# TRY plant trait database – enhanced coverage and open access

Jens Kattge, Gerhard Bonisch, Sandra Díaz, Sandra Lavorel, Iain Colin Prentice, Paul W. Leadley, Susanne Tautenhahn, Gijsbert Werner, Tuomas Aakala, Mehdi Abedi, et al.

► To cite this version:

Jens Kattge, Gerhard Bonisch, Sandra Díaz, Sandra Lavorel, Iain Colin Prentice, et al. TRY plant trait database – enhanced coverage and open access. Global Change Biology, 2020, 26 (1), pp.119-188. 10.1111/gcb.14904 . hal-02434220

HAL Id: hal-02434220

https://amu.hal.science/hal-02434220

Submitted on 9 Nov 2020

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# TRY plant trait database – enhanced coverage and open access

Article in Global Change Biology · January 2020

DOI: 10.1111/gcb.14904

CITATIONS

67

READS

6,352

729 authors, including:

<figure><img src="image_2.png" /><figcaption>Jens Kattge</figcaption></figure>

Max Planck Institute for Biogeochemistry Jena

272 PUBLICATIONS 16,342 CITATIONS

SEE PROFILE

<figure><img src="image_3.png" /><figcaption>Gerhard Bönisch</figcaption></figure>

Max Planck Institute for Biogeochemistry Jena

62 PUBLICATIONS 5,593 CITATIONS

SEE PROFILE

<figure><img src="image_4.png" /><figcaption>Sandra Diaz</figcaption></figure>

National University of Cordoba, Argentina

319 PUBLICATIONS 41,469 CITATIONS

SEE PROFILE

<figure><img src="image_5.png" /><figcaption>Sandra Lavorel</figcaption></figure>

CNRS

418 PUBLICATIONS 54,082 CITATIONS

SEE PROFILE

Some of the authors of this publication are also working on these related projects:

<figure><img src="image_6.png" /></figure>

Testing Enrichment Planting in Fragmented Tropical Landscapes View project

<figure><img src="image_7.png" /></figure>

Succession after fire in Central Siberian boreal forests View project
# TRY plant trait database – enhanced coverage and open access

**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); AXA Research Fund

**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-environmental 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.

**KEYWORDS**

data coverage, data integration, data representativeness, functional diversity, plant traits, TRY plant trait database

<footnote>A list of authors and their affiliations appears in the Appendix.</footnote>

<footnote>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.</footnote>

<footnote>© 2019 The Authors. *Global Change Biology* published by John Wiley & Sons Ltd</footnote>
# 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 diversity, 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–policy interface (Díaz et al., 2016; McGill, Enquist, Weiher, & 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 inference 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 review 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 remarkable success, e.g. the Ecological Flora of the British Islands

(Fitter & Peat, 1994), the Seed Information Database (Royal Botanical Gardens KEW, 2008), BIOPOP (Poschlod, Kleyer, Jackel, Dannemann, & Tackenberg, 2003), GLOPNET (Wright et al., 2004), BioIFlor (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 regions (BioIFlor, 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 community-driven program. Two factors were key to the success of TRY: the support and trust of leaders in the field of functional plant 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 database growing, the right to request data was coupled to data contribution. 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 opportunity 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><img src="image_8.png" /><figcaption>**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)</figcaption></figure>
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://creativecommons.org/licenses/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 Web/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 custodian (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/TryWeb/Data.php), which offers climate and soil data for TRY measurement sites, standardized

<figure><img src="image_9.png" /><figcaption>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://clarivate.com/products/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</figcaption></figure>
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 applications 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, climate change, plant traits, functional diversity, carbon cycle, community, 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 database 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 increasing 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 analyses presented in this paper are based on versions 1–5 of the TRY database (Table 1). TRY version 5, released on 26 March 2019, contains 387 data sets providing 11.8 million trait records, accompanied 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 coverage (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 nature as database of databases, a ‘second generation of data pooling’ (M. Westoby, personal communication, 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 measurements, which improve the availability of plant trait data for the research 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 original) or quantitative (continuous) traits (Kattge et al., 2011). Some traits are rather stable within species (mostly categorical traits), and some of these can be systematically compiled from species checklists and floras (e.g. Weigelt, König, & Kreft, 2019).

<table><caption>TABLE 1 TRY database versions</caption><thead><tr><th>Version</th><th>Data acquisition and import</th><th>Data release</th><th>Status</th></tr></thead><tbody><tr><td>1</td><td>October 2007-July 2009</td><td>October 2008-April 2011</td><td>Restricted, give-and-take</td></tr><tr><td>2</td><td>July 2009-April 2011</td><td>April 2011-December 2014</td><td>Restricted, give-and-take</td></tr><tr><td>3</td><td>April 2011-April 2014</td><td>December 2014-July 2017</td><td>Optionally open access</td></tr><tr><td>4</td><td>April 2014-February 2017</td><td>July 2017-March 2019</td><td>Optionally open access</td></tr><tr><td>5</td><td>February 2017-March 2019</td><td>March 2019-</td><td>Open access</td></tr><tr><td>6</td><td>March 2019-</td><td></td><td>Open access</td></tr></tbody></table>

<table><caption>TABLE 2 Data coverage from TRY version 1 to 5</caption><thead><tr><th>Version</th><th>Trait records</th><th>Entities</th><th>Trait records per entity</th><th>Traits</th><th>Average number of records per trait</th><th>Species</th><th>Geo-referenced trait records</th><th>Sites</th><th>Ancillary data</th></tr></thead><tbody><tr><td>1</td><td>2,077,640</td><td>1,110,303</td><td>1.87</td><td>661</td><td>3,143</td><td>57,591</td><td>682,108</td><td>8,276</td><td>4,439,783</td></tr><tr><td>2</td><td>2,376,231</td><td>1,207,669</td><td>1.97</td><td>743</td><td>3,198</td><td>65,746</td><td>871,582</td><td>8,513</td><td>4,758,033</td></tr><tr><td>3</td><td>5,783,482</td><td>2,246,967</td><td>2.57</td><td>1,149</td><td>5,033</td><td>92,146</td><td>2,201,242</td><td>11,844</td><td>11,834,960</td></tr><tr><td>4</td><td>7,162,252</td><td>3,435,238</td><td>2.08</td><td>1,981</td><td>3,615</td><td>141,461</td><td>2,978,776</td><td>16,480</td><td>14,644,354</td></tr><tr><td>5</td><td>11,850,781</td><td>5,102,993</td><td>2.37</td><td>2,091</td><td>5,668</td><td>279,875</td><td>4,952,839</td><td>20,953</td><td>35,516,190</td></tr></tbody></table>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

**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

However, most traits relevant to ecology and earth system sciences are characterized by intraspecific variability and trait-environment relationships (mostly quantitative traits). Both kinds of traits are compiled in the TRY database, 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 covariates is important but often tedious and expensive: researchers 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 themselves may be relatively simple, the selection of the adequate entity (e.g., a representative plant in a community, or a representative leaf on a tree) and obtaining the relevant ancillary data (taxonomic identification, soil and climate properties, disturbance 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 measurements but also causes a high risk of errors, which need to be corrected a posteriori, requiring substantial human work. The
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 improved number and availability of trait data on roots allowed for a project on root trait functionality in a whole-plant context (https://www.idiv.de/en/sdiv/working_groups/wg_pool/sroot.html), which motivated additional mobilization of root trait data.

* The promising coverage of plant trait data from tundra regions in the TRY database encouraged the inclusion of plant traits in an analysis of tundra ecosystem change, scaling shrub expansion from site to biome (Bjorkman, Myers-Smith, Elmendorf, Normand, Rüger, et al., 2018). In the context of the project, a large number of additional trait data were mobilized by the Tundra Traits Team (Bjorkman, Myers-Smith, Elmendorf, Normand, Thomas, et al., 2018), which have recently been contributed to the TRY database.

* Moreno-Martínez et al. (2018) estimated the worldwide variation of several leaf traits to improve the parameterization of global vegetation models and remote sensing approaches predicting, for example, gross primary productivity. Due to the low representation of traits for crop species in the TRY database, they could not provide estimates for major agricultural regions (see white spots in figure 5 of Moreno-Martínez et al., 2018). The identification of these gaps motivated mobilization of trait data for crop plants and agro-ecosystems (Engemann et al., 2016; Martin, Hale, et al., 2018; Martin & Isaac, 2015), which were then contributed to the TRY database.

* Trait data on plant growth form (tree, shrub, herb, etc.) were compiled by TRY, extended and consolidated in the context of the BIEN initiative (Engemann et al., 2016; Enquist, Condit, Peet, Schildhauer, & Thiers, 2016) and then contributed to the development of the GIFT database (Weigelt, König, & Kreft, 2019). The upgraded plant growth form data were contributed again to TRY.

* Plant species richness is unequally distributed across the globe, with the highest species richness observed in the tropics (von Humboldt, 1817). The highest numbers of species with measurements in TRY are also found in the tropics, but as well the largest gap relative to reported species richness: less than 1% of estimated species richness is represented in TRY (Jetz et al., 2016). This principal and systematic mismatch of data coverage and representativeness has contributed to motivate the development of a 'global biodiversity observatory' of in situ measurements and space-borne remote sensing that tracks temporal changes in plant functional traits around the globe to fill critical knowledge gaps, aid in the assessment of global environmental change and improve predictions of future change (Jetz et al., 2016).

* TRY is involved in the sPlot initiative to establish a global vegetation-plot database (www.idiv.de/en/splot.html). sPlot supports the analysis of plant communities across the world's biomes by combining vegetation-plot data with traits from the TRY database (Bruelheide et al., 2019). This has resulted, for the first time, in global analyses of plant functional community data (Bruelheide et al., 2018). In contrast to single species measurements or trait values aggregated in grid cells, using vegetation-plot data allows understanding the role of traits for biotic interactions and community assembly processes. In turn, trait data measured in the context of sPlot are contributed to TRY.

integration of these data from different sources into a consistent data set requires a carefully designed workflow with sufficient 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 respect 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 measurements have been taken) increased by a factor of about 6 for trait
## BOX 2 The data integration workflow for the TRY database version 5

### Data acquisition

In the context of the TRY initiative, data acquisition so far relies on active contributions by the community—data sets need to be sent by email or uploaded at the TRY website (https://www.try-db.org/TryWeb/Submission.php). From time to time (2007, 2013 and 2019), TRY sends out calls for data contributions to the community. However, so far there has been no systematic screening of public data repositories like DRYAD or PANGAEA for plant trait data.

### Data integration

The basic principle of data integration in the TRY database is to preserve the original trait and ancillary data and annotate these with complementing and consolidated information. Data integration consists of three major components: data consolidation, complementation and quality assurance. We here provide a brief overview; a detailed description can be found in Supporting Information and on the TRY website (https://www.try-db.org/TryWeb/Database.php).

### Data consolidation

The data structure is transformed into the entity-attribute-value (EAV) model and the OBOE schema (Madin et al., 2007) as used in the TRY database: a long table of trait records and ancillary data where all trait records and ancillary data measured on the same entity (individual plant or plant organ on which the measurements have been taken) are linked by a unique identifier. Plant taxonomy is consolidated using the Taxonomic Names Resolution Service (TNRS; http://tnrs.iplantcollaborative.org; Boyle et al., 2013) with a taxonomic backbone based on the Plant List (http://www.theplantlist.org), Missouri Botanical Garden's Tropicos database (http://www.tropicos.org), the Global Compositae Checklist (https://www.compositae.org/checklist), the International Legume Database and Information Service (http://www.ildis.org), and USDA's Plants Database (http://plants.usda.gov). Trait names and definitions are consolidated across all data sets, based on the TOP thesaurus of plant characteristics (Garnier et al., 2017) or the plant trait handbook (Pérez-Harguindeguy et al., 2013), if possible. For continuous traits with more than 1,000 records, units are standardized and trait values recalculated. Most relevant ancillary data—geo-references, measurement date, exposition, maturity, and health—are consolidated across data sets and, if possible, to external standards, like the decimal representation of latitude and longitude, or ISO 8601 (YYYY-MM-DD) for the date.

### Data complementation

After consolidation, additional trait values are derived from contributed trait data where possible; for example, leaf nitrogen content per area from leaf nitrogen content per dry mass and specific leaf area (SLA) if both were measured on the same entity.

### Data quality assurance

Continuous traits with >1,000 records in the database are subject to a three-step process: (a) Systematic errors, like a wrong unit for a given trait for all records of a specific data set, are identified across data sets with semi-automated procedures and corrected. (b) Z-scores are calculated for each standardized trait value to indicate outliers and potential errors of individual trait records. (c) Duplicate trait records are identified based on consolidated trait names, taxonomy, units and values. Geo-references are checked against the ESA CCI Land Cover Map of Global Water Bodies (https://www.esa-landcover-cci.org/?q=node/162) to assure at least that the provided locations are on land.

After a data set has been integrated into the TRY database, the data set custodian is asked for feedback; that is, whether consolidated trait names are appropriate and consolidated values correct. Data are reformatted for data release and format errors (i.e. tabs and line breaks in database cells) are corrected. Finally, the original and consolidated data (including flags for outliers and duplicates) are released on request as tab-delimited text files.

records and 5 for entities from TRY version 1 (2.1 million trait records measured on 1.1 million entities) to TRY version 5 (11.8 million trait records measured on 5.0 million entities). The average number of trait records per entity increased from 1.9 to 2.4 (Table 2).

## 3.3 | Traits

The number of traits has grown steadily from TRY version 1 to 5, apart from a steep step from TRY version 3 to 4 (Table 2). This step was caused by the contribution of the FRED database, which added

about 700 new traits for roots. Data coverage across traits is characterized by long-tail distributions: a small number of traits is well covered by records and species, while the majority of traits has only very low coverage of records and species (Figure 3). However, the number of continuous traits with more than 1,000 records (which are subject to intense data quality assurance during integration) has increased from about 200 in TRY version 1 to 600 in TRY version 5 (Figure 3b). The number of traits with data for more than 100 species has increased from 300 to 700 (Figure 3c). In parallel, the number of records per trait and species ('intraspecific retakes') has increased from almost zero traits with an average more than 10 records per
trait-species combination in TRY version 1 to more than 500 in TRY version 5 (Figure 3f).

The traits with the best species coverage in TRY version 5 are mostly categorical (Table 3). The categorical traits used for the

<table><caption>TABLE 3 Traits with best species coverage. The 30 traits covering the highest number of species in the TRY database version 5 and the number of species represented for these traits in TRY version 1. Data type: cat = categorical; con = continuous. Sorted by the number of species in TRY 5</caption><thead><tr><th rowspan="2">Trait name</th><th rowspan="2">Data type</th><th colspan="2">Number of species</th></tr><tr><th>TRY 1</th><th>TRY 5</th></tr></thead><tbody><tr><td>Plant growth form</td><td>cat</td><td>31,327</td><td>263,357</td></tr><tr><td>Plant woodiness</td><td>cat</td><td>14,628</td><td>79,298</td></tr><tr><td>Leaf type</td><td>cat</td><td>7,934</td><td>62,904</td></tr><tr><td>Leaf compoundness</td><td>cat</td><td>7,998</td><td>57,922</td></tr><tr><td>Leaf photosynthesis pathway</td><td>cat</td><td>15,609</td><td>37,315</td></tr><tr><td>Leaflet number per leaf</td><td>con</td><td>0</td><td>30,296</td></tr><tr><td>Plant height vegetative</td><td>con</td><td>13,899</td><td>28,944</td></tr><tr><td>Leaf phenology type</td><td>cat</td><td>14,622</td><td>28,514</td></tr><tr><td>Species tolerance to frost</td><td>cat</td><td>2,180</td><td>28,122</td></tr><tr><td>Seed dry mass</td><td>con</td><td>14,602</td><td>27,022</td></tr><tr><td>Species occurrence range: native versus invasive</td><td>cat</td><td>11,313</td><td>25,067</td></tr><tr><td>Plant lifespan</td><td>cat, con</td><td>7,617</td><td>24,712</td></tr><tr><td>Dispersal syndrome</td><td>cat</td><td>7,528</td><td>21,717</td></tr><tr><td>Plant nitrogen fixation capacity</td><td>cat</td><td>10,504</td><td>18,247</td></tr><tr><td>Leaf area<sup>a</sup></td><td>con</td><td>8,873</td><td>16,663</td></tr><tr><td>Leaf area per leaf dry mass (specific leaf area)<sup>b</sup></td><td>con</td><td>7,879</td><td>16,460</td></tr><tr><td>Plant resprouting capacity</td><td>cat</td><td>3,320</td><td>15,997</td></tr><tr><td>Seed germination rate (germination efficiency)</td><td>con</td><td>6,698</td><td>15,822</td></tr><tr><td>Plant life form sensu Raunkiaer</td><td>cat</td><td>7,710</td><td>15,766</td></tr><tr><td>Pollination syndrome</td><td>cat</td><td>4,064</td><td>15,631</td></tr><tr><td>Leaf shape</td><td>cat</td><td>3,191</td><td>15,594</td></tr><tr><td>Flower sex</td><td>cat</td><td>3,572</td><td>13,735</td></tr><tr><td>Leaf distribution arrangement type</td><td>cat</td><td>3,998</td><td>13,130</td></tr><tr><td>Leaf nitrogen content per leaf dry mass</td><td>con</td><td>6,291</td><td>12,238</td></tr><tr><td>Stem specific density</td><td>con</td><td>9,813</td><td>11,001</td></tr><tr><td>Flower colour</td><td>cat</td><td>4,747</td><td>10,507</td></tr><tr><td>Seed storage behaviour</td><td>cat</td><td>10,161</td><td>10,161</td></tr><tr><td>Fruit type</td><td>cat</td><td>3,644</td><td>9,573</td></tr><tr><td>Leaf margin type</td><td>cat</td><td>0</td><td>9,179</td></tr><tr><td>Wood growth ring distinction</td><td>cat</td><td>5,121</td><td>9,103</td></tr></tbody></table>

classification of PFTs—plant woodiness, plant growth form, leaf type (broadleaved vs. needle-leaved), leaf phenology type (deciduous vs. evergreen), leaf photosynthesis pathway (C3, C4, CAM)—are still among the best covered. However, the number of species characterized for each of these traits has substantially increased from TRY version 1 to 5, most significantly for plant growth form from 31,327 to 263,357 species, supported by the contribution from the GIFT database (Weigelt, König, & Kreft, 2019).

The quantitative traits with the highest species coverage are still the six traits which were already prominent in TRY version 1 and involved in the analysis of the global spectrum of plant form and function (Díaz et al., 2016): plant height, seed mass, leaf area, leaf area per dry mass, leaf nitrogen content per dry mass and stem specific density (SSD). However, in general, the coverage of continuous traits already present in TRY 1 has substantially improved. This facilitates a more robust characterization of frequency distributions (Figure 4). In most cases, the range of observed trait values did not change much from TRY version 1 to 5, but the shapes of frequency distributions became more regular and pronounced, especially for multimodal frequency distributions like plant height and leaf Δ13C. Noteworthy, the examples in Figure 4 lack several kinds of traits because they are missing relevant numbers of trait records, like roots (only one trait), flowers and dead plant material (litter), secondary metabolites or data related to trophic interactions.

The 30 traits that were most often requested (Table 4) are dominated by continuous traits related to the global spectrum of plant form and function (Díaz et al., 2016), the leaf economics spectrum (Wright et al., 2004) and rooting depth. Only seven categorical traits are among these 30 traits. This indicates a switch between well covered—categorical—traits (Table 3) and most frequently requested—continuous—traits (Table 4). The first five most documented traits are categorical whereas among the 10 most requested traits, only one is categorical. However, within continuous traits, there is, in general, a good match between traits characterized for most species and traits most often requested. To some extent this may be influenced by the amount of available data for the individual traits. However, a noteworthy exception is rooting depth, as 10% of requests ask for this trait, while it is 'only' covered for 3,886 species, mostly contributed via the Global Dataset of Maximum Rooting Depth (Fan, Miguez-Macho, Jobbágy, Jackson, & Otero-Casal, 2017). This mismatch indicates a demand for more data on the most relevant below-ground traits.

## 3.4 | Species

From TRY version 1 to 4, the number of species increased slowly, but almost doubled to version 5 due to the contribution of plant growth form data from the GIFT database, which added about 100,000 new species. As in the case of traits, the data coverage for species is characterized by long-tail distributions: few species are covered well by measurements and traits, while the majority

<footnote><sup>a</sup>In case of compound leaves: leaflet, undefined if petiole is included or excluded.</footnote>

<footnote><sup>b</sup>Undefined if petiole is included or excluded.</footnote>
<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

**FIGURE 4** Frequency distributions of observations for 15 ecologically relevant and well sampled continuous traits from different plant organs. Grey: TRY version 1; white: TRY version 5. (a) Plant height, (b) Seed mass, (c) SSD: stem dry mass per stem fresh volume (stem specific density), (d) Leaf area, (e) SLA: leaf area per leaf dry mass (specific leaf area), (f) Leaf N mass: leaf nitrogen content per leaf dry mass (leaf nitrogen concentration), (g) LDMC: leaf dry mass per leaf fresh mass (leaf dry matter content), (h) Leaf P mass: leaf phosphorus content per leaf dry mass, (i) Leaf C mass: leaf carbon content per leaf dry mass, (j) Leaf N area: leaf nitrogen content per leaf area, (k) Leaf P area: leaf phosphorus content per leaf area, (l) Leaf CN: leaf carbon content per leaf nitrogen content, (m) Leaf 13C: leaf 13C carbon isotope signature, (n) Leaf 15N: leaf 15N nitrogen isotope signature, (o) SRL: root length per root dry mass (specific root length)
<table><caption>TABLE 4 Most often requested traits. The 30 traits with the highest number of requests (status 1 October 2019). Number of requests and in parentheses the percentage relative to all 7,330 requests</caption><thead><tr><th>Trait</th><th>Data type</th><th>Number of requests</th></tr></thead><tbody><tr><td>Leaf area per leaf dry mass (specific leaf area or 1/LMA)<sup>a</sup></td><td>con</td><td>2,977 (41%)</td></tr><tr><td>Plant height vegetative</td><td>con</td><td>2,159 (29%)</td></tr><tr><td>Leaf nitrogen (N) content per leaf dry mass</td><td>con</td><td>1,938 (26%)</td></tr><tr><td>Leaf area<sup>b</sup></td><td>con</td><td>1,676 (23%)</td></tr><tr><td>Plant growth form</td><td>cat</td><td>1,625 (22%)</td></tr><tr><td>Seed dry mass</td><td>con</td><td>1,580 (22%)</td></tr><tr><td>Leaf nitrogen (N) content per leaf area</td><td>con</td><td>1,221 (17%)</td></tr><tr><td>Leaf phosphorus (P) content per leaf dry mass</td><td>con</td><td>1,170 (16%)</td></tr><tr><td>Plant lifespan (longevity)</td><td>con</td><td>1,168 (16%)</td></tr><tr><td>Leaf dry mass per leaf fresh mass (leaf dry matter content)</td><td>con</td><td>1,147 (16%)</td></tr><tr><td>Leaf phenology type</td><td>cat</td><td>1,047 (14%)</td></tr><tr><td>Leaf carbon (C) content per leaf dry mass</td><td>con</td><td>973 (13%)</td></tr><tr><td>Dispersal syndrome</td><td>cat</td><td>958 (13%)</td></tr><tr><td>Stem specific density</td><td>con</td><td>951 (13%)</td></tr><tr><td>Leaf photosynthesis rate per leaf area</td><td>con</td><td>896 (12%)</td></tr><tr><td>Leaf dry mass (single leaf)</td><td>con</td><td>896 (12%)</td></tr><tr><td>Leaf photosynthesis pathway</td><td>cat</td><td>874 (12%)</td></tr><tr><td>Leaf thickness</td><td>con</td><td>852 (12%)</td></tr><tr><td>Plant nitrogen (N) fixation capacity</td><td>con</td><td>833 (11%)</td></tr><tr><td>Leaf carbon/nitrogen (C/N) ratio</td><td>con</td><td>817 (11%)</td></tr><tr><td>Plant life form sensu Raunkiaer</td><td>cat</td><td>801 (11%)</td></tr><tr><td>Leaf lifespan (longevity)</td><td>con</td><td>790 (11%)</td></tr><tr><td>Root rooting depth</td><td>con</td><td>733 (10%)</td></tr><tr><td>Plant growth rate</td><td>con</td><td>727 (10%)</td></tr><tr><td>Leaf type</td><td>cat</td><td>727 (10%)</td></tr><tr><td>Leaf phosphorus (P) content per leaf area</td><td>con</td><td>719 (10%)</td></tr><tr><td>Plant functional type</td><td>cat</td><td>710 (10%)</td></tr><tr><td>Plant woodiness</td><td>cat</td><td>708 (10%)</td></tr><tr><td>Leaf photosynthesis rate per leaf dry mass</td><td>con</td><td>701 (10%)</td></tr><tr><td>Plant reproductive phenology timing</td><td>con</td><td>672 (9%)</td></tr></tbody></table>

of species has only very low data coverage (Figure 3d-f). The species characterized by the most traits tend to be northern temperate trees or globally distributed pasture species (Table 5). Out of the top 30 species with the best trait coverage, 27 (90%) originate in Central or Northern Europe.

