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

Gonzalez-Redin, J. ; Luque, S. ; Poggio, L. ; Smith, R. ; Gimona, A.

Type de document
Article de revue scientifique à comité de lecture
Langue
Anglais
Affiliation de l'auteur
JHI JAMES HUTTON INSTITUTE ABERDEEN GBR ; IRSTEA GRENOBLE UR EMGR FRA ; JHI JAMES HUTTON INSTITUTE ABERDEEN GBR ; CEH CENTRE FOR ECOLOGY AND HYDROLOGY PENICUIK GBR ; JHI JAMES HUTTON INSTITUTE ABERDEEN GBR
Année
2016
Résumé / Abstract
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.
Source
Environmental Research, vol. 144, num. Part B, p. 15 - 26

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