Alex presented his work on modelling metabarcoding data at the 2023 STEM for Britain Competition
His poster described the novel modelling framework, introduced in the corresponding paper, and showed results from a data set on ingested DNA from a survey in China.
For the last term of the 22/23 academic year, we have an exciting plan for SE@K and guest talks
The visit was part of their Swedish research council funded project on modelling overcoverage in population registers, in collaboration with Dr Eleonora Mussino.
The first paper from this project is already under review, using multiple systems estimation to estimate the number of immigrants who are present but have not been observed in any register each year.
They are currently developing capture-recapture-recovery models with temporary emigration as an alternative approach for estimating the probability that an individual is there given that they weren’t observed.
A new modelling framework for fast Bayesian inference in large occupancy data sets by Alex, Emily, Eleni and Byron just published in Biometrics.
The model accounts for spatio-temporal autocorrelation and gives robust inference on species presence, as evidence by the simulation results in the paper and the two case studies on one common and widespread and one rare species, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years.
Spring term starts with the second half of Python training by Lena :-). Plan for the term in terms of sessions (and cake) below!
The SE@K group are meeting weekly for research lunch and socialising 🙂
Autumn term
First week was about introductions and a tasty lunch at Dolce Vita!
Second week was about staff talking about their research interests, with Diana talking to the group about parameter redundancy.
Third, fourth & fifth week were about research students talking about their research, with Tommy using randomised response techniques to estimate the proportion of people in the group who like/liked their supervisor (!), Milly talking about distance sampling, Ioannis talking about Bayesian non-parametrics and ABC and Lena talking about predator-prey models!
The term went on with learning about Gaussian processes from Alex, about PDEs from Eduard and about Python from Lena!
The one-day workshop, organised by the group in Lille, consisted of a series of talks by researchers from the Universities of Lille, Ghent and Kent.
Eleni gave talks on her work on modelling BeeWalk data and on modelling eDNA data.
Alex Diana, Eleni Matechou, Jim E. Griffin, Yadvendradev Jhala, Qamar Qureshi
Capture-recapture (CR) data and corresponding models have been used extensively to estimate the size of wildlife populations when detection probability is less than 1. When the locations of traps or cameras used to capture or detect individuals are known, spatially-explicit CR models are used to infer the spatial pattern of the individual locations and population density. Individual locations, referred to as activity centers (ACs), are defined as the locations around which the individuals move. These ACs are typically assumed to be independent, and their spatial pattern is modeled using homogeneous Poisson processes. However, this assumption is often unrealistic, since individuals can interact with each other, either within a species or between different species. In this article, we consider a vector of point processes from the general class of interaction point processes and develop a model for CR data that can account for interactions, in particular repulsions, between and within multiple species. Interaction point processes present a challenge from an inferential perspective because of the intractability of the normalizing constant of the likelihood function, and hence standard Markov chain Monte Carlo procedures to perform Bayesian inference cannot be applied. Therefore, we adopt an inference procedure based on the Monte Carlo Metropolis Hastings algorithm, which scales well when modeling more than one species. Finally, we adopt an inference method for jointly sampling the latent ACs and the population size based on birth and death processes. This approach also allows us to adaptively tune the proposal distribution of new points, which leads to better mixing especially in the case of non-uniformly distributed traps. We apply the model to a CR data-set on leopards and tigers collected at the Corbett Tiger Reserve in India. Our findings suggest that between species repulsion is stronger than within species, while tiger population density is higher than leopard population density at the park.
PROJECT TITLE:
Supervisors: This is a joint project between the University of Kent and Butterfly Conservation and the PhD student will be supervised by a team with expertise in Statistics, Statistical Ecology, Citizen Science, and Butterfly Monitoring
University of Kent: Dr Eleni Matechou, Dr Diana Cole, Prof Byron Morgan
Butterfly Conservation: Dr Emily Dennis, Dr Richard Fox
Application Process
Deadline for applications: midnight 17th of April 2023
Applicants should follow the University of Kent’s online application process.
Please create an account and add your personal details as requested. Subsequently, you need to select your starting date (September 2023) and write your personal statement (see below). Choose “Other” for source of funding and “Definite” for funding. Add details of your qualifications, and then in the Research Information Tab, write “Dr Eleni Matechou” under supervisor and the title of the project (“Spatio-temporal models for large citizen science data sets“) as the research topic. You do not need to add a research proposal.
As part of the process, you need to provide the following:
o details of your qualifications;
o two academic references;
o a personal statement
The statement must be maximum 500 words detailing (1) your reason for applying for a doctoral studentship (i.e, why do you want to pursue doctoral studies) and (2) your fit with the proposed project (how your educational/professional/personal background has prepared you well to undertake research in this topic).
Please email Dr Eleni Matechou (e.matechou@kent.ac.uk) if you are interested in applying for the project or have any questions about the project or the application process.
Person specification
We seek a candidate with a strong quantitative background, for example an MSc in Statistics or an MSc with high statistics content, or a background in ecological modelling. Experience coding in R, or similar, is essential. An interest in conservation and ecology is advantageous. Quantitative ecologists are encouraged to apply.
The University of Kent requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree. For more information on English language requirements, please visit this page.
Scientific 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 (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:
Training
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.
Research excellence
The student will join the thriving Statistical Ecology @ Kent research group, being supervised by leading researchers in statistics and statistical ecology. They will also be members of the UK-wide National Centre for Statistical Ecology. They will attend London Taught Course Centre training, NCSE seminars, and SE@K specialist training and they will present research results at a range of appropriate national and international conferences. There will be ample opportunity for independent development, with the student gaining transferable knowledge of modern data science and statistics.
Scholarship Information
VC scholars will receive the following:
o 2023/24 rates to be announced.
Relevant literature
Diana, A., Dennis, E. B., Matechou, E., Morgan, B. J. T. (2022) Fast Bayesian inference for large occupancy datasets. Biometrics, . ISSN 0006-341X. (In press) (KAR id:98286)
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
, , , , & (2022) A general modeling framework for open wildlife populations based on the Polya tree prior. Biometrics, 00, 1– 13. https://doi.org/10.1111/biom.13756
Griffin, J. E., Matechou, E., Buxton, A. S., Bormpoudakis, D., & Griffiths, R. A. (2020). 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), 69(2), 377-392.
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.
