Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China
Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, th...
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ftdoajarticles:oai:doaj.org/article:fb624e65456f4bc8a8c561c27d43ba59 2023-05-15T18:40:33+02:00 Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China Yanzheng Yang Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng 2019-07-01T00:00:00Z https://doi.org/10.3389/fpls.2019.00908 https://doaj.org/article/fb624e65456f4bc8a8c561c27d43ba59 EN eng Frontiers Media S.A. https://www.frontiersin.org/article/10.3389/fpls.2019.00908/full https://doaj.org/toc/1664-462X 1664-462X doi:10.3389/fpls.2019.00908 https://doaj.org/article/fb624e65456f4bc8a8c561c27d43ba59 Frontiers in Plant Science, Vol 10 (2019) trait covariations trait–climate relationships Gaussian mixture model vegetation modeling vegetation sensitivity Plant culture SB1-1110 article 2019 ftdoajarticles https://doi.org/10.3389/fpls.2019.00908 2022-12-30T21:58:46Z Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, these traits vary under different climatic conditions. Therefore, it is urgent to quantify trait covariations, including those among specific leaf area (SLA), area-based leaf nitrogen (Narea), and leaf area index (LAI) (in 580 species across 218 sites in this study), and explore new classification methods that can be applied to model vegetation dynamics under future climate change scenarios. We use a redundancy analysis (RDA) to derive trait–climate relationships and employ a Gaussian mixture model (GMM) to project vegetation distributions under different climate scenarios. The results show that (1) the three climatic variables, mean annual temperature (MAT), mean annual precipitation (MAP), and monthly photosynthetically active radiation (mPAR) could capture 65% of the covariations of three functional traits; (2) tropical, subtropical and temperate forest complexes expand while boreal forest, temperate steppe, temperate scrub and tundra shrink under future climate change scenarios; and (3) the GMM classification based on trait covariations should be a powerful candidate for building new generation of DGVM, especially predicting the response of vegetation to future climate changes. This study provides a promising route toward developing reliable, robust and realistic vegetation models and can address a series of limitations in current models. Article in Journal/Newspaper Tundra Directory of Open Access Journals: DOAJ Articles Frontiers in Plant Science 10 |
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op_collection_id |
ftdoajarticles |
language |
English |
topic |
trait covariations trait–climate relationships Gaussian mixture model vegetation modeling vegetation sensitivity Plant culture SB1-1110 |
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trait covariations trait–climate relationships Gaussian mixture model vegetation modeling vegetation sensitivity Plant culture SB1-1110 Yanzheng Yang Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China |
topic_facet |
trait covariations trait–climate relationships Gaussian mixture model vegetation modeling vegetation sensitivity Plant culture SB1-1110 |
description |
Dynamic global vegetation models (DGVMs) suffer insufficiencies in tracking biochemical cycles and ecosystem fluxes. One important reason for these insufficiencies is that DGVMs use fixed parameters (mostly traits) to distinguish attributes and functions of plant functional types (PFTs); however, these traits vary under different climatic conditions. Therefore, it is urgent to quantify trait covariations, including those among specific leaf area (SLA), area-based leaf nitrogen (Narea), and leaf area index (LAI) (in 580 species across 218 sites in this study), and explore new classification methods that can be applied to model vegetation dynamics under future climate change scenarios. We use a redundancy analysis (RDA) to derive trait–climate relationships and employ a Gaussian mixture model (GMM) to project vegetation distributions under different climate scenarios. The results show that (1) the three climatic variables, mean annual temperature (MAT), mean annual precipitation (MAP), and monthly photosynthetically active radiation (mPAR) could capture 65% of the covariations of three functional traits; (2) tropical, subtropical and temperate forest complexes expand while boreal forest, temperate steppe, temperate scrub and tundra shrink under future climate change scenarios; and (3) the GMM classification based on trait covariations should be a powerful candidate for building new generation of DGVM, especially predicting the response of vegetation to future climate changes. This study provides a promising route toward developing reliable, robust and realistic vegetation models and can address a series of limitations in current models. |
format |
Article in Journal/Newspaper |
author |
Yanzheng Yang Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng |
author_facet |
Yanzheng Yang Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng |
author_sort |
Yanzheng Yang |
title |
Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China |
title_short |
Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China |
title_full |
Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China |
title_fullStr |
Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China |
title_full_unstemmed |
Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China |
title_sort |
trait-based climate change predictions of vegetation sensitivity and distribution in china |
publisher |
Frontiers Media S.A. |
publishDate |
2019 |
url |
https://doi.org/10.3389/fpls.2019.00908 https://doaj.org/article/fb624e65456f4bc8a8c561c27d43ba59 |
genre |
Tundra |
genre_facet |
Tundra |
op_source |
Frontiers in Plant Science, Vol 10 (2019) |
op_relation |
https://www.frontiersin.org/article/10.3389/fpls.2019.00908/full https://doaj.org/toc/1664-462X 1664-462X doi:10.3389/fpls.2019.00908 https://doaj.org/article/fb624e65456f4bc8a8c561c27d43ba59 |
op_doi |
https://doi.org/10.3389/fpls.2019.00908 |
container_title |
Frontiers in Plant Science |
container_volume |
10 |
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1766229935643426816 |