Primary biodiversity data and the
Post-2020 Global Biodiversity
Framework
Andrew Rodrigues, David Bloom, Paula Zermoglio, Robert Guralnick, Tim Hirsch,
Jillian Campbell, Natasha Ali, Simon Ferrier, Aidin Niamir, Maria Cecilia Londoño,
Yanina Sica

Version 90e1d79, 2022-06-23 09:21:48 UTC

Table of Contents
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1. Data models and standards for improved usability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2. Spatial, temporal and taxonomic biases in the data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
3. Transparency of data and methodologies used in indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
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Suggested citation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Licence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Persistent URI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Document control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
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Preface
For decades scientists and policymakers have called for more effective and efficient methods tomonitor and address the global challenges of biodiversity loss and environmental degradation(Carpenter et al. 2006, Pereira & Cooper 2006, Navarro et al. 2017). The Convention on Biological
Diversity (CBD) is developing a post-2020 global biodiversity framework to support an accelerated
push for action with a new set of goals and targets that will replace the Aichi targets. The First Draftof the framework (CBD 2021a) built upon the mission of the Strategic Plan for Biodiversity 2011-2020(CBD 2010), retains the 2050 Vision and proposed four long-term goals with associated outcomes for2030 and 2050, including 21 action targets through which the goals should be achieved.
As part of the post-2020 framework, Parties to the CBD will agree on a subset of headline
quantitative indicators that will allow Parties to monitor progress towards the goals and action
targets (UNEP-WCMC & BIP 2020). These indicators should apply to all countries and allow
prioritization of capacity and resource needs. A sufficient supply of high-quality data and a range ofEssential Biodiversity Variables (EBVs), a framework that defines a minimum set of critical variables
required to study, report and manage biodiversity change (Jetz et al. 2019), would need to be inplace to ensure the comparability of headline indicators across the globe and allow scalable
reporting across space and time (CBD 2021b). These need to be supported by common
methodologies and data standards, and biodiversity observation networks and information facilities.
GBIF commissioned a study in 2020 to analyse those indicators (synthetic or derived metrics)
available at that time that make use of primary biodiversity data (raw observations) to support the
post-2020 biodiversity framework. The study reviewed and characterized the sources of primarybiodiversity data, identifying where data use is redundant, and how GBIF might mobilize data more
effectively to support the implementation of the new post-2020 framework. To ensure a robust
monitoring framework three key elements need to be addressed: the fitness of data models andstandards for the development of indicators; biases within the data that could prevent the utility ofindicators at different reporting scales; and a lack of transparency in the way in which data is applied
in indicators, as well as in the provenance of the data generating indicator results. The main
conclusions of the study are set out in this paper and will be addressed in an online consultation.

1. Data models and standards for improved
usability
Over the last two decades there have been enormous efforts to mobilize biodiversity data, whichhave resulted in the availability of massive amounts of published data that can be readily discovered,accessed and freely used for onward applications. The process of building meaningful EBVs that can
inform indicators needs data that can be reliably tracked across not just organism, space and time
but also provenance; the latter includes relevant, complete and searchable metadata about theinventory process and the methods that produced those data. Much of the data shared throughbiodiversity data platforms lack one or more of those four components, which limits or excludes
their use in the creation of EBVs and biodiversity indicators. Furthermore, much of the data currently
shared correspond to incidental records and lack any defined inventory or survey methods.
For EBVs that use multi-variable analyses to aggregate and homogenize data across species, space1and time, a taxon name, an event date and a set of coordinates are not enough to account for any
bias or deficiencies in the available data. One way to help to overcome these biases is to publish
occurrence and event records with metadata that describes the collection methodology andprocesses that are as rich as possible. However, this type of correction will only be useful for certain
types of analyses. Many species occurrence records that represent only the presence of the species(i.e. incidental records) will still not be useful for EBVs that must account for data that enables
inference about absence of species. For these EBVs, well-documented monitoring or inventory event
data is needed.
As more monitoring data becomes available, expanded best practice guidelines should include, but
may not be limited to, how to share quality metadata containing details of the sampling methods
employed, the scope, and descriptions and provenance of the collected data. To make this practical,
biodiversity data platforms will need to review and amend current data-sharing standards andpractices, and upgrade their infrastructures to host and display new types of data and data formats
such as is the case with GBIF´s ongoing consultation to review its current data model. An example
of a new standard that is under review for implementation is the Humboldt extension to Darwin Core
(Guralnick et al. 2017, Sica & Zermoglio 2021). Furthermore, data publishing institutions could be
encouraged to create “sub-collections” of their data that meet these metadata requirements thatthey could publish separately from their larger corpus of data. An increased focus on the publicationof past and current monitoring and inventory datasets with the expressed purpose of supporting
EBV and biodiversity indicator creation would require strengthened ties with the research andmonitoring communities that produce those data.

