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Methods Ecol Evol. 2021;12:996–1007.wileyonlinelibrary.com/journal/mee3
Received: 30 October 202 0
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Accepted: 15 Februar y 2021
DOI: 10.1111/2041-210X.13593
RESEARCH ARTICLE
A standardisation framework for bio- logging data to advance
ecological research and conservation
Ana M. M. Sequeira1 | Malcolm O'Toole1 | Theresa R. Keates2 | Laura H. McDonnell3 |
Camrin D. Braun4,5 | Xavier Hoenner6 | Fabrice R. A. Jaine7,8 | Ian D. Jonsen8 |
Peggy Newman9 | Jonathan Pye10 | Steven J. Bograd11 | Graeme C. Hays12 |
Elliott L. Hazen11 | Melinda Holland13 | Vardis M. Tsontos14 | Clint Blight15 |
Francesca Cagnacci16 | Sarah C. Davidson17,1 8 | Holger Dettki19 | Carlos M. Duarte20 |
Daniel C. Dunn21 | Victor M. Eguíluz22 | Michael Fedak15 | Adrian C. Gleiss23 |
Neil Hammerschlag3,24 | Mark A. Hindell25 | Kim Holland26 | Ivica Janekovic27 |
Megan K. McKinzie28,29 | Mônica M. C. Muelbert25,30 | Chari Pattiaratchi27 |
Christian Rutz31 | David W. Sims32,33,34 | Samantha E. Simmons35 |
Brendal Townsend10 | Frederick Whoriskey10 | Bill Woodward29 | Daniel P. Costa36 |
Michelle R. Heupel37 | Clive R. McMahon7,2 5 | Rob Harcourt8 | Michael Weise38
1Oceans Institute and School of Biologica l Sciences, Universit y of Western Australia, C rawley, WA, A ustralia; 2Dep artm ent of Ocean Scien ces, Uni versit y of
Califo rnia Santa Cruz, Santa Cruz, C A, USA; 3Leonard and Jay ne Abess Center for Ecosystem Science and Policy, Universit y of Miami, Coral G ables , FL, USA;
4School of Aquatic and Fishery Sciences , Univer sity of Washington, Seattle, WA, USA; 5B iolog y Department, Woods H ole Oceanogra phic Ins titut ion, Woods
Hole, MA , USA; 6CSIRO Oceans and Atmosphe re, Hobart, TAS , Austr alia; 7Integrated Marine Obser ving Sys tem (IMOS) Animal Tracking Facility, Sydney
Instit ute of Marine Science, Mosman, NSW, Australia; 8Depa rtment of Biological S cience s, Macquarie Un iversi ty, Sydney, NSW, Australia; 9Atlas of Liv ing
Australia, Me lbourne Museum, Car lton, VIC, Aus tralia; 10Ocean Tracking N etwor k, Dalh ousie University, Halifax, NS, C anada; 11NOAA Environm ental Research
Divisio n, Sout hwest Fisheries Scien ce Center, Monterey, CA , USA; 12S chool of L ife and Env ironmental Sciences, Deakin University, Ge elong, VIC, Austra lia;
13Wildli fe Computers, Redmond, WA, USA ; 14NASA Jet Propulsion L abor atory, Pasadena, CA , USA; 15SMRU Instrument ation, Scottish Oceans Insti tute, St
Andrews, UK; 16D epar tment of B iodive rsity and Molecular Ecology, Research an d Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Trento,
Italy; 17Department of Migration, Ma x Planck Instit ute of Animal Behavior, Radolfzell, Germa ny; 18Centre for th e Advanced Study of Collec tive Behaviour,
University of Konstanz, Konstanz, Germany; 19 Swedish University of Agricultural S ciences, SLU Swedish Species Information Centre, Uppsala, Swede n; 20Red
Sea Rese arch Centre (RSRC) and Computational Bioscience Research Center (CBRC), King Ab dullah U niversity of Science and Technolog y, Thuwal , Saudi
Arabia; 21Schoo l of Earth an d Environm ental Sciences , University of Queenslan d, St Lucia, QL D, Australia; 22Instituto de Física Interdisciplinar y Sistemas
Comple jos IFISC (CSIC- UIB), Palma de Mallorca, Spa in; 23Cent re for Sus tainable Aquatic Ecosystems , Harr y Butler Institute, Murdoch University, Murdo ch,
WA, Aust ralia; 24 Rosens tiel School of Marine & Atmospher ic Science, Miam i, FL, USA; 25Inst itute fo r Antar ctic and Marin e Studie s, University of Tasmania,
Hobar t, TAS, Australia; 26Hawaii Instit ute of Marine Biology, University of Hawaii, M anoa, H I, USA; 27Oceans Graduate School and the UWA Oceans Institute,
The Universit y of Western Austr alia, Crawley, WA, Au stralia; 28Monterey Bay Aq uarium Research Insti tute (MBARI), Moss Landing, CA , USA; 29U.S. Animal
Telemetr y Netwo rk (ATN), NOA A Integrated Ocean Observing System , Silver S pring, MD, USA; 30Institu te of Marine Science, Federal Universit y of São Paulo
(IMar/UNIFE SP), Santos, Brazil; 31Centre for Biologica l Diversity, Scho ol of Biology, University of St Andrews, St Andrews, UK; 32Marine Biolo gical A ssociation
of the United Kingd om, The L abor atory, Plymout h, UK; 33Ocean and E arth Science , Nation al Oceanograp hy Centre Southampton, Univer sity of Southampton,
Southampton, UK; 34Centre for Biologic al Scie nces, University of Southamp ton, Southampton, UK; 35U.S. Marine Mammal Com mission , Bethesda, MD, USA;
36Institute of Marine Sciences, Department of Eco logy and Evolutionary Biolog y, University of C alifor nia Sant a Cruz, Santa Cruz, CA, USA; 37Integrated Marine
Obser ving Sys tem, University of Tasmania , Hobar t, TAS, Au stralia and 38Of fice of Naval Rese arch, A rling ton, VA, USA
This is an op en access arti cle under the ter ms of the Creative Commons Attribution N onCom mercial License, which p ermit s use, distribution an d reproduction
in any medium, provided the original work is properl y cited an d is not use d for comm ercial purposes.
