Spatio-temporal models for large sets of citizen science data to inform conservation policy for UK Lepidoptera


Spatio-temporal models for large sets of citizen science data to inform conservation policy for UK Lepidoptera


Project Description


Dr Eleni Matechou (Durrell Institute of Conservation and Ecology, University of Kent) – Contact me

Dr Emily Dennis (Butterfly Conservation)

Professor Byron Morgan (University of Kent, SMSAS)

Dr Diana Cole (University of Kent, SMSAS)


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.

Lepidoptera 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 student 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.


The student 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 student 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

We seek a candidate with a strong quantitative background, for example a degree in Statistics or a degree with high statistics content, or a background in demographic modelling. Experience coding in R, or similar, is essential. An interest in conservation and ecology is advantageous. Quantitative ecologists are encouraged to apply.


[1] Altwegg, R. & Nichols, J.D. (2019) Occupancy models for citizen-science data. Methods in Ecology & Evolution, 10, 8-21.

[2] Guillera-Arroita, G. (2017) Modelling of species distributions, range dynamics and communities under imperfect detection: advances, challenges and opportunities. Ecography, 40, 281-295.

[3] Fox, R., et al. (2015) The State of the UK’s Butterflies 2015. Butterfly Conservation and the Centre for Ecology & Hydrology, Wareham, Dorset.

[4] Randle, Z. et al (2019) Atlas of Britain & Ireland’s Larger Moths. Pisces Publications, Newbury.

[5] Isaac, N.J.B., van Strien, A.J., August, T.A., de Zeeuw, M.P. & Roy, D.B. (2014) Statistics for citizen science: extracting signals of change from noisy ecological data. Methods in Ecology & Evolution, 5, 1052-1060.

[6] Dennis, E.B., Morgan, B.J.T, Freeman, S.N., Ridout, M.S., Brereton, T.M., Fox, R., et al. (2017)

Efficient occupancy model-fitting for extensive citizen-science data. PLoS ONE 12(3): e0174433.

[7] Besbeas, P. Freeman, S. N., Morgan, B. J. T. and Catchpole, E. A. (2002) Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics, 58, 540-547.

[8] Matechou, E., Morgan, B. J. T., Pledger, S., Collazo, J. A. and Lyons, J. E. (2013) Integrated analysis of capture-recapture-resighting data and counts of unmarked birds at stop-over sites. Journal of Agricultural, Biological and Environmental Statistics, 18, 120-135.

[9] Johnston, A., Moran, N., Musgrove, A., Fink, D., and Baillie, S. R. (2020) Estimating species distributions from spatially biased citizen science data. Ecological Modelling, 422.

[10] Conn, P. B., Thorson, J. T., and Johnson, D. S. (2017) Confronting preferential sampling in wildlife surveys: diagnosis and model-based triage. Methods in Ecology and Evolution, 8(11):1535–1546.

[11] Roy, D.B. & Sparks, T.H. (2001) Phenology of British butterflies and climate change. Global Change Biology, 6, 407-416.

[12] Altermatt, F. (2009) Climatic warming increases voltinism in European butterflies and moths. Proceedings of the Royal Society B, 277, 1281-1287.

[13] Guillera-Arroita, G., Morgan, B. J. T., Ridout, M., and Linkie, M. (2011) Species occupancy modeling for detection data collected along a transect. Journal of Agricultural, Biological, and Environmental Statistics, 16, 301–317.

[14] Diana, A., Matechou E., Griffin, J.E., 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)

[15] Matechou, E., & Caron, F. (2017) Modelling individual migration patterns using a Bayesian nonparametric approach for capture–recapture data. The Annals of Applied Statistics, 11, 21-40.

[16] Griffin, J.E., Matechou, E., Buxton, A.S., Bormpoudakis, D., & Griffiths, R.A. (2019) Modelling environmental DNA

Key Information

  • This project has been shortlisted for funding by the ARIES NERC DTP and will start on 1st October 2022. The closing date for applications is 23:59 on 12th January 2022.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship, which covers fees, stipend (£15,609 p.a. for 2021-22) and research funding. International applicants (EU and non-EU) are 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 non-academic experience, and our recruitment process considers potential with the same weighting as past 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.
  • ARIES is required by our funders to collect Equality and Diversity Information from all of our applicants. The information you provide will be used solely for monitoring and statistical purposes; it will remain confidential, and will be stored on the UEA sharepoint server. Data will not be shared with those involved in making decisions on the award of Studentships, and will have no influence on the success of your application. It will only be shared outside of this group in an anonymised and aggregated form. You will be ask to complete the form by the University to which you apply.

Applications are open

Apply now