Power law scaling relationships link canopy structural complexity and height across forest types

Abstract

Forest canopy structural complexity (CSC), an emergent ecosystem property, plays a critical role in controlling ecosystem productivity, resource acquisition and resource use‐efficiency; yet is poorly characterized across broad geographic scales and is difficult to upscale from the plot to the landscape. Here, we show that the relationship between canopy height and CSC can be explained using power laws by analysing lidar‐derived CSC data from 17 temperate forest sites spanning over 17 degrees of latitude. Across three plant functional types (deciduous broadleaf, evergreen needleleaf and mixed forests), CSC increases as an approximate power law of forest height. In evergreen needleleaf forests, increases in canopy height do not result in increases in complexity to the same magnitude as in other forest types. We attribute differences in the slope of height:complexity relationships among forest types to: (a) the limited diversity of crown architectures among evergreen conifer trees relative to broadleaf species; (b) differences in how vertical forest layering develops with height; and (c) competitive exclusion by needleleaf species. We show support for these potential mechanisms with an analysis of 4,324 individual trees from across 18 National Ecological Observatory Network sites showing that crown geometry‐to‐tree height relationships differ consistently between broadleaf and needleleaf species. Power law relationships between forest height and CSC have broad implications for modelling, scaling and mapping forest structural attributes. Our results suggest that forest research and management should consider the nonlinearity in scaling between forest height and CSC and that the nature of these relationships may differ by forest type. A free Plain Language Summary can be found within the Supporting Information of this article.

Scale dependency of lidar‐derived forest structural diversity

“…We found differences in REA among forest types for FSD measures of CC, forest height and leaf area (Figure 4). ENF sites have a higher CC REA (~30–115 m) than DBF sites (~20–30 m; Figure 4), whereas MF sites fall within the range of ENF sites (~100 m), DBF or MF (Atkins et al, 2022). The starkest difference observed is that REA for CC is much higher in needleleaf forests (ENF) than either mixed (MF) or broadleaf forests (DBF), but the REA for variability in CC is much lower in needleleaf forests (ENF).…”

“…Rasters of each calculated lidar metric, for each site, were mosaicked and then sampled using a uniformly spaced sampling grid of 16 sampling points per 1 × 1 km tile for grains of less than 250 m, 4 sampling points per 1 × 1 km tile for 500 m grain size and 1 sampling point per 1 × 1 km tile for the 1000 m grain via the sampleRegular function from the raster package (Hijmans et al, 2020). Data were then filtered to pixels with a minimum of 5 m canopy height (based on the 99th percentile return height) and 25% CC, motivated by the definition of what constitutes a ‘forest’ in Hansen et al (2010) and the lidar structural analysis method outlined in (Atkins et al, 2022). In all, 100 randomly sampled points for each spatial grain between 5 and 250 m were retrained for further analysis, as were all data at the 500 and 1000 m grain ( n = 36 and 9, respectively).…”

Vegetation canopy height estimation in dynamic tropical landscapes with TanDEM‐X supported by GEDI data

“…The canopy height estimation of GEDI and TanDEM‐X suggests a potential approach to estimate aboveground biomass on a regional to even global scale with high resolution (Caicoya et al, 2016; Choi et al, 2021; Dubayah et al, 2020). In addition to aboveground biomass, the vegetation canopy height is an important proxy for ecosystem structure and biodiversity (Atkins et al, 2021; Fahey et al, 2019; Lang et al, 2022). It can be assumed that the combination of GEDI and TanDEM‐X data could support the assessment of biodiversity with wall‐to‐wall canopy height information on a regional to global scale (Dubayah et al, 2020).…”

“…The vegetation canopy height is a relevant proxy for aboveground biomass and other structural attributes of vegetation (Asner & Mascaro, 2014; Dubayah et al, 2020; Réjou‐Méchain et al, 2015). Furthermore, canopy height and its heterogeneity are considered as indicators for ecosystem structure and its complexity as an essential biodiversity variable (Atkins et al, 2021; Fahey et al, 2019; Lang et al, 2022). For instance, it was found that information about the spatial distribution of canopy height supports the prediction of biodiversity variables on global scale (Feng et al, 2020; Gatti et al, 2017).…”

Tallo: A global tree allometry and crown architecture database

“…Large, tall trees play a disproportionately big role in shaping carbon cycling on land, as they store the vast majority of the aboveground biomass in a given patch of forest (Bastin et al, 2018; Lutz et al, 2018; Slik et al, 2013). Tree height is also a key axis of habitat structural complexity and plays a major role in determining habitat diversity and the buffering effect that forest canopies exert on local microclimates (Atkins et al, 2022; de Frenne et al, 2021; Jucker, Bongalov, et al, 2018; Jucker, Hardwick, et al, 2018). However, tall trees are predicted to be among the most vulnerable to climate change, as they are particularly prone to hydraulic stress (Bennett et al, 2015; McDowell & Allen, 2015; Olson et al, 2018; Stovall et al, 2019), making it critical to identify the environmental conditions under which tall trees can thrive.…”