Aboveground Biomass Stocks Research Articles

Age-Based Stratification to Estimate Aboveground Biomass (AGB) and Carbon Stocks of Rubber Plantations in Tripura

Age-Based Stratification to Estimate Aboveground Biomass (AGB) and Carbon Stocks of Rubber Plantations in Tripura

Biomass and Carbon Stocks in Post-Agriculture Secondary Forest in Manokwari, West Papua, Indonesia

Vegetations in post-agriculture forests (PAF) have the potential to absorb carbon dioxide and store it in to the form of biomass. However, information on carbon stocks in post-agriculture secondary forest in Papua, Indonesia still needs to be available. This study aimed to analyze the density of vegetation and its relationship to the carbon stock of aboveground vegetation biomass on PAF in the lowland of Manokwari, West Papua. This study was conducted on PAF aged 4 and 7 years using a one-factor, completely randomized design. Carbon stock is calculated using an allometric equation based on a diameter at breast height of 1.30 m (dbh ≥5cm). The results showed that 60% of the relative density of trees on the two PAF’s was composed by Premna odorata, Piper aduncum, and Macaranga tanarius. Carbon stocks on PAF aged 4 and 7 years reach 550 kg/ha and 140 kg/ha respectively. There is an increase in aboveground biomass and carbon stock with relative tree density and, tree diameter, which is significantly affected by the age of the PAF (P<0,002). The low carbon stock on PAF aged four years indicates that there has been a significant decrease in soil fertility after several years of cultivation by the local Papuan community. Therefore, there must be a difference in land use management between natural secondary forest and post-agriculture forest.

Understanding Soil Respiration Dynamics in Temperate Forests in Northwestern Mexico

Temperate mixed forests in Mexico are considered highly important ecosystems because of their high levels of biodiversity and capacity to store carbon. The aim of this study was to evaluate temporal and between-forest soil respiration (CO2 efflux) variability, and to assess the effect of vegetation diversity metrics on soil CO2 fluxes in mixed-uneven-aged forests in Durango, Northwestern Mexico. Soil CO2 efflux, soil moisture, and soil temperature were measured in three temperate forest types. A generalized linear model (GLM) was fitted to analyze the relationship between soil CO2 fluxes and stand variables, diversity metrics, soil moisture, and soil temperature. Furthermore, a two-way analysis of variance was used to assess the effect of forest type, month of the year, and their interaction on soil respiration. Annual average, minimum, and maximum soil CO2 efflux rate values were 3.81 (±2.94), 2.28 (±1.47), and 7.97 (±2.94) µmol m−2 s−1, respectively. Soil respiration was positively related to species richness, aboveground biomass, and quadratic mean diameter; however, forest type did not contribute to understanding the dynamics of soil CO2 fluxes. The results highlight the importance of seasonality, species diversity and aboveground biomass stocks to preserve the ecosystem processes driving soil respiration in temperate forests.

Carbon Biomass Estimation Using Vegetation Indices in Agriculture–Pasture Mosaics in the Brazilian Caatinga Dry Tropical Forest

Remote sensing is valuable for estimating aboveground biomass (AGB) stocks. However, its application in agricultural and pasture areas is limited compared with forest areas. This study quantifies AGB in agriculture–pasture mosaics within Brazil’s Campo Maior Complex (CMC). The methodology employs remote sensing cloud processing and utilizes an estimator to incorporate vegetation indices. The results reveal significant changes in biomass values among land use and land cover classes over the past ten years, with notable variations observed in forest plantation, pasture, sugar cane, and soybean areas. The estimated AGB values range from 0 to 20 Mg.ha−1 (minimum), 53 to 419 Mg.ha−1 (maximum), and 19 to 57 Mg.ha−1 (mean). In Forest formation areas, AGB values range from approximately 0 to 278 Mg.ha−1, with an average annual value of 56.44 Mg.ha−1. This study provides valuable insights for rural landowners and government officials in managing the semiarid territory and environment. It aids in decision making regarding agricultural management, irrigation and fertilization practices, agricultural productivity, land use and land cover changes, biodiversity loss, soil degradation, conservation strategies, the identification of priority areas for environmental restoration, and the optimization of resource utilization.

