Partitioning direct and indirect human-induced effects on carbon sequestration of managed coniferous forests using model simulations and forest inventories

Temperate forest ecosystems have recently been identified as an important net sink in the global carbon budget. The factors responsible for the strength of the sinks and their permanence, however, are less evident. In this paper, we quantify the present carbon sequestration in Thuringian managed coniferous forests. We quantify the effects of indirect human‐induced environmental changes (increasing temperature, increasing atmospheric CO₂ concentration and nitrogen fertilization), during the last century using BIOME‐BGC, as well as the legacy effect of the current age‐class distribution (forest inventories and BIOME‐BGC). We focused on coniferous forests because these forests represent a large area of central European forests and detailed forest inventories were available. The model indicates that environmental changes induced an increase in biomass C accumulation for all age classes during the last 20 years (1982–2001). Young and old stands had the highest changes in the biomass C accumulation during this period. During the last century mature stands (older than 80 years) turned from being almost carbon neutral to carbon sinks. In high elevations nitrogen deposition explained most of the increase of net ecosystem production (NEP) of forests. CO₂ fertilization was the main factor increasing NEP of forests in the middle and low elevations. According to the model, at present, total biomass C accumulation in coniferous forests of Thuringia was estimated at 1.51 t C ha^?1 yr^?1 with an averaged annual NEP of 1.42 t C ha^?1 yr^?1 and total net biome production of 1.03 t C ha^?1 yr^?1 (accounting for harvest). The annual averaged biomass carbon balance (BCB: biomass accumulation rate‐harvest) was 1.12 t C ha^?1 yr^?1 (not including soil respiration), and was close to BCB from forest inventories (1.15 t C ha^?1 yr^?1). Indirect human impact resulted in 33% increase in modeled biomass carbon accumulation in coniferous forests in Thuringia during the last century. From the forest inventory data we estimated the legacy effect of the age‐class distribution to account for 17% of the inventory‐based sink. Isolating the environmental change effects showed that these effects can be large in a long‐term, managed conifer forest.     

Litter Fall Dynamics of Restored Mangroves (Rhizophora mucronata Lamk. and Sonneratia alba Sm.) in Kenya

Mangrove forests are active carbon sinks and important for nutrient cycling in coastal ecosystems. Restoration of degraded mangrove habitats enhances return of ecosystem goods and services, including carbon sequestration. Our objective was to assess the restoration of primary productivity of reforested mangrove stands in comparison with natural reference stands in Gazi Bay, Kenya. Litter fall data were collected in nine Rhizophora mucronata and Sonneratia alba monospecific stands by use of litter traps over 2 years. Litter was emptied monthly, dried, sorted, and weighed. The reforested and natural stands showed seasonality patterns only in the production of reproductive material. Leaves constituted the highest percentage to total litter fall. Litter productivity rates for the R. mucronata stands were not significantly different and ranged from 6.61–10.15 to 8.36–11.02 t ha^?1 yr^?1 for the restored and natural stands, respectively. The productivity of 5 years R. mucronata stands reached 5.22 t ha^?1 yr^?1 and was significantly different from other stands. Litter productivity rates for S. alba stands was 7.77–7.85 for the restored stands and 10.15 t ha^?1 yr^?1 for the natural stand but differences were not significant. Our results indicate that plantations of at least 11 years have attained litter productivity rates comparable to the natural forests. This suggests that productivity of replanted mangroves is likely to reach complete recovery by this age under the prevailing environmental conditions.     

Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park,Vietnam

Forest biomass is one of the key measurement for carbon budget accounting, carbon flux monitoring, and climate change studies. Hence, it is essential to develop a credible approach to estimate forest biomass and carbon stocks. Our study applied Sentinel-2 satellite imagery combined with field-measured biomass using Random Forest (RF), a machine learning regression algorithm, to estimate forest aboveground biomass (AGB) in Yok Don National Park, Vietnam. A total of 132 spectral and texture variables were extracted from Sentinel-2 imagery (February 7, 2017) to predict AGB of the National Park using RF algorithm. It was found that a combination of 132 spectral and texture variables could predict AGB with an R² value of 0.94, RMSE of 34.5 Mgha⁻¹ and %RMSE of 18.3%. RF regression algorithm was further used to reduce the number of variables in such a way that a minimum number of selected variables can be able to estimate AGB at a satisfactory level. A combination of 11 spectral and texture variables was identified based on out-of-bag (OOB) estimation to develop an easy-to-use model for estimating AGB. On validation, the model developed with 11 variables was able to predict AGB with R² = 0.81, RMSE = 36.67 Mg ha⁻¹ and %RMSE of 19.55%. The results found in the present study demonstrated that Sentinel-2 imagery in conjunction with RF-based regression algorithm has the potential to effectively predict the spatial distribution of forest AGB with adequate accuracy.     

