Publishing DNA-derived data throughbiodiversity data platforms
Anders F. Andersson, Andrew Bissett, Anders G. Finstad, Frode Fossøy, Marie
Grosjean, Michael Hope, Thomas S. Jeppesen, Urmas Kõljalg, Daniel Lundin, R.
Henrik Nilsson, Maria Prager, Cecilie Svenningsen, Dmitry Schigel
Version b121e78, 2022-07-08 13:18:47 UTC

Table of Contents
Colophon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Suggested citation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Authors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
License . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Persistent URI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Document control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Cover image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.1. Rationale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2. Target audiences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3. Introduction to DNA-derived occurrence data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3.1. Environmental DNA as a source for DNA-derived occurrence data . . . . . . . . . . . . . . . . . . . . . 6
1.3.2. DNA-metabarcoding: sequence-derived data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.3. Metagenomic: sequence-derived data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.4. qPCR/ddPCR: occurrence data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.4. Introduction to biodiversity publishing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.5. Processing workflows: from sample to ingestible data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.6. Taxonomy of sequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.7. Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
2. Data packaging and mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1. Categorization of your data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1.1. Category I: DNA-derived occurrences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.2. Category II: Enriched occurrences. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.3. Category III: Targeted species detection (qPCR/ddPCR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.1.4. Category IV: Name references . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.1.5. Category V: Metadata-only datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2. Data mapping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.1. Mapping metabarcoding (eDNA) and barcoding data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.2. Mapping ddPCR / qPCR data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3. Future prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Colophon
Suggested citation
Andersson AF, Bissett A, Finstad AG, Fossøy F, Grosjean M, Hope M, Jeppesen TS, Kõljalg U, Lundin D,
Nilsson RN, Prager M, Svenningsen C & Schigel D (2021) Publishing DNA-derived data throughbiodiversity data platforms. v1.0 Copenhagen: GBIF Secretariat. https://doi.org/10.35035/doc-vf1anr22.

Authors• Anders F. Andersson, anders.andersson@scilifelab.se, Science for Life Laboratory, Departmentof Gene Technology, KTH Royal Institute of Technology, 17121 Stockholm, Sweden• Andrew Bissett, Andrew.Bissett@csiro.au, CSIRO O&A, GPO box 1533, Hobart, Tasmania, 7000,
Australia• Anders G. Finstad, anders.finstad@ntnu.no, Department of Natural History, Centre for
Biodiversity Dymamics, Norwegian University of Science and Technology, Trondheim, Norway• Frode Fossøy, Frode.Fossoy@nina.no, Norwegian institute for nature research (NINA), P.O. Box5685 Torgarden, NO-7485 Trondheim, Norway• Marie Grosjean, mgrosjean@gbif.org, Global Biodiversity Information Facility, Universitetsparken
15, 2100 København Ø, Denmark• Michael Hope, Michael.Hope@csiro.au, Atlas of Living Australia, CSIRO National Collections &Marine Infrastructure, GPO Box 1700, Canberra ACT 2601, Australia.
• Thomas

S.

Jeppesen,tsjeppesene@gbif.org,

GlobalBiodiversity

Information

Facility,

Universitetsparken 15, 2100 København Ø, Denmark• Urmas Kõljalg, urmas.koljalg@ut.ee, Natural History Museum and Botanical Garden, Universityof Tartu, 46 Vanemuise Street, 51003 Tartu, Estonia.
• Daniel Lundin, daniel.lundin@lnu.se, Centre for Ecology and Evolution in Microbial model
Systems - EEMiS, Linnaeus University, SE-39182 Kalmar, Sweden• R. Henrik Nilsson, henrik.nilsson@bioenv.gu.se, University of Gothenburg, Department of
Biological and Environmental Sciences, Box 461, 405 30 Göteborg, Sweden• Maria Prager, maria.prager@scilifelab.se, Science for Life Laboratory, Department of Ecology,
Environment and Plant Sciences, Stockholm University; Department of Microbiology, Tumor andCell Biology, Karolinska Institutet• Cecilie Svenningsen, cssvenningsen@snm.ku.dk, Natural History Museum of Denmark,
University of Copenhagen, Øster Voldgade 5-7, 1350 Copenhagen, Denmark• Dmitry Schigel, dschigel@gbif.org, Global Biodiversity Information Facility, Universitetsparken
15, 2100 København Ø, Denmark1

Contributors
Valuable discussions with members of ELIXIR, iBOL, GGBN, GLOMICON, and OBIS networks
contributed to compilation of this draft. We are especially grateful for inputs and encouragementfrom Kessy Abarenkov, Andrew Bentley, Matt Blissett, Pier Luigi Buttigieg, Kyle Copas, Camila A. Plata
Corredor, Gabriele Dröge, Torbjørn Ekrem, Tobias Guldberg Frøslev, Birgit Gemeinholzer, Quentin
Groom, Tim Hirsch, Donald Hobern, Hamish Holewa, Corinne Martin, Raissa Meyer, Chris Mungall,
Daniel Noesgaard, Corinna Paeper, Pieter Provoost, Tim Robertson, Saara Suominen, Maxime
Sweetlove, Andrew Young, John Waller, Ramona Walls, John Wieczorek, Lucie Zinger who havecontributed to the GBIF community review process.

LicenseThe document Publishing DNA-derived data through biodiversity data platforms is licensed under
Creative Commons Attribution-ShareAlike 4.0 Unported License.

Persistent URI
https://doi.org/10.35035/doc-vf1a-nr22

Document control
Version 1.0.1 released on 27 March 2022.

Abstract
When genetic information is used to describe or classify a taxon, most users will foresee its use in
the context of molecular ecology or phylogenetic research. It is important to realize that a sequencewith coordinates and a timestamp is a valuable biodiversity occurrence which is useful in a muchbroader context than its original purpose. To realize this potential, DNA-derived data needs to bediscoverable through biodiversity data platforms. This guide will teach you the principles andapproaches of exposing “sequences with dates and coordinates” in the context of broader
biodiversity data. The guide covers choices of particular schemas and terms, common pitfalls andgood practice, without going into platform-specific details. It will benefit anyone interested in better
exposure of DNA-derived data through general biodiversity data platforms, including national
biodiversity portals.

Cover image
Velvet shank (Flammulina velutipes), Kursk, Russian Federation. Photo 2020 Oleg Ryzhkov via
iNaturalist research-grade observations, licensed under CC BY-NC 4.0.

Preface
The work on this guide started from discussions at the biodiversity_next conference in 20192

consolidating inputs from various resources, such as:• Final Report on Environomics Future Science Platform Project• ALA blog post eDNA records now available on ALA• Environmental DNA (eDNA) in the ALA• ALA eDNA data template• Norwegian Criteria for depositing eDNA samples and data, including vouchered specimens• Molecular biodiversity data in SBDI, Sweden• GBIF resources (How) can I publish molecular/sequence/DNA based data to GBIF?
• Molecular data in GBIF
• GBIF quick guide to publishing data and detailed guides to publishing data
• How to publish data to GBIF, as well as DwC/extension field overview.
• Genomic Biodiversity Interest Group

1. Introduction
1.1. Rationale
The last 20 years have brought an increased understanding of the immense power of molecularmethods for documenting the diversity of life on earth. Seemingly lifeless and mundane substrates
such as soil and sea water turn out to abound with life–although perhaps not in a way that the casual
observer may immediately appreciate. DNA-based studies have shown that organism groups such as
fungi, insects, oomycetes, bacteria and archaea are everywhere, although we often cannot observe
them physically (Debroas et al. 2017). The benefits of molecular methods are not restricted to themicroscopic world: there are many organisms, such as some fish species, which can at least
theoretically be observed physically but for which it is very costly, labour-intensive, and perhaps
invasive to seek to do so (Boussarie et al. 2018). In such situations, DNA data enable us to record thepresence (and past presence) of these organisms non-invasively and with minimal effort. Thesedevelopments mean that we do not always need tangible, physical manifestations of all organisms
present at some site in order to record them. All organisms, whether or not they are physicallyobservable, may be important when it comes to understanding biodiversity, ecology and biologicalconservation.
DNA-derived data enable us to record inconspicuous or otherwise unobservable taxa that fall below
the radar of vetted protocols for field work, checklists, depositions into natural science collections,etc. The current maturity of DNA methodologies enables us to record the presence of theseorganisms to a level of detail that exceeds that of macroscopic observations of organisms in general.
However, bearing in mind that DNA methodologies comes with their own problems and biases, it isimportant to use this moment to define and agree how we should record and report on an organism
as present in some substrate or locality through molecular data. Doing so will help avoid significant
inefficiencies that have been reported in other domains, in which the lack of standards and guidance
has led to very heterogeneous and largely incomparable bodies of data (Leebens-Mack et al. 2006;
Yilmaz et al. 2011; Nilsson et al. 2012). Moreover, clear documentation of the computational

3

processing from raw sequence reads to deduced species observation, will enable reanalysis when
improved methods appear.
DNA-derived occurrence data of species should be as standardized and reproducible as possible,
regardless of whether or not the detected species have formal scientific names. In some cases, suchoccurrence records will hint at previously unknown geographical and ecological properties ofdescribed species, thus enriching our body of knowledge on these taxa. In other cases, the data may
allow us to amalgamate and visualize information on currently undescribed species, potentiallyspeeding up their eventual formal description. The ability to collect usable data even for unnamed
species adds significantly to the many ways in which GBIF and other biodiversity data platforms
index the living world, and make this knowledge available to all and for a variety of purposes,
including biodiversity conservation. Recent estimates suggest that at least 85 per cent of all extant
species are undescribed (Mora et al. 2011; Tedesco et al. 2014). Existing data standards have been
designed for the minority of taxa that have been described. Good practices for dealing with DNAderived data will help to characterize occurrences of all organisms, whether described or not.
This guide sets out the ways in which DNA-derived occurrence data should be reported forstandardized inclusion in GBIF and other biodiversity data platforms. It does not express any view on
the issue of access and benefit sharing for digital sequence information, the subject of extensive
discussion through the Convention on Biological Diversity (CBD). However, it is worth noting thatgenetic barcodes and metabarcodes are typically genes or non-coding DNA fragments, which are notsuitable for commercial exploitation. As the archiving of sequences through International
Nucleotide Sequence Database Collaboration (INDSC) is a widespread norm in sequence-based
research, publication of occurrence data originating from sequences does not involve publishing
new sequences. In most cases these have already been placed in a public genetic repository. This
guide therefore addresses the added value possible from deriving spatio-temporal occurrence data
and dna-based names from dna data, rather than the value of the genetic information itself. In
addition to dealing with sequence-derived data, this guide also includes suggestions for publishingoccurrence data of species derived from qPCR or ddPCR analyses.
Reporting DNA-derived occurrences in an open and reproducible way brings many benefits: notably,
it increases citability, highlights the taxa concerned in the context of biological conservation andcontributes to taxonomic and ecological knowledge. Additionally, it also provides a mechanism tostore occurrence records of undescribed species. When this yet to be described taxon is finally linked
to a new Linnaean name, all these linked occurrence records will be immediately available. Each ofthese benefits provides a strong rationale for professionals to adopt the practices outlined in thisguide, helping them to highlight a significant proportion of extant biodiversity, hasten its discovery
and integrate it into biological conservation and policy-making.

1.2. Target audiences
This guide has been developed for multiple target audiences: students planning a first DNA-based
study, researchers with old sequences and abundance tables they want to revive or preserve,
biodiversity data specialists who are new to DNA-derived occurrences, and bioinformaticians familiar
with sequence data but new to biodiversity data platforms. The guide does not directly target usersof molecular data in biodiversity data platforms, but such users may find section 1.7 on Outputs
particularly interesting. The authors' intention is to provide guidance on publishing data andassociated attributes from genetic sequence through general biodiversity data platforms.

4

The flowchart outlines the processing steps involved in publishing amplicon-derived molecularbiodiversity data in repositories such as GBIF and national biodiversity data platforms, including
those built on the ALA platform. This guide’s focus is primarily on the steps following the arrival of
raw FASTQ sequences from the sequencing step. By familiarizing themselves with theflowchart—and noting any steps that appear familiar or unclear—users will be able to navigate thecontent included in the guide.

Figure 1. Overall workflow for DNA sequence-derived biodiversity data as described in this guide.

We have done our best to present the information in this guide so that it is useful for each of theaudiences described above, but background reading (e.g. GBIF quick guide to data publishing) may
be required in certain cases.