Quantifying carbon Stock and tree species diversity of green infrastructure of Varanasi, India

The world is undergoing rapid urbanization and experiencing its negative impacts, often due to the loss of urban green infrastructure. This study focuses on the green infrastructure of Varanasi city, India, and analyses current tree species diversity, and carbon storage in aboveground and belowground biomass and soil. The study calculated the biomass of urban green infrastructure because it serves as a carbon stock reservoir. As random sampling, data were collected from 24 sample plots across various urban green infrastructure sites via rigorous fieldwork. The biomass was then recorded using a non-destructive approach and a standard equation by King et al.2006. The diversity of tree species was recorded across urban green infrastructure sites, and was found to be higher in the BHU site, and lower in the MA site. The Pielou’s evenness index and Margellef’s richness index were found to be higher in the BHU site, while they were found to be lower in UPAC and MA sites, respectively. Aboveground biomass and total carbon stock were found to be high in the BLW site, with values of 1939.84 ton/ha and 7806 ton/ha, respectively, with trees having a larger girth circumference being the primary contributors. The findings of this study prove a better understanding of tree species diversity, biomass, and carbon stock of different green infrastructure sites of Varanasi city and generate evidence on how urban green space preservation and green infrastructure development may help to the countries' green economic transformation and sustainable, resilient, and low-carbon cities.

Decadal soil warming decreased vascular plant above and belowground production in a subarctic grassland by inducing nitrogen limitation.

Below and aboveground vegetation dynamics are crucial in understanding how climate warming may affect terrestrial ecosystem carbon cycling. In contrast to aboveground biomass, the response of belowground biomass to long-term warming has been poorly studied. Here, we characterized the impacts of decadal geothermal warming at two levels (on average +3.3°C and +7.9°C) on below and aboveground plant biomass stocks and production in a subarctic grassland. Soil warming did not change standing root biomass and even decreased fine root production and reduced aboveground biomass and production. Decadal soil warming also did not significantly alter the root-shoot ratio. The linear stepwise regression model suggested that following 10 yr of soil warming, temperature was no longer the direct driver of these responses, but losses of soil N were. Soil N losses, due to warming-induced decreases in organic matter and water retention capacity, were identified as key driver of the decreased above and belowground production. The reduction in fine root production was accompanied by thinner roots with increased specific root area. These results indicate that after a decade of soil warming, plant productivity in the studied subarctic grassland was affected by soil warming mainly by the reduction in soil N.

Estimation of Aboveground Biomass Stock in Tropical Savannas Using Photogrammetric Imaging

The use of photogrammetry technology for aboveground biomass (AGB) stock estimation in tropical savannas is a challenging task and is still at a preliminary stage. This work aimed to use metrics derived from point clouds, constructed using photogrammetric imaging obtained by an RGB camera on board a remotely piloted aircraft (RPA), to generate a model for estimating AGB stock for the shrubby-woody stratum in savanna areas of Central Brazil (Cerrado). AGB stock was estimated using forest inventory data and an allometric equation. The photogrammetric digital terrain model (DTM) was validated with altimetric field data, demonstrating that the passive sensor can identify topographic variations in sites with discontinuous canopies. The inventory estimated an average AGB of 18.3 (±13.3) Mg ha−1 at the three sampled sites. The AGB model selected was composed of metrics used for height at the 10th and 95th percentile, with an adjusted R2 of 93% and a relative root mean squared error (RMSE) of 16%. AGB distribution maps were generated from the spatialization of the metrics selected for the model, optimizing the visualization and our understanding of the spatial distribution of forest AGB. The study represents a step forward in mapping biomass and carbon stocks in tropical savannas using low-cost remote sensing platforms.

Aboveground biomass and carbon stock estimation using UAV photogrammetry in Indonesian mangroves and other competing land uses

