A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates
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A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates. / Forio, Marie Anne Eurie; Burdon, Francis J.; De Troyer, Niels; Lock, Koen; Witing, Felix; Baert, Lotte; De Saeyer, Nancy; Risnoveanu, Geta; Popescu, Cristina; Kupilas, Benjamin; Friberg, Nikolai; Boets, Pieter; Johnson, Richard K.; Volk, Martin; McKie, Brendan G.; Goethals, Peter L. M.
In: Science of the Total Environment, Vol. 810, 152146, 2022.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates
AU - Forio, Marie Anne Eurie
AU - Burdon, Francis J.
AU - De Troyer, Niels
AU - Lock, Koen
AU - Witing, Felix
AU - Baert, Lotte
AU - De Saeyer, Nancy
AU - Risnoveanu, Geta
AU - Popescu, Cristina
AU - Kupilas, Benjamin
AU - Friberg, Nikolai
AU - Boets, Pieter
AU - Johnson, Richard K.
AU - Volk, Martin
AU - McKie, Brendan G.
AU - Goethals, Peter L. M.
PY - 2022
Y1 - 2022
N2 - Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach-and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones.
AB - Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach-and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones.
KW - Learning environment
KW - Stakeholders engagement
KW - Catchment management
KW - Water resource management
KW - Forest riparian buffers
KW - Nature-based solution
KW - Restoration
KW - Social learning
KW - WATER-QUALITY
KW - LAND-USE
KW - STONEFLIES PLECOPTERA
KW - ORGANISM GROUPS
KW - MACROINVERTEBRATES
KW - FRAMEWORK
KW - TEMPERATURE
KW - MANAGEMENT
KW - SERVICE
KW - FOREST
U2 - 10.1016/j.scitotenv.2021.152146
DO - 10.1016/j.scitotenv.2021.152146
M3 - Journal article
C2 - 34864036
VL - 810
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 152146
ER -
ID: 346780036