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.