Mangrove ecosystem is one of coastal wetlands that have experienced significant anthropogenic driven degradation and conversion, which have resulted in substantial carbon releases to the atmosphere and the oceans. However, carbon stock assessments and monitoring for mangroves are mostly confined to in situ methodologies, which has limited further understanding of wider-scale spatial variations and additional driving factors of carbon loss. Here,we report a novel application using unmanned aerial vehicles (UAVs) to estimate and compare aboveground biomass (AGB) carbon stocks of mangrove forests with two alternate replacement commercial land uses (oil palm and coconut plantations) in Lubuk Kertang and Pulau Sembilan, North Sumatra, Indonesia. We generated high-accuracy digital surface models (DSMs) and digital terrain models (DTMs), as well as elevation data from a global navigation satellite system (GNSS). Through analysis, we highlight how mangrove forest conversion to oil palm and coconut plantations significantly decreases the aboveground biomass carbon (AGB) and carbon stocks (AGC) of selected mangrove ecosystems in Indonesia. In this study, spatially mapped biomass carbon measurements were found to be more accurate when cross-validated with in situ measurements. AGC stocks were found to be over 10- to 12-fold that of converted oil palm and coconut plantations across selected study sites. The generated biomass carbon stock product provides new knowledge of carbon stock distribution in Indonesia across mangrove forests and two alternative commercial land use types within similar topographical landscapes. This combined methodological approach may be a useful tool to assist with blue carbon inventories as well as for selecting specific mangrove conservation and restoration areas across larger spatial scales.

Seeing the Forest for the Trees: Mapping Cover and Counting Trees from Aerial Images of a Mangrove Forest Using Artificial Intelligence

Mangrove forests provide valuable ecosystem services to coastal communities across tropical and subtropical regions. Current anthropogenic stressors threaten these ecosystems and urge researchers to create improved monitoring methods for better environmental management. Recent efforts that have focused on automatically quantifying the above-ground biomass using image analysis have found some success on high resolution imagery of mangrove forests that have sparse vegetation. In this study, we focus on stands of mangrove forests with dense vegetation consisting of the endemic Pelliciera rhizophorae and the more widespread Rhizophora mangle mangrove species located in the remote Utría National Park in the Colombian Pacific coast. Our developed workflow used consumer-grade Unoccupied Aerial System (UAS) imagery of the mangrove forests, from which large orthophoto mosaics and digital surface models are built. We apply convolutional neural networks (CNNs) for instance segmentation to accurately delineate (33% instance average precision) individual tree canopies for the Pelliciera rhizophorae species. We also apply CNNs for semantic segmentation to accurately identify (97% precision and 87% recall) the area coverage of the Rhizophora mangle mangrove tree species as well as the area coverage of surrounding mud and water land-cover classes. We provide a novel algorithm for merging predicted instance segmentation tiles of trees to recover tree shapes and sizes in overlapping border regions of tiles. Using the automatically segmented ground areas we interpolate their height from the digital surface model to generate a digital elevation model, significantly reducing the effort for ground pixel selection. Finally, we calculate a canopy height model from the digital surface and elevation models and combine it with the inventory of Pelliciera rhizophorae trees to derive the height of each individual mangrove tree. The resulting inventory of a mangrove forest, with individual P. rhizophorae tree height information, as well as crown shape and size descriptions, enables the use of allometric equations to calculate important monitoring metrics, such as above-ground biomass and carbon stocks.

Drought diminishes aboveground biomass accumulation rate during secondary succession in a tropical forest on Hainan Island, China

With rising temperatures and altered annual precipitation patterns, climatic factors are likely to play an increasingly important role in controlling the biomass accumulation rate of tropical secondary forests. However, the relative importance of biotic and abiotic factors and demographic progresses (growth, mortality, and recruitment) driving biomass dynamics in these ecosystems remain largely unclear, especially in tropical Asia. We explored aboveground biomass (AGB) dynamics in old-growth and secondary tropical forests on Hainan Island, China, across two consecutive five-year intervals (2010–2020). Drought frequency was higher in the second vegetation census interval. We studied the relationships of AGB and its three demographic components with drought frequency, tree diversity, soil nutrient and microorganismal abundance, and successional stage. We found that AGB accumulation rate decreased as AGB increased gradually during secondary succession. The different drought frequency between the two intervals was the dominant factor predicting the net change of AGB. With high-frequency droughts, the loss of AGB intensified and the increment rate of AGB decreased, especially in old-growth forests during second interval (2015–2020). Considering the impact of the three demographic progresses on net change in AGB, the relative influence of mortality was the largest (61.1%), followed by growth (38.3%) and recruitment (0.6%). Successional stage was another major factor affecting AGB and its dynamics, showing a positive relationship with AGB stock and a negative relationship with net AGB change. Species richness and fungal richness had a significant positive association with AGB, while soil available nutrient had a significant negative relationship. Overall, our results show that drought frequency had a strong impact on AGB dynamics, and that the resilience of AGB accumulation to drought decreased in later successional stages. Thus, with the intensification of global climate change, the effects of high-frequency drought events should be taken into account when studying the factors affecting AGB recovery in tropical secondary forests.

