Herpetology Notes, volume 11: 805-808 (2018) (published online on 27 September 2018)

Modelling the potential distribution of diadema (Schlegel, 1837) (Serpents: ) in

Morteza Moadab1, Jamil Zargan2,*, Eskandar Rastegar-pouyani1 and Ashkan Hajinourmohammadi2

Abstract. The diadem Spalerosophis diadema (Schlegel, 1837) is found in most arid and semi-arid regions of Iran. However, little is known about ecology and lifestyle of this in the area. In the present study, we used MaxEnt software to predict suitable habitats and determine the factors influencing its distribution pattern. Four variables have the most importance in the distribution of S. diadema: 1) average temperature of the coldest quarter of the year (Bio11), 2) precipitation of the warmest quarter of the year (Bio 18), 3) precipitation of the wettest month (Bio 13), and 4) slope. Considering the distribution areas of S. diadema and its closely relates species, S. microlepis, and effective factors on their distribution in Iran, it can be concluded that geographical separation and the type of habitat have the most important roles in their speciation.

Keywords: Spalerosophis diadema, MaxEnt, snake, Iran

Introduction Indo- (occupied by S. d. diadema) (Marx, 1959; Baig and Masroor, 2008). The Genus Spalerosophis is one of the members of the Species Distribution Modelling (SDM) is one of the family Colubridae that is distributed from North important subjects in ecology, because the method is in the West through Arabia, Iran, Pakistan to central directly linked to biological conservation aims (Graham in the east (Baig and Masroor, 2008; Sindaco et al., 2004). SDM is based on environmental variables et al., 2013; Uetz, 2015). Six species of this genus and species occurrence records, and is a useful tool have, so far, been described: Spalerosophis arenarius for conservation biology, evolutionary ecology and (Boulenger, 1890) and S. atriceps (Fischer, 1885) from invasive-species management perspectives (Elith et al., North India and Pakistan, S. diadema (Schlegel, 1837) 2006; Elith et al., 2011; Phillips et al., 2006; Rissler et from the western Sahel and to central and al., 2007). Maximum Entropy (MaxEent) algorithm Northwest India, S. dolichospilus (Werner, 1923) from is a useful and popular tool for predicting the habitat Maghreb, S. josephscorteccii (Lanza, 1964) from suitability of a given species (Phillips et al., 2006), and Galgalo Oasis in Northwest Somalia and S. microlepis allows us to predict the habitat suitability and potential (Jan, 1865) from West Iran (Sindaco et al., 2013; Uetz, distribution of species and find effective environmental 2015). Spalerosophis diadema is a polytypic species factors on the species distribution. In this study, we and consists of three : S. d. cliffordi (Schlegel, present a habitat suitability model of Spalerosophis 1837), S. d. schirazianus (Jan, 1865) and S. d. diadema diadema in Iran and also refer to the most important (Schlegel, 1837) (Marx, 1959). Saharo-Sindian range environmental variables that are effective on the species area consists of three distinct regions: Afro-Arabia distribution. (occupied by S. d. cliffordi), Irano-turan (Iran segment inhabited by S. d. schirazianus and S. d. cliffordi) and Materials and methods In total, 33 occurrence records of S. diadema were obtained from three resources: www.GBIF.org website (the global biodiversity information facility); Sabzevar 1 Department of Biology, Faculty of science, Hakim Sabzevari University Herpetological Collection (SUHC) and our University, Sabzevar, Iran 2 Biological science department, Faculty of basic science, Imam field expeditions during 2014–2016 (Table 1). The Hossein comprehensive University, Tehran, Iran 19 bioclimatic layers with 30-arc-seconds resolution * Corresponding author. E-mail: [email protected] were downloaded from the worldclim website (www. 806 Morteza Moadab et al.

