.47%), erosion/stony
(5.60%), degraded forest (33.85%), Black Pine forest (31.47), and Red Pine
forest (0.21%).
Methods
A flowchart of the methodology is given in Figure 2. Digital geological and soilmaps of the study area were obtained from the General Directorate of Mineral
Research and Exploration (MTA) and the General Directorate of Rural Af-
fairs (KHGM), respectively. Topographical and forest stand maps were digi-
tized in UNIX Arc/Info 7.0.4 and PC Arc/Info 3.5 software (ESRI 1994; ESRI
1997). The LANDSAT-TM image, acquired on August 21st 2000, was utilized
to develop land cover and normalized difference vegetative index (NDVI) maps
in Erdas Imagine 8.5 software (ERDAS 1997). Supervised classification
method (maximum likelihood parametric rule), 4-5-3 band combination, and
statistical filtering (7 · 7) were used to develop a land cover map. The unsigned
8-bit NDVI model was utilized to establish NDVI classes. Arc/View 3.2 soft-
ware (ESRI 1996) and Inverse Distance Weighted (IDW) method were em-
ployed to produce the interpolated surfaces (grid maps) of climatic(temperature, precipitation, and potential evapotranspiration (PET)), and
additional soil (K2O, P2O5, organic matter, pH, salt, CaCO3, saturation and
texture) variables. To conduct spatial analysis, all developed maps were con-
verted to grid themes by using 30 · 30 m grid size in Arc/View 3.2. Universal
Transverse Mercator (UTM) projection system (spheroid international-1909,
datum: European-1950, zone: 36) was applied to all map data.
Georeferenced point data (791 points) were collected to classify LANDSAT-
TM image and to conduct accuracy assessment (Figure 3a). Land-use (FAO
1990) and formation classes (UNESCO 1973) were utilized to identify the main
land-use and vegetation types in the field. Detailed plant data for diversity
indices were collected from the established quadrats (Figure 3b). The number
of quadrats was determined as 56 considering the quadrat surveys of Magurran
(1981), and the quadrat sites were established according to stratified randomsampling design (McGrew and Monroe 1993). The size of each quadrat was
20 · 50 m following Grossman et al. (2003). Plant parameters collected from
each quadrat were (1) species component, (2) NS, (3) species cover (%), and (4)
species density (number of plant/m2). From the quadrats, soil samples were
also taken according to the certain soil sampling methods (Atesalp 1976; Ulgen
and Yurtsever 1995). On the map of study area, total 570 points were deter-
mined to aggregate climatic data (Figure 3c). The LOCCLIM software
(Grieser 2002) was employed with the digital elevation model (DEM) to
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calculate the best estimates of focused climatic variables in each determined
point. Climatic variables were investigated in both ‘annual’ and ‘seasonal’
basis. The time period between May and September was taken as seasonal
because of low precipitation and high PET values within this period.
Total 752 plant specimens, pressed and dried following the rules and defi-
nitions explained by Davis and Heywood (1965), were identified in the
ANKARA Herbarium of Ankara University. The Davis’ Flora of Turkey and
the East Aegean Island Vol. 1–10 (Davis 1965–1988) were used as the mainreference throughout the herbarium studies. Species diversity indices were
calculated for each quadrat at the end of this work. The formulas
H ¢ = P
( pi loge pi ) and D =P
( pi 2) were employed to calculate Shannon–
Wiener and Simpson indices, respectively (Barbour et al. 1987; Molles 1999). In
both formulas, pi values indicate the proportional abundance of the i th species
in a quadrat. On the other hand, NS index has no formula, and it was deter-
mined by using the total species number in a quadrat.
Figure 2. The flowchart of the methodology (the rounded rectangles indicate the analyses and
processes, rectangles show output products).
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Spatial analyses and model development were conducted in four steps
(Figure 2). Kaiser–Meyer–Olkin (KMO)–Bartlett tests were conducted to test
the suitability of the data for factor analysis. Then, principle component
Figure 3. Georeferenced point data for supervised classification (a), established quadrats (b), and
established point data to derive climatic variables (c).
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analysis (PCA) with varimax rotation was applied for data reduction (SPSS
2001). Multiple regression, regressing a variable on a series of independent
variables (Sokal and Rohlf 1995), was chosen to formulate the relationships.
This was achieved by applying the linear regression with ‘enter’ method in
SPSS-11 software (SPSS 2001). Applying the models, species richness maps
were produced in Arc/View 3.2. The reliability of the maps was tested by
residual maps and ecological interpretations. Residuals were calculated by
using the observed and computed values of indices in each quadrat, and IDW
method was employed to map them. To evaluate different indices in the same
base, interpolating surfaces of the residuals were developed by using standard
deviation values of each index.
Results
Plant species
Total 239 species belonging to 45 families were determined in the study area.
