- Associate Investigators
- Kate Helmstedt
Dr Kate Helmstedt
Associate Investigator, Lecturer
Queensland University of Technology
I use decision theory and operations research to improve the outcomes achieved for biodiversity from conservation management. Ecological systems are incredibly complex, and changing how those systems interact and evolve can have unexpected implications. I build mathematical models of coupled ecological, land-use, and economic systems to understand the mechanisms driving success, failure, and efficiency of management actions. Carefully, transparently, and defensibly planning management and policy interventions while acknowledging those complexities and the associated risks helps achieve better outcomes for the environment and society.
PhD (University of Queensland)
BSc Maths (University of Queensland)
Invited talks, refereed proceedings and other conference outputs
Adams, M. P., Sisson S., O'Brien K. R., Helmstedt K. J., Baker C. M., Koh E. J. Y., et al. (2020). Propagating uncertainty through to model forecasts: deterministic Lotka-Volterra systems as a case study. Quantitative Ecology Virtual Meeting 2020.
Holloway, J., Mengersen KL., & Helmstedt K. J. (2018). Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals. IAOS-OECD Better Statistics for Better Lives.
Coutts, S. R., Helmstedt K. J., & Bennett J. R. (2018). Invasion lags: The stories we tell ourselves and our inability to infer process from pattern. (Roura-Pascual, N., Ed.).Diversity and Distributions. 24(2), 244-251. doi: 10.1111/ddi.12669
Helmstedt, K. J., & Possingham H.. P. (2017). Costs are key when reintroducing threatened species to multiple release sites. Animal Conservation. 20(4), 331 - 340. doi: 10.1111/acv.2017.20.issue-4
Publicly available softwares
Adams, M. P., Sisson S. A., Helmstedt K. J., Baker C. M., Holden M. H., Plein M., et al. (2020). Code and selected model outputs for "Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data". doi: 10.6084/m9.figshare.11492928