• age of first flowering;
  • buoyancy;
  • clonal traits;
  • canopy height;
  • dispersal;
  • functional ecology;
  • plant life span;
  • plant functional traits;
  • seed weight;
  • seed number;
  • SLA;
  • soil seed bank


  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information
  • 1
    An international group of scientists has built an open internet data base of life-history traits of the Northwest European flora (the LEDA-Traitbase) that can be used as a data source for fundamental research on plant biodiversity and coexistence, macro-ecological patterns and plant functional responses.
  • 2
    The species-trait matrix comprises referenced information under the control of an editorial board, for ca. 3000 species of the Northwest European flora, combining existing information and additional measurements. The data base currently contains data on 26 plant traits that describe three key features of plant dynamics: persistence, regeneration and dispersal. The LEDA-Traitbase is freely available at
  • 3
    We present the structure of the data base and an overview of the trait information available.
  • 4
     Synthesis. The LEDA Traitbase is useful for large-scale analyses of functional responses of communities to environmental change, effects of community trait composition on ecosystem properties and patterns of rarity and invasiveness, as well as linkages between traits as expressions of fundamental trade-offs in plants.


  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information

The immense variation in plant form and life history has always intrigued botanists, plant geographers and ecologists. From the middle of the 19th century, interest in disentangling relationships between plant biological traits and the environment has steadily developed, resulting in a wealth of descriptions of plant morphology as adaptations to climate and soil factors (Du Rietz 1931). This interest evolved into compilations of biological knowledge for individual plant species (e.g. Kirchner et al. 1908–1936; the Biological Flora of the British Isles series published in this journal; Rabotnov 1974–1990 for Russia). A further step has been taken more recently, to build up digital data bases to synthesise information on plant traits. For instance, the GLOPNET data base (Wright et al. 2004) covers chemical, structural and physiological traits of leaves for a large number of species worldwide. Seed weight data are now available for > 104 species (Flynn et al. 2006), and other data bases offer bibliographic data for selected communities (e.g. APIRS for aquatic plants ( or provide taxonomic information together with some selected traits (e.g. USDA 2006).

Within Europe, knowledge of traits for individual species is growing fast, but information remains scattered over many sources, including dozens of different journals, large monographs and floras. The sources are available in various languages and distributed across many countries, collected and stored in different ways, and are not mutually integrated. Standardisation of trait definitions and measurements is often poor among species and studies. Trait data can also be retrieved from various data bases. However, currently accessible data bases are often restricted to certain regions, and cover only a limited number of species or traits.

A trans-national initiative has therefore aimed at designing and filling a species-trait matrix for the NW European flora that would be freely retrievable on the Worldwide Web (Knevel et al. 2003). The LEDA Traitbase (, which uses a European consolidated species list, is concerned with pooling existing data bases, compiling new information from published data and closing knowledge gaps through extensive new measurements across several NW European countries. It consists of a relational data base linking species with traits and reference information about data source, location, habitat and trait measurement protocol on three core sets of traits: (i) persistence (vegetative) traits such as leaf, stem and clonal growth characteristics; (ii) regeneration traits such as seed production, seed longevity and (iii) dispersal traits such as seed weight, dispersal vectors, floating capacity and vertical terminal velocity of propagules.

The general objectives of the LEDA project were announced in Knevel et al. (2003). The present article describes the scope and architecture of the data base, the methods of collecting data and the plant life-history traits that are covered by the LEDA Traitbase. Additionally, a brief overview of applications illustrates the value of trait data bases in general, and the LEDA Traitbase in particular, for research in functional ecology.

Framework of the LEDA Traitbase

  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information

traits in the leda traitbase

Traits covered by the LEDA Traitbase were selected according to two major criteria: (1) relevance for persistence, regeneration and dispersal as key functions for survival in patterned landscapes and (2) trait data available for the flora of Northwest-Europe, either in published sources or in unpublished data bases maintained by the project partners. As LEDA was designed as a compilation of data for a large number of species, we had to exclude traits for which only a small number of records for the Northwest European flora could be expected (e.g. relative growth rate or leaf life span). Table 1 shows an overview of the traits in the LEDA Traitbase together with associated functions and selected references, whereas Table 2 describes the categories or units of measurement, and actual number of species and records for the traits (version 1, 2007). More detailed information on the trait definitions is available in the Appendix S1 in Supporting Information.

