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https://doi.org/10.1038/s41559-018-0667-3
1Department of Theoretical and Computational Ecology, Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam,
Amsterdam, The Netherlands. 2CyVerse, University of Arizona, Tucson, AZ, USA. 3Woodrow Wilson International Center for Scholars, Washington DC,
USA. 4University of Montana, W. A. Franke Department of Forestry and Conservation, Missoula, MT, USA. 5Max Planck Institute for Biogeochemistry,
Jena, Germany. 6German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany. 7Plazi, Bern, Switzerland. 8Area
de Conservacion, Seguimiento y Programas de la Red, Organismo Autonomo Parques Nacionales, Ministerio de Agricultura y Pesca, Madrid, Spain.
9Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy. 10Institute of Environmental Sciences, Leiden
University, Leiden, The Netherlands. 11Systems Ecology, Department of Ecological Science, Vrije Universiteit, Amsterdam, The Netherlands. 12USA National
Phenology Network, University of Arizona, Tucson, AZ, USA. 13Instituto Español de Oceanografía, Centro Oceanográfico de Baleares, Palma de Mallorca,
Spain. 14National Ecological Observatory Network, Battelle Ecology, Boulder, CO, USA. 15Department of Ecology and Evolutionary Biology, University of
Colorado, Boulder, CO, USA. 16Franklin Institute, University of Alcala, Madrid, Spain. 17Department of Biology, University of Southern Denmark, Odense M,
Denmark. 18Laboratoire d’Ecologie Alpine, CNRS - Université Grenoble Alpes, Grenoble, France. 19Marine Biological Association of the United Kingdom,
Plymouth, Devon, UK. 20Institute of Biology, Martin Luther University Halle Wittenberg, Halle (Saale), Germany. 21Department of Life Sciences, Imperial
College London, Ascot, Berkshire, UK. 22CSIRO and Atlas of Living Australia, Canberra, Australian Capital Territory, Australia. 23Smithsonian Tropical
Research Institute, Ancon, Panama. 24Department of Zoology, Oxford University, Oxford, UK. 25Department of Animal and Plant Sciences, University of
Sheffield, Sheffield, UK. 26Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Queensland, Australia. 27Evolutionary
Demography Laboratory, Max Plank Institute for Demographic Research, Rostock, Germany. 28Global Biodiversity Information Facility (GBIF), Secretariat,
Copenhagen, Denmark. 29Smithsonian Institution, National Museum of Natural History, Washington DC, USA. 30Department of Natural Resources,
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands. 31Department of Environmental
Science, Macquarie University, New South Wales, Australia. 32Florida Museum of Natural History, University of Florida, Gainesville, FL, USA.
*e-mail: wdkissling@gmail.com
In 2013, the Group on Earth Observations Biodiversity Observation
Network (GEO BON) introduced the framework of Essential
Biodiversity Variables (EBVs) to derive coordinated measure-
ments critical for detecting and reporting biodiversity change1.
Through this process, 22 candidate EBVs were proposed and orga-
nized within six classes (‘genetic composition’, ‘species populations’,
‘species traits’, ‘community composition’, ‘ecosystem structure’ and
‘ecosystem function’)1. These EBVs provide a foundation for assess-
ing progress towards national and international policy goals, includ-
ing the 20 Aichi Biodiversity Targets developed by the Parties to the
United Nations (UN) Convention on Biological Diversity (CBD)
and the 17 Sustainable Development Goals (SDGs) identified by the
UN 2030 Agenda for Sustainable Development2. EBVs are concep-
tually located on a continuum between primary data observations
(‘raw data’) and synthetic or derived metrics (‘indicators’), and can
be represented as ‘data cubes’ with several basic dimensions (for
example, time, space, taxonomy or Earth observation data types)3–5.
Hence, EBVs allow derivation of biodiversity indicators (for exam-
ple, trends of biodiversity change) such as those developed for the
Aichi Biodiversity Targets, with several EBVs (for example, spe-
cies population abundance) informing multiple targets1,6. Specific
EBVs in the classes species populations, ecosystem structure and
Towards global data products of Essential
Biodiversity Variables on species traits
W.DanielKissling 1*, RamonaWalls2, AnneBowser3, MatthewO.Jones4, JensKattge 5,6,
DonatAgosti7, JosepAmengual8, AlbertoBasset9, PeterM.vanBodegom10,
JohannesH.C.Cornelissen11, EllenG.Denny12, SaludDeudero13, WilliEgloff7, SarahC.Elmendorf14,15,
EnriqueAlonso García16, KatherineD.Jones14, OwenR.Jones17, SandraLavorel18, DanLear19,
LaetitiaM.Navarro6,20, SamraatPawar 21, RebeccaPirzl22, NadjaRüger6,23, SofiaSal21,
RobertoSalguero-Gómez24,25,26,27, DmitrySchigel 28, Katja-SabineSchulz 29, AndrewSkidmore 30,31
and RobertP.Guralnick32
Essential Biodiversity Variables (EBVs) allow observation and reporting of global biodiversity change, but a detailed framework
for the empirical derivation of specific EBVs has yet to be developed. Here, we re-examine and refine the previous candidate
set of species traits EBVs and show how traits related to phenology, morphology, reproduction, physiology and movement
can contribute to EBV operationalization. The selected EBVs express intra-specific trait variation and allow monitoring of how
organisms respond to global change. We evaluate the societal relevance of species traits EBVs for policy targets and demon-
strate how open, interoperable and machine-readable trait data enable the building of EBV data products. We outline collection
methods, meta(data) standardization, reproducible workflows, semantic tools and licence requirements for producing species
traits EBVs. An operationalization is critical for assessing progress towards biodiversity conservation and sustainable develop-
ment goals and has wide implications for data-intensive science in ecology, biogeography, conservation and Earth observation.
