Original Research

Temporal modelling of Lymnaea natalensis (Krauss, 1848) in tropical aquatic habitats

Opeyemi G. Oso, Joseph O. Sunday, Alexander B. Odaibo
Onderstepoort Journal of Veterinary Research | Vol 90, No 1 | a2023 | DOI: https://doi.org/10.4102/ojvr.v90i1.2023 | © 2023 Opeyemi G. Oso, Joseph O. Sunday, Alexander B. Odaibo | This work is licensed under CC Attribution 4.0
Submitted: 25 March 2022 | Published: 10 May 2023

About the author(s)

Opeyemi G. Oso, Department of Zoology, Faculty of Science, University of Ibadan, Ibadan, Nigeria
Joseph O. Sunday, Parasitology Unit, Department of Zoology, Kwara State University, Malete, Kwara State, Nigeria
Alexander B. Odaibo, Department of Zoology, Faculty of Science, University of Ibadan, Ibadan, Nigeria

Abstract

Lymnaea natalensis is the only snail intermediate host of Fasciola gigantica, the causative agent of fascioliasis, in Nigeria. The species also serves as intermediate host for many other African trematode species of medical and veterinary importance, and it is found throughout the country. However, there is no detailed information on the factors that influence its distribution and seasonal abundance in the tropical aquatic habitats in Nigeria. This study used the geographic information system and remotely sensed data to develop models for predicting the distribution of L. natalensis in South-Western Nigeria. Both land surface temperature (LST) and normalised difference vegetation index (NDVI) were extracted from Landsat satellite imagery; other variables (slope and elevation) were extracted from a digital elevation model (DEM) while rainfall data were retrieved from the European Meteorology Research Programme (EMRP). These environmental variables were integrated into a geographic information system (GIS) to predict suitable habitats of L. natalensis using exploratory regression. A total of 1410 L. natalensis snails were collected vis-à-vis 22 sampling sites. Built-up areas recorded more L. natalensis compared with farmlands. There was no significant difference in the abundance of snails with season (p > 0.05). The regression models showed that rainfall, NDVI, and slope were predictors of L. natalensis distribution. The habitats suitable for L. natalensis were central areas, while areas to the north and south were not suitable for L. natalensis.

Contribution: The predictive risk models of L. natalensis in the study will be useful in mapping other areas where the snail sampling could not be conducted.

 


Keywords

geographic information system (GIS/RS); modelling; risk map; Lymnaea natalensis (L. natalensis); rain forest

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