Tropical Forest Biomass Research Articles

Evolutionary diversity impacts tropical forest biomass and productivity through disturbance‐mediated ecological pathways

Abstract Significant research efforts have been made to uncover links between biodiversity and biomass productivity in forest ecosystems. However, the causal link between these two ecosystem components, and the underlying mediation role of disturbance, are yet poorly understood for hyper‐diverse tropical forests, because multiple ecological mechanisms are sequentially or simultaneously in play, leading to contradictory results in observational studies. Here, we introduce a novel framework for inferring the expected effects of evolutionary diversity on biomass stocks and productivity within forest ecosystems using observational field data. This framework involves an analytical decomposition of stand biomass into three key components: the number of trees, the mean size of trees and the mean wood density. Through this approach, we can distinguish structure‐ and compositional‐based diversity effects, which likely have distinct ecological origins. We tested this framework in one of the oldest tropical forest experiments, where different levels of silvicultural disturbances were applied in the 1980s, with regular monitoring since then. Our results revealed that disturbance history mediates the effect of evolutionary diversity on forest biomass dynamics and that several Biodiversity Ecosystem Function (BEF) relationships may be hidden behind the composite biomass variable. We specifically found an overall significant negative relationship between evolutionary diversity and biomass productivity soon after disturbances (~5–8 years), mostly via mean tree size, despite a positive evolutionary diversity effect on mean wood density. This result reflects that the productivity of disturbed forests is driven by a few dominant and disturbance‐prone species with low wood density and large potential stature, and not by niche complementarity among species. However, these effects rapidly vanished with time, with non‐significant overall effect of evolutionary diversity on productivity both ~30 years after disturbance and in the undisturbed plots. Synthesis. By disentangling the effects of evolutionary diversity on the different components of forest biomass, our framework unveiled how evolutionary diversity impacts forest productivity through different ecological mechanisms, and suggests that it plays a major role, albeit mainly negative, only soon after a disturbance.

Effects of species and tree size diversity on above-ground biomass in diverse heterogeneous tropical evergreen forests, Quangbinh Province, north central Vietnam

Effects of species and tree size diversity on above-ground biomass in diverse heterogeneous tropical evergreen forests, Quangbinh Province, north central Vietnam

Estimating forest aboveground biomass in tropical forests with Landsat time-series data and recurrent neural network

ABSTRACT The insensitivity and saturation of sensor signals significantly limit the application of Landsat data on estimating biomass in tropical forests with a high level of biomass. The previous studies with single-date Landsat data have lower accuracies in tropical forests compared to boreal and temperate forests. Landsat time-series data provide a promising opportunity to improve the accuracy by enhancing the relationship between Landsat spectral reflectance and forest aboveground biomass with disturbance and recovery dynamics. Compared to the single-date image, Landsat time-series data can capture abrupt spectral changes (e.g. harvesting and fire) and show the regrowth process in forested pixels. However, very limited studies take advantage of Landsat time-series data to estimate aboveground biomass in tropical forests. Recurrent neural networks (RNNs) are powerful deep learning techniques to capture time dependencies in sequence data. However, the application of RNNs in estimating forest biomass has not been explored yet. Therefore, we integrate the long short-term memory network (LSTM) and the fully connected neuron network (FNN) to establish an RNN-FNN model for estimating forest biomass with Landsat time-series imagery and airborne LiDAR data. We compared the proposed model with the commonly used Random Forest model and linear regression model which are implemented with single-date data. The results show that the RNN-FNN model can deal with Landsat time-series sequence data to enhance the relationship between Landsat spectral reflectance and forest aboveground biomass. The proposed model achieves the R2 of 0.63 and RMSE of 25.5 Mg/ha, which significantly outperformed the Random Forest model and linear regression model with Landsat single-date data. This study demonstrates the value of RNN and Landsat time-series imagery in estimating forest biomass for tropical forests.

Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates

Satellite Derived Trait Data Slightly Improves Tropical Forest Biomass, NPP and GPP Estimates

Estimation of above ground biomass in tropical heterogeneous forests in India using GEDI

Quantifying above ground biomass (AGB) and its spatial distribution can significantly contribute to monitor carbon stocks as well as the carbon storage dynamics in forests. For effective forest monitoring and management in the case of complex tropical Indian forests, there is a need to obtain reliable estimates of the amount of carbon sequestration at regional as well as national levels, but the estimation of biomass is quite challenging. The main objective of the study is to validate the usefulness of the gridded above ground biomass density (AGBD) estimates (ton/ha) of the spaceborne LiDAR Global Ecosystem Dynamics Investigation data (GEDI L4B, Version 2) across two tropical heterogeneous forests in India, Betul and Mudumalai forests. Methodology includes, for each forest area, a linear regression model which predicts AGB from Sentinel-2 MSI data was developed using ground reference data and comparing it with the GEDI AGBD values. The AGB model for the Betul forests in Central India had a RMSE of 13.9 ton/ha, relative RMSE = 8.7% and a R2 of 0.88, with a bias of −0.28 ton/ha, and a comparison between modelled AGB and GEDI gridded AGBD at 1 km resolution show a relatively strong correlation (0.66) and no or little bias. It also found that GEDI AGBD footprint value is underestimated compared to the AGB according to the Sentinel-2 model. For the Mudumalai forest in southern India, the AGB model had an RMSE of 29.1 ton/ha, relative RMSE = 10.8%, and an R2 of 0.79 and a bias of −0.022. The correlation between modelled AGB and gridded GEDI AGBD was 0.84, and GEDI AGBD is underestimated compared to AGB from the Sentinel-2 model. The field values of AGB of Betul lies between 42.2 ton/ha and 238.8 ton/ha and for the Mudumalai forests, the AGB lies between 75.9 ton/ha and 353.6 ton/ha. The results indicates that the GEDI gridded AGBD underestimates AGB, and that the model used to produce the gridded AGBD product needs to be adjusted to provide reliable information on the carbon balance and its changes over time for the type of Indian forests that exists in the two test areas.

More Accurately Estimating Aboveground Biomass in Tropical Forests With Complex Forest Structures and Regions of High‐Aboveground Biomass

AbstractAccurately estimating aboveground biomass (AGB) in tropical forests is vital for managing the threats posed by deforestation, degradation, and climate change. However, challenges persist in accurately estimating AGB in high AGB regions. This study aims to accurately estimate the AGB of regions with high AGB by using spatial statistical analyses based on AGB estimates made by machine‐learning fusion of multisource data. We hypothesize that incorporating dominant auxiliary factors in the analysis increases the estimation accuracy. This study focuses on tropical forests located in Longyan, Fujian Province, China, covering an area of 19,028 km2. Multisource data are used, including airborne laser scanning, the Shuttle Radar Topography Mission digital elevation model, the Landsat Operational Land Imager, and the National Forest Inventory. Based on GeogDetector's spatial covariance matrix and the spatial similarity principle, we identify key auxiliary factors (dominant tree species, canopy closure, and herbaceous cover) and investigated how auxiliary variables can improve estimation accuracy. Empirical Bayesian kriging regression prediction introduces the main auxiliary factors to refine AGB estimates. These refinements significantly enhance the accuracy of AGB estimates, particularly for high AGB, resulting in a 0.1 increase in R2, a 7.0% reduction in root mean square error, a 13.5% reduction in mean square error, and a 6.6% reduction in mean absolute error when compared with the AGB estimates obtained by using machine learning to fuse multisource data. Thus, incorporating spatial statistical analysis into the integration of multisource data and machine learning for AGB estimation can enhance the accuracy of high‐AGB estimates in intricate forest structures, resulting in precise AGB maps.

Effects of successional age, plot size, and tree size on the relationship between diversity and aboveground biomass in tropical dry forests

Effects of successional age, plot size, and tree size on the relationship between diversity and aboveground biomass in tropical dry forests

Estimating aboveground biomass of tropical urban forests with UAV-borne hyperspectral and LiDAR data

Urban trees and forests can contribute to climate change mitigation by sequestering carbon in their living tissues, with aboveground biomass (AGB) playing a pivotal role. This study explores the capability of UAV-borne hyperspectral and LiDAR data for estimating AGB in tropical urban forests. Structural attributes of trees, such as diameter at breast height (DBH), total height, and wood density, were collected from over 5600 individuals, forming a comprehensive AGB dataset. Our methodology included two primary AGB estimation strategies: an area-based strategy that correlated AGB with hyperspectral and canopy height data across various grid sizes and an individual tree crown (ITC)-based method that integrated canopy height, spectral signatures of individual trees and crown area. The findings indicate that increasing the grid size from 10 m to 50 m improved the R2 from 0.24 ± 0.04–0.61 ± 0.13, mainly due to reduced border effects. Furthermore, integrating canopy height and hyperspectral data enhanced the R2 of AGB estimates from 0.61 ± 0.13–0.70 ± 0.09 for a 50 × 50 m grid. Crucially, wavelengths centered at the green peak and red-edge were identified as key bands in AGB retrieval. Integrating hyperspectral and LiDAR data did not significantly enhance results for individual trees, where AGB was closely linked to tree height and crown area. This study underscores the potential of utilizing integrated UAV-borne sensors for biomass assessment in urban forest settings.

