developing snakebite risk model using venomous snake ... · 06.04.2020 · 22 snakebite envenoming...

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1 Title

2 Developing snakebite risk model using venomous snake habitat

3 suitability as an indicating factor: An application of species distribution

4 models in public health research

5 Authors:

6 Masoud Yousefi 1 ⃰, Anooshe Kafash 1, Ali Khani2, Nima Nabati3

7 Affiliations:

8 1 Department of Environmental Science, Faculty of Natural Resources, University of

9 Tehran, Iran

10 2 Khorasan-e-Razavi Provincial Office of the Department of the Environment, Mashhad,

11 Iran.

12 3Shahid Sadoughi Hospital, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.

13 *Corresponding author:

14 Masoud Yousefi Email address: [email protected]

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16

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18

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20

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2

21 Abstract

22 Snakebite envenoming is an important public health problem in Iran, despite its risk not

23 being quantified. This study aims to use venomous snakes’ habitat suitability as an

24 indicator of snakebite risk, to identify high-priority areas for snakebite management

25 across the country. Thus, an ensemble approach using five distribution modeling

26 methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy

27 Modeling Generalized Linear Models, and Random Forest was applied to produce a

28 spatial snakebite risk model for Iran. To achieve this, four venomous snakes’ habitat

29 suitability (Macrovipera lebetina, Echis carinatus, Pseudocerastes persicus and Naja

30 oxiana) were modeled and then multiplied. These medically important snakes are

31 responsible for the most snakebite incidents in Iran. Multiplying habitat suitability

32 models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi

33 province) has the highest snakebite risk in the country. In addition, villages that were at

34 risk of envenoming from the four snakes were identified. Results revealed that 51,112

35 villages are at risk of envenoming from M. lebetina, 30,339 from E. carinatus, 51,657

36 from P. persicus and 12,124 from N. oxiana. This paper demonstrates application of

37 species distribution modeling in public health research and identified potential snakebite

38 risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating

39 factor. Results of this study can be used in snakebite and human–snake conflict

40 management in Iran. We recommend increasing public awareness of snakebite



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41 envenoming and education of local people in areas which identified with the highest

42 snakebite risk.

43 Introduction

44 Snakebite envenoming is known as an important public health problem and the cause of

45 medical emergencies around the globe [1−14]. On Earth, between 421,000 and 1.2

46 million people are envenomed by venomous snakes annually and around 125,000 deaths

47 per year are attributable to snakebite envenoming [1, 6, 14, 15]. Snakebite envenoming is

48 mostly described as a neglected public health issue in the tropics, [2, 6, 16, 17] however,

49 it is also an important challenge for public health in temperate areas like Iran [18].

50 Iran is home to 81 snake species [19, 14] of which 25 are venomous (Nine species are sea

51 snakes and 16 species are terrestrial snakes). Macrovipera lebetina, Echis carinatus,

52 Pseudocerastes persicus and Naja oxiana are widespread in Iran and are responsible for

53 the most snakebite incidents in the country [18, 21]. A study reported 53,787 cases of

54 snake bites between 2002 and 2011 in Iran [18]. Despite considerable research into the

55 phylogeny, taxonomy, morphology and ecology of venomous snakes in Iran [22, 31]

56 snakebite envenoming has received less attention [18, 32]. In fact, snakebite is an

57 important uninvestigated public health problem and conservation challenge in Iran

58 [33−35]. Thus, more effort should be made to identify areas with high snakebite risk and

59 reduce envenoming risk from snakes.

60 Species distribution models (SDMs) have found an important application in

61 biodiversity research [36−38]. They are employed in studying habitat suitability [39−41],



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62 identifying environmental drivers of species distribution [42−45] and predicting impacts

63 of climate change on biodiversity [46−51]. SDMs are successfully used to identify

64 suitable habitats of species even in areas with no distribution records [52−54]. Thus,

65 these models can be used to identify suitable habitats of venomous snakes as proxies of

66 snakebite risk [12, 55−57] in data poor regions like Iran.

67 The main goal of this paper was to apply SDMs and produce a spatial risk model

68 for snakebite in Iran. To do this, firstly, five distribution modeling methods [38]:

69 Generalized Boosted Models, Generalized Additive Models, Maximum Entropy

70 Modeling Generalized Linear Models, and Random Forest and distribution data of M.

71 lebetina, E. carinatus, P. persicus and N. oxiana were to produce their habitat suitability

72 models. Secondly, the five habitat suitability models [38−58] of each species were

73 combined by ensemble approach and finally the four species ensemble models were

74 multiplied to identify potential snakebite risk. We also in addition, number of villages

75 that are at risk of envenoming by these four snakes determined in Iran.