According to Davis (1965–1988) and the records of Turkey’s Plant Database
(TUBIVES 2003); 14 species were detected as endemic for the study area. NS
recognized in each family is stated in Table 1. Leguminosae, Compositae,
Labiatae, Rosaceae, Cruciferae, and Gramineae families have more species
comparing to the others. The full list of identified species was given in
Appendix 1
Table 1. NS recognized in each family.
Family No. of species Family No. of species Family No. of species
Leguminosae 37 Ranunculaceae 3 Iridaceae 1
Compositae 34 Cistaceae 3 Acanthaceae 1
Labiatae 28 Papaveraceae 3 Anacardiaceae 1
Rosaceae 15 Fagaceae 3 Chenopodiaceae 1
Cruciferae 10 Santalaceae 2 Convolvulaceae 1
Graminae 10 Illecebraceae 2 Coryllaceae 1
Liliaceae 9 Rhamnaceae 2 Crassulaceae 1
Boraginaceae 8 Geraniaceae 2 Equisetaceae 1Scrophulariaceae 8 Linaceae 2 Euphorbiaceae 1
Caryophyllaceae 7 Berberidaceae 2 Globulariaceae 1
Umbelliferae 7 Cyperaceae 2 Guttiferae 1
Campanulaceae 5 Paeoniaceae 2 Malvaceae 1
Rubiaceae 5 Pinaceae 2 Orchidaceae 1
Cupressaceae 4 Valerianaceae 2 Polygalaceae 1
Plumbaginaceae 4 Dipsacaceae 1 Urticaceae 1
Number of determined plant species in this study were given with their families in this table. In this way, overall
results about the recognized species were summarized efficiently. Details about the determined species were also
given in Appendix 1.
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Remote sensing data
Supervised classification obtained 92.16% overall accuracy with a Kappa
coefficient of 0.8828, and produced a reliable result. Moreover, NDVI map
delineated the areas where the plants dominated. Therefore, these two map
layers supplied valuable spatial information that can be effective on plant
species diversity. The grid maps of land cover and NDVI were given in Fig-
ure 4 with the original LANDSAT-TM (band 3) image.
Data reduction
Initial results of KMO measure of sampling adequacy indicated that factor
analysis would be an appropriate statistics for data reduction. The best solu-
tion was found after the second pass with the removal of (1) aspect, (2) slope,
(3) P2O5, (4) K2O, (5) salt, (6) erosion, and (7) seasonal maximum temperature.
After the removal of these seven variables the KMO measure of sampling
adequacy was increased for all indices (Table 2).
Total five factors were determined according to their Eigenvalues in the
second pass (Table 3). Consequently, stable models were produced. The sta-
bility of the models can be seen in the generic differentiation of the factors and
their responsible variables. For instance, the first component consists of ele-
vation and climatic variables for all indices that is reasonable because of the
clear relationship between elevation and climatic factors. Similarly, classes
derived from satellite images (NDVI and land cover classes) take part in thefifth component of all indices.
Modeling
The results of linear (multiple) regression were summarized in Table 4. The
Analysis of Variance (ANOVA) showed the acceptability of the models from a
statistical perspective, and the model summary reported the strength of the
relationship between the models and the dependent variables (Table 4). Large
values of the multiple correlation coefficient (R) indicated a strong relationship.
Table 2. Kaiser–Meyer–Olkin and Bartlett’s Test results for the 22 variables in second pass.
Second pass SWI index SIMP index Number of species
KMO measure of sampling adequacy 0.808 0.811 0.808
Bartlett’s test of sphericity Approx. v2 2502.219 2454.079 2426.494
df 210 210 210
Significance 0.000 0.000 0.000
This table summarized the KMO measure of sampling adequacy results after the second pass with
the removal of (1) aspect, (2) slope, (3) P2O5, (4) K2O, (5) salt, (6) erosion, and (7) seasonal
maximum temperature variables. Increasing KMO and Bartlett’s Test results (0.808 for Shannon–
Wiener, 0.811 for Simpson and 0.808 for NS) for the last 22 variables and the low significance levels
(0.00 for all indices) indicated the suitability of the for data reduction.
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Moreover, the significance values of the F statistic are less than 0.05 in all models,
which means that the variation explained by the models is not due to chance. The
unstandardized coefficients were defined as the coefficients of the estimated
regression model, and they were used in the developed models (Table 5).
Figure 4. Original LANDSAT-TM (band 3) image (a), land cover map (b), and NDVI (8 bit)
classes (c) of the study area.
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Mapping
The developed grid themes (complementary data set) and map calculator
functions of Arc/View 3.2 were employed throughout the application process
of the three models. With the power of GIS, mathematical operations
were easily conducted on grid themes. Consequently, species diversity maps of
focused indices were developed (Figure 5).
Discussion
The reliability of species diversity maps was questioned in two ways. These
were (1) mapping residuals to predict the locations where the models work
perfectly and (2) logical interpretations in ecological point of view.
