Hemicelluloses are the second most abundant polysaccharide in nature after cellulose. So far, the chemical heterogeneity of cell-wall hemicelluloses and the relatively large sample-volume required in ...existing methods represent major obstacles for large-scale, cross-species analyses of this important plant compound. Here, we apply a new micro-extraction method to analyse hemicelluloses and the ratio of ‘cellulose and lignin’ to hemicelluloses in different tissues of 28 plant species comprising four plant functional types (broad-leaved trees, conifers, grasses and herbs). For this study, the fiber analysis after Van Soest was modified to enable the simultaneous quantitative and qualitative measurements of hemicelluloses in small sample volumes. Total hemicellulose concentrations differed markedly among functional types and tissues with highest concentration in sapwood of broad-leaved trees (31% d.m. in
Fraxinus excelsior) and lowest concentration between 10 and 15% d.m. in leaves and bark of woody species as well as in roots of herbs. As for total hemicellulose concentrations, plant functional types and tissues exhibited characteristic ratios between the sum of cellulose plus lignin and hemicelluloses, with very high ratios (>4) in bark of trees and low ratios (<2) in all investigated leaves. Additional HPLC analyses of hydrolysed hemicelluloses showed xylose to be the dominant hemicellulose monosaccharide in tissues of broad-leaved trees, grasses and herbs while coniferous species showed higher amounts of arabinose, galactose and mannose. Overall, the micro-extraction method permitted for the simultaneous determination of hemicelluloses of various tissues and plant functional types which exhibited characteristic hemicellulose concentrations and monosaccharide patterns.
Phenological transitions determine the timing of changes in land surface properties and the seasonality of exchanges of biosphere-atmosphere energy, water, and carbon. Accurate mechanistic modeling ...of phenological processes is therefore critical to understand and correctly predict terrestrial ecosystem feedbacks with changing atmospheric and climate conditions. However, the phenological components in the land model of the US Department of Energy's (DOE) Energy Exascale Earth System Model (ELM of E3SM) were previously unable to accurately capture the observed phenological responses to environmental conditions in a well-studied boreal peatland forest. In this research, we introduced new seasonal-deciduous phenology schemes into version 1.0 of ELM and evaluated their performance against the PhenoCam observations at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment in northern Minnesota from 2015 to 2018. We found that phenology simulated by the revised ELM (i.e., earlier spring onsets and stronger warming responses of spring onsets and autumn senescence) was closer to observations than simulations from the original algorithms for both the deciduous conifer (Larix laricina) and mixed shrub layers. Moreover, the revised ELM generally produced higher carbon and water fluxes (e.g., photosynthesis and evapotranspiration) during the growing season and stronger flux responses to warming than the default ELM. A parameter sensitivity analysis further indicated the significant contribution of phenology parameters to uncertainty in key carbon and water cycle variables, underscoring the importance of precise phenology parameterization. Furthermore, this phenological modeling effort demonstrates the potential to enhance the E3SM representation of land-climate interactions at broader spatiotemporal scales, especially under anticipated elevated CO2 and warming conditions.
Both elevated atmospheric carbon dioxide (CO
2
) and nitrogen (N) deposition may induce changes in C:N ratios in plant tissues and mineral soil. However, the potential mechanisms driving the ...stoichiometric shifts remain elusive. In this study, we examined the responses of C:N ratios in both plant tissues and mineral soil to elevated CO
2
and N deposition using data extracted from 140 peer-reviewed publications. Our results indicated that C:N ratios in both plant tissues and mineral soil exhibited consistent increases under elevated CO
2
regimes whereas decreases in C:N ratios were observed in response to experimental N addition. Moreover, soil C:N ratio was less sensitive than plant C:N ratio to both global change scenarios. Our results also showed that the responses of stoichiometric ratios were highly variable among different studies. The changes in C:N ratio did not exhibit strong correlations with C dynamics but were negatively associated with corresponding changes in N content. These results suggest that N dynamics drive stoichiometric shifts in both plant tissues and mineral soil under both elevated CO
2
and N deposition scenarios.
Methane emissions from boreal and arctic wetlands, lakes, and rivers are
expected to increase in response to warming and associated permafrost thaw.