Table 1.  Overview of the traits in the LEDA Traitbase, their functional significance and related publications
The LEDA traitsFunctional significance and related publications
 Canopy heightCompetitive ability (Westoby et al. 2002)
 Leaf distribution along the stem, branching, shoot growth formCompetitive ability (Barkman 1988)
 Leaf mass, leaf size, specific leaf area, leaf dry matter contentGrowth rate, competitive ability, stress tolerance (Westoby et al. 2002)
 Woodiness, stem specific densityGrowth rate, investment in supporting structure (Ryser 1996)
 Clonal growth organs, persistence of connection between parent and offspring shoots, number of offspring shoots per parent shoot per year, lateral spreadCompetitive ability, persistence, clonal integration, storage (De Kroon & Van Groenendael 1997; Klimeš & Klimešová 2000; Vesk & Westoby 2004)
 Bud bank – vertical distribution and seasonalityResponse to disturbance (Bellingham & Sparrow 2000; Klimešová & Klimeš 2007)
 Plant growth form, plant life span, age of first floweringResponse to disturbance, establishment, invasiveness (Raunkiaer 1937; Rejmánek & Richardson 1996)
 Seed number, seed shedding Response to disturbance, establishment, dispersal (Leishman 2001; Bruun & Poschlod 2006)
 Seed weight, size and shapeDispersal, establishment (Grime et al. 1988; Westoby et al. 2002)
 Seed bank longevityStorage effects, response to disturbance (Bekker et al. 1998)
 Morphology of dispersal unit, seed releasing heightWind dispersal, ecto- and endozoochorous dispersal (Van der Pijl 1972)
 Dispersal vectorsSpectra of dispersal vectors for plants (Bonn et al. 2000)
 Terminal velocityWind-dispersal (Tackenberg et al. 2003)
 Attachment capacity of the dispersal unit, digestion survival Ecto- and endozoochorous dispersal (Couvreur et al. 2004; Römermann et al. 2005)
 BuoyancyDispersal in running water (Danvind & Nilsson 1997)
Table 2.  Contents of the LEDA Traitbase, version 1
Trait name in data standardsNumber of speciesNumber of recordsCatalogue NumberCategory or unit(s) of measurement
Plant growth form2334 3154 1Phanerophyte
   9vascular semi-parasite
  10vascular parasite
Canopy height2893 4934 m
Plant life span2219 4293 1summer annuals
   2winter annuals
   3strict monocarpic biennials and poly-annuals
   4short-lived perennials (< 5 years)
   5medium- lived perennials (5–50 years)
   6long-lived perennials (> 50 years)
   7perennials without any further detailed information
Age of first flowering1521 2530 1< 1 year
   21 and 5 years
   3> 5 years
Leaf mass1665 4472 mg
Specific leaf area (SLA)2019 5941 mm2 mg−1
Leaf size2054 5590 mm2
Leaf dry matter content (LDMC)1735 3451 mg g−1
Woodiness & Stem specific density3152 5300 1woody
   1.1hard wood
   1.2soft wood
   3herbaceous (non-woody)
   g cm−3
Shoot growth form3118 5386 1lianas, climbers and scramblers
   2stem erect
   3stem ascending to prostrate
   4stem prostrate
   5free-floating plants
   6emergent, attached to the substrate
   7floating leaves, attached to the substrate
   8submerged, attached to the substrate
Branching2878 4055 1yes
Leaf distribution along the stem3491 5355 1rosette/tufted plant
   3leaves distributed regularly along the stem
   4shoot scarcely foliated
   5tufts and crowns, leaves concentrated as a rosette at the top of taller shoot or stem
Bud bank: vertical layers2442 6052 1no buds per shoot (not applicable)
   1.1no buds per shoot, below soil surface, < –10 cm
   1.2no buds per shoot, below soil surface, 0 < x < –10 cm
   1.3no buds per shoot at soil surface
   1.4no buds per shoot, above soil surface, 0 > x > 10 cm