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ecosystem function are now being developed by GEO BON working
groups7. However, other EBV classes have received less attention,
and the research community has yet to fully coalesce efforts to
develop the conceptual and empirical frameworks for those vari-
ables and their associated data products.
Species traits are a key component of biodiversity because they
determine how organisms respond to disturbances and changing
environmental conditions, with impacts at a population level and
beyond8–10. Within the EBV framework, the EBV class ‘species traits’
has yet to be formally conceptualized in detail and therefore cannot
yet be made operational. In line with previous work8,11,12, we here
define a species trait as any phenological, morphological, physi-
ological, reproductive or behavioural characteristic of an individual
that can be assigned to a species (Box 1). Because the building of
EBV data products requires standardization and harmonization
of raw measurements1,3,5, we further define species traits EBVs as
standardized and harmonized measurements of species’ character-
istics that allow monitoring of intra-specific trait changes within
species populations across space and time (Box 1). Specific species
traits selected for EBVs (for example, body mass, plant height and
specific leaf area as examples of morphological traits) allow quanti-
fication of how species respond to global change including climate
change, biological invasions, overexploitation and habitat frag-
mentation8,13–16 (Box 1). The time frame of species traits responses
should be policy relevant, that is, intra-specific trait changes should
be detectable within a decade rather than only seasonally, annu-
ally or over evolutionary time scales6. This is needed because EBVs
will feed into biodiversity change indicators (Box 1) that allow the
assessment of progress towards policy goals including the SDGs and
Aichi Biodiversity Targets as well as National Biodiversity Strategies
and Action Plans (NBSAPs). They can also help to inform global
and regional assessments of the Intergovernmental Platform on
Biodiversity and Ecosystem Services (IPBES)1,17. Other aspects of
species traits that reflect traits expressions at the community or eco-
system level are not considered here as they belong to other EBV
classes (Box 1). To our knowledge there are currently no global data
products available that allow direct measurement and monitoring of
trait changes within species populations across time17.
Here, we develop the conceptual and empirical basis for species
traits EBVs to help to operationalize the development of global EBV
Box 1 | Definition and societal relevance of species traits EBVs
A species trait can be dened as any phenological, morphological,
physiological, reproductive or behavioural characteristic of a spe-
cies that can be measured at an individual level11,91. Hence, species
traits can be quantied by measuring characteristics of individuals
(for example, timing of owering, body lengths of sh individuals,
stem heights and diameters of tree individuals, leaf nitrogen and
chlorophyll content) or parts of individuals (for example, area of
an individual leaf).
Individual variation in trait measurements can be summarized
at dierent hierarchical levels, for instance at the population level
(for example, mean body length of a sh species population), at the
species level (for example, intra-specic variability of body lengths
of a sh species across its entire geographic range), or across
multiple species (for example, as community-weighted means91
or as spectral trait variation when using airborne or spaceborne
remote sensing43,92). Quantifying trait variation across multiple
species (that is, within a community, ecosystem or landscape) is
highly relevant for mapping and monitoring ecosystem processes
and functional diversity43,51. However, such community- and
ecosystem-level trait variation is mainly relevant for the EBV
classes ‘community composition’, ‘ecosystem structure’ and
‘ecosystem function’1, but not for ‘species traits’ because it does not
allow attribution of trait variation to the species level1.
A key aspect of EBV development is to standardize,
aggregate and harmonize data across time (for example,
temporal resolution), space (for example, spatial resolution
and geographic extent) and biological organization (for
example, taxonomy or Earth observation data type)3–5. Species
traits EBVs can therefore be dened as standardized and
harmonized data of phenological, morphological, physiological,
reproductive or behavioural trait measurements that can be
quantied at the level of individual organisms. To distinguish
species traits EBVs from other EBV classes, we constrain them
to trait measurements that allow quantication of trait changes
within species populations (that is, intra-specic variation).
Hence, trait measurements of individuals or populations must
be attributable to the taxonomic level of a species (rather than
to communities, landscapes or ecosystems). Alternatively (as in
the case of micro-organisms), individuals might be identied
at the level of operational taxonomic units (OTUs), that is,
grouped by DNA sequence similarity rather than by a classical
Linnaean taxonomy. Hence, taxonomic information, as well as
time and location of trait data collection, is key for monitoring
intra-specic trait changes.
e societal relevance of EBVs becomes crucial when assessing
progress towards biodiversity targets and policy goals1,2. Species
traits EBVs can be important for such targets, including the 20
Aichi Biodiversity Targets developed by Parties to the UN CBD
and the 17 SDGs identied by the UN 2030 Agenda for Sustainable
Development. For instance, the impact of harvesting large sh
individuals for commercial sheries could be monitored by trait
measurements that quantify changes in mean or maximum body
size (for example, body length at rst maturity) in economically
important sh populations15,79. is would allow deriving size-
based indicators (for example, trends of maximal sh body
lengths over time) and hence measuring overexploitation and
unsustainable harvesting as specied in Aichi Target 6 (sustainable
harvesting of sh and invertebrate stocks and aquatic plants) or
SDG 2 (sustainable food production).
Species traits are also important for understanding the
response of organisms to their environment (‘response traits’)8.
For instance, phenological trait information (for example, related
to changes in timing of bird egg laying, phytoplankton population
peaks, or plant leang, owering and fruiting) can be an early
indicator of climate change impacts21 and has relevance for SDG
13 (combating climate change and its impacts). Other examples
include trait measurements related to movement behaviour (for
example, dispersal distances and pathways, animal home range
size) and reproduction (for example, fruit and seed size). ese
trait measurements can be of societal relevance, for instance if
they determine the success of alien invasive species16, describe
how organisms respond to habitat fragmentation14, or indicate
how species adapt to global change drivers93. is information
is directly related to Aichi Target 5 (habitat loss and forest
fragmentation) and Aichi Target 9 (invasive species control), but
has yet to be developed into indicators.
