Aboveground Carbon Storage Research Articles

Climate feedback from plant physiological responses to increasing atmospheric CO2 in Earth system models.

Plant physiological responses to increasing atmospheric CO2 concentration (iCO2), including enhanced photosynthesis and reduced stomatal conductance, impact regional and global climate. Here, I describe recent advances in understanding these effects through Earth system models (ESMs). Idealized simulations of a 1% annual iCO2 show that despite fertilization, CO2 physiological forcing contributes to 10% of warming and at least 30% of future precipitation decline in Amazonia. This reduces aboveground vegetation carbon storage and triggers positive carbon-climate feedback. ESM simulations indicate that reduced transpiration and increased heat stress from iCO2 could amplify meteorological drought and wildfire risks. Understanding these climate feedbacks is essential for improving carbon accounting in natural climate solutions, such as avoiding deforestation and reforestation, as iCO2 complicates assessing their climate benefits.

Understory plant biodiversity is inversely related to carbon storage in a high carbon ecosystem.

Given that terrestrial ecosystems globally are facing the loss of biodiversity from land use conversion, invasive species, and climate change, effective management requires a better understanding of the drivers and correlates of biodiversity. Increasingly, biodiversity is co-managed with aboveground carbon storage because high biodiversity in animal species is observed to correlate with high aboveground carbon storage. Most previous investigations into the relationship of biodiversity and carbon co-management do not focus on the biodiversity of the species rich plant kingdom, which may have tradeoffs with carbon storage. To examine the relationships of plant species richness with aboveground tree biomass carbon storage, we used a series of generalized linear models with understory plant species richness and diversity data from the USDA Forest Service Forest Inventory and Analysis dataset and high-resolution modeled carbon maps for the Tongass National Forest. Functional trait data from the TRY database was used to understand the potential mechanisms that drive the response of understory plants. Understory species richness and community weighted mean leaf dry matter content decreased along an increasing gradient of tree biomass carbon storage, but understory diversity, community weighted mean specific leaf area, and plant height at maturity did not. Leaf dry matter content had little variance at the community level. The decline of understory plant species richness but not diversity to increases in aboveground biomass carbon storage suggests that rare species are excluded in aboveground biomass carbon dense areas. These decreases in understory species richness reflect a tradeoff between the understory plant community and aboveground carbon storage. The mechanisms that are associated with observed plant communities along a gradient of biomass carbon storage in this forest suggest that slower-growing plant strategies are less effective in the presence of high biomass carbon dense trees in the overstory.

Community identification and carbon storage monitoring of Heritiera littoralis with UAV hyperspectral imaging

The Heritiera littoralis, an important rare semi-mangrove species found in coastal protective forests, plays a crucial role in carbon storage and cycling within mangrove wetland ecosystems. Despite its importance, previous studies have overlooked remote monitoring of its carbon storage. Taking the world’s oldest and largest natural community of H. littoralis in Shenzhen, China, as the study area, this research pioneers the use of UAV hyperspectral imaging to identify the H. littoralis community and estimate its above-ground, below-ground, and total carbon storage. The impacts of five geo-environmental factors (elevation, slope, aspect, vegetation communities, and inland distance from the coastline) on the spatial variability of carbon storage using SHapley Additive exPlanations (SHAP) and multiscale geographically weighted regression (MGWR) methods were also investigated. The results demonstrate that the first derivative bands within the red edge and near-infrared regions, the anthocyanin reflectance index 2 (ARI2) and newly-developed three-band VIs, were sensitive features for community identification and carbon storage estimation within the H. littoralis community. Among the four machine learning models (eXtreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR)), the best identification accuracy for the H. littoralis community was achieved by XGBoost. Among the four machine learning models (XGBoost, RF, SVM and kernel ridge regression (KRR)), RF model achieved the best performance in estimating above-ground (R2 = 0.749, RMSE=1.723 kg/m2, EV (explained variance) = 0.704), below-ground (R2 = 0.636, RMSE=0.6 kg/m2, EV=0.606) and total carbon storage (R2 = 0.613, RMSE=2.592 kg/m2, EV=0.597). SHAP and MGWR analysis showed that elevation and inland distance from the coastline were key factors influencing the spatial variability of carbon storage. In conclusion, this study showcases significant advantages in community identification and carbon storage monitoring of rare mangrove communities, providing valuable insights for biodiversity conservation efforts and management within the H. littoralis ecosystem.

