Improving use of citizen science data in ecological surveillance with hidden process models.

Studentship strategic priority area: Mathematical Biology
Keywords: Surveillance, statistics, ecosystem health, marine mammals, environmental change

This project will explore new ways of collecting and analyzing surveillance data within the context of marine animal biology. Marine animals have become stranded along UK coasts for centuries but the reasons for these events remain unclear. Discovery of a stranded animal often leads to questions regarding why such events happen, and what they indicate about the health of our oceans. Long-term accumulation of stranding data allows the investigation of trends in stranding numbers, indices of health and causes of mortality and can provide essential baseline information to detect emerging diseases, unusual mortality events, and anthropogenic impacts. However, extrapolating the strandings record to the at-sea population is challenging as reported cases are a complex function of biological, physical, and social processes. This study seeks to improve the statistical modelling techniques for strandings data to facilitate their use for monitoring and to reduce uncertainty in quantifying anthropogenic impacts on marine populations. It will interrogate the strandings databases available for the UK, and other countries bordering the North Sea, to model spatiotemporal patterns of strandings incorporating multiple data sources, and examine how these novel insights can help inform management decisions and develop more robust future monitoring strategies. This has application to wider opportunistic wildlife surveillance schemes and offers tangible benefit to the work of the monitoring and consenting bodies collaborating on this project.

The studentship with provide training in data visualization and integration. In particular, in the use of a general class of latent variable approaches that distinguish between observation and process models to analyze imperfect data, and especially in relation to diverse citizen-science schemes where the information on survey effort is scant. Science with impact has become an inseparable part of academic careers, and this studentship will provide training simultaneously in both an academic and non-academic environment (SMASS), thereby acquiring a considerable head start in this area over other PhD graduates. Citizen science data sets are a rapidly expanding and seriously underexploited source of environmental monitoring information, certain to become considerably more important in future environmental research where a strong statistics and modeling background will open up career development opportunities.