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TRY plant trait database – enhanced coverage and open access

Authors:

Abstract

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait– nvironmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.
Glob Change Biol. 2020;26:119–188.
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  119wileyonlinelibrary.com/journal/gcb
Received: 15 August 2019 
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  Accepted: 12 September 2019
DOI : 10.1111/gcb .1490 4
INVITED PRIMARY RESEARCH ARTICLE
TRY plant trait database – enhanced coverage and open access
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,
provided the original work is properly cited.
© 2019 The Auth ors. Global Change Biology published by John Wiley & Sons Ltd
A list of authors a nd their affiliation s appears in the Appen dix.
Abstract
Plant traits—the morphological, anatomical, physiological, biochemical and
phenological characteristics of plants—determine how plants respond to environ-
mental factors, affect other trophic levels, and influence ecosystem properties and
their benefits and detriments to people. Plant trait data thus represent the basis for a
vast area of research spanning from evolutionary biology, community and functional
ecology, to biodiversity conservation, ecosystem and landscape management, resto-
ration, biogeography and earth system modelling. Since its foundation in 2007, the
TRY database of plant traits has grown continuously. It now provides unprecedented
data coverage under an open access data policy and is the main plant trait database
used by the research community worldwide. Increasingly, the TRY database also sup-
ports new frontiers of trait-based plant research, including the identification of data
gaps and the subsequent mobilization or measurement of new data. To support this
development, in this article we evaluate the extent of the trait data compiled in TRY
and analyse emerging patterns of data coverage and representativeness. Best spe-
cies coverage is achieved for categorical traits—almost complete coverage for ‘plant
growth form’. However, most traits relevant for ecology and vegetation modelling
are characterized by continuous intraspecific variation and trait–environmental re-
lationships. These traits have to be measured on individual plants in their respective
environment. Despite unprecedented data coverage, we observe a humbling lack of
completeness and representativeness of these continuous traits in many aspects.
We, therefore, conclude that reducing data gaps and biases in the TRY database
remains a key challenge and requires a coordinated approach to data mobilization
and trait measurements. This can only be achieved in collaboration with other
initiatives.
KEYWORDS
data coverage, data integration, data representativeness, functional diversity, plant traits, TRY
plant trait database
Correspondence
Jens Kattge, Max Planck Institute for
Biogeochemistry, Hans Knöll Str. 10, 07745
Jena, Germany.
Email: jkattge@bgc-jena.mpg.de
Funding information
Max Planck Institute for Biogeochemistry;
Max Planck Society; German Centre for
Integrative Biodiversity Research (iDiv)
Halle-Jena-Leipzig; International Programme
of Biodiversity Science (DIVERSITAS);
International Geosphere-Biosphere
Programme (IGBP); Future Earth; French
Foundation for Biodiversity Research (FRB);
GIS ‘Climat, Environnement et Société'
France; UK Natural Environment Research
Council (NERC); A XA Research Fund
120 
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1 | INTRODUCTION
Plant traits—the morphological, anatomical, physiological, bio-
chemical and phenological characteristics of plants measurable at
the individual plant level (Violle et al., 2007)—reflect the outcome
of evolutionary and community assembly processes responding to
abiotic and biotic environmental constraints (Valladares, Gianoli, &
Gomez, 2007). Traits and trait syndromes (recurrent coordinated
expressions of multiple traits) determine how plants perform and
respond to environmental factors (Grime, 1974; Wright et al., 2017),
affect other trophic levels (Lavorel et al., 2013; Loranger et al., 2012,
2013), and provide a link from species richness to functional diver-
sity, which influences ecosystem properties and derived benefits
and detriments to people (Aerts & Chapin, 2000; Díaz et al., 2004,
2007; Garnier & Navas, 2012; Grime, 2001, 2006; Lavorel et al.,
2015; Lavorel & Garnier, 2002). In the context of the Global Earth
Observation Biodiversity Observation Network (GEO BON) species
traits are considered an Essential Biodiversity Variable to inform
policy about biodiversity change (Kissling et al., 2018; Pereira et al.,
2013). A focus on traits and trait syndromes, therefore, provides
a crucial basis for quantitative and predictive ecology, ecologically
informed landscape conservation and the global change science–
po lic y inte rfac e (Díaz et al., 2016 ; McGill , En quist, Weihe r, & Westoby,
2006; Westoby & Wright, 2006). To fully realize this potential, plant
trait data not only need to be available and accessible in appropriate
quantity and quality but also representative for the scales of infer-
ence and research questions (König et al., 2019). Here we analyse
where the TRY plant trait database stands with respect to coverage
and representativeness after 12 years of operation. We further re-
view the mechanisms and emergent dynamics helping to increase
both.
