Distribution of biomass dynamics in relation to tree size in forests across the world

Tracking tree demography and forest dynamics at scale using remote sensing

Capturing how tree growth and survival vary through space and time is critical to understanding the structure and dynamics of tree-dominated ecosystems. However, characterising demographic processes at scale is inherently challenging, as trees are slow-growing, long-lived and cover vast expanses of land. We used repeat airborne laser scanning data acquired across 25 km2 of semi-arid, old-growth temperate woodland in Western Australia to track the height growth, crown expansion and mortality of 42 213 individual trees over 9 yr. We found that demographic rates are constrained by a combination of tree size, competition and topography. After initially investing in height growth, trees progressively shifted to crown expansion as they grew larger, while mortality risk decreased considerably with size. Across the landscape, both tree growth and survival increased with topographic wetness, resulting in vegetation patterns that are strongly spatially structured. Moreover, biomass gains from woody growth generally outpaced losses from mortality, suggesting these old-growth woodlands remain a net carbon sink in the absence of wildfires. Our study sheds new light on the processes that shape the dynamics and spatial structure of semi-arid woody ecosystems and provides a roadmap for using emerging remote sensing technologies to track tree demography at scale.

Sparse large trees in secondary and planted forests highlight the need to improve forest conservation and management

Large trees are essential for carbon storage and biodiversity conservation. While an increasing number of studies have focused on large trees in primary forests, little is known about them in secondary and planted forests. We surveyed 86,936 trees in secondary forests and 91,294 trees in planted forests in Zhejiang, China, to investigate the distribution patterns and determinants of large trees in these forests. We found a mean density of large trees (DBH ≥ 30 cm) of 15 ± 13 stems ha-1 in secondary forests and 11 ± 9 stems ha-1 in planted forests. Moreover, the mean density of trees with DBH ≥ 60 cm was 0.36 stems ha-1, indicating that large trees are particularly rare in secondary and planted forests. These large trees were primarily occurred in secondary forests that living in high-elevation area with less human exploitation and colder and wetter climates, and in planted forests with higher species richness and lower tree density. In addition, the density of large trees in these forests significantly increased with tree species richness and decreased with increasing tree density. These results indicate that the sparse large trees were the legacy of historical human activities in the studied area, but currently, the development of large trees is still limited by the improper forest structure characterized by low species diversity and high tree density. To better conserve large trees, there is an urgent need for enhanced conservation policies for secondary forests, such as establishing forest parks for forests with large trees, and implementing near-natural forest management practices for planted forests, which include planting mixed native tree species and maintaining moderate tree density.

Capturing woody aboveground biomass historical change and potential under climate change using Landsat time-series for afforestation in dryland of China

Capturing long-term dynamics and the potential under climate change of woody aboveground biomass (AGB) is imperative for calculating and raising carbon sequestration of afforestation in dryland. It is always been a great challenge to accurately capture AGB dynamics of sparse woody vegetation mixed with grassland using only Landsat time-series, resulting in changing trajectory of woody AGB estimates cannot accurately reflect woody vegetation growth regularity in dryland. In this study, surface reflectance (SR) sensitive to woody AGB was firstly selected and interannual time-series of composited SR was smoothed using S-G filter for each pixel, and then optimal machine learning algorithm was selected to estimate woody AGB time-series. Pixels that have reached AGB potential were detected based on the AGB changing trajectory, and the potential was spatial-temporal extended using random forest model combining environmental variables under current climate condition and CMIP6 climate models. Results show that: 1) minimum value composite based on NIRv during Jul.-Sep. is more capable of explaining woody AGB variation in dryland (R = 0.87, p < 0.01), and Random Forest (RF) model has the best performance in estimating woody AGB (R2 = 0.75, RMSE = 4.74 t·ha-1) among sis commonly used machine learning models. 2) Annual woody AGB estimates can be perfectly fitted with a logistic growth curve (R2 = 0.97, p < 0.001) indicating explicit growth regularity of woody vegetation, which provides physiological foundation for determining woody AGB potential. 3) Woody AGB potential can be accurately simulated by RF combining environmental variables (R2 = 0.95, RMSE = 2.89 t·ha-1), and current woody AGB still has a potential of small increase, whereas the overall losses of woody AGB potential were observed in 2030, 2040 and 2050 under CMIP6 SSP-RCP scenarios.

