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Bugmann H, Seidl R. The evolution, complexity and diversity of models of long-term forest dynamics. THE JOURNAL OF ECOLOGY 2022; 110:2288-2307. [PMID: 36632361 PMCID: PMC9826524 DOI: 10.1111/1365-2745.13989] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 08/01/2022] [Indexed: 06/17/2023]
Abstract
To assess the impacts of climate change on vegetation from stand to global scales, models of forest dynamics that include tree demography are needed. Such models are now available for 50 years, but the currently existing diversity of model formulations and its evolution over time are poorly documented. This hampers systematic assessments of structural uncertainties in model-based studies.We conducted a meta-analysis of 28 models, focusing on models that were used in the past five years for climate change studies. We defined 52 model attributes in five groups (basic assumptions, growth, regeneration, mortality and soil moisture) and characterized each model according to these attributes. Analyses of model complexity and diversity included hierarchical cluster analysis and redundancy analysis.Model complexity evolved considerably over the past 50 years. Increases in complexity were largest for growth processes, while complexity of modelled establishment processes increased only moderately. Model diversity was lowest at the global scale, and highest at the landscape scale. We identified five distinct clusters of models, ranging from very simple models to models where specific attribute groups are rendered in a complex manner and models that feature high complexity across all attributes.Most models in use today are not balanced in the level of complexity with which they represent different processes. This is the result of different model purposes, but also reflects legacies in model code, modelers' preferences, and the 'prevailing spirit of the epoch'. The lack of firm theories, laws and 'first principles' in ecology provides high degrees of freedom in model development, but also results in high responsibilities for model developers and the need for rigorous model evaluation. Synthesis. The currently available model diversity is beneficial: convergence in simulations of structurally different models indicates robust projections, while convergence of similar models may convey a false sense of certainty. The existing model diversity-with the exception of global models-can be exploited for improved projections based on multiple models. We strongly recommend balanced further developments of forest models that should particularly focus on establishment and mortality processes, in order to provide robust information for decisions in ecosystem management and policymaking.
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Affiliation(s)
- Harald Bugmann
- Forest Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems ScienceETH ZurichZürichSwitzerland
- Ecosystem Dynamics and Forest ManagementTechnical University of MunichFreisingGermany
| | - Rupert Seidl
- Ecosystem Dynamics and Forest ManagementTechnical University of MunichFreisingGermany
- Berchtesgaden National ParkBerchtesgadenGermany
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2
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Zhao X, Tang X, Du J, Pei X, Chen G, Xu T. A data-driven estimate of litterfall and forest carbon turnover and the drivers of their inter-annual variabilities in forest ecosystems across China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 821:153341. [PMID: 35085631 DOI: 10.1016/j.scitotenv.2022.153341] [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: 11/15/2021] [Revised: 01/18/2022] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
Abstract
Strong influences of climate and land-cover changes on terrestrial ecosystems urgently need to re-estimate forest carbon turnover time (τforest), i.e., the residence time of carbon (C) in the living forest carbon reservoir in China, to reduce uncertainties in ecosystem carbon sinks under ongoing climate change. However, in absence of accurate carbon loss (e.g., forest litterfall), τforest estimate based on the non-steady-state assumption (NSSA) in forest ecosystems across China is still unclear. In this study, thus, we first compiled a litterfall dataset with 1025 field observations, and applied a Random Forest (RF) algorithm with the linkage of gridded environmental variables to predict litterfall from 2000 to 2019 with a fine spatial resolution of 1 km and a temporal resolution of one year. Finally, τforest was also estimated with the data-driven litterfall product. Results showed that RF algorithm could well predict the spatial and temporal patterns of forest litterfall with a model efficiency of 0.58 and root mean square error of 78.7 g C m-2 year-1. Mean litterfall was 205.4 ± 1.1 Tg C year-1 (mean ± standard error) with a significant increasing trend of 0.65 ± 0.14 Tg C year-2 from 2000 to 2019 (p < 0.01), indicating an increasing carbon loss from litterfall. Mean τforest was 26.2 ± 0.1 years with a significant decreasing trend of -0.11 ± 0.02 years (p < 0.01) from 2000 to 2019. Climate change dominated the inter-annual variability of τforest in high latitude areas, and land-cover change dominated the regions with intensive human activities. These findings suggested that carbon loss from vegetation to the atmosphere becomes more quickly in recent decades, with significant implication for vegetation carbon cycling-climate feedbacks. Meanwhile, the developed litterfall and τforest datasets can serve as a benchmark for biogeochemical models to accurately estimate global carbon cycling.
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Affiliation(s)
- Xilin Zhao
- College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Xiaolu Tang
- College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, Sichuan, China; State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, China.