Species traits EBVs can therefore provide critical information
for monitoring biodiversity change, which cannot be captured by
measuring changes in species distributions alone or ecosystem
structure and functioning. Moreover, dierent species traits dier
in their importance across policy targets and each species traits
EBV contains important information with societal and policy
relevance that cannot be substituted by other species traits EBVs
(Supplementary Note 2).
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data products. We start by critically re-examining the current set of
candidate species traits EBVs (phenology, body mass, natal dispersal
distance, migratory behaviour, demographic traits and physiologi-
cal traits). We then explore how trait data are collected, how they
can be standardized and harmonized and what bottlenecks cur-
rently prevent them from becoming findable, accessible, interoper-
able and reusable (FAIR guiding principles)18. We further outline
workflow steps to produce EBV data products of species traits,
using an example of plant phenology. Our perspective provides a
conceptual framework with practical guidelines for building global,
integrated and reusable EBV data products of species traits. This
will promote the use of species trait information in national and
international policy assessments and requires significant advance-
ments and new tools in ecology, biogeography, conservation and
environmental science. Beyond the direct relevance to species traits
EBVs, our perspective further explores cross-cutting issues related
to data-intensive science, interoperability, and legal and policy
aspects of biodiversity monitoring and Earth observation that will
help to advance the EBV framework.
A critical re-examination
GEO BON has proposed six candidate EBVs in the EBV class spe-
cies traits (Supplementary Table 1): phenology, body mass, natal
dispersal distance, migratory behaviour, demographic traits and
physiological traits. These candidate EBVs were discussed in detail
during a three-day experts’ workshop in Amsterdam (March 2017)
organized by the GLOBIS-B project (http://www.globis-b.eu/)19. We
suggest several key improvements of that initial list of candidate
species traits EBVs.
Identified inconsistencies. We identified several inconsistencies in
the proposed candidate list of species traits EBVs (summarized in
Supplementary Table 2). First, some previously listed measurements
— such as ocean and river flows, extent of wetlands and net pri-
mary productivity — do not occur at the species level (Box 1) and
should therefore be placed within community or ecosystem-scale
EBV classes such as community composition, ecosystem function
or ecosystem structure. Second, several candidate EBVs (for exam-
ple, body mass and natal dispersal distance) are narrowly defined
compared to other candidate EBVs (for example, phenology, demo-
graphic traits, physiological traits), resulting in an inconsistent
scope across EBVs. Third, a few candidate EBVs represent a similar
category but are split into different EBVs (for example, both natal
dispersal distance and migratory behaviour are aspects of move-
ment behaviour), and should therefore be represented together.
Fourth, the candidate EBV ‘demographic traits’ reflects population-
level quantities that cannot be measured on individual organisms
(for example, population growth rate, generation time, survival
rate). These population-level metrics are derived from data that are
captured by the EBV population structure by age/size/stage class
belonging to another EBV class (species populations). It is therefore
inconsistent to capture the same set of underlying measurements in
two different EBV classes.
Suggestions for improvement. Based on our assessment, we sug-
gest reducing the initial candidate list to five species traits EBVs
(Fig. 1): phenology (timing of periodic biological events), mor-
phology (dimensions, shape and other physical attributes of
organisms), reproduction (sexual or asexual production of new
individual organisms), physiology (chemical or physiological func-
tions promoting organism fitness) and movement (spatial mobility
of organisms) (see overview in Fig. 1 and detailed description in
Supplementary Note 1). This improves the previous classification
of species traits EBVs by standardizing the breadth and scope of
EBVs, better recognizing the importance and relevance of repro-
ductive traits and excluding ecosystem variables that cannot be
measured at the scale of the individual and are thus not species-spe-
cific traits (Supplementary Note 1). These five species traits EBVs
provide a conceptual framework for the EBV class species traits
and are relevant to the Aichi Biodiversity Targets and SDGs (Fig. 1,
Supplementary Table 3). Because GEO BON has the main respon-
sibility for developing EBVs, we suggest that the new GEO BON
working group on species traits (as recommended in the GEO BON
implementation plan 2017–20207) should take our suggestions into
consideration when updating the EBV class species traits.
Collecting trait data
Many trait databases have recently emerged that support assembling
trait measurements from published literature, specimen collections,
in situ collections and close-range, airborne or spaceborne remote
sensing (for examples see Supplementary Table 4). Nevertheless, the
total demand for species traits in the EBV context is still unmet for
the following reasons.
Aggregated species-level trait values are not sufficient. Many
ongoing trait data collections assemble species trait information
from published literature (Fig. 2). When aggregated to the species-
level without location and time information (for example, mean
species body length for morphology, or typical month of flowering
or fruiting for phenology), this information does not allow mea-
surement of trait changes within species populations over space
or time, and hence lacks the ability to yield species traits EBVs
(Fig. 2, Box 1). However, if the variation in the aggregated trait (that
is, variance) can be calculated from a sufficiently large sample, then
changes in species populations over time (or space) can be statis-
tically estimated15,20–22. Nevertheless, many projects aggregate trait
data at the species level from multiple sources such as published
and unpublished trait datasets, natural history collections, citizen
science projects and text mining23–28. These trait data remain limited
in their application for species traits EBVs if they do not keep the
resolution of the original data in terms of space, time and individual
measurement information. The lack of individual or population
measures therefore makes it difficult to assess intra-specific trait
changes and the drivers and scales at which they operate.
