Mapping local and global variability in plant trait distributions

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been...

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Autores Principales: Butler, Ethan E., Datta, Abhirup, Flores-Moreno, Habacuc, Chen, Ming, Wythers, Kirk R., Fazayeli, Farideh, Banerjee, Arindam, Atkin, Owen K., Kattge, Jens, Amiaud, Bernard, Blonder, Benjamin, Boenisch, Gerhard, Bond-Lamberty, Ben, Brown, Kerry A., Byun, Chaeho, Campetella, Giandiego, Cerabolini, Bruno E. L., Cornelissen, Johannes H. C., Craine, Joseph M., Craven, Dylan, de Vries, Franciska T., Díaz, Sandra, Domingues, Tomas F., Forey, Estelle, González-Melo, Andrés, Gross, Nicolas, Han, Wenxuan, Hattingh, Wesley N., Hickler, Thomas, Jansen, Steven, Kramer, Koen, Kraft, Nathan J. B., Kurokawa, Hiroko, Laughlin, Daniel C., Meir, Patrick, Minden, Vanessa, Niinemets, Ülo, Onoda, Yusuke, Peñuelas, Josep, Read, Quentin, Sack, Lawren, Schamp, Brandon, Soudzilovskaia, Nadejda A., Spasojevic, Marko J., Sosinski, Enio, Thornton, Peter E., Valladares, Fernando, van Bodegom, Peter M., Williams, Mathew, Wirth, Christian, Reich, Peter B.
Formato: Artículo (Article)
Lenguaje:Inglés (English)
Publicado: National Academy of Sciences 2017
Materias:
Acceso en línea:https://repository.urosario.edu.co/handle/10336/22879
https://doi.org/10.1073/pnas.1708984114
id ir-10336-22879
recordtype dspace
spelling ir-10336-228792022-05-02T12:37:14Z Mapping local and global variability in plant trait distributions Butler, Ethan E. Datta, Abhirup Flores-Moreno, Habacuc Chen, Ming Wythers, Kirk R. Fazayeli, Farideh Banerjee, Arindam Atkin, Owen K. Kattge, Jens Amiaud, Bernard Blonder, Benjamin Boenisch, Gerhard Bond-Lamberty, Ben Brown, Kerry A. Byun, Chaeho Campetella, Giandiego Cerabolini, Bruno E. L. Cornelissen, Johannes H. C. Craine, Joseph M. Craven, Dylan de Vries, Franciska T. Díaz, Sandra Domingues, Tomas F. Forey, Estelle González-Melo, Andrés Gross, Nicolas Han, Wenxuan Hattingh, Wesley N. Hickler, Thomas Jansen, Steven Kramer, Koen Kraft, Nathan J. B. Kurokawa, Hiroko Laughlin, Daniel C. Meir, Patrick Minden, Vanessa Niinemets, Ülo Onoda, Yusuke Peñuelas, Josep Read, Quentin Sack, Lawren Schamp, Brandon Soudzilovskaia, Nadejda A. Spasojevic, Marko J. Sosinski, Enio Thornton, Peter E. Valladares, Fernando van Bodegom, Peter M. Williams, Mathew Wirth, Christian Reich, Peter B. Nitrogen Phosphorus Article Bayes theorem Concentration (parameters) Data base Environment Evergreen Leaf area Leaf litter Model Nonhuman Plant Prediction Priority journal Ecosystem Geography Plant dispersal Quantitative trait Spatial analysis Statistical model Ecosystem Environment Geography Plant dispersal Plants Spatial analysis Bayesian modeling Climate Global Plant traits Spatial statistics heritable statistical Models Quantitative trait Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration - specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ?50×50-km cells across the entire vegetated land surface. We do this in several ways - without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means. 2017 2020-05-25T23:58:31Z info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 10916490 00278424 https://repository.urosario.edu.co/handle/10336/22879 https://doi.org/10.1073/pnas.1708984114 eng info:eu-repo/semantics/openAccess application/pdf National Academy of Sciences instname:Universidad del Rosario
institution EdocUR - Universidad del Rosario
collection DSpace
language Inglés (English)
topic Nitrogen
Phosphorus
Article
Bayes theorem
Concentration (parameters)
Data base
Environment
Evergreen
Leaf area
Leaf litter
Model
Nonhuman
Plant
Prediction
Priority journal
Ecosystem
Geography
Plant dispersal
Quantitative trait
Spatial analysis
Statistical model
Ecosystem
Environment
Geography
Plant dispersal
Plants
Spatial analysis
Bayesian modeling
Climate
Global
Plant traits
Spatial statistics
heritable
statistical
Models
Quantitative trait
spellingShingle Nitrogen
Phosphorus
Article
Bayes theorem
Concentration (parameters)
Data base
Environment
Evergreen
Leaf area
Leaf litter
Model
Nonhuman
Plant
Prediction
Priority journal
Ecosystem
Geography
Plant dispersal
Quantitative trait
Spatial analysis
Statistical model
Ecosystem
Environment
Geography
Plant dispersal
Plants
Spatial analysis
Bayesian modeling
Climate
Global
Plant traits
Spatial statistics
heritable
statistical
Models
Quantitative trait
Butler, Ethan E.
