Forest Carbon Balance Research Articles

Defaunation impacts on the carbon balance of tropical forests.

The urgent need to mitigate and adapt to climate change necessitates a comprehensive understanding of carbon cycling dynamics. Traditionally, global carbon cycle models have focused on vegetation, but recent research suggests that animals can play a significant role in carbon dynamics under some circumstances, potentially enhancing the effectiveness of nature-based solutions to mitigate climate change. However, links between animals, plants, and carbon remain unclear. We explored the complex interactions between defaunation and ecosystem carbon in Earth's most biodiverse and carbon-rich biome, tropical rainforests. Defaunation can change patterns of seed dispersal, granivory, and herbivory in ways that alter tree species composition and, therefore, forest carbon above- and belowground. Most studies we reviewed show that defaunation reduces carbon storage 0-26% in the Neo- and Afrotropics, primarily via population declines in large-seeded, animal-dispersed trees. However, Asian forests are not predicted to experience changes because their high-carbon trees are wind dispersed. Extrapolating these local effects to entire ecosystems implies losses of ∼1.6 Pg CO2 equivalent across the Brazilian Atlantic Forest and 4-9.2 Pg across the Amazon over 100 years and of ∼14.7-26.3 Pg across the Congo basin over 250 years. In addition to being hard to quantify with precision, the effects of defaunation on ecosystem carbon are highly context dependent; outcomes varied based on the balance between antagonist and mutualist species interactions, abiotic conditions, human pressure, and numerous other factors. A combination of experiments, large-scale comparative studies, and mechanistic models could help disentangle the effects of defaunation from other anthropogenic forces in the face of the incredible complexity of tropical forest systems. Overall, our synthesis emphasizes the importance of-and inconsistent results when-integrating animal dynamics into carbon cycle models, which is crucial for developing climate change mitigation strategies and effective policies.

Bark Thickness of Forest-forming Species: Modeling and Comparative Analysis

The need to assess the carbon-depositing capacity of a stand, an individual tree and its components is constantly emphasized in the world literature, since mitigation of the effects of climate change has become the highest priority. Since the carbon concentration differs in different tree species and in different components of the tree, including in the stem wood and bark, it is necessary to develop species-specific mathematical models for estimating the proportion of bark in trunks to improve the accuracy of knowledge about the carbon balance of forests. The relationship of bark thickness with both age and diameter of the tree trunk is known, but with respect to its regional variability, the data are contradictory. According to the actual data from 1100 model trees of five forest-forming species, allometric models of a mixed type have been developed. They include as independent variables the age and diameter of the trunk, as well as a binary variable characterising the influence of the growing area itself on the bark thickness of Scots pine, silver birch and Siberian larch, as well as the difference in the thickness of the bark between Siberian spruce and Siberian fir in case of joint growth in mature stands of the taiga zone. At a statistically significant level, it was found that the bark thickness of Scots pine and silver birch trees in the steppe zone is significantly greater compared to the taiga zone, and the bark thickness of Siberian larch trees in the forest-tundra is significantly greater compared to the steppe zone. The bark thickness of Siberian spruce trees is significantly less than that of Siberian fir trees. The ranking of species by bark thickness is included in the paper.

Construction of Remote Sensing Quantitative Model for Biomass of Deciduous Broad-Leaved Forest in Mazongling Nature Reserve Based on Machine Learning

As an important forest type, deciduous broad-leaved forest is crucial for estimating forest carbon sequestration capacity and evaluating forest carbon balance. This study focuses on the natural deciduous broad-leaved forest of Mazongling Nature Reserve in Jinzhai County of China. WorldView-2 images were selected as data source. 36 candidate factors including vegetation indices, texture features, and topographic factors were used for modelling. Three machine learning algorithms (i.e., random forest, k-nearest neighbor, and artificial neural network) were used to establish the optimal quantitative retrieval model for natural deciduous broad-leaved biomass. Results showed that the ANN model was the best predictor with R2 = 0.69 and RMSE = 31.53 (Mg·ha−1). Combining the ANN model with the complete spatial coverage of remote sensing data, we developed a distribution map of natural deciduous broad-leaved biomass in the Mazongling forest farm. The estimated average biomass of the study area was 90.34 ± 47.96 Mg·ha−1. In addition, the influence of light saturation on model accuracy is also discussed. This study confirms that remote sensing data in temporal and spatial space can improve the model estimation accuracy.