76

77 Materials and methods

78 Occurrence data

79 Distribution records of the M. lebetina, E. carinatus, P. persicus and N. oxiana (Fig 1)

80 were obtained from published books and papers [31, 59−66], and online databases (GBIF

81 and VertNet). These four snakes were selected because they are responsible for the most

82 snakebite incidents in Iran [18] and have the widest distribution range across the country



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83 [19−20]. By combining presence records from the two sources 89 distribution records

84 were obtained for M. lebetina, 68 records for E. carinatus, 54 records for P. persicus and

85 37 records for N. oxiana (Fig 2).

86

87 Fig 1. Photos of the four medically important venomous snakes modeled in this

88 research to map snakebite in Iran. Saw-scaled Viper, Echis carinatus (a), Central

89 Asian Cobra, Naja oxiana (b), Levantine Viper, Macrovipera lebetina (c), and Persian

90 Horned Viper, Pseudocerastes persicus (d). Photos by Masoud Yousefi.

91

92 Fig 2. Distribution records of Macrovipera lebetina, Echis carinatus, Naja oxiana,

93 and Pseudocerastes persicus in a topographic overview of Iran. Provinces numbers

94 should read as follows; (1) West Azerbaijan, (2) East Azerbaijan, (3) Ardabil, (4)

95 Kurdistan, (5) Zanjan, (6) Gilan, (7) Kermanshah, (8) Hamedan, (9) Qazvin, (10) Alborz,

96 (11) Mazandaran, (12) Ilam, (13) Lorestan, (14) Markazi, (15) Qom, (16) Tehran, (17)

97 Khuzestan, (18) Chahar Mahaal and Bakhtiari, (19) Isfahan, (20) Semnan, (21) Golestan,

98 (22) Khorasan-e-Shomali (Northern Khorasan), (23) Khorasan-e-Razavi, (24) Yazd, (25)

99 Khorasan-e-Jonobi (Southern Khorasan), (26) Kohgiluyeh and Boyer-Ahmad, (27) Fars,

100 (28) Kerman, (29) Sistan and Baluchestan, (30) Bushehr, (31) Hormozgan.

101

102 Environmental data



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103 Seven uncorrelated environmental variables (Table 1) related to climate, topography,

104 vegetation, and human footprint were used to develop the four snakes’ habitat suitably

105 models [12, 17, 28, 55, 67−70]. Climatic variables were downloaded from the WorldClim

106 database at 30-seconds spatial resolution [71]. Normalized Difference Vegetation Index

107 (NDVI) was considered as an indicator of resource availability [72]. Snakebite risk is

108 associated with human population density and activities [55], thus, human footprint index

109 was included in models [69]. Human footprint index was produced by combining data on

110 the extent of built environments, population density, electric infrastructure, crop lands,

111 pasture lands, roads, railways, and navigable waterways [70]. Topographic heterogeneity

112 was used as topography variable by measuring the standard deviation of elevation values

113 in area grid cells of 1km from a 90 m resolution in the Raster package [73] in the R

114 environment v.3.4.3 [74]. Elevation layer was obtained from the Shuttle Radar

115 Topography Mission (SRTM) elevation model [75].

116 Table 1. List of Environmental variables. Name, source and the variance inflation

117 factor (VIF) for the seven environmental variables which were used for developing

118 habitat suitability of venomous snakes in Iran.

Variable Description (abbreviation) References VIF

Climate Temperature seasonality (bio4) [71] 1.438

Annual precipitation (bio12) [71] 2.871

Precipitation seasonality (bio15) [71] 2.389

Precipitation of driest quarter (bio17) [71] 3.049



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Topography Topographic heterogeneity (SD) [75] 1.315

Vegetation Normalized Difference Vegetation

Index (NDVI)

[72] 3.33

Anthropogenic Human Footprint (HFP) [69-70] 1.466

119

120 Snakebite risk modeling

121 An ensemble approach [38-58] was applied to model habitat suitability of the M.

122 lebetina, E. carinatus, P. persicus and N. oxiana, using five methods: Generalized

123 Boosted Models (GBM; [76]), Generalized Additive Models (GAM; [77]), Maximum

124 Entropy modeling (Maxent; [78]), Generalized Linear Models (GLM; [79]), Random

125 Forest (RF; [80]). Since these methods need background data points we generated a

126 randomly drawn sample of 10,000 background points (e.g., pseudo-absence points) from

127 the extent of the study area using the PresenceAbsence package [81]. Then areas

128 associated with high snakebite risk in Iran were identified by multiplying habitat

129 suitability models of the four species.

