Net primary productivity (NPP) is a fundamental component of global carbon cycling. However, the global partitioning between aboveground (ANPP) and belowground NPP (BNPP) remains poorly quantified, which limits our understanding of carbon allocation strategies and ecosystem responses to environmental change. In this study, a resource-constrained framework was developed to consistently estimate ANPP and BNPP from resource constraints (e.g., light, water, and nutrient). Model performance was evaluated using 937 field observations from 1939 to 2014, including 252 paired ANPP and BNPP records, achieving Nash-Sutcliffe efficiency (NSE) values of 0.76 for ANPP, 0.49 for BNPP, and 0.52 for total NPP. The framework estimated global ANPP and BNPP at 25.36 and 22.01 Pg C yr-1, respectively, with BNPP accounting for about 46.5% of total productivity. Across biomes, long-term mean ANPP ranged from 129 to 1088 g C m-2 yr-1, and BNPP from 153 to 781 g C m-2 yr-1. Further analysis using an interpretable machine learning approach revealed that temperature was identified as the dominant control of fBNPP (BNPP/NPP) in forests, while vapor pressure deficit and aridity index were most influential in non-forest ecosystems. Our findings provide consistent assessment of above- and belowground NPP partitioning. These results support the resource-constrained framework as a robust and ecologically interpretable tool for simulating NPP partitioning, which helps improve the representation of carbon allocation in terrestrial biosphere models and enhances our understanding of ecosystem responses to a change climate.

Plants assimilate carbon through photosynthesis (gross primary productivity, GPP) while losing water via transpiration (Trans), with both processes responding nonlinearly to temperature. Although the air temperature optimum of GPP ( T opt GPP ) is well studied, the thermal response of Trans ( T opt Trans ) remains unknown. Here, using global eddy covariance observations and sap flow measurements along with simulations from an Earth system model, we find that T opt Trans is consistently higher than T opt GPP across biomes and climates, indicating greater heat tolerance in Trans. Despite a strong correlation, their divergence suggests carbon uptake is more vulnerable to warming than water loss. Machine learning identifies maximum air temperature as the key driver of both optima, while their difference ( Δ T opt ) is associated with vegetation water content. The Earth system model predicts spatial patterns of T opt Trans and T opt GPP that align with observations, but the model significantly underestimates the magnitudes of T opt Trans , T opt GPP and Δ T opt . These results reveal a critical decoupling of carbon-water coordination under heat stress, with ecosystems sustaining Trans beyond T opt GPP to cool leaves, but ultimately reducing Trans to conserve water.

Drought stress affects soil nutrient metabolism, microbial network complexity and soil multifunctionality (SMF), but the mechanisms underlying how nutrient metabolism and microbial network complexity control SMF in subtropical plantations under natural drought conditions remain unclear. This study investigated the soil physicochemical property, enzyme activity, gene abundance, microbial diversity, community composition, cooccurrence network, and SMF in rhizosphere soil of A. odoratissima plantations. Results showed that natural drought decreased the absolute abundance of carbon fixation and degradation genes, but enhanced the absolute abundance of some nitrogen metabolic genes, promoted CO2 and CH4 emissions, strengthened microbial carbon limitation and reduced soil organic carbon and total nitrogen contents, ultimately reduced SMF. Natural drought decreased the absolute abundance of Pseudomonadota, Betaproteobacteria, Chitinophagales, Verrucomicrobiae, Silvibacterium, Dactylosporangium, Acidobacterium and Streptacidiphilus, but enhanced the absolute abundance of Methylocystis, Candidatus_sulfotelmatobacter, Conexibacter, Luteitalea, Alloacidobacterium and Candidatus_acidoferrum, reduced bacterial diversity and fungal beta-diversity, simplified microbial network complexity and weakened metabolic function, thereby directly reducing SMF. Partial least squares path model further revealed that carbon metabolism and microbial network complexity were the dominant environmental factors reducing SMF. These findings indicate that nutrient metabolism and microbial network complexity drive the decrease in SMF in rhizosphere soil of A. odoratissima plantations under natural drought.

