Data from: How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue
Data files
Nov 01, 2023 version files 103.87 MB
- 
              
                alldata_n100_a000_hivar.csv
                7.30 MB
- 
              
                alldata_n100_a000_lowvar.csv
                6.85 MB
- 
              
                alldata_n100_a035_hivar.csv
                6.51 MB
- 
              
                alldata_n100_a035_lowvar.csv
                5.62 MB
- 
              
                alldata_n20_a000_hivar.csv
                4.24 MB
- 
              
                alldata_n20_a000_lowvar.csv
                3.60 MB
- 
              
                alldata_n20_a035_hivar.csv
                3.47 MB
- 
              
                alldata_n20_a035_lowvar.csv
                3.38 MB
- 
              
                bholt_robust_check.csv
                7.12 MB
- 
              
                longsims_n100_a000_hivar.csv
                2.43 MB
- 
              
                longsims_n100_a000_lowvar.csv
                2.79 MB
- 
              
                longsims_n100_a035_hivar.csv
                4.15 MB
- 
              
                longsims_n100_a035_lowvar.csv
                2.72 MB
- 
              
                longsims_n20_a000_hivar.csv
                1.35 MB
- 
              
                longsims_n20_a000_lowvar.csv
                1.17 MB
- 
              
                longsims_n20_a035_hivar.csv
                2.06 MB
- 
              
                longsims_n20_a035_lowvar.csv
                1.16 MB
- 
              
                nloci_robust_check.csv
                7.11 MB
- 
              
                README.md
                6.01 KB
- 
              
                sig2e_robust_check.csv
                4.13 MB
- 
              
                theta_robust_check.csv
                6.02 MB
- 
              
                w_max_robust_check.csv
                12.32 MB
- 
              
                wfitn_robust_check.csv
                8.36 MB
Abstract
Following severe environmental change that reduces mean population fitness below replacement, populations must adapt to avoid eventual extinction, a process called evolutionary rescue. Models of evolutionary rescue demonstrate that initial size, genetic variation, and degree of maladaptation influence population fates. However, many models feature populations that grow without negative density dependence or with constant genetic diversity despite precipitous population decline, assumptions likely to be violated in conservation settings. We examined the simultaneous influences of density-dependent growth and erosion of genetic diversity on populations adapting to novel environmental change using stochastic, individual-based simulations. Density dependence decreased the probability of rescue and increased the probability of extinction, especially in large and initially well-adapted populations that previously have been predicted to be at low risk. Increased extinction occurred shortly following environmental change, as populations under density dependence experienced more rapid decline and reached smaller sizes. Populations that experienced evolutionary rescue lost genetic diversity through drift and adaptation, particularly under density dependence. Populations that declined to extinction entered an extinction vortex, where small size increased drift, loss of genetic diversity, and the fixation of maladaptive alleles, hindered adaptation, and kept populations at small densities where they were vulnerable to extinction via demographic stochasticity.
https://doi.org/10.5061/dryad.zgmsbccjj
Simulation code and data from the simulated experiment in the manuscript "How density dependence, genetic erosion, and the extinction vortex impact evolutionary rescue." Data is in .csv format and code is in .R format.
Description of the data and file structure
There are two main types of .csvs with results for main text and a third type .csv for the robustness analysis. These files are described below:
alldata_n{20/100}_a{000/035}_{hi/low}var.csv
These files are the "main" batch of simulations with all relevant variables recorded. There are 4000 per parameter combination. All trials were run for up to 15 timesteps. The name implies the treatment: initial size (n) 20 or 100, alpha (a) 0 or 0.0035, and genetic variation (var) high or low.
Columns:
- trialtrial number (replicate) 1 through 4000 within that treatment
- gengeneration (time step); here presented as 1-16 because the initial generation is recorded as- 1; our analysis makes the processing step of subtracting 1 from the- gencolumn such that populations are initialized at step zero and simulations run until at most time step 15
