How does fluctuating selection affect pesticide effectiveness?


Can living pathogens help sustainably overcome the tremendous evolutionary power of pests?

Pathogens provide some of the strongest selection pressures in nature, driving continual dynamic coevolutionary interactions with hosts. Because pathogens tend to show specificity in their ability to infect hosts, genetic variation for resistance to pathogens is typically high: any one host is unlikely to be able to resist all extant genotypes of pathogen circulating in a population. Host-pathogen coevolution is therefore key to fundamental questions about how selection shapes diversity, but it also may provide a tantalising solution to the long-standing problem of pesticide resistance evolution.

This PhD studentship will combine studies of long-term laboratory evolution with Next Generation Sequencing methods to explore how diverse selection can help address the global challenge of pesticide resistance evolution.

Insect pests consume 10-20% of globally produced food during growth or in storage, and therefore represent a substantial threat to global food security. Conventional pest control using synthetic pesticides can be problematic because their use often conflicts with other sustainable development goals by damaging nontarget organisms and disrupting natural food webs. Moreover, in spite of intensive research and development, insects continue to evolve resistance to synthetic pesticides with predictable regularity, eluding even the most ingenious attempts to circumvent evolution. Populations of some pests, such as the cotton boll moth (Figure 1), have evolved resistance to nearly all synthetic pesticides, and can quickly cause significant economic losses, while simultaneously prompting the overuse of ineffective chemicals by farmers who are desperate to control the damage.

Recently, we have proposed an innovative and evolutionarily sustainable approach to pest control that harnesses rather than resists the enormous evolutionary potential of pest populations. It relies on the observation that the performance of pest genotypes towards genetically complex challenges (e.g., the infective ability of pathogens and the similarly complex defences of host plants) depends strongly on environmental context. This suggests that we can preserve genetic diversity for susceptibility to biological agents by ensuring that pests experience different challenges (both in terms of fungal pathogens and host plants) at a landscape scale.

To explore the long-term effects of pathogen and host plant diversity on the evolution of resistance (alongside many other traits that might trade-off with it), this studentship will evaluate the results on an ongoing laboratory evolution experiment (Figure 2) that manipulates the consistency of selection in replicate lines, using the cotton boll moth, H. armigera.

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Image Captions

Figure 1: The cotton boll moth, Helicoverpa armigera (Lepidoptera: Noctuidae) is one of the world’s most destructive crop pests. Its relatively recent invasion of South America has led to significant control challenges because it is tolerant to many synthetic pesticides. It is highly polyphagous and well suited to manipulations of landscape heterogeneity that seek to make selection for pesticide-resistance inconsistent. Photo credit: Gyorgy Csoka, Hungary Forest Research Institute,

Figure 2: Experimental design to generate consistent vs. inconsistent selection across treatments. Replicate lines of moths experience either consistent exposure to fungal pathogens (none in control on left, consistently the same isolate in the middle) and host plant (e.g., soybean, in green), or are split among all combinations of fungal isolate and host plant (right). The random mating of emerging moths at the right should mimic the challenge of evolution in a diverse landscape. The studentship will investigate the life-history and genomic consequences of long term evolution in these contrasting conditions.


This PhD studentship contributes to broader, integrative efforts to study the ecological and evolutionary effects of landscape diversity on crop pests. It has three main aims:

1. To quantify the phenotypic consequences of long-term evolution in response to constant or fluctuating exposure to fungal pathogens.
Theory predicts that pathogen resistance should be costly and accompanied by trade-offs, but the strength and nature of these trade-offs is difficult to quantify and predict. The replicate evolution lines will provide a rare opportunity to explore how increased investment in alternate immune mechanisms imposes costs on other aspects of life history. This project will therefore provide opportunities to confront fundamental life history theory concerning how resources are allocated in the presence of consistent or fluctuating selection.

