Terrestrial Carbon Stocks Research Articles

Using matrix models to estimate aboveground forest biomass dynamics in the eastern USA through various combinations of LiDAR, Landsat, and forest inventory data

The ability to harmonize data sources with varying temporal, spatial, and ecosystem measurements (e.g. forest structure to soil organic carbon) for creation of terrestrial carbon baselines is paramount to refining the monitoring of terrestrial carbon stocks and stock changes. In this study, we developed and examined the short- (5 years) and long-term (30 years) performance of matrix models for incorporating light detection and ranging (LiDAR) strip samples and time-series Landsat surface reflectance high-level data products, with field inventory measurements to predict aboveground biomass (AGB) dynamics for study sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC). The rows and columns of the matrix were stand density (i.e. number of trees per unit area) sorted by inventory plot and by species group and diameter class. Through model comparisons in the short-term, we found that average stand basal area (B) predicted by three matrix models all fell within the 95% confidence interval of observed values. The three matrix models were based on (i) only field inventory variables (inventory), (ii) LiDAR and Landsat-derived metrics combined with field inventory variables (LiDAR + Landsat + inventory), and (iii) only Landsat-derived metrics combined with field inventory variables (Landsat + inventory), respectively. In the long term, predicted AGB using LiDAR + Landsat + inventory and Landsat + inventory variables had similar AGB patterns (differences within 7.2 Mg ha−1) to those predicted by matrix models with only inventory variables from 2015–2045. When considering uncertainty derived from fuzzy sets all three matrix models had similar AGBs (differences within 7.6 Mg ha−1) by the year 2045. Therefore, the use of matrix models enabled various combinations of LiDAR, Landsat, and field data, especially Landsat data, to estimate large-scale AGB dynamics (i.e. central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories. These findings suggest that the use of Landsat data alone incorporating elevation (E), plot slope (S) and aspect (A), and site productivity (C) could produce suitable estimation of AGB dynamics (ranging from 67.1–105.5 Mg ha−1 in 2045) to actual AGB dynamics using matrix models. Such a framework may afford refined monitoring and estimation of terrestrial carbon stocks and stock changes from spatially explicit to spatially explicit and spatially continuous estimates and also provide temporal flexibility and continuity with the Landsat time series.

Impacts of Land Cover Changes on Ecosystem Carbon Stocks Over the Transboundary Tumen River Basin in Northeast Asia

Understanding the effects of land cover changes on ecosystem carbon stocks is essential for ecosystem management and environmental protection, particularly in the transboundary region that has undergone marked changes. This study aimed to examine the impacts of land cover changes on ecosystem carbon stocks in the transboundary Tumen River Basin (TTRB). We extracted the spatial information from Landsat Thematic Imager (TM) and Operational Land Imager (OLI) images for the years 1990 and 2015 and obtained convincing estimates of terrestrial biomass and soil carbon stocks with the InVEST model. The results showed that forestland, cropland and built-up land increased by 57.5, 429.7 and 128.9 km2, respectively, while grassland, wetland and barren land declined by 24.9, 548.0 and 43.0 km2, respectively in the TTRB from 1990 to 2015. The total carbon stocks encompassing aboveground, belowground, soil and litter layer carbon storage pools have declined from 831.48 Tg C in 1990 to 831.42 Tg C in 2015 due to land cover changes. In detail, the carbon stocks decreased by 3.13 Tg C and 0.44 Tg C in Democratic People’s Republic of Korea (North Korea) and Russia, respectively, while increased by 3.51 Tg C in China. Furthermore, economic development, and national policy accounted for most land cover changes in the TTRB. Our results imply that effective wetland and forestland protection policies among China, North Korea, and Russia are much needed for protecting the natural resources, promoting local ecosystem services and regional sustainable development in the transnational area.