Ecosystem carbon stocks of mangrove forests along the Pacific and Caribbean coasts of Honduras

Among the many ecosystem services provided by mangrove ecosystems, their role in carbon (C) sequestration and storage is quite high compared to other tropical forests. Mangrove forests occupy less than 1 % of tropical forested areas but account for approximately 3 % of global carbon sequestration by tropical forests. Yet there remain many areas where little data on the size and variation of mangrove C stocks exist. To address this gap and examine the range of C stocks in mangroves at landscape scales, we quantified C stocks of Honduran mangroves along the Pacific and Caribbean coasts and the Bay Islands. We also examined differences in ecosystem C stocks due to size and structure of mangrove vegetation found in Honduras. Ecosystem C stocks ranged from 570 Mg C ha^?1 in the Pacific coast to ~1000 Mg C ha^?1 in Caribbean coast and the Bay Islands. Ecosystem C stocks on the basis of mangrove structure were 1200, 800 and 900 Mg C ha^?1, in low, medium and tall mangroves, respectively. We did not find significant differences in ecosystem C stocks on the basis of location (Pacific coast, Caribbean coast and Bay Islands) or mangrove type (low, medium and tall). Mangrove soils represented the single largest pool of total C in these ecosystems, with 87, 81 and 94 % at the Pacific coast, Caribbean coast and the Bay Islands, respectively. While there were no significant differences in total ecosystem stocks among mangrove types, there were differences in where carbon is stored. Mangrove soils among low, medium and tall mangroves contained 99, 93 and 80 % of the total ecosystem C stocks. In addition, we found a small yet significant negative correlation between vegetation C pools and pore water salinity and pH at the sampled sites. Conversion of mangroves into other land use types such as aquaculture or agriculture could result in loses of these soil C reserves due to mineralization and oxidation. Coupled with their other ecosystem services, an understanding of the size of mangrove ecosystem C stocks underscores their values in the formulation of conservation and climate change mitigation strategies in Central America.     

Carbon stocks in artificially and naturally regenerated mangrove ecosystems in the Mekong Delta

Mangrove forests cover a small fraction of the earth’s surface, but contribute disproportionately to ecosystem services, including carbon (C) storage. These forests are being rapidly degraded as demand for economic development grows. In recognition of the multiple benefits of mangrove forests, rehabilitation of degraded forests is being carried out in many regions. This study assesses the potential for restored mangrove forests in Vietnam to sequester and store C, by characterizing two different mangrove restoration areas in the Mekong Delta region. The Can Gio Mangrove Biospheres Reserve (CGMBR) in Ho Chi Minh City was highly degraded during the Vietnam War and was subsequently replanted between 1978 and 1998. The Kien Vang Protection Forest (KVPF) in Ca Mau Province was similarly degraded during the war, but unlike CGMBR, it has experienced natural regeneration over the last 35 years. We find that vegetation structure between two sites are not different significantly, though CGMBR has richer mangrove species diversity than KVPF. The mean of total ecosystem C stocks in planted mangroves of CGMBR (889 ± 111 MgC ha^?1) is not significantly different compare to natural regeneration forests of KVPF (844 ± 58 MgC ha^?1). Our findings suggest that after 35 years, both anthropogenically and naturally regenerated mangroves appear to store similar levels of C.     

Belowground carbon storage of Micronesian mangrove forests

Belowground carbon storage was examined for mangrove forests on Pohnpei Island, Micronesia. Stored carbon in a coral reef-type mangrove habitat consisting of a 2 m thick mangrove peat layer, which is a type of mangrove habitat in tropical Pacific islands, was estimated at 1300 t C ha^–1. The carbon burial rate during the phase of gradual sea-level rise, which was calculated at 93 g m^–2 year^–1 between 1800 and 1380 years bp using the medians of the radiocarbon ages, was significantly higher than that between 1380 years bp and present in a stable sea-level phase.     