1.3. Introduction to DNA-derived occurrence data
DNA-derived biological occurrence data include information derived from DNA from individualorganisms, but also from environmental DNA (eDNA, i.e. DNA extracted from environmental
samples, Thomsen & Willerslev 2015) and from bulk samples comprising many individuals (e.g.
plankton samples or Malaise trap samples consisting of multiple individuals from many species).
Currently, the greatest volume of DNA-derived occurrence data derives from eDNA. Since analyticalmethods and end products are largely similar for all sample sources, the discussion below will focus
on eDNA (§ 2.1.1 and § 2.1.2), noting that the outline is applicable to the other sources. Surveys often
utilize targeted sequencing of taxonomically and phylogenetically informative genetic markers, but
can also use, for example, qPCR-based approaches that do not directly result in DNA sequence data(§ 2.1.3 and § 2.2.2). This guide may appear heavy in DNA related terms; if this is the case, consult the
Glossary before reading further.

51.3.1. Environmental DNA as a source for DNA-derived occurrence data
Environmental DNA has been in use as a term since 1987, when it was first used to describe DNA
from microbes in sediment samples (Ogram et al. 1987). eDNA is now more broadly used to describe
a complex mix of DNA from different organisms (Taberlet et al. 2018 and 2012). Thus, eDNA includes
all DNA extracted from a specific environmental sample, regardless of substrate and which species it
contains. It may be extracted from a wide range of sources, including skin and hair cells, saliva, soil,
faeces, and from living or recently dead organisms (Pietramellara et al. 2009). Environmental DNA
often sufficiently represents all organisms in a given sample. In practice, however, the presence of
DNA in the environmental sample depends on the habitat selection, the body size, morphology andactivity level of the organism, and the sampling methods used to capture it (Taberlet et al. 2018) as
well as the stage of DNA degradation.

Figure 2. Caricature of sampling processes comparing data collection by A) traditional ecological/biodiversity
sampling methods, and B) eDNA-based studies, here exemplified by metabarcoding. This is a simplified
representation. For eDNA, most of the steps up to sequencing will involve technical or biological replications to
identify contamination and false positives as well as false negative results, making the structure of data andmetadata hierarchical. However, studies will often include both types of sampling. For example, if the 'Reference
Library' used in B) does not contain all relevant species from a given group of organisms, it will be necessary to
go back to A). It may also be that 'Resolving against Reference Library' produced unexpected or unlikely results,
in which case further studies using traditional methodology will be required to determine whether the species
identified by bioinformatic analysis can be verified.

eDNA is thus a sample type, not a method, including DNA derived from any environmental sample
rather than from the capture and sequencing of a targeted individual. Such sample types includes
water, soil, sediment and air, but also gut content samples and tissue (plant/animal) where the hostDNA is not targeted (Taberlet et al. 2018). A number of analytical methods exist for studying
environmental DNA. These can be divided into two main classes: 1) those which aim to detect a
specific organism and 2) those which describe an assemblage or a community of a range oforganisms. Different methods of analysis will generate different types and volumes of data. Mostoften DNA concentrations are low, and technical and biological replicates should be used to validate
species detection.
Several studies show that, for water samples, analyses based on eDNA may have a higher probabilityof finding rare and hard to survey species than conventional methods (Thomsen et al. 2012; Biggs et
al. 2015; Valentini et al. 2016; Bessey et al. 2020). The same may be true in other environments.
Therefore, eDNA may be suitable for monitoring rare red list species and undesirable alien species
that often have low densities and that are difficult to detect with conventional methods because6sometimes DNA traces can still be detected, although the actual organism is no longer present there.
Environmental DNA methods are able to detect cryptic organisms, especially those that are small
and unable to be detected by the naked eye (e.g. bacteria and fungi). In addition, eDNA can also be
used for observation of many species simultaneously, and may describe entire biologicalcommunities or major components of them (Ekrem & Majaneva 2019).
Some studies show a relationship between the amount of DNA for a given species in anenvironmental sample and the biomass of the species in the environment. One can therefore
potentially also think of environmental DNA allowing a so-called semi-quantitative estimate (indirecttarget) for organism biomass, both from environmental samples and bulk samples (Takahara et al.
2012; Thomsen et al. 2012; Andersen et al. 2012; Ovaskainen et al. 2013; Lacoursière-Roussel et al.
2016; Thomsen et al. 2016; Valentini et al. 2016; Fossøy et al. 2019; Yates et al. 2019; Doi et al. 2017).
However, other studies show little correlation between environmental DNA quantity and estimatedpopulation density (Knudsen et al. 2019). PCR, quantification, mixing and other biases are frequently
debated. For example, moult, reproduction and mass death can contribute to increased levels ofcrustacean environmental DNA in water, while turbidity and poor water quality reduce the amount ofdetectable environmental DNA (Strand et al. 2019). Therefore we encourage data publishers to
supply both read counts per ASV as well as total read count in samples, as this is necessary
information for users to make their own conclusions on presence/absence and abundance.

1.3.2. DNA-metabarcoding: sequence-derived data
The generation of sequence-derived data is currently increasing fast due to the development of DNAmetabarcoding. This method utilizes general primers to generate thousands to millions of short
DNA-sequences for a given group of organisms with the help of high-throughput sequencing (HTS,
alt. next-generation sequencing (NGS)). By comparing each DNA-sequence to a reference databasesuch as GenBank (Benson et al. 2006 ), BOLD (Ratnasingham et al. 2007), or UNITE (Nilsson et al.
2009), each sequence can be assigned to a species or higher rank taxon identity. DNAmetabarcoding is used for samples originating from both terrestrial and aquatic environments,
including water, soil, sediments, biofilms, plankton, bulk samples and faces, simultaneously
identifying hundreds of species (Ruppert et al. 2019).
The identification and classification of organisms from sequence data and marker-based surveys
depends on access to a reference library of sequences taken from morphologically identified
specimens that are matched against the newly generated sequences. The efficacy of classification
depends on the completeness (coverage) and the reliability of reference libraries, as well as the tools
used to carry out the classification. These are all moving targets, making it essential to apply
taxonomic expertise and caution in the interpreting results (§ 1.6). Availability of of all verified
amplicon sequence variants (Сallahan et al. 2017) allow for precise reinterpretation of data, intraspecific population genetic analyses (Sigsgaard et al. 2019) and is likely to increase identification
accuracy.

1.3.3. Metagenomic: sequence-derived data
Sequence derived diversity data may also be generated using amplification free metagenomic
methods whereby all DNA in a sample is targeted for sequencing (Tyson & Hugenholtz 2005), rather
than specific amplicons or barcodes, as described above. Sequence derived diversity data obtained
from metagenomic sequencing can be in the form of sequence matches to annotated gene7

databases (as above) or as (near) complete metagenome assembled genomes (MAGs). While
metabarcoding methods still dominate in terms of sequence derived diversity information,
metagenomic data is becoming more important, as evidenced by the growing number of MAGS andtheir utility in informing phylogeny and taxonomy (Parks et al. 2020); discussion of the rapidly
evolving methods associated with metagenome analysis is beyond the scope of this document. This
document uses metabarcoding as the model for discussion around concepts and methods for
publishing sequence derived diversity data, and while the bioinformatic pathways will differ for
metagenomic data, the end result (a sequence, often in the form of a contig/assembly) is congruent
with the concepts suggested for metabarcoding data (i.e., sample specific, sample collection, data
generation and processing workflow metadata should be captured).

1.3.4. qPCR/ddPCR: occurrence data
For the detection of specific species in eDNA-samples, most analyses include species-specific primersand qPCR (Quantitative Polymerase Chain Reaction) or ddPCR (Droplet-Digital Polymerase Chain
Reaction). These methods do not generate DNA-sequences, and the occurrence data are completelydependent on the specificity of the primers/assays. Hence, there are strict recommendations for how
to validate such assays and the requirements for publishing data (Bustin et al. 2009, Huggett et al.
2013), as well as the readiness for assays in routine monitoring (Thalinger et al. 2020). Analyses of
eDNA-samples using qPCR requires few resources and can be done in most DNA-laboratories. The
first example of using eDNA water samples utilized qPCR for detecting the invasive American
Bullfrog (Rana catesbeiana) (Ficetola et al. 2008), and qPCR analyses of eDNA water samples are
regularly used for detecting specific species of fish, amphibians, molluscs, crustaceans and more, as
well as their parasites (Hernandez et al. 2020, Wacker et al. 2019, Fossøy et al. 2019, Wittwer et al.
2019). eDNA-detections using qPCR thus generate important occurrence data for single species.

1.4. Introduction to biodiversity publishing
Publishing biodiversity data is largely a process of making species occurrence data findable,
accessible, interoperable and reusable, in accordance with the FAIR principles (Wilkinson et al. 2016).
Biodiversity data platforms help expose and discover genetic sequence data as biodiversity
occurrence records alongside other types of biodiversity data, such as museum collection specimens,
citizen science observations, and classical field surveys. The structure, management and storage foreach original data source will vary according to the needs of each community. The biodiversity data
platforms support data discovery, access and reuse by making these individual datasets compatible
with each other, addressing taxonomic, spatial and other inconsistencies in the available biodiversity
data. Making data available through single access points supports large-scale data-intensive
research, management, and policy. The compatibility between datasets is reached through theprocess of standardization.
A number of data standards are in use for general biodiversity data (https://www.gbif.org/
standards), and a separate set of standards for genetic sequence data (see MIxS and GGBN). This
guide reflects some ongoing efforts for increasing the compatibility between standards for general
biodiversity and genetic data. Standards often highlight the subsets of fields which are mostimportant or most frequently applicable. These subsets may be referenced as “cores”. The preferred
format for publishing data in the GBIF and ALA networks is currently the Darwin Core Archive (DwCA) using the Darwin Core (DwC) data standard. In practice, this is a compressed folder (a zip file)containing data files, in standard comma- or tab-delimited text format, a metadata file (eml.xml) that8

describes the data resource, and a metafile (meta.xml) that specifies the structure of files and data
fields included in the archive. Standardized packaging ensures that the data can travel between
systems using specific data exchange protocols. Section 2 of this guide provides recommendations
for the mapping of the data files, while guidelines and tools for constructing the xml files can befound here: TDWG, GBIF, and ALA.
A central part of the standardization process is the mapping of fields, which is required to transformthe original field (column) structure in a source-data export into a standard field structure.
Standardization may also affect the content of the individual fields within each record, for example,
by recalculating coordinates to a common system, rearranging date elements, or mapping thecontents of fields a standard set of values, often called a vocabulary. The process of standardization
also provides an opportunity to improve data quality, for example, by filling in omissions, correcting
typos and extra spaces and handling inconsistent use of fields. Such improvements enhance thequality of data and increase its suitability for reuse, but at the same time, data published in any state
are better than data that remain unpublished and inaccessible. Standartization is typically applied toa copy or to an export from the data source, leaving the original untouched.

Figure 3. Outline of a platform for reporting and publishing DNA sequences and associated metadata (greenbox) based on existing systems and data standards (grey boxes). An envisioned system for regular (based onmachine-to-machine reading of data) update of results (white box) can either read and update the Darwin Core
Archive or various administration systems. The data transfer between the various elements (black arrows) will
require various degrees of data transformation and harmonization and may include either mechanical or
human quality assessment.

Once a dataset has been through these standardization and data quality processes, it should beplaced in an accessible online location and associated with relevant metadata. Metadata–data or
information about the dataset includes key parameters that describe the dataset and further
improve its discoverability and reuse. Metadata should include other important elements such as
authorship, Digital Object Identifiers (DOIs), organizational affiliations and other provenanceinformation, as well as procedural and methodological information about how the dataset wascollected and curated. We encourage to provide a description of workflow details and versions
including quality control in the methods section in the EML file.

9

Datasets and their associated metadata are indexed by each data portal: this process enables users
to query, filter and process data through APIs and web portals. Unlike journal publications, datasets
may be dynamic products that go through multiple versions, with an evolving number of records andmutable metadata fields under the same title and DOI.
Note that holders of genetic sequence data are expected to upload and archive genetic sequence
data in raw sequence data repositories such as NCBI’s SRA, EMBL’s ENA or DDBJ. The sequencearchival topic is not covered here, but e.g. Penev et al. (2017) provide a general overview of theimportance of data submission and guidelines in connection with scientific publication. Biodiversity
data platforms such as ALA, GBIF, and most national biodiversity portals are not archives orrepositories for raw sequence reads and associated files. We do, however, stress the importance of
maintaining links between such primary data and derived occurrences in Section 2.

1.5. Processing workflows: from sample to ingestible
data
Metabarcoding data can be produced from a number of different sequencing platforms (Illumina,
PacBio, Oxford Nanopore, Ion Torrent, etc.) that rely on different principles for readout andgeneration of data that differ with respect to read length, error profile, whether sequences are single
or paired-end, etc. Currently the Illumina short-read platform is the most widely adopted and as suchis the basis of the descriptions here. However, the bioinformatics processing of the data follows thesame general principles (QC, denoising, classification) regardless of the sequencing technology used
(Hugerth et al. 2017, Figure 2).

