Belowground Carbon Storage Research Articles

Spatial-temporal analysis of carbon benefit and land use for low-carbon development strategies in Shandong Province, China

Abstract Assessing the carbon sequestration capacity of regional ecosystems is essential for achieving carbon neutrality goals. However, existing research often fails to comprehensively evaluate the spatial-temporal dynamic changes of ecosystem carbon emissions and absorption. This study introduces a Carbon Benefit Index (CBI) to assess carbon neutrality potential and classifies Shandong's 16 cities into four regions based on their carbon storage and emissions profiles. We conducted an in-depth analysis of ecosystem carbon benefits in Shandong Province from 2000 to 2020 using the multimodel random forest ensemble method, which enhances the accuracy of carbon sink simulations across terrestrial ecosystems. Our results showed that from 2000 to 2020, Shandong’s carbon emissions increased by 1.45×10⁸ tons (a 203.8% rise), while carbon storage decreased by 3.40×10⁷ tons (a 2.05% decline). Compared to previous studies, our findings underscore the significance of both above-ground and below-ground carbon storage. Grey correlation analysis of land use, anthropogenic CO₂ emissions, and ecosystem carbon storage revealed that cultivated and forest lands were most significantly correlated with carbon storage, whereas built-up areas were most closely linked to carbon emissions. The CBI analysis and classification of the 16 cities into four categories highlights the spatial-temporal heterogeneous of the carbon efficiency, and diverse roles cities play in the province's overall carbon balance, informing city-specific, targeted carbon reduction strategies. The study emphasizes the need for spatially differentiated, comprehensive carbon accounting to improve carbon efficiency. Based on these findings, we propose tailored low-carbon improvement strategies for different regions. This research not only contributes to existing literature by incorporating below-ground carbon storage but also provides valuable insights for policy and land management, with practical implications for promoting sustainable development and advancing efforts toward carbon neutrality.

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.

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.

A new data-driven map predicts substantial undocumented peatland areas in Amazonia

Abstract Tropical peatlands are among the most carbon-dense terrestrial ecosystems yet recorded. Collectively, they comprise a large but highly uncertain reservoir of the global carbon cycle, with wide-ranging estimates of their global area (441 025–1700 000 km2) and below-ground carbon storage (105–288 Pg C). Substantial gaps remain in our understanding of peatland distribution in some key regions, including most of tropical South America. Here we compile 2413 ground reference points in and around Amazonian peatlands and use them alongside a stack of remote sensing products in a random forest model to generate the first field-data-driven model of peatland distribution across the Amazon basin. Our model predicts a total Amazonian peatland extent of 251 015 km2 (95th percentile confidence interval: 128 671–373 359), greater than that of the Congo basin, but around 30% smaller than a recent model-derived estimate of peatland area across Amazonia. The model performs relatively well against point observations but spatial gaps in the ground reference dataset mean that model uncertainty remains high, particularly in parts of Brazil and Bolivia. For example, we predict significant peatland areas in northern Peru with relatively high confidence, while peatland areas in the Rio Negro basin and adjacent south-western Orinoco basin which have previously been predicted to hold Campinarana or white sand forests, are predicted with greater uncertainty. Similarly, we predict large areas of peatlands in Bolivia, surprisingly given the strong climatic seasonality found over most of the country. Very little field data exists with which to quantitatively assess the accuracy of our map in these regions. Data gaps such as these should be a high priority for new field sampling. This new map can facilitate future research into the vulnerability of peatlands to climate change and anthropogenic impacts, which is likely to vary spatially across the Amazon basin.

The spatial distribution of tree-tree interaction effects on soil microbial biomass and respiration.

