Vegetation Nitrogen Research Articles

High-resolution maps of vegetation nitrogen density on the Tibetan Plateau: An intensive field-investigation

Nitrogen (N) is a vital macronutrient in plant growth and development that plays a crucial role in the regulation of numerous physiological processes. The Tibetan Plateau is among the most species-diverse vegetation zones in the world, and is sensitive to climate change; however, research on vegetation N in the region remains limited. This study used field grid-sampling of 2040 plant communities to investigate the spatial variation and driving factors of vegetation N on the Tibetan Plateau. The results yielded an average N content, density and storage in vegetation of 8.48 mg g−1, 27.02 g m−2, and 29.84Tg, respectively. The ratio-based optimal partitioning hypothesis appears to be more suitable than the isometric allocation hypothesis to explain variation in vegetation N on the Tibetan Plateau. Variation in vegetation N density, was influenced by several environmental factors of which the most significant was radiation. Based on these results, a Random Forest model was used to predict a N density distribution map at 1 km resolution, achieving an accuracy (R2) of 0.72 (aboveground N density), 0.61 (belowground N density), and 0.69 (total vegetation N density). Trends for high densities were predicted in the southeast and low densities in the northwest of the region. Our findings and maps could be used to provide key N cycle parameters, contributing to future remote sensing, radar analyses, modeling and ecological management.

Estimating Plant Nitrogen by Developing an Accurate Correlation between VNIR-Only Vegetation Indexes and the Normalized Difference Nitrogen Index

Nitrogen is crucial for plant physiology due to the fact that plants consume a significant amount of nitrogen during the development period. Nitrogen supports the root, leaf, stem, branch, shoot and fruit development of plants. At the same time, it also increases flowering. To monitor the vegetation nitrogen concentration, one of the best indicators developed in the literature is the Normalized Difference Nitrogen Index (NDNI), which is based on the usage of the spectral bands of 1510 and 1680 nm from the Short-Wave Infrared (SWIR) region of the electromagnetic spectrum. However, the majority of remote sensing sensors, like cameras and/or satellites, do not have an SWIR sensor due to high costs. Many vegetation indexes, like NDVI, EVI and MNLI, have also been developed in the VNIR region to monitor the greenness and health of the crops. However, these indexes are not very well correlated to the nitrogen content. Therefore, in this study, a novel method is developed which transforms the estimated VNIR band indexes to NDNI by using a regression method between a group of VNIR indexes and NDNI. Training is employed by using VNIR band indexes as the input and NDNI as the output, both of which are calculated from the same location. After training, an overall correlation of 0.93 was achieved. Therefore, by using only VNIR band sensors, it is possible to estimate the nitrogen content of the plant with high accuracy.

Disentangling the effects of phosphorus, nitrogen and species identity on the vegetation reflectance spectrum

AbstractQuestionsEffective and successful restoration of species‐rich semi‐natural grasslands requires knowledge of the soil nutrient status, including soil phosphorus availability. Plants are solid indicators of soil nutrient status, because their growth reflects nutrient availability integrated over a certain period. The use of reflectance spectroscopy to estimate vegetation nutrient content has the potential to offer a fast and efficient approach to provide information about soil nutrient availability for plants. Here, we investigated the effect and relative importance of vegetation phosphorus content compared with vegetation nitrogen content and species identity in explaining variation in vegetation reflectance.LocationPot experiment mimicking Western European grassland communities on a restoration trajectory.MethodsWe combined a pot experiment with a broad range of mesotrophic grassland species growing along a soil phosphorus gradient with a multivariate modelling approach. We measured vegetation spectra and vegetation phosphorus and nitrogen content in monocultures and mixtures.ResultsAlthough vegetation biochemistry explained a considerable part of the variation in vegetation reflectance, we found no pronounced absorption features for vegetation nitrogen and phosphorus content across the reflectance spectrum. The relatively large effect of species and community identity suggest that other drivers, for example vegetation architecture, overruled the effect of vegetation biochemistry on the reflectance spectrum. Our findings indicate that species detection and indirect case‐specific estimation of nutrients is possible, especially in structurally less‐complex canopies such as monospecific grass swards.ConclusionsDisentangling the specific drivers of variation in spectral reflectance is challenging. Many studies confound the effect of species identity on the vegetation reflectance spectrum with the effect of vegetation biochemistry. Here, we showed the importance of explicitly taking species identity into account. Gaining insight into light–vegetation interactions and the in‐depth integration of ecological theory in remote sensing are the way forward.

