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

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 publication in JABES: Statistical Development of Animal Density Estimation Using Random Encounter Modelling by Natoya Jourdain, Diana Cole, Martin Ridout and Marcus Rowcliffe.

Abstract:

Camera trapping is widely used in ecological studies to estimate animal density, although these studies are largely restricted to animals that can be identified to the individual level. The random encounter model, developed by Rowcliffe et al. (J Anal Ecol 45(4):1228–1236, 2008), estimates animal density from camera-trap data without the need to identify animals. Although the REM can provide reliable density estimates, it lacks the potential to account for the multiple sources of variance in the modelling process. The density estimator in REM is a ratio, and since the variance of a ratio estimator is intractable, we examine and compare the finite sample performance of many approaches for obtaining confidence intervals via simulation studies. We also propose an integrated random encounter model as a parametric alternative, which is flexible and can incorporate covariates and random effects. A data example from Whipsnade Wild Animal Park, Bedfordshire, south England, is used to demonstrate the application of these methods.

The paper Model averaging in ecology: a review of Bayesian, information‐theoretic, and tactical approaches for predictive inference, first published on the the 2nd of May 2018, was among the top cited articles in recent publication history for Ecological Monographs.

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.

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

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.

Ming Zhou has  received an honourable mention by the British and Irish Region of the International Biometric Society as part of their Young Biometrician Award 2019 for her paper “Removal models accounting for temporary emigration” (Biometrics 2019; 75, 24-35).

https://biometricsociety.org.uk/news/news-03-07-2019-16-24

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.

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.