Ecosystem Carbon Stocks Research Articles

American alligators (Alligator mississippiensis) as wetland ecosystem carbon stock regulators

Blue carbon refers to organic carbon sequestered by oceanic and coastal ecosystems. This stock has gained global attention as a high organic carbon repository relative to other ecosystems. Within blue carbon ecosystems, tidally influenced wetlands alone store a disproportionately higher amount of organic carbon than other blue carbon systems. North America harbors 42% of tidally influenced global wetland area, which has been identified as a critical carbon stock in the context of climate change mitigation. However, quantified associations between vertebrate biota and carbon sequestration within ecosystems are in their infancy and have been incidental, given that microbial trophic levels are thought to drive nutrient dynamics. Here, we assess the relationship between American alligator (Alligator mississippiensis) demography and tidally influenced wetland soil carbon stock among habitats at continental, biogeographically-relevant, and local scales. We used soil core profile data from the Smithsonian’s Coastal Carbon Network and filtered for continuous core profiles in tidally influenced wetland areas along the Gulf and Atlantic Coasts of the United States. Results indicate that American alligator presence is positively correlated with soil carbon stock across habitats within their native distribution. Further, American alligator demographic variables are positively correlated with soil carbon stock at local scales. These conclusions are concordant with previous findings that apex predators, through trophic cascade theory, play a key role in regulating soil carbon stock and that alligators are functional apex predators in carbon dynamics and a key commercialized natural resource.

Ecosystem carbon storage, allocation and carbon credit values of major forest types in the central Himalaya

Himalayan forests are crucial global carbon reservoirs that contribute significantly to carbon mitigation efforts. Although situated within a single climatic zone, Himalayan forests include diverse forest types within a short distance due to variations in altitude, mountain range, slope, and aspect. This study aimed to estimate ecosystem carbon storage (including plant biomass, deadwood, litter, and soil organic carbon [SOC]) and allocation and to evaluate carbon sequestration and carbon credit potential in chir-pine plants (Pinus roxburghii Sarg.), deodar (Cedrus deodara [Roxb.] G. Don), oak (Quercus leucotrichophora A. Camus), and sal (Shorea robusta [Roth]) forests in the central Himalaya. Volumetric equations were utilized across diverse tree species and supplemented by field sampling, particularly by employing the quadrat method to quantify tree biomass. The carbon stocks within ecosystems varied considerably, ranging between 122.44 and 306.44 Mg C ha−1, with discernible differences among forest types, with oak forests exhibiting the highest carbon stock, followed by deodar and sal forests, and pine forests showing the lowest. The allocation of ecosystem carbon stocks among the different components, including trees (21%–34%), soil (64%–77%), deadwood (0.9%–0.35%), and litter (0.46%–1.20%), demonstrated significant variability. The Mantel test revealed the significant influence of environmental factors on carbon storage. Carbon dioxide (CO2) sequestration ranged from 448.98 (pine forest) to 1123.16 (oak forest) Mg CO2 ha−1, while carbon credit values ranged from 1346.96 EUR ha−1 (pine forests) to 3379.49 EUR ha−1 (oak forest). In this study, dominant trees in various forest types contributed to higher carbon storage in their biomass and forest soil, resulting in greater carbon credits. The present research evaluated ecosystem carbon storage, CO2 sequestration potential, and carbon credit valuation for major forests in the central Himalaya. By incorporating these findings into forest management plans and strategies, the carbon sequestration potential and carbon trading of the central Himalayan forest ecosystem in India can be enhanced.

The GrassSyn dataset: Soil organic carbon stocks in Brazilian grassy ecosystems.

