2017 ESA Annual Meeting (August 6 -- 11)

COS 115-10 - Fighting Queensland Fruit Fly in-silico: Modelling an insect pest to inform management strategies

Wednesday, August 9, 2017: 4:40 PM
B113, Oregon Convention Center
Florian Schwarzmueller, Hazel Parry and Nancy A. Schellhorn, Agriculture & Food, CSIRO, Brisbane, Australia
Background/Question/Methods

Highly mobile and polyphagous pests, such as the Queensland Fruit Fly (Bactrocera tryoni, QFly) are a major threat to agricultural systems worldwide. In order to minimize economic losses, farmers need to develop on-farm best-management practices (BMP), get engaged in area-wide management approaches (AWM) and need to access new technologies, such as the Sterile Insect Technique (SIT). Ecological modelling can contribute by informing BMP’s, and showing the level of AWM adoption needed for suppression so that SIT can be cost effective.

In this project, we are focussed on QFly, the major horticultural pest in Australia. We use existing information about the biology and ecology of QFly and combine it with information about the phenology and distribution of their hosts to mimic fruit fly populations on a regional level. We can then implement different management strategies, varying levels of adoption and community engagement to look at management effectiveness. We can also explore the levels of AWM adoption needed in a landscape to get fly suppression that is low enough for SIT to be effective, and thus become a viable option for QFly treatment.

Results/Conclusions

Fruit producing regions of Australia are highly variable with different proportions of summer, autumn and winter fruits. Therefore, the model has to be tailored for specific regions, especially in terms of actual host distribution and phenology. In the more theoretical approach presented here, we can however show some general results: (1) we show that the population density of QFly is landscape dependent; higher amount or diversity of fruit or a higher amount of urban areas in a landscape support higher numbers of flies. (2) On-farm BMP can reduce fly numbers significantly when adopted by a enough growers (AWM). (3) SIT in urban areas only can further reduce fly numbers in adjacent rural areas when embedded in AWM.

These findings can be coupled with economic analyses about costs of the different management strategies and the benefits of a reduction in fly numbers. This will inform region specific guidelines and action plans and can help get people engaged in the idea of AWM.

Fighting highly mobile polyphagous pests through AWM is a complicated task that requires experts in entomology, biosecurity, economy and social science. Spatially explicit modelling is one tool to combine their existing knowledge, identify important gaps, explore management solutions, and get community groups involved.