DOI: 10.5061/dryad.6m905qg8s
title: Data from: Warm acclimation reduces the sensitivity of Drosophila species to heat stress at ecologically relevant scales

description: Thermal acclimation is presumed to affect heat tolerance, though it is
 unclear how this should impact populations under realistic natural
 conditions. In this study, we quantified how thermal acclimation affect
 heat tolerance landscapes in Drosophila and, as a consequence, their
 predicted mortality in the field based on simulations with the dynamic
 landscape. We measured the thermal tolerance of four Drosophila species
 (D. repleta, D. hydei, D. simulans, and D. virilis) acclimated to five
 constant temperatures along a gradient. We then combined this information
 with field temperatures to construct dynamic tolerance landscapes for
 these species and examine how survival varies over the course of a year.
 Our analyses reveal the effect of acclimation on an ecologically relevant
 scale, specifically through the study of cumulative mortality under
 natural thermal regimes. We show that different species exhibit a common
 strategy in response to thermal challenges during acclimation, resulting
 in a trade-off between increasing critical temperature (CTmax) and thermal
 sensitivity (z). Furthermore, we show that while acclimation presents a
 relatively modest improvement in thermal tolerance during short ramping
 laboratory trials, this response becomes stronger when tolerance estimates
 are translated into ecologically relevant timescales, such as annual
 survival. Our results indicate that acclimation to warm conditions can
 substantially increase their thermal tolerance, contradicting the idea
 that thermal acclimation in ectotherms has only a minor effect. Our work
 applies novel approaches to studying thermal tolerance and aims to
 highlight the role of acclimation in ameliorating the impact of global
 warming.
description: Adult <em>Drosophila</em> were collected in Santiago,
 Chile, and identified based on morphology. Laboratory lines of four
 species (<em>D. hydei</em>, <em>D. repleta</em>,
 <em>D. virilis</em>, and <em>D. simulans</em>)
 were established and maintained under controlled conditions at 21°C. Flies
 were acclimated to five different temperatures (18, 21, 24, 27, and 30°C),
 and their development was closely monitored. From each
 acclimation group, a random selection of adult flies was made. After
 briefly anesthetizing and sexing them, the flies were allowed to recover
 for 1-2 days before heat tolerance experiments. For each species and
 acclimation temperature, heat tolerance assays were performed with 10
 males and 10 females. The flies were placed in vials and submerged in
 water baths at four critical temperatures (36, 38, 40, and 42°C), with
 knockdown times recorded. A total of 1,482 individuals were tested across
 59 assays. Thermal tolerance landscapes for each
 species were estimated using thermal death time (TDT) curves. These
 landscapes were then applied to a dynamic model to predict survival under
 field conditions. The model calculates instantaneous survival as
 temperatures vary over time, converting constant temperature survival
 probabilities to variable conditions. Hourly field temperature data
 (2014-2018) from Santiago was interpolated to 1-minute intervals, and the
 annual cycle (July to June) was analyzed to evaluate the effect of Austral
 summer temperatures on survival.
description: ## Description of the data and file structure # **Warm acclimation reduces
 the sensitivity of *Drosophila* species to heat stress at ecologically
 relevant scales**
 [https://doi.org/10.5061/dryad.6m905qg8s](https://doi.org/10.5061/dryad.6m905qg8s) Adult *Drosophila* were collected in Santiago, Chile, and four species were reared in the lab at 21°C. Flies were acclimated to five temperatures (18, 21,24, 27 and 30°C), and heat tolerance assays were conducted on 10 males and 10 females per species, testing knockdown times at four critical temperatures (36, 38, 40 and 42°C). Thermal tolerance landscapes were estimated using TDT curves and applied to a dynamic model to predict survival under variable field conditions. Hourly temperature data from Santiago (2014-2018) was analyzed to assess the impact of Austral summer temperatures on survival. ### Files and variables The attached files contain the data and R code to replicate the analyses and figures presented in the paper, including those from the Supplementary Material. It should be noted that the code routine to obtain the thermal tolerance estimates requires around 3 hours to complete. For this reason, processed thermal tolerance and viability data are provided as "out.RData", "out_low.RData" and "out_up.RData" . **CODE (2 files in total)** 1. **Baeza_IcazaEA2024_code.R**: R script to replicate analyses and figures in the paper and Supplementary Material. 2. **Thermal_landscape_functions.R**: Extra functions to get temperature survival using thermal landscapes and dynamic models from Rezende et al. (2020). **DATA (10 files in total)** **1. Excel TDT AS.xlsx:** Comprises raw data on thermal death time curves. Columns: * ID: treatment identification code. * Sex: flies sex (male, female) * Tacc: Acclimation temperature in ºC * Tko: Critical temperature in ºC * t0: initial time of experiment (HH:MM:SS) * tko: final time of experiment (HH:MM:SS) * date: date of the experiment (dd/mm) * VideoID: name of the video file corresponding to each experimental trial Note that missing values in **Excel TDT AS.xlsx** data file have been filled as "NA" **2. Distribucion_Brncic.xlsx:** Distribution of studied species in Chile. Columns: * sp: species name * x: latitude (º) * y: longitude (º) * fig: Figure from where data have been collected * panel: panel from where data have been collected (north, center, south) **3. Temp_2014.csv, Temp_2015.csv, Temp_2016.csv, Temp_2017.csv, Temp_2018.csv**: Data files with thermal regimes in Santiago during 2014, 2015, 2016, 2017 and 2018. This file includes hourly field temperature data between 2014 and 2018 from the Chilean Agricultural Research Institute database (https://agrometeorologia.cl/). Columns: * row: number of sample. * date: Date and time (d/m/y hh:mm). * Stgo: Temperature in Santiago region (ºC). * Ch: Temperature in Chillan region (ºC). * PM: Temperature in Puerto Montt region (ºC). * Val: Temperature in Valparaiso region (ºC). * Cur: Curico region  (ºC). Note that missing vales have been filled a "-". **4. out.RData, out_low.RData, out_up.RData**: These files contain the survival results obtained from the simulations of the dynamic thermal tolerance models (from L611 to L645 in the R code *Baeza_IcazaEA2024.R *). The dataset *out.RData* includes the simulation results for four different species and acclimation temperatures. The datasets *low.RData* and *out_up.RData* contain the lower and upper confidence interval values of the simulations, respectively. Briefly, these files include a dataframe per species and acclimation temperature with three columns: date,  temperature (ta; ºC) and survival  (surv; %). These datasets can be generated by running lines L611 to L645 in the R code *Baeza_IcazaEA2024.R*, but they are provided here because each simulation takes approximately 57 minutes to complete. From line 648 onward in *Baeza_IcazaEA2024.R*, these datasets are loaded using `load("out.RData")` to visualize the results.  # Code/software All analyses were performed using R software.