The impact of spongy moth (Lymantria dispar dispar) defoliation on carbon balance of a temperate deciduous forest in North America

Temperate deciduous forests are an important contributor to the global carbon (C) sink. However, changes in environmental conditions and natural disturbances such as insect infestations can impact carbon sequestration capabilities of these forests. While, insect infestations are expected to increase in warmer future climates, there is a lack of knowledge on the quantitative impact of these natural disturbances on the carbon balance of temperate deciduous forests. In 2021, a record-breaking defoliation, caused by the spongy moth (Lymantria dispar dispar (LDD), formerly knows as the gypsy moth) occurred in eastern North America. In this study, we assess the impact of this spongy moth defoliation on carbon uptake in a mature oak-dominated temperate forest in the Great Lakes region in Canada, using eddy covariance flux data from 2012 to 2022. Study results showed that the forest was a large C sink with mean annual net ecosystem productivity (NEP) of 207 ± 77 g C m–2 yr−1 from 2012 to 2022, excluding 2021, which experienced the infestation. Over this period mean annual gross ecosystem productivity (GEP) was 1,398 ± 137 g C m–2 yr−1, while ecosystem respiration (RE) was 1,209 ± 139 g C m–2 yr−1. However, in 2021 due to defoliation in the early growing season, annual GEP of the forest declined to 959 g C m–2 yr−1, while annual RE increased to 1,345 g C m–2 yr−1 causing the forest to become a large source of C with annual NEP of -351 g C m–2 yr−1. The forest showed a rapid recovery from this major disturbance event, with annual GEP, RE, and NEP values of 1,671, 1,287, and 298 g C m–2 yr−1, respectively in 2022 indicating that the forest was once again a large C sink. This study demonstrates that major transient natural disturbances under changing climate can have a significant impact on forest C dynamics. The extent to which North American temperate forests will remain a major C sink will depend on the severity and intensity of these disturbance events and the rate of recovery of forests following disturbances. Lay abstractTemperate deciduous forests play an important role in carbon sequestration from the atmosphere. However, the impact of climate change, extreme weather, and disturbance events can alter the extent to which these forests sequester carbon, in some cases shifting their role from being a carbon sink to becoming a carbon source to the atmosphere. In 2021, a spongy moth infestation severely defoliated a mature oak-dominated temperate forest north of Lake Erie, Ontario, Canada, turning the forest from a 10-year mean carbon sink of about 2 tons of carbon per hectare to a carbon source of about 3.5 tons of carbon per hectare. Our analysis indicates that meteorological conditions during the early growing season might have influenced the severity of this infestation. Specifically, the prevalence of dry and warm weather conditions enabled the moth to survive and thrive longer. This study shows the significant influence of natural disturbances on forest carbon dynamics as temperatures continue to rise due to climate change. The future role forests play in carbon sequestration will be determined by the severity of disturbance events and the effectiveness of forests to recover in the aftermath of these events.

Partitioning seasonal stem carbon dioxide efflux into stem respiration, bark photosynthesis, and transport-related flux in Scots pine.

Stem CO2 efflux is an important component of the carbon balance in forests. The efflux is considered to principally reflect the net result of two dominating and opposing processes: stem respiration and stem photosynthesis. In addition, transport of CO2 in xylem sap is thought to play an appreciable role in affecting the net flux. This work presents an approach to partition stem CO2 efflux among these processes using sap-flux data and CO2-exchange measurements from dark and transparent chambers placed on mature Scots pine (Pinus sylvestris) trees. Seasonal changes and monthly parameters describing the studied processes were determined. Respiration contributed most to stem net CO2 flux, reaching up to 79% (considering the sum of the absolute values of stem respiration, stem photosynthesis, and flux from CO2 transported in xylem sap to be 100%) in June, when stem growth was greatest. The contribution of photosynthesis accounted for up to 13% of the stem net CO2 flux, increasing over the monitoring period. CO2 transported axially with sap flow decreased towards the end of the growing season. At a reference temperature, respiration decreased starting around midsummer, while its temperature sensitivity increased during the summer. A decline was observed for photosynthetic quantum yield around midsummer together with a decrease in light-saturation point. The proposed approach facilitates modeling net stem CO2 flux at a range of time scales.

