Atmospheric phosphorus deposition amplifies carbon sinks in simulations of a tropical forest in Central Africa

Nutrient Addition Enhances the Temperature Sensitivity of Soil Carbon Decomposition Across Forest Ecosystems

Atmospheric nitrogen (N) and phosphorus (P) depositions have been shown to alter nutrient availability in terrestrial ecosystems and thus largely influence soil carbon cycling processes. However, the general pattern of nutrient-induced changes in the temperature response of soil carbon decomposition is unknown. Yet, understanding this pattern is crucial in terms of its effect on soil carbon-climate feedback. Here, we report that N and P additions significantly increase the temperature sensitivity of soil organic carbon decomposition (Q10) by sampling soils from 36 sites across China's forests. We found that N, P, and their co-addition (NP) significantly increased the Q10 by 11.3%, 11.5%, and 23.9%, respectively. The enhancement effect of nutrient addition on Q10 was more evident in soils from warm regions than in those from cold regions. Moreover, we found that nutrient-induced changes in substrate availability and initial substrate and nutrient availability mainly regulated nutrient addition effects. Our findings highlight that N and P deposition enhances the temperature response of soil carbon decomposition, suggesting that N and P deposition should be incorporated into Earth system models to improve the projections of soil carbon feedback to climate change.

Nitrogen and phosphorus additions alter soil N transformations in a Metasequoia glyptostroboides plantation

Introduction

Nitrogen (N) and phosphorus (P) enrichment due to anthropogenic activities can significantly affect soil N transformations in forest ecosystems. However, the effects of N and P additions on nitrification and denitrification processes in Metasequoia glyptostroboides plantations, and economically important forest type in China, remain poorly understood.

Methods

This study investigated the responses of soil nitrification and denitrification rates, as well as the abundances of nitrifiers and denitrifiers, to different levels of N and P additions in a 6-year nutrient addition experiment in a M. glyptostroboides plantation.

Results

Stepwise multiple regression analysis was used to identify the main predictors of nitrification and denitrification rates. The results showed that moderate N addition (N2 treatment, 2.4 mol·m-2) stimulated nitrification rates and abundances of ammonia-oxidizing archaea (AOA) and bacteria (AOB), while excessive N and P additions inhibited denitrification rates and reduced the abundance of nirS-type denitrifiers. AOB abundance was the main predictor of nitrification rates under N additions, whereas microbial biomass carbon and nirS gene abundance were the key factors controlling denitrification rates. Under P additions, tree growth parameters (diameter at breast height and crown base height) and AOB abundance were the primary predictors of nitrification and denitrification rates.

Discussion

Our study reveals complex interactions among nutrient inputs, plant growth, soil properties, and microbial communities in regulating soil N transformations in plantation forests. This study also offers valuable insights for formulating effective nutrient management strategies to enhance the growth and health of M. glyptostroboides plantations under scenarios of increasing elevated nutrient deposition.

Rare earth elements as a tool to study the foliar nutrient uptake phenomenon under ambient and elevated atmospheric CO2 concentration

The ability of plants to uptake nutrients from mineral dust lying on their foliage may prove to be an important mechanism by which plants will cope with increasing CO2 levels in the atmosphere. This mechanism had only recently been reported and was shown to compensate for the projected dilution in plants ionome. However, this phenomenon has yet to be thoroughly studied, particularly in terms of the expected trends under different dust types and varying atmospheric CO2 concentrations, as projected by the IPCC. We treated plants grown under ambient (415 ppm) and elevated CO2 (850 ppm) conditions with either desert dust, volcanic ash, and fire ash analogues by applying it solely on plant foliage and studied their Rare Earth Elements concentrations and patterns. The Rare Earth Elements compositions of the treated plants originated from the dust application, and their incorporation into the plants led to a significant increase in plants vitality, evident in increased photosynthetic activity and biomass. Two trends in the foliar nutrient uptake mechanism were revealed by the Rare Earth Elements, one is that different treatments affected the plant in decreasing order volcanic ash > desert dust > fire ash. The second trend is that foliar intake becomes more significant under elevated CO2, an observation not previously seen. This testifies that the use of Rare Earth Elements in the study of foliar nutrient uptake, and other biological mechanisms is fundamental, and that foliar pathways of nutrient uptake will indeed become more dominant with increasing CO2 under expected atmospheric changes.

