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Data from: Causation without correlation: parasite-mediated frequency-dependent selection and infection prevalence

Citation

Lively, Curtis; Ben-Ami, Frida; Xu, Julie (2021), Data from: Causation without correlation: parasite-mediated frequency-dependent selection and infection prevalence, Dryad, Dataset, https://doi.org/10.5061/dryad.w9ghx3fqp

Abstract

Parasite-mediated selection is thought to maintain genetic diversity for resistance in natural host populations.  We might thus expect to find a strong positive correlation between host genetic diversity and infection prevalence across field populations.  Here we used computer simulations to examine host-parasite coevolution in 20 simi-isolated populations, using a broad range of values for both parasite virulence and parasite fecundity.  We found that the correlation between host genetic diversity and infection prevalence can be significantly positive for intermediate values of parasite virulence and fecundity.  But the correlation can also be weak and statistically non-significant, even when parasite-mediated, frequency-dependent selection is the sole force maintaining host diversity.  Hence correlational analyses of field populations, while useful, may underestimate the role of parasites in maintaining host diversity.

Methods

This is a computer simulation written in Excel.  It includes a data set from a representative run, assuming virulence was equal to 0.7 with a mean parasite fecunity equal to 15.  Virulence and parasite fecundity can be reset by the user (see usage notes).   

Usage Notes

Directions for simulation

1) Download and open the .xlsm file.

2) A dialog box will pop up.  Click "enable macro"

3) The file will open, and a "bootstrap" dialog box will pop up.  Close the box.

4) To change parameter values, click on the "data1" tab.

5) You will see a list of parameter values on the left-hand side of the sheet.

6) We left the birth rate of uninfected hosts (bu) at 10, but it could be changed. 

7) To change the birth rate of infected hosts (bi), simply change the bold blue entry next to "bi".
     Virulence is calculated as 1 - (bi/bu). 

8) To change the mean realized fecundity of parasites, change "MeanBeta".

9) To change the Standard Deviation for the birth rate of uninfected hosts, change "STD_bu".

10) Other variables can be changed in the sim, such as the probability of infected (or uninfected) migrants, but we left them as in "data1" 

11) The simulation can be run at any time for a single generation by pressing "control +"

12) To iterate the simulation for multiple generations, click on Excel's developer tab at the top of the page.

13) Then click on the "Visual Basic" icon on the top left-hand side.

14) The macro will open up.  Press the run icon (which is a triangle shaped icon pointed to the right)

15) The "Bootstrap" dialog box pops up.  Select either 100, 1000, or 10000 iterations.  The macro will begin running automatically.  1000 iterations takes about 30 mins.  

16) When to run has completed, close the bootstrap box.

17) The macro will have created a new sheet (to the left of "data1") with the data.  The new tab is named after the date and time.  We suggest renaming the tab as the virulence, e.g., Vir = 0.4.  

18) The data are in columns.  The definitions of the column headers are

"Div-Prev" = the correlation between host clonal diversity and prevalence of infection. 

"Prev-Beta" = the correlation between prevalence of infection and mean parasite fecundity (which varies among populations). 

"Div-Beta" = the correlation between host clonal diversity and mean parasite fecundity.

"Mean_Inf" = the average prevalence of infection across the 20 populations.

"STD_Inf" = the Standard Deviation of prevalence across the 20 populations.

"Mean_Diversity" = the average clonal diversity across the 20 populations.

"STD_Diversity" = the observed Standard Deviation of clonal diversity across the 20 populations.

"Pvalue_Div-Prev" = the P-value for the correlation between diversity and prevalence.

"Pvalue_Prev-Beta" = the P-value for the correlation between prevalence and parasite fecundity.

"Pvalue_Div-Beta" = the P-value for the correlation between host diversity and parasite fecundity.

"Pvalue_Div-STDbu" = the P-value for the correlation between host diversity and the Standard Deviation in the birth rate of uninfected hosts.

"starting_Freq_A_pop1" = the randomly assigned frequency of allele A in population 1.  (This is test of the random number generator.)

"starting_bu_AX_pop1" = the randomly assigned fecundity for uninfected hosts having genotype AX in population 1.  (This is test of the random number generator.)

19) To rerun the simulation, return to tab "data1" to change parameter values.   Then follow the directions above for running the macro. 

Notes.

The "Calcs" sheets are the calculations for each of the 20 populations.  e.g., "Calcs (2)" gives the calculations for Population 2.

The simulation allows for sexual reproduction, but we set the recombination rate to 0, making the hosts effectively clonal. 

Funding

National Science Foundation, Award: DEB-1906465

US-Israel Binational Science Foundation, Award: 2011011