EGU22-7796
https://doi.org/10.5194/egusphere-egu22-7796
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

A Bayesian Belief Network model assessing the multi-scale effects of riparian vegetation on stream invertebrates

Marie Anne Eurie Forio1, Francis J. Burdon2, Felix Witing3, Geta Risnoveanu4, Benjamin Kupilas5, Nikolai Friberg5, Martin Volk3, Brendan Mckie2, and Peter Goethals1
Marie Anne Eurie Forio et al.
  • 1Aquatic Ecology Research Unit, Department of Animal Sciences and Aquatic Ecology, Ghent University
  • 2Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences
  • 3Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research-UFZ
  • 4Research Institute of the University of Bucharest
  • 5Norwegian Institute for Water Research (NIVA)

Despite the benefits of riparian vegetation, they are limitedly implemented in water management – which is partly due to the lack of information on their effectiveness. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. Tools used in social learning activities are of paramount importance in the learning process. We developed a Bayesian belief network (BBN) model as a learning tool to simulate and assess the reach- and segment-scale effects of riparian vegetation properties and subcatchment-scale land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and land use information from geographic information system (GIS) data and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania and Sweden). We modelled the ecological water quality, expressed as Average Score Per Taxon, as a function of different riparian variables using the BBN modelling approach. The collected data were used to populate the conditional probability table of the BBN model. The model simulations provided insights into the usefulness of both reach- and segment-scale riparian vegetation attributes in enhancing ecological water quality. We assessed the strengths and limitations of the BBN model for application as a learning tool. Despite some weaknesses, the BBN model has great potential in workshop activities to stimulate key learning processes that help inform the management of riparian zones.

How to cite: Forio, M. A. E., Burdon, F. J., Witing, F., Risnoveanu, G., Kupilas, B., Friberg, N., Volk, M., Mckie, B., and Goethals, P.: A Bayesian Belief Network model assessing the multi-scale effects of riparian vegetation on stream invertebrates, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7796, https://doi.org/10.5194/egusphere-egu22-7796, 2022.