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Curvature in metabolic scaling

Author

Listed:
  • Tom Kolokotrones

    (Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Van Savage

    (David Geffen School of Medicine at the University of California at Los Angeles, Los Angeles, California 90024, USA)

  • Eric J. Deeds

    (Harvard Medical School, Boston, Massachusetts 02115, USA)

  • Walter Fontana

    (Harvard Medical School, Boston, Massachusetts 02115, USA)

Abstract

Making body size toe the line The relationship between an organism's metabolic rate and its body mass has fascinated biologists since Max Kleiber first proposed, in 1932, that metabolic rate scales across species with body mass raised to the power of 3/4. This 'scaling exponent' has been recalculated many times since, with some estimating the exponent to be close to 2/3 and others nearer to 3/4. A new analysis suggests that the relationship does not follow a straight line on a logarithmic scale, so is not a pure power law at all. Attempts to fit a straight line to what is really a curve produce 'scaling exponents' that are highly dependent on the data used. Data sets dominated by small organisms tend to produce exponents of 2/3 while those dominated by large organisms will produce 3/4.

Suggested Citation

  • Tom Kolokotrones & Van Savage & Eric J. Deeds & Walter Fontana, 2010. "Curvature in metabolic scaling," Nature, Nature, vol. 464(7289), pages 753-756, April.
    • Handle: RePEc:nat:nature:v:464:y:2010:i:7289:d:10.1038_nature08920
    DOI: 10.1038/nature08920
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    Citations

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    Cited by:

    1. Nathan P Myhrvold, 2016. "Dinosaur Metabolism and the Allometry of Maximum Growth Rate," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-35, November.
    2. Elif Tekin & David Hunt & Mitchell G Newberry & Van M Savage, 2016. "Do Vascular Networks Branch Optimally or Randomly across Spatial Scales?," PLOS Computational Biology, Public Library of Science, vol. 12(11), pages 1-28, November.
    3. Xu, Meng & Jiang, Mengke & Wang, Hua-Feng, 2021. "Integrating metabolic scaling variation into the maximum entropy theory of ecology explains Taylor's law for individual metabolic rate in tropical forests," Ecological Modelling, Elsevier, vol. 455(C).
    4. Michail Fragkias & José Lobo & Deborah Strumsky & Karen C Seto, 2013. "Does Size Matter? Scaling of CO2 Emissions and U.S. Urban Areas," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    5. Hsiehchen, David & Espinoza, Magdalena & Hsieh, Antony, 2016. "Hypoallometric scaling in international collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 188-193.
    6. Mitchell G Newberry & Daniel B Ennis & Van M Savage, 2015. "Testing Foundations of Biological Scaling Theory Using Automated Measurements of Vascular Networks," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-18, August.
    7. Milotti, Edoardo & Vyshemirsky, Vladislav & Stella, Sabrina & Dogo, Federico & Chignola, Roberto, 2017. "Analysis of the fluctuations of the tumour/host interface," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 587-594.
    8. Chen, Shi & Bao, Forrest Sheng, 2015. "Linking body size and energetics with predation strategies: A game theoretic modeling framework," Ecological Modelling, Elsevier, vol. 316(C), pages 81-86.
    9. Witting, Lars, 2017. "The natural selection of metabolism and mass selects allometric transitions from prokaryotes to mammals," Theoretical Population Biology, Elsevier, vol. 117(C), pages 23-42.
    10. David G Jenkins & Pedro F Quintana-Ascencio, 2020. "A solution to minimum sample size for regressions," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-15, February.

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