Impacts of wildfires on boreal forest ecosystem carbon dynamics from 1986 to 2020

Wildfires significantly change boreal forest ecosystem carbon balance through both direct combustion and post-fire carbon dynamics. Affected vegetation influences soil thermal regime and carbon cycling by impacting the surface energy balance of boreal forests. This study uses a process-based biogeochemistry model to quantify carbon budget of North American boreal forests during 1986–2020 based on satellite-derived burn severity data. During the study period, burn severity generally increases. Fires remove ecosystem carbon of 2.4 Pg C and reduce net ecosystem production (NEP) from 32.6 to 0.8 Tg C yr−1, making the forest ecosystems lose 3.5 Pg C, shifting a carbon sink to a source. The canopy’s cooling effect leads to lower soil temperature and lower net primary production due to lower nitrogen mineralization and uptake. Post-fire NEP decreases from 1.6 to 0.8 Tg C yr−1. This reduction accounts for 50% of the simulated NEP when the effects of fire-affected canopy are not considered. Our study highlights the importance of wildfires and their induced-canopy changes in soil thermal and ecosystem carbon dynamics of boreal forests.

Temperature variations impacting leaf senescence initiation pathways alter leaf fall timing patterns in northern deciduous forests

Simulating the timing of leaf fall in large scale is crucial for accurate estimation of ecosystem carbon sequestration. However, the limited understanding of leaf senescence mechanisms often impedes the accuracy of simulation and prediction. In this study, we employed the advanced process-based models to fit remote sensing-derived end dates of the growing season (EOS) across deciduous broadleaf forests in the Northern Hemisphere, and revealed the spatial pattern associated with two leaf senescence pathways (i.e., either photoperiod- or temperature- initiated leaf senescence) and their potential effects on EOS prediction. The results show that the pixel-specific optimum models effectively fitted all EOS time series. Leaf senescence in 67.6 % and 32.4 % of pixels was initiated by shortening daylength and declining temperature, respectively. Shortening daylength triggered leaf senescence occurs mainly in areas with shorter summer daylength and/or warmer autumns, whereas declining temperature induced leaf senescence appears primarily in areas with longer summer daylength and/or colder autumns. The strong dependence of leaf senescence initiation cues on local temperature conditions implies that the ongoing increase in autumn temperature has the potential to alter the leaf senescence initiation, shifting from temperature cues to photoperiod signals. This shift would occur in 26.2–49.6 % of the areas where leaf senescence is initiated by declining temperature under RCP 4.5 and 8.5 scenarios, while forest areas where leaf senescence is induced by shortening daylength may expand northward. The overall delaying of the currently predicted EOS would therefore slow down by 4.5–10.3 % under the two warming scenarios. This implies that the adaptive nature of plants will reduce the overestimation of changes in carbon exchange capacity between ecosystems and atmosphere. Our study offers novel insights into understanding the mechanism of leaf senescence and improving the estimation of autumn phenology and ecosystem carbon balance in the deciduous broadleaf forests.

Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

LIFE CYCLE ASSESSMENT OF LOW CARBON SANDWICH PANELS WITH WOOD FIBRE CORE

AbstractWood is a popular material in civil engineering due to its renewability and ability to store carbon during the growing in forests. However, the LCA of wood‐based materials is currently based on the balance of carbon without considering the dynamic changes of forests over time and the effects of climate change, like wildfires, on this balance. Hence, this paper is the first step towards the development of a dynamic LCA of forests considering climate change effects on the carbon balance of forests. The paper focusses on the life cycle analysis of sandwich panels with a core made of wood fibers, based on current databases, and aims to illustrate the importance of biogenic carbon in the whole life cycle of such panels, particularly when compared with other traditional panels.

