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Nian D, Bathiany S, Sakschewski B, Drüke M, Blaschke L, Ben-Yami M, von Bloh W, Boers N. Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 947:174378. [PMID: 38960201 DOI: 10.1016/j.scitotenv.2024.174378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 06/20/2024] [Accepted: 06/27/2024] [Indexed: 07/05/2024]
Abstract
Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.
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Affiliation(s)
- Da Nian
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany.
| | - Sebastian Bathiany
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; Earth System Modelling, School of Engineering and Design, Technical University Munich., Munich 80333, Germany
| | - Boris Sakschewski
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
| | - Markus Drüke
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; Deutscher Wetterdienst, Hydrometeorologie, Frankfurter Str., 135, 63067 Offenbach, Germany
| | - Lana Blaschke
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; Earth System Modelling, School of Engineering and Design, Technical University Munich., Munich 80333, Germany
| | - Maya Ben-Yami
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; Earth System Modelling, School of Engineering and Design, Technical University Munich., Munich 80333, Germany
| | - Werner von Bloh
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany
| | - Niklas Boers
- Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany; Earth System Modelling, School of Engineering and Design, Technical University Munich., Munich 80333, Germany; Department of Mathematics and Global Systems Institute, University of Exeter, Exeter EX4 4QF, UK
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Heilmayr R, Dudney J, Moore FC. Drought sensitivity in mesic forests heightens their vulnerability to climate change. Science 2023; 382:1171-1177. [PMID: 38060640 DOI: 10.1126/science.adi1071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 10/26/2023] [Indexed: 12/18/2023]
Abstract
Climate change is shifting the structure and function of global forests, underscoring the critical need to predict which forests are most vulnerable to a hotter and drier future. We analyzed 6.6 million tree rings from 122 species to assess trees' sensitivity to water and energy availability. We found that trees growing in wetter portions of their range exhibit the greatest drought sensitivity. To test how these patterns of drought sensitivity influence vulnerability to climate change, we predicted tree growth through 2100. Our results suggest that drought adaptations in arid regions will partially buffer trees against climate change. By contrast, trees growing in the wetter, hotter portions of their climatic range may experience unexpectedly large adverse impacts under climate change.
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Affiliation(s)
- Robert Heilmayr
- Environmental Studies Program, University of California, Santa Barbara, Santa Barbara, CA, USA
- Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Joan Dudney
- Environmental Studies Program, University of California, Santa Barbara, Santa Barbara, CA, USA
- Bren School of Environmental Science and Management, University of California, Santa Barbara, Santa Barbara, CA, USA
| | - Frances C Moore
- Department of Environmental Science and Policy, University of California, Davis, Davis, CA, USA
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3
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Varghese R, Cherukuri AK, Doddrell NH, Doss CGP, Simkin AJ, Ramamoorthy S. Machine learning in photosynthesis: Prospects on sustainable crop development. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 335:111795. [PMID: 37473784 DOI: 10.1016/j.plantsci.2023.111795] [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: 05/03/2023] [Revised: 07/10/2023] [Accepted: 07/13/2023] [Indexed: 07/22/2023]
Abstract
Improving photosynthesis is a promising avenue to increase food security. Studying photosynthetic traits with the aim to improve efficiency has been one of many strategies to increase crop yield but analyzing large data sets presents an ongoing challenge. Machine learning (ML) represents a ubiquitous tool that can provide a more elaborate data analysis. Here we review the application of ML in various domains of photosynthetic research, as well as in photosynthetic pigment studies. We highlight how correlating hyperspectral data with photosynthetic parameters to improve crop yield could be achieved through various ML algorithms. We also propose strategies to employ ML in promoting photosynthetic pigment research for furthering crop yield.
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Affiliation(s)
- Ressin Varghese
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Aswani Kumar Cherukuri
- School of Information Technology and Engineering, VIT University, Vellore 632014, Tamil Nadu, India
| | | | - C George Priya Doss
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India
| | - Andrew J Simkin
- School of Biosciences, University of Kent, Canterbury CT2 7NJ, UK; School of Life Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
| | - Siva Ramamoorthy
- School of Bio Sciences and Technology, VIT University, Vellore 632014, Tamil Nadu, India.
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4
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Richardson K, Steffen W, Lucht W, Bendtsen J, Cornell SE, Donges JF, Drüke M, Fetzer I, Bala G, von Bloh W, Feulner G, Fiedler S, Gerten D, Gleeson T, Hofmann M, Huiskamp W, Kummu M, Mohan C, Nogués-Bravo D, Petri S, Porkka M, Rahmstorf S, Schaphoff S, Thonicke K, Tobian A, Virkki V, Wang-Erlandsson L, Weber L, Rockström J. Earth beyond six of nine planetary boundaries. SCIENCE ADVANCES 2023; 9:eadh2458. [PMID: 37703365 PMCID: PMC10499318 DOI: 10.1126/sciadv.adh2458] [Citation(s) in RCA: 119] [Impact Index Per Article: 119.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 07/12/2023] [Indexed: 09/15/2023]
Abstract
This planetary boundaries framework update finds that six of the nine boundaries are transgressed, suggesting that Earth is now well outside of the safe operating space for humanity. Ocean acidification is close to being breached, while aerosol loading regionally exceeds the boundary. Stratospheric ozone levels have slightly recovered. The transgression level has increased for all boundaries earlier identified as overstepped. As primary production drives Earth system biosphere functions, human appropriation of net primary production is proposed as a control variable for functional biosphere integrity. This boundary is also transgressed. Earth system modeling of different levels of the transgression of the climate and land system change boundaries illustrates that these anthropogenic impacts on Earth system must be considered in a systemic context.
