Statistical decomposition of passive and active phenotypic plasticity in traits under homeostatic regulation

Phenotypic plasticity enables organisms to maintain an optimal phenotype in the face of environmental change. Phenotypic plasticity is often viewed as being either passive or active. Yet many traits exhibit what we term mixed plasticity, where both passive effects of the environment and active physiological regulation shape trait dynamics. Indeed, this is a key characteristic of traits that are under homeostatic regulation, where organisms attempt to reverse environmental disturbance of the phenotype. Here, we develop a novel model that decomposes the temporal dynamics of homeostatic or mixed plasticity traits into parameters that describe the passive effect of the environment, the rate at which active regulation is adjusted (i.e., rate of plasticity), and the asymptotic magnitude of active regulation (i.e., capacity for plasticity). We apply this model to a dataset comprising 653 experiments documenting time-course changes in ion concentrations and osmolality in aquatic organisms ..., , # Data from: Statistical decomposition of passive and active phenotypic plasticity in traits under homeostatic regulation

Dataset DOI: [10.5061/dryad.j9kd51cr4](10.5061/dryad.j9kd51cr4)

## Description of the data and file structure

A literature search was conducted to obtain a relatively comprehensive and representative sample of the published literature documenting the time-course of acclimation responses to salinity change in aquatic organisms. Such data come from experiments where organisms are first kept at one level of salinity before being abruptly shifted to a new salinity. Phenotypic measurements are taken at intervals from before the shift and for a certain period following this shift.

Missing values in the data files are given as \"NA\".

### Files and variables

#### File: salinity_data.txt

**Description:** Raw time-series data of phenotypic traits following a shift in salinity obtained from the publications

##### Variables

* Experiment: Originally assigned experiment nu...,