Operationalizing an ecosystem services-based approach using Bayesian Belief Networks: An application to riparian buffer strips

Abstract

4The interface between terrestrial and aquatic ecosystems contributes to the provision of key ecosystem 5 services including improved water quality and reduced flood risk. We develop an ecological-economic 6 model using a Bayesian Belief Network (BBN) to assess and value the delivery of ecosystem services from 7 riparian buffer strips. By capturing the interactions underlying ecosystem processes and the delivery of 8 services we aim to further the operationalization of ecosystem services approaches. The model is 9 developed through outlining the underlying ecological processes which deliver ecosystem services. 10Alternative management options and regional locations are used for sensitivity analysis. 11We identify optimal management options but reveal relatively small differences between impacts of 12 different management options. We discuss key issues raised as a result of the probabilistic nature of the 13 BBN model. Uncertainty over outcomes has implications for the approach to valuation particularly where 14 preferences might exhibit non-linearities or thresholds. The interaction between probabilistic outcomes 15 and the statistical nature of valuation estimates suggests the need for further exploration of sensitivity in 16 such models. Although the BBN is a promising participatory decision support tool, there remains a need to 17 understand the trade-off between realism, precision and the benefits of developing joint understanding of 18 the decision context. 19

Applications of Bayesian Networks as Decision Support Tools for Water Resource Management under Climate Change and Socio-Economic Stressors: A Critical Appraisal

“…Model simulation data from different types of models included a system dynamics model (e.g., [16]), a two-dimensional habitat simulation model (e.g., [115]) and a groundwater flow model (e.g., [59]). Qualitative data (e.g., expert knowledge and stakeholder opinion) are frequently used to calibrate and validate the BNs where empirical data are lacking [15,21]. For example, expert knowledge was used to identify the environmental factors (e.g., land use, rainfall, storage level, agricultural areas) affecting the three key water quality parameters (turbidity, colour and crypto) in the reservoirs of New South Wales, Australia, and then quantify the relationships between environmental factors and these water quality variables through populating the CPTs for the model [47].…”

Delineating and mapping riparian areas for ecosystem service assessment

“…Establishing a common riparian framework is not impossible. McVittie et al () proposed a model applied to riparian areas that integrated physical attributes (land cover, soil type, rainfall), terrestrial and aquatic process (e.g., erosion and river flow), and management intervention using Bayesian Belief Networks. Thus, the parameters introduced will ultimately aim to outline the fundamental ecological processes that deliver ecosystem services within riparian areas.…”

Diagnosing agrosilvopastoral practices using Bayesian networks

“…Frayer et al (2014) applied BNs to identify proximate causes and underlying drivers that influence the decisions of farm households in Yunnan province, China, to plant trees on former cropland. Although not focusing on agricultural technology adoption, BNs have also been employed to model ecosystem service delivery of farm and forest management options (Barton et al 2008;Gret-Regamey et al 2013;McVittie et al 2015).…”