Although ecosystem management and restoration are known to enhance carbon storage, limited knowledge of ecosystem-specific soil organic carbon (SOC) stocks and processes hinders the development of climate-ready, biodiversity-focused policies. Baseline SOC stocks data for specific ecosystems is essential. This paper aims to: (i) examine SOC stock variability across major grassy ecosystems in Brazil and (ii) discuss data limitations and applications. We compiled the Grassland Synthesis Working Group dataset, which comprehensively aggregates SOC stocks data from published studies on main Brazil's grassy ecosystems. Our dataset results from systematic literature review and regional soil sampling datasets. The dataset provides spatially explicit SOC stocks, physical soil properties, and ancillary information from 182 studies (1996-2021) across 803 sites, spanning 35° latitude and 28° longitude. The dataset, structured in relational tables, reports soil C stocks and ancillary soil parameters at depths up to 100cm. SOC stocks vary by grassy ecosystem types and sampling depth, with subtropical grasslands (Campos Gerais, South Brazilian highland grasslands, and Pampa) showing the highest SOC stocks across all depth layers (SOC 0-30cm: 64.5-162.8 Mg C ha-1; SOC 0-100cm: 137.6-224.7 Mg C ha-1). The tropical Cerrado and Amazon grassy ecosystems exhibit high SOC stocks, particularly in subsurface layers (SOC 0-30cm: 53.6 and 38.3 Mg C ha-1; SOC 0-100cm: 109.8 and 121.4 Mg C ha-1, respectively). Our data analysis shows high carbon stocks in natural/seminatural ecosystems, but some ecosystems are undersampled. The dataset on SOC stocks in grassy ecosystems could greatly aid Brazil's national greenhouse gas inventory.

Assessment of Ecosystem Health and Carbon Stocks in the Seagrass Meadows of Mengiat Beach, Bali, Indonesia

Seagrass meadows provide many important ecosystem services. They are now recognized as blue carbon ecosystems that are crucial in the mitigation of global climate change. This study was conducted at Mengiat Beach in Bali, Indonesia, where there are extensive seagrass meadows along the shorelines, but also considerable anthropogenic activity that pose threats to the ecosystem. The objectives of this study were to: (1) describe the seagrass community at Mengiat Beach; (2) assess the health status of the seagrass ecosystem; and (3) estimate carbon stocks stored within the ecosystem. Vegetation analysis was conducted to describe the seagrass community in terms of density, cover, biomass and species importance. Spatial Sentinel-2 satellite data with unsupervised classification was used to determine the extent of seagrass meadows. Carbon stocks in sediment and biomass were estimated using the loss on ignition method. The seagrass community at Mengiat Beach consists of at least five different species, dominated by Cymodocea rotundata. The meadows are characterized by high density (588 ind.m-2) and good cover (60.7%). They are considered healthy, with good ecological quality, as indicated by a SEQI (Seagrass Ecological Quality Index) of 0.69. The seagrass ecosystem stores a significant amount of carbon, with 99.23% of it stored in sediment. Total carbon stock in sediment and seagrass biomass is estimated at 133.39 MgC.ha-1. When extrapolated to the total seagrass area of 43.21 ha, the meadows at Mengiat Beach store a total carbon stock of 5.76 GgC, highlighting their potential as high-carbon reservoirs and importance in climate change mitigation efforts.

Analysis of Annual Spatiotemporal Variations in Carbon Stock in the Urban Agglomeration of the Middle Yangtze River Basin, China

Terrestrial ecosystem carbon stock (TECS) is critical to socioeconomic development and ecosystem services and is jointly affected by land use and cover and climate change. However, the dynamics of long-term annual TECS levels in urban agglomeration remain largely unknown, and research mostly ignores the spatial heterogeneity of climate factors, compromising sustainable environmental management and land planning strategies. To this end, we integrated field observations of carbon density, land use, and climate factors to map the annual distribution of TECS and analyzed their spatiotemporal variations and policy implications in the urban agglomeration of the middle Yangtze River Basin in China from 1990 to 2020. The results showed that 43,855.47 km2 of the land of the urban agglomeration changed from 1990 to 2020, accounting for 12.54% of the study area. The farmland and forest land area fluctuated and reduced, and the construction land area increased significantly. The increase in construction land was mainly from farmland and forest land. The TECS in urban agglomerations underwent a remarkable change, the overall trend fluctuated downward, and the maximum interannual variation was 1560 Tg. The transfer of construction land, farmland, forest land, shrubs, grassland, and other land mainly caused the change in carbon storage. Due to abnormal climate change, the urban agglomeration in some areas illustrated carbon storage with a spatially aggregated distribution. When considering the impact of climate change on carbon density, the TECS changes of land types other than forest land were found to be consistent with the area change but more significant due to climate change. The research results can provide reference data for regional land management policy formulation and realization of “dual carbon” goals.

