Data from: Methodological artefacts cause counter-intuitive evolutionary conclusions in a simulation study

von Schmalensee, Loke1 ; Ittonen, Mats1 ; Brødsgaard Shoshan, Anna1; Roberts, Kevin1; Siemers, Isabelle1; Süess, Philip1; Wiklund, Christer1; Gotthard, Karl1

Published Jan 12, 2024 on Dryad. https://doi.org/10.5061/dryad.cz8w9gj9k

Data files

Jan 12, 2024 version files 39.47 MB

both_500.RData

7.01 MB
ecuador.txt

3.76 MB
gsto_500.RData

7.01 MB
none_500.RData

6.98 MB
README.md

3.01 KB
spain.txt

6.29 MB
sweden.txt

1.41 MB
tde_500.RData

7.01 MB

Abstract

In their simulation study, Garcia-Costoya et al. (2023) conclude that evolutionary constraints might aid populations facing climate change. However, we are concerned that this conclusion is largely a consequence of the simulated temperature variation being too small, and, most importantly, that uneven limitations to standing variation disadvantage unconstrained populations.

README: Data from: Methodological artefacts cause counter-intuitive evolutionary conclusions in a simulation study

These are the data that we used for the figures in our article. They are parts of four previously published datasets:

Garcia-Costoya, G., Williams, C.E., Faske, T.M., Moorman, J.D. & Logan, M.L. (2023). Data from: Evolutionary constraints mediate extinction risk under climate change. Dryad Digital Repository. Available at: https://doi.org/10.5061/dryad.2fqz612t3.

Montejo-Kovacevich, G. (2020). Data from: Microclimate buffering and thermal tolerance across elevations in a tropical butterfly. Zenodo. Available at: https://doi.org/10.5281/ZENODO.3634105.

Vives-Ingla, M., Sala-Garcia, J., Stefanescu, C., Casadó-Tortosa, A., Garcia, M., Peñuelas, J. et al. (2023). Data from: Interspecific differences in microhabitat use expose insects to contrasting thermal mortality. Zenodo. Available at: https://doi.org/10.5281/ZENODO.7358091.

von Schmalensee, L., Hulda Gunnarsdóttir K., Näslund, J., Gotthard, K. & Lehmann, P. (2021). Data from: Thermal performance under constant temperatures can accurately predict insect development times across naturally variable microclimates. Dryad Digital Repository. Available at: https://doi.org/10.5061/dryad.gtht76hm5.

Data from Garcia-Costoya et al. (2023)

The files 'both_500.RData', 'gsto_500.RData', 'none_500.RData', and 'tde_500.RData' are unmodified data files from Garcia-Costoya et al. 2023. We quote the relevant part of the original authors' metadata below:

[t]he raw data for all individuals generated as part of the initial populations faced with climate change scenarios in our simulations. In all cases, each file indicates genetic correlation and initial population size (and carrying capacity) in the format geneticcorrelation_populationsize.RData. Initial population sizes are either 50,500, or 5000. Genetic correlations are abbreviated as:

none: For no genetic correlations.

gsto: For the generalist-specialist trade-off (GSTO).

tde: For the thermodynamic effect (TDE).

both: For both the GSTO and the TDE acting together.

Microclimate data

Our file equador.txt contains the columns 'value' (renamed to 'temp'; temperaure in °C), 'alt_height_slope' (renamed to 'site'), and date.time (renamed to 'date_time'; date in YYYY-MM-DD format) from the file fig1.1.logger.hourly.means.csv in Montejo-Kovacevich (2020)

Our file spain.txt contains the columns 'TEMP' (renamed to 'temp'; temperaure in °C), 'site', and 'Winter_time' (renamed to 'date_time'; date in YYYY-MM-DD format) from sensors_hourly.csv in Vives-Ingla et al. (2023).

Our file sweden.txt contains the columns 'cage' (renamed to 'site'; temperaure in °C), 'temp', and 'date.time' (renamed to 'date_time'; date in YYYY-MM-DD format) from von Schmalensee et al. (2021).

R code

The file figure_script.R contains all (annotated) code used for producing the figures in our article.