Residual from regression is simply the difference between observed and
computed value (Berry and Marble 1968; McGrew and Monroe 1993), and is a
good indicator to show where the models work perfectly or imperfectly. In
general, low residual values indicate the robust models. Produced residual maps
were given in Figure 5. The percent area covered by each distinct residual class
indicated the credibility of three models. The less predictive areas for three
models covered small percentages (SWI: 7.82%, SIMP: 6.60% and NS: 7.84%),
while the strongly predictive areas contained significant parts (SWI: 64.85%,
SIMP: 68.12%, and NS: 67.57%). Moderately predictive areas were alsodetermined as approximately one fourth of the total area (SWI: 27.33%, SIMP:
25.28% and NS: 24.59%) for each index. Considering strongly and moderately
predictive areas together, it seems that each model runs very well in itself.
Overall results of the study indicated that Simpson model worked inversely
comparing the Shannon–Wiener and NS models (Figure 5). Low Simpson (0–
0.42) high Shannon–Wiener (2.70–3.59 and 3.60
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T a b l e 4 .
T h e s t a t i s t i c s o f l i n e a r ( m u l t i p l e ) r e g r e s s i o n .
S h a n n o n – W i e n e r
A N O V A b
M o d e
l 1
S u m o f s q u a r e s
d f
M e a n s q u a r e
F
S i g n i fi c a n c e
R e g r e
s s i o n
2 9 . 1 7 3
2 0
1 . 4 5 9
1 7 . 0 5 5
0 . 0 0 0 a
R e s i d
u a l
2 . 9 9 3
3 5
0 . 0 8 6
T o t a l
3 2 . 1 6 7
5 5 M
o d e l s u m m a r y b
M o d e
l 1
R
R 2
A d j u s t e d R 2
S t a n d a r d e r r o r
0 . 9 5 2 a
0 . 9 0 7
0 . 8 5 4
0 . 2 9 2 4 5 2
S i m p s o n
A N O V A b
M o d e
l 1
S u m o f s q u a r e s
d f
M e a n s q u a r e
F
S i g n i fi c a n c e
R e g r e
s s i o n
2 . 6 6 9
2 0
0 . 1 3 3
5 . 0 2 7
0 . 0 0 0 a
R e s i d
u a l
0 . 9 2 9
3 5
0 . 0 2 7
T o t a l
3 . 5 9 8
5 5 M
o d e l s u m m a r y b
M o d e
l 1
R
R 2
A d j u s t e d R 2
S t a n d a r d e r r o r
0 . 8 6 1 a
0 . 7 4 2
0 . 5 9 4
0 . 1 6 2 9 3 8
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N u m b e r o f S P .
A N O V A b
M o d e
l 1
S u m o f s q u a r e s
d f
M e a n s q u a r e
F
S i g n i fi c a n c e
R e g r e
s s i o n
1 1 9 8 . 6 9 5
2 0
5 9 . 9 3 5
2 . 0 2 6
0 . 0 3 3
R e s i d
u a l
1 0 3 5 . 4 3 0
3 5
2 9 . 5 8 4
T o t a l
2 2 3 4 . 1 2 5
5 5 M
o d e l s u m m a r y b
M o d e l 1
R
R 2
A d j u s t e d R 2
S t a n d a r d e r r o r
0 . 7 3 2 d
0 . 5 3 7
0 . 2 7 2
5 . 4 3 9
T h e r e s u l t s o f l i n e a r ( m u l t i p l e )
r e g r e s s i o n p r o d u c e d f o r e a c h i n d e x w e r e u n
i fi e d i n t h i s t a b l e . T h e a n a l y s i s o f v a r i a n c e (
A N O V A ) s h o w e d t h e a c c e p t t a b i l i t y o f
t h e m o d e l s f r o m a s t a t i s t i c a l p e r s p e c t i v e , a n d t h e m o d e l s u m m a r y r e p o r t e d
t h e s t r e n g t h o f t h e r e l a t i o n s h i p b e t w e e n t h e m o d e l s a n d t h e d e p e n d e n t v a r i a b l e s .
L a r g e v a l u e s o f t h e m u l t i p l e c o
r r e l a t i o n c o e ffi c i e n t ( R ) i n d i c a t e d a s t r o n g r e l a t i o n s h i p . M o r e o v e r , t h e s i g n i fi c a n c e v a l u e s o f t h e F s t a t i s t i c a r e l e s s t h a n 0 . 0 5 i n
a l l m o d e l s , w h i c h m e a n s t h a t t
h e v a r i a t i o n e x p l a i n e d b y t h e m o d e l s i s n o t d u e t o c h a n c e .
N o t e : F o r a b b r e v i a t i o n s s e e T a b l e 3 .
a P r e d i c t o r s : ( C o n s t a n t ) , S P V S D
, G E O , P H , S L D P T , O R G M , N D V I , M I N
T A , T E X T R , S O I L G , C A C O 3 , P R C P S , S T
R , P E T A N , M E T S ,
P R C P A , M A X T A , M I N T S , E
L E V , P E T S E , M E T A .
b D e p e n d e n t v a r i a b l e : S W I .
c D e p e n d e n t v a r i a b l e : S i m p s o n .
d P r e d i c t o r s : ( C o n s t a n t ) , P E T S E , O R G M , N D V I , G E O , S L D P T , P H , S P V
S D , T E X T R , S O I L G , C A C O 3 , P R C P S , S T R , M E T S , P R C P A ,
M A X T A , M I N T A , P E T A N , M
I N T S , E L E V , M E T A .
e D e p e n d e n t v a r i a b l e : N u m b e r
o f s p e c i e s .