A tree-based approach to biomass estimation from remote sensing data in a tropical agricultural landscape

Agricultural land now exceeds forests as the dominant global biome. Because of their global dominance, and potential expansion or loss, methods to estimate biomass and carbon in agricultural areas are necessary for monitoring global terrestrial carbon stocks and predicting carbon dynamics. Agricultural areas in the tropics have substantial tree cover and associated above ground biomass (AGB) and carbon. Active remote sensing data, such as airborne LiDAR (light detection and ranging), can provide accurate estimates of biomass stocks, but common plot-based methods may not be suitable for agricultural areas with dispersed and heterogeneous tree cover. The objectives of this research are to quantify AGB of a tropical agricultural landscape using a tree-based method that directly incorporates the size of individual trees, and to understand how landscape estimates of AGB from a tree-based method compare to estimates from a plot-based method. We use high-resolution (1.12 m) airborne LiDAR data collected on a 9280-ha region of the Azuero Peninsula of Panama. We model individual tree AGB with canopy dimensions from the LiDAR data. We apply the model to individual tree crown polygons and aggregate AGB estimates to compare with previously developed plot-based estimates. We find that agricultural trees are a distinct and dominant part of our study site. The tree-based approach estimates greater AGB in pixels with low forest cover than the plot-based approach, resulting a 2-fold difference in landscape AGB estimates between the methods for non-forested areas. Additionally, one third of the total landscape AGB exists in areas having <10% cover, based on a global tree cover product. Our study supports the continued use and development of allometric models to predict individual tree biomass from LiDAR-derived canopy dimensions and demonstrates the potential for spatial information from high-resolution data, such as relative isolation of canopies, to improve allometric models.

Variation in ecosystem services with rapid urbanization: A study of carbon sequestration in the Beijing–Tianjin–Hebei region, China

Land use and land cover change (LUCC) has had a profound effect on terrestrial carbon stocks, though its impact on the terrestrial carbon cycle remains uncertain. LUCC and rapid urbanization have occurred in the Beijing–Tianjin–Hebei (BTH) region over the past decades, which have greatly influenced regional carbon stocks. Based on the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, this paper estimated the temporal and spatial variation of carbon stocks in the BTH region from 1990 to 2015 and explored the relationship between carbon sequestration and urbanization level at the county scale. Our results showed: (1) There was a built–up land expansion (5795 km2) at the expense of other land use types (−5548 km2, including cultivated land, woodland and grassland). (2) The total carbon stock of the BTH region decreased from 40.55 × 108 t in 1990 to 40.04 × 108 t in 2015. The soil organic matter stock accounted for more than 60% of the total carbon stock. (3) Carbon density decreased from northwest to southeast in the BTH region. Meanwhile, carbon stocks for 134 county districts decreased (carbon sources), while the other 26 county districts increased (carbon sinks) from 1990 to 2015. (4) The urbanization level had significant negative correlations with carbon sequestration in the BTH region at a county scale. Our results provide a unique perspective on LUCC and ecosystem services, which can influence the implementation of regional planning and policy.

Identifying effects of land use cover changes and climate change on terrestrial ecosystems and carbon stocks in Mexico

Abstract Land use cover change (LUCC) has a crucial role in global environmental change, impacting both ecosystem services and biodiversity. Evaluating the trends and possible alternatives of LUCC allows quantification and identification of the hotspots of change. Therefore, this study aims to answer what the most vulnerable ecosystems and the carbon stocks losses to LUCC are under two socioeconomic and climate change (CC) scenarios–Business as Usual (BAU) and Green. The scenarios integrate the Representative Concentration Pathways, and the Shared Socioeconomic Pathways, with a spatially explicit LUCC. Distance to roads and human settlements are the most explicative direct drivers of LUCC. The projections include thirteen categories of natural and anthropogenic covers at a fine resolution for Mexico for the two scenarios. The results show that 83% of deforestation in the country has taken place in tropical dry forests, scrublands, temperate forests, and tropical evergreen forests. Considering the range of distribution of natural vegetation and the impacts of LUCC and CC, tropical dry and evergreen forests, followed by other vegetation and cloud forests are shown to be most vulnerable. By 2011, anthropogenic covers accounted for 26% of the country’s cover, and by 2050, according to the BAU scenario, they could account for 37%. The Green scenario suggests a feasible reduction to 21%. In 1985, Mexico had 2.13 PgC in aboveground biomass, but the LUCC would be responsible for 1–2% of LUCC global emissions, and by 2100, it may account for up to 5%. However, if deforestation were reduced and regeneration increased (Green scenario), carbon stocks would reach 2.14 PgC before 2050. Therefore, identifying which natural covers are the most vulnerable to LUCC and CC, and characterizing the principal drivers of ecosystems loss are crucial to prioritizing areas for implementing actions addressing resources to combat the loss of ecosystems and carbon stocks.

Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico

Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model’s predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model’s predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter.

Extensive loss of past permafrost carbon but a net accumulation into present-day soils.

Atmospheric concentrations of carbon dioxide increased between the Last Glacial Maximum (LGM, around 21,000 years ago) and the preindustrial era1. It is thought that the evolution of this atmospheric carbon dioxide (and that of atmospheric methane) during the glacial-to-interglacial transition was influenced by organic carbon that was stored in permafrost during the LGM and then underwent decomposition and release following thaw2,3. It has also been suggested that the rather erratic atmospheric δ13C and ∆14C signals seen during deglaciation1,4 could partly be explained by the presence of a large terrestrial inert LGM carbon stock, despite the biosphere being less productive (and therefore storing less carbon)5,6. Here we present an empirically derived estimate of the carbon stored in permafrost during the LGM by reconstructing the extent and carbon content of LGM biomes, peatland regions and deep sedimentary deposits. We find that the total estimated soil carbon stock for the LGM northern permafrost region is smaller than the estimated present-day storage (in both permafrost and non-permafrost soils) for the same region. A substantial decrease in the permafrost area from the LGM to the present day has been accompanied by a roughly 400-petagram increase in the total soil carbon stock. This increase in soil carbon suggests that permafrost carbon has made no net contribution to the atmospheric carbon pool since the LGM. However, our results also indicate potential postglacial reductions in the portion of the carbon stock that is trapped in permafrost, of around 1,000petagrams, supporting earlier studies7. We further find that carbon has shifted from being primarily stored in permafrost mineral soils and loess deposits during the LGM, to being roughly equally divided between peatlands, mineral soils and permafrost loess deposits today.

Constraining estimates of global soil respiration by quantifying sources of variability.

Quantifying global soil respiration (RSG ) and its response to temperature change are critical for predicting the turnover of terrestrial carbon stocks and their feedbacks to climate change. Currently, estimates of RSG range from 68 to 98PgCyear-1 , causing considerable uncertainty in the global carbon budget. We argue the source of this variability lies in the upscaling assumptions regarding the model format, data timescales, and precipitation component. To quantify the variability and constrain RSG , we developed RSG models using Random Forest and exponential models, and used different timescales (daily, monthly, and annual) of soil respiration (RS ) and climate data to predict RSG . From the resulting RSG estimates (range=66.62-100.72Pg), we calculated variability associated with each assumption. Among model formats, using monthly RS data rather than annual data decreased RSG by 7.43-9.46Pg; however, RSG calculated from daily RS data was only 1.83Pg lower than the RSG from monthly data. Using mean annual precipitation and temperature data instead of monthly data caused +4.84 and -4.36Pg C differences, respectively. If the timescale of RS data is constant, RSG estimated by the first-order exponential (93.2Pg) was greater than the Random Forest (78.76Pg) or second-order exponential (76.18Pg) estimates. These results highlight the importance of variation at subannual timescales for upscaling to RSG. The results indicated RSG is lower than in recent papers and the current benchmark for land models (98PgCyear-1 ), and thus may change the predicted rates of terrestrial carbon turnover and the carbon to climate feedback as global temperatures rise.

Predator-prey mass ratio drives microbial activity under dry conditions in Sphagnum peatlands.