Net primary productivity allocation and cycling of carbon along a tropical forest elevational transect in the Peruvian Andes

The net primary productivity, carbon (C) stocks and turnover rates (i.e. C dynamics) of tropical forests are an important aspect of the global C cycle. These variables have been investigated in lowland tropical forests, but they have rarely been studied in tropical montane forests (TMFs). This study examines spatial patterns of above‐ and belowground C dynamics along a transect ranging from lowland Amazonia to the high Andes in SE Peru. Fine root biomass values increased from 1.50 Mg C ha^?1 at 194 m to 4.95 ± 0.62 Mg C ha^?1 at 3020 m, reaching a maximum of 6.83 ± 1.13 Mg C ha^?1 at the 2020 m elevation site. Aboveground biomass values decreased from 123.50 Mg C ha^?1 at 194 m to 47.03 Mg C ha^?1 at 3020 m. Mean annual belowground productivity was highest in the most fertile lowland plots (7.40 ± 1.00 Mg C ha^?1 yr^?1) and ranged between 3.43 ± 0.73 and 1.48 ± 0.40 Mg C ha^?1 yr^?1 in the premontane and montane plots. Mean annual aboveground productivity was estimated to vary between 9.50 ± 1.08 Mg C ha^?1 yr^?1 (210 m) and 2.59 ± 0.40 Mg C ha^?1 yr^?1 (2020 m), with consistently lower values observed in the cloud immersion zone of the montane forest. Fine root C residence time increased from 0.31 years in lowland Amazonia to 3.78 ± 0.81 years at 3020 m and stem C residence time remained constant along the elevational transect, with a mean of 54 ± 4 years. The ratio of fine root biomass to stem biomass increased significantly with increasing elevation, whereas the allocation of net primary productivity above‐ and belowground remained approximately constant at all elevations. Although net primary productivity declined in the TMF, the partitioning of productivity between the ecosystem subcomponents remained the same in lowland, premontane and montane forests.     

Non‐native mangroves support carbon storage,sediment carbon burial,and accretion of coastal ecosystems

Mangrove forests play an important role in climate change adaptation and mitigation by maintaining coastline elevations relative to sea level rise, protecting coastal infrastructure from storm damage, and storing substantial quantities of carbon (C) in live and detrital pools. Determining the efficacy of mangroves in achieving climate goals can be complicated by difficulty in quantifying C inputs (i.e., differentiating newer inputs from younger trees from older residual C pools), and mitigation assessments rarely consider potential offsets to CO₂ storage by methane (CH₄) production in mangrove sediments. The establishment of non‐native Rhizophora mangle along Hawaiian coastlines over the last century offers an opportunity to examine the role mangroves play in climate mitigation and adaptation both globally and locally as novel ecosystems. We quantified total ecosystem C storage, sedimentation, accretion, sediment organic C burial and CH₄ emissions from ~70 year old R. mangle stands and adjacent uninvaded mudflats. Ecosystem C stocks of mangrove stands exceeded mudflats by 434 ± 33 Mg C/ha, and mangrove establishment increased average coastal accretion by 460%. Sediment organic C burial increased 10‐fold (to 4.5 Mg C ha^?1 year^?1), double the global mean for old growth mangrove forests, suggesting that C accumulation from younger trees may occur faster than previously thought, with implications for mangrove restoration. Simulations indicate that increased CH₄ emissions from sediments offset ecosystem CO₂ storage by only 2%–4%, equivalent to 30–60 Mg CO₂‐eq/ha over mangrove lifetime (100 year sustained global warming potential). Results highlight the importance of mangroves as novel systems that can rapidly accumulate C, have a net positive atmospheric greenhouse gas removal effect, and support shoreline accretion rates that outpace current sea level rise. Sequestration potential of novel mangrove forests should be taken into account when considering their removal or management, especially in the context of climate mitigation goals.     

Carbon of Chaillu forests based on a phytosociological analysis in Republic of Congo,more than meets the eye?