Natural history collections offer historical data that remain unde-
rutilized. Museum and herbarium specimens allow study of indi-
viduals’ traits in species populations of the recent past29. Specimen
collections can therefore be an important source for individual-level
trait measurements through time (Fig. 2). For example, specimens
have been used to document temporal changes in morphology (for
example, bird and beetle body size30,31) and phenology (for example,
timing of flowering32,33) during the past century. Billions of speci-
mens are available for study, but efforts to digitize and store trait
data associated with specimens are still in their infancy29. Hence,
trait data from digitized specimen collections remain underuti-
lized and are currently too often constrained and biased in space,
time and number of individuals25. New ways to digitize biocol-
lections and to automate trait data extraction from specimens are
needed25, and analyses must take into account the constraints and
biases inherent in these data34.
In situ monitoring of traits is promising but labour intensive. A
promising approach for developing species traits EBVs is to collect
in situ trait data through monitoring schemes (Fig. 2). These include
repeated trait measurements (for example, of animal body size,
plant size, lichen length, flower and fruit phenology, leaf morphol-
ogy and chemistry) with standardized protocols using long-term
ecological research sites35,36 or national and international moni-
toring programmes and citizen science networks20,37,38. Such sites
and networks can monitor a comprehensive set of trait measure-
ments for targeted species or sites through time and at continental
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extents38,39, but remain costly and labour intensive. The future
collection of trait data time series through in situ monitoring there-
fore requires prioritization according to global and regional biodi-
versity and sustainability goals, and a robust temporal replication
and spatial/environmental stratification of the sampling design40.
Remote sensing observations are promising but often not spe-
cies specific. Airborne, spaceborne and close-range remote sensing
techniques are promising tools (Fig. 2) because they can extend the
geographic and temporal dimensions of trait measurements consid-
erably9,41–43. Increasingly, ground-based light detection and ranging
(that is, terrestrial LiDAR) is automating in situ data collection and
allows retrieval of species trait information for individual plants
(for example, height44 and leaf water content45). Moreover, sen-
sor-derived trait data can provide individual- or population-level
trait measurements from close-range instruments such as camera
traps, phenology cameras46,47, field spectrometers48, wireless sensor
networks, unmanned aerial vehicle (UAV) and aircraft mounted
instruments such as airborne LiDAR and hyperspectral sensors49,50.
Combining airborne LiDAR and imaging spectroscopy also allows
mapping of individual-level variation in morphological and physio-
logical traits (for example, canopy height, leaf chlorophyll and water
content) at regional scales43. For species traits EBVs, the remotely
sensed trait measurements require fine enough spatial resolution to
attribute them to an individual or population of a particular species
(Box 1). A synergy of hyperspectral and LiDAR remote sensing with
airborne sensors has great potential for developing species traits
EBVs, but is not available at a global extent. Spaceborne remote
sensing systems can provide global coverage, but they still show a
large deficit for providing an operational combination of data at
high spatial and spectral resolution9,42,51. In other words, spaceborne
instruments are in their infancy for monitoring species traits due to
limitations with very high spatial resolution (pixel area) and spectral
resolution (high number and small width of spectral bands), though
new spaceborne imaging spectrometers and LiDAR are planned
which will go some way towards closing this gap42,52,53. Further
developments in instrumentation and data52, planned satellite sen-
sor missions53, species-level spectral library databases (for example,
EcoSIS; https://ecosis.org) and spectranomics54,55 — the coupling of
spectroscopy with plant phylogeny and canopy chemistry — will
further enhance the ability to retrieve species-specific trait data.
Standardizing trait data
A current bottleneck for integrating trait datasets from multiple
sources is that measurements, data and metadata are not sufficiently
standardized. We highlight three focal areas to improve this.
Standardizing protocols for measuring traits. The use of stan-
dardized measurement protocols during the phase of trait data
collection is foundational for integrating data into EBV data
Phenology Physiology Reproduction Movement Morphology
Examples
1 year
1 to 5 years
1 to >10 years
1 to >10 years
1 to >10 years
Genetic
composition
Species
populations
Species
traits
Community
composition
Ecosystem
function
Ecosystem
structure
EBV
classes
Species traits
EBVs
Presence, absence,
abundance or duration
of seasonal activities
of organisms
Dimensions
(for example, volume,
mass and height), shape,
other physical attributes
of organisms
Sexual or asexual
production of new
individual organisms
(‘offspring’) from parents
Chemical or physical
functions promoting
organism fitness and
responses to environment
Behaviours related
to the spatial mobility
of organisms
Definition
Timing of breeding,
flowering, fruiting,
emergence,
host infection
and so on
Body mass, plant height,
cell volume, leaf area,
wing length, colour
and so on
Age at maturity, number
of offspring, lifetime
reproductive output
Thermal tolerance,
disease resistance,
stoichiometry
(for exmaple,
chlorophyll content)
Natal dispersal distance,
migration routes, cell
sinking of phytoplankton
Temporal
sensitivity
Aichi: –
SDG: 13, 15
Aichi: 6, 15
SDG: 2, 14
Aichi: 6, 9, 12
SDG: 14, 15
Aichi: 8, 10, 15
SDG: –
Aichi: 9
SDG: –
Societal
relevance
Fig. 1 | A framework for EBVs on species traits. We suggest five EBVs within the EBV class ‘species traits’, comprising (1) phenology, (2) morphology,
(3) reproduction, (4) physiology and (5) movement. For each EBV, a definition, examples of species trait measurements, temporal sensitivity and societal
relevance are given. Societal relevance refers to those Aichi Biodiversity Targets and SDGs to which the specific EBV is of highest relevance (for details on
societal relevance see Supplementary Note 2 and Supplementary Table 2). Photo credits: Katja-Sabine Schulz.