Understanding nutrient fluxes across ecosystems is critical for sustainable land management, climate adaptation, and food security. However, current ecosystem models face challenges in integration, data consistency, and scalability across spatial and temporal domains. This review outlines the rationale and design principles for a unified modelling framework to support integrated nutrient budgeting. We identify key barriers—including model coupling, semantic alignment, data harmonization, and temporal-spatial scaling—and discuss opportunities to overcome them through modular design, interdisciplinary collaboration, and open-source platforms. Building on these insights, we propose the rationale and design principles for a unified, scale-aware modeling framework that supports flexible coupling among vegetation, soil, and hydrological components while explicitly accounting for uncertainty and data limitations. The framework emphasizes transparent variable semantics, scale-aware information exchange, and the integration of observational constraints from monitoring networks and remote sensing products. By balancing process complexity with operational usability, it addresses the needs of both researchers and decisionmakers, enabling more robust, transparent, and interoperable approaches to terrestrial nutrient budgeting across scales. This work establishes a foundation for advancing integrated, user-oriented nutrient modeling capable of supporting scientific inquiry, land management, and policy development under global environmental change.

Abstract Disturbances profoundly impact forest capacity for sequestering and storing carbon, and the recovery rates after disturbances vary geographically. Many terrestrial carbon models inadequately simulate the effects of forest disturbances on biomass due to their reliance on equilibrium assumptions, the absence of historical disturbance data, as well as homogeneous vegetation parameters. In this study, we developed a new hybrid machine learning framework to optimize the spatial distribution of the parameters of a process‐based model, the integrated biosphere simulator (IBIS), across global forest regions. High‐resolution satellite‐derived products of gross primary productivity (GPP), leaf area index (LAI), forest age, and biomass were used as references to optimize parameters of both fast and slow carbon processes in the IBIS model. By integrating age maps and spatially explicit growth curves for biomass accumulation, our model was able to better represent the biomass dynamics in regenerating forests after disturbances. Our findings underscored the role of spatially optimized parameters in accurately simulating GPP, LAI, and biomass across global forests, with particular gains from accounting for age structures. The optimized IBIS model shows superior performance in reproducing biomass gradients across climate zones, compared with global satellite‐derived products and simulations from multiple dynamic global vegetation models (DGVMs). The optimization revealed a greater carbon sequestration potential in regrowing forests after disturbances. Our framework provides a new strategy for using forest age data to improve the accuracy of large‐scale forest carbon simulations in DGVMs.

Soil mineral-associated organic carbon (MAOC) constitutes over 50% of total soil organic carbon (SOC) and serves as a key determinant of its long-term stabilization. Although soil pH is recognized as a "master variable" in biogeochemical cycles, it remains unclear how soil pH influences MAOC and SOC at large spatial scales. To address this knowledge gap, we conducted a continental-scale survey in China and synthesized global observations from literature, totaling 1300 independent observations. We analyzed the relationships between SOC and MAOC across pH gradients and assessed the contributions of other environmental factors to MAOC variability. Our results showed that higher SOC content is generally positively correlated with higher MAOC. However, this relationship is strongly influenced by soil mineralogy, texture, and climatic conditions. We identified distinct pH-dependent pathways controlling MAOC formation. In acidic soils (pH ≤ 5.5), Fe/Al (hydr)oxides explain most of the variation in MAOC content and in MAOC fraction (the ratio of MAOC to SOC). In neutral-to-alkaline soils (pH > 6.5), however, clay content and mean annual temperature emerge as the primary regulators. Net primary productivity (NPP) augments the particulate organic carbon (POC) pool, thereby diluting the MAOC fraction. This divergence challenges the universal applicability of clay-centric SOC models in acidic soils. Our findings provide a conceptual framework for biogeochemical models, urging the integration of pH-dependent metal oxide dynamics to improve predictions of SOC persistence under diverse future scenarios.

