Distilled from

Kattge, J., S. Díaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bönisch, E. Garnier, M. Westoby, P. B. Reich, I. J. Wright, J. H. C. Cornelissen, C. Violle, S. P. Harrison, P. M. Van Bodegom, M. Reichstein, B. J. Enquist, N. A. Soudzilovskaia, D. D. Ackerly, M. Anand, O. Atkin, M. Bahn, T. R. Baker, D. Baldocchi, R. Bekker, C. C. Blanco, B. Blonder, W. J. Bond, R. Bradstock, D. E. Bunker, F. Casanoves, J. Cavender-Bares, J. Q. Chambers, F. S. Chapin, J. Chave, D. Coomes, W. K. Cornwell, J. M. Craine, B. H. Dobrin, L. Duarte, W. Durka, J. Elser, G. Esser, M. Estiarte, W. F. Fagan, J. Fang, F. Fernández-Méndez, A. Fidelis, B. Finegan, O. Flores, H. Ford, D. Frank, G. T. Freschet, N. M. Fyllas, R. V Gallagher, W. A. Green, A. G. Gutierrez, T. Hickler, S. Higgins, J. G. Hodgson, A. Jalili, S. Jansen, C. Joly, A. J. Kerkhoff, D. Kirkup, K. Kitajima, M. Kleyer, S. Klotz, J. M. H. Knops, K. Kramer, I. Kühn, H. Kurokawa, D. Laughlin, T. D. Lee, M. Leishman, F. Lens, T. Lenz, S. L. Lewis, J. Lloyd, J. Llusià, F. Louault, S. Ma, M. D. Mahecha, P. Manning, T. Massad, B. Medlyn, J. Messier, A. T. Moles, S. C. Müller, K. Nadrowski, S. Naeem, Ü. Niinemets, S. Nöllert, A. Nüske, R. Ogaya, J. Oleksyn, V. G. Onipchenko, Y. Onoda, J. Ordoñez, G. Overbeck, W. A. Ozinga, S. Patiño, S. Paula, J. G. Pausas, J. Peñuelas, O. L. Phillips, V. Pillar, H. Poorter, L. Poorter, P. Poschlod, A. Prinzing, R. Proulx, A. Rammig, S. Reinsch, B. Reu, L. Sack, B. Salgado-Negret, J. Sardans, S. Shiodera, B. Shipley, A. Siefert, E. Sosinski, J.-F. Soussana, E. Swaine, N. Swenson, K. Thompson, P. Thornton, M. Waldram, E. Weiher, M. White, S. White, S. J. Wright, B. Yguel, S. Zaehle, A. E. Zanne, and C. Wirth. 2011. TRY - a global database of plant traits. Global Change Biology 17:2905–2935

Kattge, J., S. Díaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bönisch, E. Garnier, M. Westoby, P. B. Reich, I. J. Wright, J. H. C. Cornelissen, C. Violle, S. P. Harrison, P. M. Van Bodegom, M. Reichstein, B. J. Enquist, N. A. Soudzilovskaia, D. D. Ackerly, M. Anand, O. Atkin, M. Bahn, T. R. Baker, D. Baldocchi, R. Bekker, C. C. Blanco, B. Blonder, W. J. Bond, R. Bradstock, D. E. Bunker, F. Casanoves, J. Cavender-Bares, J. Q. Chambers, F. S. Chapin, J. Chave, D. Coomes, W. K. Cornwell, J. M. Craine, B. H. Dobrin, L. Duarte, W. Durka, J. Elser, G. Esser, M. Estiarte, W. F. Fagan, J. Fang, F. Fernández-Méndez, A. Fidelis, B. Finegan, O. Flores, H. Ford, D. Frank, G. T. Freschet, N. M. Fyllas, R. V Gallagher, W. A. Green, A. G. Gutierrez, T. Hickler, S. Higgins, J. G. Hodgson, A. Jalili, S. Jansen, C. Joly, A. J. Kerkhoff, D. Kirkup, K. Kitajima, M. Kleyer, S. Klotz, J. M. H. Knops, K. Kramer, I. Kühn, H. Kurokawa, D. Laughlin, T. D. Lee, M. Leishman, F. Lens, T. Lenz, S. L. Lewis, J. Lloyd, J. Llusià, F. Louault, S. Ma, M. D. Mahecha, P. Manning, T. Massad, B. Medlyn, J. Messier, A. T. Moles, S. C. Müller, K. Nadrowski, S. Naeem, Ü. Niinemets, S. Nöllert, A. Nüske, R. Ogaya, J. Oleksyn, V. G. Onipchenko, Y. Onoda, J. Ordoñez, G. Overbeck, W. A. Ozinga, S. Patiño, S. Paula, J. G. Pausas, J. Peñuelas, O. L. Phillips, V. Pillar, H. Poorter, L. Poorter, P. Poschlod, A. Prinzing, R. Proulx, A. Rammig, S. Reinsch, B. Reu, L. Sack, B. Salgado-Negret, J. Sardans, S. Shiodera, B. Shipley, A. Siefert, E. Sosinski, J.-F. Soussana, E. Swaine, N. Swenson, K. Thompson, P. Thornton, M. Waldram, E. Weiher, M. White, S. White, S. J. Wright, B. Yguel, S. Zaehle, A. E. Zanne, and C. Wirth. 2011. TRY - a global database of plant traits. Global Change Biology 17:2905–2935

This dataset consists of 31 plant functional trait maps at 1 km resolution with global extent. Plant functional traits are predicted as community-weighted means based on a synthesis of crowdsourced biodiversity data (GBIF species observations, sPlot vegetation surveys, and in situ trait measurements from the TRY trait database) modeled as a function of Earth observation data, including surface reflectance, climatic variables, soil properties, canopy height, and vegetation optical depth.