Spatial-spectral attention state-space fusion network for tree carbon estimation using multi-source data

Abstract Amid rapid industrialization and urbanization, global warming and the urban heat island effect are intensifying. There is an urgent need for rapid and accurate monitoring of carbon storage in urban vegetation to assess and mitigate these environmental issues. While methods exist for estimating above-ground carbon (AGC) stocks in individual trees using deep learning, fewer studies integrate multi-source remote sensing data with these techniques. This study introduces a network that leverages spatial-spectral attention and state-space models, called the spatial-spectral attention state-space fusion network (S4FNet). S4FNet utilizes UAV hyperspectral imaging (HSI) data, light detection and ranging (LiDAR) data, and RGB data to estimate AGC storage in individual trees. This study conducted experiments at two sites, the South and North areas of Shenzhen University’s Yuehai Campus, Shenzhen City, Guangdong Province, China, and compared S4FNet with eight traditional machine learning methods and a deep learning method. The experimental results demonstrate that S4FNet achieves the best performance across all metrics, confirming its excellent capability in predicting AGC stocks using multi-source remote sensing data.

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning.

The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province. Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019). The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019. This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Regional mangrove vegetation carbon stocks predicted integrating UAV-LiDAR and satellite data

Using satellite RS data predicting mangrove vegetation carbon stock (MVC) is the popular and efficient approach at a large scale to protect mangroves and promote carbon trading. Satellite data have performed poorly in predicting MVC due to saturation issues. UAV-LiDAR data overcomes these limitations by providing detailed structural vegetation information. However, how to cross-scale integration of UAV-LiDAR and satellite RS data and the selection of features and machine learning methods hampered the practitioner in making a lightweight but efficient model to predict the MVC. Our study integrated UAV-LiDAR, Sentinel-1, and Sentinel-2 to extract spectral, structural, and textural features at the regional scale. We estimated the influences of different combinations between three vegetation features and machine learning methods (Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Regression Tree (GBDT), and Extreme Gradient Regression Tree (XGBOOST)) on the results of MVC prediction, and constructed a framework for estimating mangrove vegetation aboveground (ACG) and belowground (BCG) carbon storage in Zhanjiang, the largest mangrove area of China. Our research shows: 1) Compared to using satellite remote sensing (RS), integrating UAV and satellite RS data and fusing multiple vegetation features significantly improved the accuracy of mangrove vegetation carbon stock (MVC) predictions. 2) Structural features, particularly canopy height retrieved from UAV and satellite RS, are essential indicators for predicting MVC. Combined with spectral and structural features, regional MVC was precisely predicted. 3)Although the influence of different machine learning methods on MVC prediction was not significant, XGBOOST demonstrated relatively high precision. We recommend that mangrove practitioners integrate UAV and satellite RS data to predict MVC at a regional scale. Importantly, governments should prioritize the application of UAV-LiDAR in forestry monitoring and establish a long-term mangrove monitoring database to aid in estimating blue carbon resources and promoting blue carbon trading.

Assessment of Pinus halepensis Forests’ Vulnerability Using the Temporal Dynamics of Carbon Stocks and Fire Traits in Tunisia