Abstract Gross primary productivity (GPP) and evapotranspiration (ET) represent two fundamental processes in coupled water and carbon cycles. The strong regulation of ecosystem carbon and water fluxes by stomata is well understood at the leaf level. However, the coupling is complex at regional or ecosystem scales. The objective of this study is to understand key environmental factors that control both water and carbon fluxes at regional scales and develop a robust resource‐constrained framework (RCF) for estimating climatology of ecosystem carbon and water fluxes consistently. Water balance data from 1927 catchments were obtained to parameterize the model and independent observations from 107 flux stations were used to validate the method. Results demonstrated robust model performance with Nash–Sutcliffe efficiency (NSE) of 0.65 for GPP and NSE of 0.55 for ET against independent flux observations. The RCF approach estimated global mean GPP and ET at 1,141 g C m−2 a−1 and 530 mm a−1, respectively, corresponding to an annual terrestrial carbon uptake of 142.4 Pg C a−1. Further analysis identified the ecosystem responsive regimes, with about 40% land areas energy‐responsive, 40% water‐responsive, and 20% co‐responsive for both GPP and ET across the globe. This study reveals consistent estimates of GPP and ET by disentangling the spatial interplay of energy and water constraints. The RCF approach provides a transparent and scalable approach to jointly estimate and attribute carbon and water fluxes, offering new insights into ecosystem functioning and a pathway to improve ecosystem modeling.

Understanding spatiotemporal vegetation dynamics and their driving factors is critical for ecological conservation and ecosystem functioning under environmental change. However, the relative roles of climate change and human activities in shaping long-term vegetation dynamics remain unclear. Here, we integrated multi-source remote sensing data to construct a long-term NDVI dataset and investigated vegetation dynamics in the Yellow River Basin from 1982 to 2020 under the Grain for Green Project. Sensitivity analysis and residual trend analysis were applied to quantify the contributions of climate factors (temperature, precipitation, and solar radiation) and human activities. Our results revealed a significant increasing trend of vegetation dynamics from 1982 to 2020, with the significantly increased area rising from 28.86% (1982-2000) to 49.59% (2000-2020). Temperature and precipitation were significant positive with vegetation change, and account for 65.27% of the vegetation change. Human activities substantially promoted vegetation growth, with their positive contribution increasing from 42.35% to 68.92% between the two periods. Moreover, the area experiencing a significant increase in vegetation cover attributable to human activities expanded by 22.55% after 2000. This study highlights the critical role of climate change and human activity in driving the spatiotemporal vegetation dynamics, and provide a scientific reference for ecological conservation and land management strategies.

Estimates of CO2 emissions from land-use change strongly influence the inferred land carbon sink but remain highly uncertain. One key source of this uncertainty is nitrogen (N) limitation and its interactions with climate and CO2 emissions from land-use change. Here we developed and implemented a land-use change scheme into the Australian Community Atmosphere Biosphere Land Exchange model (CABLE) and conducted simulations using a full factorial design combining changes in land use, climate, and atmospheric CO2, both with and without nitrogen limitation. CABLE with nitrogen limitation simulates global gross primary production (GPP), soil respiration, plant biomass, and soil carbon, with correlation coefficients from 0.6 to 0.9 against the benchmark data. From 1960 to 2020, estimated cumulative CO2 emissions from land-use change were 119 Pg C without nitrogen limitation and 73 Pg C with nitrogen limitation, compared to 100 +/- 35 Pg C from 20 dynamic global vegetation models and 87 +/- 24 Pg C from three bookkeeping approaches reported in the Global Carbon Budget 2023. We further partitioned total emissions into direct emissions from land-use change and indirect emissions arising from interactions with climate, nitrogen deposition, and atmospheric CO2. Nitrogen limitation reduced global direct CO2 emissions from land-use change by 0.12 Pg C yr(-)(1) and indirect emissions by 0.24 Pg C yr(-)(1) during 1701-1959, and by 0.4 Pg C yr(-)(1) for both emissions during 1960-2020. Nitrogen limitation reduced soil carbon inherited from lands when converted to secondary forests, which contributed over 75 % of the total emission reduction, while reduced regrowth of secondary forests contributed < 25 % of the total reduction. Furthermore, nitrogen limitation had the strongest effect on the interaction between land-use change and atmospheric CO2. Therefore, accounting for nitrogen limitation in both direct and indirect emissions from land-use change is critical for understanding emission drivers, improving model accuracy, and informing climate policy.

