Data from: Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the Latent Environmental Threshold Model
Buoro, Mathieu, Centre d'Ecologie Fonctionnelle et Evolutive
Gimenez, Olivier, Centre d'Ecologie Fonctionnelle et Evolutive
Prévost, Etienne, INRA, UMR Ecobiop, Pôle d'hydrobiologie Quartier Ibarron, 64310 Saint Pée s/ Nivelle, France
Published Feb 04, 2014 on Dryad.
Cite this dataset
Buoro, Mathieu; Gimenez, Olivier; Prévost, Etienne (2014). Data from: Assessing adaptive phenotypic plasticity by means of conditional strategies from empirical data: the Latent Environmental Threshold Model [Dataset]. Dryad. https://doi.org/10.5061/dryad.ft612j0h
Conditional strategies are the most common form of discrete phenotypic plasticity. In a conditional strategy, the phenotype expressed by an organism is determined by the difference between an environmental cue and a threshold, both of which may vary among individuals. The Environmental Threshold model (ETM) has been proposed as a mean to understand the evolution of conditional strategies, but has been surprisingly seldom applied to empirical studies. A hindrance for the application of the ETM is that often, the proximate cue triggering the phenotypic expression and the individual threshold are not measurable, and can only be assessed using a related observable cue. We describe a new statistical model that can be applied in this common situation. The Latent Environmental Threshold Model (LETM) allows for a measurement error in the phenotypic expression of the individual environmental cue and a purely genetically determined threshold. We show that coupling our model with quantitative genetic methods allows an evolutionary approach including an estimation of the heritability of conditional strategies. We evaluate the performance of the LETM with a simulation study and illustrate its utility by applying it to empirical data on the size-dependent smolting process for stream-dwelling Atlantic salmon juveniles.
This data file contains: a script for simulated data sets using R software (ScriptR_LETM_SIM.R), a script (ScriptR_LETM_SAT.R) to analyze data collected in the field (Data_SAT.RData) using R and OpenBUGS software.