Katrien Van der Biest

A GIS plug-in for Bayesian belief networks

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. Spatial heterogeneity of ES delivery requires spatially explicit accounting methods.Limited availability of primary data promotes the use of knowledge-based BBN models.The proposed GIS BBN plug-in offers a standardized approach to model ES delivery.Diverse probabilistic output maps can be produced to support decision making.The preferred type of output map depends mainly on end-user requirements.

CICES going Local : Ecosystem Services Classification Adapted for a Highly Populated Country

Abstract Multiple classification systems for ecosystem services (ES) make comparison and integration between studies and assessments very difficult. With the fast-growing number of ecosystem services assessment and valuation studies, there is a need to identify generally agreed definitions and to design a common base that will enable comparisons between ecosystem services assessments at different places. The recently developed Common International Classification for Ecosystem Services (CICES) is aiming to fill this gap. One advantage of the CICES approach is that it allows adjustment to local conditions. Through an iterative consultation round with Belgian experts from administrations, policy support units, and research centers CICES has been adapted to the needs of a highly populated country, where multifunctional land use is very common. The goal of CICES-Be is to introduce a common reference base for ecosystem services in Belgium, which is locally adapted and compatible with an international standard.

The ecosystem services valuation tool and its future developments

Abstract Although methodologies for classification, quantification, and valuation of ecosystem services are improving drastically, applications of the ecosystem services concept in day-to-day decision-making processes remain limited, especially at the planning level. Nevertheless, spatial planning decisions would benefit from systematic considerations of their effects on ecosystem services. Assessing the impacts of policy on a wide range of ecosystem services contributes to more cost-effective policy implementation, establishing win-win situations across different environmental domains. The “nature value explorer” (natuurwaardeverkenner in Dutch) is a web application developed to explore the quantity and value of ecosystem services in Flanders, Belgium, as part of environmental impact assessments. The tool estimates the impact of land-use and land-cover change on regulating and cultural ecosystem services. The web application is successful in drawing the interest of policy makers and is used in several cases to support decisions in infrastructural projects as well as nature restoration projects.

Chapter 20 – EBI—An Index for Delivery of Ecosystem Service Bundles∗

The Ecosystem service Bundle Index (EBI) was developed in response to the urgent need for tools that allow rapid and transparent, yet scientific underpinned assessment of ecosystem services. The index is based on a Bayesian network environment in which data on the biophysical conditions and land use properties that drive service delivery are combined to determine the level of service provision. The index points out service optimization opportunities as discrepancies between actual land use and the ecosystem’s biophysical potential. The model can be used for scenario building and offers opportunities to spatially distribute services in a most beneficial way. The EBI was developed as a prototype and tested in a pilot study area using three interacting ecosystem services: carbon sequestration, agricultural production and wood production.