Modelling landscape-level approaches for mitigating the evolution of pesticide resistance


   School of Biological & Environmental Sciences

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  Dr Alexander Duthie, Prof OE Gaggiotti, Dr Rebecca Boulton, Dr M Tinsley  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

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.

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 [5] 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:

https://iapetus2.ac.uk/studentships/modelling-landscape-level-approaches-for-mitigating-the-evolution-of-pesticide-resistance-2/


Biological Sciences (4)

Funding Notes

IAPETUS2 Studentship Competition 2024. IAPETUS2 is a fully funded doctoral traineeship programme funded as part of the NERC DTP2 process.
In order to address historical imbalances in the higher education sector, IAPETUS2 is committed to recruiting a diverse, representative community of researchers in Environmental Science. The DTP has developed an Equality, Diversity and Inclusion policy to further this. This includes the Widening Participation Scheme, which identifies Home applicants from underrepresented groups. The DTP aims to give up to 30% of interview places to those eligible for this scheme. For more, please see the IAPETUS2 website.

References

[1] Gould, F., Brown, Z. S., and Kuzma, J. (2018). Wicked evolution: Can we address the sociobiological dilemma of pesticide resistance? Science, 360:728–732.
[2] Lykogianni, M., Bempelou, E., Karamaouna, F., and Aliferis, K. A. (2021). Do pesticides promote or hinder sustainability in agriculture? The challenge of sustainable use of pesticides in modern agriculture. Science of the Total Environment, 795:148625.
[3] Saikai, Y., Hurley, T. M., and Mitchell, P. D. (2021). An agent-based model of insect resistance management and mitigation for Bt maize: a social science perspective. Pest Management Science, 77:273–284.
[4] Sudo, M., Takahashi, D., Andow, D. A., Suzuki, Y., and Yamanaka, T. (2018). Optimal management strategy of insecticide resistance under various insect life histories: Heterogeneous timing of selection and interpatch dispersal. Evolutionary Applications, 11:271–283.
[5] Duthie, A. B., Mangan, R., McKeon, C. R., Tinsley, M. C., and Bussière, L. F. (2022). resevol: an R package for spatially explicit models of pesticide resistance given evolving pest genomes. PLOS Computational Biology (Accepted). bioRxiv preprint: https://www.biorxiv.org/content/10.1101/2022.08.22.504740v1
[6] Raymond, B., Sayyed, A. H., and Wright, D. J. (2005). Genes and environment interact to determine the fitness costs of resistance to Bacillus thuringiensis. Proceedings of the Royal Society B: Biological Sciences, 272: 1519–1524. https://doi.org/10.1098/rspb.2005.3103
[7] Jensen, K., Ko, A. E., Schal, C., and Silverman, J. (2016). Insecticide resistance and nutrition interactively shape life-history parameters in German cockroaches. Scientific Reports, 6: 1–7. https://doi.org/10.1038/srep28731
[8] Jacomb, F., Marsh, J., and Holman, L. (2016). Sexual selection expedites the evolution of pesticide resistance. Evolution, 70: 2746–2751. https://doi.org/10.1111/evo.13074
[9] Ffrench-Constant, R. H. (2013). The molecular genetics of insecticide resistance. Genetics, 194:807–815.

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