Identifying drivers and mechanisms of climate-related tree mortality

Abstract

Eastern New South Wales (NSW), Australia, is a region prone to frequent and intense droughts, with an unprecedented episode of high temperatures and low rainfall in 2017–2019 culminating in the 2019-2020 summer triggering massive canopy dieback of eucalypt forests throughout the region. Subsequent high rainfall in 2020 offered a unique and rare opportunity to study the process of vegetation recovery following severe drought impact. Several studies have documented the impact of the 2019-2020 event on canopy browning associated with declining forest health. However, pinpointing the exact environmental triggers and physiological responses that drive tree stress and guide recovery remains challenging due to complex ecological interactions unfolding over time particularly in heterogeneous environments. This research addresses these complexities by quantitatively linking ground- based observations of eucalypt drought-stress with spectral observations from remote sensing, employing advanced machine learning (ML) and comparing outcomes with a process- based ecosystem model to unravel drought and recovery dynamics and underlying ecological response mechanisms in eucalypt ecosystems. This research comprises three interconnected studies. Chapter 2 established relationships between plot-based canopy health observations and satellite-derived spectral indices, scaled these to map regional drought impacts, and validated results against the citizen-science dataset, confirming severe drought stress in approximately 13% of eastern NSW forests. This approach demonstrated the Normalized Burn Ratio (NBR) index as a reliable tool for establishing long-term baselines to monitor drought responses. In Chapter 3, I employed Artificial Neural Networks (ANN) to link field-validated z- scores NBR index to dynamic and static spatio-temporal environmental variables hypothesised to represent key drought-stress and recovery drivers. Findings showed lagged, cumulative climatic stresses and landscape factors such as wetness index, aspect, and soil properties governed drought impacts and subsequent recovery, and that these effects vary across bioregions according to local environmental conditions. Chapter 4 aimed to elucidate the mechanistic drivers of drought stress by integrating ecosystem model simulations with machine-learning inferences and the field-calibrated spectral index from previous sub- projects; this unified approach revealed hydraulic-impairment signatures, rapid upper-soil moisture loss and carbon depletion, at high-severity sites and deeper soil-moisture buffering at moderate-severity locations, while exposing our model’s shortcomings (static trait parameterization and absence of root plasticity) which lead us to hypothesize that incorporate hydraulics traits may enhance the model’s predictive accuracy under severe drought. These interconnected studies provide scalable methodologies for drought assessments, empowering policymakers to prioritize vulnerable ecosystems and allocate resources toward forest resilience-building interventions.

Date of Thesis submission	2025
Original language	English
Awarding Institution	Western Sydney University
Sponsors	Australian Government Research Training Program Scholarship
Supervisor	Benjamin Smith (Supervisor) & Ali Mansourian (Supervisor)