Climate change determines the sign of productivity trends in US forests

Forests are integral to the global land carbon sink, which has sequestered ~30% of anthropogenic carbon emissions over recent decades. The persistence of this sink depends on the balance of positive drivers that increase ecosystem carbon storage-e.g., CO2 fertilization-and negative drivers that decrease it-e.g., intensifying disturbances. The net response of forest productivity to these drivers is uncertain due to the challenge of separating their effects from background disturbance-regrowth dynamics. We fit non-linear models to US forest inventory data (113,806 plot remeasurements in non-plantation forests from ~1999 to 2020) to quantify productivity trends while accounting for stand age, tree mortality, and harvest. Productivity trends were generally positive in the eastern United States, where climate change has been mild, and negative in the western United States, where climate change has been more severe. Productivity declines in the western United States cannot be explained by increased mortality or harvest; these declines likely reflect adverse climate-change impacts on tree growth. In the eastern United States, where data were available to partition biomass change into age-dependent and age-independent components, forest maturation and increasing productivity (likely due, at least in part, to CO2 fertilization) contributed roughly equally to biomass carbon sinks. Thus, adverse effects of climate change appear to overwhelm any positive drivers in the water-limited forests of the western United States, whereas forest maturation and positive responses to age-independent drivers contribute to eastern US carbon sinks. The future land carbon balance of forests will likely depend on the geographic extent of drought and heat stress.

Research on Estimating and Evaluating Subtropical Forest Carbon Stocks by Combining Multi-Payload High-Resolution Satellite Data

Forest carbon stock is an important indicator reflecting the structure of forest ecosystems and forest quality, and an important parameter for evaluating the carbon sequestration capacity and carbon balance of forests. It is of great significance to study forest carbon stock in the context of current global climate change. To explore the application ability of multi-loaded, high-resolution satellite data in the estimation of subtropical forest carbon stock, this paper takes Huangfu Mountain National Forest Park in Chuzhou City as the study area, extracts remote sensing features such as spectral features, texture features, backscattering coefficient, and other remote sensing features based on multi-loaded, high-resolution satellite data, and carries out correlation analyses with the carbon stock of different species of trees and different age groups of forests. Regression models for different tree species were established for different data sources, and the optimal modeling factors for multi-species were determined. Then, three algorithms, namely, multiple stepwise regression, random forest, and gradient-enhanced decision tree, were used to estimate carbon stocks of multi-species, and the predictive ability of different estimation models on carbon stocks was analyzed using the coefficient of determination (R2) and the root mean square error (RMSE) as indexes. The following conclusions were drawn: for the feature factors, the texture features of the GF-2 image, the new red edge index of the GF-6 image, the radar intensity coefficient sigma, and radar brightness coefficient beta of the GF-3 image have the best correlation with the carbon stock; for the algorithms, the random forest and gradient-boosting decision tree have the better effect of fitting and predicting the carbon stock of multi-tree species, among which gradient-boosting decision tree has the best effect, with an R2 of 0.902 and an RMSE of 10.261 t/ha. In summary, the combination of GF-2, GF-3, and GF-6 satellite data and gradient-boosting decision tree obtains the most accurate estimation results when estimating forest carbon stocks of complex tree species; multi-load, high-resolution satellite data can be used in the inversion of subtropical forest parameters to estimate the carbon stocks of subtropical forests. The multi-loaded, high-resolution satellite data have great potential for application in the field of subtropical forest parameter inversion.

Aboveground Forest Biomass Estimation Using Tent Mapping Atom Search Optimized Backpropagation Neural Network with Landsat 8 and Sentinel-1A Data

Accurate forest biomass estimation serves as the foundation of forest management and holds critical significance for a comprehensive understanding of forest carbon storage and balance. This study aimed to integrate Landsat 8 OLI and Sentinel-1A SAR satellite image data and selected a portion of the Shanxia Experimental Forest in Jiangxi Province as the study area to establish a biomass estimation model by screening influencing factors. Firstly, we extracted spectral information, vegetation indices, principal component features, and texture features within 3 × 3-pixel neighborhoods from Landsat 8 OLI. Moreover, we incorporated Sentinel-1’s VV (vertical transmit–vertical receive) and VH (vertical transmit–horizontal receive) polarizations. We proposed an ensemble AGB (aboveground biomass) model based on a neural network. In addition to the neural network model, namely the tent mapping atom search optimized BP neural network (Tent_ASO_BP) model, partial least squares regression (PLSR), support vector machine (SVR), and random forest (RF) regression prediction techniques were also employed to establish the relationship between multisource remote sensing data and forest biomass. Optical variables (Landsat 8 OLI), SAR variables (Sentinel-1A), and their combinations were input into the four prediction models. The results indicate that Tent_ ASO_ BP model can better estimate forest biomass. Compared to pure optical or single microwave data, the Tent_ASO_BP model with the optimal combination of optical and microwave input features achieved the highest accuracy. Its R2 was 0.74, root mean square error (RMSE) was 11.54 Mg/ha, and mean absolute error (MAE) was 9.06 Mg/ha. Following this, the RF model (R2 = 0.54, RMSE = 21.33 Mg/ha, MAE = 17.35 Mg/ha), SVR (R2 = 0.52, RMSE = 17.66 Mg/ha, MAE = 15.11 Mg/ha), and PLSR (R2 = 0.50, RMSE = 16.52 Mg/ha, MAE = 12.15 Mg/ha) models were employed. In conclusion, the BP neural network model improved by tent mapping atom search optimization algorithm significantly enhanced the accuracy of AGB estimation in biomass studies. This will provide a new avenue for large-scale forest resource surveys.