The objectives were to quantify aboveground, belowground and dead wood carbon pools near Mayoko in the Chaillu massif of Republic of Congo and explore relationships between carbon storage and plant diversity of all growth forms. A total of 190 plots (25 m by 25 m) were sampled (5072 stems, 211 species) and data analysed using recommended central-African forest allometric equations. Mean stem diameter at breast height was 33.6 cm, mean basal area 47.7 m² ha⁻¹ and mean density of individuals 407 ha⁻¹. Mean aboveground carbon (AGC) ranged from 13.93–412.66 Mg C ha⁻¹, belowground carbon from 2.86–96.97 Mg C ha⁻¹ and dead wood from 0.00–7.59 Mg C ha⁻¹. The maximum AGC value recorded in a plot was 916 Mg C ha⁻¹. The analysis performed using phytosociological association as basis rather than broad vegetation type is unique. AGC values for undisturbed terra firme forest sites featured among the highest recorded for African tropical forests. Considering only tree diversity, a weak, yet significant, relationship existed between AGC and species richness, Shannon-Wiener index of diversity and Fisher's alpha. However, if diversity of all plant growth forms is considered, no relationship between carbon and plant diversity existed.     

Quantifying tree carbon stock in historically conserved Seminary Hills urban forest of Nagpur,India

Urban forests help regulating flow of ecosystem services and are efficient to sequester atmospheric carbon. Tree carbon stock in urban forests and green spaces can help improving human well-being. Nagpur being one of the fastest growing urban agglomerate in India that has faced rapid loss of green spaces in last three decades. Present study assessed tree biomass carbon storage potential of a historically conserved large (67.41 ha) Seminary Hills Reserve forest of Nagpur. A total of 150 quadrats of 100 m² were laid to understand the vegetation structure and tree biomass storage. Overall structure and composition of the forest was assessed while, non-destructive biomass estimation was carried out using tree volume eqs. A total of 27 tree species belonging to 12 plant families were observed from the forest with only 6 tree species being dominant and remaining 21 being rare in occurrence. The maximum tree carbon storage was observed in dominant tree species of Hardwickia binata (76.30 t C ha^?1) followed by 17.04 t C ha^?1 in Tectona grandis and 1.19 t C ha^?1 in Boswellia serrata. Carbon stock in other co-dominant species was reported in Terminalia bellirica (76.57 kg C ha^?1), Gardenia resinifera (1118.6 g C ha^?1) and Terminalia arjuna (84.8 g C ha^?1). Total carbon stock of dominant tree species present in Seminary Hills urban forest was 94.53 ± 39.6 t C ha^?1. The study intends to bring focus ecosystem benefits from Urban Forests in growing urban sprawls of India and the need to include their vital role in urban planning.     

Including GHG emissions from mangrove forests LULUC in LCA: a case study on shrimp farming in the Mekong Delta,Vietnam

Purpose

Mangrove forests have been recognized as important regulators of greenhouse gases (GHGs), yet the resulting land use and land-use change (LULUC) emissions have rarely been accounted for in life cycle assessment (LCA) studies. The present study therefore presents up-to-date estimates for GHG emissions from mangrove LULUC and applies them to a case study of shrimp farming in Vietnam.

Methods

To estimate the global warming impacts of mangrove LULUC, a combination of the International Panel for Climate Change (IPCC) guidelines, the Net Committed Emissions, and the Missed Potential Carbon Sink method were used. A literature review was then conducted to characterize the most critical parameters for calculating carbon losses, missed sequestration, methane fluxes, and dinitrogen monoxide emissions.

Results and discussion

Our estimated LUC emissions from mangrove deforestation resulted in 124 t CO₂ ha^?1 year^?1, assuming IPCC’s recommendations of 1 m of soil loss, and 96% carbon oxidation. In addition to this, 1.25 t of carbon would no longer be sequestered annually. Discounted over 20 years, this resulted in total LULUC emissions of 129 t CO₂ ha^?1 year^?1 (CV = 0.441, lognormal distribution (ln)). Shrimp farms in the Mekong Delta, however, can today operate for 50 years or more, but are 1.5 m deep (50% oxidation). In addition to this, Asian tiger shrimp farming in mixed mangrove concurrent farms (the only type of shrimp farm that resulted in mangrove deforestation since 2000 in our case study) resulted in 533 kg methane and 1.67 kg dinitrogen monoxide per hectare annually. Consequently, the LULUC GHG emissions resulted in 184 and 282 t CO₂-eq t^?1 live shrimp at farm gate, using mass and economic allocation, respectively. These GHG emissions are about an order of magnitude higher than from semi-intensive or intensive shrimp farming systems. Limitations in data quality and quantity also led us to quantify the uncertainties around our emission estimates, resulting in a CV of between 0.4 and 0.5.

