Ocean Biodiversity Informatics - NIO Bioinformatics Centre

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11 Oct 2005


“Ocean Biodiversity Informatics” enabling a new era in marine biology
research and management

Mark J. Costello

and Edward Vanden Berghe

Leigh Marine Laboratory, University of Auckland, P.O.Box 349, Warkworth, New
Zealand (


Flemish Marine Data and Information Centre, Flanders Marine Institute,
Wandelaarkaai 7, B8400 Oostende, Belgium (wardvdb@vliz.be)


For several hundred years marine biology has been based on

natural history, and
during the 20

century addressed ecology and evolution. In recent decades, genetic
and molecular sciences have brought new insights to marine biology. In parallel
physical oceanography has become a global science using satellites a
nd other remote
sensing technology to compliment traditional sampling, and plans for real
sharing of data are underway as part of the Global Ocean Observing System (GOOS).
This growth in physical data led to the Intergovernmental Oceanographic
sion’s (IOC) International Oceanographic Data and Information Exchange
(IODE) programme establishing a network of national ocean data centres (NODC)
around the world. However, with the exception of genetic data, marine biology data
remained scattered and
often unpublished (Grassle 2000, Myers 2000, Seller et al.
2005). This may have reflected the lack of opportunities for publication of raw data.
However, the internet has reduced costs of data publication, and marine biology has
entered the information a
ge with other sciences (International Council for Science
2004). In this paper we define the scope, challenges and future prospects for the new
field of Ocean Biodiversity Informatics.

Need for data access

Never before has the need for rapid access to
data at regional and global scales been
so important. Recent analyses of ocean scale data has shown major shifts in plankton
distribution due to climatic factors (e.g. Stevens et al. this volume), global over
fishing (Pauly et al. 2003, Pauly and Watson 2
003), many
fold reductions in
abundance of large fish (e.g. Myers and Worm 2003), profound changes in ecosystem
structure due to indirect effects of fisheries that may be irreversible (Jackson et al.
2001, Frank et al. 2005), and as yet unexplained 62 mill
ion year (my) cycles of
marine genera richness in the 542 my fossil record (Kirchner and Weil 2002, Rhode
and Muller 2005). Without informatics
aided analyses, and supporting large
databases, the global
nature of these phenomena would not have been


Species are being introduced by human activities around the world with socio
economic impacts on local fisheries and aquaculture. These species may not be
recognised as introductions because only a fraction of marine species have so far be
described. The ability to identify species from anywhere in the world is particularly
important for detection of introductions that may prove economically harmful. Online
species indentification guides provide immediate access to more people who have
internet access (e.g.
). In addition, electronic keys helpfully allow
users to select whichever characteristics of the animal or plant they can recognise with
confidence; rather than be forced to
choose one or two characters at each step in a
11 Oct 2005


dichotomous keys where one error or oversight can lead to lost time and
misidentification. The management of these invasive species requires rapid access to
identification and ecological information from othe
r parts of the world. On
line tools
such as the Kansas Geological Survey Mapper (e.g. Guinotte et al. this volume), and
Desktop GARP (e.g. Wiley et al. 2003), can be used to predict potential
environmental suitability for candidate invasive species. Othe
r modelling approaches
may be less automated, such as that used by Kaschner et al. (this volume) to predict
habitats for marine mammals. It seems likely that ocean biodiversity informatics will
provide a suite of modelling options appropriate for differen
t types of data and

Local patterns of biodiversity have their origins, and may still be maintained by,
ecosystem processes at regional and global scales. Thus selecting areas for fishery
stock management and conservation require knowledge of
biodiversity patterns at all
spatial scales. At present, most conservation focuses on national scale patterns
because of regulatory obligations and limited availability of data at larger geographic
scales. Ideally, conservation should operate at ecologic
ally and evolutionarily
relevant scales.

