95th ESA Annual Meeting (August 1 -- 6, 2010)

COS 88-9 - Modeling the malaria vector: Population dynamics of Anopheles mosquitoes with respect to temperature

Thursday, August 5, 2010: 10:50 AM
320, David L Lawrence Convention Center
Lindsay M. Beck-Johnson, Biology, Colorado State University, Fort Collins, CO, Ottar N. Bjornstad, Biology, Pennsylvania State University, William A. Nelson, Biology, Queen's University, Kingston, ON, Canada, Andrew F. Read, Center for Infectious Disease Dynamics and Departments of Biology & Entomology, The Pennsylvania State University, University Park, PA and Matthew B. Thomas, Entomology, Penn State University, University Park, PA
Background/Question/Methods   Anopheles spp. mosquitoes represent the most important vectors for the Plasmodium parasites that cause malaria. The age structure of the adult mosquito population is a major determinate of the ability of any given population to vector malaria. The reason for this is that the parasite requires a week or two, depending on temperature, to develop before it is transmissible by the mosquito. Temperature also affects most of the life processes in mosquitoes. However, it is unclear how different temperature conditions affect longevity and the dynamics of mosquito populations. This type of information will become increasingly important in the face of climate change. To explore the population dynamics of Anopheles mosquitoes we developed a temperature-dependent delayed differential equation model where all rates are assumed to scale with temperature and /or density. We parameterize the model using our own experimental data as well as data from the literature.

Results/Conclusions   The model allows us to explore dynamics under both constant and fluctuating temperatures. Because of temperature dependence in various rates (eg. temperature may enhance mortality of adults but also speed up the extrinsic incubation period of Plasmodium), the results are sometimes non-intuitive and variability may sometimes be as important as mean temperature. The mathematical formalism we develop allows a quantitative and analytic approach to predicting how climate change may change vector capacity.