10Figure 4. Outline of bioinformatic processing of metabarcoding data.

Typically, the DNA sequences are first pre-processed by removing primer sequences and, dependingon the sequencing method used, low quality bases, usually toward the 5’ and 3’ sequence ends.
Sequences not fulfilling requirements on length, overall quality, presence of primers, tags etc. areremoved.
The pre-processed sequences can then be assigned a taxon by comparing them against reference
databases. When reference databases are incomplete, sequences classification can be done without
taxonomic identifications, either by clustering sequences into operational taxonomic units based on
their similarity (OTUs; Blaxter et al. 2005) or by denoising the data, i.e. explicitly detecting andexcluding PCR/sequencing errors sequences to produce amplicon sequence variants (ASV; also
referred to as zero radius OTU (zOTU)). Denoising attempts to correct errors that have been
introduced in the PCR and/or sequencing steps, such that the denoised sequences are the set of
unique biologically real sequences present in the original sequence mixture. In case of paired-end
sequences, the forward and reverse sequences may be denoised separately before merging or else
merged prior to denoising. ASVs in the resulting set can differ by as little as one base which isindicative of inter- or intraspecific species variation. Operationally, ASVs may be thought of as OTUs
without defined radius and while denoising algorithms are typically very good, they do not entirely
remove the problems of over-splitting or lumping sequences.
The PCR used for generating the sequencing library can result in the generation of artefactual
sequences in the form of chimeras; a single sequence that originates from multiple parent
sequences. Such sequences can be detected bioinformatically and removed, and this is typically done
11

after OTU clustering or denoising.
Finally, the pre-processed sequences, OTUs or ASVs, are taxonomically classified by comparing them
to a database of annotated sequences (often referred to as reference libraries, see § 1.6). As with theprevious steps, several alternative methods are available. Most of these are either based on aligning
the metabarcoding sequences to the reference sequences or on counting shared k-mers (short exact
sequences).
Several open source tools and algorithms exist for bioinformatic processing of metabarcoding data
(QIIME2 (Bolyen et al. 2019), DADA2 (Callahan et al. 2016), SWARM (Mahé et al. 2014), USEARCH(Edgar 2010), Mothur (Schloss et al. 2009), LULU (Frøslev et al. 2017), PROTAX (Somervuo et al.
2016)). Given the existence of many popular and well used workflows, we make somerecommendations below on analysing data for submission to biodiversity data platforms. This is not
to suggest that these are the best methods or most appropriate for all purposes but is an attempt toencourage submission of relatively standardized data that may readily be compared via theplatforms. If possible, a well documented and maintained workflow should be used (e.g. nfcore/ampliseq pipeline). Metadata should include workflow details and versions either in themetadata method steps or as a reference in the SOP field in the DNA derived data extension (seemapping in Table 4). Sequence data should be deposited in an appropriate nucleotide archive(NCBI’s SRA: Leinonen et al. 2011) or EMBL’s ENA (Amid et al. 2020)) and data submitted to thebiodiversity platform should include the biosample ID obtained from the archive (see data mappingin § 2.2). Making use of these sample IDs will reduce the chances of duplication and ensure sequence
data are readily obtainable should opportunities for re-analysis arise, as reference libraries andbioinformatic tools improve. The core end-product of these pipelines is typically a file of counts ofindividual OTUs or ASVs in each sample along with the taxonomy assigned to these. This is
generated either in tabular format or in the BIOM format (McDonald et.al 2012). OTU or ASV
sequences are also usually provided in the FASTA format (Pearson & Lipman 1988).

1.6. Taxonomy of sequences
Taxonomic annotation of sequences is a critical step in the processing of molecular biodiversity
datasets, as scientific names are key to accessing and communicating information about theobserved organisms. The accuracy and precision of such sequence annotation will depend on theavailability of reliable reference databases and libraries across all branches of the tree of life, which
in turn will require joint efforts from taxonomists and molecular ecologists. Public sequence
databases should always be used knowingly of the fact that they suffer from various shortcomings
related to, e.g., taxonomic reliability and lack of standardized metadata vocabularies (Hofstetter et
al. 2019; Durkin et al. 2020).
Species, as described by taxonomists, are central to biology and attempts at characterizing
biodiversity may therefore make use of the end products of taxonomic research. However, unlike
DNA sequence data, taxonomic outputs are not always readily amenable to direct algorithmic orcomputational interpretation: classical taxonomy is a human-driven process which includes manualsteps of taxon delimitation, description and naming, culminating in a formal publication inaccordance to the international Codes of Nomenclature. As discussed in previous chapters, DNA
sequence-based surveys are very effective at detecting hard to observe species and will often identify
the presence of organisms currently outside traditional Linnaean taxonomic knowledge. While theseguidelines do not address the publication of alternative species checklists derived from sequence12

data, the disconnection between traditional taxonomy and eDNA efforts is undesirable. Therefore weoffer the following recommendations to readers of this guide.
As taxonomy is central to the discovery of biodiversity data, it is highly recommended that any eDNA
sequencing efforts should seek to include relevant taxonomic expertise in their study. It will similarly
be beneficial if eDNA sequencing studies are able to allocate a portion of their budget to generation
and release of reference sequences from previously unsequenced type specimens or otherimportant reference material from the local herbarium, museum, or biological collection.
Taxonomists, too, can contribute towards this goal by always including relevant DNA sequences witheach new species description (Miralles et al. 2020) and by targeting the many novel biologicalentities unravelled by eDNA efforts (e.g. Tedersoo et al. 2017).
Most current biodiversity data platforms are organized around traditional name lists and taxonomic
indexes. As DNA sequence-derived occurrences are rapidly becoming a significant source of
biodiversity data, and as official taxonomy and nomenclature for such data lags, it is recommended
that data providers and platforms should continue to explore and include more flexible
representations of taxonomy into their taxonomic backbones. These new representations include
molecular reference databases (e.g., GTDB, BOLD, UNITE) that recognize sequence data as referencematerial for previously unclassified organisms. Additionally, we suggest other commonly usedmolecular databases (e.g., PR2, RDP, SILVA) should develop stable identifiers for taxa and makereference sequences available for those taxa, to allow their use as taxonomic references.
In contrast to classical taxonomy, which is a heavily manual process, clustering DNA sequences into
taxonomic concepts relies on algorithmic analysis of similarity and other signals (such as phylogeny
and probability), as well as some human editing. The resulting OTUs vary in stability, presence ofreference sequences and physical material, alignments and cut-off values, and OTU identifiers suchas DOIs (Nilsson et al. 2019). Even more importantly, they vary in scale, from local study- or projectspecific libraries to global databases that enable broader cross-study comparison. In contrast to thecentralization and codification of Linnaean taxa that are formally described in research publications,
OTUs are distributed across multiple evolving digital reference libraries that differ in taxonomic
focus, barcode genes and other factors. By associating standard sequences with identified reference
specimens, BOLD and UNITE are establishing an essential mapping layer for linking ASVs and OTUs
with the foundations of the Linnaean taxonomic framework. The GBIF backbone taxonomy includes
identifiers for UNITE Species Hypotheses as well as Barcode index numbers which allows indexing ofspecies occurrence data taxonomically annotated at the OTU level for Fungi and Animals (GBIF
secretariat 2018, Grosjean 2019).
Algorithms for taxonomic annotation of eDNA will typically assign each unique sequence to thenearest taxonomic group in a reference set, based on some criteria for relatedness and confidence.
For poorly known groups of organisms, such as prokaryotes, insects and fungi, the annotation may
be a non-Linnaean placeholder name for a (cluster-based) taxon, and this taxon may represent a
species or even a taxonomic unit above the species level. No reference database contains all species
in a given group due to the many unknown, unidentified, and yet undescribed species on earth.
Frequent neglections of this fact has been the source of numerous taxonomic misidentifications
during the last 30 years.
During import into the biodiversity platform, the taxonomic resolution for these occurrences may be
reduced even further, as the reference set used for annotation may not be included in the taxonomic
index of that platform. Records assigned to a well defined cluster within a genus are likely to be13

treated as undefined records of that genus. The inclusion of the underlying sequence within or byreference in each record will allow future users to potentially identify the organism to a greater levelof granularity, particularly as reference libraries improve over time. In cases where the underlying
sequence cannot be included as part of the submitted data, we advocate deposition of a (scientific orplaceholder) name of the taxon plus an MD5 checksum of the sequence as a unique taxon ID (see §2.2, “Data mapping”). MD5 checksums are unidirectional hash algorithms commonly used forverifying file integrity (ref). In this case, they would provide a unique and repeatable representationof the original sequence that would nevertheless not allow the sequence itself to be recovered. Thismay be required in cases where sensitivity exists around access. MD5 checksums enable efficientquery to determine whether the same exact sequence has been recovered in other eDNA efforts, but
it is not a complete replacement of the sequence as MD5s do not enable further analyses. Twosequences differing by even a single base will get two completely different MD5 checksums, suchthat BLAST-style sequence similarity searches will not work.

1.7. Outputs
The purpose of exposing DNA-derived data through biodiversity platforms is to enable reuse of
these data alongside other biodiversity data types. It is very important to keep this reuse in mind
when preparing your data for publication. Ideally, the metadata and data should tell a complete
story in such a way that new, uninformed users can use this evidence without any additional
consultations or correspondence. Biodiversity data platforms provide search, filtering, browsing,visualizations, data access, and data citation functionality. For metabarcoding data we encourage
users

to

configurefiltersfororganismQuantity,organismQuantityType,sampleSizeValue,

sampleSizeUnit. These can be used exclude singletons and/or set thresholds for relative number ofsequence reads within a sample (§ 2.2.1). Users can often choose data-output formats (e.g. DwC-A,
CSV) and then process, clean and transform data into the shape and format needed for the analyses.
At GBIF.org or through the GBIF API, registered users can search, filter, and download biodiversity
data in the following three formats:• Simple: a simple, tab-delimited format which includes only the GBIF-interpreted version of thedata, as a result of the indexing process. This is suitable for quick tests and direct import into
spreadsheets.
• Darwin Core Archive: richer format that includes both the interpreted data and the original
verbatim version provided by the publisher (prior to indexing and interpretation by GBIF).
Because it includes all the metadata and issue flags, this format provides a richer view of thedownloaded dataset.
• Species list: a simple table format that includes only an interpreted list of unique species names
from a dataset or query result.
Regardless of the selected format, each GBIF user download receives a reusable link to the queryand a data citation that includes a DOI. This DOI-based citation system provides the means ofrecognizing and crediting uses to datasets and data originators, improving both the credibility andtransparency of the findings based on the data. It is essential to follow data citation
recommendations and use DOIs, as good data citation culture is not only the academic norm, butalso a powerful mechanism for crediting acknowledging and, therefore, incentivizing data
publishers.

14

2. Data packaging and mappingThis chapter focuses on practical details on turning your data export into a dataset indexed by abiodiversity data platform. § 2.1 will help you understand what is the optimal mapping schema foryour data at hand. § 2.2 describes these mappings in detail.
This guide combines the standards for general biodiversity data publishing with genetic DNA-derived
biodiversity data (Figure 5). This “do-section” stops at providing mapping recommendations fordifferent types of DNA-derived data.
Data packaging and publishing pathways vary from platform to platform and are described ingeneral documentation. One of the widespread ways to package data files is currently DwC-A, wheredata tables are arranged in a star schema, with records (rows) in peripheral extension files pointing
to a single record in the central core file (Figure 5). The different types of core files (e.g. occurrence
and sampling-event) correspond to different classes of datasets. Although DNA-derived datasets
often are event-based in nature, i.e. hundreds or even thousands of quantified sequenceoccurrences may derive from a single sampling event and thus share most metadata attributes, thecurrent recommendation is to publish data as Occurrence core (Category I or II) with the DNA
derived data extension. This approach compensates for limitations of the DwC star schema, which
would not allow any occurrence-level data in extension files (such as processed barcode sequences)to point to records in an event core file. We do, however, recommend including an eventID for eachcore record, to indicate the association between occurrences derived from the same sampling event.

Figure 5. Zoom in of DwC-A / IPT from figure 3 in chapter 1.2. The choice of core entity is mainly a matter offitting data to the data import mechanism (ingestion) of the biodiversity data platforms. Most data could beformulated as either Occurrence, Event or Taxon core, but as only the core can have extensions, this will affectthe choice. It is for example not possible to extend occurrences with DNA sequences if data are packaged using
Event core.

