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\title{DNA barcoding and taxonomy: dark taxa and dark texts}


\author{Roderic Page}
\affil{Affiliation not available}



\date{\today}

\maketitle




\subsection*{Summary}\label{summary}

Both classical taxonomy and DNA barcoding are engaged in the task of
digitising the living world. Much of the taxonomic literature remains
undigitised. The rise of open access publishing this century, and the
freeing of older literature from the shackles of copyright has greatly
increased the online availability of taxonomic descriptions, but much of
the literature of the mid- to late twentieth century remains offline
(``dark texts''). DNA barcoding is generating a wealth of computable
data that in many ways is much easier to work with than classical
taxonomic descriptions, but many of the sequences are not identified to
species level. These ``dark taxa'' hamper the classical method of
integrating biodiversity data using shared taxonomic names. Voucher
specimens are a potential common currency of both the taxonomic
literature and sequence databases, and could be used to help link names,
literature, and sequences. An obstacle to this approach is the lack of
stable, resolvable specimen identifiers. The paper concludes with an
appeal for a global ``digital dashboard'' to assess the extent to which
biodiversity data is available online.

Keywords: DNA barcoding, taxonomy, dark taxa, dark texts, digitisation

\section*{Introduction}\label{introduction}

As with many fields, digitisation is having huge impact on the study of
biodiversity. Museums and herbaria are engaged with turning physical,
analogue specimens into digital objects, whether these are strings of
A's, G's, C's and T's from DNA sequencing machines, or pixels obtained
from a digital camera. Libraries and commercial publishers are
converting physical books and articles into images, which are then
converted into strings of letters using optical character recognition
(OCR). Despite the sometimes contentious relationship between
morphological and molecular taxonomy, there are striking parallels
between the formation of DNA sequence databases in the twentieth century
and the rise of natural history museums in the preceding centuries
\cite{Strasser_2008, Strasser_2011}.

Viewed in this way, both classical taxonomy and genomics are in the
business of digitising life. Some of the challenges faced are similar,
for example algorithms developed for pairwise sequence alignment have
applications in extracting articles from OCR text \cite{Page_2011}.
But in other respects the two fields are very different. Sequence data
is approximately doubling every 18 months \cite{citeulike:11981561}, whereas
the number of new taxa described each year has remained essentially
constant since the 1980s (see below). A challenge for sequence databases
is how to handle exponential growth of data; for taxonomy the challenge
is often how to make a dent in the vast number of objects that don't
have a digital representation \cite{Ari_o_2010}. This paper explores
some of these issues, focusing on taxonomy and DNA barcoding.

\section*{Taxonomy}\label{taxonomy}

Among the many challenges faced by taxonomy is the difficulty of
determining the size of the task it faces. Estimates of the number of
species on Earth are uncertain and inconsistent, and show no signs of
converging \cite{Caley_2014}. Some estimates based on models of
taxonomic effort suggest that two-thirds of all species have already
been described \cite{Costello_2011}. Analyses that use the number of
authors per species description as a proxy for effort \cite{Joppa_2011}
ignore the global trend for an increasing number of authors per paper
\cite{Aboukhalil_2014}, and assume that the effort required per species
description has remained constant over time. An alternative
interpretation is that the quality of taxonomic description is
increasing over time \cite{Sangster_2014}, reflecting both increased
thoroughness and the availability of new technologies
\cite{Stoev_2013,Akkari_2015}.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image-5/image-7}
\caption{{Numbers of new animal names published each year for animals as a whole, and various taxonomic groups. Data from ION and BioNames.%
}}
\end{center}
\end{figure}

Rather than try and estimate an unknown (the number of species remaining
to be described), here I focus on the current state of taxonomic
knowledge. Given that we lack a comprehensive, global index of all
species descriptions, discovering what we know about what we know is not
entirely straightforward. For zoology the nearest we have is the Index
of Organism Names (ION, \url{http://www.organismnames.com}), which is
based on Zoological Record. Figure 1 shows the numbers of new taxonomic
names covered by the International Code on Zoological Nomenclature
(animals plus some protozoan groups) that have been described each year
based on data from ION, cleaned and augmented in BioNames
\cite{Page_2013}. These data show an increase in overall numbers over
time, with dips around the times of the two World Wars, followed by an
essentially constant number each year since the mid-twentieth century.
The pattern varies across taxa, some taxa show increasing numbers per
year, but other taxonomic groups are essentially static or in decline,
even in groups thought to be hyperdiverse such as nematodes
\cite{Blaxter_2003}.