Tall Bornean forests experience higher canopy disturbance rates than those in the eastern Amazon or Guiana shield

The future of tropical forests hinges on the balance between disturbance rates, which are expected to increase with climate change, and tree growth. Whereas tree growth is a slow process, disturbance events occur sporadically and tend to be short-lived. This difference challenges forest monitoring to achieve sufficient resolution to capture tree growth, while covering the necessary scale to characterize disturbance rates. Airborne LiDAR time series can address this challenge by measuring landscape scale changes in canopy height at 1 m resolution. In this study, we present a robust framework for analysing disturbance and recovery processes in LiDAR time series data. We apply this framework to 8000 ha of old-growth tropical forests over a 4-5-year time frame, comparing growth and disturbance rates between Borneo, the eastern Amazon and the Guiana shield. Our findings reveal that disturbance was balanced by growth in eastern Amazonia and the Guiana shield, resulting in a relatively stable mean canopy height. In contrast, tall Bornean forests experienced a decrease in canopy height due to numerous small-scale (<0.1 ha) disturbance events outweighing the gains due to growth. Within sites, we found that disturbance rates were weakly related to topography, but significantly increased with maximum canopy height. This could be because taller trees were particularly vulnerable to disturbance agents such as drought, wind and lightning. Consequently, we anticipate that tall forests, which contain substantial carbon stocks, will be disproportionately affected by the increasing severity of extreme weather events driven by climate change.

Mangrove Health Index, Community Structure and Canopy Cover in Small Islands of Bunaken National Park, Indonesia: Insights into Dominant Mangrove Species and Overall Mangrove Condition

Mangrove ecosystems are crucial for protecting littoral regions, preserving biodiversity and sequestering carbon. The implementation of effective conservation and management strategies requires a comprehensive understanding of mangrove community structure, canopy coverage and overall health. This investigation focused on four small islands located within the Bunaken National Park in Indonesia: Bunaken, Manado Tua, Mantehage and Nain. Utilising the line transect quadrant method and hemispherical photography, the investigation comprised a total of 12 observation stations. Nain had the greatest average canopy coverage at 76.09%, followed by Mantehage, Manado Tua and Bunaken at 75.82%, 71.83% and 70.01%, respectively. Mantehage had the maximum species density, with 770.83 ind/ha, followed by Bunaken, Nain and Manado Tua with 675 ind/ha, 616.67 ind/ha and 483.34 ind/ha, respectively. The predominant sediment type observed was sandy mud and the mangrove species identified were Avicennia officinalis (AO), Bruguiera gymnorrhiza (BG), Rhizophora apiculata (RA), R. mucronata (RM), and Sonneratia alba (SA). On the small islands, S. alba emerged as the dominant mangrove species based on the importance value index (IVI). In addition, the Mangrove Health Index revealed that only 6.79% of the region exhibited poor health values, while 50% of the region was categorised as being in outstanding condition. These findings indicate that the overall condition of mangroves on these islands was relatively favourable.

Latitudinal variation and driving factors of above-ground carbon proportion of large trees in old-growth forests across China

Large trees play a vital role in forest carbon stocks, dominating the distribution of community biomass. However, climate change and deforestation are reducing large trees globally, resulting in regional differences in their contribution to carbon stocks. Here, we examined the latitudinal change pattern and drivers of large trees' contributions to stand carbon stocks. Above-ground carbon storage was calculated for 530 plots in old-growth forests across China. Linear regression was used to calculate latitudinal variation in the proportion of above-ground carbon in large trees (i.e., AGC proportion). Variance partitioning and multiple linear regression were used to calculate the relative importance of species diversity, stand structure, functional traits, and environmental factors to AGC proportion. The study found that AGC proportion decreased with increasing latitude, averaging at 64.44 %. Stand structure, particularly the coefficient of variation of DBH, was identified as the key drivers of the AGC proportion. The number of common species (Hill's 1D) had no direct effect on the AGC proportion, while wood density, maximum tree height, and leaf nitrogen-to‑phosphorus ratio showed negative effects. The mass-ratio effects on AGC proportion were stronger than diversity effects. Climate variables primarily affected the AGC proportion through stand variables. These results indicate that simultaneously managing high diversity and AGC proportion may pose challenges. Moreover, considering the substantial contribution of large trees to carbon stocks, their storage capacity and sensitivity to environmental changes exert significant control over forest carbon cycles. Therefore, preserving and enhancing the carbon sink function of old-growth forests in the face of climate change and disturbance may depend primarily on protecting existing large trees and soon-to-be large-diameter trees.

