Ming participated in postgraduate research festival

Ming presented a poster titled “Optimal Design for Removal Sampling” at the postgraduate research festival that took place at the University of Kent.

She presented her work in which she investigates removal models accounting for temporary emigration analytically and examines how to optimally allocate a fixed level of total sampling effort in terms of maximising the Fisher information.


SE@K students run masterclasses for SMSAS

Three SE@K students (Ming, Marina and Alex) took part in masterclasses organised by the SMSAS outreach officer, Joe Watkins. For details about master classes and other outreach events at SMSAS see here

The classes took place on two days and involved four sessions: i) Introduction to probability and statistics ii) Removal modelling, iii) Occupancy modelling and iv) Capture-recapture modelling. They were attended by some of the most enthusiastic and engaged year 9 students in the local area.

All sessions were interactive and the participating year 9 students had the opportunity to replicate real-life sampling techniques for monitoring populations of lizards, penguins and birds. These involved digging for lizards in the sand, looking for hidden penguins and marking birds. No animals were hurt in the process as they were all made out of plastic!


NCSE Summer 2017 meeting at the University of Kent

The Statistical Ecology @ Kent (SE@K) group will host the next NCSE summer meeting, which is returning to the University of Kent after 10 years and will take place in the w/c 26th of June 2017.

Click for  Programme and  Abstracts.

The meeting will include an invited talk by Professor Robert P Freckleton, University of Sheffield, and a one-day workshop by Professor David Borchers, University of St Andrews, and Professor Finn Lindgren, University of Edinburgh, on “Spatial Point Process Modelling with INLAbru”.

Meeting Location:

Registration will take place in the foyer of the Grimond building on the University of Kent campus, at 2pm on Monday, 26th June. The talks and workshop will take place in Grimond Lecture Theatre 2 (GLT2). The location indicated can be found on the University map at

Drinks Reception:

There will be a drinks reception on Monday at 5.20pm in Grimond Foyer.

Meeting dinner:

The meeting dinner will take place on Tuesday at 7pm.  It will be free for NCSE members, excluding drinks and take place in a private room in Café du Soleil in Canterbury; see


On Thursday there will be a workshop  on Spatial Point Process Modelling with INLAbru by David Borchers and Finn Lindgren. Participants in the workshop will need to bring their own laptop and install the packages listed in: workshop preparation

Travel to Canterbury:

A campus map and travel instructions can be found at


There are various possibilities, including Air B&B. The University campus is on a hill outside Canterbury. There are buses regularly connecting the campus to the city, and the walk takes less than 30 minutes each way. General information is to be found at:

– We should be able to make a block booking in student accommodation on campus; this is probably going to be an economic option. In order to do this we will need to know how many people would be interested. If you are interested then let us know by the 7 April deadline.

– A B&B at the entrance to the campus is the City of Canterbury:


Tuesday afternoon will be free for an outing.

The first railway in the world to issue season tickets ran from Canterbury to Whitstable, opening in 1830. We propose a walk along the line of this historic railway (the Crab and Winkle line) for those who may be interested. From the campus the 6-mile walk to Whitstable passes through  woods and  countryside. At Whitstable it would be possible to take refreshment at local pubs around the harbour, and possibly sample Whitstable oysters. If walking back is unattractive then there are regular buses.

The walk will leave Grimond Foyer at 12.30 and go via the campus shop to buy yourself a packed lunch.

There is no shortage of alternative activities for individuals. Canterbury contains a UNESCO World Heritage site, comprising the Cathedral, St Augustine’s Abbey and St. Martin’s church.  See In addition there are possibilities of visiting Faversham, home to Shepherd Neame brewery, Britain’s oldest brewer, and Dover and Leeds castles.

Bird watching is possible at various local sites, including at Stodmarsh Natural Nature Reserve, which we visited when the last NCSE summer meeting was held in Canterbury. This is home to Bearded reedlings, Marsh harriers, Cetti’s warblers, Bitten and many more. The reserve is 20 minutes drive from the campus. Blean woods is walking distance from the campus, and is one of just a few sites in the UK where there is a chance of seeing the rare Heath Fritillary butterfly, weather permitting.




NERC-funded advanced short course run by SE@K

The course took place 9-13 January 2017 at the University of Kent and was run by SE@K’s Diana, Eleni, and Rachel and DICE’s Richard Griffiths with the help of SE@K PhD students  Alex, Anita, Marina and Ming.

