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Biol Rev Camb Philos Soc. 2018 Feb;93(1):600-625. doi: 10.1111/brv.12359. Epub 2017 Aug 2.

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale.

Author information

1
Department Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, 1090 GE, Amsterdam, The Netherlands.
2
TEAM Network, Moore Center for Science, Conservation International, 2011 Crystal Dr. Suite 500, Arlington, VA, 22202, U.S.A.
3
Woodrow Wilson International Center for Scholars, 1300 Pennsylvania Ave, NW Washington, DC, 20004, U.S.A.
4
Biodiversity Conservation Group, German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany.
5
Institute of Biology, Martin Luther University Halle-Wittenberg, Halle, Germany.
6
Instituto de Ecología, Universidad Mayor de San Andrés (UMSA), Campus Universitario, Cota cota, La Paz, Bolivia.
7
Estación Biológica de Doñana EBD-CSIC, Américo Vespucio s.n, 41092, Sevilla, Spain.
8
Councillor of State of the Kingdom of Spain and Honorary Researcher of the Franklin Institute of the University of Alcalá, Madrid, Spain.
9
University of Florida Museum of Natural History, University of Florida at Gainesville, Gainesville, FL, 32611-2710, U.S.A.
10
Biological Records Centre, Centre for Ecology & Hydrology, Maclean Building, Benson Lane, Crowmarsh Gifford, OX10 8BB, Wallingford, U.K.
11
Cornell Lab of Ornithology, Cornell University, 158 Sapsucker Woods Rd, Ithaca, NY, 14850, U.S.A.
12
Institute of Zoology, Zoological Society of London, Regent's Park, NW1 4RY, London, U.K.
13
UPMC Université Paris 06, Muséum National d'Histoire Naturelle, CNRS, CESCO, UMR 7204, Sorbonne Universités, 61 rue Buffon, 75005, Paris, France.
14
Department of Conservation Biology, UFZ-Helmholtz Centre for Environmental Research, Permoserstr. 15, 04318, Leipzig, Germany.
15
Department of Marine Sciences, Göteborg University, Box 463, SE-40530, Göteborg, Sweden.
16
Gothenburg Global Biodiversity Centre, Box 461, SE-405 30, Göteborg, Sweden.
17
CNR-Institute of Biomembranes and Bioenergetics, Amendola 165/A Street, 70126, Bari, Italy.
18
Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500AE, Enschede, The Netherlands.
19
Land and Water, Commonwealth Scientific and Industrial Research Organisation (CSIRO), PO Box 1600, Canberra, Australian Capital Territory, 2601, Australia.
20
Plazi, Zinggstr. 16, 3007, Bern, Switzerland.
21
Decision and Policy Analysis (DAPA), International Center for Tropical Agriculture (CIAT), AA6713, Cali, Colombia.
22
Instituto Alexander von Humboldt, CALLE 28A # 15-09, Bogota D.C., Colombia.
23
Institute of Marine Biology, Biotechnology and Aquaculture, Hellenic Centre for Marine Research, Thalassokosmos, Former US Base at Gournes, 71003, Heraklion, Crete, Greece.
24
School of Engineering and Applied Science, Aston University, Aston Triangle, B4 7ET, Birmingham, U.K.
25
Knowledge Management Unit, Joint Research Centre of the European Commission, Via Enrico Fermi, 21027, Varese, Italy.
26
School of BioSciences (Building 143), University of Melbourne, Melbourne, VIC, 3010, Australia.
27
Global Biodiversity Information Facility Secretariat, Universitetsparken 15, 2100, København Ø, Denmark.
28
Department of Biosciences, Biotechnology and Biopharmaceutics, University of Bari "A. Moro", via Orabona 4, 70125, Bari, Italy.
29
Department for Ecosystem Research & Environmental Information Management, Umweltbundesamt GmbH, Spittelauer Lände 5, 1090, Vienna, Austria.
30
Department of Forest Sciences, University of Eastern Finland, Joensuu Science Park, Länsikatu 15, FI-80110, Joensuu, Finland.
31
ECOLAB, Université de Toulouse, CNRS, INPT, UPS, Toulouse, France.
32
Centre for Integrative Biology, University of Trento, Via Sommarive 9, 38123, Trento, Italy.
33
NSW Office of Environment and Heritage, PO Box A290, Sydney South, NSW, 1232, Australia.
34
Australian Museum, 6 College Street, Sydney, NSW, 2000, Australia.
35
Consultant, Data Policy and Management, P.O. Box 305, Callicoon, NY, 12723, U.S.A.
36
Massive Connections, 2410 17th St NW, Apt 306, Washington, DC, 20009, U.S.A.
37
School of Computer Science & Informatics, Cardiff University, Queens Buildings, 5 The Parade, Cardiff, CF24 3AA, U.K.

Abstract

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to 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 emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.

KEYWORDS:

big data; biodiversity monitoring; data interoperability; ecological sustainability; environmental policy; global change research; indicators; informatics; metadata; research infrastructures

PMID:
28766908
DOI:
10.1111/brv.12359
[Indexed for MEDLINE]

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