Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling

Abstract

The geometric structure of tree branches has been hypothesized to relate to the mechanical safety and efficiency of resource transport within a tree. As such, the topology of tree architecture links physical properties within a tree and influences the interaction of the tree with its environment. Prior work suggests the existence of general principles which govern tree architectural patterns across of species and bio-geographical regions. In particular, West, Brown and Enquist (WBE; 1997) and Savage et al.(2010) derive scaling exponents (branch radius scaling ratio α and branch length scaling ratio β) from symmetrical branch parameters and from these, an architecture-based metabolic scaling rate (θ) for the whole tree. With this key scaling exponent, the metabolism (e.g., number of leaves, respiration, etc.) of a whole tree, or potentially a group of trees, can be estimated allometrically. Until now, branch parameter values have been measured manually; either from standing live trees or from harvested trees. Such measurements are time consuming, labour intensive and susceptible to subjective errors.Remote sensing, and specifically terrestrial LiDAR (TLS), is a promising alternative, being objective, scalable, and able to collect large quantities of data without destructive sampling. In this paper, we calculated branch length, branch radius, and architecture-based metabolic rate scaling exponents by first using TLS to scan standing trees and then fitting quantitative structure models (TreeQSM ) models to 3D point clouds from nine trees in a tropical forest in Guyana. To validate these TLS-derived scaling exponents, we compared them with exponents calculated from direct field measurements of all branches > 10 cm at four scales: branch-level, cumulative branch order, tree-level and plot-level.We found a bias on the estimations of α and β exponents due to a bias on the reconstruction of the branching architecture. Although TreeQSM scaling exponents predicted similar θ as the manually measured exponents, this was due to the combination of α and β scaling exponents which were both biased. Also, the manually measured α and β scaling exponents diverged from the WBE's theoretical exponents suggesting that trees in tropical environments might not follow the predictions for the symmetrical branching geometry proposed by WBE. Our study provides an alternative method to estimate scaling exponents at both the branch-and tree-level in tropical forest trees without the need for destructive sampling. Although this approach is based on a limited sample of nine trees in Guyana, it can be implemented for large-scale plant scaling assessments. These new data might improve our current understanding of metabolic scaling without harvesting trees.

Comparison of Mature Douglas-Firs’ Crown Structures Developed with Two Quantitative Structural Models Using TLS Point Clouds for Neighboring Trees in a Natural Regime Stand

“…With the human-aid computation, Cyclone is believed to generate more accurate BD estimates, especially for complex understory canopy, because the noise and leaf points are manually excluded from the pipe model construction. TreeQSM could accurately construct pipe models for the broadleaf tree stems and branches [42,44,68,69], particularly for branches and stem diameters greater than 10 cm [68]. For branches smaller than 10 cm in diameter, TreeQSM was prone to overestimation [44,67].…”

Tropical tree size–frequency distributions from airborne lidar

“…However, in most natural tropical ecosystems that deviate from a steady‐state condition due to past disturbance and climate induced responses, and particularly for large trees, the theory may not be able to predict the observations (Muller‐Landau et al 2006 a , Farrior et al 2016). Furthermore, empirical studies suggest that there are significant intraspecific (Poorter et al 2003, Bohlman and O’Brien 2006) and intersite variability (Coomes et al 2003, Muller‐Landau et al 2006 b , Lau et al 2019) in tree crown architectural development in tropical forests that cannot be readily predicted by scaling theory. In particular, the tree diameter–height relationship is subject to strong intersite variability, which might be related to environmental conditions, floristic composition, nutrient availability, and soil type (Nogueira et al 2006, Feldpausch et al 2011, Banin et al 2012, Molto et al 2014).…”

Large‐Scale Geographical Variations and Climatic Controls on Crown Architecture Traits

“…With the assistance of individual tree segmentation and stem‐leaf segmentation algorithms, detailed crown architecture traits are also becoming possible to acquire in a repeatable and accurate way (Disney, ; Jin, Su, Gao, et al, ; Jin, Su, Wu, et al, ; Li et al, ; Li et al, ; Moorthy et al, ; Tao, Guo, et al, ; Tao, Wu, et al, ), which provides a new tool to observe changes in tree architecture. For example, Jackson et al () modeled the motion of tree crowns in the wind from TLS data; Puttonen et al () found that TLS could be used to monitor overnight sleep movements of tree branches and argued that TLS could be used to support chronobiology studies; Magney et al () proved that TLS could be used to investigate canopy light regime and reveal patterns of photosynthetic partitioning; Lau et al () used TLS technique to validate the crown architecture‐based metabolic scaling exponents of tropical trees. It is believed that TLS can lead the advances in understanding how tree architectures are influenced by environmental stresses (Malhi et al, ).…”

“…Note that DBH, CBH, BD, and θ represent diameter at breast height, crown base height, branch diameter, and insertion angle, respectively. photosynthetic partitioning; Lau et al (2019) used TLS technique to validate the crown architecture-based metabolic scaling exponents of tropical trees. It is believed that TLS can lead the advances in understanding how tree architectures are influenced by environmental stresses (Malhi et al, 2018).…”