The influence of temperature and age-dependent survival on Anopheles gambiae s.s. : life-history and population dynamics

• Main Author: Christiansen-Jucht, Céline
• Other Authors: Parham, Paul ; Basáñez, María-Gloria ; Koella, Jacob
• Published: Imperial College London 2016
• Subjects: 616.9
• Online Access: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.689147

Malaria transmission depends on the life-history parameters, and population dynamics, of its vectors, and particularly on the survival of adult Anopheles mosquitoes, the main vector of malaria in Africa. These dynamics are sensitive to climatic and environmental factors, and temperature is a particularly important driver, though the exact extent of this sensitivity is not well established. Some data currently exist on the influence of constant and fluctuating environmental temperature on Anopheles gambiae s.s. survival but very few exist examining other life-history traits, and none exist on the influence of larval temperature on adult life-history. This thesis conducts a detailed investigation into the effect of temperature on life-history parameters of Anopeheles gambiae s.s., and uses experimental results to inform the structure and parameterisation of a suite of mathematical models of mosquito population dynamics. Increasing environmental temperature during the larval stages is shown to decrease larval survival, and significantly increase adult mortality. Higher environmental temperature during the adult stages also significantly lowers adult survival. The data also highlight that mosquitoes senesce, and that age-dependent survival models fit experimental data best. Environmental temperature during the larval and adult stages is also shown to have an effect on a number of other life-history parameters. The set of novel mathematical models of climate-driven mosquito population dynamics developed here are motivated by the experimental data suggesting that in An. gambiae, mortality is temperature and age dependent, and show that age-dependent models consistently fit the data better than the reference model.