While nitrogen (N) fertilization alters nutrient uptake dynamics in subtropical afforestation tree species, the species-specific adjustments in growth and nutrient-acquisition strategies remain unclear, limiting predictions of their carbon (C) sequestration potential. We investigated seedling responses of three representative species (Pinus massoniana, Schima superba, and Ormosia pinnata) to two-year N addition in a controlled pot experiment. We quantified shifts in rhizosphere microbial communities, carboxylate concentrations, and root morphology, and analyzed their associations with seedling N and phosphorus (P) acquisition. N addition increased above- and below-ground biomass as well as N and P uptake in P. massoniana and S. superba, whereas O. pinnata showed no significant growth response. These differences corresponded to contrasting nutrient-acquisition patterns identified by redundancy analysis (RDA). For P. massoniana, enhanced nutrient uptake was primarily associated with rhizosphere carboxylates, whereas in S. superba it was positively correlated with rhizosphere microbial biomass. In contrast, O. pinnata exhibited the highest N and P acquisition capacity despite showing minimal plasticity in its rhizosphere functional traits. Given its identity as a leguminous N-fixing species, symbiotic N-fixation likely offers an alternative strategy for sustained nutrient acquisition. Our results demonstrate significant interspecific differences in nutrient-acquisition strategies among these afforestation species, and highlight how root morphology, rhizosphere carboxylates, and microorganisms are differentially associated with nutrient uptake under N fertilization. This study underscores the importance of incorporating species-specific strategies into afforestation management to optimize ecosystem sustainability in subtropical China.

Photosynthesis, quantified as gross primary productivity (GPP), and evapotranspiration (ET) are two fundamental processes regulating terrestrial carbon and water cycles. Quantifying GPP and ET is essential for understanding ecosystem functioning and land-atmosphere feedbacks under changing climatic conditions. However, most existing methods estimate GPP and ET independently without ensuring inherent consistency in vegetation physiological processes. Moreover, coupled estimation of GPP and ET under varying soil moisture conditions remains a major challenge, primarily because most terrestrial ecosystem models do not consider dynamic soil moisture feedbacks. In this study, a fully coupled ecosystem model was developed by explicitly considering interactions and feedbacks among photosynthesis, evapotranspiration, stomatal conductance, and soil moisture dynamics. Long-term flux observations from 32 sites across diverse ecosystems were collected to evaluate the model performances. Results showed the model reproduced daily GPP and ET robustly across all sites. Further validation of water use efficiency showed robust performance with R & sup2; of 0.51 at monthly scales and 0.79 at annual scales. Incorporating dynamic soil moisture feedbacks substantially improved the model performance compared to the default non-coupled model, with NSE values (Nash-Sutcliffe efficiency coefficient) increasing from 0.56 +/- 0.39 to 0.64 +/- 0.24 for GPP and from 0.41 +/- 0.71 to 0.65 +/- 0.22 for ET, particularly under dry soil moisture conditions (normalized SWC < 0.3). Our findings highlight the importance of incorporating time-varying soil moisture feedbacks into modeling terrestrial carbon and water fluxes. The coupled model provides a robust framework for assessing ecosystem responses to drought and climate variability, enhancing our understanding of climate change impacts on water resource and carbon management.

Abstract. Termites are major detritivores in tropical and subtropical ecosystems, yet their contributions to the terrestrial carbon cycle remains absent from process-based soil organic carbon (SOC) models. Here, we present a termite carbon module that explicitly represents termite-mediated litter consumption and transfer of ingested carbon into gaseous (CO2, CH4) and SOC pools. The module integrates biome-specific termite biomass with spatially explicit productivity inputs to quantify termite-driven carbon fluxes within a mass-balance framework. Model simulations show that termites act as spatially heterogeneous carbon processors, accelerating litter turnover while modifying the pathways through which carbon is redistributed between atmospheric and SOC pools. Global sensitivity analysis identifies termite biomass and ingestion capacity as the dominant controls on flux magnitude, whereas carbon partitioning governs the fate of processed carbon. Including termite-mediated pathways in SOC models provides a mechanism for representing faunal controls on decomposition, soil carbon formation, and trace gas emissions, particularly in tropical and seasonally dry ecosystems. Globally, we estimate termites process 1569.4 ± 800.4 Tg C yr-1, releasing 864.7 ± 444.5 Tg C yr-1 as CO2 and 7.9 ± 4.9 Tg C yr-1 as CH4, while transferring 689.3 ± 367.4 Tg C yr-1 into labile and mineral-associated SOC. Explicit representation of termite-driven carbon fluxes will therefore be important for improving predictions of litter decomposition, SOC formation, and terrestrial carbon-climate feedbacks.

