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

Abstract

Crown architecture is a critical component for a tree to interact with the ambient environment and to compete with neighbors. However, little is known regarding how climate variability may shape crown architecture traits across large geographical extents and whether crown architecture traits have coordinated variations with trunk and leaf traits to climate gradients. Here we used Quercus mongolica trees as an example, used the cutting‐edge terrestrial laser scanning technique to accurately characterize their crown architecture traits, and explored their variabilities along with environmental variability across large climate gradients in northern China. Our results showed that there are significant spatial variations in trunk, crown, and leaf traits even for the same genetic group across large environmental gradients. Tree height and leaf size had tight covariations with precipitation (|R|> 0.8, p < 0.01). We also observed coordinated variations among crown architecture traits related to canopy shape (e.g., primary branch insertion angle, chord length ratio), trunk traits (e.g., tree height), leaf traits (e.g., specific leaf area), and climate variability, highlighting there are likely fundamental evolutionary strategies regulating these covariations. With a projected drier and hotter climate scenario in this region, our results further suggest trees are expected to transit from a “tree shape” to a “shrub shape,” with large ecological and ecophysiological impacts on this region.

Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective

“…Accurate 3D tree structural attributes also provide the opportunity to validate hypotheses in current vegetation dynamic models. For example, Su et al [256] used TLS data to prove that canopy architecture displays a strong spatial variability along climate gradients and to reject the current hypothesis in vegetation dynamic models of a constant ratio between canopy height and canopy size. Tao et al [257] found that canopy height exhibits a hump-shaped correlation with water availability, rejecting the prevailing assumption that canopy height follows a linear correlation with water availability.…”

“…to significantly different carbon modeling results. These studies suggest that interpreting the spatial and temporal dynamics of forest structure and incorporating them into the current dynamic vegetation models are necessary and important research directions [256]. Moreover, the vertical dimension provided by lidar data enables much more comprehensive and accurate descriptions of animal habitats [259].…”

“…In conjunction with optical remote sensing and field surveys, lidar has played a crucial role in the development of 3D ecological observations and modeling. Lidar data have allowed researchers to gain new insights and gather finer details about ecological processes [256], [257]. Nevertheless, increasing demands have emerged to expand our ecological observations from 3D (i.e., X, Y, and Z) to multiple dimensions (i.e., X, Y, Z and the spectral and temporal dimensions) to compensate for the weaknesses of single-type or single-date remote sensing data.…”

Three-dimensional quantification of tree architecture from mobile laser scanning and geometry analysis

“…To date, we are aware of no study that has investigated the relationship between tree crown complexity and seed dispersal strategy. This is because tree architecture and structural complexity are difficult to quantify (Su et al 2020;Guzmán et al 2020). While Xu et al (2019) did find a relationship between tree parameters of height-stem diameter relationships and seed dispersal type in a subtropical montane moist forest (with wind-dispersed strategies common in largestatured tree species, and animal mediated dispersal more common in understory species), their study did not consider crown complexity.…”

Assessing wood properties in standing timber with laser scanning

“…;Hackenberg et al 2015) -Calibration of allometric tree volume or growth equations (both empirical and processbased)(Côté et al 2018;Saarinen et al 2017;Stovall et al 2017) -Retrieval of tree-specific wood quality indicators(Höwler et al 2017;Kankare et al 2014;Kretschmer et al 2013;Stängle et al 2014;Thies et al 2004) -Tree species identification, based on tree morphology (Åkerblom et al 2017) -Analyses of forest canopy space occupation and competition(Bayer et al 2013;Hess et al 2018;Metz et al 2013;Su et al 2020) -Estimating the distribution of photosynthetically active radiation within and below the canopies(Cifuentes et al 2017;Côté et al 2009) -Estimations of architecture-based metabolic scaling exponents…”