Plants extend root deeper rather than increase root biomass triggered by critical age and soil water depletion

Exploring plant root characteristics is important to understand the aboveground plant growth and ecosystem, but has rarely been conducted because of the difficulties in obtaining root information. This study aims to clarify the root distribution and rooting strategy under the combined control of vegetation types and rainfall gradients. We compiled 64 plant root and 81 soil water profiles up to 10 m deep with plant ages of up to 40 years old in China's Loess Plateau, and then fitted the shape and extinction coefficients (β) and proposed the relation of D95/D50 (ratio of depth corresponding to 95 % of total biomass to that corresponding to 50 % of total biomass) to β to characterize the rooting strategy. The cumulative root biomass increase from shallow- to deep-rooted plants, and from >550 mm to <450 mm precipitation gradients. The root system parameters have large spatial variability, dominated by vegetation type but supplemented by climate. The negative correlation between D95/D50 and β indicated a tradeoff between rooting depths and root biomass. The plants would change rooting strategy from increasing root biomass to increasing rooting depths when the plant stand age and soil water depletion degree are >25.7 ± 3.6 years and 35.7 % ± 15.1 %, respectively. These results reveal a clear plant rooting strategy that extends root deeper rather than increases root biomass triggered by critical age and soil water depletion.

Density affects plant size in the Gobi Desert

Plant size is a crucial functional trait with substantial implications in agronomy and forestry. Understanding the factors influencing plant size is essential for ecosystem management and restoration efforts. Various environmental factors and plant density play significant roles in plant size. However, how plant size responds to mean annual precipitation (MAP), mean annual temperature (MAT), and density in the arid areas remains incomplete. To address this knowledge gap, we conducted comprehensive vegetation surveys in the Gobi Desert in northwestern China with a MAP below 250 mm. We also collected climate data to disentangle the respective influences of climate and density on the community-weighted plant height, crown length, and crown width. Our observations revealed that the community-weighted mean plant height, crown length, and width demonstrated a positive association with MAT but negative relationships with both MAP and density. These patterns can be attributed to the predominance of shrubs over herbs in arid regions, as shrubs tend to be larger in size. The proportion of shrubs increases with MAT, while it decreases with MAP and density, resulting in higher plant height and larger crown dimensions. Although both MAP and MAT affect plant size in the Gobi Desert, our findings highlight the stronger role of plant density in regulating plant size, indicating that the surrounding plant community and competition among individuals are crucial drivers of plant size patterns. Our findings provide valuable guidance for nature-based solutions for vegetation restoration and ecosystem management, highlighting the importance of considering plant density as a key factor when designing and implementing restoration strategies in arid areas.

A new approach to derive productivity of tropical forests using radar remote sensing measurements

Deriving gross & net primary productivity (GPP & NPP) and carbon turnover time of forests from remote sensing remains challenging. This study presents a novel approach to estimate forest productivity by combining radar remote sensing measurements, machine learning and an individual-based forest model. In this study, we analyse the role of different spatial resolutions on predictions in the context of the Radar BIOMASS mission (by ESA). In our analysis, we use the forest gap model FORMIND in combination with a boosted regression tree (BRT) to explore how spatial biomass distributions can be used to predict GPP, NPP and carbon turnover time (τ) at different resolutions. We simulate different spatial biomass resolutions (4 ha, 1 ha and 0.04 ha) in combination with different vertical resolutions (20, 10 and 2 m). Additionally, we analysed the robustness of this approach and applied it to disturbed and mature forests. Disturbed forests have a strong influence on the predictions which leads to high correlations (R2 > 0.8) at the spatial scale of 4 ha and 1 ha. Increased vertical resolution leads generally to better predictions for productivity (GPP & NPP). Increasing spatial resolution leads to better predictions for mature forests and lower correlations for disturbed forests. Our results emphasize the value of the forthcoming BIOMASS satellite mission and highlight the potential of deriving estimates for forest productivity from information on forest structure. If applied to more and larger areas, the approach might ultimately contribute to a better understanding of forest ecosystems.