<table><caption>TABLE 5 Species with best trait coverage. The 30 species with the highest number of traits in the TRY database version 5 and number of traits represented for these species in TRY version 1 and 5. Sorted by the number of traits in TRY 5</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Plant growth form</th><th colspan="2">Number of traits</th></tr><tr><th>TRY 1</th><th>TRY 5</th></tr></thead><tbody><tr><td>Pinus sylvestris</td><td>Tree</td><td>264</td><td>569</td></tr><tr><td>Fagus sylvatica</td><td>Tree</td><td>237</td><td>517</td></tr><tr><td>Picea abies</td><td>Tree</td><td>252</td><td>475</td></tr><tr><td>Quercus robur</td><td>Tree</td><td>194</td><td>435</td></tr><tr><td>Acer saccharum</td><td>Tree</td><td>139</td><td>430</td></tr><tr><td>Betula pendula</td><td>Tree</td><td>265</td><td>429</td></tr><tr><td>Achillea millefolium</td><td>Herb</td><td>209</td><td>403</td></tr><tr><td>Acer pseudoplatanus</td><td>Tree</td><td>186</td><td>397</td></tr><tr><td>Trifolium pratense</td><td>Herb</td><td>181</td><td>395</td></tr><tr><td>Quercus rubra</td><td>Tree</td><td>190</td><td>388</td></tr><tr><td>Dactylis glomerata</td><td>Herb</td><td>193</td><td>387</td></tr><tr><td>Plantago lanceolata</td><td>Herb</td><td>156</td><td>386</td></tr><tr><td>Vaccinium vitis-idaea</td><td>Shrub</td><td>189</td><td>382</td></tr><tr><td>Trifolium repens</td><td>Herb</td><td>173</td><td>380</td></tr><tr><td>Fraxinus excelsior</td><td>Tree</td><td>196</td><td>378</td></tr><tr><td>Acer platanoides</td><td>Tree</td><td>186</td><td>378</td></tr><tr><td>Quercus petraea</td><td>Tree</td><td>194</td><td>368</td></tr><tr><td>Poa pratensis</td><td>Herb</td><td>195</td><td>366</td></tr><tr><td>Holcus lanatus</td><td>Herb</td><td>178</td><td>364</td></tr><tr><td>Tilia cordata</td><td>Tree</td><td>153</td><td>362</td></tr><tr><td>Calluna vulgaris</td><td>Shrub</td><td>190</td><td>360</td></tr><tr><td>Lotus corniculatus</td><td>Herb</td><td>153</td><td>360</td></tr><tr><td>Pseudotsuga menziesii</td><td>Tree</td><td>141</td><td>356</td></tr><tr><td>Medicago lupulina</td><td>Herb</td><td>145</td><td>351</td></tr><tr><td>Festuca rubra</td><td>Herb</td><td>175</td><td>347</td></tr><tr><td>Sorbus aucuparia</td><td>Tree</td><td>197</td><td>335</td></tr><tr><td>Phleum pratense</td><td>Herb</td><td>179</td><td>335</td></tr><tr><td>Quercus ilex</td><td>Tree</td><td>195</td><td>333</td></tr><tr><td>Betula papyrifera</td><td>Tree</td><td>126</td><td>332</td></tr><tr><td>Vaccinium uliginosum</td><td>Shrub</td><td>169</td><td>330</td></tr></tbody></table>

## 3.5 | Entity × trait and species × trait matrices

The trait data in the TRY database can be represented by two two-dimensional matrices: the entity × trait matrix, with entities in rows and traits in columns; and the species × trait matrix, with species in rows and traits in columns. Both matrices are characterized as large but sparse: high numbers of entities, species and traits in TRY make the two matrices large, but many cells in the matrices are empty. From TRY version 1 to 5 the size of the matrices has grown by a factor of 15, but at the same time the number of trait records to fill

<footnote><sup>a</sup>Undefined if petiole is included or excluded.</footnote>

<footnote><sup>b</sup>In case of compound leaves: leaflet, undefined if petiole is included or excluded.</footnote>
the cells increased only by a factor of about 5 and thus the matrices became even sparser: the fractional coverage decreased from 0.4% to 0.1% (entity × trait) and 1.4% to 0.4% (species × trait; Figure 5a). This sparsity together with the observed long-tail distributions has consequences, especially for multivariate analyses. Given that on average only two to three traits of the 2,091 traits in TRY version 5 are measured on an individual plant (entity), a multivariate analysis based on individuals is indeed practically impossible, as mentioned by Shan et al. (2012). Even after aggregation at the species level, the decline of the number of species with complete trait coverage when adding a new trait, for example for multivariate analysis, is surprisingly high (Figure 5b). Additionally, the final number of species represented in the analysis is determined by the trait with the lowest species coverage. Therefore multivariate analyses with more than about six traits are still very much limited by the number of species. The same applies when species have to be classified by several categorical traits, like for example in the context of PFTs.

## 3.6 | Ancillary data

The numbers of ancillary data, geo-referenced trait records and trait records with measurement date increased by a factor of almost 10 from TRY version 1 to TRY version 5 (Table 2). The ratio of ancillary data to trait records, therefore, increased from TRY version 1 to 5 from 2:1 to 3:1 and the fraction of geo-referenced trait records from about 33% to 42% (Table 2). The number of geo-referenced trait records with information on measurement date that could be standardized to year, month and day increased from 290,000 in TRY version 1 (15% of all trait records) to 2.5 million records in TRY version 5 (20%). The increasing ratio of ancillary data to trait records indicates growing awareness for the relevance of environmental conditions during plant growth and trait measurements. In this context, geo-references (and date) are crucial, as they allow trait records to be related to information on climate, soil or biome type from external sources.

The geographic coverage of trait measurements has substantially improved from TRY version 1 (8,276 measurement sites representing 1,260 1° × 1° grid-cells) to TRY version 5 (20,953 sites representing

<figure><img src="image_31.png" /></figure>

**FIGURE 5** (a) Comparison of the species × trait matrix from TRY version 1 and version 5. The sizes of the boxes represent the numbers of species and traits. Blue: missing data; bright colour: the presence of data (yellow to red indicating an increasing number of measurements). For visibility, only 5% of traits and 0.05% of species are shown (randomly selected, with TRY version 1 as a subsample of TRY version 5; ordering of species and traits by submission date). 'Plant growth form' with entries for almost all species has been selected coincidentally. (b) Multivariate analyses: the decline of the number of common species with an increasing number of traits. Green: the six best covered traits in TRY version 5 (categorical traits: plant growth form, plant woodiness, leaf type, leaf compoundness, leaf photosynthetic pathway, leaflet number per leaf); blue: six traits representing plant form and function in Díaz et al. (2016); red: the best covered quantitative traits representing each of the six plant parts (see Figure 6); shoot (plant height vegetative), reproductive organs (seed dry mass), whole plant (plant lifespan), leaves (SLA), roots (rooting depth) and dead material (litter decomposition rate)
<figure><img src="image_32.png" /><figcaption>**FIGURE 6** Geographic coverage of measurement sites in TRY version 1 (red) and additional measurement sites in TRY version 5 (blue)</figcaption></figure>

<figure><img src="image_33.png" /><figcaption>**FIGURE 7** Distribution of trait records among plant parts. Different sizes of circles indicate different data coverage in TRY version 1 (left, 2.1 million trait records) and TRY version 5 (right, 11.8 million trait records)</figcaption></figure>

3,320 1° × 1° grid-cells; Table 2; Figure 6). Europe still has the highest density of measurement sites, but TRY version 5 also provides good coverage for the United States and China. The number of measurement sites has substantially improved for several other regions as well, for example Central America, Russia, Asia and parts of central Africa. However, there are still obvious gaps in boreal regions (Canada, East Russia) and some parts of the tropics and subtropics, particularly in Africa (Figure 6).

representativeness of plant trait data in the TRY database along five key dimensions: (a) Are trait data in TRY well distributed among plant parts? (b) Are the species in TRY and for individual traits representative for global plant growth forms and functional types? (c) Are the species in TRY and for individual traits representative according to phylogeny? (d) Does the geographic distribution of species richness in TRY represent the estimated pattern of global species richness? (e) Is data coverage sufficient to represent intraspecific variation?

## 3.7 | Data completeness and representativeness

To progress from a description of data coverage towards an analysis of representativeness, we need a baseline for comparison. At the global scale, this information has been lacking. Reference data sets have become available only recently for plant growth form (Weigelt, König, & Kreft, 2019) and phylogeny (Smith & Brown, 2018) representing about 260,000 and 356,000 of the 400,000 extant species. Together with estimations for the global distribution of plant species richness (Kier et al., 2005), it seems now possible and timely to explicitly address

## 3.8 | Distribution of trait records among plant parts

Trait records in the TRY database are very unequally distributed among different plant parts (Figure 7): leaves and the whole plant are well represented; shoot and reproductive organs are moderately represented; roots and dead material (morphological and chemical feature of litter and coarse woody debris, but also decomposition rates) are not well represented. Within reproductive organs, seeds are better represented than floral traits, mostly due to contributions from the Seed Information Database. The skewed distribution of
trait records to the different parts of the plants has only changed little from TRY version 1 to 5. However, the fraction of records for root traits has substantially increased (from 0.7% to 2.0%), due to the contribution of the FRED database.

## 3.9 | Plant growth form and PFTs

The most basic approach to characterize functional groups of plant species and their impact on vegetation and ecosystem function is the plant growth form, with its simplest classification to herbs, shrubs and trees. We compare here the fraction of the different plant growth forms for trait measurements in TRY version 5 to a comprehensive baseline of plant growth form for >280,000 of the extant 400,000 species, which is currently developed in the context of the GIFT database project.

In GIFT, about 50% of species are currently assigned to herbs, 30% to trees and 20% to shrubs (Figure 8). This distribution is well reflected by the species in TRY (excluding data from the GIFT database). However, the six best covered continuous traits in TRY indicate that this distribution is very much trait dependent, with a bias towards trees versus herbs, while the fraction of shrubs is surprisingly constant and close to the fraction in the GIFT database (Figure 8). The overrepresentation of trees is most obvious for SSD, which is not surprising because SSD is a more general concept derived from wood density, a trait relevant for forestry, timber industry and estimates of forest vegetation biomass. However, the tendency of relatively more data for trees compared to other growth forms is also obvious for SLA, leaf nitrogen content per dry mass and leaf area, but opposite for root length per root dry mass (specific root length), which is frequently reported for herbs.

Apart from plant growth form, three additional categorical traits are relevant to determine PFTs commonly used in global vegetation models: leaf type (broadleaved vs. needle-leaved) and leaf phenology type (deciduous vs. evergreen) for tree species, and photosynthetic pathway (C3, C4, CAM) for herbaceous species. TRY provides leaf type and leaf phenology type, for about 10% of the estimated

130,000 tree species worldwide and photosynthetic pathway for about 6% of estimated 200,000 herb species.

## 3.10 | Phylogeny

Smith and Brown (2018) published a series of broadly inclusive seed plant phylogenies. Here we chose the most comprehensive phylogeny (ALLMB), containing 356,305 taxa, as a baseline to visualize the coverage of TRY in a phylogenetic context. Taxa in ALLMB were cut to binomials and consolidated using the TNRS with TPL, GCC, ILDIS, TROPICOS and USDA as the taxonomic backbone (the same approach as for TRY). After consolidation, we could match the taxa in the phylogeny to 208,406 of the 279,875 taxa in TRY. Higher level taxonomy is based on Zanne et al. (2014).

Visually, the 208,000 species with data in TRY are well distributed across the 350,000 species represented in the phylogeny of seed plants (Figure 9). An ancestral state reconstruction (ASR) of species trait number confirms that the long-tail distribution previously seen at the species level also holds in a phylogenetic context: some clades are covered very well (bright colours), while most clades have lower data coverage (dark colours). The ASR additionally shows how deep in phylogeny data gaps are rooted. This indicates the potential for, and limits to, phylogenetically or taxonomically informed gap-filling (Schrodt et al., 2015). Examples of high-coverage clades are (parts of) the Pinales, Poales and Asterales. When looking at the six best-covered continuous traits individually, we find these too are well distributed across the phylogeny (Figure 9).

## 3.11 | Geographic distribution of species richness

Jetz et al. (2016) reported a latitudinal gradient in disparities between plant species with regional measurements in TRY and estimated species richness, with the largest gap observed in the tropics, because these are especially rich in species. To address this in more detail, we

**FIGURE 8** Fraction of species with plant growth forms herb, shrub and tree in the GIFT database, in TRY version 5 (excl. GIFT) and for the six best covered continuous traits in TRY version 5: plant height, seed dry mass, leaf area per leaf dry mass (SLA), leaf N per dry mass, leaf area and stem specific density (SSD), and one well covered root trait: specific root length (SRL). In parentheses: the number of species with data for the trait and the number of species for which the growth form could be determined as tree, shrub or herb

<figure><img src="image_34.png" /></figure>
<figure><img src="image_35.png" /><figcaption>**FIGURE 9** Trait coverage per species projected on a global phylogeny. The presence of trait data for plant species in the TRY database version 5 matched to the global ALLMB phylogeny published by Smith and Brown (2018). Rings surrounding the phylogeny indicate from inside outwards: (i) number of traits per species (innermost ring); (ii) presence of data for six of the best covered continuous traits, specifically: leaf area, leaf area per leaf dry mass (SLA), leaf nitrogen content per dry mass (LeafN), seed dry mass, plant height and stem specific density (SSD). Colours of phylogeny branches represent an ancestral state reconstruction of the number of traits per species. White and grey circles indicate periods of 50 million years. For visibility, only 5% of species (randomly selected) are presented</figcaption></figure>

here ask if the TRY database provides trait information for a relevant number of plant species in the different regions worldwide. To characterize regions in an ecologically meaningful way we adopt the ecoregions introduced by Olson et al. (2001), which are defined as relatively large units of land containing a distinct assemblage of natural communities and species, with boundaries that approximate the original extent of natural communities before major land-use change. The ecoregions are nested within biomes with biotic communities formed in response to a shared physical climate, most importantly temperature and rainfall. We compare the number of species, which have trait

measurements in an ecoregion in TRY version 5 to species numbers per ecoregion estimated by Kier et al. (2005). This approach accounts to some extent for intraspecific trait variation, as it counts only species with at least one trait measurement in the given ecoregion.

The 839 ecoregions defined by Olson et al. (2001) are very different in size, from 6 km² (San Felix-San Ambrosio Islands temperate forests) to 4,639,920 km² (Sahara desert) with species numbers ranging from 0 (St. Peter and St. Paul rocks and the Maudlandia Antarctic desert) to 10,000 (Borneo lowland rain forests). The TRY database contains no trait measurements for 271 mostly small ecoregions and
up to 1,400 species for some ecoregions in Europe (Alps conifer and mixed forests) and tropical South America (Napo moist forests, Tapajos-Xingu moist forests). In general, high absolute numbers of species with trait measurements for ecoregions are found in Europe,

East Asia, Oceania, Australia, tropical South America and the United States (Figure 10a). East Asia, Oceania and tropical South America are also the regions with the highest numbers of species per ecoregions estimated by Kier et al. (2005; Figure 10b).

<figure><img src="image_36.png" /></figure>

**FIGURE 10** Geographic representativeness: (a) the number of species with at least one trait measurement in an ecoregion in TRY version 5; (b) number of species per ecoregion estimated by Kier et al. (2005); (c) fraction of species represented in TRY version 5 versus number of species per ecoregion estimated by Kier et al. (2005)
<figure><img src="image_37.png" /></figure>

**FIGURE 11** Data coverage enabling examination of intraspecific variation: impact of the minimum number of individuals per species (a) and measurement sites per species (b) on the number of species available for analyses of intraspecific variation. Measurement sites were classified as different if they differed at least by 0.01 degrees in latitude or longitude. LDMC: leaf dry matter content; Leaf 13C: Leaf carbon (C) isotope signature (leaf Δ13C); Leaf C mass: leaf carbon content per leaf dry mass; Leaf N area: leaf nitrogen content per leaf area; Leaf N mass: leaf nitrogen content per leaf dry mass; Leaf P mass: leaf phosphorus content per leaf dry mass; SLA: leaf area per leaf dry mass (specific leaf area); SSD: stem specific density

The best relative coverage in TRY (Figure 10c) is provided for the Marielandia Antarctic tundra (two species estimated and in TRY) and for a large ecoregion in Central Russia (West Siberian taiga, 900 species estimated, 885 in TRY). The species in the Russian ecoregion are measured at several sites relatively well distributed across the ecoregion, but dominated by just one trait, 'mycorrhiza infection intensity' contributed by the mycorrhizal intensity database (Akhmetzhanova et al., 2012). Some other ecoregions are also well covered with data for more than 50% of estimated species (Southeast Australia temperate savanna, Qaidam Basin semi-desert, Córdoba forests and mountain grasslands). Apart from these individual ecoregions spread across the world, large parts of Europe are well covered, with trait data for about 30% of the species number estimated by Kier et al. (2005). Some ecoregions in East Asia, Australia, tropical South America, the Sahara, and the United States are also well covered, providing data for about 20% of estimated species richness. Very low relative coverage (<2%) is observed for major parts of Canada, Africa, western Asia (Iran, Iraq, Pakistan, Afghanistan) and major parts of India.

## 3.12 | Intraspecific variation

Understanding and predicting intraspecific variation for a relevant number of traits and species is still in its infancy. Given that TRY is collecting trait measurements on individual plants, the TRY data set might be suited to address these questions. A precondition for such analyses is a minimum number of measurements on different individual plants at different sites per trait and species. To assess this issue, we plotted

the number of species for which TRY version 5 contains a minimum number of individual measurements (Figure 11a) and the number of species for which TRY version 5 contains measurements from a minimum number of individual sites (Figure 11b) for the 15 best-covered continuous traits. By increasing the minimum number of individuals or sites, the number of species available for analysis decreases sharply (more than exponentially), whereas the exact slope is trait specific. The characterization of intraspecific variation in Kattge et al. (2011) relied on at least 20 individuals per species. Based on this criterion, TRY 5 provides information for hundreds to thousands of species for 14 out of the 15 traits (Figure 11a). Assuming a more realistic limit of 100 individuals per species, SLA and LDMC are sufficiently covered for about 300 species, and four other traits for more than 100 species. Assuming a minimum number of 200 individuals per species, four traits still allow for an analysis of 100 species, and nine traits allow for the analysis of intraspecific variation of more than 10 species. However, the numbers are more humbling if the environmental context is taken into account (Figure 11b). If we assume that trait records from a minimum number of 50 sites per species are necessary to represent intraspecific variation, four traits are sufficiently covered for about 100 species. If 100 sites are necessary, no trait is covered by data for more than 10 species.

## 4 | DISCUSSION

Plant trait data provide a wealth of information directly relevant in several scientific contexts, from conservation, ecology and evolution to earth system sciences. To fully realize this potential, the TRY
initiative was initiated in 2007 as a 'database of databases' and leading groups in the field of functional plant ecology joined forces for this community-driven program. The TRY database now provides an unprecedented number of consolidated plant trait data, which have become easily accessible at the TRY Data Portal under an open access data policy.

The TRY database is well accepted by the scientific community and has facilitated progress in different aspects of research, for example in global vegetation modelling from static PFTs to a more continuous representation of biodiversity (e.g. Peaucelle, Bellassen, Ciais, Peñuelas, & Viovy, 2016; Sakschewski et al., 2015, 2016; Verheijen et al., 2013, 2015), extending macroecology and biodiversity by functional aspects (e.g. Bjorkman, Myers-Smith, Elmendorf, Normand, Rüger, et al., 2018; Bruelheide et al., 2018; Craven et al., 2018; Newbold et al., 2015), linking soil characteristics to vegetation attributes (e.g. Boeddinghaus et al., 2019; de Vries et al., 2012; Delgado-Baquerizo et al., 2018) and providing data for global maps of plant traits (e.g. Butler et al., 2017; Moreno-Martínez et al., 2018). The central keywords of the cluster analysis in Figure 2 (biodiversity, climate change, plant traits, functional diversity, carbon cycle, community, vegetation and environmental filtering) seem to reflect the expectation that improved knowledge of plant functional diversity, mediated by plant traits, contributes to a better understanding of vegetation feedbacks to climate change and drivers and consequences of plant biodiversity loss.

Data coverage of the TRY database is characterized by four attributes: (a) long-tail distributions, (b) sparse matrices, (c) increasing number of ancillary data per trait record and (d) increasing geographic coverage. So far the size of the two sparse matrices (entity × trait and species × trait) has increased faster than the number of trait records to fill the matrices. Therefore the sparseness of the matrices has increased from TRY version 1 to 5 (the fractional coverage declined). Rather than converging in a small number of traits, the scientific community continues to measure a large, diverse number of traits, following equally diverse motivations.

However, given the number of species has a natural limit and assuming the number of traits will continue to grow, but more slowly, once the most obvious ones have been covered, we expect that the sparseness of the entity × trait matrix will become stable: new data adding new rows for entities, but not many new columns for traits. In comparison, the sparseness of the species × trait matrix should decline in the future; new data will mostly contribute to filling the matrix and increasing the number of species with data per trait. This reduced sparseness of the species × traits matrix will systematically improve the applicability of trait data for macroecology and earth system modelling and will facilitate multivariate analyses for an increasing number of traits. In parallel, the number of records per species-trait combination is increasing: between TRY 1 and 5 it already doubled and will further increase in the future. This increasing number of records per species-trait combination will improve data coverage for analyses of intraspecific trait variation and trait-environment relationships accounting for intraspecific variation. It is noteworthy that the matrix will not only become more complete,

but the traits will increasingly be able to inform each other. The 'usual suspects' (i.e. the best covered continuous traits) might not be masters of all traits, but they surely will be very useful as baseline traits and provide a background against which other—maybe more influential—traits can be analysed for coverage, representativeness, orthogonality, etc.

## 4.1 | Data completeness and representativeness

Despite unprecedented and continuously growing data coverage, we observe a humbling lack of completeness and representativeness in many aspects. The best species coverage is achieved for categorical traits relevant to determine PFTs commonly used in global vegetation models. For the traits 'woodiness' and 'plant growth form', even full species coverage is within reach, due to the contribution of data from the GIFT database. For the first time, this provides a global baseline for these traits, which are relevant to understand basic patterns of variation for several other traits (Díaz et al., 2016). With this baseline, future analyses will be able to address representativeness in addition to coverage, which will substantially contribute to better understand the global pattern of plant traits relevant for biodiversity and ecosystem function (König et al., 2019).

