Natural and planted forests are two major forms of vegetation restoration and are often assumed to exhibit distinct evapotranspiration (ET) patterns. We investigated ET dynamics in a natural secondary oak forest and a nearby planted black locust forest over a ten-year period in a semiarid region. ET was estimated by the soil water budget method, canopy phenology and micrometeorological variables were concurrently monitored in both forests. The growing seasons during 2012–2021 were divided into four distinct periods based on the critical soil water storage (SWS_crit) and the onset of the rainy season. ET of the two forests exhibited similar inter-annual variations. The seasonal dynamics of ET for both forests displayed a bimodal pattern, jointly driven by climatic and vegetation factors. As water availability improved, the responses of ET to reference evapotranspiration (ET₀) strengthened in both forests. The ET amount in the natural forest exceeded that of the planted forest only in the moistest period. This difference may be attributed to contrasting root growth strategies, whereby the natural oak forest tends to use more water under moist conditions but restricts water use during severe drought. These long-term results indicate that, despite contrasting origins, natural and planted forests can converge in ET under seasonal water variations with highly variable precipitation. Such water-use adaptability may be an important factor in restoration success of the black locust and may offer potential insights for forest management in forest ecosystems with similar semiarid climates.

Rising concentrations of atmospheric CO₂ (c_a) increase plant photosynthesis (A_n) and reduce stomatal conductance (g_s). This increases the intrinsic water-use efficiency (iWUE = A_n/ g_s), a major proxy of tree adaptation to climate change. However, whether an increase in iWUE leads to a concomitant increase in tree growth remains in dispute, prompting interest in theoretical links between iWUE and tree productivity. Here using an optimality theory for kinetics of stomatal aperture, we establish an envelope delineating maximal relative increases in tree productivity that can be inferred/expected from relative increases in iWUE. The resulting expressions are used to interpret relations between iWUE (an observable proxy) and tree growth (the target variable), using available experimental data from manipulation experiments and tree-ring isotopes. While rising c_a increases iWUE, proportional increases in tree growth are unlikely given ameliorating environmental (for example, rising atmospheric dryness) and anatomical/physiological (for example, tree height) influences.

Predicting litter decomposition remains a central challenge in biogeochemical modeling. Traditional models of litter decomposition lack the chemical resolution necessary to capture intra-molecular interactions like chemical protection of carbohydrates and proteins by lignins, which creates a bottleneck during decomposition, and do not describe microbial responses specific to compound classes. Solid-state 13C Nuclear Magnetic Resonance (NMR) spectroscopy offers molecular-level resolution of litter composition, which can help constrain decomposition models—yet its potential has not been fully exploited. Here, we develop a chemically resolved litter decomposition model informed by 13C NMR-derived compound classes—carbohydrates, proteins, lignins, lipids, and carbonyls—to evaluate how litter chemistry and microbial physiology influence decomposition dynamics. We synthesized data from 17 studies reporting temporal changes in litter mass and NMR spectra across 89 litter types. Four model scenarios were tested, varying in their treatment of lignin protection and the metabolic cost of oxidative enzyme production. Calibrating four model scenarios yielded strong agreement with observed mass loss across chemical classes (Nash–Sutcliffe modeling efficiency > 0.95). While the inclusion of a lignin-specific rate modifier did not universally enhance predictive accuracy, it enabled mechanistic representation of microbial trade-offs between enzyme allocation and substrate decomposition. In scenarios incorporating enzyme production costs, the initial carbon use efficiency and the efficiency of nitrogen recycling at microbial senescence were reduced, consistent with theoretical expectations under nutrient-limited conditions. These findings demonstrate that integrating molecular-scale litter chemistry into litter decomposition models allows explicit representation of substrate specific microbial processing of diverse compounds and metabolic tradeoffs.

Carbon (C) turnover in forest soils is jointly controlled by abiotic factors, such as temperature, soil moisture, and soil properties, as well as microbial activity, which drive organic matter decomposition and its associated CO₂ fluxes. While microbial biomass is recognized as a predictor of soil respiration, the role of microbial community composition and diversity in shaping C dynamics under field conditions remains underrepresented in models. Here, we quantify the relative contributions of microbial biomass, taxonomic diversity, and community composition to soil heterotrophic respiration (Rh) across five managed Scots pine forests in Europe. Integrating microbial variables (fungal and bacterial PLFA, ITS and 16S sequencing) with abiotic drivers substantially improved model performance, explaining ~20% more variance in Rh than models based solely on abiotic drivers. Among microbial predictors, the fungi:bacteria ratio and bacterial community composition were consistently strong predictors of Rh. Fungal effects were modulated by soil temperature, with fungal diversity negatively and fungal biomass positively correlated with respiration at high temperatures. A structural equation model further showed that soil properties influenced Rh indirectly through their effects on microbial communities. Together, our results demonstrate that microbial community composition improves predictions of soil respiration beyond abiotic factors alone, and provide insights into how biomass, diversity, and composition interact with environmental drivers to regulate soil C cycling.