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The relationships between the indices and elevation can be recognized when
the elevation (Figure 1) and diversity maps (Figure 5) were examined together.
A direct relationship between the elevation and indices was detected for
Shannon–Wiener and NS models. On the other hand, this relationship turned
an inverse character in Simpson model. Depending on these results, animportant question arises: which model has the capacity to delineate real sit-
uation in the field? In basic, there are two general concepts: (1) a monotonic
decrease in species richness with increasing elevation (Stevens 1992; Huston
1994; Rahbek 1995; Brown and Lomolino 1998) and (2) a peak in richness at
intermediate elevations (800–1400 m) exemplified by a hum-shaped distribu-
tion (McCoy 1990; Rahbek 1997; Fleishman et al. 1998). Considering the
elevation range (144–1740 m) of the study area, Simpson model might be
found reasonable within the first concept. On the other hand, Shannon–Wiener
Figure 5. Plant species diversity and residual maps of Shannon–Wiener (a), Simpson (b), and NS
(c) models.
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T a b l e 5 .
D e v e l o p e d m o d e l s o f e a c h i n d e x ( S h a n n o n – W i e n e r , S i m p s o n , a n d n u m b e r o f s p e c i e s ) .
M
o d e l s
S h a n n o n – W i e n e r i n d e x =
2 2 . 2 9
6 +
( 0 . 0 0 8 * E L E V ) +
( 1 . 6 8 8 * M E T A ) +
( 0 . 9 4 4 * M I N T S ) +
( 0 . 0 9 7 * P R C P A ) +
( 0 . 2 2 6 * G E O ) +
( 0 . 0 7 1 * O R G M ) +
( 0 . 0 2 0 * C A C O 3 ) + ( 0 . 0 0 5 * S O I L G ) + ( 0 . 1 3 9 * S P V S D )
( 0 . 9 8 1 * M E T S )
( 1 . 0 6 8 * M A X T A )
( 0 . 2 8 9 * M I N T A )
( 0 . 1 4 3 * P R C P S )
( 0 . 1 6 8 * P E T A N )
( 0 . 0 3 2 * P E T S E )
( 0 . 0 0 4 * S T R )
( 0 . 0 0 8 * T E X T R )
( 0 . 3 7 8 * P H )
( 0 . 0 8 1 * S L D P T )
( 0 . 0 0 2 * N D V I )
S i m p s o n i n d e x =
1 0 . 4 2 4 + ( 0 . 4 3 7 * M I N T A ) + ( 0 . 0 8 7 * P R C P S ) + ( 0 . 0 2 8
* P E T S E ) + ( 0 . 0 7 9 * T E X T R ) + ( 0 . 0 0 4 *
O R G M ) + ( 0 . 0 3 0 * S L D P T ) + ( 0 . 2 6 9 *
P H ) +
( 0 . 0 0 1 * N D V I )
( 0 . 0 0
2 * E L E V )
( 0 . 0 0 9 * M E T A )
( 0 . 2 2 0 *
M E T S )
( 0 . 2 9 3 * M A X T A )
( 0 . 4 2 8 * M
I N T S )
( 0 . 0 6 4 * P R C P A )
( 0 . 0 2 7 *
P E T A N )
( 0 . 0 0 3 * S T R ) ( 0 . 0 6 4 * G E O )
( 0 . 0 0 1 * S O I L G )
( 0 . 0 1 1 * C A C O 3 )
( 0 . 0 5 3 * S P V S D )
N u m b e r o f s p e c i e s i n d e x =
2 4 4 . 8 0 4 + ( 0 . 1 3 6 * E L E V ) + ( 0 . 3 4 0 * O R G M
) + ( 0 . 1 7 5 * C A C O 3 ) + ( 4 . 9 3 5 * T E X T R ) + ( 1 1 . 5 6 5 * S O I L G ) + ( 1 . 0 9 9 * G E O ) +
( 0 . 0 2 6 * N D V I ) + ( 0 . 3 6 5 * S P V S D ) + ( 2 3 . 0 8 9 * M E T A ) + ( 4 . 7 6 6 * M E T S ) + ( 3 . 8 0 4 * M A X T A ) + ( 6 . 6 9 7 * M I N T A ) + ( 1 . 1 5 2 * P R C P S ) + ( 3 . 9 3 2 * P E T S E )
( 5 . 6 4 2 * P H )
( 0 . 3 9 5 * S T R )
( 0 . 4 0 2 * S L D P T )
( 2 . 0 7 9 * M I N T S )
( 0 . 2 0 4 * P R C P A )
( 1 0 . 7 7 0 * P E T A N )
T h i s t a b l e s t a t e s t h e m o d e l s ( r e g r e s s i o n e q u a t i o n s ) a c c o r d i n g t o t h e r e s u l t s
o f m u l t i p l e r e g r e s s i o n . I n t h e e q u a t i o n s , t h e u n d e r s t a n d a r d i z e d c o e ffi c i e n t s a r e t h e
c o e ffi c i e n t s o f t h e e s t i m a t e d r e g r e s s i o n m o d e l . E a c h m o d e l a l s o h a s a c o n s t a n t v a l u e s u c h a s ; 2 2 . 2 9 6 f o r S h a n n o n – W i e n e r , 1 0 . 4 2 4 f o r S i m p s o n , a n d 2 4 4 . 8 0 4 f o r
n u m b e r o f s p e c i e s .
