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.
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All datasets used in this study are publicly available from the referenced sources.
The source code for the Bayesian DLM usedto identify the EWS is available at https://github.com/YanlanLiu/early-warning-signal-DLM.
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We thank J.S. Clark, M. West and C. Rundel for discussions and insightful suggestions. M.K. acknowledges support from the National Science Foundation (NSF, grant nos. EAR-1454983 and EAR-1331846). G.K. acknowledges support from the NSF (grant nos. EAR-1344703, AGS-1644382, IOS-1754893 and DGE-1068871). A.P. acknowledges support from the NSF (grant nos. EAR-1331846, DGE-1068871 and EAR-1316258). The publication cost was shared by The University of Alabama–Alabama Water Institute.
The authors declare no competing interests.
Peer review information Nature Climate Change thanks Christopher Schwalm and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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