DOI: 10.5061/dryad.2jm63xt1g
title: Data from: Evolution of phenotypic plasticity during environmental fluctuations

description: Evolution in fluctuating environments is predicted to disfavor genetic
 canalization and instead select for alternative strategies, such as
 phenotypic plasticity or possibly bet-hedging, depending on the accuracy
 of environmental cues and type of fluctuations. While these two
 alternatives are often contrasted in theoretical studies, their evolution
 are seldom studied together in empirical work. We used experimental
 evolution in the nematode worm Caenorhabditis remanei to
 simultaneously study the evolution of plasticity and bet-hedging in
 environments differing only in their temperature variability, where one
 regime is exposed to faster temperature cycles between 20°C and 25°C, with
 little autocorrelation between parent and offspring environment, while the
 other regime had slowly increasing temperature with high autocorrelation
 in temperature between parent and offspring. We exposed worms for 30
 generations to either fluctuating or slowly increasing temperature, these
 two environments had the same average temperature over evolutionary time.
 After experimental evolution, we scored size at sexual maturity and
 fitness in full siblings reared in two different temperatures, optimal
 20°C and mildly stressful 25°C. The datasets contain data on adult size,
 total reproduction and individual fitness of full-sibs from the two
 evolutionary selection regimes, where each regime was replicated in 6
 replicate populations. Experimental evolution in the fluctuating
 environment resulted in the evolution of increased body size plasticity
 but not increased bet-hedging, compared to evolution in the slowly
 changing environment. Plasticity followed the temperature size rule as
 size decreased with increasing temperature and this plastic response was
 adaptive. In addition, we documented substantial standing genetic
 variation in body size, which represents a potential for further
 evolutionary change.
description: <em>Experimental evolution</em> We
 used <em>C. remanei</em> nematode worms, strain SP8 which has
 been lab adapted at 20°C and subsequently exposed to 30 generations of
 experimental evolution in two regimes (<em>Increased
 warming</em> and <em>Fast temperature cycles</em>). The
 experimental evolution has been previously described in detail in
 Lind <em>et al.</em>, (2020). Briefly, in
 the <em>Increased warming </em>experimental evolution regime,
 the temperature gradually raised from 20°C to 25°C, which is a novel and
 mildly stressful temperature. This gradual change over 30 generations
 represents an increase of 0.1°C every 2.13 days and results in a
 correlated parental and offspring environment. In the second
 regime,<em> Fast temperature cycles</em>, the temperature
 fluctuated every second generation between 20°C and 25°C, resulting in 14
 temperature shifts but no exposure to the intermediate temperatures. The
 environmental change is deterministic (every second generation), but since
 parents and offspring would end up in either the same or in different
 temperature, it represents uncorrelated parental and offspring
 environment. The generation time in <em>C. remanei</em> is
 temperature dependent; 4 days long in 20°C and 3.4 days long in 25°C.
 Despite these differences, the average temperature and the total
 chronological time of experimental evolution were identical for both
 regimes, at 22.5°C and 110 days, respectively. Each
 evolutionary regime consisted of six replicate populations. The
 populations were maintained on 92 mm Petri dishes poured with NGM agar in
 climate chambers set to 60% relative humidity. In order to prevent
 bacterial and fungal contamination, the agar and bacterial LB contained
 the antibiotics streptomycin and kanamycin, and the fungicide nystatin.
 The plates were seeded with 2 ml of an antibiotic-resistant OP50-1 (pUC4K)
 strain of <em>E. coli</em> (Stiernagle, 2006) that served as a
 source of food. Every 1-2 days, a piece of agar containing approximately
 150 worms of mixed ages was cut and transferred to a new plate containing
 fresh bacteria. This resulted in populations with overlapping generations
 that were maintained in a constant exponential growth phase. After the
 experimental evolution, populations were expanded for two generations and
 frozen in -80°C for later revival and subsequent phenotypic
 assays. <strong><em>Experimental
 set-up</em></strong> Each replicate
 population of each of the two selection regimes was run in a separate
 block resulting in 12 experimental blocks in total. For logistical
 reasons, we focus on females, since they are responsible for population
 growth rate and their fitness is straightforward to measure.
 Briefly, populations were revived from freezing and exposed to
 25°C for 3 generations, to avoid any maternal effects associated with
 freezing. The third generation was split into eight families, each family
 consisting of one male and one female worm. From each family, we randomly
 picked eight offspring females (full siblings) and placed four females in
 20°C and four in 25°C. Since our focus was evolution in females, their
 fitness was assessed by mating them with standardized males from the
 ancestral line. For the detailed description of the experimental set up,
 see supplementary figure 2.
 <strong><em>Phenotypic
 assays</em></strong> <em>Daily
 reproduction</em> Female and male worms were
 transferred to a new plate every 24 hours, and viable offspring were
 counted two days later. The female worm was discarded after dying, or when
 reproduction ended.<em> </em>
 <em>Body size</em> Worms in 20°C
 reach their peak size at day 4 of adulthood (Lind <em>et
 al.</em>, 2016). The peak size in 25°C is on day 2 of adulthood,
 which was determined during pilot assays (supplementary figure 3).