This version of the maps includes traits sourced from all available plant functional types (PFT). PFT-specific maps are in progress and will be available in the future.

Note: In addition to using the coefficient of variation and area of applicability layers of the maps, we encourage all who wish to use these maps to take particular note of the model performance for the traits of interest, which can be found both in the raster metadata as well as in Table A.1. of the manuscript. The model performance varies significantly across the traits, and so care should be used.

GeoTIFF multilayer raster description

Each GeoTIFF is in Equal Area Scalable Earth projection (EPSG:6933). In addition to the trait predictions, two quality bands are provided. Model performance metrics are also included in the raster metadata.

Trait table

Crowsourced biodiversity data:

• https://www.try-db.org/TryWeb/Home.php" target="_blank" rel="noopener">TRY Trait database
• https://gbif.org" target="_blank" rel="noopener">Global Biodiversity Information Facility (GBIF)
• https://www.idiv.de/research/projects/splot/" target="_blank" rel="noopener">sPlot

These maps have been created in the framework of the PANOPS project. More information on this project is available at:

• https://uni-freiburg.de/enr-geosense/research/panops/
• https://gepris-extern.dfg.de/gepris/projekt/504978936?language=en

Are non-native plants abundant because they are non-native, and have advantages over native plants, or because they possess ‘fast’ resource strategies, and have advantages in disturbed environments? This question is central to invasion biology but remains unanswered. We quantified the relative importance of resource strategy and origin in 69,441 plots across the conterminous United States containing 11,280 plant species. Non-native species had faster economic traits than native species in most plant communities (77%, 86%, and 82% of plots for leaf nitrogen concentration, specific leaf area, and leaf dry matter content). Non-native species also had distinct patterns of abundance, but these were not explained by their fast traits. Compared to functionally similar native species, non-native species (1) were more abundant in plains and deserts, indicating the importance of biogeographical origin, and less abundant in forested ecoregions, (2) were more abundant where co-occurring species had..., Plant cover data were compiled from multiple sources including state and federal land management agencies, which used a variety of methods for measuring plant cover within plots. See Petri et al. (2022) for details on data compilation. Plant growth form data were obtained from the USDA Plants database. Data from the TRY database (https://www.try-db.org/TryWeb/Home.php) included leaf nitrogen content, leaf dry matter content, and specific leaf area, and were averaged to the species level prior to being combined with plant cover datasets. Code used in associated analyses, described in Blumenthal et al. (2025), is available at: https://github.com/DiezJ/PAINLES-repository., # Relative cover and leaf economic traits for native and non-native plants across five U.S. ecoregions

Dataset DOI: 10.5061/dryad.rjdfn2zpq

Description of the data and file structure:

This dataset contains eight data files, each one accompanied by a separate data dictionary for clarity. The files are available in comma-delimited format. They are a supplement to:

Blumenthal et al. 2025, “Why are non-native plants successful? Consistently fast economic traits and novel origin jointly explain abundance across U.S. ecoregions.†New Phytologist.

Data on plant cover, taxonomy, origin, and growth form were obtained from Petri et al. (2022), and combined with trait data from the TRY database (Kattge et al. 2020) as described in Blumenthal et al. (2025).

Kattge J, Bönisch G, DÃaz S, Lavorel S, Prentice IC, Leadley P, Tautenhahn S, Werner GD, Aakala T, Abedi M. et al. 2020. TRY plant trait database–enhanced coverage and open access. Global Change Biol...,