Mid‐ to high‐latitude peatlands are a major terrestrial carbon stock but become carbon sources during droughts, which are increasingly frequent as a result of climate warming. A critical question within this context is the sensitivity to drought of peatland microbial food webs. Microbiota drive key ecological and biogeochemical processes, but their response to drought is likely to impact these processes. Peatland food webs have, however, been little studied, especially the response of microbial predators. We studied the response of microbial predators (testate amoebae, ciliates, rotifers, and nematodes) living in Sphagnum moss carpet to droughts, and their influence on lower trophic levels and on related microbial enzyme activity. We assessed the impact of reduced water availability on microbial predators in two peatlands using experimental (Linje mire, Poland) and natural (Forbonnet mire, France) water level gradients, reflecting a sudden change in moisture regime (Linje), and a typically drier environment (Forbonnet). The sensitivity of different microbial groups to drought was size dependent; large sized microbiota such as testate amoebae declined most under dry conditions (−41% in Forbonnet and −80% in Linje). These shifts caused a decrease in the predator–prey mass ratio (PPMR). We related microbial enzymatic activity to PPMR; we found that a decrease in PPMR can have divergent effects on microbial enzymatic activity. In a community adapted to drier conditions, decreasing PPMR stimulated microbial enzyme activity, while in extreme drought experiment, it reduced microbial activity. These results suggest that microbial enzymatic activity resulting from food web structure is optimal only within a certain range of PPMR, and that different trophic mechanisms are involved in the response of peatlands to droughts. Our findings confirm the importance of large microbial consumers living at the surface of peatlands on the functioning of peatlands, and illustrate their value as early warning indicators of change.

Abiotic and biotic factors modulate plant biomass and root/shoot (R/S) ratios in grassland on the Loess Plateau, China

Plant biomass and the root/shoot ratio (R/S) are key parameters for estimating terrestrial ecosystem carbon (C) stocks. However, how environmental driving factors (abiotic and biotic factors) modulate plant biomass and R/S has not been well investigated on the Loess Plateau. Here, we tested the impacts of abiotic and biotic driving factors on plant biomass and R/S and whether they are in accordance with optimal partitioning theory in natural grassland in this region. The results showed that above-ground biomass (AGB) and below-ground biomass (BGB) were 63.96 g·m−2 and 311.18 g·m−2, respectively, and that R/S ranged from 0.13 to 0.46, with high spatial heterogeneity. There was a strong positive linear relationship between AGB and BGB (p < 0.05) in accordance with optimal partitioning theory. A principal component analysis (PCA) indicated that the topographic properties (Slope position, Slope gradient and Altitude) were negatively correlated with the soil physical properties (Ec,Electric conductivity; BD, Bulk density; ST, Soil temperature; and SM, Soil moisture) and positively correlated with the soil chemical properties (SOC, Soil organic carbon; TN, Total nitrogen; SMBC, Soil microbial biomass carbon and SMBN, Soil microbial biomass nitrogen), while soil total phosphorus (TP) was not correlated with the soil physical properties (p > 0.05). Structural equation modeling (SEM) suggested that R/S is indirectly driven by plant properties (Height, Density, Coverage), which are determined by soil and topographic properties. However, only 5% of R/S was explained by the soil physical properties and topographic properties, suggesting that these factors had no significant effect on R/S. The data do, however, provide information for quantifying C stocks in natural grassland on the Loess Plateau. Further, ecologists should focus on mechanistic and fresh approaches to understanding the abiotic and biotic factors influencing plant biomass and R/S.