2. To quantify genomic and transcriptomic divergence across lines using state-of-the-art next-generation methods.
We will monitor changes over time in the allele frequencies for several populations using cost-effective poolseq techniques. Single nucleotide polymorphism (SNP) frequencies will be identified and the consistency of changes across replicates used to identify changes due to each pathogen and host plant and to quantify what proportion of the genome shows pathogen x host plant interactions. We will also examine differences in the trajectory of candidate SNPs under different selection regimes.
To analyse expression, we will rear caterpillars from alternate evolution lines on each of the host plants, infect them with the two pathogens, and use RNAseq to detect differential expression in response to infection, host plant and evolutionary history. We predict less consistent responses under variable selection history, demonstrating a reduced capacity of directional evolution.

3. To compare the phenotypic differences across selection lines with associations between phenotypic traits in wild populations.
Agricultural systems are unusual in that they represent atypically homogeneous conditions under which pests can evolve, and therefore agricultural pest populations could conceivably be more similar to lab evolution lines than is generally true for other species. In this project, the student will assess whether phenotypic associations observable in natural systems can be explained by the same kinds of genetic remodelling that occurred in the selection experiment, providing a unique opportunity to validate observations from lab experiments in a field setting.

Project Timeline

Year 1

Initial PhD training, planning, phenotyping of selection lines.

Year 2

Secondment to University of St. Andrews, bioinformatics training, genomic analyses.

Year 3

Field work in Brazil, contrast of selection and genetic associations across lab and field settings.

Year 3.5

Further analysis, thesis, MS preparation.

& Skills

The project involves training in several important ‘in-demand’ transferable skills identified by NERC: Modelling; Multi-disciplinarity; Data Management; Numeracy; Fieldwork; Sustainability Science and Microbiology. In addition, the student will learn state-of-the-art methods for detecting evolution in lab experiments and receive training in general bioinformatics techniques, particularly during a secondment to the University of St. Andrews. During field work, the student will join an international team of investigators working across the diverse disciplines of agronomy, ecology, evolution, economics and social science.

References & further reading

You can find out more about our research work investigating how ecological heterogeneity in agroecosystems can limit the evolution of resistance to pest control interventions here:

Bussiere, L.F., Hunt, J., Stolting, K.N., Jennions, M.D., & Brooks, R. (2008). Mate choice for genetic quality when environments vary: suggestions for empirical progress. Genetica, 134, 69-78.
Cory, J.S. (2017). Evolution of host resistance to insect pathogens. Curr. Opin. Insect Sci., 21, 54-59.
Hunt, J., Bussiere, L.F., Jennions, M.D., & Brooks, R. (2004). What is genetic quality? Trends Ecol. Evol., 19, 329-333.
Kofler R., Orozco-terWengel P., De Maio N., Pandey R.V., Nolte V., Futschik A., Kosiol C., Schlotterer C. (2011). Poolation: a toolbox for population genetic analysis of next generation sequencing data from pooled individuals. PLoS One 6 (1): e15925.
Tinsley, M.C., Blanford, S. & Jiggins, F.M (2006). Genetic variation in Drosophila melanogaster pathogen susceptibility. Parasitology, 132, 767-773.
Topa H., Jónás Ã., Kofler R., Kosiol C., Honkela A. (2014). Gaussian process test for high-throughput sequencing time series: application to experimental evolution. Bioinformatics 31 (11): 1762-1770.
Veltsos, P., Fang, Y., Cossins, A.R., Snook, R.R. & Ritchie, M.G. (2017). Mating system manipulation and the evolution of sex-biased gene expression in Drosophila. Nature Comm. 8: 2072.
Wiberg, R.A.W., Morrissey, M.M., Gaggiotti, O.E. & Ritchie, M.G. (2017). Identifying consistent allele frequency differences in studies of stratified populations. Methods Ecol. Evol. 8: 1899-1909.

Further Information

Luc Bussiere, BES, University of Stirling
Stirling, UK, FK19 0AY, tel: +44 (0)1786 467758

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