| | - Jie Du
- Jiuzhaigou Nature Reserve Administration, Aba Tibetan and Qiang Autonomous Prefecture, Jiuzhai 623402, Sichuan, China
| | - Xiangjun Pei
- College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, Sichuan, China; State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, China; State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, Sichuan, China
| | - Guo Chen
- College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, Sichuan, China; State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, China
| | - Tingting Xu
- College of Ecology and Environment, Chengdu University of Technology, Chengdu 610059, Sichuan, China; State Environmental Protection Key Laboratory of Synergetic Control and Joint Remediation for Soil & Water Pollution, Chengdu University of Technology, Chengdu 610059, China
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3
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Han X, Gao C, Liang B, Cui J, Xu Q, Schulz A, Liesche J. Evidence for conifer sucrose transporters' functioning in the light-dependent adjustment of sugar allocation. TREE PHYSIOLOGY 2022; 42:488-500. [PMID: 35020944 DOI: 10.1093/treephys/tpab149] [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: 04/06/2021] [Accepted: 11/16/2021] [Indexed: 06/14/2023]
Abstract
Sucrose is the central unit of carbon and energy in plants. Active intercellular transport of sucrose is mediated by sucrose transporters (SUTs), genes for which have been found in the genomes of all land plants. However, they have only been assigned functions in angiosperm species. Here, we cloned two types of SUTs from two gymnosperms, the conifers Cedrus deodara (Roxb. G. Don) and Pinus massoniana Lambert, and analyzed their sucrose transport activities. Uptake of the fluorescent sucrose-analog esculin into tobacco epidermis cells expressing the conifer SUT confirmed their transport ability. To determine their function in planta, we investigated their mRNA abundance in relation to photosynthesis and sugar levels in leaves, inner bark, wood and roots. Combined with measurements of protein abundance and immunolocalization of C. deodara SUTs, our results suggest a role for CdSUT1G and CdSUT2 in supporting phloem transport under varying environmental conditions. The implications of these findings regarding conifer physiology and SUT evolution are discussed.
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Affiliation(s)
- Xiaoyu Han
- College of Life Sciences, Northwest A&F University, Yangling 712100, China
- Biomass Energy Center for Arid and Semi-arid Lands, Northwest A&F University, Yangling 712100, China
| | - Chen Gao
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg 1870, Denmark
| | - Buyou Liang
- College of Life Sciences, Northwest A&F University, Yangling 712100, China
| | - Jingxuan Cui
- College of Life Sciences, Northwest A&F University, Yangling 712100, China
| | - Qiyu Xu
- College of Life Sciences, Northwest A&F University, Yangling 712100, China
- Biomass Energy Center for Arid and Semi-arid Lands, Northwest A&F University, Yangling 712100, China
| | - Alexander Schulz
- Department of Plant and Environmental Sciences, University of Copenhagen, Frederiksberg 1870, Denmark
| | - Johannes Liesche
- College of Life Sciences, Northwest A&F University, Yangling 712100, China
- Biomass Energy Center for Arid and Semi-arid Lands, Northwest A&F University, Yangling 712100, China
- Institute for Molecular Physiology, Heinrich-Heine-University Düsseldorf, Düsseldorf 40225, Germany
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4
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Pompa-García M, González-Cásares M, Gazol A, Camarero JJ. Run to the hills: Forest growth responsiveness to drought increased at higher elevation during the late 20th century. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 772:145286. [PMID: 33578149 DOI: 10.1016/j.scitotenv.2021.145286] [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: 12/10/2020] [Revised: 01/14/2021] [Accepted: 01/14/2021] [Indexed: 06/12/2023]
Abstract
Climate warming is expected to enhance forest growth in cold-limited biomes while triggering reductions in drought-limited biomes. However, as temperature raises, it is unclear how temperature- and drought-growth couplings shift across elevation gradients in different biomes. We still lack comprehensive analyses on how altitude modulates the influence of temperature and drought on tree growth during the second half of the 20th century when climate warming accelerated. We compared the worldwide responses of tree growth (RWI, ring-width indices) to two of its major climatic constraints, growing-season minimum temperatures and drought (SPEI index), across biomes and elevation gradients during two periods with different warming rates (1960-1980 vs. 1980-2000). We found a decrease in the correlations of minimum temperatures with growth, but a strengthening of drought-growth relationships. However, these patterns varied across biomes because correlations between growth and temperature decreased in temperate forests and woodland shrubland, while correlations between growth and SPEI increased in boreal forests and decreased in temperate forests. Differences in growth responsiveness to climate between the two periods were more marked for mid-latitude forests situated between 1200 and 1600 m. The slopes of the relationships between growth-temperature correlations and elevation decreased in late spring and midsummer. The slopes of the relationships between growth-drought correlations and elevation increased in temperate forests and woodland shrubland suggesting that drought impacts are "climbing" in these biomes. Temperature controls on forest growth are relaxing as the climate warms, while drought is becoming a more significant constraint for tree growth, particularly for mid-elevation forests and in drought-prone woodland and shrubland. The strengthening of drought-growth coupling should be considered in vegetation models to reduce the uncertainty on forest climate mitigation.
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Affiliation(s)
- Marín Pompa-García
- Facultad de Ciencias Forestales, Universidad Juárez del Estado de Durango, Durango, Mexico.
| | | | - Antonio Gazol
- Instituto Pirenaico de Ecología (IPE-CSIC), Zaragoza, Spain.
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5
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He Y, Wang X, Wang K, Tang S, Xu H, Chen A, Ciais P, Li X, Peñuelas J, Piao S. Data-driven estimates of global litter production imply slower vegetation carbon turnover. GLOBAL CHANGE BIOLOGY 2021; 27:1678-1688. [PMID: 33423389 DOI: 10.1111/gcb.15515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 12/10/2020] [Accepted: 01/05/2021] [Indexed: 06/12/2023]
Abstract
Accurate quantification of vegetation carbon turnover time (τveg ) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τveg could only be estimated based on net primary productivity under the steady-state assumption. Here, we applied a machine-learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation-based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year-1 . By contrast, land-surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τveg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation-based τveg , modelled τveg tended to underestimate τveg at high latitudes. Our empirically derived gridded datasets of litter production and τveg will help constrain global vegetation models and improve the prediction of global carbon cycle.
