2016: Jane Elith (UMelb)
Jane specialised in species distribution models, statistical models that describe relationships between the occurrence or abundance of species and the environment. These are used by both academics and practitioners, and Jane has been particularly interested in technical details of the methods and their appropriateness for the data and questions to which they are often applied. She has tested methods and explored their uncertainties, helped to develop and extend methods appropriate for typical data types, and worked on a broad range of practical applications. Jane is particularly keen on providing typical users with good technical advice for how to apply the methods, so a strong focus of her research has been writing guides and tutorials for methods, and working with statisticians and computer scientists to progress techniques and understanding.
Jane Elith is a principal researcher in the Quantitative Ecology group at the University of Melbourne. Her undergraduate degree was in Agricultural Science in the 1970’s. She returned to study in the late 1990’s, finishing her PhD in species modelling in 2003. Since then she has worked as a postdoc and ARC Future Fellow, and is now an Associate Professor in the School of BioSciences. Jane’s work on species distribution modelling is highly cited, and has been recognized with the 2015 Prime Minister’s early career researcher prize for Life Scientist of the Year, the 2016 Academy of Science Fenner Medal.
Knowledge about where plants, animals and diseases occur is limited by available data for the vast majority of species. Yet understanding species distributions is critical when managing threatened species, controlling threatening processes, predicting changes in distribution, and managing landscapes and biological invasions. Species distribution models, statistical models that describe relationships between the occurrence and abundance of species and the environment, are now widely used for making these predictions. Associate Professor Jane Elith will talk about the models, their application, and challenges and progress in using these with typically available data.