DOI: 10.5061/dryad.7d7wm37rc
title: Data and code from: Phenotypic memory drives population growth and extinction risk in a noisy environment

description: Random environmental fluctuations pose major threats to wild
 populations. As patterns of environmental noise are themselves altered by
 global change, there is growing need to identify general mechanisms
 underlying their effects on population dynamics.
 This notably requires understanding and predicting
 population responses to the color of environmental noise, i.e.
 its temporal autocorrelation pattern. Here, we show
 experimentally that environmental autocorrelation has a large influence on
 population dynamics and extinction rates, which can be predicted
 accurately provided that a memory of past
 environment is accounted for. We exposed near to 1000 lines of
 the microalgae Dunaliella salina to randomly fluctuating
 salinity, with autocorrelation ranging from negative to highly positive.
 We found lower population growth, and twice as
 many extinctions, under lower autocorrelation. These
 responses closely matched predictions based on a tolerance curve with
 environmental memory, showing that non-genetic inheritance
 can be a major driver of population dynamics in randomly
 fluctuating environments.  
description: Six strains (or mix of two strains) of the green
 microalgae <em>Dunaliella salina </em>have been exposed to 164
 independant, randomly fluctuating time series of salinity, with four
 autocorrelation treatments. Populations were transferred twice a week for
 37 transfers, and population sizes were measured after each transfer by
 spectrometry (optical density and fluorescence), and once a week by flow
 cytometry. Optical density, fluorescence and cytometer
 counts are used to analyse Dunaliela population dynamics in stochastic
 environment. All measurements are integrated into a state-space models,
 assuming logistic growth between each dilution and transfer, using the R
 package TMB (Template Model Builder). The provided code
 estimates: - the parameters of the distribution of
 growth rates in autocorrelated environments - the
 parameters of the function relating growth to current and previous
 salinity.
description: Users can perform the analysis by running the R script
 (PDFE_analysis.R) after the installation of all packages mentioned in the
 preamble, then running the Mathematica notebook
 (moments_predicted.nb)  This
 publication contains:  <br> - 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.