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Affiliation(s)
- Yue He
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Xuhui Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Kai Wang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Shuchang Tang
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Hao Xu
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Anping Chen
- Department of Biology, Colorado State University, Fort Collins, CO, USA
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA CNRS UVSQ, Gif Sur Yvette, France
| | - Xiangyi Li
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
| | - Josep Peñuelas
- CREAF, Barcelona, Spain
- Global Ecology Unit CREAF-CSIC-UAB, CSIC, Barcelona, Spain
| | - Shilong Piao
- Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China
- Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
- Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China
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6
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Irauschek F, Barka I, Bugmann H, Courbaud B, Elkin C, Hlásny T, Klopcic M, Mina M, Rammer W, Lexer MJ. Evaluating five forest models using multi-decadal inventory data from mountain forests. Ecol Modell 2021. [DOI: 10.1016/j.ecolmodel.2021.109493] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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7
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Tei S, Sugimoto A. Excessive positive response of model-simulated land net primary production to climate changes over circumboreal forests. PLANT-ENVIRONMENT INTERACTIONS (HOBOKEN, N.J.) 2020; 1:102-121. [PMID: 37283728 PMCID: PMC10168094 DOI: 10.1002/pei3.10025] [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: 12/19/2019] [Revised: 05/17/2020] [Accepted: 05/22/2020] [Indexed: 06/08/2023]
Abstract
Land carbon cycle components in an Earth system model (ESM) play a crucial role in the projections of forest ecosystem responses to climate/environmental changes. Evaluating models from the viewpoint of observations is essential for an improved understanding of model performance and for identifying uncertainties in their outputs. Herein, we evaluated the land net primary production (NPP) for circumboreal forests simulated with 10 ESMs in Phase 5 of the Coupled Model Intercomparison Project by comparisons with observation-based indexes for forest productivity, namely, the composite version 3G of the normalized difference vegetation index (NDVI3g) and tree-ring width index (RWI). These indexes show similar patterns in response to past climate change over the forests, i.e., a one-year time lag response and smaller positive responses to past climate changes in comparison with the land NPP simulated by the ESMs. The latter showed overly positive responses to past temperature and/or precipitation changes in comparison with the NDVI3g and RWI. These results indicate that ESMs may overestimate the future forest NPP of circumboreal forests (particularly for inland dry regions, such as inner Alaska and Canada, and eastern Siberia, and for hotter, southern regions, such as central Europe) under the expected increases in both average global temperature and precipitation, which are common to all current ESMs.
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Affiliation(s)
- Shunsuke Tei
- Arctic Research CenterHokkaido UniversitySapporoJapan
| | - Atsuko Sugimoto
- Arctic Research CenterHokkaido UniversitySapporoJapan
- Graduate School of Environmental ScienceHokkaido UniversitySapporoJapan
- Global Station for Arctic ResearchGlobal Institution for Collaborative Research and EducationHokkaido UniversitySapporoJapan
- North‐Eastern Federal UniversityYakutskRussia
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8
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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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9
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Franklin O, Harrison SP, Dewar R, Farrior CE, Brännström Å, Dieckmann U, Pietsch S, Falster D, Cramer W, Loreau M, Wang H, Mäkelä A, Rebel KT, Meron E, Schymanski SJ, Rovenskaya E, Stocker BD, Zaehle S, Manzoni S, van Oijen M, Wright IJ, Ciais P, van Bodegom PM, Peñuelas J, Hofhansl F, Terrer C, Soudzilovskaia NA, Midgley G, Prentice IC. Organizing principles for vegetation dynamics. NATURE PLANTS 2020; 6:444-453. [PMID: 32393882 DOI: 10.1038/s41477-020-0655-x] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 04/02/2020] [Indexed: 06/11/2023]
Abstract
Plants and vegetation play a critical-but largely unpredictable-role in global environmental changes due to the multitude of contributing processes at widely different spatial and temporal scales. In this Perspective, we explore approaches to master this complexity and improve our ability to predict vegetation dynamics by explicitly taking account of principles that constrain plant and ecosystem behaviour: natural selection, self-organization and entropy maximization. These ideas are increasingly being used in vegetation models, but we argue that their full potential has yet to be realized. We demonstrate the power of natural selection-based optimality principles to predict photosynthetic and carbon allocation responses to multiple environmental drivers, as well as how individual plasticity leads to the predictable self-organization of forest canopies. We show how models of natural selection acting on a few key traits can generate realistic plant communities and how entropy maximization can identify the most probable outcomes of community dynamics in space- and time-varying environments. Finally, we present a roadmap indicating how these principles could be combined in a new generation of models with stronger theoretical foundations and an improved capacity to predict complex vegetation responses to environmental change.
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Affiliation(s)
- Oskar Franklin
- International Institute for Applied Systems Analysis, Laxenburg, Austria.
- Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden.
| | - Sandy P Harrison
- Department of Geography and Environmental Science, University of Reading, Reading, UK
| | - Roderick Dewar
- Plant Sciences Division, Research School of Biology, The Australian National University, Canberra, Australia
- Institute for Atmospheric and Earth System Research/Physics, University of Helsinki, Helsinki, Finland
| | - Caroline E Farrior
- Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA
| | - Åke Brännström
- International Institute for Applied Systems Analysis, Laxenburg, Austria
- Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden
| | - Ulf Dieckmann
- International Institute for Applied Systems Analysis, Laxenburg, Austria
- Department of Evolutionary Studies of Biosystems, The Graduate University for Advanced Studies (Sokendai), Hayama, Japan
| | - Stephan Pietsch
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Daniel Falster
- Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - Wolfgang Cramer
- Institut Méditerranéen de Biodiversité et d'Ecologie Marine et Continentale (IMBE), Aix Marseille Université, CNRS, IRD, Avignon Université, Technopôle Arbois-Méditerranée, Aix-en-Provence, France
| | - Michel Loreau
- Centre for Biodiversity, Theory, and Modelling, Theoretical and Experimental Ecology Station, CNRS, Moulis, France
| | - Han Wang
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
| | - Annikki Mäkelä
- Forest Sciences, University of Helsinki, Helsinki, Finland
| | - Karin T Rebel
- Copernicus Institute of Sustainable Development, Environmental Sciences, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
| | - Ehud Meron
- Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Sede Boqer Campus, Israel
- Department of Physics, Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Stanislaus J Schymanski
- Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
| | - Elena Rovenskaya
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Benjamin D Stocker
- Department of Environmental Systems Sciences, ETH Zurich, Zurich, Switzerland
- CREAF, Cerdanyola del Vallès, Spain
| | - Sönke Zaehle
- Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Jena, Germany
| | - Stefano Manzoni
- Department of Physical Geography, Stockholm University, Stockholm, Sweden
- Bolin Centre for Climate Research, Stockholm, Sweden
| | - Marcel van Oijen
- Centre for Ecology and Hydrology (CEH-Edinburgh), Bush Estate, Penicuik, UK
| | - Ian J Wright
- Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ, Gif-sur-Yvette, France
| | - Peter M van Bodegom
- Environmental Biology Department, Institute of Environmental Sciences, CML, Leiden University, Leiden, The Netherlands
| | - Josep Peñuelas
- CREAF, Cerdanyola del Vallès, Spain
- CSIC, Global Ecology Unit CREAF-CSIC-UAB, Bellaterra, Spain
| | - Florian Hofhansl
- International Institute for Applied Systems Analysis, Laxenburg, Austria
| | - Cesar Terrer
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA
| | - Nadejda A Soudzilovskaia
- Environmental Biology Department, Institute of Environmental Sciences, CML, Leiden University, Leiden, The Netherlands
| | - Guy Midgley
- Department Botany & Zoology, Stellenbosch University, Stellenbosch, South Africa
| | - I Colin Prentice
- Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing, China
- Department of Biological Sciences, Macquarie University, North Ryde, New South Wales, Australia
- AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Silwood Park Campus, Ascot, UK
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10
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Collalti A, Tjoelker MG, Hoch G, Mäkelä A, Guidolotti G, Heskel M, Petit G, Ryan MG, Battipaglia G, Matteucci G, Prentice IC. Plant respiration: Controlled by photosynthesis or biomass? GLOBAL CHANGE BIOLOGY 2020; 26:1739-1753. [PMID: 31578796 DOI: 10.1111/gcb.14857] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2018] [Accepted: 10/01/2019] [Indexed: 06/10/2023]
Abstract
Two simplifying hypotheses have been proposed for whole-plant respiration. One links respiration to photosynthesis; the other to biomass. Using a first-principles carbon balance model with a prescribed live woody biomass turnover, applied at a forest research site where multidecadal measurements are available for comparison, we show that if turnover is fast the accumulation of respiring biomass is low and respiration depends primarily on photosynthesis; while if turnover is slow the accumulation of respiring biomass is high and respiration depends primarily on biomass. But the first scenario is inconsistent with evidence for substantial carry-over of fixed carbon between years, while the second implies far too great an increase in respiration during stand development-leading to depleted carbohydrate reserves and an unrealistically high mortality risk. These two mutually incompatible hypotheses are thus both incorrect. Respiration is not linearly related either to photosynthesis or to biomass, but it is more strongly controlled by recent photosynthates (and reserve availability) than by total biomass.
