Trait spectra have been used in various branches of ecology to explain and predict patterns of species distributions. Several categorical and continuous traits have been proposed as relevant for ectomycorrhizal fungi, but a spectrum that unifies co-varying traits remains to be established and tested. Here, we propose a nitrogen acquisition and carbon use trait spectrum for ectomycorrhizal fungi in nitrogen-limited forests, which encompasses several morphological, physiological, and metabolic traits. Using a simple stoichiometric model, the trait spectrum is linked to the concept of apparent carbon use efficiency and resolves the contradiction that species with high supply of host carbon can maintain nitrogen transfer despite building large mycelial biomass. We suggest that ectomycorrhizal fungal species are distributed along this spectrum, with lifestyles ranging from ‘absorbers’ with a niche in high productive forests with high availability of soluble nitrogen to ‘miners’ with the ability to exploit organic matter in forests with low nitrogen availability. Further, we propose ways to test the outlined trait spectrum empirically.

Soils are the largest terrestrial carbon (C) pool on the planet, and targeted grassland management has the potential to increase grassland C sequestration. Appropriate land management strategies, such as organic matter addition, can increase soil C stocks and improve grasslands' resilience to drought by improving soil water retention and infiltration. However, soil carbon dynamics are closely tied to vegetation responses to management and climatic changes, which affect roots and shoots differently. This study presents findings from a 3-year field experiment on two Swedish grasslands that assessed the impact of compost amendment and experimental drought on plant biomass and soil C to a depth of 45 cm. Aboveground biomass and soil C content (% C) increased compared with untreated controls in compost-amended plots; however, because bulk density decreased, there was no significant effect on soil C stocks. Experimental drought did not significantly reduce plant biomass compared to control plots, but it stunted the increase in aboveground biomass in compost-treated plots and led to changes in root traits. These results highlight the complexity of ecosystem C dynamics and the importance of considering multiple biotic and abiotic factors across spatial scales when developing land management strategies to enhance C sequestration.

Carbon use efficiency (CUE) of microbial communities in soil quantifies the proportion of organic carbon (C) taken up by microorganisms that is allocated to growing microbial biomass as well as used for reparation of cell components. This C amount in microbial biomass is subsequently involved in microbial turnover, partly leading to microbial necromass formation, which can be further stabilized in soil. To unravel the underlying regulatory factors and spatial patterns of CUE on a large scale and across biomes (forests, grasslands, croplands), we evaluated 670 individual CUE data obtained by three commonly used approaches: (i) tracing of a substrate C by 13C (or 14C) incorporation into microbial biomass and respired CO2 (hereafter 13C-substrate), (ii) incorporation of 18O from water into DNA (18O-water), and (iii) stoichiometric modelling based on the activities of enzymes responsible for C and nitrogen (N) cycles. The global mean of microbial CUE in soil depends on the approach: 0.59 for the 13C-substrate approach, and 0.34 for the stoichiometric modelling and for the 18O-water approaches. Across biomes, microbial CUE was highest in grassland soils, followed by cropland and forest soils. A power-law relationship was identified between microbial CUE and growth rates, indicating that faster C utilization for growth corresponds to reduced C losses for maintenance and associated with mortality. Microbial growth rate increased with the content of soil organic C, total N, total phosphorus, and fungi/bacteria ratio. Our results contribute to understanding the linkage between microbial growth rates and CUE, thereby offering insights into the impacts of climate change and ecosystem disturbances on microbial physiology with consequences for C cycling.

Long-term terrestrial ecosystem development is characterized by declining soil phosphorus (P) and a corresponding increase in biological P limitation. The function of P-limited ecosystems relies on efficient use of P by soil microorganisms, but the physiological strategies used by microorganisms to manage P scarcity during ecosystem development are unknown. Here, by applying recent advances in soil metabolomic techniques to samples collected from a ~700,000-year chronosequence of ecosystem development in eastern Australia, we show that soil microbial physiological strategies for P efficiency include a high proportion of non-phosphorous membrane lipids along with substantial intracellular carbon storage. These strategies—which proliferate during primary succession and are maximized in retrogressive, P-depleted ecosystems—uphold microbial carbon limitation, triple modelled P-mineralization potential and can conserve close to double the P contained in the aboveground biomass of vegetation. These findings transform our understanding of terrestrial ecosystems by revealing a strong yet overlooked interplay between the ecophysiology of soil microorganisms and the long-term trajectory of ecosystem development.

