A GIS plug-in for Bayesian belief networks: Towards a transparent software framework to assess and visualise uncertainties in ecosystem service mapping |
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| Affiliation: | 1. Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Jozef Plateaustraat 22, B-9000 Ghent, Belgium;2. Unit Environmental Modelling, Flemish Institute for Technological Research, Boeretang 200, B-2240 Mol, Belgium;3. Ecosystem Management Research Group, University of Antwerp, Universiteitsplein 1C, B-2610 Wilrijk, Belgium;1. ICMC–USP, São Carlos, Brazil;2. TU Wien, Austria;3. VRVIS Research Center, Austria;1. U.S. Environmental Protection Agency, Office of Research and Development, Center for Environmental Solutions and Emergency Response, Land Remediation Technology Division, Environmental Decision Analytics Branch, Cincinnati, OH, USA;2. U.S. Environmental Protection Agency, Office of Research and Development, Gulf Breeze, FL, USA;1. Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia;2. Agronomy and Soil Science, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia;1. CENSUI, Comillas, 39520, Cantabria, Spain;2. Basque Centre for Climate Change (BC3), Sede Building 1, UPV Scientific Campus, 48940, Leioa, Spain;1. School of BioSciences, The University of Melbourne, Parkville, VIC 3010, Australia;2. School of Ecosystem and Forest Sciences, The University of Melbourne, 500 Yarra Boulevard, Burnley, VIC 3121, Australia;3. Faculty of Information Technology, Monash University, PO Box 197, Caulfield East, VIC 3145, Australia;4. Department of Biology, University of Central Florida, 4000 Central Florida Blvd, Orlando, FL 32816-2368, USA;5. Division of Water Resources, St. Johns River Water Management District, PO Box 1429, Palatka, FL 32178-1429, USA;1. Australian Rivers Institute, Griffith School of Environment, Griffith University, Nathan, Queensland, Australia;2. Griffith Climate Change Response Program, Australia;3. Griffith School of Engineering, Griffith University, Gold Coast, Queensland, Australia |
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| Abstract: | The complexity and spatial heterogeneity of ecosystem processes driving ecosystem service delivery require spatially explicit models that take into account the different parameters affecting those processes. Current attempts to model ecosystem service delivery on a broad, regional scale often depend on indicator-based approaches that are generally not able to fully capture the complexity of ecosystem processes. Moreover, they do not allow quantification of uncertainty on their predictions. In this paper, we discuss a QGIS plug-in which promotes the use of Bayesian belief networks for regional modelling and mapping of ecosystem service delivery and associated uncertainties. Different types of specific Bayesian belief network output maps, delivered by the plug-in, are discussed and their decision support capacities are evaluated. This plug-in, used in combination with firmly developed Bayesian belief networks, has the potential to add value to current spatial ecosystem service accounting methods. The plug-in can also be used in other research domains dealing with spatial data and uncertainty. |
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| Keywords: | BBN ES Spatial modelling Decision support Uncertainty maps Uncertainty analysis |
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