DOI: 10.5061/dryad.bvq83bkg8
title: Data from: Testing metabolic cold adaptation and the climatic variability hypotheses across the latitudinal range of a widespread, supratidal water beetle

description: 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.
description: In the file: <strong>metabolic_dates.xlsx</strong>.
 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:<strong> thermal_limits.xlsx</strong>.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).
description: # Data from: Testing metabolic cold adaptation and the climatic
 variability hypotheses across the latitudinal range of a widespread,
 supratidal water beetle
 [https://doi.org/10.5061/dryad.bvq83bkg8](https://doi.org/10.5061/dryad.bvq83bkg8) We carried out a common garden laboratory experiment, with reciprocal acclimations between two populations of water beetles, and measured the metabolic rates and thermal limits of specimens following acclimation. Acclimation treatments were chosen to reproduce the range of daily temperature fluctuations that each population typically experiences in May (when maximal adult activity and reproduction take place) in the supratidal zone of the two localities (Plymouth, with colder temperatures; and Murcia, with warmer temperatures). Metabolic rates were measured by closed respirometry and thermal tolerance limits estimated through thermography. ## Description of the data and file structure The data matrix used to analyse the metabolic rates presents a column with: (i) the treatment temperatures at which metabolic rates were measured; (ii) the Arrhenius transformation of metabolic rates, which presents the rates logarithmically transformed as a function of inverse temperature, (kT)1, where k is the Boltzmann constant (eV K1), and T is the absolute temperature (K); iii) the localities to which each population belongs; iv) the type of acclimation to which they were subjected (northern acclimation, with a colder temperature cycle; and southern acclimation, with a warmer temperature cycle); v) metabolic rates; vi) metabolic rates corrected with the neperian logarithm: 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).  The thermal limits analysis data matrix contains a column with: i) the locality to which each population belongs; ii) the acclimation to which they were subjected; iii) the value obtained for supercooling; iv) the value obtained for heat coma. 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.  ## Code/Software All the analyses were carried out using R software, version 4.2.3 (2023-03-01, Development Core Team, 2023).