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Synthesising trait observations and knowledge across the Tree of Life remains a grand challenge for biodiversity science. Despite the well-recognised importance of traits for addressing ecological and evolutionary questions, trait-based approaches still struggle with several basic data requirements to deliver openly accessible, reproducible, and transparent science. Here, we introduce the Open Traits Network (OTN) – a decentralised alliance of international researchers and institutions focused on collaborative integration and standardisation of the exponentially increasing availability of trait data across all organisms. The OTN embraces the use of Open Science principles in trait research, particularly open data, open source, and open methodology protocols and workflows, to accelerate the synthesis of trait data across the Tree of Life. Increased efforts at all levels – from individual scientists, research networks, scientific societies, funding agencies, to publishers – are necessary to fully exploit the opportunities offered by Open Science in trait research. Democratising access to data, tools and resources will facilitate rapid advances in the biological sciences and our ability to address pressing environmental and societal demands.
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1
The Open Traits Network: Using Open Science principles to accelerate trait-based
science across the Tree of Life
Gallagher, R. V.1, Falster, D. S.2, Maitner, B. S.3, Salguero-Gómez , R.4,5,6, Vandvik,
V.7,8, Pearse, W. D.9, Schneider, F. D.10, Kattge, J.11,12, Alroy, J.1, Ankenbrand, M. J.13,14,
Andrew, S. C.15, Balk, M.16, Bland, L. M.17, Boyle, B. L.3, Bravo-Avila, C. H.18,19,
Brennan, I.20, Carthey, A. J. R.21, Catullo, R.20, Cavazos, B. R.22, Chown, S.23, Fadrique,
B.24, Feng, X.3, Gibb, H.25, Halbritter, A. H.7,8, Hammock, J.26, Hogan, J. A.27, Holewa,
H.15, Hope, M.15, Iversen, C. M.28, Jochum, M.29,30,31, Kearney, M.32, Keller, A.13, Mabee,
P.33, Madin, J. S.34, Manning, P.35, McCormack, L.36, Michaletz, S. T.37, Park, D. S.38,
Penone, C.29, Perez, T. M.24,39, Pineda-Munoz, S.40, Poelen, J.41, Ray, C. A.42, Rossetto,
M.43, Sauquet, H.43,44, Sparrow, B.45, Spasojevic, M. J.46, Telford, R. J.7,8, Tobias, J. A.47,
Violle, C.48, Walls, R.49, Weiss, K. C. B.42, Westoby, M.1, Wright, I. J.1, Enquist, B. J.3,50
1Department of Biological Sciences, Macquarie University, Sydney, NSW, Australia
, 2Evolution and Ecology Research Centre, School of Biological, Earth and
Environmental Sciences, University of New South Wales, Sydney,
Australia, 3Department of Ecology and Evolutionary Biology, University of Arizona,
Tucson, Arizona, USA, 4Department of Zoology, Oxford University, Oxford,
UK, 5Centre for Biodiversity and Conservation Science, University of Queensland,
Queensland, Australia, 6Evolutionary Demography Laboratory, Max Plank Institute for
Demographic Research, Rostock, Germany, 7Department of Biological Sciences,
University of Bergen, Bergen, Norway, 8Bjerknes Centre for Climate Research,
University of Bergen, N-5020 Bergen, Norway, 9Ecology Center and Department of
Biology, Utah State University, Utah, USA, 10Niederstr. 23 64285 Darmstadt,
Germany, 11Max Planck Institute for Biogeochemistry, Jena, Germany,12German Centre
for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig,
Germany,13Department of Bioinformatics, Biocenter, University of Wuerzburg,
Wuerzburg, Germany, 14Center for Computational and Theoretical Biology, Biocenter,
University of Wuerzburg, Wuerzburg, Germany,15Commonwealth Scientific and
Industrial Research Organisation (CSIRO), Canberra, Australia, 16Bio5 Institute,
University of Arizona, Tucson, Arizona, USA, 17School of Life and Environmental
Sciences, Centre for Integrative Ecology, Deakin University, Victoria,
Australia, 18Department of Biology, University of Miami, Coral Gables, Florida,
2
USA, 19Fairchild Tropical Botanic Garden, Coral Gables, Florida, USA, 20Research
School of Biology, Australian National University, Canberra, Australia, 21Department of
Biological Sciences, Macquarie University, North Ryde, NSW, Australia , 22Department
of BioSciences, Program in Ecology and Evolutionary Biology, Rice University,
Houston, Texas, USA, 23School of Biological Sciences, Monash University, Victoria,
Australia, 24Department of Biology, University of Miami, Florida, USA, 25Department of
Ecology, Environment and Evolution and Centre for Future Landscapes, La Trobe
University, Victoria,26National Museum of Natural History, Smithsonian Institution,