   1.5no buds per shoot, above soil surface, > 10 cm
   21–10 buds per shoot
   2.11–10 buds per shoot, below soil surface, < –10 cm
   2.21–10 buds per shoot, below soil surface, 0 < x < –10 cm
   2.31–10 buds per shoot at soil surface
   2.41–10 buds per shoot, above soil surface, 0 > x > 10 cm
   2.51–10 buds per shoot, above soil surface, > 10 cm
   3> 10 buds per shoot
   3.1> 10 buds per shoot, below soil surface, < –10 cm
   3.2> 10 buds per shoot, below soil surface, 0 < x < –10 cm
   3.3> 10 buds per shoot at soil surface
   3.4> 10 buds per shoot, above soil surface, 0 > x > 10 cm
   3.5> 10 buds per shoot, above soil surface, > 10 cm
Bud bank – seasonality2468 6203 1seasonal
   1.1seasonal, above-ground
   1.2seasonal, below-ground
   2.1perennial, above-ground
   2.2perennial, below-ground
   3seasonal & potential
   3.1seasonal & potential, above-ground
   3.2seasonal & potential, below-ground
   4perennial & potential
   4.1perennial & potential, above-ground
   4.2perennial & potential, below-ground
Clonal growth organs1958 55401717 categories hierarchical classified according to their placement (above, at or below soil surface) and again subdivided to their origin (stem, root or leaf origin) (see Data Standards)
Life span of a shoot1737 4233 1monocyclic (1 year)
   2dicyclic or polycyclic (> 1 year)
Persistence of connection between parent and offspring shoots1834 4683 1< 1 year
   21–2 years
   3> 2 years
Number of offspring shoots per parent shoot per year1740 4263 1< 1 shoot/parent shoot/year
   21 shoot/parent shoot/year
   32–10 shoots/parent shoot/year
   4> 10 shoots/parent shoot/year
Lateral spread 555 1089 1< 0.01 m year−1
   20.01–0.25 m year−1
   30.25 m year−1
   4dispersable diaspores
Seed number1767 6165 number of seeds per ramet
Seed crop frequency 196  201 1more than once a year
   2once a year
   3once in 2 years
   4once in > 2 years
   5not applicable
Seed shedding1640 3331 month of the year (1–12)
Seed weight2025 7239 mg
Seed size2401 6578 length, width and height (mm)
Seed shape2401 6578 calculated from seed length, width and height (unitless, see Data Standards)
Soil seed bank type147944353 transient – short-term persistent – long-term persistent
Seed bank longevity index147944353 short-lived (0) – long-lived (1)
Soil seed bank density147944353 per m2
Diaspore type categories2082 4162 1vegetative dispersule
   2generative dispersule
Morphology of dispersal unit2082 4162 1nutrient containing structures
   5balloon structures
   5.1open balloons
   5.2closed balloons
   8flat appendages
   8.1small flat appendages
   8.2large flat appendages
   9elongated appendages
   9.1one short elongated appendage
   9.2two or more short elongated appendages
   9.3one long elongated appendage
   9.4two or more long elongated appendages
   9.1–4additional info: hooked structures
  10no appendages
  10.1seed with coarse surface, no appendages
  10.2seed with smooth surface, no appendages
  11other specialisations
Seed release height2586 3921 m
Terminal velocity seeds1328 2592 m s−1
Buoyancy 989 8081 number or % of floating seeds
Epizoochory 192  559 number or % of attached seeds
Endozoochory 149  179 number or % seeds that survived ingestion
Dispersal data obtained from literature295613920 14 dispersal type categories (see Data Standards), 32 dispersal vector categories (see Data Standards)
Habitat characteristics1401 1401 Categories referring to soil moisture, acidity, substrate, type, nutrient status. Water column acidity, alkalinity, and sediment redox potential for aquatic plants

