Metabolic phenotype mediates the outcome of competitive interactions in a response-surface field experiment

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

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'.

Photo Phenosizer, a rapid machine learning-based method to measure cell dimensions in fission yeast

Cell metrics such as area, length, and width provide informative data about cell cycle dynamics. Factors that affect these dimensions include environmental conditions and genotypic differences. Fission yeast ( Schizosaccharomyces pombe ) is a rod-shaped ascomycete fungus in which cell cycle progression is linked to changes in cell length. Microscopy work to obtain these metrics places considerable burdens on time and effort. We now report on Photo Phenosizer (PP), a machine learning-based methodology that measures cell dimensions in fission yeast. It does this in an unbiased, automated manner and streamlines workflow from image acquisition to statistical analysis. Using this new approach, we constructed an efficient and flexible pipeline for experiments involving different growth media (YES and EMM) and treatments (Untreated and MMS) as well as different genotypes ( cut6-621 versus wildtype). This methodology allows for the analysis of larger sample sizes and faster image processing relative to manual segmentation. Our findings suggest that researchers using PP can quickly and efficiently determine cell size differences under various conditions that highlight genetic or environmental disruptions.