Data from: Warm acclimation reduces the sensitivity of Drosophila species to heat stress at ecologically relevant scales
Data files
Feb 20, 2025 version files 350.61 MB
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Baeza_IcazaEA2024_code.R
50.70 KB
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Distribucion_Brncic.xlsx
18.17 KB
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Excel_TDT_AS.xlsx
104.72 KB
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out_low.RData
118.56 MB
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out_up.RData
113.69 MB
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out.RData
116.37 MB
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README.md
4.10 KB
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Temp_2014.csv
324.08 KB
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Temp_2015.csv
366.56 KB
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Temp_2016.csv
367.75 KB
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Temp_2017.csv
366.87 KB
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Temp_2018.csv
387.50 KB
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Thermal_landscape_functions.R
5.09 KB
Abstract
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 of the data and file structure
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)
- Baeza_IcazaEA2024_code.R: R script to replicate analyses and figures in the paper and Supplementary Material.
- 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.
Adult Drosophila were collected in Santiago, Chile, and identified based on morphology. Laboratory lines of four species (D. hydei, D. repleta, D. virilis, and D. simulans) 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.