Authors: Amanda K Pettersen, Dustin J Marshall, and Craig R White
Published in: The Journal of Experimental Biology
Metabolic rate reflects an organism’s capacity for growth, maintenance and reproduction, and is likely to be a target of selection. Physiologists have long sought to understand the causes and consequences of within-individual to among-species variation in metabolic rates – how metabolic rates relate to performance and how they should evolve.
Traditionally, this has been viewed from a mechanistic perspective, relying primarily on hypothesis-driven approaches. A more agnostic, but ultimately more powerful tool for understanding the dynamics of phenotypic variation is through use of the breeder’s equation, because variation in metabolic rate is likely to be a consequence of underlying microevolutionary processes.
Here we show that metabolic rates are often significantly heritable, and are therefore free to evolve under selection. We note, however, that ‘metabolic rate’ is not a single trait: in addition to the obvious differences between metabolic levels (e.g. basal, resting, free-living, maximal), metabolic rate changes through ontogeny and in response to a range of extrinsic factors, and is therefore subject to multivariate constraint and selection.
We emphasize three key advantages of studying metabolic rate within a quantitative genetics framework: its formalism, and its predictive and comparative power.
We make several recommendations when applying a quantitative genetics framework: (i) measuring selection based on actual fitness, rather than proxies for fitness; (ii) considering the genetic covariances between metabolic rates throughout ontogeny; and (iii) estimating genetic covariances between metabolic rates and other traits.
A quantitative genetics framework provides the means for quantifying the evolutionary potential of metabolic rate and why variance in metabolic rates within populations might be maintained.
Pettersen AK, Marshall DJ, White CR (2018) Understanding variation in metabolic rate, The Journal of Experimental Biology, PDF DOI