130 To quantify snakebite risk in Iran in more detail number of villages that are at risk

131 of envenoming from the four snakes determined and area of snakebite risk calculated in

132 the Raster package [73]. For this continuous habitat suitability models were converted to

133 suitable/unsuitable maps using maximum test sensitivity with a specificity threshold

134 [82]), and then overlaying 185,000 (population in these villages range from less than 50

135 to 5000 individuals) villages with each snake model.



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136 Model performance

137 Assessing model performance is an important step in distribution modeling studies [37].

138 Several metrics were introduced for estimating the predictive powers of models [37, 78,

139 83, 84]. Three metrics were considered to assess the produced habitat suitability models’

140 performances. The true skills statistic (TSS), area under the receiver operating

141 characteristic curve (AUC), and the Boyce index [37, 83, 85].

142 Analyses were carried out using the packages GISTools (https://rdrr.io/cran/

143 GISTools/), dismo (https://rdrr.io/cran/dismo/), biomod2 (https://cran.rproject.org/web/

144 packages/ biom od2/index.html), maptools (http://r-forge.r-project.org/projects/maptools/)

145 , SDMTools (htt ps://cran.r-project.org/web/packages/SDMTools/index.html), and ecospat

146 (https://cran.rproject.org/web/packages/ecospat/ecospat.pdf), in the R environment

147 (v.3.4.3).

148 Results

149 Model performance

150 All models developed in this study performed well based on the three model performance

151 evaluation metrics, AUC, TSS and Boyce index (See figs 3-6).

152 Habitat suitability

153 Echis carinatus

154 Based on ensemble model, southern part of Iran, north of Persian Gulf and vast areas in

155 central parts of the country are identified to have highest suitability for E. carinatus (Fig



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156 3). Precipitation seasonality and NDVI and were the most important determinant of

157 habitat suitability of the species across the Iran.

158

159 Fig 3. Variables importance (a), models performance (b) and habitat suitability

160 model (c) of Echis carinatus in Iran.

161 Macrovipera lebetina

162 The most suitable habitats of M. lebetina are located in Zagros Mountains, Alborz

163 Mountains, Kopat-Dagh Mountains as well as in some isolated mountains in central Iran

164 (Fig 4). Precipitation seasonality, precipitation of the driest quarter, and human footprint

165 were the most important predictors of suitable habitats for the species.

166

167 Fig 4. Variables importance (a), models performance (b) and habitat suitability (c)

168 of Macrovipera lebetina in Iran.

169 Pseudocerastes persicus

170 Central, southwest and northeast of Iran have highest suitability for the P. persicus.

171 While, northern parts of the country are not suitable for this species (Fig 5). Results

172 showed that precipitation seasonality and NDVI and were the most important determinant

173 of habitat suitability of the species across the Iran.

174


176 of Pseudocerastes persicus in Iran.

177 Naja oxiana



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178 Naja oxiana’s most suitable habitats are located in north east of Iran around Kopat-Dagh

179 Mountains as well as some isolated patches in western parts of the country (Fig 6).

180 Precipitation of the driest quarter was the most important predictor of suitable habitats of

181 the species.

182


184 of Naja oxiana in Iran.

185

186 Snakebite risk model

187 The four venomous snakes’ ensemble habitat suitability models were combined to

188 develop a snakebite risk model for Iran (Fig 7). Results showed that Khorasan-e-Razavi,

189 east of Semnan, north of Khorasan-e-Jonobi and south of Khorasan-e-Shomali provinces

190 have highest snakebite risk in Iran. West of Khorasan-e-Razavi province has high

191 suitability for the four venomous snakes.

192

193 Fig 7. Snakebite envenoming risk model in Iran. The snakebite risk model was

194 developed based on combined habitat suitability models’ of Macrovipera lebetina, Echis

195 carinatus, Pseudocerastes persicus and Naja oxiana.

196 Villages at risk of envenoming

197 Number of villages that are at the risk of envenoming by each of the four snakes (Table 2,

198 Fig 8) were determined. Results revealed that 51,112 villages are at risk of envenoming



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199 from M. lebetina, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N.

200 oxiana. Area of envenoming risk by each species was estimated (Table 2), M. lebetina

201 and N. oxiana are identified with largest (362,558 km2) and smallest (121,803 km2) area,

202 respectively.