2.1. Categorization of your data
For the purpose of this guide, we categorize data into five categories, linked by a key ID field (15eventID), equivalent to the standards for general biodiversity data, and include fields relevant for
DNA-derived data (see § 2.2, “Data mapping”). These five categories seek to reflect the mostcommonly used molecular approaches to biodiversity characterization and are I) DNA-derived
occurrences, II) enriched occurrences, III) targeted species detection, IV) name references and V)
metadata only. Examine the decision tree and proceed to the correct section below.
Table 1. A decision tree for DNA-derived data categorization.

 Is your data (meta)barcoding or qPCR based?
(Meta)barcoding

qPCR

↓

↓

 Does data consist of digitized genetic material, or sequences, Category III
associated with location and time?

Targeted species detection

Yes

No

↓

↓

 Is the genetic material the only
evidence of a given organism orcommunity?

 Is the dataset a list of DNAbased names?

Yes

No

Yes

No

↓

↓

↓

↓

Category I

Category II

Category IV

Category V

DNA-based

Enriched

Name

Metadata-only

occurrences

occurrencesreferences

16Category II
Category V

Category III

Category I

Category IV
Figure 6. Visual representation of categories I-V.

2.1.1. Category I: DNA-derived occurrences
This category concerns data where a DNA sequence or detection through PCR is the only evidence
for the presence of a given organism or community. In other words, the data cannot be traced backto an observable specimen. This is the case for many metagenomics, metabarcoding and eDNA
studies.
Examples of DNA-derived occurrence datasets• MGnify (2019) Impact of rainforest transformation on phylogenetic and functional diversity of soilprokaryotic communities in Sumatra (Indonesia). Sampling event dataset https://doi.org/
10.15468/osp7hi accessed via GBIF.org on 2020-04-16.
• MGnify (2020) Marine metagenomes from the bioGEOTRACES project. Sampling event dataset
https://doi.org/10.15468/oifcho accessed via GBIF.org on 2020-04-16.
• Bessey C, Jarman SN, Berry O et al. (2020) Maximizing fish detection with eDNA metabarcoding.
Environmental DNA: 1–12. https://doi.org/10.1002/edn3.74 (Atlas of Living Australia website athttps://collections.ala.org.au/public/show/dr14581. Accessed 24 June 2020)
For guidance on how to format and share these datasets, see § 2.2.1. General guidelines for Darwin
Core occurrence datasets are also available through the DwC-A template for occurrence datasets
and Data quality requirements for occurrences.

2.1.2. Category II: Enriched occurrences
If some genetic material is, or can be, associated with an observation or a specimen, we will
categorize this type of data as “enriched occurrences”. In this context, the sequences are not the only
evidence of occurrences. One can always trace the information back to a vouchered specimen or17

observed organism. This category includes barcoding datasets and some DNA metabarcoding
datasets with reference material for example. For more guidance on barcoding, follow Centre for
Biodiversity Genomics, University of Guelph (2021).
Examples of Enriched occurrence datasets• The International Barcode of Life Consortium (2016) International Barcode of Life project (iBOL).
Occurrence dataset https://doi.org/10.15468/inygc6 accessed via GBIF.org on 2020-04-16.
• Takamura K (2019) Chironomid Specimen records in the Chironomid DNA Barcode Database.
Version 1.9. National Institute of Genetics, ROIS. Occurrence dataset https://doi.org/10.15468/
hxhow5 accessed via GBIF.org on 2020-04-16.
• Bessey C, Jarman SN, Stat M, Rohner CA, Bunce M, Koziol A, Power M, Rambahiniarison JM, Ponzo
A, Richardson AJ & Berry O (2019) DNA metabarcoding assays reveal a diverse prey assemblage
for Mobula rays in the Bohol Sea, Philippines. Ecology and Evolution 9 (5) 2459-2474.
https://doi.org/10.1002/ece3.4858,(Atlasof

LivingAustralia

websiteathttps://collections.ala.org.au/public/show/dr11663. Accessed 24 June 2020)
For guidance on how to format and share these datasets, see § 2.2.1. General guidelines for Darwin
Core occurrence datasets are also available through the DwC-A template for occurrence datasets
and Data quality requirements for occurrences.

2.1.3. Category III: Targeted species detection (qPCR/ddPCR)
This category concerns data where a specific (qPCR/ddPCR) assay is used to detect the presence (orabsence) of a DNA sequence specific to the target organism in an environmental sample. In this casethe occurrence record may not even contain sequence data, as it is the process itself that determines
the occurrence. With qPCR/ddPCR analyses for targeted species detection, many studies also reportabsence of that specific species for a given sample. Absence data is highly dependent on thedetection limit of the specific assay, as well as field and lab protocols. As for DNA-metabarcoding
data there is an issue of both false negatives and false positives, and it is important that sufficient
information is reported for evaluating the records.
Examples of targeted species occurrence datasets• Strzelecki, Joanna; Feng, Ming; Berry, Olly; Zhong, Liejun; Keesing, John; Fairclough, David;
Pearce, Alan; Slawinski, Dirk; Mortimer, Nick. Location and transport of early life stages ofWestern Australian Dhufish Glaucosoma hebraicum. Floreat, WA: Fisheries Research andDevelopment Corporation; 2013. http://hdl.handle.net/102.100.100/97533 (Atlas of Living
Australia website at https://collections.ala.org.au/public/show/dr8131. Accessed 22 July 2020)
For guidance on how to format and share these datasets, see § 2.2.2. General guidelines for Darwin
Core occurrence datasets are also available through the DwC-A template for occurrence datasets
and Data quality requirements for occurrences.

2.1.4. Category IV: Name references
This category corresponds to DNA-derived names, derived from clustering or denoising (errorcorrection based models), such as stable non-Linnaean Operational Taxonomic Units (OTU),

18

Amplicon Sequence Variant (ASV) and Barcode Index Numbers (BIN)—in other words, any reference
to taxa or provisional names that are defined outside of the Linnaean taxonomy. Numerous projects
produce local project- or study-specific libraries of OTUs, and although it is technically possible topublish these as checklists, they have limited to no value for data linking or interpretation; as aresult, we do not encourage their publication through biodiversity data platforms. However, theinclusion of the widely adopted, stable, global, digitally referenceable OTUs into Linnaean taxonomic
backbones is critically important for indexing unnamed “dark” biodiversity. GBIF have accumulated
experience in integrating such large and global reference libraries of OTUs into the GBIF taxonomic
backbone, which allows the display of OTUs under the nearest parent taxon which has a Latin name(Figure 7).

Figure 7. OTUs from UNITE (fungi, above) and from BOLD (arthropods, below) are displayed in the GBIF
backbone taxonomy under their corresponding parent taxa which have Latin names. Multiple individuallyobserved occurrences of cryptic biodiversity become discoverable together with non-genetic evidence through asingle access point.

19

Examples of Name references checklists• The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL)
Barcode Index Numbers (BINs). Checklist dataset https://doi.org/10.15468/wvfqoi accessed via
GBIF.org on 2020-04-16.
• PlutoF (2019). UNITE - Unified system for the DNA based fungal species linked to theclassification. Version 1.2. Checklist dataset https://doi.org/10.15468/mkpcy3 accessed via
GBIF.org on 2020-04-16.
This guide does not provide mapping recommendations for global OTU checklists / reference
libraries (Category IV), and publishing referenceable (project- or study-specific) OTU libraries aschecklists is discouraged. For guidance on how to format and share OTU checklists, see the following
general Darwin Core guidelines in DwC-A template for checklists and Data quality requirements forchecklists. General guidelines for MIxS checklists. For advice on how to map global reference
libraries of OTUs for inclusion in the GBIF taxonomic backbone, contact the GBIF help desk.

2.1.5. Category V: Metadata-only datasets
Metadata are data about the data and is a description of the dataset in broad terms, such as authors,author affiliations, original research purpose of the dataset, DOI(s), taxonomic scope, temporal
scope, and geographical scope. Information regarding laboratory methods and general sequencing
methods is included in this category. This category includes datasets or collections that cannot bemade available online at the moment, e.g. undigitized work.
Examples of Metadata-only datasets• Collins E, Sweetlove M (2019). Arctic Ocean microbial metagenomes sampled aboard CGC Healy
during the 2015 GEOTRACES Arctic research cruise. SCAR - Microbial Antarctic Resource System.
Metadata dataset https://doi.org/10.15468/iljmun accessed via GBIF.org on 2020-04-16.
• Cary S C (2015). New Zealand Terrestrial Biocomplexity Survey. SCAR - Microbial Antarctic
Resource System. Metadata dataset https://doi.org/10.15468/xnzrhq accessed via GBIF.org on
2020-04-16.
Mapping recommendations for metadata-only DNA-derived datasets (Category V) is the same as forany other metadata-only datasets, and this guide does not provide any specific mapping
recommendations for metadata. Please follow general recommendations of biodiversity data portals,
paying attention to required and recommended metadata. Descriptions of field, lab, andbioinformatics steps should be as detailed as possible. Describing your methods as method steps in
the EML metadata makes them display on the dataset homepage in GBIF (https://www.gbif.org/
dataset/3b8c5ed8-b6c2-4264-ac52-a9d772d69e9f#methodology Frøslev T, Ejrnæs R (2018). BIOWIDEeDNA Fungi dataset. Danish Biodiversity Information Facility. Occurrence dataset https://doi.org/
10.15468/nesbvx accessed via GBIF.org on 2021-07-06). However, if a structured and possibly moredetailed method description is already published somewhere (e.g. at protocols.io or NEON protocols
collection), it is straightforward to provide a link through the MIxS SOP field (see § 2.2.1).

2.2. Data mapping
While core files store ubiquitous data on the 'what, where and when' of a record, extension files are20

used to describe the specifics of a certain type of observation. We propose using the DNA derived
data extension to complement occurrence data derived from either barcoding, metabarcoding(eDNA) or qPCR/ddPCR. The DNA derived data extension builds on the Minimum information
standards developed by the Genomic Standards Consortium (GSC) and applied by the ENA for
submission of eDNA sample metadata, for example. We are following and have contributed to theguidelines proposed by the Sustainable DwC-MIxS interoperability task group under TDWG. To
improve indexing and search we have opted to split some MIxS terms, for instance separating
forward and reverse primer sequences and names. Furthermore, some fields from the GGBN
standard and fields from the MIQE (minimum information for the publication of quantitative realtime PCR) guidelines for qPCR and ddPCR data have been included to make it applicable for a wide
range of DNA-derived data.
As a first step in preparing your data for publishing, you should make sure your field names / column
headers follow the Darwin Core data standard. In many cases this is straightforward, such asrenaming your lat or latitude field to decimalLatitude. However, the Darwin Core Standard is quite
flexible and some terms are used in different ways, depending on the type of data. An example ofthis are the fields organismQuantity and organismQuantityType, which could be used to describe thenumber of individuals, per cent biomass or a score on the Braun-Blanquet Scale, as well as thenumber of reads of an ASV within a sample. Therefore, we here provide tables of required andrecommended fields with descriptions and examples (Table 1, Table 2, Table 3 and Table 4).
Additional fields and extensions (such as extended Measurement or Fact (eMoF)) are applicable.
When a sequence is derived from an organism (e.g. a parasite, gut contents, epibiont etc.) the
observation may be linked to the observation of the host organism. This can be achieved using the(Resource Relation extension) of Darwin Core (e.g. https://www.gbif.org/species/143610775/
verbatim). Perhaps the most important recommendation is to use globally unique (when available)
and other permanent identifiers for as many data fields and parameters as possible (in all ID fields inthe tables below).

