Testing the Metabolic Scaling Theory of tree growth
Article first published online: 18 SEP 2009
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Journal of Ecology
Volume 97, Issue 6, pages 1369–1373, November 2009
How to Cite
Coomes, D. A. and Allen, R. B. (2009), Testing the Metabolic Scaling Theory of tree growth. Journal of Ecology, 97: 1369–1373. doi: 10.1111/j.1365-2745.2009.01571.x
- Issue published online: 13 OCT 2009
- Article first published online: 18 SEP 2009
- Received 5 June 2009; accepted 13 August 2009 Handling Editor: Susan Schwinning
- asymmetric competition;
- metabolic ecology model;
- scaling laws;
- SMA line-fitting;
- type II regression;
- WBE theory
1. Metabolic Scaling Theory (MST) predicts a ‘universal scaling law’ of tree growth. Proponents claim that MST has strong empirical support: the size-dependent growth curves of 40 out of 45 species in a Costa Rican forest have scaling exponents indistinguishable from the MST prediction.
2. Here, we show that the Costa Rican study has been misinterpreted. Using Standardized Major Axis (SMA) line-fitting to estimate scaling exponents, we find that four out of five species represented by more than 100 stems have scaling exponents that deviate significantly from the MST prediction. On the other hand, sample sizes were too small to make strong inferences in the cases of 33 species represented by fewer than 50 stems.
3. Recently, it has been argued that MST is useful for predicting average scaling exponents, even if individual species do not conform to the theory. We find that the mean scaling exponent of the Costa Rican trees is greater than predicted (across-species mean = 0.44), and hypothesize that scaling exponents in natural forests will generally be greater than predicted, because the theory fails to model asymmetric competition for light.
4. Synthesis. We highlight shortcomings in the interpretation of data used in support of a key MST prediction. We recommend that future research into biological scaling should compare the merits of alternative models rather than focusing attention on tests of a single theory.