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.

This study assessed the impact of grazing, with and without cattle, on carbon pools in an Australian semi-arid woodland using field measurements and simulations of the Full Carbon Accounting Model (FullCAM). Field measurements included living and dead trees, shrubs and grasses, litter and coarse woody debris (aboveground) and biocrust and soil to 10 cm depth (belowground). Root biomass was estimated as a shoot-to-root ratio. Total carbon (above + below ground) in cattle-excluded grazed sites (75.0 +/- 0.08 t ha-1) was similar to cattle-included grazed sites (72.4 +/- 0.08 t ha-1), with approximately 16-21 % of the total measured carbon stored in the soil. However, cattle grazing reduced biocrust carbon from 0.24 t ha-1 to 0.06 t ha-1, emphasizing the vulnerability of biocrusts to hard-hooved trampling by large and heavy cattle. The field measurements were used to calibrate model parameters within FullCAM so that simulated carbon pools aligned with field-measured carbon stocks. Following the calibration step, grazing scenarios, with and without cattle, were simulated from 1900 to 2300, including periodic wildfire occurrences. These simulations showed lower live tree carbon stock in cattle-included grazing treatment that led to a gradual decline in total carbon. The total carbon stock under the cattle-excluded grazing scenario remained between 90 and 103 t ha-1 from 1900 to 2100, but decreased to 86-100 t ha-1 following the introduction of cattle-included grazing conditions. Our results highlight the degrading impacts of cattle-included grazing on biocrusts, which in turn undermine soil stability and carbon retention. The application of FullCAM to simulate carbon pools in semi-arid woodland ecosystems over decades and centuries extends the interpretation of results from short-term studies in plausible ways.

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.

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.

We implement natural capital accounts for ecosystem services (ES) using the System of Environmental-Economic Accounting-Ecosystem Accounting (SEEA EA) framework and a case study from the Murray-Darling Basin, Australia. Extended ES supply-use tables are presented that allow for the simultaneous reporting on multiple intermediate and final ES alongside data already available in traditional national economic accounts. We cover the ES of crop provisioning, grazed biomass provisioning, water supply, soil erosion control, recreational fishing, and carbon sequestration and storage. This study shows that extended ES supply-use tables using physical and monetary measures can provide feasible, although not necessarily complete, links between information recorded in the SEEA EA and traditional national economic accounts. It provides an integration of intermediate ES, final ES and produced goods and services in a single table and helps to distinguish benefits from the inputs that create these benefits. Going forward, more integrated recording of the economy and ecosystems' contributions to wellbeing is needed to better understand the benefits derived from nature.

During 2017-2019 south-eastern Australia experienced intense drought that threatened and damaged ecosystems and helped enable the Black Summer wildfires of 2019-2020. These wildfires burnt through about 7 M ha of mainly temperate Eucalyptus forests, releasing the equivalent of Australia's total net annual greenhouse gas emissions. For the state of Victoria, these fires burnt through about 1 M ha of highly productive forests, where 50 % of the area was burnt with high to extreme wildfire severity. This study reports the recovery of Eucalyptus sieberi dominated forests three years after the Black Summer wildfires, and five years after prescribed burn applied in May 2018, during the drought. Wildfire affected sites were burnt with high and low-to-moderate severity, while previously prescribed burnt sites were burnt at low-to-moderate severity and were not reburnt in the 2020 wildfires. For wildfire affected sites, 30 % of small diameter trees (10-20 cm) continued to die three years after fires, irrespective of fire severity. Three years after wildfire, live tree carbon reached 92 % of pre-fire level for low-to-moderate severity sites and 76 % for high severity sites, indicating longer recovery with more severe wildfire. For prescribed burnt sites, tree mortality significantly increased two years after the burn and the number of live trees thereafter remained stable to five years post fire. This trend was reflected in the nonrecovery of live tree carbon reaching only 95 % of pre-fire level five years after the burn. Recruitment of new Eucalyptus seedlings was observed in sites burnt in wildfire regardless of fire severity (900 +/- 316 trees ha(-1)), but not in prescribed burnt sites. This was possibly driven by the differences in rainfall after fires, rather than fire type. For other forest carbon pools such as surface litter and CWD, recovery to pre-fire levels was observed within six months after fires. Moreover, for low-to-moderate severity burnt sites litter loads had doubled pre-fire mass three years after fire and continued to increase five years post fire. Results from this study suggest that both fire severity and post-fire weather conditions play an important role in determining the rate of forest aboveground carbon recovery. Our findings indicate that as fire severity increases, the time required for recovery of live tree carbon to pre-fire level is longer. Even low impact prescribed burns during prolonged drought can lead to ongoing mortality that reduces the rate of carbon recovery. These observations indicate that increases in fire occurrence and severity associated with climate change can reduce the carbon carrying capacity of these highly productive temperate forests. The implication of this work is that the presumed recovery of aboveground carbon in forests following fires may be over-estimated or might not occur at all.

