Keryn I Paul, Stephen H Roxburgh, Jerome Chave, Jacqueline R England, Ayalsew Zerihun, Alison Specht, Tom Lewis, Lauren T Bennett, Thomas G Baker, Mark A Adams, Dan Huxtable, Kelvin D Montagu, Daniel S Falster, Mike Feller, Stan Sochacki, Peter Ritson, Gary Bastin, John Bartle, Dan Wildy, Trevor Hobbs, John Larmour, Rob Waterworth, Hugh T L Stewart, Justin Jonson, David I Forrester, Grahame Applegate, Daniel Mendham, Matt Bradford, Anthony O'Grady, Daryl Green, Rob Sudmeyer, Stan J Rance, John Turner, Craig Barton, Elizabeth H Wenk, Tim Grove, Peter M Attiwill, Elizabeth Pinkard, Don Butler, Kim Brooksbank, Beren Spencer, Peter Snowdon, Nick O'Brien, Michael Battaglia, David M Cameron, Steve Hamilton, Geoff McAuthur, Jenny Sinclair

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 prediction above-ground biomass. 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; multi-stemmed 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 generalisation (species-specific, plant functional type) were validated using whole-plot harvest data from 17 contrasting stands (range: 9 to 356 Mg ha(-1) ). Losses in efficiency of prediction were < 1% if generalised models were used in place of species-specific models. Furthermore, application of generalised multi-species 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). This article is protected by copyright. All rights reserved.