© 2021 The Authors . Methods in Ecolog y and Evolution published by John Wiley & Sons Ltd on b ehalf of B ritish Ecologic al Soci ety. This article has bee n
contrib uted to by US G overnm ent employees an d their wo rk is in the public domain in t he USA .
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1 | INTRODUCTION
Bio- logging is a powerful set of methods that enables the collec-
tion of data about animal movement, behaviour, physiolog y and the
physical environment (Hussey et al., 2015; Kays et al., 2015; Rut z &
Hays, 2009). The rapid development and use of devices (hereafter
‘tags’) to collect, store and transmit bio- logging data began following
the launch of the Argos satellite data collection and location system
in the 1970s (Thums et al., 2018). Over the subsequent 50 years, the
use of acoustic telemetry, light- based geolocation, and other forms
of data logging and transmission have matured and become stan-
dard methods to understand animal distributions, habitat use, and
population connectivity. Data are being generated at unprecedented
rates, providing oppor tunities to conduct synthetic studies (Figure 1;
Block et al., 2011; Davidson et al., 2020; Hindell et al., 2020; Queiroz
et al., 2019; Sequeira et al., 2018; Tucker et al., 2018) and address con-
servation challenges, such as those resulting from global environmen-
tal change (Brett et al., 2020; Hays et al., 2019; McGowan et al., 2017;
Sequeira et al., 2019) as well as from extreme events (e.g. a global
pandemic; Bates et al., 2020; Rutz et al., 2020). However, managing
these data is challenging. Despite the growing number of collabora-
tive regional and global initiatives launched to compile existing bio-
logging data (Harcourt et al., 2019), there are no widely adopted data
and metadata standards, and most existing bio- logging data remain
undiscoverable and inaccessible. The lack of universal standards for
bio- logging datasets hampers progress in ecological research, burden-
ing researchers with technical and administrative hurdles each time
data are shared and re- used (Campbell et al., 2016). Problems range
Correspondence
Ana M. M. S equei ra
Email: ana.sequeira@uwa.edu.au
Funding information
The Pew Charitable Trusts; Office of
Naval Research Global, G rant/Award
Number : N00014- 19- 1- 2573; Australian
Research Council, Gra nt/Award Number:
DP2101030 91
Handling Editor: Edward Codling
Abstract
1. Bio- logging data obtained by tagging animals are key to addressing global conser-
vation challenges. However, the many thousands of existing bio- logging datasets
are not easily discoverable, universally comparable, nor readily accessible through
existing repositories and across platforms, slowing down ecological research and
effective management. A set of universal standards is needed to ensure discover-
ability, interoperability and effective translation of bio- logging data into research
and management recommendations.
2. We propose a standardisation framework adhering to existing data principles
(FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency,
Responsibility, User focus, Sustainability and Technology) and involving the use of
simple templates to create a data flow from manufacturers and researchers to
compliant repositories, where automated procedures should be in place to prepare
data availability into four standardised levels: (a) decoded raw data, (b) curated
data, (c) interpolated data and (d) gridded data. Our framework allows for inte-
gration of simple tabular arrays (e.g. csv files) and creation of sharable and inter-
operable network Common Data Form (netCDF) files containing all the needed
information for accuracy- of- use, rightful attribution (ensuring data providers keep
ownership through the entire process) and data preservation security.
3. We show the standardisation benefits for all stakeholders involved, and illustrate the
ap p lic a t ion of our frame work by fo cusi ng on marin e anim als an d by prov i din g exam p les
of the workflow across all data levels, including filled templates and code to process
data between levels, as well as templates to prepare netCDF files ready for sharing.
4. Adoption of our framework will facilitate collection of Essential Ocean Variables
(EOVs) in support of the Global Ocean Observing System (GOOS) and inter-
governmental assessments (e.g. the World Ocean Assessment), and will provide a
starting point for broader efforts to establish interoperable bio- logging data for-
mats across all fields in animal ecology.
KEYWORDS
bio- logging template, data accessibility and interoperability, data standards, metadata
templates, movement ecology, sensors, telemetry, tracking
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from acute issues with merging disparate datasets, through to the
lack of an overarching framework that ensures (a) accuracy- of- use,
(b) rightful attribution and ownership, and (c) data preservation se-
curity. The latter is especially relevant for older data not currently in
use, but potentially invaluable as baseline for future work. Adoption
of a framework to standardise bio- logging data will promote effi-
cient data collation, usage and sharing consistent with FAIR (Findable,
Accessible, Interoperable and Reusable) (Wilkinson et al., 2019) and
TRUST (Transparency, Responsibility, User focus, Sustainability and
Technology; Lin et al., 2020) data principles, and enable compliance
with requirements of publishers and funding agencies.