Datta, Abhirup
Flores-Moreno, Habacuc
Chen, Ming
Wythers, Kirk R.
Fazayeli, Farideh
Banerjee, Arindam
Atkin, Owen K.
Kattge, Jens
Amiaud, Bernard
Blonder, Benjamin
Boenisch, Gerhard
Bond-Lamberty, Ben
Brown, Kerry A.
Byun, Chaeho
Campetella, Giandiego
Cerabolini, Bruno E. L.
Cornelissen, Johannes H. C.
Craine, Joseph M.
Craven, Dylan
de Vries, Franciska T.
Díaz, Sandra
Domingues, Tomas F.
Forey, Estelle
González-Melo, Andrés
Gross, Nicolas
Han, Wenxuan
Hattingh, Wesley N.
Hickler, Thomas
Jansen, Steven
Kramer, Koen
Kraft, Nathan J. B.
Kurokawa, Hiroko
Laughlin, Daniel C.
Meir, Patrick
Minden, Vanessa
Niinemets, Ülo
Onoda, Yusuke
Peñuelas, Josep
Read, Quentin
Sack, Lawren
Schamp, Brandon
Soudzilovskaia, Nadejda A.
Spasojevic, Marko J.
Sosinski, Enio
Thornton, Peter E.
Valladares, Fernando
van Bodegom, Peter M.
Williams, Mathew
Wirth, Christian
Reich, Peter B.
Mapping local and global variability in plant trait distributions
description Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration - specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ?50×50-km cells across the entire vegetated land surface. We do this in several ways - without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.
format Artículo (Article)
author Butler, Ethan E.
Datta, Abhirup
Flores-Moreno, Habacuc
Chen, Ming
Wythers, Kirk R.
Fazayeli, Farideh
Banerjee, Arindam
Atkin, Owen K.
Kattge, Jens
Amiaud, Bernard
Blonder, Benjamin
Boenisch, Gerhard
Bond-Lamberty, Ben
Brown, Kerry A.
Byun, Chaeho
Campetella, Giandiego
Cerabolini, Bruno E. L.
Cornelissen, Johannes H. C.
Craine, Joseph M.
Craven, Dylan
de Vries, Franciska T.
Díaz, Sandra
Domingues, Tomas F.
Forey, Estelle
González-Melo, Andrés
Gross, Nicolas
Han, Wenxuan
Hattingh, Wesley N.
Hickler, Thomas
Jansen, Steven
Kramer, Koen
Kraft, Nathan J. B.
Kurokawa, Hiroko
Laughlin, Daniel C.
Meir, Patrick
Minden, Vanessa
Niinemets, Ülo
Onoda, Yusuke
Peñuelas, Josep
Read, Quentin
Sack, Lawren
Schamp, Brandon
Soudzilovskaia, Nadejda A.
Spasojevic, Marko J.
Sosinski, Enio
Thornton, Peter E.
Valladares, Fernando
van Bodegom, Peter M.
Williams, Mathew
Wirth, Christian
Reich, Peter B.
author_facet Butler, Ethan E.
Datta, Abhirup
Flores-Moreno, Habacuc
Chen, Ming
Wythers, Kirk R.
Fazayeli, Farideh
Banerjee, Arindam
Atkin, Owen K.
Kattge, Jens
Amiaud, Bernard
Blonder, Benjamin
Boenisch, Gerhard
Bond-Lamberty, Ben
Brown, Kerry A.
Byun, Chaeho
Campetella, Giandiego
Cerabolini, Bruno E. L.
Cornelissen, Johannes H. C.
Craine, Joseph M.
Craven, Dylan
de Vries, Franciska T.
Díaz, Sandra
Domingues, Tomas F.
Forey, Estelle
González-Melo, Andrés
Gross, Nicolas
Han, Wenxuan
Hattingh, Wesley N.
Hickler, Thomas
Jansen, Steven
Kramer, Koen
Kraft, Nathan J. B.
Kurokawa, Hiroko
Laughlin, Daniel C.
Meir, Patrick
Minden, Vanessa
Niinemets, Ülo
Onoda, Yusuke
Peñuelas, Josep
Read, Quentin
Sack, Lawren
Schamp, Brandon
Soudzilovskaia, Nadejda A.
Spasojevic, Marko J.
Sosinski, Enio
Thornton, Peter E.
Valladares, Fernando
van Bodegom, Peter M.
Williams, Mathew
Wirth, Christian
Reich, Peter B.
author_sort Butler, Ethan E.
title Mapping local and global variability in plant trait distributions
title_short Mapping local and global variability in plant trait distributions
title_full Mapping local and global variability in plant trait distributions
title_fullStr Mapping local and global variability in plant trait distributions
title_full_unstemmed Mapping local and global variability in plant trait distributions
title_sort mapping local and global variability in plant trait distributions
publisher National Academy of Sciences
publishDate 2017
url https://repository.urosario.edu.co/handle/10336/22879
https://doi.org/10.1073/pnas.1708984114
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