Turkish Journal of Zoology
Turk J Zool
(2016) 40: 262-271
© TÜBİTAK
doi:10.3906/zoo-1504-10
http://journals.tubitak.gov.tr/zoology/
Research Article
Ensemble distribution modeling of the Mesopotamian spiny-tailed lizard, Saara loricata
(Blanford, 1874), in Iran: an insight into the impact of climate change
1
1,
2
1
3,4
Anooshe KAFASH , Mohammad KABOLI *, Gunther KOEHLER , Masoud YOUSEFI , Atefe ASADI
Department of Environmental Sciences, Faculty of Natural Resources, College of Agriculture and Natural Resources,
University of Tehran, Karaj, Iran
2
Senckenberg Research Institute and Natural History Museum, Frankfurt am Main, Germany
3
Department of Energy and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran
4
Institute of Systematics, Evolution, and Biodiversity, National Museum of Natural History and Pierre and Marie Curie University,
Paris, France
1
Received: 11.04.2015
Accepted/Published Online: 08.12.2015
Final Version: 05.02.2016
Abstract: Modeling the distribution patterns of species is a generally efficient tool for understanding their ecological characteristics.
In this study, we used the ensemble predictions of the best performing models in order to project the probability of the Mesopotamian
spiny-tailed lizard’s presence in southwestern Iran. The models used in our study showed that two of the variables had the highest
importance in describing the distribution of this species. These two were the annual mean temperature and the maximum temperature
in the warmest month of the year. All of the models used in this study reached AUC values of above 0.9 (RF (AUC = 1), GBM (AUC =
0.99), MARS (AUC = 0.98), GLM (AUC = 0.97), and Maxent (AUC = 0.95)), indicating good overall prediction accuracy. The accuracy
of random forest (RF) was the highest. The most suitable areas for the presence of this species in Iran were located in Bushehr, Khuzestan,
southern Ilam, and western Kermanshah provinces. Furthermore, we modeled the extent of the suitable areas under a climate change
scenario, where the results showed a potential increase in the area of suitable habitats for the species in the future. An overlay of the
Iranian Conservation Network with the habitat suitability map showed poor representation (13% overlap) of the species in the network
of nationally protected areas.
Key words: Species distribution models, habitat suitability, Saara loricata, climatic factors, Iranian Conservation Network
1. Introduction
Species distribution models (SDMs) are being used for
many purposes in biogeography, conservation biology, and
ecology (Elith et al., 2006, 2011; Elith and Leathwick, 2007,
2009; Rodriguez et al., 2007; Thuiller, 2007). Detection of
the suitable habitats along with the determination of the
most important factors influencing species distribution
are among the most application of SDMs (Phillips et al.,
2006; Peterson et al., 2011; Kalle et al., 2013). Furthermore,
SDMs could lead to the detection of new possible habitats
for rare and endangered species in remote areas and to
evaluation of the protected area’s effectiveness (Engler et
al., 2004; Araújo et al., 2007; Zhang et al., 2009; Lomba et
al., 2010; McKenna et al., 2013; Sousa-Silva et al., 2014),
which have important roles in planning and conservation
strategies (Cayuela et al., 2009; Domínguez-Vega et al.,
2012; Velásquez-Tibatá et al., 2012).
Species modeling techniques are widely used to
estimate the potential impacts of climate change (Guisan
*Correspondence: [email protected]
262
and Thuiller, 2005; Liu et al., 2011; Velásquez-Tibatá et al.,
2012), which has been one of the key factors threatening
biodiversity and affecting distribution ranges of many
species during the last century (Chen et al., 2011). In
addition, climate change has been determined as one of
the most important factors affecting the future distribution
patterns of biodiversity (Araújo et al., 2006; Bellard et al.,
2012; Duckett et al., 2013; Chapman et al., 2014).
Over the last decades, several algorithms have been
developed to model species distributions (Guisan and
Thuiller, 2005; Elith et al., 2006; Thuiller et al., 2009).