The capacity of forests to sequester carbon in both above- and belowground compartments is a crucial tool to mitigate rising atmospheric carbon concentrations. Belowground carbon storage in forests is strongly linked to soil microbial communities that are the key drivers of soil heterotrophic respiration, organic matter decomposition and thus nutrient cycling. However, the relationships between tree diversity and soil microbial properties such as biomass and respiration remain unclear with inconsistent findings among studies. It is unknown so far how the spatial configuration and soil depth affect the relationship between tree richness and microbial properties. Here, we studied the spatial distribution of soil microbial properties in the context of a tree diversity experiment by measuring soil microbial biomass and respiration in subtropical forests (BEF-China experiment). We sampled soil cores at two depths at five locations along a spatial transect between the trees in mono- and hetero-specific tree pairs of the native deciduous species Liquidambar formosana and Sapindus saponaria. Our analyses showed decreasing soil microbial biomass and respiration with increasing soil depth and distance from the tree in mono-specific tree pairs. We calculated belowground overyielding of soil microbial biomass and respiration - which is higher microbial biomass or respiration than expected from the monocultures - and analysed the distribution patterns along the transect. We found no general overyielding across all sampling positions and depths. Yet, we encountered a spatial pattern of microbial overyielding with a significant microbial overyielding close to L. formosana trees and microbial underyielding close to S. saponaria trees. We found similar spatial patterns across microbial properties and depths that only differed in the strength of their effects. Our results highlight the importance of small-scale variations of tree-tree interaction effects on soil microbial communities and functions and are calling for better integration of within-plot variability to understand biodiversity-ecosystem functioning relationships.

Early changes in carbon uptake and partitioning moderate belowground carbon storage in a perennial grain

There is increasing interest in perennial crops to build soil carbon (C), but the mechanisms underlying soil C accrual in perennial croplands remain unclear, especially over time in the first years of perennial crop growth. To address this gap, research is needed that directly tracks intra-annual C fluxes through crop-microbial-soil pools, evaluating the capacity of perennial crops to build soil C over intra-decadal time periods. We conducted a 13C isotope-tracer study to compare within-season C uptake and crop-microbial-soil C partitioning patterns between 1-year-old (IWG-1) and 2-year-old (IWG-2) stands of a novel perennial grain crop, intermediate wheatgrass (IWG; Thinopyrum intermedium (Host) Barkworth and Dewey). We compared these to a common annual grain crop, spring wheat (Triticum aestivum L.). Crop shoots, roots, soil, and soil respired-C were sampled ten times over a 90-day chase period. We also measured the incorporation of recently assimilated 13C into soil microbial biomass (13C PLFA) and functional groups over the first 7 days post-label application. Overall, IWG-1 assimilated almost 1670 mg 13C m−2 during the study period, nearly twice that of IWG-2 or wheat, but neither IWG system retained significant amounts of new C in soil. Rather, a higher proportion of assimilated new C was retained in IWG-1 in root tissues (14%) and arbuscular mycorrhizal fungi when compared to other cropping systems, while IWG-2 retained almost 50% of total assimilated C in aboveground crop tissues. We expect the shift from new C retention in belowground root-mycorrhizal networks to aboveground tissues is associated with a shift from an acquisitive to conservative growth strategy that occurs between the first and second IWG production years. The observed shift in C partitioning patterns and potential change in growth strategy limited the allocation and retention of new C in soil as IWG aged, adding valuable context to our understanding of why perennial grain crop establishment seldom leads to significant carbon gains in the first several years following establishment.

Assessing belowground carbon storage after converting a temperate permanent grassland into a bamboo (Phyllostachys) plantation

AbstractBamboo (Phyllostachys sp.) is considered a sustainable resource that can replace fossil fuel‐based products. Its additional ability to sequester organic carbon in the soil (SOC) makes it a promising nature‐based solution for combating climate change. However, bamboo's soil C storage potential may vary considerably between species or growing conditions and needs to be better quantified, especially in temperate climates where data are lacking. In the present research, the SOC dynamics of plots converted from grassland to plantations of three bamboo species (i.e. Phyllostachys nigra, Phyllostachys aurea and Phyllostachys aureosulcata), planted 12 years ago on podzol (World Reference Base classification) in the Belgian Campine region, have been studied. Soil and root samples were taken until a depth of 40 cm using a 10 cm interval. Besides, the total belowground C stability (mgCO2‐C g−1 C h−1) was assessed by measuring during 3 months the carbon dioxide (CO2) efflux relative to the belowground C stock. Based on an equivalent soil mass, only P. aureosulcata, the species with the highest culm basal area, had a significant (p < .001) SOC increase of 5.0 kg C m−2 (relative increase of +94%) as compared with grassland. Considering the sum of C stocks in the soil, roots and leaf litter, all bamboo species showed significant (p < .001) C storage, i.e. +3.6 kg C m−2 (+64%), +5.3 kg C m−2 (+94%) and +8.6 kg C m−2 (+151%) for P. nigra, P. aurea and P. aureosulcata, respectively. In addition, bamboo's relative basal CO2 efflux (0.007, 0.006 and 0.008 mgCO2‐C g−1 C h−1, respectively) was remarkably lower than in the grassland (0.012 mgCO2‐C g−1 C h−1), though it was only significant for P. aurea. This study highlights that converting temperate permanent grassland into Phyllostachys bamboo plantation can result in net and rapid organic C storage by increasing the total belowground C stability and C input. Further research regarding the net CO2 balance of bamboo‐derived products is still required to fully assess its climate change mitigation potential.

