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A standardisation framework for bio‐logging data to advance ecological research and conservation



Bio‐logging data obtained by tagging animals is key to addressing global conservation 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. This slows down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability, and effective translation of bio‐logging data into research and management recommendations. 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: (i) decoded raw data, (ii) curated data, (iii) interpolated data, and (iv) gridded data. Our framework allows for integration of simple tabular arrays (e.g., csv files) and creation of sharable and interoperable 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. We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, providing data examples, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing. 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 formats across all fields in animal ecology.
Methods Ecol Evol. 2021;12:996–
Received: 30 October 202 0 
Accepted: 15 Februar y 2021
DOI: 10.1111/2041-210X.13593
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
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contrib uted to by US G overnm ent employees an d their wo rk is in the public domain in t he USA .
Methods in Ecology and Evoluon
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
Ana M. M. S equei ra
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
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.
bio- logging template, data accessibility and interoperability, data standards, metadata
templates, movement ecology, sensors, telemetry, tracking
Methods in Ecology and Evoluon
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 -
(b) (c)
Methods in Ecology and Evoluon
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 ( 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
We hosted a workshop at the OceanObs'19 conference in Honolulu,
Hawaii (ocean, 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;
AODN— Australian Ocean Data Network portal,
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,, the Ocean
Biogeographic Information System (OBIS, and the Atlas of
Living Australia (ALA,, 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 ( 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;
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 ( ct/dmac/)
providing free access to surface drifter data (
gdp/), (b) developed a full set of universal data standards for Argo
floats (argod entat ion) and (c) defined procedures
to access data ( There are also meta- repositories,
such as Coastwatch (coast, 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).
Methods in Ecology and Evoluon
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
Methods in Ecology and Evoluon
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. 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
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 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 - track ing- netwo rk/biolo gging _stand ardiz ation)
Methods in Ecology and Evoluon
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
Methods in Ecology and Evoluon
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
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
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; 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 ( 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 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 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 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.
Methods in Ecology and Evoluon
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
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 - 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;
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).
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.
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.
The peer review history for this article is available at https://publo ns.
com/publo n/10.1111/2 041- 210 X.13593.
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 - track
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... Tag quantity and study design should be determined by the ability to draw meaningful conclusions and based on well-developed hypotheses, and, ideally, not simply availability or opportunity. Further improvements can be made by pooling information from across studies to generate larger data sets, made possible from the establishment and collaboration of multi-institutional programs (e.g., TOPP, see Block et al., 2011;GSMP, see Queiroz et al., 2019), open data policies, and online repositories (i.e.., Integrated Marine Observing System [IMOS] and Ocean Tracking Network [OTN]) that continue to be vital to increasing outputs from tagging events (Sequeira et al., 2019b;Harcourt et al., 2019;Sequeira et al., 2021). Ongoing telemetry networks like the Animal Tracking Network (ATN) hosted by the Integrated Ocean Observing Systems (IOOS) have been responsible for large accumulations of shark tracking data (Coffey et al., 2017;Queiroz et al., 2019;White et al., 2019b;Bruce et al., 2019). ...
... Many existing programs have developed the required infrastructure needed for large data repositories that would include elasmobranch tracking data, such as MoveBank ( Initiatives including the Spatial Ecological Analysis of Mega-vertebrate Population (OBIS-SEAMAP), Migratory Connectivity (MiCO), Marine Megafauna Movement Analytical Program (MMMAP), and the Global Shark Movement Project (GSMP) have also begun to address persistent challenges of data sharing pertaining to ownership rights (Sequeira et al., 2019b(Sequeira et al., , 2021. However, as Sequeira et al. (2019b) and Harcourt et al. (2019) pointed out, a lack of universal data standards, misinterpretations of data, limited funding, opaque resource sovereignty, and analytical difficulties of pooled data continue to create hurdles for data collection and require innovative solutions (see Sequeira et al., 2019bSequeira et al., , 2021 for recommendations and Harcourt et al., 2019 for a list of marine data repositories). ...
