Building essential biodiversity variables(EBVs) of species distribution and abundanceat a global scale
To support thedevelopment of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify11 key workflow steps that will operationalize the process of building EBV data products within and across researchinfrastructures worldwide. These workflow steps take multiple...
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| Định dạng: | BB |
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| Ngôn ngữ: | eng |
| Thông tin xuất bản: |
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/4502 |
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| Tóm tắt: | To support thedevelopment of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify11 key workflow steps that will operationalize the process of building EBV data products within and across researchinfrastructures worldwide. These workflow steps take multiple sequential activities into account, including identificationand aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modellingof integrated data. We illustrate these steps with concrete examples from existing citizen science and professionalmonitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living PlanetIndex and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial andaquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable andaccessible metadata, and we provide an overview of current data and metadata standards. Several challenges remainto be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous,multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emergingmethods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques andsatellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, executionof workflows and the production process/cycle as well as approaching technical interoperability among researchinfrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturingconsistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free fromrestrictions on use, modification and sharing. |
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