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A methodology for adaptable and robust ecosystem services assessment

PLoS ONE

By:
, , , , , , and
DOI: 10.1371/journal.pone.0091001

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Abstract

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.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
A methodology for adaptable and robust ecosystem services assessment
Series title:
PLoS ONE
DOI:
10.1371/journal.pone.0091001
Volume
9
Issue:
3
Year Published:
2014
Language:
English
Publisher:
PLoS ONE
Contributing office(s):
Geosciences and Environmental Change Science Center
Description:
e91001; 18 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
PLoS ONE