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products. Good examples of comprehensive protocols for stan-
dardized measurements of morphological, reproductive, physi-
ological and behavioural traits exist for vascular plants56,57 and
terrestrial invertebrates58. However, such comprehensive defi-
nitions of measurement protocols are still missing for most
traits and taxa, and some remain little-known and difficult to
access59. This is particularly true for remote sensing measure-
ments of species traits (for example, leaf chlorophyll concentra-
tion and canopy chlorophyll content) where the instrumentation
and required pre-processing of data to derive information on
species-specific traits may vary considerably even within the
same class of sensors (for example, within different types of
spectrometers, phenology cameras or LiDAR instruments). A
coordinated effort is therefore needed to develop and harmo-
nize standardized measurement protocols for various taxa and
across data types, sensors and regions, and to support consistent
monitoring across political boundaries.
Standardizing trait terminology. Aggregating trait data from
multiple sources requires standardized lists of trait terms or con-
trolled vocabularies (that is, carefully selected lists of words and
phrases)11,27,60,61. For instance, in the marine domain the formal-
ization of a standardized list of trait terms and definitions has
been achieved across a wide range of taxa26,60. Similar examples
exist for other taxa and realms, for example, the thesaurus of plant
characteristics11. Nevertheless, comprehensive trait vocabularies
that provide standardized terms, definitions, units and synonyms
for trait data and their metadata remain scarce. The further devel-
opment and linking of such trait vocabularies is therefore needed
to achieve semantic interoperability and facilitate integration of
trait datasets11,23,27,62.
Ontologies. Integrating trait data from disparate sources requires
mapping trait data to ontologies23,25,61,63–66, that is, to semantic mod-
els that allow formal descriptions of the relationships among trait
concepts and vocabulary terms (Box 2). For trait data in partic-
ular, not only information about the occurrence of a species and
the identification process needs to be reported, but also informa-
tion about the entity (that is, whether specific parts of organisms,
individual organisms, populations or species are measured), the
measurement focus (for example, mass, length or area), the mea-
surement units (for example, plant height in m, leaf nitrogen con-
tent in mg g–1, photosynthetic rate in μ mol m2 s–1) and the protocols
used. Because many traits exhibit phenotypic plasticity, informa-
tion about the individuals’ living conditions before trait measure-
ments (for example, if a plant was exposed to direct sunlight or
shaded in the understory) is also essential to understand and inter-
pret trait measurements67. Such reporting can be standardized by
connecting two types of ontology: (1) observation and measure-
ment ontologies for traits and environmental conditions and (2)
ontologies for entities and qualities (Box 2). Various examples of
both types of ontology already exist (Box 2), but their wider inte-
gration for developing comprehensive species traits data products
has not yet been achieved.
Making trait data open and machine-readable
A workflow-oriented production of EBVs requires trait datasets and
their metadata to be openly accessible and machine-readable3,18.
Although openness and sharing of biodiversity data are improv-
ing68–70 and trait databases increasingly develop data management
policies around open access principles (see Supplementary Note
3 for an assessment of openness of individual species traits datas-
ets), the actual levels of open and FAIR18 access to trait data are still
Trait data aggregation
Increasing temporal frequency of observations
Published literature Specimen collections In situ monitoring Remote sensing
Specific trait databases
(BIOTIC, Biotraits,
COMPADRE, COMADRE,
FRED, PolyTraits
and so on)
Digitized biocollections with
specimen-related trait data
from museums and herbaria
(for example, VertNet)
Examples of
trait databases
Monitoring networks with
focus on species traits
(for example, NEON,
Pan European
Phenology, USA-NPN)
Close-range measurements (for example, from
PhenoCam, wireless sensor networks, camera traps)
and airborne (for example, UAV or aeroplane) or
spaceborne (satellite) data collections
(including LiDAR, imaging spectroscopy)
Aggregation of trait data from multiple sources
(for example, TRY, EMODnet, TraitBank)
Current
limitations
for use in
species
trait EBVs
• Wide variation in collection and sampling methods
• Often aggregated (mean) trait values per species
• Few individual or population level trait measurements
available through time
• Costly and labour intensive
• Only few systematic and
temporally contiguous in situ
collections available
• Spatial resolution makes attribution of trait
information to species or population level difficult
• Limited coupling of high-resolution data
(for example, PhenoCam, UAV LiDAR)
with species identification
Fig. 2 | Methods for trait data collection with examples of trait databases and limitations for developing EBVs. Several methods are used to assemble
comprehensive trait databases, for example, from published literature, specimen collections, in situ monitoring and remote sensing (close-range, airborne
and spaceborne). These methods can be ordered along a gradient of increasing temporal frequency of observations. Aggregation of trait data from
multiple sources often does not provide measurements repeated in time and hence typically does not allow monitoring of trait changes within species
populations. More information about trait databases (abbreviations) is provided in Supplementary Table 3.
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lagging behind the ideal, although remote-sensing data are increas-
ingly freely available, especially through space agencies (for exam-
ple, NASA and the European Space Agency). Here, we highlight two
key steps for enhancing openness and machine-driven integration
of trait datasets.
Use standardized copyright waivers and licences. Waivers and
licences support legal interoperability by clearly defining the condi-
tions for both creation and use of combined or derivative data prod-
ucts, and allow users to legally access and use data without seeking
additional authorization from the rights holders71. Many trait datas-
ets do not yet use standardized copyright waiver or licence informa-
tion such as those published through the Creative Commons (CC)
framework72. In the context of EBVs, the formal designation with a
CC0 copyright waiver or an open CC BY licence have been recom-
mended because they minimize constraints on legal interoperability
that emerge from restrictions on data use, modification and shar-
ing3. Although a waiver of copyright through CC0 makes sharing and
reuse much easier, the appropriate ‘attribution’ and maintenance of
data provenance is important in a scientific context18, and the CC BY
licence provides the opportunity for acknowledgement and citation.
Provide standardized and machine-readable metadata. Many
trait datasets are already available through web portals and other
developed infrastructures (Supplementary Table 4), but access
to standardized and machine-readable trait data and metadata
remains a key bottleneck for technical and legal interoperability.
