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A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management

Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES...

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Autores principales: Bruen, Michael, Hallouin, Thibault, Christie, Michael, Matson, Ronan, Siwicka, Ewa, Kelly, Fiona, Bullock, Craig, Feeley, Hugh B., Hannigan, Edel, Kelly-Quinn, Mary
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012763/
https://www.ncbi.nlm.nih.gov/pubmed/35171345
http://dx.doi.org/10.1007/s00267-022-01595-x
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author Bruen, Michael
Hallouin, Thibault
Christie, Michael
Matson, Ronan
Siwicka, Ewa
Kelly, Fiona
Bullock, Craig
Feeley, Hugh B.
Hannigan, Edel
Kelly-Quinn, Mary
author_facet Bruen, Michael
Hallouin, Thibault
Christie, Michael
Matson, Ronan
Siwicka, Ewa
Kelly, Fiona
Bullock, Craig
Feeley, Hugh B.
Hannigan, Edel
Kelly-Quinn, Mary
author_sort Bruen, Michael
collection PubMed
description Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes “small group” workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both.
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spelling pubmed-90127632022-05-02 A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management Bruen, Michael Hallouin, Thibault Christie, Michael Matson, Ronan Siwicka, Ewa Kelly, Fiona Bullock, Craig Feeley, Hugh B. Hannigan, Edel Kelly-Quinn, Mary Environ Manage Article Models of ecological response to multiple stressors and of the consequences for ecosystem services (ES) delivery are scarce. This paper describes a methodology for constructing a BBN combining catchment and water quality model output, data, and expert knowledge that can support the integration of ES into water resources management. It proposes “small group” workshop methods for elucidating expert knowledge and analyses the areas of agreement and disagreement between experts. The model was developed for four selected ES and for assessing the consequences of management options relating to no-change, riparian management, and decreasing or increasing livestock numbers. Compared with no-change, riparian management and a decrease in livestock numbers improved the ES investigated to varying degrees. Sensitivity analysis of the expert information in the BBN showed the greatest disagreements between experts were mainly for low probability situations and thus had little impact on the results. Conversely, in our applications, the best agreement between experts tended to occur for the higher probability, more likely, situations. This has implications for the practical use of this type of model to support catchment management decisions. The complexity of the relationship between management measures, the water quality and ecological responses and resulting changes in ES must not be a barrier to making decisions in the present time. The interactions of multiple stressors further complicate the situation. However, management decisions typically relate to the overall character of solutions and not their detailed design, which can follow once the nature of the solution has been chosen, for example livestock management or riparian measures or both. Springer US 2022-02-16 2022 /pmc/articles/PMC9012763/ /pubmed/35171345 http://dx.doi.org/10.1007/s00267-022-01595-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bruen, Michael
Hallouin, Thibault
Christie, Michael
Matson, Ronan
Siwicka, Ewa
Kelly, Fiona
Bullock, Craig
Feeley, Hugh B.
Hannigan, Edel
Kelly-Quinn, Mary
A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management
title A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management
title_full A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management
title_fullStr A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management
title_full_unstemmed A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management
title_short A Bayesian Modelling Framework for Integration of Ecosystem Services into Freshwater Resources Management
title_sort bayesian modelling framework for integration of ecosystem services into freshwater resources management
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9012763/
https://www.ncbi.nlm.nih.gov/pubmed/35171345
http://dx.doi.org/10.1007/s00267-022-01595-x
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