diff --git a/README.md b/README.md index 32f2c8e..a539483 100644 --- a/README.md +++ b/README.md @@ -11,7 +11,7 @@ ## Introduction -R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see Gaynor et al. 2023. To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations and two life-stages (reproductively immature and reproductively mature). Stay-tune for v2.0.0, which will include population genetic simulations. +R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see [Gaynor et al. 2023](https://doi.org/10.1101/2023.03.29.534764). To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations and two life-stages (reproductively immature and reproductively mature). Stay-tune for v2.0.0, which will include population genetic simulations. ## Installation @@ -22,7 +22,7 @@ devtools::install_github("mgaynor1/AutoPop") ## References -Gaynor ML, Kortessis N, Soltis DE, Soltis PS, and Ponciano JM. 2023. Dynamics of mixed-ploidy populations under demographic and environmental stochasticities. *In Review*. [Preprint Available Soon]() +Gaynor ML, Kortessis N, Soltis DE, Soltis PS, and Ponciano JM. 2023. Dynamics of mixed-ploidy populations under demographic and environmental stochasticities. *In Review*. [Preprint Available](https://doi.org/10.1101/2023.03.29.534764) Gaynor ML, Soltis DE, Soltis PS, and Ponciano JM. Modeling the dynamics of multiple origins and gene flow in autopolyploids. *In prep.* diff --git a/docs/articles/Introduction.html b/docs/articles/Introduction.html index bdacc25..85f471f 100644 --- a/docs/articles/Introduction.html +++ b/docs/articles/Introduction.html @@ -84,7 +84,8 @@

Summary

AutoPop is an R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, -see Gaynor et al. 2023. To summarize, this package contains a stochastic +see Gaynor et +al. 2023. To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations. Specifically, this model includes three cytotypes (diploids, triploids, diff --git a/docs/articles/Maturation_files/figure-html/maturation-1.png b/docs/articles/Maturation_files/figure-html/maturation-1.png index 8fc7e9b..59db715 100644 Binary files a/docs/articles/Maturation_files/figure-html/maturation-1.png and b/docs/articles/Maturation_files/figure-html/maturation-1.png differ diff --git a/docs/articles/Reproduction.html b/docs/articles/Reproduction.html index 063cffb..2b2d71d 100644 --- a/docs/articles/Reproduction.html +++ b/docs/articles/Reproduction.html @@ -204,15 +204,17 @@

Types of Gametes Produced\(c_{i,j}\) is the total number of the ith cytotype that is reproductively mature (j=2) at time t. This figure is -Figure A1 in Gaynor et al. 2023.

+Figure A1 in Gaynor +et al. 2023.

Gamete Union

The remaining parameters are associated with selfing rate (\(s\)) and mating choice (\(mc\)).

-

Figure 4: Figure A1 in Gaynor et al. 2023. This is a -visual display of reproduction including gamete formation (\(gam.vec\), \(b\), and \(v\)) and the union of gametes via selfing +

Figure 4: Figure A1 in Gaynor et al. 2023. +This is a visual display of reproduction including gamete formation +(\(gam.vec\), \(b\), and \(v\)) and the union of gametes via selfing or outcrossing (\(s\) and \(mc\)). The number of gametes of each type is calculated based on the number of mature individuals of each cytotype (\(c_{2,2}, c_{3,2}, c_{4,2}\)), the diff --git a/docs/articles/Reproduction_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/Reproduction_files/figure-html/unnamed-chunk-1-1.png index 869eef0..ac0cbf5 100644 Binary files a/docs/articles/Reproduction_files/figure-html/unnamed-chunk-1-1.png and b/docs/articles/Reproduction_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/articles/Reproduction_files/figure-html/unnamed-chunk-2-1.png b/docs/articles/Reproduction_files/figure-html/unnamed-chunk-2-1.png index 165a46c..ceb8404 100644 Binary files a/docs/articles/Reproduction_files/figure-html/unnamed-chunk-2-1.png and b/docs/articles/Reproduction_files/figure-html/unnamed-chunk-2-1.png differ diff --git a/docs/articles/Survival.html b/docs/articles/Survival.html index 27b00a1..3ebf96b 100644 --- a/docs/articles/Survival.html +++ b/docs/articles/Survival.html @@ -145,8 +145,9 @@

Mature survival and \(\beta = (1-\mu)*\frac{\mu*(1-\mu)}{(\sigma^{2} - 1)}\).

Note, \(0 \le env.ci < 1\), see -Gaynor et al. 2023 for details. As env.ci increases, we see the variance -in probability of survival also increase (Figure 2). +Gaynor et +al. 2023 for details. As env.ci increases, we see the variance in +probability of survival also increase (Figure 2). Additionally, as the mature survival increase, so does the sampled probability of survival.

Figure 2: Expected survival probability when we sample diff --git a/docs/articles/Survival_files/figure-html/immature-1.png b/docs/articles/Survival_files/figure-html/immature-1.png index 4c8a2a1..3f86ae6 100644 Binary files a/docs/articles/Survival_files/figure-html/immature-1.png and b/docs/articles/Survival_files/figure-html/immature-1.png differ diff --git a/docs/articles/Survival_files/figure-html/mature-1.png b/docs/articles/Survival_files/figure-html/mature-1.png index 3e4ceda..467fbf5 100644 Binary files a/docs/articles/Survival_files/figure-html/mature-1.png and b/docs/articles/Survival_files/figure-html/mature-1.png differ diff --git a/docs/index.html b/docs/index.html index c89bc05..7c52d56 100644 --- a/docs/index.html +++ b/docs/index.html @@ -76,7 +76,7 @@

Introduction

-

R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see Gaynor et al. 2023. To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations and two life-stages (reproductively immature and reproductively mature). Stay-tune for v2.0.0, which will include population genetic simulations.