Some of these include the generalized linear model (GLM;
McCullagh and Nelder, 1989), generalized boosting model
(GBM; Ridgeway 1999), multivariate adaptive regression
splines (MARS; Friedman, 1991), random forest (RF;
Breiman, 2001), maximum entropy (Maxent; Phillips et
al., 2006), support vector machines (SVMs; Guo et al.,
2005), and artificial neural networks (ANNs; Manel et al.,
1999; Spitz and Lek, 1999; Moisen and Frescino, 2002).
KAFASH et al. / Turk J Zool
However, they considerably vary in performance and
spatial predictions of species distributions (Grenouillet et
al., 2011). Some studies have suggested that the accuracy
of species distribution predictions could be substantially
improved by applying consensus methods (Araújo et al.,
2005, 2011; Araújo and New, 2007; Crossman and Bass,
2008; Thuiller et al., 2009).
Very little is known about biodiversity and species
distributions in the southwestern and western parts of
Iran, where the Iran–Iraq war (1980–1990) took place. The
Mesopotamian spiny-tailed lizard is an herbivorous lizard
inhabiting foothills, open valleys, and plains in the warm
and dry areas of the southwestern and western parts of the
country (Anderson, 1999; Rastegar-Pouyani et al., 2006).
The Mesopotamian spiny-tailed lizard is categorized as
Least Concern in the IUCN Red List (Papenfuss et al.,
2009), while many populations of this species are at the
risk of local extinction due to illegal collection and habitat
fragmentation as a result of human activities (Kafash et
al., 2014b). The distribution, the environmental factors
determining distribution, and the effectiveness of the
Iranian Conservation Network (ICN) are poorly known
for this species in conservational efforts (Anderson,
1999). Assessing the impacts of climate change on the
Mesopotamian spiny-tailed lizard yielded contradicting
results depending on the used model (i.e. habitats for the
species are likely to expand under the predicted climate
change using the maximum entropy model, whereas it was
found to decline using the Bioclim model; Kafash et al.,
2014a). Hence, we concluded that more studies are needed
to accurately determine the impacts of climate change on
the species.
The habitat of this species is characterized by high
temperature and low level of rainfall, as is typical for other
species in this genus (Wilms, 2005; Wilms and Böhme,
2007). Given the habitat characteristics of this species as
well as other related species, the climatic factors could
potentially play an important role in determination of the
species presence or absence (Anderson, 1999). The present
study was carried out to identify the potential suitable
habitats and geographic distribution of this species as
well as determine the effects of climatic, topographic,
and anthropogenic factors on its presence; to assess the
representation of the Mesopotamian spiny-tailed lizard
by the ICN to determine whether it is well presented in
the national network of protected areas; and to predict its
future distribution under the predicted climate change.
2. Materials and methods
2.1. Species distribution data
The species occurrence data were collected during field
sampling in 2012 and 2013, supplemented by additional
data provided by other Iranian herpetologists’ observations
in recent years (Figure 1). Based on this approach, the data
entered into the model could be potentially considered
to be free from errors, including any uncertainty in the
correct identification of the species in the field as well as
errors due to incorrectly located species in the field due to
literature records (Liu et al., 2011).
2.2. Environmental data
In this study, environmental variables were used for two
modeling approaches. One was a general model to predict
the current potential species distribution using three types
of variables: climatic variables, topography, and land use/
land cover (Lu/Lc) types (Table 1). The other one was a
climate-only model to evaluate the possible changes to
the species’ habitat under climate change scenarios. Shrub
lands, rangelands, and agricultural lands were extracted
from the land cover maps provided by the Forests,
Rangelands, and Watershed Management Organization
of Iran. In order to provide continuity for the variables
extracted from the related layer, Euclidean distance
was calculated using ArcGIS 9.3. Altitude and slope are
generally considered to be maximum. Climatic variables
were extracted from the WorldClim database (Hijmans et
al., 2005). The database consisted of 19 climatic variables
for the earth, based on the interpolation of meteorological
data between 1950 and 2000. Due to a high autocorrelation
Table 1. Environmental variables used to develop the distribution model for the Mesopotamian spiny-tailed lizard.
