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Building essential biodiversity variables ( EBV s) of species distribution and abundance at a global scale
Author(s) -
Kissling W. Daniel,
Ahumada Jorge A.,
Bowser Anne,
Fernandez Miguel,
Fernández Néstor,
García Enrique Alonso,
Guralnick Robert P.,
Isaac Nick J. B.,
Kelling Steve,
Los Wouter,
McRae Louise,
Mihoub JeanBaptiste,
Obst Matthias,
Santamaria Monica,
Skidmore Andrew K.,
Williams Kristen J.,
Agosti Donat,
Amariles Daniel,
Arvanitidis Christos,
Bastin Lucy,
De Leo Francesca,
Egloff Willi,
Elith Jane,
Hobern Donald,
Martin David,
Pereira Henrique M.,
Pesole Graziano,
Peterseil Johannes,
Saarenmaa Hannu,
Schigel Dmitry,
Schmeller Dirk S.,
Segata Nicola,
Turak Eren,
Uhlir Paul F.,
Wee Brian,
Hardisty Alex R.
Publication year - 2018
Publication title -
biological reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.993
H-Index - 165
eISSN - 1469-185X
pISSN - 1464-7931
DOI - 10.1111/brv.12359
Subject(s) - biodiversity , population , sampling (signal processing) , abundance (ecology) , computer science , operationalization , scale (ratio) , data science , data mining , geography , environmental resource management , ecology , cartography , biology , environmental science , filter (signal processing) , computer vision , philosophy , demography , epistemology , sociology
Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of E ssential B iodiversity V ariables ( EBV s) 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 ‘ B ig D ata’ 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 EBV s by the G roup on E arth O bservations B iodiversity O bservation N etwork ( 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 e B ird, the T ropical E cology A ssessment and M onitoring network, the L iving P lanet I ndex and the B altic S ea 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.