Carbon stocks provide information that is essential for analyzing the role of forests in global climate mitigation, yet they are highly vulnerable to wildfires in Mediterranean ecosystems. These carbon stocks’ exposure to fire is usually estimated from specific allometric equations relating tree height and diameter to the overall amount of aboveground carbon storage. Assessments of vulnerability to fire additionally allow for specific fire resistance (bark thickness, crown basal height) and post-fire recovery traits (cone mass for regeneration, and fine branches or leaves mass for flammability) to be accounted for. These traits are usually considered as static, and their temporal dynamic is rarely assessed, thus preventing a full assessment of carbon stocks’ vulnerability and subsequent cascading effects. This study aimed to measure the pools of carbon stocks of individual trees varying between 30 and 96 years old in the Djbel Mansour Aleppo pine (Pinus halepensis) forest in semi-arid central Tunisia in the southern range of its distribution to fit a sigmoid equation of the carbon pools and traits recovery according to age as a vulnerability framework. Allometric equations were then developed to establish the relationships between fire vulnerability traits and dendrometric independent variables (diameter at breast height, height, and live crown length) for further use in regional vulnerability assessments. The total carbon stocks in trees varied from 29.05 Mg C ha−1 to 92.47 Mg C ha−1. The soil organic carbon stock (SOC) at a maximum soil depth of 0–40 cm varied from 31.67 Mg C ha−1 to 115.67 Mg C ha−1 at a soil depth of 0–70 cm. We could identify an increasing resistance related to increasing bark thickness and basal crown height with age, and enhanced regeneration capacity after 25 years of age with increasing cone biomass, converging toward increasing vulnerability and potential cascading effects under shorter interval fires. These results should be considered for rigorous forest carbon sequestration assessment under increasing fire hazards due to climate and social changes in the region.

Aboveground Spatiotemporal Carbon Storage Model in the Changing Landscape of Jatigede, West Java, Indonesia

Land use and land cover (LULC) change is the variable with the maximum influence on carbon storage in terrestrial ecosystems, due to a fundamental alteration of the ecosystem, structure, function, and variability over time. Understanding the dynamics of aboveground carbon stocks in underway constructions and urban expansions is crucial to provide a basis for land use management and planning. The objective of this study was to analyze the spatiotemporal dynamics of aboveground carbon storage and assess how the LULC change is affected by human intervention, as well as how aboveground carbon stocks respond to these changes in the tropical highland landscape of Jatigede. In this study, changes in aboveground carbon stocks were investigated between 2014 and 2021 by using the integrated valuation of ecosystem services and tradeoffs (InVEST) model. The results revealed that the total aboveground carbon stock decreased between 2014 and 2021. Forests showed the greatest decline in the aboveground carbon stock in terms of space. The primary cause of the reduction in the aboveground carbon stock was the conversion of vegetated land to agricultural and urban land cover. The aboveground carbon stock change was also caused by the continuing construction, which resulted in the extension of construction zones. However, an increase in the aboveground carbon stock was mostly observed in mixed gardens that were close to forest areas. The preservation of mixed gardens as a tree-based agroforestry system can be suggested for enhancing the aboveground carbon stock, as mixed gardens play a significant role in carbon storage in the midst of the increasingly massive deforestation due to the expansion of urban areas.

Organic soil greenhouse gas flux rates in hemiboreal old-growth Scots pine forests at different groundwater levels

Tree biomass and soils (especially organic soils) are significant carbon pools in forest ecosystems, therefore forest management practices, in order to ensure carbon storage in these pools and to mitigate climate change, are essential in reaching climate neutrality goals set by the European Union. Overall studies have focused on diverse aspects of forest carbon storage and greenhouse gas (GHG) fluxes from mineral soils, and recently also from organic soils. However, the information about old-growth forests and the long-term effects of drainage on GHG fluxes of organic soils is missing. Additionally, a large proportion of Scots pine (Pinus sylvestris L.) forests on organic soils in the hemiboreal region are drained to regulate groundwater level and to improve above-ground carbon storage. The study aims to assess the intra-annual dynamics of soil carbon dioxide (CO2) and methane (CH4) fluxes in hemiboreal old-growth Scots pine stands on organic soils with diverse groundwater levels. Six old-growth stands (130–180 years old) were evaluated. In old-growth forests, the main source of soil CO2 emissions is ground vegetation and tree roots (autotrophic respiration), while heterotrophic respiration contributes to almost half (41%) of the total forest floor ecosystem (soil) respiration. The total forest floor respiration and soil heterotrophic respiration are mainly affected by soil temperature, with minor but statistically significant contribution of groundwater level (model R2 = 0.78 and R2 = 0.56, respectively). The CO2 fluxes have a significant, yet weak positive relationship with groundwater level (RtCO2 R2 = 0.06 RhCO2 R2 = 0.08). In contrast, total soil CH4 uptake or release depends primarily on groundwater level fluctuations, with a minor but significant contribution of soil temperature (model R2 = 0.67). CH4 flux has high variability between stands.