Data, information and knowledge

Data and associated metadata (background information about the data) are the
foundation of science; the what, where, when, who, and how. The interpretation of
these facts leads to
information and theories that create knowledge. At present,
marine biology delivers many papers that provide statistics, graphs and models
derived from often unpublished data. While the importance of most of these
syntheses, models and theories will fade

in time, the value of the data increases in
time as it becomes harder to replace. The digitization of historical data from paper
files can cost only ≤ 0.5% of the original field surveys (Zeller et al. 2005), and reveal
new insights into human interaction
s with natural resources (e.g. Lotze and Milewski

Most data collection is paid for directly or indirectly through public funds to
ultimately benefit society through research, development and resource management.
The failure to publish raw data u
ndermines science, including the management of
natural resources, by impeding independent analysis, reuse and combination of
different datasets. The calls by international scientific organisations such as IOC and
ICSU (International Council for Science 20
04) to make data publicly available are
being ignored by many scientists, and are thus being repeated at international
conferences (Box 1). For example, NODC contain less than half of the oceanographic
data collected in their countries (Krohnke et al. 200
5), and few of the marine papers in
top journals publish their data. Scientists, funding agencies, institutions and
publishers must require the publication of data in user accessible form. Archives that
are not compromised by hardware and software change
s, and facilitate data re
use, are
also required.

Scope of ocean biodiversity informatics

Biodiversity informatics is the computer technologies that enable the management and
analysis of biodiversity data and information (Bisby 2000), and has many benef
its and
positive outcomes (Box 2). The Convention on Biological Diversity defines
biodiversity as the variety of life within species (e.g. populations), between species
11 Oct 2005


(e.g. communities) and of ecosystems (i.e. ecological and environmental interactions)
(Costello 2001). Related fields include bioinformatics, phyloinformatics, species
informatics, ecoinformatics and geoinformatics. Bioinformatics is generally restricted
to molecular and genetic data that do not involve species names as the core element,
and encompasses phyloinformatics, which concerns the phylogenetic relationships
between taxa (e.g. Tree of Life initiative). Species, eco

and geo
informatics concern
species level, ecological, ecosystem and geographic aspects. They deal with concepts
scribed as words, such as species, habitats and places, rather than numerical or
biochemical data. It is to these challenges that biodiversity informatics provides the
most novel contributions and solutions.

Ocean biodiversity informatics (OBI) is an in
terdisciplinary activity based on data
associated with marine species and their environment. It includes traditional database
design and function, as well as data exchange standards, schema and protocols, and
exploration, visualization, analysis and publi
cation software. While primary goals are
free and open access to data over the internet, some project
specific or sensitive (e.g.
location of threatened species) may be withheld. The use of open
source software is
preferred (e.g. XML, MapServer) because
this can be modified for special purposes
and freely shared, but standard proprietary software is also used (e.g. Oracle,
Microsoft Access, ARCIMS).


With the advent of online data exchange, standard data exchange protocols,
middleware (or wrap
pers) that cross
map one database to another, and common
vocabularies of terminology have become more in demand than when databases were
isolated and centralized. Standard categories and definitions are also required for the
metadata that describes datase
ts (“discovery metadata”) and data records. Whereas
links between web pages are by hypertext mark
up language (HTML), the extensible
up language (XML) provides a more formal structure for data exchange

Data exchange

A standard list of da
ta fields (48 data elements) for exchanging data on species
distribution records has been established called Darwin Core. This has been expanded
in a backward
compatible manner by OBIS and Mammal Networked Information
system (MANIS) for marine and mammal s
pecializations respectively. The most
widely used biological data exchange protocol is DiGIR (Distributed Generic
Information Retrieval). The Access to Biological
Collections Data (ABCD) schema is
more complex and comprehensive (about 300 data elements) t
han Darwin Core, and
is used with the BioCASE data exchange protocol. A protocol building on and
combining DiGIR and BioCASE is under development

called TAPIR, the
Access Protocol for Information Retrieval



Metadata needs to be standardised to facilitate reporting and standard definitions of
terminology. Such controlled vocabularies exist, as provided e.g. by the Global
Change Metadata Standard (GCMD), ISO 19115 and the Federal Geographic Data

(FGDC), but need to be expanded to cover marine biology and ecology.
The searching of metadata is improved by knowing the relationships between words,
such as if a word naming a concept is equal to, a subset (or child) of, or related to
11 Oct 2005


another word in so
me other way. This field of informatics “ontology” is well
established in information science and used by librarians, but little known by marine
biologists and ecologists. Ontologies include dictionaries, controlled vocabularies,
thesauri, and classifica
tions. Classifications can indicate taxonomic phylogenies, and
relationships between habitats and place names, and may or may not be hierarchical.
They aid capture of information from the literature as well as datasets, and are the
mechanism for creating

a “semantic web” (www.semanticweb.org). However, their
construction requires collaboration between ontology and marine biodiversity
“domain” experts, and is being facilitated by the Marine Metadata Initiative (MMI).