30 participants travelled from all around the UK for the course which involved lectures, R practicals, talks by Humphrey Crick from Natural England and Rufus Howard from IEMA, round table discussions on ecological challenges and the role of statistical modelling in dealing with some of these challenges and 1-1 sessions with the course organisers for all participants who wanted to discuss their studies and data.

On Tuesday Richard kicked off the day by discussing the types of population data that we need in conservation practice. Humphrey  then went on to discuss problems in modern conservation and the role of statistical modelling. Finally Rufus Howard talked about Big Data (Gaps) in EIA. A round table discussion then  focused on obstacles to incorporating statistical models and priorities of ecologists.

The rest of the week focused on statistical methods used in statistical ecology including abundance estimation, capture-recapture, occupancy modeling, distance sampling, citizen science data, modelling movement, species interaction models, spatial models and integrated modelling.

The course was successful at introducing complex statistical ideas, exposing participants to a wide range of statistical techniques and discussing state-of-the-art statistical methods. Below is a list of comments received about the course:

“Attending the course has enabled me to see how my data can be utilised in a better way..”


“Great materials and trainers”


“There were difficult concepts but it was very well presented and very accessible”


“Training was fantastic throughout”


“Good course and definitely beneficial”


“Great comprehensive course addressing many scenarios and how to deal with different datasets”


“I really appreciated the level of explanation given by all of the speakers”


“Incredibly well presented”


“Really great overview of the topics and great practical session”


“1-1 meetings were a great idea”


“I thought all lecturers were really very good and explained things very clearly and simply with good repetition”.



Diana gave talk as part of Statistical Ecology seminar double bill

On Thursday 6th October Diana gave a talk as part of Statistical Ecology seminar double bill. The other speaker was Ruth King from the University of Edinburgh. The Seminar was jointly hosted by the Royal Statistical Society (RSS) Glasgow Local Groups, the RSS Environmental Statistics Section and the Boyd Orr Centre for Population and Ecosystem Health.

The talk was entitled Parameter Redundancy and Identifiability in Ecological Models and talk slides are available here: Talk Slides

Conferences/Meetings/Workshops, News

Model Averaging meeting

15th of September 2016, 2-4.45pm

Maths Lecture Theatre
School of Mathematics, Statistics and Actuarial Science
University of Kent

Jointly organised by the Environmental Statistics Section of the RSS and the East Kent local RSS group

The meeting is free and open to all but please register your intention to attend (for hospitality purposes)

Doodle poll

Meeting organiser: Dr Eleni Matechou


  • Professor Richard Chandler, UCL, 2-2.45pm
The interpretation of climate model ensembles
Almost all projections of future climate, and its impacts, rely at some level on the outputs of numerical models (simulators) of the climate system. These simulators represent the main physical and chemical (and, in some cases, biological) processes in the atmosphere and oceans. However, different simulators give different projections of future climate – indeed, the choice of simulator can be the dominant source of uncertainty in some applications. It is therefore becoming common practice to consider the outputs from several different simulators when making and using climate projections. The question then arises: how should the information from different simulators be combined? There are many challenging statistical issues here. Two key ones are (a) that simulators cannot be considered as independent (for example, many of them share common pieces of computer code); and (b) that no single simulator is uniformly better than another so that simple techniques, such as assigning weights to simulators, are not defensible. This talk will review the issues involved and present a Bayesian framework for resolving them. The ideas will be illustrated by considering projections of future global temperature. The talk is based on joint work with Marianna Demetriou.


  • Professor Jonty Rougier, University of Bristol, 2.45-3.30pm
Ensemble averaging and mean squared error
In fields such as climate science, it is common to compile an ensemble of different simulators for the same underlying process.  It is an interesting observation that the ensemble mean often out-performs at least half of the ensemble members in mean squared error (measured with respect to observations).  This despite the fact that the ensemble mean is typically ‘less physical’ than the individual ensemble members (the state space not being convex).  In fact, as demonstrated in the most recent IPCC report, the ensemble mean often out-performs all or almost all of the ensemble members.  It turns out that that this is likely to be a mathematical result based on convexity and asymptotic averaging, rather than a deep insight about climate simulators.  I will outline the result and discuss its implications.