21

2.2.1. Mapping metabarcoding (eDNA) and barcoding data
This section provides mapping recommendations for Categories I and II.
Table 2. Recommended fields for Occurrence core for Metabarcoding data

Field nameExamplesDescription

RequiredbasisOfRecord

MaterialSample

The specific nature of the data record - a subtype of the dcterms:type. For DNA-

Requiredderived occurrences, (see Category I and Category III) use MaterialSample. For
enriched occurrences use PreservedSpecimen or LivingSpecimen as appropriate.

occurrenceID

urn:catalog:UWBM:

A unique identifier for the occurrence, allowing the same occurrence to be

Bird:89776

recognized across dataset versions as well as through data downloads and use. May

Required

be a global unique identifier or an identifier specific to the data set.

eventID

urn:uuid:a964765b- An identifier for the set of information associated with an Event (something thatHighly22c4-439a-jkgt-2

recommended

occurs at a place and time). May be a global unique identifier or an identifier specificto the data set.

eventDate

2020-01-05

Date when the event was recorded. Recommended best practice is to use a date that Required
conforms to ISO 8601-1:2019. For more information, check https://dwc.tdwg.org/
terms/#dwc:eventDate

recordedBy"Oliver P. Pearson | A list (concatenated and separated) of names of people, groups, or organizations
Anita K. Pearson"

Highlyresponsible for recording the original Occurrence. The recommended best practice is recommended
to separate the values with a vertical bar (' | '). Including information about theobserver improves the scientific reproducibility (Groom et al. 2020).

organismQuantity

33

Number of reads of this sequence variant in the sample

Highlyrecommended

organismQuantityTy DNA sequence
pe
reads

Should always be “DNA sequence reads”

sampleSizeValue

Total number of reads in the sample. This is important since it allows calculating theHighlyrelative abundance of the sequence variant within the sample.

recommended

22

1233890

Highlyrecommended

Field nameExamplesDescription

RequiredsampleSizeUnit

DNA sequence

Should always be “DNA sequence reads”

HighlymaterialSampleID

reads

recommended

https://www.ncbi.nl An identifier for the MaterialSample (as opposed to a particular digital record of theHighlym.nih.gov/

material sample). Use the biosample ID if one was obtained from a nucleotide

recommended

biosample/

archive. In the absence of a persistent global unique identifier, construct one from a15224856

combination of identifiers in the record that will most closely make thehttps://www.ebi.ac.

materialSampleID globally unique.

uk/ena/browser/
view/
SAMEA3724543
urn:uuid:a964805b33c2-439a-beaa6379ebbfcd03

samplingProtocol

UV light trap

The name of, reference to, or description of the method or protocol used during a

Recommended

sampling Event. https://dwc.tdwg.org/terms/#dwc:samplingProtocol

associatedSequence https://www.ncbi.nl A list (concatenated and separated) of identifiers (publication, global uniquesm.nih.gov/nuccore/ identifier, URI) of genetic sequence information associated with the Occurrence.
MK405371

Recommended

Could be used for linking to archived raw barcode reads and/or associated genome
sequences, e.g. in a public repository.

identificationRema RDP annotation
rks
confidence (atlowest specified

Specification of taxonomic identification process, ideally including data on applied

Recommended

algorithm and reference database, as well as on level of confidence in the resulting
identification.

taxon): 0.96, againstreference database:
GTDB

23

Field nameExamplesDescription

identificationRefe https://www.ebi.ac. A list (concatenated and separated) of references (publication, global uniquerencesuk/metagenomics/ identifier, URI) used in the Identification. Recommended best practice is to separate
pipelines/4.1
the values in a list with space vertical bar space ( | ).

RequiredRecommended

https://github.com/
terrimporter/
CO1ClassifierdecimalLatitude

60.545207

The geographic latitude (in decimal degrees, using the spatial reference system

Highlygiven in geodeticDatum) of the geographic centre of a Location. Positive values arerecommended

north of the Equator, negative values are south of it. Legal values lie between -90 and
90, inclusive.

decimalLongitude

24.174556

The geographic longitude (in decimal degrees, using the spatial reference system

Highlygiven in geodeticDatum) of the geographic centre of a Location. Positive values arerecommended

east of the Greenwich Meridian, negative values are west of it. Legal values lie
between -180 and 180, inclusive.

taxonID

ASV:7bdb57487bee0 For eDNA data, it is recommended to use an MD5 hash of the sequence and prepend Highly
22ba30c03c3e7ca50 it with “ASV:”. See also § 1.6.

recommended, ife1

DNA_sequence is
not provided

scientificName

Gadus morhua L.

Latin name of the closest known taxon (species or higher) or an OTU identifier from

1758,

BOLD or UNITE

Required

BOLD:ACF1143

kingdom

Animalia

Higher taxonomy

Highlyrecommended

phylumChordata

Higher taxonomy

Recommended

class

Actinopterygii

Higher taxonomy

Recommended

order

Gadiformes

Higher taxonomy

Recommended24

Field nameExamplesDescription

RequiredfamilyGadidae

Higher taxonomy

Recommended

genus

Gadus

Higher taxonomy

Recommended25

Table 3. Recommended fields from the DNA derived data extension (a selection) for metabarcoding data

Field nameExamplesDescription

DNA_sequence

TCTATCCTCAATTATA The DNA sequence (ASV). Taxonomic interpretation of the sequence depends on theRequiredHighlyGGTCATAATTCACCA technology and reference library available at the time of publication. Hence, the most recommended
TCAGTAGATTTAGGA objective taxonomic handle is the sequence which can be reinterpreted in the future.
ATTTTCTCTATTCATA
TTGCAGGTGTATCAT
CAATTATAGGATCAA
TTAATTTTATTGTAAC
AATTTTAAATATACATACAAAAACTCATTCA
TTAAACTTTTTACCATTATTTTCATGATCAG
TTCTAGTTACAGCAATTCTCCTTTTATTATC
ATTA

sop

https://www.protoc Standard operating procedures used in assembly and/or annotation of genomes,

Recommended

ols.io/view/emp-its- metagenomes or environmental sequences.
illumina-ampliconprotocol-pa7dihn

target_gene

A reference to a well documented protocol, e.g. using protocols.io

16S rRNA, 18S rRNA, Targeted gene or marker name for marker-based studies

Highlynif, amoA, rpo

recommended

target_subfragment V6, V9, ITSName of subfragment of a gene or markerImportant to e.g. identify special regions

Highlyon marker genes like the hypervariable V6 region of the 16S rRNA generecommended

pcr_primer_forward GGACTACHVGGGTW Forward PCR primer that was used to amplify the sequence of the targeted gene,
TCTAAT

locus or subfragment.

pcr_primer_reverse GGACTACHVGGGTW Reverse PCR primer that was used to amplify the sequence of the targeted gene,
TCTAAT26

locus or subfragment.

Highlyrecommended
Highlyrecommended

Field nameExamplesDescription

Required

pcr_primer_name_fo jgLCO1490
rward

Name of the forward PCR primer

Highlypcr_primer_name_re jgHCO2198
verse

Name of the reverse PCR primer

recommended
Highlyrecommended

pcr_primer_referen https://doi.org/
Reference for the primersce
10.1186/1742-9994-

Highlyrecommended

10-34

env_broad_scale

forest biome

Equivalent to env_biome in MIxS v4[ENVO:01000174]

In this field, report which major environmental system your sample or specimen

Recommended

came from. The systems identified should have a coarse spatial grain, to provide thegeneral environmental context of where the sampling was done (e.g. were you in thedesert or a rainforest?). We recommend using subclasses of ENVO’s biome class:
http://purl.obolibrary.org/obo/ENVO_00000428

env_local_scale

litter layer

Equivalent to env_feature in MIxS v4[ENVO:01000338]

In this field, report the entity or entities which are in your sample or specimen´s local

Recommended

vicinity and which you believe have significant causal influences on your sample orspecimen. Please use terms that are present in ENVO and which are of smaller
spatial grain than your entry for env_broad_scale.

env_medium

soil[ENVO:00001998 Equivalent to env_material in MIxS v4]

Recommended

In this field, report which environmental material or materials (pipe separated)
immediately surrounded your sample or specimen prior to sampling, using one ormore subclasses of ENVO´s environmental material class:
http://purl.obolibrary.org/obo/ENVO_00010483

lib_layout

PairedEquivalent to lib_const_meth in MIxS v4

Recommended

Specify whether to expect single, paired, or other configuration of reads

seq_meth

Illumina HiSeq 1500 Sequencing method used; e.g. Sanger, pyrosequencing, ABI-solid

Highlyrecommended27

Field nameExamplesDescription

Required

otu_class_appr"95% ANI;85% AF;

Cutoffs and approach used when clustering new UViGs in "species-level" OTUs. Note

Highlygreedy incremental that results from standard 95% ANI / 85% AF clustering should be provided alongside recommended
clustering"

OTUS defined from another set of thresholds, even if the latter are the ones primarily
used during the analysis

otu_seq_comp_appr
otu_db"blastn;2.6.0+;e-Tool and thresholds used to compare sequences when computing "species-level"

Highlyvalue cutoff: 0.001"

OTUs

recommended"NCBI Viral

Reference database (i.e. sequences not generated as part of the current study) used

HighlyRefSeq;83",

to cluster new genomes in "species-level" OTUs, if any

recommended"UNITE;8.2"

28

2.2.2. Mapping ddPCR / qPCR data
This section provides mapping recommendations for Category III.
Table 4. Recommended fields for Occurrence core for ddPCR/qPCR data

Field nameExamplesDescription

RequiredbasisOfRecord

MaterialSample

The specific nature of the data record - a subtype of the dcterms:type. For DNA-

Requiredderived occurrences (see Category I and Category III), use MaterialSample.

occurrenceStatus

Present, Absent

A statement about the presence or absence of a taxon at a location.

eventID

urn:uuid:a964765b- An identifier for the set of information associated with an Event (something thatHighly22c4-439a-jkgt-2

recommended

occurs at a place and time). May be a global unique identifier or an identifier specific

Requiredto the dataset.

eventDate

2020-01-05

Date when the event was recorded. Recommended best practice is to use a date that Required
conforms to ISO 8601-1:2019. For more information, check https://dwc.tdwg.org/
terms/#dwc:eventDate

recordedBy"Oliver P. Pearson | A list (concatenated and separated) of names of people, groups, or organizations
Anita K. Pearson"

Highlyresponsible for recording the original Occurrence. The recommended best practice is recommended
to separate the values with a vertical bar (' | '). Including information about theobserver improves the scientific reproducibility (Groom et al. 2020).

organismQuantity

50

Number of positive droplets/chambers in the sample

Highlyrecommended for
ddPCR, dPCR

organismQuantityTy ddPCR droplets
pedPCR chambers

The partition type

Highlyrecommended for
ddPCR, dPCR

sampleSizeValue

20000

The number of accepted partitions (n), e.g. meaning accepted droplets in ddPCR or

Highlychambers in dPCR.

recommended for
ddPCR, dPCR

29

Field nameExamplesDescription

RequiredsampleSizeUnit

ddPCR droplets

The partition type, should be equal to the value in organismQuantityType

HighlydPCR chambers

recommended for
ddPCR, dPCR

materialSampleID

https://www.ncbi.nl An identifier for the MaterialSample (as opposed to a particular digital record of theHighlym.nih.gov/

material sample). Use the biosample ID if one was obtained from a nucleotide

recommended

biosample/

archive. In the absence of a persistent global unique identifier, construct one from a15224856

combination of identifiers in the record that will most closely make theurn:uuid:a964805b-

materialSampleID globally unique.

33c2-439a-beaa6379ebbfcd03

samplingProtocol

UV light trap

The name of, reference to, or description of the method or protocol used during a

Recommended

sampling Event. https://dwc.tdwg.org/terms/#dwc:samplingProtocol

decimalLatitude

60.545207

The geographic latitude (in decimal degrees, using the spatial reference system

Highlygiven in geodeticDatum) of the geographic centre of a Location. Positive values arerecommended

north of the Equator, negative values are south of it. Legal values lie between -90 and
90, inclusive.

decimalLongitude

24.174556

The geographic longitude (in decimal degrees, using the spatial reference system

Highlygiven in geodeticDatum) of the geographic centre of a Location. Positive values arerecommended

east of the Greenwich Meridian, negative values are west of it. Legal values lie
between -180 and 180, inclusive.

scientificName

Gadus morhua L.

Latin name of the closest known taxon (species or higher) or an OTU identifier from

1758,

BOLD or UNITE

Required

BOLD:ACF1143

kingdom

Animalia

Higher taxonomy

Highlyrecommended

phylum30

Chordata

Higher taxonomy

Recommended

Field nameExamplesDescription

Requiredclass

Actinopterygii

Higher taxonomy

Recommended

order

Gadiformes

Higher taxonomy

Recommended

familyGadidae

Higher taxonomy

Recommended

genus

Gadus

Higher taxonomy

Recommended31

Table 5. Recommended fields from the DNA derived data extension (a selection) for ddPCR/qPCR data

Field nameExamplesDescription

Requiredsop

https://www.protoc Standard operating procedures used in assembly and/or annotation of genomes,

Highlyols.io/view/

metagenomes or environmental sequences.

recommended

protocol-for-dna-

A reference to a well documented protocol, e.g. using protocols.io

extraction-andquantitative-pcr-dvwie7ce
https://doi.org/
10.17504/
protocols.io.vwie7ceannealingTemp60

The reaction temperature during the annealing phase of PCR.

Required if
annealingTemp was
suppliedannealingTempUnit

Degrees Celsius

Highlyrecommended

pcr_cond

initialDescription of reaction conditions and components of PCR in the form of "initialdenaturation:94_3;a denaturation:94degC_1.5min; annealing=…"

Highlyrecommended

nnealing:50_1;elong
ation:72_1.5;final
elongation:72_10;35

probeReporter

FAM

Type of fluorophore (reporter) used. Probe anneals within amplified target DNA.