Life-cycle impact assessment of hardwood forest resources in the eastern United States

To explore the carbon sequestration potential of hardwood forests in the eastern United States, the forest vegetation simulator (FVS) and life cycle assessment (LCA) were integrated to analyze the forest carbon dynamics for the four subregions of the eastern United States: northeast (NE), mid-Atlantic (MA), southeast (SE), and north central (NC). This study quantitatively assessed current forest management practices for timber production and their associated life-cycle environmental impacts. The system boundary was selected to be consistent with the A1 module (extraction and upstream production) required by an Environmental Product Declaration (EPD) for wood products. The results indicate that uneven-aged (UA) forest management yields higher carbon stocks and growth than even-aged (EA) management across all subregions. In contrast, clearcutting under EA management results in higher carbon removal. It was found that fuel consumption-related greenhouse gas (GHG) emissions for manual and mechanized harvesting systems for both management types ranged between 9.13 and 12.15 kg of CO2 equivalent per cubic meter (kg CO2e/m3), with an average of 11 kg CO2e/m3 of hardwood timber harvested across all subregions. It is estimated that 63–187 megajoules (MJ) of energy is needed to produce 1 m3 of hardwood sawlogs. The extraction and loading processes contributed more to the total GHG emissions than the felling and processing within the system boundary. The study concludes that UA management led to higher forest carbon and net carbon balance (excluding carbon stock) compared to EA management in the eastern U.S. hardwood forests. Forest management strategies should be determined based on the ecological goal of increasing forest carbon stock and the economic goal of maximizing revenue from the timber market. The findings of this study have implications for policymakers and forest managers in mitigating climate change and carbon sequestration through sustainable forest management for timber production.

Chronic warming and dry soils limit carbon uptake and growth despite a longer growing season in beech and oak.

Progressively warmer and drier climatic conditions impact tree phenology and carbon cycling with large consequences for forest carbon balance. However, it remains unclear how individual impacts of warming and drier soils differ from their combined effects and how species interactions modulate tree responses. Using mesocosms, we assessed the multiyear impact of continuous air warming and lower soil moisture alone or in combination on phenology, leaf-level photosynthesis, nonstructural carbohydrate concentrations, and aboveground growth of young European beech (Fagus sylvatica L.) and Downy oak (Quercus pubescens Willd.) trees. We further tested how species interactions (in monocultures and in mixtures) modulated these effects. Warming prolonged the growing season of both species but reduced growth in oak. In contrast, lower moisture did not impact phenology but reduced carbon assimilation and growth in both species. Combined impacts of warming and drier soils did not differ from their single effects. Under warmer and drier conditions, performances of both species were enhanced in mixtures compared to monocultures. Our work revealed that higher temperature and lower soil moisture have contrasting impacts on phenology vs. leaf-level assimilation and growth, with the former being driven by temperature and the latter by moisture. Furthermore, we showed a compensation in the negative impacts of chronic heat and drought by tree species interactions.

A dataset of monthly and yearly litterfall production in different succession stages of evergreen broad-leaved forests in Tiantong (2008 – 2018)

Litter production is an important part of the matter cycle of forest ecosystems, which plays an important role in maintaining forest soil fertility and promoting ecosystem nutrient cycling. Additionally, it serves as an important indicator for the quantification of ecosystem function such as forest productivity and carbon balance. The data in the dataset are from two observation plots in Zhejiang Tiantong National Forest Ecosystem National Observation and Research Station. The community succession types in the above two pilots belong to the middle-late stage and mature stage of evergreen broad-leaved forest succession, respectively. The projected area of each plot is 50 m × 50 m. There are 27 litter traps of 0.5 m2 in each plot. We collected and counted the data in terms of litter components, i.e. branches, leaves, flowers (fruits), bark and debris). The dataset includes the monthly and annual production of each component and the total amount of litter in the two plots from 2008 to 2018. The data have been checked against the original paper records, as well as the dynamic trend of data in the same period, so as to ensure data quality control and accuracy assessment. This dataset can provide basic information for understanding the changes of ecosystem structure and function during the succession process of evergreen broad-leaved forests. Moreover, it can also provide data support for studying the impact of regional climate change on the forest ecosystem and the assessment of carbon balance in the zone of evergreen broad-leaved forests.

