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

(a) Harmonizing measurement units from different data sources

Measurement units present a multi-layered consideration for building EBV-ready data sets, particularly those relating to population abundance. First, abundance is not easily derived from population density when the surveyed area is not reported. Second, measurements of abundance are different for different types of organisms: most vertebrates are usually counted as individuals, plants and fish are usually measured as biomass or percentage cover, and aquatic meiofauna in terms of unit volume. Third, constraints in the way data are collected produce data sets with a great variety of units for measuring abundance. Some of these issues could be overcome by developing a set of well-validated correction factors, or by combining abundance data from different sources (e.g. using geometric means) (Buckland et al., 2011). However, this requires at least that metadata are sufficiently informative (see Section VI).

(b) Dealing with different spatial scales

Spatial scale, i.e. resolution (grain size) and extent, is a pervasive issue in ecology (Chave, 2013). Most metrics in ecology depend on the spatial resolution at which they are measured, including species abundance and distribution. For building EBV-ready data sets, it is necessary that the spatial units of sampling are well defined and that data can be converted and standardized to consistent spatial extents and resolutions. Population data sets collected at high spatial resolutions can provide sensitive metrics of occurrence and abundance patterns across scales, but high-resolution information rarely covers broad spatial extents. More often, EBV data products will need some type of rescaling to combine observations collected at different spatial scales. The rescaling procedure can have important consequences for the consistency of the EBV data product because few ecological measurements increase in direct proportion with spatial scale (e.g. probability of occupancy does not scale linearly with sampled area). There is a growing theoretical and methodological literature that explores scaling relationships of distribution and abundance information across resolutions and extents (e.g. Storch, Marquet & Brown, 2007; Nichols et al., 2008; Keil, Wilson & Jetz, 2014; Pagel et al., 2014; Azaele et al., 2015). However, such methods have not yet been applied in the wider context of building global data products from multi-source observational data. It therefore remains unclear whether such approaches can be generalized and how they can be implemented within EBV workflows.

(c) Correcting for imperfect detection

Imperfect detection needs to be accounted for when estimating species distributions and population parameters (Kéry & Schaub, 2012), and is particularly important in monitoring applications. A species that is not detected in a survey might be either not there (true absence) or undetected (false absence). A range of statistical models and tools has been developed to correct for imperfect detection and to address different data types (Guillera-Arroita, 2017). In general, accounting for imperfect detection requires information about the observation process (e.g. number of visits, observers, or detection methods), meaning that sampling detection covariates and accurate metadata on data collection and processing is crucial for building EBV-ready data sets. For example, distance sampling corrects for detection probability in transect counts as a function of distance and other covariates (Buckland et al., 2015). If data structures allow separate estimation of detection and occupancy parameters, the detection probability can be estimated statistically (MacKenzie et al., 2006; Royle & Dorazio, 2009). Hence, methods for imperfect detection play a crucial role in building EBV data products.

(d) Interpolation and extrapolation

Original observations from which species distribution and abundance EBVs can be derived are by necessity sparse and heterogeneous. Moreover, for most biodiversity data sets the distribution of search effort across space and time is irregular, and the data available in a particular area or time of year may be limited (Kelling et al., 2015). This means that in addition to aggregating and harmonizing data (i.e. producing EBV-ready data sets), a significant amount of spatial and temporal interpolation (e.g. gap filling) and extrapolation (i.e. prediction beyond sampled space or time) might be required for producing derived and modelled EBV data products. One possibility is to apply geostatistical models for spatial interpolation (e.g. kriging or co-kriging) to estimate abundance or occupancy in places where sample data is limited (Meng, Liu & Borders, 2013). Another possibility is to use SDMs for filling in the geographical or temporal gaps between locations with data (Elith & Leathwick, 2009; Fink et al., 2010). Whilst most of these methods are commonly used and well suited for interpolation, their use for extrapolation is prone to error and high uncertainty (Elith & Leathwick, 2009).