1.1 | A global database of plant traits—A
brief history
Before the foundation of TRY in 2007, several research groups
had already developed major plant trait databases with remark-
able success, e.g. the Ecological Flora of the British Islands
(Fitter & Peat, 1994), the Seed Information Database (Royal
Botanical Ga rdens KE W, 20 08), BIO P O P (P oschlod, Kl e y e r, Ja c kel,
Dan n em a nn , & Ta c ken b er g, 20 03 ), GL OP N ET (Wri g ht et al., 20 04),
BiolFlor (Klotz, Kühn, & Durka, 2002, 2017), LEDA (Kleyer et al.,
2008), BROT (Paula et al., 2009), USDA PLANTSdata (Green,
2009) and BRIDGE (Baraloto, Timothy Paine, Patino, et al., 2010).
However, these databases were either focused on particular re-
gions (BiolFlor, LEDA, BIOPOP, BROT, USDA Plants, Ecological
Flora of the British Islands, BRIDGE) or specific traits (GLOPNET,
SID). A ‘database of databases’ was in discussion for some time,
but it had been impossible to secure long-term funding for
such a project. Finally, at a joint workshop of the International
Geosphere-Biosphere Program (IGBP) and DIVERSITAS, the TRY
database (TRY—not an acronym, rather a statement of sentiment;
https ://www.try-db.org; Kattge et al., 2011) was proposed with
the explicit assignment to improve the availability and accessibility
of plant trait data for ecology and earth system sciences. The Max
Planck Institute for Biogeochemistry (MPI-BGC) offered to host
the database and the different groups joined forces for this com-
munity-driven program. Two factors were key to the success of
TRY: th e suppor t and trus t of le a ders in the fie l d of functi o nal pla nt
ecology submitting large databases and the long-term funding by
the Max Planck Society, the MPI-BGC and the German Centre for
Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, which
has enabled the continuous development of the TRY database.
At the time of the foundation of TRY, data sharing was not yet a
common practice in ecology (Kattge et al., 2011; Reichman, Jones,
& Schildhauer, 2011). This was an important obstacle for scientific
progress. The first important step of the initiative was, therefore, to
jointly develop a data sharing policy. This was based on permission
of data set owners and a ‘give-and-take’ system: to keep the TRY
databa se growing, the right to reques t data was coupled to data con-
tribution. Exceptions were data requests for vegetation modelling
projects, as modellers typically do not own plant trait data. At an
open workshop in 2013, the members decided to offer the opportu-
nity to make data publicly available and trait data contribution was
no longer a requirement for data access. In 2014, this decision was
implemented in the TRY Data Portal and was immediately followed
by an ‘explosion’ of the number of data requests (Figure 1a): TRY
FIGURE 1 TRY performance statistics, status 1 July 2019. (a) Cumulative numbers of data sets and publications (left axis) and data
requests (right axis); light grey vertical bars indicate calls for data contribution; the red vertical bar indicates the date of opening TRY to the
public. (b) Number of citations for publications using trait data via TRY (Google Scholar)
  
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KATTGE E T Al.
started to serve more than 1,000 requests per year, so that as of
July 2019, about 700 million trait records accompanied by 3 billion
ancillary data have been released for 7,000 requests, submitted by
more than 5,000 registered users. Since 2019, the TRY database is
open access under a Creative Commons Attribution license (CC BY
4.0, https ://creat iveco mmons.org/licen ses/by/4.0): anyone can use
and redistribute data received via TRY under the only condition of
appropriate citation of the TRY database and the references of con-
tributing data sets. Restriction of data access now is the exception
and limited to 2(+2) years, after which the data sets become public.