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Affiliation(s)
- Alessio Collalti
- Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Rende (CS), Italy
- Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, Viterbo, Italy
| | - Mark G Tjoelker
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Günter Hoch
- Department of Environmental Sciences - Botany, University of Basel, Basel, Switzerland
| | - Annikki Mäkelä
- Institute for Atmospheric and Earth System Research (INAR), Faculty of Science and Faculty of Agriculture and Forestry, University of Helsinki, Helsinki, Finland
| | - Gabriele Guidolotti
- Institute of Research on Terrestrial Ecosystem, National Research Council of Italy (CNR-IRET), Rome, Italy
| | - Mary Heskel
- Department of Biology, Macalester College, Saint Paul, MN, USA
| | - Giai Petit
- Department of Land, Environment, Agriculture and Forestry, University of Padova, Padua, Italy
| | - Michael G Ryan
- Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA
- USDA Forest Service, Rocky Mountain Experiment Station, Fort Collins, CO, USA
| | - Giovanna Battipaglia
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania "L. Vanvitelli", Caserta, Italy
| | - Giorgio Matteucci
- Institute for Agriculture and Forestry Systems in the Mediterranean, National Research Council of Italy (CNR-ISAFOM), Rende (CS), Italy
| | - Iain Colin Prentice
- AXA Chair of Biosphere and Climate Impacts, Department of Life Sciences, Imperial College London, Ascot, UK
- Department of Biological Sciences, Macquarie University, North Ryde, NSW, Australia
- Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China
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11
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Forkel M, Drüke M, Thurner M, Dorigo W, Schaphoff S, Thonicke K, von Bloh W, Carvalhais N. Constraining modelled global vegetation dynamics and carbon turnover using multiple satellite observations. Sci Rep 2019; 9:18757. [PMID: 31822728 PMCID: PMC6904745 DOI: 10.1038/s41598-019-55187-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 11/25/2019] [Indexed: 12/02/2022] Open
Abstract
The response of land ecosystems to future climate change is among the largest unknowns in the global climate-carbon cycle feedback. This uncertainty originates from how dynamic global vegetation models (DGVMs) simulate climate impacts on changes in vegetation distribution, productivity, biomass allocation, and carbon turnover. The present-day availability of a multitude of satellite observations can potentially help to constrain DGVM simulations within model-data integration frameworks. Here, we use satellite-derived datasets of the fraction of absorbed photosynthetic active radiation (FAPAR), sun-induced fluorescence (SIF), above-ground biomass of trees (AGB), land cover, and burned area to constrain parameters for phenology, productivity, and vegetation dynamics in the LPJmL4 DGVM. Both the prior and the optimized model accurately reproduce present-day estimates of the land carbon cycle and of temporal dynamics in FAPAR, SIF and gross primary production. However, the optimized model reproduces better the observed spatial patterns of biomass, tree cover, and regional forest carbon turnover. Using a machine learning approach, we found that remaining errors in simulated forest carbon turnover can be explained with bioclimatic variables. This demonstrates the need to improve model formulations for climate effects on vegetation turnover and mortality despite the apparent successful constraint of simulated vegetation dynamics with multiple satellite observations.
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Affiliation(s)
- Matthias Forkel
- Technische Universität Dresden, Institute of Photogrammetry and Remote Sensing, Helmholtzstr. 10, 01069, Dresden, Germany.
| | - Markus Drüke
- Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, Potsdam, Germany
| | - Martin Thurner
- Senckenberg Biodiversity and Climate Research Centre (SBiK-F), Senckenberg Gesellschaft für Naturforschung, Senckenberganlage 25, 60325, Frankfurt am Main, Germany
| | - Wouter Dorigo
- TU Wien, Department of Geodesy and Geoinformation, Gusshausstr. 27-29, Vienna, Austria
| | - Sibyll Schaphoff
- Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, Potsdam, Germany
| | - Kirsten Thonicke
- Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, Potsdam, Germany
| | - Werner von Bloh
- Potsdam Institute for Climate Impact Research, Telegraphenberg A 62, Potsdam, Germany
| | - Nuno Carvalhais
- Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, Jena, Germany
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12
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Yu K, Smith WK, Trugman AT, Condit R, Hubbell SP, Sardans J, Peng C, Zhu K, Peñuelas J, Cailleret M, Levanic T, Gessler A, Schaub M, Ferretti M, Anderegg WRL. Pervasive decreases in living vegetation carbon turnover time across forest climate zones. Proc Natl Acad Sci U S A 2019; 116:24662-24667. [PMID: 31740604 PMCID: PMC6900527 DOI: 10.1073/pnas.1821387116] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Forests play a major role in the global carbon cycle. Previous studies on the capacity of forests to sequester atmospheric CO2 have mostly focused on carbon uptake, but the roles of carbon turnover time and its spatiotemporal changes remain poorly understood. Here, we used long-term inventory data (1955 to 2018) from 695 mature forest plots to quantify temporal trends in living vegetation carbon turnover time across tropical, temperate, and cold climate zones, and compared plot data to 8 Earth system models (ESMs). Long-term plots consistently showed decreases in living vegetation carbon turnover time, likely driven by increased tree mortality across all major climate zones. Changes in living vegetation carbon turnover time were negatively correlated with CO2 enrichment in both forest plot data and ESM simulations. However, plot-based correlations between living vegetation carbon turnover time and climate drivers such as precipitation and temperature diverged from those of ESM simulations. Our analyses suggest that forest carbon sinks are likely to be constrained by a decrease in living vegetation carbon turnover time, and accurate projections of forest carbon sink dynamics will require an improved representation of tree mortality processes and their sensitivity to climate in ESMs.