A comparison of approaches to quantify carbon for ecosystem service assessments through time

Monitoring of global climate regulation ecosystem services is needed to inform national accounts, meet emission targets, and evaluate nature-based climate solutions. As carbon monitoring is context-dependent, the most useful methodological approach will depend on the spatial extent and resolution, temporal frequency, baseline, available data, funding, and dominant drivers of change, all of which will impact results and interpretation. Here, focusing on above and belowground carbon storage and sequestration, we review four groups of methods for estimating trends in carbon over time: (1) field-based measurements, (2) land cover maps with reference carbon values by land cover type, (3) statistical and machine learning models linking field measurements to remotely sensed data, and (4) mass balance models representing key carbon pools and flows between them. We discuss strengths, limitations, and best practices for each method to assist researchers in implementing an approach or critically evaluating whether an existing carbon dataset can be used for a different project. The best methods often account for spatial variability of carbon, ecosystem interconnections, and temporal stability of carbon stocks against future environmental changes. Effective carbon monitoring can help determine optimal conservation, restoration, and/or land management interventions with win-win outcomes for both conservation and nature-based climate solutions.

Spatio-Temporal Variation and Prediction of Carbon Storage in Terrestrial Ecosystems in the Yellow River Basin

The accurate estimation of a regional ecosystem’s carbon storage and the exploration of its spatial distribution and influencing factors are of great significance for ecosystem carbon sink function enhancements and management. Using the Yellow River Basin as the study area, we assessed the changes in regional terrestrial ecosystem carbon storage through geographically weighted regression modeling based on a large number of measured sample sites, explored the main influencing factors through geographic probe analysis, and predicted the carbon sequestration potentials under different scenarios from 2030 to 2050. The results showed that (1) the total carbon storage in the Yellow River Basin in 2020 was about 8.84 × 109 t. Above-ground biological carbon storage, below-ground biological carbon storage, and soil carbon storage accounted for 6.39%, 5.07%, and 89.70% of the total ecosystem carbon storage, respectively. From 2000 to 2020, the carbon storage in the basin showed a trend in decreasing and then increasing, and the carbon storage in the west was larger than in the east and larger in the south than in the north. (2) Forest ecosystem was the main contributor to the increase in carbon storage in the Yellow River Basin. Elevation, temperature, and precipitation were the main factors influencing the spatial pattern of carbon storage. (3) The ecological conservation scenario had the best carbon gain effect among the four future development scenarios, and appropriate ecological conservation policies could be formulated based on this scenario in the future to help achieve the goals of carbon sequestration and sink increase.

Effects of Tree Competition on Biomass Allocation of Stump and Coarse Roots of Larix olgensis of Different Site Classes

Site class is a quantitative indicator used to evaluate site quality. It reflects site conditions, mainly climate, the suitability of soil for tree species and soil fertility. Despite the economic and ecological importance of tree competition and site class in sustainable forest management, there has been little research on its impact on the stump and coarse root biomass allocation within plantations. The stump and coarse roots were divided into five components ((stump disc (SD), stump knot (SK), coarse roots (>10 cm in diameter) (CR1), medium coarse roots (5–10 cm) (CR2) and fine coarse roots (2–5 cm) (CR3)), and the biomass of each component was obtained via the weighing method. It was found that the biomass of SD, CR1, CR2 and CR3 was mainly affected by competition (p ≤ 0.01). In the three site classes, the biomass of CR3 increased significantly with the increase in the competition index (CI) (p < 0.01); the biomass of CR1 decreased gradually. In site V, the biomass of SK, sapwood and heartwood increased significantly with the increase in CI. The results show that competition affects the allocation of stump and coarse root biomass mainly by changing the coarse root biomass. The development of stump knots is greatly influenced by site class. This study provides a reference for solving the competition mechanism underlying larch wood forest development, which will in turn promote more effective utilization of larch wood forests. This study also provides a scientific basis for accurately estimating the belowground biomass and carbon storage of artificial plantation forests.