(e) Quantifying uncertainties

Uncertainties in EBV data products might derive from uncertainties in the underlying raw data (e.g. when producing EBV-ready data sets) or from the applied models (e.g. when producing derived and modelled EBV data). Data uncertainties are evident in all key dimensions of species distribution and abundance EBVs, e.g. due to inaccuracy and imprecision in raw data collection (Table 1). This can relate to the accuracy of geographic coordinates, the precision of time of collection, or spelling errors and orthographic variants in taxon names. Several tools have already been applied to quantify uncertainties of geographic coordinates, sampling dates and taxon names (Guralnick et al., 2006; Belbin et al., 2013; Lepage, Vaidya & Guralnick, 2014; Enquist et al., 2016; Meyer et al., 2016). However, assessments of data uncertainties are often lacking and high-throughput processing tools for quantifying uncertainties must be developed for EBV data products.

Data uncertainties can be further exacerbated by uncertainties in models, e.g. through covariates, model fitting and parameter estimation (Beale & Lennon, 2012). Different methods can account for uncertainty from different sources when deriving indicators. The most common way is to use the data to generate pseudo-replicates of the parameters from bootstrapping and then to propagate this distribution into aggregated indicators (O'Brien et al., 2010). Another is to use hierarchical models (Gelman et al., 2013) or to employ Monte Carlo Markov Chains to solve simultaneously for the parameters and calculate the derived indicators as a by-product under a Bayesian framework (Royle & Dorazio, 2009; Ahumada et al., 2013). Such methods are important when building EBV data products.

(a) Citizen science

One opportunistic way to improve data coverage is to mobilize more data from species monitoring projects and field observation campaigns (Stephenson et al., 2017; Proença et al., in press). This includes citizen science projects (Chandler et al., in press; Dickinson et al., 2010) which can have several advantages over traditional field surveys (Fig. 2). Assuring the usefulness of citizen-science data for EBV data products requires careful design of data-input and management procedures and recording of associated information such as sampling effort, species absence and other data-collection variables (Sullivan et al., 2014; Isaac & Pocock, 2015; Kelling et al., 2015). Citizen science data may also require additional cleaning to protect the privacy of volunteers, and additional metadata documentation to meet conditions of attribution (Bowser, Wiggins & Stevenson, 2013).

(b) Sensor networks

The in situ collection of species distribution and abundance information can be accelerated by building networked sensor instruments (Porter et al., 2005). For instance, networks of camera traps allow monitoring the spatiotemporal dynamics of terrestrial birds and mammals in tropical forests (Kays et al., 2009; Jansen et al., 2014). Such camera-trap networks have been successfully applied in many terrestrial systems to measure species occupancy and abundance (Burton et al., 2015), but only recently have they been deployed in marine systems (Bicknell et al., 2016). Extending the use of camera traps within marine, terrestrial and freshwater research domains can provide valuable new data and insightful images on species distributions and abundances. Lessons from terrestrial systems (e.g. imperfect detection, effective sampling area, multi-species inference) will help to facilitate a successful transition of such methods to other environments (Bicknell et al., 2016).

Beyond camera traps, the development of new networked sensors for automatic species recognition based on sound detection (Jeliazkov et al., 2016) also offers novel possibilities to calculate large-scale temporal trends of species distribution and abundances. Comprehensive tracking of animal movements from space may further enable the distributed monitoring of species occurrence (Kays et al., 2015), although this approach might largely be limited to terrestrial and marine birds and mammals (Kissling, 2015). Overall, sensor networks have many advantages over traditional surveys (Fig. 2). They allow a less costly and much more comprehensive sampling than field observations.

(c) DNA-based techniques

Recent progress in molecular techniques related to high-throughput DNA sequencing has disclosed unprecedented perspectives for monitoring biodiversity (Creer et al., 2016). In particular, DNA (meta-)barcoding (i.e. analysis of one or a few orthologous but variable DNA regions) or metagenomics (i.e. shotgun sequencing of genomic fragments) are rapid and cost-effective means for taxonomic identification of hundreds or thousands of organisms in both terrestrial and aquatic environments (Bourlat et al., 2013; Segata et al., 2013; Creer et al., 2016; Leray & Knowlton, 2016). These approaches can provide presence/absence data for macro- and micro-organisms from given locations at a particular time. However, the DNA-based techniques currently do not allow reliable estimation of absolute population abundances because the number of sequence reads is relative to the sum of the abundances of the other species in a given sample (Aylagas, Borja & Rodríguez-Ezpeleta, 2014).

