Global relationships in tree functional traits

Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits under...

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Published inNature communications Vol. 13; no. 1; pp. 3185 - 12
Main Authors Maynard, Daniel S., Bialic-Murphy, Lalasia, Zohner, Constantin M., Averill, Colin, van den Hoogen, Johan, Ma, Haozhi, Mo, Lidong, Smith, Gabriel Reuben, Acosta, Alicia T. R., Aubin, Isabelle, Berenguer, Erika, Boonman, Coline C. F., Catford, Jane A., Cerabolini, Bruno E. L., Dias, Arildo S., González-Melo, Andrés, Hietz, Peter, Lusk, Christopher H., Mori, Akira S., Niinemets, Ülo, Pillar, Valério D., Pinho, Bruno X., Rosell, Julieta A., Schurr, Frank M., Sheremetev, Serge N., da Silva, Ana Carolina, Sosinski, Ênio, van Bodegom, Peter M., Weiher, Evan, Bönisch, Gerhard, Kattge, Jens, Crowther, Thomas W.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 08.06.2022
Nature Publishing Group
Nature Portfolio
Subjects
631/158
631/158/2455
704/158/852
Bark
Biodiversity
Biodiversity and Ecology
biogeography
Botanics
Dominant traits
ecology
Ecology, environment
Economics
ecophysiology
Ecosystems
Environmental Sciences
Forests
Humanities and Social Sciences
Leaves
Life Sciences
Machine learning
Moisture effects
Morphology
multidisciplinary
Physiology
Plant Bark - physiology
Plant Leaves - physiology
Plant Roots - physiology
Science
Science (multidisciplinary)
Seeds
Seeds - physiology
Sparsity
Systematics, Phylogenetics and taxonomy
Trees
Trees - physiology
Uniqueness
Vegetal Biology
Wood - physiology
Woody plants
Biogeography
Ecophysiology
Ecology
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Abstract Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide. Understanding patterns in woody plant trait relationships and trade-offs is challenging. Here, by applying machine learning and data imputation methods to a global database of georeferenced trait measurements, the authors unravel key relationships in tree functional traits at the global scale.
AbstractList Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.
Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.Understanding patterns in woody plant trait relationships and trade-offs is challenging. Here, by applying machine learning and data imputation methods to a global database of georeferenced trait measurements, the authors unravel key relationships in tree functional traits at the global scale.
Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide. Understanding patterns in woody plant trait relationships and trade-offs is challenging. Here, by applying machine learning and data imputation methods to a global database of georeferenced trait measurements, the authors unravel key relationships in tree functional traits at the global scale.
Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not experienced by herbaceous plants. Despite a wealth of studies exploring trait relationships across the entire plant kingdom, the dominant traits underpinning these unique aspects of tree form and function remain unclear. Here, by considering 18 functional traits, encompassing leaf, seed, bark, wood, crown, and root characteristics, we quantify the multidimensional relationships in tree trait expression. We find that nearly half of trait variation is captured by two axes: one reflecting leaf economics, the other reflecting tree size and competition for light. Yet these orthogonal axes reveal strong environmental convergence, exhibiting correlated responses to temperature, moisture, and elevation. By subsequently exploring multidimensional trait relationships, we show that the full dimensionality of trait space is captured by eight distinct clusters, each reflecting a unique aspect of tree form and function. Collectively, this work identifies a core set of traits needed to quantify global patterns in functional biodiversity, and it contributes to our fundamental understanding of the functioning of forests worldwide.
Understanding patterns in woody plant trait relationships and trade-offs is challenging. Here, by applying machine learning and data imputation methods to a global database of georeferenced trait measurements, the authors unravel key relationships in tree functional traits at the global scale.
ArticleNumber 3185
Author González-Melo, Andrés
Ma, Haozhi
da Silva, Ana Carolina
Crowther, Thomas W.
Maynard, Daniel S.
Boonman, Coline C. F.
Kattge, Jens
Catford, Jane A.
Dias, Arildo S.
Sosinski, Ênio
Zohner, Constantin M.
Mori, Akira S.
Niinemets, Ülo
van den Hoogen, Johan
Acosta, Alicia T. R.
Hietz, Peter
Rosell, Julieta A.
Smith, Gabriel Reuben
Sheremetev, Serge N.
Bönisch, Gerhard
Weiher, Evan
Mo, Lidong
Pinho, Bruno X.
Berenguer, Erika
Cerabolini, Bruno E. L.
Aubin, Isabelle
van Bodegom, Peter M.
Averill, Colin
Bialic-Murphy, Lalasia
Lusk, Christopher H.
Pillar, Valério D.
Schurr, Frank M.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/35676261$$D View this record in MEDLINE/PubMed
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Keywords Biogeography
Ecophysiology
Ecology
Language English
License 2022. The Author(s).
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Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
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Snippet Due to massive energetic investments in woody support structures, trees are subject to unique physiological, mechanical, and ecological pressures not...
Understanding patterns in woody plant trait relationships and trade-offs is challenging. Here, by applying machine learning and data imputation methods to a...
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SubjectTerms 631/158
631/158/2455
704/158/852
Bark
Biodiversity
Biodiversity and Ecology
biogeography
Botanics
Dominant traits
ecology
Ecology, environment
Economics
ecophysiology
Ecosystems
Environmental Sciences
Forests
Humanities and Social Sciences
Leaves
Life Sciences
Machine learning
Moisture effects
Morphology
multidisciplinary
Physiology
Plant Bark - physiology
Plant Leaves - physiology
Plant Roots - physiology
Science
Science (multidisciplinary)
Seeds
Seeds - physiology
Sparsity
Systematics, Phylogenetics and taxonomy
Trees
Trees - physiology
Uniqueness
Vegetal Biology
Wood - physiology
Woody plants
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Title Global relationships in tree functional traits
URI https://link.springer.com/article/10.1038/s41467-022-30888-2
https://www.ncbi.nlm.nih.gov/pubmed/35676261
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https://hal.inrae.fr/hal-03710390
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https://pubmed.ncbi.nlm.nih.gov/PMC9177664
https://doaj.org/article/eae734ba19874ab39ab9e8bf37f3a395
Volume 13
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