... Initiatives including the Spatial Ecological Analysis of Mega-vertebrate Population (OBIS-SEAMAP), Migratory Connectivity (MiCO), Marine Megafauna Movement Analytical Program (MMMAP), and the Global Shark Movement Project (GSMP) have also begun to address persistent challenges of data sharing pertaining to ownership rights (Sequeira et al., 2019b(Sequeira et al., , 2021. However, as Sequeira et al. (2019b) and Harcourt et al. (2019) pointed out, a lack of universal data standards, misinterpretations of data, limited funding, opaque resource sovereignty, and analytical difficulties of pooled data continue to create hurdles for data collection and require innovative solutions (see Sequeira et al., 2019bSequeira et al., , 2021 for recommendations and Harcourt et al., 2019 for a list of marine data repositories). ...
Satellite telemetry as a tool in marine ecological research continues to adapt and grow and has become increasingly popular in recent years to study shark species on a global scale. A review of satellite tag application to shark research was published in 2010, provided insight to the advancements in satellite shark tagging, as well as highlighting areas for improvement. In the years since, satellite technology has continued to advance, creating smaller, longer lasting, and more innovative tags, capable of expanding the field. Here we review satellite shark tagging studies to identify early successes and areas for rethinking moving forward. Triple the amount of shark satellite tagging studies have been conducted during the decade from 2010 to 2020 than ever before, tracking double the number of species previously tagged. Satellite telemetry has offered increased capacity to unravel ecological questions including predator and prey interactions, migration patterns, habitat use, in addition to monitoring species for global assessments. However, <17% of the total reviewed studies directly produced results with management or conservation outcomes. Telemetry studies with defined goals and objectives produced the most relevant findings for shark conservation, most often in tandem with secondary metrics such as fishing overlap or management regimes. To leverage the power of telemetry for the benefit of shark species, it remains imperative to continue improvements to tag function and maximize the outputs of tagging efforts including increasing data sharing capacity and standardization across the field, as well as spatial and species coverage. Ultimately, this review offers a status report of shark satellite tagging and the ways in which the field can continue to progress.
... Such analyses would not only provide insight into the conservation and management of marine species but also reveal how their behavior and physiology have diversified and converged over evolutionary time. A critical component for future advancements is greater accessibility and standardization of biologging data sets collected by different research groups (140). This will lead to many discoveries by ensuring that research groups are aware of existing data sets in a format in which they can be combined easily with other data sets. ...
Addressing important questions in animal ecology, physiology, and environmental science often requires in situ information from wild animals. This difficulty is being overcome by biologging and biotelemetry, or the use of miniaturized animal-borne sensors. Although early studies recorded only simple parameters of animal movement, advanced devices and analytical methods can now provide rich information on individual and group behavior, internal states, and the surrounding environment of free-ranging animals, especially those in marine systems. We summarize the history of technologies used to track marine animals. We then identify seven major research categories of marine biologging and biotelemetry and explain significant achievements, as well as future opportunities. Big data approaches via international collaborations will be key to tackling global environmental issues (e.g., climate change impacts), and curiosity about the secret lives of marine animals will also remain a major driver of biologging and biotelemetry studies.
... These are often driven by constraints on the number of individual tags that can be deployed, and ultimately relies on the restrictions of capturing animals to tag and the funding of different projects. However, with the improvement and reduces costs of GPS tags it would be highly beneficial for the community to aim to develop some standard minimum sampling rates and durations in an attempt to make diverse datasets more compatible (Campbell et al, 2016;Sequeira et al, 2021). Tag failure must also be considered in future studies, and it is critical that studies report all data including tag failures and discrepancies from methods and results. ...