Conversion of tropical forests to water buffalo pastures in lower Amazonia: Carbon losses and social carbon costs

AbstractTropical peat swamp forests provide many important ecosystem services, especially their function as global carbon sinks. These carbon‐rich wetlands are widespread in South America, yet few studies have examined carbon stocks or losses due to land use change. In the lower Amazon, they are being converted to pastures largely utilized by domestic water buffalo (Bubalus bubalis). We quantified carbon stocks in intact peat forests and recently converted pastures (<10 years) at the Lago Piratuba Biosphere Reserve (LPBR) in the lower Amazon of Brazil. The soils of intact forests were typified by shallow organic (peat) horizons at the soil surface. The mean total ecosystem carbon stock (TECS) in intact forests was 354 ± 28 Mg C ha−1. In contrast, the TECS of disturbed sites was significantly lower (p = 0.02) with a mean of 248 ± 17 Mg C ha−1. We estimated greenhouse gas (GHG) emissions from water buffalo (due to enteric fermentation and manure deposition) to be 7.5 Mg CO2e ha−1 year−1. Considering GHG emissions from this land use, the social carbon costs (SCCs) arising from the degradation of coastal Amazon peatlands are as high as US$2742 ha−1 year−1. The SCC of meat produced from this land use is as high as US$100/kg of meat produced, which far exceeds the economic returns from livestock. Based on the estimated numbers of water buffalo for the southern portion of the LPBR and the time since initial disturbance, the annual GHG emissions from this land use are estimated to be 602,846 Mg CO2e year−1 with an SCC as high as US$111,526,524 million year−1. This land use also eliminates opportunity values and services of carbon storage and biodiversity that would be possible from a regenerating biosphere reserve.

Estimation of carbon stocks of tropical mangrove forests along the Carigara Bay in Leyte, Philippines

Mangrove forest ecosystems are known to sequester large quantities of carbon, becoming a significant carbon source when disturbed. This paper presents a quantification in aboveground (standing trees, palm, shrub, standing dead trees, downed wood and litter), belowground (root and soil) and ecosystem carbon stocks in mangrove forests along the Carigara Bay in Leyte, Philippines. The carbon stocks in the different mangrove forest types (fringe and riverine) and zones (landward, middleward, and seaward/along water) were compared. Further, the relationship between environmental factors (eg, interstitial soil salinity, soil water content and soil depth) and ecosystem carbon stocks was examined. The study yielded an ecosystem carbon stock of 558.02±51.13Mg ha-1, partitioned into aboveground and belowground carbon stocks of 251.96±31.08 and 306.06±28.50Mg ha-1, respectively. The ecosystem carbon stocks of the riverine (805.89±80.57Mg ha-1) greatly exceeded that of the fringe mangrove forests (310.15±24.59Mg ha-1). In general, biomass and soil both store a similar proportion of carbon, corresponding to 57% and 43%, respectively. In addition, regression analysis revealed that soil depth was a reasonable predictor of ecosystem carbon stocks, whereby increasing ecosystem carbon stocks were associated with deeper soil deposits. Overall, the study’s results highlight the exceptionally high amount of carbon stored in the mangrove ecosystems, indicating their potential role in climate change mitigation.

Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle-Light Detection and Ranging and Machine Learning.

Eucalyptus is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of Eucalyptus biomass is important for plantation forest management and the prediction of carbon stock in terrestrial ecosystems. In this study, the performance of predictive biomass regression equations and machine learning algorithms, including multivariate linear stepwise regression (MLSR), support vector machine regression (SVR), and k-nearest neighbor (KNN) for constructing a predictive forest AGB model was analyzed and compared at individual tree and stand scales based on forest parameters extracted by Unmanned Aerial Vehicle-Light Detection and Ranging (UAV LiDAR) and variables screened by variable projection importance analysis to select the best prediction method. The results of the study concluded that the prediction model accuracy of the natural transformed regression equations (R2 = 0.873, RMSE = 0.312 t/ha, RRMSE = 0.0091) outperformed that of the machine learning algorithms at the individual tree scale. Among the machine learning models, the SVR prediction model accuracy was the best (R2 = 0.868, RMSE = 7.932 t/ha, RRMSE = 0.231). In this study, UAV-LiDAR-based data had great potential in predicting the AGB of Eucalyptus trees, and the tree height parameter had the strongest correlation with AGB. In summary, the combination of UAV LiDAR data and machine learning algorithms to construct a predictive forest AGB model has high accuracy and provides a solution for carbon stock assessment and forest ecosystem assessment.