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Affiliation(s)
- Kailiang Yu
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112;
| | - William K Smith
- School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721
| | - Anna T Trugman
- School of Biological Sciences, University of Utah, Salt Lake City, UT 84112
- Department of Geography, University of California, Santa Barbara, CA 93106
| | | | - Stephen P Hubbell
- The Morton Arboretum, Lisle, IL 60532
- Department of Ecology and Evolutionary Biology, University of California, Los Angeles, CA 90095
| | - Jordi Sardans
- Consejo Superior de Investigaciones Científicas, Global Ecology Unit (Center for Ecological Research and Forestry Applications-Consejo Superior de Investigaciones Científicas-Universitat Autònoma de Barcelona), 08193 Bellaterra (Catalonia), Spain
- Center for Ecological Research and Forestry Applications, 08193 Cerdanyola del Vallès (Catalonia), Spain
| | - Changhui Peng
- Department of Biological Sciences, University of Quebec at Montreal, Montréal, QC H3C 3J7, Canada
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest Agriculture and Forestry University, Yangling, 712100 Shaanxi, China
| | - Kai Zhu
- Department of Environmental Studies, University of California, Santa Cruz, CA 95064
| | - Josep Peñuelas
- Consejo Superior de Investigaciones Científicas, Global Ecology Unit (Center for Ecological Research and Forestry Applications-Consejo Superior de Investigaciones Científicas-Universitat Autònoma de Barcelona), 08193 Bellaterra (Catalonia), Spain
- Center for Ecological Research and Forestry Applications, 08193 Cerdanyola del Vallès (Catalonia), Spain
| | - Maxime Cailleret
- The Swiss Federal Institute for Forest Snow and Landscape Research (WSL) 8903 Birmensdorf, Switzerland
- UMR RECOVER, University of Aix-Marseille, Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture, 13182 Aix-en-Provence, France
| | - Tom Levanic
- Slovenian Forestry Institute, 1000 Ljubljana, Slovenia
| | - Arthur Gessler
- The Swiss Federal Institute for Forest Snow and Landscape Research (WSL) 8903 Birmensdorf, Switzerland
- Institute of Terrestrial Ecosystems, Eidgenössische Technische Hochschule Zürich, 8092 Zürich, Switzerland
| | - Marcus Schaub
- The Swiss Federal Institute for Forest Snow and Landscape Research (WSL) 8903 Birmensdorf, Switzerland
| | - Marco Ferretti
- The Swiss Federal Institute for Forest Snow and Landscape Research (WSL) 8903 Birmensdorf, Switzerland
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13
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Thrippleton T, Hülsmann L, Cailleret M, Bugmann H. Projecting Forest Dynamics Across Europe: Potentials and Pitfalls of Empirical Mortality Algorithms. Ecosystems 2019. [DOI: 10.1007/s10021-019-00397-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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14
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Ge R, He H, Ren X, Zhang L, Yu G, Smallman TL, Zhou T, Yu SY, Luo Y, Xie Z, Wang S, Wang H, Zhou G, Zhang Q, Wang A, Fan Z, Zhang Y, Shen W, Yin H, Lin L. Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: A perspective from long-term data assimilation. GLOBAL CHANGE BIOLOGY 2019; 25:938-953. [PMID: 30552830 DOI: 10.1111/gcb.14547] [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: 05/20/2018] [Revised: 11/30/2018] [Accepted: 11/30/2018] [Indexed: 06/09/2023]
Abstract
It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback.
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Affiliation(s)
- Rong Ge
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Honglin He
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoli Ren
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Li Zhang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - Guirui Yu
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China
| | - T Luke Smallman
- School of GeoSciences, University of Edinburgh, Edinburgh, UK
| | - Tao Zhou
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China
| | - Shi-Yong Yu
- Large Lakes Observatory, University of Minnesota Duluth, Duluth, Minnesota
| | - Yiqi Luo
- Center for Ecosystem Science and Society (Ecoss) and Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona
| | - Zongqiang Xie
- Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Silong Wang
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
| | - Huimin Wang
- Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Guoyi Zhou
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
| | - Qibin Zhang
- Institute of Botany, Chinese Academy of Sciences, Beijing, China
| | - Anzhi Wang
- Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang, China
| | - Zexin Fan
- Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China
| | - Yiping Zhang
- Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China
| | - Weijun Shen
- South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, China
| | - Huajun Yin
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu, China
| | - Luxiang Lin
- Key Laboratory of Tropical Forest Ecology, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla, China
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15
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Babst F, Bouriaud O, Poulter B, Trouet V, Girardin MP, Frank DC. Twentieth century redistribution in climatic drivers of global tree growth. SCIENCE ADVANCES 2019; 5:eaat4313. [PMID: 30746436 PMCID: PMC6357745 DOI: 10.1126/sciadv.aat4313] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 12/06/2018] [Indexed: 05/19/2023]
Abstract
Energy and water limitations of tree growth remain insufficiently understood at large spatiotemporal scales, hindering model representation of interannual or longer-term ecosystem processes. By assessing and statistically scaling the climatic drivers from 2710 tree-ring sites, we identified the boreal and temperate land areas where tree growth during 1930-1960 CE responded positively to temperature (20.8 ± 3.7 Mio km2; 25.9 ± 4.6%), precipitation (77.5 ± 3.3 Mio km2; 96.4 ± 4.1%), and other parameters. The spatial manifestation of this climate response is determined by latitudinal and altitudinal temperature gradients, indicating that warming leads to geographic shifts in growth limitations. We observed a significant (P < 0.001) decrease in temperature response at cold-dry sites between 1930-1960 and 1960-1990 CE, and the total temperature-limited area shrunk by -8.7 ± 0.6 Mio km2. Simultaneously, trees became more limited by atmospheric water demand almost worldwide. These changes occurred under mild warming, and we expect that continued climate change will trigger a major redistribution in growth responses to climate.