Decreased grazing and/or cessation of land clearing across Australia’s rangelands are being used to promote carbon sequestration through regeneration of woody biomass, predominately in Acacia aneura (mulga) woodlands. Changes in carbon stock are predicted using the carbon accounting model FullCAM. We collated datasets to assess the level of confidence in applying FullCAM to mulga regeneration across south-western Queensland and north-western New South Wales, with respect to model accuracy, specificity, and comprehensiveness. We found that FullCAM predictions were moderately accurate, with independent verification sites (N = 102) indicating model efficiencies of 48–70% and bias of −3.50 to −0.99 Mg DM ha−1, depending on calculation method. To ensure accuracy and to reduce risks of over-prediction, it is recommended FullCAM should be limited to sites with regeneration ages of ≤25 years and with levels of pre-existing above-ground biomass less than approximately 5 Mg DM ha−1. The paucity of data from mulga ecosystems in central and western Australia was identified as an important research gap. Regarding specificity, FullCAM has been calibrated to average rates of regeneration, generalised across a range of vegetation types, disturbance histories, and grazing management practices. This generalisation ensures accuracy when applied over broad spatial domains, but may limit the model’s accuracy at specific locations. For example, at the site scale, long-term grazing exclosure experiments (N = 34) have shown a wide range of regeneration outcomes (−0.52 to 1.85 Mg DM ha−1 year−1, with an average of 0.29 Mg DM ha−1 year−1), with site-scale contributors to this variability including the proportion of mulga in the total biomass, and the degree of change in grazing intensity (e.g. exclusion of livestock only, cf. exclusion of livestock plus native and feral animals). Regarding model comprehensiveness, new field data suggest that FullCAM could be extended to include standing dead pools of woody biomass, which contribute, on average, 17% of total woody biomass in mulga woodlands.

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.

Abstract Managing vegetation to sequester carbon in biomass requires estimates to meet standards for accuracy, with methods that are transparent, verifiable and cost‐effective. Allometric models are commonly used to predict biomass from non‐destructive field inventory data. Although a number of studies have addressed biomass error propagation, none have provided a general set of methods for linking errors all the way from initial allometric model development through to the final site‐based biomass prediction, for both above‐ and below‐ground biomass. Error sources in total biomass (above‐ + below‐ground) were quantified using a combination of analytical and Monte Carlo methods, illustrated with four contrasting case studies using either site‐ and‐species‐specific, species‐specific or generalised allometric models. Sampling error was found to be the most important contributor to site‐level biomass uncertainty, arising from the interaction between spatial variability and the field sampling design. The contribution of allometric model covariance to total error was also quantified, with errors in the determination of moisture content during allometric model development identified as a potentially important yet often overlooked error source. Application of different allometric models to the same inventory data suggested the error from generalised models was no greater than that from site‐ or species‐specific models, with increases in the generalised model prediction error balanced by decreases in other error sources associated with the increased sample size on which generalised models are based. Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. To reduce costs while maintaining acceptable accuracy, the use of generalised allometric models is recommended, with the caveat that additional biomass sampling for model validation may be required to limit the potential for biased predictions.

Background Tropical savannas represent a large proportion of the area burnt each year globally, with growing evidence that management to curtail fire frequency and intensity in some of these regions can contribute to mitigation of climate change. Approximately 25% of Australia’s fire-prone tropical savanna region is currently managed for carbon projects, contributing significantly to Australia’s National Greenhouse Gas Inventory. Aims To improve the accuracy of Australia’s national carbon accounting model (FullCAM) for reporting of fire emissions and sequestration of carbon in savanna ecosystems. Methods Field data from Australian savannas were collated and used to calibrate FullCAM parameters for the prediction of living biomass, standing dead biomass and debris within seven broad vegetation types. Key results Revised parameter sets and improved predictions of carbon stocks and fluxes across Australia’s savanna ecosystems in response to wildfire and planned fire were obtained. Conclusions The FullCAM model was successfully calibrated to include fire impacts and post-fire recovery in savanna ecosystems. Implications This study has expanded the capability of FullCAM to simulate both reduced emissions and increased sequestration of carbon in response to management of fire in tropical savanna regions of Australia, with implications for carbon accounting at national and project scales.

Greenhouse gas (GHG) accounting of emissions from land use, land-use change, and forestry necessarily involves consideration of landscape fire. This is of particular importance for Australia given that natural and human fire is a common occurrence, and many ecosystems are adapted to fire, and require periodic burning for plant regeneration and ecological health. Landscape fire takes many forms, can be started by humans or by lightning, and can be managed or uncontrolled. We briefly review the underlying logic of greenhouse gas accounting involving landscape fire in the 2020 Australian Government GHG inventory report. The treatment of wildfire that Australia chooses to enact under the internationally agreed guidelines is based on two core assumptions (a) that effects of natural and anthropogenic fire in Australian vegetation carbon stocks are transient and they return to the pre-fire level relatively quickly, and (b) that historically and geographically anomalous wildfires in forests should be excluded from national anthropogenic emission estimates because they are beyond human control. It is now widely accepted that anthropogenic climate change is contributing to increased frequency and severity of forest fires in Australia, therefore challenging assumptions about the human agency in fire-related GHG emissions and carbon balance. Currently, the national inventory focuses on forest fires; we suggest national greenhouse gas accounting needs to provide a more detailed reporting of vegetation fires including: (a) more detailed mapping of fire severity patterns; (b) more comprehensive emission factors; (c) better growth and recovery models from different vegetation types; (d) improved understanding how fires of different severities affect carbon stocks; and (e) improved analysis of the human agency behind the causes of emissions, including ignition types and fire-weather conditions. This more comprehensive accounting of carbon emissions would provide greater incentives to improve fire management practices that reduce the frequency, severity, and extent of uncontrolled landscape fires.

