Dr Erin Peterson is the owner of EP Consulting in Brisbane, where she leads a small team of scientists working at the interface of natural resource management, geographic information science (GIS), and environmental statistics. Erin has over 20 years of experience as a government and academic research scientist, and independent consultant. Her research and consulting work is primarily focused on the next generation of environmental monitoring in terrestrial, marine, and river ecosystems using new technologies. This includes innovative methods of data capture; the analysis of near real-time data collected using in situ sensors; and accounting for data quality and uncertainty when combining different data sources, including citizen-science data. The results of these projects have been used by government, industry, and NGOs to support condition and trend assessments, spatial prioritization of management actions, evaluations of the effectiveness of management actions, and natural and anthropogenic impact assessments under current and future land-use and climate scenarios.
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.
ACEMS research combines state-of-art methodologies for coral reef data collection, remote sensing and statistical modelling to predict the future ecological status of the reefs within the Great Barrier Reef as the incidence of multiple disturbances continues to increase.
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.
Enormous effort is invested in monitoring the Great Barrier Reef (GBR), but data collection is currently fragmented over dozens of publicly and privately funded organisations, with data collected using different methods and for different purposes. As a result, the data are rarely analysed together.
Invited talks, refereed proceedings and other conference outputs
Peterson, E., Mengersen KL., Vercelloni J., & Brown R.
(2019). Combining citizen science and professional monitoring data to conserve and sustainably use marine resources. 62nd ISI World Statistics Congress.
Thilan, P., Peterson E., Menendez P., M. Caley J., Drovandi C., Mellin C., et al.
(2019). Optimisation of coral reef monitoring using Bayesian adaptive design methods. Bayes on the Beach 2019.
Peterson, E., Hoef J. Ver, Hooten M., Hanks E., & Fortin M-J.
(2019). Estimating parameters of landscape resistance using spatial autoregressive models. The 10th International Association for Landscape Ecology World Congress.
Isaak, D. J., Hoef J. M. Ver, Peterson E., Horan D., & Nagel D.
(2017). Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams. Canadian Journal of Fisheries and Aquatic Sciences. 74(2), 147-156. doi: 10.1139/cjfas-2016-0247
Turschwell, M. P., Peterson E., Balcombe S. R., & Sheldon F.
(2016). To aggregate or not? Capturing the spatio-temporal complexity of the thermal regime. Ecological Indicators. 67, 39-48. doi: 10.1016/j.ecolind.2016.02.014
Ashley Steel, E.., Sowder C., & Peterson E.
(2016). Spatial and temporal variation of water temperature regimes on the Snoqualmie river network. JAWRA Journal of the American Water Resources Association. 52(3), 769-787. doi: 10.1111/1752-1688.12423
Isaak, D. J., Young M. K., Luce C. H., Hostetler S. W., Wenger S. J., Peterson E., et al.
(2016). Slow climate velocities of mountain streams portend their role as refugia for cold-water biodiversity. Proceedings of the National Academy of Sciences. 113(16), 4374-4379. doi: 10.1073/pnas.1522429113