A Bayesian network to simulate macroinvertebrate responses to multiple stressors in lowland streams

WATER RESEARCH

卷:194

Article

de Vries, Jip¹  Kraak, Michiel H. S.¹  Skeffington, Richard A.²  Wade, Andrew J.²  Verdonschot, Piet F. M.^1,3 

[1] Univ Amsterdam, Inst Biodivers & Ecosyst Dynam, Freshwater & Marine Ecol, POB 94248, NL-1090 GE Amsterdam, Netherlands

[2] Univ Reading, Dept Geog & Environm Sci, Reading, Berks, England

[3] Wageningen UR, Wageningen Environm Res, POB 47, NL-6700 AA Wageningen, Netherlands

关键词: Bayesian network;    Ecological water quality;    Macroinvertebrate;    Multiple stressors;    Stream restoration;   

DOI  :  10.1016/j.watres.2021.116952

来源: Elsevier

【 摘 要 】

Aquatic ecosystems are affected by multiple environmental stressors across spatial and temporal scales. Yet the nature of stressor interactions and stressor-response relationships is still poorly understood. This hampers the selection of appropriate restoration measures. Hence, there is a need to understand how ecosystems respond to multiple stressors and to unravel the combined effects of the individual stressors on the ecological status of waterbodies. Models may be used to relate responses of ecosystems to environmental changes as well as to restoration measures and thus provide valuable tools for water management. Therefore, we aimed to develop and test a Bayesian Network (BN) for simulating the responses of stream macroinvertebrates to multiple stressors. Although the predictive performance may be further improved, the developed model was shown to be suitable for scenario analyses. For the selected lowland streams, an increase in macroinvertebrate-based ecological quality (EQR) was predicted for scenarios where the streams were relieved from single and multiple stressors. Especially a combination of measures increasing flow velocity and enhancing the cover of coarse particulate organic matter showed a significant increase in EQR compared to current conditions. The use of BNs was shown to be a promising avenue for scenario analyses in stream restoration management. BNs have the capacity for clear visual communication of model dependencies and the uncertainty associated with input data and results and allow the combination of multiple types of knowledge about stressor-effect relations. Still, to make predictions more robust, a deeper understanding of stressor interactions is required to parametrize model relations. Also, sufficient training data should be available for the water type of interest. Yet, the application of BNs may now already help to unravel the contribution of individual stressors to the combined effect on the ecological quality of water bodies, which in turn may aid the selection of appropriate restoration measures that lead to the desired improvements in macroinvertebrate-based ecological quality. ? 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

【 授权许可】

Free