In contrast to establi
shed physical ocean and genetic data management, the common
element of all parts biodiversity informatics is species names. The application of
some species names changes over time, such as when a species is discovered to
contain several species, or to hav
e been described under different names. The
Linnaean system of species nomenclature is the best available with well developed
rules, although codes and common names can sometimes have supplementary value
(Froese 1999). Similarly, place names change over
time and the same names may be
used for different locations. Available gazetteers may find locations of some marine
place names, but they do not yet intelligently link these locations to databases to
integrate data. Ecological nomenclatures are also compl
ex, with terminology for
habitats and what defines ecosystems varying significantly.

Informatics should reduce duplication errors by making species names and
descriptions more readily available online. Having an online register of all species
names as
suggested by Costello et al. (2005) may soon become a reality (Polaszek et
al. 2005), enabling more rapid identification and avoiding the re
description of
species. The first step towards this, having a checklist of all described species is well
by initiatives such as Species 2000 (www.species2000.org), Integrated
Taxonomic Information System (
) and the European Register of Marine
Species (http://www.marbef.org/data/erms.php).

A key challenge in bi
odiversity informatics is the management of species names.
Name management is well established in information systems, and the first
biodiversity data exchange protocol (Z39.50) was used in bibliographic searches by
libraries (Vieglas et al. 2000). It re
organised data from a database into a standard
(Darwin Core) format that was accessible through an interface called The Species
Analyst. It has largely been superseded by DiGIR and ABCD.

GBIF’s Electronic Catalogue of names which includes the Catalogue
of Life (CoL), a
joint publication by Species 2000 and ITIS whose marine taxa are supported by OBIS.
CoL has listed about 1/3 of the estimated 1.75 million described species (Bisby et al.
2005). A parallel initiative, UBio, is capturing all used species
names from the
literature and relating this to higher taxa. This will facilitate location of information
in libraries and online sources, and if linked with the currently valid names in CoL
will greatly aid access to biological information.


Centralised databases

11 Oct 2005


The first informatics approaches to biodiversity data management were single
centralized databases, sometimes called “data silos”. These have advantages in a
single data structure and nomenclature, and are the best approach

where the data is
largely required within the host institution, and when a host is willing to undertake its
management. Examples include FishBase, AlgaeBase (Nic Donncha and Guiry
2002), Hexacorallia (Fautin 2000), CephBase (Wood et al. 2000), MedOBIS
rvantidis et al. this volume), BioOcean (Fabri et al. this volume) and the Integrated
Taxonomic Information system (ITIS). However, when a database becomes larger
and requires many participants, then centralized systems place a heavy technical,
, and financial burden on a single organization (Merali and Giles 2005). A
centralised database may allow online access to the scientists who maintain the data
(i.e. a “data warehouse”), while the host institute focuses on technical aspects of data
ment; this model is in use by the European Register of Marine Species
(Costello 2000, Costello 2004, Costello et al. this volume).

Networked databases

Some recent biodiversity informatics initiatives such as Species 2000 (Bisby et al.
2005), the Ocean B
iogeographic Information System (OBIS) (Zhang and Grassle
2003, Costello et al. 2005a), and Global Biodiversity Information Facility (GBIF)
(Edwards et al. 2000), are federations of databases distributed in many organizations
around the world. Distributed

data systems have financial, quality control, ownership,
and community building advantages over centralized structures. The funding costs
are distributed, data is maintained at source by those best qualified to update and
improve it, and data ownership i
ssues are minimized as the custodian retains control
over what data is shared. Building a scientific community to support and develop the
data system is promoted because the data sources can remain directly involved in the
initiative. The central web sit
e or “portal” that connects to all the datasets can thus
concentrate on portal function rather than raw data collection and management. The
costs of hardware, software and expertise are similarly distributed, and know
how can
be shared amongst the partici