Despite advantages, several problems exist with DNA-based surveys. Common marker sequences (e.g. 18S, 28S and 16S rRNA genes, or the ribosomal internal transcribed spacer) may not yield sufficient variation for species identification. Mitochondrial genes (e.g. cytochrome oxidase I) can identify species, but their use in large-scale and long-term projects is constrained by tremendous variation in primer use, amplification steps and sequencing platforms (Bucklin et al., 2016). Most species also have insufficient reference sequences in public archives such as GenBank, and a universal taxonomic system that allows combining the operational taxonomic units (OTUs) with the traditional Linnaean names is still lacking (Kõljalg et al., 2016). Nevertheless, DNA-based techniques have many advantages for assessing species distributions when compared to in situ field observations, including identification of cryptic organisms or incomplete individuals (Fig. 2).

(d) Satellite remote sensing

Satellite remote sensing can play a crucial role in building EBV data products, including those on species distributions and population abundances (Pereira et al., 2013; Skidmore et al., 2015; Lausch et al., 2016; Pettorelli et al., 2016). Nevertheless, measuring species distributions and population abundances from satellite remote sensing has various restrictions when identifying individual plants or animals. One key bottleneck is spatial resolution. For instance, the accurate identification of individual animals such as large wildlife in open savannah habitats (Yang et al., 2015) or penguins on ice (Witharana & Lynch, 2016) requires very high-resolution satellite images with a spatial resolution preferably below 1 m. However, such global remote-sensing products with very high resolution are currently only available through commercial satellite operators, and are costly. As new spaceborne hyperspectral instruments become available, species distribution monitoring from space will become increasingly common and viable, especially for monitoring plant species that characteristically dominate specific vegetation types. The planned launches of next-generation satellites such as EnMAP (http://www.enmap.org/) will allow scaling up towards global monitoring.

Future research should widen the applications for mapping species distributions and abundances from both airborne and spaceborne spatial imagery to a larger number of animal and plant species from diverse habitats and biomes. This has several advantages compared to in situ observations, including a much more consistent and contiguous data collection at broad spatial scales (Fig. 2). With appropriate ground-truthing, this makes satellite remote sensing an ideal method for understanding biodiversity change at national, continental and global scales (Schimel, Asner & Moorcroft, 2013).

(a) EBV-useable data sets

The first series of workflow steps is aimed at gathering EBV-useable data sets (orange in Fig. 3). This process begins with identifying and importing the relevant raw data (step 1, Fig. 3). For in situ species distribution and abundance data, raw observations are currently mostly derived from natural history collections, national and international monitoring programs, or research surveys including those utilizing citizen science. For some of these, the Global Biodiversity Information Facility (GBIF) serves as a global aggregation infrastructure that speeds up the discovery and retrieval process. For a workflow implementation the available raw data need to contain not only relevant measurements on species distributions or abundances (e.g. opportunistic incidence records, checklists, density estimates), but also accessible metadata (e.g. in relation to spatial and temporal extent and resolution, measurement units, sampling strategy, taxonomy) that facilitate an automatic and standardized process of data extraction and processing (e.g. Fegraus et al., 2011).

In a second step, data-sharing agreements and licenses (if available) must be checked and enforced (step 2, Fig. 3). Not all data are freely available or useable, and some uses may be restricted in certain contexts (e.g. commercial use) or through certain conditions (e.g. requiring citation or another form of attribution). While including standardized and machine-readable data licenses would allow this step to be integrated with step 1, machine-readable licenses are not currently consistently applied to biodiversity data. Licenses and other key considerations related to legal interoperability are discussed below.

In a third step, a basic data consistency and completeness check must be performed to identify whether the data are complete and whether they have time formats, measurement units, spatial information and species identity descriptors that can be mapped to known standards (step 3, Fig. 3). This is important because available data sets are often incomplete or archived in a way that partially or entirely prevents their reuse (Roche et al., 2015). These first three steps allow to compile data into EBV-useable data sets for subsequent processing in a standardized way.

