- Climatic influences on insect popluation dynamics
- Eco-climatic assessment of the risk of establishment of alien invasive species
- Application of ecological informatics in bioprotection and biosecurity
- Spatial analysis and modelling spatial spread of insect populations
- Modelling insect phenology
- Insect, plant, climate interactions.
- Paini, D.R., Worner, S.P., Thomas, M.B., Cook, D.C. 2010. Using a self organising map to predict invasive species: sensitivity to data errors and a comparison with expert opinion. Journal of Applied Ecology, 47: 290-298.
- Pitt, J.P.W., Worner, S.P., Suarez, A.V. 2009. Predicting Argentine ant spread over the Heterogeneous landscape using a spatially explicit stochastic model. Ecological Applications, 19: 1176-1186.
- Schliebs, S., Platel, M.D., Worner, S.P., Kasabov, N. 2009. Integrated feature and parameter optimisation for an evolving spiking neural network: Exploring Heterogeneous Probabilistic Models. Neural Networks, 22: 623-632.
- Watts, M.J., Worner, S.P. 2009. Estimating the risk of insect species invasion: Kohonen self-organising maps versus k-means clustering. Ecological Modelling, 220: 821-829.
- Lankin, G.O., Worner, S.P., Teulon, D.A.J. 2008. An ensemble model for predicting Rhopalosiphum padi (Hemiptera: Aphididae) abundance. Entomologia Experimentalis et Applicata, 129: 308-315.
- Worner, S.P., Gevrey, M. 2006. Modelling global insect species assemblages to determine risk of invasion. Journal of Applied Ecology, 43: 858-867.
The current research of Assoc Prof Sue Worner's team involves ecoclimatic assessment of the distribution and spread of invasive species, particularly insects. Sue's research methods comprise the analysis and modelling of relationships between species ecology and climatic influences to estimate their potential distribution and abundance in new regions. Novel analysis and prediction methods from the disciplines of ecological informatics and computational intelligence, as well as standard approaches, are used.
Such methods include the application of artificial neural networks (ANN) and Bayesian techniques for modelling and prediction, particularly in the area of biosecurity research, bio-protection, and climate change. Sue's group are currently developing spatially explicit models that link individual based models (IBM) of species spread with spatial information through GIS technology to support management and research in biosecurity and other disciplines.
Previous research in population modelling has concerned timing prediction (phenology) and modelling insect population dynamics to improve pest and biological control practices. Projects in biological control and species spread have extended to modelling predator-prey, parasite-host interactions and species dispersal. Other research interests involve the use of geostatistics and simulation to investigate the effect of species dispersion and the heterogeneous environment on sampling and monitoring procedures, and the study of invertebrate community diversity and species relationships in the largely unexplored native forest canopy.