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A presence-only habitat suitability model for Persian leopard
Panthera pardus saxicolor in Golestan National Park, Iran
Author(s): Behnaz Erfanian , Seyed Hamed Mirkarimi , Abdolrassoul Salman Mahini &
Hamid Reza Rezaei
Source: Wildlife Biology, 19(2):170-178. 2013.
Published By: Nordic Board for Wildlife Research
DOI: http://dx.doi.org/10.2981/12-045
URL: http://www.bioone.org/doi/full/10.2981/12-045
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Original article
Wildl. Biol. 19: 170-178 (2013)
DOI: 10.2981/12-045
ÓWildlife Biology, NKV
www.wildlifebiology.com
A presence-only habitat suitability model for Persian leopard
Panthera pardus saxicolor in Golestan National Park, Iran
Behnaz Erfanian, Seyed Hamed Mirkarimi, Abdolrassoul Salman Mahini & Hamid Reza Rezaei
Top predators such as leopard Panthera spp. are often associated with high biodiversity, so the protection of their habitats
is one of the most effective ways to conserve biodiversity globally. In this paper, we use ecological niche factor analysis
(ENFA), a presence-only environmental habitat-envelope based method to create habitat suitability maps for Persian
leopard in Golestan National Park (GNP), Iran. The Persian leopard Panthera pardus saxicolor is an endangered sub-
species on the IUCN Red List of Threatened Species. During a one-year field study in 2009, we recorded 120 leopard
locations and related these to 14 environmental variables. Our analysis shows that the Persian leopard in this area lives
within a very narrow range of conditions and therefore may require rather specific habitat protection and management in
this area.
Key words: ecological niche factor analysis, Golestan National Park, habitat suitability, Panthera pardus saxicolor, Persian
leopard
Behnaz Erfanian, Seyed Hamed Mirkarimi, Abdolrassoul Salman Mahini & Hamid Reza Rezaei, Department of
Environmental Sciences, Gorgan University of Agricultural Sciences and Natural Resources, Gorgan,Iran - e-mail addresses:
b_erfaniyan@yahoo.com (Behnaz Erfanian); mirkarimi.hamed@gmail.com (Seyed Hamed Mirkarimi); a_mahini@
yahoo.com (Abdolrassoul Salman Mahini); hamid.r.rezaei@gmail.com (Hamid Reza Rezaei)
Corresponding author: Behnaz Erfanian
Received 1 May 2012, accepted 28 December 2012
Associate Editor: John P. Ball
Knowledge of the distribution of threatened species
and their habitat requirements is an important ele-
ment of conservation biology (Engler et al. 2004).
Management for threatened species, ecosystem res-
toration, species reintroductions, population viabil-
ity analyses and resolving conflicts between humans
and wildlife often rely on habitat suitability model-
ling (LeLay et al. 2001, Hirzel et al. 2001). Multivar-
iate models are commonly used to define habitat
suitability and combined with geographical infor-
mation systems (GIS) allow researchers to create
potential distribution maps (Guisan & Zimmermann
2000).
Presence-only modelling techniques are increasing-
ly being used to study the distribution of many
different organisms (Robertson et al. 2001, Hirzel et
al. 2001). One of these alternative techniques is
ecological niche factor analysis (ENFA; Hirzel et al.
2002). Because the results of ENFA analysis are
straightforward and often easily interpreted (Rood et
al. 2010), and because the software is readily avail-
able, we chose to use ENFA for assessing the habitat
of the Persian leopard Panthera pardus saxicolor.The
canonical depiction of the species’ niche relative to its
environment allows one to evaluate which part of the
available habitat is occupied and to assess to which
extent the available habitat is utilised (Titeux et al.
