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Abstract Increased plant growth under elevated carbon dioxide (CO₂) slows the pace of climate warming and underlies projections of terrestrial carbon (C) and climate dynamics. However, this important ecosystem service may be diminished by concurrent changes to vegetation carbon‐to‐nitrogen (C:N) ratios. Despite clear observational evidence of increasing foliar C:N under elevated CO₂, our understanding of potential ecological consequences of foliar stoichiometric flexibility is incomplete. Here, we illustrate that when we incorporated CO₂‐driven increases in foliar stoichiometry into the Community Land Model the projected land C sink decreased two‐fold by the end of the century compared to simulations with fixed foliar chemistry. Further, CO₂‐driven increases in foliar C:N profoundly altered Earth's hydrologic cycle, reducing evapotranspiration and increasing runoff, and reduced belowground N cycling rates. These findings underscore the urgency of further research to examine both the direct and indirect effects of changing foliar stoichiometry on soil N cycling and plant productivity. 

Abstract Alpine tundra ecosystems are highly vulnerable to climate warming but are governed by local‐scale abiotic heterogeneity, which makes it difficult to predict tundra responses to environmental change. Although land models are typically implemented at global scales, they can be applied at local scales to address process‐based ecological questions. In this study, we ran ecosystem‐scale Community Land Model (CLM) simulations with a novel hillslope hydrology configuration to represent topographically heterogeneous alpine tundra vegetation across a moisture gradient at Niwot Ridge, Colorado, USA. We used local observations to evaluate our simulations and investigated the role of topography and aspect in mediating patterns of snow, productivity, soil moisture, and soil temperature, as well as the potential exposure to climate change across an alpine tundra hillslope. Overall, our simulations captured observed gradients in abiotic conditions and productivity among heterogeneous, hydrologically connected vegetation communities (moist, wet, and dry). We found that south facing aspects were characterized by reduced snowpack and drier and warmer soils in all communities. When we extended our simulations to the year 2100, we found that earlier snowmelt altered the timing of runoff, with cascading effects on soil moisture, productivity, and growing season length. However, these effects were not distributed equally across the tundra, highlighting potential vulnerabilities of alpine vegetation in dry, wind‐scoured, and south facing areas. Overall, our results demonstrate how land model outputs can be applied to advance process‐based understanding of climate change impacts on ecosystem function. 

Abstract Plant element stoichiometry and stoichiometric flexibility strongly regulate ecosystem responses to global change. Here, we tested three potential mechanistic drivers (climate, soil nutrients, and plant taxonomy) of both using paired foliar and soil nutrient data from terrestrial forested National Ecological Observatory Network sites across the USA. We found that broad patterns of foliar nitrogen (N) and foliar phosphorus (P) are explained by different mechanisms. Plant taxonomy was an important control over all foliar nutrient stoichiometries and concentrations, especially foliar N, which was dominantly related to taxonomy and did not vary across climate or soil gradients. Despite a lack of site‐level correlations between N and environment variables, foliar N exhibited intraspecific flexibility, with numerous species‐specific correlations between foliar N and various environmental factors, demonstrating the variable spatial and temporal scales on which foliar chemistry and stoichiometric flexibility can manifest. In addition to plant taxonomy, foliar P and N:P ratios were also linked to soil nutrient status (extractable P) and climate, especially actual evapotranspiration rates. Our findings highlight the myriad factors that influence foliar chemistry and show that broad patterns cannot be explained by a single consistent mechanism. Furthermore, differing controls over foliar N versus P suggests that each may be sensitive to global change drivers on distinct spatial and temporal scales, potentially resulting in altered ecosystem N:P ratios that have implications for processes ranging from productivity to carbon sequestration. 

