Developing ecosystem service indicators: experiences and lessons learned from sub-global assessments and other initiatives

People depend upon ecosystems to supply a range of services necessary for their survival and well-being. Ecosystem service indicators are critical for knowing whether or not these essential services are being maintained and used in a sustainable manner, thus enabling policy makers to identify the policies and other interventions needed to better manage them. As a result, ecosystem service indicators are of increasing interest and importance to governmental and inter-governmental processes, including amongst others the Convention on Biological Diversity (CBD) and the Aichi Targets contained within its strategic plan for 2011-2020, as well as the emerging Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES). Despite this growing demand, assessing ecosystem service status and trends and developing robust indicators is o!en hindered by a lack of information and data, resulting in few available indicators. In response, the United Nations Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), together with a wide range of international partners and supported by the Swedish International Biodiversity Programme (SwedBio)*, undertook a project to take stock of the key lessons that have been learnt in developing and using ecosystem service indicators in a range of assessment contexts. The project examined the methodologies, metrics and data sources employed in delivering ecosystem service indicators, so as to inform future indicator development. This report presents the principal results of this project

Testing the generalized functional translation model

The purpose of this study was to evaluate the generalized functional model that allows someone to work at the same intensity during a training session as was achieved during a graded exercise test (GXT) by downregulating the workload to account for cardiovascular drift. This also tested for the accuracy of the new generalized translation model as a way to downregulate GXT workload to a specific training workload. Earlier studies found that in order for patients to work at the same relative workload during training as during a GXT, there needs to be a translation (downregulation) from GXT results to steady state exercise. We have recently developed a generalized translation model that combines the results of earlier experimental studies. Subjects (n=20) performed a GXT and three, 20-minute training bouts at easy, moderate and hard intensities on a treadmill. Average values for HR and RPE achieved during the workout were compared to target values for all three intensity levels, using repeated measures ANOVA. When significant differences were observed, pair-wise comparisons were made using Tukey’s test. It was found that the generalized prescription model slightly, but significantly, over predicted target RPE at 60% HRR and 70% HRR workloads but seems to be accurate at the 80% HRR workload