Variable
Variable description
Reference
Topography
Slope
U.S. Geological Survey
Annual mean temperature (analmeant), annual precipitation (analprec),
precipitation of wettest month (wettest), precipitation of driest month (driest),
maximum temperature of warmest month (warmest), minimum temperature of
coldest month (coldest), and temperature seasonality (tmpseas)
Hijmans et al., 2005
Agricultural lands (agri), shrub lands (shrub), rangelands (range)
Forests, Rangelands, and
Watershed Management
Organization
Climate
Land cover
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KAFASH et al. / Turk J Zool
between these climatic variables, we extracted their values
from the occurrence points of the species and screened
them for low correlated variables (r < 0.7). Regarding
the grid size of climatic variables at approximately 1 km2
precision (30 s × 30 s), all other environmental variables
were prepared with the same grid size.
2.3. Assessment of the climate change effect on species’
habitat
In order to assess the impact of climatic change on the
species’ habitat, we first constructed the correlation
coefficient matrix between 19 climate variables. We then
developed several climatic models using seven lowcorrelated variables for the present and future climate
conditions for the year 2070 (average for 2061–2080).
We used the output from the general circulation model
CCSM4 from the Intergovernmental Panel on Climate
Change (IPCC) 4th Assessment Report (https://www.
ipcc.ch/publications_and_data/publications_ipcc_
fourth_assessment_report_synthesis_report.htm). Based
on different inputs of greenhouse gas emission drivers
(i.e. population and economic growth, and technological
choices), land use changes, environmental policy options,
and adaptation processes (Solomon et al., 2007), the IPCC
recommended four representative concentration pathways
(RCPs) that became a part of the IPCC Fifth Assessment
Report finalized in 2014 (https://www.ipcc.ch/report/
ar5/). Each pathway is defined by a radiative forcing value,
which describes the change in the amount of energy
entering the atmosphere and the quantity reflected back,
and is expressed in watts per square meter of surface (W/
m2). We considered RCPs 2.6 and 8.5, which described
a possible future range of energy state for the earth on
the basis of different trends in climate change drivers.
We calculated the entire climatically suitable areas for
the species in the present climatic condition and future
climactic change scenarios using ArcGIS 9.3. Finally, we
compared the suitable areas in the present and the future
to show any increase or decrease in the suitable range size
under the effect of climate change on the species’ habitat.
2.4. Data analysis
We considered five modeling techniques, including
maximum entropy (Maxent; Phillips et al., 2006), the
generalized linear model (GLM; McCullagh and Nelder,
1989), the generalized boosting model (GBM; Ridgeway
1999), multivariate adaptive regression splines (MARS;
Friedman, 1991), and random forest (RF; Breiman, 2001).
Species distribution modeling was performed using
the BIOMOD platform (Thuiller et al., 2009) for R (R
Development Core Team, 2011).
In order to evaluate the model performance, the
available data were randomly divided into two subsets:
80% of the data was used for model calibration and the
remaining 20% was used for model evaluation. To obtain
264
a measure of the SDM accuracy, the most highly applied
metric of accuracy measurement for SDMs, the area under
the curve (AUC), was applied (Fourcade et al., 2013). The
AUC acts as a measure for the model’s discrimination ability
to detect present points from absent ones in a manner that
is independent of suitability thresholds. A model with no
detectability is expected to represent an AUC of 0.5, while
a model with a very high detectability has an AUC value
close to 1. The total area of suitable habitat was calculated
using ArcGIS 9.3 software for all the models.
