DOI: 10.5061/dryad.2jm63xt3v
title: Environmental predictability drives different routes to adaptation

description: Climate change is altering thermal environments, yet we know little about
 how environmental predictability shapes species' adaptive responses.
 Different species may rely on plasticity or evolution to survive
 environmental change, but how these strategies depend on environmental
 predictability remains unclear. Experimental evidence that distinguishes
 between plastic and evolutionary responses to different patterns of
 environmental variability has been lacking. Here we present the first
 experimental demonstration that compares adaptive responses to predictable
 versus unpredictable thermal variation disentangling plastic from
 evolutionary changes. Using Drosophila melanogaster populations evolved
 for 11 generations under constant, predictably fluctuating, and randomly
 fluctuating thermal regimes, we assessed survival and fecundity: a
 plasticity assay testing flies directly from their evolutionary
 environments to capture total phenotypic responses, and a common garden
 assay after two generations of standardized rearing to isolate genetic
 changes. Strikingly, environmental predictability shaped divergent
 life-history strategies that were only revealed by comparing our two
 assays. Populations from predictably fluctuating environments evolved
 enhanced survival over generations, but this benefit was only visible in
 the common garden assay, not when tested directly from their evolutionary
 environment. Conversely, populations from randomly fluctuating
 environments showed reduced survival in the plasticity assay and
 consistently lower fecundity in the common garden assay, though this
 reproductive cost was completely masked in the plasticity assay. These
 contrasting responses demonstrate that environmental predictability
 fundamentally determines life-history evolution: predictable variation
 favors investment in stress-resistant longevity, while unpredictable
 variation imposes both immediate survival costs and constitutive
 reproductive constraints. Our findings challenge the traditional view that
 environmental variation uniformly selects for increased plasticity,
 instead revealing that the predictability of environmental change
 determines both the target and mechanism of adaptation. As climate change
 increases environmental variability and reduces environmental
 predictability, these insights provide crucial guidance for predicting
 species persistence and developing effective conservation strategies.
description: # Environmental predictability drives different routes to adaptation
 Dataset DOI:
 [10.5061/dryad.2jm63xt3v](https://doi.org/10.5061/dryad.2jm63xt3v) ##
 Description of the data and file structure README for Data and Code
 Associated with: "Environmental Predictability Drives Different
 Routes to Adaptation" Vinton et al. - Evolution Letters #### CONTENTS
 1\. Fly_Data_Dryad.csv - Experimental data 2\. Analysis_Code.R - R script
 to reproduce all statistical analyses DATA FILE: Fly_Data_Dryad.csv ####
 DESCRIPTION This dataset contains survival and fecundity measurements from
 an experimental evolution study using Drosophila melanogaster. Flies were
 maintained in three thermal regimes (constant, predictably fluctuating,
 and randomly fluctuating) for 11 generations. Fitness assays were
 conducted at generations 4, 7, and 10. Two assay types were performed: \-
 Plasticity Assay (PA): Flies tested directly from their evolutionary
 environment \- Common Garden Assay (CG): Flies reared for 3 generations at
 constant 27°C before testing, to isolate genetic changes from plastic
 responses **COLUMN DEFINITIONS** * Age : Days since start of assay
 (integer) - Assay : Type of fitness assay ``` "PA" = Plasticity
 Assay "CG" = Common Garden Assay ``` * Parent_Gen : Generation
 of selection when parental flies were collected ``` 4, 7, or 10 ``` *
 Incubator_Temp : Evolutionary thermal environment ``` "27" =
 Constant 27°C "Flux" = Predictably fluctuating (daily 21-33°C
 cycle) "Random" = Randomly fluctuating (unpredictable daily
 cycle) ``` * Pop : Replicate population (1-9) ``` Populations 1-3: Random
 treatment Populations 4-6: Constant (27°C) treatment Populations 7-9:
 Predictable (Flux) treatment ``` * WB_Temp : Test temperature in water
 bath (°C) ``` 23, 25, 27, 29, or 31 ``` * Vial_Replicate : Unique
 identifier for each vial - Male_Num : Number of males alive at observation
 * Female_Num : Number of females alive at observation - Egg_Count : Total
 eggs counted in vial at observation - Male_Em – Number of male individuals
 that successfully emerged (eclosed) from eggs. - Female_Em – Number of
 female individuals that successfully emerged. - Sum – Likely sum of male
 and female numbers (Male_Num + Female_Num). - Sum_Em – Sum of male and
 female emergences (Male_Em + Female_Em). - Missing values are indicated as
 blank cells or NA. **EXPERIMENTAL DESIGN** \- Species: Drosophila
 melanogaster \- Selection duration: 11 generations \- Thermal regimes: \*
 Constant: 27°C continuous \* Predictable: Daily cycle of 21°C (12h) and
 33°C (12h) \* Random: Daily cycle with temperatures randomized weekly
 (mean 27°C) \- Replicate populations: 3 per treatment (9 total) \- Test
 temperatures: 23, 25, 27, 29, 31°C \- Vial setup: 2 males + 2 females per
 vial, 4 replicate vials per combination \- Measurements: Survival counts
 and egg counts taken twice weekly ANALYSIS CODE: Analysis_Code.R R script
 to reproduce statistical analyses reported in the manuscript, including
 Cox proportional hazards models for survival and negative binomial GLMs
 for fecundity. **REQUIREMENTS** R version 4.0 or later Required packages:
 survival, dplyr, MASS **USAGE** 1\. Place Fly_Data_Dryad.csv in your
 working directory 2\. Update the file path in the script if needed 3\. Run
 the entire script to reproduce all analyses **ANALYSES INCLUDED** 1\.
 Plasticity Assay Survival (Cox proportional hazards model) \- Results
 correspond to Supplementary Table S3 \- Model: surv_obj ~ Parent_Gen *
 WB_Temp + Incubator_Temp 2\. Common Garden Survival (Cox proportional
 hazards model) \- Results correspond to Supplementary Table S6 \- Model:
 surv_obj ~ Parent_Gen * Incubator_Temp + WB_Temp 3\. Plasticity Assay
 Fecundity (Negative binomial GLM) \- Results correspond to Supplementary
 Table S9 \- Model: Egg_Count_Per_Female ~ Age + Parent_Gen + WB_Temp +
 Age:WB_Temp 4\. Common Garden Fecundity (Negative binomial GLM) \- Results
 correspond to Supplementary Table S12 \- Model: Egg_Count_Per_Female ~ Age
 + Parent_Gen + WB_Temp + Incubator_Temp + Age:WB_Temp **NOTE ON OUTPUT
 LABELS** R codes factor levels alphabetically, so in model output: \-
 Incubator_Temp reference level = "27" (Constant) \-
 Incubator_Temp1 = "Flux" (Predictably fluctuating) \-
 Incubator_Temp2 = "Random" (Randomly fluctuating) CITATION If
 you use these data, please cite: [Vinton et. al, 2025, Evolution Letters]
 CONTACT For questions about the data or analysis, please contact:
 [avinton@usc.edu](mailto:avinton@usc.edu)