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Accurate ground-based estimation of the carbon stored in terrestrial ecosystems is critical to quantifying the global carbon budget. Allometric models provide cost-effective methods for biomass prediction. But do such models vary with ecoregion or plant functional type? We compiled 15 054 measurements of individual tree or shrub biomass from across Australia to examine the generality of allometric models for above-ground biomass prediction. This provided a robust case study because Australia includes ecoregions ranging from arid shrublands to tropical rainforests, and has a rich history of biomass research, particularly in planted forests. Regardless of ecoregion, for five broad categories of plant functional type (shrubs; multistemmed trees; trees of the genus Eucalyptus and closely related genera; other trees of high wood density; and other trees of low wood density), relationships between biomass and stem diameter were generic. Simple power-law models explained 84-95% of the variation in biomass, with little improvement in model performance when other plant variables (height, bole wood density), or site characteristics (climate, age, management) were included. Predictions of stand-based biomass from allometric models of varying levels of generalization (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9-356 Mg ha(-1) ). Losses in efficiency of prediction were <1% if generalized models were used in place of species-specific models. Furthermore, application of generalized multispecies models did not introduce significant bias in biomass prediction in 92% of the 53 species tested. Further, overall efficiency of stand-level biomass prediction was 99%, with a mean absolute prediction error of only 13%. Hence, for cost-effective prediction of biomass across a wide range of stands, we recommend use of generic allometric models based on plant functional types. Development of new species-specific models is only warranted when gains in accuracy of stand-based predictions are relatively high (e.g. high-value monocultures)., (© 2015 John Wiley & Sons Ltd.)

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. • FullCAM simulates carbon follows between live biomass, standing dead, debris and soil. • FullCAM is used at local, regional and continental scales, and daily, monthly or annual steps. • FullCAM is used for native forests, woodlands and savannas, as well as plantations. [ABSTRACT FROM AUTHOR]

The clearing of natural vegetation for agriculture has reduced the capacity of natural systems to provide ecosystem functions. Ecological restoration can restore desirable ecosystem functions, such as creating habitat for animal conservation and carbon sequestration as woody biomass. In order to maintain these beneficial ecosystem functions, restoration projects need to mature into self‐perpetuating communities. Here we compared the ecological attributes of two types of restoration, "active" tree plantings with "passive" natural forest regeneration ("natural regrowth") to existing remnant vegetation in a cleared agricultural landscape. Specifically, we measured differences between forest categories in factors that may predict future restoration failure or ecosystem collapse: aboveground plant biomass and biomass accrual over time (for regrowing stands), plant density and size class distributions, and diversity of functional groups based on seed dispersal and growth strategy traits. We found that natural regrowth and planted forests were similar in many ecological characteristics, including biomass accrual. Despite this, planted stands contained fewer tree recruit and shrub individuals, which may be due to limited recruitment in plantings. If this continues, these forests may be at risk of collapsing into nonforest states after mature trees senesce. Lower shrub density and richness of mid‐story trees may lead to lower structural complexity in planting plots, and alongside lower richness of fleshy‐fruited plant species may reduce animal resources and animal use of the restored stand. In our study region, natural regrowth may result in restored woodland communities with greater conservation and carbon mitigation value. [ABSTRACT FROM AUTHOR]

To combat global warming and biodiversity loss, we require effective forest restoration that encourages recovery of species diversity and ecosystem function to deliver essential ecosystem services, such as biomass accumulation. Further, understanding how and where to undertake restoration to achieve carbon sequestration and biodiversity conservation would provide an opportunity to finance ecosystem restoration under carbon markets. We surveyed 30 native mixed‐species plantings in subtropical forests and woodlands in Australia and used structural equation modeling to determine vegetation, soil, and climate variables most likely driving aboveground biomass accrual and bird richness and investigate the relationships between plant diversity, aboveground biomass accrual, and bird diversity. We focussed on woodland and forest‐dependent birds, and functional groups at risk of decline (insectivorous, understorey‐nesting, and small‐bodied birds). We found that mean moisture availability strongly limits aboveground biomass accrual and bird richness in restoration plantings, indicating potential synergies in choosing sites for carbon and biodiversity purposes. Counter to theory, woody plant richness was a poor direct predictor of aboveground biomass accrual, but was indirectly related via significant, positive effects of stand density. We also found no direct relationship between aboveground biomass accrual and bird richness, likely because of the strong effects of moisture availability on both variables. Instead, moisture availability and patch size strongly and positively influenced the richness of woodland and forest‐dependent birds. For understorey‐nesting birds, however, shrub cover and patch size predicted richness. Stand age or area of native vegetation surrounding the patch did not influence bird richness. Our results suggest that in subtropical biomes, planting larger patches to higher densities, ideally using a diversity of trees and shrubs (characteristics of ecological plantings) in more mesic locations will enhance the provision of carbon and biodiversity cobenefits. Further, ecological plantings will aid the rapid recovery of woodland and forest bird richness, with comparable aboveground biomass accrual to less diverse forestry plantations. [ABSTRACT FROM AUTHOR]