2.5. Assessment of the species representation by the
Iranian Conservation Network
In order to assess the degree of protection granted to the
Mesopotamian spiny-tailed lizard in the conservation
areas, we overlaid the habitat suitability map of the species
with the map of the conservation networks of Iran using
spatial analysis tools in ArcGIS 9.3.
3. Results
3.1. SDMs for Mesopotamian spiny-tailed lizard
We obtained the records of the Mesopotamian spiny-tailed
lizard from 18 localities in Iran (Figure 1). The results of
the habitat suitability model for S. loricata showed the most
suitable habitats for this species to be in Bushehr, central
and northern Khuzestan, southern Ilam, and western
Kermanshah provinces (Figure 2). We found climatic
variables, such as annual mean temperature and maximum
temperature in the warmest month of the year, among the
most important variables affecting the distribution of this
species in Iran (Table 2). In addition, slope and distance
to agricultural lands were among the most important
nonclimatic variables affecting the distribution of this
species.
All of the models used in this study reached an AUC
value of above 0.9, indicating good overall prediction
accuracy (Table 3). The accuracy of RF was the highest
(AUC = 1), followed by GBM (AUC = 0.99), MARS (AUC
= 0.98) GLM (AUC = 0.97), and Maxent (AUC = 0.95).
3.2. The effects of climate change on species habitat
All of the climatic-only models reached an AUC value above
0.9, indicating good overall prediction accuracy, with the
highest accuracy observed in RF (Table 3). The ensemble
model showed that in current climatic conditions and in
those anticipated for the year 2070, the most important
climatic variable for prediction of this species’ presence
would be the annual mean temperature (35–55 °C) (Figure
3). According to the ensemble model, 19.48% of the study
area was suitable for the species at present (Figure 3A).
However, under the 2.6 and 8.5 scenarios of the CCSM
for the future, this area was predicted to be increased to
25.48% and 26.41% of the study area, respectively (Figures
3B and 3C).
KAFASH et al. / Turk J Zool
Figure 1. The presence records (black dots) used for the development of a maximum entropy model for
predicting the habitat suitability of the Mesopotamian spiny-tailed lizard.
3.3. Effectiveness of Iranian Conservation Network
The results of the assessment of the ICN’s effectiveness in
protecting suitable habitats for the Mesopotamian spinytailed lizard indicated that only 13% of the suitable habitats
are covered by the ICN (Figure 4). Furthermore, the results
showed several highly suitable patches distributed far from
the protected area, mostly located in Bushehr, the south of
Khuzestan, Ilam, and Kermanshah provinces.
4. Discussion
4.1. SDMs and environment factors for the Mesopotamian
spiny-tailed lizard
The present distribution of any type of organism is
generally attributed to a set of historical and ecological
factors (Monge-Nájera, 2008). The modeling results
of this study showed that the main factors influencing
the presence of the Mesopotamian spiny-tailed lizard
were climatic variables (annual mean temperature). This
confirmed the assumption that climatic parameters were
the most important factors affecting the distribution of
this species.
High temperature has been defined as one of the most
important features for the species’ habitat (Wilms and
Böhme, 2007). In our study, habitat suitability increased
where the annual mean temperature reached 35–55 °C in
Khuzestan and Bushehr provinces. Because this species
deposits eggs underground, it could also depend on
suitable temperatures for egg incubation (Madjnoonian
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KAFASH et al. / Turk J Zool
Figure 2. The habitat suitability map of the Mesopotamian spiny-tailed lizard in southwestern Iran.
et al., 2005). This factor was also considered as a critical
factor in the life cycle of arid and semiarid dwelling lizards,
such as the Mesopotamian spiny-tailed lizard.
266
Furthermore, this species is found in plains and flat
areas with relatively low slopes of less than 10°, while it
avoids areas with high slopes (Anderson, 1999; Rastegar-
KAFASH et al. / Turk J Zool
Table 2. Variable importance in the five models used in the present study (for variable descriptions, see Table 1).