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Affiliation(s)
- Katherine Richardson
- Globe Institute, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Will Steffen
- Australian National University, Canberra, Australia
| | - Wolfgang Lucht
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jørgen Bendtsen
- Globe Institute, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Sarah E. Cornell
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Jonathan F. Donges
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Markus Drüke
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Ingo Fetzer
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
- Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Govindasamy Bala
- Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bangalore, Karnataka – 560012, India
| | - Werner von Bloh
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Georg Feulner
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Stephanie Fiedler
- GEOMAR Helmholtz Centre for Ocean Research Kiel and Faculty for Mathematics and Natural Sciences, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Dieter Gerten
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Department of Geography, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tom Gleeson
- Department of Civil Engineering, University of Victoria, Victoria, British Columbia, Canada
- School of Earth and Ocean Sciences, University of Victoria, Victoria, British Columbia, Canada
| | - Matthias Hofmann
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Willem Huiskamp
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Matti Kummu
- Water and Development Research Group, Aalto University, Espoo, Finland
| | - Chinchu Mohan
- GEOMAR Helmholtz Centre for Ocean Research Kiel and Faculty for Mathematics and Natural Sciences, Christian-Albrechts-University Kiel, Kiel, Germany
- Global Institute for Water Security, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
- Waterplan (YC S21), San Francisco, CA, USA
| | - David Nogués-Bravo
- Globe Institute, Faculty of Health, University of Copenhagen, Copenhagen, Denmark
| | - Stefan Petri
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Miina Porkka
- Water and Development Research Group, Aalto University, Espoo, Finland
| | - Stefan Rahmstorf
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany
| | - Sibyll Schaphoff
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Kirsten Thonicke
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
| | - Arne Tobian
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
| | - Vili Virkki
- Water and Development Research Group, Aalto University, Espoo, Finland
| | - Lan Wang-Erlandsson
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
- Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
| | - Lisa Weber
- GEOMAR Helmholtz Centre for Ocean Research Kiel and Faculty for Mathematics and Natural Sciences, Christian-Albrechts-University Kiel, Kiel, Germany
| | - Johan Rockström
- Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany
- Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden
- Institute for Environmental Science and Geography, University of Potsdam, Potsdam, Germany
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Beigaitė R, Tang H, Bryn A, Skarpaas O, Stordal F, Bjerke JW, Žliobaitė I. Identifying climate thresholds for dominant natural vegetation types at the global scale using machine learning: Average climate versus extremes. GLOBAL CHANGE BIOLOGY 2022; 28:3557-3579. [PMID: 35212092 PMCID: PMC9302987 DOI: 10.1111/gcb.16110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 01/13/2022] [Indexed: 05/08/2023]
Abstract
The global distribution of vegetation is largely determined by climatic conditions and feeds back into the climate system. To predict future vegetation changes in response to climate change, it is crucial to identify and understand key patterns and processes that couple vegetation and climate. Dynamic global vegetation models (DGVMs) have been widely applied to describe the distribution of vegetation types and their future dynamics in response to climate change. As a process-based approach, it partly relies on hard-coded climate thresholds to constrain the distribution of vegetation. What thresholds to implement in DGVMs and how to replace them with more process-based descriptions remain among the major challenges. In this study, we employ machine learning using decision trees to extract large-scale relationships between the global distribution of vegetation and climatic characteristics from remotely sensed vegetation and climate data. We analyse how the dominant vegetation types are linked to climate extremes as compared to seasonally or annually averaged climatic conditions. The results show that climate extremes allow us to describe the distribution and eco-climatological space of the vegetation types more accurately than the averaged climate variables, especially those types which occupy small territories in a relatively homogeneous ecological space. Future predicted vegetation changes using both climate extremes and averaged climate variables are less prominent than that predicted by averaged climate variables and are in better agreement with those of DGVMs, further indicating the importance of climate extremes in determining geographic distributions of different vegetation types. We found that the temperature thresholds for vegetation types (e.g. grass and open shrubland) in cold environments vary with moisture conditions. The coldest daily maximum temperature (extreme cold day) is particularly important for separating many different vegetation types. These findings highlight the need for a more explicit representation of the impacts of climate extremes on vegetation in DGVMs.
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Affiliation(s)
- Rita Beigaitė
- Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland
| | - Hui Tang
- Natural History MuseumUniversity of OsloOsloNorway
- Department of GeosciencesUniversity of OsloOsloNorway
| | - Anders Bryn
- Natural History MuseumUniversity of OsloOsloNorway
| | | | - Frode Stordal
- Department of GeosciencesUniversity of OsloOsloNorway
| | - Jarle W. Bjerke
- Norwegian Institute for Nature ResearchFRAM – High North Research Centre for Climate and the EnvironmentTromsøNorway
| | - Indrė Žliobaitė
- Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland
- Finnish Museum of Natural HistoryUniversity of HelsinkiHelsinkiFinland
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