(b) EBV-ready data sets

In the next sequence of workflow steps, harmonized (EBV-ready) data sets can be produced that are standardized. They will have undergone rudimentary quality control, with corrections applied where relevant (green in Fig. 3). This requires aggregation of data sets from different sources (step 4, Fig. 3). Combining different types of data (e.g. opportunistic population counts and population time series, or population time series from different sources) into the same EBV is not trivial because each data type represents different sampling schemes, measurement units and spatial and temporal resolutions. It might therefore be most feasible to combine different data sets that have the same data type or those collected for similar purposes.

Another key step is to match taxonomic names (Boyle et al., 2013), e.g. to relate synonyms to accepted species names if they derive from different taxonomic treatments (step 5, Fig. 3). As a minimum, this requires standardized lists or backbone taxonomies such as those from the Catalogue of Life, the World Register of Marine Species, or those directly maintained by specific research communities (e.g. AmphibiaWeb, ReptileBase, Mammal Species of the World, iPlant). Nevertheless, such a process can be complicated (requiring additional tools) because of numerous taxonomic revisions and inconsistencies over time. Tools such as Avibase (Lepage et al., 2014), which provides taxon concept mapping across different bird taxonomic resources, may be particularly relevant, but are rare outside well-studied taxonomic groups or geographic regions.

A data quality check and cleaning of data is then needed (step 6, Fig. 3). This includes identifying and annotating errors, outliers, wrong identifications and duplicates which may arise from merging heterogeneous data sets (Fernández et al., 2015; Meyer et al., 2016). Many e-Science infrastructures have already developed tools for cleaning and correcting outliers, errors and duplicates, e.g. in relation to taxonomy and geo-referencing (Constable et al., 2010; Kelling et al., 2011; Belbin et al., 2013; Enquist et al., 2016; La Salle et al., 2016). Of course, not all issues can be automatically detected or corrected, and community assistance is needed to help improve the quality and cleaning of the data (e.g. Constable et al., 2010; Kelling et al., 2011).

The six steps described above are fundamental for producing EBV-useable and EBV-ready data sets (Fig. 3).

(c) Derived and modelled EBV data products

A further set of workflow steps must be performed if EBVs are being interpolated or extrapolated (e.g. for gap filling) or further processed with specific analysis tools (blue in Fig. 3). This requires checking data coverage and fitness for purpose, and then creating input files for subsequent modelling (step 7, Fig. 3). This process should allow for the custom selection of specific taxa or groups of taxa required for a particular purpose, as well as the ability flexibly to aggregate data spatially into grid cells or temporally into time bands of a specific resolution and extent.

The next workflow step is to choose an appropriate analytical tool (e.g. a specific statistical model) and, if required, relevant covariates (step 8, Fig. 3). This depends on the type of data (e.g. presence-only, presence–absence, abundance) and the specific purpose (e.g. inter- or extrapolation, spatial prediction, temporal modelling of occupancy). The choice can include various species distribution, occupancy and abundance models (Fink et al., 2010; Conn et al., 2015; Beaudrot et al., 2016). For some (but not all) model applications, various detection and habitat covariates might be needed (Fink et al., 2010; Beaudrot et al., 2016).

The analytical tools can then be applied (step 9, Fig. 3). A next step is to calculate and report uncertainty of the analysis (step 10, Fig. 3). A final step of the workflow is to generate visualizations that can be provided together with the underlying data (step 11, Fig. 3). The last two steps might not always be needed to produce a derived and modelled EBV data product.

(d) Publishing EBV data products

A relevant and important aspect of the workflow is to publish the EBV data products and associated metadata (Fig. 3). This should include not only the EBV data products but also the raw data sets used (or links to them), the scripts, analytical tools and software applied, machine-readable workflow metadata, and licensing information. In addition, dashboards with numbers and summary statistics, geospatial layers, maps and animations for occurrence and abundance predictions, predictive performance metrics and predictor importance information for the covariates that were used in the models, and conditions for reuse should also be published. Each data product should have a persistent identifier, such as a Digital Object Identifier (DOI), so that it is traceable and provides appropriate credit (Hugo et al., 2017). The content and structure of metadata should be in a standardized form to allow accessibility via machine-to-machine interactions, but this must still be developed for EBV data products (see Section VI).

