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

Autor: Nicholas C. Wegner, Nicholas K. Dulvy, Yangfan Zhang, Tanya S. Prinzing
Rok vydání: 2020
Předmět:
Zdroj: Ecology and Evolution
Ecology and Evolution, Vol 11, Iss 15, Pp 9987-10003 (2021)
ISSN: 2045-7758
Popis: 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 (
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 paid to the consequences of statistical choices on the estimation of MMR. We show that MMR estimates are sensitive to the type of statistical analysis used to generate them and provide a framework for model selection.
Databáze: OpenAIRE
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