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Affiliation(s)
- Flurin Babst
- Dendro Sciences Group, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
- Department of Ecology, W. Szafer Institute of Botany, Polish Academy of Sciences, ul. Lubicz 46, 31-512 Kraków, Poland
- Laboratory of Tree-Ring Research, University of Arizona, 1215 E. Lowell St., Tucson, AZ 85721, USA
- Corresponding author.
| | - Olivier Bouriaud
- Stefan cel Mare University of Suceava, Strada Universitătii 13, Suceava 720229, Romania
| | | | - Valerie Trouet
- Laboratory of Tree-Ring Research, University of Arizona, 1215 E. Lowell St., Tucson, AZ 85721, USA
| | - Martin P. Girardin
- Laurentian Forestry Centre, Canadian Forest Service, Natural Resources Canada, Quebec, QC G1V4C7, Canada
- Centre d’étude de la forêt, Université du Québec à Montréal, C.P. 8888, succ. Centre-ville, Montréal, QC H3C 3P8, Canada
| | - David C. Frank
- Dendro Sciences Group, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
- Laboratory of Tree-Ring Research, University of Arizona, 1215 E. Lowell St., Tucson, AZ 85721, USA
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16
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Estimation of Above-Ground Biomass over Boreal Forests on Siberia Using Updated In Situ, ALOS-2 PALSAR-2, and RADARSAT-2 Data. REMOTE SENSING 2018. [DOI: 10.3390/rs10101550] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The estimation of above-ground biomass (AGB) in boreal forests is of special concern as it constitutes the highest carbon pool in the northern hemisphere. In particularly, monitoring of the forests in the Russian Federation is important as some regions have not been inventoried for many years. This study explores the combination of multi-frequency, multi-polarization, and multi-temporal radar data as one key approach to provide an accurate estimate of forest biomass. The data from L-band Advanced Land Observing Satellite 2 (ALOS-2) Phased Array L-Band Synthetic Aperture Radar 2 (PALSAR-2), together with C-band RADARSAT-2 data, were applied for AGB estimation. Backscatter coefficients from L- and C-band radar were used independently and in combination with a non-parametric model to retrieve AGB data for a boreal forest in Siberia (Krasnoyarskiy Kray). AGB estimation was performed using the random forests machine learning algorithm. The results demonstrated that high estimation accuracies can be achieved at a spatial resolution of 0.25 ha. When the L-band data alone were used for the retrieval, a corrected root-mean-square error (RMSEcor) of 29.4 t ha−1 was calculated. A marginal decrease in RMSEcor was observed when only the filtered L-band backscatter data, without ratio and texture, were used (29.1 t ha−1). The inclusion of the C-band data reduced the over and underestimation; the bias was reduced from 5.5 t ha−1 to 4.7 t ha−1; and a RMSEcor of 30.2 t ha−1 was calculated.
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17
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McDowell N, Allen CD, Anderson-Teixeira K, Brando P, Brienen R, Chambers J, Christoffersen B, Davies S, Doughty C, Duque A, Espirito-Santo F, Fisher R, Fontes CG, Galbraith D, Goodsman D, Grossiord C, Hartmann H, Holm J, Johnson DJ, Kassim AR, Keller M, Koven C, Kueppers L, Kumagai T, Malhi Y, McMahon SM, Mencuccini M, Meir P, Moorcroft P, Muller-Landau HC, Phillips OL, Powell T, Sierra CA, Sperry J, Warren J, Xu C, Xu X. Drivers and mechanisms of tree mortality in moist tropical forests. THE NEW PHYTOLOGIST 2018; 219:851-869. [PMID: 29451313 DOI: 10.1111/nph.15027] [Citation(s) in RCA: 169] [Impact Index Per Article: 28.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 12/19/2017] [Indexed: 05/22/2023]
Abstract
Tree mortality rates appear to be increasing in moist tropical forests (MTFs) with significant carbon cycle consequences. Here, we review the state of knowledge regarding MTF tree mortality, create a conceptual framework with testable hypotheses regarding the drivers, mechanisms and interactions that may underlie increasing MTF mortality rates, and identify the next steps for improved understanding and reduced prediction. Increasing mortality rates are associated with rising temperature and vapor pressure deficit, liana abundance, drought, wind events, fire and, possibly, CO2 fertilization-induced increases in stand thinning or acceleration of trees reaching larger, more vulnerable heights. The majority of these mortality drivers may kill trees in part through carbon starvation and hydraulic failure. The relative importance of each driver is unknown. High species diversity may buffer MTFs against large-scale mortality events, but recent and expected trends in mortality drivers give reason for concern regarding increasing mortality within MTFs. Models of tropical tree mortality are advancing the representation of hydraulics, carbon and demography, but require more empirical knowledge regarding the most common drivers and their subsequent mechanisms. We outline critical datasets and model developments required to test hypotheses regarding the underlying causes of increasing MTF mortality rates, and improve prediction of future mortality under climate change.
