Leveraging Signatures of Plant Functional Strategies in Wood Density Profiles of African Trees to Correct Mass Estimations From Terrestrial Laser Data

Abstract

Wood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. this functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends. Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived speciesaverage WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests. Terrestrial plants account for 83% of the living carbon on Earth 1 , of which tropical forests are estimated to account for close to half 2 , principally contained within woody plant parts. Tropical forests are therefore becoming a key element in international carbon trading schemes despite obvious difficulties in accurately estimating stocks

Plant traits controlling growth change in response to a drier climate

“…It has been previously hypothesised that ρ constitutes a major control on growth rates (Chave et al ., 2009; Falster et al ., 2018; Phillips et al , 2019) and on the growth–mortality trade‐off in trees (Wright et al ., 2010; Ruger et al ., 2018). However, almost all the studies that demonstrate this in tropical forests use species averaged measures of wood density, which can generate fundamental problems with predicting trait–growth relationships (Y. Yang et al ., 2018), in part through ignoring the large within‐species variation in wood density (Plourde et al ., 2015; Lehnebach et al ., 2019; Momo et al ., 2020). We argue that many of the trade‐offs which may exist within the WES justifying a link between ρ and growth may break down at local scales when individual tree‐by‐tree data are used (e.g.…”

“…Furthermore, close‐to‐zero correlations between growth rates and ρ on an individual basis may be being caused by both high intraspecific and within‐tree variation in ρ (Table 2; Fig. S5; Plourde et al ., 2015; Lehnebach et al ., 2019; Momo et al ., 2020), alongside substantial variations in growth rates and their responses to environmental variables between trees of different sizes (Rowland et al ., 2015a). We note, however, that determining which measure of wood density to use to represent the whole‐tree average is complex (Lehnebach et al ., 2019; Momo et al ., 2020) and such correlations may become more accurate at the stand scale if such an estimate could be accurately derived.…”

Assessing wood properties in standing timber with laser scanning

“…Similarly, carbon storage and sinks are calculated from the forest biomass, using fixed wood density and carbon content factors (Penman et al 2003) that are specific to single species or genera. However, wood density varies significantly between and within species and individuals, stands, and geographic regions (Duncanson et al 2019;Momo et al 2020;Stephenson et al 2014).…”

Consequences of vertical basic wood density variation on the estimation of aboveground biomass with terrestrial laser scanning

“…When sampling or felling is simply not possible, as is often the case, researchers and forest managers rely on earlier published work in wood density repositories (Zanne et al 2009;Falster et al 2015;Martínez-Sancho et al 2020) or unpublished locally established representative density data. Many tree species show substantial stump-to-tip and pith-to-bark trends in specific wood density, which is closely intertwined with tree life history strategies and functional type (Momo et al 2020; MacFarlane 2020), further complicating the accurate quantification of weighted basic wood density. To some extent, pragmatic sampling rules have been established to approximate weighted basic wood density from a single measurement (Wahlgren and Fassnacht 1959;Wiemann and Williamson 2012;Bastin et al 2015;Wassenberg et al 2015;Momo et al 2020), the reliability of such density estimates remains uncertain.…”

“…Many tree species show substantial stump-to-tip and pith-to-bark trends in specific wood density, which is closely intertwined with tree life history strategies and functional type (Momo et al 2020; MacFarlane 2020), further complicating the accurate quantification of weighted basic wood density. To some extent, pragmatic sampling rules have been established to approximate weighted basic wood density from a single measurement (Wahlgren and Fassnacht 1959;Wiemann and Williamson 2012;Bastin et al 2015;Wassenberg et al 2015;Momo et al 2020), the reliability of such density estimates remains uncertain. Furthermore, as TLS systems capture the whole 3D structure of objects, the resulting tree volume includes bark, whereas basic density measurements often exclude any bark tissues.…”