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EASTBIO Standing out in the crowd: understanding the role of individual variation to improve agricultural pest control.


School of Biological Sciences

Dr T Bodey , Prof J Travis , Dr P Walsh Wednesday, January 06, 2021 Competition Funded PhD Project (Students Worldwide)

About the Project

Supervisors:

Dr Thomas Bodey (University of Aberdeen)
https://www.abdn.ac.uk/people/thomas.bodey/
https://abdn.pure.elsevier.com/en/persons/thomas-bodey

Professor Justin Travis (University of Aberdeen)
https://www.abdn.ac.uk/sbs/people/profiles/justin.travis
https://abdn.pure.elsevier.com/en/persons/justin-travis

Dr Patrick Walsh (University of Edinburgh)
https://www.ed.ac.uk/profile/patrick-walsh

Effective strategies for pest control are a vital component of food security. Crop pests are estimated to result in >£50billion of damage and reduce global yields by 20-40% annually. Current approaches for pest control have had notable successes, but there is substantial scope for improvement. These methods target populations, assuming all pest individuals behave ecologically identically, and respond to control similarly. Yet there is a growing realisation that relaxing assumptions around ‘average individuals’ has important implications for fundamental and applied topics. This project addresses how inter-individual variabilities in behaviours and responses to control influence the effectiveness and optimisation of control strategies. This project is ideal for an individual keen to apply emerging ecological concepts to one of the most important current applied environmental science questions.

Individual-level variation is ubiquitous across species. Understanding individual responses is essential for explaining population-level patterns and impacts, yet this approach is rarely applied in pest management[1]. For example, the motivations of different individuals can vary temporally (e.g. energy reserves) or spatially (density of competitors or resources), and over multiple timeframes. Targeting pest control by addressing the problem from an individual perspective is key to effective long-term management, reducing unsustainable practices that can damage human and animal health and the wider environment[2]. This will ensure food production while retaining multiple management options, and maintaining broader ecosystem services.

To address these varying motivators requires a multi-pronged strategy, permitting effective responses to different individual proclivities and changing situations e.g. scent lures during reproduction, switching to food baits during resource scarcity[1]. However, how strategies should be implemented in time (e.g. simultaneously or sequentially) or space (clustered, dispersed, random) is entirely unknown.

This project sits at the interface between behavioural ecology and agricultural systems science, and will address these key knowledge gaps through a multi-disciplinary approach spanning individual-based modelling (IBM) that will inform, and be informed by, mesocosm and fieldwork experiments that assess the effectiveness of control strategies under increasing real-world complexity. The central approaches will be:
i) Modelling - extend the field-leading RangeShifter platform developed at UoA[3] (https://rangeshifter.github.io/RangeshiftR-tutorials/) to create IBMs examining the interactions between individuals and control methodologies, incorporating plasticity and variation in responses to determine the effectiveness of different implementation scenarios.
ii) Mesocosms –using an established flour beetle system at UoA, populations of individuals with quantified behavioural parameters e.g. bold-shy continuum, functional consumption response, will be used to experimentally inform and test model parameters and outcomes.
iii) Fieldwork – understanding developed from i) and ii) will be applied to real-world scenarios, with potential systems including key agricultural and forestry pests e.g. rodents and bark beetles (UK), possums and wasps (New Zealand).

The candidate will gain a range of transferable skills, making them competitive for a career in research or more widely including expertise in i) modelling and analytical skills; ii) effective experimental design; iii) fieldwork; iv) scientific writing; v) disseminating results to multiple audiences. The project will suit a student with a background including strong numerical or modelling skills and with interests in applied or behavioural ecology.

Application Procedure:

http://www.eastscotbiodtp.ac.uk/how-apply-0

Please send your completed EASTBIO application form, along with academic transcripts to Alison McLeod at . Two references should be provided by the deadline using the EASTBIO reference form. Please advise your referees to return the reference form to .

Funding Notes

This 4 year PhD project is part of a competition funded by EASTBIO BBSRC Doctoral Training Partnership View Website. This opportunity is open to UK and International students and provides funding to cover stipend and UK level tuition (limited funding is available to provide international tuition fees). Please refer to UKRI website and Annex B of the UKRI Training Grant Terms and Conditions for full eligibility criteria.

Candidates should have (or expect to achieve) a minimum of a 2:1 UK Honours degree, or the equivalent qualifications gained outside the UK, in a relevant subject.

References

[1] Simon-Delso N, Amaral-Rogers V, Belzunces LP, Bonmatin JM, Chagnon M, Downs C et al. (2015) Systemic insecticides (neonicotinoids and fipronil): trends, uses, mode of action and metabolites Environmental Science and Pollution Research
doi.org/10.1007/s11356-014-3470-y

[1] Garvey P, Banks P, Suraci, JP, Bodey TW, Glen AS, Jones CJ, McArthur C, Norbury GL, Price CJ, Russell JC, Sih A (2020) Leveraging motivations, personality, and sensory cues for vertebrate pest management Trends in Ecology & Evolution
doi.org/10.1016/j.tree.2020.07.007

[3] Bocedi G, Palmer SCF, Pe’er G, Heikkinen RK, Matsinos YG, Watts K, Travis JMJ (2014) RangeShifter: a platform for modelling spatial eco‐evolutionary dynamics and species' responses to environmental changes Methods in Ecology and Evolution
doi.org/10.1111/2041-210X.12162
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