Temperature and nutrition do not interact to shape the evolution of metabolic rate

Abstract

Metabolic cold adaptation, or Krogh's rule, is the controversial hypothesis that predicts a monotonically negative relationship between metabolic rate and environmental temperature for ectotherms living along thermal clines measured at a common temperature. Macrophysiological patterns consistent with Krogh's rule are not always evident in nature, and experimentally evolved responses to temperature have failed to replicate such patterns. Hence, temperature may not be the sole driver of observed variation in metabolic rate. We tested the hypothesis that temperature, as a driver of energy demand, interacts with nutrition, a driver of energy supply, to shape the evolution of metabolic rate to produce a pattern resembling Krogh's rule. To do this, we evolved replicate lines of Drosophila melanogaster at 18, 25 or 28°C on control, low-calorie or low-protein diets. Contrary to our prediction, we observed no effect of nutrition, alone or interacting with temperature, on adult female and male metabolic rates. Moreover, support for Krogh's rule was only in females at lower temperatures. We, therefore, hypothesize that observed variation in metabolic rate along environmental clines arises from the metabolic consequences of environment-specific life-history optimization, rather than because of the direct effect of temperature on metabolic rate. This article is part of the theme issue ‘The evolutionary significance of variation in metabolic rates’.

Experimental evolution under combined thermal-nutritional stress inDrosophila melanogasterresults in evolved shifts in larval thermotolerance and thermal plasticity

“…Two hundred female D. melanogaster were collected in Duranbah, Queensland (28.3° S, 153° E) in 2018 to initiate isofemale lines (Alton et al . 2024).…”

“…These selection conditions reflect combinations of thermal and nutritional changes that may be expected in the future under climate change; 18°C and 25°C represent the current average Australian winter and summer temperatures respectively (Alton et al 2024), while 28°C represents a 3°C increase of the average summer temperature equalize density across all selection regimes. Animals were allowed to develop on their respective diet and temperatures until adult eclosion.…”

“…In this study, we aimed to understand how long-term adaptation to combined thermal-nutritional stress affects larval thermotolerance and thermal plasticity. We assessed basal and induced thermotolerance of larvae from nine D. melanogaster experimental evolution lines that were forced adapted to combinations of three temperatures (18°C, 25°C and 28°C) and three diets (standard, diluted and low protein:high carbohydrate (low P:C) diets) using laboratory natural selection (Alton et al . 2024).…”

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

“…B 379: 20220498 generations, such that both cool-and warm-reared lines had the same metabolic rate. Alton et al [74] conducted an experimental evolution study to separate the evolutionary responses of metabolic rates to temperature and nutrition. While they found no effect of nutrition (including its interaction with temperature) on the evolution of metabolic rates, they offer solutions for future studies to address this question using protocols that allow for selection on lifehistory strategies that are often otherwise controlled for in experimental evolution studies (table 1).…”

“…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 ] …”