Tropical forests are increasingly affected by drought, yet the factors that control post-drought ecosystem resilience—the capacity to withstand disturbances—are not fully understood. Here we use temporal autocorrelation of satellite-derived vegetation greenness to quantify ecosystem resilience following 142,444 severe drought events across tropical forests from 2003 to 2022. We show that resilience declined in 68.8% of areas after droughts, particularly in dry environments, whereas 20.3% of areas with increased resilience were located in moist tropical forests. More intense and prolonged droughts led to a pronounced decline in resilience. Mean annual precipitation was identified as the most important regulator influencing resilience changes after drought, while soil phosphorus was the most consistent regulator across forest biomes, exhibiting widespread mitigating effects on resilience loss. Along decreasing precipitation gradients, the mitigating effect of soil phosphorus on post-drought resilience loss intensified. These findings provide insights into how tropical forests respond to drought and offer practical guidance for region-specific, adaptive forest management under a changing climate. Drought disturbances are reducing the recovery capacity of tropical forests, especially in drier conditions, but soil phosphorus can mitigate this impact, according to a satellite-based analysis of ecosystem resilience.

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.

Upholding stable terrestrial ecosystems is integral to supporting climate regulation and planetary security. Yet, while aboveground ecosystem stability is widely described, global-scale patterns in belowground ecosystem stability and how it connects to aboveground stability remain virtually unknown. Here, we assembled a global dataset including high-resolution information on annual estimates of soil respiration from 4,544 communities and associated aboveground ecosystem productivity over the past four decades (1985-2018). We found that ecosystems with greater stability in aboveground productivity had greater long-term stability in soil respiration, with a positive and significant connection between above- and belowground stability being especially strong in arid environments. Stable temperatures played a crucial role in reinforcing the stability and coupling of above- and belowground ecosystems. Our work provides new evidence of, and insights into, the local to global connections of stability of above- and belowground biological activity, and identifies a fundamental role of temperature stability in maintaining this stability under a changing climate.

The ecosystem water use efficiency (WUE) plays a critical role in many aspects of the global carbon cycle, water management, and ecological services. However, the response mechanisms and driving processes of WUE need to be further studied. This research was conducted based on Gross Primary Productivity (GPP), Evapotranspiration (ET), meteorological station data, and land use/cover data, and the methods of Ensemble Empirical Mode Decomposition (EEMD), trend variation analysis, the Mann–Kendall Significant Test (M-K test), and Partial Correlation Analysis (PCA) methods. Our study revealed the spatio-temporal trend of WUE and its influencing mechanism in the Yellow River Basin (YRB) and compared the differences in WUE change before and after the implementation of the Returned Farmland to Forestry and Grassland Project in 2000. The results show that (1) the WUE of the YRB showed a significant increase trend at a rate of 0.56 × 10−2 gC·kg−1·H2O·a−1 (p < 0.05) from 1982 to 2018. The area showing a significant increase in WUE (47.07%, Slope > 0, p < 0.05) was higher than the area with a significant decrease (14.64%, Slope < 0, p < 0.05). The region of significant increase in WUE in 2000–2018 (45.35%, Slope > 0, p < 0.05) was higher than that of 1982–2000 (8.23%, Slope > 0, p < 0.05), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC·kg−1·H2O) > Cropland WUE (0.972 gC·kg−1·H2O) > Grassland WUE (0.805 gC·kg−1·H2O) under different land cover types. Forest ecosystem WUE has the highest rate of increase (0.79 × 10−2 gC·kg−1·H2O·a−1) from 2000 to 2018. Forest ecosystem WUE increased by 0.082 gC·kg−1·H2O after 2000. (3) precipitation (37.98%, R > 0, p < 0.05) and SM (10.30%, R > 0, p < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 70.39% of the WUE exhibited an increasing trend, which is mainly attributed to the simultaneous increase in GPP and ET, and the rate of increasing GPP is higher than the rate of increasing ET. This study could provide a scientific reference for policy decision-making on the terrestrial carbon cycle and biodiversity conservation.