Since 2014, the TRY Data Portal (https ://www.try-db.org/Try
We b/dp.php) has become the central access point of the TRY
database: the portal organizes data uploads, searches and requests,
and enables interaction between data contributors, management
and users. The portal provides an account for each data set custo-
dian (the individual who directly contributed the data set), which
provides precise bookkeeping about the use of his or her trait data
via TRY. The TRY Data Portal also provides a link to the TRY File
Archive (https ://www.try-db.org/TryWe b/Data.php), which offers
climate and soil data for TRY measurement sites, standardized
FIGURE 2 Cluster analysis of keywords from peer-reviewed publications using plant trait data via TRY. The size of the circles and letters
indicates the frequency of the keywords, colours indicate the eight clusters around the central keywords (from largest to smallest cluster):
biodiversity (red), climate change (dark green), plant traits (dark blue), functional diversity (light green), carbon cycle (violet), community (light
blue), vegetation (orange) and environmental filtering (brown). The analysis is based on 190 publications with DOIs compiled by ISI Web of
Science (https ://clari vate.com/produ cts/web-of-science). The analysis was performed with VOSviewer version 1.6.11 (https ://www.vosvi
ewer.com) using the default settings and only minor editing of selected terms. The clustering technique used by VOSviewer is discussed by
Waltman, van Eck, and Noyons (2010). Due to limited space not all central keywords of small clusters are displayed. Material to display the
results in detail using the VOSviewer software is provided in the Supporting Information
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categorical traits relevant to attribute species to plant functional
types (PFTs), and provides the opportunity to publish plant trait data
sets and receive a DOI.
Trait data via TRY contributed to at least 250 scientific applica-
tions and publications (Figure 1a), among these 202 peer-reviewed
publications in 83 different scientific journals, covering a broad range
of topics, from ‘Landscape and Urban planning’ to ‘Geoscientific
Model Development’. Twenty publications were directly related to
vegetation model development, while 230 were empirical studies.
A cluster analysis of keywords from the peer-reviewed publications
shows eight clusters around the central keywords biodiversity, cli-
mate change, plant traits, functional diversity, carbon cycle, commu-
nity, vegetation and environmental filtering (Figure 2). Citations of
publications using trait data via TRY have increased exponentially,
leading to about 10,000 citations and an h-factor of 46 for the TRY
database (Figure 1b).
During 12 years of development, versions 1–5 of the TRY data-
base have been released with an increasing number of contributed
data sets and trait records (Tables 1 and 2; Figure 1a). Currently,
TRY is working on version 6. As of July 2019, the TRY database
comprised 588 data sets from 765 data contributors (Table A1).
The dynamics of the number of data sets in TRY indicates an in-
creasing success of calls to the scientific community for data
contribution in 2007, 2013 and 2019. When the manuscript was
submitted, data contributions responding to the call in 2019 were
not yet fully integrated into the TRY database. Therefore all anal-
yses presented in this paper are based on versions 1–5 of the TRY
dat abase (Tab le 1). TRY ver sion 5, released on 26 March 2019, con-
tains 387 data sets providing 11.8 million trait records, accompa-
nied by 35 million ancillary data, for 2,091 traits and 280,000 plant
taxa, mostly at the species level (Table 2). Data coverage is still
driven by a few large (often integrated) databases, but increasingly
small data sets (mostly primary data) contribute to the overall cov-
erage (Figure 3a). Plant trait data in TRY can be traced to >10,000
original references. This highlights the breadth of data integrated
in the TRY database and its nat ure as database of databa ses, a ‘sec-
ond generation of data pooling’ (M. Westoby, personal communi-
cation, August 24, 2009).
We now observe a tendency that new trait-based research is
increasingly planned against the background of the TRY database.
Coverage and availability of trait data in TRY stimulate trait-based
research, which then often leads to the identification of unexpected
data gaps. This motivates data mobilization and/or new measure-
ments, which improve the availability of plant trait data for the re-
search community, and—if contributed to TRY—help the database
grow. Examples for such a ‘feed-forward data integration loop’ are
provided in Box 1.
To support this process, in this article, we take stock of the data
compiled in the TRY database and present emerging patterns of data
coverage and representativeness with a focus on the identification
of principal and systematic gaps. Finally, we discuss ways forward
and the potential future role of the TRY initiative for the research
community.
2 | MATERIALS AND METHODS
2.1 | Plant trait data in the TRY database
Plant traits can be classified as categorical (qualitative and or-
dinal) or quantitative (continuous) traits (Kattge et al., 2011).
So me trait s ar e ra the r stabl e withi n specie s (mos tly cate gor ical
traits), and some of these can be systematic ally compiled from
s p ec ie s ch ec kl i st s an d f l or as (e .g . We ig el t , Kö ni g, & Kr ef t, 2 019 ) .
