Seasonal dynamics of snail populations in coastal Kenya: Model calibration and snail control

• Published in: Advances in water resources Vol. 108; pp. 397 - 405
• Main Authors: Gurarie, D., King, C.H., Yoon, N., Wang, X., Alsallaq, R.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-10-2017; Elsevier Science Ltd
• Subjects: Biology; Calibration; Coastal environments; Control; Dynamic models; Dynamics; Environment models; Environmental factors; Gastropoda; Habitats; Infections; Maturation; Model, theoretical; Nonlinear dynamics; Population biology; Population dynamics; Populations; Precipitation; Predictive control; Rain; Rainfall; Schistosomiasis; Seasonal rainfall; Seasonal variability; Seasonal variation; Seasonal variations; Seasons; Shedding; Snails; Studies; Kenya; Model, theoretical; Nonlinear dynamics; Schistosomiasis; Population biology
• ISSN: 0309-1708; 1872-9657
• DOI: 10.1016/j.advwatres.2016.11.008

•Snail population biology in seasonal habitats is driven by rainfall and food resource.•Field data shows significant time lags between “peak rainfall” and “peak snail” numbers.•Resource-limited snail population models can explain such patterns and fit the data.•Two proposed models emphasize different components of snail biology and environment.•Model analysis yields optimal seasonal timing of snail population control.
A proper snail population model is important for accurately predicting Schistosoma transmission. Field data shows that the overall snail population and that of shedding snails have a strong pattern of seasonal variation. Because human hosts are infected by the cercariae released from shedding snails, the abundance of the snail population sets ultimate limits on human infection. For developing a predictive dynamic model of schistosome infection and control strategies we need realistic snail population dynamics. Here we propose two such models based on underlying environmental factors and snail population biology. The models consist of two-stage (young–adult) populations with resource-dependent reproduction, survival, maturation. The key input in the system is seasonal rainfall which creates snail habitats and resources (small vegetation). The models were tested, calibrated and validated using dataset collected in Msambweni (coastal Kenya). Seasonal rainfall in Msambweni is highly variable with intermittent wet - dry seasons. Typical snail patterns follow precipitation peaks with 2–4-month time-lag. Our models are able to reproduce such seasonal variability over extended period of time (3-year study). We applied them to explore the optimal seasonal timing for implementing snail control.