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Invariant scaling relations across tree-dominated communities

Author

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  • Brian J. Enquist

    (University of Arizona
    National Center for Ecological Analysis and Synthesis, University of California)

  • Karl J. Niklas

    (Cornell University)

Abstract

Organizing principles are needed to link organismal, community and ecosystem attributes across spatial and temporal scales. Here we extend allometric theory—how attributes of organisms change with variation in their size—and test its predictions against worldwide data sets for forest communities by quantifying the relationships among tree size–frequency distributions, standing biomass, species number and number of individuals per unit area. As predicted, except for the highest latitudes, the number of individuals scales as the -2 power of basal stem diameter or as the -3/4 power of above-ground biomass. Also as predicted, this scaling relationship varies little with species diversity, total standing biomass, latitude and geographic sampling area. A simulation model in which individuals allocate biomass to leaf, stem and reproduction, and compete for space and light obtains features identical to those of a community. In tandem with allometric theory, our results indicate that many macroecological features of communities may emerge from a few allometric principles operating at the level of the individual.

Suggested Citation

  • Brian J. Enquist & Karl J. Niklas, 2001. "Invariant scaling relations across tree-dominated communities," Nature, Nature, vol. 410(6829), pages 655-660, April.
    • Handle: RePEc:nat:nature:v:410:y:2001:i:6829:d:10.1038_35070500
    DOI: 10.1038/35070500
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    Citations

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    Cited by:

    1. Picard, Nicolas, 2021. "The role of spatial competitive interactions between trees in shaping forest patterns," Theoretical Population Biology, Elsevier, vol. 142(C), pages 36-45.
    2. Wiegand, Kerstin & Saltz, David & Ward, David & Levin, Simon A., 2008. "The role of size inequality in self-thinning: A pattern-oriented simulation model for arid savannas," Ecological Modelling, Elsevier, vol. 210(4), pages 431-445.
    3. Amsalu Abich & Mesele Negash & Asmamaw Alemu & Temesgen Gashaw, 2022. "Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects," Land, MDPI, vol. 11(6), pages 1-23, May.
    4. James A Lutz & Andrew J Larson & Mark E Swanson & James A Freund, 2012. "Ecological Importance of Large-Diameter Trees in a Temperate Mixed-Conifer Forest," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-15, May.
    5. Hongying Li & Zhongwen Huang & Junyi Gai & Song Wu & Yanru Zeng & Qin Li & Rongling Wu, 2007. "A Conceptual Framework for Mapping Quantitative Trait Loci Regulating Ontogenetic Allometry," PLOS ONE, Public Library of Science, vol. 2(11), pages 1-10, November.
    6. Nilsen, Erlend B. & Finstad, Anders G. & Næsje, Tor F. & Sverdrup-Thygeson, Anne, 2013. "Using mass scaling of movement cost and resource encounter rate to predict animal body size–Population density relationships," Theoretical Population Biology, Elsevier, vol. 86(C), pages 23-28.
    7. Taleb, Nassim Nicholas, 2009. "Errors, robustness, and the fourth quadrant," International Journal of Forecasting, Elsevier, vol. 25(4), pages 744-759, October.
    8. Matthew H Bonds & Andrew P Dobson & Donald C Keenan, 2012. "Disease Ecology, Biodiversity, and the Latitudinal Gradient in Income," PLOS Biology, Public Library of Science, vol. 10(12), pages 1-12, December.
    9. Ma, Ping & Han, Xiao-Hui & Lin, Yue & Moore, John & Guo, Yao-Xin & Yue, Ming, 2019. "Exploring the relative importance of biotic and abiotic factors that alter the self-thinning rule: Insights from individual-based modelling and machine-learning," Ecological Modelling, Elsevier, vol. 397(C), pages 16-24.
    10. Raquel Fernandes Araujo & Jeffrey Q Chambers & Carlos Henrique Souza Celes & Helene C Muller-Landau & Ana Paula Ferreira dos Santos & Fabiano Emmert & Gabriel H P M Ribeiro & Bruno Oliva Gimenez & Adr, 2020. "Integrating high resolution drone imagery and forest inventory to distinguish canopy and understory trees and quantify their contributions to forest structure and dynamics," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-16, December.
    11. Brolly, Matthew & Woodhouse, Iain H., 2012. "A “Matchstick Model” of microwave backscatter from a forest," Ecological Modelling, Elsevier, vol. 237, pages 74-87.
    12. Clough, Brian J. & Russell, Matthew B. & Domke, Grant M. & Woodall, Christopher W. & Radtke, Philip J., 2016. "Comparing tree foliage biomass models fitted to a multispecies, felled-tree biomass dataset for the United States," Ecological Modelling, Elsevier, vol. 333(C), pages 79-91.

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