However, there are limitations to a purely distributed system in that the s
peed of
response decreases with system growth, the availability of the potential data is
variable as some sources may be off
line, the portal is ignorant of the data conte
nt so
it cannot develop advanced data handling and search tools, users get no feedback as to
why ‘zero’ returns occur (may be no data or temporarily no data). The s
olution is to
“crawl” the data sources and “cache” the data at intervals. Thus the data ca
n be
classified and indexed, for example geographically and taxonomically. For example,
OBIS Index initiated by Tony Rees (CSIRO) is a subset of all data available from
the cache that can be classified, and allows calculation of statistics on availabl
e data.
By resolving records in the cache to one record per geographic grid
square it reduces
data volume and allows more rapid online search and mapping. It allows “near
matches” to account for misspellings, and users can search down taxonomic
. Because users are more aware of the data content, they can customise their


Initially, most users of ocean biodiversity informatics are probably scientists. This is
essential because their use of the data is a key aspect of quality cont
rol, and their
involvement will improve the functionality of the systems. It is also critical that the
11 Oct 2005


systems have the confidence of the scientific community, as without that, further
investment of experts’ time and government funding will decline. Univ
ersity and
school students and their teachers will make up greater numbers of users but it
takes time to develop awareness within this community. Most users of FishBase, the
best developed publicly available marine database, now fall into this catego
ry (Froese
pers. comm.). To attract these users, systems must have authoritative and credible
content. Exciting tools may elicit ‘wow’ factor and attract first time users, but content
will result in repeat and longer
term usage.


ity assurance is especially challenging when the use of the data cannot be
predefined. The value of data is dependant on the purpose to which they are put.
Knowing a species occurs in the Pacific Ocean is useful at a global scale but
somebody in New Zeal
and would want to know where in that ocean it occurs so they
can judge whether their discovery is a range extension.

The completeness of a product is a function of its stated content and the needs of the
user. Unfortunately naïve users may not apprecia
te that so little of the marine
environment has been explored, that many species remain to be discovered, and that
of what has been observed only a fraction has been described and published in any
format. Setting too high goals for a product may delay its

completion and publication,
but setting interim goals that allow a step
wise publication provides a service for users
and demonstrates progress. For example, a simple checklist of species is of more
value when seen as the first step in a process where it

provides the backbone for
linking to synonyms, distribution data, identification information, and published

The early steps in quality control begin at the point of data collection. This is
followed by procedures to minimise additional erro
rs that may arise in documentation,
digitisation, archiving, and publishing (either on paper or electronically). Because the
opportunity for errors increases with the number of steps in handling the data, it is
critical for raw data to be available in thei
r basic form as well as any synthesised
forms. Present ocean biodiversity information systems may serve data from
authoritative sources, but less credible sources, such as amateur websites and
student’s web pages also exist. Quality control includes adeq
uate metadata,
standardised format of data (e.g. consistent placement of rows and columns in a
table), and standard pre
defined terminology. Procedures include checks for missing
values, scanning for impossible and anomalous values, mapping and graphing f
outliers, and calculations to check records match expected numbers. Checking for
outliers and irregularities needs expert intervention to avoid removing remarkable but
true discoveries. The best quality control comes from the use of the data and this
hould be facilitated by the publication process. User feedback must be encouraged,
and this form of peer
review could become a pre
requisite of data publication as it is
for publication of papers.

Conventional statistical analyses require presence and
absence data. However, being
certain of a species absence is challenging in ecology because many observations are
limited in same and time, and all sampling methods are biased. For example, without
the use of underwater video the abundance of deep
sea co
ral reefs on the continental
shelf of Europe would have remained unknown, although some reefs are 40 * 8 km in
11 Oct 2005


area (Costello et al. 2005b). Thus ecological studies often limit analyses to presence
only data. Museum collection data is also biased by spec
imens of rare species and
excludes absence information. However, protocols to convert presence
only data to
absence may be possible based on known sampling and survey methods.
Such tools would increase the utility of online data but require high
compliance with
metadata standards that have yet to be established.

Data quality indices could be developed based on evidence that steps in a standard
Quality Assurance process were conducted. Data suitability is a different issue and is
dependant not o
n the data, but on purpose it is required for. An objective method for
scoring data reliability has been used for FishBase (Froese et al. 1999).


The challenges facing ocean biodiversity informatics are not just technology.
Arguably, the grea
test obstacle is the lack of a data publication culture in marine
biology. Government agencies may make data available as a public service, but
unless required by funding agencies, there is no incentive for scientists to publish their
data. Science journ
als prefer synthesis and statistics of data, but an increasing number
allow data to be published as online appendices. These appendices could be
published to a standard format for data exchange and hence facilitate interoperability.
Such standards exist
and are in use by OBIS, GBIF and others. It is normal practice
in taxonomy to lodge type specimens in museums prior to publication, and in genetics
to enter data into GenBank. It should be a similar requirement of journals that
ecological data is similar
ly made publicly available prior to publication (International
Council for Science 2004).