Washington DC, USA, 27International Center for Tropical Botany, Department of
Biological Sciences, Florida International University, Florida, USA,28Climate Change
Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory,
Tennessee, USA, 29Institute of Plant Sciences, University of Bern, Bern,
Switzerland, 30German Centre for Integrative Biodiversity Research (iDiv)
Halle?Jena?Leipzig, Leipzig, Germany, 31Leipzig University, Institute of Biology,
Leipzig, Germany, 32School of BioSciences, The University of Melbourne, Victoria,
Australia,33Department of Biology, University of South Dakota, South Dakota,
USA, 34Hawai‘i Institute of Marine Biology, University of Hawai‘i at Manoa, Hawai‘i,
USA, 35Senckenberg Biodiversity and Climate Research Centre (SBiK-F),
Frankfurt,, 36Center for Tree Science, The Morton Arboretum, Illinois,
USA, 37Department of Botany and Biodiversity Research Centre, University of British
Columbia, British Columbia, Canada,38Department of Organismic and Evolutionary
Biology and Harvard University Herbaria, Harvard University, Cambridge,
Massachusetts, USA, 39Fairchild Tropical Botanic Garden, Florida, USA, 40School of
Biological Sciences, School of Earth & Atmospheric Sciences, Georgia Institute of
Technology, Georgia, USA, 41400 Perkins Street Apt 104, Oakland, California, United
States, 42School of Life Sciences, Arizona State University, Tempe, Arizona,
USA, 43National Herbarium of New South Wales, Royal Botanic Gardens and Domain
Trust, Sydney, NSW, Australia, 44Ecologie Systématique Evolution, Univ. Paris-Sud,
CNRS, AgroParisTech, Universite? Paris-Saclay, Orsay, France, 45TERN / School of
Biological Sciences, Faculty of Science, The University of Adelaide, Adelaide,
Australia, 46Department of Evolution, Ecology, and Organismal Biology, University of
California Riverside, California, USA, 47Department of Life Sciences, Imperial College
London, Silwood Park, UK, 48CEFE, CNRS, Univ Montpellier, Université Paul Valéry
Montpellier 3, EPHE, IRD, Montpellier, France, 49CyVerse, University of Arizona,
Tucson, AZ, USA, 50Santa Fe Institute, Santa Fe, New Mexico, USA
3
ABSTRACT
Synthesising trait observations and knowledge across the Tree of Life remains a grand
challenge for biodiversity science. Despite the well-recognised importance of traits for
addressing ecological and evolutionary questions, trait-based approaches still struggle
with several basic data requirements to deliver openly accessible, reproducible, and
transparent science. Here, we introduce the Open Traits Network (OTN) a
decentralised alliance of international researchers and institutions focused on
collaborative integration and standardisation of the exponentially increasing availability
of trait data across all organisms. The OTN embraces the use of Open Science principles
in trait research, particularly open data, open source, and open methodology protocols
and workflows, to accelerate the synthesis of trait data across the Tree of Life. Increased
efforts at all levels from individual scientists, research networks, scientific societies,
funding agencies, to publishers are necessary to fully exploit the opportunities offered
by Open Science in trait research. Democratising access to data, tools and resources will
facilitate rapid advances in the biological sciences and our ability to address pressing
environmental and societal demands.
4
INTRODUCTION
Traits, broadly speaking, are attributes or characteristics of organisms. Traits related to
functionsuch as leaf size, body mass, or growth formare often used to understand
how organisms interact with the environment and with other species via key vital rates
such as survival, development, and reproduction1-6. Trait-based approaches have long
been used in systematics and macroevolution to delineate taxa and reconstruct ancestral
morphology and function7-9 and to link candidate genes to phentoypes10-12. The broad
appeal of the trait concept is its ability to facilitate quantitative comparisons of biological
form and function and to mechanistically link organismal responses to abiotic and biotic
factors using measurements that are, in principle, relatively easy to capture across large
numbers of individuals. For example, appropriately chosen and defined traits can help
identify different lineages that share similar life-history strategies for a given
environmental regime13,14. Thus, documenting and understanding the diversity and
composition of traits in ecosystems directly contributes to our understanding of
organismal and ecosystem processes, functionality, productivity, and resilience in the
face of environmental change15-20.
