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Zheng Y, Zhao W, Chen A, Chen Y, Chen J, Zhu Z. Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170439. [PMID: 38281630 DOI: 10.1016/j.scitotenv.2024.170439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Revised: 01/10/2024] [Accepted: 01/23/2024] [Indexed: 01/30/2024]
Abstract
Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m-2 d-1 °C-1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.
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Affiliation(s)
- Yaoyao Zheng
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China; Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Weiqing Zhao
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China; Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Anping Chen
- Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, USA
| | - Yue Chen
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China; Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Jiana Chen
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China; Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China
| | - Zaichun Zhu
- School of Urban Planning and Design, Shenzhen Graduate School, Peking University, Shenzhen 518055, China; Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China, Shenzhen Graduate School, Peking University, Shenzhen 518055, China.
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Li D, Li X, Li Z, Fu Y, Zhang J, Zhao Y, Wang Y, Liang E, Rossi S. Drought limits vegetation carbon sequestration by affecting photosynthetic capacity of semi-arid ecosystems on the Loess Plateau. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:168778. [PMID: 38008313 DOI: 10.1016/j.scitotenv.2023.168778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/28/2023] [Accepted: 11/20/2023] [Indexed: 11/28/2023]
Abstract
Drought is the driver for ecosystem production in semi-arid areas. However, the response mechanism of ecosystem productivity to drought remains largely unknown. In particular, it is still unclear whether drought limits the production via photosynthetic capacity or phenological process. Herein, we assess the effects of maximum seasonal photosynthesis, growing season length, and climate on the annual gross primary productivity (GPP) in vegetation areas of the Loess Plateau using multi-source remote sensing and climate data from 2001 to 2021. We found that maximum seasonal photosynthesis rather than growing season length dominates annual GPP, with above 90 % of the study area showing significant and positive correlation. GPP and maximum seasonal photosynthesis were positively correlated with self-calibrating Palmer Drought Severity Index (scPDSI), standardized precipitation and evapotranspiration index (SPEI) in >95 % of the study area. Structural equation model demonstrated that both drought indices contributed to the annual GPP by promoting the maximum seasonal photosynthesis. Total annual precipitation had a positive and significant effect on two drought indices, whereas the effects of temperature and radiation were not significant. Evidence from wood formation data also confirmed that low precipitation inhibited long-term carbon sequestration by decreasing the maximum growth rate in forests. Our findings suggest that drought limits ecosystem carbon sequestration by inhibiting vegetation photosynthetic capacity rather than phenology, providing a support for assessing the future dynamics of the terrestrial carbon cycle and guiding landscape management in semi-arid ecosystems.
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Affiliation(s)
- Dou Li
- College of Ecology, Lanzhou University, Lanzhou 730000, China; Center for Pan-third Pole Environment, Lanzhou University, Lanzhou 730000, China; State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaoxia Li
- State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.
| | - Zongshan Li
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
| | - Yang Fu
- Center for Pan-third Pole Environment, Lanzhou University, Lanzhou 730000, China; College of Earth and Environment Science, Lanzhou University, Lanzhou 730000, China
| | - Jingtian Zhang
- State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 10049, China
| | - Yijin Zhao
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Yafeng Wang
- State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Eryuan Liang
- State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Sergio Rossi
- Laboratoire sur les écosystèmes terrestres boréaux, Département des Sciences Fondamentales, Université du Québec à Chicoutimi, Chicoutimi G7H2B1, Canada
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Chen L, Keski-Saari S, Kontunen-Soppela S, Zhu X, Zhou X, Hänninen H, Pumpanen J, Mola-Yudego B, Wu D, Berninger F. Immediate and carry-over effects of late-spring frost and growing season drought on forest gross primary productivity capacity in the Northern Hemisphere. GLOBAL CHANGE BIOLOGY 2023; 29:3924-3940. [PMID: 37165918 DOI: 10.1111/gcb.16751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 03/27/2023] [Indexed: 05/12/2023]
Abstract
Forests are increasingly exposed to extreme global warming-induced climatic events. However, the immediate and carry-over effects of extreme events on forests are still poorly understood. Gross primary productivity (GPP) capacity is regarded as a good proxy of the ecosystem's functional stability, reflecting its physiological response to its surroundings. Using eddy covariance data from 34 forest sites in the Northern Hemisphere, we analyzed the immediate and carry-over effects of late-spring frost (LSF) and growing season drought on needle-leaf and broadleaf forests. Path analysis was applied to reveal the plausible reasons behind the varied responses of forests to extreme events. The results show that LSF had clear immediate effects on the GPP capacity of both needle-leaf and broadleaf forests. However, GPP capacity in needle-leaf forests was more sensitive to drought than in broadleaf forests. There was no interaction between LSF and drought in either needle-leaf or broadleaf forests. Drought effects were still visible when LSF and drought coexisted in needle-leaf forests. Path analysis further showed that the response of GPP capacity to drought differed between needle-leaf and broadleaf forests, mainly due to the difference in the sensitivity of canopy conductance. Moreover, LSF had a more severe and long-lasting carry-over effect on forests than drought. These results enrich our understanding of the mechanisms of forest response to extreme events across forest types.