Modelling the soil C impacts of cover crops in temperate regions

CONTEXTAgricultural land management decisions are based on numerous considerations. Belowground carbon (C) storage for both ecosystem health and greenhouse gas (GHG) management is a growing motivation. Observed heterogeneity in soil C storage in croplands may be driven by various environmental, climatic and management factors. Farm system models can indicate which practices will drive C storage, provided the practice is well parameterised and the land manager can provide necessary input data. OBJECTIVEWe aimed to predict soil C impacts of temperate cover cropping using simple models suitable for broad farmer use and decision support. METHODSThe dataset used was initially compiled for a meta-analysis (McClelland et al., 2021) to quantify soil C response to cover crop treatments relative to a non-cover cropped system. It contains 181 data points from 40 existing studies in temperate climates. Environmental, climatic and management indicators were regressed pairwise to predict annual soil C stock change under cover cropping relative to no cover cropping. We also included the IPCC tier 1 methodology and meta-analysis response ratios in our model comparison.The ease of reliable measurement and monitoring across the modelled indicators was also considered because the best-correlated relationships are squandered if data constraints risk decision-makers being unable to use the model. RESULTS AND CONCLUSIONSUsing an extended test dataset to consider priorities for model users, several regression models outperformed the IPCC tier 1 methodology. In particular, two regression models reliably predicted negative changes in soil C, which IPCC and meta-analysis factor approaches could not. A single variable regression model based on cover crop biomass (dry matter) production was the best combination of statistical power, biological relevance and parsimony. In temperate climates, we predicted an increase in soil C stocks as long as cover crop biomass production exceeded 1.3 Mg ha−1 yr−1. SIGNIFICANCEOur final model can be applied with estimated user input data, and avoids the need for baseline soil C as an input; this makes it relatively accessible for farmers. Parsimonious models for soil C change under land management practices can be effective and are an opportunity to increase access to soil C management information for farmers.

Beech Leaf Disease Severity Affects Ectomycorrhizal Colonization and Fungal Taxa Composition.

Beech leaf disease (BLD) is an emerging forest infestation affecting beech trees (Fagus spp.) in the midwestern and northeastern United States and southeastern Canada. BLD is attributed to the newly recognized nematode Litylenchus crenatae subsp. mccannii. First described in Lake County, Ohio, BLD leads to the disfigurement of leaves, canopy loss, and eventual tree mortality. Canopy loss limits photosynthetic capacity, likely impacting tree allocation to belowground carbon storage. Ectomycorrhizal fungi are root symbionts, which rely on the photosynthesis of autotrophs for nutrition and growth. Because BLD limits tree photosynthetic capacity, ECM fungi may receive less carbohydrates when associating with severely affected trees compared with trees without BLD symptoms. We sampled root fragments from cultivated F. grandifolia sourced from two provenances (Michigan and Maine) at two timepoints (fall 2020 and spring 2021) to test whether BLD symptom severity alters colonization by ectomycorrhizal fungi and fungal community composition. The studied trees are part of a long-term beech bark disease resistance plantation at the Holden Arboretum. We sampled from replicates across three levels of BLD symptom severity and compared fungal colonization via visual scoring of ectomycorrhizal root tip abundance. Effects of BLD on fungal communities were determined through high-throughput sequencing. We found that ectomycorrhizal root tip abundance was significantly reduced on the roots of individuals of the poor canopy condition resulting from BLD, but only in the fall 2020 collection. We found significantly more ectomycorrhizal root tips from root fragments collected in fall 2020 than in spring 2021, suggesting a seasonal effect. Community composition of ectomycorrhizal fungi was not impacted by tree condition but did vary between provenances. We found significant species level responses of ectomycorrhizal fungi between levels of both provenance and tree condition. Of the taxa analyzed, two zOTUs had significantly lower abundance in high-symptomatology trees compared with low-symptomatology trees. These results provide the first indication of a belowground effect of BLD on ectomycorrhizal fungi and contribute further evidence to the role of these root symbionts in studies of tree disease and forest pathology.