\subsection*{Digitising the taxonomic
literature}\label{digitising-the-taxonomic-literature}

The rate of progress in biodiversity research is controlled by two
factors, the speed with which we can discover and describe biodiversity,
and the speed with which we can communicate that information
\cite{Pentcheff_2010}. Unlike most biological disciplines, the entire
corpus of taxonomic literature since the mid 18th century remains a
vital resource for current day research. In this way taxonomy is similar
to the digital humanities, which has not just ``big data'' but ``long
data'' \cite{Aiden2013}. Not only is this because of the rules of
nomenclature that dictate (with some exceptions) that the name to use
for a species is the oldest one published, it reflects the uneven effort
devoted to the study of different taxonomic groups \cite{MAY_1988}.
For poorly known groups the bulk of our knowledge of their biology may
reside in the primary taxonomic literature.

Digitisation is one step towards making taxonomic information available.
Many commercial publishers have, on the face of it, done the taxonomic
community a great service by digitising whole back catalogues of
relatively obscure journals. However, digitisation is not the same as
access, and many commercial publishers keep this scanned literature
behind paywalls. In some fields legal issues around access have been
side-stepped by constructing a ``shadow'' dataset that summarises key
features of the data while still restricting access to the data itself.
For example, by extracting phrases comprising a set of \emph{n} words
(\emph{n}-grams) from Google Books it is possible to create a data set
that contains valuable information without exposing the full text
\cite{Michel_2010}. However for taxonomic work, there does not seem to
be an obvious way to extract a shadow. Agosti and colleagues
\cite{Agosti_2009, Patterson_2014} have explored ways to extract core facts from the
literature and re-purpose these without violating copyright, though how
much of their conclusions can be generalised across different national
and international legal systems remains untested.

Apart from commercial digitisation of the scientific literature, two
other developments are accelerating access to taxonomic information. The
first is the rise of open access publishing, notably journals such as
\emph{ZooKeys} that support sophisticated markup of the text
\cite{Penev_2010}. This is increasing the number of recently-described
species that are published in a machine-readable form that can then be
subject to further processing \cite{Miller_2015}. At the same time, the
\href{http://www.biodiversitylibrary.org}{Biodiversity Heritage Library
(BHL)} \cite{Gwinn_2009} has embarked on large-scale digitisation of
legacy taxonomic literature. Although initially focussing on out of
copyright literature (i.e., pre-1923 in the United States), BHL is
increasingly getting permission from copyright holders to scan more
recent literature as well. Coupled with tools such as BioStor
\cite{Page_2011} to locate and extract articles within the scanned
volumes BHL is fast becoming the largest available open access archive
of biodiversity literature.

To quantify the extent to which the taxonomic literature has been
digitised, for each decade I counted the number of publications of new
names in animals both with and without a digital identifier (such as a
DOI, a PDF, a Handle, or a URL to BioStor). The recent taxonomic
literature is mostly digital: for the years 2010-15 60\% of publications
have a digital identifier, the bulk of these having a DOI. However,
prior to the 21st century more publications lack identifiers than have
them, with the 1970s being the least digitalised decade (Fig. 2).\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}
\caption{{Difference between number of publications publishing taxonomic names that have any digital identifier versus those that don't, plotted against decade. Data from BioNames.%
}}
\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image-3/image-tail}
\caption{{Number of taxonomic names published in each journal plotted against rank
order for that journal. Note the distinctiveness of the first journal
(\emph{Zootaxa}). Data from BioNames.%
}}
\end{center}
\end{figure}

\subsection*{The long tail of taxonomic
literature}\label{the-long-tail-of-taxonomic-literature}

Another challenge presented by the taxonomic literature is that it is
highly decentralised, being spread across numerous journals (Fig. 3).
What is striking is the dominance of animal taxonomy by the
``megajournal'' \emph{Zootaxa}, and yet this journal has published only
15\% of the new names that have been minted since 2000. The taxonomic
literature has a very ``long tail'' of small, often obscure journals
that contain a few taxonomic publications. Long tails require
significant effort to index \cite{edwards_thorne_1993}, although the Zoological
Record claims 90\% coverage of the taxonomic literature
\cite{thorne_2003}, in some taxa there may be significantly greater gaps
\cite{bouchet_rocroi_1992}. Conversely, if we set our sites lower, then long
tail distributions mean that we can get a substantial fraction of the
names from a small number of journals (the ``low hanging fruit'').
Indeed, the first 20\% of the journals in Fig. 3 contain 80\% of the
names in Bionames that are linked to a publication. Unfortunately, many
of these journals are not currently available digitally (Fig. 2).

The picture that emerges from our knowledge of the taxonomic literature
is the recent literature is mostly digital, identified with DOIs, and
some of it is open access. But much of our fundamental knowledge of the
world's biodiversity, particularly that published in the mid to late
20th century remains digitally inaccessible (Fig. 2). Between the 21st
century trend towards digitisation and open access, and the removal of
restrictions pre-1923 as copyright expires, lies a great body of 20th
century work that will require considerable effort to make available.