Bio- logging is used for a broad range of taxa across terrestrial
and marine ecosystems (Hussey et al., 2015; Kays et al., 2015). The
high diversity of marine animals, ranging from small seabirds and
fishes to the giant blue whale Balaenoptera musculus, and with high
mobility in three- dimensional space, has sparked a wide variety of
engineering solutions, sensors and approaches to enable attach-
ment of instruments and recovery of data. These include ‘store-
on- board’ tags that need to be recovered for data retrieval (Gleiss
et al., 2009; Watanabe & Sato, 20 08), and data- relay technologies
(Hussey et al., 2015) for radio- transmitting or pop- up archival tags
(Block et al., 1998). The data obtained can range from coarse tem-
poral and spatial resolution (e.g. light- level- based geolocation), to
pre cise locati on data in space an d ti me (e.g . GP S; Global Position ing
System), to ver y high- resolution pseudo- tracks from daily diary
instruments (Wilson et al., 2008). Moreover, marine bio- logging
dataset s can include concurrent data on horizontal and vertical
movements (i.e. in depth; similar to altitude in terrestrial bio- logging
data), as well as physical measurements from ancillary sensors
(Williams et al., 2020). The latter include detailed oceanographic
FIGURE 1 Value of synthesising bio- logging data. Effor ts to integrate animal tracking results from across multiple studies can deliver
fundamental insights into the ecology of diverse species as well as providing import ant information to help conser vation. (a) Tracking results
for >2,600 individuals across 50 marine species at global scale have shown similarities in the global movement pat terns across taxa linked
to habitat . Each colour represents different taxa: blue = seals, pink = sea turtles, light green = sea birds, dark green = sharks; re- drawn
from Sequeira et al. (2018) by Dr Jorge Rodríguez. (b) A nesting leatherback turtle Dermochelys coriacea. Collated tracking results for adult
leatherback tur tles from tracking studies across the Atlantic and Pacific have identified overlap hotspots bet ween pelagic longline fishing
intensity and tur tle foraging and have also revealed how foraging success varies between ocean basins and is linked to reproductive output
and conservation status (Bailey et al., 2012; Fossette et al., 2014; Roe et al., 2014). Photo courtesy of Tom Doyle. (c) A blue shark Prionace
glauca. Tracking thousands of pelagic sharks has revealed high overlap between fisheries and shark space use in the global ocean and has
highlighted the impor tance of marine- protected areas for this group (Queiroz et al., 2019). Photo courtesy of Jeremy Stafford- Deitsch.
However, even the largest animal tracking studies still only use a small fraction of the tracking data that have been collec ted (Hays &
Hawkes, 2018). (d) The increase in the annual number of published satellite tracking studies across various taxa. The number of publications
each year was obtained from Web of Science using the search terms ‘sea turtle satellite tracking’, ‘seal satellite tracking’, ‘whale satellite
tracking’, ‘seabird satellite tracking’ and ‘fish satellite tracking’. The plot conveys the ever- increasing number of published satellite tracking
studies. For legend to colours, see panel (e). (e) The number of Argos ids issued each year for satellite tracking studies with different
marine taxa. Each satellite tag is programmed with an Argos id number. Although some Argos ids are reused while others may be unused,
the number of Argos ids issued will broadly reflect the number of satellite tags deployed. As of May 2020, circa 50,00 0 Argos ids have
been issued for marine animal tracking, including around 10,000 for sea turtle tracking, 4,500 for cetaceans, 6,0 00 for seabirds, 6,000 for
pinnipeds and >20,000 for fish. Data on the number of Argos ids are supplied by CLS (https://www.argos - system.org/)
(a)
(d)
(e)
(b) (c)
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conduc tivit y, tempe rature an d de pth (CT D) dat a tha t can be us ed to
improve the outputs of ocean models (Moore et al., 2011; Roquet
et al., 2013). Size constraints specific to CTD packages currently
restrict their use to marine megafauna (i.e. the larger marine verte-
brates). Such marine megafauna play an important role in collect-
ing relevant data for a range of Essential Ocean Variables (EOVs)
(Miloslavich et al., 2018; Muller- Karger et al., 2018), including
temperature, salinity, fluorescence (proxy for chlorophyll- a) and
dissolved oxygen, across a range of ecosystems. These ecosys-
tems range from shallow coastal areas to the deep open ocean, and
from the tropics to the poles, including ice- covered areas that are
otherwise inaccessible to humans (Harcourt et al., 2019; Moore
et al., 2011; Treasure et al., 2017). An example of the latter includes
the near real- time temperature and salinity profile data collected
by elephant seals that is made freely available daily via the Global
Telecommunication System (GTS) of the World Meteorological
Organization (wmo.int) for immediate use by weather forecasters
and ship operators (Roquet et al., 2014). Marine megafauna are
therefore strong candidates to become a key data contributor to
the Global Ocean Observing System (GOOS), and indeed recently
the GOOS- Steering Committee has endorsed and included AniBOS
(A nim al Bor ne Oc ean Se nso rs) as one of its gl oba l net works th at wi ll
provide a cost- effective and complementary observing capability
through using animals as ‘ocean samplers’ (Harcourt et al., 2019).
However, successful integration of datasets is strongly dependent
on improving data standardisation.
Here, we provide a framework designed to facilitate stan-
dardisation of bio- logging data, including three data and meta-
data templates that can readily be used by manufacturers and
re s e arc her s to up l oad dat a to com p l ian t re p osi t orie s. We pr o pos e
th at comp liant repo sitor ie s auto ma te pr ocess in g bio- log gin g dat a
into four levels (described below) compiled to maximise interop-
erability and facilitate scientific discovery. Such outcomes will
be key to improve conservation management and lead to policy
development. Although our focus here is on marine bio- logging
data, our objective is to contribute to standardising bio- logging
datasets across all taxa and ecosystems, which is also one of the
stated goals of the International Bio- Logging Society (bio- loggi
ng.net).