- npopulation size at that time step
- gbarmean genotype in the population
- zbarmean phenotype in the population
- wbarmean intrinsic fitness within the population
- pfemproportion of the population that is female
- p.fix.posproportion of loci that are at fixation for the positive allele
- p.fix.negproportion of loci that are at fixation for the negative allele
- vadditive genetic variance within the population
- n.pop0initial size treatment (20/100)
- alphadensity dependence parameter (0/0.0035)
- low.varboolean for if the population is in the low genetic variance treatment
- ext.genthe generation at which the population went extinct; populations that did not go extinct have value- 15here
- extinctboolean for if the population went extinct during the simulation or not
Note that in this data file, data for a trial is truncated at extinction. That is, the time step in which they went extinct (went to size zero) is not included, and neither are time steps after that. For analysis that treated extinct populations as size zero, we added this data to the data frame.
longsim_n{20/100}_a{000/035}_{hi/low}var.csv
The "long" simulations lasting up to 50 generations. There are 1000 simulated trials per treatment here. Naming conventions are the same here as for the main simulation output described above.
The fields are the same as above, but without the columns ext.gen and extinct.
{variable}_robust_check.csv
Output data for the robustness checks. One file for each of the six variables tested (sig2e, theta, w_max, wfitn, nloci, and bholt - the final of these, bholt is the Beverton-Holt density dependence). Each file contains the eight-treatment simulated experiment, run at several levels of the specified variables.
Fields:
- gengeneration/time step (as specified above)
- npopulation size
- alphadensity dependence parameter (0/0.0035)
- n.pop0initial size treatment (20/100)
- low.varboolean for if the population is in the low genetic variance treatment
- trialtrial number - note that unlike in the- alldatascripts, the- trialis not repeated across treatments here
- sig.e(file- sig2e_robust_check.csvonly) standard deviation of environmental contribution to phenotypic variance in the trial
- theta(files- theta_robust_check.csvand- w_max_robust_check.csvonly) value of environmental change used in the trial
- w.max(file- w_max_robust_check.csvonly) value of w_max (W_{max} in the manuscript) (maximum intrinsic fitness) used in the trial
- wfitn(file- wfitn_robust_check.csvonly) value of- wfitn(w in manuscript), the variable quantifying the width of the selection surface used in the trial
- n.loci(file- nloci_robust_check.csvonly) value of number of loci (m in manuscript) used in the trial
- delta(file- bholt_robust_check.csvonly) value of delta, the degree of overcompensation used in the trial
- beta(file- bholt_robust_check.csvonly) value of beta, measuring strength of density dependence in Beverton-Holt model (see Supporting Information) used in the trial
Code/Software
All simulations were run in R version 4.1.2. Simulations and analysis use the packages dplyr (v. 1.0.7), tidyr (1.1.3), and analysis additonally uses rstanarm (v. 2.21.2) for the Bayesian modeling. Plots are generated with ggplot2 (v. 3.3.4) and cowplot (v. 1.1.1) . The robustness analysis was run with the package parallel (v. 4.1.2) but can be run in serial with modification to the code (changing mclapply to lapply).
Each of the .csv files above is generated by a single R script. Each script begins by sourcing a file (sim_functions.R) with wrapper functions for implementing the simulations. The scripts then generate a mesh of parameter combinations for the simulations, runs a large batch of simulations, and then aggregates the output into a summary data frame for exporting.
The files take the following forms:
- The main batch of simulations (starting with alldata_) are run by the filessim_alldata_n{20/100}_a{000/035}_{hi/low}var.R
- The batch of longer (up to 50 generation) simulations are run by the files longsim_n{20/100}_a{000/035}_{hi/low}var.R
- The robustness checks are run by the files robust_check_{varname}.R
Sharing/Access information
All associated data and code can also be found on GitHub at the following address: https://github.com/melbourne-lab/evo_rescue_ndd_erosion. The files for analyzing this data are also stored here.
Contact information
Scott Nordstrom (scottwatsonnordstrom@gmail.com)
Data enclosed is generated from individual-based stochastic simulation; code to generate and analyze this data is included with the repository.