Low black carbon concentration in agricultural soils of central and northern Ethiopia

Soil carbon (C) represents the largest terrestrial carbon stock and is key for soil productivity. Major fractions of soil C consist of organic C, carbonates and black C. The turnover rate of black C is lower than that of organic C, and black C abundance decreases the vulnerablility of soil C stock to decomposition under climate change. The aim of this study was to determine the distribution of soil C in different pools and impact of agricultural management on the abundance of different species. Soil C fractions were quantified in the topsoils (0–15cm) of 23 sites in the tropical highlands of Ethiopia. The sites in central Ethiopia represented paired plots of agroforestry and adjacent control plots where cereal crops were traditionally grown in clayey soils. In the sandy loam and loam soils of northern Ethiopia, the pairs represented restrained grazing with adjacent control plots with free grazing, and terracing with cereal-based cropping with adjacent control plots without terracing. Soil C contained in carbonates, organic matter and black C along with total C was determined. The total C median was 1.5% (range 0.3–3.6%). The median proportion of organic C was 85% (range 53–94%), 6% (0–41%) for carbonate C and 6% (4–21%) for black C. An increase was observed in the organic C and black C fractions attributable to agroforestry and restrained grazing. The very low concentration of the relatively stable black C fraction and the dominance of organic C in these Ethiopian soils suggest vulnerability to degradation and the necessity for cultivation practices maintaining the C stock.

Wetland flux controls: how does interacting water table levels and temperature influence carbon dioxide and methane fluxes in northern Wisconsin?

Wetlands play a disproportionately large role in global terrestrial carbon stocks, and from 1 year to the next individual wetlands can fluctuate between carbon sinks and sources depending on factors such as hydrology, temperature, and land use. Although much research has been done on short-term seasonal to annual wetland biogeochemical cycles, there is a lack of experimental evidence concerning how the reversibility of wetland hydrological changes will influence these cycles over longer time periods. Five years of drought-induced declining water table at Lost Creek, a shrub fen wetland in northern Wisconsin, coincided with increased ecosystem respiration (Reco) and gross primary production (GPP) as derived from long-term eddy covariance observations. Since then, however, the average water table level at this site has increased, providing a unique opportunity to explore how wetland carbon fluxes are affected by interannual air temperature differences as well as changing water table levels. Water table level, as measured by water discharge, was correlated with Reco and GPP at interannual time scales. However, air temperature had a strong correlation with Reco, GPP, and net ecosystem productivity (NEP) at monthly time scales and correlated with NEP at inter-annual time scales. Methane flux was strongly temperature-controlled at seasonal time scales, increasing an order of magnitude from April to July. Annual methane emissions were 51 g C m−2. Our results demonstrate that over multi-year timescales, water table fluctuations can have limited effects on wetland net carbon fluxes and instead at Lost Creek annual temperature is the best predictor of interannual variation.

Variation of main terrestrial carbon stocks at the landscape-scale are shaped by soil in a tropical rainforest

Economic incentives to offset carbon dioxide (CO2) emissions associated with deforestation and other human activities affecting forest ecosystems depend on robust estimates of changes in forest carbon (C) stocks. Such stocks are difficult to assess in heterogeneous landscapes where the soil properties and the forest structure and functionality vary in space and time. Here we show that geopedological mapping is useful to quantify the above- and belowground C stocks in the different land units of the Lacandon tropical rainforest, southeast Mexico. We used an ordination method to recognize major gradients in the soil and we applied regression analyses to identify relationships between soil properties and AGB. Total forest C stocks differed among land units (287 to 478MgCha−1 in limestone mountains and fluvial terraces, respectively). Soil constrains like rooting depth (ranging from 0.13 to 1.34m), available water storage capacity (ranging from 32.3 to 161.4Lm−2) and Al saturation in the ion exchange complex (0 to 22.6% Alsat) were correlated with the aboveground biomass (AGB) C stock by affecting the stem size and density of trees. Soil organic carbon (SOC) in the solum represented 22 to 46% of the total forest C stock in the different landscape units, of which 28 to 45% was stored below 30cm depth. Therefore, an accurate assessment of forest C stocks must consider not only the variation between land units with contrasting soil properties, but also the solum depth. Our results indicate that stratified sampling based on geopedologic mapping is useful to allocate incentives assessment of C storage at relatively low costs and with reasonable effort.