Mismatch of N release from the permafrost and vegetative uptake opens pathways of increasing nitrous oxide emissions in the high Arctic.

Biogeochemical cycling in permafrost-affected ecosystems remains associated with large uncertainties, which could impact the Earth's greenhouse gas budget and future climate policies. In particular, increased nutrient availability following permafrost thaw could perturb the greenhouse gas exchange in these systems, an effect largely unexplored until now. Here, we enhance the terrestrial ecosystem model QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system), which simulates fully coupled carbon (C), nitrogen (N) and phosphorus (P) cycles in vegetation and soil, with processes relevant in high latitudes (e.g., soil freezing and snow dynamics). In combination with site-level and satellite-based observations, we use the model to investigate impacts of increased nutrient availability from permafrost thawing in comparison to other climate-induced effects and CO2 fertilization over 1960 to 2018 across the high Arctic. Our simulations show that enhanced availability of nutrients following permafrost thaw account for less than 15% of the total Gross primary productivity increase over the time period, despite simulated N limitation over the high Arctic scale. As an explanation for this weak fertilization effect, observational and model data indicate a mismatch between the timing of peak vegetative growth (week 26-27 of the year, corresponding to the beginning of July) and peak thaw depth (week 32-35, mid-to-late August), resulting in incomplete plant use of nutrients near the permafrost table. The resulting increasing N availability approaching the permafrost table enhances N loss pathways, which leads to rising nitrous oxide (N2 O) emissions in our model. Site-level emission trends of 2mg N m-2 year-1 on average over the historical time period could therefore predict an emerging increasing source of N2 O emissions following future permafrost thaw in the high Arctic.

Thawing Permafrost as a Nitrogen Fertiliser: Implications for Climate Feedbacks

Studies for the northern high latitudes suggest that, in the near term, increased vegetation uptake may offset permafrost carbon losses, but over longer time periods, permafrost carbon decomposition causes a net loss of carbon. Here, we assess the impact of a coupled carbon and nitrogen cycle on the simulations of these carbon fluxes. We present results from JULES-IMOGEN—a global land surface model coupled to an intermediate complexity climate model with vertically resolved soil biogeochemistry. We quantify the impact of nitrogen fertilisation from thawing permafrost on the carbon cycle and compare it with the loss of permafrost carbon. Projections show that the additional fertilisation reduces the high latitude vegetation nitrogen limitation and causes an overall increase in vegetation carbon uptake. This is a few Petagrams of carbon (Pg C) by year 2100, increasing to up to 40 Pg C by year 2300 for the RCP8.5 concentration scenario and adds around 50% to the projected overall increase in vegetation carbon in that region. This nitrogen fertilisation results in a negative (stabilising) feedback on the global mean temperature, which could be equivalent in magnitude to the positive (destabilising) temperature feedback from the loss of permafrost carbon. This balance depends on the future scenario and initial permafrost carbon. JULES-IMOGEN describes one representation of the changes in Arctic carbon and nitrogen cycling in response to climate change. However there are uncertainties in the modelling framework, model parameterisation and missing processes which, when assessed, will provide a more complete picture of the balance between stabilising and destabilising feedbacks.

