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).

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Exchanges of carbon and nitrogen between the atmosphere and terrestrial ecosystems involve a complex set of interactions affected by both natural and management processes. Understanding these processes is important for managing ecosystem productivity and sustainability. Management processes also affect the net outcome of exchanges of greenhouse gases between terrestrial ecosystems and the atmosphere. In developing a national carbon accounting system (NCAS) for Australia to account for emissions and removal of greenhouse gases to and from the atmosphere, a carbon:nitrogen mass balance ecosystem model (FullCAM) was developed. The FullCAM model is a hybrid of empirical and process modelling. The approach enables application to a wide range of natural resource management issues, because it is at land-management-relevant spatial and temporal resolution and captures the main process and management drivers. The scenario-prediction capability can be used to determine the emissions consequences of different management activities. Because, in Australia, emissions of greenhouse gases are closely related to the retention of dead organic matter and the availability of nitrogen for plant growth, the carbon and nitrogen cycling as modelled are good indicators of ecosystem productivity and condition. The NCAS also emphasizes the advantages of a comprehensive and integrated approach to developing a continental scale ecosystem-modelling system that has relevance both to estimation of greenhouse gas emissions and sustainable management of natural resources.