2006, Braunisch et al. 2008). Note that ENFA is more
suited to determine a species potential distribution
rather than its realised distribution (Jimenez-Val-
verde et al. 2008).According to the latest assessment
of leopard status for the 2008 IUCN Red List of
Threatened Species, the Persian leopard should be
classified as ’endangered’under the category C2a(i)
(Khorozyan et al. 2005, Khorozyan 2008). Iran has
been the stronghold of the Persian leopard population
170 ÓWILDLIFE BIOLOGY 19:2 (2013)
in the Middle East where the range of approximately
885,300 km
2
provides home for an estimated 550-850
individuals (Kiabi et al. 2002, Khorozyan 2008).
The most urgent threat to the Persian leopard is the
ever increasing fragmentation of the population into
the patchy network of distant and often too small
subpopulations. According to Ghoddousi et al.
(2008), not a single subpopulation across the entire
range is believed to contain more than 100 adult
individuals and only a handful of protected areas (all
concentrated in Iran) are large enough to maintain
viable subpopulations of Persian leopards. Prey
reduction because of poaching, infrastructure devel-
opment, disturbance and habitat loss (collection of
edible plants and mushrooms, mining, road con-
struction, deforestation, wild fire and livestock
grazing) are the principal factors of the fragmenta-
tion, leaving vast tracts of habitats unsuitable for
resident Persian leopard subpopulations (Fumagalli
2007, Khorozyan 2008, Ghoddousi et al. 2008). In
some areas, Persian leopards regularly attack do-
mestic livestock because of reductions in natural
prey, and then clash with rural people who try to
eliminate predators by poisoning prey remains
(Farhadinia et al. 2007). Previous research on the
Persian leopard in Iran has focused on the species’
population size and status (Kiabi et al. 2002,
Farhadinia et al. 2009), reproductive ecology (Far-
hadinia et al. 2009), genetic diversity (Farhadinia
2009), territorial marking (Ghoddousi et al. 2008),
habitat modelling (Omidi 2008), status analysis and
distribution mapping in Iran (Sanei 2007).
Despite the need for efficient habitat protection
efforts, more ecological studies on the Persian
leopard in Golestan National Park (GNP) are
needed. In fact, our study is the first attempt to
determine Persian leopard habitat suitability in GNP
to date; no field study has been carried out in this
region. Hence, in the present study, we used ENFA
to: 1) develop predictive habitat suitability maps for
Persian leopards, 2) identify the environmental
variables important in describing the habitat for this
subspecies and 3) quantify the extent and location of
potential Persian leopard habitat available for con-
servation action in GNP.
Material and methods
Study area
The GNP is located in the northeastern part of Iran
near the border with Turkmenistan and covers an
area of about 919 km
2
. It is considered one of Iran’s
most important nationalparks because of its natural
assets such as its verdant, virgin forests. The park is
located east of the Caspian Sea between 37824’Nand
55858’E(37.4038Nand55.9768E; Madjnoonian et al.
1999; Fig. 1). The vegetation of the park can be
divided into two zones: the Hyrcanian forest in east
Alborz (the western section of the park with a high
humidity) and the Iran-Turanian vegetation (the
eastern section of the park where it is dry; Javanshir
1976). Its maximum and minimum elevations are the
summit of Divarkaji and the Tangrah at approxi-
mately 2,411 m and 450 m altitudes, respectively. The
park includes mountainous areas, hills, fields and
plains. The mountainous areas of the park are mostly
located in the northern and western parts, with
altitude reducing gradually towards the steppe-
covered east. The average annual precipitation is
400 mm and annual average temperature is 11.98C
from April to October and 10.58CfromDecemberto
March.
GNP is the most important protected area of Iran
with great habitats for Persian leopards because of its
unique natural situation and the well-chosen location
Figure 1. Geographical location of the study
area in the Golestan National Park, Iran.
ÓWILDLIFE BIOLOGY 19:2 (2013) 171
(Agili 2005). One of the unresolved problems of the
park at the moment is the Asian highway, which runs
through thepark and divides it into a northern and a
southern part, possibly hampering leopard move-
ment (see Fig. 1).
Leopard distribution data
During a one-year field study in 2009, we recorded
120 detailed point locations of the Persian leopard.
