Societal Impact Statement Climate change is contributing to vegetation changes that threaten life support systems. Yet, inherent climatic variability and past and present human actions—such as clearing, burning and grazing regimes—also alter vegetation and complicate understanding of vegetation change. Australian ecosystems exemplify such complexity. To predict future vegetation changes, proactively guide management and ensure persistent drivers do not disrupt intended outcomes, we need to untangle the effects of these various change drivers on vegetation. Such attribution of change, which is rarely done, requires historical context, long‐term datasets of vegetation and environmental drivers and integrating data with process‐based understanding. Summary Climate change is expected to affect vegetation: associated rising atmospheric CO2, higher temperatures and more variable and extreme rainfall regimes can all cause major shifts in vegetation composition, structure and function. Such effects need to be detected to confirm understanding and to inform models that can predict future vegetation change and guide management efforts. However, many change drivers—some related to, and others distinct from, climate change—simultaneously affect vegetation. These drivers include altered land management practices and shifts in fire and grazing regimes. Untangling the signals of climate‐change‐induced vegetation change from these other drivers of variation poses significant challenges. These challenges are amplified in regions with high interdecadal climate variability and enduring legacies of shifting human activities. Here, we assess attempts to detect and attribute vegetation change across Australia, a continent that exemplifies such complexities. We develop a scheme to classify attribution efforts according to whether they consider (1) qualitative or quantitative evidence, (2) mechanistic explanations and (3) alternative plausible change drivers. While a significant body of evidence demonstrates vegetation change in Australia, we find that it is difficult to confidently attribute changes to recent climate shifts—noting that few studies have attempted to do so. Several recommendations emerge that may improve attribution worldwide, including explicitly considering attribution strength, committing to long‐term monitoring of vegetation and change drivers and recognising multiple drivers of change, especially past and present human influences. Finally, achieving the strongest level of attribution requires linking observations and mechanistic models.

Pyrogenic carbon (PyC) is a highly stable fraction of soil organic carbon that plays an important role in the global carbon budget and biogeochemical processes. However, the stocks and dynamics of PyC in soils remain insufficiently characterized, largely due to longstanding challenges in obtaining accurate and reliable assessments. On Melville Island, northern Australia, fire is a common land management tool likely to influence both soil total organic carbon (TOC) and PyC. This study compared PyC to 50 cm depth and TOC to 100 cm depth in soils under three land uses (native Eucalyptus forests, Eucalyptus pellita plantations and Acacia mangium plantations) with differing recent fire frequencies. This study also compared the performance of nitric acid-hydrogen peroxide digestion (PyCKMD) and mid-infrared spectroscopy (PyCMIRS) in estimating PyC, using hydrogen pyrolysis (PyCHypy) as the reference technique. The results showed strong evidence that TOC stocks (0-10 cm soil) were lower under E. pellita plantation (18 Mg ha(-1)) than either A. mangium plantations or native forests (each about 25 Mg ha(-1)) (p < 0.01), and in all cases declined with increasing soil depth. PyChypy stock was distributed relatively evenly to 50 cm depth in each land use, at about 4-5 Mg ha(-1) per 10 cm depth interval. Although native forests were subject to prescribed burns approximately every two years (compared with fire exclusion over 15+ years in the plantations), the ratio of PyChypy to TOC (PyChypy/TOC) under native forests was lower than under A. mangium and E. pellita, suggesting that repeated fire events may lead to re-combustion of PyC. PyChypy/TOC increased with increasing soil depth, strongly suggesting the preferential translocation of PyC into deeper soil layers relative to TOC in the sandy and highly porous soils of Melville Island. Among the three methods used to estimate PyC, PyCKMD was not well correlated with PyCHypy, while PyCMIRS, based on the available calibration database, explained 23 % of the variation in PyCHypy. Compared to direct measurements by Hypy, MIRS tended to underestimate PyC in soil with TOC less than 45 g kg(-1) and overestimate PyC in soil with TOC greater than 45 g kg(-1). Expanded calibration datasets are required to improve the performance of MIRS for estimating PyC in the sandy forest soils of tropical Australia. Further work is required to quantify PyC beyond 50 cm deep soil, to track temporal changes in soil PyC across different land uses and fire histories, and to evaluate the performance of different methods in quantifying PyC across diverse soil types.

