AOBPreview published online on November 16, 2004
Annals of Botany, doi:10.1093/aob/mci028
© 2004 by Annals of Botany Company
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1 Department of Plant Biology, Cornell University, Ithaca, NY 14853, USA
* To whom correspondence should be addressed.
• Background and Aims Intraspecific relationships between below- and above-ground biomass (MB and MA, respectively) have been studied extensively to evaluate environmental effects on growth and development at the level of the individual plant. However, no current theoretical model for this relationship exists for broad interspecific trends. The aims of this paper are to provide a model and to test its predictions using a recently assembled, large database (1406 data entries for 257 species). • Methods An allometric model was derived to predict the relationship between MB and MA for non-woody and woody plants based on previously developed scaling relationships for leaf, stem and root standing biomass and annual growth rates. The predictions of this model were tested by comparing the numerical values of predicted scaling exponents (the slopes of log-log regression curves) with those observed for the database. • Key Results and Conclusions For non-woody plants and the juveniles of woody species, the model predicts an isometric scaling relationship (i.e. MB
Revised September 1, 2004
Accepted September 21, 2004
Article
Modelling Below- and Above-ground Biomass for Non-woody and Woody Plants
KARL J. NIKLAS, E-mail: kjn2{at}cornell.edu
Abstract 
MA). For woody plants, a complex scaling function is predicted. But, for a particular set of biologically reasonable conditions, the model predicts MB MA across woody plants. These predictions accord reasonably well with observed statistical trends when non-woody and woody plants are studied separately (n = 1061 and 345 data entries, respectively). Although the reliability of regression formulas to estimate MB based on MA measurements increased with increasing plant size, estimates of MB can be as much as two orders of magnitude off, even when using regression formulas with r2 >> 0·90 and F >> 53 000.
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