Department of Zoology, Faculty of Science, University of Ibadan, Ibadan.
Onderstepoort J Vet Res. 2023 May 10;90(1):e1-e13. doi: 10.4102/ojvr.v90i1.2023.
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.
大蜗牛是唯一的蜗牛中间宿主巨型圆口吸虫,在尼日利亚,它是片形吸虫病的病原体。该物种也是许多其他具有医学和兽医重要性的非洲吸虫物种的中间宿主,在全国范围内都有发现。然而,关于影响其在尼日利亚热带水生栖息地分布和季节性丰度的因素,尚无详细信息。本研究使用地理信息系统和遥感数据,开发了用于预测尼日利亚西南部大蜗牛分布的模型。从陆地卫星图像中提取了地表温度(LST)和归一化差异植被指数(NDVI);从数字高程模型(DEM)中提取了其他变量(坡度和海拔),而降雨量数据则从欧洲气象研究计划(EMRP)中检索。这些环境变量被整合到地理信息系统(GIS)中,以使用探索性回归来预测大蜗牛的适宜栖息地。总共收集了 1410 只大蜗牛,涉及 22 个采样点。与农田相比,建成区记录的大蜗牛更多。蜗牛的丰度与季节之间没有显著差异(p 0.05)。回归模型表明,降雨量、NDVI 和坡度是大蜗牛分布的预测因子。适合大蜗牛的栖息地是中心区域,而北部和南部的区域则不适合大蜗牛生存。贡献:研究中大蜗牛的预测风险模型将有助于绘制其他无法进行蜗牛采样的区域的地图。