دورية أكاديمية
Using weighted expert judgement and nonlinear data analysis to improve Bayesian belief network models for riverine ecosystem services.
| العنوان: | Using weighted expert judgement and nonlinear data analysis to improve Bayesian belief network models for riverine ecosystem services. |
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| المؤلفون: | Penk MR; Department of Zoology and Trinity Centre for the Environment, Trinity College Dublin, Dublin, Ireland; School of Biology and Environmental Science & UCD Earth Institute, University College Dublin, Dublin, Ireland. Electronic address: penkm@tcd.ie., Bruen M; Dooge Centre for Water Resources Research, School of Civil Engineering & UCD Earth Institute, University College Dublin, Dublin, Ireland., Feld CK; Faculty of Biology-Department of Aquatic Ecology and Centre for Water and Environmental Research, University of Duisburg-Essen, Essen, Germany., Piggott JJ; School of Biology and Environmental Science & UCD Earth Institute, University College Dublin, Dublin, Ireland., Christie M; Aberystwyth Business School, Aberystwyth University, Aberystwyth, UK., Bullock C; School of Architecture, Planning and Environmental Policy, University College Dublin, Ireland., Kelly-Quinn M; Department of Zoology and Trinity Centre for the Environment, Trinity College Dublin, Dublin, Ireland. |
| المصدر: | The Science of the total environment [Sci Total Environ] 2022 Dec 10; Vol. 851 (Pt 1), pp. 158065. Date of Electronic Publication: 2022 Aug 15. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Amsterdam, Elsevier. |
| مواضيع طبية MeSH: | Data Analysis* , Ecosystem*, Bayes Theorem ; Conservation of Natural Resources/methods ; Environmental Monitoring/methods ; Phosphates ; Rivers |
| مستخلص: | Rivers are a key part of the hydrological cycle and a vital conduit of water resources, but are under increasing threat from anthropogenic pressures. Linking pressures with ecosystem services is challenging because the processes interconnecting the physico-chemical, biological and socio-economic elements are usually captured using heterogenous methods. Our objectives were, firstly, to advance an existing proof-of-principle Bayesian belief network (BBN) model for integration of ecosystem services considerations into river management. We causally linked catchment stressors with ecosystem services using weighted evidence from an expert workshop (capturing confidence among expert groups), legislation and published literature. The BBN was calibrated with analyses of national monitoring data (including non-linear relationships and ecologically meaningful breakpoints) and expert judgement. We used a novel expected index of desirability to quantify the model outputs. Secondly, we applied the BBN to three case study catchments in Ireland to demonstrate the implications of changes in stressor levels for ecosystem services in different settings. Four out of the seven significant relationships in data analyses were non-linear, highlighting that non-linearity is common in ecosystems, but rarely considered in environmental modelling. Deficiency of riparian shading was identified as a prevalent and strong influence, which should be addressed to improve a broad range of societal benefits, particularly in the catchments where riparian shading is scarce. Sediment load had a lower influence on river biology in flashy rivers where it has less potential to settle out. Sediment interacted synergistically with organic matter and phosphate where these stressors were active; tackling these stressor pairs simultaneously can yield additional societal benefits compared to the sum of their individual influences, which highlights the value of integrated management. Our BBN model can be parametrised for other Irish catchments whereas elements of our approach, including the expected index of desirability, can be adapted globally. Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Bayesian belief network; Ecosystem function; Environmental management; Multi-criteria decision support; Multiple stressors; Nature's contribution to people |
| المشرفين على المادة: | 0 (Phosphates) |
| تواريخ الأحداث: | Date Created: 20220818 Date Completed: 20221019 Latest Revision: 20221019 |
| رمز التحديث: | 20240628 |
| DOI: | 10.1016/j.scitotenv.2022.158065 |
| PMID: | 35981597 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1879-1026 |
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| DOI: | 10.1016/j.scitotenv.2022.158065 |