For instance, licence and citation information is often not available
in standardized and machine-readable form (for example, by using
hyperlinks or embedded code, Supplementary Note 3) and many
research projects publish their trait data on file hosting services
(for example, Figshare, Dryad, Zenodo and so on) where no data
and metadata standards are forced upon the uploaded material27.
Moreover, metadata on the level of individual trait records is usually
missing and data provenance is rarely documented (Supplementary
Note 3). Hence, sufficient, consistent and well-documented meta-
data in a standardized form should be provided to successfully
integrate trait measurements into workflows for building EBV data
products of species traits.
A workflow for integrating EBV-relevant trait data
The production of species traits EBVs can only be achieved if mul-
tiple trait datasets are harmonized and combined into open, acces-
sible and reusable products3. However, most trait data are currently
stored in siloed resources and not available in an interoperable and
machine-readable format. We therefore outline a generalized work-
flow for integrating EBV-relevant trait data (Fig. 3) and show how
this workflow is currently applied to produce a new integrated plant
phenology dataset (Box 3).
Collecting and provisioning trait data. The first part of the work-
flow represents the collection and initial processing of raw measure-
ments of traits (for example, on flower and leaf phenology) following
standardized sampling protocols, for example, by people (specimen
collection and in situ observations) or close-range, airborne and
spaceborne remote sensing (Fig. 3, top). After collection, raw data
are validated through data quality assurance (QA, for example, by
following standard protocols for trait data cleaning) and quality
control (QC, for example, normalizing trait distributions, check-
ing for outliers) (Fig. 3, top). Metadata about trait data collection
and validation processes (for example, description of protocols) and
about the dataset itself (for example, specimen IDs, ownership and
licensing) need to be associated with the data when bundling the
trait datasets (Box 3). Most currently existing trait datasets are only
published in repositories with little metadata documentation and
data standardization, but efforts to integrate them into more com-
prehensive data products are beginning to emerge.
Converting trait data into interoperable formats. To achieve inte-
grated trait data products, data and metadata from different sources
have to be standardized (Fig. 3, middle). This involves converting all
data to comparable units and formats, the mapping of trait data to
ontologies and automated reasoning over mapped data to discover
new facts (Fig. 3, middle). The use of ontologies, for example, the
Plant Phenology Ontology (PPO)73 for flower and leaf phenology
traits (Box 3), provides a formal, generalized, logical structure that
helps to automate integration across different datasets. Ontologies
can also be used to further improve quality of trait data integration
through inferring new facts through machine reasoning (see Box 3
for examples). This process converts trait datasets into fully interop-
erable formats and enables future researchers as well as machines to
interpret the data.
Providing integrated and reusable trait data products via web
services. To make an integrated trait data product FAIR18 (see
above), a public domain designation (for example, CC0) or an open
access licence (for example, CC BY) should be applied and provided
together with other metadata in a machine-readable format (Fig. 3,
Box 2 | Semantic tools for reporting trait measurements
Reporting trait data is best accomplished using two types of on-
tologies (that is, semantic models): those that describe the pro-
cesses, inputs and outputs around data collection, and those that
systematically describe the traits themselves. e rst type of
ontology standardizes observation and measurement data that is
important for capturing how trait measurements were performed
(for example, protocols), metadata on taxon, sampling location,
sampling time and so on, and tracking data provenance. A key
example is the Extensible Observation Ontology (OBOE), which
captures the semantics of observational datasets, including eld,
experimental, simulation and monitoring data94. Similarly, the
Biological Collections Ontology (BCO) allows sampling, speci-
men collection and observations to be reported in a standard-
ized way95. For geospatial data, the Observations and Measure-
ments (O&M) ontology allows interoperability with sensor data
and could be valuable to report information such as optical traits
related to plant function51. Further progress is still needed to cre-
ate interoperability across dierent observation ontologies and
develop easy-to-use implementations. Moreover, comprehensive
denitions of measurement protocols and methods are lacking.
e second type of ontology (that is, semantic models for
describing traits) is most commonly based on the Entity–Quality
(E–Q) model63. e E–Q model provides a framework for
adequately describing the entity (for example, a leaf of a plant, of
individual organisms, populations or species) and the quality of that
entity being measured, such as mass, length or area. Standardized
trait data must also include information on how they are measured
(for example, protocols), and the units used for coding the trait
value96. While the E–Q model was originally developed for the
description of phenotypes in the eld of biomedicine63, there are
now many applications to ecological trait data. Examples for plant
traits include the esaurus of Plant Characteristics (TOP)11, the
Flora Phenotype Ontology (FLOPO)64, the Plant Trait Ontology
(TO)65 and the PPO73. Similar examples can be found for animal
traits61,66,97. In addition, trait measurements should also be linked
to descriptions of the environment in which the individuals have
been living67, for example, using the Environment Ontology
(ENVO)98. e combination of trait ontologies with observation
process ontologies provides a strong basis for standardizing how
traits are measured, compiled, shared and made semantically
interoperable (see Box 3).
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bottom). In the best case, licence information should be available for
each trait record and original source (Box 3). Further, it is impor-
tant that data structures of trait data products align with seman-
tic web standards (for example, multi-layered, relational databases
rather than two-dimensional data tables). Hence, trait data products
Box 3 | Example of a workflow integrating plant phenology
data
e USA National Phenology Network (USA-NPN)20 and the
Pan-European Phenology Network (PEP725)75 are two separate
networks with diering protocols for capturing plant phenology
traits (for example, timing of leang, owering and fruiting) at
continental scales. e networks mobilize scientists and volun-
teers to collect data according to phenology trait or phase deni-
tions. In addition, the National Ecological Observatory Network
(NEON)99 gathers trait measurements of many taxa (including
leaf and ower phenology) across multiple eld sites in the US.
