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Improving tropical forest current biomass estimates can help more accurately evaluate ecosystem services in tropical forests. The Global Ecosystem Dynamics Investigation (GEDI) lidar provides detailed 3D forest structure and height data, which can be used to improve above‐ground biomass estimates. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare stand biomass predicted by GEDI data with the observed data of 2,102 inventory plots in tropical forests and find that adding a remotely sensed (RS) trait map of leaf mass area (LMA) significantly (P < 0.001) improves field biomass predictions, but by only a small amount (r2 = 0.01). However, it may also help reduce the bias of the residuals because there was a negative relationship between both LMA (r2 of 0.34) and percentage of phosphorus (%P, r2 = 0.31) and residuals. Leaf spectral data (400–1,075 nm) from 523 individual trees along a Peruvian tropical forest elevation gradient predicted Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r2 = 0.01 and LMA predicts DBH with an r2 = 0.04. Other data sets may offer further improvements and max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of 0.76 (N = 66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N = 21), leaf traits predicted with remote sensing are better at predicting fluxes than structure variables. Overall, trait maps, especially future improved ones produced by Surface Biology Geology, may improve biomass and carbon flux predictions by a small but significant amount. Plain Language Summary: Improving predictions of tropical forest biomass can help us to fight climate change. In this paper, we tried to improve tropical forest biomass predictions of satellite lidar (Global Ecosystem Dynamics Investigation) by adding remote sensed estimates of leaf traits. Leaf traits like leaf mass area or phosphorus slightly improved predictions of forest biomass with both a ground data set and a remotely sensed data set. Further, remotely sensed trait data could help explain the differences in the prediction of remotely sensed biomass compared with field derived biomass (residuals). Maximum temperature, but not soil fertility, also improved biomass predictions. Remotely sensed leaf traits were better than structure, like tree height, for predicting net primary production (NPP) and gross primary production (GPP). Future improved hyperspectral satellite data may be used to further improve predictions of biomass, NPP and GPP. Key Points: Both field measured and remotely sensed trait data improved biomass predictions, but only by a small amountIn the Amazon region, maximum temperature, but not soil fertility, improved biomass predictionsTrait data was better than structure data for predicting tropical forest net primary production and gross primary production [ABSTRACT FROM AUTHOR]

Fine roots constitute a significant component of the net primary productivity (NPP) of forest ecosystems but are much less studied than aboveground NPP. Comparisons across sites and regions are also hampered by inconsistent methodologies, especially in tropical areas. Here, we present a novel dataset of fine root biomass, productivity, residence time, and allocation in tropical old‐growth rainforest sites worldwide, measured using consistent methods, and examine how these variables are related to consistently determined soil and climatic characteristics. Our pantropical dataset spans intensive monitoring plots in lowland (wet, semi‐deciduous, and deciduous) and montane tropical forests in South America, Africa, and Southeast Asia (n = 47). Large spatial variation in fine root dynamics was observed across montane and lowland forest types. In lowland forests, we found a strong positive linear relationship between fine root productivity and sand content, this relationship was even stronger when we considered the fractional allocation of total NPP to fine roots, demonstrating that understanding allocation adds explanatory power to understanding fine root productivity and total NPP. Fine root residence time was a function of multiple factors: soil sand content, soil pH, and maximum water deficit, with longest residence times in acidic, sandy, and water‐stressed soils. In tropical montane forests, on the other hand, a different set of relationships prevailed, highlighting the very different nature of montane and lowland forest biomes. Root productivity was a strong positive linear function of mean annual temperature, root residence time was a strong positive function of soil nitrogen content in montane forests, and lastly decreasing soil P content increased allocation of productivity to fine roots. In contrast to the lowlands, environmental conditions were a better predictor for fine root productivity than for fractional allocation of total NPP to fine roots, suggesting that root productivity is a particularly strong driver of NPP allocation in tropical mountain regions. [ABSTRACT FROM AUTHOR]

A prominent signal of the Anthropocene is the extinction and population reduction of the megabiota—the largest animals and plants on the planet. However, we lack a predictive framework for the sensitivity of megabiota during times of rapid global change and how they impact the functioning of ecosystems and the biosphere. Here, we extend metabolic scaling theory and use global simulation models to demonstrate that (i) megabiota are more prone to extinction due to human land use, hunting, and climate change; (ii) loss of megabiota has a negative impact on ecosystem metabolism and functioning; and (iii) their reduction has and will continue to significantly decrease biosphere functioning. Global simulations show that continued loss of large animals alone could lead to a 44%, 18% and 92% reduction in terrestrial heterotrophic biomass, metabolism, and fertility respectively. Our findings suggest that policies that emphasize the promotion of large trees and animals will have disproportionate impact on biodiversity, ecosystem processes, and climate mitigation. Human-driven losses of megafauna and megaflora may have disproportionate ecological consequences. Here, the authors combine metabolic scaling theory and global simulation models to show that past and continued reduction of megabiota have and will continue to decrease ecosystem and biosphere functioning. [ABSTRACT FROM AUTHOR]

