Assessing the reliability of predicted plant trait distributions at the global scale
AIM: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant trait...
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ftpubmed:oai:pubmedcentral.nih.gov:7319484 2023-05-15T15:12:18+02:00 Assessing the reliability of predicted plant trait distributions at the global scale Boonman, Coline C. F. Benítez‐López, Ana Schipper, Aafke M. Thuiller, Wilfried Anand, Madhur Cerabolini, Bruno E. L. Cornelissen, Johannes H. C. Gonzalez‐Melo, Andres Hattingh, Wesley N. Higuchi, Pedro Laughlin, Daniel C. Onipchenko, Vladimir G. Peñuelas, Josep Poorter, Lourens Soudzilovskaia, Nadejda A. Huijbregts, Mark A. J. Santini, Luca 2020-03-20 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319484/ http://www.ncbi.nlm.nih.gov/pubmed/32612452 https://doi.org/10.1111/geb.13086 en eng John Wiley and Sons Inc. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319484/ http://www.ncbi.nlm.nih.gov/pubmed/32612452 http://dx.doi.org/10.1111/geb.13086 © 2020 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. CC-BY Glob Ecol Biogeogr Research Papers Text 2020 ftpubmed https://doi.org/10.1111/geb.13086 2020-07-05T00:51:08Z AIM: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty. LOCATION: Global. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions. RESULTS: Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait–environment relationships and trait–trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%. MAIN CONCLUSIONS: Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high‐quality data for traits that mostly respond to large‐scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using ... Text Arctic PubMed Central (PMC) Arctic Global Ecology and Biogeography 29 6 1034 1051 |
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Research Papers Boonman, Coline C. F. Benítez‐López, Ana Schipper, Aafke M. Thuiller, Wilfried Anand, Madhur Cerabolini, Bruno E. L. Cornelissen, Johannes H. C. Gonzalez‐Melo, Andres Hattingh, Wesley N. Higuchi, Pedro Laughlin, Daniel C. Onipchenko, Vladimir G. Peñuelas, Josep Poorter, Lourens Soudzilovskaia, Nadejda A. Huijbregts, Mark A. J. Santini, Luca Assessing the reliability of predicted plant trait distributions at the global scale |
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Research Papers |
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AIM: Predictions of plant traits over space and time are increasingly used to improve our understanding of plant community responses to global environmental change. A necessary step forward is to assess the reliability of global trait predictions. In this study, we predict community mean plant traits at the global scale and present a systematic evaluation of their reliability in terms of the accuracy of the models, ecological realism and various sources of uncertainty. LOCATION: Global. TIME PERIOD: Present. MAJOR TAXA STUDIED: Vascular plants. METHODS: We predicted global distributions of community mean specific leaf area, leaf nitrogen concentration, plant height and wood density with an ensemble modelling approach based on georeferenced, locally measured trait data representative of the plant community. We assessed the predictive performance of the models, the plausibility of predicted trait combinations, the influence of data quality, and the uncertainty across geographical space attributed to spatial extrapolation and diverging model predictions. RESULTS: Ensemble predictions of community mean plant height, specific leaf area and wood density resulted in ecologically plausible trait–environment relationships and trait–trait combinations. Leaf nitrogen concentration, however, could not be predicted reliably. The ensemble approach was better at predicting community trait means than any of the individual modelling techniques, which varied greatly in predictive performance and led to divergent predictions, mostly in African deserts and the Arctic, where predictions were also extrapolated. High data quality (i.e., including intraspecific variability and a representative species sample) increased model performance by 28%. MAIN CONCLUSIONS: Plant community traits can be predicted reliably at the global scale when using an ensemble approach and high‐quality data for traits that mostly respond to large‐scale environmental factors. We recommend applying ensemble forecasting to account for model uncertainty, using ... |
format |
Text |
author |
Boonman, Coline C. F. Benítez‐López, Ana Schipper, Aafke M. Thuiller, Wilfried Anand, Madhur Cerabolini, Bruno E. L. Cornelissen, Johannes H. C. Gonzalez‐Melo, Andres Hattingh, Wesley N. Higuchi, Pedro Laughlin, Daniel C. Onipchenko, Vladimir G. Peñuelas, Josep Poorter, Lourens Soudzilovskaia, Nadejda A. Huijbregts, Mark A. J. Santini, Luca |
author_facet |
Boonman, Coline C. F. Benítez‐López, Ana Schipper, Aafke M. Thuiller, Wilfried Anand, Madhur Cerabolini, Bruno E. L. Cornelissen, Johannes H. C. Gonzalez‐Melo, Andres Hattingh, Wesley N. Higuchi, Pedro Laughlin, Daniel C. Onipchenko, Vladimir G. Peñuelas, Josep Poorter, Lourens Soudzilovskaia, Nadejda A. Huijbregts, Mark A. J. Santini, Luca |
author_sort |
Boonman, Coline C. F. |
title |
Assessing the reliability of predicted plant trait distributions at the global scale |
title_short |
Assessing the reliability of predicted plant trait distributions at the global scale |
title_full |
Assessing the reliability of predicted plant trait distributions at the global scale |
title_fullStr |
Assessing the reliability of predicted plant trait distributions at the global scale |
title_full_unstemmed |
Assessing the reliability of predicted plant trait distributions at the global scale |
title_sort |
assessing the reliability of predicted plant trait distributions at the global scale |
publisher |
John Wiley and Sons Inc. |
publishDate |
2020 |
url |
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319484/ http://www.ncbi.nlm.nih.gov/pubmed/32612452 https://doi.org/10.1111/geb.13086 |
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Arctic |
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Arctic |
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Arctic |
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Arctic |
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Glob Ecol Biogeogr |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319484/ http://www.ncbi.nlm.nih.gov/pubmed/32612452 http://dx.doi.org/10.1111/geb.13086 |
op_rights |
© 2020 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
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CC-BY |
op_doi |
https://doi.org/10.1111/geb.13086 |
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Global Ecology and Biogeography |
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6 |
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