Rangelands cover vast areas and are subject to episodic droughts, regular fires and grazing pressure, all of which affect woody plant health. Consequently, standing dead and senescent woody plants are key components of these ecosystems. Stand-level, woody aboveground biomass (AGB), an important component of the terrestrial carbon budget, is typically scaled using individual-based allometric relationships with predictor variables such as stem diameter (D) or crown area (CA) measured through on-ground inventories of woody plants. Current data are lacking to allow assessment of the influence of woody plant condition on allometry and subsequent scaling of AGB. To address this, we undertook field measurements across 431 Australian rangeland sites to improve understanding of the variation in the condition of woody plants across rangelands, including how condition of different plant functional types (PFTs) affects overall stand condition, how stem diameter-crown area allometry varies with condition and PFT, and the sensitivity of allometry-predicted AGB to variance in condition. Field measurements included stem diameter, crown width and vigour, and health scores of live and standing dead woody plants. Over one-quarter of individual woody plants were either dead or senescing across all sites. D-CA allometric relationships differed among PFTs, with those for trees differing from those of shrubs and multi-stemmed acacias. For a given stem diameter, allometry-predicted crown area declined as health score decreased. Further, across all sites, allometry-predicted AGB was reduced when corrections were applied to account for standing dead and large senescing eucalypt trees relative to existing allometric equations. Our findings suggest that if traditional allometric relationships developed for live, healthy woody plants are applied to predict AGB in these ecosystems, substantial over-estimations may result, particularly for stands with a high proportion of large woody plants of poor condition. Results will inform ongoing improvements to the accuracy of stand-level biomass estimates in rangelands.

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.

Background Frequent fire characterises Australia’s tropical savannas and strongly influences vegetation structure and carbon (C) stocks. Fire management is widely used to reduce greenhouse gas emissions, but long-term effects of fire timing and frequency on woody C accumulation remain uncertain. Aims To quantify stand-level woody C stocks and assess cumulative multi-decadal effects of fire timing and frequency in northern Australian savannas. Methods Field measurements and historical biomass and basal area data from three long-term (17–30-year) fire trials were analysed. Treatments included unburnt controls and burned plots with up to four fire frequencies within a 6-year range and two fire seasons (early and late dry season). Carbon stocks were quantified for standing biomass, coarse woody debris and litter. Key results Total woody C accumulation was greater under early dry season fire than late dry season fire regimes. Longer between-fire intervals (particularly late dry season) may increase woody C stocks, although effect sizes were uncertain. Conclusions Early dry season burning was associated with greater long-term woody C accumulation than late dry season burning. Implications are that early dry season fire management remains important for reducing savanna fire emissions, but optimal fire regimes should consider site-specific conditions and broader ecological objectives.

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.

Climate and elevational gradients influence the spatial variation of tree diversity and community composition across tropical forest ecosystems. The present study examines how water–energy dynamics regulate tree species diversity and community turnover along the elevational gradient of Sri Lanka, a tropical island biodiversity hotspot, using data from 100 National Forest Inventory plots. We tested two hypotheses: that tree diversity declines with elevation in response to interacting reductions in temperature and precipitation, and that community compositional turnover is continuous and gradual rather than punctuated by abrupt transitions along the elevation gradient. Generalized additive models revealed a significant hump-shaped relationship between Shannon diversity and elevation, peaking at approximately 800 m a.s.l., with climate variables jointly explaining 39.7

