New paper: Removal modelling in ecology – A systematic review

SE@K members Oscar and Rachel have published a paper reviewing the current state-of-the-art in modelling removal data. This work is part of the the EPSRC project EP/S020470/1 “Modelling removal and re-introduction data for improved conservation”.

The paper is available in full here: https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0229965

Removal modelling in ecology: A systematic review

Oscar Rodriguez de Rivera and Rachel McCrea

Removal models were proposed over 80 years ago as a tool to estimate unknown population size. More recently, they are used as an effective tool for management actions for the control of non desirable species, or for the evaluation of translocation management actions. Although the models have evolved over time, in essence, the protocol for data collection has remained similar: at each sampling occasion attempts are made to capture and remove individuals from the study area. Within this paper we review the literature of removal modelling and highlight the methodological developments for the analysis of removal data, in order to provide a unified resource for ecologists wishing to implement these approaches. Models for removal data have developed to better accommodate important features of the data and we discuss the shift in the required assumptions for the implementation of the models. The relative simplicity of this type of data and associated models mean that the method remains attractive and we discuss the potential future role of this technique.


New paper: A Test for the Underlying State-Structure of Hideen Markov Models – Partially Observed Capture-Recapture Data

Former SE@K PhD student Anita Jeyam, Rachel and Roger Pradel (Montpellier) have had a paper published presenting a new test for determining the underlying state structure of Hidden Markov models.  This is an exciting piece of work which provides the foundation for addressing a complex topic for general HMMs.

The full paper is available open access here: https://www.frontiersin.org/articles/10.3389/fevo.2021.598325/full

A Test for the Underlying State-Structure of Hideen Markov Models – Partially Observed Capture-Recapture Data

Hidden Markov models (HMMs) are being widely used in the field of ecological modeling,
however determining the number of underlying states in an HMM remains a challenge.
Here we examine a special case of capture-recapture models for open populations,
where some animals are observed but it is not possible to ascertain their state (partial
observations), whilst the other animals’ states are assigned without error (complete
observations). 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 observed in the complete observations. We demonstrate the good performance
of the test using simulation and through application to a data set of Canada Geese.


New paper: Modeling Recruitment of Birth Cohorts to the Breeding Population: A Hidden Markov Approach

Rachel McCrea and co-authors from St Andrews and Edinburgh have published a paper on modelling the recruitment process of gray seals.

The full paper is available open access here: https://www.frontiersin.org/articles/10.3389/fevo.2021.600967/full

Modeling Recruitment of Birth Cohorts to the Breeding Population: A Hidden Markov Approach

Worthington, King, McCrea, Smout and Pomeroy

Long-term capture-recapture studies provide an opportunity to investigate the population dynamics of long-lived species through individual maturation and adulthood and/or
time. We consider capture-recapture data collected on cohorts of female gray seals
(Halichoerus grypus) born during the 1990s and later observed breeding on the Isle
of May, Firth of Forth, Scotland. Female gray seals can live for 30+ years but display
individual variability in their maturation rates and so recruit into the breeding population
across a range of ages. Understanding the partially hidden process by which individuals
transition from immature to breeding members, and in particular the identification of
any changes to this process through time, are important for understanding the factors
affecting the population dynamics of this species. Age-structured capture-recapture
models can explicitly relate recruitment, and other demographic parameters of interest,
to the age of individuals and/or time. To account for the monitoring of the seals from
several birth cohorts we consider an age-structured model that incorporates a specific
cohort-structure. Within this model we focus on the estimation of the distribution of
the age of recruitment to the breeding population at this colony. Understanding this
recruitment process, and identifying any changes or trends in this process, will offer
insight into individual year effects and give a more realistic recruitment profile for the
current UK gray seal population model. The use of the hidden Markov model provides an
intuitive framework following the evolution of the true underlying states of the individuals.
The model breaks down the different processes of the system: recruitment into the
breeding population; survival; and the associated observation process. This model
specification results in an explicit and compact expression for the model with associated
efficiency in model fitting. Further, this framework naturally leads to extensions to more
complex models, for example the separation of first-time from return breeders, through
relatively simple changes to the mathematical structure of the model.



Guy Bronze Medal awarded to Rachel McCrea

Rachel has been awarded the 2020 Guy Bronze Medal by the Royal Statistical Society (RSS).

The Guy Medal in Bronze has been awarded for her innovative and novel work in statistical ecology, with particular reference to the development of goodness-of-fit tests and model selection strategies for complex ecological data. Important areas include (multi-state) capture-recapture-type models and integrated models. Notable publications include: the 2017 JRSSC paper ‘A new strategy for diagnostic model assessment in capture-recapture’, which identified a direct relationship between particular diagnostic tests and score tests; and the 2020 JRSSC paper ‘Diagnosing heterogeneity in transition probabilities in multistate capture-recapture data’, which developed new tests to identify unmodelled transition heterogeneity.

