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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

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ABSTRACT. The ability to incorporate and manage the different drivers of land-use change in a modeling process is one of the key challenges because they are complex and are both quantitative and qualitative in nature. This paper uses Bayesian belief networks (BBN) to incorporate characteristics of land managers in the modeling process and to enhance our understanding of land-use change based on the limited and disparate sources of information. One of the two models based on spatial data represented land managers in the form of a quantitative variable, the area of individual holdings, whereas the other model included qualitative data from a survey of land managers. Random samples from the spatial data provided evidence of the relationship between the different variables, which I used to develop the BBN structure. The model was tested for four different posterior probability distributions, and results showed that the trained and learned models are better at predicting land use than the uniform and random models. The inference from the model demonstrated the constraints that biophysical characteristics impose on land managers; for older land managers without heirs, there is a higher probability of the land use being arable agriculture. The results show the benefits of incorporating a more complex notion of land managers in land-use models, and of using different empirical data sources in the modeling process. Future research should focus on incorporating more complex social processes into the modeling structure, as well as incorporating spatiotemporal dynamics in a BBN.