Highlights • Over 2000 measurements of plant belowground biomass were compiled. • Accurate generic allometric models could be developed. • These models were validated across stands where whole-plot excavation data were available. • Gains in prediction efficiencies using species-specific models were negligible. Abstract Accurate quantification of below-ground biomass (BGB) of woody vegetation is critical to understanding ecosystem function and potential for climate change mitigation from sequestration of biomass carbon. We compiled 2054 measurements of planted and natural individual tree and shrub biomass from across different regions of Australia (arid shrublands to tropical rainforests) to develop allometric models for prediction of BGB. We found that the relationship between BGB and stem diameter was generic, with a simple power-law model having a BGB prediction efficiency of 72–93% for four broad plant functional types: (i) shrubs and Acacia trees, (ii) multi-stemmed mallee eucalypts, (iii) other trees of relatively high wood density, and; (iv) a species of relatively low wood density, Pinus radiata D. Don. There was little improvement in accuracy of model prediction by including variables (e.g. climatic characteristics, stand age or management) in addition to stem diameter alone. We further assessed the generality of the plant functional type models across 11 contrasting stands where data from whole-plot excavation of BGB were available. The efficiency of model prediction of stand-based BGB was 93%, with a mean absolute prediction error of only 6.5%, and with no improvements in validation results when species-specific models were applied. Given the high prediction performance of the generalised models, we suggest that additional costs associated with the development of new species-specific models for estimating BGB are only warranted when gains in accuracy of stand-based predictions are justifiable, such as for a high-biomass stand comprising only one or two dominant species. However, generic models based on plant functional type should not be applied where stands are dominated by species that are unusual in their morphology and unlikely to conform to the generalised plant functional group models. [ABSTRACT FROM AUTHOR]

Reforestation of agricultural land with mixed-species environmental plantings of native trees and shrubs contributes to abatement of greenhouse gas emissions through sequestration of carbon, and to landscape remediation and biodiversity enhancement. Although accumulation of carbon in biomass is relatively well understood, less is known about associated changes in soil organic carbon (SOC) following different types of reforestation. Direct measurement of SOC may not be cost effective where rates of SOC sequestration are relatively small and/or highly spatially-variable, thereby requiring intensive sampling. Hence, our objective was to develop a verified modelling approach for determining changes in SOC to facilitate the inclusion of SOC in the carbon accounts of reforestation projects. We measured carbon stocks of biomass, litter and SOC (0–30 cm) in 125 environmental plantings (often paired to adjacent agricultural sites), representing sites of varying productivity across the Australian continent. 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 SOC and its fractions. Uncertainties in both measured and modelled results meant that efficiencies of prediction of SOC across the 125 contrasting plantings were only moderate, at 39–68%. Data-informed modelling nonetheless improved confidence in outputs from scenario analyses, confirming that: (i) reforestation on agricultural land highly depleted in SOC (i.e. previously under cropping) had the highest capacity to sequester SOC, particularly where rainfall was relatively high (> 600 mm year − 1 ), and; (ii) decreased planting width and increased stand density and the proportion of eucalypts enhanced rates of SOC sequestration. These results improve confidence in predictions of SOC following environmental reforestation under varying conditions. The calibrated model will be a useful tool for informing land managers and policy makers seeking to understand the dynamics of SOC following such reforestation. [ABSTRACT FROM AUTHOR]

• Biomass, litterfall, decomposition and soil carbon data were collated. • Data was sourced from decades of research in plantations and farm forestry. • These data informed the model predicting carbon in tree plantings across Australia. • Biomass, litterfall and decomposition data first constrained the model. • The model was then efficiently calibrated for prediction of litter and soil carbon. 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. [ABSTRACT FROM AUTHOR]

Establishment of mallee eucalypt plantings on cleared agricultural land is currently the predominant method of reforestation for carbon sequestration in Australia. Investment in establishing and maintaining such plantings relies on having a cost-effective approach for providing un-biased estimates of yield in biomass and carbon sequestration. The Australian Government’s forest carbon accounting model (FullCAM) had not previously been calibrated for mallee eucalypt plantings and, in many circumstances, substantially under-estimated of biomass for these plantings. Our objective was to improve model applicability and reliability of estimates of carbon sequestration. To achieve this, we first collected and analysed above-ground biomass data from 257 mallee eucalypt plantings (or 744 observations, when including the multiple measurements made at some planting sites) to determine the key factors influencing growth. Plantings were categorised according to species, planting configuration (block or belt plantings) and stand density. Each category of planting had significantly different rates of growth, with the rates of sequestration of above-ground biomass carbon being relatively high when established in densely-stocked, two-row belts. These categories of plantings then provided the basis for calibration (estimation of appropriate modifiers) of FullCAM growth curves. Overall model efficiency was 63%, and there was no apparent bias when the model was applied to the various planting categories. Thus, modelled estimates of biomass accumulation will be reliable on average but at any particular location will be highly uncertain, with either substantial under- or over-prediction possible. For some categories of mallee eucalypt plantings, and for plantings with access to ground-water or established in non-productive soils, there were insufficient observations to provide confidence in new calibrations specific for these circumstances. Moreover, application of the calibrations provided here are applicable for prediction of sequestration of biomass carbon of relatively young (<15-year-old) stands, with more data needed for prediction of longer-term rates of growth. [ABSTRACT FROM AUTHOR]