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

One of many first textbooks during this rising very important box of ecology. such a lot of ecology is ready metabolism: the ways in which organisms use strength and fabrics. The power necessities of people -- their metabolic premiums -- differ predictably with their physique dimension and temperature. Ecological interactions are exchanges of power and fabrics among organisms and their environments. So metabolic cost impacts ecological methods in any respect degrees: participants, populations, groups and ecosystems. every one bankruptcy makes a speciality of a special method, point of association, or type of organism. It lays a conceptual beginning and provides empirical examples. jointly, the chapters offer an built-in framework that holds the promise for a unified idea of ecology. The booklet is meant to be obtainable to upper-level undergraduate, and graduate scholars, but in addition of curiosity to senior scientists. Its easy-to-read chapters and transparent illustrations can be utilized in lecture and seminar classes. jointly they make for an authoritative therapy that may encourage destiny generations to review metabolic ecology.

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This approach had limited impact because Kooijman’s models are very complex, with too many parameters and functions for most applications (but see Kooijman 2000; van der Meer 2006). By the 1990s, with the excitement of the molecular revolu‑ tion and no theoretical, mechanistic explanation for the quarter powers, research on allometry had dimin‑ ished, and much of it focused on scaling of biomechan‑ ics and hydrodynamics rather than metabolism. 5 THE MODELS OF WEST, BROWN, AND ENQUIST In the mid-1990s Geoffrey West, James Brown, and Brian Enquist (WBE) began an interdisciplinary physics-biology collaboration.

3 Statistical issues related to using regression analysis and comparative phylogenetic methods to estimate the values of exponents and normalization constants. Many of these are addressed by White, Xiao, Isaac, and Sibly (Chapter 1), who point out that statistical methods are constantly being refined, and many issues are far from settled. Moreover, the many empirical studies that used different methods have rarely been re-evaluated using standardized methodology. Suffice it to say that reported empirical scaling relationships should be interpreted cautiously, with the warning that biologi‑ cal reality may not be as straightforward as some authors have implied.

Enquist and Niklas 2001; Enquist et al. 2009). This is a reasonable way to visualize these data, but it suffers from the fact that bins with zero individuals must be excluded from the analysis due to the log-transformation. , at large diameters), thus impacting the visual perception of the form of the distribution. To address this problem we recommend using normalized logarithmic binning (sensu White et al. , Kerr and Dickie 2001). This approach involves binning the data into equal logarithmic width bins (either by log-transforming the data prior to constructing the his‑ togram or by choosing the bin edges to be equal loga‑ rithmic distances apart) and then dividing the counts in each bin by the linear width of the bin prior to graphing (Fig.

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