Environmental effects on genetic variance are likely to constrain adaptation in novel environments

Walter, Gregory1 ; Monro, Keyne1; Terranova, Delia2; la Spina, Enrico3; Majorana, Maria3; Pepe, Giuseppe3; Clark, James4; Cozzolino, Salvatore2; Cristaudo, Antonia3; Hiscock, Simon5; Bridle, Jon6

Published Dec 14, 2023 on Dryad. https://doi.org/10.5061/dryad.k6djh9wdf

Data files

Dec 14, 2023 version files 3.52 MB

README.md

3.87 KB
Walter_etal_2024_EvolutionLetters.csv

3.52 MB

Abstract

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.

README: Environmental effects on genetic variance are likely to constrain adaptation in novel environments

https://doi.org/10.5061/dryad.k6djh9wdf

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) if you are interested in analysing these data as they are part of ongoing projects, including https://doi.org/10.1101/2021.02.04.429835.