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Abstract The concepts of resilience and ecosystem services broaden the opportunities for assessing sustainability of social-ecological systems (SESs). The lack of operational frameworks for assessing the resilient provision of ecosystem services by SESs impedes greater integration of resilience thinking in natural resource governance. The greatest challenge so far has been to understand the capacity of the SES to (re)organize itself and sustain the flow of benefits from nature to people under various global and local pressures and trade-offs between ecosystem services users. To assess the resilience of an SES within a single framework, we propose a new approach which is a combination of: (1) the Driver-Pressure-State-Impact-Response (DPSIR) framework; (2) social-ecological indicators; and (3) scenario building. Practical application of the approach is demonstrated with the example of European polar and altitudinal treeline areas. The DPSIR framework analyzes causal relationships between the components of the SES. Social-ecological indicators quantify processes in the SES and estimate trends in the DPSIR factors. Combined top-down and bottom-up scenarios envision plausible development paths of the SES in the future based on expected global environmental and social changes which create context specific dynamics between DPSIR factors at specific localities. The proposed approach represents the analytical framework of the European Cooperation in Science and Technology (COST) action SENSFOR (Enhancing the resilience capacity of SENSitive mountain FORest ecosystems under environmental change) and can be applied to promote systemic resilience thinking in any SES.
Abstract Motivation: The BioTIME database contains raw data on species identities and abundances in ecological assemblages through time. These data enable users to calculate temporal trends in biodiversity within and amongst assemblages using a broad range of metrics. BioTIME is being developed as a community‐led open‐source database of biodiversity time series. Our goal is to accelerate and facilitate quantitative analysis of temporal patterns of biodiversity in the Anthropocene. Main types of variables included: The database contains 8,777,413 species abundance records, from assemblages consistently sampled for a minimum of 2 years, which need not necessarily be consecutive. In addition, the database contains metadata relating to sampling methodology and contextual information about each record. Spatial location and grain: BioTIME is a global database of 547,161 unique sampling locations spanning the marine, freshwater and terrestrial realms. Grain size varies across datasets from 0.0000000158 km2 (158 cm2) to 100 km2 (1,000,000,000,000 cm2). Time period and grain: BioTIME records span from 1874 to 2016. The minimal temporal grain across all datasets in BioTIME is a year. Major taxa and level of measurement: BioTIME includes data from 44,440 species across the plant and animal kingdoms, ranging from plants, plankton and terrestrial invertebrates to small and large vertebrates. Software format: .csv and .SQL.