Classification: Mathematics in Science and Technology
Fully funded PhD position in developing data-informed mathematical models for locust hopper band movement. Project Summary: Effective control of locust outbreaks relies on the early detection and monitoring of locust hopper bands before flying swarms form. To reduce the environmental impact of locust control operations (and reduce their cost), there has been a shift from blanket spraying to the targeted application of barrier treatment using biopesticides. Their successful application, however, requires a better ability to predict locust band movement. This project will focus on modelling the collective movement of locust hopper bands (thousands to millions of organisms). We will improve on current models through the inclusion of variation of individual animal characteristics such as hunger and epigenetic phase (solitary vs. gregarious) and resource distribution (notably food and water). We will use field and laboratory observations to tune the model's parameters and validate its predictive efficacy, by applying techniques from data science. Our data-informed models will be able to predict the behaviour of the bands in response to different control measures, allowing us to develop strategies for optimal application of barrier treatment. This project is part of the national Analytics for the Australian Grains Industry (AAGI) initiative that will provide the student with extended collaborative opportunities and professional development activities.
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