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Affiliation(s)
- Liang Chen
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Sarita Keski-Saari
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
- Department of Geographical and Historical Studies, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Sari Kontunen-Soppela
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Xudan Zhu
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Xuan Zhou
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Heikki Hänninen
- State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Hangzhou, China
| | - Jukka Pumpanen
- Department of Environmental and Biological Sciences, Kuopio Campus, University of Eastern Finland, Kuopio, Finland
| | - Blas Mola-Yudego
- School of Forest Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
| | - Di Wu
- Department of Environmental and Biological Sciences, Kuopio Campus, University of Eastern Finland, Kuopio, Finland
| | - Frank Berninger
- Department of Environmental and Biological Sciences, Joensuu Campus, University of Eastern Finland, Joensuu, Finland
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Zhu XJ, Yu GR, Chen Z, Zhang WK, Han L, Wang QF, Chen SP, Liu SM, Wang HM, Yan JH, Tan JL, Zhang FW, Zhao FH, Li YN, Zhang YP, Shi PL, Zhu JJ, Wu JB, Zhao ZH, Hao YB, Sha LQ, Zhang YC, Jiang SC, Gu FX, Wu ZX, Zhang YJ, Zhou L, Tang YK, Jia BR, Li YQ, Song QH, Dong G, Gao YH, Jiang ZD, Sun D, Wang JL, He QH, Li XH, Wang F, Wei WX, Deng ZM, Hao XX, Li Y, Liu XL, Zhang XF, Zhu ZL. Mapping Chinese annual gross primary productivity with eddy covariance measurements and machine learning. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 857:159390. [PMID: 36243072 DOI: 10.1016/j.scitotenv.2022.159390] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 10/05/2022] [Accepted: 10/08/2022] [Indexed: 06/16/2023]
Abstract
Annual gross primary productivity (AGPP) is the basis for grain production and terrestrial carbon sequestration. Mapping regional AGPP from site measurements provides methodological support for analysing AGPP spatiotemporal variations thereby ensures regional food security and mitigates climate change. Based on 641 site-year eddy covariance measuring AGPP from China, we built an AGPP mapping scheme based on its formation and selected the optimal mapping way, which was conducted through analysing the predicting performances of divergent mapping tools, variable combinations, and mapping approaches in predicting observed AGPP variations. The reasonability of the selected optimal scheme was confirmed by assessing the consistency between its generating AGPP and previous products in spatiotemporal variations and total amount. Random forest regression tree explained 85 % of observed AGPP variations, outperforming other machine learning algorithms and classical statistical methods. Variable combinations containing climate, soil, and biological factors showed superior performance to other variable combinations. Mapping AGPP through predicting AGPP per leaf area (PAGPP) explained 86 % of AGPP variations, which was superior to other approaches. The optimal scheme was thus using a random forest regression tree, combining climate, soil, and biological variables, and predicting PAGPP. The optimal scheme generating AGPP of Chinese terrestrial ecosystems decreased from southeast to northwest, which was highly consistent with previous products. The interannual trend and interannual variation of our generating AGPP showed a decreasing trend from east to west and from southeast to northwest, respectively, which was consistent with data-oriented products. The mean total amount of generated AGPP was 7.03 ± 0.45 PgC yr-1 falling into the range of previous works. Considering the consistency between the generated AGPP and previous products, our optimal mapping way was suitable for mapping AGPP from site measurements. Our results provided a methodological support for mapping regional AGPP and other fluxes.
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Affiliation(s)
- Xian-Jin Zhu
- College of Agronomy, Shenyang Agricultural University, Shenyang 110866, China; Liaoning Panjin Wetland Ecosystem National Observation and Research Station, Shenyang 110866, China
| | - Gui-Rui Yu
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhi Chen
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wei-Kang Zhang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Lang Han
- Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin,300072, China
| | - Qiu-Feng Wang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Shi-Ping Chen
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Shao-Min Liu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Hui-Min Wang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jun-Hua Yan
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Jun-Lei Tan
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Fa-Wei Zhang
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
| | - Feng-Hua Zhao
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Ying-Nian Li
- Northwest Institute of Plateau Biology, Chinese Academy of Sciences, Xining 810008, China
| | - Yi-Ping Zhang
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Pei-Li Shi
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Jiao-Jun Zhu
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Jia-Bing Wu
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Zhong-Hui Zhao
- Central South University of Forestry and Technology, Changsha 410004, China
| | - Yan-Bin Hao
- University of the Chinese Academy of Sciences, Beijing 100049, China
| | - Li-Qing Sha
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Yu-Cui Zhang
- Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050021, China
| | | | - Feng-Xue Gu
- Institute of Environmental and sustainable development in agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zhi-Xiang Wu
- Rubber research institute, Chinese Academy of tropical agricultural sciences, Haikou 570100, China
| | - Yang-Jian Zhang
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Zhou
- Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
| | - Ya-Kun Tang
- Northwest A&F University, Yangling 712100, China
| | - Bing-Rui Jia
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Yu-Qiang Li
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Qing-Hai Song
- Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China
| | - Gang Dong
- Shanxi University, Taiyuan 030006, China
| | - Yan-Hong Gao
- Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
| | - Zheng-De Jiang
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
| | - Dan Sun
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Jian-Lin Wang
- Qingdao Agricultural University, Qingdao 266109, China
| | - Qi-Hua He
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
| | - Xin-Hu Li
- Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Fei Wang
- Inner Mongolia Agricultural University, Hohhot 010018, China
| | - Wen-Xue Wei
- Institute of Subtropical Agriculture Chinese Academy of Sciences, Changsha 410125, China
| | - Zheng-Miao Deng
- Institute of Subtropical Agriculture Chinese Academy of Sciences, Changsha 410125, China
| | - Xiang-Xiang Hao
- Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Yan Li
- Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
| | - Xiao-Li Liu
- Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Xi-Feng Zhang
- Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
| | - Zhi-Lin Zhu
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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Xie M, Li L, Liu B, Liu Y, Wan Q. Responses of terrestrial ecosystem productivity and community structure to intra-annual precipitation patterns: A meta-analysis. FRONTIERS IN PLANT SCIENCE 2023; 13:1088202. [PMID: 36699850 PMCID: PMC9868929 DOI: 10.3389/fpls.2022.1088202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
INTRODUCTION The productivity and community structures of terrestrial ecosystems are regulated by total precipitation amount and intra-annual precipitation patterns, which have been altered by climate change. The timing and sizes of precipitation events are the two key factors of intra-annual precipitation patterns and potentially drive ecosystem function by influencing soil moisture. However, the generalizable patterns of how intra-annual precipitation patterns affect the productivity and community structures of ecosystems remain unclear. METHODS We synthesized 633 observations from 17 studies and conducted a global meta-analysis to investigate the influences of intra-annual precipitation patterns on the productivity and community structures of terrestrial ecosystems. By classifying intra-annual precipitation patterns, we also assess the importance of the magnitude and timing of precipitation events on plant productivity. RESULTS Our results showed that the intra-annual precipitation patterns decreased diversity by 6.3% but increased belowground net primary productivity, richness, and relative abundance by 16.8%, 10.5%, and 45.0%, respectively. Notably, we found that the timing uniformity of precipitation events was more important for plant productivity, while the plant community structure benefited from the increased precipitation variability. In addition, the relationship between plant productivity and community structure and soil moisture dynamic response was more consistent with the nonlinear model. COMCLUSIONS The patterns of the responses of plant productivity and community structure to altered intra-annual precipitation patterns were revealed, and the importance of the timing uniformity of precipitation events to the functioning of production systems was highlighted, which is essential to enhancing understanding of the structures and functions of ecosystems subjected to altered precipitation patterns and predicting their changes.
