grants, News

Bruno and Eleni visited Stockholm

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.

grants, News

Eleni awarded Knowledge Transfer Partership with NatureMetrics

NatureMetrics is an innovative, science-based, women-led SME that commercialises environmental DNA-based biodiversity monitoring solutions at scale.  They are world leaders in delivering powerful, scalable biodiversity data collected safely and sustainably using environmental DNA.  NatureMetrics work to develop end-to-end and automated tools for biodiversity detection in the field, to be used by non-experts.


This KTP aims to develop and integrate new statistical techniques for addressing two challenges associated with biodiversity surveys using environmental DNA: accounting for error and noise in environmental DNA surverys, and optimising survey design. These techniques will improve the business’s decision-support tools for nature conservation and restoration, facilitating access to new markets.


NatureMetrics’ mission is to bridge the gap between molecular techniques and environmental management by using cutting-edge DNA analysis to monitor biodiversity and measure natural capital in the environment and this KTP will enable them to achieve this mission.


Specifically, the aim of this KTP is to embed new knowledge and capabilities of Bayesian hierarchical statistical models developed by academics at the University of Kent (Dr Eleni Matechou) and University College London (Professor Jim Griffin). Through this KTP, these techniques will be integrated into NatureMetrics’ analysis workflows, enabling them to increase confidence levels around the presence/absence or relative abundance of surveyed biological communities (i.e. collections of species at a site) in downstream products.  This will optimise the cost and effort needed to collect and analyse samples.



NERC-funded project on “Integrating new statistical frameworks into eDNA survey and analysis at the landscape scale”

The NERC-funded project, led by Dr Eleni Matechou, with Dr Alex Bush, University of Lancaster, Professor Jim Griffin, Statistical Science, UCL, Professor Richard Griffiths, Durrell Institute of Conservation and Ecology, University of Kent, and Professor Doug Yu, UEA as co-Investigators, is part of NERC’s Strategic Priorities Fund on Landscape Decisions: Towards a new framework for using land assets programme.

The RA on the project will be in place until 31st January 2022 and will work on meeting the four model development and two implementation and knowledge exchange objectives of the project.

Model Development (MD) objectives:
MD1 Account for sampling and analysis errors and taxonomic uncertainty in DNA-based data
Single-species (barcoding) and multi-species (metabarcoding) DNA data sets have more complicated properties than data
collected using traditional techniques using visual, aural or physical observations. For example, eDNA surveys are prone to
false-positive and false-negative errors at both the sampling and analysis stages as well as to taxonomic mismatches. We
will develop new statistical models that account for the probabilistic nature of eDNA data, reliably quantifying all levels of
uncertainty in eDNA surveys.

MD2 Identify important landscape predictors for site-specific species composition and metacommunity structure
The probability of species presences, species interactions and metacommunity structures is affected by landscape
characteristics. Accounting for these effects is necessary to obtain an understanding of the system and how to maintain it
or improve it. Failing to account for important effects can lead to inaccurate inference and biased results. However, the
strength and direction of these effects are typically unknown and we will develop a novel Bayesian modelling framework
and sophisticated algorithms for inferring landscape effects efficiently at the species and metacommunity levels using
eDNA data.

MD3 Perform simulations to identify the optimal study design for DNA-based surveys under different scenarios
The power to correctly infer the metacommunity structure and the landscape effects that shape it using eDNA data
depends on the number of sites sampled, the number of samples collected from each site, the number of PCR replicates
performed for each sample, as well as on landscape characteristics. We will perform extensive simulations to identify the
optimal study design under different scenarios and levels of error to provide practitioners with informed guidelines on how
to design eDNA surveys.

MD4 Identify the effects of errors in survey data or suboptimal study design on the selection of conservation priorities (e.g.
high-value habitat or restoration priorities)

Several conservation planning software exist that aim to infer the most efficient allocation of resources. Our Bayesian
model defined by MD1-2 as well as the power analysis resulting from MD3 provide a valuable predictive tool for species
presences and metacommunity structures, with corresponding measures of uncertainty, across the landscape. Using the
predictions generated by the fitted model in our case study system of UK ponds, we will obtain and compare different
solutions provided by planning software that are frequently employed in decision-making processes and assess how and if
decision-making is informed when uncertainty in inference is explicitly accounted for.

Implementation and Knowledge Exchange (IKE) objectives:

IKE1 Develop R-Shiny apps to implement MD1-4, making them accessible to users
The new statistical framework for eDNA data (MD1-4) will be implemented into R-Shiny apps and be accompanied by
examples of data analyses and corresponding interpretation to enable practitioners with limited understanding of statistics
and no prior knowledge of programming to employ our methods when analysing eDNA data.

IKE2 Disseminate project outputs and software to users via training workshops and project partners
We will organise two training workshops to take place in the last quarter of the project and after MD1-4 and IKE1 are
complete to disseminate the new methods to research users. These will complement ongoing – and fully subscribed –
training workshops that the research team have been running for practitioners in the general area of eDNA and statistical

A more detailed description of the project can be found here


Eleni awarded Royal Society International Exchanges grant

Dr Eleni Matechou has been awarded £12000 for the project entitled “A novel statistical modelling framework for ecological data collected on migration routes” as part of the Royal Society’s International Exchanges Scheme to collaborate with Prof Alessio Farcomeni, Sapienza – University of Rome, Italy

The project will start on the 19th August 2019 and last for 2 years.


NERC Advanced Training Short Course Grant

Rachel, Diana and Eleni with Richard Griffiths  have been awarded £53,584.00 funding for a NERC Advanced Training Short Course `Statistical models for wildlife population assessment and conservation’ for 2018 and 2019, following the successful course in January 2017.


grants, Prizes

SE@K project awarded Sciences Faculty Competition scholarship

The project, titled  Studying migration patterns of UK bird populations using Bayesian nonparametric models, was proposed by Dr Eleni Matechou in collaboration with Dr Alison Johnston from the British Trust for Ornithology and Professor Jim Griffin from SMSAS.

Summary of the proposal: The PhD student on this collaborative project will develop and use novel and sophisticated statistical models, namely Bayesian nonparametric models, to understand patterns of bird migration within the UK. The data to be analysed refer to bird species that breed in the UK and spend the winter in Africa. These are collected by the BTO as part of the Constant Effort Sites (CES) monitoring scheme. The analyses will describe the migration patterns, phenology, population sizes and distribution of these species. Links between these demographic parameters and environmental covariates will be explored to explain the mechanisms leading to patterns and changes (for example, climate change leading to earlier migration). The results will also be used to inform conservation management strategies. As well as a number of scientific manuscripts describing the statistical models and the ecological processes, the student will also produce freely-available software that will be used by the BTO in the future and by any interested researchers to fit the models to their own data.