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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2019-07-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fpls.2019.00908/full |
| _version_ | 1811267287567564800 |
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| author | Yanzheng Yang Yanzheng Yang Jun Zhao Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng Changhui Peng |
| author_facet | Yanzheng Yang Yanzheng Yang Jun Zhao Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng Changhui Peng |
| author_sort | Yanzheng Yang |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-04-12T20:59:49Z |
| format | Article |
| id | doaj.art-fb624e65456f4bc8a8c561c27d43ba59 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-12T20:59:49Z |
| publishDate | 2019-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-fb624e65456f4bc8a8c561c27d43ba592022-12-22T03:16:52ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2019-07-011010.3389/fpls.2019.00908458489Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in ChinaYanzheng Yang0Yanzheng Yang1Jun Zhao2Jun Zhao3Pengxiang Zhao4Hui Wang5Boheng Wang6Shaofeng Su7Mingxu Li8Liming Wang9Qiuan Zhu10Zhiyong Pang11Changhui Peng12Changhui Peng13College of Forestry, Northwest A&F University, Yangling, ChinaState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaState Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaInstitute of Surface-Earth System Science, Tianjin University, Tianjin, ChinaCollege of Forestry, Northwest A&F University, Yangling, ChinaDepartment of Biology Sciences, Institute of Environment Sciences, University of Québec at Montreal, Montreal, QC, CanadaDynamic 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.https://www.frontiersin.org/article/10.3389/fpls.2019.00908/fulltrait covariationstrait–climate relationshipsGaussian mixture modelvegetation modelingvegetation sensitivity |
| spellingShingle | Yanzheng Yang Yanzheng Yang Jun Zhao Jun Zhao Pengxiang Zhao Hui Wang Boheng Wang Shaofeng Su Mingxu Li Liming Wang Qiuan Zhu Zhiyong Pang Changhui Peng Changhui Peng Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China Frontiers in Plant Science trait covariations trait–climate relationships Gaussian mixture model vegetation modeling vegetation sensitivity |
| title | 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_short | 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 |
| topic | trait covariations trait–climate relationships Gaussian mixture model vegetation modeling vegetation sensitivity |
| url | https://www.frontiersin.org/article/10.3389/fpls.2019.00908/full |
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