Most terrestrial plants disperse by seeds, yet the relationship between seed mass, seed dispersal traits, and plant dispersion is poorly understood. We quantified seed traits for 48 species of native and introduced plants from grasslands of western Montana, USA, to investigate the relationships between seed traits and plant dispersion patterns. Additionally, because the linkage between dispersal traits and dispersion patterns might be stronger for actively dispersing species, we compared these patterns between native and introduced plants. Finally, we evaluated the efficacy of a global trait database, the TRY plant traits database, versus locally collected data for examining these questions. This archive contains species-level data used in analyses, including species metadata (origin, growth form), mean values of measured seed traits (size metrics and type of dispersal structures), two metrics of dispersion (local and broad scales, respectively) derived from grassland surveys in the study region, and information on the seed mass accessed from the TRY traits database. Note that the latter seed mass data could not be included in the archive, but can be acquired directly from the TRY plant traits database (https://www.try-db.org/TryWeb/Home.php). Methods Our study took place in semi-arid grasslands of the Intermountain Region in western Montana, U.S.A. The native system is dominated primarily by bluebunch wheatgrass (Pseudoroegneria spicata) with other grasses and a great variety of forbs diversifying the system, but it is heavily invaded by exotics. We identified our study species, comprised of 23 native and 25 exotic species, to reflect a range of dispersion patterns by using data from 620 1-m2 vegetation plots from 31 grassland sites spread over 20,000 km2 of western Montana. Plant dispersion patterns were defined at a local-scale by the proportion of plots occupied within a site and at a broad-scale by the proportion of sites occupied per species. For each species, we collected at least 50 seeds from each of 10 plants at each of 3 locations in Missoula and Lake County, Montana in either 2020 or 2021. Collection locations were chosen opportunistically based on species presence and hence differed by species. Although these locations did not align with sites surveyed for species dispersion per se, they were generally drawn from the central portion of the study area. Seeds were stored in a laboratory under ambient conditions until measurements were taken, at which point they were cleaned by hand and sorted based primarily on visual characteristics to remove potentially non-viable seeds. To determine the mean seed mass per species, we weighed a fixed number of samples (three or four) from each of the three locations. The number of seeds weighed per sample was set per species to ensure a total mass >1.5 mg, the minimum reading needed for an accuracy of 2% per the specifications of the balance. For 32 of our 48 species, only 10 seeds were needed to reach this minimum. For remaining species, we increased the number of seeds included per sample in increments of 10 (range 20–150 seeds/sample) until the minimum mass was reached. Seed mass included the entire diaspore (e.g., endosperm, seed coat, awns, and dispersal appendages) to ensure that all species could be treated in the same way (e.g., dispersal appendages such as wings would have been very difficult to remove from small-seeded species). Though the inclusion of dispersal appendages potentially biases seed mass estimates for this subset of species, we note that this bias should be small relative to the large variation in seed mass across species. Indeed, estimates for three exotic species (Lactuca serriola, Taraxacum officinale, and Tragopogon dubius) with pappuses showed that these structures increased seed mass measures by <12%. For the remaining measurements, we used a ProgRes C10 camera (Jenoptik, CCD/CMOS) to create images of 20 seeds per species drawn from the 3 sampling locations (n=6 from two locations and n=8 from the third, chosen randomly). We used the images to obtain the following measurements for each seed via ImageJ software (Rasband 1997-2018): seed length (maximum), seed width (maximum), and seed surface area. These seed measurements excluded dispersal structures. Mean values per species for all seed size measurements are included in the species-level dataset archived here. Finally, we inspected seeds to determine whether seeds of each species possessed dispersal structures including pappuses, awns, wings, or plumes. For smaller-seeded species, we accomplished this using the seed images and also checked the literature to assure that dispersal structures were not missed. To enable comparison of empirical seed measures to those available in online trait databases, we used the TRY plant trait database (accessed 22 September – 7 October 2022), a global database integrating ~700 datasets including other major collective databases. This database included seed mass data for 44 of our 48 species but contained insufficient data to evaluate the other seed traits (i.e., length, width, and surface area) we measured (i.e., for only 2-40% of our study species). Importantly, 63% of n=831 seed mass records obtained from the TRY database could not be used in analyses. This is because these contained duplicate data that resulted from the consolidation of many datasets with common sources. See the publication for a full description of our process for identifying duplicate values. Remaining seed mass values from the TRY database were averaged to generate the mean estimate used in analyses. See the archive for sample size information per species.

Specific leaf area and leaf nitrogen concentration of 108 native and 73 naturalised New Zealand trees compiled from the TRY online database (Kattge et al. 2011) and internally-maintained data sets within Manaaki Whenua. Only data sets containing information for both of these leaf traits are included. These data were used to compare nitrogen content per leaf area in Brandt et al. (in prep). Kattge et al. (2011) TRY - a global database of plant traits. Global Change Biology 17:2905-2935. http://www.try-db.org

The LT-Brazil data set contains observations of leaf mass per area, leaf N and P concentration per unit mass, and leaf N:P ratio from native woody species across the Brazilian territory, encompassing information of biome, vegetation, taxonomic data, geographical coordinates, climatic parameters, as well as soil properties. We compiled data from several geographical coordinates in native vegetation distributed across all biomes (i.e., Amazônia, Caatinga, Cerrado, Mata Atlântica, Pampa, and Pantanal) found in Brazil. Our compilation was focused on native woody plants (i.e., trees, shrubs, subshrubs, and lianas), excluding monocots, palm trees, herbs, and hemiparasitic plants. The compiled data set covers c. 9% of woody angiosperm species of Brazil. Unidentified or mixed species were also considered when met our eligibility criteria. Contributions to expand this database can be performed through our repository at GitHub (https://github.com/emariano-git/lt-brazil.git). Major versions of the LT-Brazil data set will also be made available via the TRY Plant Trait Database (https://www.try-db.org).

This data package contains xylem hydraulic properties data, both newly extracted from publications plus tropics-specific data extracted from the "Xylem Functional Traits Database" (Choat et al., 2012; Gleason et al., 2016), which is available on the TRY plant trait database (www.try-db.org). Measurements include xylem hydraulic conductivity per unit sapwood (Ks) and per unit leaf (Kl) area, water potential at 12%, 50%, and 88% loss of conductivity (P12, P50, and P88, respectively), leaf mass per area, wood density, and maximum rate of photosynthesis. Data span both natural tropical forests and plantations and rainfall gradients from deciduous dry tropical forests to everwet tropical forests, and come from countries all across the pantropics. Data is included in the attached zip file as three CSV files. This dataset was originally published on the NGEE Tropics Archive and is being mirrored on ESS-DIVE for long-term archival Acknowledgement: This research was supported as part of NGEE-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under contract no. DE-SC0012704.

Compared to other plant life forms, epiphytes remain understudied. Understanding the responses of epiphytes to changing environmental conditions is necessary to predict changes in ecosystem functioning especially in subtropical and tropical regions. We investigated the functional traits of epiphytes along a large elevation gradient on Mount Kilimanjaro, Tanzania. We measured traits of co-occurring trees and terrestrial non-tree life forms, and compared changes in community-weighted means of traits (CWM) and trait spread, the range of observed trait values. We chose traits linked to growth and persistence: leaf area, specific leaf area, leaf dry matter content, stem specific density, plant height, leaf carbon, leaf nitrogen, and leaf phosphorus. For most traits, differences in community-weighted means between life forms exceeded differences within life forms along the elevation gradient. Many CWM showed linear changes with elevation, but no response and unimodal patterns were also freque...