(a) Constraints on legal interoperability

Key constraints on legal interoperability emerge from restrictions on data use, modification and sharing. For instance, legal interoperability is constrained when data sets are protected by different intellectual property rights (IPRs), e.g. U.S. copyright law or E.U. Database protection. Other restrictions on data access and re-use can come from national security regulations, protection of endangered species, other types of confidentiality, individual use agreements (e.g. contracts, licenses and disclaimers) as well as incompatible data policies of different data sets for the same species. In many situations, individual researchers and research teams act as if data were proprietary, and the use of data is not necessarily designated as open. When data sets are made accessible only on a case-by-case basis or if they are kept secret, legal interoperability is not achievable.

Restrictions on data access, use and sharing therefore have severe consequences for building EBV data products. For instance, including copyrighted or otherwise restricted raw data in EBV data products can impede quality control, limit data aggregation and restrict re-usability. Building EBV data products that involve proprietary data or sensitive data (e.g. because of threatened or endangered species) could lead to situations where different results are produced by different researchers. Moreover, legal interoperability becomes a key issue when combining data from multiple sources because the most restrictive access rights of an included data set will dictate the access rights of the whole EBV data product. Existing legal mechanisms therefore need to be promoted and enforced to ensure open access and legal interoperability (RDA-CODATA Legal Interoperability Interest Group, 2016).

(b) The need for common-use licenses

One of the most efficient approaches that help to assure legal interoperability are common-use licenses. The best-known set of common-use licenses are the Creative Commons (CC) licenses (Carroll, 2006). Six CC licenses describe different conditions for re-use, and one designation (CC0) supports a full waiver of copyright in favour of placing work in the public domain (Table 5). When combining data from multiple sources with different CC licenses, the resulting EBV data product will incorporate the accumulated restrictions imposed by each source of data. Since not all licenses are compatible with each other (Table 5), this can have severe consequences for aggregating data from multiple sources. For instance, if one data set licensed with CC0 (i.e. placed in the public domain) is combined with data licensed as CC BY (i.e. attribution only) and also combined with data licensed as CC BY-NC (i.e. with attribution and only for non-commercial uses), then the resulting EBV data product will contain the most stringent license type (here, CC BY-NC). This is problematic because it limits the re-use of the EBV data product by imposing conditions of attribution and non-commercial use. Moreover, two of the six CC license types are incompatible with each other because they do not permit modification (Table 5).

Table 5. Overview of Creative Commons (CC) licenses and designations (https://creativecommons.org/) and their compatibility when combining data from multiple sources. Combining data from multiple sources with different licenses will result in the most stringent license type. Several licenses are compatible with each other, but those that do not permit modification are not compatible with others. The most flexible is the CC0 designation, which represents a full waiver of copyright in favour of placing work in the public domain. For the building of Essential Biodiversity Variable data products, CC0 and CC BY are recommended because they either impose no restrictions on re-use (CC0) or only require attribution (CC BY)

License symbol	License name	Description	Compatible with
	No Rights Reserved [CC0]	Copyright holder chooses to opt out of copyright, placing work in the public domain	Any
	Attribution [CC BY]	Permits access and use; including modification; for any purpose; with attribution	Any
	Attribution-ShareAlike [CC BY-SA]	Permits access and use; including modification; for any purposes; with attribution. All derivative works must use this license	CC0, CC BY
	Attribution-NonCommercial [CC BY-NC]	Permits access and use; including modification; with attribution. Does not permit commercial use	CC0, CC BY
	Attribution-NonCommercial-ShareAlike [CC BY-NC-SA]	Permits access and use; including modification; with attribution. Does not permit commercial use. All derivative works must use this license	CC0, CC BY, CC BY-NC
	Attribution-NoDerivs [CC BY-ND]	Permits access and use; for any purpose; with attribution. Does not permit modification	None
	Attribution-NonCommercial-NoDerivs [CC BY-NC-ND]	Permits access and use; with attribution. Does not permit modification; does not permit commercial use	None