Many trait data now available in LEDA for Northwest Europe had already attracted considerable attention in functional ecology (e.g. canopy height, seed number, seed mass, see Table 1) and are, at least in part, available elsewhere (e.g. Ellenberg et al. 1991 for life form; Flynn et al. 2006 for seed mass). For other traits, the LEDA Traitbase may be a unique source of data. For instance, seed bank longevity of many species was poorly known previously. The LEDA Project has improved this knowledge quite substantially from 21 071 records on 1189 species in the data base of Thompson et al. (1997) to 44 353 records covering 1787 species in total in the LEDA Traitbase (Table 2).

The LEDA Traitbase also includes data on clonal growth and dispersal traits that are rarely available elsewhere. The morphological traits characterising clonal growth serve as indicators for vegetative multiplication, persistence and vegetative regeneration subsequent to damage (Klimešet al. 1997; Klimešová & Martínková 2004). The data available in the LEDA Traitbase that are related to clonal growth encompass a categorisation of clonal growth organs, bud bank vertical distribution and seasonality (Klimešová & Klimeš 2007), life span of a shoot, persistence of the connections between parent and offspring shoots, lateral spread and number of offspring shoots produced per year and per parent shoot. These traits indicate speed of lateral spread, rate of clonal multiplication and duration of possibility of mutual support inside interconnected parts of a clone.

Seed dispersal influences many key aspects of the biology of plants, but is inherently hard to measure (Cain et al. 2000). Since every species may be dispersed through different vectors and to different distances, we have measured traits related to dispersal potential (Poschlod et al. 2005). Terminal velocity is a relevant predictor for wind dispersal potential. If vertical air velocity exceeds ‘terminal velocity’ then the seed can be uplifted and dispersed for larger distances (Nathan et al. 2002; Tackenberg et al. 2003). Combined with terminal velocity, seed release height is important in modelling wind dispersal. One key factor in epizoochory (dispersal by means of seeds attached to external parts of an animal) is the capacity of seeds to remain attached to fur, i.e. the attachment potential (Couvreur et al. 2004; Römermann et al. 2005). Other dispersal traits covered by the LEDA Traitbase include endozoochory (seeds dispersed after passing through the digestive tract of an animal), buoyancy (floating capacity), morphology of the dispersal unit and information about dispersal types as well as dispersal vectors of plants.

the leda geographical range and taxonomic core

The geographical range of the LEDA project (Fig. 1) roughly covers NW-Europe from the North Cape, Norway, to the Loire in France, and from the eastern borders of both Finland and Germany to the west coast of Ireland. Plant species present in Austria, Switzerland, Iceland, Poland, the Baltic States, Czech Republic, Slovakia and Hungary overlap with those in the core LEDA area by 50–80%, indicating the wide range of possible users of the Traitbase.


Figure 1. Geographical range covered by the data base. Dark grey: core regions; light grey: overlap > 50% with the national floras.

Download figure to PowerPoint

Selection of the 3000 priority vascular plant species for which we collected data was made according to the species frequencies in the core countries, i.e. UK, The Netherlands and Germany, disregarding alpine species and extremely rare species.

The taxonomic core of the LEDA Traitbase consists of one synonymised plant list at the species level, complete with authorities. The list was collated from the national plant lists available for the geographical range of the LEDA project (see Appendix S1). Species names and grouping of the species in higher taxa, however, cannot be considered as a stable reference system because taxonomies are subject to research and are changed frequently. When collating existing data bases and retrieving data from published literature different ‘taxonomic concepts’ (sensu Geoffroy & Berendsohn 2003) inevitably get merged. The resulting loss in data quality can, however, be expected have little impact on the LEDA trait data base for the following reasons: (1) Only a few taxonomic groups in the flora of Northwest Europe are still under profound revision, and (2) Floras were used, which are interconnected by the species checklist from the SynBioSys-Europe project (Schaminée et al. 2007).

methods of collecting data

In LEDA, the following trait data bases were collated: Ecoflora (Fitter & Peat 1994), Electronic Comparative Plant Ecology (Hodgson et al. 1995), Biological traits of vascular plants data base (Kleyer 1995), CLOPLA (Klimešová & Klimeš 2006), the Soil seed bank data base (Thompson et al. 1997), the Dutch Botanical Database (CBS 1997 with updates), DIASPORUS (Bonn et al. 2000), seed mass data from BiolFlor (Klotz et al. 2002), and BioPop (Poschlod et al. 2003; Jackel et al. 2006).

The remaining data were derived from literature dating back to the 19th Century. For many traits in the LEDA Traitbase, we expected more data to be available in the literature than we were actually able to retrieve. A large field sampling campaign was used to obtain data identified as missing in the literature: collecting and measuring standards are described in Knevel et al. (2005,; see also Cornelissen et al. 2003). Very rare species had to be excluded because sampling effort increased with rarity or because extraction of plant material from the field was prohibited by conservation authorities. Age of first flowering could not be determined during field collections and was therefore compiled solely from the literature.

