A methodology for adaptable and robust ecosystem services assessment.

Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research...

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Autores principales:	Ferdinando Villa, Kenneth J Bagstad, Brian Voigt, Gary W Johnson, Rosimeiry Portela, Miroslav Honzák, David Batker
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4d8c793eb9fa490089ab9855a0b86b7b
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spelling	oai:doaj.org-article:4d8c793eb9fa490089ab9855a0b86b7b2021-11-18T08:28:22ZA methodology for adaptable and robust ecosystem services assessment.1932-620310.1371/journal.pone.0091001https://doaj.org/article/4d8c793eb9fa490089ab9855a0b86b7b2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24625496/?tool=EBIhttps://doaj.org/toc/1932-6203Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant "one model fits all" paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES--both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.Ferdinando VillaKenneth J BagstadBrian VoigtGary W JohnsonRosimeiry PortelaMiroslav HonzákDavid BatkerPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 3, p e91001 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ferdinando Villa Kenneth J Bagstad Brian Voigt Gary W Johnson Rosimeiry Portela Miroslav Honzák David Batker A methodology for adaptable and robust ecosystem services assessment.
description	Ecosystem Services (ES) are an established conceptual framework for attributing value to the benefits that nature provides to humans. As the promise of robust ES-driven management is put to the test, shortcomings in our ability to accurately measure, map, and value ES have surfaced. On the research side, mainstream methods for ES assessment still fall short of addressing the complex, multi-scale biophysical and socioeconomic dynamics inherent in ES provision, flow, and use. On the practitioner side, application of methods remains onerous due to data and model parameterization requirements. Further, it is increasingly clear that the dominant "one model fits all" paradigm is often ill-suited to address the diversity of real-world management situations that exist across the broad spectrum of coupled human-natural systems. This article introduces an integrated ES modeling methodology, named ARIES (ARtificial Intelligence for Ecosystem Services), which aims to introduce improvements on these fronts. To improve conceptual detail and representation of ES dynamics, it adopts a uniform conceptualization of ES that gives equal emphasis to their production, flow and use by society, while keeping model complexity low enough to enable rapid and inexpensive assessment in many contexts and for multiple services. To improve fit to diverse application contexts, the methodology is assisted by model integration technologies that allow assembly of customized models from a growing model base. By using computer learning and reasoning, model structure may be specialized for each application context without requiring costly expertise. In this article we discuss the founding principles of ARIES--both its innovative aspects for ES science and as an example of a new strategy to support more accurate decision making in diverse application contexts.
format	article
author	Ferdinando Villa Kenneth J Bagstad Brian Voigt Gary W Johnson Rosimeiry Portela Miroslav Honzák David Batker
author_facet	Ferdinando Villa Kenneth J Bagstad Brian Voigt Gary W Johnson Rosimeiry Portela Miroslav Honzák David Batker
author_sort	Ferdinando Villa
title	A methodology for adaptable and robust ecosystem services assessment.
title_short	A methodology for adaptable and robust ecosystem services assessment.
title_full	A methodology for adaptable and robust ecosystem services assessment.
title_fullStr	A methodology for adaptable and robust ecosystem services assessment.
title_full_unstemmed	A methodology for adaptable and robust ecosystem services assessment.
title_sort	methodology for adaptable and robust ecosystem services assessment.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/4d8c793eb9fa490089ab9855a0b86b7b
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A methodology for adaptable and robust ecosystem services assessment.

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