Regional Carbon Predictions in a Temperate Forest Using Satellite Lidar

Large uncertainties in terrestrial carbon stocks and sequestration predictions result from insufficient regional data characterizing forest structure. This study uses satellite waveform lidar from ICESat to estimate regional forest structure in central New England, where each lidar waveform estimates fine-scale forest heterogeneity. ICESat is a global sampling satellite, but does not provide wall-to-wall coverage. Comprehensive, wall-to-wall ecosystem state characterization is achieved through spatial extrapolation using the random forest machine-learning algorithm. This forest description allows for effective initialization of individual-based terrestrial biosphere models making regional carbon flux predictions. Within 42/43.5 N and 73/71.5 W, aboveground carbon was estimated at 92.47 TgC or 45.66 MgC ha−1, and net carbon fluxes were estimated at 4.27 TgC yr−1 or 2.11 MgC ha−1 yr−1. This carbon sequestration potential was valued at 47% of fossil fuel emissions in eight central New England counties. In preparation for new lidar and hyperspectral satellites, linking satellite data and terrestrial biosphere models are crucial in improving estimates of carbon sequestration potential counteracting anthropogenic sources of carbon.

Vegetation ecology and carbon sequestration potential of shrubs in tropics of Chhattisgarh, India.

Tropical forests are well known to have great species diversity and contribute substantial share in terrestrial carbon (C) stocks worldwide. Shrubs are long-neglected life form in the forest ecosystem, playing many roles in the forest and human life. Shrub has great impact on vegetation attributes which in turn modify the C storage and capture. In the present investigation, an attempt has been made to explore the dynamics of shrub species in four fire regimes, viz. high, medium, low, and no fire zones of Bhoramdeo Wildlife Sanctuary of Kawardha forest division (Chhattisgarh), India. The variations in structure, diversity, biomass, productivity, and C sequestration potential in all the sites were quantified. The density and basal area of shrub varied from 1250 to 3750 individuals ha-1 and 2.79 to 4.92m2ha-1, respectively. The diversity indices showed that the value of Shannon index was highest in medium fire zone (3.77) followed by high, low, and no fire zones as 3.25, 3.12, and 2.32, respectively. The value of Simpson's index or concentration of dominance (Cd) ranged from 0.08 to 0.20, species richness from 0.56 to 1.58, equitability from 1.41 to 1.44, and beta diversity from 1.50 to 4.20, respectively. The total biomass and C storage ranged from 6.82 to 15.71 and from 2.93 to 6.76t ha-1, respectively. The shrub density, importance value index (IVI), and abundance to frequency ratio (A/F) significantly correlated between high fire and medium fire zone. The basal area was found to be significantly positively correlated between high fire and medium fire, and low and no fire zones, respectively. Two-way cluster analysis reflected various patterns of clustering due to influence of the forest fire which showed that some species have distant clustering while some have smaller cluster. Principal component analysis (PCA) reflects variable scenario with respect to shrub layer. Ventilago calyculata and Zizyphus rotundifolia showed higher correlation between themselves in terms of basal area (BA). The total shrub production was 1.59-3.53tha-1year-1 while the C sequestration potential of 0.71-1.57tha-1year-1 under different fire regimes. Shrub community in the medium fire zone reflected higher productivity and higher C sequestration in comparison to other fire zone. Among the different plant parts, the biomass accumulation ratio was highest in the root of shrub community among various fire regimes. Screening of species for restoration and different land-use pattern on the basis of biomass accumulation and carbon sequestering potential would be an effective strategy for decision-making in sustainable forest management.