Root traits in response to frequent fires: Implications for belowground carbon dynamics in fire-prone savannas.

Predicting how belowground carbon storage reflects changes in aboveground vegetation biomass is an unresolved challenge in most ecosystems. This is especially true for fire-prone savannas, where frequent fires shape the fraction of carbon allocated to root traits for post-fire vegetation recovery. Here I review evidence on how root traits may respond to frequent fires and propose to leverage root traits to infer belowground carbon dynamics in fire-prone savannas. Evidently, we still lack an understanding of trade-offs in root acquisitive vs. conservative traits in response to frequent fires, nor have we determined which root traits are functionally important to mediate belowground carbon dynamics in a frequently burned environment. Focusing research efforts along these topics should improve our understanding of savanna carbon cycling under future changes in fire regimes.

Assessment of aboveground, belowground, and total biomass carbon storage potential of Bambusa vulgaris in a tropical moist forest in Ghana, West Africa

This article reports on a study conducted to assess the carbon storage potential of Bambusa vulgaris, the predominant bamboo species in Ghana. The study aimed to fill a knowledge gap on the potential of bamboo to sequester carbon for climate change mitigation in Ghana. Unlike previous studies that only focused on aboveground biomass, this study assessed belowground, litter, and coarse wood carbon pools. Allometric parameters and models were used to measure the aboveground biomass, while other carbon pools were directly measured. The results showed that the aboveground biomass of B. vulgaris had a carbon stock of 42.85 ± 9.32 Mg C ha−1, which was 73% of the total biomass carbon stock. The carbon stocks of belowground, coarse wood and litter were 8.57, 3.02, and 4.25 Mg C ha−1, respectively. The study also found that B. vulgaris had a high carbon dioxide sequestration potential of 215.39 Mg CO2e ha−1 compared to 147–275 Mg CO2e ha−1 for trees in general. The findings suggest that B. vulgaris could contribute to Ghana's transition to a low-carbon economy through carbon stock monitoring, reporting, and policy development to minimise the impact of climate change. Moreover, the inclusion of relevant carbon pools, including coarse wood and litter, in forest carbon estimates should be encouraged to provide a comprehensive understanding of the plant carbon cycle.

Modeling present and future ecosystem services and environmental justice within an urban-coastal watershed

Globally, urban-coastal areas are expected to experience substantial landscape shifts as a result of climate change induced sea level rise. Such changes will impact valuable ecosystem services. We employed sea level rise projections and land cover change mapping to develop a model which quantified present-day carbon sequestration, aboveground carbon storage, and belowground carbon storage ecosystem services and predicted the impact of sea level rise and accretion through 2100 on future ecosystem services in the urban-coastal Jamaica Bay, New York (USA) watershed. Our model predicted that future carbon sequestration, aboveground carbon storage, and belowground carbon storage potential in our watershed will be significantly impacted in wetlands and natural coastal-fringe habitat and have losses up to 0.16%, 15%, and 51%, respectively. We paired our present-day ecosystem services model results with data on socio-economic need (access to open space and poverty level of each census tract in the watershed) and used multivariate clustering analysis to identify clusters in which planning and restoration may help to address issues of ecological conservation and environmental justice. Our work addresses the need for better understanding of urban-coastal ecosystem service flows and the potential impact of future landscape change on these services. Our results provide support to increase coastal resilience through informed design, planning, and management of these ecologically and socially significant landscapes.

Can seasonal fire management reduce the risk of carbon loss from wildfires in a protected Guinea savanna?

