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

Forio, Marie Anne Eurie and Burdon, Francis and De Troyer, Niels and Lock, Koen and Witing, Felix and Baert, Lotte and De Saeyer, Nancy and Risnoveanu, Geta and Popescu, Cristina and Kupilas, Benjamin and Friberg, Nikolai and Boets, Pieter and Johnson, Richard and Volk, Martin and Mckie, Brendan and Goethals, Peter L. M. (2022). A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates. Science of the Total Environment. 810 , 152146
[Research article]

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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.

Authors/Creators:Forio, Marie Anne Eurie and Burdon, Francis and De Troyer, Niels and Lock, Koen and Witing, Felix and Baert, Lotte and De Saeyer, Nancy and Risnoveanu, Geta and Popescu, Cristina and Kupilas, Benjamin and Friberg, Nikolai and Boets, Pieter and Johnson, Richard and Volk, Martin and Mckie, Brendan and Goethals, Peter L. M.
Title:A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates
Series Name/Journal:Science of the Total Environment
Year of publishing :2022
Volume:810
Article number:152146
Number of Pages:11
Publisher:ELSEVIER
ISSN:0048-9697
Language:English
Publication Type:Research article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution-Noncommercial-No Derivative Works 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 1 Natural sciences > 105 Earth and Related Environmental Sciences > Environmental Sciences (social aspects to be 507)
Keywords:Learning environment, Stakeholders engagement, Catchment management, Water resource management, Forest riparian buffers, Nature-based solution, Restoration, Social learning
URN:NBN:urn:nbn:se:slu:epsilon-p-115677
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-115677
Additional ID:
Type of IDID
DOI10.1016/j.scitotenv.2021.152146
Web of Science (WoS)000740224700006
ID Code:26788
Faculty:NJ - Fakulteten för naturresurser och jordbruksvetenskap
Department:(NL, NJ) > Dept. of Aquatic Sciences and Assessment
Deposited By: SLUpub Connector
Deposited On:21 Jan 2022 08:26
Metadata Last Modified:21 Jan 2022 08:31

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