Demographic feedbacks during evolutionary rescue can slow or speed adaptive evolution
Data files
Jan 26, 2024 version files 702.18 MB
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azure.zip
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blue.zip
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cerulean.zip
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main_figure_01.R
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main_figure_02.R
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main_figure_03.R
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main_figure_04.R
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main_figure_05.R
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no_genes.zip
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organic.zip
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QG_full_generalized_organic.R
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QG_full_generalized.R
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QG_make_organic_pops.R
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QG_make_pops.R
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QG_no_genes_steady_state.R
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QG_no_genes.R
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QG_selection_tester.R
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README.md
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selection_test.zip
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supp_fig_02.R
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supp_fig_03.R
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supp_fig_04_inset.R
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supp_fig_04_main.R
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supp_fig_05.R
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supp_fig_06.R
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supp_fig_07.R
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supp_fig_08.R
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supp_fig_09.R
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supp_fig_10.R
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supp_figure_01.R
Abstract
Populations declining toward extinction can persist via genetic adaptation in a process called evolutionary rescue. Predicting evolutionary rescue has applications ranging from conservation biology to medicine, but requires understanding and integrating the multiple effects of a stressful environmental change on population processes. Here we derive a simple expression for how generation time, a key determinant of the rate of evolution, varies with population size during evolutionary rescue. Change in generation time is quantitatively predicted by comparing how intraspecific competition and the source of maladaptation each affect the rates of births and deaths in the population. Depending on the difference between two parameters quantifying these effects, the model predicts that populations may experience substantial changes in their rate of adaptation in both positive and negative directions, or adapt consistently despite severe stress. These predictions were then tested by comparison to the results of individual-based simulations of evolutionary rescue, which validated that the tolerable rate of environmental change varied considerably as described by analytical results. We discuss how these results inform efforts to understand wildlife disease and adaptation to climate change, evolution in managed populations, and treatment resistance in pathogens.
README: R code and simulation outputs
https://doi.org/10.5061/dryad.fttdz090j
All results in this paper are based on simulation. This package includes the simulation code, outputs, and the scripts used in analyses and making figures.
Description of the data and file structure
The basic algorithm is contained in the file QG_full_generalized.R. However, several variants were used throughout the paper. Below is a list of the files used in preparing each figure.
All scripts the analyze simulation data require a path set to the location of a directory include the archived outputs, which are each identified by name (azure, blue, cerulean, no_genes, selection_test, organic).
Each of these simulation data directories contains a table file which primarily serves to associate the initial random number seed with each replicate. For each replicate, a file is generated with the allele effects (e.g., azure_effects_0001.txt) and another file is produced with the complete genotype of each individual in the starting population (e.g., azure_pop_0001.txt). These files are human-readable but should not be modified; they are generated by the script QG_make_pops.R.
Required packages include color pallets (R packages wesanderson and viridis) and the doParallel package for running simulations in parallel.
Figure 1:
main_figure_01.R
Figure 2:
main_figure_02.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure 3:
main_figure_03.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure 4:
main_figure_04.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure 5:
main_figure_05.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure S1:
supp_fig_01.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure S2:
supp_fig_02.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure
Archived folder blue
Archived folder cerulean
Figure S3:
supp_fig_03.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure S4:
supp_fig_04_main.R
supp_fig_04_inset.R
QG_full_generalized_organic.R
QG_make_organic_pops.R
Archived folder organic (contains all relevant simulation outputs)
Figure S5:
supp_fig_05.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure S6:
supp_fig_06.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Figure S7:
supp_fig_07.R
QG_no_genes_steady_state.R
Archived folder no_genes
Figure S8:
supp_fig_08.R
QG_no_genes.R
Archived folder no_genes
Figure S9:
supp_fig_09.R
QG_selection_tester.R
Archived folder selection_test
Figure S10:
supp_fig_10.R
QG_full_generalized.R
QG_make_pops.R
Archived folder azure (contains all relevant simulation outputs)
Sharing/Access information
- n/a
Code/Software
All code described above.