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

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.