Most traits directly relevant for ecology and vegetation modeling are characterized by intraspecific variation and trait-environmental relationships; for these traits, completeness at the global scale is impossible and representativeness is challenging. We find that in the current version of the TRY database, these traits are biased against roots, floral traits and dead organic material, like litter or coarse woody debris. Plant growth forms and phylogeny are generally well represented, but there are significant biases for individual traits. The global distribution of species richness is only marginally reflected by trait measurements. We observe a general bias towards temperate biomes. In contrast to Jetz et al. (2016), the tropics do not stand out as especially underrepresented in our analysis; apart from Europe, all continents contain major regions that are very sparsely represented in TRY.

So far we addressed representativeness in a geographic context only based on species richness, the number of species observed in an ecoregion. To address representativeness in an ecologically more meaningful context, species identity and species abundance should be taken into account. Both aspects are relevant to community attributes and ecosystem function. There is ample evidence in the literature of the high influence, at the level of community structure and ecosystem dynamics, of species that represent a large proportion of the total local biomass (consisting of large individuals and/or large total cover). Such species have a particularly large impact on the community weighted mean trait value (Garnier et al., 2004), and particularly large individual trees may overrule remaining trees' attributes (Ali et al., 2019). Initial evidence indicates that abundant species are better covered in TRY than rare species. Bruelheide et al. (2019) show that the 25% most dominant or most frequent species
compiled in the sPlot vegetation-plot database are better represented by trait data in the TRY database than species observed on the plots overall. We also checked this for the 227 hyperdominant species of the Amazonia tree flora identified by ter Steege et al. (2013). After the consolidation of species names via TNRS, all 227 hyperdominant species are present in the TRY database, with on average 69 traits per species, which is far above average (see Figure 3e). We therefore conclude that the coverage of trait data in TRY is biased towards the more abundant species in the respective ecoregions—which is reasonable and welcome for many kinds of analyses.

We have reported that intraspecific variation in space is increasingly well covered, but variation in time is hard to estimate. Nevertheless, intraspecific variation in time is relevant for several traits to characterize the seasonal variation of plant and ecosystem function (Xu & Baldocchi, 2003; Xu & Griffin, 2006) and long-term trends to inform policy about biodiversity change (Kissling et al., 2018). About half of the geo-referenced trait records have information on the sampling date that could be standardized to year, month and day, but systematic replicates over time ('time series') are rare (but see e.g. the 'Photosynthesis Traits Database'; Xu & Baldocchi, 2003). In principle, 'non-time series' data allow detection of trait changes over time (Craine et al., 2018), but these analyses are very challenging, as most traits demonstrate stronger variation in geographic space along climate and soil gradients than over time. In addition, the variations of traits on different time scales (diurnal, seasonal, inter-annual variation and long-term trends) are superimposed and hard to disentangle. Apart from this, there is a need to collect and report repeated trait measurements from the same location or population to monitor biodiversity change and inform policy, for example in the context of GEO BON (Kissling et al., 2018).

## 4.2 | Ways forward

Figure 1 shows the most obvious way to mobilize additional trait data: the TRY initiative should regularly send calls for data contribution to the wider scientific community, that is the network of more than 6,000 researchers contributing and using trait data via TRY. These calls should be combined with regular publications of respective reference papers. This can be combined with (a) a systematic collection of data sets from public data repositories, which is becoming more effective with the general move by many journals to require that authors make their data open access; and (b) systematic extraction of trait data from the ecological literature, floras and herbarium specimens, which is a promising task, especially for its potential to open a window into the past. In parallel, TRY should further support the 'feed-forward data integration loop' outlined above: using trait data via TRY, identifying gaps, mobilizing and/or measuring new data, contributing additional data to TRY. This has proven very effective for focused data mobilization. If relevant gaps are detected, TRY can also send specific calls to the community.

As TRY has been designed as a community cyber-infrastructure based on the idea of incentive-driven data sharing (Kattge, Díaz, &

Wirth, 2014), the collaboration and data exchange with other plant trait databases will continue to be the key to achieve a comprehensive representation of plant traits. TRY is, therefore, collaborating with many more recent trait database initiatives, such as, for example, FRED, GIFT, BIEN and the Tundra Trait Team, and since the early days of TRY–GLOPNET, LEDA, SID, BioIFlor, BIOPOP, BROT, the Ecological Flora of the British Isles, eHALOPH, USDA PLANTSdata, BRIDGE and many others. Importantly, these collaborations need to provide mutual benefit. Based on these collaborations, the TRY database may serve as a central node for plant traits in an overarching network of trait databases, currently emerging in the context of the Open Traits Network (Gallagher et al., in press). Finally, new techniques and approaches are gradually becoming available, which may substantially change how plant trait data are collected: remote sensing, citizen sciences, microbiological and molecular screening, etc.

## 4.3 | Towards a third generation of plant trait data integration and sharing

We expect that the combination of (a) systematic involvement of the TRY network towards extraction and mobilization of legacy and recent trait data from public repositories, ecological literature, floras and herbaria, (b) facilitation of the 'feed-forward data integration loop' and (c) intensified collaboration of all plant trait-related initiatives, including new approaches and techniques, will be effective towards an increasingly comprehensive representation of plant traits. After the development of integrated databases focused on specific regions or topics, and the development of a 'database of databases', such a joined effort might be leading towards a third generation of plant trait data integration and sharing.

## 5 | CONCLUSION

TRY has received institutional support since 2007 and is still growing considerably in quantity and quality. While TRY may be considered a success and potentially a role model for database initiatives, it is important to realize that this development needed time and patience. It took until 2011 for the first TRY publications to appear because the early years of TRY were mostly devoted to the development of the database, organizing the community process towards a joint data sharing policy and building trust. This process involved initially dozens and later hundreds of scientist when it came to agree on moving towards open access. These dynamics do not fit into 3 year funding cycles as typically offered by national funding agencies. A key lesson of TRY is that the development of a database that is trusted by the community and accepted for its service and quality also needs the trust of the funders, that is long-term support, at the scale of decades rather than years. It also needs journals that are willing to accept long author lists and extended references lists to adequately acknowledge the original contributions that are the building blocks of communal databases.
ACKNOWLEDGEMENTS

We would like to thank Stephen Long for the invitation to contribute to the special issue celebrating the 25th anniversary of Global Change Biology and the Executive Editor Rachel Shekar for her extraordinary support and patience handling this manuscript. We also thank the publisher for excellent support. We thank the two anonymous reviewers for valuable suggestions, which helped to substantially improve the manuscript. The TRY database is hosted, developed and maintained at the Max Planck Institute for Biogeochemistry (MPI-BGC) in Jena, Germany, in collaboration with the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. The TRY database receives additional funding by the Max Planck Society via the Max Planck Fellow Program for Christian Wirth. In addition, the TRY initiative has been supported by the International Programme of Biodiversity Science (DIVERSITAS), the International Geosphere-Biosphere Programme (IGBP), Future Earth, the French Foundation for Biodiversity Research (FRB), and GIS 'Climat, Environnement et Société' France. The TRY initiative is grateful for major support by Linda Maack and the IT Department at the MPI-BGC. We would like to thank all data contributors to TRY, who are not co-authors of this paper, that is Pierre Meerts, Jennifer Powers, Nina Koele, Henrik Balslev, John Briggs, Michael White and Robin Chazdon. V.O. thanks RSF (#19-14-00038). S.D. thanks CONICET, FONCyT and IAI. Finally, the TRY initiative is very grateful to all the numerous scientists not mentioned here measuring plant traits: without their work the TRY database would not exist.

CONFLICT OF INTEREST

All authors declare no conflict of interest.

AUTHOR CONTRIBUTION

Jens Kattge, Gerhard Bönisch, Sandra Díaz, Sandra Lavorel, Iain Colin Prentice, Paul Leadley and Christian Wirth developed the concept and draft manuscript. Susanne Tautenhahn and Gijsbert Werner contributed analyses and plots for Figures 9, 10 and 11. The other authors contributed plant trait data and/or supported data curation and analysis. All 729 authors contributed to writing.

ORCID

Jens Kattge ID https://orcid.org/0000-0002-1022-8469

Sandra Díaz ID https://orcid.org/0000-0003-0012-4612

Susanne Tautenhahn ID https://orcid.org/0000-0002-2753-3443

Gijsbert D. A. Werner ID https://orcid.org/0000-0002-5426-2562

Tuomas Aakala ID https://orcid.org/0000-0003-0160-6410

Mehdi Abedi ID https://orcid.org/0000-0002-1499-0119

Alicia T. R. Acosta ID https://orcid.org/0000-0001-6572-3187

George C. Adamidis ID https://orcid.org/0000-0001-8704-6623

Masahiro Aiba ID https://orcid.org/0000-0002-5966-1562

Cécile H. Albert ID https://orcid.org/0000-0002-0991-1068

Julio M. Alcántara ID https://orcid.org/0000-0002-8003-7844

Carolina Alcázar C ID https://orcid.org/0000-0002-9366-8098

Izabela Aleixo ID https://orcid.org/0000-0001-9220-8965

Hamada Ali ID https://orcid.org/0000-0002-7062-9344

Christian Ammer ID https://orcid.org/0000-0002-4235-0135

Carolyn Anderson ID https://orcid.org/0000-0003-4211-5765

Deborah Mattos Guimarães Apgaua ID https://orcid.org/0000-0002-6303-6989

Tia-Lynn Ashman ID https://orcid.org/0000-0002-9884-5954

Gregory P. Asner ID https://orcid.org/0000-0001-7893-6421

Michael Aspinwall ID https://orcid.org/0000-0003-0199-2972

Owen Atkin ID https://orcid.org/0000-0003-1041-5202

Isabelle Aubin ID https://orcid.org/0000-0002-5953-1012

Lars Baastrup-Spohr ID https://orcid.org/0000-0001-8382-984X

Khadijeh Bahalkeh ID https://orcid.org/0000-0003-1485-0316

Michael Bahn ID https://orcid.org/0000-0001-7482-9776

William J. Baker ID https://orcid.org/0000-0001-6727-1831

Jan P. Bakker ID https://orcid.org/0000-0001-7475-5906

Dennis Baldocchi ID https://orcid.org/0000-0003-3496-4919

Jennifer Baltzer ID https://orcid.org/0000-0001-7476-5928

Jos Barlow ID https://orcid.org/0000-0003-4992-2594

Diego R. Barneche ID https://orcid.org/0000-0002-4568-2362

Zdravko Baruch ID https://orcid.org/0000-0002-7264-4812

Denis Bastianelli ID https://orcid.org/0000-0002-6394-5920

John Battles ID https://orcid.org/0000-0001-7124-7893

William Bauerle ID https://orcid.org/0000-0003-3090-234X

Marijn Bauters ID https://orcid.org/0000-0003-0978-6639

Michael Beckmann ID https://orcid.org/0000-0002-5678-265X

Hans Beeckman ID https://orcid.org/0000-0001-8954-6277

Carl Beierkuhnlein ID https://orcid.org/0000-0002-6456-4628

Gavin Belfry ID https://orcid.org/0000-0003-3405-5950

Michael Belluau ID https://orcid.org/0000-0001-6707-546X

Mirela Beloiu ID https://orcid.org/0000-0002-3592-8170

Raquel Benavides ID https://orcid.org/0000-0003-2328-5371

Lahcen Benomar ID https://orcid.org/0000-0001-9301-5655

Mary Lee Berdugo-Lattke ID https://orcid.org/0000-0002-6662-6458

Erika Berenguer ID https://orcid.org/0000-0001-7357-8805

Rodrigo Bergamin ID https://orcid.org/0000-0002-2405-9977

Joana Bergmann ID https://orcid.org/0000-0002-2008-4198

Marcos Bergmann Carlucci ID https://orcid.org/0000-0002-5868-7090

Logan Berner ID https://orcid.org/0000-0001-8947-0479

Markus Bernhardt-Römermann ID https://orcid.org/0000-0002-2740-2304

Christof Bigler ID https://orcid.org/0000-0003-3757-6356

Anne D. Bjorkman ID https://orcid.org/0000-0003-2174-7800

Carolina Blanco ID https://orcid.org/0000-0002-8959-2633

Benjamin Blonder ID https://orcid.org/0000-0002-5061-2385

Dana Blumenthal ID https://orcid.org/0000-0001-7496-0766

Kelly T. Bocanegra-González ID https://orcid.org/0000-0001-7177-5856

Pascal Boeckx ID https://orcid.org/0000-0003-3998-0010

Katrin Böhning-Gaese ID https://orcid.org/0000-0003-0477-5586

Laura Boisvert-Marsh ID https://orcid.org/0000-0002-0939-8196

William Bond ID https://orcid.org/0000-0002-3441-2084

Ben Bond-Lamberty ID https://orcid.org/0000-0001-9525-4633

Arnoud Boom ID https://orcid.org/0000-0003-1299-691X

Coline C. F. Boonman ID https://orcid.org/0000-0003-2417-1579

Kauane Bordin ID https://orcid.org/0000-0003-3871-6293

Elizabeth H. Boughton ID https://orcid.org/0000-0003-0932-280X

Vanessa Boukili ID https://orcid.org/0000-0002-5950-2123
Sandra Bravo ID https://orcid.org/0000-0001-6098-7255

Marco Richard Brendel ID https://orcid.org/0000-0003-4356-4597

Martin R. Broadley ID https://orcid.org/0000-0003-3964-7226

Kerry A. Brown ID https://orcid.org/0000-0002-6342-8966

Helge Bruelheide ID https://orcid.org/0000-0003-3135-0356

Federico Brumnich ID https://orcid.org/0000-0002-0502-6694

Hans Henrik Bruun ID https://orcid.org/0000-0003-0674-2577

David Bruy ID https://orcid.org/0000-0002-7339-0375

Solveig Franziska Bucher ID https://orcid.org/0000-0002-2303-4583

Nina Buchmann ID https://orcid.org/0000-0003-0826-2980

Robert Buitenwerf ID https://orcid.org/0000-0003-3356-2301

Daniel E. Bunker ID https://orcid.org/0000-0002-7234-3403

Sabina Burrascano ID https://orcid.org/0000-0002-6537-3313

David F. R. P. Burslem ID https://orcid.org/0000-0001-6033-0990

Bradley J. Butterfield ID https://orcid.org/0000-0003-0974-9811

Chaeho Byun ID https://orcid.org/0000-0003-3209-3275

Marcia Marques ID https://orcid.org/0000-0002-1003-9596

Marina C. Scalon ID https://orcid.org/0000-0003-2069-8226

Marco Caccianiga ID https://orcid.org/0000-0001-9715-1830

Marc Cadotte ID https://orcid.org/0000-0002-5816-7693

Maxime Cailleret ID https://orcid.org/0000-0001-6561-1943

James Camac ID https://orcid.org/0000-0003-4785-0742

Jesús Julio Camarero ID https://orcid.org/0000-0003-2436-2922

Giandiego Campetella ID https://orcid.org/0000-0001-6126-522X

Juan Antonio Campos ID https://orcid.org/0000-0001-5992-2753

Roberto Canullo ID https://orcid.org/0000-0002-9913-6981

Michele Carbognani ID https://orcid.org/0000-0001-7701-9859

Fabio Carvalho ID https://orcid.org/0000-0002-6305-5602

Fernando Casanoves ID https://orcid.org/0000-0001-8765-9382

Bastien Castagneyrol ID https://orcid.org/0000-0001-8795-7806

Jane A. Catford ID https://orcid.org/0000-0003-0582-5960

Jeannine Cavender-Bares ID https://orcid.org/0000-0003-3375-9630

Bruno E. L. Cerabolini ID https://orcid.org/0000-0002-3793-0733

Marco Cervellini ID https://orcid.org/0000-0002-0853-2330

Eduardo Chacón-Madrigal ID https://orcid.org/0000-0002-8328-5456

Kenneth Chapin ID https://orcid.org/0000-0002-8382-4050

F. Stuart Chapin ID https://orcid.org/0000-0002-2558-9910

Stefano Chelli ID https://orcid.org/0000-0001-7184-8242

Si-Chong Chen ID https://orcid.org/0000-0002-6855-2595

Anping Chen ID https://orcid.org/0000-0003-2085-3863

Paolo Cherubini ID https://orcid.org/0000-0002-9809-250X

Francesco Chianucci ID https://orcid.org/0000-0002-5688-2060

Brendan Choat ID https://orcid.org/0000-0002-9105-640X

Kyong-Sook Chung ID https://orcid.org/0000-0002-4464-4698

Milan Chytrý ID https://orcid.org/0000-0002-8122-3075

Daniela Ciccarelli ID https://orcid.org/0000-0001-9715-9779

Luisa Conti ID https://orcid.org/0000-0001-8047-1467

David Coomes ID https://orcid.org/0000-0002-8261-2582

Johannes H. C. Cornelissen ID https://orcid.org/0000-0002-2346-1585

William K. Cornwell ID https://orcid.org/0000-0003-4080-4073

Piermaria Corona ID https://orcid.org/0000-0002-8105-0792

Joseph Craine ID https://orcid.org/0000-0001-6561-3244

Dylan Craven ID https://orcid.org/0000-0003-3940-833X

Anikó Csecserits ID https://orcid.org/0000-0002-0538-4520

Katarina Cufar ID https://orcid.org/0000-0002-7403-3994

Matthias Cuntz ID https://orcid.org/0000-0002-5966-1829

Ana Carolina da Silva ID https://orcid.org/0000-0002-1285-640X

Kyla M. Dahlin ID https://orcid.org/0000-0002-6016-2605

Matteo Dainese ID https://orcid.org/0000-0001-7052-5572

Igor Dalke ID https://orcid.org/0000-0001-5711-9916

Michele Dalle Fratte ID https://orcid.org/0000-0002-7907-1586

Anh Tuan Dang-Le ID https://orcid.org/0000-0002-9794-0669

Jiri Danihelka ID https://orcid.org/0000-0002-2640-7867

Masako Dannoura ID https://orcid.org/0000-0003-0389-871X

Samantha Dawson ID https://orcid.org/0000-0001-5165-9591

Angel De Frutos ID https://orcid.org/0000-0002-5250-9488

Jonathan R. De Long ID https://orcid.org/0000-0002-7951-4818

Benjamin Dechant ID https://orcid.org/0000-0001-5171-2364

Nicolas Delpierre ID https://orcid.org/0000-0003-0906-9402

Géraldine Derroire ID https://orcid.org/0000-0001-7239-2881

Arildo S. Dias ID https://orcid.org/0000-0002-5495-3435

Milton Hugo Diaz-Toribio ID https://orcid.org/0000-0003-1675-2699

Panayiotis G. Dimitrakopoulos ID https://orcid.org/0000-0002-8374-4392

Mark Dobrowolski ID https://orcid.org/0000-0001-5586-4023

Pavel Dřevojan ID https://orcid.org/0000-0003-0802-3509

Ning Dong ID https://orcid.org/0000-0003-0793-8854

Leandro Duarte ID https://orcid.org/0000-00003-1771-0407

Stefan Dullinger ID https://orcid.org/0000-0003-3919-0887

Walter Durka ID https://orcid.org/0000-0002-6611-2246

Olga Dymova ID https://orcid.org/0000-0003-2008-6350

Anna E-Vojtkó ID https://orcid.org/0000-0001-6370-680X

Rolf Lutz Eckstein ID https://orcid.org/0000-0002-6953-3855

Hamid Ejtehadi ID https://orcid.org/0000-0002-6128-2481

James Elser ID https://orcid.org/0000-0002-1460-2155

Thaise Emilio ID https://orcid.org/0000-0001-5415-1822

Kristine Engemann ID https://orcid.org/0000-0003-1431-1726

Mohammad Bagher Erfanian ID https://orcid.org/0000-0002-8671-9037

Adriane Esquivel-Muelbert ID https://orcid.org/0000-0001-5335-1259

Marc Estiarte ID https://orcid.org/0000-0003-1176-8480

Tomas F. Domingues ID https://orcid.org/0000-0003-2857-9838

William F. Fagan ID https://orcid.org/0000-0003-2433-9052

Jaime Fagúndez ID https://orcid.org/0000-0001-6605-7278

Daniel S. Falster ID https://orcid.org/0000-0002-9814-092X

Ying Fan ID https://orcid.org/0000-0002-0024-7965

Emmanuele Farris ID https://orcid.org/0000-0002-9843-5998

Fatih Fazlioglu ID https://orcid.org/0000-0002-4723-3640

Yanhao Feng ID https://orcid.org/0000-0003-0460-4883

Fernando Fernandez-Mendez ID https://orcid.org/0000-0001-8693-790X

Joice Ferreira ID https://orcid.org/0000-0002-4008-2341

Alessandra Fidelis ID https://orcid.org/0000-0001-9545-2285

Jennifer Firn ID https://orcid.org/0000-0001-6026-8912

Timothy J. Flowers ID https://orcid.org/0000-0002-2712-9504

Dan F. B. Flynn ID https://orcid.org/0000-0002-2978-5257

Estelle Forey ID https://orcid.org/0000-0001-6082-3023

Cristiane Forgiarini ID https://orcid.org/0000-0002-0319-7430

Louis François ID https://orcid.org/0000-0001-8292-8360
Marcelo Frangipani https://orcid.org/0000-0001-8050-8282

