Food security is a global challenge reflected across two United Nations Sustainable Development Goals (SDGs 2 & 3). One critical component to meeting this challenge is the management of agricultural pests. Successful pest management is regularly thwarted by the rapid evolution of pest resistance to pesticides [1, 2]. Consequently, pesticide resistance is a widespread problem despite the ongoing development of various strategies to disrupt or weaken it. Such strategies often manage when and where pesticides are applied to ensure that some susceptibility to pesticides persists in the pest population [3, 4]. Testing different pesticide application strategies can be time-consuming and expensive in real agricultural systems, and such tests are constrained in their ability to control for pest characteristics such as genome properties and trait distributions. Instead, agent-based models (ABMs) can be used to simulate the social-ecological dynamics of pesticide application regimes and pest eco-evolutionary responses to determine the most effect strategies for overcoming pesticide resistance evolution [3, 5]. This project will use ABMs to develop theory on pesticide resistance management across a range of pesticide application regimes, pest life histories, and pest genome properties.
Pest management strategies are often developed on the
premise that uniform and constant pesticide application will cause sustained
directional selection for pesticide resistance. Hence, by relaxing selection
using designated refuge areas where no pesticide is applied, alleles conferring
susceptibility to pesticides can be maintained in the pest population [1, 3].
Alternatively, a combination of different types of pesticides applied in
combination, or at different times and in different locations, might also be
effective for maintaining susceptibility to any single pesticide . Such
approaches with refuges or multiple pesticides might be especially advantageous
if there are trade-offs in fitness related pest traits [6, 7]. For example, if
pesticide resistance incurs a cost to pest reproduction or longevity.
Consequently, the efficacy of management strategies can depend on the complex
relationships among pest life histories , mating systems , and genetics
. This project will investigate management strategies given these
relationships using ABM computer simulations in the newly developed resevol R
package [5, https://bradduthie.github.io/resevol/].
The primary aim of the project is to develop new theory on pest management through the use of agent-based models (ABMs). This new theory will integrate knowledge from the literature and ABM simulations to predict the general consequences of different pest management strategies under different evolutionary, ecological, and environmental conditions.
During this project, the student will join the development team of the resevol R package and learn how to develop social-ecological theory using agent-based models (ABMs). They will work with the primary supervisor to identify important theoretical questions, then design and simulate appropriate ABMs to address those questions using state of the art modelling software. The student will thereby develop expertise in model development, use, and analysis. All coding will be done collaboratively with the lead supervisor using GitHub, and software developed will be published open access.
The resevol R package  is a highly flexible tool for simulating ABMs of pest evolutionary and ecological dynamics under different regimes of crop and pesticide use. Simulations are run on spatially explicit landscapes with a potentially high level of terrain detail and pre-specified farm locations. Crops and pesticides can be rotated over time for each farm independently, which can simulate different management strategies applied across the full agricultural landscape. Pests are modelled to have evolving traits that have a pre-specified covariance structure and means. These traits can affect multiple aspects of life history including movement, reproduction, metabolism, feeding ability on specific crops, and resistance to specific pesticides. Pest traits are polygenic, quantitative traits produced from explicit and unique agent genomes that have an arbitrary number of loci in which alleles undergo mutation, recombination, selection, and drift. This complexity allows simulations to fully track all aspects of pest evolution and ecology. The PhD student will have the opportunity to apply the resevol R package and develop their own model or case study to evaluate different crop and pesticide application regimes for different agricultural pest systems and provide policy recommendations for pest resistance management.
Informal enquiries are strongly encouraged and can be
sent to Brad Duthie ([Email Address Removed]) as early as possible. Further
information about the project can be found here: