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New paper: Estimating abundance from multiple sampling capture-recapture data via a multi-state multi-period stopover model

Rachel McCrea, Richard Griffiths (DICE) and collaborators at the Universities of St Andrews and Edinburgh have had a paper published in Annals of Applied Statistics.

 

The article presents an exciting piece of work using Hidden Markov model structure for the fitting of multi-season, multi-state stopover capture-recapture models.  The framework is general, with many existing capture-recapture models being a special case.

The paper can be accessed here.

Abstract

Capture-recapture studies often involve collecting data on numerous capture occasions over a relatively short period of time. For many study species this process is repeated, for example, annually, resulting in capture information spanning multiple sampling periods. To account for the different temporal scales, the robust design class of models have traditionally been applied providing a framework in which to analyse all of the available capture data in a single likelihood expression. However, these models typically require strong constraints, either the assumption of closure within a sampling period (the closed robust design) or conditioning on the number of individuals captured within a sampling period (the open robust design). For real datasets these assumptions may not be appropriate. We develop a general modelling structure that requires neither assumption by explicitly modelling the movement of individuals into the population both within and between the sampling periods, which in turn permits the estimation of abundance within a single consistent framework. The flexibility of the novel model structure is further demonstrated by including the computationally challenging case of multi-state data where there is individual time-varying discrete covariate information. We derive an efficient likelihood expression for the new multi-state multi-period stopover model using the hidden Markov model framework. We demonstrate the significant improvement in parameter estimation using our new modelling approach in terms of both the multi-period and multi-state components through both a simulation study and a real dataset relating to the protected species of great crested newts, Triturus cristatus.

 

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Invited Speaker at the Royal Statistical Society Annual Conference, Belfast

Rachel McCrea presented her work at an invited session at the annual Royal Statistical Society conference held in Belfast earlier this month.  She spoke in a session on Applications of Hidden Markov Models in Ecology.

A test for the underlying state-structure of partial observations in Hidden Markov models

Rachel McCrea*, Anita Jeyam* and Roger Pradel**

*University of Kent, **CNRS Montpellier

Hidden Markov models are prominent in current ecological statistics literature due to being a flexible means by which to describe many existing ecological models.  Multievent capture-recapture models are widely used for modelling observations that are assigned to states with uncertainty and are a type of Hidden Markov model where underlying states are observable.  We focus on the special case of partial observations, where some animals are observed but it is not possible to ascertain their state, whilst the other animals’ states are assigned without error. We propose a mixture test of the underlying state structure generating the partial observations, which assesses whether they are compatible with the set of states directly observed in the capture-recapture experiment.We confirmed the theoretical properties of the test using simulation; the test also worked well on a dataset of Canada Geese, Branta canadensis, in which we artificially created partial observations, indicating good results for real-life applications.

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Conferences/Meetings/Workshops

Workshop on sampling, analysing and interpreting eDNA data

The workshop will take place on the 19th of September and is FREE but places are limited, and registration will close when all places are filled, or on 6 September (whichever is the earlier). Please sign up here providing your name and email.

Workshop facilitators:
AB: Dr Andrew Buxton (ARC/Newt Conservation Partnership; DICE, University of Kent)
RG: Professor Richard Griffiths (DICE, University of Kent)
EM: Dr Eleni Matechou (School of Mathematics, Statistics and Actuarial Science, University of Kent)
AD: Alex Diana (School of Mathematics, Statistics and Actuarial Science, University of Kent)

Enquiries:
Dr Eleni Matechou – E.Matechou@kent.ac.uk

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grants

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.

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Alex presented his work at the Bayesian non-parametric conference in Oxford

Alex gave a presentation during the 12th BNP conference, held in Oxford during the last week of June.

He talked about his work on the Hierarchical Dependent Dirichlet Process, which allows him to jointly model capture-recapture data collected at different sites and across different years, sharing information between data sets and increasing the power to identify covariate effects and to detect trends.

 

 

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