Model name
GLM
GBM
RF
MARS
Maxent
Agri
0.000
0.012
0.009
0.161
0.047
Analmeant
1.000
0.639
0.233
0.410
0.948
Analprec
0.136
0.020
0.065
0.353
0.000
Coldets
0.492
0.005
0.127
0.881
0.000
Driest
0.178
0.028
0.004
0.000
0.004
Range
0.000
0.000
0.000
0.000
0.000
Shrub
0.099
0.039
0.058
0.083
0.000
Slope
0.120
0.232
0.040
0.025
0.093
Tmpseas
0.000
0.000
0.076
0.626
0.000
Warmest
0.470
0.015
0.204
0.670
0.000
Wettest
0.482
0.304
0.108
0.447
0.030
Table 3. The AUC values of the five models used in the present study.
Model name
GLM
GBM
RF
MARS
Maxent
Current general model
0.973
0.999
1.000
0.980
0.952
Current climatic-only model
0.975
0.996
1.000
0.968
0.946
Future_2.6 climatic model
0.960
0.991
1.000
0.970
0.944
Future_8.5 climatic model
0.967
0.998
1.000
0.972
0.940
Pouyani et al., 2006). Therefore, it can be inferred that
highland areas with a high slope might act as an effective
barrier limiting the distribution of the species expansion
(Anderson, 1999). The most important physiographical
barrier for this species was the Zagros Mountains that
prevented the species from entering the Central Iranian
Plateau.
The distance from the agricultural lands was identified
as an effective variable for the habitat suitability of the
species. We found a sharp suitability decrease with an
increase in the distance to agricultural lands (up to 250 m).
A distance of more than 250 m could potentially increase
the risk of predation due to the long distance to burrows
that play an important role in the species’ survival. It was
inferred that agricultural lands could provide a good food
supply during spring and summer, especially in summer
when the high temperature (35–55 °C) reduces vegetation
resources.
4.2. The impacts of climate changes
Many studies have recently attempted to develop models
and methods to predict the potential effects of future
climate changes on biodiversity (Dawson et al., 2011;
McMahon et al., 2011; Bellard et al., 2012). At least 10%
of species could potentially face extinction by 2100, due
to climate changes (Maclean and Wilson, 2011). However,
similar studies related to monitoring and recording climate
changes among the Iranian herpetofauna are scarce
(Kafash et al., 2013, 2014a). Our modeling revealed that
the optimal habitat for this species will likely increase due
to climate changes, which suggests that the species climate
niche could benefit from the predicted future climate
conditions (Bellard et al., 2012). Results of assessing the
impacts of climate change on the Mesopotamian spinytailed lizard using ensemble modeling were in accordance
with results of the maximum entropy model, whereas it
did not match the results of the Bioclim model (Kafash et
267
KAFASH et al. / Turk J Zool
Figure 3. Model of habitat suitability for the species based on the present climatic data (A) and 2.6 (B) and 8.5 (C) scenarios of the
CCSM for the future.
al., 2014a). Because of the better predictive performance
of ensemble models (Araújo and New, 2007), we
concluded that the maximum entropy model had better
ability in prediction of the impacts of climate change
on biodiversity compared to the Bioclim model. Since
Mesopotamian spiny-tailed lizards inhabit southwestern
Iran and are well adapted to warm climates (Anderson,
1999), increasing temperature under climate change could
cause an increase in the habitat suitability. Shifting and
268
shrinking species ranges have been shown in the literature
under the predicted climate changes (Parmesan and Yohe,
2003; Chapman et al., 2014); however, here we presented
evidence of possible future range expansion under climate
change for a desert dwelling lizard in southwestern Iran.
We further speculated that there could be similar patterns
present, sympatric with the Mesopotamian spiny-tailed
lizard.
KAFASH et al. / Turk J Zool
Figure 4. Overlay of Iranian Conservation Network with the habitat suitability map of the Mesopotamian
spiny-tailed lizard.
