Summary
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 Po2' or 'Pc'). For almost 30 years, the principal way in which Pc has been estimated has been via piecewise 'broken stick' regression. Broken stick regression (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 data collected using traditional methods in earlier studies can be compared to data collected using 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, the 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.