N o t e : F o r a b b r e v i a t i o n s s e e T a b
l e 3 .
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and NS models could be found acceptable according to the second concept. So,
the question remains as to which diversity index reflected the reality. According
to The Ecological Society of America Committee on Land Use (Dale et al.
2000); Shannon’s index of diversity has greater sensitivity to rare cover types
and it needs to be given greater importance during interpretation. However,
Simpson’s index of diversity might be preferred in landscapes where a single
dominant land cover type is of interest. Therefore, the appropriateness of the
models depends on the aims what the decision-makers seek. Shannon–Wiener
and NS Models might be useful to detect the areas where rare and endangered
species in focus. On the other hand, Simpson model could be best fit to
determine the areas where dominant species in point of concentration.
Conclusion
In this study, we tested the modeling and mapping capabilities of some diversity
indices by using a new approach. The forest ecosystem was handled as a whole,
and the relationship between the plant biodiversity and the factors effective on
ecosystem were investigated. The complementary data about topography,
geology, soil, forest, climate, land cover, and NDVI supplied very important
information, and played the backbone role at the spatial analysis and modeling
stages. The importance of quantitative field data was also emphasized. The
results showed that plant diversity can be modeled by using index values and
complementary data set. Both GIS and RS are important tools at the analysisand visualizing (mapping) stages. According to the results; both Shannon–
Wiener and NS models could be successful to reveal the richness aspect of
species diversity, while Simpson model might be acceptable to delineate the
evenness aspect indicating single dominant land cover types. Although this
study suggested an applicable method, it is implied that researchers should be
cautious to select appropriate index according to their aims.
Acknowledgements
The authors wish to thank the following individuals for their contributions in
various parts of this research study: Vedat Toprak, Lutfi Suzen, Unal Sorman,
and Zuhal Akyurek from Middle East Technical University (METU); OsmanKetenoglu from Ankara University (AU); Ali Mermer, Ediz Unal, Tuncay
Porsuk, Oztekin Urla, and Hakan Yildiz from the GIS and RS Department of
Central Research Institute for Field Crops (CRIFC-GIS and RS); Murat
Cetiner and Irfan Artuc from the Nallihan Forest Management District
(NFMD). Thanks are also due to METU Research Fund for making financial
assistance, Soil and Fertilizer Research Institute for analyzing soil samples, and
the Keeper of the Ankara (ANK) Herbarium for making the herbarium
facilities available.
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Appendix 1 List of identified species in the study area (endemics were stated in bold and marked
with are asterisk (*))