4.3. Effectiveness of Iranian Conservation Networks
Potential distribution modeling is a key to identifying
species distribution. Consequently, it is essential in
biodiversity management and conservation (Whittaker
et al., 2005; Williams et al., 2009) as it could reveal the
efficiency of conservation networks in a country. Our
results showed that this species, as well as 18 cohabitant
species (Anderson, 1999; Latifi, 2000; Rastegar-Pouyani
et al., 2006), were poorly represented by the ICN (13%
overlap). This could be a concern for a species with such
an unknown ecology under the risk of local extinction. For
this reason, a systematic in depth review is suggested for
the ICN.
Acknowledgment
The authors are grateful to Mr Omid Mozaffari for
providing some information about the areas that the
species inhabited.
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KAFASH et al. / Turk J Zool
References
Anderson SC (1999). The Lizards of Iran. Oxford, UK: Society for the
Study of Amphibians and Reptiles.
Araújo MB, Alagador D, Cabeza M, Nogues-Bravo D, Thuiller W
(2011). Climate change threatens European conservation
areas. Ecol Lett 14: 484–492.
Araújo MB, Lobo JM, Moreno JC (2007). The effectiveness of Iberian
protected areas in conserving terrestrial biodiversity. Conserv
Biol 21: 1423–1432.
Araújo MB, New M (2007). Ensemble forecasting of species
distributions. Trends Ecol Evol 22: 42–47.
Araújo MB, Thuiller W, Pearson RG (2006). Climate warming and
the decline of amphibians and reptiles in Europe. J Biogeogr
33: 1712–1728.
Araújo MB, Whittaker RJ, Ladle RJ, Erhard M (2005). Reducing
uncertainty in projections of extinction risk from climate
change. Glob Change Biol 14: 529–538.
Bellard C, Bertelsmeier C, Leadley P, Thuiller W Courchamp F
(2012). Impacts of climate change on the future of biodiversity.
Ecol Lett 15: 365–377.
Breiman L (2001). Random forests. Mach Learn 45: 5–32.
Cayuela L, Golicher DJ, Newton AC, Kolb M, Alburquerque FS, Arets
EJMM, Alkemade JRM, Pérez AM (2009). Species distribution
modeling in the tropics: problems, potentialities, and the role
of biological data for effective species conservation. Trop
Conserv Sci 2: 319–352.
Chen IC, Hill JK, Ohlemüller R, Roy DB, Thomas CD (2011). Rapid
range shifts of species associated with high levels of climate
warming. Science 333:1024–1026.
Crossman ND, Bass DA (2008). Application of common predictive
habitat techniques for post-border weed risk management.
Divers Distrib 14: 213–224.
Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011).
Beyond predictions: biodiversity conservation in a changing
climate. Science 332: 53–58.
Domínguez-Vega H, Monroy-Vilchis O, Balderas-Valdivia CJ,
Gienger CM, Ariano-Sánchez D (2012). Predicting the
potential distribution of the beaded lizard and identification
of priority areas for conservation. J Nat Conserv 20: 247–253.
Duckett PE, Wilson PD, Stow AJ (2013). Keeping up with the
neighbours: using a genetic measurement of dispersal and
species distribution modelling to assess the impact of climate
change on an Australian arid zone gecko (Gehyra variegata).
Diversity Distrib 19: 964–976.
Elith J, Graham CH, Anderson RP, Dudík M, Ferrier S, Guisan
A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A et
al. (2006). Novel methods improve prediction of species’
distributions from occurrence data. Ecography 29: 129–151.
Elith J, Leathwick J (2007). Predicting species distributions from
museum and herbarium records using multiresponse models
fitted with multivariate adaptive regression splines. Divers
Distrib 13: 265–275.
270
Elith J, Leathwick JR (2009). Species distribution models: ecological
explanation and prediction across space and time. Annu Rev
Ecol Evol Syst 40: 677–697.