HighlyPolymerase activity degrades the probe that has annealed to the template, and therecommended

probe releases the fluorophore from it and breaks the proximity to the quencher,thus allowing fluorescence of the fluorophore.

32

Field nameExamplesDescription

RequiredprobeQuencher

NFQ-MGB

Type of quencher used. The quencher molecule quenches the fluorescence emitted

Highlyby the fluorophore when excited by the cycler’s light source as long as fluorophore

recommended

and the quencher are in proximity, quenching inhibits any fluorescence signals.

ampliconSize

83

The length of the amplicon in basepairs

Highlyrecommended

thresholdQuantific 0.3
ationCycle

Threshold for change in fluorescence signal between cycles

baselineValue

The number of cycles when fluorescence signal from the target amplification is

qPCR: Highly

below background fluorescence not originated from the real target amplification.

recommended

15

quantificationCycl 37.9450950622558
eqPCR: Highly
recommended

The number of cycles required for the fluorescent signal to cross a given value
threshold above the baseline. Quantification cycle (Cq), threshold cycle (Ct), crossing
point (Cp), and take-off point (TOP) refer to the same value from the real-time
instrument. Use of quantification cycle (Cq), is preferable according to the RDML
(Real-Time PCR Data Markup Language) data standard

automaticThreshold no
QuantificationCycle

Whether the threshold was set by instrument or manuallyautomaticBaselineV no
alue

Whether baseline value was set by instrument or manuallycontaminationAsses no
sment

Whether DNA or RNA contamination assessment was done or not

estimatedNumberOfC 10300
opies

Number of target molecules per µl. Mean copies per partition (?) can be calculatedusing the number of partitions (n) and the estimated copy number in the total
volume of all partitions (m) with a formula ?=m/n.

amplificationReact 22
ionVolume

PCR reaction volume

amplificationReact µl
ionVolumeUnit

Unit used for PCR reaction volume. Many of the instruments require preparation of amuch larger initial sample volume than is actually analysed.

33

Field nameExamplesDescription

pcr_analysis_softw BIO-RAD
are
QuantaSoft

The program used to analyse the d(d)PCR runs.

experimentalVariance

Multiple biological replicates are encouraged to assess total experimental variation.

Required

When single dPCR experiments are performed, a minimal estimate of variance due tocounting error alone must be calculated from the binomial (or suitable equivalent)
distribution.

target_gene

16S rRNA, 18S rRNA, Targeted gene or marker name for marker-based studies

Highlynif, amoA, rpo

recommended

target_subfragment V6, V9, ITSName of subfragment of a gene or markerImportant to identify, for example, special Highly
regions on marker genes like the hypervariable V6 region of the 16S rRNA gene

pcr_primer_forward GGACTACHVGGGTW Forward PCR primer that was used to amplify the sequence of the targeted gene,
TCTAAT

locus or subfragment.

pcr_primer_reverse GGACTACHVGGGTW Reverse PCR primer that was used to amplify the sequence of the targeted gene,
TCTAAT

recommended
Highlyrecommended
Highlylocus or subfragment.

recommended

pcr_primer_name_fo jgLCO1490
rward

Name of the forward PCR primer

Highlypcr_primer_name_re jgHCO2198
verse

Name of the reverse PCR primer

recommended
Highlyrecommended

pcr_primer_referen https://doi.org/
Reference for the primersce
10.1186/1742-9994-

Highlyrecommended

10-34

env_broad_scale

forest biome

Equivalent to env_biome in MIxS v4[ENVO:01000174]

In this field, report which major environmental system your sample or specimen
came from. The systems identified should have a coarse spatial grain, to provide thegeneral environmental context of where the sampling was done (e.g. were you in thedesert or a rainforest?). We recommend using subclasses of ENVO´s biome class:
http://purl.obolibrary.org/obo/ENVO_00000428

34

Recommended

Field nameExamplesDescription

Required

env_local_scale

litter layer

Equivalent to env_feature in MIxS v4

Recommended[ENVO:01000338]

In this field, report the entity or entities which are in your sample or specimen´s local
vicinity and which you believe have significant causal influences on your sample orspecimen. Please use terms that are present in ENVO and which are of smaller
spatial grain than your entry for env_broad_scale.

env_medium

soil

Equivalent to env_material in MIxS v4[ENVO:00001998]

In this field, report which environmental material or materials (pipe separated)

Recommended

immediately surrounded your sample or specimen prior to sampling, using one ormore subclasses of ENVO´s environmental material class:
http://purl.obolibrary.org/obo/ENVO_00010483

concentration

67.5

Concentration of DNA (weight ng/volume µl) see also http://terms.tdwg.org/wiki/

Recommended

ggbn:concentration

concentrationUnit

ng/µl

Unit used for concentration measurement see also http://terms.tdwg.org/wiki/

Recommended

ggbn:concentrationUnit

methodDeterminatio Nanodrop, Qubit
nConcentrationAndR
atios

Description of method used for concentration measurement see also

ratioOfAbsorbance2 1.89
60_230

Ratio of absorbance at 260 nm and 230 nm assessing DNA purity (mostly secondary

Recommended

http://terms.tdwg.org/wiki/ggbn:methodDeterminationConcentrationAndRatios
Recommended

measure, indicates mainly EDTA, carbohydrates, phenol), (DNA samples only). see
also http://terms.tdwg.org/wiki/ggbn:ratioOfAbsorbance260_230

ratioOfAbsorbance2 1.91
60_280

Ratio of absorbance at 280 nm and 230 nm assessing DNA purity (mostly secondary

Recommended

measure, indicates mainly EDTA, carbohydrates, phenol), (DNA samples only). see
also http://terms.tdwg.org/wiki/ggbn:ratioOfAbsorbance260_280

samp_collect_devic biopsy, niskinebottle, push core

The method or device employed for collecting the sample

Recommended35

Field nameExamplesDescription

Required

samp_mat_processfiltering of

Any processing applied to the sample during or after retrieving the sample from

Recommended

seawater, storing

environment. This field accepts OBI, for a browser of OBI (v 2018-02-12) terms pleasesamples in ethanol

see http://purl.bioontology.org/ontology/OBI

samp_size

5 litreAmount or size of sample (volume, mass or area) that was collected

Recommended

size_frac0-0.22 micrometer

Filtering pore size used in sample preparation

Recommended

pcr_primer_lod

51

The assay’s ability to detect the target at low levels

Highlyrecommended

pcr_primer_loq

184

The assay’s ability to quantify copy number at low levels

Highlyrecommended36

3. Future prospects
The present interest in exposing DNA-derived data through biodiversity data platforms is very high,and it is very likely that the demand will grow. Our aim is for the mapping recommendations
provided here to remain valid and evolve slowly, even as packaging and indexing by biodiversity data
platforms may develop more rapidly. The authors are aware of but did not yet consult the BOLD
Handbook, BIOM format and http://edamontology.org/page.
We suggest that data platforms such as ALA and GBIF work towards adopting data formats that
support more complex relational and hierarchical data. Examples could be the Frictionless Data
Format and the more domain-specific Biological Observation Matrix (BIOM) format. The latter isused by several bioinformatic tools (QIIME2, Mothur, USEARCH etc.), and hence could help
publishers skip a step in converting data into DwC-A format. A more flexible data format than thecurrent DwC star schema is crucial for allowing hierarchical sampling events and material samples aswell as attaching sequence data to individual occurrences within a sampling event.
Biodiversity data platforms will also need to enable researchers to easily include or exclude DNAderived occurrence data from their query results. The data formats suggested above could open
opportunities for a richer classification of the types of evidence on which a specific occurrence record
is based. However, for the time being there is a lack of an appropriate value in the BasisOfRecord
vocabulary for these data types. We suggest, as a pragmatic immediate solution, that theBasisOfRecord is extended with a value such as “DNA”, “DNA-derived”, or similar. As described above,
DNA-derived data may come from well-documented sampling or individual organisms, may bebacked by preserved physical material or not, and may result from genetic sequencing or other DNA
detection methods, such as qPCR. Biodiversity data platforms and TDWG should provide the meansof differentiating between these data types and their origins.
We also recommend that the data platforms index the actual sequences, or at least a MD5 checksumof these, to facilitate searches for ASVs across datasets. If ASVs are provided, MD5s should be
generated by the biodiversity discovery platforms; if ASVs are not provided, MD5s need to be
mandatory.
As mentioned in § 1.6 and § 2.1.4, we encourage the biodiversity data platforms to continue work on
adopting relevant molecular taxonomic reference databases into their taxonomic backbones.
Broader application of other methods and technologies, such as Oxford Nanopore, PacBio andshotgun sequencing, will likely trigger the need for adjustments to this guide to accommodate
specific new data and metadata fields.

Glossary
Atlas of Living Australia (ALA)The ALA is a web-based platform that pulls together Australian biodiversity data from multiple
sources, making it accessible and reusable to anyone (see https://www.ala.org.au/about-ala/).
The open infrastructure platform developed by the ALA is also used by several other countries fortheir own national biodiversity data platform (see https://living-atlases.gbif.org/).

37

Amplicon Sequence Variant (ASV)
Unique DNA sequence derived from high-throughput sequencing and denoising, and assumed torepresent a biologically real sequence variant. See also Operational Taxonomic Unit (OTU) and(Callahan et al. 2017).
Application Programming Interface (API)Set of protocols and tools for interaction and data transmission between different computer
applications.
Barcode Index Numbers (BINs)
Species-level Operational Taxonomic Units (OTUs) derived from clustering of the cytochrome coxidase I (COI) gene in animals. Each BIN is assigned a globally unique identifier, and is madeavailable in searchable database within the Barcode of Life Data System (BOLD).
Barcode of Life Data System (BOLD)
BOLD is the reference database maintained by the Centre for Biodiversity Genomics in Guelph on
behalf of the International Barcode of Life Consortium (IBOL). It hosts data on barcode reference
specimens and sequences for eukaryote species, particularly COI for animals, and maintains the
Barcode Index Number (BIN; Ratnasingham & Hebert 2013) system, identifiers for OTUs ofapproximately species rank, based on clusters of closely similar sequences.
Biodiversity data platform
General online resource to discover and access biodiversity data derived from various sources,
such as natural history collections, citizen science, ecology and monitoring projects, and genetic
sequences. Can be global (GBIF) or national (ALA).
Clustering
In taxonomic classification, the process of grouping organisms together according to somesimilarity criterion. See Operational Taxonomic Unit.
Community (bulk) DNA
DNA from bulk samples (e.g. plankton samples or Malaise trap samples consisting of several
individuals from many species). For the purpose of this guide, bulk sample DNA is included in the
eDNA concept.
Darwin Core Archive (DwC-A)
Compressed (ZIP) file format for exchange of biodiversity data compiled in accordance with theDarwin Core (DwC) standard. Essentially a self-contained set of interconnected CSV files and an
XML document describing included files and data columns, and their mutual relationships.
Darwin Core (DwC) standard
Standard for sharing and publishing biodiversity data, originating from the Biodiversity
Information Standards (TDWG) community. In principle, a set of terms used for describing
different entities of biodiversity observations, such as sampling events, occurrences and taxa.
Current Darwin Core terms are described in the Quick Reference Guide.
Data vocabulary
Set of preferred terms or concepts with specific, well-defined meanings and interrelationships,