Microbial functional diversity covaries with permafrost thaw‐induced environmental heterogeneity in tundra soil

Permafrost soil in high latitude tundra is one of the largest terrestrial carbon (C) stocks and is highly sensitive to climate warming. Understanding microbial responses to warming-induced environmental changes is critical to evaluating their influences on soil biogeochemical cycles. In this study, a functional gene array (i.e., geochip 4.2) was used to analyze the functional capacities of soil microbial communities collected from a naturally degrading permafrost region in Central Alaska. Varied thaw history was reported to be the main driver of soil and plant differences across a gradient of minimally, moderately, and extensively thawed sites. Compared with the minimally thawed site, the number of detected functional gene probes across the 15-65cm depth profile at the moderately and extensively thawed sites decreased by 25% and 5%, while the community functional gene β-diversity increased by 34% and 45%, respectively, revealing decreased functional gene richness but increased community heterogeneity along the thaw progression. Particularly, the moderately thawed site contained microbial communities with the highest abundances of many genes involved in prokaryotic C degradation, ammonification, and nitrification processes, but lower abundances of fungal C decomposition and anaerobic-related genes. Significant correlations were observed between functional gene abundance and vascular plant primary productivity, suggesting that plant growth and species composition could be co-evolving traits together with microbial community composition. Altogether, this study reveals the complex responses of microbial functional potentials to thaw-related soil and plant changes and provides information on potential microbially mediated biogeochemical cycles in tundra ecosystems.

Environmental driving factors affecting plant biomass in natural grassland in the Loess Plateau, China

Plant biomass is a key parameter for estimating terrestrial ecosystem carbon (C) stocks, which varies greatly as a result of specific environmental conditions. Here, we tested environmental driving factors affecting plant biomass in natural grassland in the Loess Plateau, China. We found that above-ground biomass (AGB) and below-ground biomass (BGB) had a similar change trend in the order of Stipa bungeana>Leymus secalinus>Artemisia sacrorum>Artemisia scoparia, whereas shoot ratio (R/S) displayed an opposite change trend. There was a significantly positive linear relationship between the AGB and BGB, regardless of plant species (p<0.05). Furthermore, more than 50% of the AGB were found in 20–50cm of plant height in Compositae plants (A. sacrorum, A. scoparia), whereas over 60% of the AGB were found in 20–80cm of plant height in Gramineae plants (S. bungeana, L. secalinus). For each plant species, more than 75% of the BGB was distributed in 0–10cm soil depth, and 20% was distributed in 10–20cm soil depth, while less than 5% was distributed in 20–40cm soil depth. Further, AGB and BGB were highly affected by environmental driving factors (soil properties, plant traits, topographic properties), which were identified by the structural equation model (SEM) and the generalized additive models (GAMs). In addition, AGB was directly affected by plant traits, and BGB was directly affected by soil properties, and soil properties associated with plant traits that affected AGB and BGB through interactive effects were 9.12% and 3.59%, respectively. However, topographic properties had a weak influence on ABG and BGB (as revealed by the lowest total pathway effect). Besides, soil organic carbon (SOC), soil microbial biomass carbon (MBC), and plant height had a higher relative contribution to AGB and BGB. Our results indicate that environmental driving factors affect plant biomass in natural grassland in the Loess Plateau.

Optimal climate for large trees at high elevations drives patterns of biomass in remote forests of Papua New Guinea.

Our ability to model global carbon fluxes depends on understanding how terrestrial carbon stocks respond to varying environmental conditions. Tropical forests contain the bulk of the biosphere's carbon. However, there is a lack of consensus as to how gradients in environmental conditions affect tropical forest carbon. Papua New Guinea (PNG) lies within one of the largest areas of contiguous tropical forest and is characterized by environmental gradients driven by altitude; yet, the region has been grossly understudied. Here, we present the first field assessment of aboveground biomass (AGB) across three main forest types of PNG using 193 plots stratified across 3,100-m elevation gradient. Unexpectedly, AGB had no direct relationship to rainfall, temperature, soil, or topography. Instead, natural disturbances explained most variation in AGB. While large trees (diameter at breast height>50cm) drove altitudinal patterns of AGB, resulting in a major peak in AGB (2,200-3,100m) and some of the most carbon-rich forests at these altitudes anywhere. Large trees were correlated to a set of climatic variables following a hump-shaped curve. The set of "optimal" climatic conditions found in montane cloud forests is similar to that of maritime temperate areas that harbor the largest trees in the world: high ratio of precipitation to evapotranspiration (2.8), moderate mean annual temperature (13.7°C), and low intra-annual temperature range (7.5°C). At extreme altitudes (2,800-3,100 m), where tree diversity elsewhere is usually low and large trees are generally rare or absent, specimens from 18 families had girths >70cm diameter and maximum heights 20-41m. These findings indicate that simple AGB-climate-edaphic models may not be suitable for estimating carbon storage in forests where optimal climate niches exist. Our study, conducted in a very remote area, suggests that tropical montane forests may contain greater AGB than previously thought and the importance of securing their future under a changing climate is therefore enhanced.