The Effect of Grazing Intensity on Vegetation Coverage and Nitrogen Mineralization Kinetics of Steppe Rangelands of Iran (Case Study: Nodoushan Rangelands, Yazd, Iran)

Livestock grazing can affect the cycling of nutritional elements in soil by making changes to the vegetation coverage. This study aimed to investigate the effect of rangeland exploitation on vegetation coverage and nitrogen kinetics. To this end, three experimental sites of light, moderate, and heavy grazing in Nodoushan rangelands of Yazd province were selected. The vegetation properties were then measured through systematic random sampling method and three to five bases along the transect were sampled from the current year growth of the dominant plants in the region. The soil samples were collected from 0–15 cm depth in five replications and mixed together to obtain a single composite soil sample on each site. In the first stage, nitrogen (N), carbon (C), C/N, cellulose, hemicellulose, and lignin of the sampled plant as well as nitrogen, carbon, lime, soil texture, saturation moisture percentage, pH, and electrical conductivity (EC) of the soil were measured. As the soil properties did not differ for light and moderate grazing soils, different treatments were conducted on the dominant species of light and heavy grazing sites with 1% organic carbon added to the rangeland soil. Nitrogen mineralization treatments were selected based on vegetation changes that, with increasing livestock grazing intensity, changed the predominance of plant composition from Artemisia sieberi and steppe to percentage Artemisia sieberi and Peganum harmala. The treatments included control, 100% Artemisia sieberi, 75% Artemisia sieberi and 25% Peganum harmala, 50% Artemisia sieberi and 50% Peganum harmala, 25% Artemisia sieberi and 75% Peganum harmala, and 100% Peganum harmala. The soil samples were incubated for pure nitrogen mineralization in three replications of 3 months. The results of nitrogen mineralization revealed that the immobilization of the treated soil with higher Artemisia sieberi and lower Peganum harmala was done at a more rapid rate during the first week. The immobilization was slowly reduced by the third week and then followed a growing rate. Overall, the results show that an increase in grazing intensity was associated with a change in vegetation coverage toward Peganum harmala species, the biochemical characteristics of which elevated the levels of pure nitrogen mineralization in soil.

Estimation of Aboveground Vegetation Nitrogen Contents in the Yellow River Estuary Wetland Using GaoFen-1 Remote Sensing Data

Wang, J.B.; Ren, G.B.; Lin, Z.Y.; Wang, A.D.; Hu, Y.B.; Li, X.M.; Wu, P.Q.; Ma, Y., and Zhang, J., 2020. Estimation and analysis of nitrogen contents in the yellow river estuary wetland using Gaofen-1 remote sensing data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 1-10. Coconut Creek (Florida), ISSN 0749-0208.Nitrogen storage is a key indicator of vegetation growth, and the spatial distribution of nitrogen storage provides important information about the ecological status of wetland ecosystems. In this study, the aboveground vegetation nitrogen content (AVNC) in the Yellow River estuary wetland is estimated based on GaoFen-1 wide field of view (GF-1 WFV) image. Based on the spatial distribution of vegetation and the fractional vegetation coverage (FVC), the characteristics of AVNC are analyzed. On the basis of this study, the best model for estimating AVNC based on vegetation indices is the sample rate index (SRI), whose determination coefficient (R2) is 0.72 and root mean square error (RMSE) is 1.5 g/m2. Per the results of this model, the total AVNC of the study area is obtained, which is 620.6 tons.The total ANVC for freshwater Phragmites australis (FPA), Tamarix chinesis shrub (TCS), Spartina alterniflora (SA), Suaeda salsa (SS), and tidal flat Phragmites australis (TFPA) communities are 296.2 t, 115.3 t, 97.1 t, 86.3 t and 25.7 t, respectively. The average AVNC per unit area for SA, FPA, TCS, TFPA, and SS communities are 4.2 g/m2, 4.8 g/m2, 3.1 g/m2, 1.7 g/m2 and 1.4 g/m2, respectively. The correlation between FVC and AVNC is also analyzed, from which, the spatial distribution of AVNC and FVC is found to be in good agreement. Results of this study thus provide a valuable reference for determining AVNC in coastal wetland ecosystems with implications for ecological health and other ecological characteristics.

The Influence of Selected Habitat and Agronomic Factors on the Yield of Flax (Linum usitatissimum L.)