Functional traits are important indicators for the socio-economic value of
ecosystems and their services. They are increasingly used to model food and energy
security, and to inform conservation decision-making21-25. Traits are also key to
improving modelling of earth systems and their responses to climate change by linking
energy and resource fluxes between organisms and the environment26,27. In short, trait
data can help bridge disciplines within biology, and link biology to the physical sciences
and human systems.
In recent decades there has been an acceleration in the collection, compilation,
and availability of trait data for a variety of organisms. Substantial trait databases now
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exist for plants28-31, reptiles32,33, invertebrates31,34-37, fish38,39, corals40, birds31,41,42,
amphibians43, mammals31,42,44,45, and fungi31 (see also
https://github.com/traitecoevo/fungaltraits) and parallel efforts are no doubt underway
for other taxa. Though considerable effort has been made to quantify traits for some key
groups (e.g., Fig. 1), substantial work remains. In order to advance knowledge by
developing and testing theory in biodiversity science much greater effort is needed to
combine and integrate data46. There are two central questions that together define the
scope and nature of the challenge to trait science. First, how can we most effectively
advance the synthesis of trait data within and across disciplines to address questions of
global significance (Box 1)? Second, how can we best deliver accurate and
understandable biodiversity knowledge to non-academic audiences who should have
equitable access to quality data on the traits of Earths’ species?
Figure 1. Mammal, bird, and plant phylogenies coloured according to the number of
traits for which we have data for each species and lineage. Trait data were downloaded
from 33,42,47, the number of traits present across these datasets for each species counted,
and then mapped onto single phylogenies from the posteriors of 45,48, and a random
subset of plant species within a single phylogeny from49. Terminal branches (representing
species) and ancestral lineages (using ancestral state reconstruction50) were then coloured
according to the number of (reconstructed) traits. Note that this is an exploratory
analysis, conducted purely to show the variation across taxonomic groups in the amount
of available trait data.
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Using Open Science principles to accelerate trait-based science
This is an exciting time to advocate for greater coordination and transparency in trait-
based research. Indeed, across the sciences, Open Science principles are rapidly emerging
and being adopted. Open Science principles (Fig. 2) outline a movement towards making
all aspects of the scientific process transparent and accessible to a wide audience51,52.
Figure 2. The six core principles of Open Science which guide the Open Traits Network.
In this context, knowledge is considered open if anyone can freely access, use, modify,
and share it subject, at most, to measures that preserve provenance and openness
(http://opendefinition.org/). Several pronouncements about Open Science principles
have already been made, including the Berlin (https://openaccess.mpg.de/Berlin-
Declaration), Bouchout (http://www.bouchoutdeclaration.org/declaration/) and
Denton Declarations (https://openaccess.unt.edu/denton-declaration) on open access to
science data. Other initiatives champion some open practices such as the Bari Manifesto
on interoperability53 and the FORCE 11 network, which developed the 'Joint
Declaration of Data Citation Principles’
(https://www.force11.org/datacitationprinciples) and ‘FAIR’ principles
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(https://www.force11.org/group/fairgroup/fairprinciples). The FAIR principles address
several of the major challenges facing trait-based research, namely making data
Findable, Accessible, Interoperable, and Reusable.
While the adoption of Open Science Principles has the potential to rapidly
advance global trait synthesis by democratising access to data, tools, and resources,
numerous hurdles remain. Trait-based science continues to struggle with realising these
principles because of several issues including: 1) a lack of readily available, machine-
accessible primary data released under a clear license arrangement; 2) the need for
standardised protocols, handbooks or metadata formats for data collection,
documentation and management, but see54-56; and 3) the complexity of integrating
existing legacy data from disparate sources (e.g., specimens, published literature, citizen
science initiatives57,58, large scale digitisation efforts (e.g., Biodiversity Heritage Library)
which will have systematic differences in the error rates, validation, context,
reproducibility, and objectivity relative to field-collected trait observations.
For many researchers and institutions, Open Science principles are increasingly
becoming part of their everyday approach to trait research. Connecting those wishing to
transition to an Open Science model whether individuals, research groups, or
institutions to those already actively adopting these principles will facilitate transfer of
skills and knowledge. These connections could be made via model examples, standards,
and networks. Trait science has clear potential to rapidly increase its taxonomic,
phylogenetic, and spatial scope, by stronger advocacy of Open Science and greater
connection between researchers, institutions, publishers, and funding bodies.