These distribution data come from field observations
and interviews with biologists and park managers
from regional environmental agencies and all were
verified by visits to the locations at which Persian
leopards were reported. Prey species distribution
data were also obtained by means of field observa-
tions using GPS and map-based interviews with
rangers and staff from the environmental office in
GNP. Sampling for evidence of Persian leopard
presence was done using tracks, scrapes and scats.
We only recorded spots carrying certain signs of the
Persian leopard being present: each spot was visited,
and based on the certainty of the remains or signs of
this animal the records were refined. The Persian
leopard presence data are shown in Figure 2.
Environmental data
ENFA needs two types of data to calculate habitat
suitability: a map of locations where the species has
been detected and a set of quantitative raster maps
describing the environment as used by the species
under investigation. Independent eco-geographical
variables (EGV) quantitatively describe relevant
characteristics for each grid cell. These may be to-
pographical features (e.g. altitude and slope), eco-
logical data (e.g. frequency of forests and nitrate
concentration), or human infrastructures (e.g. dis-
tance to the nearest town and road density). A
function of the EGV is then calibrated so as to classify
as correctly as possible the cells as suitable or
unsuitable for the species. The details of the function
and its calibration depend on the analysis (Hirzel et al.
2001). Using expert knowledge and an extensive
literature review, we selected 14 variables that might
act as direct or surrogate determinants of the current
distribution of the Persian leopard in GNP (Table 1):
four accounted for environmental traits (habitat
structure and geomorphology) and 10 for human
impacts and main prey species. Altitude, slope and
aspect were calculated from a digital elevation model
of 10 m pixel width. The normalised difference veg-
etation index (NDVI) variable that accounted for
habitat structure was derived from Landsat TM
images (acquired in August 2007) originally in 30330
mega pixels. These we then resampled to 10310 mega
pixels. Studies such as Salman Mahini (2007) and
Boelman et al. (2011) indicate a positive relationship
Figure2. Presence data of t hePersian leopard
in the GNP, Iran.
Table 1. Variables used in the spatial modelling of Persian leopard
habitat in Golestan National Park, Iran.
Variables Description and sources
GEOMORPHOLOGY
Altitude Digital elevation model (DEM)
Slope Slope steepness (%)
SAspect Based on DEM
HUMAN IMPACT
Distance to road (m) -
Distance to village (m) -
Distance to spring (m) -
Distance to river (m) -
Distanceto agricultural land (m) -
HABITAT STRUCTURE
NDVI Based on red and near infrared
bands of Landsat TM, 2007
PREY SPECIES
Goitered gazelle, red deer,
wild boar, wild goat and
wild sheep
Presence points (interview with
game guards in the field)
172 ÓWILDLIFE BIOLOGY 19:2 (2013)
between NDVI and habitat structure as it shows
density of vegetation on the ground. Variables that
might potentially account for the human impacts on
the Persian leopard territories were distance to road
(Asian highway and dirt roads), distance to villages,
distance to springs, distance to agricultural lands and
distance to rivers. The major prey species of the
Persian leopard in the area were goitered gazelle
Gazella subgutturosa,reddeerCervus elaphus,wild
boar Sus scrofa, wild goat Capra aegagrus and wild
sheep Ovis orientalis. We used the presence points for
these prey acquired through interview with game
guards in the field.
Topographical data (i.e. altitude, slope and aspect)
were quantitative and used directly in the ENFA
model, but habitat structure, human impact and prey
factors were qualitative and were transformed into
frequency and distance variables before calculation
(Hirzel et al. 2002). Distance variables expressed the
distance between the focal cell and the closest cell
belonging to a given category. Frequency variables
described the proportion of cells from a given
category within a circle of a 150 m radius around
the focal cell. This radius was applied within
Biomapper during our analyses. After the prepara-
tion of environmental variables and conversion of
’Presence’points to raster grids (10310 mega pixels),
the data were normalised through a Box-Cox trans-
formation (Hirzel et al. 2002). The common resolu-
tion of the maps used for the analysis was set at 10 m.
