{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T10:34:55Z","timestamp":1791974095579,"version":"4.4.0"},"reference-count":67,"publisher":"AOSIS","issue":"1","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Onderstepoort j. vet. res."],"abstract":"<jats:p>Lymnaea natalensis\u00a0is the only snail intermediate host of\u00a0Fasciola 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\u00a0L. natalensis\u00a0in 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\u00a0L. natalensis\u00a0using exploratory regression. A total of 1410\u00a0L. natalensis\u00a0snails were collected\u00a0vis-\u00e0-vis\u00a022 sampling sites. Built-up areas recorded more\u00a0L. natalensis\u00a0compared with farmlands. There was no significant difference in the abundance of snails with season (p\u00a0 0.05). The regression models showed that rainfall, NDVI, and slope were predictors of\u00a0L. natalensis\u00a0distribution. The habitats suitable for\u00a0L. natalensis\u00a0were central areas, while areas to the north and south were not suitable for\u00a0L. natalensis.Contribution:\u00a0The predictive risk models of\u00a0L. natalensis\u00a0in the study will be useful in mapping other areas where the snail sampling could not be conducted.\u00a0<\/jats:p>","DOI":"10.4102\/ojvr.v90i1.2023","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T09:39:24Z","timestamp":1683797964000},"source":"Crossref","is-referenced-by-count":4,"title":["Temporal modelling of Lymnaea natalensis (Krauss, 1848) in tropical aquatic habitats"],"prefix":"10.4102","volume":"90","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0961-3749","authenticated-orcid":false,"given":"Opeyemi G.","family":"Oso","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1601-0516","authenticated-orcid":false,"given":"Joseph 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