Seed dispersal is one of the most important ecosystem services globally. It shapes plant populations, encourages forest succession, and has multiple, indirect benefits for humans, yet it is one of the most threatened processes in plant regeneration, worldwide. The restricted movement of local frugivores, through habitat fragmentation, is one of the main threats to seed dispersal. These restrictions alter the behaviour associated with movements before, during and after interacting with fruits and seeds. Consequently, there have been recent calls for animal movement and behaviour to be better integrated with seed dispersal studies to enable researchers to fully understand the processes that determine seed rain. To assess the current use of animal tracking in frugivory studies and to provide a baseline for future studies, we provide a comprehensive review and synthesis on the existing primary literature of global tracking studies that monitor movement of frugivorous animals. Specifically, we identify studies that estimate dispersal distances and how they vary with morphological and environmental traits. We show that over the last two decades there has been a large increase in frugivore tracking studies that determine seed dispersal distances. However, gaps across taxa and geographic distribution still exist. Furthermore, we found that certain morphological and environmental traits can be used to predict seed dispersal distances. We demonstrate that an increase in body mass significantly increases the estimated seed dispersal mean and maximum distances, as does species flight ability. Our results also suggest that protected areas have a positive effect on mean seed dispersal distances when compared to unprotected areas. We anticipate that this review act as a reference for future frugivore tracking studies to build upon, specifically to understand the drivers of movement, and to interpret how seed dispersal and other ecosystem services will be impacted by human disturbance and land use changes.
... Desde la tecnología VHF (Kenward 2001), ARGOS y GPS (Kays et al. 2015, Tomkiewicz et al. 2010, Weimerskirch 2009), la tecnología GPS inversa (Weiser et al. 2016 y los sistemas satelitales, como ICARUS, dedicados al seguimiento de animales en todo el mundo (Wikelski et al. 2007) hasta el desarrollo de sensores, que incluye los acelerómetros ). El marcaje de individuos ha revolucionado nuestra comprensión de la ecología de los animales silvestres , Yoda 2019, Sequeira et al. 2021. Los sensores más utilizados, los acelerómetros (Yoda et al. 1999), son una herramienta muy potente para el estudio del movimiento y el comportamiento animal (Ropert-Coudert & Wilson 2005), pues detectan movimientos sutiles, incluyendo comportamientos de vigilancia , Kröschel et al. 2017, y son utilizados tanto en entornos terrestres como acuáticos (Bidder et al. 2014). ...
Comprender los cambios en el uso del espacio y el comportamiento reproductivo de las especies, sobre todo de aquellas que se encuentran amenazadas, es crucial no solo para mejorar el conocimiento sobre las mismas, sino para poder aplicar medidas de conservación que eviten su declive y posible extinción. Esta tesis doctoral tiene como objetivo profundizar en la ecología espacial y el comportamiento reproductivo de la avutarda hubara canaria (Chlamydotis undulata fuertaventurae; en adelante, hubara canaria), subespecie endémica de las islas Canarias, catalogada como amenazada a nivel global por la UICN y en peligro de extinción a nivel nacional. Mediante los datos obtenidos con el marcaje y seguimiento de individuos adultos marcados con emisores GSM/GPRS, se pretende conocer los patrones y procesos biológicos y ecológicos que ayuden a diseñar nuevas estrategias de conservación. Tras la Introducción (Capítulo I) y la Metodología General (Capítulo II), la tesis doctoral está estructurada en 4 capítulos de resultados. El Capítulo III (pendiente de publicar). En el Capítulo IV, se aborda el patrón de migración de la subespecie. Los resultados muestran la existencia de migración parcial en la población de estudio, siendo aproximadamente un tercio los individuos que se mueven a otras zonas una vez concluida la reproducción y mostrando una gran fidelidad, tanto a las zonas reproductivas como no reproductivas. Estos patrones de migración parecen estar producidos por las diferencias en la productividad vegetal entre las zonas reproductivas y las no reproductivas, según estimaciones derivadas de los índices NDVI y SAVI. Estos resultados sugieren que la migración parcial se produce como un mecanismo de adaptación a la distinta abundancia de alimento en verano en las diferentes zonas. Además, se profundiza sobre la cantidad de áreas no reproductivas que son utilizadas, observando que una de las zonas no reproductivas es seleccionada por más de la mitad de los individuos migradores. Esta zona presenta un mosaico de parcelas con vegetación natural, mayoritariamente de aulagas (Launaea arborescens), con barbechos y cultivos en regadío. Un resultado relevante es que la mayoría de los vuelos migratorios se realizaron en horario nocturno. En el Capítulo V, se investigan la selección de recursos y las áreas de campeo de los individuos. Se caracterizan el tamaño y la forma de los territorios utilizados, diferenciando entre individuos reproductores y no