2 | MATERIALS AND METHODS
We hosted a workshop at the OceanObs'19 conference in Honolulu,
Hawaii (ocean obs19.net), to develop a plan for global standardisation
of marine bio- logging datasets. The workshop was attended by 28
representatives from national and regional tagging networks, manu-
facturing companies, and intergovernmental bodies, and the group
was subsequently extended to include other key members from the
bio- logging community. We recognised the common goal to improve
the quality and consistency of processes, measurements, data, and
applications through agreed procedures, evolving into and contribut-
ing to best practices (cf. Pearlman et al., 2019; Tanhua et al., 2019).
2.1 | Progress to date and lessons learned on bio-
logging data standardisation
Varying levels of data standardisation have been achieved by exist-
ing repositories storing spatially discrete acoustic telemetry data
(e.g. OTN— Ocean Tracking Network, ocean track ingne twork.org;
AODN— Australian Ocean Data Network portal, portal.aodn.org.
au). Such standardisation is crucial for acoustic data (resulting from
detection of animal- borne transmitters through static receiver sta-
tions) to match detections across acoustic networks around the
world that are managed by dif ferent user groups. Although these
repositories are not yet fully interoperable, templates for report-
ing acoustic tracking data enable integration and rapid dat a sharing
among researchers and existing networks (Bangley et al., 2020). For
satellite and archival telemetry data, standardisation is more chal-
lenging given the many heterogeneous data file formats that result
from the large number of sensors used, existing manufacturers, as
well as settings and applications for different tags.
Several biogeographic data aggregators, such as the Global
Biodiversity Information Facility (GBIF, gbif.org), the Ocean
Biogeographic Information System (OBIS, obis.org) and the Atlas of
Living Australia (ALA, ala.org.au), use the Darwin Core body of stan-
dards for data interoperability. Darwin Core is a glossary of terms
well- suited for spatiotemporal biodiversity data maintained by the
Biodiversity Information Standards Group (tdwg.org). However, it has
limited capacity for capturing instrument metadata, and does not eas-
ily accommodate the multiple different intraspecific and interspecific
behaviours (often expressed by metrics recorded by multiple sensors)
that occur in a bio- logging study. To address this issue, OBIS has de-
veloped a schema (OBIS- Event- Data schema) relevant to acoustic and
satellite telemetry data (De Pooter et al., 2017; github.com/tdwg/dwc-
for- biolo gging). Another more recent development is the nc- eTAG, a
file format and metadata specification for the production of archive
quality, standards- based netCDF (network Common Data Form) data
files for dif ferent types of electronic tags (Tsontos et al., 2020).
The bio- logging community can leverage these standardisation
efforts as well as from learning the standardisation methods al-
ready achieved by other established networks (e.g. the Argo floats
and Lagrangian drifters) to fast- track bio- logging data standardisa-
tion consistent with the GOOS’ Framework for Ocean Observation
(FOO) (Lindstrom et al., 2012). For example, physical oceanogra-
phers have (a) established a permanent Data Management and
Communications (DMAC) Centre (ioos.noaa.gov/proje ct/dmac/)
providing free access to surface drifter data (aoml.noaa.gov/phod/
gdp/), (b) developed a full set of universal data standards for Argo
floats (argod atamgt.org/Docum entat ion) and (c) defined procedures
to access data (argo.ucsd.edu). There are also meta- repositories,
such as Coastwatch (coast watch.noaa.gov), that serve as meta- hosts
by linki ng and tr anslating oc eanog raphic data files fr om ot he r repos-
itories, and which could be used as an example for a meta- repository
of bio- logging data, particularly relevant for linking telemetry and
oceanographic data collected by marine megafauna acting as ocean
samplers (Harcourt et al., 2019; Treasure et al., 2017).
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2.2 | Standardisation of bio- logging data is needed
at multiple levels
Our proposed workflow (Figure 2) aims to advance the standardi-
sation of bio- logging data, using as a starting point three simple
templates (in comma- separated values; i.e. .csv format), which are
fully described in the Templates section in Supporting Information.
First, a Device Metadata template (Table S1) should be completed
by manufacturers or companies supporting tag data acquisition
and decoding. This template, which comprises information pertain-
ing to the instrument used, will be essential to complete the upload
of original, decoded bio- logging data to repositories with relevant
metadata about the device. Second, a Deployment Metadata tem-
plate (Table S2) should be completed by the researchers deploying
the tag devices, to encapsulate information about the animal tagged,
tagging protocols followed and tag settings. This template provides
FIGURE 2 Flow for standardisation of bio- logging data from tag to search engine. Standardisation of bio- logging data will need a
concerted and coordinated ef fort across manufacturers, researchers and repositories. It is crucial that the standardisation procedure
starts as close as possible to the time of data production. Manufacturers will need to provide a Device metadata template (Table S1) to
researchers, and will have the crucial role of creating a data file output option in their data processing software that allows export in a
compliant standardised format as we specify here for upload to repositories. This step will be vital, as the current heterogeneity of files
provided by the many existing manufacturers present s a major bottleneck for st andardisation. Researchers will then have a central role in
starting and maintaining data flow after deployment of bio- logging devices, by engaging in the data uploading process and providing the
Deployment metadata template (Table S2) where specification of ‘permission- to- use’ (e.g. acknowledgement, consultation or co- authorship)
is to be included. Despite the central role of researchers in establishing the data flow, the framework we propose is also prepared for direc t
upload of data by the manufacturer (indicated by the dashed arrow on the left) as it would be required for near real- time data availabilit y
at the repositories. Data are to be uploaded in a standardised format (Table S3) to facilitate data ingestion by repositories, and once at the
repositories, bio- logging data and metadata are to be used and kept together during translation into data products (Levels I through to IV;
refer to Figure 3 for details) that are to be easily discoverable through a global search engine acting as a meta- repositor y. Independent users
will be able to use this meta- repository to search data and obtain specific data- level products accompanied by the respective metadata to
translate it into synthesis product s useful for management and conservation while abiding to the ‘permission- to- use’ specifications made by
the researcher at the beginning of the process
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essential information and context for translation of data into derived
products and to enable assessment of possible biases during analysis
(Kilkenny et al., 2010; Webster & Rutz, 2020). Clear description of
conditions for data usage should be specified by the researchers in
this template (e.g. including specific requirements for acknowledge-
ment, attribution of ownership or need for co- authorship in resulting
outcomes) or alternatively, default to existing licensing types (e.g.
