|Home||About TRY||Data Portal||Feedback||Registration|
|Database||Datasets||Members||Workshops||Requests||Publications||Steering Committee||News Archive||Administration|
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 6 is available, which is based on more than 700 contributed datasets and provides 15 million trait records for 305,000 plant taxa (mostly species) and 2661 traitsDevelopment 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 Model and 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.
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, classified by plant growth form (based on König et al. 2019, Taseski et al. 2019 and Weigelt et al. 2019), and for all data. The maximum deviation of each trait record is provided with data releases. All deviations are available at the TRY File Archive: https://www.try-db.org/TryWeb/Data.php#90.
Export Data Format
For export data are reformatted to tab delimited text with UTF-16 Latin1_swedish_ci encoding.
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.
König, C., P. Weigelt, J. Schrader, A. Taylor, J. Kattge, and H. Kreft (2019) Biodiversity data integration - The significance of data resolution and domain. PLoS Biology 17:e3000183
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
Taseski, G., Beloe, C. J., Gallagher, R. V., Chan, J. Y., Dalrymple, R. L., and Cornwell, W. K. (2019) A global growth‐form database for 143,616 vascular plant species. Ecology 100(3):e02614. 10.1002/ecy.2614
The Taxonomic Name Resolution Service [Internet]. iPlant Collaborative. Version 4.0 [Accessed: 17 April 2017]. Available from: http://tnrs.iplantcollaborative.org
Weigelt, P., C. König, and H. Kreft (2019) GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography. bioRxiv doi.org/10.1101/535005
|Disclaimer||Page calls: 35253||Gerhard Boenisch, Jens Kattge, created 2012-01-16, modified 2022-12-05|