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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National Academy of Sciences
2017
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Acceso en línea: | https://repository.urosario.edu.co/handle/10336/22879 https://doi.org/10.1073/pnas.1708984114 |
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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 |
_version_ |
1740173045606121472 |
score |
12,131701 |