Grégoire T. Freschet https://orcid.org/0000-0002-8830-3860

Ellen L. Fry https://orcid.org/0000-0001-7513-2006

Nikolaos M. Fyllas https://orcid.org/0000-0002-5651-5578

Guilherme G. Mazzochini https://orcid.org/0000-0002-6932-8544

Rachael Gallagher https://orcid.org/0000-0002-4680-8115

Pablo García-Palacios https://orcid.org/0000-0002-6367-4761

Verónica Gargaglione https://orcid.org/0000-0002-0797-1868

Eric Garnier https://orcid.org/0000-0002-9392-5154

Jose Luis Garrido https://orcid.org/0000-0002-6859-4234

André Luís de Gasper https://orcid.org/0000-0002-1940-9581

Guillermo Gea-Izquierdo https://orcid.org/0000-0003-0148-3721

David Gibson https://orcid.org/0000-0002-0308-7506

Andrew N. Gillison https://orcid.org/0000-0003-2781-1500

Aelton Giroldo https://orcid.org/0000-0001-7850-0011

Sean Gleason https://orcid.org/0000-0002-5607-4741

Mariana Gliesch https://orcid.org/0000-0003-3226-6762

Erika Gonzalez-Akre https://orcid.org/0000-0001-8305-6672

Jose L. Gonzalez-Andujar https://orcid.org/0000-0003-2356-4098

Ana González-Robles https://orcid.org/0000-0003-3135-8905

Bente Jessen Graae https://orcid.org/0000-0002-5568-4759

Elena Granda https://orcid.org/0000-0002-9559-4213

Sarah Graves https://orcid.org/0000-0003-3805-4242

Thomas Gregor https://orcid.org/0000-0001-7692-8348

Nicolas Gross https://orcid.org/0000-0001-9730-3240

Greg R. Guerin https://orcid.org/0000-0002-2104-6695

Alvaro G. Gutiérrez https://orcid.org/0000-0001-8928-3198

Lillie Haddock https://orcid.org/0000-0003-3432-6191

Jefferson Hall https://orcid.org/0000-0003-4761-9268

Wenxuan Han https://orcid.org/0000-0001-8678-7147

Sandy P. Harrison https://orcid.org/0000-0001-5687-1903

Joseph E. Hawes https://orcid.org/0000-0003-0053-2018

Pengcheng He https://orcid.org/0000-0001-9531-531X

Jacob Mason Heberling https://orcid.org/0000-0003-0756-5090

Aveliina Helm https://orcid.org/0000-0003-2338-4564

Bruno Hérault https://orcid.org/0000-0002-6950-7286

Ana-Maria Heres https://orcid.org/0000-0002-1839-1770

Katharina Herz https://orcid.org/0000-0001-5676-3646

Myriam Heuertz https://orcid.org/0000-0002-6322-3645

Peter Hietz https://orcid.org/0000-0002-0458-6593

Pedro Higuchi https://orcid.org/0000-0002-3855-555X

Andrew L. Hipp https://orcid.org/0000-0002-1241-9904

Maria Hock https://orcid.org/0000-0001-8401-7309

James Aaron Hogan https://orcid.org/0000-0001-9806-3074

Karen Holl https://orcid.org/0000-0003-2893-6161

Olivier Honnay https://orcid.org/0000-0002-4287-8511

Enqing Hou https://orcid.org/0000-0003-4864-2347

Nate Hough-Snee https://orcid.org/0000-0003-4581-0931

Knut Anders Hovstad https://orcid.org/0000-0002-7108-0787

Tomoaki Ichie https://orcid.org/0000-0002-1979-5806

Boris Igić https://orcid.org/0000-0002-6302-8030

Estela Illa https://orcid.org/0000-0001-7136-6518

Marney Isaac https://orcid.org/0000-0002-5975-833X

Masae Ishihara https://orcid.org/0000-0002-8365-7766

Leonid Ivanov https://orcid.org/0000-0001-6900-5086

Larissa Ivanova https://orcid.org/0000-0003-2363-9619

Colleen M. Iversen https://orcid.org/0000-0001-8293-3450

Jordi Izquierdo https://orcid.org/0000-0002-3949-1295

Robert B. Jackson https://orcid.org/0000-0001-8846-7147

Benjamin Jackson https://orcid.org/0000-0001-6405-8111

Hervé Jactel https://orcid.org/0000-0002-8106-5310

Andrzej M. Jagodzinski https://orcid.org/0000-0001-6899-0985

Steven Jansen https://orcid.org/0000-0002-4476-5334

Thomas Jenkins https://orcid.org/0000-0002-3036-971X

Anke Jentsch https://orcid.org/0000-0002-2345-8300

Guo-Feng Jiang https://orcid.org/0000-0002-3221-8608

Jesper Liengaard Johansen https://orcid.org/0000-0001-7602-316X

David Johnson https://orcid.org/0000-0003-2299-2525

Eric J. Jokela https://orcid.org/0000-0002-1819-1285

Carlos Alfredo Joly https://orcid.org/0000-0002-7945-2805

Gregory J. Jordan https://orcid.org/0000-0002-6033-2766

Grant Stuart Joseph https://orcid.org/0000-0002-8731-9828

Decky Junaedi https://orcid.org/0000-0001-5846-6135

Robert R. Junker https://orcid.org/0000-0002-7919-9678

Eric Justes https://orcid.org/0000-0001-7390-7058

Jeffrey Kane https://orcid.org/0000-0003-1453-9608

Zdenek Kaplan https://orcid.org/0000-0003-1707-7461

Teja Kattenborn https://orcid.org/0000-0001-7381-3828

Elizabeth Kearsley https://orcid.org/0000-0003-0046-3606

Anne Kempel https://orcid.org/0000-0002-0563-901X

Tanaka Kenzo https://orcid.org/0000-0002-4856-7204

Mohammed I. Khalil https://orcid.org/0000-0002-9310-2780

Nicole L. Kinlock https://orcid.org/0000-0002-2917-5133

Wilm Daniel Kissling https://orcid.org/0000-0002-7274-6755

Kaoru Kitajima https://orcid.org/0000-0001-6822-8536

Thomas Kitzberger https://orcid.org/0000-0002-9754-4121

Rasmus Kjøller https://orcid.org/0000-0002-2027-4119

Tamir Klein https://orcid.org/0000-0002-3882-8845

Michael Kleyer https://orcid.org/0000-0002-0824-2974

Jitka Klimešová https://orcid.org/0000-0003-0123-3263

Joice Klipel https://orcid.org/0000-0003-3936-9692

Brian Kloepfel https://orcid.org/0000-0003-0215-4671

Takashi Kohyama https://orcid.org/0000-0001-7186-8585

Fumito Koike https://orcid.org/0000-0002-6588-6485

Johannes Kollmann https://orcid.org/0000-0002-4990-3636

Kimberly Komatsu https://orcid.org/0000-0001-7056-4547

Christian König https://orcid.org/0000-0003-0585-5983

Nathan J. B. Kraft https://orcid.org/0000-0001-8867-7806

Koen Kramer https://orcid.org/0000-0002-1402-2775

Holger Kreft https://orcid.org/0000-0003-4471-8236

Ingolf Kühn https://orcid.org/0000-0003-1691-8249

Dushan Kumarathunge https://orcid.org/0000-0003-1309-4731

Jonas Kuppler https://orcid.org/0000-0003-4409-9367

Hiroko Kurokawa https://orcid.org/0000-0001-8778-8045

Yoko Kurosawa https://orcid.org/0000-0002-1419-7575

Shem Kuyah https://orcid.org/0000-0001-7692-585X
Jean-Paul Laclau https://orcid.org/0000-0002-2506-214X

Eric Lamb https://orcid.org/0000-0001-5201-4541

Andrea Lamprecht https://orcid.org/0000-0002-8719-026X

Daniel J. Larkin https://orcid.org/0000-0001-6378-0495

Daniel Laughlin https://orcid.org/0000-0002-9651-5732

Yoann Le Bagousse-Pinguet https://orcid.org/0000-0002-5615-5541

Guerric le Maire https://orcid.org/0000-0002-5227-958X

Peter C. le Roux https://orcid.org/0000-0002-7941-7444

Elizabeth le Roux https://orcid.org/0000-0001-8468-8284

Tali Lee https://orcid.org/0000-0002-3933-6607

Frederic Lens https://orcid.org/0000-0002-5001-0149

Simon L. Lewis https://orcid.org/0000-0002-8066-6851

Yuanzhi Li https://orcid.org/0000-0002-5258-877X

Jeremy W. Lichstein https://orcid.org/0000-0001-5553-6142

Jun Ying Lim https://orcid.org/0000-0001-7493-2159

Juan Carlos Linares https://orcid.org/0000-0001-8375-6353

Daijun Liu https://orcid.org/0000-0002-0993-0832

Udayangani Liu https://orcid.org/0000-0002-6192-1319

Madelon Lohbeck https://orcid.org/0000-0002-3959-1800

Álvaro López-García https://orcid.org/0000-0001-8267-3572

Gabriela Lopez-Gonzalez https://orcid.org/0000-0002-0814-8830

Zdeňka Lososová https://orcid.org/0000-0001-9152-7462

Frédérique Louault https://orcid.org/0000-0002-9430-4261

Balázs A. Lukács https://orcid.org/0000-0002-3163-1281

Michele Lussu https://orcid.org/0000-0002-1313-4732

Camilla Maciel Rabelo Pereira https://orcid.org/0000-0002-7027-7430

Vincent Maire https://orcid.org/0000-0002-3245-2568

Annikki Mäkelä https://orcid.org/0000-0001-9633-7350

Harri Mäkinen https://orcid.org/0000-0002-1820-6264

Ana Claudia Mendes Malhado https://orcid.org/0000-0003-3621-779X

Azim Mallik https://orcid.org/0000-0002-6275-6255

Peter Manning https://orcid.org/0000-0002-7940-2023

Stefano Manzoni https://orcid.org/0000-0002-5960-5712

Zuleica Marchetti https://orcid.org/0000-0002-9241-9217

Vinicius Marcílio-Silva https://orcid.org/0000-0002-5276-2838

Eric Marcon https://orcid.org/0000-0002-5249-321X

Michela Marignani https://orcid.org/0000-0002-8420-5454

Lars Markesteijn https://orcid.org/0000-0003-3046-3121

Adam Martin https://orcid.org/0000-0002-7207-4004

Cristina Martínez-Garza https://orcid.org/0000-0002-9310-564X

Jordi Martínez-Vilalta https://orcid.org/0000-0002-2332-7298

Tereza Mašková https://orcid.org/0000-0001-8386-5146

Kelly Mason https://orcid.org/0000-0003-3426-3178

Itay Mayrose https://orcid.org/0000-0002-8460-1502

James McCarthy https://orcid.org/0000-0003-3060-1678

M. Luke McCormack https://orcid.org/0000-0002-8300-5215

Katherine McCulloh https://orcid.org/0000-0003-0801-3968

Ian R. McFadden https://orcid.org/0000-0002-4508-7272

Mara Y. McPartland https://orcid.org/0000-0002-1242-0438

Belinda Medlyn https://orcid.org/0000-0001-5728-9827

Zia Mehrabi https://orcid.org/0000-0001-9574-0420

Patrick Meir https://orcid.org/0000-0002-2362-0398

Felipe P. L. Melo https://orcid.org/0000-0002-1271-3214

Maurizio Mencuccini https://orcid.org/0000-0003-0840-1477

Céline Meredieu https://orcid.org/0000-0002-2950-2253

Julie Messier https://orcid.org/0000-0003-1637-6793

Sean T. Michaletz https://orcid.org/0000-0003-2158-6525

Chrysanthi Michelaki https://orcid.org/0000-0001-9217-0063

Ruben Milla https://orcid.org/0000-0001-8912-4373

Jesse E. D. Miller https://orcid.org/0000-0002-2316-779X

Vanessa Minden https://orcid.org/0000-0002-4933-5931

Ray Ming https://orcid.org/0000-0002-9417-5789

Karel Mokany https://orcid.org/0000-0003-4199-3697

Angela T. Moles https://orcid.org/0000-0003-2041-7762

Attila Molnár https://orcid.org/0000-0001-7096-9579

Jane Molofsky https://orcid.org/0000-0001-7927-516X

Arnaud Monty https://orcid.org/0000-0003-1910-6748

Álvaro Moreno-Martínez https://orcid.org/0000-0003-2990-7768

Marco Moretti https://orcid.org/0000-0002-5845-3198

Akira S. Mori https://orcid.org/0000-0002-8422-1198

Shigeta Mori https://orcid.org/0000-0001-5604-2443

Dave Morris https://orcid.org/0000-0002-5739-0594

Jane Morrison https://orcid.org/0000-0002-4055-4679

Ladislav Mucina https://orcid.org/0000-0003-0317-8886

Christopher D. Muir https://orcid.org/0000-0003-2555-3878

Sandra Cristina Müller https://orcid.org/0000-0002-6316-2897

François Munoz https://orcid.org/0000-0001-8776-4705

Isla H. Myers-Smith https://orcid.org/0000-0002-8417-6112

Balachandran Natesan https://orcid.org/0000-0001-6396-3865

Luka Negoita https://orcid.org/0000-0003-3225-7575

Andrew S. Nelson https://orcid.org/0000-0001-8715-7825

Eike Lena Neuschulz https://orcid.org/0000-0001-7526-2580

Jian Ni https://orcid.org/0000-0001-5411-7050

Georg Niedrist https://orcid.org/0000-0002-7511-6273

Ülo Niinemets https://orcid.org/0000-0002-3078-2192

Rachael Nolan https://orcid.org/0000-0001-9277-5142

Henning Nottebrock https://orcid.org/0000-0003-2238-2700

Yann Nouvellon https://orcid.org/0000-0003-1920-3847

Alexander Novakovskiy https://orcid.org/0000-0003-4105-7436

Kristin Odden Nystuen https://orcid.org/0000-0003-1027-469X

Kevin O'Hara https://orcid.org/0000-0002-3800-9188

Andrew O'Reilly-Nugent https://orcid.org/0000-0003-2071-6279

Simon Oakley https://orcid.org/0000-0002-5757-7420

Walter Oberhuber https://orcid.org/0000-0002-5197-7044

Toshiyuki Ohtsuka https://orcid.org/0000-0002-9032-7933

Ricardo Oliveira https://orcid.org/0000-0001-8044-3458

Kinga Öllerer https://orcid.org/0000-0003-3142-0000

Mark E. Olson https://orcid.org/0000-0003-3715-4567

Vladimir Onipchenko https://orcid.org/0000-0002-1626-1171

Yusuke Onoda https://orcid.org/0000-0001-6245-2342

Renske E. Onstein https://orcid.org/0000-0002-2295-3510

Ivika Ostenon https://orcid.org/0000-0001-9043-6083

Gianluigi Ottaviani https://orcid.org/0000-0003-3027-4638

Sarah Otto https://orcid.org/0000-0003-3042-0818

Gerhard E. Overbeck https://orcid.org/0000-0002-8716-5136

Wim A. Ozinga https://orcid.org/0000-0002-6369-7859
C. E. Timothy Paine ID https://orcid.org/0000-0001-8705-3719

Robin J. Pakeman ID https://orcid.org/0000-0001-6248-4133

Aristotelis C. Papageorgiou ID https://orcid.org/0000-0001-6657-7820

Meelis Pärtel ID https://orcid.org/0000-0002-5874-0138

Susana Paula ID https://orcid.org/0000-0001-5405-6155

Juraj Paule ID https://orcid.org/0000-0001-5375-7689

Harald Pauli ID https://orcid.org/0000-0002-9842-9934

Juli G. Pausas ID https://orcid.org/0000-0003-3533-5786

Begoña Peco ID https://orcid.org/0000-0003-2149-1438

Josep Penuelas ID https://orcid.org/0000-0002-7215-0150

Antonio Perea ID https://orcid.org/0000-0001-8351-9358

Pablo Peri ID https://orcid.org/0000-0002-5398-4408

Ana Carolina Petisco-Souza ID https://orcid.org/0000-0002-2393-6210

Alessandro Petraglia ID https://orcid.org/0000-0003-4632-2251

Any Mary Petritan ID https://orcid.org/0000-0003-3683-1108

Oliver L. Phillips ID https://orcid.org/0000-0002-8993-6168

Simon Pierce ID https://orcid.org/0000-0003-1182-987X

Valério D. Pillar ID https://orcid.org/0000-0001-6408-2891

Jan Pisek ID https://orcid.org/0000-0003-0396-2072

Hendrik Poorter ID https://orcid.org/0000-0001-9900-2433

Angelika Portsmuth ID https://orcid.org/0000-0002-5100-2888

Peter Poschlad ID https://orcid.org/0000-0003-4473-7656

A. Shafer Powell ID https://orcid.org/0000-0002-9622-0061

Sally A. Power ID https://orcid.org/0000-0002-2723-8671

Giacomo Puglielli ID https://orcid.org/0000-0003-0085-4535

Petr Pyšek ID https://orcid.org/0000-0001-8500-442X

Valerie Raevel ID https://orcid.org/0000-0002-9631-114X

Courtenay A. Ray ID https://orcid.org/0000-0002-2276-5915

Peter B. Reich ID http://orcid.org/0000-0003-4424-662X

Douglas E. B. Reid ID https://orcid.org/0000-0002-0967-5122

Maxime Réjou-Méchain ID https://orcid.org/0000-0003-2824-267X

Victor Resco de Dios ID https://orcid.org/0000-0002-5721-1656

Sabina Ribeiro ID https://orcid.org/0000-0002-4504-3050

Sarah Richardson ID https://orcid.org/0000-0002-4097-0381

Matthias C. Rillig ID https://orcid.org/0000-0003-3541-7853

Fiamma Riviera ID https://orcid.org/0000-0003-2505-8180

Elisabeth M. R. Robert ID https://orcid.org/0000-0002-3611-7265

Bjorn Robroek ID https://orcid.org/0000-0002-6714-0652

Adam Roddy ID https://orcid.org/0000-0002-4423-8729

Arthur Vinicius Rodrigues ID https://orcid.org/0000-0003-2656-558X

Alistair Rogers ID https://orcid.org/0000-0001-9262-7430

Emily Rollinson ID https://orcid.org/0000-0003-3428-2352

Victor Rolo ID https://orcid.org/0000-0001-5854-9512

Christine Römermann ID https://orcid.org/0000-0003-3471-0951

Dina Ronzhina ID https://orcid.org/0000-0003-0854-0223

Christiane Roscher ID https://orcid.org/0000-0001-9301-7909

Julieta A. Rosell ID https://orcid.org/0000-0001-5741-8027

Milena Fermina Rosenfield ID https://orcid.org/0000-0002-1799-2822

David B. Roy ID https://orcid.org/0000-0002-5147-0331

Samuel Royer-Tardif ID https://orcid.org/0000-0001-7809-6485

Nadja Rüger ID https://orcid.org/0000-0003-2371-4172

Ricardo Ruiz-Peinado ID https://orcid.org/0000-0003-0126-1651

Sabine B. Rumpf ID https://orcid.org/0000-0001-5909-9568

Masahiro Ryo ID https://orcid.org/0000-0002-5271-3446

Lawren Sack ID https://orcid.org/0000-0002-7009-7202

Beatriz Salgado-Negret ID https://orcid.org/0000-0002-3103-9878

Roberto Salguero-Gomez ID https://orcid.org/0000-0002-6085-4433

Ignacio Santa-Regina ID https://orcid.org/0000-0002-7312-3605

Ana Carolina Santacruz-García ID https://orcid.org/0000-0002-3340-4859

Joaquim Santos ID https://orcid.org/0000-0002-2160-4968

Jordi Sardans ID https://orcid.org/0000-0003-2478-0219

Brandon Schamp ID https://orcid.org/0000-0002-6885-2029

Michael Scherer-Lorenzen ID https://orcid.org/0000-0001-9566-590X

Matthias Schleuning ID https://orcid.org/0000-0001-9426-045X

Bernhard Schmid ID https://orcid.org/0000-0002-8430-3214

Marco Schmidt ID https://orcid.org/0000-0001-6087-6117

Sylvain Schmitt ID https://orcid.org/0000-0001-7759-7106

Julio V. Schneider ID https://orcid.org/0000-0002-9823-6569

Simon D. Schowanek ID https://orcid.org/0000-0001-6036-7507

Julian Schrader ID https://orcid.org/0000-0002-8392-211X

Franziska Schrodt ID https://orcid.org/0000-0001-9053-8872

Frank Schurr ID https://orcid.org/0000-0002-4518-0602

Galia Selaya Garvizu ID https://orcid.org/0000-0003-1920-7450

Marina Semchenko ID https://orcid.org/0000-0001-6196-3562

Julia C. Sfair ID https://orcid.org/0000-0003-3823-7233

Christine S. Sheppard ID https://orcid.org/0000-0002-7847-2310

Serge Sheremetiev ID https://orcid.org/0000-0002-0318-6766

Satomi Shiodera ID https://orcid.org/0000-0002-6849-8568

Bill Shipley ID https://orcid.org/0000-0002-7026-3880

Tanvir Ahmed Shovon ID https://orcid.org/0000-0003-3311-1797

Alrun Siebenkäs ID https://orcid.org/0000-0001-9676-7094

Carlos Sierra ID https://orcid.org/0000-0003-0009-4169

Vasco Silva ID https://orcid.org/0000-0003-2729-1824

Mateus Silva ID https://orcid.org/0000-0002-4281-3400

Tommaso Sitzia ID https://orcid.org/0000-0001-6221-4256

Henrik Sjöman ID https://orcid.org/0000-0002-5526-6303

Martijn Slot ID https://orcid.org/0000-0002-5558-1792

Nicholas G. Smith ID https://orcid.org/0000-0001-7048-4387

Darwin Sodhi ID https://orcid.org/0000-0003-2119-2681

Pamela Soltis ID https://orcid.org/0000-0001-9310-8659

Douglas Soltis ID https://orcid.org/0000-0001-8638-4137

Ben Somers ID https://orcid.org/0000-0002-7875-107X

Grégory Sonnier ID https://orcid.org/0000-0002-0614-7033

Mia Vedel Sørensen ID https://orcid.org/0000-0002-2225-6117

Enio Egon Sosinski ID https://orcid.org/0000-0001-6310-9474

Nadejda A. Soudzilovskaia ID https://orcid.org/0000-0002-9584-2109

Alexandre F. Souza ID https://orcid.org/0000-0001-7468-3631

Marko Spasojevic ID https://orcid.org/0000-0003-1808-0048

Marta Gaia Sperandii ID https://orcid.org/0000-0002-2507-5928

Amanda B. Stan ID https://orcid.org/0000-0001-6111-0578

James Stegen ID https://orcid.org/0000-0001-9135-7424

Klaus Steinbauer ID https://orcid.org/0000-0002-3730-9920

Jörg G. Stephan ID https://orcid.org/0000-0001-6195-7867

Dejan B. Stojanovic ID https://orcid.org/0000-0003-2967-2049

Jens-Christian Svenning ID https://orcid.org/0000-0002-3415-0862

Ivana Svitková ID https://orcid.org/0000-0002-5671-0330
Marek Svitok https://orcid.org/0000-0003-2710-8102

Miroslav Svoboda https://orcid.org/0000-0003-4050-3422

Nathan Swenson https://orcid.org/0000-0003-3819-9767

Marcelo Tabarelli https://orcid.org/0000-0001-7573-7216

Kentaro Takagi https://orcid.org/0000-0002-1321-2841

Ulrike Tappeiner https://orcid.org/0000-0002-0195-7459

Rubén Tarifa https://orcid.org/0000-0002-0288-1978

Simon Tauugourdeau https://orcid.org/0000-0001-6561-3228

Cagatay Tavsanoglu https://orcid.org/0000-0003-4447-6492

Mariska te Beest https://orcid.org/0000-0003-3673-4105

Leho Tedersoo https://orcid.org/0000-0002-1635-1249

Nelson Thiffault https://orcid.org/0000-0003-2017-6890

Dominik Thom https://orcid.org/0000-0001-8091-6075

Evert Thomas https://orcid.org/0000-0002-7838-6228

Peter E. Thornton https://orcid.org/0000-0002-4759-5158

Wilfried Thuiller https://orcid.org/0000-0002-5388-5274

Lubomír Tichý https://orcid.org/0000-0001-8400-7741

David Tissue https://orcid.org/0000-0002-8497-2047

Mark G. Tjoelker https://orcid.org/0000-0003-4607-5238

David Yue Phin Tng https://orcid.org/0000-0001-5135-0922

Joseph Tobias https://orcid.org/0000-0003-2429-6179

Péter Török https://orcid.org/0000-0002-4428-3327

Tonantzin Tarin https://orcid.org/0000-0001-5125-579X

José M. Torres-Ruiz https://orcid.org/0000-0003-1367-7056

Béla Tóthmérész https://orcid.org/0000-0002-4766-7668

Martina Treurnicht https://orcid.org/0000-0002-0853-0247

Franck Trolliet https://orcid.org/0000-0003-1472-2592

Volodymyr Trotsiuk https://orcid.org/0000-0002-8363-656X

James L. Tsakalos https://orcid.org/0000-0001-5067-196X

Ioannis Tsiripidis https://orcid.org/0000-0001-9373-676X

Vladimir Usoltsev http://orcid.org/0000-0003-4587-8952

Matthew Vadeboncoeur https://orcid.org/0000-0002-8269-0708

Fernando Valladares https://orcid.org/0000-0002-5374-4682

Jana Vamosi https://orcid.org/0000-0002-2376-0729

Peter M. van Bodegom https://orcid.org/0000-0003-0771-4500

Michiel van Breugel https://orcid.org/0000-0003-2778-7803

Elisa Van Cleemput https://orcid.org/0000-0002-5305-9749

Stephni van der Merwe https://orcid.org/0000-0002-6385-8536

Masha T. van der Sande https://orcid.org/0000-0002-6845-2308

Mark van Kleunen https://orcid.org/0000-0002-2861-3701

Koenraad Van Meerbeek https://orcid.org/0000-0002-9260-3815

Kim André Vanselow https://orcid.org/0000-0003-3299-6220

Laura Varone https://orcid.org/0000-0002-9989-5283

Maribel Yesenia Vasquez Valderrama https://orcid.org/0000-0001-7839-7229

Kiril Vassilev https://orcid.org/0000-0003-4376-5575

Mark Vellend https://orcid.org/0000-0002-2491-956X

Erik J. Veneklaas https://orcid.org/0000-0002-7030-4056

Hans Verbeeck https://orcid.org/0000-0003-1490-0168

Kris Verheyen https://orcid.org/0000-0002-2067-9108

Alexander Vibrans https://orcid.org/0000-0002-8789-5833

Ima Vieira https://orcid.org/0000-0003-1233-318X

Jaime Villacís https://orcid.org/0000-0001-7752-8506

Cyrille Violle https://orcid.org/0000-0002-2471-9226

Katrin Wagner https://orcid.org/0000-0003-4326-1086

Anthony P. Walker https://orcid.org/0000-0003-0557-5594

Martyn Waller https://orcid.org/0000-0003-3876-2492

Han Wang https://orcid.org/0000-0003-2482-1818

Feng Wang https://orcid.org/0000-0003-3595-515X

Weiqi Wang https://orcid.org/0000-0001-8503-0978

Harry Watkins https://orcid.org/0000-0002-4038-7145

James T. Weedon https://orcid.org/0000-0003-0491-8719

Liping Wei https://orcid.org/0000-0002-4296-9851

Patrick Weigelt https://orcid.org/0000-0002-2485-3708

Evan Weiher https://orcid.org/0000-0002-5375-9964

Aidan W. Wells https://orcid.org/0000-0002-2209-3282

Camilla Wellstein https://orcid.org/0000-0001-6994-274X

Mark Westoby https://orcid.org/0000-0001-7690-4530

Philip John White https://orcid.org/0000-0003-0827-288X

Mathew Williams https://orcid.org/0000-0001-6117-5208

Daniel E. Winkler https://orcid.org/0000-0003-4825-9073

Ian J. Wright https://orcid.org/0000-0001-8338-9143

S. Joseph Wright https://orcid.org/0000-0003-4260-5676

Justin Wright https://orcid.org/0000-0002-9102-5347

Bruno X. Pinho https://orcid.org/0000-0002-6588-3575

Fabiano Ximenes https://orcid.org/0000-0003-0516-9414

Toshihiro Yamada https://orcid.org/0000-0001-9307-6255

Keiko Yamaji https://orcid.org/0000-0003-2875-5619

Ruth Yanai https://orcid.org/0000-0001-6987-2489

Amy E. Zanne https://orcid.org/0000-0001-6379-9452

David Zelený https://orcid.org/0000-0001-5157-044X

Jingming Zheng https://orcid.org/0000-0002-8517-9999

Ji Zheng https://orcid.org/0000-0003-4936-1168

Kasia Ziemińska https://orcid.org/0000-0001-7750-4758

Chad R. Zirbel https://orcid.org/0000-0002-9289-1722

Irié Casimir Zo-Bi https://orcid.org/0000-0003-0982-8579

Gerhard Zotz https://orcid.org/0000-0002-6823-2268

REFERENCES

Aerts, R., & Chapin, F. S. (2000). The mineral nutrition of wild plants revisited: A re-evaluation of processes and patterns. *Advances in Ecological Research*, 30, 1-67. https://doi.org/10.1016/s0065-2504(08)60016-1

