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Open AccessJournal ArticleDOI

A global Fine-Root Ecology Database to address below-ground challenges in plant ecology.

TLDR
This Viewpoint addresses the need for a centralized fine-root trait database, and introduces the Fine-Root Ecology Database (FRED), which so far includes > 70 000 observations encompassing a broad range of root traits and also includes associated environmental data.
Abstract
Variation and tradeoffs within and among plant traits are increasingly being harnessed by empiricists and modelers to understand and predict ecosystem processes under changing environmental conditions. While fine roots play an important role in ecosystem functioning, fine-root traits are underrepresented in global trait databases. This has hindered efforts to analyze fine-root trait variation and link it with plant function and environmental conditions at a global scale. This Viewpoint addresses the need for a centralized fine-root trait database, and introduces the Fine-Root Ecology Database (FRED, http://roots.ornl.gov) which so far includes > 70 000 observations encompassing a broad range of root traits and also includes associated environmental data. FRED represents a critical step toward improving our understanding of below-ground plant ecology. For example, FRED facilitates the quantification of variation in fine-root traits across root orders, species, biomes, and environmental gradients while also providing a platform for assessments of covariation among root, leaf, and wood traits, the role of fine roots in ecosystem functioning, and the representation of fine roots in terrestrial biosphere models. Continued input of observations into FRED to fill gaps in trait coverage will improve our understanding of changes in fine-root traits across space and time.

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Citations
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Journal ArticleDOI

TRY plant trait database : Enhanced coverage and open access

Jens Kattge, +754 more
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.
Journal ArticleDOI

Evolutionary history resolves global organization of root functional traits

TL;DR: The analysis of a global dataset of 10 functionally important root traits in metabolically active first-order roots suggests that plants have evolved thinner roots since they first emerged in land ecosystems, which has enabled them to markedly improve their efficiency of soil exploration per unit of carbon invested and to reduce their dependence on symbiotic mycorrhizal fungi.
Journal ArticleDOI

Multiple facets of biodiversity drive the diversity–stability relationship

TL;DR: It is found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony in the performance of co-occurring species and enhances ecosystem stability directly, albeit weakly.
References
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Journal ArticleDOI

Advances in the rhizosphere: stretching the interface of life

TL;DR: The rhizosphere is a multifaceted, complex ‘melting pot’ of components and processes affecting plant growth and development, and thus ecosystem functioning, and the challenge now is to reveal the functionality of these multifacetted interactions.
Journal ArticleDOI

Sensitivity of four ecological models to adjustments in fine root turnover rate

TL;DR: In this article, the sensitivity of four models to adjustments in fine root turnover in forested systems: CENTURY, ED2, MC1, and LANDCARB was tested.
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Model behavior of arbuscular mycorrhizal fungi: predicting soil carbon dynamics under climate change1

TL;DR: This commentary argues that a more explicit incorporation of microbial mechanisms can increase the accuracy of ecosystem-scale models that inform the larger-scale Earth system models, to improve projections of global climate change.
Journal ArticleDOI

Variation in trait trade-offs allows differentiation among predefined plant functional types: implications for predictive ecology

TL;DR: This analysis quantitatively demonstrates how structural differences between PFTs are reflected in functional differences described by particular traits, and provides the foundation for important applications for predictive ecology.
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