Improving the rigor and Usefulness of Scenarios and Models for Biodiversity and Ecosystem Services Assessment

Abstract

In April 2012, over 100 governments agreed to establish IPBES, the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, as the leading intergovernmental body for assessing the state of the planet’s biodiversity, its ecosystems and the essential services they provide to society. Among many other activities, the methodological assessment of scenarios and models of biodiversity and ecosystem services was initiated in order to provide expert advice on the use of such methodologies in all activities under the Platform to ensure the policy relevance of its deliverables. In particular, the work focused on providing critical analyzes of the state-of-the-art and best practices for using scenarios and models in assessments, policy design and policy implementation relevant to biodiversity and ecosystem services; on proposing means for addressing gaps in data, knowledge, methods and tools relating to scenarios and models; and on recommendations for action by IPBES to implement and encourage those best practices, engage in capacity-building, and mobilize indigenous and local knowledge. The target audiences are not only those working within IPBES but also the scientific community and funding agencies, as well as policymakers and implementers at local to global scales, and people employing scenarios and models for decision support. Because of on-going research and rapid progress on many aspects of scenario analysis and modelling of biodiversity and ecosystem services, there is a need to continually update the review of available policy support tools and methodologies for scenario analysis and modelling. The goal is to point the way forward for additional research and development that is required to take the use of models and scenarios to a whole new level of rigor and utility. Here, some relevant gaps in data availability and data access as well as gaps and shortages from the modelling point of view will be identified and discussed. The need to have guidelines for verification and validation of models, and for assessing and managing uncertainty in scenario analysis and modelling will be analyzed