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.Raw data from the Sable Systems International Field Metabolic system was processed using the two macro scripts uploaded here: extract-means-Matt-120sec.txt and VO2-VCO2-H2O-120sec_per_cage.txt, Data: analysis_data_final_2July2020.csv is a comma delimited file containing respirometry data for male and female cactus mice for 3 treatment groups (i) baseline/diurnally variable envrionmental conditions, (ii), constant hot and dry conditions, (iii) constant cool and wet conditions. Columns: Sex = M/F indicating Male/Female EE = energy expenditure H2Omg = relative water loss (RWL; mg) RQ = respiratory quotient (ratio of VCO2 / VO2) Animal_ID = individual animal identifier Deg_C = temperature in Celcius weight = animal weight in grams (g) experiment = eperimental treatment group (BL = baseline, Hot, Cold) StartTime = time of measurment initiation (format = "%H:%M:%S") SD_VCO2 = standard deviation (SD) of VCO2 SD_VO2 = standard deviation (SD) of VO2 VO2 = VO2 estimate for a particular recording VCO2 = VCO2 estimate for a particular recording StartDate = Date (format = "%Y-%m-%d") of a particular recording hour = integer indicating the hour of a particular 120s recording all_noOL_F.csv and all_noOL_M.csv = Female and male subsets of analysis_data_final_2July2020.csv with outliers (> 3 standard deviations) removed Once processed, csv's can be analyzed following scripts available at: github.com/jpcolella/peer_respo/ Funding provided by: National Institutes of HealthCrossref Funder Registry ID: http://dx.doi.org/10.13039/100000002Award Number: 1R35GM128843: National Institute of General Medical SciencesDetailed 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