Temperature significantly impacts ectotherm physiology, with thermal and metabolic traits varying with latitude but the drivers of this variation remain unclear, despite obvious consequences in the face of ongoing global change. This study explores metabolic cold adaptation (MCA) and the climatic variability hypothesis (CVH) to evaluate local adaptation and phenotypic plasticity of metabolic rates and thermal limits in two populations of the supratidal rockpool beetle Ochthebius lejolisii from localities experiencing contrasting thermal variability. Reciprocal acclimation was conducted under spring temperature regimes of both localities, incorporating local diurnal variation. Metabolic rates were measured by closed respirometry, and thermal tolerance limits estimated through thermography. In line with MCA, the northern population (colder climate) showed higher metabolic rates and Q10s at lower temperatures than the southern population. As predicted by the CVH, the southern population (more variable climate) showed higher upper thermal tolerance but only the northern population was able to acclimate upper thermal limits. This pattern suggests the existence of trade-offs in thermal adaptation in this species, likely increasing the vulnerability of populations on Mediterranean coasts to the projected increases in extreme temperatures under ongoing climate change.

In the file: metabolic_dates.xlsx. The data represented in columns A to G: A) the acclimatisation regime to which individuals were subjected with C for the coldest regime, and W for the warmest regime. B) the metabolic rates calculated in rate nmol/h mg C) the temperature at which the metabolic rates were calculated (in °C); D) the population to which each experimental group belongs (1Mur = Murcia; 2Ply = Plymouth); E) the neperian logarithm of the metabolic rate; F) Arrhenius transformation of the metabolic rates, where k is the Boltzmann constant (eV K1), and T is the absolute temperature (K); G) contains the weight of each group of organisms after the experiment (mg). 

In the file: thermal_limits.xlsx.The first column shows the population of each individual, the second column shows the acclimatisation regime (cold = c; warm = w), the third column shows the supercooling temperature and the last column shows the heat coma temperature, in both cases in °C. 

A generalized linear regression model was used to compare metabolic rates between populations, acclimation treatments and their interaction. The body mass of the five individuals’ group was included as covariate. Upper and lower thermal limits were compared between populations and acclimation treatments (including their interaction) using an ANOVA and post hoc analyses with Bonferroni p-adjustment. Normality and homoscedasticity of the data were first checked using Shapiro-Wilk and Levene tests, respectively.

All the analyses were carried out using R software, version 4.2.3 (2023-03-01, Development Core Team, 2023).