Metabolic scaling as an emergent outcome of variation in metabolic rate

Abstract

The allometric scaling of metabolic rate and what drives it are major questions in biology with a long history. Since the metabolic rate at any level of biological organization is an emergent property of its lower-level constituents, it is an outcome of the intrinsic heterogeneity among these units and the interactions among them. However, the influence of lower-level heterogeneity on system-level metabolic rate is difficult to investigate, given the tightly integrated body plan of unitary organisms. In this context, social insects such as honeybees can serve as important model systems because unlike unitary organisms, these superorganisms can be taken apart and reassembled in different configurations to study metabolic rate and its various drivers at different levels of organization. This commentary discusses the background of such an approach and how combining it with artificial selection to generate heterogeneity in metabolic rate with an analytical framework to parse out the different mechanisms that contribute to the effects of heterogeneity can contribute to the various models of metabolic scaling. Finally, the absence of the typical allometric scaling relationship among different species of honeybees is discussed as an important prospect for deciphering the role of top-down ecological factors on metabolic scaling. This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.

Consequences of the cost of living: is variation in metabolic rate evolutionarily significant?

“…Despite its potential importance for explaining among-individual variation in metabolic rate, the role of group phenotypic composition in driving metabolic rate variation is currently underexplored. Naug [76] advocates for the use of social insects such as honeybees as an experimental tool in this context, since they can be used to create groups containing different configurations of metabolic rate individuals, so allowing elucidation of potential eco-evolutionary feedbacks as drivers of metabolic rate variation (table 1). Metabolic rate data collected in the laboratory can help to assess a species average physiological niche and predict potential species distributions across space and time.…”

“…Alternatively, structural equation modelling (path analysis) may be used to tease apart direct and indirect effects [ 28 , 73 ] producing comparable estimates of heritability of metabolic rates breeding design, artificial selection or experimental evolution experiments Box 3 in [ 27 ] isolating the effects of a single factor on the evolution of metabolic rate when comparing populations experimental work manipulating those variables, e.g. common garden and reciprocal transplant [ 74 ] explaining clines in metabolic rate in nature two complementary approaches: (i) artificial selection to generate replicate lines that differ in metabolic rate, to assess relative fitness across treatments representing environmental clines; (ii) laboratory natural selection to observe how metabolic rates evolve under different environments while keeping generation times similar across treatments, and allowing for natural variation in population size [ 74 ] accounting for ‘group phenotypic composition’ as a potential driver of metabolic rate variation artificial selection approach that modifies the composition of metabolic phenotypes within groups, to observe evolutionary trajectories in metabolism [ 75 , 76 ] avoiding collinearity between mass and metabolism when estimating selection on metabolic rates use mass-independent metabolic rate to estimate selection on metabolic rates that are independent of body size [ 63 ] …”