Paul Keryn I, Roxburgh Stephen H, Chave Jerome, England Jacqueline R, Zerihun Ayalsew, Specht Alison, Lewis Tom, Bennett Lauren T, Baker Thomas G, Adams Mark A, Huxtable Dan, Montagu Kelvin D, Falster Daniel S, Feller Mike, Sochacki Stan, Ritson Peter, Bastin Gary, Bartle John, Wildy Dan, Hobbs Trevor, Larmour John, Waterworth Rob, Stewart Hugh T L, Jonson Justin, Forrester David I, Applegate Grahame, Mendham Daniel, Bradford Matt, O'Grady Anthony, Green Daryl, Sudmeyer Rob, Rance Stan J, Turner John, Barton Craig, Wenk Elizabeth H, Grove Tim, Attiwill Peter M, Pinkard Elizabeth, Butler Don, Brooksbank Kim, Spencer Beren, Snowdon Peter, O'Brien Nick, Battaglia Michael, Cameron David M, Hamilton Steve, McAuthur Geoff, Sinclair Jenny
CSIRO Agriculture and CSIRO Land and Water, GPO Box 1700, Canberra, ACT, 2601, Australia.
UMR 5174 Laboratoire Evolution et Diversité Biologique, CNRS & Université Paul Sabatier, Toulouse, 31062, France.
Glob Chang Biol. 2016 Jun;22(6):2106-24. doi: 10.1111/gcb.13201. Epub 2016 Mar 29.
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).
准确地基于地面估算陆地生态系统中储存的碳对于量化全球碳预算至关重要。异速生长模型为生物量预测提供了经济有效的方法。但是这些模型会因生态区域或植物功能类型而有所不同吗?我们收集了来自澳大利亚各地的15054个单株树木或灌木生物量的测量数据,以检验异速生长模型在预测地上生物量方面的通用性。这提供了一个有力的案例研究,因为澳大利亚包括从干旱灌木丛到热带雨林的各种生态区域,并且有丰富的生物量研究历史,特别是在人工林中。无论生态区域如何,对于五类广泛的植物功能类型(灌木;多干树;桉属及其近缘属的树木;其他高木材密度的树木;以及其他低木材密度的树木),生物量与茎直径之间的关系是通用的。简单的幂律模型解释了生物量变化的84 - 95%,当纳入其他植物变量(高度、树干木材密度)或场地特征(气候、年龄、管理)时,模型性能几乎没有改善。使用来自17个对比林分(范围:9 - 356 Mg ha⁻¹)的全地块收获数据,对不同泛化水平(物种特异性、植物功能类型)的异速生长模型进行基于林分的生物量预测验证。如果使用通用模型代替物种特异性模型,预测效率损失<1%。此外,在测试的53个物种中,92%的物种应用通用多物种模型在生物量预测中未引入显著偏差。此外,林分水平生物量预测的总体效率为99%,平均绝对预测误差仅为13%。因此,为了在广泛的林分中进行具有成本效益的生物量预测,我们建议使用基于植物功能类型的通用异速生长模型。只有当基于林分的预测准确性提高相对较高时(例如高价值单一栽培),才值得开发新的物种特异性模型。