The global spectrum of plant form and function dataset (Diaz et al. 2022; Diaz et al. 2016; TRY 2022, accessed 15-May-2025) provides mean trait values for (i) plant height; (ii) stem specific density; (iii) leaf area; (iv) leaf mass per area; (v) leaf nitrogen content per dry mass; and (vi) diaspore (seed or spore) mass for 46,047 taxa.

Here I provide a dataset where the taxa covered by that database were standardized to World Flora Online (Borsch et al. 2020; taxonomic backbone version 2023.12) by matching names with those in the Agroforestry Species Switchboard (Kindt et al. 2025; version 4). Taxa for which no matches could be found were standardized with the WorldFlora package (Kindt 2020), using similar R scripts and the same taxonomic backbone data as those used to standardize species names for the Switchboard. Where still no matches could be found, taxa were matched with those matched previously with a harmonized data set for TRY 6.0 (Kindt 2024).

References

• Díaz, S., Kattge, J., Cornelissen, J.H.C. et al. The global spectrum of plant form and function: enhanced species-level trait dataset. Sci Data 9, 755 (2022). https://doi.org/10.1038/s41597-022-01774-9
• Díaz, S., Kattge, J., Cornelissen, J. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016). https://doi.org/10.1038
• TRY. 2022. The global spectrum of plant form and function dataset. https://www.try-db.org/TryWeb/Data.php#81
• Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. https://doi.org/10.1002/tax.12373
• Roeland Kindt, Ilyas Siddique, Ian Dawson, Innocent John, Fabio Pedercini, Jens-Peter B. Lillesø, Lars Graudal. 2025. The Agroforestry Species Switchboard, a global resource to explore information for 107,269 plant species. bioRxiv 2025.03.09.642182; doi: https://doi.org/10.1101/2025.03.09.642182
• Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. Applications in Plant Sciences 8(9): e11388. https://doi.org/10.1002/aps3.11388
• Kindt, R. (2024). TRY 6.0 - Species List from Taxonomic Harmonization – Matches with World Flora Online version 2023.12 (2024.10b) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13906338

Funding

The development of this dataset was supported by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, and by the Bezos Earth Fund to the Quality Tree Seed for Africa in Kenya and Rwanda project.

Specific leaf area and leaf nitrogen concentration of 108 native and 73 naturalised New Zealand trees compiled from the TRY online database (Kattge et al. 2011) and internally-maintained data sets within Manaaki Whenua. Only data sets containing information for both of these leaf traits are included. These data were used to compare nitrogen content per leaf area in Brandt et al. (in prep).

Kattge et al. (2011) TRY - a global database of plant traits. Global Change Biology 17:2905-2935. http://www.try-db.org

The complement dataset pertaining to the species functional traits has been archived in the TRY database and is publicly available from https://www.try-db.org

Forest understories play a vital role in ecosystem functioning and the provision of ecosystem services. However, the extent to which environmental conditions drive dominant ecological strategies in forest understories at the continental scale remains understudied. Here, we used ~29,500 forest vegetation plots sampled across Europe and classified into 25 forest types to explore the relative role of macroclimate, soil pH, and tree canopy cover in driving abundance-weighted patterns in the leaf economic spectrum (LES) and plant size spectrum (PSS) of forest understories (shrub and herb layers). We calculated LES using specific leaf area (SLA) and leaf dry matter content (LDMC), and PSS using plant height and seed mass of vascular plant species found in the understories. We found that forest understories had more conservative leaf economics in areas with more extreme mean annual temperatures (mainly Fennoscandia and the Mediterranean Basin), more extreme soil pH, and under more open canopies. Warm and summer-dry regions around the Mediterranean Basin and areas of Atlantic Europe also had taller understories with heavier seeds than continental temperate or boreal areas. Understories of broadleaved deciduous forests, such as Fagus forests on non-acid soils, or ravine forests, more commonly hosted species with acquisitive leaf economics. In contrast, some coniferous forests, such as Pinus, Larix, and Picea mire forests, or Pinus sylvestris light taiga, and sclerophyllous forests, more commonly hosted species with conservative leaf economics. Our findings highlight the importance of macroclimate and soil factors in driving trait variation of understory communities at the continental scale and the mediator effect of canopy cover on these relationships. We also provide the first maps and analyses of LES and PSS of forest understories across Europe and give evidence that the understories of European forest types are positioned along different major axes of trait variation.

Methods All vegetation data used in this study were obtained from the European Vegetation Archive (EVA; http://euroveg.org; project number 82). Further details on data collection and associated databases can be found on the EVA website.

Trait data used in the study were obtained from the TRY database (https://www.try-db.org/TryWeb/Home.php). See "Material and Methods" in the reference paper for details on the processing of these data.

This data is used in the article: Castillo-Lorenzo, Breman, Gomez Barreiro and Viruel (2024). Current status of global conservation and characterisation of wild and cultivated Brassicaceae genetic resources. GigaScience (DOI: 10.1093/gigascience/giae050)Data gathered for 1242 wild Brassicaeea species related to cultivated species. The data includes conservation status, ex situ conservation records, classification of crop wild relatives and crop related to, phyisio and phenotipic traits described and published and chromosome number.The information was gathered from World Checklist of Vascular Plants (https://powo.science.kew.org/); USDA-GRIN Global (https://npgsweb.ars-grin.gov/gringlobal/taxon/taxonomysearchcwr); TRY plant trait database (https://www.try-db.org/TryWeb/dp.php); Harlan and De Wet CWR inventory (https://www.cwrdiversity.org/checklist); Genesys-pgr (https://www.genesys-pgr.org); NCBI (https://www.ncbi.nlm.nih.gov/); Wild Germplasm of Brassica (Part II: Chromosome number, Warwick et al., 2009); Brassibase (https://brassibase.cos.uni-heidelberg.de/?action=cyto); the Plant DNA C-values database (https://cvalues.science.kew.org/) and plant CCDB database (https://taux.evolseq.net/CCDB_web). Conservation status was obtained from IUCN red list (https://www.iucnredlist.org/) and ThreatSearch tool from the BGCI (https://members.bgci.org/data_tools/threatsearch).(databases accessed between September and December 2022).

