Abstract

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. This study aims to use venomous snakes’ habitat suitability as an indicator of snakebite risk, to identify high-priority areas for snakebite management across the country. Thus, an ensemble approach using five distribution modelling methods: Generalized Boosted Models, Generalized Additive Models, Maximum Entropy Modelling, Generalized Linear Models, and Random Forest was applied to produce a spatial snakebite risk model for Iran. To achieve this, four venomous snakes’ habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and then multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. Multiplying habitat suitability models of the four snakes showed that the northeast of Iran (west of Khorasan-e-Razavi province) has the highest snakebite risk in the country. In addition, villages that were at risk of envenoming from the four snakes were identified. Results revealed that 51,112 villages are at risk of envenoming from M. lebetinus, 30,339 from E. carinatus, 51,657 from P. persicus and 12,124 from N. oxiana. Precipitation seasonality was identified as the most important variable influencing distribution of the P. persicus, E. carinatus and M. lebetinus in Iran. Precipitation of the driest quarter was the most important predictor of suitable habitats of the N. oxiana. Since climatic variables play an important role in shaping the distribution of the four venomous snakes in Iran, thus their distribution may alter with changing climate. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating factor. Results of this study can be used in snakebite and human–snake conflict management in Iran. We recommend increasing public awareness of snakebite envenoming and education of local people in areas which identified with the highest snakebite risk.

Highlights

  • Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified

  • All models developed in this study performed well based on the three model performance evaluation metrics, AUC, true skills statistic (TSS) and Boyce index

  • With this research the first snakebite envenoming risk model was produced at fine resolution (~ 1 km2) in Iran by modeling and multiplying habitat suitability of four medically important venomous snakes which are responsible for the most snakebite incidents in the c­ ountry[18]

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Summary

Introduction

Snakebite envenoming is an important public health problem in Iran, despite its risk not being quantified. Four venomous snakes’ habitat suitability (Macrovipera lebetinus, Echis carinatus, Pseudocerastes persicus and Naja oxiana) were modelled and multiplied. These medically important snakes are responsible for the most snakebite incidents in Iran. This paper demonstrates application of species distribution modelling in public health research and identified potential snakebite risk areas in Iran by using venomous snakes’ habitat suitability models as an indicating factor. Species Distribution Models (SDMs) have found an important application in biodiversity ­research[33,34,35] They are employed in studying habitat ­suitability[36,37,38], identifying environmental drivers of species ­distribution[39,40,41,42,43,44] and predicting impacts of climate change on ­biodiversity[45,46,47,48,49,50]. These models can be used to identify suitable habitats of venomous snakes as proxies of snakebite ­risk[12,56,57,58] in data poor regions like Iran

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