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Data from: Plasticity via feedback reduces the cost of developmental instability

Citation

Matthey-Doret, Remi (2020), Data from: Plasticity via feedback reduces the cost of developmental instability, Dryad, Dataset, https://doi.org/10.5061/dryad.0gb5mkkzz

Abstract

Costs of plasticity are thought to have important physiological and evolutionary consequences. A commonly predicted cost to plasticity is that plastic genotypes are likely to suffer from developmental instability. Adaptive plasticity requires that the developing organism can in some way sense what environment it is in or how well it is performing in that environment. These two information pathways—an “environmental signal” or a “performance signal” that indicates how well a developing phenotype matches the optimum in the current environment—can differ in their consequences for the organism and its evolution. Here, we consider how developmental instability might emerge as a side-effect of these two distinct mechanisms. Because a performance cue allows a regulatory feedback loop connecting a trait to a feedback signal, we hypothesized that plastic genotypes using a performance signal would be more developmentally robust compared to those using a purely environmental signal. Using a numerical model of a network of gene interactions, we show that plasticity comes at a cost of developmental instability when the plastic response is mediated via an environmental signal, but not when it is mediated via a performance signal. We also show that a performance signal mechanism can evolve even in a constant environment, leading to genotypes pre-adapted for plasticity to novel environments even in populations without a history of environmental heterogeneity.

Methods

Data collected through numerical simulations.

Usage Notes

Here are explanations of the meaning of each column in the dataset:

meanP: Mean phenotypic value over all replicated development of the genotype of interest.

sdP: Standard deviation in phenotypic value among all replicated development of the genotype of interest.

meanW: Mean fitness over all replicated development of the genotype of interest.

sdW: Standard deviation in fitness among all replicated development of the genotype of interest.

trait_optima: Optimal trait in the environment considered.

nbGenes: Number of genes.

Project: A coded name of the set of simulation. It is kept here in the daat for backward verification. Each project correspond to a unique treatment.

Replicate: Simulation index in the project.

Generation: Generation sampled.

Environment: Type of environment.

Signal: Type of signal.

Funding

Swiss National Science Foundation, Award: P1SKP3_168393

Natural Sciences and Engineering Research Council of Canada, Award: RGPIN-2016-03779

Swiss National Science Foundation, Award: P1SKP3_168393