CONTEXT: Agroforestry is globally recognised as a key nature-based solution for achieving net zero and biodiversity goals. However, land use decisions at the farm level often focus on single outcomes, missing the broader public and private benefits. There is a growing need for frameworks that make these multiple benefits explicit and connect local decisions with global imperatives. OBJECTIVE: To develop and demonstrate farm-level natural capital accounting (NCA) as a mechanism for informing agroforestry decisions on farms, enabling alignment of local actions with both local and global sustainability needs. METHODS: Farm-level NCA was implemented across nine Tasmanian farms (total 12,420 ha), using spatial models to predict key ecosystem services from agroforestry (biodiversity habitat, carbon sequestration, forest products, shade, shelter, pollination, and agricultural productivity). The study established 221 ha of new trees in various configurations and developed forward-looking natural capital accounts to be used alongside financial accounts. RESULTS AND CONCLUSIONS: The new plantings delivered marked improvements in both private and public benefits. Private benefits included increased animal welfare and productivity (25 ha more shade, 501 ha more shelter), and 52,000 m3 additional timber, with only a small loss of production area (12 virtual ha). Public benefits included 102 kt COQ-e sequestered, 716 ha of additional threatened species habitat, and 11.4 ha of new pollinator habitat. The NCA tools allow strategic planning of agroforestry assets to better meet farm, value chain, and public/environmental objectives. SIGNIFICANCE: This is the first demonstration of forward-looking, farm-level NCA for agroforestry, providing a practical framework to integrate natural capital into farm decision-making. The approach enables transparent accounting of multiple benefits, supporting both local farm viability and global sustainability targets, and offers a pathway for better informed farm decision-making to deliver global benefits at scale.

Australia is dominated by 6 million km2 of rangelands that contribute significantly to the livestock industry and carbon (C) market; yet, few studies have quantified the influence of grazing management on woody biomass and C stocks across these rangelands. Here, we quantified stand-level C stocks (Mg C ha−1) in live aboveground biomass (AGB), live belowground biomass (BGB), standing dead mass, coarse woody debris (CWD) and litter of rangeland vegetation at 46 long-term (average 40 year) grazing trials comprising grazed control plots paired with exclosures protected from grazers. Four major woody vegetation types were considered, namely, those dominated by Eucalyptus species, Acacia aneura, other Acacia species, and other species. Most C stocks (except litter C) were highest in Eucalyptus-dominated vegetation and lowest in the ‘other species’ types. There was high variability and few significant grazing effects on C stocks. There was a trend of higher AGB-C, BGB-C, woody basal area (m2 ha−1) and litter C with grazing exclusion at sites with mean annual precipitation of >300 mm, but the magnitude of increase was highly variable. The impact of the type of grazer was highly variable, although there was a trend of higher woody biomass C where rabbits and domestic livestock were excluded than with exclusion of domestic livestock only. The relative effects of grazing declined as the woody basal area of grazed control plots increased. Our results showed that although grazing exclusion can facilitate C stock accumulation in woody vegetation, it is difficult to predict when and where this may occur. To enable greater clarity, future studies may need to collect additional information, including data on soil water availability and current and historical grazing intensity.

Woody plantings are widely promoted to ameliorate biodiversity loss in agricultural landscapes. New market mechanisms are rapidly emerging to expedite such efforts, but limited tools and data to account for benefits achieved hamper their implementation. Using data from 204 primary studies and 1206 paired comparisons, we present a global meta-analysis of the biodiversity benefits of woody plantings in agricultural landscapes, in a biodiversity and ecosystem accounting framework. Consistent with emerging biodiversity and ecosystem accounting methods, we express results as agricultural field:natural reference and planting:natural reference ratios to estimate the biodiversity values of agricultural fields and plantings, respectively. Mean biodiversity abundance and species richness for agricultural fields were 0.40 of those for natural reference sites, compared with 0.62 for plantings averaging 20 years old, indicating a mean biodiversity benefit of 0.22. These values varied significantly among taxonomic groups, with unexpectedly high values for agricultural fields driven by high means for invertebrates. Variation among studies was substantial, and biodiversity values for plantings increased with higher diversity and native dominance of plantings and lower management intensity. Critically, estimates of biodiversity benefits based on abundance versus richness were comparable, but estimates using compositional measures typically implied substantially lower benefits, likely owing to effects of species identity. Our study operationalises approaches for quantifying the benefit of plantings for biodiversity and ecosystem accounting, and emphasises the need to use compositional measures for realistic estimates of biodiversity benefits.

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.