  • Coffee Break, 3.30-4pm


  • Dr Kate Searle, CEH, 4-4.45pm
Ecology isn’t rocket science….it’s harder: a practitioner’s perspective on the development of ecological analyses in complex systems
Understanding how environmental drivers influence the seasonal dynamics and abundance of species is key to developing predictive models for populations over space and time. This is particularly important in disease ecology where the phenology and seasonal abundance of vector species and hosts is a key determinant of disease risk. In this talk I will present a summary of collaborative research between ecologists and statisticians aimed at developing models that describe and predict the seasonal abundance of key insect vector species. These models were particularly challenging because insect vectors tend to show rapid rises and falls in population size across orders of magnitude, and may also be entirely absent from some regions at certain times of the year. Developing a model that is robust to these data-driven challenges required some novel statistical developments (and a lot of suffering on the part of both the ecologists and the statisticians!). This seminar will describe the evolution of our modelling approaches over a four-year study, culminating in a (reasonably) successful modelling framework for drawing inference and predictions in highly eruptive populations. Finally, I will highlight difficulties in applying model averaging techniques within the context of ecological models with typically low explanatory power.


NERC Advanced Training Course

Statistical models for wildlife population assessment and conservation


9-13 January 2017


University of Kent


Deadline for applications: 5pm on Friday 14th of October 2016.

We have 30 fully-funded places (inc. travel and accommodation) and priority is given to NERC-funded PhD students but if spaces remain we are able to offer the funded places to other PhD students and early-career researchers.

Please e-mail a completed Application Form to

Within the environmental sector there is currently a shortage of practitioners equipped with the statistical modelling skills to carry out reliable population assessments. Consequently, environmental impact assessments (EIAs) and development mitigation projects often use population assessment protocols that are not fit-for-purpose1. The skills shortage arises because (1) recent advances in statistical models for population assessment are largely confined to the academic sector with little penetration to the end-users; and (2) although many postgraduate programmes have a statistical modelling training component, this often fails to expose PhD students to new models in the area and the potential applications these have for conservation practice2. This training programme will provide a cohort of PhD students and early career researchers/practitioners with the relevant modelling skills required for a career that involves wildlife population assessment for conservation.


  1. Griffiths, Foster, Wilkinson and Sewell (2015). Science, statistics and surveys: a herpetological perspective. Journal of Applied Ecology. doi: 10.1111/1365-2664.12463
  2. McCrea and Morgan (2015). Analysis of capture-recapture data. Chapman & Hall/CRC Press, Florida.


Proposed programme of the course


The workshop will focus on ecological questions that arise in conservation practice and use real case study data. Training will include individual-based models, such as capture-recapture, but will also embrace scenarios more frequently used in EIA, such as batch-marked, presence/absence, site occupancy and counts. Applications will include newts, butterflies, birds, bees, beetles, ibex and bats. Each module will be accompanied by a practical computer session using R and each module builds on the last so that delegates build a portfolio of statistical skills.


Training outcomes:  By the end of the course, attendees will be able to:

  • construct, interpret and fit relevant stochastic models, use different methods of inference, understand the pros and cons of Bayesian and classical methods and the use of prior information;
  • personalise R code to undertake modelling of their own research data;
  • understand data needs for animal population assessments for EIAs and conservation;
  • analyse animal population data to meet both conservation and commercial needs.

Draft timetable:


Module 1: Background in statistics and R (Monday PM)
  • Likelihood and probability theory
  •  Bayesian inference
  • Basic model assessment (AIC/absolute GOF)
  • Practical session: Introduction


Plenary session and Round table discussions (Tuesday AM)


Module 2: Understanding statistical uncertainty (Tuesday PM)
  • Imperfect detection
  • Data types, relationships and summaries.
  • Introduction to data sets/case studies (bees, butterflies, newts, mallards etc)
  • Practical session: converting format of data and summarising complex data.


Module 3: Model fitting and assessment (Wednesday AM+PM)
  • Estimating abundance
  • M0,Mtbh, removal
  • CR/RR
  • Occupancy
  • Practical session: model fitting, optimisation, use of packages.


Module 4: Modern challenges (Thursday AM)
  • Citizen science data
  • Small/sparse data and big data issues
  •  Cost-effectiveness in study design and statistical power.
  • Informative prior information.
  • Practical session: power analyses and adapting models


Module 5: Advanced stochastic modelling (Thursday PM)
  • modelling movement
  •  state uncertainty
  • species interaction
  • spatial models
  • integrated modelling
  • Practical session: use of Rjags, Bayesian graphical models using MCMC.


One-to-one consultation sessions (Friday AM)


Module 6: Advanced aspects of R (Friday AM)
  • Practical session: self-lead worksheets
  • Multistate examples
  • PR diagnosis
  • Diagnostic GOF testing