Akhmetzhanova, A. A., Soudzilovskaia, N. A., Onipchenko, V. G., Cornwell, W. K., Agafonov, V. A., Selivanov, I. A., & Cornelissen, J. H. C. (2012). A rediscovered treasure: Mycorrhizal intensity database for 3000 vascular plant species across the former Soviet Union. *Ecology*, 93(3), 689-690. https://doi.org/10.1890/11-1749.1

Ali, A., Lin, S. L., He, J. K., Kong, F. M., Yu, J. H., & Jiang, H. S. (2019). Big-sized trees overrule remaining trees' attributes and species richness as determinants of aboveground biomass in tropical forests. *Global Change Biology*, 25(8), 2810-2824. https://doi.org/10.1111/gcb.14707

Baraloto, C., Timothy Paine, C. E. T., Patino, S., Bonal, D., Herault, B., & Chave, J. (2010). Functional trait variation and sampling strategies in species-rich plant communities. *Functional Ecology*, 24(1), 208-216. https://doi.org/10.1111/j.1365-2435.2009.01600.x

Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Normand, S., Rüger, N., Beck, P. S. A., ... Weiher, E. (2018). Plant functional trait change across a warming tundra biome. *Nature*, 562(7725), 57-62. https://doi.org/10.1038/s41586-018-0563-7
Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Normand, S., Thomas, H. J. D., Alatalo, J. M., ... Zamin, T. (2018). Tundra Trait Team: A database of plant traits spanning the tundra biome. *Global Ecology and Biogeography*, 27(12), 1402-1411. https://doi.org/10.1111/geb.12821

Boeddinghaus, R. S., Marhan, S., Berner, D., Boch, S., Fischer, M., Hölzel, N., ... Manning, P. (2019). Plant functional trait shifts explain concurrent changes in the structure and function of grassland soil microbial communities. *Journal of Ecology*, 107(5), 2197-2210. https://doi.org/10.1111/1365-2745.13182

Boyle, B., Hopkins, N., Lu, Z., Raygoza Garay, J. A., Mozzherin, D., Rees, T., ... Enquist, B. J. (2013). The taxonomic name resolution service: An online tool for automated standardization of plant names. *BMC Bioinformatics*, 14(1). https://doi.org/10.1186/1471-2105-14-16

Bruelheide, H., Dengler, J., Jiménez-Alfaro, B., Purschke, O., Hennekens, S. M., Chytrý, M., ... Zverev, A. (2019). sPlot - A new tool for global vegetation analyses. *Journal of Vegetation Science*, 30(2), 161-186. https://doi.org/10.1111/jvs.12710

Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro, B., Hennekens, S. M., ... Jandt, U. (2018). Global trait-environment relationships of plant communities. *Nature Ecology & Evolution*, 2(12), 1906-1917. https://doi.org/10.1038/s41559-018-0699-8

Butler, E. E., Datta, A., Flores-Moreno, H., Chen, M., Wythers, K. R., Fazayeli, F., ... Reich, P. B. (2017). Mapping local and global variability in plant trait distributions. *Proceedings of the National Academy of Sciences of the United States of America*, 114(51), E10937-E10946. https://doi.org/10.1073/pnas.1708984114

Craine, J. M., Elmore, A. J., Wang, L., Aranibar, J., Bauters, M., Boeckx, P., ... Zmudczyńska-Skarbek, K. (2018). Isotopic evidence for oligotrophication of terrestrial ecosystems. *Nature Ecology & Evolution*, 2(11), 1735-1744. https://doi.org/10.1038/s41559-018-0694-0

Craven, D., Eisenhauer, N., Pearse, W. D., Hautier, Y., Isbell, F., Roscher, C., ... Manning, P. (2018). Multiple facets of biodiversity drive the diversity-stability relationship. *Nature Ecology & Evolution*, 2(10), 1579-1587. https://doi.org/10.1038/s41559-018-0647-7

deVries, F. T., Manning, P., Tallowin, J. R. B., Mortimer, S. R., Pilgrim, E. S., Harrison, K. A., ... Bardgett, R. D. (2012). Abiotic drivers and plant traits explain landscape-scale patterns in soil microbial communities. *Ecology Letters*, 15(11), 1230-1239. https://doi.org/10.1111/j.1461-0248.2012.01844.x

Delgado-Baquerizo, M., Fry, E. L., Eldridge, D. J., deVries, F. T., Manning, P., Hamonts, K., ... Bardgett, R. D. (2018). Plant attributes explain the distribution of soil microbial communities in two contrasting regions of the globe. *New Phytologist*, 219(2), 574-587. https://doi.org/10.1111/nph.15161

Díaz, S., Hodgson, J. G., Thompson, K., Cabido, M., Cornelissen, J. H. C., Jalili, A., ... Zak, M. R. (2004). The plant traits that drive ecosystems: Evidence from three continents. *Journal of Vegetation Science*, 15(3), 295-304. https://doi.org/10.1658/1100-9233(2004)015[0295:tpttd]eJ2.0:co;2

Díaz, S., Kattge, J., Cornelissen, J. H. C., Wright, I. J., Lavorel, S., Dray, S., ... Gorné, L. D. (2016). The global spectrum of plant form and function. *Nature*, 529(7585), 167-171. https://doi.org/10.1038/nature16489

Díaz, S., Lavorel, S., de Bello, F., Quétier, F., Grigulis, K., & Robson, T. M. (2007). Incorporating plant functional diversity effects in ecosystem service assessments. *Proceedings of the National Academy of Sciences of the United States of America*, 104(52), 20684-20689. https://doi.org/10.1073/pnas.0704716104

Engemann, K., Sandel, B., Boyle, B., Enquist, B. J., Jørgensen, P. M., Kattge, J., ... Svenning, J. C. (2016). A plant growth form dataset for the New World. *Ecology*, 97(11), 3243-3243. https://doi.org/10.1002/ecy.1569

Enquist, B., Condit, R., Peet, R., Schildhauer, M., & Thiers, B. (2016). Cyberinfrastructure for an integrated botanical information network to investigate the ecological impacts of global climate change on plant

biodiversity. *PeerJ Preprints*, 4, e2615v2. https://doi.org/10.7287/peerj.preprints.2615v2

Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B., & Otero-Casal, C. (2017). Hydrologic regulation of plant rooting depth. *Proceedings of the National Academy of Sciences of the United States of America*, 114(40), 10572-10577. https://doi.org/10.1073/pnas.1712381114

Fitter, A. H., & Peat, H. J. (1994). The ecological flora database. *Journal of Ecology*, 82(2), 415. https://doi.org/10.2307/2261309

Gallagher, R. V., Falster, D. S., Maitner, B., Salguero-Gomez, R., Vandvik, V., Pearse, W., ... Enquist, B. J. (in press). The open traits network: Using open science principles to accelerate trait-based science across the tree of life. *Nature Ecology & Evolution*.

Garnier, E., Cortez, J., Billés, G., Navas, M.-L., Roumet, C., Debussche, M., ... Toussaint, J.-P. (2004). Plant functional markers capture ecosystem properties during secondary succession. *Ecology*, 85, 2630-2637. https://doi.org/10.1890/03-0799

Garnier, E., & Navas, M.-L. (2012). A trait-based approach to comparative functional plant ecology: Concepts, methods and applications for agroecology. A review. *Agronomy for Sustainable Development*, 32, 365-399. https://doi.org/10.1007/s13593-011-0036-y

Garnier, E., Stahl, U., Laporte, M.-A., Kattge, J., Mougenot, I., Kühn, I., ... Klotz, S. (2017). Towards a thesaurus of plant characteristics: An ecological contribution. *Journal of Ecology*, 105(2), 298-309. https://doi.org/10.1111/1365-2745.12698

Green, W. (2009). USDA PLANTS Compilation, version 1, 09-02-02. (http://bricol.net/downloads/data/PLANTSdatabase/) NRCS: The PLANTS Database (http://plants.usda.gov, 1 Feb 2009). Baton Rouge, LA: National Plant Data Center.

Grime, J. P. (1974). Vegetation classification by reference to strategies. *Nature*, 250, 26-31. https://doi.org/10.1038/250026a0

Grime, J. P. (2001). *Plant strategies, vegetation processes, and ecosystem properties*. Chichester, UK: John Wiley & Sons.

Grime, J. P. (2006). Trait convergence and trait divergence in herbaceous plant communities: Mechanisms and consequences. *Journal of Vegetation Science*, 17(2), 255-260. https://doi.org/10.1111/j.1654-1103.2006.tb02444.x

Iversen, C. M., McCormack, M. L., Powell, A. S., Blackwood, C. B., Freschet, G. T., Kattge, J., ... Violle, C. (2017). A global Fine-Root Ecology Database to address below-ground challenges in plant ecology. *New Phytologist*, 215(1), 15-26. https://doi.org/10.1111/nph.14486

Jetz, W., Cavender-Bares, J., Pavlick, R., Schimel, D., Davis, F. W., Asner, G. P., ... Ustin, S. L. (2016). Monitoring plant functional diversity from space. *Nature Plants*, 2(3). https://doi.org/10.1038/nplants.2016.24

Kattge, J., Díaz, S., Lavorel, S., Prentice, I. C., Leadley, P., Bönisch, G., ... Wirth, C. (2011). TRY - A global database of plant traits. *Global Change Biology*, 17(9), 2905-2935. https://doi.org/10.1111/j.1365-2486.2011.02451.x

Kattge, J., Díaz, S., & Wirth, C. (2014). Of carrots and sticks: Commentary. *Nature Geoscience*, 7(11), 778-779. https://doi.org/10.1038/ngeo2280

Kattge, J., Ogle, K., Boenisch, G., Díaz, S., Lavorel, S., Madin, J., ... Wirth, C. (2011). A generic structure for plant trait databases. *Methods in Ecology and Evolution*, 2(2), 202-213. https://doi.org/10.1111/j.2041-210X.2010.00067.x

Kier, G., Mutke, J., Dinerstein, E., Ricketts, T. H., Küper, W., Kreft, H., & Barthlott, W. (2005). Global patterns of plant diversity and floristic knowledge. *Journal of Biogeography*, 32(7), 1107-1116. https://doi.org/10.1111/j.1365-2699.2005.01272.x

Kissling, W. D., Walls, R., Bowser, A., Jones, M. O., Kattge, J., Agosti, D., ... Guralnick, R. P. (2018). Towards global data products of Essential Biodiversity Variables on species traits. *Nature Ecology & Evolution*, 2(10), 1531-1540. https://doi.org/10.1038/s41559-018-0667-3

Kleyer, M., Bekker, R. M., Knevel, I. C., Bakker, J. P., Thompson, K., Sonnenschein, M., ... Peco, B. (2008). The LEDA Traitbase: A database of life-history traits of the Northwest European flora. *Journal of Ecology*, 96(6), 1266-1274. https://doi.org/10.1111/j.1365-2745.2008.01430.x
Klotz, S., Kühn, I., & Durka, W. (2002). *BIOLFLOR – Eine Datenbank zu biologisch-ökologischen Merkmalen der Gefäßpflanzen in Deutschland – Schriftenreihe für Vegetationskunde* 38. Bonn, Germany: Bundesamt Für Naturschutz.

König, C., Weigelt, P., Schrader, J., Taylor, A., Kattge, J., & Kreft, H. (2019). Biodiversity data integration – The significance of data resolution and domain. *PLoS Biology*, 17(3), e3000183. https://doi.org/10.1371/journal.pbio.3000183

Lavorel, S., Colloff, M. J., McIntyre, S., Doherty, M. D., Murphy, H. T., Metcalfe, D. J., ... Williams, K. J. (2015). Ecological mechanisms underpinning climate adaptation services. *Global Change Biology*, 21(1), 12–31. https://doi.org/10.1111/gcb.12689

Lavorel, S., & Garnier, E. (2002). Predicting changes in community composition and ecosystem functioning from plant traits: Revisiting the Holy Grail. *Functional Ecology*, 16(5), 545–556. https://doi.org/10.1046/j.1365-2435.2002.00664.x

Lavorel, S., Storkey, J., Bardgett, R. D., de Bello, F., Berg, M. P., Le Roux, X., ... Harrington, R. (2013). A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services. *Journal of Vegetation Science*, 24(5), 942–948. https://doi.org/10.1111/jvs.12083

Loranger, J., Meyer, S. T., Shipley, B., Kattge, J., Loranger, H., Roscher, C., & Weisser, W. W. (2012). Predicting invertebrate herbivory from plant traits: Evidence from 51 grassland species in experimental monocultures. *Ecology*, 93(12), 2674–2682. https://doi.org/10.1890/12-0328.1

Loranger, J., Meyer, S. T., Shipley, B., Kattge, J., Loranger, H., Roscher, C., ... Weisser, W. W. (2013). Predicting invertebrate herbivory from plant traits: Polycultures show strong nonadditive effects. *Ecology*, 94(7), 1499–1509. https://doi.org/10.1890/12-2063.1

Madin, J., Bowers, S., Schildhauer, M., Krivov, S., Pennington, D., & Villa, F. (2007). An ontology for describing and synthesizing ecological observation data. *Ecological Informatics*, 2, 279–296. https://doi.org/10.1016/j.ecoinf.2007.05.004

Martin, A. R., Hale, C. E., Cerabolini, B. E. L., Cornelissen, J. H. C., Craine, J., Gough, W. A., ... Tirona, C. K. F. (2018). Inter- and intraspecific variation in leaf economic traits in wheat and maize. *AoB PLANTS*, 10(1), https://doi.org/10.1093/aobpla/ply006

Martin, A. R., & Isaac, M. E. (2015). REVIEW: Plant functional traits in agroecosystems: A blueprint for research. *Journal of Applied Ecology*, 52(6), 1425–1435. https://doi.org/10.1111/1365-2664.12526

McGill, B. J., Enquist, B. J., Weiher, E., & Westoby, M. (2006). Rebuilding community ecology from functional traits. *Trends in Ecology & Evolution*, 21(4), 178–185. https://doi.org/10.1016/j.tree.2006.02.002

Moreno-Martínez, Á., Camps-Valls, G., Kattge, J., Robinson, N., Reichstein, M., van Bodegom, P., ... Running, S. W. (2018). A methodology to derive global maps of leaf traits using remote sensing and climate data. *Remote Sensing of Environment*, 218, 69–88. https://doi.org/10.1016/j.rse.2018.09.006

Newbold, T., Hudson, L. N., Hill, S. L. L., Contu, S., Lysenko, I., Senior, R. A., ... Purvis, A. (2015). Global effects of land use on local terrestrial biodiversity. *Nature*, 520(7545), 45–50. https://doi.org/10.1038/nature14324

Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., ... Kassem, K. R. (2001). Terrestrial Ecoregions of the world: A new map of life on earth. *BioScience*, 51(11), 933. https://doi.org/10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2

Paula, S., Arianoutsou, M., Kazanis, D., Tavsanoglu, Ç., Lloret, F., Buhk, C., ... Pausas, J. G. (2009). Fire-related traits for plant species of the Mediterranean Basin. *Ecology*, 90(5), 1420. https://doi.org/10.1890/08-1309.1

Peacelle, M., Bellassen, V., Ciais, P., Peñuelas, J., & Viovy, N. (2016). A new approach to optimal discretization of plant functional types in a process-based ecosystem model with forest management: A case study for temperate conifers. *Global Ecology and Biogeography*, 26(4), 486–499. https://doi.org/10.1111/geb.12557

Pereira, H. M., Ferrier, S., Walters, M., Geller, G. N., Jongman, R. H. G., Scholes, R. J., ... Wegmann, M. (2013). Essential biodiversity variables. *Science*, 339(6117), 277–278. https://doi.org/10.1126/science.1229931

Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., ... Cornelissen, J. H. C. (2013). New handbook for standardised measurement of plant functional traits worldwide. *Australian Journal of Botany*, 61(3), 167–234. https://doi.org/10.1071/bt12225

Poschlod, P., Kleyer, M., Jackel, A.-K., Dannemann, A., & Tackenberg, O. (2003). *BIOPOP* – A database of plant traits and internet application for nature conservation. *Folia Geobotanica*, 38(3), 263–271. https://doi.org/10.1007/bf02803198

Reichman, O. J., Jones, M. B., & Schildhauer, M. P. (2011). Challenges and opportunities of open data in ecology. *Science*, 331(6018), 703–705. https://doi.org/10.1126/science.1197962

Royal Botanical Gardens KEW. (2008). Seed Information Database (SID). Version 7.1. Retrieved from http://data.kew.org/sid/

Sakschewski, B., von Bloh, W., Boit, A., Poorter, L., Peña-Claros, M., Heinke, J., ... Thonicke, K. (2016). Resilience of Amazon forests emerges from plant trait diversity. *Nature Climate Change*, 6(11), 1032–1036. https://doi.org/10.1038/nclimate3109

Sakschewski, B., vonBloh, W., Boit, A., Rammig, A., Kattge, J., Poorter, L., ... Thonicke, K. (2015). Leaf and stem economics spectra drive diversity of functional plant traits in a dynamic global vegetation model. *Global Change Biology*, 21(7), 2711–2725. https://doi.org/10.1111/gcb.12870

Schrodt, F., Kattge, J., Shan, H., Fazayeli, F., Joswig, J., Banerjee, A., ... Reich, P. B. (2015). BHPMF – A hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. *Global Ecology and Biogeography*, 24(12), 1510–1521. https://doi.org/10.1111/geb.12335

Shan, H., Kattge, J., Reich, P., Banerjee, A., Schrodt, F., & Reichstein, M. (2012). *Gap filling in the plant kingdom – Trait prediction using hierarchical probabilistic matrix factorization*. Paper presented at the International Conference on Machine Learning (ICML), Edinburgh.

Smith, S. A., & Brown, J. W. (2018). Constructing a broadly inclusive seed plant phylogeny. *American Journal of Botany*, 105(3), 302–314. https://doi.org/10.1002/ajb2.1019

ter Steege, H., Pitman, N. C. A., Sabatier, D., Baraloto, C., Salomão, R. P., Guevara, J. E., ... Silman, M. R. (2013). Hyperdominance in the Amazonian tree flora. *Science*, 342(6156). https://doi.org/10.1126/science.1243092

Valladares, F., Gianoli, E., & Gomez, J. M. (2007). Ecological limits to plant phenotypic plasticity. *New Phytologist*, 176, 749–763. https://doi.org/10.1111/j.1469-8137.2007.02275.x

Verheijen, L. M., Aerts, R., Brovkin, V., Cavender-Bares, J., Cornelissen, J. H. C., Kattge, J., & van Bodegom, P. M. (2015). Inclusion of ecologically based trait variation in plant functional types reduces the projected land carbon sink in an earth system model. *Global Change Biology*, 21(8), 3074–3086. https://doi.org/10.1111/gcb.12871

Verheijen, L. M., Brovkin, V., Aerts, R., Bönisch, G., Cornelissen, J. H. C., Kattge, J., ... van Bodegom, P. M. (2013). Impacts of trait variation through observed trait-climate relationships on performance of an Earth system model: A conceptual analysis. *Biogeosciences*, 10(8), 5497–5515. https://doi.org/10.5194/bg-10-5497-2013

Violle, C., Navas, M. L., Vile, D., Kazakou, E., Fortunel, C., Hummel, I., & Garnier, E. (2007). Let the concept of trait be functional! *Oikos*, 116(5), 882–892. https://doi.org/10.1111/j.2007.0030-1299.15559.x

von Humboldt, A.. (1817) De distributione geographica plantarum secundum coeli temeriem et altitudinem montium: Prolegomena. Lutetiae Parisiorum. In Libraria Graeco-Latino-Germanica. https://doi.org/10.5962/bhl title.118581

Waltman, L., vanEck, N. J., & Noyons, E. C. M. (2010). A unified approach to mapping and clustering of bibliometric networks. *Journal of Informetrics*, 4(4), 629–635. https://doi.org/10.1016/j.joi.2010.07.002
Weigelt, P., König, C., & Kreft, H. (2019). GIFT – A global inventory of
floras and traits for macroecology and biogeography. *Journal of
Biogeography*. https://doi.org/10.1111/jbi.13623

Westoby, M., & Wright, I. J. (2006). Land-plant ecology on the basis of functional traits. *Trends in Ecology & Evolution*, 21(5), 261–268.
https://doi.org/10.1016/j.tree.2006.02.004

Wright, I. J., Dong, N., Maire, V., Prentice, I. C., Westoby, M., Díaz, S., ... Wilf, P. (2017). Global climatic drivers of leaf size. *Science*, 357(6354), 917-921. https://doi.org/10.1126/science.aal4760

Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers, F., ... Villar, R. (2004). The worldwide leaf economics spectrum. *Nature*, 428(6985), 821–827. https://doi.org/10.1038/nature02403

Xu, C. Y., & Griffin, K. L. (2006). Seasonal variation in the temperature response of leaf respiration in *Quercus rubra*: Foliage respiration and leaf properties. *Functional Ecology*, 20(5), 778–789. https://doi.org/10.1111/j.1365-2435.2006.01161.x

Xu, L. K., & Baldocchi, D. D. (2003). Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (*Quercus douglasii*) under prolonged summer drought and high temperature. *Tree Physiology*, 23(13), 865–877. https://doi.org/10.1093/treephys/23.13.865

Zanne, A. E., Tank, D. C., Cornwell, W. K., Eastman, J. M., Smith, S. A., FitzJohn, R. G., ... Beaulieu, J. M. (2014). Three keys to the radiation of angiosperms into freezing environments. *Nature*, 506(7486), 89–92. https://doi.org/10.1038/nature12872

DATASET REFERENCES

Abakumova, M., Zobel, K., Lepik, A., & Semchenko, M. (2016). Plasticity in plant functional traits is shaped by variability in neighbourhood species composition. *New Phytologist*, 211(2), 455–463. https://doi.org/10.1111/nph.13935

Abedi, M., Bartelheimer, M., & Poschlod, P. (2012). Aluminium toxic effects on seedling root survival affect plant composition along soil reaction gradients – A case study in dry sandy grasslands. *Journal of Vegetation Science*, 24(6), 1074–1085. https://doi.org/10.1111/jvs.12016

Adamidis, G. C., Kazakou, E., Fyllas, N. M., & Dimitrakopoulos, P. G. (2014). Species adaptive strategies and leaf economic relationships across serpentine and non-serpentine habitats on Lesbos, Eastern Mediterranean. *PLoS ONE*, 9(5), e96034. https://doi.org/10.1371/journal.pone.0096034

Adler, P. B., Milchunas, D. G., Lauenroth, W. K., Sala, O. E., & Burke, I. C. (2004). Functional traits of graminoids in semi-arid steppes: A test of grazing histories. *Journal of Applied Ecology*, 41(4), 653–663. https://doi.org/10.1111/j.0021-8901.2004.00934.x

Adler, P. B., Salguero-Gomez, R., Compagnoni, A., Hsu, J. S., Ray-Mukherjee, J., Mbeau-Ache, C., & Franco, M. (2013). Functional traits explain variation in plant life history strategies. *Proceedings of the National Academy of Sciences of the United States of America*, 111(2), 740–745. https://doi.org/10.1073/pnas.1315179111

Adriaenssens, S. (2012). Dry deposition and canopy exchange for temperate tree species under high nitrogen deposition. (PhD), Ghent University, Ghent, Belgium.

