(Rosemary) Jane Elith

University of Melbourne


Primary Section: 64, Human Environmental Sciences
Secondary Section: 63, Environmental Sciences and Ecology
Membership Type: International Member (elected 2020)

Biosketch

Jane Elith is a quantitative ecologist, influential in her field for her work on species distribution modelling. Her guides and novel tools for modelling species and ecological communities have been utilised by government and environmental management agencies in many jurisdictions. People at the interface between environmental management and science draw extensively on her research to manage invasive species, conserve biodiversity, and plan strategic land-use. Elith was born in Melbourne, Australia and lived in other Australian cities and in Yorkshire, England, during her schooling. She finished a Bachelor of Agricultural Science with first class honours at the University of Melbourne in 1977, and after working in academia for three years then left to become a full-time mother to three sons for 11 years. She returned to academia, first tutoring then doing a part-time PhD in quantitative ecology at the University of Melbourne, completing in 2003. She then worked as a research fellow for several years until becoming faculty in 2015. Elith has received several awards, including the 2015 Prime Minister’s prize for Life Scientist of the Year and the 2016 Fenner Medal from the Australian Academy of Science, and was elected as a Fellow of the Australian Academy of Science in 2017. Professor Elith has been a subject editor for scientific journals in the fields of ecology, plant and animal species distribution and biogeography and teaches specialist courses in spatial modelling.

Research Interests

Jane Elith is broadly interested in biodiversity modelling. She has specialised in species distribution models, focussing on the technical aspects of models, and the interface between the science and its applications. She is interested in how well common methods predict given typical data types, how to test predictive performance in ways relevant to end-use, developing new methods for typical scenarios, how to deal with uncertainty in decision-making based on these models, combining pattern- and process-based methods, and explaining methods well for end-users so they know the strengths and limitations of their models.

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