creativecommons.org). These two metadata templates include a
range of metadata fields common to all types of bio- logging devices
and resulting data, but are flexible enough to accommodate specific
subsets unique to each data type (see Supporting Information). A
third Input Data template including all data fields needed when using
diff erent tag ty pes sho uld als o be filled to en sure data set s ar e sta nd-
ardised and to facilitate data ingestion by repositories (Table S3).
This template should be filled by researchers collecting the data, or
directly by those acting as the first contact point for data, which
depending on the data type could include manufacturers or raw data
decoders (e.g. for data collected by satellite).
Our long- term vision for standardising bio- logging data is the
development of a suite of dynamic repositories with identical
protocols for data archiving and processing, resulting in interoper-
able data and metadata. Such interoperable formats will maintain
standardisation and data flow as new data are collected (Figure 2).
We note that much of the infrastructure needed for implementation
already exists, including procedures, standardised vocabularies and
formats. Therefore, standardisation could be achieved by improv-
ing the uptake of existing infrastructure, and by implementing pro-
cesses and procedures similar to those used in other fields where
data are constantly being updated. A relevant example of the latter
is the prod uct levels used by th e rem ot e sen si ng comm uni ty (e.g . the
US National Aeronautics and Space Administration Ocean Biology
Processing Group; NASA Oceancolor— oceancolor.gsfc.nasa.gov/
products). They provide a framework for organising data at various
levels, ranging from raw unprocessed instrumental data files (Level
0) to gridded data products with different levels of processing (up
to Levels 3 and 4). Such data organisation is directly relevant to bio-
logging and we have identified four equivalent levels at which bio-
logging data could be standardised in repositories to satisfy most
user needs (detailed in Figure 3). Our levels of standardisation start
with already decoded bio- logging data (Level 1), instead of raw,
FIGURE 3 Diagram of data processing from Level I through to Level IV at the repositories. Example of data flow for horizontal bio- logging
movement datasets. The translation of uploaded data into data products (Levels I– IV) should occur in a reproducible manner across all existing
repositories to facilitate integration and interoperability of Level I– IV datasets across repositories. We therefore suggest that this be an
automated and standardised process across repositories, where specific processing scripts and definitions for filters, interpolation intervals, and
gridding are adopted across repositories (refer to the example we provide in github.com/ocean- tracking- network/biologging_standardization).
Full documentation for the data processing settings used should be made available by repositories, including description of the filters used (e.g.
speed filter), uncertainty associated with locations provided (e.g. error ellipses), track processing method, interpolation time interval, location
uncertainty post- processing, temporal and spatial resolution for gridding. At each level, all metadata attributes should be retained to allow tracing
of the same datasets in different formats, with DeploymentID being the key to match data with metadat a. The data should be downloadable
(where permissions allow) through netCDF files built using standardised CDL files and standardised controlled vocabularies compliant with the
Climate Forecast (CF) metadata convention (see example provided on github.com/ocean - track ing- netwo rk/biolo gging _stand ardiz ation)
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unprocessed data files obtained from t ags (equivalent to Level 0 in
ocea n co lor prod ucts ). This is bec ause th e Level 0 data ar e of ten su b-
ject to proprietary rights from manufacturers, and standardisation
could become an impediment to innovation of protocols for data
storage and transmission.
2.2.1 | Level I— Decoded sensor data
De cod e d sen s or da t a , that is, de cryp t ed lo w - l eve l inf o r mat ion obtai n e d
directly from sensors after decoding Level 0 data, are critical to en-
suring original and complete bio- logging datasets remain archived for
future analysis and processing, particularly as downstream methods
evolve. Researchers should transfer transmitted and archival data to
repositories that share standardised procedures to receive individual
datasets. This procedure should involve a step where the researcher
assists in flagging (but not removing) meaningful versus erroneous or
irrelevant data (e.g. measurements representing the tag deployment
vs. pre- deployment). Level I data should include all data provided by
the tag, with the relevant data flags. It is desirable that such data are
made available immediately at the repository for visualisation in near
real time (Sequeira et al., 2019), which can be made possible if upload
is made directly by the first point of contact for the data (i.e. manufac-
turers). This visualisation should be made possible even if data access
needs to follow a predefined embargo period, as is already practiced
in some existing repositories (e.g. AODN, where some data can have
a 2- year embargo despite most data being made open access imme-
diately). Indeed, aggregation or delayed release of bio- logging data
might be needed to protect endangered species, and also to allow re-
searchers the opportunity to first publish their findings. Organisation
of Level 1 data will also offer a straightforward option for users who
are unable to process their data further (e.g. due to time constraints),
but want to securely archive their data. Once at the repositories, we
suggest that the Level I data and metadata be translated to processed
products (Levels II through to IV ) in an automated, st andardised way
as described below, with clear documentation provided at each step.
