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KESS2 Scholarship: Utilising Earth Observational (EO) techniques and data sets to detect and monitor insect pest disturbance events.

  • Full or part time
  • Application Deadline
    Thursday, January 30, 2020
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

Project Description

Utilising Earth Observational (EO) techniques and data sets to detect and monitor insect pest disturbance events, impacts on tree health and rates of tree mortality within forest ecosystems.

Insect disturbance events within forest ecosystems are becoming increasingly more common, mainly in response to changing climate patterns, or through the accidental introduction of invasive pests into new areas. This project will contribute to the development of novel techniques to predict insect outbreaks, monitor existing pest populations and the resulting defoliation or tree mortality that they may cause, as well as having the potential to improve the response and management approaches through the early detection of symptoms induced by new and emerging pests.

Current ground-based assessments of tree growth, canopy condition, rates of defoliation and assessing population densities of specific insect pests are notoriously labour intensive, time consuming and often need to be conducted over multiple years.
Earth Observational (EO) techniques and the resulting data sets that these methodologies generate now make it feasible for the early detection of major disturbance events, particularly those caused by forest insect pests. There have been numerous studies that have demonstrated how EO techniques can be used to map and monitor insect outbreaks and the resulting damage incurred. Hence in light of the current threats that are facing UK forests from both invasive wood boring insects (e.g. Asian longhorn beetle, Ips typographus, emerald ash borer, bronze birch borer), as well as concern over defoliating insects in both deciduous and coniferous forests (e.g. oak lace bug, spruce budworm moths etc) there is now a real need to further develop and utilise these novel EO techniques to monitor forest health more effectively.

Applicants

The successful applicant will initially use FR existing data sets to calibrate historical EO information with previous ground-based assessments. They will then develop the project to:
• Identify whether EO techniques can be used as early detection systems that can highlight imminent pest outbreak hotspots e.g. can they detect individual or small clusters of tree mortality caused by, for example, the bark beetle Dendroctonus micans.
• Assess the feasibility of using EO techniques to monitor existing insect pest issues, such as the green spruce aphid (Elatobium abietinum), by identifying areas/regions that are becoming more susceptible under changing climatic conditions.
• Examine the potential for EO data to help quantify tree growth losses caused by significant defoliation events linked to high insect pest ‘outbreak’ episodes.
• Determine whether it is possible to remotely identify forest areas that are susceptible to specific environmental conditions (e.g. drought) that may predispose forest stands to insect (and disease) issues.
This is an opportunity to join a growing and vibrant team of postgraduates and staff at Bangor studying various aspects of forestry, agriculture, and land use, and to work with scientists at Forest Research, Great Britain’s principal organisation for forestry and tree related research (www.forestresearch.gov.uk).
Supervisors: Dr Rubén Valbuena, Bangor University (), Dr David Williams (Forest Research), and Dr Andy Smith (Bangor University).
The project is expected to begin in February 2020 and to continue for three years. To apply for this fully funded post please email a current CV and covering letter (2 A4 pages maximum) to , cc’d to Penny Dowdney by 30th January 2020.

Funding Notes

KESS 2 East is a pan-Wales higher level skills initiative led by Bangor University on behalf of the HE sector in Wales. It is part funded by the Welsh Government’s European Social Fund (ESF) convergence programme for East Wales.

How good is research at Bangor University in Agriculture, Veterinary and Food Science?
(joint submission with Aberystwyth University)

FTE Category A staff submitted: 18.20

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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