Respirometry data for cactus mice (Peromyscus eremicus) corresponding to Colella et al. 2021

Metabolism is a complex phenotype shaped by natural environmental rhythms, as well as behavioral, morphological, and physiological adaptations. Metabolism has been historically studied under constant environmental conditions, but new methods of continuous metabolic phenotyping now offer a window into organismal responses to dynamic environments, and enable identification of abiotic controls and the timing of physiological responses relative to environmental change. We use indirect calorimetry to characterize metabolic phenotypes of the desert-adapted cactus mouse (Peromyscus eremicus) in response variable environmental conditions that mimic their native environment versus those recorded under constant warm and constant cool conditions, while using a constant photoperiod and full access to resources. We found significant sexual dimorphism, with males being more prone to dehydration than females. Under circadian environmental variation, most metabolic shifts occurred prior to physical environmental change and the timing was disrupted under both constant temperature-humidity treatments. The ratio of CO2 produced to O2 consumed (the respiratory quotient) reached greater than 1.0, only during the light phase under diurnally variable conditions, a pattern that strongly suggests that lipogenesis is contributing to the production of energy and endogenous water. Our results are consistent with historical descriptions of circadian torpor in this species (torpid by day, active by night), but reject the hypothesis that torpor is initiated by food restriction or negative water balance.

Methods
Detailed methods are available in Colella et al. 2021.

Raw respirometry data were collected on a Sable Systems International Field Metabolic System (FMS) at the University of New Hampshire. Fourteen individuals were measured for 3 days under baseline diurnal conditions and 14 additional individuals were measured under constant hot and constant cold conditions.

Raw data include:

4 Baseline ExpeData (.exp) files: RepRec_02-20-2020_cat.exp, RepRec_02-26-2020_cat.exp, RepRec_03-10-2020_cat.exp, RepRec_03-15-2020_cat.exp

14 Cold ExpeData files: 20_april_2020_cat1.exp, 20_april_2020_cat2.exp, 20_april_2020_cat3.exp, 20_april_2020_cat4.exp, 7June20_0006_cat1.exp, 7June20_0013_cat2.exp, 7June20_0019_cat3.exp, 7June20_0025_cat4.exp, 30_april20_0098_cat4.exp, 30_april20_0105_cat5.exp, 30_april20_0111_cat6.ex, 27_april_2020_cat1.exp, 27_april_2020_cat2.exp, 27_april_2020_cat3.exp

15 Hot Expedata files: 3June20_0006_cat1.exp

3June20_0013_cat2.exp, 3June20_0020_cat3.exp, 3June20_0027_cat4.ex, 29May20_0007_cat1.exp, 29May20_0018_cat2.exp, 29May20_0027_cat3.ex, 25may20_0006_cat1.exp, 25may20_0014_cat2.exp, 25may20_0021_cat3.exp, 25may20_0027_cat4.ex, 18_may20_0007_cat1.exp,, 18_may20_0014_cat2.exp,18_may20_0021_cat3.exp, 18_may20_0027_cat4.exp

Raw data were processed through 2 macro files (extract-means-Matt-120sec.txt and VO2-VCO2-H2O-120sec_per_cage.txt, available here) to create: analysis_data_final_2July2020.csv which was used in all downstream analyses. all_noOL_F.csv and all_noOL_M.csv represent subsets of analysis_data_final_2July2020.csv that include only females and males, respectively, without outliers that were more than 3 standard deviations from the mean (per: github.com/jpcolella/removeOL.R). All additional analyses detailed in the manuscript were conducted in R using scripts available at github.com/jpcolella/peer_respo.