reproductores y también entre diferentes periodos del año (estación reproductiva y no reproductiva). El tamaño de las áreas de campeo varió en función de la temporada y el estado reproductivo del individuo. Los resultados mostraron que durante la época reproductiva ambos sexos utilizaron casi exclusivamente terrenos con vegetación natural, seleccionando como hábitats de alimentación matorrales de baja densidad, pastos y barbechos verdes. Sin embargo, durante la época no reproductiva (mayo-octubre), se desplazaron a matorrales de alta densidad, pero también en parte a tierras cultivadas y barbechos verdes, mostrando preferencia por las parcelas de regadío. Esta investigación aporta datos sobre los requerimientos ecológicos de la hubara canaria, que son importantes para el diseño de planes de conservación. En el Capítulo VI, se aborda la actividad nocturna (detectada previamente en el capítulo IV), investigando el tipo y la intensidad de dicha actividad. Los resultados muestran que, a pesar de que la hubara se considera un ave exclusivamente diurna, presenta cierta actividad por la noche. Los machos realizan comportamientos reproductivos, con mayor o menor intensidad en función de la fase lunar. La intensidad de la vocalización es mayor en las noches de luna llena, alcanzando niveles similares a los del amanecer, momento del día en el que hasta la fecha se había registrado la máxima actividad sexual en esta especie. Estos resultados sugieren que la luz de la luna puede ayudar a los machos que se exhiben no solo a detectar a los depredadores, sino a comunicarse visualmente con las hembras que se acercan, pudiendo llegar a lograr cópulas nocturnas sin la interferencia de machos vecinos. Tras los capítulos de resultados, se expone la Discusión General (Capitulo VII) y las Conclusiones (Capítulo VIII). Esta tesis pone de manifiesto la necesidad de comprender el uso del espacio y el comportamiento reproductivo de las aves para poder realizar una gestión adecuada. La hubara canaria se encuentra en un preocupante estado de conservación, debido principalmente a la fragmentación del hábitat, las molestias antropogénicas y la mortalidad no natural causada por choques con tendidos eléctricos y telefónicos y con vehículos. Estos problemas no parece que vayan a resolverse en breve, y por ello, las implicaciones para la conservación que se desprenden de esta tesis deberían tenerse en cuenta y aplicarse en un futuro próximo, para así evitar el declive de un ave endémica de las islas.
... 7. The marine ecosystem offers an opportunity to address many key ecological questions-from the influence of memory or learning, and social interactions, to prey distribution, and the impact of global change (Hays et al., 2016). Marine animals with sensors can potentially act as sentinels and record environmental variables in regions in the ocean not commonly sampled, which will allow us to better understand climate and ocean variability (McMahon et al., 2021). With a coastline of~7,500 km, there is immense potential to track the movement of various marine animals to provide relevant information for the conservation of species and ecologically sensitive marine zones of India. ...
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The field of animal movement ecology has advanced by leaps and bounds in the past few decades with the advent of sophisticated technology, advanced analytical tools, and multiple frameworks and paradigms to address key ecological problems. Unlike the longer history and faster growth of the field in North America, Europe, and Africa, movement ecology in Asia has only recently been gaining momentum. Here, we provide a review of the field from studies based in India over the last 11 years (2011–2021) curated from the database, Scopus, and search engine, Google Scholar. We identify current directions in the research objectives, taxa studied, tracking technology and the biogeographic regions in which animals were tracked, considering the years since the last systematic review of movement ecology research in the country. As an indication of the growing interest in this field, there has been a rapid increase in the number of publications over the last decade. Class Mammalia continues to dominate the taxa tracked, with tiger and leopard being the most common species studied across publications. Invertebrates and other small and medium-sized animals, as well as aquatic animals, in comparison, are understudied and remain among the important target taxa for tracking in future studies. As in the previous three decades, researchers have focussed on characterising home ranges and habitat use of animals. There is, however, a notable shift to examine the movement decision of animals in human-modified landscapes, although efforts to use movement ecology to understand impacts of climate change remain missing. Given the biogeographic and taxonomic diversity of India, and the fact that the interface between anthropogenic activity and wildlife interactions is increasing, we suggest ways in which the field of movement ecology can be expanded to facilitate ecological insights and conservation efforts. With the advancement of affordable technologies and the availability of analytical tools, the potential to expand the field of movement ecology, shift research foci, and gain new insights is now prime.