38

facilitating data exchange and reuse.
ddPCR (droplet digital Polymerase Chain Reaction)
Droplet digital PCR. Method for measuring absolute amount of DNA (number of copies) of one
marker in a sample. See also qPCR.
Denoising
In metabarcoding, method for separation of true biological sequences (see ASVs) from spurious
sequence variants caused by PCR amplification and sequencing error.
Digital Object Identifier (DOI)
Long-lasting reference used to uniquely identify (and locate) digital information objects, such as a
biodiversity data set or a scientific publication.
DNA barcoding and metabarcoding (amplicon sequencing)Use of short, standardized DNA fragments to identify individual organisms via sequencing.
Metabarcoding combines barcoding with high-throughput DNA sequencing, using universal
primers to amplify and sequence large groups of organisms in eDNA samples.
DNA marker
A DNA fragment used as a marker of some property (e.g., taxonomic affiliation). May, but does
not have to, be a gene or a part of a gene.
DNA metabarcoding database
Database containing DNA sequences (DNA barcodes) from previously recovered or studiedorganisms. The reference sequences were ideally generated from individuals of described, wellstudied species-with the type specimen serving as the ideal-or higher taxonomic level (e.g., genus,
family), but may also stem from eDNA sequencing efforts. It is wise not to trust “reference
sequences” blindly.
DNA probe
A short, synthetic single-stranded DNA fragment with fluorescent labelling that binds to aselected region of target DNA (marker) during PCR. Increases specificity and can be used inaddition to primers in qPCR and ddPCR to detect and quantify a genetic marker.
European Bioinformatics Institute (EMBL-EBI)
Intergovernmental organization for bioinformatics research and services, part of the European
Molecular Biology Laboratory (EMBL), providing eg. (raw) sequence reads and assembly data via
the European Nucleotide Archive (ENA).
Environmental DNA (eDNA)
DNA from an environmental sample, e.g. soil, water, air or host organism. An often useddefinition is that environmental DNA is the genetic material (DNA) obtained from environmental
samples without any obvious evidence of biological source material (Thomsen and Willerslev
2015).
European Nucleotide Archive (ENA)
European repository for nucleotide sequences, covering raw sequencing data, sequence assembly39information and functional annotation. Includes the Sequence Read Archive (SRA), and ismaintained by the European Bioinformatics Institute (EMBL-EBI), as part of the International
Nucleotide Sequence Database Collaboration (INSDC).
FASTQ
Text-based standard for storing molecular sequences and associated quality measures derivingfrom High-throughput sequencing (HTS). For each sequence position, single ASCII-characters areused to represent base call (identified nucleotide) and score, respectively.
Global Biodiversity Information Facility (GBIF)International network and research infrastructure, mainly focused on mobilizing and providing
open access to global biodiversity data.
Global Genome Biodiversity Network (GGBN)International network of institutions concerned with efficient sharing and usage of genomic
biodiversity samples and associated metadata, e.g. promoting the Darwin Core-compatible GGBN
Data Standard.
Global Positioning System (GPS)Satellite navigation system operated by the United States Space Force.
High-throughput sequencing (HTS)
Different technologies for massively parallel sequencing, producing millions of DNA sequencereads from library preparations of genetic material, rather than targeting single amplicons as intraditional Sanger sequencing. Also called Next Generation Sequencing (NGS).
Ingestion
Process of importing data from heterogeneous sources, such as local databases, text files or
spreadsheets, to a common destination system, such as an online biodiversity data platform, forstorage and further analysis. Typically includes steps of extraction, transformation (cleaning) andloading (ETL).
Indexing
Organization of information in accordance with a specific schema or structure, making data easierto access and present.
International Nucleotide Sequence Database Collaboration (INSDC)
Joint effort of the DNA Databank of Japan (DDBJ), EMBL and NCBI to provide global public accessto nucleotide sequence data and associated information.
Metagenomics
PCR-free sequencing of random genomic fragments in a mixed sample.
Minimum Information about any (x) Sequence (MIxS) standard
Family of standards (checklists) for sequence metadata, developed by the Genomic Standards
Consortium (GSC).

40

molecular Operational Taxonomic Unit (mOTU)
See Operational Taxonomic Unit (OTU).
National Center for Biotechnology Information (NCBI)
Division of United States National Library of Medicine (NLM) housing important bioinformatics
resources, such as the GenBank database of DNA sequences, and the Sequence Read Archive(SRA) of high throughput sequencing data.
Next Generation Sequencing (NGS)
See High-throughput sequencing (HTS).
Occurrence
An existence of an Organism (sensu http://rs.tdwg.org/dwc/terms/Organism) at a particularplace at a particular time.
Operational Taxonomic Unit (OTU)
Cluster of organisms based on similarity in specific DNA marker sequence(s), used for taxonomic
classification. Includes, for example, Species Hypothesis in UNITE, and Barcode Index Numbers in
the Barcode of Life Data System (BOLD). Amplicon Sequence Variants (ASVs) may be consideredanalogous to zero radius OTUs (zOTUs).
Polymerase Chain Reaction (PCR)
Technique for fast amplification and detection of specific fragments of target DNA (or RNA)
sequences. Amplified regions are determined by the pair of PCR primers used in the reaction.
Pipeline
In bioinformatics, a set of algorithms or tools applied in a predefined workflow to process e.g.
High-throughput sequencing (HTS) data.
Primers (PCR primers)
Short, synthetic, single-stranded DNA fragments that bind to a selected region of target DNA(marker) to initiate replication during PCR. A pair of primers is necessary for the polymerase
enzyme to amplify the selected marker.
qPCR (quantitative Polymerase Chain Reaction)
Quantitative PCR. Method that measures relative DNA quantity of a marker in a sample. See also
ddPCR.
SampleMaterial (water, soil, gut content, etc) obtained for analysis.
Sequence alignment
Bioinformatic process of comparing and arranging two or more molecular (DNA, RNA or protein)sequences to detect similarities caused by e.g. evolutionary relatedness.
Species Hypothesis (SH)Species-level Operational Taxonomic Unit (OTU) as defined in the UNITE database and sequence
management environment, for Fungi.

41

Specimen
An individual animal, plant, fungus, etc. used as an example of its species or type for scientificstudy or display.
Sequence Read Archive (SRA)
Public repository of high throughput (NGS) sequencing data, with instances operated by the
National Center for Biotechnology Information (NCBI), the European Bioinformatics Institute(EMBL-EBI), and the DNA Data Bank of Japan (DDBJ). Includes both raw (non-denoised)sequencing output and sequence alignments. One of three components of the European
Nucleotide Archive (ENA), and previously known as the Short Read Archive.
Target-capture sequencing
Sequencing of DNA fragments isolated with hybridization probes.
UNITE
UNITE is a web-based sequence management environment centred on the eukaryotic nuclear
ribosomal ITS region. All public sequences are clustered into species hypotheses (SHs), which areassigned unique DOIs. An SH-matching service outputs various elements of information,
including what species are present in eDNA samples, whether these species are potentially
undescribed new species, other studies in which they were recovered, whether the species arealien to a region, and whether they are threatened. The DOIs are connected to the taxonomic
backbone of the PlutoF platform and GBIF, such that they are accompanied by a taxon namewhere available. The data used in UNITE are hosted and managed in PlutoF. Data are represented
through a range of standards, primarily Darwin Core, MIxS, and DMP Common Standard; partial
support is available for EML, MCL, and GGBN. PlutoF exports data primarily through the CSV andFASTA formats. PlutoF can also be used to publish data in GBIF (using the DwC format) and toprepare GenBank submission files. It is furthermore possible to download species lists from yourdata and download your project as a JSON document with project data in hierarchicallystructured.
Zero radius otu (zOTU)
See ASV.

42

References▪ Amid C, Alako BT, Balavenkataraman Kadhirvelu V, Burdett T, Burgin J, Fan J, Harrison PW, Holt S,
Hussein A, Ivanov E & Jayathilaka S (2020) The European Nucleotide Archive in 2019. Nucleic acids
research 48(D1): D70-D76. https://doi.org/10.1093/nar/gkz1063▪ Andersen K, Bird KL, Rasmussen M, Haile J, Breuning-Madsen H, Kjaer KH, Orlando L, Gilbert MTP
and Willerslev E (2012) Meta-Barcoding of ‘Dirt’ DNA from Soil Reflects Vertebrate Biodiversity.
Molecular Ecology 21(8): 1966–79. https://doi.org/10.1111/j.1365-294X.2011.05261.x▪ Benson DA, Karsch-Mizrachi I, Lipman DJ, Ostell J, Wheeler DL (2006) GenBank, Nucleic Acids
Research, 34(1): D16–D20, https://doi.org/10.1093/nar/gkj157▪ Bessey C, Jarman SN, Berry O et al. (2020) Maximizing fish detection with eDNA metabarcoding.
Environmental DNA: 1–12. https://doi.org/10.1002/edn3.74▪ Biggs J, Ewald N, Valentini A, Gaboriaud C, Dejean T, Griffiths RA, Foster J, et al. (2015) Using eDNA
to Develop a National Citizen Science-Based Monitoring Programme for the Great Crested Newt
(Triturus cristatus). Biological Conservation 183: 19–28. https://doi.org/10.1016/
j.biocon.2014.11.029▪ Blaxter M, Mann J, Chapman T, Thomas F, Whitton C, Floyd R & Abebe E (2005) Defining
operational taxonomic units using DNA barcode data. Philosophical Transactions of the Royal
Society B: Biological Sciences 360(1462): 1935-1943. https://doi.org/10.1098/rstb.2005.1725▪ Bolyen E, Rideout JR, Dillon MR et al. (2019) Reproducible, interactive, scalable and extensible
microbiome data science using QIIME 2. Nat Biotechnol 37: 852–857. https://doi.org/10.1038/
s41587-019-0209-9▪ Boussarie G, Bakker J, Wangensteen OS, Mariani S, Bonnin L, Juhel JB, Kiszka JJ, Kulbicki M, Manel
S, Robbins WD & Vigliola L (2018) Environmental DNA illuminates the dark diversity of sharks.
Science Advances 4(5): eaap9661. https://doi.org/10.1126/sciadv.aap9661▪ Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, … & Wittwer CT (2009). The
MIQE Guidelines: M inimum I nformation for Publication of Q uantitative Real-Time PCR E
xperiments. https://doi.org/10.1373/clinchem.2008.112797▪ Callahan B, McMurdie P & Holmes S (2017) Exact sequence variants should replace operationaltaxonomic units in marker-gene data analysis. The ISME Journal 11: 2639–2643. https://doi.org/
10.1038/ismej.2017.119▪ Callahan B, McMurdie P, Rosen M et al. (2016) DADA2: High-resolution sample inference from
Illumina amplicon data. Nat Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869▪ Centre for Biodiversity Genomics, University of Guelph (2021) The Global Taxonomy Initiative
2020: A Step-by-Step Guide for DNA Barcoding. Technical Series No. 94. Secretariat of theConvention on Biological Diversity, Montreal, 66 pp. https://www.cbd.int/doc/publications/cbdts-94-en.pdf▪ Convention on Biological Diversity (2020) Report of the ad hoc Technical Expert Group on Digital
Sequence Information On Genetic Resources, 17-20 March 2020. Montreal, Canada.
https://www.cbd.int/doc/c/ba60/7272/3260b5e396821d42bc21035a/dsi-ahteg-2020-01-07en.pdf▪ Debroas D, Domaizon I, Humbert JF, Jardillier L, Lepère C, Oudart A & Taïb N (2017) Overview of43freshwater microbial eukaryotes diversity: a first analysis of publicly available metabarcoding
data. FEMS Microbiology Ecology 93(4): fix023. https://doi.org/10.1093/femsec/fix023▪ Doi H, Fukaya K, Oka SI, Sato K, Kondoh M & Miya M (2019) Evaluation of Detection Probabilities
at the Water-Filtering and Initial PCR Steps in Environmental DNA Metabarcoding Using a
Multispecies Site Occupancy Model. Scientific Reports 9(1): 3581. https://doi.org/10.1038/s41598019-40233-1▪ Durkin L, Jansson T, Sanchez M, Khomich M, Ryberg M, Kristiansson E, Nilsson RH (2020) When
mycologists describe new species, not all relevant information is provided (clearly enough).
MycoKeys 72: 109-128. https://doi.org/10.3897/mycokeys.72.56691▪ Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST, Bioinformatics
26(19): 2460–2461, https://doi.org/10.1093/bioinformatics/btq461▪ Ekrem T & Majaneva M (2019) DNA-Metastrekkoding Til Undersøkelser Av Invertebrater I
Ferskvann. NTNU Vitenskapsmuseet Naturhistorisk Notat. https://ntnuopen.ntnu.no/ntnu-xmlui/handle/11250/2612638.
▪ Elbrecht V & Leese F (2015) Can DNA-based ecosystem assessments quantify species abundance?
Testing primer bias and biomass—sequence relationships with an innovative metabarcoding
protocol. PLoS ONE 10(7): e0130324. https://doi.org/10.1371/journal.pone.0130324▪ Ficetola GF, Miaud C, Pompanon F, & Taberlet P (2008). Species detection using environmental
DNA from water samples. Biology letters, 4(4), 423-425. https://doi.org/10.1098/rsbl.2008.0118▪ Fossøy F, Brandsegg H, Sivertsgård R, Pettersen O, Sandercock BK, Solem Ø, Hindar K & Tor AM(2019) Monitoring Presence and Abundance of Two Gyrodactylid Ectoparasites and Their
Salmonid Hosts Using Environmental DNA. Environmental DNA. https://doi.org/10.1002/edn3.45.
▪ Frøslev TG, Kjøller R, Bruun HH et al. (2017) Algorithm for post-clustering curation of DNA
amplicon data yields reliable biodiversity estimates. Nat Commun 8, 1188 . https://doi.org/
10.1038/s41467-017-01312-x▪ Groom Q, Güntsch A, Huybrechts P, Kearney N, Leachman S, Nicolson N, Page RDM, Shorthouse
DP, Thessen, AE, Haston E. People are essential to linking biodiversity data. 2020. Database2020:baaa072 https://doi.org/10.1093/database/baaa072.
▪ Hernandez C, Bougas B, Perreault‐Payette A, Simard A, Côté G, & Bernatchez L (2020). 60 specificeDNA qPCR assays to detect invasive, threatened, and exploited freshwater vertebrates andinvertebrates in Eastern Canada. Environmental DNA, 2(3): 373-386. https://doi.org/10.1002/
edn3.89▪ Hofstetter, V, Buyck, B, Eyssartier, G, Schnee S, Gindro K (2019) The unbearable lightness ofsequenced-based identification. Fungal Diversity 96, 243–284. https://doi.org/10.1007/s13225019-00428-3▪ Huggett JF, Foy CA, Benes V, Emslie K, Garson JA, Haynes R, … & Bustin SA (2013). The Digital
MIQE Guidelines: M inimum I nformation for Publication of Q uantitative Digital PCR E
xperiments. Clinical chemistry, 59(6), 892-902. https://doi.org/10.1373/clinchem.2013.206375▪ Hugerth LW, Andersson AF (2017) Analysing Microbial Community Composition throughAmplicon Sequencing: From Sampling to Hypothesis Testing. Frontiers in Microbiology 8: 1561.
https://doi.org/10.3389/fmicb.2017.01561▪ Knudsen SW, Ebert RB, Hesselsøe M, Kuntke F, Hassingboe J, Mortensen PB, Thomsen PF et al44