The LEDA editorial board ensures that each entry in the Traitbase has a full reference to its original source, whether a published book, article, data base or recent measurement according to the LEDA standards. Also, newly measured data are referenced according to the field site, including georeference information and habitat characteristics. When habitat characteristics were missing for data from other sources, these were derived from indicator values (Ellenberg et al. 1991).

technical structure of the data base

The LEDA Traitbase is a combination of a relational data base holding the trait data and several web applications allowing for input, access and analysis of trait-related data (see Appendix S1). Users only need a Web browser to query the LEDA Traitbase. After query execution, a table containing the selected records will be displayed within the web browser, either as individual records with bibliographic reference or as aggregated values, e.g. the average of all SLA records for a species. Registered users may upload or otherwise compose a list of species names to constrain their queries, and they may instruct the system to deliver the query results to their e-mail address. For example, to obtain a GIS link, the mapping module FloraMap of the German online plant atlas ( is accessible from within the LEDA web query application. This allows access to further trait data, species distribution data and related information (in German for version 1). Hence, the analysis of the spatial distribution of traits (e.g. Kühn et al. 2006) can be facilitated easily.

Applications of the LEDA Traitbase

  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information

Potential applications of the LEDA Traitbase cover the whole range of functional ecology and phylogenetic ecology, the fusion of ecology and evolutionary history (e.g. Grime 2006; McGill et al. 2006; Westoby 2006). A major field of functional ecology is the analysis of changes in community trait composition in response to environmental change to reveal functional response traits (Lavorel & Garnier 2002). Understanding how persistence, regeneration and dispersal traits respond to environmental change is essential for the prediction of species change in many ecological applications (e.g. landscape planning, restoration, mitigation of plant invasions). For instance, the LEDA Traitbase has been used to show that the predictability of local species composition from environmental conditions is constrained by dispersal traits (Ozinga et al. 2005). Dispersal traits were further used to assess wind dispersal potential or external animal dispersal in plants (Tackenberg et al. 2003). LEDA Traitbase data were also used to model relationships between plant traits, soil fertility and disturbance by land use (Kleyer 2002; Kühner & Kleyer 2008), and bud bank traits were used to explain the regeneration of biennials and perennials following disturbance of urban plant communities (Latzel et al. 2008).

Potential applications of the LEDA Traitbase include the analysis of changes in ecosystem functions (e.g. productivity, carbon sequestration) in response to changes in biodiversity and community composition based on the concomitant changes in ‘functional effect traits’. Changes in traits such as longevity, leaf dry matter content, leaf nitrogen content or woodiness can affect the productivity of plant communities (Garnier et al. 2007), nutrient cycling (Eviner et al. 2006), or soil carbon sequestration (De Deyn et al. 2008). Response and effect traits are linked when changes in species composition translate into modifications of ecosystem properties (Chapin et al. 2000). For instance, seed production may be essential for the response of plant species to strong disturbances and at the same time an essential resource for animals. On the other hand, while seeds may be important for the response to disturbance, leaf and stem traits may be more important for the effect of plant species on biomass decomposition. By coupling the LEDA Traitbase with data sets that combine species abundances with environmental information and ecosystem properties, response and effect traits and linkages between these can be identified (Suding et al. 2008).

LEDA data were also used for the analysis of relationships between traits and distribution patterns of rarity and endangerment of plant species (Smart et al. 2005; Römermann et al. 2008). Specifically, there has been a long quest for traits that make species invasive (e.g. Kühn et al. 2004; Moles et al. 2008, see Pyšek & Richardson 2007 for a review) or influence commonness and rarity in weeds (e.g. Lososováet al. 2008) or urban plant species (Thompson & McCarthy, in press).

Functional diversity, i.e. the value and range of plant functional traits in a given community (Tilman 2001), has been proposed as an important feature of communities, for instance to provide resilience in relation to regime shifts in terrestrial and aquatic communities (Folke et al. 2004). Functional diversity can be recorded at different biological levels, e.g. within species and between species in a community. Intraspecific diversity can be extracted from the LEDA Traitbase either by retrieving the original individual records or from aggregated information such as minimum and maximum values or standard deviations. For rare species or native species of natural landscapes without agricultural land use, the number of trait records is still small. More records will be needed throughout the geographical and environmental range of the species to assess the full extent of trait variability. Interspecific diversity can be measured with various indices (e.g. Mason et al. 2005) by collating LEDA data aggregated per trait and species to vegetation relevés.

