Vegetation structural complexity and biodiversity in the Great Smoky Mountains

Abstract

Vegetation structural complexity and biodiversity tend to be positively correlated, but understanding of this relationship is limited in part by structural metrics tending to quantify only horizontal or vertical variation, and that do not reflect internal structure. We developed new metrics for quantifying internal vegetation structural complexity using terrestrial LiDAR scanning and applied them to 12 NEON forest plots across an elevational gradient in Great Smoky Mountains National Park, USA. We asked (1) How do our newly developed structure metrics compare to traditional metrics? (2) How does forest structure vary with elevation in a high-biodiversity, high topographic complexity region? (3) How do forest structural metrics vary in the strength of their relationships with vascular plant biodiversity? Our new measures of canopy density (Depth) and structural complexity (σDepth), and their canopy height-normalized counterparts, were sensitive to structural variations and effectively summarized horizontal and vertical dimensions of structural complexity. Forest structure varied widely across plots spanning the elevational range of GRSM, with taller, more structurally complex forests at lower elevation. Vascular plant biodiversity was negatively correlated with elevation and more strongly positively correlated with vegetation structure variables. The strong correlations we observed between canopy structural complexity and biodiversity suggest that structural complexity metrics could be used to assay plant biodiversity over large areas in concert with airborne and spaceborne platforms.

TLSLeAF: automatic leaf angle estimates from single‐scan terrestrial laser scanning

“…With an easy and automatic method of capturing leaf angle, these new measurements can be linked to biome or plant functional type for use in functional ecological models for refined photosynthetic rates, competition, ecosystem flux, and light interception. For example, more accurate and independent angular estimates of canopy PAVD through directly measured G ‐functions will improve estimates of canopy complexity – a strong predictor of ecosystem biodiversity (Walter et al ., 2021). Likewise, more direct characterization of leaf angle will directly impact remotely sensed estimates of PAI, from ground‐based (hemispherical photography or TLS) to air and space‐borne (multispectral optical; LiDAR) sensors (Tang et al ., 2012; Hancock et al ., 2019; Dubayah et al ., 2020).…”

“…Before voxelization, an optional topographic normalization step can be included, detailed in Walter et al (2021). In short, a ground surface model is reconstructed from the single-scan TLS data and subtracted from the vertical dimension of the TLS point cloud (Roussel et al, 2020), effectively normalizing by topographysee Walter et al (2021) for details.…”

“…Before voxelization, an optional topographic normalization step can be included, detailed in Walter et al (2021). In short, a ground surface model is reconstructed from the single-scan TLS data and subtracted from the vertical dimension of the TLS point cloud (Roussel et al, 2020), effectively normalizing by topographysee Walter et al (2021) for details. Topographic normalization was included in TLSLEAF to allow for vertical profiles of mean leaf angle and LAD, which can be reconstructed with a simple summary operation (e.g.…”

Assessing Low-Cost Terrestrial Laser Scanners for Deriving Forest Structure Parameters

“…Since the early 2000s, terrestrial laser scanning (TLS) has matured into a robust, and tractable means of measuring forest structural attributes using light detection and ranging (LiDAR) from the ground [1]. TLS has been used to model branch architecture [2], quantify leaf angle distributions [3,4], assess habitat quality [5], estimate fuel loads [6][7][8], map forest microtopograpy [9,10], examine biodiversity gradients [11,12], and estimate forest biomass [13][14][15][16][17][18]. Forest biomass carbon storage and productivity and can be inferred from estimates of above-ground biomass [19,20].…”

Testing the Height Variation Hypothesis with the R rasterdiv Package for Tree Species Diversity Estimation

“…The HVH assumes that the higher the variation in tree height, calculated with LiDAR data, the more complex the overall structure of the forest and the higher the tree species diversity (see Figure A1 in Appendix A for visual explanation of the HVH). Forest structure and its vertical heterogeneity has been considered in many studies a good proxy of tree species diversity: the higher the complexity, the higher the number of available niches that can host more tree species [32][33][34][35][36]. The vertical spatial distribution of the forest canopy indeed plays a crucial role in structuring spatial-temporal patterns of various forest resources and it has been considered an essential driver of different ecosystem functions such as habitat diversification and environmental heterogeneity [37].…”