2023 Early Hearing Detection & Intervention Conference
March 5-7, 2023 • Cincinnati, OH
5/25/2021 | 8:30 AM - 10:30 AM | A BAYESIAN BELIEF NETWORK-BASED LEARNING TOOL FOR UNDERSTANDING LOCAL AND LONGITUDINAL EFFECTS OF RIPARIAN VEGETATION ON STREAM INVERTEBRATES | Virtual Platform
A BAYESIAN BELIEF NETWORK-BASED LEARNING TOOL FOR UNDERSTANDING LOCAL AND LONGITUDINAL EFFECTS OF RIPARIAN VEGETATION ON STREAM INVERTEBRATES
Despite the benefits of riparian forests, they are rarely implemented in water management, which is partly due to 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. Tools used in workshop activities are crucial to the learning process. We developed a Bayesian belief network (BBN) model applied as a learning tool to simulate and assess the local and longitudinal effects of riparian vegetation and land use on stream invertebrates. We surveyed local riparian condition, extracted longitudinal riparian and 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 quality, expressed as Average Score Per Taxon index, as a function of different riparian variables using the BBN modelling approach. The model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing ecological quality. We assessed the strengths and limitations of the BBN model for application as a learning tool. Despite some weaknesses, the BBN model proved to be a valuable learning tool.
- Nature-based solutions
- Adaptive management
- Management
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Presenters/Authors
Marie Anne Eurie Forio
(), Aquatic Ecology Research Unit, Department of Animal Sciences and Aquatic Ecology, Ghent University, Belgium , Marie.Forio@UGent.be;
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Francis Burdon
(), Swedish University of Agricultural Sciences, Uppsala, Sweden, francis.burdon@slu.se;
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Felix Witing
(), Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research - UFZ, Leipzig, Germany, felix.witing@ufz.de;
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Geta Risnoveanu
(), Department of Systems Ecology and Sustainability, University of Bucharest, Romania , geta.risnoveanu@g.unibuc.ro;
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Benjamin Kupilas
(), Norwegian Institute for Water Research (NIVA), Oslo, Norway , benjamin.kupilas@niva.no;
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Nikolai Friberg
(), Norwegian Institute for Water ResearchNorwegian Institute for Water Research (NIVA), Oslo, Norway , Nikolai.Friberg@niva.no;
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Martin Volk
(), Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany, martin.volk@ufz.de;
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Brendan McKie
(), Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences, Uppsala, Sweden, brendan.mckie@slu.se;
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Peter Goethals
(), Aquatic Ecology Research Unit, Department of Animal Sciences and Aquatic Ecology, Ghent University, Belgium , Peter.Goethals@UGent.be;
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