... Even within species, nuanced differences between studies in the behaviour or habitat use of the tagged individuals, or the fitting procedures of the researchers (where there are few good options for objective assessment), could also feasibly lead to different impacts being detected. This may only be better understood with wider reporting of evaluations as a record for other researchers (Bodey et al 2018), especially where negative impacts are detected; this is in line with calls to continue to improve processes and reporting of other aspects of biologging (Campbell et al 2016, Lameris & Kleyheeg 2017, Sequeira et al 2021. loggers was provided by Lech Iliszko. ...
Biologging is a routine technique for investigating movements and behaviours of birds in ecology, needing harnesses to attach devices to birds when long-term data of high spatial resolution are required. Evaluating the impacts on individuals of those devices and their attachment methods is important to maintain both animal welfare and the validity of data. In two independent trials of harnesses on Black-legged Kittiwake Rissa tridactyla, in Norway and the UK, harnesses were constructed from Teflon ribbon and deployed on breeding adults using two different attachment methods, a leg-loop (UK; n = 3) and thoracic cross-strap harness (UK; n = 3, Norway; n = 2). The birds were later recaptured and the harness fit and bird condition inspected. We found acute impacts from the harnesses of varying extent and severity, including abrasion and small lesions where the device or harness was sitting. Generally, the thoracic harness design caused more severe impacts, but some signs of feather abrasion were also evident on leg-loop individuals. While there was an apparently larger impact of thoracic harnesses on daily mass loss between recaptures compared with untagged or leg-loop individuals, the low sample sizes denied us statistically meaningful results. Continual appraisal in biologging studies is important, especially when studying a novel combination of species and attachment methods, even if those methods have been demonstrated to be safe and effective with similar taxa. Given the degree of abrasion reported here, we would not recommend the use of thoracic harnesses for Kittiwakes. However, leg-loops may be a viable alternative if different materials or design are used, provided that the impacts can be closely monitored and reported.
... It is envisioned that data from shark-borne packages will be passed to the Animal Tracking Network (ATN) Data Assembly Center (DAC) and from there to the Global Telemetry System (GTS) of the World Meteorological Office (WMO). Many operational oceanographic and meteorological models rely on data from the GTS and efforts are underway to standardize animal-derived data to expedite their assimilation into the system [17]. Depending on satellite and Wildlife Computers Mote coverage, it is possible for data transmitted from a shark to be processed by the Wildlife Computers Data Portal and exported to the DAC or other data assembly sites (such as PacIOOS) with a latency of as little as 15 min. ...
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Background Many regions of the ocean are under-sampled in terms of their biology and physical structure. Increasingly sophisticated animal-borne electronic tags are capable of measuring and transmitting in situ environmental data such as ocean temperature–depth profiles. This has the potential to significantly augment the volume of data acquired from under-sampled regions of the ocean. These data would enhance interpretation of animal behavior and distribution and could be used to inform oceanographic and meteorological models. Building on results obtained from marine mammals and turtles, we present a case study of depth–temperature profiles obtained from a tagged tiger shark. Results During a 102-day deployment, 1350 geolocations were obtained from a shark from waters around Oahu, Hawaii. Of these, 520 were associated with depth–temperature profiles—some of which were from depths exceeding 500 m. Delay between profile creation and transmission to satellite or land-based receiver averaged 8.9 h (range: 35 s–43 h, median 6.32 h). The profiles were in close agreement with profiles extracted from nearby locations in an operational ROMS model. Land-based receivers played a significant role in augmenting data throughput obtained via satellites. Conclusions Shark-borne transmitters offer a viable option for collecting ocean profiles with reporting latencies that make them suitable for operational oceanography. They can significantly increase sampling frequency (especially subsurface) and sample geographic areas that are otherwise difficult to monitor with Lagrangian methods such as Argo floats. They sample locations and depths that are important to the animal and which in some cases may be biological hotspots. The resolution of the data is comparable with that derived from traditional platforms. By targeting appropriate species of shark, different areas of the ocean could be monitored at significantly higher rates than is currently the case.
