Analytical methods matter too: Establishing a framework for estimating maximum metabolic rate for fishes.

Tanya S Prinzing,Nicholas K Dulvy,Nicholas C Wegner,Yangfan Zhang

doi:10.1002/ece3.7732

Abstract

Advances in experimental design and equipment have simplified the collection of maximum metabolic rate (MMR) data for a more diverse array of water‐breathing animals. However, little attention has been given to the consequences of analytical choices in the estimation of MMR. Using different analytical methods can reduce the comparability of MMR estimates across species and studies and has consequences for the burgeoning number of macroecological meta‐analyses using metabolic rate data. Two key analytical choices that require standardization are the time interval, or regression window width, over which MMR is estimated, and the method used to locate that regression window within the raw oxygen depletion trace. Here, we consider the effect of both choices by estimating MMR for two shark and two salmonid species of different activity levels using multiple regression window widths and three analytical methods: rolling regression, sequential regression, and segmented regression. Shorter regression windows yielded higher metabolic rate estimates, with a risk that the shortest windows (<1‐min) reflect more system noise than MMR signal. Rolling regression was the best candidate model and produced the highest MMR estimates. Sequential regression models consistently produced lower relative estimates than rolling regression models, while the segmented regression model was unable to produce consistent MMR estimates across individuals. The time‐point of the MMR regression window along the oxygen consumption trace varied considerably across individuals but not across models. We show that choice of analytical method, in addition to more widely understood experimental choices, profoundly affect the resultant estimates of MMR. We recommend that researchers (1) employ a rolling regression model with a reliable regression window tailored to their experimental system and (2) explicitly report their analytical methods, including publishing raw data and code.

Highlights

Metabolic rate is the rate at which organisms convert food and materials from their environment into energy to fuel their biological processes
We used each of three analytical methods to estimate maximum metabolic rate (MMR) for each individual: (1) rolling regression with 1- to 5-min sampling window widths, (2) sequential regression with 1-and 2-min window widths, and (3) segmented regression
Across four species of varying activity level and body mass, we found that (1) smaller regression windows yielded higher estimates of MMR, (2) MMR was best estimated using a rolling regression model with a 1- to 2-min window, and (3) the time-point at which MMR occurs is often at least two minutes into the oxygen depletion trace and, may be missed with certain analytical methods, such as with sequential regression or too short of a postexercise monitoring period

Summary

Introduction

Metabolic rate is the rate at which organisms convert food and materials from their environment into energy to fuel their biological processes. The analytical process of estimating MMR from the experimental oxygen consumption data immediately following exhaustive exercise or air exposure—the statistical algorithm used to regress oxygen consumption over time—has not been systematically tested or standardized, despite recent recognition that these analytical choices affect MMR estimates (Little et al, 2020; Norin & Clark, 2016; Zhang et al, 2019, 2020). Details concerning the analytical approach used to estimate MMR are not clearly reported, and when provided, there is usually little or no explanation as to why those specific methods were chosen. These unknowns and lack of consistency potentially bias MMR estimates and makes comparison between studies difficult

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