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Testing the metabolic theory of ecology

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https://ir.library.oregonstate.edu/concern/articles/1g05fc14j

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  • The metabolic theory of ecology (MTE) predicts the effects of body size and temperature on metabolism through considerations of vascular distribution networks and biochemical kinetics. MTE has also been extended to characterise processes from cellular to global levels. MTE has generated both enthusiasm and controversy across a broad range of research areas. However, most efforts that claim to validate or invalidate MTE have focused on testing predictions. We argue that critical evaluation of MTE also requires strong tests of both its theoretical foundations and simplifying assumptions. To this end, we synthesise available information and find that MTE's original derivations require additional assumptions to obtain the full scope of attendant predictions. Moreover, although some of MTE's simplifying assumptions are well supported by data, others are inconsistent with empirical tests and even more remain untested. Further, although many predictions are empirically supported on average, work remains to explain the often large variability in data. We suggest that greater effort be focused on evaluating MTE's underlying theory and simplifying assumptions to help delineate the scope of MTE, generate new theory and shed light on fundamental aspects of biological form and function.
  • This is the publisher’s final pdf. The published article is copyrighted by John Wiley & Sons, Inc. and can be found at: http://onlinelibrary.wiley.com/journal/10.1111/%28ISSN%291461-0248
  • Keywords: fractal, scaling theory, Allometry, WBE model, kleiber curve, metabolism, metabolic theory
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  • Price, C. A., Weitz, J. S., Savage, V. M., Stegen, J., Clarke, A., Coomes, D. A., Dodds, P. S. ... (2012). Testing the metabolic theory of ecology. Ecology Letters, 15, 1465-1474. doi:10.1111/j.1461-0248.2012.01860.x
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  • 15
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  • 12
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  • CAP is supported by a Discovery Early Career Research Award (DECRA) from the Australian Research Council (ARC). JSW is supported by a Career Award at the Scientific Interface from the Burroughs Wellcome Fund. VMS was supported by UCLA Biomathematics start-up funds and by NSF DEB Award 1021010. JCS was supported by a NSF Postdoctoral Fellowship in Biological Informatics (DBI-0906005). RSE is supported by the Netherlands Organization for Scientific Research (NWO). AJK was supported by NSF UBM Award DMS-0827208. KAM was supported by National Science Foundation grant IBN 09-19871.
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