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ15N) in plants (δP) and soil (δS). Together with pairwise δP and δS measurements from 18 forest sites covering diverse climates and thousands of δP and δS observations worldwide, we show that the spatial distribution of the fraction of symbiotic BNF relative to the total external N acquisition by plants (f BNFs) is primarily controlled by temperature (29%) and mycorrhizal fungi (14%), with colder climate and higher ectomycorrhizal fungi abundance leading to a lower f BNFs. We find a large discrepancy between the spatial distributions of isotope-based BNF and those simulated by using Earth System Models (ESMs) in the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6). Moreover, we constrain the global total BNF from natural terrestrial ecosystems as 78.2-89.8 Tg N yr-1, suggesting a ≥18% underestimation of the global BNF in CMIP6 models. In addition to the temperature dependence found in previous laboratory studies, our isotope-based study suggests a competitive relationship between BNF and mycorrhizal N uptake as another important control mechanism. This complex interplay remains unresolved in ESMs and has the potential to improve BNF simulations in the next phase of CMIP.

Acid rain is believed to exacerbate phosphorus (P) limitation in tropical forests, but how tropical trees respond and adapt to acid-induced P limitation, particularly after long-term acid rain events, remains poorly understood. We conducted a 12-year simulated acid rain (SAR) experiment by irrigating plots with water of different pH values (i.e., 3.0, 3.5, 4.0, and 4.5 as a control) in a tropical forest in southern China. Five tree species associated with either ectomycorrhizal (ECM) or arbuscular mycorrhizal fungi (AMF) were chosen to examine the changes of P fractions in their rhizosphere soils and green leaves. In ECM tree rhizospheres, SAR treatments significantly increased labile P by 27.3 % (p < 0.05) and decreased occluded P by 11.7 % (p < 0.05), which were positively correlated with increased phosphodiesterase activity and related gene abundance. However, in AMF trees, SAR treatments significantly reduced rhizosphere available P and foliar P by 45.9 % and 28.7 % (p < 0.05 for both), respectively. In response, AMF trees exhibited greater plasticity in foliar P fractions than ECM trees, shifting from structural P (phospholipids and phosphorylated proteins) to metabolic P (P-containing metabolites and nucleic acid P) fractions under SAR treatments. These findings suggest that, to cope with acid-induced P limitation, ECM trees tend to adopt an acquisitive nutrient-use strategy for greater P mobilization, while AMF trees favor a conservative strategy with more efficient foliar P utilization.

The Full Carbon Accounting Model (FullCAM) simulates carbon (C) pools of live biomass, standing dead mass, debris and soil, the flows among them and the atmosphere, and the influences of fire and harvesting disturbances under Australian conditions. It is regularly used by governments, landowners, companies and researchers, at continental, regional and local scales. Recently, FullCAM was calibrated for seven categories of native tropical savanna vegetation. However, for non-savanna native vegetation, calibrated parameters are available for only two general vegetation categories, based on whether the annual rainfall exceeds or falls below 500 mm. These two categories are too broad to capture the large range of growth conditions, vegetation structures and species assemblages that occur across Australia’s native woody vegetation. Here, our objective was to improve FullCAM’s ability to model variation in C pools and post-disturbance recovery among eight native vegetation categories, from shrublands to rainforests, for which there were differences in biomass allocation, litterfall and/or decomposition. To do this, we calibrated FullCAM for each vegetation type, including 14 parameters that were calculated directly from field observations and 17 that were calibrated using a dataset containing about 9300 field plots with measurements of at least one woody vegetation C stock. New parameters (compared with the two general parameter sets) reduced bias from 77 to 25 % (averaged across C stocks), and root mean square error from 44 to 30 Mg C ha-1. Model accuracy could be further improved (i) by focusing on sites with a known disturbance history, (ii) calibrating as many vegetation categories as possible (instead of eight categories generalising across many species), and (iii) adding more detail to growth calculations to quantify factors that may not be adequately represented by FullCAM’s growth equation.