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A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates

Marie Anne Eurie Forio (UGent) , Francis J. Burdon, Niels De Troyer (UGent) , Koen Lock (UGent) , Felix Witing, Lotte Baert (UGent) , Nancy De Saeyer (UGent) , Geta Rîșnoveanu, Cristina Popescu, Benjamin Kupilas, et al.
(2022) SCIENCE OF THE TOTAL ENVIRONMENT. 810.
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Abstract
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.
Keywords
Pollution, Waste Management and Disposal, Environmental Chemistry, Environmental Engineering, Learning environment, Stakeholders engagement, Catchment management, Water resource management, Forest riparian buffers, Nature-based solution, Restoration, Social learning, ECOLOGICAL WATER-QUALITY, LAND-USE, STONEFLIES PLECOPTERA, ORGANISM GROUPS, MACROINVERTEBRATES, FRAMEWORK, TEMPERATURE, MANAGEMENT, SERVICE, FOREST

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Citation

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MLA
Forio, Marie Anne Eurie, et al. “A Bayesian Belief Network Learning Tool Integrates Multi-Scale Effects of Riparian Buffers on Stream Invertebrates.” SCIENCE OF THE TOTAL ENVIRONMENT, vol. 810, 2022, doi:10.1016/j.scitotenv.2021.152146.
APA
Forio, M. A. E., Burdon, F. J., De Troyer, N., Lock, K., Witing, F., Baert, L., … Goethals, P. (2022). A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates. SCIENCE OF THE TOTAL ENVIRONMENT, 810. https://doi.org/10.1016/j.scitotenv.2021.152146
Chicago author-date
Forio, Marie Anne Eurie, Francis J. Burdon, Niels De Troyer, Koen Lock, Felix Witing, Lotte Baert, Nancy De Saeyer, et al. 2022. “A Bayesian Belief Network Learning Tool Integrates Multi-Scale Effects of Riparian Buffers on Stream Invertebrates.” SCIENCE OF THE TOTAL ENVIRONMENT 810. https://doi.org/10.1016/j.scitotenv.2021.152146.
Chicago author-date (all authors)
Forio, Marie Anne Eurie, Francis J. Burdon, Niels De Troyer, Koen Lock, Felix Witing, Lotte Baert, Nancy De Saeyer, Geta Rîșnoveanu, Cristina Popescu, Benjamin Kupilas, Nikolai Friberg, Pieter Boets, Richard K. Johnson, Martin Volk, Brendan G. McKie, and Peter Goethals. 2022. “A Bayesian Belief Network Learning Tool Integrates Multi-Scale Effects of Riparian Buffers on Stream Invertebrates.” SCIENCE OF THE TOTAL ENVIRONMENT 810. doi:10.1016/j.scitotenv.2021.152146.
Vancouver
1.
Forio MAE, Burdon FJ, De Troyer N, Lock K, Witing F, Baert L, et al. A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates. SCIENCE OF THE TOTAL ENVIRONMENT. 2022;810.
IEEE
[1]
M. A. E. Forio et al., “A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates,” SCIENCE OF THE TOTAL ENVIRONMENT, vol. 810, 2022.
@article{8732650,
  abstract     = {{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.}},
  articleno    = {{152146}},
  author       = {{Forio, Marie Anne Eurie and Burdon, Francis J. and De Troyer, Niels and Lock, Koen and Witing, Felix and Baert, Lotte and De Saeyer, Nancy and Rîșnoveanu, Geta and Popescu, Cristina and Kupilas, Benjamin and Friberg, Nikolai and Boets, Pieter and Johnson, Richard K. and Volk, Martin and McKie, Brendan G. and Goethals, Peter}},
  issn         = {{0048-9697}},
  journal      = {{SCIENCE OF THE TOTAL ENVIRONMENT}},
  keywords     = {{Pollution,Waste Management and Disposal,Environmental Chemistry,Environmental Engineering,Learning environment,Stakeholders engagement,Catchment management,Water resource management,Forest riparian buffers,Nature-based solution,Restoration,Social learning,ECOLOGICAL WATER-QUALITY,LAND-USE,STONEFLIES PLECOPTERA,ORGANISM GROUPS,MACROINVERTEBRATES,FRAMEWORK,TEMPERATURE,MANAGEMENT,SERVICE,FOREST}},
  language     = {{eng}},
  pages        = {{11}},
  title        = {{A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates}},
  url          = {{http://doi.org/10.1016/j.scitotenv.2021.152146}},
  volume       = {{810}},
  year         = {{2022}},
}
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