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About the TRY Database
The TRY Database integrates plant trait data from several hundreds of trait datasets in one consistent format. While there is a constant acquisition of trait datasets, new versions of the TRY database are released on a regular basis. Currently TRY version 4 is available, which is based on 329 contributed datasets and provides 6.9 million trait records for 148,333 plant taxa (mostly species) and 1832 traits.
TRY version 4 not only provides more trait records, but is also based on improved data curation:
Development and curation of the TRY database is under the responsibility of Jens Kattge and Gerhard Boenisch at the Max Planck Institute for Biogeochemistry. Basic information about the TRY database has been published in Kattge et al. (2011a,b).
Data Management System
The integration of the various sources in one consistent framework is possible due to a generic database structure, which is consistent with the Extensible Observation Ontology (OBOE, Madin et al. 2007) and the Entity-Quality model (Mungall et al. 2010), two commonly accepted semantic models for observational data in ecology (Garnier et al. 2017).
The TRY database is managed in the context of a LAMP framework, which is based on Linux operating system, Apache HTTP Server, MySQL relational database management system and PHP programming language (for more information about the LAMP software bundle
Data submitted to the TRY initiative are cleaned and curated before they are made available. However, all original information as contributed to the initiative is preserved when adding cleaned or standardized information.
Trait data: Trait names are standardized conforming the standards of the TOP Thesaurus of Plant Characteristics (http://top-thesaurus.org, Garnier et al. 2017). Whenever the definition of a trait is available in the TOP Thesaurus, the link is provided. Numerical trait values are standardized to one appropriate unit.
Auxilliary data: Geo-reference data are standardized to longitude and latitude values in decimal format and checked against ESA CCI Land Cover Map of Global Water Bodies (https://www.esa-landcover-cci.org/?q=node/162). Sampling or measurement date is standardized to a common format (yyyy-mm-dd). Information about conditions during plant growth (natural environment vs. experimental) and maturity of plants and organs (juvenile vs. mature) is consolidated.
When two traits are mathematically convertible without additional information, e.g. leaf mass per area (LMA) and leaf specific area (SLA), or leaf water content (LWC) and leaf dry matter content (LDMC), the data for one of the two traits are complemented by the other, and the other trait is omitted.
In case of leaf traits, mass-based and area-based data are related via SLA. If two of the three traits are obtained within one observation the third trait is being calculated and this value is added to the database.
In the context of qualitative or categorical traits textual information is standardized and complemented. So far we have developed a species specific look-up table for plant growth form, woodiness, leaf type, leaf phenology and photosynthetic pathway covering up to 40.000 or the 69.000 plant species in the TRY database. The table is available for download: here.
Information about climate and soil at the sampling sites has been derived from global databases (WorldClim, Koeppen-Geiger Climate Regions, The Harmonized World Soil Database). The table is available for download here.
Identification of Duplicates
Duplicates of numerical trait records are identified by trait name, accepted species name and the logarithm of the standardized trait value rounded to three digits. The information about duplicates is added to the database.
Identification of Outliers
The standardized values of numerical traits are checked for inconsistencies and if necessary corrected in cooperation with the data owners. Outliers are then identified in terms of number of standard deviation of the trait value from the respective trait mean on species, genus and family level and for all data. This information is added to the database.
Export Data Format
For export data are reformatted to tab delimited text with UTF-16 encoding and Latin-1 supplement.
Boyle, B. et al. 2013. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinformatics 14:16. doi:10.1186/1471-2105-14-16
Garnier, E., Stahl, U., Laporte, M.-A., Kattge, J., Mougenot, I., Kühn, I., Laporte, B., Amiaud, B., Ahrestani, F.S., Bönisch, G., Bunker, D.E., Cornelissen, J.H.C., Díaz, S., Enquist, B.J., Gachet, S., Jaureguiberry, P., Kleyer, M., Lavorel, S., Maicher, L., Pérez-Harguindeguy, N., Poorter, H., Schildhauer, M., Shipley, B., Violle, C., Weiher, E., Wirth, C., Wright, I.J. & Klotz, S. (2017) Towards a thesaurus of plant characteristics: an ecological contribution. Journal of Ecology, 105, 298-309.
Kattge, J., K. Ogle, G. Boenisch, S. Diaz, S. Lavorel, J. Madin, K. Nadrowski, S. Noellert, K. Sartor, and C. Wirth (2011a) A generic structure for plant trait databases. Methods in Ecology and Evolution 2:202-213.
Kattge, J. et al. (2011b) TRY - a global database of plant traits. Global Change Biology 17:2905-2935.
Madin, J. S., S. Bowers, M. P. Schildhauer, and M. B. Jones (2008) Advancing ecological research with ontologies. Trends in Ecology & Evolution 23:159-168.
Mungall, C.J., Gkoutos, G.V., Smith, C.L., Haendel, M.A., Lewis, S.E. & Ashburner, M. (2010) Integrating phenotype ontologies across multiple species. Genome Biology, 11, R2
The Taxonomic Name Resolution Service [Internet]. iPlant Collaborative. Version 4.0 [Accessed: 17 April 2017]. Available from: http://tnrs.iplantcollaborative.org
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