Tree allometry and improved estimation of carbon stocks and balance in tropical forests

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dc.contributor.author Chave, J
dc.contributor.author Andalo, C
dc.contributor.author Brown, S
dc.contributor.author Cairns, MA
dc.contributor.author Chambers, JQ
dc.contributor.author Eamus, D
dc.contributor.author Fölster, H
dc.contributor.author Fromard, F
dc.contributor.author Higuchi, N
dc.contributor.author Kira, T
dc.contributor.author Lescure, J-P
dc.contributor.author Nelson, BW
dc.contributor.author Ogawa, H
dc.contributor.author Puig, H
dc.contributor.author Riéra, B
dc.contributor.author Yamakura, T
dc.date.accessioned 2009-12-21T02:29:28Z
dc.date.issued 2005-08
dc.identifier.citation Oecologia, 2005, 145 (1), pp. 87 - 99
dc.identifier.issn 0029-8549
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/3628
dc.description.abstract Tropical forests hold large stores of carbon, yet uncertainty remains regarding their quantitative contribution to the global carbon cycle. One approach to quantifying carbon biomass stores consists in inferring changes from long-term forest inventory plots. Regression models are used to convert inventory data into an estimate of aboveground biomass (AGB). We provide a critical reassessment of the quality and the robustness of these models across tropical forest types, using a large dataset of 2,410 trees ≥ 5 cm diameter, directly harvested in 27 study sites across the tropics. Proportional relationships between aboveground biomass and the product of wood density, trunk cross-sectional area, and total height are constructed. We also develop a regression model involving wood density and stem diameter only. Our models were tested for secondary and old-growth forests, for dry, moist and wet forests, for lowland and montane forests, and for mangrove forests. The most important predictors of AGB of a tree were, in decreasing order of importance, its trunk diameter, wood specific gravity, total height, and forest type (dry, moist, or wet). Overestimates prevailed, giving a bias of 0.5-6.5% when errors were averaged across all stands. Our regression models can be used reliably to predict aboveground tree biomass across a broad range of tropical forests. Because they are based on an unprecedented dataset, these models should improve the quality of tropical biomass estimates, and bring consensus about the contribution of the tropical forest biome and tropical deforestation to the global carbon cycle. © Springer-Verlag 2005.
dc.language eng
dc.relation.isbasedon 10.1007/s00442-005-0100-x
dc.title Tree allometry and improved estimation of carbon stocks and balance in tropical forests
dc.type Journal Article
dc.parent Oecologia
dc.journal.volume 1
dc.journal.volume 145
dc.journal.number 1 en_US
dc.publocation New York, USA en_US
dc.identifier.startpage 87 en_US
dc.identifier.endpage 99 en_US
dc.cauo.name SCI.Faculty of Science en_US
dc.conference Verified OK en_US
dc.for 0607 Plant Biology
dc.personcode 000006
dc.percentage 100 en_US
dc.classification.name Plant Biology en_US
dc.classification.type FOR-08 en_US
dc.description.keywords Biomass
dc.description.keywords Carbon
dc.description.keywords Plant allometry
dc.description.keywords Tropical forest
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc true
utslib.collection.history School of the Environment (ID: 344)
utslib.collection.history School of the Environment (ID: 344)
utslib.collection.history Closed (ID: 3)


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