 Photographs of worms were taken during their peak size using a Lumenera
 Infinity2-5C digital microscope camera mounted on a Leica M165C
 stereomicroscope. Body size was measured from the photographs
 using <em>ImageJ</em> <em>1.46r</em> (https://imagej.nih.gov/ij/) as total cross-section area. <em>Individual fitness</em> We used the age-specific reproduction data to calculate rate-sensitive individual fitness λind for each individual (McGraw & Caswell, 1996), which is analogous to the intrinsic rate of population growth. Individual fitness was calculated by constructing a population projection matrix for each individual, and then calculating the dominant eigenvalue of this matrix, following McGraw & Caswell, (1996). Since we kept the population size and age structure constant during experimental evolution, individual fitness is the most appropriate fitness measure for this study (Mylius & Diekmann, 1995). <em>Thermal reaction norms</em> To test whether the degree of phenotypic plasticity has evolved, we used linear mixed-effect models to separately estimate the thermal reaction norms of body size and individual fitness, using the package <em>lme4</em> (Bates <em>et al.</em>, 2015) in R. The models included either body size (area) or individual fitness (λind) as response variables. The full model included three fixed effects: mean-standardized temperature as a covariate, the experimental evolution regime as a categorical factor, and their interaction. We expect this interaction to be significant if the degree of plasticity has evolved. Experimental line and dam identity were included as random effects. Significance of the fixed effects was evaluated using Wald χ2 tests. Pseudo-R2 values were calculated as the squared correlation coefficient between fitted values from the model and observed values. <em>Selection</em> To test if temperature responses in size are adaptive, we estimated the selection on body size and compared it to the observed temperature response. Selection on body size (area) was estimated using mixed-effect models in R with the package <em>lme4</em> and individual fitness (λind) as the response variable. The full model included the following fixed effects: area, area2, temperature, experimental evolution regime and all interactions except for interactions involving both area and area2 together. Experimental line was included as random effect. Significance of fixed effects was evaluated using Wald χ2 tests.  From the full model, we obtained temperature-specific estimates of the slope and the squared term between λind and body size. For each temperature, the optimal size (i.e. the area that maximizes fitness) was calculated as: -b/(2×c), where b = the temperature-specific slope from the full model (i.e. the linear selection gradient, estimating the relationship between body area and λind) and c = the temperature-specific squared term from the full model (i.e. the quadratic selection gradient, estimating the relationship between area2 and λind). Confidence intervals of the temperature-specific optimal sizes were generated by bootstrapping, implemented in the <em>boot</em> package using 10 000 bootstrap replicates. <em>Within family coefficient of variance</em> To test whether the degree of diversifying bet-hedging has evolved, we tested whether the experimental evolution regimes differed in the mean within family variance within temperatures. For each family, we used the trait values of the offspring (within a temperature) to calculate the within family variance. To account for differences in trait means, we used within family means to mean-standardize the variance by calculating the within family coefficient of variance (CV): CV = σ/μ, where σ = within family standard deviation, and μ = within family mean. An ANOVA was used within each temperature to test if the evolution regimes differed in their mean within family CV. Since it is more difficult to detect differences in variances than differences in means, we also performed power calculations on our ability to detect whether within-family CVs differ between the selective regimes. Balanced one-way ANOVA power calculations were performed to estimate the effect sizes possible to detect with power ranging from 0.70 to 0.95. Effect sizes, η2, were obtained for our sample size of N = 48 per selection regime and a significance level of 0.05. η2 is calculated as the sum of squared explained by the treatment (here: selection regime) divided by the total sum of squares and has an equivalent interpretation as an R2. <em>Genetic variance and correlations</em> For body size, genetic variance and genetic correlations across temperature were estimated using animal models in the package <em>MCMCglmm</em>. Univariate models were used to estimate genetic variance, whereas bivariate models were used to estimate genetic correlations, both models using Gaussian family for trait distribution. An inverse Wishart prior with parameters V = 1 and nu = 0.02 were used in both univariate and bivariate models. Pedigree data linking offspring to parents, based on full-sib relationships, was included in the models. Convergence of the models were ensured by evaluating diagnostic plots of posterior distributions, using the convergence diagnostic half-width test by Heidelberger and Welch (1983), and by ensuring that the autocorrelation between MCMC samples was close to zero. For univariate models, body area was used as response variable. Temperature (categorical), experimental evolution regime, and their interaction, were included as fixed effects. Genetic variance (VG), variance due to differences between experimental lines, and residual variances were estimated separately as random effects in the full model for each temperature-by-evolution regime combination. The full model ran for 4.2×106 MCMC iterations, 0.2×106 samples were discarded as burnin, and the thinning interval was 4000, resulting in a sample size of 1000 MCMC-samples. Reduced models, subset by temperature-by-evolution regime combination, were used to assess statistical significance of VG, by comparing deviance information criterion (DIC) of models with versus without genetic variance included. Broad sense heritability (H2 = VG/VP, where VP = total phenotypic variance after accounting for variance due to experimental line effects) and broad sense evolvability (I2 = VG/mean2, Hansen <em>et al.