RESUMEN
Knowing the distribution of venomous snakes of medical importance is essential to identify areas at risk for snakebites. Thus, we used an integrative approach based on the application of geographic distribution data of venomous snakes, species distribution modeling (SDM), spatial organization of snakebites, and information on human population density for mapping the potential distribution of snakes and identifying areas at risk of snakebites in the state of Maranhão (mid-northern Brazil). From a compiled database of venomous snake records deposited in biological collections and the literature, we predict the potential distribution of venomous snakes in Maranhão, a state whose diversity and geographic distribution of venomous snake species are poorly known. With this, we constructed potential distribution maps for each venomous snake species with at least one occurrence record within state boundaries, as well as generalized maps by family (Viperidae and Elapidae) and the total number of venomous snakes in Maranhão State. We also obtained data on the number of snakebites recorded in each municipality of Maranhão over a decade (2009-2019) and we ran a Generalized Linear Model to test for relationships between the number of venomous snakebites, the area of occurrence of snakes, and human population density. We obtained 1046 records of venomous snake species for Maranhão, represented by 17 viperid and elapid species. Most of the records were from Viperidae (mostly Bothrops atrox and B. marajoensis) and were concentrated mainly in the Amazon of the northern portion of the state. The models showed accurate predictive performance for all modeled species. The entire area of Maranhão exhibits environmental conditions for the occurrence of venomous snakes, with higher suitability indices in the northern region, in the Amazon rainforest. The number of snakebites was positively correlated with the interaction between high-risk areas (i.e., greater distribution of venomous snakes) and human population density. Our study is a pioneer in using species distribution modeling in mid-northern Brazil to address the scarcity of data on snakebite-causing species, directly contributing to the theme of neglected tropical diseases of the World Health Organization.