Predicting drought-induced tree mortality in Swiss beech forests hinges upon predisposing and inciting factors

Abstract

The increase in the frequency and severity of drought-induced tree mortality in many European low-elevation forests poses considerable challenges to forest management and requires an understanding of its causes. We propose a novel framework for integrating the factors underlying drought-induced tree mortality in a dynamic vegetation model. We evaluate whether this framework accurately reproduces drought-related mortality in six mesic beech-dominated stands in 2018-2020, and over multiple years in a xeric Scots pine-dominated stand in Switzerland. Additionally, we investigate its behavior along a large climatic gradient in central Europe. We employ a three-step approach. First, we evaluate multiple drought indices for capturing tree growth responses to extreme drought. We find that in contrast to widespread indices such as SPI and SPEI, the ForClim drought index captures growth responses to drought intensity during summer, the growing period, and annually. Second, we assess in detail the capability of the ForClim soil moisture model to simulate soil water dynamics, comparing it to the mechanistic soil-vegetation-atmosphere model LWFBrook90. The ForClim soil moisture model adequately simulates soil water dynamics, particularly in extreme drought years. Third, based on Manions Decline Disease Theory, we develop a novel mortality sub-model that combines predisposing and inciting factors. Its integration in ForClim captures drought-induced mortality events in the mesic beech forests as well as the multi-year mortality at the xeric Scots pine-dominated stand. Along a climatic gradient in central Europe, the model provides good quantifications of Potential Natural Vegetation. The novel framework to capture drought-related tree mortality is simple yet produces accurate results. The underlying hypothesis regarding the factors leading to drought-induced tree mortality is promising but requires further tests for its generality.