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Affiliation(s)
- Mingyu Xie
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Xinjiang Key Laboratory of Desert Plant Roots Ecology and Vegetation Restoration, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lei Li
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Xinjiang Key Laboratory of Desert Plant Roots Ecology and Vegetation Restoration, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bo Liu
- Shandong Provincial Key Laboratory of Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi, China
| | - Yalan Liu
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Xinjiang Key Laboratory of Desert Plant Roots Ecology and Vegetation Restoration, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Qian Wan
- State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Xinjiang Key Laboratory of Desert Plant Roots Ecology and Vegetation Restoration, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China
- Cele National Station of Observation and Research for Desert-Grassland Ecosystems, Cele, China
- University of Chinese Academy of Sciences, Beijing, China
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Zhang W, Yu G, Chen Z, Zhu X, Han L, Liu Z, Lin Y, Han S, Sha L, Wang H, Wang Y, Yan J, Zhang Y, Gharun M. Photosynthetic capacity dominates the interannual variation of annual gross primary productivity in the Northern Hemisphere. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 849:157856. [PMID: 35934043 DOI: 10.1016/j.scitotenv.2022.157856] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Revised: 07/09/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
Annual gross primary productivity (AGPP) of terrestrial ecosystems is the largest carbon flux component in ecosystems; however, it's unclear whether photosynthetic capacity or phenology dominates interannual variation of AGPP, and a better understanding of this could contribute to estimation of carbon sinks and their interactions with climate change. In this study, observed GPP data of 494 site-years from 39 eddy covariance sites in Northern Hemisphere were used to investigate mechanisms of interannual variation of AGPP. This study first decomposed AGPP into three seasonal dynamic attribute parameters (growing season length (CUP), maximum daily GPP (GPPmax), and the ratio of mean daily GPP to GPPmax (αGPP)), and then decomposed AGPP into mean leaf area index (LAIm) and annual photosynthetic capacity per leaf area (AGPPlm). Furthermore, GPPmax was decomposed into leaf area index of DOYmax (the day when GPPmax appeared) (LAImax) and photosynthesis per leaf area of DOYmax (GPPlmax). Relative contributions of parameters to AGPP and GPPmax were then calculated. Finally, environmental variables of DOYmax were extracted to analyze factors influencing interannual variation of GPPlmax. Trends of AGPP in 39 ecosystems varied from -65.23 to 53.05 g C m-2 yr-2, with the mean value of 6.32 g C m-2 yr-2. Photosynthetic capacity (GPPmax and AGPPlm), not CUP or LAI, was the main factor dominating interannual variation of AGPP. GPPlmax determined the interannual variation of GPPmax, and temperature, water, and radiation conditions of DOYmax affected the interannual variation of GPPlmax. This study used the cascade relationship of "environmental variables-GPPlmax-GPPmax-AGPP" to explain the mechanism of interannual variation of AGPP, which can provide new ideas for the AGPP estimation based on seasonal dynamic of GPP.
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Affiliation(s)
- Weikang Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Guirui Yu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Zhi Chen
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xianjin Zhu
- College of Agronomy, Shenyang Agricultural University, Shenyang 100161, China
| | - Lang Han
- School of Earth System Science, Tianjin University, Tianjin 300072, China; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhaogang Liu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yong Lin
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shijie Han
- School of Life Science, Henan University, Kaifeng 475004, China
| | - Liqing Sha
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China
| | - Huimin Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanfen Wang
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Junhua Yan
- Key Laboratory of Vegetation Restoration and Management of Degraded Ecosystems, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
| | - Yiping Zhang
- CAS Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Menglun 666303, China
| | - Mana Gharun
- Department of Environmental Systems Science, ETH Zürich, Switzerland; Institute of Landscape Ecology, University of Münster, Germany
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7
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Wang S, Chen W, Fu Z, Li Z, Wang J, Liao J, Niu S. Seasonal and Inter-Annual Variations of Carbon Dioxide Fluxes and Their Determinants in an Alpine Meadow. FRONTIERS IN PLANT SCIENCE 2022; 13:894398. [PMID: 35812942 PMCID: PMC9260316 DOI: 10.3389/fpls.2022.894398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/20/2022] [Indexed: 06/15/2023]
Abstract
The alpine meadow is one of the most important ecosystems on the Qinghai-Tibet Plateau (QTP) due to its huge carbon storage and wide distribution. Evaluating the carbon fluxes in alpine meadow ecosystems is crucial to understand the dynamics of carbon storage in high-altitude areas. Here, we investigated the carbon fluxes at seasonal and inter-annual timescales based on 5 years of observations of eddy covariance fluxes in the Zoige alpine meadow on the eastern Tibetan Plateau. We found that the Zoige alpine meadow acted as a faint carbon source of 94.69 ± 86.44 g C m-2 y-1 during the observation periods with large seasonal and inter-annual variations (IAVs). At the seasonal scale, gross primary productivity (GPP) and ecosystem respiration (Re) were positively correlated with photosynthetic photon flux density (PPFD), average daily temperature (Ta), and vapor pressure (VPD) and had negative relationships with volumetric water content (VWC). Seasonal variations of net ecosystem carbon dioxide (CO2) exchange (NEE) were mostly explained by Ta, followed by PPFD, VPD, and VWC. The IAVs of GPP and Re were mainly attributable to the IAV of the maximum GPP rate (GPPmax) and maximum Re rate (Remax), respectively, both of which increased with the percentage of Cyperaceae and decreased with the percentage of Polygonaceae changes across years. The IAV of NEE was well explained by the anomalies of the maximum CO2 release rate (MCR). These results indicated that the annual net CO2 exchange in the alpine meadow ecosystem was controlled mainly by the maximum C release rates. Therefore, a better understanding of physiological response to various environmental factors at peak C uptake and release seasons will largely improve the predictions of GPP, Re, and NEE in the context of global change.