Effects of salvage logging after forest fire on Siberian larch regeneration and ecosystem carbon stocks at the drought limit of the boreal forest in Mongolia

Post-fire salvage logging is widely applied in Mongolia's boreal forests with the intent to prevent intact forests from logging. The rationale behind this approach is the assumption that the additional disturbance caused by the removal of standing deadwood after stand-replacing fire is of no further significance for the already heavily disturbed ecosystem. However, while there is a global debate on effects of salvage logging for regeneration success, biodiversity, and soil health, little evidence has been collected from strongly drought-limited southern boreal forests of Central Asia. Comparing sites with and without salvage logging, we investigated forests of Siberian larch (Larix sibirica) ca. 20 years after stand-replacing fire and asked whether postfire salvage logging affected regeneration density, terminal shoot length and radial stem increment, ecosystem carbon stock densities, and reduced organic layer depth and compacted the soil. The biomass of the larch regeneration was significantly reduced by salvage logging, while tree growth was not affected. The ecosystem carbon stock density of burnt forest without salvage logging was 202 Mg C ha−1 and thus even in the lower range of intact larch forests from Mongolia, whereas burnt forests with salvage logging had organic carbon stock densities (104 Mg C ha−1) that were lower than those of unburned grasslands in the forest-steppe. These results show that removing deadwood from burnt forest is not insignificant, but has the potential to delay forest recovery and strongly reduces organic carbon storage. However, we did not find significant reductions in soil organic carbon stocks or soil compaction. Nonetheless, our findings raise the question of whether careful management of intact forests (especially by selective felling under a continuous-cover forestry regime) would be a more ecologically sustainable alternative than post-fire salvage logging.

The contribution of tropical forests to climate change mitigation. Biomass estimation techniques a necessary tool in their assessment

Climate change is a global problem caused by human activities such as burning fossil fuels and deforestation. This leads to indicators of climate change like increased flooding, sea level rise, and water stress. Tropical forests play a crucial role in mitigating climate change through photosynthesis, storing large amounts of carbon in their biomass. Two methods, destructive and non-destructive, are commonly used to estimate the biomass of tropical forests. There are five components of biomass in these ecosystems, with most of it found aboveground. Belowground biomass is estimated based on aboveground biomass. About 50 % of the dry biomass in forest ecosystems is carbon. Allometric equations are used to estimate biomass and volume based on tree diameter and height. Different equations have been developed for different species and locations. Carbon stocks in forest ecosystems are present in both aboveground and belowground parts.

Acclimation of Photosynthesis to CO2 Increases Ecosystem Carbon Storage due to Leaf Nitrogen Savings.

Photosynthesis is the largest flux of carbon between the atmosphere and Earth's surface and is driven by enzymes that require nitrogen, namely, ribulose-1,5-bisphosphate (RuBisCO). Thus, photosynthesis is a key link between the terrestrial carbon and nitrogen cycle, and the representation of this link is critical for coupled carbon-nitrogen land surface models. Models and observations suggest that soil nitrogen availability can limit plant productivity increases under elevated CO2. Plants acclimate to elevated CO2 by downregulating RuBisCO and thus nitrogen in leaves, but this acclimation response is not currently included in land surface models. Acclimation of photosynthesis to CO2 can be simulated by the photosynthetic optimality theory in a way that matches observations. Here, we incorporated this theory into the land surface component of the Energy Exascale Earth System Model (ELM). We simulated land surface carbon and nitrogen processes under future elevated CO2 conditions to 2100 using the RCP8.5 high emission scenario. Our simulations showed that when photosynthetic acclimation is considered, photosynthesis increases under future conditions, but maximum RuBisCO carboxylation and thus photosynthetic nitrogen demand decline. We analyzed two simulations that differed as to whether the saved nitrogen could be used in other parts of the plant. The allocation of saved leaf nitrogen to other parts of the plant led to (1) a direct alleviation of plant nitrogen limitation through reduced leaf nitrogen requirements and (2) an indirect reduction in plant nitrogen limitation through an enhancement of root growth that led to increased plant nitrogen uptake. As a result, reallocation of saved leaf nitrogen increased ecosystem carbon stocks by 50.3% in 2100 as compared to a simulation without reallocation of saved leaf nitrogen. These results suggest that land surface models may overestimate future ecosystem nitrogen limitation if they do not incorporate leaf nitrogen savings resulting from photosynthetic acclimation to elevated CO2.

