Modelling forest dynamics using integral projection models (IPMs) and repeat LiDAR

O_LIEstimating the life histories and population dynamics of trees is important for predicting how forests respond to climate change and disturbances. To do so, ecologist often use stage-structured population models, which explicitly account for individual heterogeneity. However, the data needed to parameterise these models is typically constrained by time- and labour-intensive field methods. C_LIO_LIDespite growing access to remote sensing imagery that could potentially satiate these data-hungry population models, these data remain underutilised in population ecology. Here, we demonstrate a pipeline that integrates repeat-LiDAR data with a type of structured demographic model (Integral Projection Model, IPM) to examine forest-wide dynamics in response to environmental drivers. Using Australias Great Western Woodlands as a case study, we model the survival and growth of [~]40,000 eucalypt trees over a decade to estimate their life expectancy and characterise multiple stages of growth (height growth and crown expansion). C_LIO_LIOur results indicate distinct responses of small trees (where vital rates are predicted by height) and large trees (which invested predominantly in expanding crown area) to proxies for competition and soil moisture (local canopy density and topographic wetness index, respectively). Parameterising structured population models using LiDAR data therefore offers a step-change in perspective towards more ecologically meaningful forest dynamics. C_LIO_LITo broaden the application of this pipeline, we highlight three priorities: (1) Application to more complex systems such as mixed species stands and dense, multilayered canopies); (2) Incorporating complete life histories, including reproduction and early life stages; and (3) Using in long-term or comparative studies through improved availability of high-quality, comparable, repeat-LiDAR surveys. By combining remote sensing data with detailed insights from field- based studies, this pipeline represents a scalable framework for guiding forest management and conservation decisions. C_LI