Why do forest productivity and biomass decline with elevation? To address this question, research to date generally has focused on correlative approaches describing changes in woody growth and biomass with elevation., We present a novel, mechanistic approach to this question by quantifying the autotrophic carbon budget in 16 forest plots along a 3300 m elevation transect in Peru., Low growth rates at high elevations appear primarily driven by low gross primary productivity ( GPP), with little shift in either carbon use efficiency ( CUE) or allocation of net primary productivity ( NPP) between wood, fine roots and canopy. The lack of trend in CUE implies that the proportion of photosynthate allocated to autotrophic respiration is not sensitive to temperature. Rather than a gradual linear decline in productivity, there is some limited but nonconclusive evidence of a sharp transition in NPP between submontane and montane forests, which may be caused by cloud immersion effects within the cloud forest zone. Leaf-level photosynthetic parameters do not decline with elevation, implying that nutrient limitation does not restrict photosynthesis at high elevations., Our data demonstrate the potential of whole carbon budget perspectives to provide a deeper understanding of controls on ecosystem functioning and carbon cycling. [ABSTRACT FROM AUTHOR]

Has land surface cover in South America been impacted by the loss of most large herbivores following the severe Pleistocene and Early Holocene megafauna extinctions on this continent? Here, we estimate how mean savanna woody biomass may have changed in the Americas following these extinctions by creating an empirical model to understand how large herbivores impact savanna woody biomass. To create this empirical model, we combine a large recently published dataset of savanna woody cover from Lehmann et al. (2014) (n = 2154 plots) with estimates of mammals ranges and weights from the IUCN database. We evaluate how variables such as number of megaherbivores (mammal species ≥ 1000 kg), log10 sum species weights, and total number of mammal species predict changes to woody cover by using both ordinary least squares regression analysis (OLS) and simultaneous auto-regressive (SAR) analysis to control for spatial autocorrelation. Both number of megaherbivores and log10 sum species weights, which both disproportionately weight for megaherbivores, significantly explained much (∼ 5-13%) variance in woody cover, but the third variable weighting all animals equally, did not. We then combined these biotic variables with abiotic variables such as temperature, precipitation, and fire frequency to create a model predicting 36% of the variance of savanna woody cover. We used this model combined with estimated range maps of extinct South American megafauna to estimate that had those South American megafauna not gone extinct, total savanna woody cover in South America could possibly have decreased by ∼ 29% and that savannas would likely have been more open like current African savannas. [ABSTRACT FROM AUTHOR]

Background:The woody biomass residence time (τw) of an ecosystem is an important variable for accurately simulating its biomass stocks. Methods and results:We reviewed published data from 177 forest plots across the tropics and found a six-fold variation (23–129 years) in τwacross our dataset, with a median τwof ca. 50 years. This value is similar to the median default value across 21 vegetation models for tropical forests, although the range of values used in models is large (20 to 200 years). Conclusions:The notion of a constant τwacross all tropical forests may be of limited utility, given the large observed variation in τw. We found that while there was little relationship between climate variables and τw, there was evidence that edaphic factors exerted a strong influence on τw. In both the Neotropics and the Paleotropics, τwwas highest in heavily weathered soils, suggesting that low soil fertility and/or non-limiting soil physical conditions exert a critical influence on τw. There is considerable uncertainty in how τwwill be affected by global environmental change, especially by increased atmospheric CO2. Even small changes in τwcould significantly reduce the future tropical forest carbon sink predicted by many vegetation models. [ABSTRACT FROM PUBLISHER]

Forest dynamics and tree species composition vary substantially between Paleotropical and Neotropical forests, but these broad biogeographic regions are treated uniformly in many land models. To assess whether these regional differences translate into variation in productivity and carbon (C) storage, we compiled a database of climate, tree stem growth, litterfall, aboveground net primary production (ANPP), and aboveground biomass across tropical rainforest sites spanning 33 countries throughout Central and South America, Asia, and Australasia, but excluding Africa due to a paucity of available data. Though the sum of litterfall and stem growth (ANPP) did not differ between regions, both stem growth and the ratio of stem growth to litterfall were higher in Paleotropical forests compared to Neotropical forests across the full observed range of ANPP. Greater C allocation to woody growth likely explains the much larger aboveground biomass estimates in Paleotropical forests (~29%, or ~80 Mg DW/ha, greater than in the Neotropics). Climate was similar in Paleo‐ and Neotropical forests, thus the observed differences in C likely reflect differences in the evolutionary history of species and forest structure and function between regions. Our analysis suggests that Paleotropical forests, which can be dominated by tall‐statured Dipterocarpaceae species, may be disproportionate hotspots for aboveground C storage. Land models typically treat these distinct tropical forests with differential structures as a single functional unit, but our findings suggest that this may overlook critical biogeographic variation in C storage potential among regions. [ABSTRACT FROM AUTHOR]