Complex Realities, Simple Signals? Global Evaluation of Early Warning Signals for Forest Mortality Events

Abstract. Forests around the world are increasingly experiencing large-scale regional mortality events as a result of droughts and heat waves. Despite their considerable impacts on the material, non-material, and regulatory contributions of forest ecosystems to people, these mortality events remain difficult to predict. Temporal Early Warning Signals (EWS) based on the concept of Critical Slowing Down (CSD) have been applied widely to remotely sensed vegetation indices. These EWS have often been interpreted as indicators of resilience loss. In order to be of practical use in real-world ecosystem management, such EWS must demonstrate the capacity to reliably and robustly predict upcoming forest mortality events. Previous work has applied EWS for local cases of mortality, but to date, there is no global assessment of EWS on remotely sensed vegetation indices of forest mortality events. The objective of this study is threefold: 1) to provide an overview of various types of EWS as applied to forest mortality events in case studies around the world, 2) to empirically assess the effectiveness of different EWS in predicting globally distributed forest mortality events driven by droughts and heat waves using remote sensing time series, and 3) to conduct a driver analysis to evaluate which factors associated with the methodological setup, the characteristics of the mortality event and climatic conditions explain the performance of different EWS. We find that most previous work in predicting forest mortality events is based on tree ring data. In remote sensing applications, there is a significant lack of robust evaluation of CSD-based EWS using control cases. Our empirical analysis indicates that all of the EWS that were evaluated in this study are ineffective and lack the necessary robustness to serve as predictors of drought-induced forest mortality events. The primary factor that determines trends in EWS is the methodological setup employed. We conclude by calling for more caution in the application of system-agnostic CSD-based EWS, increased efforts to assess accuracy and uncertainty, and more consideration of the system characteristics and actual needs of ecosystem managers when assessing forest resilience and early warning systems.