However, the lack of appropriate land cover ...datasets for scaling
field-measured methane emissions to circumpolar scales has contributed to a
large uncertainty for our understanding of present-day and future methane
emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset
(BAWLD), a land cover dataset based on an expert assessment, extrapolated
using random forest modelling from available spatial datasets of climate,
topography, soils, permafrost conditions, vegetation, wetlands, and surface
water extents and dynamics. In BAWLD, we estimate the fractional coverage of
five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes
(17 % of the global land surface). Land cover classes were defined using
criteria that ensured distinct methane emissions among classes, as indicated
by a co-developed comprehensive dataset of methane flux observations. In
BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain)
with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland
classes, covering ∼ 28 % each of the total wetland area,
while the highest-methane-emitting marsh and tundra wetland classes occupied
5 % and 12 %, respectively. Lakes, defined to include all lentic open-water
ecosystems regardless of size, covered 1.4 × 106 km2
(6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake
area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %,
respectively. Small (<0.1 km2) glacial, peatland, and yedoma
lakes combined covered 17 % of the total lake area but contributed
disproportionally to the overall spatial uncertainty in lake area with a
95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with
high-methane-emitting headwaters that drain organic-rich landscapes.
Distinct combinations of spatially co-occurring wetland and lake classes
were identified across the BAWLD domain, allowing for the mapping of
“wetscapes” that have characteristic methane emission magnitudes and
sensitivities to climate change at regional scales. With BAWLD, we provide a
dataset which avoids double-accounting of wetland, lake, and river extents
and which includes confidence intervals for each land cover class. As such,
BAWLD will be suitable for many hydrological and biogeochemical modelling
and upscaling efforts for the northern boreal and arctic region, in
particular those aimed at improving assessments of current and future
methane emissions. Data are freely available at
https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).
Soil organic matter (SOM) is heterogeneous in structure and has been considered to consist of various pools with different intrinsic turnover rates. Although those pools have been conceptually ...expressed in models and analyzed according to soil physical and chemical properties, separation of SOM into component pools is still challenging. In this study, we conducted inverse analyses with data from a long-term (385 days) incubation experiment with two types of soil (from plant interspace and from underneath plants) to deconvolute soil carbon (C) efflux into different source pools. We analyzed the two datasets with one-, two- and three-pool models and used probability density functions as a criterion to judge the best model to fit the datasets. Our results indicated that soil C release trajectories over the 385 days of the incubation study were best modeled with a two-pool C model. For both soil types, released C within the first 10 days of the incubation study originated from the labile pool. Decomposition of C in the recalcitrant pool was modeled to contribute to the total CO
2
efflux by 9–11 % at the beginning of the incubation. At the end of the experiment, 75–85 % of the initial soil organic carbon (SOC) was modeled to be released over the incubation period. Our modeling analysis also indicated that the labile C-pool in the soil underneath plants was larger than that in soil from interspace. This deconvolution analysis was based on information contained in incubation data to separate carbon pools and can facilitate integration of results from incubation experiments into ecosystem models with improved parameterization.
Emerging leaves in evergreen tree species are supplied with carbon (C) from the previous year's foliage. In deciduous trees, no older leaves are present, and the early phase of leaf development must ...rely on C reserves from other tissues. How soon developing leaves become autotrophic and switch from being C sinks to sources has rarely been studied in mature forest trees, and simultaneous comparisons of species are scarce. Using a canopy crane and a simple (13)CO(2)-pulse-labelling technique, we demonstrate that young leaves of mature trees in three European deciduous species (Fagus sylvatica L., Quercus petraea (Matt.) Liebl., Tilia platyphyllos Scop.) start assimilating CO(2) at a very early stage of development (10-50% expanded). One month after labelling, all leaves were still strongly (13)C enriched, suggesting that recent photosynthates had been incorporated into slow turnover pools such as cellulose or lignin and thus had contributed to leaf growth. In line with previous studies performed at the same site, we found stronger incorporation of recent photosynthates into growing tissues of T. platyphyllos compared with F. sylvatica and Q. petraea. Non-structural carbohydrate (NSC) concentrations analysed for one of the three study species (F. sylvatica) showed that sugar and starch pools rapidly increased during leaf development, suggesting that newly developed leaves soon produce more NSC than can be used for growth. In conclusion, our findings indicate that expanding leaves of mature deciduous trees become C autonomous at an early stage of development despite the presence of vast amounts of mobile carbohydrate reserves.
