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.

Emily Dennis (Butterfly Conservation) and Byron Morgan (SE@K) have recently published a paper in Ecological Indicators exploring multi-species abundance and occupancy indices using Functional Data Analysis tools.

The full paper can be accessed here.

Functional data analysis of multi-species abundance and occupancy data sets

Emily B.Dennis, Byron J.T.Morgan, RichardFox, David B.Roy and Tom M.Brereton

Multi-species indicators are widely used to condense large, complex amounts of information on multiple separate species by forming a single index to inform research, policy and management. Much detail is typically lost when such indices are constructed. Here we investigate the potential of Functional Data Analysis, focussing upon Functional Principal Component Analysis (FPCA), which can be easily carried out using standard R programs, as a tool for displaying features of the underlying information. Illustrations are provided using data from the UK Butterflies for the New Millennium and UK Butterfly Monitoring Scheme databases. The FPCAs conducted result in a huge simplification in terms of dimensional reduction, allowing species occupancy and abundance to be reduced to two and three dimensions, respectively. We show that a functional principal component arises for both occupancy and abundance analyses that distinguishes between species that increase or decrease over time, and that it differs from percentage trend, which is a simplification of complex temporal changes. We find differences in species patterns of occupancy and abundance, providing a warning against routinely combining both types of index within multi-species indicators, for example when using occupancy as a proxy for abundance when insufficient abundance data are available. By identifying the differences between species, figures displaying functional principal component scores are much more informative than the simple bar plots of percentages of significant trends that often accompany multi-species indicators. Informed by the outcomes of the FPCA, we make recommendations for accompanying visualisations for multi-species indicators, and discuss how these are likely to be context and audience specific. We show that, in the absence of FPCA, using mean species occupancy and total abundance can provide additional, accessible information to complement species-level trends. At the simplest level, we suggest using jitter plots to display variation in species-level trends. We encourage further application to other taxa, and recommend the routine augmentation of multi-species indicators in the future with additional statistical procedures and figures, to serve as an aid to improve communication and understanding of biodiversity metrics, as well as reveal potentially hidden patterns of behaviour and guide additional directions for investigation.