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Affiliation(s)
- Song Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Weinan Chen
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Zheng Fu
- Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA-CNRS-UVSQ, UMR8212, Gif-sur-Yvette, France
| | - Zhaolei Li
- College of Resources and Environment, and Academy of Agricultural Sciences, Southwest University, Chongqing, China
| | - Jinsong Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Research, Chinese Academy of Sciences, Beijing, China
| | - Jiaqiang Liao
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Shuli Niu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
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8
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Lv Y, Zhao XQ, Zhang SR, Zhang JG, Yue KT, Meng BP, Li M, Cui WX, Sun Y, Zhang JG, Chang L, Li JR, Yi SH, Shen MH. Herbaceous Dominant the Changes of Normalized Difference Vegetation Index in the Transition Zone Between Desert and Typical Steppe in Inner Mongolia, China. FRONTIERS IN PLANT SCIENCE 2022; 12:832044. [PMID: 35197991 PMCID: PMC8859413 DOI: 10.3389/fpls.2021.832044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/29/2021] [Indexed: 06/14/2023]
Abstract
Asymmetric responses of aboveground net primary productivity (ANPP) to precipitation were identified as a signal to predict ecosystem state shifts at temperate grassland zones in Inner Mongolia, China. However, mechanism studies were still lacking. This study hypothesized that the enhanced growth and newly emerged herbaceous after increased precipitation resulted in the highest asymmetry at the transition zone between desert and typical steppe. We monitored the responses of the normalized difference vegetation index (NDVI) of different species to precipitation events using un-manned aerial vehicle technology to test this hypothesis. NDVI and species richness were measured twice at fixed points in July and August with a time interval of 15 days. Results showed that: (1) From July to August, NDVI in the transition zone increased significantly after precipitation (P < 0.05), but NDVI in both the desert and typical steppe showed a non-significant change (P > 0.05). (2) In the transition zone, NDVI increases from the shrub and herbaceous contributed to 37 and 63% increases of the site NDVI, respectively. (3) There was a significant difference in species richness between July and August in the transition zone (P < 0.05), mainly caused by the herbaceous (Chenopodiaceae, Composite, Convolvulaceae, Gramineae, Leguminosae, and Liliaceae), which either emerged from soil or tillers growth from surviving plants. This study demonstrated that herbaceous dominant the changes of NDVI in the transition zone, which provides a scientific basis for the mechanism studies of ANPP asymmetric response to precipitation and warrants long-term measurements.
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Affiliation(s)
- Yanyan Lv
- Institute of Fragile Eco-Environment, Nantong University, Nantong, China
- School of Geographic Science, Nantong University, Nantong, China
| | - X. Q. Zhao
- School of Geographic Science, Nantong University, Nantong, China
| | - S. R. Zhang
- School of Geographic Science, Nantong University, Nantong, China
| | - J. G. Zhang
- School of Geographic Science, Nantong University, Nantong, China
| | - K. T. Yue
- School of Geographic Science, Nantong University, Nantong, China
| | - B. P. Meng
- Institute of Fragile Eco-Environment, Nantong University, Nantong, China
- School of Geographic Science, Nantong University, Nantong, China
| | - M. Li
- Institute of Fragile Eco-Environment, Nantong University, Nantong, China
- School of Geographic Science, Nantong University, Nantong, China
| | - W. X. Cui
- Inshanbeilu Grassland Eco-Hydrology National Observation and Research Station, Beijing, China
- Institute of Water Resources and Hydropower Research, Beijing, China
| | - Y. Sun
- Institute of Fragile Eco-Environment, Nantong University, Nantong, China
- School of Geographic Science, Nantong University, Nantong, China
| | - J. G. Zhang
- Institute of Fragile Eco-Environment, Nantong University, Nantong, China
- School of Geographic Science, Nantong University, Nantong, China
| | - L. Chang
- College of Urban Environment, Lanzhou City University, Lanzhou, China
| | - J. R. Li
- Inshanbeilu Grassland Eco-Hydrology National Observation and Research Station, Beijing, China
- Institute of Water Resources and Hydropower Research, Beijing, China
| | - S. H. Yi
- Institute of Fragile Eco-Environment, Nantong University, Nantong, China
- School of Geographic Science, Nantong University, Nantong, China
| | - M. H. Shen
- School of Geographic Science, Nantong University, Nantong, China
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9
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Wu L, Liu H, Liang B, Zhu X, Cao J, Wang Q, Jiang L, Cressey EL, Quine TA. A process-based model reveals the restoration gap of degraded grasslands in Inner Mongolian steppe. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 806:151324. [PMID: 34749967 DOI: 10.1016/j.scitotenv.2021.151324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/24/2021] [Accepted: 10/26/2021] [Indexed: 06/13/2023]
Abstract
Due to the influence of climate change and extensive grazing, a large proportion of steppe grassland has been degraded worldwide. The Chinese government initiated a series of grassland restoration programs to reverse the degradation. However, the limiting factors and the restoration potential remain unknown. Here we present a process-based model to assess the restoration gap (RG) defined as maximum biomass differences between non-degraded and degraded grasslands with different degrees of soil and vegetation degradation. The process-based model Agricultural Production Systems Simulator (APSIM) was evaluated utilizing observation data from both typical and meadow steppes under natural conditions in terms of phenology, dynamics of above-ground biomass and soil water content. Scenario analysis and sensitivity analysis were subsequently performed to address the RG and controlling factors during 1969-2018. The results showed that the calibrated model performed well with r > 0.75 and model efficiency factor EF > 0.5 for all the simulation components. According to our model results, the RG was larger in typical steppe compared to that of meadow steppe and it increased with increasing soil and/or vegetation degradation, to ~60% under extremely degraded scenarios. Both soil and vegetation degradation led to reduced water use efficiency, with an elevated proportion of soil evaporation to evapotranspiration (Es/ET), however, the limiting factor for RG varied. The degradation of soil water holding capacity contributed more to RG regardless of climate conditions for typical steppe in all years and for meadow steppe in dry years. In wet years the importance of vegetation coverage reduction increased for RG in meadow steppe, where the relative importance of vegetation coverage (valued at 62.8%) was 25.6% higher than that of soil degradation. Our results demonstrated the importance of considering climate variations when developing protection and restoration programs for grassland ecosystems.
