Process-based pest risk mapping using Bayesian networks and GIS.

Abstract

Abstract Predicting the potential distribution of invasive alien pests (i.e. habitat suitability modelling) and their potential spread from existing populations (i.e. habitat susceptibility modelling) is critical to guide management responses at local, regional and national scales. We use the management of Chilean needle grass (Nassella neesiana) invasion in a 260,791 km2 part of eastern Australia as an example to describe a process-based approach for making such predictions with publicly available soft ware (e.g. Netica and ESRI products). The approach is deductive, with causal relationships captured in a Bayesian network and represented spatially at fine resolution using a geographic information system (GIS). Pest risk responses to changing environments, such as land-use change, climate change or altered flood regimes, and to management interventions can be tested through scenario analysis. Predictive risk mapping of invasive aliens is often knowledge-constrained; therefore, our approach seeks to capture the best available knowledge from often disparate sources in a transparent and explicit manner. For Chilean needle grass, we elicited process understanding from experts through a participatory approach, integrated an existing bioclimatic model and obtained our own field data. Our model, thereby, represents a hypothesis of what determines the distribution, abundance and spread of Chilean needle grass in the modelled region. Specifically, the model forecasts the likelihood of the weed reaching a threshold density (e.g. in this case, >30% ground cover) as defined by the experts. This approach to likelihood estimation contrasts with the presence/absence predictions of most other models. Modelling was done at a sufficiently fine spatial resolution (i.e. 30 m) to capture relevant invasion dynamics. Finally, we illustrate how validation can be used to give end users confidence in model predictions and to identify important knowledge gaps and uncertainties. We demonstrate how the resulting pest risk maps for Chilean needle grass can guide management decisions.