Introducing the Open Traits Network
The Open Traits Network (OTN) is a decentralised network accessible to any
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international researcher, institution, or research infrastructure provider focused on
collaboration and standardization in the collection of trait data based on Open Science
principles (Fig. 2). The primary goal of the OTN is to increase cross-community
exchange of ideas, tools, resources, and data.
Network diagrams can represent the way researchers interact when building
interlinked trait sources and databases; they show how individual nodes (researchers,
groups, institutions) are connected (Fig. 3).
Figure 3. Architectures of three alternative networks in which research groups (nodes) interact in
collecting and organising trait data. Black nodes are individuals, groups or institutions pursuing trait
projects. Light green nodes are those synthesising data and protocols, where node size is proportional to
available resources. Dark green nodes are synthesising nodes, which benefit from the flow of standardised
trait data and knowledge. (A) Groups are disconnected and decentralised, risking duplication of effort
(often the status quo); (B) Groups are inflexibly linked to a centralised repository, potentially limiting
innovation; (C) The Open Traits Network, which is represented by orange connecting lines. Nodes are
linked together within biological domains (e.g., plants, corals) and/or disciplines (e.g., systematics,
palaeobiology), allowing for more efficient and specialised decisions about trait collection. However, data
synthesis across domains or disciplines is then facilitated by joining nodes based on common workflows
and data sharing protocols which adhere to the guiding principles of the Open Traits Network. Central
synthesising nodes may be, for example, the Smithsonian Encyclopedia of Life https://eol.org/.
In many instances, groups building smaller-scale databases do so in isolation,
using their own tools and workflows tailored to their research question (Fig. 3A). These
decentralised researchers are often best placed to determine which traits are most useful
in their study system and how they should be compiled. However, there is little formal
support or interaction across this style of network, so researchers often collect redundant
9
data, use different data structures, and may develop similar tools for data collection,
cleaning, and integration in isolation, which can lead to duplication of effort. There are
many small, isolated, and heterogeneous data sources of this sort, increasing the
disconnect between pools of trait data59.
For some organisms, centralised hubs already exist which aggregate and
standardise trait data from across disparate sources (e.g.,29,40,60-63) (Fig. 3B). These trait
repository hubs have increasingly become the main access point for trait data, but they
remain mostly isolated from each other, limiting the sharing of expertise. Other large-
scale informatic challenges have also followed the centralised and connected network
model (e.g., the Global Biodiversity Information Facility https://www.gbif.org/;
GenBank https://www.ncbi.nlm.nih.gov/genbank/) and mandate strict data exchange
protocols to facilitate synthesis. Standardized, controlled vocabularies such as Darwin
Core and Humboldt Core have been essential to the explosive growth of biodiversity
data because they facilitate free exchange of information using common data
formats46,58,64. Further, ontologies necessary to represent traits have been developed (e.g.,
Uberonthe multispecies anatomy ontology for animals65, TOP the Thesaurus of
Plant characteristics66) that enable the integration with genetic and environmental data
through corresponding ontologies (e.g., Gene Ontology67; Environmental Ontology68).
Although the centralised and connected model (Fig. 3B) has clear advantages, it can lack
the flexibility to dynamically connect trait data where ontologies and exchange formats
do not exist. The result is that established trait networks will remain isolated and
disconnected.
The OTN (represented by the orange connections in Fig. 3C) maintains the key
advantages of a decentralised network (e.g., taxon/discipline specific decision-making)
while enhancing the level of connectivity among groups, allowing for easier aggregation
10
and sharing of expertise, tools, and data. These network characteristics may also buffer
against node loss (e.g., due to lack of funding). Decentralised and connected networks are
also known to be characterised by socially-mediated improvements in learning69. The
OTN is, in part, about capitalising on existing robust connections within disciplines to
more efficiently disseminate granular knowledge about traits.
Principles of the Open Traits Network
The OTN can strengthen and bridge between current initiatives in trait science and
encourage efficient new enterprises by serving as a platform for sharing principles,
methods, tools, examples, and approaches to support the wider traits community in
developing its scientific practice. The OTN is based on core principles of Open Science
(Fig. 2), namely:
1. Openly sharing data, methods, protocols, code, and workflows;
2. Appropriate citation of original data collectors, providing scholarly credit;
3. Provision of appropriate metadata together with trait observations;
4. Collection of trait data following reproducible, standardised methods and
protocols (when available) or commitment to their development;
5. Providing training resources in trait collection and database construction using
Open Science principles.