Carbon farming presents an opportunity for the land sector to generate income and transition to more sustainable land management practices. In Australia, establishing a carbon project and earning carbon credits is complex, with project proponents needing to satisfy eligibility requirements and adhere to rigorous measurement, verification, and reporting protocols of approved methods. To address these challenges, a human centered design (HCD) approach was used to deliver a digital solution, serving landowners' needs related to method discovery and reconfiguring how the methodological and scientific complexity of abatement potentials was delivered. The solution, called LOOC-C (pronounced "Look-see"), supports the discovery of abatement methods that are available for a given land area and provides an initial estimate of the potential quantum of carbon sequestered/emitted and the nature of co-benefits associated with each eligible method. Reporting on LOOC-C development and its observed impact demonstrates the role that human centered digital tools have in promoting land management actions that are both sustainable and reasonable to undertake. It equally demonstrates the power of integrating environmental market and user requirements with a robust design methodology. With similar opportunities in environmental markets globally, additional applications of an HCD approach are proposed.

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.

The increasing availability of regional and global climate data presents an opportunity to build better ecological models; however, it is not always clear which climate dataset is most appropriate. The aim of this study was to better understand the impacts that alternative climate datasets have on the modelled distribution of plant species, and to develop systematic approaches to enhancing their use in species distribution models (SDMs).

Disturbance trends over recent decades indicate that climate change is resulting in increased fire severity and extent in Australia's temperate Eucalyptus forests. As disturbance cycles become shorter and more severe, empirical measurements are required to identify potential change in forest carbon (C) stock and emissions. However, such estimates are rare in the literature. The 2019-2020 wildfires burnt through 6 to 7 million ha of mainly temperate open Eucalyptus forest in south-east Australia, with top down emission estimates ranging from 97 to 130 tonnes CO2 ha-1. Study sites that had been assessed for all aboveground C pools prior to the wildfires, were burnt in January 2020 by wildfire that varied in severity. Here we quantify the impact of high and low/moderate fire severities on tree mortality, C loss and C redistribution and assess implications for future C storage in these temperate Eucalyptus forests. Higher fire severity resulted in greater overstorey tree mortality but not understorey or loss of dead standing trees than in low/moderate severity fires. High severity fires combusted almost twice as much C from live trees (42 Mg C ha-1) as low/moderate severity fires (25 Mg C ha-1), while C loss from dead standing trees was similar among fire severity classes (average 17 Mg C ha-1). Total aboveground C lost across study sites was 42 Mg C ha-1 for high and 47 Mg C ha-1 for low/moderate severity, with an average of 45 Mg C ha-1 equivalent to 15 % (high severity) and 14 % (low/moderate severity) of AGC. Extrapolating our findings to other tall to medium open Eucalyptus forests across Victoria revealed that 37.33 ± 12.25 Tg C (mean ± s.e.) or 152 ± 50 Mg CO2 ha-1 was lost to the atmosphere from the 0.9 million ha of these productive forests, equating to about 20 % of Australia's total net annual emissions.

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation 'impact (pressure)', with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into 'impact (pressure)' categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes-including land degradation, desertification and ecosystem restoration-the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions.

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.

Disturbances are important determinants of diversity, and the combination of their aspects (e.g., disturbance intensity, frequency) can result in complex diversity patterns. Here, we leverage an important approach to classifying disturbances in terms of temporal span to understand the implications for species coexistence: pulse disturbances are acute and discrete events, while press disturbances occur continuously through time. We incorporate the resultant mortality rates into a common framework involving disturbance frequency and intensity. Press disturbances can be encoded into models in two distinct ways, and we show that the appropriateness of each depends on the type of data available. Using this framework, we compare the effects of pulse versus press disturbance on both asymptotic and transient dynamics of a two-species Lotka-Volterra competition model to understand how they engage with equalizing mechanisms of coexistence. We show that press and pulse disturbances differ in transient behavior, though their asymptotic diversity patterns are similar. Our work shows that these differences depend on how the underlying disturbance aspects interact and that the two ways of characterizing press disturbances can lead to contrasting interpretations of disturbance-diversity relationships. Our work demonstrates how theoretical modeling can strategically guide and help the interpretation of empirical work.