2. Spatial, temporal and taxonomic biases inthe data
According to the UNEP-WCMC & BIP 2020, “The post-2020 global biodiversity framework will be
implemented primarily at the national level. It is therefore important that the relative roles andsuitability of both global and national indicators are considered.” The provision of data suitable forthe national-level implementation strategy of the post-2020 framework that addresses thechallenges of scalability will require biodiversity data platforms to improve the quality andcompleteness of available data. As noted in a recommendation of the 3rd meeting of the CBD’s
Subsidiary Body on Implementation (SBI3), addressing knowledge management for the GBF, this will
involve the establishment of biodiversity observation networks and information facilities, supported
by data-sharing policies, associated capacity-building and guidance, to underpin the generation of
the information needed to implement and track the goals and targets of the global biodiversity
framework (CBD 2022).
Bias refers to a systematic lack of information due to a sampling design that relies on incorrect
assumptions, which may be taxonomic, geographic, temporal or environmental. One cause of bias is
the lack of capacity for making existing data accessible from particular regions or across taxonomic
groups. A number of initiatives have taken steps to address these biases in global datasets. For
example, GBIF´s Biodiversity Information for Development (BID) and Biodiversity Information Fundfor Asia (BIFA) programmes, co-funded respectively by the European Union and the Ministry ofEnvironment, Japan, have made significant efforts to increase capacity for mobilizing data frominstitutions in Africa, the Caribbean, the Pacific and Asia, and to fill data gaps in those regions. Recent

2guidelines on the publication of DNA-derived data through GBIF allow for the integration of datafrom environmental DNA sampling, and help to increase data coverage in data-poor ecosystems andtaxonomic groups. And innovative uses of the data, such as the Bioclimatic Ecosystem ResilienceIndex (BERI) (Ferrier et al. 2020), are able to assess changes in biodiversity over time without a fulltime-series of observations and thus respond to temporal biases within the data.
There are also opportunities to mobilize more data, especially monitoring and inventory data at the
local level. Historically, much of the biodiversity science community has been focused on themobilization of data within established legacy collections, such as those in museums, laboratories
and government agencies (Guralnick et al. 2007). There has now been a shift toward monitoring andobservation projects, including citizen science. As pressure mounts to address questions about thestatus and trends of biodiversity at different scales, it is these data from local sources focused on thesmaller-scale monitoring of national parks, waterways, and wildlands - data often collected byindigenous peoples and local communities with local knowledge - that are of critical importance inefforts to fill knowledge gaps and maintain on-going monitoring (Tengö et al. 2017, Hill et al. 2020,
Brook & McLachlan 2008, Geldmann et al. 2021).
The private sector is also an important source of biodiversity data in the form of environmental
assessments, impact assessments, and other project-based analyses. Increasing numbers of private
sector actors are publishing biodiversity data through GBIF and the GBIF community is engaging
with the private sector directly through several initiatives, such as Data4Nature which targets public
development banks to encourage data sharing as part of financing conditions . In addition, somenational governments have begun to mandate private sector data publication, and financial
institutions have created incentives for commercial entities to share non-sensitive data with GBIF andother national and global repositories (Equator Principles Association 2020).

3. Transparency of data and methodologies
used in indicators
Assessing the quality of primary biodiversity data that meets the standards needed for further use inindicators is critical. Even if we were to satisfy the need for better quality data, questions would
remain about how homogeneous and repeatable the treatment of the same data can be in different
contexts. The same data, from multiple sources, is being used by distinct organizations orcollaborations to build EBVs and indicators. Stakeholders developing a given EBV or indicator treat
the data independently, apply their own filters and quality checks, and perform their own taxonomic
harmonization process, which may be more or less similar to those used by other stakeholders. If
biodiversity data platforms could prepare and share species occurrence data in advance for EBV andindicator creation, as EBV-usable datasets, or make the workflows to process data available forexample, better consistency and transparency might be achieved. GBIF is exploring ways of assisting
this process, for example through pre-filtered versions of GBIF-mediated data exported regularly to
public cloud environments.
A second opportunity of equal importance is to improve the communication pipeline between data
provider and data user. Data and communications about these data, tend to flow in one direction,from local data collection and mobilization to scientists and policymakers, with little to nocommunication in the opposite direction. GBIF and other biodiversity data platforms have madecommendable efforts to track downloads of data and to report the citations of published works back3