Allometric models for liana aboveground biomass in old-growth and secondary tropical forests of Tanzania

Lianas are common in tropical forests, where they influence forest dynamics, thus impacting the global carbon sink, with implications for climate change mitigation. Despite their increasing competitiveness with trees at the global scale, robust measurements of liana aboveground biomass (AGB) have been limited. Here we use data from destructive sampling to develop two separate allometric equations for estimating liana AGB from stem diameter in old-growth (n = 15 lianas) and secondary forests (n = 22 lianas). We compared estimates of AGB using our equations for 3141 lianas (≥ 1 cm diameter) in Tanzania's Kilombero Valley against estimates from previously published equations in other tropical regions. Our equations demonstrated stronger correlations between diameter and destructively measured AGB, than those from previously published equations (R2 = 0.86–0.89, versus R2 = 0.82–0.88). Across all stems, the average stem-level liana AGB estimated using the equation for old-growth forests was 52 % higher than that estimated by the equation for secondary forests, showing that lianas have lower biomass per unit diameter in forests impacted by disturbance. In such forests, liana stems are damaged, deformed, or cannot reach maximum height due to reduced structural support. At the scale of the forest stand, our equations estimated a mean liana AGB of 3.25 Mg ha−1 (95 % Confidence Interval: 1.52–6.96) in old-growth forests and 10.19 Mg ha−1 (5.91–17.64) in secondary forests. These estimates roughly aligned with estimates from other equations, although there was considerable variation. Depending on the equation used, mean stand-level estimates of liana AGB ranged from 2.49–9.76 Mg ha−1 in old-growth forests and 10.19–20.74 Mg ha−1 in secondary forests. Our findings show the variability in liana allometry and AGB with disturbance and successional stage, further underscoring a need for caution when comparing estimates of liana biomass across studies and regions.

Estimating forest height and above-ground biomass in tropical forests using P-band TomoSAR and GEDI observations

ABSTRACT Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA’s Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests.

Wood Basic Density in Large Trees: Impacts on Biomass Estimates in the Southwestern Brazilian Amazon

Wood basic density (WD) plays a crucial role in estimating forest biomass; moreover, improving wood-density estimates is needed to reduce uncertainties in the estimates of tropical forest biomass and carbon stocks. Understanding variations in this density along the tree trunk and its impact on biomass estimates is underexplored in the literature. In this study, the vertical variability of WD was assessed along the stems of large trees that had a diameter at breast height (DBH) ≥ 50 cm from a dense ombrophilous forest on terra firme (unflooded uplands) in Acre, Brazil. A total of 224 trees were sampled, including 20 species, classified by wood type. The average WD along the stem was determined by the ratio of oven-dry mass to saturated volume. Five models were tested, including linear and nonlinear ones, to fit equations for WD, selecting the best model. The variation among species was notable, ranging from 0.288 g cm−3 (Ceiba pentandra, L., Gaertn) to 0.825 g cm−3 (Handroanthus serratifolius, Vahl., S. Grose), with an average of 0.560 g cm−3 (±0.164, standard deviation). Significant variation was observed among individuals, such as in Schizolobium parahyba var. amazonicum (H. ex D.), which ranged from 0.305 to 0.655 g cm−3. WD was classified as low (≤0.40 g cm−3), medium (0.41–0.60 g cm−3), and high (≥0.61 g cm−3). The variability in WD along the stem differs by wood type. In trees with low-density wood, density shows irregular variation but tends to increase along the stem, whereas it decreases in species with medium- and high-density wood. The variation in WD along the stem can lead to underestimations or overestimations, not only in individual trees and species but also in total stocks when estimating forest biomass. Not considering this systematic bias results in significant errors, especially in extrapolations to vast areas, such as the Amazon.