ABSTRACT The aim of the study was to determine the impact of selected habitat and agronomic factors on the yield of straw and flax seeds of fibrous flax (Linum usitatissimum L). The research factors were: soil agricultural suitability complex, soil pH, soil type and class, length of the vegetation period, nitrogen fertilization, and forecrop. The effect of the Sielianinov hydrothermal coefficient (K) on the yield of this plant was also studied. The paper presents results from 240 exact experiments with fibrous flax of the Nike variety. The research was carried out over 20 years at 12 experimental stations of COBORU – the Research Center for Cultivar Testing in Słupia Wielka, located in Poland. The research was conducted in the period 1985–2007. Flax straw yields were more dependent on the studied factors than seed yields. The crop of straw was favorably affected by cultivation on soils of the following types: good mountain and rye complex, the rendzina type and on soils of class III a and class III b. Of the forecrop tested – rapeseed was the best, and among the nitrogen fertilization levels, the best was highest dose exceeding 60 kg/ha. The soil pH did not affect the straw yield. The highest seed yield was found on the soil of good rye complex, rendzina type, and class V. However, soil pH, forecrop and level of nitrogen fertilization did not differentiate the level of seed yield. The length of the vegetation period differentiated the level of flax yields. The flax crop was positively affected by a longer vegetation period on the soil of cereal-mountain and good wheat complex, brown type with an acid reaction, where flax was cultivated after rape forecrop and the applied nitrogen dose was up to 40 kg/ha. A significant impact of the weather conditions expressed by the Sielianinov hydrothermal coefficient (K) on straw and seed yields was observed.

Intelligent Sampling for Vegetation Nitrogen Mapping Based on Hybrid Machine Learning Algorithms

Upcoming satellite imaging spectroscopy missions will deliver spatiotemporal explicit data streams to be exploited for mapping vegetation properties, such as nitrogen (N) content. Within retrieval workflows for real-time mapping over agricultural regions, such crop-specific information products need to be derived precisely and rapidly. To allow fast processing, intelligent sampling schemes for training databases should be incorporated to establish efficient machine learning (ML) models. In this study, we implemented active learning (AL) heuristics using kernel ridge regression (KRR) to minimize and optimize a training database for variational heteroscedastic Gaussian processes regression (VHGPR) to estimate aboveground N content. Several uncertainty and diversity criteria were applied on a lookup table (LUT) composed of aboveground N content and corresponding hyperspectral reflectance simulated by the PROSAIL-PRO model. The best-performing AL criteria were Euclidian distance-based diversity (EBD) resulting in a reduction of the LUT training data set by 81% (50 initial samples plus 141 samples selected from a pool of 1000 samples). This reduced LUT was used for training VHGPR, which is not only a competitive algorithm but also provides uncertainty estimates. Validation against in situ N reference data provided excellent results with a root-mean-square error (RMSE) of 1.84 g/m2 and a coefficient of determination (R2 ) of 0.92. Mapping aboveground N content over an agricultural region yielded reliable estimates and meaningful associated uncertainties. These promising results encourage the transfer of such hybrid workflows into space and time within the frame of future operational N monitoring from satellite imaging spectroscopy data.

Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes

We present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen. Phase 3 follows a balanced-accumulation trajectory along a continuum of quasi-steady states that approaches the true steady state asymptotically. We examine the effects of three ecosystem properties on recovery: openness of the nitrogen cycle, nitrogen distribution in and turnover between vegetation and soils, and the proportion of nitrogen losses that are in a refractory form. Openness exacerbates Phase 1 nitrogen losses but speeds recovery in Phases 2 and 3. A high fraction of ecosystem nitrogen in vegetation, resulting from nitrogen turnover that is slow in vegetation but fast in soil, exacerbates Phase 1 nitrogen losses but speeds recovery in Phases 2 and 3. A high proportion of nitrogen loss in refractory form mitigates Phase 1 nitrogen losses and speeds recovery in Phases 2 and 3. Application of our conceptual framework requires empirical recognition of the continuum of quasi-steady states constituting the balanced-accumulation trajectory and a distinction between the balanced-accumulation trajectory and the true steady state.