AbstractFire is fundamental to the functioning of tropical savannas and routinely used as a management tool. Shifting prescribed burning from later to earlier in the growing season has the potential to reduce greenhouse gas emissions. However, large uncertainties surround the impact of seasonal burning on longer term plant and soil carbon sequestration. In this study, we quantify ecosystem carbon storage across burn seasons and histories in a wet‐to‐mesic Guinea tropical savanna in Mole National Park, Ghana. Aboveground (plant and litter) and belowground (soil plus roots) carbon storage was quantified across four burning seasons and histories: recent (<3 years) early‐season burns, recent late‐season burns, old (>4 years) late‐season burns, and long‐unburned (>15 years) sites. We found that recent late‐season burns significantly lowered belowground carbon storage to a depth of 17 cm compared with all other burn seasons and histories. Belowground carbon was 1.2 kg C m−2, or 27% lower, for recent late‐season burns compared with prescribed early‐season burns. However, in older late‐season burns sites, belowground carbon “recovered” after 4–13 burn years to comparable storage as long‐unburned and early‐season burn sites. For most aboveground carbon pools, there was no significant difference in carbon storage across burn seasons and histories, except higher aboveground tree carbon in long‐unburned sites. We suggest that observed changes in belowground carbon are likely due to the turnover and production of root carbon. Prescribed early‐season burning is promoted to reduce greenhouse gas emissions and our findings affirm that early‐season burning has limited impact on plant and soil carbon stocks compared with long‐unburned sites. While early‐season burning regimes will have some patches that become late‐season wildfires, our results suggest on balance early‐season burning regimes are a low‐risk land management practice in reducing plant and soil carbon storage losses and sustaining a patch‐mosaicked landscape with multiple other ecosystem service benefits for savannas.

Carbon Sequestration by Native Tree Species around the Industrial Areas of Southern Punjab, Pakistan

Industries have been a major culprit in increasing carbonaceous emissions and major contributors to global warming over the past decades. Factories in the urban periphery tend to warm cities more as compared with rural surroundings. Recently, nature-based solutions have been promoted to provide solutions related to climate adaptations and mitigation issues and challenges. Among these solutions, urban trees have proven to be an effective solution to remove air pollutants and mitigate air pollution specifically caused by carbon emissions. This work was designed to assess the role of tree species in mitigating air emissions of carbon around the vicinity of various industrial sites. For this purpose, three different industrial sites (weaving, brick kiln, and cosmetic) were selected to collect data. Selected industrial sites were divided into two areas, i.e., (a) area inside the industry and (b) area outside the industry. The samples were collected from 100 square meters inside the industries and 100 square meters outside the industries. Five different trees species comprised of four replications were selected for sampling. About twenty trees species from inside and outside of the industries were measured, making it 120 trees from all three selected industries for estimating aboveground and belowground biomass, showing their carbon estimation. The results showed that Moringa oleifera depicted overall higher total biomass from both inside (2.58, 0.56, and 4.57 Mg ha−1) and outside sites from all three selected industries. In terms of total carbon stock and carbon sequestration inside the industry sites, Syzygium cumini had the most dominant values in the weaving industry (2.82 and 10.32 Mg ha−1) and brick kiln (3.78 and 13.5 Mg ha−1), while in the cosmetic industry sites, Eucalyptus camaldulensis depicted higher carbon, stock, and sequestration values (7.83 and 28.70 Mg ha−1). In comparison, the sites outside the industries’ vicinity depicted overall lower carbon, stock, and sequestration values. The most dominant tree inside came out to be Dalbergia sisso (0.97 and 3.54 Mg ha−1) in the weaving industry sites, having higher values of carbon stock and carbon sequestration. Moringa oliefra (1.26 and 4.63) depicted dominant values in brick kiln sites, while in the cosmetic industry, Vachellia nilotica (2.51 and 9.19 Mg ha−1) displayed maximum values as compared with other species. The findings regarding belowground biomass and carbon storage indicate that the amount of soil carbon decreased with the increase in depth; higher soil carbon stock values were depicted at a 0–20 cm depth inside and outside the industries. The study concludes that forest tree species present inside and outside the vicinity of various industries have strong potential in mitigating air emissions.

Influence of land cover types on soil quality and carbon storage in Moramo Education Estate, Southeast Sulawesi, Indonesia

