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CABI Book Chapter

Pest risk modelling and mapping for invasive alien species.

Book cover for Pest risk modelling and mapping for invasive alien species.

Description

The International Pest Risk Mapping Workgroup acknowledges that advanced training and a 'tool kit' of software packages are needed to produce pest risk maps that are fully fit for purpose. This book is an initial attempt to address those needs. Invited chapters emphasize specific steps and data requirements to guide users through the development of pest risk models and maps, or components thereof....

Metrics

Chapter 12 (Page no: 171)

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

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.

Other chapters from this book

Chapter: 1 (Page no: 1) The challenge of modelling and mapping the future distribution and impact of invasive alien species. Author(s): Venette, R. C.
Chapter: 2 (Page no: 18) Mapping endangered areas for pest risk analysis. Author(s): Baker, R. Eyre, D. Brunel, S. Dupin, M. Reynaud, P. Jarošík, V.
Chapter: 3 (Page no: 35) Following the transportation trail to anticipate human-mediated invasions in terrestrial ecosystems. Author(s): Colunga-Garcia, M. Haack, R. A.
Chapter: 4 (Page no: 49) Simulation modelling of long-distance windborne dispersal for invasion ecology. Author(s): Parry, H. R. Eagles, D. Kriticos, D. J.
Chapter: 5 (Page no: 65) Using the MAXENT program for species distribution modelling to assess invasion risk. Author(s): Jarnevich, C. S. Young, N.
Chapter: 6 (Page no: 82) The NCSU/APHIS plant pest forecasting system (NAPPFAST). Author(s): Magarey, R. D. Borchert, D. M. Fowler, G. A. Hong, S. C.
Chapter: 7 (Page no: 97) Detecting and interpreting patterns within regional pest species assemblages using self-organizing maps and other clustering methods. Author(s): Worner, S. Eschen, R. Kenis, M. Paini, D. Saikkonen, K. Suiter, K. Sunil Singh Vänninen, I. Watts, M.
Chapter: 8 (Page no: 115) Modelling the spread of invasive species to support pest risk assessment: principles and application of a suite of generic models. Author(s): Robinet, C. Kehlenbeck, H. Werf, W. van der
Chapter: 9 (Page no: 131) Estimating spread rates of non-native species: the gypsy moth as a case study. Author(s): Tobin, P. C. Liebhold, A. M. Roberts, E. A. Blackburn, L. M.
Chapter: 10 (Page no: 145) Predicting the economic impacts of invasive species: the eradication of the giant sensitive plant from Western Australia. Author(s): Cook, D. C. Sheppard, A. Liu Shuang Lonsdale, W. M.
Chapter: 11 (Page no: 162) Spatial modelling approaches for understanding and predicting the impacts of invasive alien species on native species and ecosystems. Author(s): Allen, C. R. Uden, D. R. Johnson, A. R. Angeler, D. G.
Chapter: 13 (Page no: 189) Identifying and assessing critical uncertainty thresholds in a forest pest risk model. Author(s): Koch, F. H. Yemshanov, D.
Chapter: 14 (Page no: 206) Making invasion models useful for decision makers: incorporating uncertainty, knowledge gaps and decision-making preferences. Author(s): Yemshanov, D. Koch, F. H. Ducey, M.
Chapter: 15 (Page no: 223) Assessing the quality of pest risk models. Author(s): Venette, S. J.