Soil represents the largest phosphorus (P) stock in terrestrial ecosystems. Determining the amount of soil P is a critical first step in identifying sites where ecosystem functioning is potentially limited by soil P availability. However, global patterns and predictors of soil total P concentration remain poorly understood. To address this knowledge gap, we constructed a database of total P concentration of 5275 globally distributed (semi-)natural soils from 761 published studies. We quantified the relative importance of 13 soil-forming variables in predicting soil total P concentration and then made further predictions at the global scale using a random forest approach. Soil total P concentration varied significantly among parent material types, soil orders, biomes, and continents and ranged widely from 1.4 to 9630.0 (median 430.0 and mean 570.0) mg kg⁻¹ across the globe. About two-thirds (65 %) of the global variation was accounted for by the 13 variables that we selected, among which soil organic carbon concentration, parent material, mean annual temperature, and soil sand content were the most important ones. While predicted soil total P concentrations increased significantly with latitude, they varied largely among regions with similar latitudes due to regional differences in parent material, topography, and/or climate conditions. Soil P stocks (excluding Antarctica) were estimated to be 26.8 ± 3.1 (mean ± standard deviation) Pg and 62.2 ± 8.9 Pg (1 Pg = 1 × 10¹⁵ g) in the topsoil (0–30 cm) and subsoil (30–100 cm), respectively. Our global map of soil total P concentration as well as the underlying drivers of soil total P concentration can be used to constraint Earth system models that represent the P cycle and to inform quantification of global soil P availability. Raw datasets and global maps generated in this study are available at https://doi.org/10.6084/m9.figshare.14583375 (He et al., 2021).

Microbial carbon use efficiency (CUE) affects the fate and storage of carbon in terrestrial ecosystems, but its global importance remains uncertain. Accurately modeling and predicting CUE on a global scale is challenging due to inconsistencies in measurement techniques and the complex interactions of climatic, edaphic, and biological factors across scales. The link between microbial CUE and soil organic carbon relies on the stabilization of microbial necromass within soil aggregates or its association with minerals, necessitating an integration of microbial and stabilization processes in modeling approaches. In this perspective, we propose a comprehensive framework that integrates diverse data sources, ranging from genomic information to traditional soil carbon assessments, to refine carbon cycle models by incorporating variations in CUE, thereby enhancing our understanding of the microbial contribution to carbon cycling.

Most phosphorus (P) in soils is unavailable for direct biological uptake, as it is locked within primary or secondary mineral particles, adsorbed to mineral surfaces, or immobilized inside of organic material. Deciphering the composition of different P forms in soil is critical for understanding P bioavailability and its underlying dynamics. However, widely used global estimates of different soil P forms are based on a dataset containing few measurements in which many regions or soil types are unrepresented. This poses a major source of uncertainty in assessments that rely on these estimates to quantify soil P constraints on biological activity controlling global food production and terrestrial carbon balance. To address this issue, we consolidated a database of six major soil P “forms” containing 1857 entries from globally distributed (semi-)natural soils and 11 related environmental variables. These six different forms of P (labile inorganic P (Pi), labile organic P (Po), moderately labile Pi, moderately labile Po, primary mineral P, and occluded P) were measured using a sequential P fractionation method. As they do not represent precise forms of specific discrete P compounds in the soil but rather resemble operational pools, we will now refer to them as P pools. In order to quantify the relative importance of 11 soil-forming variables in predicting soil P pool concentrations and then make further predictions at the global scale, we trained random forest regression models for each of the P pools and captured observed variation with R² higher than 60 %. We identified total soil P concentration as the most important predictor of all soil P pool concentrations, except for primary mineral P concentration, which is primarily controlled by soil pH and only secondarily by total soil P concentration. When expressed in relative values (proportion of total P), the model showed that soil pH is generally the most important predictor for proportions of all soil P pools, alongside the prominent influences of soil organic carbon, total P concentration, soil depth, and biome. These results suggest that, while concentration values of P pools logically strongly depend on soil total P concentration, the relative values of the different pools are modulated by other soil properties and the environmental context. Using the trained random forest models, we predicted soil P pools' distributions in natural systems at a resolution of $\mathrm{0.5}{}^{\circ }\times \mathrm{0.5}{}^{\circ }$ . Our global maps of different P pools in soils as well as the pools' underlying drivers can inform assessments of the role of natural P availability for ecosystem productivity, climate change mitigation, and the functioning of the Earth system.

Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the cycling of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of relative abundance of soil fungi versus bacteria could drive striking differences in carbon decomposition and soil organic matter formation between different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial ecosystems has prevented the inclusion of relative abundance of soil fungi versus bacteria and the associated processes in global biogeochemical models. Here, we used a global-scale dataset of >3000 distinct observations of abundance of soil fungi versus bacteria in the surface topsoil (up to 15 cm) to generate the first quantitative and high-spatial-resolution (1 km²) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high-latitude environments with large soil carbon stocks. There was a strong nonlinear response of fungal dominance to the environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi dominated in regions with low MAT and NPP and bacteria dominated in regions with high MAT and NPP, thus representing slow vs. fast soil energy channels, respectively, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios. Raw datasets and global maps generated in this study are available at https://doi.org/10.6084/m9.figshare.19556419 (Yu, 2022).

Numerous drivers such as farming practices, erosion, land-use change, and soil biogeochemical background, determine the global spatial distribution of phosphorus (P) in agricultural soils. Here, we revised an approach published earlier (called here GPASOIL-v0), in which several global datasets describing these drivers were combined with a process model for soil P dynamics to reconstruct the past and current distribution of P in cropland and grassland soils. The objective of the present update, called GPASOIL-v1, is to incorporate recent advances in process understanding about soil inorganic P dynamics, in datasets to describe the different drivers, and in regional soil P measurements for benchmarking. We trace the impact of the update on the reconstructed soil P. After the update we estimate a global averaged inorganic labile P of 187 kgP ha⁻¹ for cropland and 91 kgP ha⁻¹ for grassland in 2018 for the top 0–0.3 m soil layer, but these values are sensitive to the mineralization rates chosen for the organic P pools. Uncertainty in the driver estimates lead to coefficients of variation of 0.22 and 0.54 for cropland and grassland, respectively. This work makes the methods for simulating the agricultural soil P maps more transparent and reproducible than previous estimates, and increases the confidence in the new estimates, while the evaluation against regional dataset still suggests rooms for further improvement.