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Testing the generality of below-ground biomass allometry across plant functional types.

Authors :
Paul, Keryn I.
Larmour, John
Specht, Alison
Zerihun, Ayalsew
Ritson, Peter
Roxburgh, Stephen H.
Sochacki, Stan
Lewis, Tom
Barton, Craig V.M.
England, Jacqueline R.
Battaglia, Michael
O'Grady, Anthony
Pinkard, Elizabeth
Applegate, Grahame
Jonson, Justin
Brooksbank, Kim
Sudmeyer, Rob
Wildy, Dan
Montagu, Kelvin D.
Bradford, Matt
Source :
Forest Ecology & Management; Jan2019, Vol. 432, p102-114, 13p
Publication Year :
2019

Abstract

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]

Details

Language :
English
ISSN :
03781127
Volume :
432
Database :
Supplemental Index
Journal :
Forest Ecology & Management
Publication Type :
Academic Journal
Accession number :
133257377
Full Text :
https://doi.org/10.1016/j.foreco.2018.08.043