Plant and Soil Nitrogen in an Ombrotrophic Peatland, Southern Canada

We examined the concentration of nitrogen (N) and δ15N in vegetation and peat in the Mer Bleue bog, Ontario, Canada. Compared with other ecosystems, N concentration in bog plant foliage is low (generally < 1.2%), with more N stored belowground than in foliar tissues. The natural abundance of 15N varies by plant functional type, indicating different strategies for nutrient acquisition. δ15N values of evergreen shrubs and trees are lower (~− 5 to ~− 9‰) than sedges and herbs (~+ 1 to ~− 1‰), likely the result of ericoid mycorrhizal fungi, which retain 15N in fungal biomass, with preferential transfer of 14N to the host plant. Results suggest that N in Sphagnum moss capitula is derived from uptake of N from decomposing peat and litter, with δ15N values of − 8 to − 2‰, rather than from N2 fixation. Soil δ15N varies with depth, related to degree of decomposition and dominant plant species litter. Foliar N plays a critical role in influencing the rate of photosynthesis and CO2 exchange. Despite paucity of available inorganic N in the system, most bog plants (that is, evergreen shrubs) have adapted with conservative growth strategies. Owing to slow decomposition, N is stored in this system tightly bound with organic matter, with C:N ratios in deep peat ranging from 33:1 to 23:1 and small fluxes into, out of and within the system. An increase in N availability, through atmospheric N deposition or water table lowering, could jeopardize the C sink function of bogs by stimulating microbial decomposition of organic matter. In the long term, an increase in N cycling would shift plant community composition from Sphagnum and evergreen dominated to deciduous species, fundamentally altering bog ecosystem function.

External and local controls on land-sea coupling assessed by stable isotopic signatures of mangrove producers in estuaries of Pacific Panama

Foliar stable isotopic signatures of nitrogen, carbon, and sulfur in mangrove vegetation from the Pacific coast of Panama were insensitive to inputs from watersheds with different area of forest land cover, and to seasonal, inter-annual, and global-scale-driven contrasts in rainfall and upwelling. N, C, and S content of mangrove vegetation were not affected by inputs from watersheds with different degrees of deforestation, but showed some influence of down-estuary transformations. While there was substantial variation that remained un-explained, isotopic signatures and nutrient contents were largely determined by species-specific features, and showed substantial small-scale variation reflecting local differences, within-estuary plant-sediment links. The ability of mangrove estuaries to erase effects of deforestation points out that conservation of these wetland ecosystems is important, because, at least in the sites we studied, transformations within mangrove estuaries were strong enough to protect water quality in receiving coastal waters.

Plastic adaptations of foraging strategies to variation in forage quality in Alpine chamois (Rupicapra rupicapra)

Foraging efficiency strongly affects individual fitness and is influenced by diverse factors such as food quality and quantity, as well as intra- and inter-specific interactions. We investigated whether Alpine chamois (Rupicapra rupicapra (Linnaeus, 1758)) in a protected area in the Swiss Alps adapted their foraging behaviour to forage availability and quality by modeling the bite and step rates of individuals on vegetation nitrogen content, relative plant cover, sex, daytime, air temperature, and slope. Vegetation characteristics were derived using remote sensing data from airborne imaging spectroscopy data sets and feeding locations determined using a theodolite. Chamois increased their bite rates with decreasing forage nitrogen content, decreasing slope, and increasing temperature. Step rates were higher at high temperatures and decreased with increasing relative plant cover. Males showed higher bite rates and lower step rates than females. Daytime had no influence on either bite or step rates. An increase in bite rate may represent a plastic adaptation of foraging behaviour to compensate for lower nutritional quality of the available vegetation. Our results show variability in foraging behaviour according to both vegetation characteristics and physical environment and emphasize the use of remote sensing data to investigate relationships between habitat and subtle behavioural adaptations in ungulates.