Albert, C. H., de Bello, F., Boulangeat, I., Pellet, G., Lavorel, S., & Thuiller, W. (2011). On the importance of intraspecific variability for the quantification of functional diversity. *Oikos*, 121(1), 116–126. https://doi.org/10.1111/j.1600-0706.2011.19672.x

Albert, C. H., Thuiller, W., Yoccoz, N. G., Soudant, A., Boucher, F., Saccone, P., & Lavorel, S. (2010). Intraspecific functional variability: Extent, structure and sources of variation. *Journal of Ecology*, 98(3), 604–613. https://doi.org/10.1111/j.1365-2745.2010.01651.x

Aleixo, I., Norris, D., Hemerik, L., Barbosa, A., Prata, E., Costa, F., & Poorter, L. (2019). Amazonian rainforest tree mortality driven by

climate and functional traits. *Nature Climate Change*, 9(5), 384–388.
https://doi.org/10.1038/s41558-019-0458-0

Ali, H. E., Reineking, B., & Münkemüller, T. (2017). Effects of plant functional traits on soil stability: Intraspecific variability matters. *Plant and Soil*, 411(1-2), 359–375. https://doi.org/10.1007/s11104-016-3036-5

Almeida, D., Domingues, T. F., Ehleringer, J., Martinelli, L. A., Cook, C., Flanagan, L., & Ometto, J. P. (2001). LBA-ECO CD-02 Leaf Water Potential, Forest and Pasture Sites, Para, Brazil: 2000–2001. Retrieved from http://daac.ornl.gov/cgi-bin/dsviewer.pl?dsوبدسته ب شریف

Apgaua, D. M. G., Ishida, F. Y., Tng, D. Y. P., Laidlaw, M. J., Santos, R. M., Rumman, R., ... Laurance, S. G. W. (2015). Functional traits and water transport strategies in lowland tropical rainforest trees. *PLoS ONE*, 10(6), e0130799. https://doi.org/10.1371/journal.pone.0130799

Atkin, O. K., Bloomfield, K. J., Reich, P. B., Tjoelker, M. G., Asner, G. P., Bonal, D., ... Zaragoza-Castells, J. (2015). Global variability in leaf respiration in relation to climate, plant functional types and leaf traits. *New Phytologist*, 206(2), 614–636. https://doi.org/10.1111/nph.13253

Aubin, I., Messier, C., Gachet, S., Lawrence, K., McKenney, D., Arseneault, A., ... Munson, A. D. (2012). TOPIC-traits of plants in Canada. Retrieved from http://cfs.cloud.nrcan.gc.ca/ctn/topic.php

Auger, S., & Shipley, B. (2012). Inter-specific and intra-specific trait variation along short environmental gradients in an old-growth temperate forest. *Journal of Vegetation Science*, 24(3), 419–428. https://doi.org/10.1111/j.1654-1103.2012.01473.x

Baastrup-Spohr, L., Sand-Jensen, K., Nicolajsen, S. V., & Bruun, H. H. (2015). From soaking wet to bone dry: Predicting plant community composition along a steep hydrological gradient. *Journal of Vegetation Science*, 26(4), 619–630. https://doi.org/10.1111/jvs.12280

Bahar, N. H. A., Ishida, F. Y., Weerasinghe, L. K., Guerrieri, R., O'Sullivan, O. S., Bloomfield, K. J., ... Atkin, O. K. (2016). Leaf-level photosynthetic capacity in lowland Amazonian and high-elevation Andean tropical moist forests of Peru. *New Phytologist*, 214(3), 1002–1018. https://doi.org/10.1111/nph.14079

Bahn, M., Wohlfahrt, G., Haubner, E., Horak, I., Michaeler, W., Rottmar, K., ... Cernusca, A. (1999). Leaf photosynthesis, nitrogen contents and specific leaf area of 30 grassland species in differently managed mountain ecosystems in the Eastern Alps. In A. Cernusca, U. Tappeiner, & N. Bayfield (Eds.), *Land-use changes in European mountain ecosystems. ECOMONT-Concept and Results* (pp. 247–255). Berlin, Germany: Blackwell Wissenschaft.

Baraloto, C., Timothy Paine, C. E., Poorter, L., Beauchene, J., Bonal, D., Domenach, A.-M., ... Chave, J. (2010). Decoupled leaf and stem economics in rain forest trees. *Ecology Letters*, 13(11), 1338–1347. https://doi.org/10.1111/j.1461-0248.2010.01517.x

Baruch, Z., & Goldstein, G. (1999). Leaf construction cost, nutrient concentration, and net CO₂ assimilation of native and invasive species in Hawaii. *Oecologia*, 121(2), 183–192. https://doi.org/10.1007/s0044 20050920

Bauerle, W. L., Oren, R., Way, D. A., Qian, S. S., Stoy, P. C., Thornton, P. E., ... Reynolds, R. F. (2012). Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. *Proceedings of the National Academy of Sciences of the United States of America*, 109(22), 8612–8617. https://doi.org/10.1073/pnas.1119131109

Bauters, M., Ampoorter, E., Huygens, D., Kearsley, E., De Haulleville, T., Sellan, G., ... Verheyen, K. (2015). Functional identity explains carbon sequestration in a 77-year-old experimental tropical plantation. *Ecosphere*, 6(10), art198. https://doi.org/10.1890/es15-0034.2

Bauters, M., Verbeeck, H., Demol, M., Bruneel, S., Taveirne, C., Van der Heyden, D., ... Boeckx, P. (2017). Parallel functional and stoichio-metric trait shifts in South American and African forest communities with elevation. *Biogeosciences*, 14(23), 5313–5321. https://doi.org/10.5194/bg-14-5313-2017
Bauters, M., Vercleyen, O., Vanlauwe, B., Six, J., Bonyoma, B., Badjoko, H., ... Boeckx, P. (2019). Long-term recovery of the functional community assembly and carbon pools in an African tropical forest succession. *Biotropica*, **51**(3), 319-329. https://doi.org/10.1111/btp.12647

Beckmann, M., Hock, M., Bruelheide, H., & Erfmeier, A. (2012). The role of UV-B radiation in the invasion of Hieracium pilosella-A comparison of German and New Zealand plants. *Environmental and Experimental Botany*, **75**, 173-180. https://doi.org/10.1016/j.envexpbot.2011.09.010

Belluau, M., & Shipley, B. (2017). Predicting habitat affinities of herbaceous dicots to soil wetness based on physiological traits of drought tolerance. *Annals of Botany*, **119**(6), 1073-1084. https://doi.org/10.1093/aob/mcw267

Belluau, M., & Shipley, B. (2018). Linking hard and soft traits: Physiology, morphology and anatomy interact to determine habitat affinities to soil water availability in herbaceous dicots. *PLoS ONE*, **13**(3), e0193130. https://doi.org/10.1371/journal.pone.0193130

Benomar, L., Lamhamedi, M. S., Pepin, S., Rainville, A., Lambert, M.-C., Margolis, H. A., ... Beaulieu, J. (2018). Thermal acclimation of photosynthesis and respiration of southern and northern white spruce seed sources tested along a regional climatic gradient indicates limited potential to cope with temperature warming. *Annals of Botany*, **121**(3), 443-457. https://doi.org/10.1093/aob/mcx174

Bergmann, J., Ryo, M., Prati, D., Hempel, S., & Rillig, M. C. (2017). Root traits are more than analogues of leaf traits: The case for diaspore mass. *New Phytologist*, **216**(4), 1130-1139. https://doi.org/10.1111/nph.14748

Berner, L. T., Alexander, H. D., Loranty, M. M., Ganzlin, P., Mack, M. C., Davydov, S. P., & Goetz, S. J. (2015). Biomass allometry for alder, dwarf birch, and willow in boreal forest and tundra ecosystems of far northeastern Siberia and north-central Alaska. *Forest Ecology and Management*, **337**, 110-118. https://doi.org/10.1016/j.foreco.2014.10.027

Bernhardt-Römermann, M., Poschlod, P., & Hentschel, J. (2018). BryForTrait - A life-history trait database of forest bryophytes. *Journal of Vegetation Science*, **29**(4), 798-800. https://doi.org/10.1111/jvs.12646

Blonder, B., Baldwin, B. G., Enquist, B. J., & Robichaux, R. H. (2015). Variation and macroevolution in leaf functional traits in the Hawaiian silversword alliance (Asteraceae). *Journal of Ecology*, **104**(1), 219-228. https://doi.org/10.1111/1365-2745.12497

Blonder, B., & Enquist, B. J. (2014). Inferring climate from angiosperm leaf venation networks. *New Phytologist*, **204**(1), 116-126. https://doi.org/10.1111/nph.12780

Blonder, B., Kapas, R. E., Dalton, R. M., Graae, B. J., Heiling, J. M., & Opedal, Ø. H. (2018). Microenvironment and functional-trait context dependence predict alpine plant community dynamics. *Journal of Ecology*, **106**(4), 1323-1337. https://doi.org/10.1111/1365-2745.12973

Blonder, B., Royer, D. L., Johnson, K. R., Miller, I., & Enquist, B. J. (2014). Plant ecological strategies shift across the cretaceous-paleogene boundary. *PLoS Biology*, **12**(9), e1001949. https://doi.org/10.1371/journal.pbio.1001949

Blonder, B., Vasseur, F., Violle, C., Shipley, B., Enquist, B. J., & Vile, D. (2015). Testing models for the leaf economics spectrum with leaf and whole-plant traits in *Arabidopsis thaliana*. *AoB PLANTS*, 7, plv049. https://doi.org/10.1093/aobpla/plv049

Blonder, B., Violle, C., Bentley, L. P., & Enquist, B. J. (2010). Venation networks and the origin of the leaf economics spectrum. *Ecology Letters*, **14**(2), 91-100. https://doi.org/10.1111/j.1461-0248.2010.01554.x

Blonder, B., Violle, C., & Enquist, B. J. (2013). Assessing the causes and scales of the leaf economics spectrum using venation networks in *Populus tremuloides*. *Journal of Ecology*, **101**(4), 981-989. https://doi.org/10.1111/1365-2745.12102

Bocanegra-González, K. T., Fernández-Méndez, F., & Galvis-Jiménez, J. D. (2015). Grupos funcionales de árboles en bosques secundarios

de la región Bajo Calima (Buenaventura, Colombia). *Boletín Científico Del Centro De Museos*, **19**(1), 17-40. https://doi.org/10.17151/bccm.2015.19.1.2

Bond-Lamberty, B., Gower, S. T., Wang, C., Cyr, P., & Veldhuis, H. (2006). Nitrogen dynamics of a boreal black spruce wildfire chronosequence. *Biogeochemistry*, **81**(1), 1-16. https://doi.org/10.1007/s10533-006-9025-7

Bond-Lamberty, B., Wang, C., Gower, S. T., & Norman, J. (2002). Leaf area dynamics of a boreal black spruce fire chronosequence. *Tree Physiology*, **22**(14), 993-1001. https://doi.org/10.1093/treephys/22.14.993

Boucher, F. C., Thuiller, W., Arnoldi, C., Albert, C. H., & Lavergne, S. (2013). Unravelling the architecture of functional variability in wild populations of *Polygonum viviparum* L. *Functional Ecology*, **27**(2), 382-391. https://doi.org/10.1111/1365-2435.12034

Boukili, V. K., & Chazdon, R. L. (2017). Environmental filtering, local site factors and landscape context drive changes in functional trait composition during tropical forest succession. *Perspectives in Plant Ecology, Evolution and Systematics*, 24, 37-47. https://doi.org/10.1016/j.ppees.2016.11.003

Brown, K. A., Johnson, S. E., Parks, K. E., Holmes, S. M., Ivoandry, T., Abram, N. K., ... Wright, P. C. (2013). Use of provisioning ecosystem services drives loss of functional traits across land use intensification gradients in tropical forests in Madagascar. *Biological Conservation*, **161**, 118-127. https://doi.org/10.1016/j.biocon.2013.03.014

Brumnich, F., Marchetti, Z. Y., & Pereira, M. S. (2019). Changes in forest diversity over a chronosequence of fluvial islands. *iForest - Biogeosciences and Forestry*, **12**(3), 306-316. https://doi.org/10.3832/ifor2737-012

Bruun, H. H. (2019). Dataset on reproductive traits of Scandinavian alpine plants. *Data in Brief*, **25**. https://doi.org/10.1016/j.dib.2019.104149

Bruy, D., Hattermann, T., Barrabé, L., Mouly, A., Barthélémy, D., & Isnard, S. (2018). Evolution of plant architecture, functional diversification and divergent evolution in the genus *Atractocarpus* (Rubiaceae) for New Caledonia. *Frontiers in Plant Science*, **9**. https://doi.org/10.3389/fpls.2018.01775

Buchanan, S., Isaac, M. E., Van den Meersche, K., & Martin, A. R. (2018). Functional traits of coffee along a shade and fertility gradient in coffee agroforestry systems. *Agroforestry Systems*, **93**(4), 1261-1273. https://doi.org/10.1007/s10457-018-0239-1

Bucher, S. F., Auerswald, K., Tautenhahn, S., Geiger, A., Otto, J., Müller, A., & Römermann, C. (2016). Inter- and intraspecific variation in stometal pore area index along elevational gradients and its relation to leaf functional traits. *Plant Ecology*, **217**(3), 229-240. https://doi.org/10.1007/s11258-016-0564-2

Burrascano, S., Copiz, R., Del Vico, E., Fagiani, S., Giarrizzo, E., Mei, M., ... Blasi, C. (2015). Wild boar rooting intensity determines shifts in understorey composition and functional traits. *Community Ecology*, **16**(2), 244-253. https://doi.org/10.1556/168.2015.16.2.12

Butterfield, B. J., & Briggs, J. M. (2010). Regeneration niche differentiates functional strategies of desert woody plant species. *Oecologia*, **165**(2), 477-487. https://doi.org/10.1007/s00442-010-1741-y

Byun, C., deBlois, S., & Brisson, J. (2012). Plant functional group identity and diversity determine biotic resistance to invasion by an exotic grass. *Journal of Ecology*, **101**(1), 128-139. https://doi.org/10.1111/1365-2745.12016

Cadotte, M. W. (2017). Functional traits explain ecosystem function through opposing mechanisms. *Ecology Letters*, **20**(8), 989-996. https://doi.org/10.1111/ele.12796

Cailleret, M., Jansen, S., Robert, E. M. R., Desoto, L., Aakala, T., Antos, J. A., ... Martinez-Vilalta, J. (2017). A synthesis of radial growth patterns preceding tree mortality. *Global Change Biology*, **23**(4), 1675-1690. https://doi.org/10.1111/gcb.13535

Campany, C. E., Martin, L., & Watkins, J. E. (2018). Convergence of ecophysiological traits drives floristic composition of early lineage
vascular plants in a tropical forest floor. *Annals of Botany*, 123(5), 793-803. https://doi.org/10.1093/aob/mcy210

Campbell, C., Atkinson, L., Zaragoza-Castells, J., Lundmark, M., Atkin, O., & Hurry, V. (2007). Acclimation of photosynthesis and respiration is asynchronous in response to changes in temperature regardless of plant functional group. *New Phytologist*, 176(2), 375-389. https://doi.org/10.1111/j.1469-8137.2007.02183.x

Campetella, G., Botta-Dukát, Z., Wellstein, C., Canullo, R., Gatto, S., Chelli, S., ... Bartha, S. (2011). Patterns of plant trait-environment relationships along a forest succession chronosequence. *Agriculture, Ecosystems & Environment*, 145(1), 38-48. https://doi.org/10.1016/j.agee.2011.06.025

Campetella, G., Chelli, S., Wellstein, C., Farris, E., Calvia, G., Simonetti, E., ... Marignani, M. (2019). Contrasting patterns in leaf traits of Mediterranean shrub communities along an elevation gradient: Measurements matter. *Plant Ecology*, 220(7-8), 765-776. https://doi.org/10.1007/s11258-019-00951-y

Carvalho, F., Brown, K. A., Waller, M. P., & Boom, A. (2019). Leaf traits interact with management and water table to modulate ecosystem properties in fen peatlands. *Plant and Soil*, 441(1-2), 331-347. https://doi.org/10.1007/s11104-019-04126-6

Carvalho, F., Brown, K. A., Waller, M. P., Bunting, M. J., Boom, A., & Leng, M. J. (2019). A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages. *PLoS ONE*, 14(5), e0216698. https://doi.org/10.1371/journal.pone.0216698

Castro-Díez, P., Puyravaud, J. P., Cornelissen, J. H. C., & Villar-Salvador, P. (1998). Stem anatomy and relative growth rate in seedlings of a wide range of woody plant species and types. *Oecologia*, 116(1-2), 57-66. https://doi.org/10.1007/s004420050563

Catford, J. A., Morris, W. K., Vesk, P. A., Gippel, C. J., & Downes, B. J. (2014). Species and environmental characteristics point to flow regulation and drought as drivers of riparian plant invasion. *Diversity and Distributions*, 20(9), 1084-1096. https://doi.org/10.1111/ddi.12225

Catford, J. A., Smith, A. L., Wragg, P. D., Clark, A. T., Kosmala, M., Cavender-Bares, J., ... Tilman, D. (2019). Traits linked with species invasiveness and community invasibility vary with time, stage and indicator of invasion in a long-term grassland experiment. *Ecology Letters*, 22(4), 593-604. https://doi.org/10.1111/ele.13220

Cavender-Bares, J., Keen, A., & Miles, B. (2006). Phylogenetic structure of Floridian plant communities depends on taxonomic and spatial scale. *Ecology*, 87(sp7), S109-S122. https://doi.org/10.1890/0012-9658(2006)87[109:psofpfc]2.0.co;2

Cerabolini, B. E. L., Brusa, G., Ceriani, R. M., De Andreis, R., Luzzaro, A., & Pierce, S. (2010). Can CSR classification be generally applied outside Britain? *Plant Ecology*, 210(2), 253-261. https://doi.org/10.1007/s11258-010-9753-6

Cerabolini, B. E. L., Pierce, S., Luzzaro, A., & Ossola, A. (2009). Species evenness affects ecosystem processes in situ via diversity in the adaptive strategies of dominant species. *Plant Ecology*, 207(2), 333-345. https://doi.org/10.1007/s11258-009-9677-1

Chacón-Madrigal, E., Wanek, W., Hietz, P., & Dullinger, S. (2018). Traits indicating a conservative resource strategy are weakly related to narrow range size in a group of neotropical trees. *Perspectives in Plant Ecology, Evolution and Systematics*, 32, 30-37. https://doi.org/10.1016/j.ppees.2018.01.003

Chain-Guadarrama, A., Imbach, P., Vilchez-Mendoza, S., Vierling, L., & Finegan, B. (2017). Potential trajectories of old-growth Neotropical forest functional composition under climate change. *Ecography*, 41, 75-89.

Chambers, J. Q., Tribuzy, E. S., Toledo, L. C., Crispim, B. F., Higuchi, N., dos Santos, J., ... Trumbore, S. E. (2004). Respiration from a tropical forest ecosystem: Partitioning of sources and low carbon use efficiency. *Ecological Applications*, 14(sp4), 72-88. https://doi.org/10.1890/01-6012

Chave, J., Coomes, D., Jansen, S., Lewis, S. L., Swenson, N. G., & Zanne, A. E. (2009). Towards a worldwide wood economics spectrum. *Ecology Letters*, 12(4), 351-366. https://doi.org/10.1111/j.1461-0248.2009.01285.x

Chen, A., Lichstein, J. W., Osnas, J. L. D., & Pacala, S. W. (2014). Species-independent down-regulation of leaf photosynthesis and respiration in response to shading: Evidence from six temperate tree species. *PLoS ONE*, 9(4), e91798. https://doi.org/10.1371/journal.pone.0091798

Chen, S.-C., Cornwell, W. K., Zhang, H.-X., & Moles, A. T. (2017). Plants show more flesh in the tropics: Variation in fruit type along latitudinal and climatic gradients. *Ecography*, 40(4), 531-538. https://doi.org/10.1111/ecog.02010

Chen, Y. H., Han, W. X., Tang, L. Y., Tang, Z. Y., & Fang, J. Y. (2013). Leaf nitrogen and phosphorus concentrations of woody plants differ in responses to climate, soil and plant growth form. *Ecography*, 36(2), 178-184. https://doi.org/10.1111/j.1600-0587.2011.06833.x

Chianucci, F., Pisek, J., Raabe, K., Marchino, L., Ferrara, C., & Corona, P. (2018). A dataset of leaf inclination angles for temperate and boreal broadleaf woody species. *Annals of Forest Science*, 75(2). https://doi.org/10.1007/s13595-018-0730-x

Choa, B., Jansen, S., Brodribb, T. J., Cochard, H., Delzon, S., Bhaskar, R., ... Zanne, A. E. (2012). Global convergence in the vulnerability of forests to drought. *Nature*, 491(7426), 752-755. https://doi.org/10.1038/nature11688

Chrobock, T., Kempel, A., Fischer, M., & van Kleunen, M. (2011). Introduction bias: Cultivated alien plant species germinate faster and more abundantly than native species in Switzerland. *Basic and Applied Ecology*, 12(3), 244-250. https://doi.org/10.1016/j.baae.2011.03.001

Chung, K.-S., Hipp, A. L., & Roalson, E. H. (2012). Chromosome number evolves independently of genome size in a clade with nonlocalized centromeres (Carex: Cyperaceae). *Evolution*, 66(9), 2708-2722. https://doi.org/10.1111/j.1558-5646.2012.01624.x

Chytrý, M., Tichý, L., Dřevojan, P., Sádlo, J., & Zelený, D. (2018). Ellenberg-type indicator values for the Czech flora. *Preslia*, 90(2), 83-103. https://doi.org/10.23855/preslia.2018.083

Ciccarelli, D. (2015). Mediterranean coastal dune vegetation: Are disturbance and stress the key selective forces that drive the psammophilous succession? *Estuarine, Coastal and Shelf Science*, 165, 247-253. https://doi.org/10.1016/j.ecss.2015.05.023

Ciocărlan, V. (2009). The illustrated Flora of Romania. Pteridophyta et Spermatopyta (in Romanian). Editura Ceres. 1141 pp.

