Battison, R. | Search bioRxiv

bioRxiv2025

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

ecology↗

bioRxiv2024

Tracking tree demography and forest dynamics at scale using remote sensing

O_LICapturing how tree growth and survival vary through space and time is critical to understanding the structure and dynamics of tree-dominated ecosystems. However, characterising demographic processes at scale is inherently challenging, as trees are slow-growing, long-lived, and cover vast expanses of land. C_LIO_LIWe used repeat airborne laser scanning data acquired over 25 km2 of semi-arid, old-growth temperate woodland in Western Australia to track the height growth, crown expansion and mortality of 42,810 individual trees over nine years. C_LIO_LIWe found that demographic rates are constrained by a combination of tree size, competition and topography. After initially investing in height growth, trees progressively shifted to crown expansion as they grew larger, while mortality risk decreased considerably with size. Across the landscape, both tree growth and survival increased with topographic wetness, resulting in vegetation patterns that are strongly spatially structured. Moreover, biomass gains from woody growth generally outpaced losses from mortality, suggesting these old-growth woodlands remain a net carbon sink in the absence of wildfires. C_LIO_LIOur study sheds new light on the processes that shape the dynamics and spatial structure of semi-arid woody ecosystems and provides a roadmap for using emerging remote sensing technologies to track tree demography at scale. C_LI

ecology↗