Below, we highlight key activities for the OTN designed to empower researchers to
gather and make better use of trait data.
Key activities for the Open Traits Network
Activity 1: Maintaining a global registry of trait-based initiatives
The OTN maintains a global registry of trait-based initiatives (https://protect-
11
au.mimecast.com/s/Fu2DCyoj8PuyX879TZG_y5?domain=docs.google.com; Table 1)
to help (i) connect the research community, (ii) identify data and knowledge gaps, (iii)
prioritise trait collection, and (iv) allow researchers to avoid inadvertent duplication of
efforts when collating trait data. The heterogeneous ways in which trait data have been
collected to date have resulted in a patchy and unrepresentative trait landscape across
trait types, taxa, regions, and times of the year70. These gaps impede synthetic analyses
across taxa, space, and ontogeny.
The OTN Registry contains information on existing datasets so that gaps can be
easily identified, and ultimately filled, through collective effort. Core information for the
registry includes trait name, geographic extent, taxonomic coverage, and temporal
period (Table 1), and existing knowledge from Ecological Metadata Language
(http://rd-alliance.github.io/metadata-directory/standards/eml-ecological-metadata-
language.html) and Darwin Core is adopted. Critically, the OTN Registry provides the
opportunity for contributors to identify where code to process and manipulate their raw
data is located (see Activity 2 below). The OTN Registry will also link to the ontology
resource OBO Foundry (http://www.obofoundry.org). Thus, the OTN registry maps to
several Open Science principles (Fig. 2; e.g., Open Source, Open Data, Open Access)
and is designed, from the ground up, to support resolving the issue of data integration.
Activity 2: Sharing reproducible workflows and tools for aggregating trait data
The OTN leverages collaborative software development via platforms like GitHub
(https://github.com/) to create a toolbox of modular open source software for access
and harmonisation and re-use of trait data, with seamless piping of data from one tool to
the next. OTN contributors have already developed several open source tools. For
instance, the traitdataform package assists R users to format their data and harmonise
12
units (http://ecologicaltraitdata.github.io/traitdataform); the code for the Coral Traits
database40 (https://github.com/jmadin/traits) could be easily modified to guide the
creation of databases on other organisms; and the FENNEC project provides a tool for
accessing and viewing community trait data as a self-hosted website service71
(https://github.com/molbiodiv/fennec). The OTN acts as a connector for interactions
between developers and the broader community seeking to synthesise trait data,
facilitating the training of scientists in all aspects of reproducible data management.
Activity 3: Advocating for a free flow of data and appropriate recognition of efforts
A goal of the OTN is to improve how researchers receive credit, via citations, for the
effort they have made to collect or synthesise primary data on species traits. Without
effective reward or motivation for collecting new trait observations or liberating legacy
data (e.g., observations from field guides, specimens, publications without data
supplements) a broad trait synthesis across the Tree of Life will remain unattainable.
Currently, however, motivation for collecting and sharing new primary data is not
strong.
The OTN can strengthen the attribution of credit to data providers via two paths.
Firstly, by encouraging citation back to primary source via CC-BY licensing. There is an
important distinction between sharing data within a network and making data publicly
available under an open license. Clear license arrangements increase visibility and
promote fair attribution/citation (e.g., using creative commons licenses such as CC-BY
or CC0). CC-BY requires attribution (i.e., citation) to the original creator, whereas CC0
doesn’t legally require users of the data to cite the source, but it does not affect the ethical
norms for attribution in scientific and research communities
(https://creativecommons.org/share-your-work/public-domain/cc0/). However,
13
identifying where credit for prior work should be directed for legacy data is complicated,
particularly where data involve a chain of expertise (e.g., when trait data are extracted
from taxonomic treatments which involve specimen collectors, digitisers, taxonomists,
and curators).
Secondly, the OTN Trait Registry (Activity 1) can be used to identify high-value
data gaps, helping would-be collectors of primary data on traits to have studies pre-
registered via the Centre for Open Science (https://cos.io/prereg/). Already as of
March 2019, 168 journals are willing to give in-principle acceptance before field or
experimental work is conducted but following pre-review of the study design.
