Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes

Gonzalez-Redin, Julen; Luque, Sandra; Poggio, Laura; Smith, Ron; Gimona, Alessandro. 2016 Spatial Bayesian belief networks as a planning decision tool for mapping ecosystem services trade-offs on forested landscapes [in special issue: The provision of ecosystem services in response to global change] Environmental Research, 144 (B). 15-26. https://doi.org/10.1016/j.envres.2015.11.009

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Abstract/Summary

An integrated methodology, based on linking Bayesian belief networks (BBN) with GIS, is proposed for combining available evidence to help forest managers evaluate implications and trade-offs between forest production and conservation measures to preserve biodiversity in forested habitats. A Bayesian belief network is a probabilistic graphical model that represents variables and their dependencies through specifying probabilistic relationships. In spatially explicit decision problems where it is difficult to choose appropriate combinations of interventions, the proposed integration of a BBN with GIS helped to facilitate shared understanding of the human–landscape relationships, while fostering collective management that can be incorporated into landscape planning processes. Trades-offs become more and more relevant in these landscape contexts where the participation of many and varied stakeholder groups is indispensable. With these challenges in mind, our integrated approach incorporates GIS-based data with expert knowledge to consider two different land use interests – biodiversity value for conservation and timber production potential – with the focus on a complex mountain landscape in the French Alps. The spatial models produced provided different alternatives of suitable sites that can be used by policy makers in order to support conservation priorities while addressing management options. The approach provided provide a common reasoning language among different experts from different backgrounds while helped to identify spatially explicit conflictive areas.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.envres.2015.11.009
UKCEH and CEH Sections/Science Areas:	Dise
ISSN:	0013-9351
Additional Keywords:	spatial Bayesian belief networks, ecosystem services, trade-offs, spatial planning, sustainable forest management, biodiversity
NORA Subject Terms:	Ecology and Environment
Date made live:	16 Feb 2017 17:20 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/516243

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