期刊论文详细信息
Frontiers in Physiology
Strong Evidence for an Intraspecific Metabolic Scaling Coefficient Near 0.89 in Fish
Mark L. Taper1  Samantha R. Csik2  Krista Kraskura2  Erika J. Eliason2  Adrian C. Stier2  Christopher L. Jerde4 
[1] Department of Biology, University of Florida, Gainesville, FL, United States;Department of Ecology, Evolution, and Marine Biology, University of California, Santa Barbara, Santa Barbara, CA, United States;Department of Ecology, Montana State University, Bozeman, MT, United States;Marine Science Institute, University of California, Santa Barbara, Santa Barbara, CA, United States;
关键词: likelihood;    evidence functions;    SIC;    standard metabolic rate;    mixed effects models;    metabolic scaling;   
DOI  :  10.3389/fphys.2019.01166
来源: DOAJ
【 摘 要 】

As an example of applying the evidential approach to statistical inference, we address one of the longest standing controversies in ecology, the evidence for, or against, a universal metabolic scaling relationship between metabolic rate and body mass. Using fish as our study taxa, we curated 25 studies with measurements of standard metabolic rate, temperature, and mass, with 55 independent trials and across 16 fish species and confronted this data with flexible random effects models. To quantify the body mass – metabolic rate relationship, we perform model selection using the Schwarz Information Criteria (ΔSIC), an established evidence function. Further, we formulate and justify the use of ΔSIC intervals to delineate the values of the metabolic scaling relationship that should be retained for further consideration. We found strong evidence for a metabolic scaling coefficient of 0.89 with a ΔSIC interval spanning 0.82 to 0.99, implying that mechanistically derived coefficients of 0.67, 0.75, and 1, are not supported by the data. Model selection supports the use of a random intercepts and random slopes by species, consistent with the idea that other factors, such as taxonomy or ecological or lifestyle characteristics, may be critical for discerning the underlying process giving rise to the data. The evidentialist framework applied here, allows for further refinement given additional data and more complex models.

【 授权许可】

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