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Affiliation(s)
- Lu Wu
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Hongyan Liu
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China.
| | - Boyi Liang
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Xinrong Zhu
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Jing Cao
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Qiuming Wang
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Lubing Jiang
- College of Urban and Environmental Science and MOE Laboratory for Earth Surface Processes, Peking University, Beijing, China
| | - Elizabeth L Cressey
- Geography, College of Life & Environmental Sciences, University of Exeter, Exeter EX4 4RJ, United Kingdom
| | - Timothy A Quine
- Geography, College of Life & Environmental Sciences, University of Exeter, Exeter EX4 4RJ, United Kingdom
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10
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Li L, Zheng Z, Biederman JA, Qian R, Ran Q, Zhang B, Xu C, Wang F, Zhou S, Che R, Dong J, Xu Z, Cui X, Hao Y, Wang Y. Drought and heat wave impacts on grassland carbon cycling across hierarchical levels. PLANT, CELL & ENVIRONMENT 2021; 44:2402-2413. [PMID: 32275067 DOI: 10.1111/pce.13767] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 03/24/2020] [Accepted: 03/31/2020] [Indexed: 06/11/2023]
Abstract
Droughts and heat waves are increasing in magnitude and frequency, altering the carbon cycle. However, understanding of the underlying response mechanisms remains poor, especially for the combination (hot drought). We conducted a 4-year field experiment to examine both individual and interactive effects of drought and heat wave on carbon cycling of a semiarid grassland across individual, functional group, community and ecosystem levels. Drought did not change below-ground biomass (BGB) or above-ground biomass (AGB) due to compensation effects between grass and non-grass functional groups. However, consistently decreased BGB under heat waves limited such compensation effects, resulting in reduced AGB. Ecosystem CO2 fluxes were suppressed by droughts, attributed to stomatal closure-induced reductions in leaf photosynthesis and decreased AGB of grasses, while CO2 fluxes were little affected by heat waves. Overall the hot drought produced the lowest leaf photosynthesis, AGB and ecosystem CO2 fluxes although the interactions between heat wave and drought were usually not significant. Our results highlight that the functional group compensatory effects that maintain community-level AGB rely on feedback of root system responses, and that plant adjustments at the individual level, together with shifts in composition at the functional group level, co-regulate ecosystem carbon sink strength under climate extremes.
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Affiliation(s)
- Linfeng Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- Environmental Futures Research Institute, School of Environment and Science, Griffith University, Brisbane, Australia
| | - Zhenzhen Zheng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Joel A Biederman
- Southwest Watershed Research Center, Agricultural Research Service, Tucson, Arizona, USA
| | - Ruyan Qian
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Qinwei Ran
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Biao Zhang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Cong Xu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Fang Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- Environmental Futures Research Institute, School of Environment and Science, Griffith University, Brisbane, Australia
| | - Shutong Zhou
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Rongxiao Che
- Institude of International Rivers and Eco-security, Yunnan University, Kunming, Yunnan, China
| | - Junfu Dong
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Zhihong Xu
- Environmental Futures Research Institute, School of Environment and Science, Griffith University, Brisbane, Australia
| | - Xiaoyong Cui
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, China
| | - Yanbin Hao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, China
| | - Yanfen Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
- CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, China
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11
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Drought Affected Ecosystem Water Use Efficiency of a Natural Oak Forest in Central China. FORESTS 2021. [DOI: 10.3390/f12070839] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Global climate models project more frequent drought events in Central China. However, the effect of seasonal drought on ecosystem water use efficiency (WUE) and water regulation strategy in Central China’s natural forests is poorly understood. This study investigated variations in WUE associated with drought in a natural oak (Quercus aliena) forest in Central China from 2017 to 2020 at several timescales based on continuous CO2 and water vapor flux measurements. Results showed that the 4-year mean gross ecosystem production (GEP), evapotranspiration (ET) and WUE of the natural oak forest was 1613.2 ± 116 g Cm−2, 637.8 ± 163.3 mm and 2.6 ± 0.68 g Ckg−1 H2O, with a coefficient of variation (CV) of 7.2%, 25.6% and 26.4%, respectively. The inter-annual variation in WUE was large, primarily due to the variation in ET caused by seasonal drought. Drought increased WUE distinctly in summer and decreased it slightly in autumn. During summer drought, surface conductance (gs) usually decreased with an increase in VPD, but the ratios of stomatal sensitivity (m) and reference conductance (gsref) were 0.21 and 0.3 molm−2s−1ln(kPa)−1 in the summer of 2019 and 2020. Strong drought can also affect ecosystem WUE and water regulation strategy in the next year. Decrease in precipitation in spring increased annual WUE. These results suggested that drought in different seasons had different effects on ecosystem WUE. Overall, our findings suggest that the natural oak forest did not reduce GEP by increasing WUE (i.e., reducing ET) under spring and summer drought, which could be due to its typical anisohydric characteristics, although it can also reduce stomatal opening during long-term drought.
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12
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Fang J, Lutz JA, Wang L, Shugart HH, Yan X. Using climate-driven leaf phenology and growth to improve predictions of gross primary productivity in North American forests. GLOBAL CHANGE BIOLOGY 2020; 26:6974-6988. [PMID: 32926493 DOI: 10.1111/gcb.15349] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Accepted: 08/25/2020] [Indexed: 06/11/2023]
Abstract
Forest ecosystems are an important sink for terrestrial carbon sequestration. Hence, accurate modeling of the intra- and interannual variability of forest photosynthetic productivity remains a key objective in global biology. Applying climate-driven leaf phenology and growth in models may improve predictions of the forest gross primary productivity (GPP). We used a dynamic non-structural carbohydrates (NSC) model (FORCCHN2) that couples leaf development and phenology to investigate the relationships among photosynthesis and environmental factors. FORCCHN2 simulates spring and autumn phenological events from heat and chilling, respectively. Leaf area index data from satellites along with climate data estimated localized phenological parameters. NSC limitation, immediate temperature, accumulated heat, and growth potential comprised a daily leaf-growth model. Functionally, leaf growth was decoupled from photosynthesis. Leaf biomass determined overall photosynthetic production. We compared this model with outputs of the other six terrestrial biospheric models and with observations from the North American Carbon Program Site Interim Synthesis in 18 forest sites. This model improved the predicted performance of yearly GPP with a 57%-210% increase in correlation (median) and up to a 102% reduction in biases (median), compared to three prognostic models and three prescribed models. At the North America continental scale, the model predicted the average annual GPP of 7.38 Pg C/year from forest ecosystems during 1985-2016. The results showed an increasing trend of GPP in North America (1.0 Pg C/decade). The inclusion of climate-driven phenology and growth has a significant potential for improving dynamic vegetation models, and promotes a further understanding of the complex relationship between environment and photosynthesis.