遥感反演植被含氮量研究进展

遥感反演植被含氮量研究进展

Root traits explain observed tundra vegetation nitrogen uptake patterns: Implications for trait‐based land models

AbstractOngoing climate warming will likely perturb vertical distributions of nitrogen availability in tundra soils through enhancing nitrogen mineralization and releasing previously inaccessible nitrogen from frozen permafrost soil. However, arctic tundra responses to such changes are uncertain, because of a lack of vertically explicit nitrogen tracer experiments and untested hypotheses of root nitrogen uptake under the stress of microbial competition implemented in land models. We conducted a vertically explicit 15N tracer experiment for three dominant tundra species to quantify plant N uptake profiles. Then we applied a nutrient competition model (N‐COM), which is being integrated into the ACME Land Model, to explain the observations. Observations using an 15N tracer showed that plant N uptake profiles were not consistently related to root biomass density profiles, which challenges the prevailing hypothesis that root density always exerts first‐order control on N uptake. By considering essential root traits (e.g., biomass distribution and nutrient uptake kinetics) with an appropriate plant‐microbe nutrient competition framework, our model reasonably reproduced the observed patterns of plant N uptake. In addition, we show that previously applied nutrient competition hypotheses in Earth System Land Models fail to explain the diverse plant N uptake profiles we observed. Our results cast doubt on current climate‐scale model predictions of arctic plant responses to elevated nitrogen supply under a changing climate and highlight the importance of considering essential root traits in large‐scale land models. Finally, we provided suggestions and a short synthesis of data availability for future trait‐based land model development.

Hyperspectral characteristic analysis for leaf nitrogen content in different growth stages of winter wheat.

The spectral characteristics in the range of visible light and near-infrared shortwave (400-1000 nm) are analyzed using the ground measured hyperspectral data and leaf nitrogen content (LNC) data of different growth stages of winter wheat, which were acquired in 2013 and 2015. First, the quantitative models for monitoring the LNC at different growth stages of winter wheat were established using the main vegetation nitrogen spectral indices. By analyzing the simulation coefficient of the models, it is demonstrated that vegetation nitrogen spectral indices, which are calculated using these data in this study, should not be an effective quantitative estimate for winter wheat LNC. Second, a method for finding representation wavebands of hyperspectral data sensitive to the LNC of winter wheat is proposed based on the spectral correlation. Using the hyperspectral data, which were acquired in 2015 and the proposed method, the representation wavebands sensitive to the LNC of winter wheat are found. The finding results show that the representative wavebands are mainly located in the purple, red and near-infrared wavelength range, but the representative wavebands are different in different stages. The red edge effects of representative wavebands are obvious. Finally, based on the acquired representation wavebands corresponding to different growth stages of winter wheat, the quantitative models for monitoring the LNC at different growth stages of winter wheat were established using data acquired in 2015 and 2013. The modeling results show that the combination of representation wavebands found to correspond to different growth stages of winter wheat are effective and credible for monitoring the LNC. So, these research results lay the foundation for accurate quantitative monitoring of winter wheat LNC.

Bacterial communities and enzymatic activities in the vegetation-activated sludge process (V-ASP) and related advantages by comparison with conventional constructed wetland

A new-developed vegetation-activated sludge process (V-ASP) was implemented for decentralized domestic wastewater treatment, and studied in lab-scale and full-scale. The main purpose of this work was the investigation of biomass activities and microbial communities in V-ASP by comparison with conventional constructed wetland (CW), to unveil the causations of its consistently higher pollutants removal efficiencies. Compared with CWs, V-ASP has greater vegetation nitrogen and phosphorus uptake rates, higher biomass and enzymatic activities, and more bacteria community diversity. The microbial community structure was comprehensively analyzed by using high-throughput sequencing. It was observed that Proteobacteria was dominated in both CWs and V-ASPs, while their subdivisions distribution was rather different. V-ASPs contained a higher nitrite-oxidizing bacteria (Nitrospira) abundances that resulted in a consistently better nitrogen removal efficiency. Hence, a long-term experiment of full-scale V-ASP displayed stably excellent capability in resistance of influent loading shocks and seasonal temperature effect.