2.2.2 | Level II— Curated data
Curated bio- logging data, that is, a quality- controlled dataset after re-
moval of invalid, inconsistent or erroneous data points, are a resource
for any analyses and fur ther processing ensuring original, unpro-
cessed data are available. Erroneous dat a include all records that are
not representative of an animal's behaviour, such as location points
obtained before the tag is deployed or after tag detachment (e.g. a
drifting tag), or other obviously impossible locations, such as those
inland for animals that are exclusively marine (Freitas et al., 2008;
Hoenner et al., 2018). These erroneous positions should be flagged by
the researcher during the processing organisation of Level I data, and
relevant information (e.g. date for the start of the track as opposed to
deployment data) should be provided through a complete Deployment
Metadata template. This template will include information to assist
in removing data that do not belong to the tracked animal (e.g. data
transmitted by a tag floating after detachment). Production of Level II
data can then be automated at the repositor y by applying relevant fil-
ters (e.g. land filter, speed filter), addressing the details provided in the
Deployment Metadata template, and clearing or removing the data
points flagged in Level I data. A clear log for all the steps employed
should be documented by the repository (Figure 3), ensuring a clean
and usable version of the original decoded data is available without
manipulation or processing for any subsequent analyses.
2.2.3 | Level III— Interpolated data
Interpolated data, that is, processed bio- logging data that include
smoothed and interpolated locations, are a resource needed often
for analyses involving bio- logging datasets. Processing data in this
way is commonly done by applying a state- space model. These
types of models are used to filter the data and estimate the ani-
mal's most probable path (Braun et al., 2018; Johnson et al., 2008;
Jonsen et al., 2005, 2020) or to infer behavioural states (Michelot
et al., 2016), which can be used to generate area- use and network
models . Th e processing of Leve l II dat a in this way leads to manipu la -
tion of the original positions so they are interpolated in equal time
intervals to display the most likely track, which does not necessarily
include all original positions and is why storage of Level II data is im-
portant. There are many dif ferent ways to apply state- space models
to data. To facilitate integration into large- scale meta- analyses and
global dataset s, we su ggest that re po si tori es inc lu de automat ed pro-
cessing to produce standardised Level III data while also providing
alternatives for user- selected interpolation parameters. Again, the
respective documentation detailing the processing used should ac-
company all resulting products.
2.2.4 | Level IV— Gridded data
Gridded data, that is, bio- logging data presented in a grid format with
a specific grid- cell size and temporal resolution, are commonly used
to harmonise behavioural data with environmental information from
ot he r so u r c e s . This pr o c e d u r e ha s be en use d in re c e n t sy n t hesis st u d -
ies (Hindell et al., 2020; Queiroz et al., 2019; Sequeira et al., 2018)
and will be needed to address key global challenges associated with
human- induced stressors (Sequeira et al., 2019). For this step, a com-
mon temporal resolution and grid- cell size should be defined aim-
ing to have standardised Level IV products readily available. This
common spatiotemporal resolution could be monthly at 1 degree x
1 degree grid- cell sizes to reduce data gaps in environmental data
collected by satellites, such as chlorophyll- a (Scal es et al., 2017), and
following result s from other recent literature (Amoroso et al., 2018;
Kroodsma et al., 2018a, 2018b; O'Toole et al., 2020). This gridding
step should be applied to dat a Levels II and III to, respectively, pro-
duce Levels IVa (gridded curated data) and IVb (gridded interpolated
data). In addition to these standardised procedures, options for the
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user to select specific spatial and temporal resolutions to grid data
Levels II and III should also be provided by the repository.
2.2.5 | Additional compliance needed at the
repositories
At the repository level, an automated mechanism should be used to
create a unique c atalogue entry (‘EntrySourceID’) when ingesting the
standardised Level I data and metadata supplied by researchers or
manufacturers (Tables S1– S3; Figure 2). Each entry will store data cor-
respon ding to on e deployme nt from on e dev ice and will inclu de gl obal
level metadata attributes relating to the Device and Deployment
templates, including Organism details, and consistent with existing
standards. The ‘EntrySourceID’ should couple the name of the reposi-
tory ingesting data, the ownerName or projectName (provided in the
Deployment template), and three key IDs contributed in the templates
(InstrumentID, DeploymentID and OrganismID), using the following
format: urn:catalog:[repository]:[ownerName/projectName]:[Instrum
entID]:[DeploymentID]:[OrganismID].
All entries should include a ‘quality flag’ describing the quality of the
data (e.g. one of five levels: no_data, bad_data, worst_quality, low_qual-
ity, acceptable_quality and best_quality), which can be used to distin-
guish datasets with differ ent data qua lity levels (e. g. geolocation vs. GP S
data) and among those, the ones that have undergone quality control
(QC) by researchers through a curation step. For acoustic telemetr y
data, where QC of the detection data is required, the ‘Detection_QC’
flags introduced by Hoenner et al. (2018), should be used where simi-
lar QC tests are implemented. These include ‘FDA_QC’, ‘Distance_QC’,
‘Velocity_QC’, ‘DetectionDistribution_QC’ and ‘DistanceRelease_QC’
(for details and definitions, refer to table 1 of that publication).
