This programme aims to use advances in ecological informatics and emerging computational technologies to increase understanding, and improve prediction, of species invasions.

A range of statistical and machine learning models, including artificial neural networks, are used to predict the potential for establishment and spread of an alien invasive species in regions or countries where it is not normally found.

Additionally, information is being gathered to design simulation experiments using our invasive species spread model and GIS technology to:

• test the performance of sampling designs to detect-low density invaders in a realistic landscape
• explore eradication strategies
• gauge the effectiveness of natural barriers to species spread.

Increased understanding, together with improved prediction of invasive alien species establishment and spread, will ultimately provide improved decision support for biosecurity so that preventative measures, eradication, mitigation and control can be prioritised.

This programme has four major projects:

Artificial neural networks and emergent pests

Modelling invasive species spread over the heterogeneous landscape

Machine learning and bioclimatic mapping and prediction

Ensemble models for prediction in bioprotection and biosecurity

Other related research