Abstract. Alam S, Ginting S, Hemon MT, Leomo S, Kilowasid LMH, Karim J, Nugroho Y, Matatula J, Wirabuana PYAP. 2022. Influence of land cover types on soil quality and carbon storage in Moramo Education Estate, Southeast Sulawesi, Indonesia. Biodiversitas 23: 4371-4376. This study investigated the influence of different land cover types on soil quality and carbon storage in Moramo Education Estate (MEE). Information is required as fundamental consideration to determine the best landscape management strategies for supporting soil conservation and climate change mitigation. Data were collected from three types of land cover generally found in this area, including forests, shrubs, and savannas. Three permanent sampling plots were randomly placed in every land cover as replicates with a size of 20 m × 20 m. Six parameters were used to describe the soil quality, i.e., soil acidity, soil organic carbon, total nitrogen, available phosphorus, exchangeable potassium, and cation exchange capacity. The above and belowground carbon storage from every plot was quantified. The soil quality and carbon storage among land cover types were compared using analysis of variance and Tukey’s honestly significant difference. Pearson’s correlation analysis was also applied to evaluate the relationship between soil quality and carbon storage. The results show that soil quality significantly differed in the exchangeable potassium and cation exchange capacity. A similar trend was also demonstrated in aboveground carbon storage. The highest average carbon storage was recorded in forests (150.50 ± 27.79 t ha?1), followed by shrubs (52.50 ± 15.02 t ha?1) and savannas (45.97 ± 4.42 t ha?1). The total carbon storage at different land covers was significantly correlated to soil acidity, available phosphorus, and cation exchange capacity. Carbon storage improved with the increased available phosphorus and cation exchange capacity. In contrast, carbon storage was negatively correlated with soil acidity. Overall, the land cover types significantly influenced soil quality and carbon storage in MEE.

Potential Factors Canceling Interannual Cycles of Shoot Production in a Moso Bamboo (Phyllostachys pubescens) Stand

Moso bamboo (Phyllostachys pubescens) forests are utilized for food, building materials, and carbon fixation in East Asia. Hence, understanding the factors that influence productivity is important. Long-term records of managed Moso bamboo forests have provided evidence for 2-year cycles of new shoot production. A widely accepted explanatory hypothesis is that the 2-year leaf life span and unequal proportions of newer and older leaves in bamboo stands are the cause of the 2-year shoot production cycle. However, 2-year cycles are not observed in all circumstances. If the 2-year leaf life span causes the biennial production cycle, why are the 2-year cycles of new shoot production not observed in some periods? By constructing an age-structured population growth model that considered the Moso bamboo leaf life span, this study aimed to clarify the possible mechanisms that could suppress the 2-year cycle of new shoot production. The simulation demonstrated that the 2-year cycle may readily disappear because of the contribution of considerable carbohydrates originating from photosynthesis in old leaves and in new leaves of zero-year-old culms, and from belowground carbon storage in roots and rhizomes. The results suggested that the contribution of photosynthesis in old leaves and in new leaves of zero-year-old culms may be overlooked at the population scale, and that belowground carbon storage in Moso bamboo rhizome systems might act as buffer to stabilize the year-to-year variations in new shoot production.

Response of microorganisms to a 5-year large-scale nitrogen loading in immature volcanic ash soil in an oak-dominated forest

Increasing atmospheric nitrogen (N) deposition in terrestrial ecosystems influences aboveground and belowground carbon (C) storage. In belowground systems, N fertilization in field experiments has often been reported to suppress soil microbial biomass and mineralization; however, the effects on soil microorganisms are not always consistent. Here, we investigated N load response of microorganisms after 2 and 5 years in organic layers and mineral soils in a temperate forest dominated by Quercus crispula using large-scale field N fertilization (9 ha, 100 kg N ha−1 year−1 for 5 years, urea) on immature volcanic ash soil (with a high buffering capacity). In the organic layers, N loading increased the total C concentration and KCl-extractable organic C content. Additionally, the amount of extractable organic C in the soil did not change after one month of laboratory incubation, possibly due to the low microbial use. These results likely indicate the accumulation of recalcitrant C (possibly due to decreased oxidase activity). Although the fungal-to-bacterial composition ratio did not change, the bacterial biomass increased by 18% and 26% in the second and fifth years, respectively, in the N-fertilized plots. Furthermore, the abundance of ammonia-oxidizing bacterial AmoA increased, which was correlated with potential nitrification. These changes may result from plant litter N content and litter quantity and subsequently change in soil environments, especially with increased soil N availability. In the mineral soils, N loading changed soil environments to a lesser extent than the organic layers; however, the fungal biomass decreased by 42% and 44% in the second and fifth years, respectively. This could be linked to a decrease in resource investment into symbionts (ectomycorrhizal fungi derived from oak roots) by underground plants. These findings suggest that in an oak-dominated forest, the influence of N loading between the organic layers and mineral soils on microorganisms varies, enhancing our understanding of belowground C dynamics.