Cornelissen, J. H. C., Diez, P. C., & Hunt, R. (1996). Seedling growth, allocation and leaf attributes in a wide range of woody plant species and types. *The Journal of Ecology*, 84(5), 755. https://doi.org/10.2307/2261337

Cornelissen, J. H. C., Quested, H. M., Gwynn-Jones, D., Van Logtestijn, R. S. P., De Beus, M. A. H., Kondratchuk, A., ... Aerts, R. (2004). Leaf digestibility and litter decomposability are related in a wide range of subarctic plant species and types. *Functional Ecology*, 18(6), 779-786. https://doi.org/10.1111/j.0269-8463.2004.00900.x

Cornwell, W. K., Cornelissen, J. H. C., Amatangelo, K., Dorrepaal, E., Eviner, V. T., Godoy, O., ... Westoby, M. (2008). Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. *Ecology Letters*, 11(10), 1065-1071. https://doi.org/10.1111/j.1461-0248.2008.01219.x

Cornwell, W. K., Wright, I. J., Turner, J., Maire, V., Barbour, M. M., Cernusak, L. A., ... Santiago, L. S. (2018). Climate and soils together regulate photosynthetic carbon isotope discrimination within C3 plants worldwide. *Global Ecology and Biogeography*, 27(9), 1056-1067. https://doi.org/10.1111/geb.12764

Craine, J. M., Elmore, A. J., Aidar, M. P. M., Bustamante, M., Dawson, T. E., Hobbie, E. A., ... Wright, I. J. (2009). Global patterns of foliar nitrogen isotopes and their relationships with climate, mycorrhizal fungi, foliar nutrient concentrations, and nitrogen availability. *New Phytologist*, 183(4), 980-992. https://doi.org/10.1111/j.1469-8137.2009.02917.x
Craine, J. M., Lee, W. G., Bond, W. J., Williams, R. J., & Johnson, L. C. (2005). Environmental constraints on a global relationship among leaf and root traits of grasses. *Ecology, 86*(1), 12–19. https://doi.org/10.1890/04-1075

Craine, J. M., Nippert, J. B., Towne, E. G., Tucker, S., Kembel, S. W., Skibbe, A., & McLauchlan, K. K. (2011). Functional consequences of climate change-induced plant species loss in a tallgrass prairie. *Oecologia, 165*(4), 1109–1117. https://doi.org/10.1007/s00442-011-1938-8

Craven, D., Braden, D., Ashton, M. S., Berlyn, G. P., Wishnie, M., & Dent, D. (2007). Between and within-site comparisons of structural and physiological characteristics and foliar nutrient content of 14 tree species at a wet, fertile site and a dry, infertile site in Panama. *Forest Ecology and Management, 238*(1–3), 335–346. https://doi.org/10.1016/j.foreco.2006.10.030

Dahlin, K. M., Asner, G. P., & Field, C. B. (2013). Environmental and community controls on plant canopy chemistry in a Mediterranean-type ecosystem. *Proceedings of the National Academy of Sciences of the United States of America, 110*(17), 6895–6900. https://doi.org/10.1073/pnas.1215513110

Dainese, M., & Bragazza, L. (2012). Plant traits across different habitats of the Italian Alps: A comparative analysis between native and alien species. *Alpine Botany, 122*, 11–21. https://doi.org/10.1007/s00035-012-0101-4

Dalke, I. V., Novakovskiy, A. B., Maslova, S. P., & Dubrovskiy, Y. A. (2018). Morphological and functional traits of herbaceous plants with different functional types in the European Northeast. *Plant Ecology, 219*(11), 1295–1305. https://doi.org/10.1007/s11258-018-0879-2

Dang-Le, A. T., Edelin, C., & Le-Cong, K. (2013). Ontogenetic variations in leaf morphology of the tropical rain forest species *Dipterocarpus alatus* Roxb. ex G. Don. *Trees, 27*(3), 773–786. https://doi.org/10.1007/s00468-012-0832-2

Dawson, S. K., Warton, D. I., Kingsford, R. T., Berney, P., Keith, D. A., & Catford, J. A. (2017). Plant traits of propagule banks and standing vegetation reveal flooding alleviates impacts of agriculture on wetland restoration. *Journal of Applied Ecology, 54*(6), 1907–1918. https://doi.org/10.1111/1365-2664.12922

deAraujo, A. C., Ometto, J. P. H. B., Dolman, A. J., Kruijt, B., Waterloo, M. J., & Ehleringer, J. R. (2012). LBA-ECO CD-02 C and N isotopes in leaves and atmospheric CO₂, *Amazonas, Brazil*. Retrieved from http://dx.doi.org/10.3334/ORNLDAAC/1097

deFrutos, Á., Navarro, T., Pueyo, Y., & Alados, C. L. (2015). Inferring resilience to fragmentation-induced changes in plant communities in a semi-arid Mediterranean ecosystem. *PLoS ONE, 10*(3), e0118837. https://doi.org/10.1371/journal.pone.0118837

De Long, J. R., Jackson, B. G., Wilkinson, A., Pritchard, W. J., Oakley, S., Mason, K. E., ... Bardgett, R. D. (2019). Relationships between plant traits, soil properties and carbon fluxes differ between monocultures and mixed communities in temperate grassland. *Journal of Ecology, 107*(4), 1704–1719. https://doi.org/10.1111/1365-2745.13160

deVries, F. T., & Bardgett, R. D. (2016). Plant community controls on short-term ecosystem nitrogen retention. *New Phytologist, 210*(3), 861–874. https://doi.org/10.1111/nph.13832

Dechant, B., Cuntz, M., Vohland, M., Schulz, E., & Doktor, D. (2017). Estimation of photosynthesis traits from leaf reflectance spectra: Correlation to nitrogen content as the dominant mechanism. *Remote Sensing of Environment, 196*, 279–292. https://doi.org/10.1016/j.rse.2017.05.019

Delpierre, N., Berveiller, D., Granda, E., & Dufrêne, E. (2015). Wood phe-nology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest. *New Phytologist, 210*(2), 459–470. https://doi.org/10.1111/nph.13771

Demey, A., Staelens, J., Baeten, L., Boeckx, P., Hermy, M., Kattge, J., & Verheyen, K. (2013). Nutrient input from hemiparasitic litter favors plant species with a fast-growth strategy. *Plant and Soil, 371*(1–2), 53–66. https://doi.org/10.1007/s11104-013-1658-4

Domingues, T. F., Martinelli, L. A., & Ehleringer, J. R. (2007). Ecophysiological traits of plant functional groups in forest and pas-ture ecosystems from eastern Amazônia, Brazil. *Plant Ecology, 193*(1), 101–112. https://doi.org/10.1007/s11258-006-9251-z

Domingues, T. F., Meir, P., Feldpausch, T. R., Saiz, G., Veenendaal, E. M., Schrodt, F., ... Lloyd, J. O. N. (2010). Co-limitation of photo-synthetic capacity by nitrogen and phosphorus in West Africa woodlands. *Plant, Cell & Environment, 33*(6), 959–980. https://doi.org/10.1111/j.1365-3040.2010.02119.x

Dong, N., Prentice, I. C., Evans, B. J., Caddy-Retalic, S., Lowe, A. J., & Wright, I. J. (2017). Leaf nitrogen from first principles: Field evidence for adaptive variation with climate. *Biogeosciences, 14*(2), 481–495. https://doi.org/10.5194/bg-14-481-2017

Dressler, S., Schmidt, M., & Zizka, G. (2014). Introducing 'African plants - A Photo Guide' - An interactive photo data-base and rapid identifica-tion tool for continental Africa. *Taxon, 63*(5), 1159–1161. https://doi.org/10.12705/635.26

Dwyer, J. M., Hobbs, R. J., & Mayfield, M. M. (2014). Specific leaf area re-sponses to environmental gradients through space and time. *Ecology, 95*(2), 399–410. https://doi.org/10.1890/13-0412.1

Ellenberg, H., & Leuschner, C. (2010). Vegetation Mitteleuropas mit den Alpen in ökologischer, dynamischer und historischer Sicht (6th ed.). Stuttgart, Germany: Ulmer.

Fagúndez, J., & Izco, J. (2008). Seed morphology of two distinct species of *Erica* L. (*Ericaceae*). *Acta Botanica Malacitana, 33*, 1–9.

Fagúndez, J., Juan, R., Fernández, I., Pastor, J., & Izco, J. (2010). Systematic relevance of seed coat anatomy in the European heathers (Ericaceae, Ericaceae). *Plant Systematics and Evolution, 284*(1–2), 65–76. https://doi.org/10.1007/s00606-009-0240-2

Falster, D. S., Duursma, R. A., Ishihara, M. I., Barneche, D. R., FitzJohn, R. G., Várhammar, A., ... York, R. A. (2015). BAAD: A biomass and allome-try database for woody plants. *Ecology, 96*(5), 1445–1445. https://doi.org/10.1890/14-1889.1

Fazlioglu, F. (2008). Numerical analysis of Taeniatherum caput-medusae collected throughout Turkey. Report from project at Middle East Technical University (METU), Ankara, Turkey.

Fazlioglu, F. (2011). A phenetics for infrageneric grouping of Limonium Mill. genus (Plumbaginaceae) in Turkey. Master thesis, Middle East Technical University (METU), Ankara, Turkey.

Fazlioglu, F., Al-Namazi, A., & Bonser, S. P. (2016). Reproductive efficiency and shade avoidance plasticity under simulated competition. *Ecology and Evolution, 6*(14), 4947–4957. https://doi.org/10.1002/ece3.2254

Fazlioglu, F., & Bonser, S. P. (2016). Phenotypic plasticity and specialization in clonal versus non-clonal plants: A data synthesis. *Acta Oecologica, 77*, 193–200. https://doi.org/10.1016/j.actao.2016.10.012

Fazlioglu, F., Wan, J. S. H., & Bonser, S. P. (2016). Testing specialization hypothesis on a stress gradient. *Austral Ecology, 42*(1), 40–47. https://doi.org/10.1111/aec.12399

Fazlioglu, F., Wan, J. S. H., & Bonser, S. P. (2018). Phenotypic plasticity and specialization along an altitudinal gradient in *Trifolium repens*. *Turkish Journal of Botany, 42*(4), 440–447. https://doi.org/10.3906/bot-1711-21

Feng, Y., & van Kleunen, M. (2014). Responses to shading of naturalized and non-naturalized exotic woody species. *Annals of Botany, 114*(5), 981–989. https://doi.org/10.1093/aob/mcu163

Findurová, A. (2018). *Variability of leaf traits SLA and LDMC in selected species of the Czech flora* (Master thesis). Brno, Czech Republic: Masaryk University.

Finegan, B., Peña-Claros, M., de Oliveira, A., Ascarrunz, N., Bret-Harte, M. S., Carreño-Rocabado, G., ... Poorter, L. (2015). Does functional trait diversity predict above-ground biomass and productivity of tropical forests? Testing three alternative hypotheses. *Journal of Ecology, 103*, 191–201.

Firn, J., McGree, J. M., Harvey, E., Flores-Moreno, H., Schütz, M., Buckley, Y. M., ... Risch, A. C. (2019). Leaf nutrients, not specific leaf
area, are consistent indicators of elevated nutrient inputs. *Nature Ecology & Evolution*, 3(3), 400–406. https://doi.org/10.1038/s41559-018-0790-1

Flowers, T. J., Santos, J., Jahns, M., Warburton, B., & Reed, P. (2017). *eHALOPH - Halophytes database* (version 3.11) accessed 2017. Retrieved from http://www.sussex.ac.uk/affiliates/halophytes

Fonseca, C. R., Overton, J. M., Collins, B., & Westoby, M. (2000). Shifts in trait-combinations along rainfall and phosphorus gradients. *Journal of Ecology*, 88(6), 964–977. https://doi.org/10.1046/j.1365-2745.2000.00506.x

Forgiarini, C., Souza, A. F., Longhi, S. J., & Oliveira, J. M. (2014). In the lack of extreme pioneers: Trait relationships and ecological strategies of 66 subtropical tree species. *Journal of Plant Ecology*, 8(4), 359–367. https://doi.org/10.1093/jpe/rtu028

Fortunel, C., McFadden, I. R., Valencia, R., & Kraft, N. J. B. (2019). Neither species geographic range size, climatic envelope, nor intra-specific leaf trait variability capture habitat specialization in a hyper-diverse Amazonian forest. *Biotropica*, 51(3), 304–310. https://doi.org/10.1111/btp.12643

Frenette-Dussault, C., Shipley, B., Léger, J.-F., Meziane, D., & Hingrat, Y. (2011). Functional structure of an arid steppe plant community reveals similarities with Grime's C-S-R theory. *Journal of Vegetation Science*, 23(2), 208–222. https://doi.org/10.1111/j.1654-1103.2011.01350.x

Freschet, G. T., Cornelissen, J. H. C., vanLogtestijn, R. S. P., & Aerts, R. (2010). Evidence of the 'plant economics spectrum' in a sub-arctic flora. *Journal of Ecology*, 98(2), 362–373. https://doi.org/10.1111/j.1365-2745.2009.01615.x

Freschet, G. T., Kichenin, E., & Wardle, D. A. (2015). Explaining within-community variation in plant biomass allocation: A balance between organ biomass and morphology above vs below ground? *Journal of Vegetation Science*, 26(3), 431–440. https://doi.org/10.1111/jvs.12259

Freschet, G. T., Swart, E. M., & Cornelissen, J. H. C. (2015). Integrated plant phenotypic responses to contrasting above- and below-ground resources: Key roles of specific leaf area and root mass fraction. *New Phytologist*, 206(4), 1247–1260. https://doi.org/10.1111/nph.13352

Freschet, G. T., Violle, C., Bourget, M. Y., Scherer-Lorenzen, M., & Fort, F. (2018). Allocation, morphology, physiology, architecture: The multiple facets of plant above- and below-ground responses to resource stress. *New Phytologist*, 219(4), 1338–1352. https://doi.org/10.1111/nph.15225

Fry, E. L., Power, S. A., & Manning, P. (2013). Trait-based classification and manipulation of plant functional groups for biodiversity-ecosystem function experiments. *Journal of Vegetation Science*, 25(1), 248–261. https://doi.org/10.1111/jvs.12068

Fyllas, N. M., Christopoulou, A., Galanidis, A., Michelaki, C. Z., Giannakopoulos, C., Dimitrakopoulos, P. G., ... Gloor, M. (2017). Predicting species dominance shifts across elevation gradients in mountain forests in Greece under a warmer and drier climate. *Regional Environmental Change*, 17(4), 1165–1177. https://doi.org/10.1007/s10113-016-1093-1

Fyllas, N. M., Patiño, S., Baker, T. R., Bielefeld Nardoto, G., Martinelli, L. A., Quesada, C. A., ... Lloyd, J. (2009). Basin-wide variations in foliar properties of Amazonian forest: Phylogeny, soils and climate. *Biogeosciences*, 6(11), 2677–2708. https://doi.org/10.5194/bg-6-2677-2009

Gachet, S., Véla, E., & Tatoni, T. (2005). BASECO: A floristic and ecological database of Mediterranean French flora. *Biodiversity and Conservation*, 14(4), 1023–1034. https://doi.org/10.1007/s10531-004-8411-5

Gallagher, R. V., & Leishman, M. R. (2012). A global analysis of trait variation and evolution in climbing plants. *Journal of Biogeography*, 39(10), 1757–1771. https://doi.org/10.1111/j.1365-2699.2012.02773.x

Gallagher, R. V., Leishman, M. R., & Moles, A. T. (2011). Traits and ecological strategies of Australian tropical and temperate climbing plants. *Journal of Biogeography*, 38(5), 828–839. https://doi.org/10.1111/j.1365-2699.2010.02455.x

García-Palacios, P., Maestre, F. T., Kattge, J., & Wall, D. H. (2013). Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes. *Ecology Letters*, 16(8), 1045–1053. https://doi.org/10.1111/ele.12137

Garnier, E., Lavorel, S., Ansquer, P., Castro, H., Cruz, P., Dolezal, J., ... Zarovali, M. P. (2007). Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: A standardized methodology and lessons from an application to 11 European sites. *Annals of Botany*, 99(5), 967–985. https://doi.org/10.1093/aob/mcl215

Giarrizzo, E., Burrascano, S., Chiti, T., de Bello, F., Lepš, J., Zavattero, L., & Blasi, C. (2016). Re-visiting historical semi-natural grasslands in the Apennines to assess patterns of changes in species composition and functional traits. *Applied Vegetation Science*, 20(2), 247–258. https://doi.org/10.1111 avsc.12288

Giroldo, A. (2016). *Pequenas plantas, grandes estrategias: adaptacões e sobrevivência no Cerrado*. PhD thesis, University of Brasília, Brasil.

Givnish, T. J., Montgomery, R. A., & Goldstein, G. (2004). Adaptive radiation of photosynthetic physiology in the Hawaiian lobelias: Light regimes, static light responses, and whole-plant compensation points. *American Journal of Botany*, 91(2), 228–246. https://doi.org/10.3732/ajb.91.2.228

Golovko, T., Dymova, O., Yatsko, Y., & Tabalenkova, G. (2011). Photosynthetic pigments apparatus in the northern plants. In M. Pessarakli (Ed.), *Handbook of plant and crop stress* (3rd ed., pp. 391–405). New York, NY: Marcel Dekker.

Gonzalez-Akre, E., McShea, W., Bourg, N., & Anderson-Teixeira, K. (2015). *Leaf traits data (SLA) for 56 woody species at the Smithsonian Conservation Biology Institute-ForestGEO Forest Dynamic Plot*. *Front Royal, Virginia*. USA. [Data set]. Version 1.0. Retrieved from http://www try-db.org

Granda, E., Baumgarten, F., Gessler, A., Gil-Pelegrin, E., Peguero-Pina, J. J., Sancho-Knapik, D. E., ... Resco de Dios, V. (2020). Day-length regulates seasonal patterns of stomatal conductance in *Quercus* species. *Plant, Cell & Environment*. https://doi.org/10.1111/pce.13665

Gubsch, M., Buchmann, N., Schmid, B., Schulze, E.-D., Lipowsky, A., & Roscher, C. (2011). Differential effects of plant diversity on functional trait variation of grass species. *Annals of Botany*, 107(1), 157–169. https://doi.org/10.1093/aob/mcq220

Guerin, G. R., Wen, H., & Lowe, A. J. (2012). Leaf morphology shift linked to climate change. *Biology Letters*, 8(5), 882–886. https://doi.org/10.1098/rsbl.2012.0458

Gutiérrez, A. G., & Huth, A. (2012). Successional stages of primary temperate rainforests of Chiloé Island, Chile. *Perspectives in Plant Ecology, Evolution and Systematics*, 14(4), 243–256. https://doi.org/10.1016/j.pees.2012.01.004

Guy, A. L., Mischkolz, J. M., & Lamb, E. G. (2013). Limited effects of simulated acidic deposition on seedling survivorship and root morphology of endemic plant taxa of the Athabasca Sand Dunes in well-watered greenhouse trials. *Botany-Botanique*, 91(3), 176–181. https://doi.org/10.1139/cjb-2012-0162

Han, W., Fang, J., Guo, D., & Zhang, Y. (2005). Leaf nitrogen and phosphorus stoichiometry across 753 terrestrial plant species in China. *New Phytologist*, 168(2), 377–385. https://doi.org/10.1111/j.1469-8137.2005.01530.x

Han, W. X., Chen, Y. H., Zhao, F. J., Tang, L. Y., Jiang, R. F., & Zhang, F. S. (2012). Floral, climatic and soil pH controls on leaf ash content in China's terrestrial plants. *Global Ecology and Biogeography*, 21(3), 376–382. https://doi.org/10.1111/j.1466-8238.2011.00677.x

Hayes, F. J., Buchanan, S. W., Coleman, B., Gordon, A. M., Reich, P. B., Thevathasan, N. V., ... Martin, A. R. (2018). Intraspecific variation in soy across the leaf economics spectrum. *Annals of Botany*, 123(1), 107–120. https://doi.org/10.1093/aob/mcy147

He, P., Wright, I. J., Zhu, S., Onoda, Y., Liu, H., Li, R., ... Ye, Q. (2019). Leaf mechanical strength and photosynthetic capacity vary independently
across 57 subtropical forest species with contrasting light requirements. *New Phytologist*, 223(2), 607–618. https://doi.org/10.1111/nph.15803

He, T., Fowler, W. M., & Causley, C. L. (2015). High nutrient-use efficiency during early seedling growth in diverse *Grevillea* species (Proteaceae). *Scientific Reports*, 5. https://doi.org/10.1038/srep17132

He, T., Lamont, B. B., & Downs, K. S. (2011). Banksias born to burn. *New Phytologist*, 191, 184–196. https://doi.org/10.1111/j.1469-8137.2011.03663.x

He, T., Pausas, J. P., Belcher, C. M., Schwilk, D. W., & Lamont, B. B. (2012). Fire-adapted traits of *Pinus* arose in the fiery Cretaceous. *New Phytologist*, 194, 751–759. https://doi.org/10.1111/j.1469-8137.2012.04079.x

Heberling, J. M., Cassidy, S. T., Fridley, J. D., & Kalisz, S. (2019). Carbon gain phenologies of spring-flowering perennials in a deciduous forest indicate a novel niche for a widespread invader. *New Phytologist*, 221(2), 778–788. https://doi.org/10.1111/nph.15404

Heberling, J. M., & Mason, N. W. H. (2018). Are endemics functionally distinct? Leaf traits of native and exotic woody species in a New Zealand forest. *PLoS ONE*, 13(5), e0196746. https://doi.org/10.1371/journal.pone.0196746

Herz, K., Dietz, S., Haider, S., Jandt, U., Scheel, D., & Bruelheide, H. (2017). Drivers of intraspecific trait variation of grass and forb species in German meadows and pastures. *Journal of Vegetation Science*, 28(4), 705–716. https://doi.org/10.1111/jvs.12534

Hickler, T. (1999). *Plant functional types and community characteristics along environmental gradients on Öland's Great Alvar (Sweden)*. Master's thesis, University of Lund, Sweden.

Hietz, P., Rosner, S., Hietz-Seifert, U., & Wright, S. J. (2016). Wood traits related to size and life history of trees in a Panamanian rainforest. *New Phytologist*, 213(1), 170–180. https://doi.org/10.1111/nph.14123

Hill, M. O., Preston, C. D., & Roy, D. B. (2004). *PLANTATT – Attributes of British and Irish plants: Status, size, life history, geography and habitats*. Huntingdon, UK: Centre for Ecology and Hydrology.

Hipp, A. L., Glasenhardt, M.-C., Bowles, M. L., Garner, M., Scharenbroch, B. C., Williams, E. W., ... Larkin, D. J. (2018). Effects of phylogenetic diversity and phylogenetic identity in a restoration ecology experiment. In R. Scherson & D. Faith (Eds.), *Phylogenetic diversity* (pp. 189–210). Cham: Springer.