Maximum stem diameter predicts liana population demography

Determining population demographic rates is fundamental to understanding differences in species' life-history strategies and their capacity to coexist. Calculating demographic rates, however, is challenging and requires long-term, large-scale censuses. Body size may serve as a simple predictor of demographic rate; can it act as a proxy for demographic rate when those data are unavailable? We tested the hypothesis that maximum body size predicts species' demographic rate using repeated censuses of the 77 most common liana species on the Barro Colorado Island, Panama (BCI) 50-ha plot. We found that maximum stem diameter does predict species' population turnover and demography. We also found that lianas on BCI can grow to the enormous diameter of 635 mm, indicating that they can store large amounts of carbon and compete intensely with tropical canopy trees. This study is the first to show that maximum stem diameter can predict plant species' demographic rates and that lianas can attain extremely large diameters. Understanding liana demography is particularly timely because lianas are increasing rapidly in many tropical forests, yet their species-level population dynamics remain chronically understudied. Determining per-species maximum liana diameters in additional forests will enable systematic comparative analyses of liana demography and potential influence across forest types.

Are absorptive root traits good predictors of ecosystem functioning? A test in a natural temperate forest

Trait-based approaches provide a useful framework to predict ecosystem functions under intensifying global change. However, our current understanding of trait-functioning relationships mainly relies on aboveground traits. Belowground traits (e.g. absorptive root traits) are rarely studied although these traits are related to important plant functions. We analyzed four pairs of analogous leaf and absorptive root traits of woody plants in a temperate forest and examined how these traits are coordinated at the community-level, and to what extent the trait covariation depends on local-scale environmental conditions. We then quantified the contributions of leaf and absorptive root traits and the environmental conditions in determining two important forest ecosystem functions, aboveground carbon storage, and woody biomass productivity. The results showed that both morphological trait pairs and chemical trait pairs exhibited positive correlations at the community level. Absorptive root traits show a strong response to environmental conditions compared to leaf traits. We also found that absorptive root traits were better predictors of the two forest ecosystem functions than leaf traits and environmental conditions. Our study confirms the important role of belowground traits in modulating ecosystem functions and deepens our understanding of belowground responses to changing environmental conditions.

Damage to living trees contributes to almost half of the biomass losses in tropical forests

Accurate estimates of forest biomass stocks and fluxes are needed to quantify global carbon budgets and assess the response of forests to climate change. However, most forest inventories consider tree mortality as the only aboveground biomass (AGB) loss without accounting for losses via damage to living trees: branchfall, trunk breakage, and wood decay. Here, we use ~151,000 annual records of tree survival and structural completeness to compare AGB loss via damage to living trees to total AGB loss (mortality + damage) in seven tropical forests widely distributed across environmental conditions. We find that 42% (3.62 Mg ha^-1  year^-1 ; 95% confidence interval [CI] 2.36-5.25) of total AGB loss (8.72 Mg ha^-1  year^-1 ; CI 5.57-12.86) is due to damage to living trees. Total AGB loss was highly variable among forests, but these differences were mainly caused by site variability in damage-related AGB losses rather than by mortality-related AGB losses. We show that conventional forest inventories overestimate stand-level AGB stocks by 4% (1%-17% range across forests) because assume structurally complete trees, underestimate total AGB loss by 29% (6%-57% range across forests) due to overlooked damage-related AGB losses, and overestimate AGB loss via mortality by 22% (7%-80% range across forests) because of the assumption that trees are undamaged before dying. Our results indicate that forest carbon fluxes are higher than previously thought. Damage on living trees is an underappreciated component of the forest carbon cycle that is likely to become even more important as the frequency and severity of forest disturbances increase.

Lianas increase lightning-caused disturbance severity in a tropical forest

Lightning is an important agent of plant mortality and disturbance in forests. Lightning-caused disturbance is highly variable in terms of its area of effect and disturbance severity (i.e. tree damage and death), but we do not know how this variation is influenced by forest structure and plant composition. We used a novel lightning detection system to quantify how lianas influenced the severity and spatial extent (i.e. area) of lightning disturbance using 78 lightning strikes in central Panama. The local density of lianas (measured as liana basal area) was positively associated with the number of trees killed and damaged by lightning, and patterns of plant damage indicated that this occurred because lianas facilitated more electrical connections from large to small trees. Liana presence, however, did not increase the area of the disturbance. Thus, lianas increased the severity of lightning disturbance by facilitating damage to additional trees without influencing the footprint of the disturbance. These findings indicate that lianas spread electricity to damage and kill understory trees that otherwise would survive a strike. As liana abundance increases in tropical forests, their negative effects on tree survival with respect to the severity of lightning-related tree damage and death are likely to increase.

