Modeling freshwater snail distribution using remotely sensed climatic variables and ridge regression

Abstract

Abstract Snails inhabit diverse ecosystems and are of veterinary and medical importance as intermediate hosts of parasitic diseases such as schistosomiasis and fascioliasis. This study investigated the relationship between remotely sensed weather variables freshwater snail species in Edu Local Government Area, Kwara State, Nigeria, a schistosomiasis-endemic region. Freshwater snails were collected from freshwater bodies, morphologically identified, and their spatial distribution assessed using QGIS software. Logistic regression (LR) was conducted to examine associations between snail presence and climatic predictors while ridge regression was applied to address multicollinearity among some variables. For Bulinus globosus , pressure, wind direction, and relative humidity emerged as significant predictors (χ 2 = 1.355, p < 0.001), with model achieving 86.7% accuracy. Bulinus jousseaumei was significantly associated with wind direction and precipitation whereas Bulinus truncatus showed a negative but nonsignificant relationship with wind direction. Wind direction, wind speed, and relative humidity had positive influences on Biom. pfeifferi although not statistically significant. For Lymnaea natalensis, minimum temperature (p = 0.009) and precipitation (p = 0.016) were strong predictors. These findings indicate that although the magnitude of predictors effect was modest, the direction and significance of some weather variables varied across species, providing valuable insights into freshwater snail ecology and their role in disease transmission. Understanding these associations can inform predictive modeling and guide targeted snail control strategies in endemic communities.