Principal Research Fellow, Principal Research Fellow
Queensland University of Technology
Erin Peterson is a Principle Research Fellow in the Queensland University of Technology Institute for Future Environments and the ARC Centre for Excellence in Mathematical and Statistical Frontiers (ACEMS), in Brisbane, Australia. Erin’s educational background and experience allow her to work at the interface of landscape ecology, geographic information science, and environmental statistics. Her research relates to broad-scale modelling; regional monitoring program design, assessment, and reporting; and the development of methods that better represent the spatial location, configuration, and connectivity of features in statistical models. Her transdisciplinary work has been published in top-tier journals, including the Journal of the American Statistical Association and Ecology Letters. She was also part of a multidisciplinary team awarded the 2016 US Forest Service Rise to the Future Award for the successful knowledge transfer of statistical methods that have become standard-practice for aquatic resource management. Erin is currently a Regional Representative of The International Environmetrics Society (TIES).
Environmental monitoring assessment and reporting
B.S. Forest Conservation, Michigan State University (1995)
M.S. Forestry, Colorado State University (2001)
Ph.D. Earth Resources, Colorado State University (2005)
ACEMS Deputy Director Kerrie Mengersen is leading a team of researchers to try to save the jaguar population in Peru. Her project combines the use of statistics, mathematics, and virtual reality technology to help leaders make informed decisions about the threatened species, and the land it depends on.
Investigation and development of virtual log models for Southern Pines will be based on analysis of data from the cores, peeled billets and approximately 60 sawn logs. We plan to predict log and stem wood properties from the breast height cores taken in the field study. Following this, applied mathematics will be used to investigate the processing of these virtual logs and predict properties for the virtual boards ‘sawn’ from these logs.