Remote Sensing Estimation of Forest Carbon Stock Based on Machine Learning Algorithms

Improving the precision of remote sensing estimation and implementing the fusion and analysis of multi-source data are crucial for accurately estimating the aboveground carbon storage in forests. Using the Google Earth Engine (GEE) platform in conjunction with national forest resource inventory data and Landsat 8 multispectral remote sensing imagery, this research applies four machine learning algorithms available on the GEE platform: Random Forest (RF), Classification and Regression Trees (CART), Gradient Boosting Trees (GBT), and Support Vector Machine (SVM). Using these algorithms, the entire Yunnan Province is classified into seven categories, including broadleaf forest, coniferous forest, mixed broadleaf-coniferous forest, water bodies, built-up areas, cultivated land, and other types. After a thorough comparison, the research reveals that the RF algorithm surpasses others in terms of accuracy and reliability, making it the most suitable choice for estimating aboveground carbon storage in forests using remote sensing data. Therefore, the study used the RF algorithm for both forest classification and the estimation of carbon storage. By extracting remote sensing factors; by using the Pearson correlation coefficient to select the most relevant factors; and by utilizing multiple linear regression, RF regression, and decision tree regression, a model for estimating aboveground carbon stocks in forests was developed. The results indicate that among the four classification algorithms, the RF classifier demonstrates superior performance, with an overall accuracy of 84.96% and a Kappa coefficient of 76.46%. In the RF regression models, the R2 values for the coniferous forest, broadleaf forest, and mixed needle-broadleaf forest models are 0.636, 0.663, and 0.638, respectively. In both RF and CART, the R2 values for the three forest-type models are greater than 0.6, indicating satisfactory model fitting performance. This study aims to explore the possibility of improving the estimation of forest carbon stocks in large-scale areas through fine land use classification. Additionally, the data sources used are completely free, and medium to low resolution can provide a better reference value for practical applications, thereby reducing the cost of utilization.

Effects of plant community structural characteristics on carbon sequestration in urban green spaces

The structural characteristics of plant communities in urban green spaces have a significant impact on their carbon sequestration function. In this study, comprehensive data were collected from 106 plant communities (each 20 m × 20 m) in Zhengzhou Green Expo Park. We assessed aboveground and soil carbon storage, alongside maintenance carbon emissions, to quantify carbon dynamics. Our primary objective was to establish a statistical model that correlates the structural attributes of plant communities with their total annual carbon sequestration. This model aims to provide a quantitative framework for optimizing community structures to maximize carbon sequestration in urban green spaces. The results showed that density and coverage were significantly and positively correlated with aboveground and soil carbon stocks. Density and mean height were significantly and positively correlated with maintenance carbon emissions. Density played a key structural role in regulating the total carbon sequestration of the plant communities, being 27.24 times more effective than coverage. The total annual carbon sequestration of the plant community reached an optimal value of 327.67 kg CO2-eq/y−1 at a density and cover of 0.15 and 1, respectively. This study provides valuable data for increasing the carbon sink ability of urban green spaces through plant structure regulation and supporting low-carbon development strategies in urban management.

Logging response alters trajectories of reorganization after loss of a foundation tree species.