CONTEXT Agroforestry provides numerous benefits to agricultural landscapes, including timber production, carbon sequestration and enhanced biodiversity. Critically, agroforestry also influences the productivity of pasture, crops, and livestock. The magnitude and direction of the effect, however, is highly variable due to factors including the type of agroforestry (e.g., windbreak, alley, silvopasture), the condition of the trees (e.g., height, age, species), planting location and climatic conditions. However, currently there is limited information that quantifies how variation in these drivers affects the influence of agroforestry on agricultural yields. OBJECTIVES In this quantitative review we aimed to determine the magnitude of the effect that silvopasture, paddock trees and linear agroforestry systems have on agricultural productivity relative to a treeless comparison. In addition, we attempted to understand how the effect of agroforestry varied with factors such as tree condition, climate, and weather. METHODS A global literature review was conducted examining two key agroforestry types (non-intercropped linear systems such as windbreaks and hedges, and dispersed pasture systems such as paddock trees and silvopasture). Agricultural productivity responses of these systems compared to a treeless control were extracted and the size of the effect was compared to a range of conditions of the agroforestry systems e.g. tree density, age, distance to tree, as well as a range of climate and weather conditions. RESULTS AND CONCLUSIONS For the agroforestry types examined we found a strong evidence base for the effects on crop/pasture growth for linear agroforestry types (windbreaks, alleys) and on pasture growth in paddock tree/silvopasture systems (crop growth was not examined in these systems). There was limited information on the effects of linear agroforestry systems on livestock production. Linear agroforestry features were generally beneficial for crop and pasture growth. By comparison, silvopasture systems resulted in a reduction in both pasture and livestock productivity, although such systems are likely to provide other benefits for mitigating risk. Tree condition was a major factor driving effect size, the most prominent drivers being paddock size in linear configurations, and tree density in silvopasture systems. Climate variables also influenced the effect of agroforestry on productivity, indicating that both local and seasonal climate variation needs be considered when predicting effect sizes. SIGNIFICANCE This study provides important baseline information for valuing the effects linear and dispersed agroforestry types have on farm productivity and predicting under what conditions these effects will be optimised. Such information will aid in designing and implementing effective agroforestry systems.

Rangelands cover vast areas of the global land surface and are important to the terrestrial carbon budget. However, carbon accounting in rangeland systems is often limited by the lack of transparent and systematic methods for assessing changes in aboveground biomass ( B AG ). Although relationships between B AG and canopy cover, C , have been investigated at site and regional scales, there are few studies across regions where the impact of a range of vegetation types and site conditions has been assessed. Here, results were compiled from extensive field measurements across 431 Australian rangeland sites (covering an area of ~6 million km 2 ) to develop empirical relationships to predict B AG from C and other structural variables. A boosted‐regression‐tree model was trained to identify the relative importance of predictor variables. Then, based on these results, a stepwise empirical log‐linear relationship was developed to estimate B AG . About 70% of the B AG could be described using C , the percentage of large trees (stem diameter >50 cm), and height. Because such detailed information is not yet available at sufficient spatial and temporal resolution, classifications based on existing maps of structural vegetation classes, using C as the single predictor variable, were explored as an alternative approach to estimate B AG . For most structural vegetation classes assessed, estimates of B AG from C were statistically significant, with Lin's concordance coefficients of 0.67–0.79 and proportional error of <36% relative to the B AG across all the classes. There was generally little improvement in model performance with the inclusion of additional explanatory variables. Overall, this study has improved our understanding of relationships between C and B AG across rangeland systems. Additionally, combining remotely sensed woody cover data with these relationships may offer a transparent and accurate approach to monitor changes in biomass carbon stocks in these ecosystems at a large spatial scale.

