Modelling Citizen-science Data for Butterflies and Moths: Where and When are they Flying?

(MATECHOU_K21ARIES)

Modelling Citizen-science Data for Butterflies and Moths: Where and When are they Flying?

(MATECHOU_K21ARIES)

Project Description

Supervisors

Dr Eleni Matechou (School of Mathematics, Statistics and Actuarial Science, University of Kent) contact me

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)

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.

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.

References

  • 1. 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
  • 2. 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)
  • 3. 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).
  • 4. 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.
  • 5. 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.

Key Information

  • This project has been shortlisted for funding by the ARIES NERC DTP and will start on 1st October 2021. The closing date for applications is 23:59 on 12th January 2021.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£15,285 p.a. for 2020-21) and research funding. For the first time in 2021/22 international applicants (EU and non-EU) will be eligible for fully-funded UKRI studentships. Please note ARIES funding does not cover visa costs (including immigration health surcharge) or other additional costs associated with relocation to the UK.
  • ARIES students benefit from bespoke graduate training and ARIES provides £2,500 to every student for access to external training, travel and conferences. Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses in the subject area.
  • ARIES is committed to equality, diversity, widening participation and inclusion in all areas of its operation. We encourage enquiries and applications from all sections of the community regardless of gender, ethnicity, disability, age, sexual orientation and transgender status. Academic qualifications are considered alongside significant relevant non-academic experience.
  • All ARIES studentships may be undertaken on a part-time or full-time basis, visa requirements notwithstanding
  • For further information, please contact the supervisor. To apply for this Studentship click on the “Apply now” link below.

Applications are Open

Apply now