Data from: Testing metabolic cold adaptation and the climatic variability hypotheses across the latitudinal range of a widespread, supratidal water beetle
Data files
Mar 25, 2024 version files 27.88 KB

metabolic_dates.xlsx

README.md

thermal_limits.xlsx
Abstract
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 tradeoffs 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.
README: 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
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 (20230301, Development Core Team, 2023).
Methods
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 padjustment. Normality and homoscedasticity of the data were first checked using ShapiroWilk and Levene tests, respectively.
All the analyses were carried out using R software, version 4.2.3 (20230301, Development Core Team, 2023).