(2019) Species-Specific Detection and Quantification of Environmental DNA from Marine Fishes in
the Baltic Sea. Journal of Experimental Marine Biology and Ecology 510: 31–45. https://doi.org/
10.1016/j.jembe.2018.09.004▪ Lacoursière-Roussel A, Rosabal M & Bernatchez L (2016) Estimating Fish Abundance and Biomass
from eDNA Concentrations: Variability among Capture Methods and Environmental Conditions.
Molecular Ecology Resources 16(6): 1401–14. https://doi.org/10.1111/1755-0998.12522▪ Leebens-Mack J, Vision T, Brenner E, Bowers JE, Cannon S, Clement MJ, Cunningham CW,
DePamphilis C, DeSalle R, Doyle JJ & Eisen JA (2006) Taking the first steps towards a standard forreporting on phylogenies: Minimum Information About a Phylogenetic Analysis (MIAPA). Omics: ajournal of integrative biology 10(2): 231-237. https://doi.org/10.1089/omi.2006.10.231▪ Leinonen R, Sugawara H, Shumway M & International Nucleotide Sequence Database
Collaboration (2011) The sequence read archive. Nucleic Acids Research 39(suppl_1): D19-D21.
https://doi.org/10.1093/nar/gkq1019▪ Mahé F, Rognes T, Quince C, de Vargas C, Dunthorn M. (2014) Swarm: robust and fast clustering
method for amplicon-based studies. PeerJ 2:e593 https://doi.org/10.7717/peerj.593▪ McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J, Wendel D, … & Caporaso JG(2012). The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying andlove the ome-ome. Gigascience, 1(1), 2047-217X. https://doi.org/10.1186/2047-217X-1-7▪ Miralles A, Bruy T, Wolcott K, Scherz MD, Begerow D, Beszteri B, Bonkowski M, Felden J,
Gemeinholzer B, Glaw F & Glöckner FO (2020) Repositories for Taxonomic Data: Where We Are
and What is Missing. Systematic Biology: syaa026. https://doi.org/10.1093/sysbio/syaa026▪ Mora C, Tittensor DP, Adl S, Simpson AG & Worm B (2011) How many species are there on Earthand in the ocean? PLoS Biology 9(8): e1001127. https://doi.org/10.1371/journal.pbio.1001127▪ Nilsson RH, Tedersoo L, Abarenkov K, Ryberg M, Kristiansson E, Hartmann M, Schoch CL,
Nylander JA, Bergsten J, Porter TM & Jumpponen A (2012) Five simple guidelines for establishingbasic authenticity and reliability of newly generated fungal ITS sequences. MycoKeys 4: 37-63.
https://doi.org/10.3897/mycokeys.4.3606▪ Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard
K, Glöckner FO, Tedersoo L, Saar I, Kõljalg U, Abarenkov K (2019) The UNITE database formolecular identification of fungi: handling dark taxa and parallel taxonomic classifications.
Nucleic Acids Research, Volume 47, Issue D1, D259–D264. https://doi.org/10.1093/nar/gky1022▪ Ogram A, Sayler GS, Barkay T (1987) The Extraction and Purification of Microbial DNA from
Sediments. Journal of Microbiological Methods. https://doi.org/10.1016/0167-7012(87)90025-x.
▪ Ovaskainen O, Schigel D, Ali-Kovero H et al. (2013) Combining high-throughput sequencing withfruit body surveys reveals contrasting life-history strategies in fungi. The ISME Journal 7:
1696–1709. https://doi.org/10.1038/ismej.2013.61▪ Parks, DH, Chuvochina, M, Chaumeil, P, Rinke C, Mussig AJ, Hugenholtz P (2020) A complete
domain-to-species taxonomy for Bacteria and Archaea. Nat Biotechnol 38, 1079–1086.
https://doi.org/10.1038/s41587-020-0501-8▪ Pearson, WR & Lipman DJ (1988) Improved tools for biological sequence comparison.
Proceedings of the National Academy of Sciences 85(8): 2444-2448. https://dx.doi.org/
10.1073%2Fpnas.85.8.2444

45

▪ Penev P, Mietchen D, Chavan VS, Hagedorn G, Smith VS, Shotton D, Tuama ÉÓ, Senderov V,
Georgiev T, Stoev P, Groom QJ, Remsen D, Edmunds SC (2017) Strategies and guidelines forscholarly publishing of biodiversity data. Research ideas and outcomes 3: e12431,
https://doi.org/10.3897/rio.3.e12431▪ Pietramellara G, Ascher J, Borgogni F, Ceccherini MT, Guerri G & Nannipieri P (2009) Extracellular
DNA in Soil and Sediment: Fate and Ecological Relevance. Biology and Fertility of Soils 45: 219235. https://doi.org/10.1007/s00374-008-0345-8.
▪ Ratnasingham S, Hebert PDN (2007) BOLD: The Barcode of Life Data System. Molecular Ecology
Notes, 7: 355-364. https://doi.org/10.1111/j.1471-8286.2007.01678.x▪ Ratnasingham S, Hebert PDN (2013). A DNA-based registry for all animal species: the Barcode
Index Number (BIN) system. PloS one, 8(7), e66213. https://doi.org/10.1371/
journal.pone.0066213▪ Ruppert KM, Kline RJ, Rahman MS (2019). Past, present, and future perspectives of environmental
DNA (eDNA) metabarcoding: A systematic review in methods, monitoring, and applications ofglobal eDNA. Global Ecology and Conservation, 17, e00547. https://doi.org/10.1016/
j.gecco.2019.e00547▪ Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, … & Weber CF (2009).
Introducing mothur: open-source, platform-independent, community-supported software fordescribing and comparing microbial communities. Applied and environmental microbiology,
75(23), 7537-7541. https://doi.org/10.1128/AEM.01541-09▪ Sigsgaard EE, Jensen MR, Winkelmann IE, Møller PR, Hansen MM, Thomsen PF (2020).
Population‐level inferences from environmental DNA—Current status and future perspectives.
Evolutionary Applications, 13(2), 245-262. https://doi.org/10.1111/eva.12882▪ Somervuo P, Koskela S, Pennanen J, Nilsson RH, Ovaskainen O (2016) Unbiased probabilistic
taxonomic classification for DNA barcoding. Bioinformatics 32(19):2920–2927, https://doi.org/
10.1093/bioinformatics/btw346▪ Strand DA, Johnsen SI, Rusch JC, Agersnap S, Larsen WB, Knudsen SW, Møller PR & Vrålstad T(2019) Monitoring a Norwegian Freshwater Crayfish Tragedy: eDNA Snapshots of Invasion,
Infection and Extinction. Journal of Applied Ecology 56(7): 1661-1673. https://doi.org/10.1111/
1365-2664.13404.
▪ Taberlet P, Bonin A, Coissac E & Zinger L (2018) Environmental DNA: For Biodiversity Researchand Monitoring. Oxford, UK: Oxford University Press. https://doi.org/10.1093/oso/
9780198767220.001.0001▪ Taberlet P, Coissac E, Hajibabaei M & Rieseberg LH (2012) Environmental DNA. Molecular Ecology
21(8): 1789–93. https://doi.org/10.1111/j.1365-294X.2012.05542.x▪ Takahara T, Minamoto T, Yamanaka H, Doi H & Kawabata Z (2012) Estimation of Fish Biomass
Using Environmental DNA. PLoS ONE 7(4): e35868. https://doi.org/10.1371/journal.pone.0035868▪ Tedersoo, L, Bahram M, Puusepp R, Nilsson RH & James TY (2017) Novel soil-inhabiting clades fillgaps in the fungal tree of life. Microbiome 5: 42. https://doi.org/10.1186/s40168-017-0259-5▪ Tedesco PA, Bigorne R, Bogan AE, Giam X, Jézéquel C & Hugueny B (2014) Estimating how many
undescribed species have gone extinct. Conservation Biology 28(5): 1360-1370. https://doi.org/
10.1111/cobi.12285

46▪ Thalinger B, Deiner K, Harper LR, Rees HC, Blackman RC, Sint D, … & Bruce K (2021). A validation
scale to determine the readiness of environmental DNA assays for routine species monitoring.
Environmental DNA. https://doi.org/10.1101/2020.04.27.063990▪ Thomsen PF, Kielgast JOS, Iversen LL, Wiuf C, Rasmussen M, Gilbert MTP Orlando L & Willerslev E(2012) Monitoring Endangered Freshwater Biodiversity Using Environmental DNA. Molecular
Ecology 21(11): 2565–73. https://doi.org/10.1111/j.1365-294X.2011.05418.x▪ Thomsen PF, Møller PR, Sigsgaard EE, Knudsen SW, Jørgensen OA & Willerslev E (2016)
Environmental DNA from Seawater Samples Correlate with Trawl Catches of Subarctic,
Deepwater Fishes. PLoS ONE 11(11): e0165252. https://doi.org/10.1371/journal.pone.0165252▪ Thomsen PF & Willerslev E (2015) Environmental DNA – An Emerging Tool in Conservation for
Monitoring Past and Present Biodiversity. Biological Conservation 183: 4–18. https://doi.org/
10.1016/j.biocon.2014.11.019▪ Tyson, GW & Hugenholtz, P (2005). Environmental shotgun sequencing. Encyclopedia of genetics,
genomics, proteomics, and bioinformatics. Edited by Lynn B. Jorde. West Sussex, UK: John Wiley &Sons.1386-1391. https://doi.org/10.1002/047001153X.g205313▪ Valentini A, Taberlet P, Miaud C, Civade R, Herder J, Thomsen PF, Bellemain E et al. (2016) NextGeneration Monitoring of Aquatic Biodiversity Using Environmental DNA Metabarcoding.
Molecular Ecology 25(4): 929–42. https://doi.org/10.1111/mec.13428▪ Wacker S, Fossøy F, Larsen BM, Brandsegg H, Sivertsgård R, & Karlsson S (2019). Downstream
transport and seasonal variation in freshwater pearl mussel (Margaritifera margaritifera) eDNA
concentration. Environmental DNA, 1(1), 64-73. https://doi.org/10.1002/edn3.10▪ Wilkinson M, Dumontier M, Aalbersberg I et al. (2016) The FAIR Guiding Principles for scientific
data management and stewardship. Scientific Data 3: 160018. https://doi.org/10.1038/
sdata.2016.18▪ Wittwer C, Stoll S, Strand D, Vrålstad T, Nowak C, & Thines M (2018). eDNA-based crayfish plaguemonitoring is superior to conventional trap-based assessments in year-round detection
probability. Hydrobiologia, 807(1), 87-97. https://doi.org/10.1007/s10750-017-3408-8▪ Yates MC, Fraser DJ & Derry AM (2019) Meta‐analysis Supports Further Refinement of eDNA for
Monitoring Aquatic Species‐specific Abundance in Nature. Environmental DNA. https://doi.org/
10.1002/edn3.7.
▪ Yilmaz P, Kottmann R, Field D, Knight R, Cole JR, Amaral-Zettler L, Gilbert JA, Karsch-Mizrachi I,
Johnston A, Cochrane G & Vaughan R (2011) Minimum information about a marker genesequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications.
Nature Biotechnology 29(5): 415. https://doi.org/10.1038/nbt.1823

47