Quantification of forest carbon flux and stock uncertainties under climate change and their use in regionally explicit decision making: Case study in Finland

Uncertainties are essential, yet often neglected, information for evaluating the reliability in forest carbon balance projections used in national and regional policy planning. We analysed uncertainties in the forest net biome exchange (NBE) and carbon stocks under multiple management and climate scenarios with a process-based ecosystem model. Sampled forest initial state values, model parameters, harvest levels and global climate models (GCMs) served as inputs in Monte Carlo simulations, which covered forests of the 18 regions of mainland Finland over the period 2015–2050. Under individual scenarios, the results revealed time- and region-dependent variability in the magnitude of uncertainty and mean values of the NBE projections. The main sources of uncertainty varied with time, by region and by the amount of harvested wood. Combinations of uncertainties in the representative concentration pathways scenarios, GCMs, forest initial values and model parameters were the main sources of uncertainty at the beginning, while the harvest scenarios dominated by the end of the simulation period, combined with GCMs and climate scenarios especially in the north. Our regionally explicit uncertainty analysis was found a useful approach to reveal the variability in the regional potentials to reach a policy related, future target level of NBE, which is important information when planning realistic and regionally fair national policy actions.

Five-year measurement data along a 1200 m elevational gradient reveals that global warming increases soil respiration

Soil respiration is a major pathway for CO2 emissions from ecosystems. Owing to its temperature dependency, the soil respiration rate is expected to increase due to global warming, particularly at high elevations. To clarify the effects of soil temperature and volumetric soil water content on soil respiration rates (RS), we examined seasonal changes in RS at five elevations of 1600–2800 m in subalpine coniferous forests in Japan for 5 years. The aboveground biomass of forest stands decreased from 282 to 29 Mg/ha as elevation increased. The monthly mean RS was lower at higher elevations from July to October. While RS was positively correlated with soil temperature at each elevation, the effect of soil water content on RS varied among the five elevations. Seasonal changes in RS could be reproduced from soil temperature and soil water content for each elevation in each year. RS at any temperature was lower at higher elevations because RS was also positively correlated with aboveground biomass. From 1600 to 2800 m, the annual RS was estimated to decrease from 2.79 to 0.74 kg CO2 year–1 m–2. The annual RS along the elevational gradient was predicted to increase by 9–12% and 30–42% under low and high greenhouse gas emission scenarios (annual mean temperature 0.76°C and 3.3°C increases), respectively, during 2095–2100 compared to the current period 2015–2020. Increased soil respiration rate will accelerate global warming via the positive feedback. Overall, our findings suggest that soil respiration evaluation is important not only for calculating the carbon balance of forest stands due to global warming but also for predicting global warming owing to the feedback of CO2 emission from soil to atmosphere.

Structural and species diversity explain aboveground carbon storage in forests across the United States: Evidence from GEDI and forest inventory data

