Individual variation in thermal plasticity and its impact on mass‐scaling

Abstract

Physiological processes vary widely across individuals and can influence how individuals respond to environmental change. Repeatability in how metabolic rate changes across temperatures (i.e. metabolic thermal plasticity) can influence mass‐scaling exponents in different thermal environments. Moreover, repeatable plastic responses are necessary for reaction norms to respond to selective forces which is important for populations living in fluctuating environments. Nonetheless, only a small number of studies have explicitly quantified repeatability in metabolic plasticity, and fewer have explored how it can impact mass‐scaling. We repeatedly measured standard metabolic rate of n = 42 delicate skinks Lampropholis delicata at six temperatures over the course of four months (N[observations] = 4952). Using hierarchical statistical techniques, we accounted for multi‐level variation and measurement error in our data in order to obtain more precise estimates of reaction norm repeatability and mass‐scaling exponents at different acute temperatures. Our results show that individual differences in metabolic thermal plasticity were somewhat consistent over time (Rslope = 0.25, 95% CI = 2.48 × 10−8 – 0.67), however estimates were associated with a large degree of error. After accounting for measurement error, which decreased steadily with temperature, we show that among individual variance remained consistent across all temperatures. Congruently, temperature specific repeatability of average metabolic rate was stable across temperatures. Cross‐temperature correlations were positive but were not uniform across the reaction norm. After taking into account multiple sources of variation, our estimates for mass‐scaling did not change with temperature and were in line with published values for snakes and lizards. This implies that repeatable plastic responses may promote thermal stability of scaling exponents. Our work contributes to understanding how energy expenditure scales with abiotic and biotic factors and the capacity for reaction norms to respond to selection.

Impact of developmental temperatures on thermal plasticity and repeatability of metabolic rate

“…While several studies have reported significant among individual variation in thermal plasticity slopes (Careau et al 2014b;Briga and Verhulst 2017), its repeatability is rarely estimated as it requires a study design that allows partitioning of within and between individual variance of slopes (Araya-Ajoy et al, 2015). Our repeatability estimates for reaction norm slopes were consistent with a previous study of the same species (R = 0.23, Kar et al 2021). Similarly, moderate repeatability of thermal sensitivity of metabolic rate has also been observed in amphipods (R = 0.38, Réveillon et al 2019).…”

“…Specifically, the average repeatability of MR in ectotherms from studies that had a measurement interval that was equal or larger than our study ( ≥ 8.5 days) was R = 0.33 (SD = 0.21, n = 18). Interestingly, repeatability of average MR in wild caught adult L. delicata (R = 0.3 -0.5, Kar et al 2021) was quite comparable or even larger. This is likely due to life stage differences that shape phenotypic among-individual variation.…”

“…where Δ%CO 2 is the maximum percentage of CO 2 in air sample above baseline, which was corrected by subtracting any 'residual' CO 2 from the initial flush from the larger of the two air samples; V chamber is the volume of the chamber (70 mL) and V lizard is the volume of the lizard. We used the mass of the lizard as a proxy for its volume (1 g = 1 ml) because of their high correlation and increased accuracy and precision in mass measurements (Friesen et al 2017;Kar et al 2021), and t is the duration of time in minutes after where the chamber had been sealed and the first air sample was taken (90 min).…”

“…This nested random effect (hereafter referred to as measurement error) estimates the variance attributed to differences among replicates. While we show in a previous study that measurement error can vary by temperature (Kar et al 2021), here we assumed that measurement error was constant across temperatures by fitting it as a random intercept as estimating a random slope resulted in model convergence issues. Heterogeneous residual variance across temperatures can also influence parameter estimates (Careau et al 2014a).…”

Great tits differ in glucocorticoid plasticity in response to spring temperature

“…Not all individuals within a single population respond to environmental variation in a similar way. Individuals can differ in how strongly they respond to variation in ambient temperature, for instance in metabolic rate (Briga & Verhulst, 2017;Careau et al, 2014;Kar et al, 2021), foraging activity (Hall & Chalfoun, 2019), or incubation behavior (Cones et al, 2021). Since glucocorticoids support the regulation of such traits (Romero & Wingfield, 2016), quantifying the extent of glucocorticoid plasticity within and among individuals of wild vertebrate populations is crucial for evaluating the potential of these mechanisms to evolve (e.g., Angelier & Wingfield, 2013;Guindre-Parker, 2020;Hau et al, 2016;Malkoc, Mentesana, et al, 2021;Taff & Vitousek, 2016;Wada & Sewall, 2014).…”