10.5061/DRYAD.7S5P5
Michel, Matt J.
University of North Carolina
Saint Louis University
Chevin, Luis-Miguel
Saint Louis University
Knouft, Jason H.
Saint Louis University
Data from: Evolution of phenotype-environment associations by genetic
responses to selection and phenotypic plasticity in a temporally
autocorrelated environment
Dryad
dataset
2014
Environmental Predictability
Linear Reaction Norm
Computer Simulaton
Environmental Sensitivity of Selection
2014-01-27T15:13:04Z
2014-01-27T15:13:04Z
en
https://doi.org/10.1111/evo.12371
8534 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Covariation between population-mean phenotypes and environmental
variables, sometimes termed a ‘phenotype-environment association’ (PEA),
can result from phenotypic plasticity, genetic responses to natural
selection, or both. PEAs can potentially provide information on the
evolutionary dynamics of a particular set of populations, but this
requires a full theoretical characterization of PEAs and their evolution.
Here, we derive formulas for the expected PEA in a temporally fluctuating
environment for a quantitative trait with a linear reaction norm. We
compare several biologically relevant scenarios, including constant versus
evolving plasticity, and the situation where an environment affects both
development and selection but at different time periods. We find that PEAs
are determined not only by biological factors (e.g., magnitude of
plasticity, genetic variation), but also environmental factors, such as
the association between the environments of development and of selection,
and in some cases the level of temporal autocorrelation. We also describe
how a PEA can be used to estimate the relationship between an optimum
phenotype and an environmental variable (i.e., the environmental
sensitivity of selection), an important parameter for determining the
extinction risk of populations experiencing environmental change. We
illustrate this ability using published data on the predator-induced
morphological responses of tadpoles to predation risk.
Simulation Code for Multiple Environmental Variables ScenarioRun using
RSimulation Code for Delay between Development and Selection ScenarioRun
in R