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Reduced resilience as an early warning signal of forest mortality

Author

Listed:
  • Yanlan Liu

    (Duke University)

  • Mukesh Kumar

    (Duke University
    University of Alabama)

  • Gabriel G. Katul

    (Duke University
    Duke University)

  • Amilcare Porporato

    (Princeton University
    Princeton University)

Abstract

Climate-induced forest mortality is being widely observed across the globe. Predicting forest mortality remains challenging because the physiological mechanisms causing mortality are not fully understood and empirical relations between climatology and mortality are subject to change. Here, we show that the temporal loss of resilience, a phenomenon often detected as a system approaches a tipping point, can be used as an early warning signal (EWS) to predict the likelihood of forest mortality directly from remotely sensed vegetation dynamics. We tested the proposed approach on data from Californian forests and found that the EWS can often be detected before reduced greenness, between 6 to 19 months before mortality. The EWS shows a species-specific relation with mortality, and is able to capture its spatio-temporal variations. These findings highlight the potential for such an EWS to predict forest mortality in the near-term.

Suggested Citation

  • Yanlan Liu & Mukesh Kumar & Gabriel G. Katul & Amilcare Porporato, 2019. "Reduced resilience as an early warning signal of forest mortality," Nature Climate Change, Nature, vol. 9(11), pages 880-885, November.
    • Handle: RePEc:nat:natcli:v:9:y:2019:i:11:d:10.1038_s41558-019-0583-9
    DOI: 10.1038/s41558-019-0583-9
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    Cited by:

    1. Florian Diekert & Daniel Heyen & Frikk Nesje & Soheil Shayegh, 2024. "Balancing the Risk of Tipping: Early Warning Systems from Detection to Management," CESifo Working Paper Series 10892, CESifo.
    2. Naoki Masuda & Kazuyuki Aihara & Neil G. MacLaren, 2024. "Anticipating regime shifts by mixing early warning signals from different nodes," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Feliu Serra-Burriel & Pedro Delicado & Fernando M. Cucchietti, 2021. "Wildfires Vegetation Recovery through Satellite Remote Sensing and Functional Data Analysis," Mathematics, MDPI, vol. 9(11), pages 1-22, June.
    4. Taylor Smith & Niklas Boers, 2023. "Global vegetation resilience linked to water availability and variability," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Yao Zhang & Pierre Gentine & Xiangzhong Luo & Xu Lian & Yanlan Liu & Sha Zhou & Anna M. Michalak & Wu Sun & Joshua B. Fisher & Shilong Piao & Trevor F. Keenan, 2022. "Increasing sensitivity of dryland vegetation greenness to precipitation due to rising atmospheric CO2," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Timothy M. Lenton & Jesse F. Abrams & Annett Bartsch & Sebastian Bathiany & Chris A. Boulton & Joshua E. Buxton & Alessandra Conversi & Andrew M. Cunliffe & Sophie Hebden & Thomas Lavergne & Benjamin , 2024. "Remotely sensing potential climate change tipping points across scales," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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