About the Project
Project Background:
At a time of biodiversity loss, including widely reported insect declines, citizen science data play a vital role in measuring changes in species’ populations and distributions and in seeking to understand the pressures influencing such changes. Butterflies and moths respond quickly to habitat and climatic change, and hence are valuable biodiversity indicators. In the UK, millions of species occurrence records for Lepidoptera have been gathered by two large citizen science recording schemes, of which the full potential has not been fully realized. Analysing recording data of this nature presents unique challenges relating to their vast quantity but also associated sampling biases. Using cutting edge modelling, this project will maximise these valuable datasets to enhance our understanding of species’ phenology (flight periods), distribution and range dynamics to help inform future conservation delivery and policy for UK butterflies and moths.
Research Methodology:
The candidate will undertake new statistical model developments applied to citizen science data. The research will involve:
- Critically assessing sampling design to determine how much data are needed to reliably estimate species’ occurrence trends – can occupancy models be used for rare species with small ranges?
- Modelling species’ phenology from citizen science data to provide new insights on variation over space and time.
- Applying state-of-the-art variable selection techniques to better describe drivers of species’ range and distribution change through suitable spatial and environmental covariates.
Training:
The candidate will develop a strong, highly transferable skillset in statistical modelling and analysis using modern statistical and computational techniques applied to large, unstructured data sets spanning multiple species, locations and years. The candidate will benefit from interactions with conservation professionals at Butterfly Conservation, including opportunities to undertake fieldwork, to better understand the data collection processes and focal taxa of the project, as well as data use for conservation delivery and policy.
Supervisory Team:
The main Supervisor for this project is Dr Eleni Matechou
https://www.kent.ac.uk/mathematics-statistics-actuarial-science/people/1039/matechou-eleni
Other supervisors are Dr Emily Dennis (Butterfly Conservation), Dr Richard Fox (Butterfly Conservation),
Professor Byron Morgan (School of Mathematics, Statistics and Actuarial Science, University of Kent), Dr Diana Cole (School of Mathematics, Statistics and Actuarial Science, University of Kent)
Person Specification:
Applicants should have a degree in a subject such as statistics, mathematics, or another scientific discipline with a substantial quantitative component. A keen interest in ecology is advantageous.
How to apply:
Interested candidates should contact Eleni Matechou to discuss the project and apply online at https://www.kent.ac.uk/courses/postgraduate/169/statistics
The closing date for applications is 23:59 on 12th January 2021.
Interviews:
There will be a two-stage interview process. The first round of interviews will take place at the end of January. Successful nominees will then participate in the second round of interviews, with the ARIES panel, on 17-18 February 2021.
References
- Dennis, E.B., Morgan, B.J.T., Freeman, S.N., Ridout, M.S., Brereton, T.M., Fox, R, Powney, G.D., Roy, D.B. (2017) Efficient occupancy model-fitting for extensive citizen-science data. PLoS ONE 12(3): e0174433. https://doi.org/10.1371/journal.pone.0174433
- Diana, A., Matechou E., Griffin, JE, Tenan, S., Volponi, S., Arnold, T., Griffiths, R. A., Pickering, J. (2020) A general modelling framework for ecological data based on the Polya Tree prior (under review)
- Griffin, J.E., Matechou, E., Buxton, A.S., Bormpoudakis, D., & Griffiths, R.A. (2019). Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors. Journal of the Royal Statistical Society: Series C (Applied Statistics).
- Dennis, E.B., Morgan, B.J.T, Freeman, S.N., Brereton, T.M. & Roy, D.B. (2016). A generalized abundance index for seasonal invertebrates. Biometrics, 71, 1305-1314.
- Dennis, E.B., Brereton, T.M., Morgan, B.J.T., Fox, R., Shortall, C.R., Prescott, T. & Foster, S. (2019). Trends and indicators for quantifying moth abundance and occupancy in Scotland. Journal of Insect Conservation, 23, 369-380.
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