1. Predicting the habitat suitability of Schistosoma intermediate host Bulinus truncatus, its predatory aquatic insect Odonata nymph, and the associated aquatic plant Ceratophyllum demersum using MaxEnt
- Author
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Marwa M. Mahmoud, Aly A. Younes, Hanaa A. El-Sherif, Fathia A. Gawish, Mohamed R. Habib, and Mohamed Kamel
- Subjects
Nymph ,Insecta ,Infectious Diseases ,Odonata ,General Veterinary ,Bulinus ,Insect Science ,Schistosoma haematobium ,Animals ,Parasitology ,General Medicine ,Ecosystem - Abstract
Schistosomiasis is one of the most important parasitic diseases in tropical and subtropical areas. Its prevalence is associated with the distribution of freshwater snails, which are their intermediate hosts. Thus, control of freshwater snails is the solution to reduce the transmission of this disease. This will be achieved by understanding the relationship between the snails and their habitats including natural enemies and associated aquatic plants as well as the factors affecting their distribution. In this study, Maximum Entropy model (MaxEnt) was used for mapping and predicting the possible geographic distribution of Bulinus truncatus snail (the intermediate host of Schistosoma haematobium), Odonata nymph (predatory aquatic insect), and Ceratophyllum demersum (the associated aquatic plant) in Egypt based on topographic and climatic factors. The models of the investigated species were evaluated using the area under receiver operating characteristic curve. The results showed that the potential risk areas were along the banks of the Nile River and its irrigation canals. In addition, the MaxEnt models revealed some similarities in the distribution pattern of the vector, the predator, and the aquatic plant. It is obvious that the predictive distribution range of B. truncatus was affected by altitude, precipitation seasonality, isothermality, and mean temperature of warmest quarter. The presence of B. truncatus decreases with the increase of altitude and precipitation seasonality values. It could be concluded that the MaxEnt model could help introducing a predictive risk map for Schistosoma haematobium prevalence and performing better management strategies for schistosomiasis.
- Published
- 2022
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