Since biodiversity often increases ecosystem functioning, changes in tree species diversity could substantially influence terrestrial carbon cycling. Yet much less is known about the relationships between forest structural diversity (i.e., the number and physical arrangement of vegetation elements in a forest) and carbon cycling, and the factors that mediate these relationships. We capitalize on spaceborne lidar data from NASA's Global Ecosystem Dynamics Investigation (GEDI) and on-the-ground forest inventory and analysis (FIA) data from 1796 plots across the contiguous United States to assess relationships among the structural and species diversity of live trees and aboveground carbon storage. We found that carbon storage was more strongly correlated with structural diversity than with species diversity, for both forest inventory-based metrics of structural diversity (e.g., height and DBH diversity) and GEDI-based canopy metrics (i.e., foliage height diversity (FHD)). However, the strength of diversity‑carbon storage relationships was mediated by forest origin and forest types. For both plot-based and GEDI-based metrics, the relationship between structural diversity (i.e., height diversity, DBH diversity, and FHD) and carbon storage was positive in natural forests for all forest types (broadleaf, mixed, conifer). For planted forests, structural diversity showed positive relationships in planted conifer forests but not in planted mixed forests. Species diversity did not show strong associations with carbon storage in natural forests but showed a positive relationship in mixed coniferous-broadleaf planted forests. Although plot-based structural diversity metrics refine our understanding of drivers of forest carbon balances at the plot scale, remotely sensed metrics such as those from GEDI can help extend that understanding to regional/national scales in a spatially continuous manner. Carbon storage showed stronger associations with plot-based structural diversity than with stand age, soil variables, or climate variables. Incorporating structural diversity into management and restoration strategies could help guide efforts to increase carbon storage and mitigate climate change as nature-based solutions.

Carbon dynamics of Western North American boreal forests in response to stand-replacing disturbances

North American boreal forests are known to be an important carbon pool in boreal ecosystems, but have experienced extensive tree mortality and carbon loss due to multiple agents of stand-replacing disturbances in recent decades. However, the impacts of these stand-replacing disturbances on forest dynamics are still unknown. We used a recently developed remote-sensing based stand-replacing disturbance product, coupled with aboveground biomass (AGB), gross primary productivity (GPP) and leaf area index (LAI) datasets to estimate the impacts of stand-replacing disturbances (e.g., fires, logging and insect outbreaks) on the carbon balance of western North American boreal forests during 2000–2012. Our results showed that fire, logging and insect outbreaks resulted in AGB losses of 23.4, 16.6, and 4.7 Tg/yr, respectively. In the post-disturbance periods, AGB did not recover to its pre-disturbed levels in the 10th year, which is longer than the recovery time of GPP and LAI. Furthermore, the losses of AGB, GPP and LAI in fire events were the dominant factors for forest recovery after stand-replacing fire. Vapor Pressure Deficit (VPD), soil clay content, temperature and precipitation were the important factors for forest recovery after stand-replacing insect outbreaks and stand-replacing logging. When removing the impact of environmental factors, our results showed a smaller magnitude of AGB, GPP and LAI loss relative to the results including these factors, although similar recovery trajectories were observed among the two results. The results have important implications for understanding the effects of stand-replacing disturbances on the carbon dynamics of boreal forests, which is required to adopt effective forest management strategies after disturbance.

Comparative Analysis and Assessment of Methodologies Applied in the Russian Federation for Calculating Greenhouse Gas Absorption by Forest Ecosystems

The assessment of the forest carbon balance is of great importance for the building of the climate policy of the Russian Federation at both national and international levels. At the same time, the results of such assessments conducted by different scientific groups vary depending on the approaches and methodologies used. This study considers the key systems for assessing the carbon balance of forest ecosystems in the Russian Federation: Integrated Land Information System, IZIS (International Institute for Applied Systems Analysis, Austria), The Carbon Budget Model of the Canadian Forest Sector, CBM-CFS (Canada), Regional Forest Carbon Budget Assessment, ROBUL (Russia), the methodology of the All-Russian Research Institute of Forestry and Mechanization of Forestry (Russia). The methodologies are compared with respect to their compliance with the IPCC requirements. The study identifies the individual characteristics of the methodologies and their application, and proposes recommendations for improving the accuracy of carbon balance estimates. The main key differences between the estimates of different scientific groups, include: compliance with the recommendations of IPCC; selection between the methods of “gain−loss” and “stock−difference”; approach to the identification of managed forests; calculation method of forest fire emissions; sources of initial data, and their reliability. The study notes the importance of scientific discussion and the necessity of compliance of the methodologies with international standards, emphasizes the problem of outdated initial data and underestimation of forest fire emissions, regardless of the chosen methodology. In general, the currently used methodology satisfactorily estimates forest carbon balance. It is recommended to improve the estimates based on remote sensing data and the second cycle of the State Forest Inventory (SFI). The implementation of the Strategy of socio-economic development of the Russian Federation with low greenhouse gas emissions until 2050 should be provided not only by changes in the method of calculating the carbon balance, but rather through real forest protection measures. Any significant adjustment to the methodology must be accompanied by an adjustment to national climate goals.