2.3 | Data format for interoperability
We suggest that all the data levels are made available at compliant
repositories (Figure 3) and formatted to ensure data and metadata are
kept together during all data exchanges. For this, a network Common
Data Form (netCDF) format combined with standardised controlled
vocabularies compliant with the Climate Forecast (CF) metadata
convention could be most useful (see netCDF section in Supporting
Information). NetCDF is a self- describing, machine- independent data
format and associated set of sof tware libraries, which supports the
creation, access, and sharing of scientific data. Such an interoper-
able data file format would facilitate exchange of bio- logging data
with associated metadata templates, and there are existing tools to
facilitate conversion from netCDF to a range of output formats, in-
cluding commonly used tabular text formats (e.g. .csv). Indeed, adop-
tion of such standard form ats by exi sting consortia such as the Ma rine
Mammals Exploring the Oceans Pole to Pole (MEOP; meop.net) has
increased data uptake by the oceanographic community, consolidating
animal- collected data as a source to GOOS networks such as AniBOS
and other end- users (Treasure et al., 2017). Recent developments,
including the nc- eTAG format (Tsontos et al., 2020), which hierarchi-
cally stores blocks of attributes by tag or feature and allows speci-
fication of metadata consistent with the latest standards and next
generation CF enhancements (github.com/Unida ta/EC- netCD F- CF),
provi de a standards- ba se d sp ecificati on to store a ran ge of bio - lo gging
data, including satellite, archival and retrieved pop- up archival (PSAT)
dat a typ es . Storing dat a as net CD F using standardised Common DATA
Language (CDL) files (see netCDF section in supplementar y informa-
tion) will allow integration of tag instrument data file collections in
web server technologies such as THREDDS Data Server (TDS; unida
ta.ucar.edu/softw are/tds), ERDDAP and OPenDAP for subsetting,
aggregation and distribution of data to the community. Repositories
should include information on how to use netCDF files and how to
convert them to other formats as needed for input to other sof tware
programs for visualisation and analysis.
2.4 | Challenges for achieving standardisation
Standardisation of bio- logging dat a is needed to manage incoming
data and to retrospectively compile the thousands of bio- logging
dataset s already in existence (Block et al., 2011; Queiroz et al., 2019;
Rop ert- Coud er t et al ., 202 0; Sequ ei ra et al ., 2018; Thums et al., 2018).
Inf rastr uc ture sup por t an d developments will be nee de d to keep pac e
with technological advances, including provisions for near real- time
da t a, mo bil e rece ive rs an d nov el tag ty p es. Inde ed, the ne ed fo r stan d-
ardisation across platforms will be exacerbated as sensor technology
develops. Defining the metadata profiles for each of the existing and
new sensors will also be necessary, and mapping common elements
across met adata schemas will be needed to enable integration across
at least a minimal subset of required attributes.
The setup of a workflow for production of archive- quality data
files at all levels is also a challenge. Although the most familiar out-
put format options that are widely used as input for analyses should
continue to be available (e.g. .csv), capacit y building to train the ecol-
ogy community in the use of netCDF data formats will be needed.
Specifically, technology and infrastructure gaps in least- developed
countries need to be addressed, for example, by engaging networks
of researchers and manufacturers in the creation of tr anslation tools,
that is, tools allowing translation between data types (e.g. Rosetta;
unida ta.ucar.edu/softw are/rosetta; a UNIDATA tool to convert tab-
ular .csv files to standards compliant netCDF files) and ‘software
carpentry’ courses (e.g. softw are- carpe ntry.org) to deliver training
in data management and analysis.
The need for automation of data processing highlights the need
to incorporate data science in ecology and to strengthen engage-
ment between scientists from different disciplines (e.g. computer
science and engineering with ecology). Machine- to- machine read-
ability is important for effective standardisation, as is the ability to
quickly visualise and analyse data across large and disparate data-
sets. For this, the coupling of metadata with different levels of pro-
cessed tracking data and environmental and oceanographic data will
need to be streamlined.
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2.5 | Advantages of standardising bio- logging data
Standardised bio- logging data will lead to major advances in (a) un-
derstanding the distribution, movement and behaviour of species, (b)
improving our capacity to make comparisons across regions and taxa
and (c) providing concomitant environmental data that place animal
behaviour information into its ecological context contributing to global
observation. Importantly, these advances will, in turn, provide informa-
tion needed for improving conservation outcomes for species at risk
from human activities. Standardisation will facilitate a broad and effec-
tive use of bio- logging data to understand ecosystem dynamics and to
establish collaborative networks of ecologists, environmental and data
scientists, and ecosystem managers. Researchers contributing dat a will
benefit from an effective framework for data storage and retrieval, al-
lowing added value to all datasets collected while ensuring right ful
attribution and accuracy- of- use. Additionally, if existing repositories
provide harmonised, archive- quality netCDF files with consistent and
well- structured metadata, data exchange can be streamlined through
the creation of a global ‘meta- repositor y’ as a search engine (i.e. a dis-
covery tool similar to datas etsea rch.resea rch.google.com).
Standardisation of bio- logging data will also facilitate standardisa-
tion and integration of other datasets, including relevant ancillary data
that can improve our understanding about ecological and evolution-
ary responses of animals to environment al change. These might in-
clud e da ta associ at ed wit h the ind iv idual 's ori gi n, physio lo gic al state or
move me n t s pr i o r to th e ta g ging pe r i o d , as we ll as diet a r y ha b i t s, grow t h
rates and breeding behaviour, and could include datasets derived from
ti ssu e sam pli ng, such as mu s cl e plu g s, fi n cl i ps , hai r s, wh is ker s or fe ath -
ers. Standardised vocabularies and data formatting options (including
the nc- eTAG format described above) can be extended to deal with
diverse ancillary information, in coordination with relevant data plat-
forms and standards from other disciplines. Additionally, standards for
netCDF- Linked Data (LD; https://binar y- array - ld.github.io/netcd f- ld)
that will enable automated cross- referencing of metadata within data
files are now emerging. Moreover, formatting data as netCDF consis-
tent with the CF standards will provide compatibilit y with the global
observing communities and likely facilitate integration with a range
of diverse environmental and oceanographic data products such as
bathymetry, satellite- derived and modelled temperatures, winds and
currents, and chlorophyll a.