This resolution represented a trade-off between accu-
racy and computation time (Chefaoui et al. 2005).
Statistical analysis
Habitat modelling
The ENFA summarises several EGVs in a few
uncorrelated factors retaining most of the informa-
tion similar to principal component analysis (PCA).
The ENFA concept has been incorporated into
Biomapper 4 (Hirzel et al. 2007) and follows the
procedures outlined by Hirzel et al. (2002). The
outputs of the ENFA included eigenvalues and
factor scores. The first factor, marginality, describes
the distance of the species optimum from the
ecological conditions in the study area (i.e. the
direction in which the species niche differs most from
the available conditions in the study area; Hirzel et al.
2002, Santos et al. 2006). The coefficients of the score
matrix as related to the marginality factors indicate
the correlation between each EGV and the margin-
ality factor. The greater the absolute value of the
coefficient the higher this EGV contributes to the
marginality. A low value (close to 0) indicates that the
species tend to live in average conditions throughout
the study area, whereas a high value (close to 1)
indicates a tendency for the species to live in extreme
habitats. A positive value means that the species
’prefers’the high values of this EGV, while a negative
value means that the species ’prefers’the low values.
The subsequent factors are called specialisation
factors and are sorted by decreasing amounts of
variance accounted for. These factors describe how
specialised the species is in comparison to the
available range of habitats in the study area (Hirzel
et al. 2002, Santos et al. 2006). Therefore, only a few
of the first factors explain the major partof the whole
information. Specialisation ranges from 1 to infinity
and thus is difficult to interpret. For this reason, it is
easier to use the tolerance factor, which measures the
choosiness of the species to the available range of
EGVs. Tolerance is defined as the inverse of special-
isation (1/S) and ranges from 0 to 1, indicating either
specialist species (stenoic) who tend to live in a very
narrow range of conditions or species that inhabit
any of the conditions in the study area (eurioic). With
the factor scores computed, a habitat suitability map
was created using the harmonic algorithm. The
number of significant factors included in the habitat
suitability map was decided according to the com-
parison of the eigenvalues to MacArthur’s broken-
stick distribution, which is the expected distribution
when breaking a stick randomly. The eigenvalues
that are larger than expected according to the broken
stick distribution may be considered ’significant’
(Hirzel et al. 2002, Hirzel et al. 2007).
Model validation and accuracy
Before using the ENFA results or habitat suitability
map (HSM), we needed to evaluate their accuracy in
describing the actual spatial response of the species
(Santos et al. 2006). The habitat suitability map was
evaluated for predictive accuracy by jackknife cross
validation (Boyce et al. 2002).
The species locations were randomly partitioned
into k mutually exclusive but identically sized sets.
Each k minus 1 partition was used to compute a
habitat suitability model and the left-out partition
was used to validate it on independent data. This
process was repeated k times, each time by leaving
out a different partition. This process resulted in k
different habitat suitability maps and the comparison
of these maps and how they fluctuated, providing an
assessment of their predictive power. The number of
ÓWILDLIFE BIOLOGY 19:2 (2013) 173
partitions used was 10. Each map was reclassified
into i bins, where each bin i covered some proportion
of the total study area (Ai) and contained some
proportion of the validation points (Ni; validation
points were the observations left out during the cross-
validation process). We used the default number of
bins (i.e. four bins). The area-adjusted frequency for
each bin was computed as Fi ¼Ni/Ai. The expected
Fi was 1 for all bins if the model fitted no better than
random. If the model was good, low valuesof habitat
suitability should have a low F (,1) and high values
ahighF(.1) with a monotonic increase between.
The monotonic nature of the curve was measured
using a Spearman rank correlation on the Fi in a
moving window, termed the continuous Boyce Index
(Boyce et al. 2002, Hirzel et al. 2004, Santos et al.
2006, Edgaonkar 2008). The Boyce Index measures
the correlation between habitat suitability values and
the area-adjusted frequency of presence points in the
habitat map. The continuous Boyce Index varies
from -1 for an inverse model to 0 for a random model
to 1 for a perfect model (Boyce et al. 2002, Hirzel et al.