Conclusions

Our results reinforce the urgency of conserving mangrove forests and the need to quantify uncertainties around LULUC emissions. It also questions mixed mangrove concurrent shrimp farming, where partial removal of mangrove forests is endorsed based upon the benefits of partial mangrove conservation and maintenance of certain ecosystem services. While we recognize that these activities limit the chances of complete removal, our estimates show that large GHG emissions from mangrove LULUC question the sustainability of this type of shrimp farming, especially since mixed mangrove farming only provide 5% of all farmed shrimp produced in Vietnam.

     

Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error

Evaluating contributions of forest ecosystems to climate change mitigation requires well‐calibrated carbon cycle models with quantified baseline carbon stocks. An appropriate baseline for carbon accounting of natural forests at landscape scales is carbon carrying capacity (CCC); defined as the mass of carbon stored in an ecosystem under prevailing environmental conditions and natural disturbance regimes but excluding anthropogenic disturbance. Carbon models require empirical measurements for input and calibration, such as net primary production (NPP) and total ecosystem carbon stock (equivalent to CCC at equilibrium). We sought to improve model calibration by addressing three sources of errors that cause uncertainty in carbon accounting across heterogeneous landscapes: (1) data‐model representation, (2) data‐object representation, (3) up‐scaling. We derived spatially explicit empirical models based on environmental variables across landscape scales to estimate NPP (based on a synthesis of global site data of NPP and gross primary productivity, n=27), and CCC (based on site data of carbon stocks in natural eucalypt forests of southeast Australia, n=284). The models significantly improved predictions, each accounting for 51% of the variance. Our methods to reduce uncertainty in baseline carbon stocks, such as using appropriate calibration data from sites with minimal human disturbance, measurements of large trees and incorporating environmental variability across the landscape, have generic application to other regions and ecosystem types. These analyses resulted in forest CCC in southeast Australia (mean total biomass of 360 t C ha^?1, with cool moist temperate forests up to 1000 t C ha^?1) that are larger than estimates from other national and international (average biome 202 t C ha^?1) carbon accounting systems. Reducing uncertainty in estimates of carbon stocks in natural forests is important to allow accurate accounting for losses of carbon due to human activities and sequestration of carbon by forest growth.     

Indonesia’s blue carbon: a globally significant and vulnerable sink for seagrass and mangrove carbon

The global significance of carbon storage in Indonesia’s coastal wetlands was assessed based on published and unpublished measurements of the organic carbon content of living seagrass and mangrove biomass and soil pools. For seagrasses, median above- and below-ground biomass was 0.29 and 1.13 Mg C ha^?1 respectively; the median soil pool was 118.1 Mg C ha^?1. Combining plant biomass and soil, median carbon storage in an Indonesian seagrass meadow is 119.5 Mg C ha^?1. Extrapolated to the estimated total seagrass area of 30,000 km², the national storage value is 368.5 Tg C. For mangroves, median above- and below-ground biomass was 159.1 and 16.7 Mg C ha^?1, respectively; the median soil pool was 774.7 Mg C ha^?1. The median carbon storage in an Indonesian mangrove forest is 950.5 Mg C ha^?1. Extrapolated to the total estimated mangrove area of 31,894 km², the national storage value is 3.0 Pg C, a likely underestimate if these habitats sequester carbon at soil depths >1 m and/or sequester inorganic carbon. Together, Indonesia’s seagrasses and mangroves conservatively account for 3.4 Pg C, roughly 17 % of the world’s blue carbon reservoir. Continued degradation and destruction of these wetlands has important consequences for CO₂ emissions and dissolved carbon exchange with adjacent coastal waters. We estimate that roughly 29,040 Gg CO₂ (eq.) is returned annually to the atmosphere–ocean pool. This amount is equivalent to about 3.2 % of Indonesia’s annual emissions associated with forest and peat land conversion. These results highlight the urgent need for blue carbon and REDD+ projects as a means to stem the decline in wetland area and to mitigate the release of a significant fraction of the world’s coastal carbon stores.     