203

204 Fig 8. Villages at risk of envenoming from Macrovipera lebetina, Echis carinatus,

205 Pseudocerastes persicus and Naja oxiana. Villages at high risk are shown with red

206 circle and villages at moderate risk are shown with yellow circle.

207

208 Table 2. Area (km2) and number of villages that are at the snakebite risk from

209 Macrovipera lebetina, Echis carinatus, Pseudocerastes persicus and Naja oxiana in

210 Iran. Results are based on the ensemble models.

Species Villages at high risk Villages at moderate risk Area

Macrovipera lebetina 9,824 41,288 362,558

Echis carinatus 5,917 24,422 321,089

Pseudocerastes persicus 2,150 49,507 655,415

Naja oxiana 3,091 9,033 121,803

211

212 Discussion

213 With this research the first snakebite envenoming risk model was produced at fine

214 resolution (~1 km2) in Iran by modeling and multiplying habitat suitability of four



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215 medically important venomous snakes which are responsible for the most snakebite

216 incidents in the country [18]. Northeastern parts of Iran were identified to have highest

217 snakebite risk in the country. Results showed that thousands of villages are located in

218 suitable ranges of the four venomous snakes, for example 51,657 villages are in the

219 suitable range of P. persicus. Thus these villages and villagers are at risk of envenoming

220 from these snakes. The snakebite risk model shows which parts of Iran are at risk of

221 envenoming from two, three or even four of snakes. All provinces of Iran, except those in

222 northwest of the country, are at risk of envenoming from at least two venomous snakes.

223 This highlights importance of snakebite envenoming as public health problem in Iran.

224 Under climate change some venomous species may expand their distribution

225 ranges [56-86], thus, envenoming risk will likely vary [12, 17, 87]. For instance, Nori et

226 al. [12] modeled distribution of five venomous snakes in Argentina for 2030 and 2080.

227 They found that the snakes’ suitable climate spaces will increase in human populated

228 areas of the country. In another study, Zacarias & Loyola [57] modeled current and future

229 distributions of 13 snakes in Mozambique and showed that venomous snake distribution

230 will change under climate change. They concluded patterns of snakebite risk may change

231 due to climatic changes [57]. Results of current research revealed that climatic variables

232 play an important role in shaping the distribution of four venomous snakes in Iran, thus

233 their distribution may alter with changing climate. It is predicted that suitable habitats of

234 Echis carinatus will increase in Iran [88]. This species is an important source of

235 snakebite in the country and its distributional range will likely increase under climate



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236 change putting more populations and human settlements at risk of envenoming by this

237 snake until 2070.

238 Venomous snake populations are declining and many of them are listed by the

239 IUCN Red List as Vulnerable, Endangered or Critically Endangered [89]. Conservation

240 of snakes especially venomous snakes is a big challenge [89] as it is not easy to convince

241 people to conserve venomous snakes which are a significant cause of human mortality

242 and morbidity [4, 6, 14, 15]. It is necessary to identify areas with high risk of snakebite

243 envenoming and prioritize those areas for snakebite risk management in each country. In

244 this study potential risk areas were identified by using habitat suitability models as an

245 indicator of snakebite risk. Future snakebite monitoring in areas with high risk can show

246 the model performance and its power in predicting envenoming events across the

247 country.

248 Results of this study can be used to reduce snakebite and venomous snake

249 conflicts with local people, farmers and shepherds in the country. Snakebite risk can be

250 reduced through community education [90-92]. We encourage education of local people

251 about snakebite prevention measures in areas with highest snakebite risk. There are

252 simple solutions to prevent snakebite envenoming in areas with high risk, like villages in

253 west of Khorasan-e-Razavi province. For example, using bed nets and protecting feet,

254 ankles and lower legs by wearing boots can significantly reduce snakebite envenoming

255 [91-93]. We also suggest that areas where snakebite envenoming risk is high should be

256 monitored to determine envenoming events and villagers in these areas must always have

257 access to antivenom supplies [14].



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258 Species distribution models are becoming important tools in public health research

259 [12, 94-103]. We encourage public hearth researchers to apply species distribution

260 models in developing snakebite risk map using venomous snakes’ habitat suitability as an

261 indicator, especially in data poor regions of the world [27, 52-54]. Our approach has the

262 potential for practical application in other countries whit high snakebite risk.

263 Acknowledgments

264 We thank Ollie Thomas for improving the English of the manuscript.

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