No. Species name Family
1 Alhagi pseudalhagi (Bieb.) Desv. Leguminosae
2 Anthyllis vulneraria L. subsp. boissieri (Sag.) Bornm. Leguminosae
3 Astragalus angustifolius Lam. subsp. angustifolius Leguminosae
4 Astragalus densifolius Lam. Leguminosae
5 Astragalus glycyphyllos L. subsp. glycyphylloides (DC.)
Matthews
Leguminosae
6 Astragalus lycius Boiss. Leguminosae
7 Astragalus macrocephalus Willd. subsp. Macrocephalus Leguminosae
8 Astragalus microcephalus Willd. Leguminosae
9 Astragalus micropterus Fischer Leguminosae
10 Astragalus squalidus Boiss. & Noe ¨ * Leguminosae
11 Astragalus trichostigma Bunge * Leguminosae
12 Chamaecytisus pygmaeus (Willd.) Rothm. Leguminosae
13 Cicer pinnatifidum Jaub. & Spach Leguminosae
14 Conorilla varia L. subsp. Varia Leguminosae
15 Dorycnium pentaphyllum Scop. subsp. anatolicum
(Boiss.) Gams
Leguminosae
16 Hedysarum varium Willd. Leguminosae
17 Lathyrus aureus (Stev.) Brandza Leguminosae
18 Lotus aegaeus (Gris.) Boiss. Leguminosae
19 Lotus corniculatus L. var. corniculatus Leguminosae
20 Lotus corniculatus L. var. tenuifolius L. Leguminosae
21 Medicago polymorpha L. var. vulgaris (Benth.) Shinners Leguminosae
22 Medicago sativa L. subsp. Sativa Leguminosae23 Medicago varia Martyn Leguminosae
24 Melilotus alba Desr. Leguminosae
25 Melilotus officinalis (L.) Desr. Leguminosae
26 Onobrychis argyrea Boiss. Subsp. argyrea Leguminosae
27 Onobrychis armena Boiss. & Huet. Leguminosae
28 Onobrychis hypargyrea Boiss. Leguminosae
29 Ononis adenotricha Boiss. var. adenotricha Leguminosae
30 Ononis spinosa L. subsp. Leiosperma (Boiss.) S ˇ irj. Leguminosae
31 Pisum sativum L. subsp. Elatius var. elatius Leguminosae
32 Trifolium arvense L. var. arvense Leguminosae
33 Trifolium barbulatum (Freyn & Sint.) Zoh.* Leguminosae
34 Trifolium repens L. var. repens Leguminosae
35 Vicia cracca L. subsp. Stenophylla Vel. Leguminosae
36 Vicia grandiflora Scop. var. grandiflora Leguminosae
37 Vicia narborensis L. var. narborensis Leguminosae38 Achillea biebersteinii Afan. Compositae
39 Achillea setacea Waldst. & Kit. Compositae
40 Acroptilon repens (L.) DC. Compositae
41 Anthemis tinctoria L. var. discoidea (All.) DC. Compositae
42 Cardopodium corymbosum (L.) Pers. Compositae
43 Carlina corymbosa L. Compositae
44 Centaurea deprassa Bieb. Compositae
45 Centaurea solstitialis L. subsp. solstitialis Compositae
46 Centaurea triumfettii All. Compositae
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Appendix 1. (Continued )
No. Species name Family
47 Centaurea urvillei DC. subsp. Urvillei * Compositae
48 Centaurea virgata Lam. Compositae
49 Chardinia orientalis (L.) O. Kuntze Compositae
50 Chondrilla juncea L. var. juncea Compositae
51 Cichorium intybus L. Compositae
52 Cirsium arvense (L.) Scop. subsp. vestitum Compositae
53 Cirsium hypoleucum DC. Compositae
54 Crepis sancta (L.) Babcock Compositae
55 Doronicum orientale Hoffm. Compositae
56 Echinops ritro L. Compositae
57 Inula oculus-christi L. Compositae
58 Lactuca serriola L. Compositae
59 Leontodon asperrimus (Willd.) J. Ball. Compositae
60 Petasites hybridus (L.) Gaertner Compositae
61 Pilosella echioides (Lumn.) C.H. & F.W.Schultz subsp.
procera (Fries) Sell & West
Compositae
62 Pilosella hoppeana (Schultes) C. H. & F.W. Schultz
subsp. testimonialis (NP.) Sell &West
Compositae
63 Scorzonera cana (C.A.Meyer) Hoffm. Compositae
64 Scorzonera laciniata L. Compositae
65 Senecio vernalis Waldst. & Kit. Compositae
66 Sonchus asper L. Hill subsp. glaucescens (Jordan) Ball. Compositae
67 Tanacetum poteriifolium (Ledeb.) Compositae
68 Tanacetum vulgare L. Compositae
69 Taraxacum seronitum (Waldst. & Kit.) Poiret in Lam. Compositae
70 Tragopogon latifolius Boiss. var. angustifolius Boiss. Compositae
71 Xeranthemum annuum L. Compositae
72 Acinos rotundifolius Pers. Labiatae
73 Ajuga chamaepitys (L.) Schreber, subsp. chia (Schreber)
Arcangeli, var. chia
Labiatae
74 Lamium macradon Boiss. & Huet Labiatae
75 Marrubium parviflorum Fisch. & Mey. subsp. oligodon
(Boiss.) Seybold *
Labiatae
76 Mentha spicata L. subsp. tomentosa (Briq.) Harley Labiatae
77 Nepeta nuda L. subsp. albiflora (Boiss.) Gams Labiatae
78 Phlomis armeniaca Willd. * Labiatae
79 Phlomis nissolii L. Labiatae
80 Prunella vulgaris L. Labiatae
81 Salvia aethiopis L. Labiatae
82 Salvia hypargeia Fisch. & Mey. Labiatae
83 Salvia sclarea L. Labiatae
84 Salvia tomentosa Miller (Syn: S. grandiflora Etl.) Labiatae
85 Salvia verticillata L. subsp. amasiaca (Freyn & Bornm.)
Bornm.