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Affiliation(s)
- Jing Fang
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
| | - James A Lutz
- Department of Wildland Resources, Utah State University, Logan, UT, USA
| | - Leibin Wang
- College of Resources and Environment Science, Hebei Normal University, Shijiazhuang, China
- Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang, China
| | - Herman H Shugart
- Department of Environmental Sciences, University of Virginia, Charlottesville, VA, USA
| | - Xiaodong Yan
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China
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13
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Luo Y, El-Madany T, Ma X, Nair R, Jung M, Weber U, Filippa G, Bucher SF, Moreno G, Cremonese E, Carrara A, Gonzalez-Cascon R, Cáceres Escudero Y, Galvagno M, Pacheco-Labrador J, Martín MP, Perez-Priego O, Reichstein M, Richardson AD, Menzel A, Römermann C, Migliavacca M. Nutrients and water availability constrain the seasonality of vegetation activity in a Mediterranean ecosystem. GLOBAL CHANGE BIOLOGY 2020; 26:4379-4400. [PMID: 32348631 DOI: 10.1111/gcb.15138] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 04/16/2020] [Indexed: 06/11/2023]
Abstract
Anthropogenic nitrogen (N) deposition and resulting differences in ecosystem N and phosphorus (P) ratios are expected to impact photosynthetic capacity, that is, maximum gross primary productivity (GPPmax ). However, the interplay between N and P availability with other critical resources on seasonal dynamics of ecosystem productivity remains largely unknown. In a Mediterranean tree-grass ecosystem, we established three landscape-level (24 ha) nutrient addition treatments: N addition (NT), N and P addition (NPT), and a control site (CT). We analyzed the response of ecosystem to altered nutrient stoichiometry using eddy covariance fluxes measurements, satellite observations, and digital repeat photography. A set of metrics, including phenological transition dates (PTDs; timing of green-up and dry-down), slopes during green-up and dry-down period, and seasonal amplitude, were extracted from time series of GPPmax and used to represent the seasonality of vegetation activity. The seasonal amplitude of GPPmax was higher for NT and NPT than CT, which was attributed to changes in structure and physiology induced by fertilization. PTDs were mainly driven by rainfall and exhibited no significant differences among treatments during the green-up period. Yet, both fertilized sites senesced earlier during the dry-down period (17-19 days), which was more pronounced in the NT due to larger evapotranspiration and water usage. Fertilization also resulted in a faster increase in GPPmax during the green-up period and a sharper decline in GPPmax during the dry-down period, with less prominent decline response in NPT. Overall, we demonstrated seasonality of vegetation activity was altered after fertilization and the importance of nutrient-water interaction in such water-limited ecosystems. With the projected warming-drying trend, the positive effects of N fertilization induced by N deposition on GPPmax may be counteracted by an earlier and faster dry-down in particular in areas where the N:P ratio increases, with potential impact on the carbon cycle of water-limited ecosystems.
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Affiliation(s)
- Yunpeng Luo
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Tarek El-Madany
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Xuanlong Ma
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
| | - Richard Nair
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Martin Jung
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Ulrich Weber
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - Gianluca Filippa
- Environmental Protection Agency of Aosta Valley, ARPA Valle d'Aosta, Aosta, Italy
| | - Solveig F Bucher
- Plant Biodiversity Group, Institute of Ecology and Evolution, Friedrich Schiller University Jena, Jena, Germany
- Michael-Stifel-Center Jena for Data-Driven and Simulation Science, Jena, Germany
| | - Gerardo Moreno
- Institute for Dehesa Research, University of Extremadura, Plasencia, Spain
| | - Edoardo Cremonese
- Environmental Protection Agency of Aosta Valley, ARPA Valle d'Aosta, Aosta, Italy
| | - Arnaud Carrara
- Fundación Centro de Estudios Ambientales del Mediterráneo (CEAM), Paterna, Spain
| | - Rosario Gonzalez-Cascon
- Department of Environment, National Institute for Agriculture and Food Research and Technology (INIA), Madrid, Spain
| | | | - Marta Galvagno
- Environmental Protection Agency of Aosta Valley, ARPA Valle d'Aosta, Aosta, Italy
| | - Javier Pacheco-Labrador
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
| | - M Pilar Martín
- Environmental Remote Sensing and Spectroscopy Laboratory (SpecLab), Institute of Economic, Geography and Demography (IEGD-CCHS), Spanish National Research Council (CSIC), Madrid, Spain
| | - Oscar Perez-Priego
- Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia
| | - Markus Reichstein
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
- Michael-Stifel-Center Jena for Data-Driven and Simulation Science, Jena, Germany
| | - Andrew D Richardson
- School of Informatics, Computing and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
- Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ, USA
| | - Annette Menzel
- Department of Ecology and Ecosystem Management, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Christine Römermann
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany
- Plant Biodiversity Group, Institute of Ecology and Evolution, Friedrich Schiller University Jena, Jena, Germany
- Michael-Stifel-Center Jena for Data-Driven and Simulation Science, Jena, Germany
| | - Mirco Migliavacca
- Department for Biogeochemical Integration, Max-Planck-Institute for Biogeochemistry, Jena, Germany
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14
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Paschalis A, Fatichi S, Zscheischler J, Ciais P, Bahn M, Boysen L, Chang J, De Kauwe M, Estiarte M, Goll D, Hanson PJ, Harper AB, Hou E, Kigel J, Knapp AK, Larsen KS, Li W, Lienert S, Luo Y, Meir P, Nabel JEMS, Ogaya R, Parolari AJ, Peng C, Peñuelas J, Pongratz J, Rambal S, Schmidt IK, Shi H, Sternberg M, Tian H, Tschumi E, Ukkola A, Vicca S, Viovy N, Wang YP, Wang Z, Williams K, Wu D, Zhu Q. Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand? GLOBAL CHANGE BIOLOGY 2020; 26:3336-3355. [PMID: 32012402 DOI: 10.1111/gcb.15024] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Accepted: 01/22/2020] [Indexed: 06/10/2023]
Abstract
Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model-data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter-model variation is generally large and model agreement varies with timescales. In severely water-limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily-monthly) timescales and reduces on longer (seasonal-annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter-model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.
