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Title: Mapping local and global variability in plant trait distributions

Abstract

Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrence ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates ofmore » functional diversity.« less

Authors:
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Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); Australian Research Council; National Science Foundation (NSF); Univ. of Minnesota, Minneapolis, MN (United States); Max Planck Society, Jena (Germany). Max Planck Inst. for Biogeochemistry; Univ. of Leipzig (Germany); Natural Environment Research Council (NERC); European Research Council (ERC); Spanish Government; Catalan Government; Wageningen Univ. and Research (Netherlands); National Natural Science Foundation of China (NNSFC); Chinese Academy of Sciences
OSTI Identifier:
1410892
Alternate Identifier(s):
OSTI ID: 1415695; OSTI ID: 1427647
Report Number(s):
PNNL-SA-121738
Journal ID: ISSN 0027-8424; KP1703020
Grant/Contract Number:
AC05-00OR22725; AC05-76RL01830; SC0012677; CE140100008; DEB-1234162; DEB-1242531; IIS-1563950; 640176; NE/M019160/1; ERC-SyG-2013-610028 IMBALANCE-P; CGL2013-48074-P; SGR 2014-274; FT110100457; NE/F002149/1; 41473068
Resource Type:
Journal Article: Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Volume: 114; Journal Issue: 51; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences, Washington, DC (United States)
Country of Publication:
United States
Language:
English
Subject:
le; gl; plant traits; Bayesian modeling; spatial statistics; global climate; 54 ENVIRONMENTAL SCIENCES; 60 APPLIED LIFE SCIENCES; global; climate

Citation Formats

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, and Reich, Peter B. Mapping local and global variability in plant trait distributions. United States: N. p., 2017. Web. doi:10.1073/pnas.1708984114.
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. United States. doi:10.1073/pnas.1708984114.
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, and Reich, Peter B. Fri . "Mapping local and global variability in plant trait distributions". United States. doi:10.1073/pnas.1708984114.
@article{osti_1410892,
title = {Mapping local and global variability in plant trait distributions},
author = {Butler, Ethan E. and Datta, Abhirup and Flores-Moreno, Habacuc and Chen, Ming and Wythers, Kirk R. and Fazayeli, Farideh and Banerjee, Arindam and Atkin, Owen K. and Kattge, Jens and Amiaud, Bernard and Blonder, Benjamin and Boenisch, Gerhard and Bond-Lamberty, Ben and Brown, Kerry A. and Byun, Chaeho and Campetella, Giandiego and Cerabolini, Bruno E. L. and Cornelissen, Johannes H. C. and Craine, Joseph M. and Craven, Dylan and de Vries, Franciska T. and Díaz, Sandra and Domingues, Tomas F. and Forey, Estelle and González-Melo, Andrés and Gross, Nicolas and Han, Wenxuan and Hattingh, Wesley N. and Hickler, Thomas and Jansen, Steven and Kramer, Koen and Kraft, Nathan J. B. and Kurokawa, Hiroko and Laughlin, Daniel C. and Meir, Patrick and Minden, Vanessa and Niinemets, Ülo and Onoda, Yusuke and Peñuelas, Josep and Read, Quentin and Sack, Lawren and Schamp, Brandon and Soudzilovskaia, Nadejda A. and Spasojevic, Marko J. and Sosinski, Enio and Thornton, Peter E. and Valladares, Fernando and van Bodegom, Peter M. and Williams, Mathew and Wirth, Christian and Reich, Peter B.},
abstractNote = {Accurate trait-environment relationships and global maps of plant trait distributions represent a needed stepping stone in global biogeography and are critical constraints of key parameters for land models. Here, we use a global data set of plant traits to map trait distributions closely coupled to photosynthesis and foliar respiration: specific leaf area (SLA), and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm); We propose two models to extrapolate geographically sparse point data to continuous spatial surfaces. The first is a categorical model using species mean trait values, categorized into plant functional types (PFTs) and extrapolating to PFT occurrence ranges identified by remote sensing. The second is a Bayesian spatial model that incorporates information about PFT, location and environmental covariates to estimate trait distributions. Both models are further stratified by varying the number of PFTs; The performance of the models was evaluated based on their explanatory and predictive ability. The Bayesian spatial model leveraging the largest number of PFTs produced the best maps; The interpolation of full trait distributions enables a wider diversity of vegetation to be represented across the land surface. These maps may be used as input to Earth System Models and to evaluate other estimates of functional diversity.},
doi = {10.1073/pnas.1708984114},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 51,
volume = 114,
place = {United States},
year = {Fri Dec 01 00:00:00 EST 2017},
month = {Fri Dec 01 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1073/pnas.1708984114

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