Root-Driven Soil Reduction in Wadden Sea Salt Marshes

The soil redox potential in wetlands such as peatlands or salt marshes exerts a strong control over microbial decomposition processes and consequently soil carbon cycling. Wetland plants can influence redox by supplying both terminal electron acceptors (i.e. oxygen) and electron donors (i.e. organic matter) to the soil system. However, quantitative insight into the importance of plant effects on wetland soil redox and associated plant traits are scarce. In a combined mesocosm and field study we investigated the impact of plants on soil reduction using IRIS (Indicator of Reduction in Soils) sticks. Vegetated plots were compared to non-vegetated plots along an elevational gradient in a salt marsh of the Wadden Sea and along an artificially created gradient in a tidal tank mesocosm experiment. Our findings from the mesocosm experiment demonstrated that vegetation both enhanced and suppressed soil reduction relative to non-vegetated control pots. The direction of the plant effect (i.e., net oxidizing or net reducing) was inversely correlated with background redox conditions. Insights from high-resolution oxygen profiling via planar optode imaging corroborated these findings. In the field study, vegetation consistently reduced the comparatively well-aerated Wadden Sea salt marsh soil. Reduction correlated positively with soil organic matter content and belowground biomass, indicating that greater availability of plant-derived electron donors, in the form of organic matter, increased soil reduction. Challenging the dominant paradigm that wetland plants primarily act as soil oxidizers, our study reveals their potential to exert a net reducing effect. The documented impact of these plant-induced changes in soil redox conditions suggests a previously overlooked role in shaping the stability of soil organic carbon stocks in wetland ecosystems with variable water tables.

Carbon Stock Estimation of Poplar Plantations Based on Additive Biomass Models

Accurate estimation of biomass and carbon stocks in forest ecosystems is critical for understanding their roles in carbon sequestration and climate change mitigation. Currently, the development of stand biomass models and carbon stock estimation at the regional scale has emerged as a prominent research priority. In this study, 225 Populus spp. (poplar) trees in Shandong Province, China, were destructively sampled to obtain the biomass of their components. Two models (MS1 and MS2) were developed using allometric equations and the seemingly unrelated regression (SUR) method to ensure additive properties across tree components. The model evaluation employed the leave-one-out jackknife (LOO) method, considering statistics such as adjusted R-squared (Ra2), root mean square error (RMSE), mean absolute percent error (MAPE), and mean absolute error (MAE). The results from our models demonstrated high accuracy, with MS2 slightly outperforming MS1 after incorporating tree height as an independent variable. The models reliably estimated component-specific biomass and carbon stocks, with distinct variations observed in the carbon content among foliage (47.14 ± 2.07%), branches (47.26 ± 2.48%), stems (47.67 ± 2.21%), and roots (46.37 ± 2.78%). Carbon stocks in poplar plantations increased with the diameter class, ranging from 5 to 35 cm and correspondingly from 3.670 to 172.491 Mg C ha−1. As the diameter class increases, the carbon allocation strategy of poplars aligns with the CSR strategy, transitioning from prioritizing growth competition to emphasizing self-stabilization. Our research proposes a robust framework for assessing biomass and carbon stocks in poplar plantations, which is essential for evidence-based forest management strategies.

Multi-Scenario Simulation of Land Use and Assessment of Carbon Stocks in Terrestrial Ecosystems Based on SD-PLUS-InVEST Coupled Modeling in Nanjing City

In the context of achieving the goal of carbon neutrality, exploring the changes in land demand and ecological carbon stocks under future scenarios at the urban level is important for optimizing regional ecosystem services and developing a land-use structure consistent with sustainable development strategies. We propose a framework of a coupled system dynamics (SD) model, patch generation land-use simulation (PLUS) model, and integrated valuation of ecosystem services and trade-offs (InVEST) model to dynamically simulate the spatial and temporal changes of land use and land-cover change (LUCC) and ecosystem carbon stocks under the NDS (natural development scenario), EPS (ecological protection scenario), RES (rapid expansion scenario), and HDS (high-quality development scenario) in Nanjing from 2020 to 2040. From 2005 to 2020, the expansion rate of construction land in Nanjing reached 50.76%, a large amount of ecological land shifted to construction land, and the ecological carbon stock declined dramatically. Compared with 2020, the ecosystem carbon stocks of the EPS and HDS increased by 2.4 × 106 t and 1.5 × 106 t, respectively, with a sizable ecological effect. It has been calculated that forest and cultivated land are the two largest carbon pools in Nanjing, and the conservation of both is decisive for the future carbon stock. It is necessary to focus on enhancing the carbon stock of forest ecosystems while designating differentiated carbon sink enhancement plans based on the characteristics of other land types. Fully realizing the carbon sink potential of each ecological functional area will help Nanjing achieve its carbon neutrality goal. The results of the study not only reveal the challenges of ecological conservation in Nanjing but also provide useful guidance for enhancing the carbon stock of urban terrestrial ecosystems and formulating land-use planning in line with sustainable development strategies.