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Affiliation(s)
- Athanasios Paschalis
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Simone Fatichi
- Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
| | - Jakob Zscheischler
- Climate and Environmental Physics, University of Bern, Bern, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
| | - Michael Bahn
- Department of Ecology, University of Innsbruck, Innsbruck, Austria
| | - Lena Boysen
- Max Planck Institute for Meteorology, Hamburg, Germany
| | - Jinfeng Chang
- Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
| | - Martin De Kauwe
- ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, Australia
| | - Marc Estiarte
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain
- CREAF, Cerdanyola del Vallès, Catalonia, Spain
| | - Daniel Goll
- Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
- Department of Geography, University of Augsburg, Augsburg, Germany
| | - Paul J Hanson
- Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Anna B Harper
- Department of Mathematics, University of Exeter, Exeter, UK
| | - Enqing Hou
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Jaime Kigel
- Institute for Plant Sciences and Genetics, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel
| | - Alan K Knapp
- Graduate Degree Program in Ecology, Department of Biology, Colorado State University, Fort Collins, CO, USA
| | - Klaus S Larsen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg C, Denmark
| | - Wei Li
- Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
| | - Sebastian Lienert
- Climate and Environmental Physics, University of Bern, Bern, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
| | - Yiqi Luo
- Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA
| | - Patrick Meir
- Research School of Biology, Australian National University, Acton, ACT, Australia
- School of Geosciences, University of Edinburgh, Edinburgh, UK
| | | | - Romà Ogaya
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain
- CREAF, Cerdanyola del Vallès, Catalonia, Spain
| | - Anthony J Parolari
- Department of Civil, Construction, and Environmental Engineering, Marquette University, Milwaukee, WI, USA
| | - Changhui Peng
- Department of Biology Sciences, University of Quebec at Montreal, Montreal, QC, Canada
| | - Josep Peñuelas
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Catalonia, Spain
- CREAF, Cerdanyola del Vallès, Catalonia, Spain
| | - Julia Pongratz
- Department of Geography, Ludwig Maximilian University of Munich, Munchen, Germany
| | - Serge Rambal
- Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), UMR5175, CNRS, Université de Montpellier, Université Paul-Valéry Montpellier, EPHE, Montpellier, France
| | - Inger K Schmidt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Frederiksberg C, Denmark
| | - Hao Shi
- International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
| | - Marcelo Sternberg
- School of Plant Sciences and Food Security, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Hanqin Tian
- International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
| | - Elisabeth Tschumi
- Climate and Environmental Physics, University of Bern, Bern, Switzerland
- Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
| | - Anna Ukkola
- ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW, Australia
| | - Sara Vicca
- Centre of Excellence PLECO (Plants and Ecosystems), Biology Department, University of Antwerp, Wilrijk, Belgium
| | - Nicolas Viovy
- Laboratoire des Sciences du Climat et de l'Environnement, Gif sur Yvette, France
| | - Ying-Ping Wang
- CSIRO Marine and Atmospheric Research and Centre for Australian Weather and Climate Research, Aspendale, Vic., Australia
| | - Zhuonan Wang
- International Center for Climate and Global Change Research, School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
| | | | - Donghai Wu
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Qiuan Zhu
- Center for Ecological Forecasting and Global Change, College of Forestry, Northwest A&F University, Xianyang, China
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15
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Li L, Zheng Z, Biederman JA, Xu C, Xu Z, Che R, Wang Y, Cui X, Hao Y. Ecological responses to heavy rainfall depend on seasonal timing and multi-year recurrence. THE NEW PHYTOLOGIST 2019; 223:647-660. [PMID: 30934122 DOI: 10.1111/nph.15832] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2018] [Accepted: 03/25/2019] [Indexed: 06/09/2023]
Abstract
Heavy rainfall events are expected to increase in frequency and severity in the future. However, their effects on natural ecosystems are largely unknown, in particular with different seasonal timing of the events and recurrence over multiple years. We conducted a 4 yr manipulative experiment to explore grassland response to heavy rainfall imposed in either the middle of, or late in, the growing season in Inner Mongolia, China. We measured hierarchical responses at individual, community and ecosystem levels. Surprisingly, above-ground biomass remained stable in the face of heavy rainfall, regardless of seasonal timing, whereas heavy rainfall late in the growing season had consistent negative impacts on below-ground and total biomass. However, such negative biomass effects were not significant for heavy rainfall in the middle of the growing season. By contrast, heavy rainfall in the middle of the growing season had greater positive effects on ecosystem CO2 exchanges, mainly reflected in the latter 2 yr of the 4 yr experiment. This two-stage response of CO2 fluxes was regulated by increased community-level leaf area and leaf-level photosynthesis and interannual variability of natural precipitation. Overall, our study demonstrates that ecosystem impacts of heavy rainfall events crucially depend on the seasonal timing and multiannual recurrence. Plant physiological and morphological adjustment appeared to improve the capacity of the ecosystem to respond positively to heavy rainfall.