Approximately ten of these participating journals regularly feature papers on trait-based
science, including BMC Ecology and Ecology and Evolution.
Activity 4: Advocating a common metadata standard across nodes
Given the highly contextual nature of trait data, metadata are as important as the
measurements themselves. The OTN provides a platform for the development of
metadata standards, controlled vocabularies, and a suite of trait ontologies which can be
recorded in the OTN Registry (Activity 1). Several initiatives have developed metadata
standards (e.g., Darwin Core64; Humboldt Core58; Ecological Metadata Language72).
However, these metadata standards are yet to be commonly applied in trait-based data
publications and syntheses. Using referencing terms from anatomy or phenotype
ontologies (e.g., the Plant Ontology73; the Vertebrate Trait Ontology74) relates traits
semantically to publicly-defined terms and allows data thus annotated to be processed
computationally75-77. Over time, the further development and implementation of
metadata standards in the OTN will help to avoid downstream issues in data re-use and
synthesis.
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Table 1. Structure of the OTN global registry of trait-based initiatives 1
Field name
Definition
Format/Values
Condition
datasetID
A unique identifier for each dataset
Integer
Required
(automatically
generated)
datasetDOI_URL
Location of the dataset or database on the
internet, e.g. the database website or the address
of the dataset on a file hosting service
Character
Required
contactName
Corresponding author or maintainer for the
dataset or database (name)
Characters separated by vertical bar space
| (if multiple names)
Required
contactEmail
Corresponding author or maintainer for the
dataset or database (email address)
Characters by vertical bar space | (if
multiple names)
Recommended
License
A legal document giving official permission to do
something with the resource
(http://purl.org/dc/terms/license); see
https://creativecommons.org/licenses/
Character
Required
traitList
List of traits. Names should correspond to
existing controlled vocabulary (when applicable)
Characters separated by vertical bar space
| (if multiple traits)
Recommended
higherGeography
A list (concatenated and separated) of geographic
names less specific than the information captured
in the locality term. (from:
http://rs.tdwg.org/dwc/terms/higherGeography)
Characters separated by vertical bar space
| (if multiple traits)
Recommended
decimalLatitude
The geographic latitude (in decimal degrees, using
the spatial reference system given in
geodeticDatum) of the geographic centre of a
Location. Positive values are north of the
Equator, negative values are south of it. Legal
Decimal degrees
Recommended
15
values lie between -90 and 90, inclusive.
(http://rs.tdwg.org/dwc/terms/decimalLatitude)
decimalLongitude
The geographic longitude (in decimal degrees,
using the spatial reference system given in
geodeticDatum) of the geographic center of a
Location. Positive values are east of the
Greenwich Meridian, negative values are west of
it. Legal values lie between -180 and 180,
inclusive.
Decimal degrees
Recommended
Taxon
A group of organisms (sensu
http://purl.obolibrary.org/obo/OBI_0100026)
considered by taxonomists to form a
homogeneous unit. (see
http://rs.tdwg.org/dwc/terms/Taxon)
Characters separated by vertical bar space
| (if multiple groups)
Required
eventDate
The date-time or interval during which an Event
occurred. For occurrences, this is the date-time
when the event was recorded. Not suitable for a
time in a geological context. (from
http://rs.tdwg.org/dwc/terms/eventDate)
Use a date that conforms to ISO
8601:2004E (see
http://rs.tdwg.org/dwc/terms/eventDate)
Recommended
paperDOIcitation
Immutable document object identifier of the
dataset or database; or actual citation (if
collection of multiple datasets, DOIs or citations
must appear in metadata)
Character
Required
Description
Description of the dataset or any other useful
information
Character
Recommended
taxaList
List of all taxa with some trait information
Characters separated by vertical bar space
| (if multiple taxa)
Recommended
usefulClasses
Any other useful information for improved
searching (e.g., life stage, body part, inferred traits
Character
Recommended
16
2
3
etc.)