Understanding how investments of carbon and mineral nutrients vary between species is central to plant ecology. Large data bases on plant traits have helped to clarify the extent to which scaling relations between traits indicate potential trade-offs or allometries (e.g. Enquist & Niklas 2002; Wright et al. 2004). Although LEDA comprises only limited data on biomass partitioning, it can produce trait correlation structures that could assist in revealing scaling relations associated with persistence, regeneration and dispersal. In contrast to such physiologically determined trade-offs, environment-induced trade-offs are often characterised by different costs and benefits along environmental gradients. LEDA data have been used to search for trade-offs between local above-ground persistence and below-ground seed persistence (Ozinga et al. 2007) and between generative and vegetative reproduction in riparian vegetation (Boedeltje et al. 2008).

These examples show that the LEDA Traitbase can assist in clarifying the role of traits and of trait variation in the response of plants to changing environments, the assembly of communities and the functioning of ecosystems. Case studies exploring these issues will most often take place at the level of a community or a landscape. Trait measurements at these levels will profit from assessing the variation of the traits under study against variation in the flora of the region or biome. This information can now be retrieved from the LEDA Traitbase for the flora of NW Europe. The LEDA Traitbase also offers the opportunity to re-analyse large vegetation data sets in terms of functional traits. For instance, it would be interesting to combine country-wide sets of relevés aggregated to syntaxonomic classes (e.g. Schaminée et al. 1995–1999) with the LEDA Traitbase to extract variation in persistence and regeneration traits of plant communities. So far, this has only been done for dispersal traits (Ozinga et al. 2005). Such community trait profiles could be used to generate better hypotheses for detailed investigations of plant trait–environment linkages (McGill et al. 2006).

Further prospects

  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information

At present the Traitbase supports a total of more than 8300 taxa of NW-Europe. Many taxa are subspecies to which no data are linked. However, the possibility exists to link data to these taxa, as well as to taxa that currently are not included in the LEDA priority list. The LEDA consortium welcomes new collaborators interested in delivering new data to the LEDA Traitbase. The LEDA standards (available at through provide baseline information on how the data should be organised. To assure data quality and consistency with the LEDA data standard, the LEDA Editorial Board will review the data before incorporating them into LEDA.

The LEDA Traitbase and its applications are designed to be extended with further traits. Adapting the data base scheme is relatively easy, since data for distinct traits are stored within distinct tables. The LEDA consortium welcomes any initiative that seeks to enlarge the LEDA Traitbase, either by extension of the geographical range or by extension of the traits that are covered by the data base. This would include the obligation to establish appropriate data standards, support additional technical effort and to take part in the reviewing process.

Moreover, we see future prospects in the collation of LEDA to various other data bases, such as plant genomics, distribution, Red Lists, plant communities, habitats and environmental factors, e.g. nutrient and disturbance data for sites with known species composition (Bekker et al. 2007; Schaminée et al. 2007). Currently, there are many initiatives across Europe and other parts of the world that intend to make available various data bases. We expect that the joint analysis of data from these different sources will greatly advance our understanding of large-scale biodiversity change.


  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information

The authors thank Heike Zimmermann, Tobias Kerrinnes, Hartmut Fridl, Susanne Bonn, Anne Krämer, Inge Lauer, Stefan Reidinger, Carsten Rüther, Tomas Flower-Ellis, Franciska Schultz, Espen Aarnes, Christina Lüst, Melanie Siemon, Christoph Reiffert and Svenja Vozenilek who assisted in measurements or data input.

The LEDA Traitbase project was funded by the European Commission through the fifth framework under the EESD programme, project number EVR1-CT-2002-40022. The authors gratefully acknowledge the help of Hans Fink and Rudolf May, German Federal Agency for Nature Conservation, Bonn, DE, in hosting and maintaining LEDA Traitbase after the end of the funding period.


  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information
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Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Framework of the LEDA Traitbase
  5. Applications of the LEDA Traitbase
  6. Further prospects
  7. Acknowledgement
  8. References
  9. Supporting Information

Appendix S1. Details regarding the LEDA trait definitions and the structure of the data base.

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JEC_1430_sm_AppS1.doc56KSupporting info item

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.