Photosynthesis, growth, and plant maintenance respiration are closely related to tree tissue nitrogen (N) concentrations. While earlier studies of the variation in tissue N concentrations and underlying controls have mostly focused on leaves, here we identify the large-scale controls of N concentration in other tree compartments for the first time. This is achieved by constructing and analysing a novel database of N concentrations in stems, roots, and branches covering all common Northern Hemisphere boreal and temperate tree genera, combined with data for leaves mostly from existing databases. This database allows us to explore the large-scale abiotic (climate, soil N concentration) and biotic controls (tree age/size, leaf type, growth rate) of tree tissue N concentration. We find that N concentrations decrease with increasing tree age (or size) and are significantly higher in deciduous compared to evergreen trees in all tissues. Low growth rates or unfavourable climate conditions (very cold or dry climate) significantly decrease leaf (the latter only for needleleaf deciduous and needleleaf evergreen trees) but not stem N concentration, indicating their effects on N allocation. Plant traits and environmental conditions together explain very large parts of the variation in tissue N concentrations. These results suggest that changes in the distribution of tree age/size, species, and extreme climate, induced by climate change, forest management, or disturbances, will have substantial consequences for the carbon (C) sequestration potential of boreal and temperate forests by altering tissue N concentrations. We expect that the expansion of tree species better adapted to dry conditions in European temperate forests will result in a higher N concentration in all tree tissues and elevated N allocation fractions to stems, which might lead to higher productivity but also higher maintenance respiration. The identified relationships need to be represented in dynamic global vegetation models (DGVMs) to estimate future effects of N limitation on the C cycle.

Microbial processes mediating the cycling of carbon and nutrients in soils are complex and thus difficult to predict with mathematical models. Such complexity arises because biological and ecological dynamics interact with physical processes in the soil to shape patterns of resource acquisition and use, and ultimately of organic matter stabilization in soil. In the article collection “Advances in Modelling Soil Microbial Dynamics” (https://www.sciencedirect.com/special-issue/10DG8MTGCCF), novel mathematical approaches to tackle these complexities are presented. This perspective summarizes their findings by highlighting theoretical advances and outstanding challenges in modelling microbial processes and their physical constraints.

Decomposition of organic matter controls the flow of carbon and nutrients in terrestrial and aquatic ecosystems. Several kinetic laws have been proposed to describe decomposition rates, but they neglect adaptation of the microbial decomposer to environmental conditions. Here we formalise decomposition as an optimal control problem by assuming that microorganisms regulate the uptake rate of a substrate to maximise their growth over the period of decomposition. The result is an optimal control problem consisting of two differential equations and auxiliary conditions that determine the optimal value of the control variable (the uptake rate), the remaining substrate at any given time, and the optimal completion time. This problem serves as a case study to illustrate the solution of differential equations and optimal control problems for students in undergraduate courses. The mathematical analysis of the problem requires rewriting the differential equations in reverse time along with the solution of a nonhomogeneous linear first order differential equation. We then return to modelling with some biologically motivated questions about how the parameters of the model representing environmental conditions and microbial functional traits affect the outcome. Finally, we discuss alternative ways to use the material with students.

Accurate representation of the chemical diversity of litter in ecosystem-scale models is critical for improving predictions of decomposition rates and stabilization of plant material into soil organic matter. In this contribution, we conducted a systematic review to evaluate how conventional characterization of plant litter quality using proximate analysis compares with molecular-scale characterization using 13C NMR spectroscopy. Using a molecular mixing model, we converted chemical shift regions from NMR into fractions of carbon (C) in five organic compound classes that are major constituents of plant material: carbohydrates, proteins, lignins, lipids, and carbonylic compounds. We found positive correlations between the acid soluble fraction and carbohydrates, and between the acid insoluble fraction and lignins. However, the acid-soluble fraction underestimated carbohydrates, and the acid insoluble fraction overestimated lignins by 243%. We identified two sources of uncertainties: i) disparities between litter chemical composition based on hydrolysability and actual chemical composition obtained from NMR and ii) conversion factors to translate proximate fractions into organic constituents. Both uncertainties are critical, potentially leading to misinterpretations of decay rates in litter decomposition models. Consequently, we recommend including explicit substrate chemistry data in the next generation of litter decomposition models.

Current biogeochemical models produce carbon–climate feedback projections with large uncertainties, often attributed to their structural differences when simulating soil organic carbon (SOC) dynamics worldwide. However, choices of model parameter values that quantify the strength and represent properties of different soil carbon cycle processes could also contribute to model simulation uncertainties. Here, we demonstrate the critical role of using common observational data in reducing model uncertainty in estimates of global SOC storage. Two structurally different models featuring distinctive carbon pools, decomposition kinetics, and carbon transfer pathways simulate opposite global SOC distributions with their customary parameter values yet converge to similar results after being informed by the same global SOC database using a data assimilation approach. The converged spatial SOC simulations result from similar simulations in key model components such as carbon transfer efficiency, baseline decomposition rate, and environmental effects on carbon fluxes by these two models after data assimilation. Moreover, data assimilation results suggest equally effective simulations of SOC using models following either first-order or Michaelis–Menten kinetics at the global scale. Nevertheless, a wider range of data with high-quality control and assurance are needed to further constrain SOC dynamics simulations and reduce unconstrained parameters. New sets of data, such as microbial genomics-function relationships, may also suggest novel structures to account for in future model development. Overall, our results highlight the importance of observational data in informing model development and constraining model predictions.

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.