Drought-induced turnover of soil microbial biomass increases nutrient subsidies for the reproduction of tropical forest

El Niño-induced drought, which is intensified by climate change, can have huge impacts on soil microbial biomass and plant productivity in tropical forests. We tested whether drought-induced turnover of soil microbial biomass can be a potential source of phosphorus (P), the limiting nutrient, for the reproduction of tropical forest trees (mast fruiting). We measured the seasonal variations in soil microbial biomass P and soil solution P concentrations including the periods before and after drought in a dipterocarp forest in Indonesia. Drought resulted in a decrease in soil microbial biomass C, N, and P, followed by a recovery after re-wetting. There was a sharp peak of soil solution P concentrations during the drought. The significant difference between soil microbial biomass P before and after drought amounted to 2.0 kg P ha−1. The potential P release from microbial turnover is not negligible compared to the additional P demand for fruit production (1.0 kg P ha−1) as well as the annual demand for litter production (2.5 kg P ha−1 year−1). In addition to the accumulation of nutrients for several non-fruiting years and their re-distribution in tree biomass, drought-induced microbial turnover can be nutrient subsidies for dipterocarp reproduction in highly-weathered soils.

Effects of lianas on forest biogeochemistry during their lives and afterlives.

Climate change and other anthropogenic disturbances are increasing liana abundance and biomass in many tropical and subtropical forests. While the effects of living lianas on species diversity, ecosystem carbon, and nutrient dynamics are receiving increasing attention, the role of dead lianas in forest ecosystems has been little studied and is poorly understood. Trees and lianas coexist as the major woody components of forests worldwide, but they have very different ecological strategies, with lianas relying on trees for mechanical support. Consequently, trees and lianas have evolved highly divergent stem, leaf, and root traits. Here we show that this trait divergence is likely to persist after death, into the afterlives of these organs, leading to divergent effects on forest biogeochemistry. We introduce a conceptual framework combining horizontal, vertical, and time dimensions for the effects of liana proliferation and liana tissue decomposition on ecosystem carbon and nutrient cycling. We propose a series of empirical studies comparing traits between lianas and trees to answer questions concerning the influence of trait afterlives on the decomposability of liana and tree organs. Such studies will increase our understanding of the contribution of lianas to terrestrial biogeochemical cycling, and help predict the effects of their increasing abundance.

Tree belowground biomass in Congo Basin forests: allometric equations and scaling with aboveground biomass

Abstract Many allometric models to predict tree aboveground biomass have been developed in tropical moist forests, but few models are available for tree belowground biomass. Theory predicts that belowground biomass scales in an isometric way with aboveground biomass. Estimates of belowground biomass could then be derived from aboveground biomass using the root:shoot ratio. Using a dataset of 118 tropical trees for which both aboveground and belowground biomass and other tree and species characteristics were measured in Gabon and Cameroon, we found a near isometric, yet significantly allometric, relationship between belowground biomass (B, in kilograms) and aboveground biomass (A, in kilograms): B = 0.324 A0.939. The root:shoot ratio was 0.20–0.22, regardless of tree size. An efficient model to predict belowground biomass from tree diameter (D, in centimeters), height (H, in meters) and wood density (ρ, in grams per cubic centimeter) was B = 0.0188 (ρD2H)0.977. A significant residual effect of species and leaf habit was found in this model, indicating that further tree and species characteristics are likely to explain additional variation in belowground biomass. Yet, the future development of belowground allometric models can benefit from the many models already developed for aboveground biomass. On the basis of this unprecedented sampling effort on tree belowground biomass in the dense tropical forests of the Congo Basin, we conclude that the scaling of belowground biomass with aboveground biomass should be the relationship to focus on.

Soil nutrients determine leaf traits and above-ground biomass in the tropical cloud forest of Hainan Island