Carbon cycling and storage in world forests: biome patterns related to forest age

Forest age, which is affected by stand‐replacing ecosystem disturbances (such as forest fires, harvesting, or insects), plays a distinguishing role in determining the distribution of carbon (C) pools and fluxes in different forested ecosystems. In this synthesis, net primary productivity (NPP), net ecosystem productivity (NEP), and five pools of C (living biomass, coarse woody debris, organic soil horizons, soil, and total ecosystem) are summarized by age class for tropical, temperate, and boreal forest biomes. Estimates of variability in NPP, NEP, and C pools are provided for each biome‐age class combination and the sources of variability are discussed. Aggregated biome‐level estimates of NPP and NEP were higher in intermediate‐aged forests (e.g., 30–120 years), while older forests (e.g., >120 years) were generally less productive. The mean NEP in the youngest forests (0–10 years) was negative (source to the atmosphere) in both boreal and temperate biomes (?0.1 and –1.9 Mg C ha^?1 yr^?1, respectively). Forest age is a highly significant source of variability in NEP at the biome scale; for example, mean temperate forest NEP was ?1.9, 4.5, 2.4, 1.9 and 1.7 Mg C ha^?1 yr^?1 across five age classes (0–10, 11–30, 31–70, 71–120, 121–200 years, respectively). In general, median NPP and NEP are strongly correlated (R²=0.83) across all biomes and age classes, with the exception of the youngest temperate forests. Using the information gained from calculating the summary statistics for NPP and NEP, we calculated heterotrophic soil respiration (R_h) for each age class in each biome. The mean R_h was high in the youngest temperate age class (9.7 Mg C ha^?1 yr^?1) and declined with age, implying that forest ecosystem respiration peaks when forests are young, not old. With notable exceptions, carbon pool sizes increased with age in all biomes, including soil C. Age trends in C cycling and storage are very apparent in all three biomes and it is clear that a better understanding of how forest age and disturbance history interact will greatly improve our fundamental knowledge of the terrestrial C cycle.     

Spatial yield estimates of fast‐growing willow plantations for energy based on climatic variables in northern Europe

Spatially accurate and reliable estimates from fast‐growing plantations are a key factor for planning energy supply. This study aimed to estimate the yield of biomass from short rotation willow plantations in northern Europe. The data were based on harvesting records from 1790 commercial plantations in Sweden, grouped into three ad hoc categories: low, middle and high performance. The predictors included climatic variables, allowing the spatial extrapolation to nearby countries. The modeling and spatialization of the estimates used boosted regression trees, a method based on machine learning. The average RMSE for the final models selected was 0.33, 0.39 and 1.91 (corresponding to R² = 0.77, 0.88 and 0.45), for the low, medium and high performance categories, respectively. The models were then applied to obtain 1×1 km yield estimates in the rest of Sweden, as well as for Norway, Denmark, Finland, Estonia, Latvia, Lithuania and the Baltic coast of Germany and Poland. The results demonstrated a large regional variation. For the first rotation under high performance conditions, the country averages were as follows: >7 odt ha^?1 yr^?1 in the Baltic coast of Germany, >6 odt ha^?1 yr^?1 in Denmark, >5 odt ha^?1 yr^?1 in the Baltic coast of Poland and between 4–5 odt ha^?1 yr^?1 in the rest. The results of this approach indicate that they can provide faster and more accurate predictions than previous modeling approaches and can offer interesting possibilities in the field of yield modeling.     

Environmental controls on net ecosystem-level carbon exchange and productivity in a Central American tropical wet forest

