EASTBIO: Developing Effective Rat Control for Rural Madagascar Landscapes: Using Individual Based Modelling Approaches to Inform Strategies to Increase Agricultural Productivity and Reduce Zoonotic Disease Risk

Supervisors:

Dr Sandra Telfer (University of Aberdeen) https://www.abdn.ac.uk/ibes/profiles/s.telfer

Professor Justin Travis (University of Aberdeen) https://www.abdn.ac.uk/people/justin.travis

Professor Xavier Lambin (University of Aberdeen) www.abdn.ac.uk/biologicalsci/staff/details/x.lambin

Across the developing world, food insecurity is greatest in rural areas, where smallholder farms predominate. Increased productivity of such farms has important positive effects on livelihoods and is essential for the global fight against hunger. The development of effective management strategies to reduce crop losses from agricultural pests requires an understanding of how pest populations will respond to control, at both the population and agricultural landscape scale.

Rodent pests contribute significantly to food insecurity. Globally, rodents annually eat and spoil cereals that could feed ~280 million people in developing countries alone. They are also important reservoirs for a range of livestock and human diseases. In some countries in Asia and Africa, increased understanding of processes contributing to variation in rodent abundance has led to the successful development of Ecologically Based Rodent Management (EBRM). EBRM targeting community actions at key times of year in specific locations can significantly reduce losses. Although traditionally most actions aim to reduce pest survival (e.g. trap barrier systems), there is increasing interest in contraceptive baits.

In Madagascar, undernourishment remains a prevalent problem. Smallholders predominate and rice is the staple food. The black rat, the major rodent pest, is found in all habitats from forest to cultivated areas and villages. Moreover, this species is a reservoir for diseases that can be transmitted to humans and/or livestock (e.g. plague, leptospirosis). EBRM is not presently practiced in Madagascar and there is an opportunity to develop strategies that improve both food security and human and livestock health.

Previous studies provide a good base knowledge of the population ecology of the black rat, as well as the epidemiology of several rodent associated diseases. However, there is an urgent need to predict landscape-level responses of populations and diseases to alternative control strategies. For example, removal of individuals through trapping or poisoning may favour increased dispersal of animals from non-control areas, possibly completely negating any benefits from control and also contributing to disease spread.

This interdisciplinary project will use existing rodent trapping data and a systems modelling approach to explore management options. Using a recently developed individual-based modelling platform, RangeShifter, which integrates population dynamics, dispersal behaviour and genetics, and can be used to simulate scenarios on spatially explicit landscapes, this project will:

(1) compare the effectiveness of targeting rat control in different habitats and different seasons, using a model parameterised with existing trapping data and landcover maps,

(2) incorporate individual disease status into RangeShifter and investigate the impact of control on the landscape epidemiology of different pathogens, including plague and leptospirosis and

(3) test model predictions against field data from control trials.

As RangeShifter incorporates evolutionary processes there is also the possibility for the student to investigate the potential impacts of the spread of resistance to rodenticide or disease.

The project will suit a student with a background in population ecology and numerical skills, who has interests in applied ecology, modelling and using interdisciplinary research to tackle global issues. The student will be expected to work closely with the Institut Pasteur de Madagascar and a range of NGO and government stakeholders. The student will be given a thorough multidisciplinary training, integrating ecological with computational modelling skills.