Hemicelluloses account for one-quarter of the global dry plant biomass and therefore are the second most abundant biomass fraction after cellulose. Despite their quantitative significance, the ...responsiveness of hemicelluloses to atmospheric carbon oversupply is still largely unknown, although hemicelluloses could serve as carbon sinks with increasing CO₂ concentrations. This study aimed at clarifying the role hemicelluloses play as carbon sinks, analogous to non-structural carbohydrates (NSC), by experimentally manipulating the plants' carbon supply. Sixteen plant species from four different plant functional types (grasses, herbs, seedlings of broad-leaved trees and conifers) were grown for 2 months in greenhouses at either extremely low (140 ppm), medium (280 ppm) or high (560 ppm) atmospheric CO₂ concentrations, thus inducing situations of massive C-limitation or -oversupply. Above and belowground biomass as well as NSC significantly increased in all species and tissues with increasing CO₂ concentrations. Increasing CO₂ concentrations had no significant effect on total hemicellulose concentrations in leaves and woody tissues in all species, except for two out of four grass species, where hemicellulose concentrations increased with atmospheric CO₂ supply. Despite the overall stable total hemicellulose concentrations, the monosaccharide spectra of hemicelluloses showed a significant increase in glucose monomers in leaves of woody species as C-supply increased. In summary, total hemicellulose concentrations in de novo built biomass seem to be largely unaffected by changed atmospheric CO₂ concentrations, while significant increases of hemicellulose-derived glucose with increasing CO₂ concentrations in leaves of broad-leaved and conifer tree seedlings showed differential responses among the different hemicellulose classes in response to varying CO₂ concentrations.
Rapid Arctic warming is expected to increase global greenhouse gas concentrations as permafrost thaw exposes immense stores of frozen carbon (C) to microbial decomposition. Permafrost thaw also ...stimulates plant growth, which could offset C loss. Using data from 7 years of experimental Air and Soil warming in moist acidic tundra, we show that Soil warming had a much stronger effect on CO
flux than Air warming. Soil warming caused rapid permafrost thaw and increased ecosystem respiration (R
), gross primary productivity (GPP), and net summer CO
storage (NEE). Over 7 years R
, GPP, and NEE also increased in Control (i.e., ambient plots), but this change could be explained by slow thaw in Control areas. In the initial stages of thaw, R
, GPP, and NEE increased linearly with thaw across all treatments, despite different rates of thaw. As thaw in Soil warming continued to increase linearly, ground surface subsidence created saturated microsites and suppressed R
, GPP, and NEE. However R
and GPP remained high in areas with large Eriophorum vaginatum biomass. In general NEE increased with thaw, but was more strongly correlated with plant biomass than thaw, indicating that higher R
in deeply thawed areas during summer months was balanced by GPP. Summer CO
flux across treatments fit a single quadratic relationship that captured the functional response of CO
flux to thaw, water table depth, and plant biomass. These results demonstrate the importance of indirect thaw effects on CO
flux: plant growth and water table dynamics. Nonsummer R
models estimated that the area was an annual CO
source during all years of observation. Nonsummer CO
loss in warmer, more deeply thawed soils exceeded the increases in summer GPP, and thawed tundra was a net annual CO
source.
Soil organic matter (SOM) is heterogeneous in structure and has been considered to consist of various pools with different intrinsic turnover rates. Although those pools have been conceptually ...expressed in models and analyzed according to soil physical and chemical properties, separation of SOM into component pools is still challenging. In this study, we conducted inverse analyses with data from a long-term (385 days) incubation experiment with two types of soil (from plant interspace and from underneath plants) to deconvolute soil carbon (C) efflux into different source pools. We analyzed the two datasets with one-, two- and three-pool models and used probability density functions as a criterion to judge the best model to fit the datasets. Our results indicated that soil C release trajectories over the 385 days of the incubation study were best modeled with a two-pool C model. For both soil types, released C within the first 10 days of the incubation study originated from the labile pool. Decomposition of C in the recalcitrant pool was modeled to contribute to the total CO 2 efflux by 9—11 % at the beginning of the incubation. At the end of the experiment, 75—85 % of the initial soil organic carbon (SOC) was modeled to be released over the incubation period. Our modeling analysis also indicated that the labile C-pool in the soil underneath plants was larger than that in soil from interspace. This deconvolution analysis was based on information contained in incubation data to separate carbon pools and can facilitate integration of results from incubation experiments into ecosystem models with improved parameterization.
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