Understanding how soil respiration responds to fluctuations in soil moisture and temperature is increasingly important as climate change intensifies drying and rewetting events and causes soil warming. In this study, we investigated whether the temperature sensitivity of soil respiration (Q₁₀) and its temperature dependence differ between drying and rewetting periods. We collated high-frequency field data on soil respiration, soil moisture, and soil temperature from the COSORE dataset (28 sites), and partitioned them into dry-down and rewetting periods. Next, random forest regressions and accumulated local effects plots were used to assess the main effect of temperature on soil respiration for both periods. This approach enables estimation of the temperature dependence of Q₁₀ without the confounding effect of soil moisture variations. Results showed that the temperature dependence of Q₁₀ differs between the drying and rewetting periods; Q₁₀ is lower during rewetting than during drying in soils with temperatures below 15 °C, but higher in soils with temperatures higher than 15 °C. No differences were found in the temperature dependences of Q₁₀ between rewetting events followed by large and small respiration pulses. We conclude that the effects of warming on soil respiration differ between dry-down and rewetting periods.

The extent to which microbial processes control soil organic carbon (SOC) dynamics remains uncertain. Carbon use efficiency (CUE), that is, the fraction of assimilated carbon allocated to growth, has been used as a key parameter but its relationship with SOC reflects carbon partitioning rather than the absolute magnitude of microbial fluxes. The microbial growth rate could provide a more mechanistic link to SOC accumulation because it quantifies biomass production and reflects necromass formation. Here we combine a global ¹⁸O–H₂O dataset (n = 268 paired observations) with outputs from four land surface models to test whether growth rate predicts SOC more strongly than CUE. In the incubation experiments, growth rates are more closely associated with SOC than CUE, although soil properties and climate explain equal or greater variance. Models reproduce the stronger role of growth rate over CUE but tend to underestimate the abiotic controls. The models also emphasize CUE as the main predictor of the SOC-to-net primary production ratio, in contrast to observations, which indicates the soil’s capacity to retain plant carbon inputs. Together, these findings identify the microbial growth rate as a diagnostic that can help bridge models with empirical data and guide a more balanced representation of microbial and mineral controls in SOC projections.

Climate change is intensifying precipitation extremes, yet how historical drought and wetting shape post-event soil carbon fluxes remains poorly understood. Using a seven-year precipitation manipulation experiment (−70% to +50%) followed by a two-year recovery period in a semi-arid grassland, we quantified legacy effects on soil respiration (Rs) and its autotrophic (Ra) and heterotrophic (Rh) components. Historical moderate wetting (+30%) generated the strongest positive legacy, increasing Rs, Rh, and Ra by 21.9%, 24.4%, and 26.9%, respectively, during the first recovery year. In contrast, historical drought treatments (−70% to −30%) produced biphasic legacies, characterized by early-season suppression followed by later-season positive responses, potentially associated with decomposition of drought-accumulated substrates and vegetation recovery. Ra generally showed stronger recovery responses than Rh, indicating contrasting sensitivities of plant- and microbially - derived respiration to moisture and carbon availability. The dominant controls of respiration also shifted over time: first-year legacies were associated mainly with microbial carbon substrates and extracellular enzyme activities, whereas second-year responses were increasingly linked to plant allocation strategy and microbial community composition. Despite these contrasting trajectories, Rs, Ra, and Rh largely converged to control levels within two years. Our results demonstrate that precipitation legacies in semi-arid grasslands are asymmetric and temporally dynamic, with important implications for predicting carbon cycling under increasing hydrological variability.

Soil moisture controls soil carbon (C) and nutrient cycling by regulating substrate and gas transport, and consequently affects microorganisms that mineralize or stabilize those substrates. Here we discuss soil moisture effects on physical, chemical, biological, and ecological processes that drive C and nutrient cycles during soil drying, rewetting, and across soil moisture regimes. These processes are conceptualized through theories and process-based models that quantify the responses of C and nutrient cycling to soil moisture variations across spatial scales (from pore to Darcy scale and along climatic gradients) and levels of biological organization (from microbial functional groups to communities).

Most soil carbon (C) models describe the effects of soil moisture on C mineralization rates using empirical response functions derived from laboratory incubations carried out on sieved soils. This pre-treatment alters the pore space structure controlling solute and oxygen diffusion and may also disturb the spatial distribution of microbial activity. Our objective was therefore to investigate the effects of disruption of the natural soil structure on the soil moisture response function for C mineralization.

We measured CO₂ emissions at soil water pressure heads ranging from zero to −600 cm for both sieved and intact soil samples taken from tilled and untilled soil horizons at a field site in northern France. The derived soil moisture response curves were then combined with a simple analytical water balance model to predict CO₂ emissions in contrasting rainfall climates. We also explored the relationships between the parameters of the moisture response function and soil physical properties as well as metrics of soil structure quantified by X-ray scanning.

Sieving significantly affected the shape of the moisture response function. In particular, the optimal degree of saturation for soil CO₂ emissions lay much closer to saturation (> 0.8) in the case of intact soil structures. The effects of repeated tillage were like those of sieving, although less pronounced. We identified relationships between some indicators of soil structure and the optimal degree of saturation, which suggests that it may be possible to derive pedotransfer functions linking soil properties to this critical model parameter. Finally, our modelling demonstrated that the use of moisture response functions derived from sieved soils in soil C models can lead to an underestimation of CO₂ emissions in wet climates.

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.