Creating bio-inspired synthetic flowers to support standardised monitoring of pollinating insects PhD

This project will support global efforts to investigate and monitor declining pollinator population by fabricating bio-inspired ‘synthetic’ flower attractants. The research will use cutting edge techniques in design including 3D printing, CAD and visualisation, in design and microfabrication to replicate flower shape, colours, smells and function. Based mainly at UKCEH, Wallingford, with periods at Cranfield University, this is an exciting opportunity for a fully-funded NERC - CENTA PhD 3.5 year studentship. CENTA is a consortium of Universities and research institutes that are working together to provide excellence in doctoral research training within the remit of the Natural Environment Research Council (NERC). Successful home-fees-eligible candidates will receive an annual stipend, set at £15,609 for 2021/22, paid directly to the student in monthly increments, full university fees and a research training support grant (RTSG) of £8,000

Sponsored by NERC through CENTA DTP, UKCEH and Cranfield University. The project has CASE support through Operation Wallacea.

Project Highlights: 

-       This project will support global efforts to investigate and monitor declining pollinator population by fabricating bio-inspired ‘synthetic’ flower attractants.

-       This interdisciplinary project will use cutting edge techniques in design including 3D printing, CAD and visualisation, in design and microfabrication to replicate flower shape, colours (including UV), smells and function (i.e. providing micro-capillary sugar solution)

-       ‘Synthetic’ flowers will be tested in controlled settings using captive insects, as well as being deployed and tested in real world settings as attractants for machine vision AI-assisted insect monitoring systems 

Overview

 Pollinators contribute to healthy and resilient ecosystems, being responsible for helping 90% of the world’s flowering plants reproduce (EPA, 2020). This is key in maintaining natural ecosystem services, in maintaining sufficient seeds and fruits for dispersal and propagation and in maintaining genetic diversity (USFC, 2020). Society is entirely dependent on ecosystem services, such as pollination, arising from the interactions between biodiversity and the physical and chemical environment. Modern agricultural systems are dependent on pollinators to ensure crop performance, whose outcome collectively contributes to national food security. Pollinating insect population decline has been linked to several drivers and challenges, including widespread use of pesticides (Woodcock et al, 2017) and changing climatic patterns (Settele et al, 2016). Given the importance of pollinators, improved knowledge is needed on their abundance and distribution worldwide. However, despite the UK supporting a world-leading scheme for systematic monitoring of insect pollinators (https://ukpoms.org.uk/, led by Carvell and partners) and other countries developing similar approaches, this relies heavily on the limited capacity of both field surveyors and taxonomists, and there are significant trade-offs between sampling intensity, capacity and cost (Breeze et al, 2020). Automated monitoring tools are showing promise for tracking changes in pollinators by using recently developed tools in machine learning (Hoye et al, 2021), potentially opening the door to large scale monitoring, especially in areas that are remote or lack taxonomic expertise (easy RIDER, co-led by August). However, one of the remaining challenges for autonomous monitoring of pollinators is the creation of standardised attractants. Existing systems use either live flowers, which can be highly variable in space and time (Hoye et al, 2021), or they use coloured sheets, which are a poor attractant as compared to flowers (Diopsis, 2020).

This research project aims to develop and evaluate bioinspired insect attractants to enhance and standardise insect monitoring systems. This aim will be supported by the following provisional research objectives:

1. Identify the attributes of a flower important for attracting insects (olfactory, visual, chemical), the forms that are most effective (e.g. colour preference), and the relative importance of features for different species groups (e.g. bees vs flies). This will be conducted by a literature review.

2. Design and manufacture of bioinspired insect attractants that can be standardised. This will make use of microfabrication methods such as 3D printing, CAD and visualisation.

3. Deploy bioinspired attractants with autonomous AI monitoring systems in experimental and real-world conditions to quantify their ability to increase the number of insects drawn to the

camera, and to standardise protocols. This will include undertaking comparative testing in biodiverse regions of the world through collaboration with Operation Wallacea, including engagement with school groups (see LoS).

Entry requirements 

Applicants should have at least a 2:1 at UK BSc level or at least a pass at UK MSc level or equivalent in a related discipline. Additional desirable personal strengths would include: Excellent CAD and visualisation skills, translating ideas into concept solutions *e.g. SolidWorks, Rhino3D or equivalent; Ability to use workshop equipment to build working prototypes; Good team-working and inter-personal skills; Strong oral, visual and written communication and presentation skills; Ability to work independently and as part of a team, and demonstrating ability to work with internal and external research teams; Ability to work in both industrial and academic setting; Analytical problem-solving skills.

Duration : 3.5 years

Start date: October 2022

Supervisors: Lead and supervision team: Dr Tom August, UKCEH (Lead); Dr Claire Carvell, UKCEH; Professor Stephen Hallett, Cranfield University; Professor Leon Williams, Cranfield University

How to apply

To apply, please follow this link and click “Apply now”.

For general enquiries about this position, including help applying, terms and conditions, etc, please contact: [Email Address Removed], quoting reference number SWEE0163.