How does the value of ecological restoration techniques affect ecosystem carbon density: A resource endowment-based perspective

In the context of carbon neutrality and carbon peaks, innovations in ecological restoration technology play a key role in carbon adsorption and maintaining ecological balance. Regional governments, tasked with formulating differentiated and rational ecologically sustainable development plans and carbon-neutral strategies tailored to various resource endowment conditions, require timely identification of the mechanisms by which ecological restoration technologies affect the carbon density of ecosystems. Based on data from 31 provinces in China from 2006 to 2021, this study employed the Spatial Durbin Model with both time and space fixed effects to examine the impact of the value of ecological restoration technologies on ecosystem carbon density under varying resource endowments. The empirical results indicate that (1) the value of ecological restoration technology has a significantly positive effect on ecosystem carbon density, particularly in neighbouring areas; (2) energy resource endowment exhibits a significantly inverted U-shaped boundary effect, whereby regions with moderate energy endowment can optimise the positive impact of ecological restoration technology; (3) the heterogeneity analysis, based on levels of regional environmental governance and forest cover, shows that in regions with less investment in environmental infrastructure and lower forest cover, ecosystem carbon density is more sensitive to the value of ecological restoration techniques, making their carbon sink effect more pronounced. This study elucidates the key role of ecological restoration techniques in enhancing carbon storage, promoting the goal of carbon neutrality, and identifying optimal pathways for increasing ecosystem carbon stocks across various regions with differing resource endowments. Moreover, it provides a reference for accelerating the achievement of carbon neutrality targets.

A Spatial–Temporal Analysis and Multi-Scenario Projections of Carbon Sequestration in Sea Islands: A Case Study of Pingtan Island

As an indispensable part of the marine ecosystem, the health status of the sea affects the stability and enhancement of the overall ecological function of the ocean. Clarifying the future land and sea utilization pattern and the impacts on the carbon stocks of island ecosystems is of great scientific value for maintaining marine ecological balance and promoting the sustainable development of the island ecosystem. Using Pingtan Island as an example, we simulate and predict changes in island utilization and carbon stocks for historical periods and multiple scenarios in 2030 via the PLUS-InVEST model and the marine biological carbon sink accounting method. The results show that (1) from 2006 to 2022, the carbon stock of Pingtan Island decreased by 7.218 × 104 t, resulting in a cumulative economic loss of approximately USD 13.35 million; furthermore, from 2014 to 2018, the implementation of many reclamation and land reclamation projects led to a severe carbon stock loss of 6.634 × 104 t. (2) By 2030, the projected carbon stock under the three different policy scenarios will be greater than that in 2022. The highest carbon stock of 595.373 × 104 t will be found in the ecological protection scenario (EPS), which will be 4.270 × 104 t more than that in 2022. With the strong carbon sequestration effect of the ocean, the total social carbon cost due to changes in island utilization is projected to decrease in 2030. (3) The factors driving changes in island utilization will vary in the design of different future scenarios. The results of this study not only provide a solid scientific basis for the sustainable development of island areas, but they also highlight the unique contribution of islands in the field of marine ecological conservation and carbon management, contributing to the realization of the dual-carbon goal.