</em>, 2003, 2011) were used to estimate the population’s evolutionary potential of body size. This was estimated for each temperature-by-evolution regime combination. Evolvability measures the expected percentage change in a trait per generation under unit strength of selection. Compared to heritability, evolvability is independent from the environmental variance and represents a measure of the evolutionary potential that is comparable across traits, populations, and species when applied to traits with a natural zero and which are strictly positive (Hansen <em>et al.</em>, 2011). Genetic correlations of body size were estimated using bivariate animal models in <em>MCMCglmm.</em> Genetic correlations was estimated separately for the two regimes. Body size was the response variable and was treated as two traits (size at 20°C and at 25°C). Random effects included genetic covariance between the temperatures, whereas VG, variance due to differences between experimental lines, and residual variances were estimated separately for each temperature. The full models ran for 2.05×106 MCMC iterations (burnin: 0.05×106 samples, thinning interval: 2000 samples), resulting in a sample size of 1000 MCMC-samples. Reduced models without genetic covariance were used to access the statistical significance of the genetic covariance, by comparing the DIC of models with versus without genetic covariance included. The genetic correlation of body size across temperatures was calculated by dividing the genetic covariance by the product of the genetic standard deviation of the two temperatures. This was done on the posterior distributions, in order to carry the error forwards in the analyses. To compare posterior distributions of H2, I2, and genetic correlations across temperatures and selection regimes, we calculated, within each MCMC sample, the pairwise differences in these measures and checked if the posterior distributions of these differences had a 95% credibility interval that included zero. Pairwise comparisons of distributions were performed between evolution regimes within temperature, and between temperatures within evolution regimes.
description: # Evolution of phenotypic plasticity during environmental fluctuations
 Dataset DOI: [10.5061/dryad.2jm63xt1g](10.5061/dryad.2jm63xt1g) ##
 Description of the data and file structure #### File:
 plasticity_data_2025-03-24.csv **Description:** Phenotypic data #####
 Variables * id: Unique ID of the individual * Block: Experimental block *
 Area_um: Maximum size (cross section area in um2) at adulthood, taken at
 adult day 2 for worms in 25C and at adult age 4 for worms in 20C * lambda:
 Individual fitness (lambda) * Sel: Experimental evolution regime (Fast =
 Fast temperature cycles; Increase = Increased warming) * Line: The
 individual replicated population (6 replicated populations per
 experimental evolution regime) * Family: The family of the individual *
 Individual: Individual identification used in assays * dam: The mother *
 Temp: Assay temperature, in Celcius * mother_area_um: Size (cross section
 area) of the mother (dam) in um2 (NA: missing data for a few mothers) ####
 File: total_reproduction_data_2025-03-24.csv **Description:** Data on
 total reproduction ##### Variables * Selection: Experimental evolution
 regime (Fast = Fast temperature cycles; Increase = Increased warming) *
 Line: The individual replicated population * Family: The family within the
 line * Temp: Assay temperature, in Celcius * FamilyLarvae: Total
 reproduction for the family (number of larvae) #### File:
 pedigree_data_2025-03-24.csv **Description:** Pedigree information for
 MCMC models ##### Variables * id: Unique ID of the individual * dam: ID of
 the Dam (NA: Not Applicable set for the parents, since the pedigree is one
 generation) * sire: ID of the Sire (NA: Not Applicable set for the
 parents, since the pedigree is one generation) #### File: Size_Photos.zip
 **Description:** Photos for measuring body size. Body size is measured as
 cross-section area (excluding the mating plug, if present). Files are
 presented as a zip file, when uncompressed it will contain 12 folders, one
 folder for each experimental evolution line. Each of these folders will
 contain subfolders with the photos from 20C, 25C or photos of the mothers.
 Next to each subfolder will be an excel file, which describes the identity
 of each file. Files are numbered (1.jpg, 2.jpg an so on) and the
 corresponding excel files have a first column "Filename" which
 give the numerical number in the file name, and the other column give all
 other information about this individual (see the variables listed below)
 ##### Variables in each excel file * Filename: the numerical name of the
 jpg photo in the subfolder corresponding to the name of the excel file.
 For example, the number "1" in the Filename column corresponds
 to a photo named "1.jpg" in the subfolder. * Regime/Selection:
 The evolutionary selection regime * Line: The individual replicated
 population * Family: The family of the individual * Individual: The
 individual name * Temperature: Assay temperature, in Celcius ##
 Code/software All statistical analyses were conducted in R 3.6.1 (R Core
 Team, 2015).