dataStandard
Automated data consolidation (e.g., Darwin Core
or Ecological Trait-Data Standard)
Character
Recommended
standardizationScripts
Links to scripts for data standardization
Character (using a standard that can be
parsed)
Recommended
17
Activity 5: Facilitating consistent approaches to measuring traits within major groups 4
5
The OTN will advocate for the development of protocols and handbooks for major 6
clades that standardise approaches to capture traits. Protocols are necessary because 7
downstream activities such as developing metadata standards (Activity 4) will be 8
impossible to create if trait measurement protocols do not exist. Some trait-research 9
communities have already adopted standardised lists of terms66 and trait data collection 10
protocols (e.g., plants28,56,78-80, invertebrates37,55,81,82, mammals44, aquatic life38,40 11
https://www.sealifebase.org/home/index.php). New protocols and handbooks may not 12
emerge rapidly and should have the flexibility to be open to innovation through a 13
commitment to creating versions and updates as techniques evolve (e.g., from83 to56, or as 14
in http://prometheuswiki.org/tiki-custom_home.php). 15
Standardising approaches to trait measurement a priori across communities of 16
researchers will reduce ambiguity when aggregating data and improve the quality of 17
resulting datasets. Also, integrating trait standardization and databasing in to taxonomic 18
workflows represents both a considerable challenge and opportunity8, but also holds the 19
promise of bridging a long disconnect between structural and functional traits. The 20
presence of a range of biodiversity collections personnel in the OTN, and an open 21
invitation for more to join, has the potential to catalyse the adoption of trait-based 22
thinking into taxonomic practices. 23
24
Concluding remarks 25
The creation of an open, trait-based view of global diversity is now possible given the 26
myriad tools and approaches to data mobilisation and aggregation, harmonisation, and 27
processing. These advances in technology are also accompanied by numerous emerging 28
opportunities to work with institutions seeking to deliver biodiversity information to the 29
18
public, and with citizen scientists84 keen to gather trait data through platforms like (e.g., 30
Zooniverse https://www.zooniverse.org/; iNaturalist https://www.inaturalist.org/). 31
The OTN aims at supporting a reciprocal exchange of expertise and outputs between 32
researchers, institutions, and citizen scientists based on Open Science principles to 33
accelerate a cross-taxa, worldwide, trait-based data resource to examine, understand, and 34
predict nature’s responses. 35
36
ACKNOWLEDGEMENTS 37
Ideas presented stem from initial discussions at three separate international meetings 38
the Australian National Climate Change Adaptation Research Facility Roundtable on 39
Species Traits, the iDigBio ALA Traits workshop, and a preliminary Open Traits 40
workshop held at the Ecological Society of America. RVG is supported by an Australian 41
Research Council DECRA Felllowship (DE170100208). DSF is supported by an 42
Australian Research Council Future Felllowship (FT160100113). WDP is supported by 43
NSF ABI-1759965, NSF EF-1802605, and USDA Forest Service agreement 18-CS-44
11046000-041. CMI was supported by the Biological and Environmental Research 45
program in the United States Department of Energy’s Office of Science. MJ was 46
supported by the German Research Foundation within the framework of the Jena 47
Experiment (FOR 1451) and by the Swiss National Science Foundation. AK received 48
financial support for MJA by the German Research Foundation (DFG KE1743/7-1). 49
STM is supported by SERDP project RC18-1346. CP is supported by the DFG Priority 50
Program 1374. 51
52
DATA AVAILABILITY STATEMENT 53
There are no original data associated with this manuscript. 54
19
BOXES 55
56
57
58
59
60
61
62
63
64
Box 1: Potential research programs that could be carried out with comprehensive
trait data across the Tree of Life
A multi-kingdom analysis of adult size exploring mechanistic constraints and
ecological correlates.
Whole-ecosystem or multi-trophic analyses of common traits which influence
function (e.g., relating traits to ecosystem processes, such as how differences in
traits and migration patterns in birds can influence soil nutrient cycling).
Prediction of community assembly processes across time and space (e.g., from
deep-time via the fossil record to present day human-dominated systems).
Reserve selection optimised for protecting function (e.g., reserve design in both
marine and terrestrial realms based functional attributes of the broad community
of organisms).
Developing better Earth System models, biophysical niche models, and process-
based mortality models through the integration of trait data.
20
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... Functional ecology requires standardized and reproducible classification schemes to characterize species' niches [76][77][78]. Rather than relying on expert opinion for the assignment of trophic groups, which often results in variable assignments, we demonstrate that the categorization of discrete trophic guilds and pairwise trophic interactions can be achieved with a quantitative, reproducible framework grounded in empirical data across biogeographic regions. We employed network analysis to partition 535 tropical coral reef fish species into 8 trophic guilds based on a synthesis of globally distributed, community-wide fish dietary analyses, and then we applied a Bayesian phylogenetic model that predicts trophic guilds based on phylogeny and body size, attaining a 5% misclassification error. ...
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