\section*{Genomics}\label{genomics}

In contrast with taxonomic knowledge, which is widely scattered, most
genomic information is highly centralised, being stored in the three
components of the International Nucleotide Sequence Database
Collaboration (INSDC), namely GenBank, EMBL, and the DDBJ
\cite{Benson_2012}. Taxonomic name ``databases'' more closely resemble
digitised library catalogues, whereas sequence databases contain the
actual sequences, which means we can compute over them. For example, a
researcher with a new sequence can discover a lot about that sequence by
a simple BLAST search \cite{Altschul_1990}, whereas a taxonomist armed
only with a name will struggle to get computable data from the name
alone.

Although the bulk of the world's sequence data is available in the
INSDC, this is not the case for DNA barcodes, most of which reside in
the Barcode of Life Data System (BOLD) \cite{RATNASINGHAM_2007,Ratnasingham_2013}. Since 2009
BOLD has released nearly 2.5 million DNA barcodes, with updates every
few months. However, many of these sequences are not currently available
in GenBank. To document this I searched for barcodes in GenBank using
two criteria. The first searched for sequences that were listed in the
Bioproject database \cite{Barrett_2011} under accession
\href{http://www.ncbi.nlm.nih.gov/bioproject/37833}{PRJNA37833}. The
second searched for sequences with the keyword ``barcode''. For both
searches the sequences were grouped by their date of publication (the
``pdat'' query parameter) that correspond to the intervals between each
BOLD data release. Plotting counts of these sequences over the same
intervals as the BOLD data releases highlights the limited data sharing
between BOLD and INSDC (Fig. 4).\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image/image}
\caption{{Comparison of numbers of DNA barcodes in each release of public data
from BOLD with the numbers of barcode sequences in the INSDC sequence
database GenBank. Data on sizes of BOLD data from
\url{http://bins.boldsystems.org/index.php/datarelease}, data from
GenBank obtained via Entrez query interface.%
}}
\end{center}
\end{figure}

\subsection*{Dark taxa}\label{dark-taxa}

As desirable as data sharing is, it is not without complications. In
2011 I coined the phrase ``dark taxa'' \citep[http://iphylo.blogspot.co.uk/2011/04/dark-taxa-genbank-in-post-taxonomic.html, see also ]{Parr_2012} to refer to
species in GenBank that lacked formal scientific names. Typically they
will have a name that comprises a genus name and some combination of
letters and numbers to make the name unique within GenBank (e.g., a
specimen code or the first letter of the last names of the researchers
that deposited the sequence). For this paper I've updated the analysis
to include sequences published up to the time of writing (Fig. 5).

The pattern shown in Fig. 5 likely reflects a combination of processes.
If most of the taxa being added to GenBank represent species that have
already been described, then the rate at which taxa can be identified
(either by taxonomists or by researchers using their outputs, such as
keys) is being outstripped by the pace of sequencing. Alternatively,
dark taxa may represent unknown species, but we lack taxonomists capable
of recognising the taxa as new (and formally describing them). If
taxonomic capacity is a limiting factor then we would expect a gradual
decline in percentage of named taxa, which is the background pattern in
Fig. 5. The growth of dark taxa might also reflect changing practices of
molecular workers, for example in DNA barcoding where large numbers of
specimens are sequenced and deposited into GenBank labelled with
specimen codes rather than taxonomic names. Indeed, the dramatic
increase in the numbers of dark taxa in 2010 is mostly due to sequences
from the BOLD project (recognised by the prefix ``BOLD'') being added.
Even if we allow for the import of unidentified BOLD sequences as a
one-off event, at present less than half the newly sequenced
invertebrate taxa being added to GenBank have been identified to species
level.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}
\caption{{Growth of dark taxa in GenBank for invertebrate sequences. For each year
the graph shows the percentage of spacies-level taxa added during that
year (based on the `edat' query field in Entrez Utilities) that do not
have formal scientific names.%
}}
\end{center}
\end{figure}

\section*{Integrating biodiversity
information}\label{integrating-biodiversity-information}

Typically integration across biodiversity databases is achieved using
taxonomic names \cite{Patterson_2010}, but the rise of dark taxa makes this
problematic for an increasing fraction of sequence-based data. Even if
we have names, these need not always mean the same thing
\cite{Kennedy_2003}. As an example, Fig 6a shows the distribution of the
lizard \emph{Morethia obscura} from the Global Biodiversity Information
Facility (GBIF). For comparison, Fig. 6b shows a geophylogeny
\cite{Page_2015} for DNA barcodes from BOLD for \emph{Morethia
obscura} which reveals considerable phylogenetic structure within
``Morethia obscura''. Specimens of this species are assigned several
distinct Barcode Index Numbers (BINs) \cite{Ratnasingham_2013} implying that
``Morethia obscura'' comprises more than one species.