Forest insect outbreaks cause large changes in ecosystem structure, composition, and function. Humans often respond to insect outbreaks by conducting salvage logging, which can amplify the immediate effects, but it is unclear whether logging will result in lasting differences in forest structure and dynamics when compared with forests affected only by insect outbreaks. We used 15 years of data from an experimental removal of Tsuga canadensis (L.) Carr. (Eastern hemlock), a foundation tree species within eastern North American forests, and contrasted the rate, magnitude, and persistence of response trajectories between girdling (emulating mortality from insect outbreak) and timber harvest treatments. Girdling and logging were equally likely to lead to large changes in forest structure and dynamics, but logging resulted in faster rates of change. Understory light increases and community composition changes were larger and more rapid in the logged plots. Tree seedling and understory vegetation abundance increased more in the girdled plots; this likely occurred because seedlings grew rapidly into the sapling- and tree-size classes after logging and quickly shaded out plants on the forest floor. Downed deadwood pools increased more after logging but standing deadwood pools increased dramatically after girdling. Understory light levels remained elevated for a longer time after girdling. Perhaps because the window of opportunity for understory species to establish was longer in the girdled plots, total species richness increased more in the girdled than logged plots. Despite the potential for greater diversity in the girdled plots, Betula lenta L. (black birch) was the most abundant tree species recruited into the sapling- and tree-size classes in both the girdled and logged plots and is poised to dominate the new forest canopy. The largest difference between the girdling and logging treatments-deadwood structure and quantity-will persist and continue to bolster aboveground carbon storage and structural and habitat diversity in the girdled plots. Human responses to insect outbreaks hasten forest reorganization and remove structural resources that may further alter forest response to ongoing climate stress and future disturbances.

Aboveground Biomass and Carbon Storage in Mangrove Forests in Southeastern Mexico

The aboveground contributions of mangroves to global carbon sequestration reinforce the need to estimate biomass in these systems. The objective was to determine the aboveground biomass storage and quantify the carbon and CO2e content in Rhizophora mangle, Avicennia germinans, and Laguncularia racemosa present in southeastern Mexico. Based on the Forest Protocol for Mexico Version 2.0 methodology, published by Climate Action Reserve, 130 circular plots were randomly selected and established in an area of 930 ha of mangrove vegetation, and the aboveground biomass and stored carbon were determined. The mangrove had a density of 3515 ± 428.5 individuals per hectare. The aboveground biomass of the three species was 120.5 Mg ha−1. The biomass of L. racemosa was 99.5 Mg ha−1, which represents 82.6% of the total biomass. The biomass of R. mangle was 20.33 Mg ha−1, and that of A. germinans was 0.32 Mg ha−1. The total carbon retained in the trees was 60.25 Mg C ha−1 and 221.1 Mg CO2e ha−1. Laguncularia racemosa generated the highest contributions of CO2e. The area of mangroves accumulated 112,065 Mg of aboveground biomass. The carbon contained in this biomass corresponds to 205,623 Mg CO2e. This mangrove contributes to mitigating the effects of climate change globally through the reduction in greenhouse gases.

Historical Trend Analysis and Future Projections of Rainfall in Amhara, Ethiopia

Understanding rainfall trends & projections is essential for water resource management. This study showed historical (1981-2020) and future (2021-2100) rainfall amounts and trends across 71 grid points separated by 0.44o in Amhara and Amhara’s five rainfall regimes (A3, A4, A5U, A5L, A6). Ground and satellite merged historical data from Ethiopia Meteorology Institute (EMI) and the Rossby Centre Regional Atmospheric Model (RCA) forced by an ensemble of the best-performed Global Circulation Models (GCMs) in Amhara, HadGEM2-ES of UK and MPI-ESM-LR of Germany, in three Representative Concentration Pathway (RCP) scenarios, RCP2.6, RCP4.5, and RCP8.5 from CORDEX project were the basis of dataset. Amhara’s annual, seasonal, and monthly historical rainfall trends mostly increased. During Jun-Sep (Kiremt), lower rainfall receiving regimes (A6, A5L, A5U) had a significant increasing trend (~ 4 mm/year), while wetter regimes (A3 & A4) had a non-significant increasing (< 1 mm/year) and decreasing (~ -0.5 mm/year) trends, respectively. Generally, compared to the climatology, the annual and Kiremt rainfall in Amhara is projected to increase in the near-term (2021-2040) and mid-term (2041-2060) and decrease in the long-term (2081-2100); Oct-Jan (Bega) rainfall is projected to increase in all future terms and all RCP scenarios while Feb-May (Belg) rainfall will be abrupt

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.