Identifying soil characteristics associated with the plant’s resource use and acquisition strategy at different scales could be a crucial step to understanding community assembly and plant strategy. There is an increasing trend that plant functional properties can be an important driver of ecosystem functioning. However, major knowledge gaps exist about how soil abiotic properties, shape species diversity, above-ground biomass (AGB) and plant functional diversity in the Bawangling tropical forest (TCF) of Hainan island. Hence we hypothesized that plant functional traits and above-ground biomass would be strongly associated with soil abiotic factors given their direct relationship to soil resource acquisition and use. Here, we used 12 plant functional traits (FTs), above-ground biomass (AGB), and five soil nutrients in the Bawangling tropical cloud forest of Hainan Island by using a polynomial regression model and multivariate correlations to show relationship and identify how plants allocate their limited resources to adapt to their surroundings. Various phytosociological attributes were assessed and an Importance Value Index (IVI) value was calculated for each species to determine the dominant species. More than half of the total variations could be attributed to interspecific variations in H, DBH, LA, LMA, and LDW. From a taxonomic perspective; we found that species-level variance was more significant for plant functional traits and soil nutrients like TN, AP, TP, and OM. On the other hand, variation in specific stem density (SSD), leaf thickness (LT), leaf phosphorus (LP) and leaf soluble sugar (LS) was an exception for these tendencies. Among soil nutrients, soil nitrogen and phosphorus significantly impact the species and functional traits. Furthermore, the soil AN and TP we also found to have a comparatively strong positive relationship with above-ground biomass (AGB) as compared with other soil nutrients. The morpho-physiological functional traits showed a trade-off between conservative and acquisitive resource usage. These variations suggested that the relationships of functional traits, AGB, and species with soil nutrients mainly AN and TP in tropical cloud forests can directly affect the growth, reproduction, and survival of the species and are beneficial for the species co-existence and maintenance of biodiversity.

Both the selection and complementarity effects underpin the effect of structural diversity on aboveground biomass in tropical forests

AbstractAimDespite mounting empirical evidence regarding the positive effects of forest structural diversity (STRDIV) on forest functioning, the underlying biotic mechanisms and controlling abiotic factors remain poorly understood. This study provides the first assessment of the interactive effects of STRDIV and diversity in species and functional traits on aboveground biomass (AGB) in natural forests in West and East Africa.LocationWest and East Africa.Time period2014–2020.Major taxa studiedWoody plants.MethodsUsing data from 276 plots and 7993 trees of 207 species distributed across various types of natural forests and major climatic zones of Africa, linear mixed‐effects and structural equation models, we have evaluated how alternative causal relationships between STRDIV and taxonomic and functional diversity attributes influence AGB, while accounting for the effects of environmental covariates. We also assessed the consistency of these relationships across floristically and environmentally homogenous forest types.ResultsWe found that the positive effects of STRDIV on AGB were underpinned by both the community‐weighted mean (CWM) of trait values (selection effects) and species richness (niche complementarity), but the relative importance of these effects varied depending on forest types. Across the forest types, STRDIV primarily mediated the effects of CWM of traits and species richness on AGB. We also found that STRDIV–AGB relationships were constrained by resource (water and nutrient) availability.Main conclusionsOur findings provide novel insights into the role of functional traits as key determinants of the effects of STRDIV on AGB in tropical forests. We suggest that forest management and climate change mitigation strategies aimed at conserving biodiversity, and fostering biomass storage through increased STRDIV should focus on maintaining high levels of functionally dominant species while also increasing tree species diversity.

Examining the Potential of Sentinel Imagery and Ensemble Algorithms for Estimating Aboveground Biomass in a Tropical Dry Forest

Accurate estimations of aboveground biomass (AGB) in tropical forests are crucial for maintaining carbon stocks and ensuring effective forest management. By combining remote sensing (RS) data with ensemble algorithms, reliable AGB estimates in forests can be obtained. In this context, the freely available Sentinel-1 (S-1 SAR) and Sentinel 2 multispectral imagery (S-2 MSI) data have a significant role in enhancing accurate AGB estimations at a lower cost, which is relevant for the tropical dry forest (TDF) regions where AGB estimation is uncertain or there is a lack of comprehensive exploration. This study aims to address this gap by presenting a cost-effective and reliable AGB estimation approach in the TDF region of Colombia. For this purpose, we modeled and compared the performance of two ensemble algorithms, random forest (RF) and extreme gradient boosting (XGBoost), to estimate AGB using three predictor categories (polarizations/textures, spectral bands/vegetation indices, and a combination of both). We then examined the modeling potential of S-1 SAR and S-2 MSI imagery in predicting forest AGB and subsequently identified the most suitable variables. To construct AGB models’ field data, we employed a clustered distributed sampling approach involving 100 subsample plots, each with an area of 400 m2. Stepwise multiple linear regression was applied to identify suitable predictors from the original satellite bands, vegetation indices, and texture metrics. To produce a map of AGB, predicted AGB values were calculated for every pixel within a specific satellite subscene using the most effective ensemble algorithm. Our study findings show that the RF model, which employed combined predictor sets, displayed superior performance when evaluated against the independent validation set. The RF model successfully estimated AGB with a high degree of accuracy, achieving an R2 value of 0.78 and an RMSE value of 42.25 Mg/ha−1. In contrast, the XGBoost model performed less accurately, obtaining an R2 value of only 0.60 and an RMSE value of 48.41 Mg/ha−1. The results also indicate that S-2 vegetation indices data were more appropriate for this purpose than S-1 texture data. Despite this, S-1 cross-polarized textures were necessary during the dry season for the combined datasets. The top predictive variables for S-2 images were cab and cw, as well as red-edge bands during the wet season. As for S-1 images, texture D_VH _Hom during the dry season was the most important variable for explaining performance. Overall, the proposed approach of using freely available Sentinel data seems to improve the accuracy of AGB estimation in heterogeneous forest cover and, as such, they should be recommended as a data source for forest AGB assessment.

