Modeling Heterothermic Fitness Landscapes in a Marsupial Hibernator Using Changes in Body Composition - Cubillos-Riffo, F (Springer Science and Business Media Deutschland GmbH, 2023)
Hibernation is an adaptive strategy that allows animals to enter a hypometabolic state, conserving energy and enhancing their fitness by surviving harsh environmental conditions. However, addressing the adaptive value of hibernation, at the individual level and in natural populations, has been challenging. Here, we applied a non-invasive technique, body composition analysis by quantitative magnetic resonance (qMR), to calculate energy savings by hibernation in a population of hibernating marsupials (Dromiciops gliroides). Using outdoor enclosures installed in a temperate rainforest, and measuring qMR periodically, we determined the amount of fat and lean mass consumed during a whole hibernation cycle. With this information, we estimated the daily energy expenditure of hibernation (DEEH) at the individual level and related to previous fat accumulation. Using model selection approaches and phenotypic selection analysis, we calculated linear (directional, β), quadratic (stabilizing or disruptive, γ) and correlational (ρ) coefficients for DEEH and fat accumulation. We found significant, negative directional selection for DEEH (β DEEH = − 0.58 ± 0.09), a positive value for fat accumulation (β FAT = 0.34 ± 0.07), and positive correlational selection between both traits (ρ DEEH × FAT = 0.24 ± 0.07). Then, individuals maximizing previous fat accumulation and minimizing DEEH were promoted by selection, which is visualized by a bi-variate selection surface estimated by generalized additive models. At the comparative level, results fall within the isometric allometry known for hibernation metabolic rate in mammals. Thus, by a combination of a non-invasive technique for body composition analysis and semi-natural enclosures, we were characterized the heterothermic fitness landscape in a semi-natural population of hibernators. © 2023, The Author(s).