to data publishers when they are made public through the use of Digital Object Identifiers (DOIs).
Improved communications build trust across the data provider network by communicating back toorganizations and individuals at the local level about the uses of data. These communications could
occur in many ways, including notifications that alert data publishers when their data have beenused in the creation of EBVs, biodiversity indicators and other high-level policy documents, using
tools similar to the GBIF citation widget. Another effective communication strategy could be thepresentation of specific examples that demonstrate how high-quality data and associated metadata
are being used to influence science and policy as a part of capacity-building activities and otherpublic events. These possibilities will remain only possibilities, however, without greatertransparency.
A third opportunity to work towards greater transparency and traceability across the entire
information supply chain is to document all steps taken to create indicators. In this complex process,it is not uncommon for the processes and analyses used to generate these synthezised data andpolicy products to remain undocumented or hidden from public view. Similarly it is equally difficult toknow exactly which data were used in the processes and how. The CBD Secretariat and UNEP-WCMC,
are currently working on standardizing the metadata requirements for the proposed headline
indicators (see example for the Species Habitat Index, UNEP-WCMC 2021); this must include clear
reporting of datasets (DOIs) used and data providers consulted to improve traceability even further.

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ecosystem resilience index. Ecological Indicators 117: 106554. https://doi.org/10.1016/
j.ecolind.2020.106554▪ Geldmann J, Deguignet M, Balmford A et al. (2021) Essential indicators for measuring site‐based
conservation effectiveness in the post‐2020 global biodiversity framework. Conservation Letterse12792. https://doi.org/10.1111/conl.12792▪ Guralnick RP, Hill AW & Lane M (2007) Towards a collaborative, global infrastructure forbiodiversity assessment. Ecology Letters 10(8): 663-672. https://doi.org/10.1111/j.14610248.2007.01063.x▪ Guralnick R, Walls R & Jetz W (2017) Humboldt Core – toward a standardized capture of biologicalinventories for biodiversity monitoring, modeling and assessment. Ecography 40: 001-012.
https://doi.org/10.1111/ecog.02942▪ Hill R, Adem C, Alangui WV et al. (2020) Working with Indigenous, local and scientific knowledge

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10.1038/s41559-019-0826-1▪ Navarro LM, Fernández N, Guerra C et al. (2017) Monitoring biodiversity change through effective
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wg2020-03-03-en.pdf▪ Secretariat of the Convention on Biological Diversity (2021b) Report of the virtual sessions of thefifth science-policy forum for biodiversity and the eighth international conference onsustainability science. https://www.cbd.int/doc/c/8cd2/6eab/663d8a4cc2d198b104225345/
sbstta-24-inf-28-en.pdf▪ Secretariat of the Convention on Biological Diversity (2022) Knowledge Management and theClearing-House Mechanism. https://www.cbd.int/doc/c/bc3c/fa25/e5d7928206fe7f3fc20aa9b1/
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5Colophon
Suggested citation
Bloom D, Zermoglio P, Guralnick R, Rodrigues A, Hirsch T, Campbell J, Ali N, Ferrier S, Niamir A,
Londoño MC & Sica Y (2022) Primary biodiversity data and the Post-2020 Global Biodiversity
Framework. GBIF Secretariat: Copenhagen. https://doi.org/10.15468/doc-pgg2-xn60

Contributors• David Bloom, VertNet: UF/Florida Museum of Natural History• Paula Zermoglio VertNet• Robert Guralnick, Florida Museum of Natural History• Andrew Rodrigues, GBIF Secretariat• Tim Hirsch, GBIF Secretariat• Simon Ferrier, CSIRO Land and Water• Aidin Niamir, Senckenberg Biodiversity and Climate Research Centre• Yanina Sica, Yale University• Natasha Ali, United Nations Environment Programme World Conservation Monitoring Centre(UNEP-WCMC)• Jillian Campbell, UN Convention on Biological Diversity• Maria Cecilia Londoño, Humboldt Institute | Group on Earth Observations Biodiversity
Observation Networks (GEO BON)
Additional contributors to subsequent versions will be credited here.

Licence
The document Primary biodiversity data and the Post-2020 Global Biodiversity Framework is licensed
under Creative Commons Attribution-ShareAlike 4.0 Unported License.

Persistent URI
https://doi.org/10.15468/doc-pgg2-xn60

Document control
Adapted from Bloom DA, Zermoglio P & Guralnick R (2021) Analysis of biodiversity data needs in the
post-2020 framework. Copenhagen: GBIF Secretariat. https://doi.org/10.35035/doc-2ph8-0403.

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Cover image
Bleeding bonnet (<em>Mycena sanguinolenta</em>), observed in Norway. Photo &copy; 2020 Kirsti
Anne Mandal via <a href="https://www.gbif.org/occurrence/2848866491">Norwegian Species
Observation Service</a>.

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