Accumulation of carbon and nitrogen in vegetation and soils of deglaciated area in Ellesmere Island, high-Arctic Canada

The amount of biomass, carbon (C), and nitrogen (N) in vegetation and soil were measured at two spatial scales in the high Arctic. At the scale of proglacial landscape, the amount of C and N in aboveground and belowground parts of vegetation, surface litter, and soil were significantly affected by the habitat (moraines vs hummocks), the relative age of the terrain after the deglaciation, and/or the vegetation. At another scale, we focused on mudboils as an agent of local disturbance in the vegetation and soil of the glacier foreland. The biomass and the amount of C and N in aboveground vegetation, surface litter, biological soil crust, and soil were generally increased with the stage of mudboils' inactivation. Biomass, C, and N in aboveground vegetation and surface litter were generally greater at moraine than at hummock, whereas those in biological soil crust and soil were greater at hummock. Principal component analysis identified two pathways, xeric and mesic ones on moraines and hummocks, respectively, of C and N accumulation both at the two spatial scales. These results suggested that the C and N accumulation was not linearly related to the time since deglaciation and that moisture condition, vegetation, and mudboil activity were locally important.

Stoichiometric characteristics of nitrogen and phosphorus in major wetland vegetation of China

Aims Little is known on the stoichiometric characteristics of nitrogen (N) and phosphorus (P) in wetland vegetation across China. This work is aimed to study the stoichiometric characteristics of wetland vegetation in China, which are of great significance for revealing the coupling relationship between vegetation and habitats. Methods Based on previous studies, a data set comprising N and P contents and N:P of wetland vegetation attained from 649 observations in 52 study sites, was classified and analyzed, which concerned vegetation organs, growth periods, plant life forms, climate zones and wetland types. Important findings The geometric mean of N, P and the N:P of different organs occurred in descending order as follows: leaf (N, 16.07 mg·g; P, 1.85 mg·g; N:P, 8.67) > aboveground organ (N, 13.54 mg·g; P, 1.72 mg·g; N:P, 7.96) > stem (N, 7.86 mg·g; P, 1.71 mg·g; N:P, 4.58). Leaf N showed a three-peak seasonal distribution, with the peaks occurring in May, July and September, whilst stem N exhibited a bimodal distribution with peaks in May and September. Leaf N:P fluctuated in conformity to N before maturity, but changed corresponding to P in senescence phase. The type of wetlands was found to be the key factor significantly impacted the stoichiometric characteristics of wetland vegetation in China. Specifically, the highest and lowest leaf N and P contents were found in the bottom lands and marshes, respectively, while the N:P displayed a reversed trend. The geometric mean of leaf N, P contents and the N:P followed the order of: tropics > temperate zone > subtropics, albeit no significant differences (p > 0.05) among them. In addition, most of the leaf N:P were less than 14, indicating that the wetland vegetation in China was under the conditions of N limitation.

Understanding the optical responses of leaf nitrogen in Mediterranean Holm oak (Quercus ilex) using field spectroscopy

The direct estimation of nitrogen (N) in fresh vegetation is challenging due to its weak influence on leaf reflectance and the overlaps with absorption features of other compounds. Different empirical models relate in this work leaf nitrogen concentration ([N]Leaf) on Holm oak to leaf reflectance as well as derived spectral indices such as normalized difference indices (NDIs), the three bands indices (TBIs) and indices previously used to predict leaf N and chlorophyll. The models were calibrated and assessed their accuracy, robustness and the strength of relationship when other biochemicals were considered. Red edge was the spectral region most strongly correlated with [N]Leaf, whereas most of the published spectral indexes did not provide accurate estimations. NDIs and TBIs based models could achieve robust and acceptable accuracies (TBI1310,1720,730: R2=0.76, [0.64,0.86]; RMSE (%)=9.36, [7.04,12.83]). These models sometimes included indices with bands close to absorption features of N bonds or nitrogenous compounds, but also of other biochemicals. Models were independently and inter-annually validated using the bootstrap method, which allowed discarding those models non-robust across different years. Partial correlation analysis revealed that spectral estimators did not strongly respond to [N]Leaf but to other leaf variables such as chlorophyll and water, even if bands close to absorption features of N bonds or compounds were present in the models.