Difficulty in balancing the global carbon budget has lead to increased attention on tropical forests, which have been estimated to account for up to one third of global gross primary production. Whether tropical forests are sources, sinks, or neutral with respect to their carbon balance with the atmosphere remains unclear. To address this issue, estimates of net ecosystem exchange of carbon (NEE) were made for 3 years (1998–2000) using the eddy‐covariance technique in a tropical wet forest in Costa Rica. Measurements were made from a 42 m tower centred in an old‐growth forest. Under unstable conditions, the measurement height was at least twice the estimated zeroplane height from the ground. The canopy at the site is extremely rough; under unstable conditions the median aerodynamic roughness length ranged from 2.4 to 3.6 m. No relationship between NEE and friction velocity (u*) was found using all of the 30‐min averages. However, there was a linear relationship between the nighttime NEE and averaged u* (R² = 0.98). The diurnal pattern of flux was similar to that found in other tropical forests, with mean daytime NEE ca. ? 18 μ mol CO₂ m^?2 s^?1 and mean nighttime NEE 4.6 μ mol CO₂ m^?2 s^?1. However, because ～ 80% of the nighttime data in this forest were collected during low u* conditions ( < 0.2 m s^?1), nighttime NEE was likely underestimated. Using an alternative analysis, mean nighttime NEE increased to 7.05 μ mol CO₂ m^?2 s^?1. There were interannual differences in NEE, but seasonal differences were not apparent. Irradiance accounted for ～ 51% of the variation in the daytime fluxes, with temperature and vapour pressure deficit together accounting for another ～ 20%. Light compensation points ranged from 100 to 207 μ mol PPFD m^?2 s^?1. No was relationship was found between 30‐min nighttime NEE and tower‐top air temperature. A weak relationship was found between hourly nighttime NEE and canopy air temperature using data averaged hourly over the entire sampling period (Q₁₀ = 1.79, R² = 0.17). The contribution of below‐sensor storage was fairly constant from day to day. Our data indicate that this forest was a slight carbon source in 1998 (0.05 to ?1.33 t C ha^?1 yr^?1), a moderate sink in 1999 (?1.53 to ?3.14 t C ha^?1 yr^?1), and a strong sink in 2000 (?5.97 to ?7.92 t C ha^?1 yr^?1). This trend is interpreted as relating to the dissipation of warm‐phase El Niño effects over the course of this study.     

Carbon sequestration following afforestation of agricultural soils: comparing oak/beech forest to short-rotation poplar coppice combining a process and a carbon accounting model

To compare the benefits for carbon (C) sequestration of afforestation with a multifunctional oak–beech forest vs. a poplar short‐rotation coppice (SRC), model simulations were run through a serial linkage of a mechanistic model and an accounting model. The process model SECRETS (Stand to Ecosystem CaRbon and EvapoTranspiration Simulator) was used to predict growth, C allocation and soil C. The output from SECRETS was used as an input for the C accounting model GORCAM (Graz Oak Ridge Carbon Accounting Model) yielding data on C sequestration in wood products, substitution of wood fuel for fossil fuel and total CO₂ emission reduction. Such C accounting based on a process model enables a more realistic calculation of forest growth, litter decomposition and soil processes. Moreover, it allows simulating the influence of climate change on the C budget. Net primary production of an oak–beech forest is low, a stable 2.5 t C ha^?1 yr^?1 after 150 years, compared to 6.2 t C ha^?1 yr^?1 for a SRC plantation. But while the yield from the SRC poplar is used as fuel and thus returns quickly to the atmosphere, the yield from the oak‐beech forest is used in long‐lasting wood products. The total C pool in the mixed forest (living biomass, wood products and soil) after 150 years amounts to 324 t C ha^?1 compared to 162 in the poplar coppice. However, when account is taken of the energy substitution, coppice culture reduces emissions with 24.3–29.3 t CO₂ ha^?1 yr^?1 while the mixed forest reduces only 6.2–7.1 t CO₂ ha^?1 yr^?1. These results demonstrate the added value of combining detailed process models with C‐accounting models to improve the predictive capacity of model simulations.     

Seventy years of continuous encroachment substantially increases ‘blue carbon’ capacity as mangroves replace intertidal salt marshes