2006). Finally, the habitat suitability map was
reclassified into two classes of suitable and unsuit-
able, and an approximation of the Persian leopard
individuals was assessed in the suitable habitat class.
Results
According to MacArthur’s broken-stick distribution
(Hirzel et al. 2002), the 14 environmental variables
considered were reduced to 10 factors (Table 2) that
explained 94% of the variance. The percentages
explained by the specialisation factor can be seen in
Table 2. The presence of Persian leopard was
positively associated with NDVI, distance to agri-
cultural lands, aspect and presence of wild boar, wild
sheep and red deer on the factor explaining margin-
ality (see Table 2), indicating a preference for these
variables.Altitude, distance to villages and frequency
of goitered gazelle had the higher coefficient of the
first factor showing that the distribution of the
species was specially restricted by these variables (see
Table 2).
According to the ecological model, Persian
leopard presented a tendency to occupy very
particular conditions compared to the whole of
the GNP (marginality score ¼0.755 and tolerance
factor ¼0.642). The 10 factors retained (out of the
14 computed) accounted for 94% of the total sum
of eigenvalues (that is, 100% of the marginality and
94% of the specialisation). The marginality factor
alone accounted for 23% of the total specialisa-
tion, a rather large value, which means that Persian
leopard displays a very restricted range in which
they utilise habitats quite different from the aver-
age conditions present in the study area. A
suitability map was built from these 10 factors for
the whole case study (Fig. 3). It is interesting to see
that the northern areas with steeper slopes and
higher elevations present lower quality habitat for
the leopards. The habitat suitability index (HSI)
ranged between 0 and 100, with 0 indicating the
Table 2. Coefficients of the variables generated in ENFA, and percentages explained by marginality (MF) and specialisation factors (SF1 -
SF9). EGVs are sorted by decreasing absolute value of coefficients on the marginality factor. The first column shows 100 percent of
marginality. On marginality factor, Positive values (þ) indicates that leopards are found in locations with higher than average cell values.
Negative values (-) indicate that leopards are found in locations with lower than average cell values. Signs of coefficients have no meaning on
the specialisation factors.
EGV MF SF1 SF2 SF3 SF4 SF5 SF6 SF7 SF8 SF9
Altitude -0.43 0.45 0.03 0.13 -0.26 0.07 0.32 0.11 0.27 -0.04
Distance to rivers -0.38 -0.08 0.13 0.19 0.29 -0.17 0.10 0.07 -0.20 0.26
Frequency of springs -0.36 -0.06 -0.01 -0.11 0.06 0.01 0.25 0.13 0.03 0.09
Distance to roads -0.36 -0.10 0.15 -0.41 0.22 0.02 -0.26 -0.64 0.05 -0.05
Frequency of wild goat 0.31 -0.31 0.46 -0.09 -0.27 0.22 0.38 0.26 -0.35 -0.02
Distance to agricultural lands 0.26 0.46 0.34 0.00 -0.57 -0.04 -0.30 0.18 -0.28 -0.12
Frequency of goitered gazelle -0.25 0.41 0.01 -0.48 -0.33 -0.54 0.07 0.24 0.20 -0.28
Frequency of wild boar 0.24 0.02 0.42 0.00 0.31 -0.11 0.28 0.10 -0.33 0.24
Frequency of wild sheep 0.22 0.15 0.28 0.35 0.10 -0.30 0.04 0.25 0.34 -0.18
Frequency of red deer 0.19 -0.35 0.04 0.06 -0.11 -0.02 0.48 -0.37 -0.07 -0.51
Slope -0.17 -0.02 0.02 -0.02 0.10 0.01 -0.30 0.26 -0.31 -0.42
Distance to villages -0.07 0.39 0.35 0.24 -0.37 -0.09 -0.25 0.07 -0.33 0.40
Aspect 0.06 -0.03 0.05 -0.52 -0.14 -0.36 0.03 0.17 0.08 0.08
NDVI 0.05 -0.06 0.51 -0.27 0.10 0.62 -0.23 0.32 0.45 0.36
174 ÓWILDLIFE BIOLOGY 19:2 (2013)
least suitable habitat and 100 the most suitable.