In Australia, the Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. Consequently, over the last 20 years, it has been routinely applied at continental, regional and local scales, and has been subject to on-going development to improve its accuracy and representativeness. Given its importance, a sensitivity analysis could facilitate our understanding of model behaviour and aid the planning of future work and data collection. In particular, the sensitivity of a given model parameter is often context dependent such that it depends on the level of other parameters or variables. Key FullCAM parameters have generally been calibrated using data from empirical studies, with differing levels of confidence based on the sample size and data quality. The objective of this study was to apply a sensitivity analysis to examine (i) the sensitivity of FullCAM carbon stock outputs to its parameters and inputs, and how the sensitivity indices compare with the sample sizes used to calculate the respective parameters, (ii) the context dependency in terms of how the sensitivity varies with age, potential maximum biomass (a key FullCAM parameter), and disturbance severity or type, and (iii) to identify the implications for further development of FullCAM for woody vegetation systems. Of the 67 parameters tested, FullCAM carbon stock outputs were most sensitive to background mortality rates, age, potential maximum biomass, climate variability, age of maximum growth, decomposition or turnover parameters, and stand structure (regenerating or mature). The context dependency of the sensitivity analysis followed a consistent pattern depending on two main conditions, such that the sensitivity was higher when (i) the carbon stock was large and (ii) the parameter had a strong influence on that carbon stock. Several strong context dependencies occurred because the size of carbon stocks often vary through space and time within an ecosystem, and different processes (i.e. parameters) are more important at different times and locations. There was a strong context dependency in relation to age and potential maximum biomass, and FullCAM simulations indicated an interaction between fire disturbances and vegetation types. Lastly, background mortality was one of the least available inputs, but one to which FullCAM outputs were very sensitive. FullCAM was applied to show that the long-term contributions of low rates of background mortality to standing dead and debris C stocks were often as large as those from frequent (every 30 years) and intense harvesting and fire events because of the regular (annual) contribution of mortality and its accumulating influence of C stocks.

The Full Carbon Accounting Model (FullCAM) is used by the Australian Government for international reporting of greenhouse gas emissions and to predict carbon stock changes for carbon abatement projects. It is also used by landowners, companies and researchers, and is therefore regularly applied at local to continental scales. FullCAM is a stand level model with daily, monthly or annual temporal resolutions, depending on the users’ preference. FullCAM has been developed over the past 20 years, however, no studies have reviewed and described the current structure, parameters, calibration, and uses of FullCAM, which was the objective of this study. We also discuss the potential for further developments that could improve FullCAM’s ability to model responses to disturbances and climate, while also increasing its local accuracy.

Large areas of Australia’s natural woodlands have been cleared over the last two centuries, and remaining woodlands have experienced degradation from human interventions and anthropogenic climate change. Restoration of woodlands is thus of high priority both for government and society. Revegetation of deforested woodlands is increasingly funded by carbon markets, with accurate predictions of site-level carbon capture an essential step in the decision making to restore. We compared predictions of carbon in above-ground biomass using both the IPCC Tier 2 modelling approach and Australia’s carbon accounting model, FullCAM, to independent validation data from ground-based measurements. The IPCC Tier 2 approach, here referred to as the FastTrack model, was adjusted to simulate carbon capture by mixed-species forests for three planting configurations: direct seeding, tubestock planting, and a mix thereof. For model validation, we collected data on above-ground biomass, crown radius, and canopy cover covering an age range of 9–35 years from 20 plantings (n = 6044 trees). Across the three planting configurations, the FastTrack model showed a bias of 2.4 tC/ha (+4.2% of the observed mean AGB), whilst FullCAM had a bias of −24.6 tC/ha (−42.9% of the observed mean AGB). About two-thirds of the error was partitioned to unsystematic error in FastTrack and about one-quarter in FullCAM, depending on the goodness-of-fit metric assessed. Model bias differed strongly between planting configurations. For the FastTrack model, we found that additional canopy cover data estimated from satellite images obtained at different years can improve the carbon capture projections. To attain the highest accuracy of carbon projection at the site level, we recommend using a model with parameters calibrated for the specific planting configuration using local representative data.