Plot-level estimates of aboveground biomass and soil organic carbon stocks from Nepal’s forest inventory

Given the contribution of deforestation and forest degradation to the global carbon cycle, forest resources are critical to mitigating the global climate change effects. Improved forest monitoring across different biomes is important to understand forest dynamics better and improve global projections of future atmospheric CO2 concentration. Better quantification of the forest carbon cycle advances scientific understanding and informs global negotiations about carbon emissions reduction. High-quality estimates of forest carbon stocks are currently scarce in many developing countries. Here, we present the most comprehensive georeferenced data set to date of plot-level forest carbon estimates for Nepal. Based on field observations from Nepal’s national forest inventory of 2010–2014; the data set includes estimates for two major forest carbon pools, aboveground biomass (AGB) and soil organic carbon (SOC) stocks from 2,009 and 1,156 inventory plots, respectively. The dataset fills an important knowledge gap about forest carbon stocks in the Central Himalayas, a region with highly heterogeneous environmental conditions and rich biodiversity that is poorly represented in existing global estimates of forest carbon.

Stand structural diversity and elevation rather than functional diversity drive aboveground biomass in historically disturbed semiarid oak forests

Forests provide biodiversity reservoirs and multiples ecosystem services such as global aboveground biomass dynamics and stock. Ecosystem functions varied according to biotic and abiotic factors, however, the effects of abiotic factors (e.g. elevation) on this relationship was rarely investigated, especially in areas impacted by human activities. We explored the relationships between aboveground biomass (AGB), a key proxy of ecosystem functioning, and plant species diversity in semiarid forests of western Iran submitted to a long history of past disturbances. Forty forest patches were sampled according to an elevation gradient (1700–2100 m) and to two types of forest cover: open cover indicating an intense past use or closed cover for more preserved forests. For each patch, the composition of woody species was recorded and woody species diversity indices (richness, Simpson, Shannon and Evenness) were calculated. We also estimated AGB and computed a wide set of community-weighted mean or functional divergence of ecological traits for woody species. We found that AGB sharply decreased with elevation (from 105.68 ± 10.29 ton/ha at 1700 m to 36.18 ± 6.43ton/ha at 2100 m) for the closed cover but variations were much less pronounced for the open cover (16.32 ± 2.97 ton/ha to 30.83 ± 1.96 ton/ha, respectively). Diversity indices (richness, Simpson, Shannon) increased with elevation for the closed cover and the other traits exhibited strong variations with elevation (either positive or negative) whereas no clear trend was visible in the open cover. Similar results were found when computing the linear relationships between AGB and the different diversity indices which were highly significant in the closed cover only. Surprisingly, the relationships were negative between AGB and most diversity indices (e.g., richness, Shannon, Simpson, Evenness). The dominant Persian oak was in fact the main contributor to AGB which was reduced in stands with a higher tree mixture, a result in line with the mass ratio hypothesis. Our results also illustrate the long-lasting effects of past conservation management on the forest cover and in shaping the relationships between forest ecosystem functions (AGB production) and biodiversity in semiarid areas.

Mangrove Resource Mapping Using Remote Sensing in the Philippines: A Systematic Review and Meta-Analysis

In spite of their importance, mangroves are still threatened by a significant reduction in global forest cover due to conversion to non-forest land uses. To implement robust policies and actions in mangrove conservation, quantitative methods in monitoring mangrove attributes are vital. This study intends to study the trend in mangrove resource mapping using remote sensing (RS) to determine the appropriate methods and datasets to be used in monitoring the distribution, aboveground biomass (AGB), and carbon stock (CS) in mangroves. A meta-analysis of several research publications related to mangrove resource mapping using RS in the Philippines has been conducted. A database was constructed containing 59 peer-reviewed articles selected using the protocol search, appraisal, synthesis, analysis, report (PSALSAR) framework and preferred reporting items for systematic reviews and meta-analysis (PRISMA). The study clarified that support vector machine (SVM) has shown to be more effective (99%) in discriminating mangroves from other land cover. Light detection and ranging (LiDAR) data also has proven to give a promising result in overall accuracy in mangrove-extent mapping (99%), AGB, and CS estimates (99%), and even species-level mapping (77%). Medium to low-resolution datasets can still achieve high overall accuracy by using appropriate algorithms or predictive models such as the mangrove vegetation index (MVI). The study has also found out that there are still few reports on the usage of high-spatial-resolution datasets, most probably due to their commercial restrictions.