UAV-based height measurement and height–diameter model integrating taxonomic effects: exploring vertical structure of aboveground biomass and species diversity in a Malaysian tropical forest

The complex stratification of tropical forests is a key feature that directly contributes to high aboveground biomass (AGB) and species diversity. This study aimed to explore the vertical patterns of AGB and tree species diversity in the tropical forest of Pasoh Forest Reserve, Malaysia. To achieve this goal, we used a combination of field surveys and drone technology to gather data on species diversity, tree height (H), and tree diameter at breast height (D). As all trees in the 6 ha plot were tagged and identified, we used the data to classify the taxonomy and calculate species diversity indices. We used unmanned aerial vehicle-based structure-from-motion photogrammetry to develop a Digital Canopy Height Model to accurately estimate H. The collected data and previous datasets were then used to develop Bayesian height–diameter (HD) models that incorporate taxonomic effects into conventional allometric and statistical models. The best models were selected based on their performance in cross-validation and then used to estimate AGB per tree and the total AGB in the plot. Results showed that taxonomic effects at the family and genus level improved the HD models and consequent AGB estimates. The AGB was the highest in the higher layers of the forest, and AGB was largely contributed by larger trees, especially specific families such as Dipterocarpaceae, Euphorbiaceae, and Fabaceae. In contrast, species diversity was the highest in the lower layers, whereas functional diversity was the highest in the middle layers. These contrasting patterns of AGB and species diversity indicate different roles of forest stratification and layer-specific mechanisms in maintaining species diversity. This study highlights the importance of considering taxonomic effects when estimating AGB and species diversity in tropical forests. These findings underscore the need for a more comprehensive understanding of the complex stratification of tropical forests and its impact on the forest ecosystem.

Alpha and beta diversity jointly drive the aboveground biomass in temperate and tropical forests.

Changes in biodiversity often affect ecosystem functioning. However, most previous biodiversity and ecosystem functioning (BEF) studies have generally been limited to very small spatial grains. Thus, knowledge regarding the biodiversity-ecosystem functioning relationships across spatial scales is lacking. Moreover, the multiscale nature of biodiversity, and specifically β diversity (i.e., spatial heterogeneity in species composition) was still largely missing in BEF studies. Here, using the vegetation and functional trait data collected from four 6-ha forest dynamics plots (FDPs) in temperate and tropical forests in China, we examine the scale-dependent relationships between tree diversity and the aboveground biomass (AGB), as well as the roles of species spatial heterogeneity in determining the AGB. In tropical forests, the effect of species richness on AGB decreased with spatial grains, while functional dominance played a stronger role at larger spatial grains. In temperate forests, positive relationship between diversity and AGB occurred at all spatial grains, especially on smaller scales. In both temperate and tropical forests, β diversity was positively correlated with AGB, but weaker than α diversity in determining AGB. Overall, complementarity and selection hypothesis play dominant role in determining AGB in temperate and tropical forests, respectively. The roles of these underlying mechanisms are more pronounced with increasing spatial scales. β diversity, a hitherto underexplored facet of biodiversity, is likely to increase ecosystem functions by species spatial turnover and should not be neglected in BEF explorations. Our findings have practical implications for forest management and demonstrate that biotic heterogeneity plays an important positive role in ecosystem functioning.

Scale dependency of the effects of landscape structure and stand age on species richness and aboveground biomass of tropical dry forests

Scale dependency of the effects of landscape structure and stand age on species richness and aboveground biomass of tropical dry forests