TABLE 1 TRY database versions
Versio n
Data
acquisition and
import Data release Status
1October 2007–
July 2009
October 2008–
April 2011
Restricted,
give-and-take
2July 2009–
April 2011
April 2011–
December 2014
Restricted,
give-and-take
3 April 2011–
April 2014
December 2014
July 2017
Optionally
open access
4April 2014–
February
2017
July 2017–March
2019
Optionally
open access
5February
2017–March
2019
March 2019– Open access
6March 2019– Open access
TABLE 2 Data coverage from TRY version 1 to 5
Versio n Trait records Entities
Tra it
records
per
entity Tr ait s
Average
number of
records
per trait Species
Geo-
referenced
trait records Sites
Ancillary
data
12,077,640 1,110,303 1.87 661 3 ,143 57, 591 682,108 8,276 4,4 3 9,78 3
22, 376 ,231 1,207, 6 6 9 1.97 74 3 3,198 65,746 871,582 8, 513 4,758,033
3 5,783,482 2,246,967 2.57 1,149 5,033 92,14 6 2,201,242 11,84 4 11,834,960
47,162,252 3,435,238 2.08 1,981 3,615 141,461 2 ,978,776 16,480 14,644,354
511,850,781 5,102,993 2.37 2,091 5,668 279, 875 4,952,839 20,953 35,516,190
  
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KATTGE E T Al.
However, most traits relevant to ecology and earth system
sciences are characterized by intraspecific variability and
trait–environment relationships (mostly quantitative traits).
Both kind s of t raits are compiled in the TRY dat abase, but with
a focus on continuous traits. These traits have to be measured
on individual plants in their particular environmental context.
Each such trait measurement has high information content
as it captures the specific response of a given genome to the
prevailing environmental conditions. The collection of these
quantitative traits and their essential environmental covari-
ates is important but often tedious and expensive: research-
ers need to travel to the objects of interest—often to remote
places—or they need to develop experiments creating specific
environmental conditions. While trait measurements them-
selves may be relatively simple, the selection of the adequate
entity (e.g., a representative plant in a community, or a repre-
sentative leaf on a tree) and obtaining the relevant ancillary
data (taxonomic identification, soil and climate properties, dis-
turbance history, etc.) may require sophisticated instruments
and a high degree of expertise and experience. Besides, these
data are most often individual measurements with a low level
of automation. This not only limits the number of measure-
ments but also causes a high risk of errors, which need to be
corrected a posteriori, requiring substantial human work. The
FIGURE 3 Trait data coverage of TRY version 1 (dark grey) and 5 (light grey). Data coverage in TRY is characterized by long-tailed rank-
size distributions: (a) rank of dataset by trait records, (b) rank of traits by number of records, (c) rank of traits by number of species, (d) rank
of species by trait records, (e) rank of species by number of traits, (f) rank of traits by number of records per species (averaged by trait).
Note that y-axes are log-scaled
10
1,000
100,000
0 100 200 300 400
Rank of datasets
No. trait records
(a) (b)
10
1,000
100,000
0 500 1,000 1,500 2,000
Rank of traits
No. species
(c) (d)
1
10
100
0 100,000 200,000
Rank of species
No. traits
(e)
10
1,000
100,000
0 500 1,000 1,500 2,000
Rank of traits
No. trait records
10
1,000
100,000
0 100,000 200,000
Rank of species
No. trait records
1
10
100
1,000
10,000
0 500 1,000 1,500 2,000
Rank of traits
No
. trait records per species
(f)
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integration of these data from different sources into a consist-
ent data set requires a carefully designed workflow with suf-
ficient data quality assurance (see Box 2: TRY data integration
workflow).
These measurements of quantitative traits are single sampling
events for particular individuals at certain locations and times,
which preserve relevant information on intraspecific variation and
provide the necessary detail to address questions at the level of
populations or communities. Within individual field campaigns or
experiments, researchers often aim to measure complete sets of
these data: all traits of interest for all individuals or species in the
analyses. However, across studies and data sets and at large scales,
the coverage of these data shows major gaps, which provide major
challenges concerning data completeness and representativeness
(König et al., 2019).
3 | RESULTS
3.1 |Data coverage
Compared to TRY database version 1 and the state reported in
Kattge et al. (2011), TRY version 5 has substantially grown with re-
spect to the number of trait records, traits, species, entities, geo-
referenced measurement sites and ancillary data (Table 2).
3.2 | Trait records and entities
The numbers of trait records (individual trait measurements) and
entities (individual plants or plant organs on which the measure-
ments have been taken) increased by a factor of about 6 for trait
BOX 1 Examples for the ‘feed-forward data integration loop’ observed in the context of the TRY database
• Iversen et al. (2017) indicated that in the TRY database only 1% of trait records were related to roots. This motivated the
development of the Fine-Root Ecology Database (FRED) specializing in the mobilization of fine-root trait records from the
literature (Iversen et al., 2017). In the meantime, the first versions of the FRED database have been contributed to the TRY
database. The im