Hogan, J. A., Valverde-Barrantes, O. J., Ding, Q., Xu, H., & Baraloto, C. (2019). *Intraspecific root and leaf trait variation with tropical forest successional status: Consequences for community-weighted patterns*. Retrieved from http://dx.doi.org/10.1101/611640

Hou, E., Chen, C., McGroddy, M. E., & Wen, D. (2012). Nutrient limitation on ecosystem productivity and processes of mature and old-growth subtropical forests in China. *PLoS ONE*, 7(12), e52071. https://doi.org/10.1371/journal.pone.0052071

Hough-Snee, N., Nackley, L. L., Kim, S.-H., & Ewing, K. (2015). Does plant performance under stress explain divergent life history strategies? The effects of flooding and nutrient stress on two wetland sedges. *Aquatic Botany*, 120, 151–159. https://doi.org/10.1016/j.aquabot.2014.03.001

Isaac, M. E., Martin, A. R., de Melo Virginio Filho, E., Rapidel, B., Roupsard, O., & Van den Meersche, K. (2017). Intraspecific trait variation and coordination: Root and leaf economics spectra in coffee across environmental gradients. *Frontiers in Plant Science*, 8(1196), https://doi.org/10.3389/fpls.2017.01196

Jager, M. M., Richardson, S. J., Bellingham, P. J., Clearwater, M. J., & Laughlin, D. C. (2015). Soil fertility induces coordinated responses of multiple independent functional traits. *Journal of Ecology*, 103(2), 374–385. https://doi.org/10.1111/1365-2745.12366

Joseph, G. S., Seymour, C. L., Cumming, G. S., Cumming, D. H. M., & Mahlangu, Z. (2014). Termite mounds increase functional diversity of woody plants in African savannas. *Ecosystems*, 17(5), 808–819. https://doi.org/10.1007/s10021-014-9761-9

Kaplan, Z., J. Danihelka, J. Chrtek, J. Kirschner, K. Kubát, M. Štech, & J. Štěpánek (Eds.) (2019). *Klíč ke květeně České republiky [Key to the flora of the Czech Republic]* (2nd ed.). Academia, Praha: Czech Republic.

Kapralov, M. V., Smith, J. A. C., & Filatov, D. A. (2012). Rubisco evolution in C4 eudicots: An analysis of Amaranthaceae Sensu Lato. *PLoS ONE*, 7(12), e52974. https://doi.org/10.1371/journal.pone.0052974

Kattenborn, T., Fassnacht, F. E., & Schmidtlein, S. (2018). Differentiating plant functional types using reflectance: Which traits make the difference? *Remote Sensing in Ecology and Conservation*, 5(1), 5–19. https://doi.org/10.1002/rse.2.86

Kattenborn, T., & Schmidtlein, S. (2019). Radiative transfer modelling reveals why canopy reflectance follows function. *Scientific Reports*, 9(1). https://doi.org/10.1038/s41598-019-43011-1

Kattge, J., Knorr, W., Raddatz, T., & Wirth, C. (2009). Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. *Global Change Biology*, 15(4), 976–991. https://doi.org/10.1111/j.1365-2486.2008.01744.x

Kavelenova, L. M., Rozno, S. A., Kireyeva, Y. V., & Smirnov, V. V. (2007). K strukturno-funktsional'nym osobennostyam list'yev drevesnykh rasteniya v nasazhdениyakh lesostepi. *Byulleten Samarskaya Luka*, 16: 3(21), 568–574.

Kearsley, E., Verbeeck, H., Hufkens, K., Van de Perre, F., Doetterl, S., Baert, G., ... Huygens, D. (2016). Functional community structure of African monodominant *Gilbertiodendron dewevrei* forest influenced by local environmental filtering. *Ecology and Evolution*, 7(1), 295–304. https://doi.org/10.1002/ece3.2589

Kempel, A., Chrobock, T., Fischer, M., Rohr, R. P., & van Kleunen, M. (2013). Determinants of plant establishment success in a multispecies introduction experiment with native and alien species. *Proceedings of the National Academy of Sciences of the United States of America*, 110(31), 12727–12732. https://doi.org/10.1073/pnas.1300481110

Kerkhoff, A. J., Fagan, W. F., Elser, J. J., & Enquist, B. J. (2006). Phylogenetic and growth form variation in the scaling of nitrogen and phosphorus in the seed plants. *The American Naturalist*, 168(4), E103–E122. https://doi.org/10.1086/507879

Khalil, M. I., Gibson, D. J., Baer, S. G., & Willand, J. E. (2018). Functional diversity is more sensitive to biotic filters than phylogenetic diversity during community assembly. *Ecosphere*, 9(3), e02164. https://doi.org/10.1002/ecs2.2164

Kichenin, E., Wardle, D. A., Peltzer, D. A., Morse, C. W., & Freschet, G. T. (2013). Contrasting effects of plant inter- and intraspecific variation on community-level trait measures along an environmental gradient. *Functional Ecology*, 27(5), 1254–1261. https://doi.org/10.1111/1365-2435.12116

Kirkup, D., Malcolm, P., Christian, G., & Paton, A. (2005). Towards a digital African flora. *Taxon*, 54(2), 457–466. https://doi.org/10.2307/25065373

Kisel, Y., Moreno-Letelier, A. C., Bogarín, D., Powell, M. P., Chase, M. W., & Barraclough, T. G. (2012). Testing the link between population genetic differentiation and clade diversification in Costa Rican orchids. *Evolution*, 66(10), 3035–3052. https://doi.org/10.1111/j.1558-5646.2012.01663.x

Kissling, W. D., Balslev, H., Baker, W. J., Dransfield, J., Göldel, B., Lim, J. Y., ... Svenning, J.-C. (2019). PalmTraits 1.0, a species-level functional trait database of palms worldwide. *Scientific Data*, 6(1), 178. https://doi.org/10.1038/s41597-019-0189-0

Klein, T., Di Matteo, G., Rotenberg, E., Cohen, S., & Yakir, D. (2012). Differential ecophysiological response of a major Mediterranean pine species across a climatic gradient. *Tree Physiology*, 33(1), 26–36. https://doi.org/10.1093/treephys/tps116

Klimešová, J., & de Bello, F. (2009). CLO-PLA: The database of clonal and bud bank traits of Central European flora. *Journal of Vegetation Science*, 20(3), 511–516. https://doi.org/10.1111/j.1654-1103.2009.01050.x

Knauer, J., Zaehle, S., Medlyn, B. E., Reichstein, M., Williams, C. A., Migliavacca, M., ... Linderson, M. L. (2017). Towards physiologically meaningful
water-use efficiency estimates from eddy covariance data. *Global Change Biology*, 24(2), 694–710. https://doi.org/10.1111/gcb.13893

Koele, N., Dickie, I. A., Oleksyn, J., Richardson, S. J., & Reich, P. B. (2012). No globally consistent effect of ectomycorrhizal status on foliar traits. *New Phytologist*, 196(3), 845–852. https://doi.org/10.1111/j.1469-8137.2012.04297.x

Koike, F. (2001). Plant traits as predictors of woody species dominance in climax forest communities. *Journal of Vegetation Science*, 12(3), 327–336. https://doi.org/10.2307/3236846

Komac, B., Pladevall, C., Domènech, M., & Fanlo, R. (2014). Functional diversity and grazing intensity in sub-alpine and alpine grasslands in Andorra. *Applied Vegetation Science*, 18(1), 75–85. https://doi.org/10.1111/avsc.12119

Kraft, N. J. B., Valencia, R., & Ackerly, D. D. (2008). Functional traits and niche-based tree community assembly in an Amazonian forest. *Science*, 322(5901), 580–582. https://doi.org/10.1126/science.1160662

Kumarathunge, D. P., Medlyn, B. E., Drake, J. E., Tjoelker, M. G., Aspinwall, M. J., Battaglia, M., ... Way, D. A. (2019). Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale. *New Phytologist*, 222(2), 768–784. https://doi.org/10.1111/nph.15668

Kuppler, J., Höfers, M. K., Trutschnig, W., Bathke, A. C., Eiben, J. A., Daehler, C. C., & Junker, R. R. (2017). Exotic flower visitors exploit large floral trait spaces resulting in asymmetric resource partitioning with native visitors. *Functional Ecology*, 31(12), 2244–2254. https://doi.org/10.1111/1365-2435.12932

Kurokawa, H., & Nakashizuka, T. (2008). Leaf herbivory and decomposability in a Malaysian tropical rain forest. *Ecology*, 89(9), 2645–2656. https://doi.org/10.1890/07-1352.1

La Pierre, K. J., & Smith, M. D. (2014). Functional trait expression of grassland species shift with short- and long-term nutrient additions. *Plant Ecology*, 216(2), 307–318. https://doi.org/10.1007/s11258-014-0438-4

Laughlin, D. C., Fulé, P. Z., Huffman, D. W., Crouse, J., & Laliberté, E. (2011). Climatic constraints on trait-based forest assembly. *Journal of Ecology*, 99(6), 1489–1499. https://doi.org/10.1111/j.1365-2745.2011.01885.x

Lavergne, S., & Molofsky, J. (2007). Increased genetic variation and evolutionary potential drive the success of an invasive grass. *Proceedings of the National Academy of Sciences of the United States of America*, 104(10), 3883–3888. https://doi.org/10.1073/pnas.0607324104

Lavorel, S., Grigulis, K., Lamarque, P., Colace, M.-P., Garden, D., Girel, J., ... Douzet, R. (2010). Using plant functional traits to understand the landscape distribution of multiple ecosystem services. *Journal of Ecology*, 99(1), 135–147. https://doi.org/10.1111/j.1365-2745.2010.01753.x

Lens, F., Endress, M. E., Baas, P., Jansen, S., & Smets, E. (2008). Wood anatomy of Rauvolfioideae (Apocynaceae): A search for meaningful non-DNA characters at the tribal level. *American Journal of Botany*, 95, 1199–1215. https://doi.org/10.3732/ajb.0800159

Lens, F., Gasson, P., Smets, E., & Jansen, S. (2003). Comparative wood anatomy of epacrids (Styphelioideae, Ericaceae s.l.). *Annals of Botany*, 91, 835–857. https://doi.org/10.1093/aob/mcg089

Lens, F., Sperry, J. S., Christman, M. A., Choat, B., Rabaey, D., & Jansen, S. (2011). Testing hypotheses that link wood anatomy to cavitation resistance and hydraulic conductivity in the genus *Acer*. *New Phytologist*, 190, 709–723. https://doi.org/10.1111/j.1469-8137.2010.03518.x

Letts, B., Lamb, E. G., Mischkolz, J. M., & Romo, J. T. (2015). Litter accumulation drives grassland plant community composition and functional diversity via leaf traits. *Plant Ecology*, 216, 357–370. https://doi.org/10.1007/s11258-014-0436-6

Lhotsky, B., Csecsérits, A., Kovács, B., & Botta-Dukát, Z. (2016). New plant trait records of the Hungarian flora. *Acta Botanica Hungarica*, 58(3-4), 397–400. https://doi.org/10.1556/abot.58.2016.3-4.8

Li, R., Zhu, S., Chen, H. Y. H., John, R., Zhou, G., Zhang, D., ... Ye, Q. (2015). Are functional traits a good predictor of global change impacts on tree species abundance dynamics in a subtropical forest? *Ecology Letters*, 18(11), 1181–1189. https://doi.org/10.1111/ele.12497

Li, X., Nie, Y., Song, X., Zhang, R., & Wang, G. (2011). Patterns of species diversity and functional diversity along a south-to north-facing slope in a sub-alpine meadow. *Community Ecology*, 12(2), 179–187.

Li, Y., & Shipley, B. (2018). Community divergence and convergence along experimental gradients of stress and disturbance. *Ecology*, 99(4), 775–781. https://doi.org/10.1002/ecy.2162

Liebergesell, M., Reu, B., Stahl, U., Freiberg, M., Welk, E., Kattge, J., ... Wirth, C. (2016). Functional resilience against climate-driven extinctions – Comparing the functional diversity of European and North American tree floras. *PLoS ONE*, 11(2), e0148607. https://doi.org/10.1371/journal.pone.0148607

Lin, Y.-S., Medlyn, B. E., Duursma, R. A., Prentice, I. C., Wang, H., Baig, S., ... Wingate, L. (2015). Optimal stomatal behaviour around the world. *Nature Climate Change*, 5(5), 459–464. https://doi.org/10.1038/nclimate2550

Lipowsky, A., Roscher, C., Schumacher, J., Michalski, S., Gubsch, M., Buchmann, N., ... Schmid, B. (2015). Plasticity of functional traits of forb species in response to biodiversity. *Perspectives in Plant Ecology, Evolution and Systematics*, 17, 66–77. https://doi.org/10.1016/j.ppees.2014.11.003

Lohbeck, M., Poorter, L., Paz, H., Pla, L., van Breugel, M., Martínez-Ramos, M., & Bongers, F. (2012). Functional diversity changes during tropical forest succession. *Perspectives in Plant Ecology, Evolution and Systematics*, 14(2), 89–96. https://doi.org/10.1016/j.ppees.2011.10.002

Louault, F., Pillar, V. D., Aufrère, J., Garnier, E., & Soussana, J. F. (2005). Plant traits and functional types in response to reduced disturbance in a semi-natural grassland. *Journal of Vegetation Science*, 16(2), 151. https://doi.org/10.1658/1100-9233(2005)016[0151:ptafti]2.0.co;2

Lukeš, P., Stenberg, P., Rautiainen, M., Möttus, M., & Vanhatalo, K. M. (2013). Optical properties of leaves and needles for boreal tree species in Europe. *Remote Sensing Letters*, 4(7), 667–676. https://doi.org/10.1080/2150704x.2013.782112

Lusk, C. H. (2019). Leaf functional trait variation in a humid temperate forest, and relationships with juvenile tree light requirements. *PeerJ*, 7, e6855. https://doi.org/10.7717/peerj.6855

Lusk, C. H., Kaneko, T., Grierson, E., & Clearwater, M. (2013). Correlates of tree species sorting along a temperature gradient in New Zealand rain forests: Seedling functional traits, growth and shade tolerance. *Journal of Ecology*, 101(6), 1531–1541. https://doi.org/10.1111/1365-2745.12152

Maire, V., Wright, I. J., Prentice, I. C., Batjes, N. H., Bhaskar, R., van Bodegom, P. M., ... Santiago, L. S. (2015). Global effects of soil and climate on leaf photosynthetic traits and rates. *Global Ecology and Biogeography*, 24(6), 706–717. https://doi.org/10.1111/geb.12296

Malhado, A. C. M., Malhi, Y., Whittaker, R. J., Ladle, R. J., terSteege, H., Fabré, N. N., ... Malhado, C. H. M. (2012). Drip-tips are associated with intensity of precipitation in the Amazon rain forest. *Biotropica*, 44(6), 728–737. https://doi.org/10.1111/j.1744-7429.2012.00868.x

Malhado, A. C. M., Malhi, Y., Whittaker, R. J., Ladle, R. J., terSteege, H., Phillips, O. L., ... Laurance, W. F. (2009). Spatial trends in leaf size of Amazonian rainforest trees. *Biogeosciences*, 6(8), 1563–1576. https://doi.org/10.5194/bg-6-1563-2009

Malhado, A. C. M., Whittaker, R. J., Malhi, Y., Ladle, R. J., terSteege, H., Butt, N., ... Ramírez, A. H., (2009). Spatial distribution and functional significance of leaf lamina shape in Amazonian forest trees. *Biogeosciences*, 6(8), 1577–1590. https://doi.org/10.5194/bg-6-1577-2009

Malhado, A. C. M., Whittaker, R. J., Malhi, Y., Ladle, R. J., Ter Steege, H., Phillips, O., ... Ramírez-Angulo, H. (2010). Are compound leaves an adaptation to seasonal drought or to rapid growth? Evidence from the
Amazon rain forest. *Global Ecology and Biogeography*, 19(6), 852–862.
https://doi.org/10.1111/j.1466-8238.2010.00567.x

Manning, P., Newington, J. E., Robson, H. R., Saunders, M., Eggers, T.,
Bradford, M. A., ... Rees, M. (2006). Decoupling the direct and indi-
rect effects of nitrogen deposition on ecosystem function. *Ecology
Letters*, 9(9), 1015–1024. https://doi.org/10.1111/j.1461-0248.2006.
00959.x

Manzoni, S., Vico, G., Porporato, A., & Katul, G. (2013). Biological con-
straints on water transport in the soil–plant–atmosphere system.
*Advances in Water Resources*, 51, 292–304. https://doi.org/10.1016/
j.adwatres.2012.03.016

Martin, A. R., Doraisami, M., & Thomas, S. C. (2018). Global patterns in wood carbon concentration across the world's trees and forests. *Nature Geoscience*, 11(12), 915–920. https://doi.org/10.1038/
s41561-018-0246-x

Martin, A. R., Hayes, F. J., Borden, K. A., Buchanan, S. W., Gordon, A. M., Isaac, M. E., & Thevathasan, N. V. (2019). Integrating nitrogen fixing structures into above- and belowground functional trait spectra in soy (Glycine max). *Plant and Soil*, 440(1-2), 53–69. https://doi.org/10.1007/s11104-019-04058-1

Martin, A. R., Rapidel, B., Roupsard, O., Van den Meersche, K., de Melo
Virginio Filho, E., Barrios, M., & Isaac, M. E. (2017). Intraspecific
trait variation across multiple scales: The leaf economics spec-
trum in coffee. *Functional Ecology*, 31(3), 604–612. https://doi.
org/10.1111/1365-2435.12790

Martínez-Garza, C., Bongers, F., & Poorter, L. (2013). Are functional traits good predictors of species performance in restoration plantings in tropical abandoned pastures? *Forest Ecology and Management*, 303, 35–45. https://doi.org/10.1016/j.foreco.2013.03.046

Messier, J., Violle, C., Enquist, B. J., Lechowicz, M. J., & B. McGill, J.
(2018). Similarities and differences in intrapopulation trait correla-
tions of co-occurring tree species: Consistent water use relationships
amidst widely different correlation patterns. *American Journal of
Botany*, 105(9), 1–14. https://doi.org/10.1002/ajb2.1146

McCarthy, J. K., Dwyer, J. M., & Mokany, K. (2019). A regional-scale assessment of using metabolic scaling theory to predict ecosystem properties. *Proceedings of the Royal Society B: Biological Sciences*, 286. https://doi.org/10.1098/rspb.2019.2221

McFadden, I. R., Bartlett, M. K., Wiegand, T., Turner, B. L., Sack, L., Valencia, R., & Kraft, N. J. B. (2019). Disentangling the functional trait correlates of spatial aggregation in tropical forest trees. *Ecology*, 100(3), e02591. https://doi.org/10.1002/ecy.2591

Medeiros, J. S., Burns, J. H., Nicholson, J., Rogers, L., & Valverde-Barrantes, O. (2017). Decoupled leaf and root carbon economics is a key component in the ecological diversity and evolutionary divergence of deciduous and evergreen lineages of genus *Rhododendron*. *American Journal of Botany*, 104(6), 803–816. https://doi.org/10.3732/ajb.1700051

Medlyn, B. E., Badeck, F. W., De Pury, D. G. G., Barton, C. V. M., Broadmeadow, M., Ceulemans, R., ... Jstbid, P. G. (1999). Effects of elevated [CO₂] on photosynthesis in European forest species: A meta-analysis of model parameters. *Plant, Cell & Environment*, 22(12), 1475–1495. https://doi.org/10.1046/j.1365-3040.1999.00523.x

Meir, P., Kruijt, B., Broadmeadow, M., Barbosa, E., Kull, O., Carswell, F., ... Jarvis, P. G. (2002). Acclimation of photosynthetic capacity to irradiance in tree canopies in relation to leaf nitrogen concentration and leaf mass per unit area. *Plant, Cell and Environment*, 25(3), 343–357. https://doi.org/10.1046/j.0016-8025.2001.00811.x

Mencuccini, M. (2003). The ecological significance of long-distance water transport: Short-term regulation, long-term acclimation and the hydraulic costs of stature across plant life forms. *Plant, Cell and Environment*, 26(1), 163–182. https://doi.org/10.1046/j.1365-3040.2003.00991.x

Messier, J., McGill, B. J., & Lechowicz, M. J. (2010). How do traits vary across ecological scales? A case for trait-based ecology. *Ecology Letters*, 13(7), 838–848. https://doi.org/10.1111/j.1461-0248.2010.01476.x

Messier, J., Violle, C., Enquist, B. J., Lechowicz, M. J., & McGill, B. J. (2018). Similarities and differences in intrapopulation trait correlations of co-occurring tree species: Consistent water use relationships amidst widely different correlation patterns. *American Journal of Botany*, 105(9), 1–14. https://doi.org/10.1002/ajb2.1146

Michaletz, S. T., & Johnson, E. A. (2006). A heat transfer model of crown scorch in forest fires. *Canadian Journal of Forest Research*, 36(11), 2839–2851. https://doi.org/10.1139/x06-158

Michelaki, C., Fyllas, N. M., Galanidis, A., Aloupi, M., Evangelou, E., Arianoutsou, M., & Dimitrakopoulos, P. G. (2019). An integrated phenotypic trait-network in thermo-Mediterranean vegetation describing alternative, coexisting resource-use strategies. *Science of the Total Environment*, 672, 583–592. https://doi.org/10.1016/j.scitotenv.2019.04.030

Milla, R., & Reich, P. B. (2011). Multi-trait interactions, not phylogeny, fine-tune leaf size reduction with increasing altitude. *Annals of Botany*, 107(3), 455–465. https://doi.org/10.1093/aob/mcq261

Miller, J. E. D., Ives, A. R., Harrison, S. P., & Damschen, E. I. (2017). Early- and late-flowering guilds respond differently to landscape spatial structure. *Journal of Ecology*, 106(3), 1033–1045. https://doi.org/10.1111/1365-2745.12849

Minden, V., Deloy, A., Volkert, A. M., Leonhardt, S. D., & Pufal, G. (2017). Antibiotics impact plant traits, even at small concentrations. *AoB PLANTS*, 9(2). https://doi.org/10.1093/aobpla/plx010

Minden, V., & Gorschlüter, J. (2016). Comparison of native and non-native Impatiens species across experimental light and nutrient gradients. *Plant Ecology and Evolution*, 149(1), 59–72. https://doi.org/10.5091/plecevo.2016.1118

Minden, V., & Kleyer, M. (2011). Testing the effect-response framework: Key response and effect traits determining above-ground biomass of salt marshes. *Journal of Vegetation Science*, 22(3), 387–401. https://doi.org/10.1111/j.1654-1103.2011.01272.x

Minden, V., & Kleyer, M. (2014). Internal and external regulation of plant organ stoichiometry. *Plant Biology*, 16(5), 897–907. https://doi.org/10.1111/plb.12155

Minden, V., & Kleyer, M. (2015). Ecosystem multifunctionality of coastal marshes is determined by key plant traits. *Journal of Vegetation Science*, 26(4), 651–662. https://doi.org/10.1111/jvs.12276

Minden, V., & Olde Venterink, H. (2019). Plant traits and species interactions along gradients of N, P and K availabilities. *Functional Ecology*, 33(9), 1611–1626. https://doi.org/10.1111/1365-2435.13387

Minden, V., Schnetger, B., Pufal, G., & Leonhardt, S. D. (2018). Antibiotic-induced effects on scaling relationships and on plant element contents in herbs and grasses. *Ecology and Evolution*, 8(13), 6699–6713. https://doi.org/10.1002/ece3.4168

Moles, A. T., Ackerly, D. D., Webb, C. O., Tweddle, J. C., Dickie, J. B., Pitman, A. J., & Westoby, M. (2005). Factors that shape seed mass evolution. *Proceedings of the National Academy of Sciences of the United States of America*, 102(30), 10540–10544. https://doi.org/10.1073/pnas.0501473102

Moles, A. T., Warton, D. I., Warman, L., Swenson, N. G., Laffan, S. W., Zanne, A. E., ... Leishman, M. R. (2009). Global patterns in plant height. *Journal of Ecology*, 97, 923–932. https://doi.org/10.1111/j.1365-2745.2009.01526.x

Moravcová, L., Pyšek, P., Jarošík, V., Havlíčková, V., & Žákravský, P. (2010). Reproductive characteristics of neophytes in the Czech Republic: Traits of invasive and non-invasive species. *Preslia*, 82, 365–390.

Moretti, M., & Legg, C. (2009). Combining plant and animal traits to assess community functional responses to disturbance. *Ecography*, 32(2), 299–309. https://doi.org/10.1111/j.1600-0587.2008.05524.x

Mori, A. S., Shiono, T., Haraguchi, T. F., Ota, A. T., Koide, D., Ohgue, T., ... Gustafsson, L. (2015). Functional redundancy of multiple forest taxa along an elevational gradient: Predicting the consequences of non-random species loss. *Journal of Biogeography*, 42(8), 1383–1396. https://doi.org/10.1111/jbi.12514