Commercial plantations and farm forestry have the potential to increase the average carbon storage in woody biomass through afforestation, integration of belt plantings into farms, or increasing the average length of harvest rotations. In Australia, such increases in carbon storage are estimated at national- and project-scale through application of the stand-level carbon accounting model FullCAM, with predictions predominantly influenced by an input layer of biomass potential and calibrated yield curve parameters. Recent revision of the biomass potential input layer in FullCAM necessitated a refinement of yield curve calibrations for plantations and farm forestry, and therefore provided an opportunity to expand the capability of the model. Here we collated and analysed 16,749 observations of yields of above-ground biomass (AGB, Mg DM ha−1) from 8,749 independent plantation stands to assess whether growth differed between regions, species, management regimes and planting configurations and inform grouping of calibration sites into 17 different categories of tree plantings across Australia, with each having unique yield curve calibrations. Eight of the categories comprised species (or hybrids) that are commonly established in Australia, while other species (for which there were less AGB data) were grouped into generic multi-species categories based on plant functional type and/or planting configuration (block or belt). In revising the model calibrations, the trade-off between model accuracy and utility was considered. Across all plantation categories, stand age and biomass potential alone explained an average of 60% of the variation in observed AGB, with little (<10%) additional variation explained by factors such as region, species in the multi-species categories, or whether considering differences in yields from new plantings vs. regrowth post coppice harvesting. Although predictions were unbiased overall, they may nonetheless provide erroneous predictions at a given site. Although individual over- and under-predictions will tend to cancel out for carbon accounting at the national-scale, at the project-scale where carbon offsets are monetarised, discounting for uncertainty may be implemented.

Understanding carbon sequestration from tree planting and management activities is important for contributing to the mitigation of climate change. Although rates of growth and resulting sequestration of carbon in live above-ground biomass are relatively well understood in various woody plantings and commercial plantations in Australia, less is known about associated changes in live below-ground biomass, and the turnover and decomposition of biomass, and hence, resulting changes in carbon stocks of litter and soil. Our objective was to develop a modelling approach for predicting these processes and the resulting changes in litter and soil carbon following the establishment and/or management of tree plantings (EMTP) to facilitate their inclusion in carbon abatement projects. We reviewed and collated data on allocation of biomass, litterfall, litter decomposition, litter/harvest residue mass and soil organic carbon (N = 5,655 estimates). Data were sourced from existing field studies of woody plantings (mixed-species environmental and mallee eucalypt), commercial plantations (hardwood and softwood) and in some cases, eucalypt-dominant native forests. After constraining a carbon accounting model to observed measures of growth, allocation of biomass, and rates of litterfall and litter decomposition, the model was calibrated to maximise the efficiency of prediction of litter and soil pools. Although large uncertainties in 'observed' results meant that precision of prediction in any given data source was relatively poor, model predictions had negligible overall bias. The size and diversity of datasets applied in the process of calibration improved overall confidence in model predictions of litter and soil carbon pools. The calibrated model will therefore be a useful tool for informing land managers and policy makers seeking to understand on-site dynamics of all carbon pools, and hence, the total contribution to carbon abatement of different EMTP projects in Australia.

Functional traits are proxies for a species' ecology and physiology and are often correlated with plant vital rates. As such they have the potential to guide species selection for restoration projects. However, predictive trait-based models often only explain a small proportion of plant performance, suggesting that commonly measured traits do not capture all important ecological differences between species. Some residual variation in vital rates may be evolutionarily conserved and captured using taxonomic groupings alongside common functional traits. We tested this hypothesis using growth rate data for 17,299 trees and shrubs from 80 species of Eucalyptus and 43 species of Acacia, two hyper-diverse and co-occurring genera, collected from 497 neighborhood plots in 137 Australian mixed-species revegetation plantings. We modeled relative growth rates of individual plants as a function of environmental conditions, species-mean functional traits, and neighbor density and diversity, across a moisture availability gradient. We then assessed whether the strength and direction of these relationships differed between the two genera. We found that the inclusion of genus-specific relationships offered a significant but modest improvement to model fit (1.6%-1.7% greater R2 than simpler models). More importantly, almost all correlates of growth rate differed between Eucalyptus and Acacia in strength, direction, or how they changed along the moisture gradient. These differences mapped onto physiological differences between the genera that were not captured solely by measured functional traits. Our findings suggest taxonomic groupings can capture or mediate variation in plant performance missed by common functional traits. The inclusion of taxonomy can provide a more nuanced understanding of how functional traits interact with abiotic and biotic conditions to drive plant performance, which may be important for constructing trait-based frameworks to improve restoration outcomes.