Modeling and Simulation of the Population Dynamics of Biomphalaria tenagophila, Snails in Tres Palmeras, Salta, Argentina

Abstract

Aims/ objectives: We have analyzed the parasitic infections, and the population dynamics of Biomphalaria tenagophila snails. The population under study lives in water bodies formed by tributaries of the river Arias in the Tres Palmeras zone in Salta, Argentina. Study Design: Longitudinal and Cross-sectional studies. Place and Duration of Study: Department of Mathematic and School of Biology for Studies of the Diversity of Invertebrates. National University of Salta, Argentina, from January 2005 to December 2007. Methodology: Samples were collected at least twice per season in the period under study, and taken to the laboratory for further analysis. Snails were observed daily during 7 days to detect emergence of cercariae. In negative cases, snails were dissected to search for hidden infections. Data was analyzed using the techniques of power spectrum, exploratory analysis, and estimation of parameters by linear fit for a logistic model. Simulation tests were also carried out in order to qualitatively describe the present dynamic model. Results: The highest prevalence corresponded to Echinostomatidae gen sp. III (2.99%), followed by Australapatemon magnacetabulum (1.44 %), while no infections by Schistosoma mansoni were found. Data showed that the most important infection frequencies were detected in the following order: 12, 18 and 3 months. Density-dependent parameters for the net growth rate were estimated as r ≈ 0:32, s ≈ 2:07. The corresponding carrying capacity was K ≈ 1069 for the annual case. Conclusion: The most important frequency being at 12 months was the parameter that best described the situation observed in the field. This frequency could explain the annual variation, and the opportunistic growth pattern of the species. The function corresponding to density-dependent net growth rate provides an estimation of the growth coefficient and so-called “crowding coefficient”. However, data showed oscillations in the fit curve. The same pattern was observed in the simulations that would be explained with the generalized logistic model. Another possible explanation for the oscillations would be a sinusoidally-variable carrying capacity. Moreover, simulations described qualitatively the annual population dynamics.