Interoperability improvements being addressed include (a) more automated ways of
merging datasets and cross
checking of nomenclatures (e.g. Froese 1997), (b)
ds of having a “Globally Unique Identifiers” (GUID) for every data record will
allow detection of duplicate records, (c) expanded schema to allow more data and
metadata to be exchanged, (d) new versions of data exchange protocols and
middleware that are mo
re comprehensive and easier to implement. With common
data sharing tools and increasing amounts of data in the public domain, the same data
can be retrieved from several sources. This may be avoided in part by selective
caching and transmitting of data,
such as where OBIS does not serve datasets to GBIF
that also enter GBIF from other sources. Automated ways of recognising and
excluding such duplication at the data record level are thus necessary. Metadata
standards are being developed for marine habita
ts, including classifications and
dictionaries. They also need to be developed to describe sampling methods so users
can appreciate bias in datasets. Fisheries scientists have special catch
related data that
requires standards to facilitate interoperabil
ity (Branton and Ricard, this volume).

Desktop Geographical Information Systems (GIS) are now standard in marine and
environmental sciences (including management), and GIS designed for operating
online are being developed (Guralnick and Neufield 2005, H
alpin et al. this volume).
Mapping data points, lines such as for satellite
tracked animals, and polygons (areas)
are available online, and ways of converting between these and comparing results to
ocean data are improving. Online semi
automated “gazette
er” tools to convert place
names to points and polygons are being developed (e.g.
) and
will improve.

11 Oct 2005


This change in culture is underway. An IOC
sponsored workshop that brought
physical oc
eanographers, biologists and data managers together in 1996, was followed
by a symposium on ocean data management in 2002 (Vanden Berghe et al. 2003). An
international conference on “Ocean Biodiversity Informatics” in 29 November

December 2004 had ove
r 170 delegates from 37 countries and 70 presentations (from
over 100 offers of papers) (
). It was sponsored by the
Intergovernmental Oceanographic Commission of UNESCO, IOC’s Internationa
Ocean Data and Information Exchange, International Council for Exploration of the
Sea, Census of Marine Life’s Ocean Biogeographic Information System, International
Association of Biological Oceanography, Taxonomic Data Working Group, Flanders
Marine Ins
titute, MarBEF (European marine biodiversity and ecosystem function
research network), the European Commission and the German Government.
Participants came from government agencies, universities, NGO, museums, and
commercial companies.


Computer technology is changing at such rapid rates that it is difficult to predict what
opportunities will be available in future years, although monitoring the commercial
sector is a good indication. Ocean biodiversity informatic
s requires an entrepreneurial
approach that seizes opportunities for technology transfer, and sees change as an
exciting opportunity rather than an impediment to development. Having a variety of
choice in hardware and software platforms may seem confusing
, but must be
recognized as the normal market
driven approach in innovation. Resources are
always limited and investments must weigh the uncertainties of more novel and
progressive approaches against the certain needs of their market. Dealing with the
certainties of future funding, what technologies and data will be available, and who
will use the data for what purposes, have parallels in any innovative business. Both
materials (types of data), technological tools, products (e.g. maps, models, derived
data), and customers are likely to change. Thus ocean biodiversity informatics
initiatives must be adaptable to change, and regularly review the way they operate.

In parallel with advancing technology, the expectations of users change, and so will
nce culture. One culture challenge is to overcome the concerns and excuses for
not making data available (Box 3). Froese et al. (2004) reviewed these for fisheries
data and noted that they can be overcome through delayed publication, data
aggregation, da
ta use agreements, disclaimers, read
only access (the norm), data
owner support and involvement, and crediting the source. The advantages of data
publication are not only to other scientists, and in the long
term to society (Box 2),
but the data providers

get more visibility, recognition, invitations, citations and
collaborations (Froese et al. 2004). Indeed, publishing data may be better for
“marketing” a scientist or organisation than publishing papers because it demonstrates
an advanced level of data m

While looking forward with imagination, there are lessons from history. One of the
greatest advances in human communication was the invention of the printing press. It
allowed mass production of information, much of it with no peer
or quality
control. The size of libraries increased, and in time edited science journals and peer
review prior to publication became established. Today, many universities use
11 Oct 2005


rankings of the citation rates of journals and papers to judge individual scien
productivity and performance, and governments use this information when
distributing research funding. We suggest the internet is a similar revolution in
information availability. A citation index for data accessed from online databases
may have s
imilar consequences for encouraging online publication by indicating data
use (Box 3).