Elith J, Phillips SJ, Hastie T, Dudik M, Chee YE, Yates CJ (2011). A
statistical explanation of MaxEnt for ecologists. Divers Distrib
17: 43–57.
Engler R, Guisan A, Rechsteiner L (2004). An improved approach for
predicting the distribution of rare and endangered species from
occurrence and pseudo-absence data. Appl Ecol 41: 263–274.
Fourcade Y, Engler JO, Besnard AG, Rödder D, Secondi J (2013).
Confronting expert-based and modelled distributions for
species with uncertain conservation status: a case study from
the corncrake (Crex crex). Biol Conserv 167: 161–171.
Friedman J (1991). Multivariate adaptive regression splines. Ann Stat
19: 1–141.
Grenouillet G, Buisson L, Casajus N, Lek S (2011). Ensemble
modelling of species distribution: the effects of geographical
and environmental ranges. Ecography 34: 9–17.
Guisan A, Thuiller W (2005). Predicting species distribution: offering
more than simple habitat models. Eco Lett 8: 993–1009.
Guo QH, Kelly M, Graham CH (2005). Support vector machines for
predicting distribution of sudden oak death in California. Ecol
Model 182: 75–90.
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005). Very
high resolution interpolated climate surfaces for global land
areas. Int J Climatol 25: 1965–1978.
Kafash A, Kaboli M, Köhler G (2014a). Predicting the impacts of
climate change on the Mesopotamian Spiny-tailed Lizard
(Saara loricata): using maximum entropy algorithm and
Bioclim. J Animal Bio 7: 75–82.
Kafash A, Yousefi M, Ahmadi M, Köhler G, Kaboli M (2013).
Predicting the impacts of climate change on the desert
dwelling reptiles of Iran (Case study Saara loricata). In: The
3rd International Conference on Environmental Planning
and Management; 29–30 October 2013; University of Tehran,
Tehran, Iran.
Kafash A, Yousefi M, Rezie A, Köhler G, Kaboli M (2014b). The
role of vegetation in burrow site selection by Mesopotamian
spiny-tailed lizard, Saara loricata (Blanford, 1875) in Mish
Dagh Protected Area, Khuzestan province. J Animal Environ
2: 31–38.
Kalle R, Ramesh T, Qureshi Q, Sankar K (2013). Predicting the
distribution pattern of small carnivores in response to
environmental factors in the Western Ghats. PLoS One 8:
e79295.
Latifi M (2000). The Snakes of Iran. Tehran, Iran: Department of the
Environment.
Liu X, Guo Z, Ke Z, Wang S, Li Y (2011). Increasing potential risk of a
global aquatic invader in Europe in contrast to other continents
under future climate change. PLoS One 6: e18429.
KAFASH et al. / Turk J Zool
Lomba A, Pellissier L, Randin C, Vicente J, Moreira F, Honrado J,
Guisan A (2010). Overcoming the rare species modelling
paradox: a novel hierarchical framework applied to an Iberian
endemic plant. Biol Conserv 143: 2647–2657.
Maclean IMD, Wilson RJ (2011). Recent ecological responses to
climate change support predictions of high extinction risk. P
Natl Acad Sci USA 108: 12337–12342.
Madjnoonian H, Kiabi BH, Danesh M (2005). Readings in
Zoogeography of Iran. Tehran, Iran: Department of
Environment.
Manel S, Dias JM, Buckton ST, Ormerod SJ (1999). Alternative
methods for predicting species distribution: an illustration
with Himalayan river birds. Appl Ecol 36: 734–747.
McCullagh P, Nelder JA (1989). Generalized Linear Models. London,
UK: Chapman and Hall.
McKenna JE, Carlson DM, Payne-Wynne ML (2013). Predicting
locations of rare aquatic species’ habitat with a combination of
species-specific and assemblage-based models. Divers Distrib
19: 503–517.
