DOI: 10.5061/dryad.k6djh9wdf
title: Environmental effects on genetic variance are likely to constrain adaptation in novel environments

description: Adaptive plasticity allows populations to cope with environmental
 variation but is expected to fail as conditions become unfamiliar. In
 novel conditions, populations may instead rely on rapid adaptation to
 increase fitness and avoid extinction. Adaptation should be fastest when
 both plasticity and selection occur in directions of the multivariate
 phenotype that contain abundant genetic variation. However, tests of this
 prediction from field experiments are rare. Here, we quantify how additive
 genetic variance in a multivariate phenotype changes across an elevational
 gradient, and test whether plasticity and selection align with genetic
 variation. We do so using two closely related, but ecologically distinct,
 sister species of Sicilian daisy (Senecio, Asteraceae) adapted to high and
 low elevations on Mount Etna. Using a paternal half-sibling breeding
 design, we generated and then reciprocally planted c.19,000 seeds of both
 species, across an elevational gradient spanning each species’ native
 elevation, and then quantified mortality and five leaf traits of emergent
 seedlings. We found that genetic variance in leaf traits changed more
 across elevations than between species. The high-elevation species at
 novel lower elevations showed changes in the distribution of genetic
 variance among the leaf traits, which reduced the amount of genetic
 variance in the directions of selection and the native phenotype. By
 contrast, the low-elevation species mainly showed changes in the amount of
 genetic variance at the novel high elevation, and genetic variance was
 concentrated in the direction of the native phenotype. For both species,
 leaf trait plasticity across elevations was in a direction of the
 multivariate phenotype that contained a moderate amount of genetic
 variance. Together, these data suggest that where plasticity is adaptive,
 selection on genetic variance for an initially plastic response could
 promote adaptation. However, large environmental effects on genetic
 variance are likely to reduce adaptive potential in novel environments.
description: All data was collected on seeds planted of two species at four
 elevations on Mt Etna, Sicily. Leaf pigment data was collected using a
 Dualex instrument (ForceA-France) and leaf morphology data was quantified
 from the software 'Lamina' that analyses scanned images of
 leaves.
description: # Environmental effects on genetic variance are likely to constrain
 adaptation in novel environments
 These data are from a
 field experiment that planted seeds of two species of *Senecio* at four
 elevations on the slopes of Mt Etna, Sicily. Seeds were produced using a
 quantitative genetic breeding design with c.80 individuals of each species
 so that additive genetic variance could be estimated for all traits
 measured. Survival was used as a proxy for fitness, and leaf trait
 measurements include leaf morphology, size and pigment content. ##
 Description of the data file: Walter\_etal\_2024\_EvolutionLetters.csv
 Below is a list of column identifiers in the .csv file. NA represent
 plants that died and could not be measured. Columns A-H are plant
 identifiers. * A: Species identifier. * B: TGSite is the transplant
 elevation (500m, 1000m, 1500m and 2000m). * C: PLANT is the individual ID
 for each plant. * D: Block is the experimental block at each elevation. *
 E: Family is the full-sibling family identifier. * F: Sire is the parent
 that donated pollen. * G: Dam is the parent that accepted pollen and
 produced the seed. * H: Grid the randomised grid position that the seed
 was planted in. Columns I-AK capture survival and life history data. * I:
 GE (binary) whether the seed germinated (1) or not (0). * J: L10 (binary)
 whether the seedling established (1) or not (0). Establishment was
 considered as having produced 10 leaves. * K: Bud (binary) whether the
 seedling reached maturity and produced a bud (1) or not (0). * L-O: Each
 column is the number of days it took to germinate (GE), establish (L10)
 and produce a bud (L10-Bud and GE-Bud). * P: totaldays is the total number
 of days each plant lived for. * Q: CensorDate is the date that the
 survival data was censored on. * R-AK: Each column is a different date on
 which mortality was scored for each plant (1=alive, 0=dead). Date format:
 DD/MM/YY Columns AL-AY are the leaf traits, all averaged across the two
 leaves measured. * AL: Leaf weight (mg). * AM: Leaf area (mm2). * AN: SLA
 = Specific Leaf Area (mg/mm2). * AO: P2A = leaf complexity
 (perimeter2/area). * AP: Nindents = Raw number of leaf indentations. * AQ:
 Nind.peri = Number of leaf indentations standardised by perimeter. * AR:
 Leaf perimeter (mm) * AS: IW_mn = Indent Width (mm) * AT: ID_mn = Indent
 Depth (mm) * AU: Circularity is how close the leaf resembles a circle. *
 AV: Chlo = Chlorophyll content in light absorbance units. * AW: Flav =
 Flavonol content in light absorbance units. * AX: Anth = Anthocyanin
 content in light absorbance units. * AY: NBI = Nitrogen balance index is
 the ratio of Chlorophyll/Flavonol contents. ## Code & model output
 (.Rdata files) R code to produce the results of Walter et al. Evolution
 Letters accompanies this dataset. So that analyses can easily be
 reconstructed, output files from the Bayesian models are included as
 .Rdata files, which include the posterior distribution of the eight
 G-matrices and their randomisations. The code used to run the Bayesian
 models is included the main code file. Below is a list of the the .Rdata
 files: G_aethn.Rdata & G_chrys.Rdata = Arrays containing the observed
 G-matrices for both species (Model 1 in the R-code). G_randomG_aethn.Rdata
 & G_randomG_chrys.Rdata = Arrays containing the randomised G-matrices
 for both species (Model 2 in the R-code). G_randomtensorG.Rdata = Array of
 G-matrices calculated on the randomisation of breeding values (Model 3 in
 the R-code). ## Sharing/Access information Please contact the
 corresponding author (Greg Walter,
 [greg.walter@monash.edu](mailto:greg.walter@monash.e)) if you are
 interested in analysing these data as they are part of ongoing projects,
 including