The predictive accuracy of the model was good as
the area-adjusted frequency cross validation ex-
hibited values below and above 1 for the low and
high suitability bins, respectively. Also, the mean
Spearman rank correlation was 0.8. Using the F
curve, the habitat suitability map was reclassified
into two classes, ’suitable’and ’unsuitable’(Fig. 4).
The HS values .2 are considered as suitable class.
These areas suitable for the Persian leopard are
located in the west, central and southeast of the
GNP. Also, the unsuitable class was assigned to
HSI values ranging from 0 to 2. Our results reveal
that the suitable habitat for the Persian leopard
comprises an area of 189 km
2
with an average
altitude of 1,234 m and a slope of 32%. Further-
more, the marginality and specialisation scores
show that the Persian leopard lives in very partic-
ular conditions in this area. Referring to previous
studies about panther home-range size by Kiabi et
al. (2002), if we choose 15 km
2
as the lowest es-
timate of the animal’s home range, then there
would be around 13 Persian leopards in the area.
For the highest estimate of the range size, i.e. 78
km
2
, the number of Persian leopards would be only
around two. Based on the field observations, hab-
itat assessment and the interviews with experienced
wildlife guards, we suggest that the actual number
of the panthers is somewhere in between two and
13.
Figure 3. Habitat suitability map for the
Persian leopard in GNP, as computed from
ENFA. The scale shows the habitat suitabil-
ity values (0 the least suitable, and 100 the
most suitable).
Figure 4. Reclassified habitat suitability map
for the Persian leopard in GNP, as computed
from ENFA.
ÓWILDLIFE BIOLOGY 19:2 (2013) 175
Discussion
Here we present the first study on Persian leopard’s
habitat suitability in GNP. Our analysis directly
provides two key measurements regarding the niche
of the focal species (i.e. marginality and specialisa-
tion factors). Furthermore, interpretation of the
factors in terms of the EGVs turns out to be very
consistent with the published literature as particu-
larly relevant for Persian leopard ecology (Hirzel et
al. 2002).
According to the habitat suitability analyses
carried out and the environmental niche descrip-
tions, we calculated that the habitat suitable for the
Persian leopard comprises an area of 189 km
2
,
averaging 1,234 m in altitude and a slope of 32%
encompassing the western, central and southeastern
parts of the GNP. Because spatial autocorrelation of
the presence data can cause biases in model predic-
tions (Jimenez-Valverde et al. 2008), we checked for
autocorrelation among input parameters in the
model, but it was not significant.
The presence of Persian leopards had a weak
negative association with the distance to villages.
Leopards are known to be bold and are commonly
found in the proximity of human settlements, where
they prey upon livestock (Odden & Wegge 2005).
These results are in agreement with previously
reported data on Persian leopard habitat use (Omidi
2008, Edgaonkar 2008, Ghoddousi et al. 2008,
Farhadinia et al. 2007). In our study, wild goat had
the strongest positive association with Persian leop-
ard occurrence. One of the main concerns for the
conservation of Persian leopards is habitat fragmen-
tation due to human land use (Acevedo et al. 2007).
We feel that this is also an issue in the GNP. Among
human interferences, the Asian highway may pre-
sumably have the largest negative impact on the
Persian leopard’s habitat and cause additional mor-
tality because it runs through the park which likely
restricts the movements of the Persian Leopard. Part
of the prey items for Persian leopard in the GNP are
found in the southern section which is divided by this
highway. So,the Persian leopard isforced to cross the
Asian highway to obtain food, causing collisions
with the traffic. In our study area, road accidents and
casualties are frequent, suggesting that the negative
impact of the main road of the GNP might be
significant. As suggested by marginality factor coef-
ficients, watercourses (i.e. springs and rivers) are
important habitat parameters for leopard and may
restrict its presence. The occurrence of Persian
leopard had a low correlation with other parameters
such as aspect, NDVI, agricultural lands, wild boar,
wild sheep and red deer. Overall, the habitat
suitability map represents an overlap between the
best habitat for Persian leopard, its preys’ habitat
(goitered gazelle, red deer, wild boar, wild goat and
wild sheep) and leopard observations in this region
during the last years.
