DOI: 10.5061/dryad.s7h44j1fz
title: Data from: Plasticity cannot fully compensate evolutionary differences in heat tolerance across fish species

description: Understanding how evolution and phenotypic plasticity contribute to
 variation in heat tolerance is crucial to predicting responses to warming.
 Here we analyze 272 thermal death time curves of 53 fish species
 acclimated to different temperatures and quantify their relative
 contributions. Analyses show that evolution and plasticity account,
 respectively, for 80.5 % and 12.4 % of the variation in elevation across
 curves, whereas their slope remained invariant. Evolutionary and plastic
 adaptive responses differ in magnitude, with heat tolerance increasing
 0.54 ºC between species and 0.32 ºC within species for every 1 ºC increase
 in environmental temperatures. After successfully predicting critical
 temperatures under ramping conditions to validate these estimates, we show
 that fish populations can only partly ameliorate the impact of warming
 waters via thermal acclimation and this deficit in plasticity could
 increase as the warming accelerates.
description: We used the database of Coutant (1972) to study the variation
 given by evolution and plasticity in heat tolerance in fish and we used
 the database of Beitinger et al. (2000) to predict critical temperatures
 from experiments under ramp conditions to validate our results. We then
 used surface water temperature data for North American lakes collected by
 Sharma et al. (2015) between 1985 and 2009 to evaluate the effect of
 increased temperature on the vulnerability of fish. Here we provide all
 this the databases and the script to replicate the analyzes and figures of
 our work. 
description: # Datasets and scripts for: Plasticity cannot fully compensate
 evolutionary differences in heat tolerance across fish species
 [https://doi.org/10.5061/dryad.s7h44j1fz](https://doi.org/10.5061/dryad.s7h44j1fz) We used the database of Coutant (1972) to study the variation given by evolution and plasticity in heat tolerance in fish and we used the database of Beitinger et al. (2000) to predict critical temperatures from experiments under ramp conditions to validate our results. We then used surface water temperature data for North American lakes collected by Sharma et al. (2015) between 1985 and 2009 to evaluate the effect of increased temperature on the vulnerability of fish. Here we provide all this the databases and the script to replicate the analyzes and figures of our work.  Main Folder: ### (1) Fish\_TDT. R R script required to replicate all analyses and figures. ### (2) Thermal\_landscape\_functions R - R script to fit the static and dynamic thermal tolerance landscapes as described in Rezende et al. (2020) (required for analysis of Figure 4 and Figure S8). ### (3) Dataset\_Coutant.xlsx Heat tolerance database in fish compiled by Coutant (1972) in Appendix ll-C ([https://www3.epa.gov/region1/npdes/merrimackstation/pdfs/ar/AR-166.pdf](https://www3.epa.gov/region1/npdes/merrimackstation/pdfs/ar/AR-166.pdf)). Note that the database starts at column 8, for this reason the R script skips the first 7 rows before starting the analysis ("read_excel("Dataset_Coutant.xlsx", sheet = "Sheet1",skip=7)"). Empty cells indicate that there is no data in that cell, the script skips those rows Empty cells in a column correspond to data that was not available and that this comes from the original source variables * sp: scientific name * sp_phylo: scientific name separated by a "_" * stage_original: state of development * length_original: length of the organism (the units are different and these should be reviewed in Appendix ll-C of Coutant 1972) * weight_original: weight of the organism (the units are different and these should be reviewed in Appendix ll-C of Coutant 1972) * sex: sex of the organism * location: location of the organism * order: taxonomic order * family: taxonomic family * class: taxonomic class * reference: source reference * extreme: whether the upper limit is Upper or Lower * temp_acc: acclimation temperature * time_acc: acclimation time (units may vary and are noted in Excel) * a: intercept of relation between logtime and temperature (see main Text) * b: slope of relation between logtime and temperature (see main Text) * N: number of measurement temperatures * r: correlation coefficient * upper_range: highest measurement temperature * lower_range: lowest measurement temperature * ld50: lethal temperature for 50% mortality * lethal_thr: critical temperature at which an organism cannot survive due to thermal exposure * page: page from which the data was taken from Appendix ll-C of Coutant 1972 * fish: This answers the question of whether the organism is a ray-finned fish (yes or no). * lat: latitude º * lon: longitude º * ott.id: species code to be able to use Open Tree of Life \**** These data are from Coutant (1972) ### (4) Dataset\_Beitinger.xlsx Heat tolerance database in fish with ramping conditions compiled by Beitinger et al (2000). Note that the database starts at column 2, for this reason the R script skips the first row before starting the analysis Empty cells indicate that there is no data in that cell, the script skips those rows Empty cells in a column correspond to data that was not available and that this comes from the original source variables * sp: scientific name * sp_phylo: scientific name separated by a "_" * order: taxonomic order * family: taxonomic family * ta: acclimation temperature * ramp: heating rate (ºC/min) * test: criterion used to determine collapse * ctmax_mean: average value of Ctmax - critical thermal maximum - (ºC) * ctmax_sd: standard desviation of Ctmax - critical thermal maximum * n: number of individuals used * loc: location of the organism * ref: reference * comment: additional comment * fun: This answers the question of whether CTmax values ​​should be calculated with a function (yes or no) \**** These data are from Beitinger et al (2000) ### (5) values.csv Environmental variables for North American lakes compiled by Sharma et al. (2020) - here we only use temperature -. variables * recordID: registry code * variable: variable being measured * year: year * sideID: measurement site ID * value: temperature value of each side and time (ºC) \**** These data are from Sharma et al. (2020) ### (6) lakeinformation.csv Information (like coordinates) for North American lakes compiled by Sharma et al. (2020) variables * siteID: measurement site ID * Lake_name: name of each lake * Other_names: other possible name of the lake * lake_or_reservoir: if is a lake or a reservoir * location: location * region: region * latitude: latitude in decimals * longitude: longitude in decimals * geospatial_accuracy_km: geospatial accuracy (km) * elevation_m: elevation (m) * mean_depth_m: mean depth (m) * max_depth_m: maximum depth (m) * surface_area_km2: surface area (km2) * volume_km3: volume (km3) * source: source from which the data was taken (satellite or in situ) * sampling_depth: sampling depth if is applicable (discrete) * sampling_time_of_day: sampling time of the day (its a range of hours) * time_period: discrete sampling period * contributor: data contributor \**** These data are from Sharma et al. (2020) References: * Beitinger, T. L., Bennett, W. A. & McCauley, R. W. (2000). Temperature tolerances of North American freshwater fishes exposed to dynamic changes in temperature. Environmental biology of fishes, 58, 237-275. [https://doi.org/10.1023/A:1007676325825](https://doi.org/10.1023/A:1007676325825) * Coutant, C. C. (1972). Water quality criteria. A report of the committee on water quality criteria. text and Appendix II-C, 410-419. Available at [https://www3.epa.gov/region1/npdes/merrimackstation/pdfs/ar/AR-166.pdf](https://www3.epa.gov/region1/npdes/merrimackstation/pdfs/ar/AR-166.pdf)(accessed on 23 January 2024). * Rezende, E. L., Bozinovic, F., Szilágyi, A. & Santos, M. (2020). Predicting temperature mortality and selection in natural Drosophila populations. Science, 369, 1242-1245. [https://doi.org/10.1126/science.aba9287](https://doi.org/10.1126/science.aba9287) * Sharma, S., Gray, D. K., Read, J. S., O’reilly, C. M., Schneider, P., Qudrat, A., et al. (2015). A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009. Scientific data, 2(1), 1-19. [https://doi.org/10.1038/sdata.2015.8](https://doi.org/10.1038/sdata.2015.8)