All three networks use data assurance and QC mechanisms, for
example, constraining trait data entry to specic formats and in-
cluding a set of consistency and completeness checks to ensure
trait data quality. eir online portals provide bundled data and
metadata on plant phenology, and the networks therefore fol-
low typical workow steps for collecting and provisioning spe-
cies traits datasets (Fig. 3 top). However, the integration of plant
phenology data products from these three sources is challenging
because these networks use dierent frameworks.
As a response to the challenge of multiple frameworks, the
PPO73 was newly developed to standardize reporting from
any in situ phenology resource, including professional and
citizen science eorts such as USA-NPN and PEP725, more
standardized surveys from NEON, and phenology data scored
from herbarium records. e PPO denes a set of hierarchically
organized ‘phenological traits’, that is, observable features of
a plant that provide phenologically relevant information such
as whether a plant has owers, how many ripe fruits are on a
plant, or whether a plant’s leaves are senescing. Denitions of
phenological traits therefore depend on classes for particular
plant structures taken from the Plant Ontology100. Phenology
terms from USA-NPN, PEP725, NEON and herbarium datasets
have been mapped to the PPO, and plant phenology data can
therefore be converted into a fully interoperable format through
standardizing data and metadata (Fig. 3 middle). An added
benet of using ontologies is that automated procedures can
produce new information from standardized data. For example,
automated reasoning tools can use the PPO to infer that any
plant that has open ower buds present must also have owers
and reproductive structures present.
To make integrated phenology trait data products accessible, a
new web platform has been created (the Global Plant Phenology
Data Portal, https://www.plantphenology.org/). Each individual
phenology record is annotated to its source (for example, USA-
NPN, PEP725 or NEON) and the licence of the source applied
to the records. To allow ecient queries, harmonized data are
processed using virtual machines run on CyVerse (formerly
iPlant Collaborative)90 and then loaded into Elasticsearch, a
distributed, RESTful search and analytics engine (https://www.
elastic.co/). is allows scalable searching of billions of trait
data points that deliver outputs from standard queries very
quickly. e backend is connected to an API which provides
simple mechanisms for building front-end queries. Such a web
platform allows open access to ne-resolution, population-level
plant phenology data from dierent regions and continents
(Fig. 3 bottom).
Apply open licence or public domain
Collecting raw data following standard protocols
1. Collecting and provisioning species trait datasets
2. Standardizing and integrating trait data and metadata
Human observations
Specimen
digitization
In situ
observations
Remote sensing
Close-range cameras, airborne
and spaceborne
Quality assurance (QA) and quality control (QC)
✓
Bundling data and metadata
<meta>
<meta>
<meta>
{---}
Publishing
siloed datasets
Publishing
siloed datasets
K
As
m
kgcd
mol
Measurement
base units
JANUARY
Dates
Data standardization
Location Controlled
vocabularies
Data
standards
Mapping data to ontologies
Inferring new facts via reasoning
3. Making trait data products and metadata accessible
Employ graph or relational database with
API and semantic web standards API
Access to trait data via web platforms
or widely used software (R, Python and so on)
Fig. 3 | A generalized workflow for integrating species trait measurements
into harmonized, open, accessible and reusable data products for
EBVs. Initial species trait measurements are collected through human
observations and remote sensing and subsequently quality checked and
bundled into datasets (1). Because such datasets often have different
sampling protocols, reporting processes and metadata descriptions, they
commonly end up as siloed datasets in file hosting services with little
metadata documentation and data standardization. To achieve integration
of different measurements and data collections, datasets must be
harmonized through standardization of data and metadata and mapped to
community-developed standards, including metadata standards, controlled
vocabularies and ontologies (2). Standardization often includes a second
QA and QC process to assure data quality across datasets (not shown).
Such harmonized data products can then be made accessible through open
licences, databases that employ semantic web standards and APIs, and
web platforms or widely used software (3).
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PersPective NATurE Ecology & EVoluTioN
should be housed with a graph database that allows on-the-fly rea-
soning via semantic queries, or with relational database if on-the-fly
reasoning is not needed (Fig. 3, bottom). In both cases, an appli-
cation programming interface (API) should allow communication
and access to the trait data product via a web platform (Box 3) or via
widely used software such as R or Python (Fig. 3, bottom).
Towards operationalizing species traits EBVs
Species traits are a key component of biodiversity, but species trait
information is currently not well represented in indicators of bio-
diversity change used for national and international policy assess-
ments2,17,74. The increasing willingness to share trait data in an open
and machine-readable way (see Supplementary Note 3), coupled
with emerging semantic tools (for example, new plant trait vocabu-
laries11, ontologies64,73 and preliminary suggestions for trait data
standards27) and a massive collection of trait data through in situ
monitoring schemes and close-range sensors (for example, for phe-
nology20,39,47,75) as well as on-going and forthcoming airborne and
spaceborne missions (including radar, optical sensors, radiometers
and spectrometers42,43,50,53,76), suggest that comprehensive data prod-
ucts on species traits are within reach in the near future. However,
a cultural shift towards more openness, interoperability and repro-
ducibility is needed within the broader science community18,19,77
— including ecologists, biogeographers, global change biologists,
biodiversity informaticians and Earth scientists — and with support
from global coordinating institutions such as GEO BON, IPBES and
the CBD.
Our refined list of species traits EBVs (Fig. 1) provides an
improved conceptual framework for how phenological, morpholog-
ical, reproductive, physiological and movement-related trait mea-
surements can represent biodiversity in the EBV context and hence
support international policies for biodiversity conservation and
sustainable development. The specific species traits EBVs contain
essential information with ecological, societal and policy relevance
for biodiversity that cannot be substituted by other species traits
EBVs (Supplementary Note 2). For instance, morphological and
physiological measurements of leaves (for example, leaf area, nitro-
gen and chlorophyll content), stems (for example, height and stem
density) and diaspores (for example, seed mass) allow quantifica-
tion of fundamental dimensions of plant ecological strategies and
how these organisms respond to competition, stress, environmen-
tal change and disturbances8,12,43,50. Phenological trait information
of amphibians (spawning), birds (egg laying), plankton (population
peaks), fish (spawning), insects (flight periods), mammals (birth
dates) and plants (flowering, fruiting, leafing) is highly relevant for
tracking changes in species’ ecology in response to climate change21
and other global changes (for example, nitrogen deposition induc-
ing delayed foliar senescence). Morphological measurements (body
sizes) of commercially relevant fish species78–80 can allow assess-
ments of sustainable food production and harvesting (Box 1).