... For example, GBIF recommends using the Ecological Metadata Language (EML) for datasets and Darwin Core for occurrence data. In addition to standardising data collection formats, meta-repositories, such as Coastwatch [277], offer the possibility of linking and translating different data bases to allow greater interoperability, as well as facilitating the search and discovery of data [278,279]. Making software for data management open access is as important as making the data itself freely available. Furthermore, greater effort is required in connecting georeferenced biodiversity data to corporate areas of operation and supply chains to facilitate science-informed decision-making by businesses. ...
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Many stakeholders, from governments to civil society to businesses, lack the data they need to make informed decisions on biodiversity, jeopardising efforts to conserve, restore and sustainably manage nature. Here we review the importance of enhancing biodiversity monitoring, assess the challenges involved and identify potential solutions. Capacity for biodiversity monitoring needs to be enhanced urgently, especially in poorer, high-biodiversity countries where data gaps are disproportionately high. Modern tools and technologies, including remote sensing, bioacoustics and environmental DNA, should be used at larger scales to fill taxonomic and geographic data gaps, especially in the tropics, in marine and freshwater biomes, and for plants, fungi and invertebrates. Stakeholders need to follow best monitoring practices, adopting appropriate indicators and using counterfactual approaches to measure and attribute outcomes and impacts. Data should be made openly and freely available. Companies need to invest in collecting the data required to enhance sustainability in their operations and supply chains. With governments soon to commit to the post-2020 global biodiversity framework, the time is right to make a concerted push on monitoring. However, action at scale is needed now if we are to enhance results-based management adequately to conserve the biodiversity and ecosystem services we all depend on.
Contemporary rates of biodiversity decline emphasize the need for reliable ecological forecasting, but current methods vary in their ability to predict the declines of real-world populations. Acknowledging that stressor effects start at the individual level, and that it is the sum of these individual-level effects that drives populations to collapse, shifts the focus of predictive ecology away from using predominantly abundance data. Doing so opens new opportunities to develop predictive frameworks that utilize increasingly available multi-dimensional data, which have previously been overlooked for ecological forecasting. Here, we propose that stressed populations will exhibit a predictable sequence of observable changes through time: changes in individuals’ behaviour will occur as the first sign of increasing stress, followed by changes in fitness-related morphological traits, shifts in the dynamics (for example, birth rates) of populations and finally abundance declines. We discuss how monitoring the sequential appearance of these signals may allow us to discern whether a population is increasingly at risk of collapse, or is adapting in the face of environmental change, providing a conceptual framework to develop new forecasting methods that combine multi-dimensional (for example, behaviour, morphology, life history and abundance) data. The authors outline a framework for predicting animal population collapse under external stressors, based on a predictable sequence of observable changes through time.
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methods used, as well as the outcome or objectives targeted for each method. In addition to summarizing the use of complementary methods and their outcomes, we discuss how they supplement acoustic telemetry and other tracking approaches. Our review shows that using additional methods to support telem-etry data helps expand the breadth of research questions that can be addressed regarding the complex and assorted factors influencing movement patterns. Doing so enables greater value in movement ecology research and adjacent fields such as population dynamics, physiology, trophic ecology, reproduction, and health and survival, to underpin management decisions. This review serves as a primer and guide for bolstering data collection and multidisciplinary Abstract Tracking the movements of aquatic animals is a primary means of understanding movement ecology and interactions with human activities such as fisheries. Despite the diverse spatiotemporal scales that various underwater tracking tools (e.g., acoustic, satellite, PIT, radio, archival telemetry) enable, there are still limitations associated with their application and ability to address diverse research questions. In many cases, supplementary methods are used to complement tracking approaches either to overcome such limitations or to optimize the data that can be collected in a study. In this review, we synthesize relevant literature between 2010 and 2019 to evaluate the different types of complementary methods used with one of the main approaches for tracking fishes-acoustic telemetry. We categorize broad and specific
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The Arctic is entering a new ecological state, with alarming consequences for humanity. Animal-borne sensors offer a window into these changes. Although substantial animal tracking data from the Arctic and subarctic exist, most are difficult to discover and access. Here, we present the new Arctic Animal Movement Archive (AAMA), a growing collection of more than 200 standardized terrestrial and marine animal tracking studies from 1991 to the present. The AAMA supports public data discovery, preserves fundamental baseline data for the future, and facilitates efficient, collaborative data analysis. With AAMA-based case studies, we document climatic influences on the migration phenology of eagles, geographic differences in the adaptive response of caribou reproductive phenology to climate change, and species-specific changes in terrestrial mammal movement rates in response to increasing temperature.
