ReadMe 

Users can perform the analysis by running the R script (PDFE_analysis.R) after the installation of all 
package mentioned in the preamble,  

then running the Mathematica notebook (moments_predicted.nb) 


This folder contains: 


- 1 dataset with population sizes:  

population_dynamics.csv 

- 1 dataset with glycerol concentrations: 

data_cinetique.csv 

- 5 C++ scripts compiled and run with the R TMB package: 

* density_dependence_cst.cpp : compute the negative loglikelihood of the dynamics of lines maintained in
constant environments. This script estimates the growth rates and carrying capacities (constant within
but varying between treatment genetic background x constant salinity). 

* logistic_data_K_unkownS_normal_autocorr_all_genotype.cpp : compute the negative loglikelihood for 
population dynamics of lines under stochastic environment, assuming a normal autocorrelated distribution
of the growth rate with parameters (mean, standard deviation and autocorrelation) that depend on the 
genetic background and the autocorrelation treatment 

* logistic_data_K_unkownS_gamma_rho_all_genotype.cpp : compute the negative loglikelihood for population
 dynamics of lines under stochastic environment, assuming a reverse gamma distribution of the growth 
rate with parameters (mean, standard deviation and autocorrelation) that depend on the genetic 
background and the autocorrelation treatment 

* logistic_data_K: compute the negative loglikelihood for population dynamics of lines under stochastic
 environment and estimate parameters for the bivariate tolerance curves (parameters depend on the 
 genetic background) 

* logistic_data_K_univariate: compute the negative loglikelihood for population dynamics of lines under
 stochastic environment and estimate parameters for the univariate tolerance curves (parameters depend 
 on the genetic background) 

- 1 R script: 

PDFE_analysis.R. Performs the population dynamics analysis. Read the data, performs the 
survival analysis, analyses salinity effect on growth rate and carrying capacity in the constant 
salinity lines, analyses r distribution and fit bivariate and univariate tolerance curves in the 
stochastic lines. Code used for the plots is also present jointly with the analysis of the moments of N
 and r (estimation by treatment + regression). 

- A Mathematica notebook: 

moments_predicted.nb: compute the predicted mean, variance, skewness and 
autocorrelation of the growth rate given the bivariate and univariate tolerance curve parameters and the
 distribution of salinity in the different autocorrelation regime. 

https://doi.org/10.5061/dryad.7d7wm37rc