We work collaboratively with researchers from a number of other Government agencies, Institutes and Universities within Australia and internationally .  Collaborators over last few years include:

Andrew Bennett (LaTrobe University/ARI) – connectivity & conservation management

Paul Boon (Victoria University) – climate change impacts on intertidal ecosystems

Mark Burgman (University of Melbourne) – decisions under uncertainty, expert elicitation

Jane Catford (University of Melbourne) – invasion ecology

Shaun Cunningham (Monash University) – floodplain condition

Tom Dietterich (Oregon State University) – topic discovery and ecosystem classification

Saso Dzeroski (University of Llubljana) – multi-objective regression and ecological modelling

Jane Elith (University of Melbourne) – species distribution modelling

Evan Dresel  (Department of Primary Industries, Vic) – groundwater dependent ecosystems

Phil Gibbons  (ANU) – vegetation condition

Ascelin Gordon (RMIT) – biodiversity off-sets, graph theoretic connectivity / systematic conservation planning

Bill Langford (RMIT) – machine learning, decision theory / uncertainty in systematic conservation planning

John Leathwick – (Department of Conservation, New Zealand Government) – spatially explicit reserve design and conservation planning

Keping Ma (Institute of Botany, Chinese Academy of Sciences) – biodiversity and vegetation ecology

Atte Moilanen (University of Helsinki) – spatially explicit reserve design, optimisation

Ann Nicholson (Monash University) – NRM investments and object oriented dynamic Bayesian networks

Ian Oliver  (Department of Environment and Conservation, NSW) – vegetation condition metrics and modelling

Laura Pollock (University of Melbourne) – intersecting plant traits & species distributions for eucalypts

Helen Regan (UC Riverside – California) – species distribution modelling and population viability

Paul Sunnucks (Monash University) – fragmentation processes, populations and genomics

Peter Vesk (University of Melbourne) – Modelling and prediction of plant and ecosystem traits