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Affiliation(s)
- Linfeng Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- Environmental Futures Research Institute, School of Environment and Science, Griffith University, Brisbane, Qld, 4111, Australia
| | - Zhenzhen Zheng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Joel A Biederman
- Southwest Watershed Research Center, Agricultural Research Service, Tucson, AZ, 85719, USA
| | - Cong Xu
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Zhihong Xu
- Environmental Futures Research Institute, School of Environment and Science, Griffith University, Brisbane, Qld, 4111, Australia
| | - Rongxiao Che
- Institude of International Rivers and Eco-security, Yunnan University, Kunming, Yunnan, 650091, China
| | - Yanfen Wang
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, 100101, China
| | - Xiaoyong Cui
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, 100101, China
| | - Yanbin Hao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China
- CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences (CAS), Beijing, 100101, China
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16
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Sun Z, Wang X, Zhang X, Tani H, Guo E, Yin S, Zhang T. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO 2 trends. THE SCIENCE OF THE TOTAL ENVIRONMENT 2019; 668:696-713. [PMID: 30856578 DOI: 10.1016/j.scitotenv.2019.03.025] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 02/25/2019] [Accepted: 03/03/2019] [Indexed: 06/09/2023]
Abstract
Remote sensing (RS)-based models play an important role in estimating and monitoring terrestrial ecosystem gross primary productivity (GPP). Several RS-based GPP models have been developed using different criteria, yet the sensitivities to environmental factors vary among models; thus, a comparison of model sensitivity is necessary for analyzing and interpreting results and for choosing suitable models. In this study, we globally evaluated and compared the sensitivities of 14 RS-based models (2 process-, 4 vegetation-index-, 5 light-use-efficiency, and 3 machine-learning-based models) and benchmarked them against GPP responses to climatic factors measured at flux sites and to elevated CO2 concentrations measured at free-air CO2 enrichment experiment sites. The results demonstrated that the models with relatively high sensitivity to increasing atmospheric CO2 concentrations showed a higher increasing GPP trend. The fundamental difference in the CO2 effect in the models' algorithm either considers the effect of CO2 through changes in greenness indices (nine models) or introduces the influences on photosynthesis (three models). The overall effects of temperature and radiation, in terms of both magnitude and sign, vary among the models, while the models respond relatively consistently to variations in precipitation. Spatially, larger differences among model sensitivity to climatic factors occur in the tropics; at high latitudes, models have a consistent and obvious positive response to variations in temperature and radiation, and precipitation significantly enhances the GPP in mid-latitudes. Compared with the results calculated by flux-site measurements, the model performance differed substantially among different sites. However, the sensitivities of most models are basically within the confidence interval of the flux-site results. In general, the comparison revealed that models differed substantially in the effect of environmental regulations, particularly CO2 fertilization and water stress, on GPP, and none of the models performed consistently better across the different ecosystems and under the various external conditions.
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Affiliation(s)
- Zhongyi Sun
- Hokkaido University, Graduate School of Agriculture, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan.
| | - Xiufeng Wang
- Hokkaido University, Research Faculty of Agriculture, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Xirui Zhang
- School of Mechanics and Electrics Engineering, Hainan University, Haikou 570228, China
| | - Hiroshi Tani
- Hokkaido University, Research Faculty of Agriculture, Kita-9 Nishi-9 Kita-Ku, Sapporo 060-8589, Japan
| | - Enliang Guo
- Inner Mongolia Normal University, College of Geographic Science, Hohhot 010022, China
| | - Shuai Yin
- Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba 3058506, Japan
| | - Tianyou Zhang
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
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17
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Contrasting the Performance of Eight Satellite-Based GPP Models in Water-Limited and Temperature-Limited Grassland Ecosystems. REMOTE SENSING 2019. [DOI: 10.3390/rs11111333] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Models constitute the primary approaches for predicting terrestrial ecosystem gross primary production (GPP) at regional and global scales. Many satellite-based GPP models have been developed due to the simple algorithms and the low requirements of model inputs. The performances of these models are well documented at the biome level. However, their performances among vegetation subtypes limited by different environmental stresses within a biome remains largely unexplored. Taking grasslands in northern China as an example, we compared the performance of eight satellite-based GPP models, including three light-use efficiency (LUE) models (vegetation photosynthesis model (VPM), modified VPM (MVPM), and moderate resolution imaging spectroradiometer GPP algorithm (MODIS-GPP)) and five statistical models (temperature and greenness model (TG), greenness and radiation model (GR), vegetation index model (VI), alpine vegetation model (AVM), and photosynthetic capacity model (PCM)), between the water-limited temperate steppe and the temperature-limited alpine meadow based on 16 site-year GPP estimates at four eddy covariance (EC) flux towers. The results showed that all the GPP models performed better in the alpine meadow, particularly in the alpine shrub meadow (R2 ≥ 0.84), than in the temperate steppe (R2 ≤ 0.68). The performance varied greatly among the models in the temperate steppe, while slight intermodel differences existed in the alpine meadow. Overall, MVPM (of the LUE models) and VI (of the statistical models) were the two best-performing models in the temperate steppe due to their better representation of the effect of water stress on vegetation productivity. Additionally, we found that the relatively worse model performances in the temperate steppe were seriously exaggerated by drought events, which may occur more frequently in the future. This study highlights the varying performances of satellite-based GPP models among vegetation subtypes of a biome in different precipitation years and suggests priorities for improving the water stress variables of these models in future efforts.
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18
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Hu Z, Guo Q, Li S, Piao S, Knapp AK, Ciais P, Li X, Yu G. Shifts in the dynamics of productivity signal ecosystem state transitions at the biome-scale. Ecol Lett 2018; 21:1457-1466. [PMID: 30019373 DOI: 10.1111/ele.13126] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 06/04/2018] [Accepted: 06/17/2018] [Indexed: 01/16/2023]
Abstract
Understanding ecosystem dynamics and predicting directional changes in ecosystem in response to global changes are ongoing challenges in ecology. Here we present a framework that links productivity dynamics and ecosystem state transitions based on a spatially continuous dataset of aboveground net primary productivity (ANPP) from the temperate grassland of China. Across a regional precipitation gradient, we quantified spatial patterns in ANPP dynamics (variability, asymmetry and sensitivity to rainfall) and related these to transitions from desert to semi-arid to mesic steppe. We show that these three indices of ANPP dynamics displayed distinct spatial patterns, with peaks signalling transitions between grassland types. Thus, monitoring shifts in ANPP dynamics has the potential for predicting ecosystem state transitions in the future. Current ecosystem models fail to capture these dynamics, highlighting the need to incorporate more nuanced ecological controls of productivity in models to forecast future ecosystem shifts.
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Affiliation(s)
- Zhongmin Hu
- School of Geography, South China Normal University, Shipai Campus, Guangzhou, 510631, China.,Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Qun Guo
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Shenggong Li
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
| | - Shilong Piao
- Department of Ecology, College of Urban and Environmental Science, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, 100871, China
| | - Alan K Knapp
- Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO, 80523, USA
| | - Philippe Ciais
- Laboratoire des Sciences du Climatet de l'Environnement, Gif-sur-Yvette, France
| | - Xinrong Li
- Shapotou Desert Research and Experiment Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China
| | - Guirui Yu
- Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.,College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100190, China
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