Population structure and carbon storage of critically endangered tree Karomia gigas (Faden) Verdic in the coastal forests of Lindi, Tanzania

Global biological loss is predominantly propelled by human activities resulting in the extinction of some important plant species while pushing others to the brink of critical danger. Better understanding of the ecology of these important species facilitates safeguarding their habitats and the development of sustainable restoration strategies. Data on vegetation, physiographic, edaphic and anthropogenic variables were collected from forty (40) concentric temporary circular plots in two forest reserves (i.e. Mitundumbea and Litipo Forest Reserves) to determine population structure and above-ground carbon storage of Karomia gigas (Faden) Verdic a critically endangered tree species in the coastal forests of Lindi, Tanzania. Within the Litipo Forest Reserve, we recorded a total of 30 K. gigas individual trees, comprising 13 seedlings, 10 saplings and 7 mature trees. The largest measured diameter at breast height (dbh) in this forest was 29.2 cm while the smallest was 0.9 cm. In contrast, the Mitundumbea Forest Reserve had 15 mature K. gigas individual trees but lacked seedlings and saplings. The largest dbh recorded in this forest stood at 59.7 cm, with the smallest dbh measurement being 5.1 cm. We overall observed significant negative correlations between the abundance of K. gigas adults and both soil organic carbon (P = 0.005) and burnt areas (P = 0.003). The total abundance of K. gigas showed a negative association with soil organic carbon (P = 0.011). Furthermore, the carbon storage of K. gigas exhibited a negative relationship with burnt areas (P = 0.005) and a positive relationship with elevation (P = 0.016). None of the assessed physiographic, edaphic, and anthropogenic variables could explain the overall abundance of K. gigas seedlings and saplings. The average above-ground carbon stored by K. gigas in Litipo forest was 3.19 ± 0.72 t ha−1, while in Mitundumbea it was 12.76 ± 1.75 t ha−1. To increase the population size and restore this once-extinct tree species, anthropogenic fires must be controlled for plant diversity conservation and environmental sustainability.

Assessing the impact of pine wilt disease on aboveground carbon storage in planted Pinus massoniana Lamb. forests via remote sensing

The continuous spread of Bursaphelenchus xylophilus (Steiner and Buhrer) Nickle, commonly known as the organism that causes pine wilt disease (PWD), has become a notable threat to forest security in East Asia and southern Europe, and an assessment of the carbon loss caused by PWD damage is important to achieving carbon neutrality. This study used satellite remote sensing and 15-year ground monitoring data to measure the impact of PWD on the carbon storage of Pinus massoniana Lamb. (P. massoniana), the conifer with the largest planted area in southern China. This study showed that the occurrence of PWD had an impact on the increase in carbon storage of P. massoniana. The infected and dead P. massoniana trees accounted for only 1.46 % of the total number of trees but caused a carbon storage loss of 1.99 t/ha, which accounted for 6.23 % of the total carbon sink in healthy P. massoniana forests over the last 15 years. The most pronounced decline in carbon storage occurred in the first five years of PWD invasion. After 10 years of clearcutting and replanting of Schima superba Gardn. et Champ., the increase in carbon storage of the reformed forest far exceeded that of the healthy forest during the same period, which was 2.04 times (10 years) and 1.56 times (15 years) that of the healthy P. massoniana forest. In addition, our study found that during the 15-year period (from the forest age of 22 to the forest age of 37), the average carbon storage of P. massoniana forest was 31.9 t/ha. This study helps to evaluate the impact of PWD on the carbon sink of pine forests and provides methodological references for analyzing the impact of biological disturbances on the carbon cycle.