McMahon SM, Harrison SP, Armbruster WS, Bartlein PJ, Beale
CM, Edwards ME, Kattge J, Midgley G, Morin X, Prentice IC
(2011). Improving assessment and modelling of climate change
impacts on global terrestrial biodiversity. Trends Ecol Evol 26:
249–259.
Moisen GG, Frescino TS (2002). Comparing five modelling
techniques for predicting forest characteristics. Ecol Model
157: 209–225.
R Development Core Team (2011). R: A Language and Environment
for Statistical Computing. Vienna, Austria: R Foundation for
Statistical Computing.
Ridgeway G (1999). The state of boosting. Comput Sci Stat 31: 172–
181.
Rodriguez JP, Brotons L, Bustamante J, Seoane J (2007). The
application of predictive modelling of species distribution to
biodiversity conservation. Divers Distrib 13: 243–251.
Solomon IS, Qin D, Manning M, Chen Z, Marquis M, Averyt KB,
Tignor M, Miller HL (2007). Climate Change: The Physical
Science Basis. Contribution of Working Group 1 to the Fourth
Assessment Report of the Intergovernmental Panel on Climate
Change (pp. 1e18). Cambridge, UK: Cambridge University
Press.
Sousa-Silva R, Alves P, Honrado J, Lomba A (2014). Improving the
assessment and reporting on rare and endangered species
through species distribution models. Glob Eco Conserv 2:
226–237.
Spitz F, Lek S (1999). Environmental impact prediction using neural
network modelling. An example in wildlife damage. Appl Ecol
36: 317–326.
Thuiller W (2007). Biodiversity – climate change and the ecologist.
Nature 448: 550–552.
Thuiller W, Lafourcade B, Engler R, Araújo MB (2009). BIOMOD –
A platform for ensemble forecasting of species distributions.
Ecography 32: 369–373.
Monge-Nájera J (2008). Ecological biogeography: a review with
emphasis on conservation and the neutral model. Gayana 72:
102–112.
Velásquez-Tibatá J, Salaman P, Graham CH (2012). Effects of climate
change on species distribution, community structure, and
conservation of birds in protected areas in Colombia. Reg
Environ Change 13: 235–248.
Papenfuss T, Shafiei Bafti S, Sharifi M, Anderson S C (2009). Saara
loricata. The IUCN Red List of Threatened Species 2009.
Gland, Switzerland: IUCN.
Whittaker RJ, Araújo MB, Paul J, Ladle RJ, Watson JE, Willis KJ
(2005). Conservation biogeography: assessment and prospect.
Divers Distrib 11: 3–23.
Parmesan C, Yohe G (2003). A globally coherent fingerprint of
climate change impacts across natural systems. Nature 421:
37–42.
Williams JN, Seo C, Thorn J, Nelson JK, Erwin S, O’Brien JM,
Schwartz MW (2009). Using species distribution models to
predict new occurrences for rare plants. Divers Distrib 15:
565–576.
Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer
E, Nakamura M, Araújo MB (2011). Ecological Niches and
Geographic Distributions. Monographs in Population Biology,
No. 49. Princeton, NJ, USA: Princeton University Press.
Phillips SJ, Anderson RP, Schapire RE (2006). Maximum entropy
modeling of species geographic distributions. Ecol Model 190:
231–259.
Rastegar-Pouyani N, Rastegar-Pouyani E, Jawaheri M (2006).
Field Guide to the Reptiles of Iran. Kermanshah, Iran: Razi
University Press.
Wilms T (2005). Uromastyx, Natural History, Captive Care and
Breeding. Offenbach am Main, Germany: Herpeton Verlag.
Wilms T, Böhme W (2007). A review of the taxonomy of the spinytailed lizards of Arabia (Reptilia: Agamidae: Leiolepidinae:
Uromastyx). Fauna of Arabia 23: 435–468.
Zhang Y, Richardson JS, Pinto X (2009). Catchment-scale effects
of forestry practices on benthic invertebrate communities in
Pacific coastal streams. J Appl Eco 46: 1292–1303.
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