Phosphorus depletion is exacerbated by increasing nitrogen loading in the Bohai sea

Phosphorus (P) is an essential nutrient for algal growth in nearshore ecosystems. In recent years, there has been a shift in nutrient dynamics in nearshore areas, leading to an exacerbation of P limitation, although the underlying mechanisms remain unclear. This study analyzed the P species and budget in the Bohai Sea (BS) from 2011 to 2020, aiming to explore the intrinsic mechanisms of P limitation in the BS. The results show that the main external source of P in the BS was river transport (89%), and the primary fate of P was burial (96%) into the sediment. Due to excessive nitrogen (N) input and biological processes in the BS, the P budget in the BS is unbalanced, resulting in an increase in the N/P ratio, particularly in nearshore areas. Nearshore areas typically have lower concentrations of dissolved inorganic P (DIP) in the water and higher concentrations of reactive P (Reac-P) in the sediments. This pattern is particularly evident in Bohai Bay and the northwest nearshore region, where harmful algal blooms occur frequently. To cope with enhanced P limitation, the biologically driven P regeneration and cycling processes within the BS are accelerated. From 2011 to 2020, the concentration of DIP in the BS during autumn increased, while the content of Reac-P in sediments slightly decreased. Historical data indicate that P depletion in the BS is intensifying and expanding, primarily due to N enrichment and algal production. N enrichment alters the structure and composition of primary production, potentially exacerbating P depletion in the BS. Excessive N may have significant impacts on the P pool, potentially influencing the stability of future coastal ecosystems.

Multi-elemental stoichiometric ratios of atmospheric wet deposition in Chinese terrestrial ecosystems

Intense human activities have significantly altered the concentrations of atmospheric components that enter ecosystems through wet and dry deposition, thereby affecting elemental cycles. However, atmospheric wet deposition multi-elemental stoichiometric ratios are poorly understood, hindering systematic exploration of atmospheric deposition effects on ecosystems. Monthly precipitation concentrations of six elements-nitrogen (N), phosphorus (P), sulfur (S), potassium (K), calcium (Ca), and magnesium (Mg)-were measured from 2013 to 2021 by the China Wet Deposition Observation Network (ChinaWD). The multi-elemental stoichiometric ratio of atmospheric wet deposition in Chinese terrestrial ecosystems was N: K: Ca: Mg: S: P = 31: 11: 67: 5.5: 28: 1, and there were differences between vegetation zones. Wet deposition N: S and N: Ca ratios exhibited initially increasing then decreasing inter-annual trends, whereas N: P ratios did not exhibit significant trends, with strong interannual variability. Wet deposition of multi-elements was significantly spatially negatively correlated with soil nutrient elements content (except for N), which indicates that wet deposition could facilitate soil nutrient replenishment, especially for nutrient-poor areas. Wet N deposition and N: P ratios were spatially negatively correlated with ecosystem and soil P densities. Meanwhile, wet deposition N: P ratios were all higher than those of ecosystem components (vegetation, soil, litter, and microorganisms) in different vegetation zones. High input of N deposition may reinforce P limitations in part of the ecosystem. The findings of this study establish a foundation for designing multi-elemental control experiments and exploring the ecological effects of atmospheric deposition.

Machine learning for accelerating process-based computation of land biogeochemical cycles

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge. We focus on the most resource-consuming step in terrestrial biosphere models (TBMs): the equilibration of biogeochemical cycles (spin-up), a prerequisite that can take up to 98% of the computational time. Through three members of the ORCHIDEE TBM family part of the IPSL Earth System Model, including versions that describe the complex interactions between nitrogen, phosphorus and carbon that do not have any analytical solution for the spin-up, we show that an unoptimized MLA reduced the computation demand by 77%-80% for global studies via interpolating the equilibrated state of biogeochemical variables for a subset of model pixels. Despite small biases in the MLA-derived equilibrium, the resulting impact on the predicted regional carbon balance over recent decades is minor. We expect a one-order of magnitude lower computation demand by optimizing the choices of machine learning algorithms, their settings, and balancing the trade-off between quality of MLA predictions and need for TBM simulations for training data generation and bias reduction. Our tool is agnostic to gridded models (beyond TBMs), compatible with existing spin-up acceleration procedures, and opens the door to a wide variety of future applications, with complex non-linear models benefit most from the computational efficiency.