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Operationalizing Plant Traits

Authors: Jens Kattge;

Operationalizing Plant Traits

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. The importance of these topics dictates the urgent need for more and better data and improved data availability and applicability, however, producing larger datasets that allow for more powerful, synthetic analyses increasingly relies on the integration of small, focused studies. Operationalizing plant functional traits has therefore been identified a key issue in plant and vegetation ecology. In 2007 the International Geosphere Bbiosphere Program (IGBP) and DIVERSITAS (together now Future Earth) initiated a global database of plant traits to make the data available for trait-based approaches in ecology and vegetation modelling. This was the start of the TRY initiative (https://www.try-db.org). In 2019 the TRY database contains about 12 million trait records for more than 300,000 plant taxa and 2000 traits. The data are publicly available under a CC BY license and so far contributed to more than 200 scientific publications. Based on experience in this bottom-up exercise, my presentation will provide a subjective view on what has been essential to make progress towards operationalizing plant traits and how far the plant trait community has progressed.

Keywords

open access, data model, open access , TRY plant trait database, framework ontology, plant traits, data integration

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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