This paper gives an overview of the value of ecosystem services of 10 main biomes expressed in monetary units. In total, over 320 publications were screened covering over 300 case study locations. Approximately 1350 value estimates were coded and stored in a searchable Ecosystem Service Value Database (ESVD). A selection of 665 value estimates was used for the analysis. Acknowledging the uncertainties and contextual nature of any valuation, the analysis shows that the total value of ecosystem services is considerable and ranges between 490 int$/year for the total bundle of ecosystem services that can potentially be provided by an ?average? hectare of open oceans to almost 350,000 int$/year for the potential services of an ?average? hectare of coral reefs. More importantly, our results show that most of this value is outside the market and best considered as non-tradable public benefits. The continued over-exploitation of ecosystems thus comes at the expense of the livelihood of the poor and future generations. Given that many of the positive externalities of ecosystems are lost or strongly reduced after land use conversion better accounting for the public goods and services provided by ecosystems is crucial to improve decision making and institutions for biodiversity conservation and sustainable ecosystem management.Peer reviewe

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. University of California Press andAmerican Institute of Biological Sciences are collaborating with JSTOR to digitize, preserve and extend access to BioScience.

No 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.

No abstract