Unstratified forests dominate the tropics especially in regions with lower fertility or higher temperatures

Abstract

The stratified nature of tropical forest structure had been noted by early explorers, but until recent use of satellite-based LiDAR (GEDI, or Global Ecosystems Dynamics Investigation LiDAR), there has been no way to quantify stratification across all tropical forests. Understanding stratification is important because by some estimates, a majority of the world’s species inhabit tropical forest canopies. Stratification can modify vertical microenvironment, and thus can affect a species’ susceptibility to global warming. A better understanding of structure could also improve predictions of biomass across the tropics. Here we find that, based on analyzing each GEDI 25m diameter footprint in tropical forests (after screening for human impact), most footprints (60-90%) do not have multiple layers of vegetation. This result is highly scale dependent, but with a 25m footprint, the most common forest structure has a minimum plant area index (PAI) at ~40m followed by an increase in PAI until ~15m followed by a decline in PAI to the ground layer (described hereafter as a one peak footprint). However, there are large geographic patterns to forest structure within the Amazon basin (ranging between 60-90% one peak) and between the Amazon (79±9sd) and SE Asia or Africa (72±14 v 73±11). The number of canopy layers is significantly correlated with tree height (r2=0.12), forest biomass (r2=0.14), maximum temperature (Tmax) (r2=0.05), vapor pressure deficit (VPD) (r2=0.03) and soil fertility proxies (e.g. total cation exchange capacity -r2=0.01). Certain boundaries, like the Pebas Formation and Ecoregions, clearly delineate continental scale structural changes. More broadly, deviation from more ideal conditions (e.g. lower fertility or higher temperatures) leads to shorter, less stratified forests with lower biomass.

Satellite derived trait data slightly improves tropical forest biomass, NPP and GPP predictions

“…In a previous paper, we had hypothesized that forest stratification (% one peak or the number of single stratum forests as a percentage of total) might improve biomass predictions better than a simple metric like rh50 (Doughty et al, 2023) because in that paper, % one peak predicted biomass better than tree height. Ecological theory suggests that a stratified forest with more large emergent trees is indicative of an older forest (Halle et al, 1980), which generally has higher biomass and carbon content.…”

“…For instance, some of the key data filters we applied were: included degrade flags of 0,3,8,10,13,18,20,23,28,30,33,38,40,43,48,60,63,68, L2A and L2B quality flags = 1 (only use highest quality data), sensitivity >= 0.98. With the GEDI data we used canopy height, height of median energy (HOME), and the number of canopy layers following Doughty et al 2023 (Doughty et al, 2023). Across all tropical forests, we created 300 by 300 m pixels containing all averaged (mean) GEDI data between 2019 and 2022.…”

“…Currently the L4A product for tropical forests uses relative height (RH) 98 and RH 50 to predict a median Above Ground Biomass (AGB) of 300 Mg Ha -1 for tropical forests (0.66 r 2 and RMSE of 10.4). Duncanson et al (2022) several peaks) correlated with biomass more strongly than tree height (Doughty et al, 2023).…”