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 [4]. 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 [4], mating systems [8], and genetics
[9]. 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. The project is divided into three initial stages presented below. These stages are flexible and might be later tailored to meet the specific interests of the student.
The first stage of the project focuses on the landscape. The student will construct an ABM with the resevol R package and run simulations that vary in landscape structure (e.g., farm locations and dimensions) and crop and pesticide rotation (e.g., random or sequential rotation of crops and pesticides). Using these simulations, the student will test how agricultural production, pest population density, and pest evolution respond to different landscape characteristics and regimes of crop and pesticide application.
The second stage of the project focuses on pest reproduction. The student will simulate pest populations with different types of reproductive systems (e.g., asexual, monoecious, or dioecious) to determine how reproductive system affects agricultural production and pest populations and which pest management regimes are most effective for each system.
The third stage of the project focuses on pest genomes. The student will investigate the effect of pest genome properties on pesticide resistance evolution. Genome properties will include loci number and effect size, recombination rate, mutation rate, and genetic constraints on pest traits. The student will use the resevol R package to vary these genome properties and determine how pest genomes modulate the efficacy of different pest management strategies.
The remainder of the project will be flexible and targeted to the student’s own research interests. This might include further development of the resevol R package with the student’s own code and simulations, application of simulations to a specific case study (e.g., the supervisory team is currently working with the agricultural pest species Helicoverpa armigera), or the development of a new model from scratch.
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