Estimating physiological tolerances - a comparison of traditional approaches to nonlinear regression techniques

Marshall, Dustin J., Bode, Michael and White, Craig R. (2013) Estimating physiological tolerances - a comparison of traditional approaches to nonlinear regression techniques. Journal of Experimental Biology, 216 12: 2176-2182. doi:10.1242/jeb.085712

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Author Marshall, Dustin J.
Bode, Michael
White, Craig R.
Title Estimating physiological tolerances - a comparison of traditional approaches to nonlinear regression techniques
Journal name Journal of Experimental Biology   Check publisher's open access policy
ISSN 0022-0949
1477-9145
Publication date 2013-06
Sub-type Article (original research)
DOI 10.1242/jeb.085712
Open Access Status File (Publisher version)
Volume 216
Issue 12
Start page 2176
End page 2182
Total pages 7
Place of publication Cambridge, United Kingdom
Publisher Company of Biologists
Collection year 2014
Language eng
Formatted abstract
Traditionally, physiologists have estimated the ability of organisms to withstand lower partial pressures of oxygen by estimating the partial pressure at which oxygen consumption begins to decrease (known as the critical P O2 or Pc). For almost 30 years, the principal way in which Pc has been estimated has been via piecewise 'broken stick' regression (BSR). BSR was a useful approach when more sophisticated analyses were less available, but BSR makes a number of unsupported assumptions about the underlying form of the relationship between the rate of oxygen consumption and oxygen availability. The BSR approach also distils a range of values into a single point with no estimate of error. In accordance with more general calls to fit functions to continuous data, we propose the use of nonlinear regression (NLR) to fit various curvilinear functions to oxygen consumption data in order to estimate Pc. Importantly, our approach is back-compatible so that estimates using traditional methods in earlier studies can be compared with data estimates from our technique. When we compared the performance of our approach relative to the traditional BSR approach for real world and simulated data, we found that under realistic circumstances, NLR was more accurate and provided more powerful hypothesis tests. We recommend that future studies make use of NLR to estimate Pc, and also suggest that this approach might be more appropriate for a range of physiological studies that use BSR currently.
Keyword Metabolism
Oxygen consumption
Statistics
Meta-analysis
Oxygen availability
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Biological Sciences Publications
 
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