Although GBIF and BOLD present rather different views of the ``same''
species, there is considerable overlap in the specimens used to
construct Figs 6a and 6b. For example, DNA barcode WAMMS012-10 was
obtained from specimen WAMR127637, which also occurs in GBIF (as
occurrence \href{http://gbif.org/occurrence/691832260}{691832260}.
Because the taxonomic concepts in GBIF and BOLD are explicitly defined
with respect to sets of specimens we can directly compare them, rather
than rely on the possibly erroneous assumption that a given taxonomic
name means the same thing in the two databases. Furthermore, as
increasing numbers of type specimens are sequenced \cite{Federhen_2014} we
can more firmly associate names with sets of specimens, leading to a
computable nomenclature where the name we assign to a set of specimens
can be determined automatically \cite{Pullan_2000}.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/figure/figure}
\caption{{Comparison of \emph{Morethia obscura} in GBIF (top) with DNA barcodes
from the same taxon in BOLD (bottom). Note the phylogeographic structure
in the sequence data, which is assigned to several different BINs imply
the existence of multiple species within ``Morethia obscura''.%
}}
\end{center}
\end{figure}

Integrating databases using specimens is attractive, but not without its
own set of problems. The biodiversity informatics community has yet to
standardise identifiers for specimens, despite numerous efforts
\cite{Guralnick_2015}, consequently there may be little apparent overlap
between specimen identifiers in different databases \cite{Guralnick_2014}.
As an example, despite the limited sharing of data between BOLD and
GBIF, there are already barcoded specimens in GBIF. To illustrate,
consider the DNA barcode GWORH520-09 from sample ``BC ZSM Lep 10234''.
GBIF doesn't have this record from BOLD, but it does have the specimen
BC ZSM Lep 10234 \citep[provided by the host institution ]{ebd55b32-cd68-46b4-85e2-105de99fecc8}. The DNA barcode from this specimen
is also in GenBank, and because that record is georeferenced it has been
ingested by GBIF as part of the Geographically tagged INSDC sequences
dataset \cite{3c8d6ba3-d69e-468a-81e2-61689418f59e}. Hence, GBIF has duplicate records for a
barcoded moth, neither provided directly by BOLD (Fig. 7). Merging and
de-duplicating specimen-based records is going to be a significant
challenge for global aggregators such as GBIF.\selectlanguage{english}
\begin{figure}[h!]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/specimens/image}
\caption{{Fig. 7 Illustration of multiple records for the same specimen of
\emph{Casbia rectaria} in GBIF. The voucher specimen for the DNA barcode
is in GBIF (provided by the institution housing the specimen). The COI
barcode sequence from this specimen is in both BOLD and GenBank, and
because the sequence is georeferenced it is GBIF as part of a dataset of
georeferenced sequences from EMBL.%
}}
\end{center}
\end{figure}

\section*{Summary}\label{summary}

Both taxonomy and barcoding are actively digitisng the living world. The
description of new animal taxa is essentially proceeding at a constant
rate, generating a steadily growing legacy of taxonomic literature into
which digitisation has made modest inroads. In contrast, sequence
databases as a whole are growing exponentially, although barcode growth
is more modest. Nucleotide sequences are ``born digital'' and readily
computable, for example they can be clustered into BINs of similar
sequences, or phylogenies of the type shown in Fig. 6. Given the obvious
overlap between the goals of classical taxonomy and barcodes, the lack
of digital overlap between these two endeavours is disconcerting. Many
barcodes lack taxonomic names (``dark taxa''), and much of the primary
taxonomic literature has not been digitised (``dark texts'').
Integrating barcodes and taxonomy at scale is going to be significant
challenge, as indeed will be integrating barcodes into mainstream
sequence databases. Mapping between databases using taxonomic names
seems the obvious approach, but the abundance of dark taxa shows this
has not been entirely successful. Alternatives such as integration via
specimens show promise, but are hampered by the lack of stable
identifiers. If we are to make process the stubborn problem of the lack
of unique, persistent identifiers, and cross links between those
identifiers needs to be tackled in earnest \cite{Page_2008}.

As a postscript, in writing this opinion piece, I have had to write
custom scripts to query various databases in an \emph{ad hoc} manner,
trying to extract and assemble information that gives insight into the
current state of biodiversity digitisation. For these analyses and
visualisations to have broader utility it would be desirable to have
some way of consistently and automatically doing these analyses, in
effect creating a dashboard of digitisation that would enable us to not
only see where we are as a field, but also suggest directions in which
we could be heading.

\section*{Acknowledgments}\label{acknowledgments}

I thank Paul Hebert for the invitation to speak at the 6th International
Barcode of Life Conference. Some of the ideas discussed here were first
developed on posts on my blog \href{http://iphylo.blogspot.com}{iPhylo}
and benefited from feedback from people who left comments on those
posts.

\section*{Competing Interests}\label{competing-interests}

I have no competing interests.

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