Labiatae
86 Salvia viridis L. Labiatae
87 Scutellaria orientalis L. subsp. macrostegia (Hausskn. ex
Bornm.) Edmondson
Labiatae
88 Sideriris montana L. subsp. montana Labiatae
89 Sideritis galatica Bornm. Labiatae
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Appendix 1. (Continued )
No. Species name Family
90 Stachys annua (L.) L. subsp. ammophila (Boiss. & Bl.)
Samuelss
Labiatae
91 Stachys annua (L.) L. subsp. a nnua var. annua * Labiatae
92 Stachys cretica L. subsp. anatolica Rech. fil. * Labiatae
93 Teucrium chamaedrys L. subsp. chamaedrys Labiatae
94 Teucrium parviflorum Schreber Labiatae
95 Teucrium polium L. Labiatae
96 Thymus leucostomus Hausskn. &Velen. var. leucostomus Labiatae
97 Thymus longicaulis C. Presl subsp. longicaulis Labiatae
98 Thymus sipyleus Boiss. subsp. sipyleus Labiatae
99 Ziziphora capitata L. Labiatae
100 Cotoneaster nummularia Fisch. & Mey. Rosaceae
101 Crataegus monogyna Jacq. subsp. monogyna Rosaceae
102 Crataegus orientalis Pallas ex Bieb. var. orientalis Rosaceae
103 Crataegus tanacetifolia (Lam.) Pers. * Rosaceae
104 Potentilla recta L. Rosaceae
105 Prunus avium (L.) L. Rosaceae
106 Prunus divaricata Ledeb. subsp. divaricata Rosaceae
107 Prunus spinosa L. subsp. dasyphylla (Schur) Domin Rosaceae
108 Pyracantha coccinea Roemer Rosaceae
109 Pyrus elaeagnifolia Pallas subsp. elaeagnifolia Rosaceae
110 Rosa canina L. Rosaceae
111 Rubus ideaus L. Rosaceae
112 Rubus sanctus Schreber Rosaceae
113 Sanguisorba minor Scop. subsp. muricata (Spach) Briq. Rosaceae
114 Sorbus umbellata (Desf.) Fritsch var. umbellata Rosaceae
115 Alyssum desertorum Stapf. var. desertorum Cruciferae
116 Alyssum murale Waldst. & Kit. var. murale Cruciferae
117 Alyssum sibiricum Willd. Cruciferae
118 Arabis nova Vill. Cruciferae
119 Barbera plantaginea DC. Cruciferae
120 Cardaria draba (L.) Desv. subsp. draba Cruciferae
121 Erysimum crassipes Fisch. & Mey. Cruciferae
122 Iberis taurica DC. Cruciferae
123 Thlaspi perfoliatum L. Cruciferae
124 Turritis glabra L. Cruciferae
125 Agropyron cristatum (L.) Geartner, subsp: pectinatum
(Bieb.) Tzvelev, var: pectinatum
Gramineae
126 Aegilops umbellulata Zhuk. Gramineae
127 Brachypodium sylvaticum (Hudson) P. Beauv Gramineae
128 Dactylis glomerata L. subsp. glomerata Gramineae
129 Festuca airoides Lam. Gramineae
130 Festuca anatolica Markgr.-Dannenb. subsp. anatolica Gramineae
131 Festuca ilgazensis Markgr.-Dannenb. Gramineae
132 Poa bulbosa L. Gramineae
133 Stipa bromoides (L.) Do ¨ rfler Gramineae
134 Stipa lessingiana Trin. & Rupr. Gramineae
135 Allium scorodoprasum L. subsp. rotundum (L.) Stearn Liliaceae
136 Gagea granatellii (Parl.) Parl. Liliaceae
137 Muscari armeniacum Leichtlin ex Baker Liliaceae
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Appendix 1. (Continued )
No. Species name Family
138 Muscari longipes Boiss. Liliaceae
139 Muscari neglectum Guss. Liliaceae
140 Muscari tenuiflorum Tausch Liliaceae
141 Ornithogalum oligophyllum E.D.Clarke. Liliaceae
142 Ornithogalum fimbriatum Willd. Liliaceae
143 Ornithogalum umbellatum L. Liliaceae
144 Adonis flammea Jacq. Ranunculaceae
145 Ranunculus argyreus Boiss. Ranunculaceae
146 Ranunculus ficaria L. subs. ficariiformis Rouy & Fouc. Ranunculaceae
147 Dianthus anatolicus Boiss. Caryophyllaceae
148 Dianthus ancyrensis Hausskn. & Bornm. * Caryophyllaceae
149 Dianthus zonatus Fenzl var. zonatus Caryophyllaceae
150 Herniaria glabra L. Caryophyllaceae
151 Minuartia hirsuta (Bieb.) Hand. & Mazz. Caryophyllaceae
152 Saponaria glutinosa Bieb. Caryophyllaceae
153 Silene supina Bieb. subsp. pruinosa (Boiss) Chowdh Caryophyllaceae
154 Astrodaucus orientalis (L.) Drude Umbelliferae
155 Coriandrum sativum L. Umbelliferae
156 Falcaria vulgaris Bernh. Umbelliferae
157 Laser trilobum (L.) Borkh. Umbelliferae
158 Malabaila secacul Banks & Sol. Umbelliferae
159 Turgenia latifolia L. Hoffm. Umbelliferae
160 Zosima absinthifolia (Vent.) Link Umbelliferae
161 Alkanna orientalis (L.) Boiss. var. orientalis Boraginaceae
162 Anchusa leptophylla Roemer & Schultes subsp. lepto-
phylla
Boraginaceae
163 Cerinthe minor L. subsp. minor Boraginaceae
164 Lithospermum officinale L. Boraginaceae
165 Onosma aucheranum DC. Boraginaceae
166 Onosma bornmuelleri Hausskn. Boraginaceae
167 Onosma isauricum Boiss. & Heldr. * Boraginaceae
168 Onosma tauricum Pallas ex Willd. var. tauricum Boraginaceae
169 Digitalis ferruginea L. subsp. ferruginea Scrophulariaceae
170 Digitalis orientalis Lam. Scrophulariaceae
171 Scrophularia scopolii [Hoppe ex] Pers. var. scopolii Scrophulariaceae
172 Verbascum cherianthifolium Boiss var. cheiranthifolium.* Scrophulariaceae
173 Verbascum glomeratum Boiss Scrophulariaceae
174 Veronica chamaedrys L. Scrophulariaceae
175 Veronica multifida L. Scrophulariaceae
176 Veronica pectinata L. var. pectinata Scrophulariaceae
177 Asyneuma limonifolium (L.) Janchen subsp. pestalozzae
(Boiss.) Damboldt.