Estimation of the Aerial Biomass of Trees with Non-Conforming Trunks (Foothills) of the Forests of the Congolese Central Basin by the Method Non-Destructive: Case of the YASIKIA Forests (Opala/Tshopo Province/DRC)

Summary: It is important to estimate the aboveground biomass and carbon stocks of trees with non-conforming trunks in the forests of the central Congolese basin to plan resource management and to assess the contribution of these forests in mitigating the effects of climate change and, this within the framework of conservation at the scale of a reserve, that for Measuring, Reporting and Verifying forest carbon stocks within the framework of national strategies for the reduction of greenhouse gas emissions linked Deforestation and Forest Degradation (REDD+). This study aimed to set up a protocol for estimating the aerial biomass and the corresponding carbon stocks of trees with particular architecture in the Yasikia forest. To succeed, a dendrometric study was carried out for all trees with non-compliant trunks. Dendrometric data were collected on 189 trees divided into 9 families and 20 species. The results of this study show that it is preferable to use the equivalent diameter for a good estimate of the biomass because taking the diameter at the end of the buttress or above, underestimates the quantity of biomass for a tree with a trunk not compliant. The equation used to estimate the woody biomass from different diameters was that of Brown et al., (1989). The results of this study show that it is preferable to use the equivalent diameter for a good estimate of the biomass because taking the diameter at the end of the buttress or above, underestimates the quantity of biomass for a tree with a trunk not compliant. The equation used to estimate the woody biomass from different diameters was that of Brown et al., (1989). The results of this study show that it is preferable to use the equivalent diameter for a good estimate of the biomass because taking the diameter at the end of the buttress or above, underestimates the quantity of biomass for a tree with a trunk not compliant. The equation used to estimate the woody biomass from different diameters was that of Brown et al., (1989).

Modeling and Mapping of Aboveground Biomass and Carbon Stock Using Sentinel-2 Imagery in Chure Region, Nepal

The concerns about climate change in recent decades have heightened the need for effective methods for assessing and reporting forest biomass and Carbon Stocks (CS) at local, national, continental, and global scales. Accurate assessment of Aboveground Biomass (AGB) is critical for the sustainable management of forests, especially in the Chure region, a fragile and young mountainous in the lesser Himalaya of Nepal. This paper presents the modeling and mapping approach and shows how medium-resolution Sentinel-2 multispectral instrument (MSI) data can be used instead of hyperspectral data in inaccessible areas of the Chure region. The data were collected and analyzed from 72 circular sample plots. 60% (43 random sample plots) were used to create the model, while the remaining 40% (29 plots) were used for model validation. This study involved calculating 12 different vegetation indices and correlating them with plot-level AGB. Five models, including linear, logarithmic, quadratic, power, and exponential, were created, but the best model was found to be the quadratic model using normalized difference vegetation indices (NDVIs) with an R2 value of 0.777 and a correlation coefficient of 0.881. The model’s AIC and BIC values were 313.60 and 320.65, respectively. The validity of the model was performed using observed and predicted AGB values, resulting in an r value of 0.9128, an R2 value of 0.8332, and an RMSE value of 10.7657 t·h−1. Finally, the developed regression equation was used to map AGB in the study area. The AGB per pixel ranges from 0 to 129.18 t·h−1, whereas the amount of CS ranges from 0 to 61.01 t·h−1. Among the different vegetation indices used in the study, NDVI was found to be more precise in estimating and mapping biomass and carbon stocks in this study. Therefore, the study recommends using the quadratic model of NDVI for accurate estimation of AGB and CS in the Chure region of Sainamaina municipality.

Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Assessment of aboveground biomass and carbon stock of subtropical pine forest of Pakistan

Aboveground biomass stock and change estimation in Amazon rainforest using airborne light detection and ranging, multispectral data, and machine learning algorithms

Developing an efficient method to accurately estimate aboveground biomass in tropical forests is critical to monitoring the carbon stock and implementing policies to reduce emissions caused by deforestation. Thus, the objective of the present study was to estimate aboveground biomass in areas of the Amazonian Forest with selective logging, using the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) machine learning algorithms, using light detection and ranging (LiDAR) data and these combined with OLI/Landsat 8 variables, as well as mapping the biomass for the years 2014 and 2017, allowing one to analyze its dynamics between the years of analysis. The RF and SVM algorithms obtained the lowest error values in all datasets. The association of the variables increased the RF performance. Analyzing the dynamics of biomass, it was observed that the oldest exploration units (2006, 2007, and 2008) have lower biomass stocks. The highest biomass losses in 2017 came from units operated between 2012 and 2013 (the most recent record). Thus, with the method used in this study, it was possible to infer that the machine learning algorithms were efficient in estimating the biomass, emphasizing the RF and the SVM.

Assessing Forest Carbon Accumulation Potential Across Different Treatments using Field Inventory Data

Deforestation and forest degradation lead to an increase in the level of carbon in the atmosphere and disrupted the global carbon cycle. The tropical forest has received a lot of interest since it contributes around 60% of the total global forest carbon. By enhancing carbon sink, tropical forests have a great potential in mitigating climate change. Assessing aboveground biomass (AGB) and carbon stock through field inventories is crucial for this purpose as it provides the most accurate result. The research was conducted at Danum Valley Conservation Area and INFAPRO in Sabah, Malaysia. An earlier study over 35 years ago at this site suggests that restored forests accrue AGB at twice the rate of regenerating forests, though the cause of this difference between treatments is unclear. Thus, this study will focus on three principal study sites which are restored, naturally regenerating and old-growth forests to determine the forest’s potential to sequester and store carbon in the forest ecosystem. These three sites were chosen because it is a well-established plot from the earlier inventory over the last seven years. The field measuring method is a non-destructive methodology. Tree parameters such as diameter at breast height (DBH), tree height and tree species diversity were collected for calculating AGB using a species-specific allometric equation. Results showed a positive correlation between tree species, diameter at breast height, and biomass/carbon stock across three different forest treatments. The active restoration increases up to 151% carbon stock whilst the old-growth forest increased by 34% and natural regeneration increased by 73%, which active restoration can be the best solution for forest treatment. The outcome of this study will increase the ability of forest authorities and the Malaysian government to effective monitoring of carbon stock for establishing reliable standard guidelines in measuring deforestation and forest degradation toward achieving sustainable forest management.

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Spatial distribution of aboveground biomass stock in tropical dry forest in Brazil

Co-benefits of biomass and biodiversity in a protected mountain forest of West Java, Indonesia

Tropical mountain forests are relatively less disturbed and store a large amount of carbon in tree biomass. A high level of species diversity compared to the boreal and temperate forests is also maintained and indicates a positive relationship with tree biomass on a small scale or at plot level. This study aimed to estimate above-ground biomass stocks (AGB) and disentangle the influence of forest structure and attributes on AGB in a small mountain forest. Forty 400 m² plots were randomly established in Takokak Nature Reserve (TNR), a 60-ha protected area at an elevation between 1150-1560 m a.s.l., located in West Java, Indonesia. All trees within the plot were identified, and their respective diameter at breast height (DBH) was measured. AGB was calculated using a global allometric model. Five independent variables, i.e., stem density, stem density of large trees (DBH >50 cm), community weighted mean wood density, rarefied species richness, and Fisher’s alpha index, were analysed using a linear model. Our results showed that AGB in TNR was comparable to other forest types in Indonesia and acted as carbon storage in the mountain regions. AGB in the TNR reached 486 Mg ha-1, of which 75% was contributed by large trees (DBH >50 cm). Three species, i.e., Liquidambar excelsa (Altingiaceae), Schima wallichii (Theaceae), and Lithocarpus sp. (Fagaceae), represent at least 70% of the total biomass in the study site. We also found that forest structures and traits, i.e., stem density, stem density of large trees, and community weighted mean wood density, drive AGB variations but not tree diversity indices. However, although diversity indices were not correlated to AGB, we found that TNR is home for endemic and threatened species on the IUCN Red List. Therefore, we suggest that the management strategies of the tropical forests should include both the conservation of the carbon stock and biodiversity.