Hence, the ideal data sets for building EBV products are those in the public domain, with no restrictions on re-use and attribution (i.e. no need to specify source and license). If conditions for re-use are limited to attribution, the only legal requirement will be to include sufficient metadata to indicate the data source and the appropriate standardized license, if one is needed. Two Creative Commons designations (CC0 and CC BY) are therefore recommended for denoting open data (Table 5), while the remaining five impose restrictions on re-use. International data-sharing principles, such as those from the Group on Earth Observations (GEOSS, http://www.earthobservations.org/dswg.php), endorse CC0 and CC BY. We recommend following this endorsement when building EBV data products.

(a) Structural formats of EBV data products

The structure and format of the three types of EBV data (EBV-useable data sets, EBV-ready data sets and derived and modelled EBV data) must be further investigated. The format and specification should facilitate interdisciplinary research and other uses. It should allow manipulation by a wide range of software tools and be accessible to a range of end users. Many established file formats currently exist, including the Darwin Core Archive (DwC-A) for raw data, the Network Common Data Format (NetCDF) widely used in Earth sciences and the Web Map Service (WMS) for geospatial data (e.g. raster and vector) from the Open Geospatial Consortium (OGC) (Hugo et al., 2017). GEO BON and the wider scientific community (e.g. data producers and product developers) should therefore develop recommendations and agreements on the most appropriate data models and file formats for EBV data products.

(b) Data storage

EBV data products must be stored and managed on a long-term, semi-permanent basis. It is presently unclear who can take the burden of the storage commitment, and how this will be funded. The means of investment will depend on whether the semi-permanent storage of EBV data products will be done centrally (i.e. in a single repository, perhaps with duplicated mirror sites) or as a network of distributed, independent but interoperable storage services. The GEO BON Secretariat may host a selection of derived and modelled EBV data, but rely on partners such as GBIF nodes to host other EBV data products, including EBV-useable and EBV-ready data sets. Any large-scale trusted data repository for EBV data products must offer quality assurance as well as publishing and archival services so that different Biodiversity Observation Networks (BONs) and other actors can contribute to, manage and publish high-quality EBV data products.

(c) Execution and implementation of workflows

Implementing the large-scale execution of workflows for EBV data production requires appropriate computing infrastructure. This probably requires workflow management systems that allow an automated execution of data-intensive scientific workflows on distributed computing infrastructures by multiple users (Deelman et al., 2009; Liu et al., 2015; Hardisty et al., 2016). Since infrastructure providers want to be flexible in terms of which computing environment and technologies they use, a workflow solution neutral to specific hardware architectures and execution models is needed. The particular software for managing these workflows is less critical than the use of a standard execution-independent mechanism of workflow representation. An open question is who can take on the responsibility and cost of implementing specific EBV workflows. It is probably not the role of data publishers, and few existing biodiversity research infrastructures are currently well funded, sustained and able to do this.

Two basic options for the technical production cycle exist. The first option is a ‘create-on-demand’ process that requires ready access to relevant raw data, the workflow and processing capacity at any time of interest. It requires that the source data for computing EBV data products are compliant with technical and semantic interoperability standards to allow on-demand machine translation and mapping of algorithms. The second option is a periodic and systematic production of EBV data products (e.g. annually). This reflects that EBV data products are published/archived as an ever-extending data archive that can be consulted for a specific place or area of interest (local, regional, national) at the time of interest. The differences between create-on-demand and periodic systematic production are fundamental for how the technical production processes are defined, how infrastructures are organized and how access and use permissions for raw data are handled. A mixture of both approaches may be needed, e.g. with the GEO BON Data Portal hosting periodic EBV data products, but on-demand EBV data products being hosted/offered by GEO BON partners.