Similarly, morphological, reproductive and physiological traits of
microbial species (for example, cell size, lifetime pattern of growth
and microbial resistance to viruses) are essential for predicting their
responses to environmental change81. A key aspect for the future
operationalization of species traits EBVs is that they should be mea-
surable with available technologies and have a proven track record
of feasibility6. We suggest that a focus on trait measurements repre-
senting plant phenology, morphology and physiology (for example,
from both in situ monitoring20,39,47,75 and remote sensing9,12,42,43,49,50,82)
as well as animal morphology15,79 and movement83 could provide a
realistic prioritization for operationalizing species traits EBVs.
Compiling the necessary data for EBVs globally remains a major
challenge, especially for species traits7,17. A key bottleneck is that the
repeated and systematic collection of in situ trait data is not only
costly and difficult but also spatially discontinuous. The global, spa-
tially contiguous and periodic nature of spaceborne remote sensing
observations therefore offers potential for building EBVs82. To date,
spaceborne remote sensing products (for example, related to land
surface phenology, canopy biochemistry and vegetation height)
allow the mapping of ecosystem structure and processes as well as
functional diversity9,43,51,84, but not the quantification of species-level
traits1,82 because the spatial resolution is not fine enough to allow
attribution of trait measurements to an individual or a population of
a single species (Box 1). With airborne remote sensing it is possible
to continuously map individual-level trait variation in morphologi-
cal and physiological traits at fine (metre) resolution across regional
scales (for example, forest trees43), often allowing assignment of trait
measurements to the species level85,86. Since species-level resolution
is required for many policy targets76, assigning trait measurements
to taxonomic information is key for monitoring intra-specific trait
changes. A deeper integration of in situ and various close-range
remote sensing trait measurements as well as a synergy of hyper-
spectral and LiDAR airborne remote sensing might help to achieve
this. An avenue for building contiguous species traits EBVs could be
to use information from Earth observation data for interpolating in
situ trait point samples for building continuous landscape maps of
trait distributions76. This would require the development of statisti-
cal and mechanistic models that allow mapping and prediction of
trait distributions across space and time87. In this context, specimens
from natural history collections could become useful for obtaining
baseline trait data for regions that have been poorly studied88.
Moving forward. Many dimensions of biodiversity still remain
invisible when measuring and monitoring global biodiversity
change2,17,76. Species traits EBVs will provide a deeper understand-
ing of the species-level responses to global change and the benefits
and services that individual species provide to humanity. For opera-
tionalizing species traits EBVs, we recommend the biodiversity
research community to support trait data harmonization, reproduc-
ible workflows, interoperability and ‘big data’ biodiversity informat-
ics for species traits19,23,27,89,90. Specifically, we suggest the following
concrete steps to facilitate the building of EBV data products of spe-
cies traits:
• Support the recording of species traits across time through
repeated and periodic collection of in situ measurements of
traits, through digitization of trait information from literature
and biocollections and through developing species traits data
products from close-range, airborne and spaceborne remote
sensing observations.
• Develop and apply standardized protocols, controlled trait
vocabularies and trait data standards when measuring, harmo-
nizing and combining trait data and metadata.
• Support the semantic integration of trait data by mapping trait
datasets to ontologies, facilitate training courses about seman-
tic standards of the World Wide Web Consortium (W3C) and
promote training tools for trait data integration within research
institutions and educational programmes of universities.
• Publish trait databases with standardized licence information in
machine-readable form and designate data as open access (for
example, through CC BY) or in the public domain (for example,
CC0). Encourage others to share trait data.
• Develop and apply reproducible statistical and mechanistic
models for integrating in situ trait data with remote sensing
observations to allow mapping and prediction of trait distribu-
tions across space and time.
• Establish consortia and interest groups on species traits. Con-
tribute to the GEO BON working group on species traits and
raise awareness of the need for semantic, technical and legal
interoperability of trait data.
• Foster the integration of species traits EBVs into biodiversity
indicators and biodiversity and sustainability goals.
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These activities — which require substantial financial and
in kind investments from universities, research infrastructures,
governments, space agencies and other bodies — will facilitate
the building of global EBV data products of species traits and
allow significant steps towards incorporating intra-specific trait
variability into global, regional and national biodiversity and
policy assessments.
Received: 25 February 2018; Accepted: 16 July 2018;
Published: xx xx xxxx
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Acknowledgements
This paper emerged from a workshop of the Horizon 2020 project GLOBIS-B (Global
Infrastructures for Supporting Biodiversity research; http://www.globisb.eu/). Financial
support came from the European Commission (grant 654003). We thank J. Konijn for
administrative support and the members of the BIOMAC lab (https://www.biomac.org/)
for discussion. L.M.N. is supported by the German Centre for Integrative Biodiversity
Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT
118). N.R. was funded by a research grant from Deutsche Forschungsgemeinschaft DFG
(RU 1536/3-1).
Author contributions
W.D.K. coordinated the study and wrote the draft manuscripts. R.W., A.B., M.O.J., J.K.,
R.P.G. and W.D.K. led the writing of sections. All authors provided substantial input into
ideas and text, and commented on draft manuscripts.
Competing interests
The authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https://doi.org/10.1038/
s41559-018-0667-3.
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