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

Marcin R. Penk*, Michael Bruen, Christian K. Feld, Jeremy J. Piggott, Michael Christie, Craig Bullock, Mary Kelly-Quinn

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
166 Downloads (Pure)

Abstract

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, particularly in the catchments where riparian shading is scarce. Sediment load had a lower influence on river biology in flashy rivers where it has less potential to settle out. Sediment interacted synergistically with organic matter and phosphate where these stressors were active; tackling these stressor pairs simultaneously can yield additional societal benefits compared to the sum of their individual influences, which highlights the value of integrated management. Our BBN model can be parametrised for other Irish catchments whereas elements of our approach, including the expected index of desirability, can be adapted globally.
Original languageEnglish
Article number158065
Number of pages11
JournalScience of the Total Environment
Volume851
Issue numberPart 1
Early online date15 Aug 2022
DOIs
Publication statusPublished - 10 Dec 2022

Keywords

  • Bayesian belief network
  • Ecosystem function
  • Environmental management
  • Multi-criteria decision support
  • Multiple stressors
  • Nature's contribution to people

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