A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management

Abstract

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes “small group” workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both.

Using weighted expert judgement and nonlinear data analysis to improve Bayesian belief network models for riverine ecosystem services

“…The BBN underwent multiple iterations of development from the initial BBN (Bruen et al, 2022). This was done, firstly, to encapsulate a broader selection of ecosystem services.…”

“…Yet aquatic ecosystem services remain poorly integrated into water resources management decisions, including in freshwaters (AQUATAP-ES, 2020;Geneletti et al, 2020). This partly relates to the paucity of practical guidance on how best to link pressures and associated stressors to impacts on ecosystem services, a key step in ecosystem services assessment frameworks (Bruen et al, 2022).…”

“…Here, our overarching aim was to develop a BBN model for integration of ecosystem services considerations into river management that would ultimately form the 'backbone' of a decision support tool (the ProgRES tool; Kelly-Quinn et al, in press). To achieve that, we advanced an existing proof-of-principle BBN (Bruen et al, 2022) to causally link catchment stressors with ecosystem services by (i) strengthening the evidence using weighted evidence from an expert workshop, and (ii) calibrating the model using national monitoring data and expert judgement. Relative to the starting BBN, we extended the range of ecosystem services in the BBN for a more integrated assessment (Grizzetti et al, 2016) and used individual environmental conditions as inputs instead of explicit management options to permit a greater flexibility.…”

Sustainable water–ecosystem management using the Bayesian network and variable relationship analysis

“…The designed model represents a valuable decision support tool to rapidly evaluate the most appropriate management plan to maintain ecosystem benefits. Bruen et al (2022) presented a method for building a nondeterministic Bayesian model by combining water quality and watershed model output, data, and expert knowledge. This framework can support the integration of ecosystem sustainability in water resource management and analyze areas of agreement and disagreement among experts.…”