DOI: 10.5061/dryad.cz8w9gj9k
title: Data from: Methodological artefacts cause counter-intuitive evolutionary conclusions in a simulation study

description: 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.
description: # 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: . Montejo-Kovacevich,
 G. (2020). Data from: *Microclimate buffering and thermal tolerance across
 elevations in a tropical butterfly.* Zenodo. Available at:
 . 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: . 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:
 ## **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: &gt; \[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:
 &gt; &gt; * `none`: For no genetic correlations. &gt; * `gsto`: For the
 generalist-specialist trade-off (GSTO). &gt; * `tde`: For the
 thermodynamic effect (TDE). &gt; * `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.