SoCal EcoServe: an online mapping tool to estimate wildfire impacts in southern California

Background Wildfires in Mediterranean-type climate regions have numerous impacts on the ecosystem services provided by native shrublands, however, quantifying these impacts is challenging. Aims We developed a reproducible method to quantify fire impacts on ecosystem services and created a tool for resource managers in southern California. Methods The SoCal EcoServe tool consists of two components: a desktop tool and an online mapping tool. We used the Alisal Fire of 2021 as a case study and quantified: aboveground live carbon storage using pre- and post-fire biomass data; water runoff, groundwater recharge and sediment erosion retention by integrating data on burn severity into hydrological and sediment erosion models; and estimated recreation services and biodiversity using pre-fire data. Key results We estimated the Alisal Fire resulted in an immediate post-fire reduction in carbon storage of 25%, of which 20% was estimated to be permanently lost. Water runoff increased by 21%, groundwater recharge 7-fold, and sediment erosion increased 24-fold. Conclusions The EcoServe tool provides an initial approximation of wildfire impacts that can support damage assessments post-fire, track carbon storage and help identify priorities for post-fire restoration. Implications We intend the tool to be used by USDA Forest Service resource managers of shurblands in southern California. However, it can provide the framework for future work in shrublands throughout the western USA.

Tree size diversity is the major driver of aboveground carbon storage in dryland agroforestry parklands

Despite the importance of agroforestry parkland systems for ecosystem and livelihood benefits, evidence on determinants of carbon storage in parklands remains scarce. Here, we assessed the direct and indirect influence of human management (selective harvesting of trees), abiotic factors (climate, topography, and soil) and multiple attributes of species diversity (taxonomic, functional, and structural) on aboveground carbon (AGC) stocks in 51 parklands in drylands of Benin. We used linear mixed-effects regressions and structural equation modeling to test the relative effects of these predictors on AGC stocks. We found that structural diversity (tree size diversity, HDBH) had the strongest (effect size β = 0.59, R2 = 54%) relationship with AGC stocks, followed by community-weighted mean of maximum height (CWMMAXH). Taxonomic diversity had no significant direct relationship with AGC stocks but influenced the latter indirectly through its negative effect on CWMMAXH, reflecting the impact of species selection by farmers. Elevation and soil total organic carbon content positively influenced AGC stocks both directly and indirectly via HDBH. No significant association was found between AGC stocks and tree harvesting factor. Our results suggest the mass ratio, niche complementarity and environmental favorability as underlying mechanisms of AGC storage in the parklands. Our findings also highlight the potential role of human-driven filtering of local species pool in regulating the effect of biodiversity on AGC storage in the parklands. We conclude that the promotion of AGC stocks in parklands is dependent on protecting tree regeneration in addition to enhancing tree size diversity and managing tall-stature trees.

High regeneration may not contribute to the forest's carbon storage: a case study in the mangrove forest of Rajang-Belawai-Paloh delta, Sarawak.

The alarming rate of the mangrove ecosystem loss poses a threat of losing valuable carbon sinks. This study was conducted to (i) determine the growth structure in different vegetation types and (ii) compare the aboveground biomass (AGB) and carbon storage in different vegetation types. The study was conducted at four vegetation types within the Rajang-Belawai-Paloh delta i.e., Matured Bakau-Berus Forest (MBBF), Bakau-Nipah Forest (BNF), Regenerating Forests (Debris pile) [RF-D], and Regenerating Forests (Machinery track) [RF-M]. Inventory plots (20 m × 20 m) are systematically located along the main waterways and smaller rivers/streams. Trees (≥ 5 cm diameter-at-breast height [DBH]), seedlings (< 2-cm stem diameter), and saplings (2-4.9-cm stem diameter) were measured. The trend of total trees per hectare is found to be decreasing across the least disturbed vegetation (MBBF) to the most disturbed vegetation (RF-M). The trends of total seedlings and saplings per hectare are found to be going upwards from the least disturbed vegetation to the most disturbed vegetation. Kruskal-Wallis H-test showed that there is a significant difference in the AGB and carbon storage between different vegetation types, χ2(2) = 43.98, p = 0.00 with the highest mean rank AGB and carbon storage in BNF (612.20 t/ha) and lowest in RF-M (287.85 t/ha). It can be concluded that although the most disturbed vegetations have higher regeneration, it may not contribute to the forest's carbon storage The naturally regenerated seedlings may not grow beyond the sapling stage unless sustainable forest management is conducted to ensure survivability and growth.