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TRY – a global database of plant traits

2011· article· en· W2162584119 on OpenAlexaff
Jens Kattge, Soledad Dı́az, Sandra Lavorel, I. Colin Prentice, Paul Leadley, Gerhard Bönisch, Éric Garnier, Mark Westoby, Peter B. Reich, Ian J. Wright, J. H. C. Cornelissen, Cyrille Violle, Sandy P. Harrison, Peter M. van Bodegom, Markus Reichstein, Brian J. Enquist, Nadejda A. Soudzilovskaia, David D. Ackerly, M. Anand, Owen K. Atkin, Michael Bahn, Timothy R. Baker, Dennis Baldocchi, R.M. Bekker, C. Blanco, Benjamin Blonder, William J. Bond, Ross A. Bradstock, Dan Bunker, Fernando Casanoves, Jeannine Cavender‐Bares, Jeffrey Q. Chambers, F. Stuart Chapin, Jérôme Chave, David A. Coomes, William K. Cornwell, Joseph M. Craine, Barbara Dobrin, Leandro Duarte, Walter Durka, James J. Elser, G. Esser, Marc Estiarte, William F. Fagan, Jinwei Fang, Fernando Fernández‐Méndez, Alessandra Fidélis, Bryan Finegan, Olivier Flores, HENRY FORD, Dorothea Frank, Grégoire T. Freschet, Nikolaos M. Fyllas, Rachael V. Gallagher, W. A. GREEN, Álvaro G. Gutiérrez, Thomas Hickler, Steven I. Higgins, J. G. Hodgson, Amir Jalili, Steven Jansen, Carlos Alfredo Joly, Andrew J. Kerkhoff, Donald W. Kirkup, Kaoru Kitajima, Michael Kleyer, Stefan Klotz, Johannes M. H. Knops, K. Krämer, Ingolf Kühn, H. Kurokawa, Daniel C. Laughlin, Tali D. Lee, Michelle R. Leishman, Frederic Lens, Tanja I. Lenz, Simon L. Lewis, Jon Lloyd, Joan Llusià, Frédérique Louault, Sai Ma, Miguel D. Mahecha, Peter Manning, Tara Joy Massad, Belinda E. Medlyn, J. Messier, Angela T. Moles, Sandra Cristina Müller, Karin Nadrowski, S. NAEEM, Ülo Niinemets, Stephanie Nöllert, Alison Nuske, Romà Ogaya, Jacek Oleksyn, V. G. Onipchenko, Yusuke Onoda, Jenny Ordóñez, Gerhard E. Overbeck, W.A. Ozinga, S. Patiño, Susana Paula, Juli G. Pausas, Josep Peñuelas, Oliver L. Phillips, Valério D. Pillar, Hendrik Poorter, Lourens Poorter, Peter Poschlod, Andréas Prinzing, Raphaël Proulx, Anja Rammig, Sabine Reinsch, Björn Reu, Lawren Sack, Beatriz Salgado‐Negret, Jordi Sardans, Satomi Shiodera, Bill Shipley, Andrew Siefert, Ênio Sosinski, Jean‐François Soussana, Emily K. Swaine, Nathan G. Swenson, Ken Thompson, Peter Thornton, Matthew Waldram, Evan Weiher, Michael A. White, Sue White, S. Joseph Wright‬, Benjamin Yguel, Sönke Zaehle, Amy E. Zanne, Christian Wirth

Bibliographic record

VenueGlobal Change Biology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de SherbrookeUniversity of Guelph
FundersNatural Environment Research CouncilSight Research UK
KeywordsDatabaseGeographyComputer science

Abstract

fetched live from OpenAlex

Abstract Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy‐in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log‐normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait‐based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0100.014
Science and technology studies0.0010.000
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.014

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.053
GPT teacher head0.253
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2,669
Published2011
Admission routes1
Has abstractyes

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