Top Canopy Height and Stem Size Variation Enhance Aboveground Biomass across Spatial Scales in Seasonal Tropical Forests

Tropical forests are biologically diverse and structurally complex ecosystems that can store a large quantity of carbon and support a great variety of plant and animal species. However, tropical forest structure can vary dramatically within seemingly homogeneous landscapes due to subtle changes in topography, soil fertility, species composition and past disturbances. Although numerous studies have reported the effects of field-based stand structure attributes on aboveground biomass (AGB) in tropical forests, the relative effects and contributions of UAV LiDAR-based canopy structure and ground-based stand structural attributes in shaping AGB remain unclear. Here, we hypothesize that mean top-of-canopy height (TCH) enhances AGB directly and indirectly via species richness and horizontal stand structural attributes, but these positive relationships are stronger at a larger spatial scale. We used a combined approach of field inventory and LiDAR-based remote sensing to explore how stand structural attributes (stem abundance, size variation and TCH) and tree species richness affect AGB along an elevational gradient in tropical forests at two spatial scales, i.e., 20 m × 20 m (small scale), and 50 m × 50 m (large scale) in southwest China. Specifically, we used structural equation models to test the proposed hypothesis. We found that TCH, stem size variation and abundance were strongly positively associated with AGB at both spatial scales, in addition to which increasing TCH led to greater AGB indirectly through increased stem size variation. Species richness had negative to negligible influences on AGB, but species richness increased with increasing stem abundance at both spatial scales. Our results suggest that light capture and use, modulated by stand structure, are key to promoting high AGB stocks in tropical forests. Thus, we argue that both horizontal and vertical stand structures are important for shaping AGB, but the relative contributions vary across spatial scales in tropical forests. Importantly, our results highlight the importance of including vertical forest stand attributes for predicting AGB and carbon sequestration that underpins human wellbeing.

Contribution of tree community structure to forest productivity across a thermal gradient in eastern Asia

Despite their fundamental importance the links between forest productivity, diversity and climate remain contentious. We consider whether variation in productivity across climates reflects adjustment among tree species and individuals, or changes in tree community structure. We analysed data from 60 plots of humid old-growth forests spanning mean annual temperatures (MAT) from 2.0 to 26.6 °C. Comparing forests at equivalent aboveground biomass (160 Mg C ha^-1), tropical forests ≥24 °C MAT averaged more than double the aboveground woody productivity of forests <12 °C (3.7 ± 0.3 versus 1.6 ± 0.1 Mg C ha^-1 yr^-1). Nonetheless, species with similar standing biomass and maximum stature had similar productivity across plots regardless of temperature. We find that differences in the relative contribution of smaller- and larger-biomass species explained 86% of the observed productivity differences. Species-rich tropical forests are more productive than other forests due to the high relative productivity of many short-stature, small-biomass species.

Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications

Rising temperatures are influencing forests on many scales, with potentially strong variation vertically across forest strata. Using published research and new analyses, we evaluate how microclimate and leaf temperatures, traits, and gas exchange vary vertically in forests, shaping tree, and ecosystem ecology. In closed-canopy forests, upper canopy leaves are exposed to the highest solar radiation and evaporative demand, which can elevate leaf temperature (T_leaf ), particularly when transpirational cooling is curtailed by limited stomatal conductance. However, foliar traits also vary across height or light gradients, partially mitigating and protecting against the elevation of upper canopy T_leaf . Leaf metabolism generally increases with height across the vertical gradient, yet differences in thermal sensitivity across the gradient appear modest. Scaling from leaves to trees, canopy trees have higher absolute metabolic capacity and growth, yet are more vulnerable to drought and damaging T_leaf than their smaller counterparts, particularly under climate change. By contrast, understory trees experience fewer extreme high T_leaf 's but have fewer cooling mechanisms and thus may be strongly impacted by warming under some conditions, particularly when exposed to a harsher microenvironment through canopy disturbance. As the climate changes, integrating the patterns and mechanisms reviewed here into models will be critical to forecasting forest-climate feedback.