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Acoustic telemetry, in which transmitters projecting ultrasonic signals carrying unique identification codes are deployed on marine and aquatic animals and detected and logged by acoustic receivers, is becoming a common tool in fisheries science. Collaboration among researchers using this technology has led to the development of telemetry networks that are capable of detecting transmitters at coastwide and even continental scales through the combined coverage of all members' receivers. Two grassroots telemetry networks in the northwest Atlantic and Caribbean, the Atlantic Cooperative Telemetry (ACT) Network and the FACT Network, began as small-scale efforts among neighboring researchers and have expanded to include shared databases of tagged animals along entire coastlines. A third telemetry network, the Ocean Tracking Network (OTN), has brought additional capacity to the ACT and FACT networks and has provided a focus for telemetry activities in Canadian waters. It has also improved the power and efficiency of telemetry research globally through collaborative, standardized methods for storing, sharing, and processing data. When used in combination with other data collected by traditional fishery research methods and emerging technologies , such as remote sensing and autonomous vehicles, data collected through acoustic telemetry networks can address fundamental but previously unanswered questions about key habitat areas and data-poor species and can yield new insights into the ecology of species that are thought to be well known. Here, we provide an overview of acoustic telemetry networks, including a history of the ACT Network, FACT Network, and OTN and a review of recent and current research that has been made possible through the connections enabled by these networks.
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Background: State-space models are important tools for quality control and analysis of error-prone animal movement data. The near real-time (within 24 h) capability of the Argos satellite system can aid dynamic ocean management of human activities by informing when animals enter wind farms, shipping lanes, and other intensive use zones. This capability also facilitates the use of ocean observations from animal-borne sensors in operational ocean forecasting models. Such near real-time data provision requires rapid, reliable quality control to deal with error-prone Argos locations. Methods: We formulate a continuous-time state-space model to filter the three types of Argos location data (Least-Squares, Kalman filter, and Kalman smoother), accounting for irregular timing of observations. Our model is deliberately simple to ensure speed and reliability for automated, near real-time quality control of Argos location data. We validate the model by fitting to Argos locations collected from 61 individuals across 7 marine vertebrates and compare model-estimated locations to contemporaneous GPS locations. We then test assumptions that Argos Kalman filter/smoother error ellipses are unbiased, and that Argos Kalman smoother location accuracy cannot be improved by subsequent state-space modelling. Results: Estimation accuracy varied among species with Root Mean Squared Errors usually <5 km and these decreased with increasing data sampling rate and precision of Argos locations. Including a model parameter to inflate Argos error ellipse sizes in the north - south direction resulted in more accurate location estimates. Finally, in some cases the model appreciably improved the accuracy of the Argos Kalman smoother locations, which should not be possible if the smoother is using all available information. Conclusions: Our model provides quality-controlled locations from Argos Least-Squares or Kalman filter data with accuracy similar to or marginally better than Argos Kalman smoother data that are only available via fee-based reprocessing. Simplicity and ease of use make the model suitable both for automated quality control of near real-time Argos data and for manual use by researchers working with historical Argos data.
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Reduced human mobility during the pandemic will reveal critical aspects of our impact on animals, providing important guidance on how best to share space on this crowded planet.
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A new framework for animal-behaviour research will help to avoid sampling bias — ten years on from the call to widen the pool of human participants in psychology studies beyond the WEIRD. A new framework for animal-behaviour research will help to avoid sampling bias — ten years on from the call to widen the pool of human participants in psychology studies beyond the WEIRD.