(a) The Darwin Core standard and the Event Core

The Darwin Core (DwC) provides a set of terms that facilitate the exchange of information about the occurrence of organisms in nature and the resulting specimens in biological collections (Wieczorek et al., 2012). As originally conceived, it is a data and metadata standard for reporting incidental observations or specimens (summarized under ‘opportunistic incidence records’ in Section IV.1). To help organize the 169 terms currently in the standard, these are grouped into several categories or classes. A list of terms and descriptions is available at http://rs.tdwg.org/dwc/terms/.

Until recently, the DwC has been insufficient to provide detailed reporting on many aspects of inventories because no DwC terms existed to report the scope, effort and completeness of surveys (which is critical information for assessing potential absence of species). New DwC terms have begun to close these gaps, including better capture of quantities found within sampling units. These include biomass or number of individuals as proxies of abundance, capture of sample effort reporting and identifiers relating parent–child relationships between events to represent hierarchical sampling designs.

In addition, a new organization of the data in the DwC has recently been proposed that is particularly suitable for EBV data products. The vast majority of records in DwC format are ‘occurrence core’ records that have broad use for reporting opportunistic incidence records in that profile format. However, there is now the possibility to make the ‘event’ the core, and to place occurrence records as related to that event (Wieczorek et al., 2014). This enables data holders to share structured survey data such as population time series or presence–absence data (GEO BON, 2016), for instance through GBIF's Integrated Publishing Toolkit (IPT) (http://www.gbif.org/sites/default/files/gbif_IPT-sample-data-primer_en.pdf). However, standard details about sampling method and effort are not required in these ‘Event Core’ fields. One possible option is to utilize extensions for various kinds of inventories, with the caveat that this may create new challenges with heterogeneous data and interpretation. In sum, new models of publishing biodiversity data extend the DwC approach from its basis in reporting opportunistic incidence records to more structured surveys with detailed methods and abundance data.

(b) The Ecological Metadata Language

The Ecological Metadata Language (EML) is an extensive metadata standard for environmental data sources (Michener et al., 1997; Jones et al., 2001; Michener & Jones, 2012). It uses controlled vocabularies (i.e. predefined, authorized terms) and specifically targets long-term observation data. Some EML modules describe the data set's spatial, temporal and taxonomic coverage, and since these metadata elements can be accessed at the data discovery stage, powerful filtering becomes possible (e.g. based on taxonomic criteria without downloading and inspecting large data sets). Other EML modules describe methods and protocols of sampling or processing or contain information on responsible persons and organizations, software resources, or access rules. EML metadata and DwC data files can be bundled into DwC archives, which are self-describing zip files produced during data publication. These are ultimately harvested by data aggregators such as GBIF. For this, data publishers use EML standard fields to describe ownership, creation and licensing (Robertson et al., 2014).

(c) Other specifications and standards

A number of other specifications and standards can serve the need for metadata associated with EBV workflows. Several ontologies have been developed with the aim of improving data aggregation and integration across the biodiversity domain, including the Biological Collections Ontology (BCO) (Walls et al., 2014), the Population and Community Ontology (PCO) (Walls et al., 2014), the Environmental Ontology (ENVO) (Buttigieg et al., 2016) and genomic standards such as the Minimum Information about any (x) Sequence (MIxS) (Yilmaz et al., 2011). For in situ biotic inventory processes, the Humboldt Core provides a vocabulary for describing spatial, temporal, environmental and taxonomic information (https://mol.org/humboldtcore/). It includes methodology and effort reporting and a way to assess how a survey and inventory was performed and what was collected (e.g. abundance information and how it was recorded). Other ontologies for describing observations and measurements based on digital or material sample evidence include the Observation and Measurements ontology (O&M) and the Extensible Observation Ontology (OBOE) (Table 6). For Earth observation data, the ISO 19115/19157 metadata standard allows detailed documentation of spatial and temporal data characteristics, measurement units, legal and licensing restrictions, data quality and provenance. The ISO standard is widely used to describe satellite products and other environmental data, and is a core component of the Global Earth Observing System of Systems (GEOSS). These standard reporting mechanisms may themselves be linked to general models for provenance such as PROV (Missier, Belhajjame & Cheney, 2013) to assure both human and machine-readable information about provenance.