Standardized names for TRY 6.0 are available from the TRY File Archive (TFA; see here: https://www.try-db.org/TryWeb/Data.php#100); these were created for the following publication:

Schellenberger Costa, D., Boehnisch, G., Freiberg, M., Govaerts, R., Grenié, M., Hassler, M., Kattge, J., Muellner-Riehl, A.N., Rojas Andrés, B.M., Winter, M., Watson, M., Zizka, A. and Wirth, C. (2023), The big four of plant taxonomy – a comparison of global checklists of vascular plant names. New Phytol, 240: 1687-1702. https://doi.org/10.1111/nph.18961

Here matched records are provided with the taxonomic backbone of World Flora Online (WFO) version 2023.01, obtained from https://zenodo.org/records/10425161.

Matches with WFO are given in the Zenodo archive in the fields for SID (=taxonID in WFO), scientificName (as in WFO) and scientificNameAuthorship (as in WFO). Fields of TRY_SpeciesID, TRY_AccSpeciesNameScientific and RecommendedScientificName were directly obtained from the TFA.

Matching was done via following steps:

1. Fungi matched in the TFA via the http://indexfungorum.org were excluded (4,099 records).

2. The matching record in WFO was obtained by matching the TFA field of TPL_ID with the WFO field of tplID. Successful matches via this method are indicated in this Zenodo archive by the field of MATCH being set to TPL ID.

3. Where matches in WFO included a non-empty acceptedNameUsageID, the currently accepted name of the taxon was obtained via this ID. Successful matches via this method are indicated in this Zenodo archive by the field of MATCH being set to TPL ID and fields of synonymID, scientificName.synonym and scientificNameAuthorship.synonym showing details for the match with the synonym.

4. Taxa that were not matched in previous steps were matched for the TFA field of MatchedName. Matching was done via the WorldFlora package version 1.14-5 (Kindt 2020). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual. Taxa at specific and infraspecific levels from the TFA where matches had only been achieved at generic levels for the MatchedName were excluded in this step.

5. Taxa that were not matched in previous steps were matched for the TFA field of TRY_AccSpeciesName. Matching was done via the WorldFlora package version 1.14-5 (Kindt 2020). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual.

6. Taxa that were not matched in previous steps were matched for the TFA field of AlternativeName. Matching was done via the WorldFlora package version 1.14-5 (Kindt 2020). Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual.

7. Taxa that were not matched in previous steps were matched for the TFA field of MatchedName against version 11 of the World Checklist of Vascular Plants. Matching was also done via the WorldFlora package version 1.14-5 (Kindt 2020), with similar scripts as shown in this Rpub. Matches expected to be acceptable after visual inspection are indicated in this Zenodo archive by the field of MATCH being set to manual and by the field of SID (and possibly synonymID) containing records where the WCVP ID is preceded by WCVP-.

8. 836 records where neither the TFA nor the previous steps managed to establish a match were removed.

9. Taxa that could not be matched in previous steps are flagged by the field of MATCH being set to NONE - no species MatchedName (where TFA also did not achieve a match at the required specific or infraspecific level) or NONE (other records where no match was achieved).

The Zenodo archive contains 406,208 records with matches via the TPL, 96,040 records with manual matches, 1,822 records where the TFA also not achieve matches at required (infra-)specific levels and 361 records where no match was achieved (among this latter category were 202 records flagged in TFA as ‘not found in WFO’ and 140 records flagged in TFA as ‘found only here’).

References

· Kattge J. 2023. TRY 6.0 - Species List from Taxonomic Harmonization. https://www.try-db.org/TryWeb/Data.php#100

· Schellenberger Costa, D., Boehnisch, G., Freiberg, M., Govaerts, R., Grenié, M., Hassler, M., Kattge, J., Muellner-Riehl, A.N., Rojas Andrés, B.M., Winter, M., Watson, M., Zizka, A. and Wirth, C. (2023), The big four of plant taxonomy – a comparison of global checklists of vascular plant names. New Phytol, 240: 1687-1702. https://doi.org/10.1111/nph.18961

· The World Flora Online Consortium, Alan Elliott, Roger Hyam, William Ulate, Mark Watson, Gregory Anderson, Giovani carlos Andrella, et al. “World Flora Online Plant List December 2023”. Zenodo, December 22, 2023. https://doi.org/10.5281/zenodo.10425161.

· Kindt R (2020). “WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data.” Applications in Plant Sciences, 8(9), e11388. https://doi.org/10.1002/aps3.11388

· Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. https://doi.org/10.1002/tax.12373

· Govaerts, R., Nic Lughadha, E., Black, N. et al. The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. Sci Data 8, 215 (2021). https://doi.org/10.1038/s41597-021-00997-6

The development of this archive supported by the Darwin Initiative to project DAREX001 of Developing a Global Biodiversity Standard certification for tree-planting and restoration, by Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia to the Provision of Adequate Tree Seed Portfolio project in Ethiopia, by the Green Climate Fund through the IUCN-led Transforming the Eastern Province of Rwanda through Adaptation project and through the Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso, by the Bezos Earth Fund to the Bezos Quality Tree Seed for Africa in Kenya and Rwanda project and by the German International Climate Initiative (IKI) to the regional tree seed programme on The Right Tree for the Right Place for the Right Purpose in Africa.