Ecosystem Carbon Stock in Iron-Metamorphic Soils with Different Types of Land Use in South Karelia

Ecosystem Carbon Stock in Iron-Metamorphic Soils with Different Types of Land Use in South Karelia

The Impact of Drought on Terrestrial Carbon in the West African Sahel: Implications for Natural Climate Solutions

AbstractTerrestrial ecosystems store more than twice the carbon of the atmosphere, and are critical to climate change mitigation efforts. This has led to a proliferation of land‐based carbon sequestration efforts, such as re/afforestation associated with the Great Green Wall in the West African Sahel (WAS GGW). However, we currently lack comprehensive assessments of the long‐term viability of these ecosystems' carbon storage in the context of increasingly severe climate extremes. The WAS is particularly prone to recurrent and disruptive extremes, exemplified by the persistent and severe late‐20th century drought. We assessed the response and recovery of WAS GGW carbon stocks and fluxes to this late‐20th century drought, and the subsequent rainfall recovery, by leveraging a suite of terrestrial ecosystem models. While multi‐model mean carbon fluxes (e.g., gross primary production, respiration) partly recovered to pre‐drought levels, modeled total (above and below ground) ecosystem carbon stock falls to as much as two standard deviations below pre‐drought levels and does not recover even ∼20 years after the maximum drought anomaly. Furthermore, to the extent that the modeled regional carbon stock recovers, it is nearly entirely driven by atmospheric CO2 trends rather than the precipitation recovery. Uncertainties in regional ecosystem carbon simulation are high, as the models' carbon responses to drought displayed a nearly 10‐standard deviation spread. Nevertheless, the multi‐model average response highlights the strong and persistent impact of drought on terrestrial carbon storage, and the potential risks of relying on terrestrial ecosystems as a “natural climate solution” for climate change mitigation.

Relationship between seagrass community structure and carbon stocks on the coasts of Karimunjawa Marine National Park, Indonesia

The seagrass ecosystem is considered one of the most effective coastal ecosystems in storing carbon. Carbon stock estimation for a certain ecosystem is highly affected by factors such as species diversity and habitat type. This study aims to investigate the relationship between plant community structure and carbon stocks in the seagrass ecosystem using a case study of six coastal sites in Karimunjawa Marine National Park, Indonesia. In this region, eight seagrass species were recorded, i.e., Enhalus acoroides, Thalassia hemprichii, Cymodocea rotundata, Halodule pinifolia, Halophila ovalis, Halophila minor, Syringodium isoetifolium, and Oceana serrulata. From the six study sites, the highest estimated carbon stock was 426.2 Mg C ha−1 (Site 5; Telaga, dominated by E. acoroides). Meanwhile, the lowest estimated carbon stock was 127.4 Mg C ha−1 (Site 4; Koin, dominated by T. hemprichii). The density of E. acoroides was positively correlated with the total seagrass biomass carbon stocks (r = 0.97; p < 0.01), while its dominance was positively correlated with sediment carbon stocks (r = 0.92; p < 0.05) and total seagrass ecosystem carbon stocks (r = 0.92; p < 0.05). Seagrass ecosystems with different community structures showed different carbon storage capacities. Seagrass ecosystems dominated by large-sized species such as E. acoroides showed higher estimated carbon stocks thus suggesting the importance of considering the variability of community structure in managing seagrass ecosystems for carbon sequestration and storage.

Impact of Land Use and Land Cover (LULC) Changes on Carbon Stocks and Economic Implications in Calabria Using Google Earth Engine (GEE).

Terrestrial ecosystems play a crucial role in global carbon cycling by sequestering carbon from the atmosphere and storing it primarily in living biomass and soil. Monitoring terrestrial carbon stocks is essential for understanding the impacts of changes in land use on carbon sequestration. This study investigates the potential of remote sensing techniques and the Google Earth Engine to map and monitor changes in the forests of Calabria (Italy) over the past two decades. Using satellite-sourced Corine land cover datasets and the InVEST model, changes in Land Use Land Cover (LULC), and carbon concentrations are analyzed, providing insights into the carbon dynamics of the region. Furthermore, cellular automata and Markov chain techniques are used to simulate the future spatial and temporal dynamics of LULC. The results reveal notable fluctuations in LULC; specifically, settlement and bare land have expanded at the expense of forested and grassland areas. These land use and land cover changes significantly declined the overall carbon stocks in Calabria between 2000 and 2024, resulting in notable economic impacts. The region experienced periods of both decline and growth in carbon concentration, with overall losses resulting in economic impacts up to EUR 357.57 million and carbon losses equivalent to 6,558,069.68 Mg of CO 2 emissions during periods of decline. Conversely, during periods of carbon gain, the economic benefit reached EUR 41.26 million, with sequestered carbon equivalent to 756,919.47 Mg of CO 2 emissions. This research aims to highlight the critical role of satellite data in enhancing our understanding and development of comprehensive strategies for managing carbon stocks in terrestrial ecosystems.