Specifically for marine bio- logging data, standardisation will
represent a step towards further integration of obser vations into
GOOS, following similar procedures already used for a broad
array of ocean sensor platforms, including gliders (Rudnick, 2016)
and acoustic platforms. Delivering standardised data streams will
provide the broader ocean community with a more efficient way
to assess the state of the world's oceans as they change and in-
form national and international assessments, including the Regular
Process, the World Ocean Assessment, assessments undertaken by
the Intergovernmental Science Policy Platform on Biodiversity and
Ecosystem Services, and Conventions on Biological Diversity and
on Migratory Species. Bio- logging provides data on multidisciplinary
EOVs that may act as ‘indicators’ to be used in national reporting
to biodiversity conventions and internationally to monitor progress
towards the UN Sustainable Development Goal 14 (SDG14; un.org/
s u s t a i n a b l e d e v e l o p m e n t / s u s t a i n a b l e - d e v e l o p m e n t - g o a l s / ) a n d t h e
new targets under the Post- 2020 Global Biodiversity Framework.
Current developments associated with the blue economy agenda
(Eikeset et al., 2018), the global aim to achieve SDG14, and the re-
quirem en t to pro vi de key obs ervations in sup por t of the UN Dec ade
of the Ocean Science (Ryabinin et al., 2019), emphasise the ne ed for
marine bio- logging data to be made readily available. Appropriate
information on movements and ecology is urgently needed to in-
form conservation of species at risk of ex tinction (Estes et al., 2016;
McCauley et al., 2015).
ACKNOWLEDGEMENTS
We are thankful to ONR and UWA OI for funding the workshop,
and to ARC for DP210103091. A.M.M.S. was funded by a 2020 Pew
Fellowship in Marine Conservation, and also supported by AIMS.
C.R. was the recipient of a Radcliffe Fellowship at the Radclif fe
Institute for Advanced Study, Harvard Universit y. We thank Suzi
Kohin and Mat thew Ruthishauser from Wildlife Computers for ear-
lier discussions and feedback on the manuscript.
AUTHORS' CONTRIBUTIONS
A.M.M.S., M.O., D.P.C ., M.R.H., C.R.M., R.H. and M.W. conceived the
study and organised the workshop; A.M.M.S., M.O., T.R.K., L.H.M.,
I.D.J., J.P., S.J.B., E.L.H., K.H., M.H., C.B., D.C.D., M.F., M.A.H.,
M.K.M., M.M.C.M., S.E.S., B.T., F.W., B.W., D.P.C., M.R.H., C.R.M.,
R.H., M.W. and F.W. attended the workshop and prepared the first
draft; A.M.M.S., M.O., T.R.K., L.H.M., C.D.B., X.H., F.R.A.J., P.N., J.P.,
S.J.B. and V.T. compiled information and prepared the templates;
J.P., P.N., T.R.K., L.H.M., C.D.B., F.R.A.J., I.J., V.T. and A.M.M.S. pre-
pared GitHub content; A.M.M.S., M.O., F.R.A.J., J.P., G.C.H., E.L.H.,
S.J.B. and M.H. prepared the figures; A.M.M.S. led the writing. All
authors contributed and edited the manuscript.
PEER REVIEW
The peer review history for this article is available at https://publo ns.
com/publo n/10.1111/2 041- 210 X.13593.
DATA AVAIL AB I LI T Y STATE MEN T
All data used in the manuscript, including ‘templates’ and associated
definition of terms, example data showing the format to be used for
data upload, code to convert between standardised data levels, CDL
and netCDF examples are available from github.com/ocean - track
ing- netwo rk/biolo gging_stand ardiz ation and Sequeira et al. (2021).
ORCID
Ana M. M. Sequeira https://orcid.org/0000-0001-6906-799X
Xavier Hoenner https://orcid.org/0000-0001-5811-1166
Fabrice R. A. Jaine https://orcid.org/0000-0002-9304-5034
Peggy Newman https://orcid.org/0000-0002-9084-5992
Graeme C. Hays https://orcid.org/0000-0002-3314-8189
Elliott L. Hazen https://orcid.org/0000-0002-0412-7178
|
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Methods in Ecology and Evoluon
SEQUEIR A Et Al.
Vardis M. Tsontos https://orcid.org/0000-0002-1723-0860
Clint Blight https://orcid.org/0000-0002-3481-7428
Sarah C. Davidson https://orcid.org/0000-0002-2766-9201
Victor M. Eguíluz https://orcid.org/0000-0003-1133-1289
Michael Fedak https://orcid.org/0000-0002-9569-1128
Mark A. Hindell https://orcid.org/0000-0002-7823-7185
Ivica Janekovic https://orcid.org/0000-0001-8388-0848
Mônica M. C. Muelbert https://orcid.org/0000-0002-5992-5994
Christian Rutz https://orcid.org/0000-0001-5187-7417
David W. Sims https://orcid.org/0000-0002-0916-7363
Samantha E. Simmons https://orcid.org/0000-0003-0326-6990
Clive R. McMahon https://orcid.org/0000-0001-5241-8917
Rob Harcourt https://orcid.org/0000-0003-4666-2934
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How to cite this article: Sequeira AMM, O'Toole M, Keates TR,
et al. A standardisation framework for bio- logging data to
advance ecological research and conservation. Methods Ecol
Evol. 2021;12:996– 1007. ht tp s://doi.org/10 .1111/2 041-
210X.13593