Photosynthesis, growth and plant maintenance respiration are closely related to tree tissue nitrogen (N) concentrations. Earlier studies of the variation in tissue N concentrations and underlying controls have mostly focused on leaves. Here we present a novel database of N concentrations in stems, roots and branches covering all common northern hemisphere boreal and temperate tree species, combined with data for leaves mostly available from existing databases. Additional information on mean annual temperature (MAT), mean annual precipitation (MAP), soil N concentration, tree height, age, and biomass is extracted from the considered studies, when available. The database can be used, for instance, for studying the controls of tree tissue N concentrations (as in Thurner et al., accepted for publication in Biogeosciences, https://doi.org/10.5194/egusphere-2024-1794), or for improving the implementation of the N cycle in dynamic global vegetation models (DGVMs), which currently usually assume fixed ratios between tissue N concentrations and thus oversimplify effects of N limitation on the carbon (C) cycle.

We collect a novel database of N concentration measurements in stems (i.e., trunks), roots and branches of northern hemisphere boreal and temperate trees by an extensive literature research. For this task, we search Web of Science for stem, root and branch nitrogen concentrations for all common boreal and temperate tree genera (for search criteria see Supporting Information S1 of the associated primary article). To a lesser extent, we also collect leaf N concentration measurements from the literature, because numerous measurements of leaf N concentration are already available from the TRY database (Kattge et al., 2020). Since measurements are rare in Russian boreal forests, we include own measurements for Larix gmelinii in the central part of the Nizhnyaya Tunguska River basin in Central Siberia (ca. 64° N 100° E; Larjavaara et al., 2017; Prokushkin et al., 2018). Moreover, data sources from the Russian and Chinese literature, the TRY database (Kattge et al., 2020) and the biomass and allometry database (BAAD; Falster et al., 2015) are considered.

Only measurements of N concentration under natural conditions (no greenhouses, no trees grown in pots, no fertilizer, and no other experiments) are included in the database. In addition, we only include studies with explicit information on the measurement location and the investigated tree species. We only analyse measurements of total root N concentration, but do not include measurements of N concentration specifically for fine roots. In cases where separate measurements are available for (stem) sapwood and heartwood, we include only N concentrations of sapwood. Replicate measurements, if available from the studies, are retained. All tissue N concentrations are expressed in g N / g dry weight. In total, the compiled database comprises 1048 stem, 267 root, 599 branch, and 5944 leaf N concentration measurements. While almost all of the stem (911 collected from literature, 1 own, 52 from TRY, 84 from BAAD), root (266 collected from literature, 1 own) and branch (all collected from literature) N concentration measurements have been collected from in total 192 studies from the literature, leaf N concentration measurements are to a large extent available from existing databases (188 collected from literature, 5 own, 5522 from TRY, 229 from BAAD). The spatial distribution of N concentration measurements is shown in Fig. 1 of the associated primary article.

Information on MAT, MAP, soil N concentration, tree height, age, and biomass is extracted from the respective studies, when available. Growth / leaf type classes categorise tree species according to their growth rate (fast-growing, slow-/medium-growing) and leaf type (broadleaf deciduous, needleleaf deciduous, needleleaf evergreen). We exclude data without information on tree species as well as broadleaf evergreen trees from the analysis since available measurements for this leaf type are scarce. Due to missing information on actual growth rates of the species at the specific measurement sites, we assign their typical growth rate (slow/medium: <= 2 feet/year; fast: > 2 feet/year) to each investigated tree species based on our expert judgement and an online research (see Supporting Information S2 of the associated primary article). As a measure of dryness, we calculate the aridity index (AI = MAP / potential evapotranspiration) from CHELSA Version 2.1 long-term climate data at the study locations (1981-2010; 30 arcsec resolution; Brun et al., 2022), as information on potential evapotranspiration is usually not available from the compiled studies.

References:

Brun P, Zimmermann NE, Hari C, Pellissier L, Karger DN. 2022. Global climate-related predictors at kilometer resolution for the past and future. Earth System Science Data 14(12): 5573-5603.

Falster DS, Duursma RA, Ishihara MI, Barneche DR, FitzJohn RG, Vårhammar A, Aiba M, Ando M, Anten N, Aspinwall MJ, et al. 2015. BAAD: a biomass and allometry database for woody plants. Ecology 96(5): 1445.

Kattge J, Bonisch G, Diaz S, Lavorel S, Prentice IC, Leadley P, Tautenhahn S, Werner GDA, Aakala T, Abedi M, et al. 2020. TRY plant trait database - enhanced coverage and open access. Glob Chang Biol 26(1): 119-188.

Larjavaara M, Berninger F, Palviainen M, Prokushkin A, Wallenius T. 2017. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci Rep 7(1): 12776.

Prokushkin A, Hagedorn F, Pokrovsky O, Viers J, Kirdyanov A, Masyagina O, Prokushkina M, McDowell W. 2018. Permafrost Regime Affects the Nutritional Status and Productivity of Larches in Central Siberia. Forests 9(6).

Related works: We sincerely thank the TRY initiative on plant traits (http://www.try-db.org) for contributing to leaf N and the Biomass And Allometry Database (BAAD; https://github.com/dfalster/baad) for contributing to leaf and stem N concentration data of this database. The TRY initiative and database is hosted, developed and maintained by J. Kattge and G. Boenisch (Max Planck Institute for Biogeochemistry, Jena, Germany). The BAAD is hosted, developed and maintained by D. Falster (University of New South Wales, Sydney, Australia).

