ARIES is a web based tool that aims to quantify ecosystem services in a manner that acknowledges the associated dynamic complexity and its consequences, but keeps its models sufficiently simple to remain manageable and adaptable to varying levels of detail and data availability. ARIES makes ecosystem service assessment less onerous for users throughout the world with the goal of assisting environmental management decisions.
ARtificial Intelligence for Ecosystem Services (ARIES) is an integrated Ecosystem Services (ES) modelling methodology that has multiple uses including (But not limited to);
Currently, ARIES prototypes are available for use in specific case study areas by experienced modellers however a new web-based ARIES Explorer (k.Explorer) aims to allow non-technical users to employ the tool by 2018.
The model has been used throughout the world including the United States, Mexico and Madagascar to address ecosystem services such as carbon sequestration, flood regulation, sediment regulation, water supply, recreation, aesthetics, fisheries and coastal protection.
The ARIES methodology has been in development since 2007 and in use, through case studies applying models that have progressed in both complexity and capability since 2010. It uses a benefit transfer approach; under this methodology, each point in the landscape is assigned ecosystem service provision and value largely according to its land use and land use change. This Value is linked to end uses and benefits to human features creating a spatial network of ecosystem services and their effects on society.
The ARIES model then identifies ecosystem service flow dynamics where values that effect society are the result of the flow of a beneficial or detrimental carrier. The carrier may be physical (e.g., water in the case of water supply or flood regulation, CO2 in carbon storage and sequestration) or informational (e.g., visual information in aesthetic services). The mode of transmission and resulting spatial patterns of ES flow are determined by the nature of the carrier, the type of benefit, landscape characteristics and the presence of human features that act as sinks for these ES.
ARIES conceptual model
For this modelling approach simulated flow trajectories are processed into different groups of mapped results. For provisioning benefits, a flow density map, displaying the amount of ecosystem benefit that has traversed each location during the course of the simulation, highlights high-value areas that are most critical to maximizing the transmission of a benefit to beneficiary groups.
For preventive benefits, flow density highlights areas where the damaging medium concentrates and can help spot areas where intervention is needed. Such maps can greatly aid planning, as in most cases it is difficult to relate flow information to either source or use areas. These spatially explicit ecosystem service maps which highlight benefit flows, sources and sinks can then inform the economic value that could be provided in ideal situations and as a result environmental management decisions.
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|Spatial Extents||Local (i.e. Catchment or District), Regional, National|
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Home page ARIES: http://aries.integratedmodelling.org/
ARIES Publications page: http://aries.integratedmodelling.org/?page_id=546
Links on Ecosystems Servies: http://aries.integratedmodelling.org/?page_id=557
Introductory study 2009: https://www.researchgate.net/profile/Gary_Johnson17/publication/228342331_ARIES_ARtificial_Intelligence_for_Ecosystem_Services_A_new_tool_for_ecosystem_services_assessment_planning_and_valuation/links/02e7e53b2e0742e75e000000/ARIES-ARtificial-Intelligence-for-Ecosystem-Services-A-new-tool-for-ecosystem-services-assessment-planning-and-valuation.pdf
Introduction to ARIES - YouTube video