Spatiotemporal Models for Better Understanding Marine Life
Increasingly large amounts of complex spatiotemporal data are being collected in the marine environment. This has enabled ocean scientists to ask questions at finer spatial and/or temporal scales than was previously possible, for example to examine variation in abundance within a particular fisheries management area instead of between areas. These new data sources, some derived from rapidly advancing digital technologies (e.g., global positioning systems for marine animal tracking) and others from hitherto under-utilized citizen science efforts (e.g., summer jellyfish sightings on Nova Scotia beaches), and the important questions that accompany them demand advancements in spatiotemporal modelling. Today I will present some methodology and computational tools that I have developed to 1) choose the correct number of behavioural states and assess goodness of fit suitable for both state space model (SSM) and hidden Markov model (HMM) frameworks for marine animal movement; and 2) incorporate spatial (and other) information into population dynamics models directly, for example to more accurately identify bycatch hotspots.
Joanna Mills Flemming is a Full Professor in the Department of Mathematics and Statistics, Dalhousie University. She is also Graduate Coordinator for its Statistics Division. She chaired the NSERC 1508 Mathematics and Statistics Evaluation Group in 2018 and has been an Associate Editor for the Canadian Journal of Statistics since 2013. Joanna is currently an Associate Director of the Canadian Statistical Sciences Institute (CANSSI) and leads one of its Collaborative Research Team projects (titled ”Towards Sustainable Fisheries: State Space Assessment Models for Complex Fisheries and Biological Data”). Her research interests centre on developing statistical methodologies and computational tools for data exhibiting spatial and/or temporal dependencies with a particular interest in the increasingly large amounts of complex spatiotemporal data being collected in the marine environment.
CIBC Auditorium, Goldberg Computer Science Building
David Langstroth firstname.lastname@example.orgSpatiotemporal Models for Better Understanding Marine Life