TY - JOUR
T1 - Using weighted expert judgement and nonlinear data analysis to improve Bayesian belief network models for riverine ecosystem services
AU - Penk, Marcin R.
AU - Bruen, Michael
AU - Feld, Christian K.
AU - Piggott, Jeremy J.
AU - Christie, Michael
AU - Bullock, Craig
AU - Kelly-Quinn, Mary
N1 - Funding Information:
This project was funded under project 2018-W-LS-37 of the Environmental Protection Agency (EPA) Research Programme 2014-2020. The EPA Research Programme is a Government of Ireland initiative funded by the Department of Communications, Climate Action and Environment . It was administered by the Environment Protection Agency, which has the statutory function of coordinating and promoting environmental research. The authors gratefully acknowledge the significant contribution from the external experts who contributed to the work and joined the authors in the project workshops, and to the EPA staff who helped retrieve the data.
Publisher Copyright:
© 2022
PY - 2022/12/10
Y1 - 2022/12/10
N2 - 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.
AB - 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.
KW - Bayesian belief network
KW - Ecosystem function
KW - Environmental management
KW - Multi-criteria decision support
KW - Multiple stressors
KW - Nature's contribution to people
UR - http://www.scopus.com/inward/record.url?scp=85136569800&partnerID=8YFLogxK
U2 - 10.1016/j.scitotenv.2022.158065
DO - 10.1016/j.scitotenv.2022.158065
M3 - Article
C2 - 35981597
AN - SCOPUS:85136569800
SN - 0048-9697
VL - 851
JO - Science of the Total Environment
JF - Science of the Total Environment
IS - Part 1
M1 - 158065
ER -