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.
Over the last two decades indigenous peoples in the lower Caquetá River basin in Colombia have experienced detrimental changes in the provision of important ecosystem services in ways that have significant implications for the maintenance of their traditional livelihoods. To assess these changes we conducted eight participatory mapping activities and convened 22 focus group discussions. We focused the analysis on two types of change: (1) changes in the location of ecosystem services provisioning areas and (2) changes in the stock of ecosystem services. The focal ecosystem services include services such as provision of food, raw materials and medicinal resources. Results from the study show that in the past two decades the demand for food and raw materials has intensified and, as a result, locations of provisioning areas and the stocks of ecosystem services have changed. We found anecdotal evidence that these changes correlate well with socio-economic factors such as greater need for income generation, change in livelihood practices and consumption patterns. We discuss the use of participatory mapping techniques in the context of marginalized and data-poor regions. We also show how this kind of information can strengthen existing ecosystem-based management strategies used by indigenous peoples in the Colombian Amazon
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.