Data from: Offspring development and life-history variation in a water flea depends upon clone-specific integration of genetic, non-genetic and environmental cues
Harney, Ewan, French National Centre for Scientific Research
Paterson, Steve, University of Liverpool
Plaistow, Stewart J., University of Liverpool
Published Mar 29, 2018 on Dryad.
Cite this dataset
Harney, Ewan; Paterson, Steve; Plaistow, Stewart J. (2018). Data from: Offspring development and life-history variation in a water flea depends upon clone-specific integration of genetic, non-genetic and environmental cues [Dataset]. Dryad. https://doi.org/10.5061/dryad.mn7k4
1. Theory predicts that offspring developmental strategies involve the integration of genetic, non-genetic and environmental ‘cues’. But it is unclear how cue integration is achieved during development, and whether this pattern is general or genotype-specific. 2. In order to test this, we manipulated the maternal and offspring environments of three genetically distinct clones of the water flea Daphnia magna taken from different populations. We then quantified the effect that the genotype, maternal environment and the offspring environment had on the development and life-histories of the three different clones. 3. Mothers responded to the same maternal environments in different ways, resulting in clone-specific maternal effects on neonate size. Offspring responses to maternal cues varied according to the trait in question and were also clone-specific. The integration of these maternal effects during development was highly context-dependent in two clones but more consistent across environments in the third. 4. Genetic, non-genetic and environmental cues contributed to offspring phenotypic variation in all three clones, but there was no general pattern linking traits to specific cues. In fact, two clones used different combinations of cues at different points in development to achieve similar phenotypic outcomes. Thus different D. magna clones integrated different combinations of cues at different points in development. Our results highlight the importance of considering variation across development and show how genotypic variation and plasticity in developmental transitions help to generate phenotypic variation. 5. Our results support the hypothesis that phenotype determination involves the integration of genetic, non-genetic and environmental cues and demonstrates that the relative contributions of different cues is highly variable.
Summary data file containing trait information for all individuals, for use with R scripts 1, 2, 3 and 4.
Data file for calculating growth coefficients for use with R script 1 (this is optional, as the summary of growth data is included in the Dmagna_ME_alltraits_summary.txt file).
Data file for calculating and plotting probabilistic maturation reaction norms (PMRNs), for use with R scripts 5 and 6.
R script 1 of 6: calculating growth coefficients, followed by multivariate analysis of variance (MANOVA), analysis of variance (ANOVA)/GLMs and post-hoc tests.
R script 2 of 6: Plotting univariate figures (those considered in the analysis of variance /GLMs from R script 1).
R script 3 of 6: Principal component analysis, together with visualisation of phenotypic change vectors in 2D sapce.
R script 4 of 6: Statistical analysis for identifying significant differences in vector lengths and angles of phenotypic change vectors (as visualised in R script 3).
R script 5 of 6: Using GLMs to statistically model probabilistic maturation reaction norms, and comparing among models to identify the best fit.
R script 6 of 6: Using best fitting GLM (identified in R script 5) in plots of probabilistic maturation reaction norms.