Simulating soil organic carbon stock as affected by land cover change and climate change, Hyrcanian forests (northern Iran)

Soil organic carbon (SOC) contains a considerable portion of the world's terrestrial carbon stock, and is affected by changes in land cover and climate. SOC modeling is a useful approach to assess the impact of land use, land use change and climate change on carbon (C) sequestration. This study aimed to: (i) test the performance of RothC model using data measured from different land covers in Hyrcanian forests (northern Iran); and (ii) predict changes in SOC under different climate change scenarios that may occur in the future. The following land covers were considered: Quercus castaneifolia (QC), Acer velutinum (AV), Alnus subcordata (AS), Cupressus sempervirens (CS) plantations and a natural forest (NF). For assessment of future climate change projections the Fifth Assessment IPCC report was used. These projections were generated with nine Global Climate Models (GCMs), for two Representative Concentration Pathways (RCPs) leading to very low and high greenhouse gases concentration levels (RCP 2.6 and RCP 8.5 respectively), and for four 20year-periods up to 2099 (2030s, 2050s, 2070s and 2090s). Simulated values of SOC correlated well with measured data (R2=0.64 to 0.91) indicating a good efficiency of the RothC model. Our results showed an overall decrease in SOC stocks by 2099 under all land covers and climate change scenarios, but the extent of the decrease varied with the climate models, the emissions scenarios, time periods and land covers. Acer velutinum plantation was the most sensitive land cover to future climate change (range of decrease 8.34–21.83tCha−1). Results suggest that modeling techniques can be effectively applied for evaluating SOC stocks, allowing the identification of current patterns in the soil and the prediction of future conditions.

Relevance of carbon stocks of marine sediments for national greenhouse gas inventories of maritime nations

BackgroundDetermining national carbon stocks is essential in the framework of ongoing climate change mitigation actions. Presently, assessment of carbon stocks in the context of greenhouse gas (GHG)-reporting on a nation-by-nation basis focuses on the terrestrial realm, i.e., carbon held in living plant biomass and soils, and on potential changes in these stocks in response to anthropogenic activities. However, while the ocean and underlying sediments store substantial quantities of carbon, this pool is presently not considered in the context of national inventories. The ongoing disturbances to both terrestrial and marine ecosystems as a consequence of food production, pollution, climate change and other factors, as well as alteration of linkages and C-exchange between continental and oceanic realms, highlight the need for a better understanding of the quantity and vulnerability of carbon stocks in both systems. We present a preliminary comparison of the stocks of organic carbon held in continental margin sediments within the Exclusive Economic Zone of maritime nations with those in their soils. Our study focuses on Namibia, where there is a wealth of marine sediment data, and draws comparisons with sediment data from two other countries with different characteristics, which are Pakistan and the United Kingdom.ResultsResults indicate that marine sediment carbon stocks in maritime nations can be similar in magnitude to those of soils. Therefore, if human activities in these areas are managed, carbon stocks in the oceanic realm—particularly over continental margins—could be considered as part of national GHG inventories.ConclusionsThis study shows that marine sediment organic carbon stocks can be equal in size or exceed terrestrial carbon stocks of maritime nations. This provides motivation both for improved assessment of sedimentary carbon inventories and for reevaluation of the way that carbon stocks are assessed and valued. The latter carries potential implications for the management of human activities on coastal environments and for their GHG inventories.