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

作者：Marcin R. Penk, Michael Bruen, Christian K. Feld, Jeremy J. Piggott, Mike Christie, Craig Bullock, Mary Kelly‐Quinn · 发表于：The Science of The Total Environment · 年份：2022 · DOI：10.1016/j.scitotenv.2022.158065 · 被引用次数：13 · 研究领域：Hydrology and Watershed Management Studies、Fish Ecology and Management Studies、Soil and Water Nutrient Dynamics

Rivers are a key part of the hydrological cycle and a vital conduit of water resources, but are under increasing threat from anthropogenic pressures. Linking pressures with ecosystem services is challenging because the processes interconnecting the physico-chemical, biological and socio-economic elements are usually captured using heterogenous methods. Our objectives were, firstly, to advance an existing proof-of-principle Bayesian belief network (BBN) model for integration of ecosystem services considerations into river management. We causally linked catchment stressors with ecosystem services using weighted evidence from an expert workshop (capturing confidence among expert groups), legislation and published literature. The BBN was calibrated with analyses of national monitoring data (including non-linear relationships and ecologically meaningful breakpoints) and expert judgement. We used a novel expected index of desirability to quantify the model outputs. Secondly, we applied the BBN to three case study catchments in Ireland to demonstrate the implications of changes in stressor levels for ecosystem services in different settings. Four out of the seven significant relationships in data analyses were non-linear, highlighting that non-linearity is common in ecosystems, but rarely considered in environmental modelling. Deficiency of riparian shading was identified as a prevalent and strong influence, which should be addressed to improve a broad range of societal benefits, pa...