Shifts in ecosystem structure have been observed over recent decades as woody plants encroach upon grasslands and wetlands globally. The migration of mangrove forests into salt marsh ecosystems is one such shift which could have important implications for global ‘blue carbon’ stocks. To date, attempts to quantify changes in ecosystem function are essentially constrained to climate‐mediated pulses (30 years or less) of encroachment occurring at the thermal limits of mangroves. In this study, we track the continuous, lateral encroachment of mangroves into two south‐eastern Australian salt marshes over a period of 70 years and quantify corresponding changes in biomass and belowground C stores. Substantial increases in biomass and belowground C stores have resulted as mangroves replaced salt marsh at both marine and estuarine sites. After 30 years, aboveground biomass was significantly higher than salt marsh, with biomass continuing to increase with mangrove age. Biomass increased at the mesohaline river site by 130 ± 18 Mg biomass km^?2 yr^?1 (mean ± SE), a 2.5 times higher rate than the marine embayment site (52 ± 10 Mg biomass km^?2 yr^?1), suggesting local constraints on biomass production. At both sites, and across all vegetation categories, belowground C considerably outweighed aboveground biomass stocks, with belowground C stocks increasing at up to 230 ± 62 Mg C km^?2 yr^?1 (± SE) as mangrove forests developed. Over the past 70 years, we estimate mangrove encroachment may have already enhanced intertidal biomass by up to 283 097 Mg and belowground C stocks by over 500 000 Mg in the state of New South Wales alone. Under changing climatic conditions and rising sea levels, global blue carbon storage may be enhanced as mangrove encroachment becomes more widespread, thereby countering global warming.     

Evaluating,predicting and mapping belowground carbon stores in Kenyan mangroves

Despite covering only approximately 138 000 km², mangroves are globally important carbon sinks with carbon density values three to four times that of terrestrial forests. A key challenge in evaluating the carbon benefits from mangrove forest conservation is the lack of rigorous spatially resolved estimates of mangrove sediment carbon stocks; most mangrove carbon is stored belowground. Previous work has focused on detailed estimations of carbon stores over relatively small areas, which has obvious limitations in terms of generality and scope of application. Most studies have focused only on quantifying the top 1 m of belowground carbon (BGC). Carbon stored at depths beyond 1 m, and the effects of mangrove species, location and environmental context on these stores, are poorly studied. This study investigated these variables at two sites (Gazi and Vanga in the south of Kenya) and used the data to produce a country‐specific BGC predictive model for Kenya and map BGC store estimates throughout Kenya at spatial scales relevant for climate change research, forest management and REDD+ (reduced emissions from deforestation and degradation). The results revealed that mangrove species was the most reliable predictor of BGC; Rhizophora muronata had the highest mean BGC with 1485.5 t C ha^?1. Applying the species‐based predictive model to a base map of species distribution in Kenya for the year 2010 with a 2.5 m² resolution produced an estimate of 69.41 Mt C [±9.15 95% confidence interval (C.I.)] for BGC in Kenyan mangroves. When applied to a 1992 mangrove distribution map, the BGC estimate was 75.65 Mt C (±12.21 95% C.I.), an 8.3% loss in BGC stores between 1992 and 2010 in Kenya. The country‐level mangrove map provides a valuable tool for assessing carbon stocks and visualizing the distribution of BGC. Estimates at the 2.5 m² resolution provide sufficient details for highlighting and prioritizing areas for mangrove conservation and restoration.     

Carbon pool densities and a first estimate of the total carbon pool in the Mongolian forest‐steppe

The boreal forest biome represents one of the most important terrestrial carbon stores, which gave reason to intensive research on carbon stock densities. However, such an analysis does not yet exist for the southernmost Eurosiberian boreal forests in Inner Asia. Most of these forests are located in the Mongolian forest‐steppe, which is largely dominated by Larix sibirica. We quantified the carbon stock density and total carbon pool of Mongolia's boreal forests and adjacent grasslands and draw conclusions on possible future change. Mean aboveground carbon stock density in the interior of L. sibirica forests was 66 Mg C ha^?1, which is in the upper range of values reported from boreal forests and probably due to the comparably long growing season. The density of soil organic carbon (SOC, 108 Mg C ha^?1) and total belowground carbon density (149 Mg C ha^?1) are at the lower end of the range known from boreal forests, which might be the result of higher soil temperatures and a thinner permafrost layer than in the central and northern boreal forest belt. Land use effects are especially relevant at forest edges, where mean carbon stock density was 188 Mg C ha^?1, compared with 215 Mg C ha^?1 in the forest interior. Carbon stock density in grasslands was 144 Mg C ha^?1. Analysis of satellite imagery of the highly fragmented forest area in the forest‐steppe zone showed that Mongolia's total boreal forest area is currently 73 818 km², and 22% of this area refers to forest edges (defined as the first 30 m from the edge). The total forest carbon pool of Mongolia was estimated at ~ 1.5?1.7 Pg C, a value which is likely to decrease in future with increasing deforestation and fire frequency, and global warming.