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As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold. Following a year-long public discussion and building on existing community consensus , several stakeholders, representing various segments of the digital repository community, have collaboratively developed and endorsed a set of guiding principles to demonstrate digital repository trustworthiness. Transparency, Responsibility, User focus, Sustainability and Technology: the TRUST Principles provide a common framework to facilitate discussion and implementation of best practice in digital preservation by all stakeholders.
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Southern Ocean ecosystems are under pressure from resource exploitation and climate change1,2. Mitigation requires the identification and protection of Areas of Ecological Significance (AESs), which have so far not been determined at the ocean-basin scale. Here, using assemblage-level tracking of marine predators, we identify AESs for this globally important region and assess current threats and protection levels. Integration of more than 4,000 tracks from 17 bird and mammal species reveals AESs around sub-Antarctic islands in the Atlantic and Indian Oceans and over the Antarctic continental shelf. Fishing pressure is disproportionately concentrated inside AESs, and climate change over the next century is predicted to impose pressure on these areas, particularly around the Antarctic continent. At present, 7.1% of the ocean south of 40°S is under formal protection, including 29% of the total AESs. The establishment and regular revision of networks of protection that encompass AESs are needed to provide long-term mitigation of growing pressures on Southern Ocean ecosystems.
Telemetry datasets are becoming increasingly large and covering a wider range of species using different technologies (GPS, Argos, light‐based geolocation). Together, such datasets hold tremendous potential to understand species' space use at broad spatial scale, through the development of species distribution or habitat suitability models (SDMs) to predict environmental dependencies of species across space and time. However, tracking datasets can be heavily biased and an assessment of how such biases affect SDM predictions, and therefore, our interpretation of animal distributions is lacking. We generated simulated tracks based on predetermined environmental values for a random predator and a central place forager, and then sampled positions from those tracks based on a combination of five common biases in tracking datasets: (a) tagging location; (b) tracking device; (c) data gaps within tracks; (d) premature tag detachment (or failure) and (e) different processing methods. We then used 240 combinations of the resulting biased simulated datasets to develop binomial generalised linear (GLM) and additive (GAM) models to estimate habitat suitability in different environmental sets (cool deep, cool coastal, warm deep and warm coastal environments). Our results show that tagging location and length of tracks have the largest effects in decreasing model performance, but that these biases can be overcome by adding a small percentage of additional, relatively less biased tracks to the dataset. In comparison, the effects from all other biases were almost negligible, including for low resolution tracking datasets for which sufficient tracks are available. We also highlight the need for a cautionary approach when using processing methods that can introduce other biases (e.g. interpolated locations). Similar trends were obtained for the random predator and the central place forager, but with relatively lower model performance for the latter. We provide evidence that even non‐GPS tracking datasets can be readily used to improve the knowledge of large‐scale space use by species without the need for detailed processing and tracking reconstruction. This is especially relevant in the current context of rapid increase in data acquisition and the urgent need to address the large spatial scale ecological consequences of global change.
Efforts to curtail the spread of the novel coronavirus (SARS-CoV2) have led to the unprecedented concurrent confinement of nearly two-thirds of the global population. The large human lockdown and its eventual relaxation can be viewed as a Global Human Confinement Experiment. This experiment is a unique opportunity to identify positive and negative effects of human presence and mobility on a range of natural systems, including wildlife, and protected areas, and to study processes regulating biodiversity and ecosystems. We encourage ecologists, environmental scientists, and resource managers to contribute their observations to efforts aiming to build comprehensive global understanding based on multiple data streams, including anecdotal observations, systematic assessments and quantitative monitoring. We argue that the collective power of combining diverse data will transcend the limited value of the individual data sets and produce unexpected insights. We can also consider the confinement experiment as a “stress test” to evaluate the strengths and weaknesses in the adequacy of existing networks to detect human impacts on natural systems. Doing so will provide evidence for the value of the conservation strategies that are presently in place, and create future networks, observatories and policies that are more adept in protecting biological diversity across the world.
Open up, share and network information so that marine stewardship can mitigate climate change, overfishing and pollution. Open up, share and network information so that marine stewardship can mitigate climate change, overfishing and pollution.