Campanulaceae
178 Asyneuma rigidum (Willd.) Grossh. subsp. rigidum Campanulaceae
179 Campanula glomerata L. Campanulaceae
180 Campanula persicifolia L. Campanulaceae
181 Legousia speculum-veneris (L.) Chaix Campanulaceae
182 Asperula stricta Boiss. subsp. latibracteata (Boiss.) Eh-
rend.
Rubiaceae
183 Cruciata taurica (Pallas ex Willd.) Ehrend. Rubiaceae
184 Galium incanum Sm. subsp. elatius (Boiss.) Ehrend. Rubiaceae
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Appendix 1. (Continued )
No. Species name Family
185 Galium palustre L. Rubiaceae
186 Galium verum subsp. verum Rubiaceae
187 Cistus laurifolius L. Cistaceae
188 Fumana aciphylla Boiss. Cistaceae
189 Helianthemum nummularium (L.) Miller. subsp. ovatum
(Viv.) Schinz & Thell
Cistaceae
190 Juniperus communis L. subsp. nana Cupressaceae
191 Juniperus excelsa Bieb. Cupressaceae
192 Juniperus foetidissima Willd. Cupressaceae
193 Juniperus oxycedrus L. subsp. oxycedrus Cupressaceae
194 Scabiosa argentea L. Dipsacaceae
195 Quercus cerris L. var. cerris Fagaceae
196 Quercus pubescens Willd. Fagaceae
197 Quercus robur L. subsp. robur Fagaceae
198 Osyris alba L. Santalaceae
199 Thesium billardieri Boiss Santalaceae
200 Paronychia dudleyi Chaudhri Illecebraceae
201 Paronychia kurdica Boiss. subsp. kurdica var. kurdica Illecebraceae
202 Iris orientalis Miller. Iridaceae
203 Acanthus hirsutus Boiss. Acanthaceae
204 Paliurus spina-christi Miller Rhamnaceae
205 Rhamnus thymifolius Bornm. * Rhamnaceae
206 Geranium robertianum L. Geraniaceae
207 Geranium tuberosum L. subsp. tuberosum Geraniaceae
208 Linum hirsitum L. subsp. anatolicum (Boiss) Hayek * Linaceae
209 Linum tenuifolium L. Linaceae
210 Fumaria cilicica Hausskn. Papaveraceae
211 Hypecoum procumbens L. Papaveraceae
212 Papaver commutatum Fisch & Mey Papaveraceae
213 Rhus coriaria L. Anacardiaceae
214 Berberis crataegina DC. Berberidaceae
215 Berberis vulgaris L. Berberidaceae
216 Salsola ruthenica Iljin Chenopodiaceae
217 Convolvulus arvensis L. Convolvulaceae
218 Corylus avellana L. var. avellana Coryllaceae
219 Sempervivum armenum Boiss. & Huet. var. armenum Crassulaceae
220 Carex flacca Schreber subsp. serrulata (Biv.) Greuter Cyperaceae
221 Carex ovalis Good. Cyperaceae
222 Equisetum palustre L. Equisetaceae
223 Euphorbia macroclada Boiss. Euphorbiaceae
224 Globularia trichosanta Fisch. & Mey. Globulariaceae
225 Hypericum perforatum L. Guttiferae
226 Malva neglecta Wallr. Malvaceae
227 Cephalanthera rubra (L.) L.C.M. Richard Orchidaceae
228 Paeonia mascula subsp. mascula Paeoniaceae
229 Paeonia peregrina Paeoniaceae
230 Pinus brutia Pinaceae
231 Pinus nigra subsp. pallasiana Pinaceae
232 Acantholimon acerosum (Willd.) Boiss Plumbaginaceae
233 Acantholimon glumaceum (Jaub. & Spach) Boiss. Plumbaginaceae
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