Abstract Accurately quantifying rates and patterns of biological nitrogen fixation (BNF) in terrestrial ecosystems is essential to characterize ecological and biogeochemical interactions, identify mechanistic controls, improve BNF representation in conceptual and numerical modelling, and forecast nitrogen limitation constraints on future carbon (C) cycling.While many resources address the technical advantages and limitations of different methods for measuring BNF, less systematic consideration has been given to the broader decisions involved in planning studies, interpreting data, and extrapolating results. Here, we present a conceptual and practical road map to study design, study execution, data analysis and scaling, outlining key considerations at each step.We address issues including defining N‐fixing niches of interest, identifying important sources of temporal and spatial heterogeneity, designing a sampling scheme (including method selection, measurement conditions, replication, and consideration of hotspots and hot moments), and approaches to analysing, scaling and reporting BNF. We also review the comparability of estimates derived using different approaches in the literature, and provide sample R code for simulating symbiotic BNF data frames and upscaling.Improving and standardizing study design at each of these stages will improve the accuracy and interpretability of data, define limits of extrapolation, and facilitate broader use of BNF data for downstream applications. We highlight aspects—such as quantifying scales of heterogeneity, statistical approaches for dealing with non‐normality, and consideration of rates versus ecological significance—that are ripe for further development. 

Vegetation processes are fundamentally limited by nutrient and water availability, the uptake of which is mediated by plant roots in terrestrial ecosystems. While tropical forests play a central role in global water, carbon, and nutrient cycling, we know very little about tradeoffs and synergies in root traits that respond to resource scarcity. Tropical trees face a unique set of resource limitations, with rock-derived nutrients and moisture seasonality governing many ecosystem functions, and nutrient versus water availability often separated spatially and temporally. Root traits that characterize biomass, depth distributions, production and phenology, morphology, physiology, chemistry, and symbiotic relationships can be predictive of plants’ capacities to access and acquire nutrients and water, with links to aboveground processes like transpiration, wood productivity, and leaf phenology. In this review, we identify an emerging trend in the literature that tropical fine root biomass and production in surface soils are greatest in infertile or sufficiently moist soils. We also identify interesting paradoxes in tropical forest root responses to changing resources that merit further exploration. For example, specific root length, which typically increases under resource scarcity to expand the volume of soil explored, instead can increase with greater base cation availability, both across natural tropical forest gradients and in fertilization experiments. Also, nutrient additions, rather than reducing mycorrhizal colonization of fine roots as might be expected, increased colonization rates under scenarios of water scarcity in some forests. Efforts to include fine root traits and functions in vegetation models have grown more sophisticated over time, yet there is a disconnect between the emphasis in models characterizing nutrient and water uptake rates and carbon costs versus the emphasis in field experiments on measuring root biomass, production, and morphology in response to changes in resource availability. Closer integration of field and modeling efforts could connect mechanistic investigation of fine-root dynamics to ecosystem-scale understanding of nutrient and water cycling, allowing us to better predict tropical forest-climate feedbacks. 

Abstract. Soil represents the largest phosphorus (P) stock in terrestrialecosystems. Determining the amount of soil P is a critical first step inidentifying sites where ecosystem functioning is potentially limited by soilP availability. However, global patterns and predictors of soil total Pconcentration remain poorly understood. To address this knowledge gap, weconstructed a database of total P concentration of 5275 globallydistributed (semi-)natural soils from 761 published studies. We quantifiedthe relative importance of 13 soil-forming variables in predicting soiltotal P concentration and then made further predictions at the global scaleusing a random forest approach. Soil total P concentration variedsignificantly among parent material types, soil orders, biomes, andcontinents and ranged widely from 1.4 to 9630.0 (median 430.0 and mean570.0) mg kg−1 across the globe. About two-thirds (65 %) of theglobal variation was accounted for by the 13 variables that we selected,among which soil organic carbon concentration, parent material, mean annualtemperature, and soil sand content were the most important ones. Whilepredicted soil total P concentrations increased significantly with latitude,they varied largely among regions with similar latitudes due to regionaldifferences in parent material, topography, and/or climate conditions. SoilP 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 underlyingdrivers of soil total P concentration can be used to constraint Earth systemmodels that represent the P cycle and to inform quantification of globalsoil P availability. Raw datasets and global maps generated in this studyare available at https://doi.org/10.6084/m9.figshare.14583375(He et al., 2021).