As Hirzel et al. (2002) suggested, ENFA is a purely
descriptive method and cannot extract causal rela-
tions. Nonetheless, it provides (at worst) important
clues about preferential conditions, and remains a
powerful tool to draw potential habitat maps. In this
respect, a limitation of the software is that it does not
yet provide confidence intervals for distribution
maps. Increasingly, conservation managers are de-
manding risk analyses that incorporate uncertainties
in model predictions. These could clearly be obtained
through the boot strapping of presence data. Though
not yet implemented in Biomapper, this procedure
will certainly provide an important and useful
extension. A second limitation, less easy to deal
with, is that ENFA only handles linear dependencies
within the species niche. Multiplicative or non-linear
interactions cannot be accommodated in the present
approach, except through transformations or non-
linear combinations of the original ecogeographical
variables. A third limitation is that some EGVs may
turn out to be constant in specialisation or in linear
combination with other EGVs, which makes species
covariance matrix (Rs) singular. This is likely to
happen with coarsely measured data or small species
data sets. Whenever this happens, Biomapper iden-
tifies the constant or correlated EGVs and removes
(one of) them from the analysis. An alternative
approach would obviously consist in improving the
field sample either by increasing the presence data set
or by measuring EGVs on a finer scale. Finally, a last
important point to emphasise is that our approach
characterises ecological niches relative to a reference
area. Marginality and specialisation are thus bound
to depend on the geographic limits of the study area.
Some species may turn out to occur at the very edge
of their distribution, and may thus appear quite
specialised in the reference set, however widespread
they might be otherwise. The niche description
methodology derived by Chefaoui et al. (2005),
based on such premises, is used in this study to
describe the realised niches of panther in GNP.
However, ENFA results usually overestimate habi-
tat suitability (Zaniewski et al. 2002, Engler et al.
2004, Acevedo et al. 2007).
176 ÓWILDLIFE BIOLOGY 19:2 (2013)
In conclusion, understanding the habitat selection
processes of wide-ranging large carnivores such as
the Persian leopard and thus the likely consequences
of habitat transformation is essential for developing
conservation strategies to improve long-term viabil-
ity. Our analyses demonstrate that ENFA is a useful
tool to explore the characteristics of the Persian
leopard’s niche as well as to produce habitat
suitability maps that can aid conservation manage-
ment. Our results indicate that Persian leopard
distribution in our study area may be especially
constrained by human impacts. Our analysis sug-
gests that the Asian highway fragments Persian leop-
ard habitat and causes death due to road accidents.
In order to protect Persian leopards and their
habitats, we suggest several conservation approaches
in this region. Firstly, preventing habitat destruction
and road widening are important for securing
viability of the species. Secondly, habitat connectiv-
ity and wildlife corridors such as overpasses and
underpasses between isolated habitats can be used to
reduce the negative effects of roads and road
mortality on wildlife populations and especially the
Persian leopard. Thirdly, measures such as ecological
compensation need to be carried out to improve the
livelihood of local residents, which will decrease the
influence of human activity and improve the quality
of Persian leopard habitats. Finally, the viability of
Persian leopard populations will be enhanced by
preserving its preferred types of habitat. More
research is needed to understand spatial connectivity
and population dynamics of the Persian leopard.
Acknowledgements - we express our gratitude to Editor-in-
Chief Ilse Storch and especially Associate Editor John P. Ball
for their careful reviewing, editing and useful comments and
suggestions on a previous version of our manuscript. We also
thank the Iranian Department of the Environment (DoE),
particularly the Golestan ProvincialOfficeofDoE,which
provided logistical and financial support for field surveys.
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