At present there is relatively little external peer
review prior to publication of material
on scholarly websites, but they are recognised as credible because of the
and people who produced them. Some online information systems, such as ERMS
and OBIS have established Editorial Boards with a similar function in quality
assurance as boards of science journals. In contrast to the scientists who volunteer
ime for editing and peer
reviewing papers for journals, their efforts directly benefit
the scientific community that retains ownership of the data. This avoids concerns
over commercial publishers or institutions profiting from their contributions. This h
been taken a step further by ERMS and Fauna Europaea (a register of all 130,000 land
animal species in Europe). They are owned by the Society for the Management of
European Biodiversity Data (
) but all

scientists who contributed to
these initiatives are honorary life
members, and elect a Council to manage the
databases (Costello 2000).

Until recently, archiving was a concern for electronic media. Tapes, diskettes,
compact disks and other media coul
d be given an ISBN number (International Serial
Book Number) and lodged in a copyright library for archiving but the media would
eventually deteriorate, and the hardware (and perhaps software) to read them may be
unavailable. Web pages are notoriously tra
nsient. However, the Internet Archives
now routinely copies web pages and archives them, for which storage capacity is no
longer a problem. However, they do not archive data that is only accessible through
search screens. Commercial search engines also c
ache web pages but delete these as
they are replaced. Procedures for database backup and mirror sites are now well
established so data will not be lost if hosted in such systems.

At present, internet access remains elusive to many people in developing
due to poor infrastructure. However, it seems probable that reduced costs of hardware
and services, and increased efficiency of satellite and wireless transmission systems,
will overcome this obstacle. Indeed, this will open the “knowledge econ
omy” to all
countries and may create a new wave of user demand and innovation at present
dominated by developed countries.

OBI is an initiative of the 21

century and will make conventional marine biodiversity
research more dynamic and comprehensive,
with a range of constantly evolving
online tools (Box 3). The consequences are positive and complementary for
traditional subjects such as taxonomy (Pennisi 2000, Costello et al. this volume),
biogeography, ecology, and resource management (Box 3).

11 Oct 2005



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Box 1. Public statement
by the 2004 conference on
Ocean Biodiversity

We note that increased
availability and sharing of

is good scientific
practice and
necessary for
advancement of
11 Oct 2005


Box 2. Some of the
ts of biodiversity

Data publication

Low cost
publication of text,
maps, images,
movies, sounds

Easier access to
data and metadata

Availability of
data and metadata

Rapid publication

Linking to many
data and
resources on

world wide web


Permits data
mining and

Combination and
sharing of data
from multiple

Data are re
for perhaps

Repatriate data and
knowledge to

analyze the
data from

between different
research groups is
promoted and

Policy makers and
11 Oct 2005


Box 3. Predictions for what Ocean Biodiversity Informatics may provide in the

Science culture


a sharing normal part of scientific process in marine biology


Data publication on
line becomes standard practice


Citation rankings of on
line publications


Recognition value on
line publication in individual’s research performance



Online mappin
g of many species against selected environmental variables


Online visualization as graphs, maps, movies and 3
D models


More automated data capture and integration option


Citation index for use of online data


Improved online data publication tools, includi
ng distribution and
identification information as text, images, sounds


Automated translations between scripts and languages


Automated and permanent archiving of scholarly websites

Data available


All valid marine species names on
line and part of the “Cat
alogue of Life”

Identification guides (descriptions and images) to all marine species on
as part of a “key of life”

Distributions of all marine species on


Search and map by marine habitats at global scales


Distributions of invasive species with
predictions of future spread

Consequences for efficiency in science


Improved quality control in identification and taxonomy


Increased rate of species being described


New discoveries and understandings of role of biodiversity in ecosystems
based on data


apid reanalysis of existing data in light of new data


Better management of fish stocks and natural resources through better
understanding of ecosystem function and health


time monitoring of environmental (e.g. satellite, in situ systems) and
al data (e.g. from video, sensors)