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.
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!
Welcome to new PhD student Milly Jones who is supervised by Diana Cole and Eleni Matechou.
She is working with data collected by Madagascar Fauna and Flora Group (MFG), investigating the biodiversity in Madagascar. She will develop multi-species temporal models for the distance data collected.
Picture shows Milly at the board explaining her PhD project to the SE@K group during our Thursday lunch time meetings.
The workshop, in Montpellier, France, was organised by Matt Silk and Olivier Gimenez and was attended by around 12 experts in statistical models for ecological data and in network modelling. 
Emily Dennis, Calliste Fagard-Jenkin and Byron Morgan have created an R package for fitting the generalized abundance index to seasonal count data. The work has been published in Ecology and Evolution in the paper “rGAI: An R package for fitting the generalized abundance index to seasonal count data”.
The paper can be found at: https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.9200
The R package is available at: https://github.com/calliste-fagard-jenkin/rGAI
Members of SE@K group attended ISEC from 27th June to 1st July. Some went to South Africa to attend in person others attended virtually.
Eleni, Alex and Ioannis gave a half day workshop on Modelling environmental DNA data.
Eleni presented work on Capture recapture models with heterogeneous temporary.
Alex gave a talk on A unifying modelling framework for metabarcoding data.
Rachel talked on Model selection for integrated population models: selecting age structure with multiple data types.
James’ presentation was on Accounting for varying spatial scales in the production of UK butterfly abundance estimates.
Diana talked about Bayesian Identifiability in Ecological Models.
Fay presented work on Assessing the success of reintroductions whilst accounting for multispecies populations.
Bryon talked about Fitting dynamic occupancy models to very large occurrence data sets using hidden Markov models.
Philip North, 21st May, 1949 – 4th June, 2021
It is our sad duty to let you know of the death of Philip North, who died on the 4th June 2021, aged 72. Philip made a huge contribution to the development of the EURING Analytical Meetings, acting as editor of the first five proceedings.
He obtained his PhD on Statistical methods in ornithology from the University of Kent in 1979. This involved the development of novel analyses of various BTO datasets, in particular devising a method of cluster analysis to determine bird territories and enhancing various aspects of survival estimation. His influential 1978 paper in Biometrics used a variety of methods, including logistic regression and point process modelling, to investigate the effects of weather on the survival of grey herons. In addition, through his enthusiasm and research highly productive links were established between the Universities of Kent and St Andrews and with the Centre d’Ecologie Fontionnelle & Evolutive, CNRS Montpellier, which, together with the BTO are continued by the current members of the Statistical Ecology at Kent research group.
Philip was an expert ornithologist and a keen birder. For example, we recall that he was one of a small number of observers who recorded the first sighting of a pallid swift in Britain and Ireland Thus he was well placed to understand both the statistical and ecological aspects of avian ecology. This combination of ornithology and statistical modeling made him an ideal contributor to the EURING Analytical Conferences, as did his role as secretary of the Mathematical Ecology Group of the British Region of the Biometric Society and the British Ecological Society.
Innovative discussions and published proceedings became key features of these EURING meetings. The first two were held in Wageningen in 1986 and at Sempach in 1989 and Philip edited the resulting proceedings single-handed, as well as jointly editing the proceedings of the meetings held in Montpellier (1992), Patuxent (1994) and Norwich (1997; see https://euring.org/meetings/analytical-meetings/analytical-meeting-proceedings ) . This was an important and sustained contribution, which appreciably advanced the theory and application of relevant aspects of statistical ecology, with wide application to both theoretical and applied problems. It also showed how data gathered by the bird ringing/banding schemes coordinated through EURING could be collected and analyzed in ways that enhanced their ecological value. It is particularly noticeable how the content of the conference papers evolved over Philip’s period as editor. The first meeting focused on the use of individual datasets to estimate specific parameters, mainly survival. By the 1997 meeting there was greater interest in model selection and applied studies, with the first signs of the developments in data integration that would follow over the next 20 years.
Philip’s wife Monica died in 2008. They are survived by their children, Robin, Geoffrey and Melissa to whom we extend our deep condolences.
An obituary has recently appeared in JRSSA https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12767
Byron Morgan and Stephen Baillie
The Think Piece, titled “Understanding ecosystems and resilience using DNA“, explores opportunities for applying advances in DNA and RNA technologies to improve understanding of ecosystem function and resilience. Eleni’s contribution in particular, written together with Doug Yu, UEA, looks at “The contribution of DNA-based methods to achieving socio-ecological resilience”.
Diana is co-author on the paper: A guide to state–space modeling of ecological time series, which was published in Ecological Monographs.
https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecm.1470
Abstract: State–space models (SSMs) are an important modeling framework for analyzing ecological time series. These hierarchical models are commonly used to model population dynamics, animal movement, and capture–recapture data, and are now increasingly being used to model other ecological processes. SSMs are popular because they are flexible and they model the natural variation in ecological processes separately from observation error. Their flexibility allows ecologists to model continuous, count, binary, and categorical data with linear or nonlinear processes that evolve in discrete or continuous time. Modeling the two sources of stochasticity separately allows researchers to differentiate between biological variation and imprecision in the sampling methodology, and generally provides better estimates of the ecological quantities of interest than if only one source of stochasticity is directly modeled. Since the introduction of SSMs, a broad range of fitting procedures have been proposed. However, the variety and complexity of these procedures can limit the ability of ecologists to formulate and fit their own SSMs. We provide the knowledge for ecologists to create SSMs that are robust to common, and often hidden, estimation problems, and the model selection and validation tools that can help them assess how well their models fit their data. We present a review of SSMs that will provide a strong foundation to ecologists interested in learning about SSMs, introduce new tools to veteran SSM users, and highlight promising research directions for statisticians interested in ecological applications. The review is accompanied by an in-depth tutorial that demonstrates how SSMs can be fitted and validated in R. Together, the review and tutorial present an introduction to SSMs that will help ecologists to formulate, fit, and validate their models.
