Modelling forest dynamics using integral projection models and repeat lidar

Abstract Estimating tree life histories and population dynamics is key to predicting how forests respond to climate change and disturbance. However, linking individual tree trajectories to whole‐forest outcomes (e.g. structural, compositional, and functional health) remains challenging. Stage‐structured demographic models offer a promising solution, but they typically require extensive field data on individual‐level vital rates (e.g. survival and growth), limiting their application at scale. Here, we demonstrate an approach that integrates repeat airborne lidar data with a structured demographic model (an integral projection model, IPM) to examine forest‐wide demography in response to environmental drivers. Using Australia's Great Western Woodlands as a case study, we model the survival, growth, and life expectancy of ~40 000 eucalypt trees over a decade. Vital rates were modelled using height for small trees and crown area for large trees, reflecting a shift in growth strategy with size. Our results indicate distinct responses of small and large trees to proxies for competition and soil moisture (local canopy density and topographic wetness index, respectively). A reduction in topographic wetness index—reflecting drier conditions—led to lower life expectancy, particularly for larger trees, which may be more vulnerable to drought. This framework enables demographic analysis at scale, using widely available lidar data, offering a scalable tool for forest monitoring, modelling, and management. We identify three priorities for broader application, including (1) mixed species stands and multilayered canopies, (2) full life cycle modelling including reproduction and early life stages, and (3) long‐term or comparative studies using high‐quality repeat lidar. By combining remote sensing data with detailed insights from field‐based studies, our study provides a scalable approach for guiding forest management and conservation decisions.