Professor Deborah Ashby, President of the Royal Statistical Society, said: “Dr McCrea has made a profound contribution to statistical ecology. The Society’s journals have published a number of noteworthy papers authored by Rachel, and her development of goodness-of-fit tests and model selection strategies has been particularly innovative.”

The medal will be presented to Rachel at the RSS Annual Conference in Bournemouth in September.


Royal Statistical Society Barnett Award for Byron Morgan

Emeritus Professor of Statistics, Byron Morgan, has been awarded the Barnett Award by the Royal Statistical Society.

Being the leading authority on age-structured modelling of capture-recapture and ring-recovery data, his joint paper was the first to model how survival probabilities were influenced by weather covariates. Another influential paper on integrating mark-recapture-recovery and census data was foundational to the internationally-embraced sub-field of Integrated Population Modelling within statistical ecology. Most recently, he has been at the forefront of developing computationally-efficient methods for co-analysis of the UK Butterfly Monitoring Scheme with citizen science data sources, to give insights to biodiversity in urban versus rural settings. Byron Morgan was also one of the co-founders and first director of the National Centre for Statistical Ecology, a virtual Centre that links up statistical ecologists in the UK, and internationally.

Professor Deborah Ashby, President of the Royal Statistical Society, said: “Professor Morgan has had a great influence on the world of statistics and statistical ecology. His innovative work on computationally efficient methods for co-analysis of the UK Butterfly Monitoring Scheme has led to great insights into biodiversity and he had been a significant figure in creating better networks of statistical ecologists.”

Byron will be presented with the award at the Royal Statistical Society Annual Conference in September 2020 where he will also give a keynote presentation.


New paper: Assessing heterogeneity in transition propensity in multistate capture–recapture data

Rachel has published a new paper in the Journal of the Royal Statistical Society with former SE@K PhD student Anita Jeyam and co-supervisor Roger Pradel from CEFE, CNRS Montpellier.

The paper is available open access so you are able to download the pdf of the full paper.  The work proposes a new test which allows you to test for heterogeneity in transition probabilities from capture-recapture data before you fit any models to the data.

Assessing heterogeneity in transition propensity in multistate capture–recapture data

Multistate capture–recapture models are a useful tool to help to understand the dynamics of movement within discrete capture–recapture data. The standard multistate capture–recapture model, however, relies on assumptions of homogeneity within the population with respect to survival, capture and transition probabilities. There are many ways in which this model can be generalized so some guidance on what is really needed is highly desirable. Within the paper we derive a new test that can detect heterogeneity in transition propensity and show its good power by using simulation and application to a Canada goose data set. We also demonstrate that existing tests which have traditionally been used to diagnose memory are in fact sensitive to other forms of transition heterogeneity and we propose modified tests which can distinguish between memory and other forms of transition heterogeneity.




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.


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.



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.


One of the top downloaded articles in Conservation Biology

A research paper written by Byron Morgan and Emily Dennis, in collaboration with Butterfly Conservation & the Centre for Ecology & Hydrology,  has been one of the top downloaded articles from the journal Conservation Biology in the 12 months following publication.

This means the work generated immediate impact and visibility and contributed significantly to the advancement of the field.

The full paper can be accessed here.

Using citizen science butterfly counts to predict species population trends

Citizen scientists are increasingly engaged in gathering biodiversity information, but trade‐offs are often required between public engagement goals and reliable data collection. We compared population estimates for 18 widespread butterfly species derived from the first 4 years (2011–2014) of a short‐duration citizen science project (Big Butterfly Count [BBC]) with those from long‐running, standardized monitoring data collected by experienced observers (U.K. Butterfly Monitoring Scheme [UKBMS]). BBC data are gathered during an annual 3‐week period, whereas UKBMS sampling takes place over 6 months each year. An initial comparison with UKBMS data restricted to the 3‐week BBC period revealed that species population changes were significantly correlated between the 2 sources. The short‐duration sampling season rendered BBC counts susceptible to bias caused by interannual phenological variation in the timing of species’ flight periods. The BBC counts were positively related to butterfly phenology and sampling effort. Annual estimates of species abundance and population trends predicted from models including BBC data and weather covariates as a proxy for phenology correlated significantly with those derived from UKBMS data. Overall, citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring methods. Although caution is urged in extrapolating from this U.K. study of a small number of common, conspicuous insects, we found that mass‐participation citizen science can simultaneously contribute to public engagement and biodiversity monitoring. Mass‐participation citizen science is not an adequate replacement for standardized biodiversity monitoring but may extend and complement it (e.g., through sampling different land‐use types), as well as serving to reconnect an increasingly urban human population with nature.