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Ensemble Distribution Modeling of the Mesopotamian Spiny-Tailed Lizard, Saara Loricata (Blanford, 1874), in Iran: an Insight Into the Impact of Climate Change

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Ensemble Distribution Modeling of the Mesopotamian Spiny-Tailed Lizard, Saara Loricata (Blanford, 1874), in Iran: an Insight Into the Impact of Climate Change Turkish Journal of Zoology Turk J Zool (2016) 40: 262-271 http://journals.tubitak.gov.tr/zoology/ © TÜBİTAK Research Article doi:10.3906/zoo-1504-10 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 1 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 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 and Thuiller, 2005; Liu et al., 2011; Velásquez-Tibatá et al., Species distribution models (SDMs) are being used for 2012), which has been one of the key factors threatening many purposes in biogeography, conservation biology, and biodiversity and affecting distribution ranges of many ecology (Elith et al., 2006, 2011; Elith and Leathwick, 2007, species during the last century (Chen et al., 2011). In 2009; Rodriguez et al., 2007; Thuiller, 2007). Detection of addition, climate change has been determined as one of the suitable habitats along with the determination of the the most important factors affecting the future distribution most important factors influencing species distribution patterns of biodiversity (Araújo et al., 2006; Bellard et al., are among the most application of SDMs (Phillips et al., 2012; Duckett et al., 2013; Chapman et al., 2014). 2006; Peterson et al., 2011; Kalle et al., 2013). Furthermore, Over the last decades, several algorithms have been SDMs could lead to the detection of new possible habitats developed to model species distributions (Guisan and for rare and endangered species in remote areas and to Thuiller, 2005; Elith et al., 2006; Thuiller et al., 2009). evaluation of the protected area’s effectiveness (Engler et Some of these include the generalized linear model (GLM; al., 2004; Araújo et al., 2007; Zhang et al., 2009; Lomba et McCullagh and Nelder, 1989), generalized boosting model al., 2010; McKenna et al., 2013; Sousa-Silva et al., 2014), (GBM; Ridgeway 1999), multivariate adaptive regression which have important roles in planning and conservation splines (MARS; Friedman, 1991), random forest (RF; strategies (Cayuela et al., 2009; Domínguez-Vega et al., Breiman, 2001), maximum entropy (Maxent; Phillips et 2012; Velásquez-Tibatá et al., 2012). al., 2006), support vector machines (SVMs; Guo et al., Species modeling techniques are widely used to 2005), and artificial neural networks (ANNs; Manel et al., estimate the potential impacts of climate change (Guisan 1999; Spitz and Lek, 1999; Moisen and Frescino, 2002). * Correspondence: [email protected] 262 KAFASH et al. / Turk J Zool However, they considerably vary in performance and study was carried out to identify the potential suitable spatial predictions of species distributions (Grenouillet et habitats and geographic distribution of this species as al., 2011). Some studies have suggested that the accuracy well as determine the effects of climatic, topographic, of species distribution predictions could be substantially and anthropogenic factors on its presence; to assess the improved by applying consensus methods (Araújo et al., representation of the Mesopotamian spiny-tailed lizard 2005, 2011; Araújo and New, 2007; Crossman and Bass, by the ICN to determine whether it is well presented in 2008; Thuiller et al., 2009). the national network of protected areas; and to predict its Very little is known about biodiversity and species future distribution under the predicted climate change. distributions in the southwestern and western parts of Iran, where the Iran–Iraq war (1980–1990) took place. The 2. Materials and methods Mesopotamian spiny-tailed lizard is an herbivorous lizard 2.1. Species distribution data inhabiting foothills, open valleys, and plains in the warm The species occurrence data were collected during field and dry areas of the southwestern and western parts of the sampling in 2012 and 2013, supplemented by additional country (Anderson, 1999; Rastegar-Pouyani et al., 2006). data provided by other Iranian herpetologists’ observations The Mesopotamian spiny-tailed lizard is categorized as in recent years (Figure 1). Based on this approach, the data Least Concern in the IUCN Red List (Papenfuss et al., entered into the model could be potentially considered 2009), while many populations of this species are at the to be free from errors, including any uncertainty in the risk of local extinction due to illegal collection and habitat correct identification of the species in the field as well as fragmentation as a result of human activities (Kafash et errors due to incorrectly located species in the field due to al., 2014b). The distribution, the environmental factors literature records (Liu et al., 2011). determining distribution, and the effectiveness of the 2.2. Environmental data Iranian Conservation Network (ICN) are poorly known In this study, environmental variables were used for two for this species in conservational efforts (Anderson, modeling approaches. One was a general model to predict 1999). Assessing the impacts of climate change on the the current potential species distribution using three types Mesopotamian spiny-tailed lizard yielded contradicting of variables: climatic variables, topography, and land use/ results depending on the used model (i.e. habitats for the land cover (Lu/Lc) types (Table 1). The other one was a species are likely to expand under the predicted climate climate-only model to evaluate the possible changes to change using the maximum entropy model, whereas it was the species’ habitat under climate change scenarios. Shrub found to decline using the Bioclim model; Kafash et al., lands, rangelands, and agricultural lands were extracted 2014a). Hence, we concluded that more studies are needed from the land cover maps provided by the Forests, to accurately determine the impacts of climate change on Rangelands, and Watershed Management Organization the species. of Iran. In order to provide continuity for the variables The habitat of this species is characterized by high extracted from the related layer, Euclidean distance temperature and low level of rainfall, as is typical for other was calculated using ArcGIS 9.3. Altitude and slope are species in this genus (Wilms, 2005; Wilms and Böhme, generally considered to be maximum. Climatic variables 2007). Given the habitat characteristics of this species as were extracted from the WorldClim database (Hijmans et well as other related species, the climatic factors could al., 2005). The database consisted of 19 climatic variables potentially play an important role in determination of the for the earth, based on the interpolation of meteorological species presence or absence (Anderson, 1999). The present 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), Climate precipitation of wettest month (wettest), precipitation of driest month (driest), Hijmans et al., 2005 maximum temperature of warmest month (warmest), minimum temperature of coldest month (coldest), and temperature seasonality (tmpseas) Forests, Rangelands, and Land cover Agricultural lands (agri), shrub lands (shrub), rangelands (range) Watershed Management Organization 263 KAFASH et al. / Turk J Zool between these climatic variables, we extracted their values a measure of the SDM accuracy, the most
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