Table 1. Records of S. diadema used in the present study evaluated using the amount of area under the curve of (SUHC stand for Sabzevar University Herpetological the receiver operator plot (AUC) as interpret here: AUC Collection). = 0.5 indicates a performance equivalent to random; AUC > 0.7 indicates useful performance, AUC > 0.8 Source of point Latitude Longitude indicates good performance and AUC ≥ 0.9 indicates GBIF 35.67 51.42 excellent performance (Manel et al., 2002). GBIF 32.17 48.94 GBIF 31.33 48.69 Results SUHC 6606 35.29 58.46 SUHC 1377 32.82 59.22 From the seven variables used to predict the habitat SUHC 214 36.55 59.91 suitability of S. diadema, four of them show a high SUHC 572 35.06 59.52 contribution percentage: BIO11 (46.3 %); BIO18 (19.2 SUHC 899 36.54 61.15 %); BIO13 (18.2 %) and slope (9.3 %) (Table 3). The SUHC 6047 31.24 57.24 final constructed average model has an AUC > 0.84 SUHC 7268 30.38 57.05 indicating it is a good model fit (Fig 1). According to SUHC 7269 29.45 55.49 this model suitable habitat for this species that should GBIF 27.28 56.46 have following conditions: 1) high winter temperatures, SUHC 1377 29.6 60.17 2) low precipitation in the summer and autumn, and 3) SUHC 5887 25.27 60.76 low slope. SUHC 1019 35.94 57.09 SUHC 1452 36.63 57.08 SUHC 1119 36.27 59.59 Discussion SUHC 1358 36.21 57.68 The species distribution model conforms to the known SUHC 1663 36.3 57.69 distribution pattern of Spalerosophis diadema, so that SUHC 1670 36.2 57.68 there is no record of this species from northern line and SUHC 1685 33.63 56.92 northwestern corner of country because these areas are SUHC 3213 31.88 54.35 predicted as inappropriate habitats. Furthermore, the SUHC 1535 29.2 53.22 centre of Iran and the Iranian provinces of Khorasan SUHC 3963 27.18 60.43 razavi, southern Khorasan, Sistan and Baluchistan, SUHC 3964 36.63 57.08 SUHC 3690 27.54 52.88 SUHC 4715 36.11 57.61 GBIF 25.27 60.78 Table 2. Bioclimatic variables used in MAXENT. SUHC 5960 31.14 61.75 SUHC 6014 33.39 60.31 SUHC 1922 30.74 59.8 Variable Description SUHC 5757 35.24 58.48 BIO1 Annual average temperature BIO2 Annual daily temperature difference BIO3 Isothermal parameter (BIO2/BIO7)(×100) BIO4 Temperature seasonality (standard deviation *100) BIO5 Maximum temperature of warmest month worldclim.org) and extracted for Iran territory by ArcGIS BIO6 Minimum temperature of coldest month (ESRI) (Table 2). To get the correlation coefficient BIO7 Annual temperature scale (BIO5-BIO6) between variables, we employed Openmodeller 1.7.0 BIO8 Average temperature of the wettest quarter of the year to obtain the grid correspondence values, and then BIO9 Average temperature of the driest quarter of the year inserted them into SPSS v. 16.0, to calculate binary BIO10 Average temperature of the warmest quarter of the year correlation. Then variables with correlation more BIO11 Average temperature of the coldest quarter of the year BIO12 Average annual precipitation than 0.75 removed from analysis (for prevention BIO13 Precipitation of the wettest month multicollinearity environmental variables) (Rissler et BIO14 Precipitation of the driest month al., 2006). Finally, seven variables selected based on BIO15 Precipitation seasonality (coefficient of variation) Bivariate (pearson) correlation coefficient for MaxEnt BIO16 Precipitation of the wettest quarter of the year analysis. Maxent was set as follow: maximum 500 BIO17 Precipitation of the driest quarter of the year iterations, convergence threshold 10-5, regularization BIO18 Precipitation of the warmest quarter of the year multiplier 2 and 10 replicates. Model accuracy can be BIO19 Precipitation of the coldest quarter of the year Modelling the potential distribution of Spalerosophis diadema in Iran 807

Figure 1. Distribution model of Spalerosophis diadema in Iran. Predicted occurrence from low likelihood (white) to high likelihood (red).

Kerman, Fars, Hormuzgan, Bushehr, Khuzestan and thermophilous species that often is found in arid regions Qom were predicted as suitable regions. From the North (open plains and deserts) and near agricultural farms toward the centre and south of the country, average (Amr and Disi, 2011). Based on our model, the Zagros temperature increases and precipitation decrease so, it Mountains are not suitable habitat of S. diadema but it is expected that central and southern regions contain can be found in both the inner and outer edges of the suitable habitats. In addition, Khorasan and Qom mountains indicating the role of slope in distribution of provinces have been predicted as an area of species this species. occurrence due to low precipitation. Hosseinzadeh et al. (2017) concluded that most As mentioned, there are two species of the genus important variable in distribution of S. microlepis Spalerosophis in Iran: Spalerosophis diadema is precipitation in the coldest quarter (bio18) and (distributed from North Africa to central India) and S. their modelling predicted the as microlepis (Endemic species of Iran). S. diadema is a suitable habitat. This species prefers humid habitat and therefore it is restricted to the Zagros Mountains, whereas S. diadema tends to live in hot and arid regions so it is not found in the Zagros and Elburz Mountains. Table 3. Relative importance of variables used in MAXENT. Considering many endemic species are found in the Zagros Mountains, it seems Zagros has an important Variable Percent contribution role in promoting isolation and speciation of the Iranian BIO11 46.3 herpetofauna (Anderson, 1968; Rastegar-Pouyani et BIO18 19.2 BIO13 18.2 al., 2010; Hosseinzadeh et al., 2014). It seems that two Slope 9.3 factors may have played a role in speciation within the BIO2 4.2 genus Spalerosophis in Iran: the Zagros Mountains as a BIO3 1.9 huge geographic barrier, and the ecological differences BIO5 0.9 in habitat. 808 Morteza Moadab et al.

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Accepted by Daniel Portik