Testing early warning metrics for drought-induced tree physiological stress and mortality

Area of Science:

• Ecology
• Climate Science
• Remote Sensing

Background:

• Climate change is increasing the frequency and severity of droughts, leading to widespread tree mortality globally.
• Predicting and detecting tree stress and mortality from drought is crucial for ecological understanding and forest management.
• Current methods lack the ability to identify at-risk trees in situ for timely intervention.

Purpose of the Study:

• To develop and validate early warning metrics for detecting drought-induced tree stress and mortality.
• To assess the predictive power of these metrics across different drought events and forest types.
• To link remote sensing metrics to underlying tree physiological stress and hydraulic function.

Main Methods:

• Utilized Landsat satellite data to develop early warning metrics from a drought event in the southwestern United States.
• Tested the developed metrics' ability to predict tree mortality during a subsequent drought in the same region.
• Validated the metrics' performance by examining an independent drought event in the Amazon rainforest.

Main Results:

• Early warning metrics successfully predicted spatial patterns of tree mortality in both the second southwestern US drought and the Amazonian drought.
• The metrics showed a strong correlation with spatial patterns of tree hydraulic stress, indicating its role in mortality.
• The findings establish a link between remote sensing, physiological stress, and tree mortality during drought.

Conclusions:

• Developed early warning metrics provide a robust foundation for predicting climate change-driven tree mortality.
• These metrics enhance our understanding of the physiological mechanisms (loss of hydraulic function) underlying drought-induced tree death.
• The validated metrics can guide forest management strategies and interventions for climate-induced disturbances.