Metabolic Ecology: A Scaling Approach by Richard M. Sibly, James H. Brown, Astrid Kodric-Brown

By Richard M. Sibly, James H. Brown, Astrid Kodric-Brown

Content material:
Chapter 1 Methodological instruments (pages 7–20): Ethan P. White, Xiao Xiao, Nick J. B. Isaac and Richard M. Sibly
Chapter 2 The Metabolic conception of Ecology and Its crucial Equation (pages 21–33): James H. Brown and Richard M. Sibly
Chapter three Stoichiometry (pages 34–47): Michael Kaspari
Chapter four Modeling Metazoan development and Ontogeny (pages 48–56): Andrew J. Kerkhoff
Chapter five existence heritage (pages 57–66): Richard M. Sibly
Chapter 6 habit (pages 67–76): April Hayward, James F. Gillooly and Astrid Kodric?Brown
Chapter 7 inhabitants and neighborhood Ecology (pages 77–85): Nick J. B. Isaac, Chris Carbone and Brian McGill
Chapter eight Predator–Prey kin and meals Webs (pages 86–98): Owen L. Petchey and Jennifer A. Dunne
Chapter nine Ecosystems (pages 99–111): Kristina J. Anderson?Teixeira and Peter M. Vitousek
Chapter 10 premiums of Metabolism and Evolution (pages 112–119): John L. Gittleman and Patrick R. Stephens
Chapter eleven Biodiversity and its full of life and Thermal Controls (pages 120–131): David Storch
Chapter 12 Microorganisms (pages 133–153): Jordan G. Okie
Chapter thirteen Phytoplankton (pages 154–163): Elena Litchman
Chapter 14 Land crops: New Theoretical instructions and Empirical customers (pages 164–187): Brian J. Enquist and Lisa Patrick Bentley
Chapter 15 Marine Invertebrates (pages 188–197): Mary I. O'connor and John F. Bruno
Chapter sixteen Insect Metabolic charges (pages 198–211): James S. Waters and Jon F. Harrison
Chapter 17 Terrestrial Vertebrates (pages 212–224): William Karasov
Chapter 18 Seabirds and Marine Mammals (pages 225–233): Daniel P. Costa and Scott A. Shaffer
Chapter 19 Parasites (pages 234–247): Ryan F. Hechinger, Kevin D. Lafferty and Armand M. Kuris
Chapter 20 Human ecology (pages 248–257): Marcus J. Hamilton, Oskar Burger and Robert S. Walker
Chapter 21 Marine Ecology and Fisheries (pages 259–270): Simon Jennings, Ken H. Andersen and Julia L. Blanchard
Chapter 22 Conservation Biology (pages 271–279): Alison G. Boyer and Walter Jetz
Chapter 23 weather swap (pages 280–292): Kristina J. Anderson?Teixeira, Felisa A. Smith and S. ok. Morgan Ernest
Chapter 24 past Biology (pages 293–301): Melanie E. Moses and Stephanie Forrest
Chapter 25 Synthesis and Prospect (pages 302–305): James H. Brown, Richard M. Sibly and Astrid Kodric?Brown

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Additional info for Metabolic Ecology: A Scaling Approach

Example text

The WBE model made several simplifying assumptions (see also Savage et al. 2008): (1) metabolic rate is proportional to the rate at which blood is pumped from the heart, because the oxygen and nutrients that fuel metabolism are delivered to the cells by the blood; (2) the arteries branch to form a symmetrical hierarchical network; (3) the network supplies all parts of the threedimensional body; (4) the terminal units, capillaries, are invariant in structure, function, and dynamics; and (5) the system has evolved under natural selection to optimize performance by minimizing the rate of energy expenditure to pump blood from the heart to the capillaries.

Here, we take the position that both critics and some members of the New Mexico group have identified significant problems with certain assumptions and predictions of WBE. However, many of these concerns can be corrected by changing the simplifying assumptions while preserving most of the overall framework that predicts the distinctive quarter-power scaling. We address three problems: 1 Fitting a network whose characteristic lengths scale as M1/4 into a geometric organism whose lengths scale as M1/3.

Most analyses in metabolic theory are causal in nature – the hypothesis is that the size and temperature of an organism determine a broad suite of dependent variables. , variability about the line not explained by measurement error; Fuller 1987; McArdle 2003) to the Y variable and therefore should be estimating the form of the relationship using OLS (Warton et al. 2006; Smith 2009). In addition to causal relationships, OLS regression is also most appropriate in cases where one wants to predict unknown values of Y based on X (Sokal and Rohlf 1995; Warton et al.

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