Epiphyte trait data for the paper Hietz et al. 2021 Putting vascular epiphytes on the traits map. Journal of Ecology

Plant functional traits impact the fitness and environmental niche of plants. Major plant functional types have been characterized by their trait spectrum, and the environmental and phylogenetic imprints on traits have advanced several ecological fields. Yet very few trait data on epiphytes, which represent almost 10% of vascular plants, are available.

We collated >80,000 mostly unpublished trait observations for 2,882 species of vascular epiphytes that were compared with non-epiphytic herbs and trees (mainly using data from www.try-db.org, which are not included in the Dryad dataset) to test hypotheses related to how the epiphytic habit affects traits, and if epiphytes occupy a distinct region in the global trait space. We also compared variation in traits among major groups of epiphytes, and investigated the coordination of traits in epiphytes, ground-rooted herbs and trees. Data include information on trait type, unit of measurement, species, individuals, location and data contributor.

Epiphytes differ from ground-rooted plants mainly in traits related to water relations. Unexpectedly, we did not find lower leaf nutrient concentrations, except for nitrogen. Mean photosynthetic rates are much lower than in ground-rooted plants and lower than expected from the nitrogen concentrations. Trait syndromes clearly distinguish epiphytes from trees and from most non-epiphytic herbs.

Among the three largest epiphytic taxa, orchids differ from bromeliads and ferns mainly by having smaller and more numerous stomata, while ferns differ from bromeliads by having thinner leaves, higher nutrient concentrations, and lower water content and water use efficiency.

Trait networks differ among epiphytes, herbs and trees. While all have central nodes represented by specific leaf area and mass-based photosynthesis, in epiphytes, traits related to plant water relations have stronger connections, and nutrients other than potassium have weaker connections to the remainder of the trait network. Whereas stem specific density reflects mechanical support related to plant size in herbs and trees, in epiphytes it mostly reflects water storage and scales with leaf water content.

Our findings advance our understanding of epiphyte ecology, but we note that currently mainly leaf traits are available. Important gaps are root, shoot and whole plant, demographic and gas exchange traits. We suggest how future research might use available data and fill data gaps.

Methods The dataset is a compilation of unpublished traits data by the authors plus some previously published data

Data collection

Plant hydraulic traits and height data were obtained from three sources: (1) field measurements of plant hydraulics for 210 forest species in China; (2) the TRY Plant Traits Database (https://www.try-db.org/TryWeb/Home.php; Kattge et al., 2020); and (3) published literature. For the latter we conducted searches on Web of Science, Google Scholar, and China National Knowledge Infrastructure (http://www.cnki.net) using keywords such as “hydraulic traits,” “xylem hydraulic conductivity,” “xylem vulnerability,” “water potential at 50% loss of hydraulic conductivity,” “xylem embolism resistance,” and “plant water conductivity.” A substantial portion of data in our study were obtained from published literature (Choat et al., 2012; Gleason et al., 2016) and the Xylem Functional Traits Database (XFT; https://xylemfunctionaltraits.org).

To minimize ontogenetic and methodological variation, we only included data that met the following criteria: (a) plants were grown in natural ecosystems, excluding greenhouse and common garden experiments; (b) measurements were made on adult plants and not on seedlings; (c) hydraulic traits were measured on terminal stem or branch segments in the sapwood at the crown; (d) trait data were calculated as the mean value for each species at the same site when data were from multiple sources; and (e) data values > 3 SD (standard deviation) were removed to reduce the effect of outliers (Carmona et al., 2021); (f) height data were reported at the same site where plant hydraulic traits were measured.

Climate data were obtained either from the original reports or from WorldClim version 2 (http://worldclim.org/version2; Fick & Hijmans, 2017; Table 1) if the original data were not available. The following variables measured at ~1 km² scale were extracted from WorldClim: mean annual wind speed (μ), mean annual precipitation, mean annual temperature, precipitation seasonality, temperature seasonality, wind seasonality (μS; coefficient of variation across monthly measurements × 100), precipitation of driest month, and minimum temperature of coldest month. The VPD data were extracted from the TerraClimate dataset (http://www.climatologylab.org/terraclimate.html; Abatzoglou et al., 2018). Annual PET (potential evapotranspiration) data were extracted from the CGIAR-CSI consortium (http://www.cgiar-csi.org/data; Zomer et al., 2008). Moisture index (MI), which is the ratio of precipitation to PET.

Data analysis

Trait and environment data were log₁₀-transformed to achieve approximate normality, except for P₅₀ and temperature data. We first calculated correlations among all climatic variables and for subsequent analyses retained only those variables with correlation coefficients lower than |0.7| (Dormann et al., 2013). We then ran independent multiple linear models for each trait of interest using the retained climatic variables. Model selection based on a corrected Akaike information criterion and using the R package glmulti (Calcagno & de Mazancourt, 2010), identified the best linear model for each trait. The R package ‘visreg’ (Breheny & Burchett, 2017) was used to visualize the partial relationships between wind speed and hydraulic traits. Two-dimensional contour plots were then used to explore and visualise how plant hydraulic traits varied simultaneously with wind speed and moisture index.

To quantify the strength of wind effects on plant hydraulics, models with wind parameters μ and μS included were compared to those without these wind parameters.