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Affiliation(s)
- Nate McDowell
- Pacific Northwest National Laboratory, Richland, WA, 99354, USA
| | - Craig D Allen
- US Geological Survey, Fort Collins Science Center, New Mexico Landscapes Field Station, Los Alamos, NM, 87544, USA
| | - Kristina Anderson-Teixeira
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, DC, 20036, USA
- Conservation Ecology Center, Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, VA, 22630, USA
| | - Paulo Brando
- Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA, 02450, USA
- Instituto de Pesquisa Ambiental de Amazonia, Lago Norte, Brasilia, Brazil
| | - Roel Brienen
- School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK
| | - Jeff Chambers
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Brad Christoffersen
- Department of Biology and School of Earth, Environmental and Marine Sciences, University of Texas Rio Grande Valley, Edinburg, TX, 78539, USA
| | - Stuart Davies
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, DC, 20036, USA
| | - Chris Doughty
- SICCS, Northern Arizona University, Flagstaff, AZ, 86001, USA
| | - Alvaro Duque
- Departmento de Ciencias Forestales, Universidad Nacional de Columbia, Medellín, Columbia
| | | | - Rosie Fisher
- National Center for Atmospheric Research, Boulder, CO, 80305, USA
| | - Clarissa G Fontes
- Department of Integrative Biology, University of California at Berkeley, Berkeley, CA, 94720, USA
| | - David Galbraith
- School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK
| | - Devin Goodsman
- Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | | | - Henrik Hartmann
- Department of Biogeochemical Processes, Max Plank Institute for Biogeochemistry, 07745, Jena, Germany
| | - Jennifer Holm
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Abd Rahman Kassim
- Geoinformation Programme, Forestry and Environment Division, Forest Research Institute Malaysia, Selangor, Malaysia
| | - Michael Keller
- International Institute of Tropical Forestry, USDA Jardin Botanico Sur, 1201 Calle Ceiba, San Juan, 00926, Puerto Rico
- Embrapa Agricultural Informatics, Parque Estacao Biologica, Brasilia DF, 70770, Brazil
- Jet Propulsion Laboratory, Pasadena, CA, 91109, USA
| | - Charlie Koven
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Lara Kueppers
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- Energy and Resources Group, University of California, Berkeley, CA, 94720, USA
| | - Tomo'omi Kumagai
- Graduate School of Agricultural and Life Sciences, The University of Tokyo, 7 Chome-3-1 Hongo, Bunkyo, Tokyo, 113-8654, Japan
| | - Yadvinder Malhi
- Environmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, OX1 2JD, UK
| | - Sean M McMahon
- Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Washington, DC, 20036, USA
| | - Maurizio Mencuccini
- ICREA, CREAF, University of Barcelona, Gran Via de les Corts Catalenes, 585 08007, Barcelona, Spain
| | - Patrick Meir
- Australian National University, Acton, Canberra, ACT, 2601, Australia
- School of Geosciences, University of Edinburgh, Old College, South Bridge, Edinburgh, EH8 9YL, UK
| | | | - Helene C Muller-Landau
- Smithsonian Tropical Research Institute, Apartado Postal, 0843-03092, Panamá, República de Panamá
| | - Oliver L Phillips
- School of Geography, University of Leeds, Woodhouse Lane, Leeds, LS2 9JT, UK
| | - Thomas Powell
- Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Carlos A Sierra
- Department of Biogeochemical Processes, Max Plank Institute for Biogeochemistry, 07745, Jena, Germany
| | - John Sperry
- University of Utah, Salt Lake City, UT, 84112, USA
| | - Jeff Warren
- Oak Ridge National Laboratory, Oak Ridge, TN, 37830, USA
| | - Chonggang Xu
- Los Alamos National Laboratory, Los Alamos, NM, 87545, USA
| | - Xiangtao Xu
- Department of Geosciences, Princeton University, Princeton, NJ, 08544, USA
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Uncertainty Quantification of Extratropical Forest Biomass in CMIP5 Models over the Northern Hemisphere. Sci Rep 2018; 8:10962. [PMID: 30026558 PMCID: PMC6053416 DOI: 10.1038/s41598-018-29227-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 07/04/2018] [Indexed: 11/24/2022] Open
Abstract
Simplified representations of processes influencing forest biomass in Earth system models (ESMs) contribute to large uncertainty in projections. We evaluate forest biomass from eight ESMs outputs archived in the Coupled Model Intercomparison Project Phase 5 (CMIP5) using the biomass data synthesized from radar remote sensing and ground-based observations across northern extratropical latitudes. ESMs exhibit large biases in the forest distribution, forest fraction, and mass of carbon pools that contribute to uncertainty in forest total biomass (biases range from −20 Pg C to 135 Pg C). Forest total biomass is primarily positively correlated with precipitation variations, with surface temperature becoming equally important at higher latitudes, in both simulations and observations. Relatively small differences in forest biomass between the pre-industrial period and the contemporary period indicate uncertainties in forest biomass were introduced in the pre-industrial model equilibration (spin-up), suggesting parametric or structural model differences are a larger source of uncertainty than differences in transient responses. Our findings emphasize the importance of improved (1) models of carbon allocation to biomass compartments, (2) distribution of vegetation types in models, and (3) reproduction of pre-industrial vegetation conditions, in order to reduce the uncertainty in forest biomass simulated by ESMs.
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Rogers BM, Solvik K, Hogg EH, Ju J, Masek JG, Michaelian M, Berner LT, Goetz SJ. Detecting early warning signals of tree mortality in boreal North America using multiscale satellite data. GLOBAL CHANGE BIOLOGY 2018; 24:2284-2304. [PMID: 29481709 DOI: 10.1111/gcb.14107] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 02/12/2018] [Indexed: 05/19/2023]
Abstract
Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.
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Affiliation(s)
| | | | - Edward H Hogg
- Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, Edmonton, AB, Canada
| | - Junchang Ju
- Biospheric Science Laboratory (Code 618), NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Jeffrey G Masek
- Biospheric Science Laboratory (Code 618), NASA Goddard Space Flight Center, Greenbelt, MD, USA
| | - Michael Michaelian
- Northern Forestry Centre, Canadian Forest Service, Natural Resources Canada, Edmonton, AB, Canada
| | - Logan T Berner
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
| | - Scott J Goetz
- School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, AZ, USA
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