To test for differences in the relationship between hydraulic traits and wind speed among species grouped into different climatic regions (i.e., dry vs. wet sites, and tropical vs. temperate regions), we used standardized major axis (SMA) analyses using the R package ‘smatr’ (Warton et al., 2012). A grouping factor was added in each SMA to test whether species groups share a common slope, with p > 0.05 indicating species groups share a common slope.

Variance partitioning analysis was performed using the ‘rdacca. hp’ R package to quantify the degree to which the effect of wind speed was independent from other climatic variables (Lai et al., 2022). The individual contribution of each predictor was estimated in this analysis. This analysis also helped to illustrate the significant values of climatic variables on plant hydraulics.

A Random Forest machine-learning algorithm (implemented using the R package ‘randomForest’) was utilized to further assess the relative importance of environmental variables for each plant hydraulic trait (Breiman, 2001). To avoid multicollinearity, this analysis only included variables with correlation coefficients lower than |0.7|. A higher value of the mean decrease in accuracy (%IncMSE) indicates the increased importance of a variable (e.g., a %IncMSE value of 50 indicates that the overall mean square error would increase by 50% if that variable were to be excluded from the analysis). This provides a measure of a variable's importance in estimating the value of the target variable across the trees in the forest.

The following data, associated with the article "Blandino et al. 2021 - Plant reproductive traits in old and recently-restored temperate forest understories", are available:

1) "species.abundance.data" . Original species abundance data from 40 forest plot

2) "seed.germination.data" . Original seed germination data from the understory species collected in the forest plots

3) "incubator.temperatures". Original data on temperatures recorded inside the incubators during the germination experiments.

4) "plant.regeneration.data". Data matrix od 18 plant regeneration traits from original measurements (seed width, embryo to endosperm ratio, time to 50% germination, final germination proportion, lag between radicle and cotyledon emergence); literature review (seed dormancy type, cold and warm stratification requirements, germination response to light, and effective germination temperature, month of flowering, clonality); and the TRY database (www.try-db.org, Kattge et al., 2011, plant height, seed dry mass, seed number per plant, seed terminal velocity and seed longevity in the soil).

5) The R script used to perform the analysis.

Distilled from

Kattge, J., S. Díaz, S. Lavorel, I. C. Prentice, P. Leadley, G. Bönisch, E. Garnier, M. Westoby, P. B. Reich, I. J. Wright, J. H. C. Cornelissen, C. Violle, S. P. Harrison, P. M. Van Bodegom, M. Reichstein, B. J. Enquist, N. A. Soudzilovskaia, D. D. Ackerly, M. Anand, O. Atkin, M. Bahn, T. R. Baker, D. Baldocchi, R. Bekker, C. C. Blanco, B. Blonder, W. J. Bond, R. Bradstock, D. E. Bunker, F. Casanoves, J. Cavender-Bares, J. Q. Chambers, F. S. Chapin, J. Chave, D. Coomes, W. K. Cornwell, J. M. Craine, B. H. Dobrin, L. Duarte, W. Durka, J. Elser, G. Esser, M. Estiarte, W. F. Fagan, J. Fang, F. Fernández-Méndez, A. Fidelis, B. Finegan, O. Flores, H. Ford, D. Frank, G. T. Freschet, N. M. Fyllas, R. V Gallagher, W. A. Green, A. G. Gutierrez, T. Hickler, S. Higgins, J. G. Hodgson, A. Jalili, S. Jansen, C. Joly, A. J. Kerkhoff, D. Kirkup, K. Kitajima, M. Kleyer, S. Klotz, J. M. H. Knops, K. Kramer, I. Kühn, H. Kurokawa, D. Laughlin, T. D. Lee, M. Leishman, F. Lens, T. Lenz, S. L. Lewis, J. Lloyd, J. Llusià, F. Louault, S. Ma, M. D. Mahecha, P. Manning, T. Massad, B. Medlyn, J. Messier, A. T. Moles, S. C. Müller, K. Nadrowski, S. Naeem, Ü. Niinemets, S. Nöllert, A. Nüske, R. Ogaya, J. Oleksyn, V. G. Onipchenko, Y. Onoda, J. Ordoñez, G. Overbeck, W. A. Ozinga, S. Patiño, S. Paula, J. G. Pausas, J. Peñuelas, O. L. Phillips, V. Pillar, H. Poorter, L. Poorter, P. Poschlod, A. Prinzing, R. Proulx, A. Rammig, S. Reinsch, B. Reu, L. Sack, B. Salgado-Negret, J. Sardans, S. Shiodera, B. Shipley, A. Siefert, E. Sosinski, J.-F. Soussana, E. Swaine, N. Swenson, K. Thompson, P. Thornton, M. Waldram, E. Weiher, M. White, S. White, S. J. Wright, B. Yguel, S. Zaehle, A. E. Zanne, and C. Wirth. 2011. TRY - a global database of plant traits. Global Change Biology 17:2905–2935

Turkey Loan Distribution: DB: ST: TRY: Credit Cards data was reported at 118,663.038 TRY mn in Jun 2018. This records an increase from the previous number of 117,432.017 TRY mn for May 2018. Turkey Loan Distribution: DB: ST: TRY: Credit Cards data is updated monthly, averaging 40,816.135 TRY mn from Dec 2002 (Median) to Jun 2018, with 187 observations. The data reached an all-time high of 118,663.038 TRY mn in Jun 2018 and a record low of 4,300.541 TRY mn in Dec 2002. Turkey Loan Distribution: DB: ST: TRY: Credit Cards data remains active status in CEIC and is reported by Banking Regulation And Supervision Agency. The data is categorized under Global Database’s Turkey – Table TR.KB017: Loan Distribution: Deposit Banks.