+

R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see Gaynor et al. 2023. To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations and two life-stages (reproductively immature and reproductively mature). Stay-tune for v2.0.0, which will include population genetic simulations.

Installation @@ -87,7 +87,7 @@

Installation

References

-

Gaynor ML, Kortessis N, Soltis DE, Soltis PS, and Ponciano JM. 2023. Dynamics of mixed-ploidy populations under demographic and environmental stochasticities. In Review. Preprint Available Soon

+

Gaynor ML, Kortessis N, Soltis DE, Soltis PS, and Ponciano JM. 2023. Dynamics of mixed-ploidy populations under demographic and environmental stochasticities. In Review. Preprint Available

Gaynor ML, Soltis DE, Soltis PS, and Ponciano JM. Modeling the dynamics of multiple origins and gene flow in autopolyploids. In prep.

diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 123af31..141ab00 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -6,7 +6,7 @@ articles: Maturation: Maturation.html Reproduction: Reproduction.html Survival: Survival.html -last_built: 2023-03-30T15:34Z +last_built: 2023-03-31T17:05Z urls: reference: http://mlgaynor.com/AutoPop/reference article: http://mlgaynor.com/AutoPop/articles diff --git a/docs/search.json b/docs/search.json index d936ec0..4c21a07 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. General Public Licenses designed make sure freedom distribute copies free software (charge wish), receive source code can get want , can change software use pieces new free programs, know can things. protect rights, need prevent others denying rights asking surrender rights. Therefore, certain responsibilities distribute copies software, modify : responsibilities respect freedom others. example, distribute copies program, whether gratis fee, must pass recipients freedoms received. must make sure , , receive can get source code. must show terms know rights. 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GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to AutoPop","text":"AutoPop R-based joint-dynamic population simulation diploids, triploids, autotetraploids. details methods, see Gaynor et al. 2023. summarize, package contains stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants overlapping generations. Specifically, model includes three cytotypes (diploids, triploids, autotetraploids), well two life stages, regards reproductive ability (immature mature), total six stages represented \\(c_{,j}\\) = {2, 3, 4} j = {1,2}. time step includes three processes: \\(\\color{green}{F_{,k}(t)}\\) number immature individuals ploidal level \\(\\) produced mature individuals ploidal level \\(k\\). \\(\\color{red}{S_{,j}(t)}\\) survival probability individuals ploidal level \\(\\) life stage \\(j\\). \\(\\color{blue}{M_{}}\\) maturation probability immature individuals ploidal level \\(\\). \\[ = \\left(\\begin{array}{cc} c_{2,1}(t) \\\\ c_{3,1}(t) \\\\ c_{4,1}(t) \\\\ c_{2,2}(t) \\\\ c_{3,2}(t) \\\\ c_{4,2}(t) \\\\ \\end{array}\\right) \\left(\\begin{array}{cc} \\color{red}{S_{2,1}(t)} & 0 & 0 & \\color{green}{F_{2,2}(t)} & \\color{green}{F_{2,3}(t)} & \\color{green}{F_{2,4}(t)}\\\\ 0 & \\color{red}{S_{3,1}(t)} & 0 & \\color{green}{F_{3,2}(t)} & \\color{green}{F_{3,3}(t)} & \\color{green}{F_{3,4}(t)}\\\\ 0 & 0 & \\color{red}{S_{4,1}(t)} & \\color{green}{F_{4,2}(t)} & \\color{green}{F_{4,3}(t)} & \\color{green}{F_{4,4}(t)}\\\\ \\color{blue}{M_{2}}& 0 & 0 & \\color{red}{S_{2,2}(t)} & 0 & 0 \\\\ 0 & \\color{blue}{M_{3}} & 0 & 0 & \\color{red}{S_{3,2}(t)} & 0\\\\ 0 & 0 & \\color{blue}{M_{4}} & 0 & 0 & \\color{red}{S_{4,2}(t)} \\\\ \\end{array}\\right) \\] matrix notation: \\[ \\begin{equation} \\mathbf{C}(t+1) = \\mathbf{}(t)\\mathbf{C}(t), \\end{equation} \\]","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"model-parameters","dir":"Articles","previous_headings":"","what":"Model Parameters","title":"Introduction to AutoPop","text":"Table 1: Summary model parameters definitions. Color indicates processes: reproduction (green), survival (red), maturation (blue).","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"simulation-example","dir":"Articles","previous_headings":"","what":"Simulation Example","title":"Introduction to AutoPop","text":"simulate multiple generations, developed function gen.iter.f.choosy(). exploring many parameter sets, recommend running model replicates parallel using foreach doParallel.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"basic-example","dir":"Articles","previous_headings":"Simulation Example","what":"Basic Example","title":"Introduction to AutoPop","text":"","code":"# Load Packages library(AutoPop) # Example Simulation example.sim <- gen.iter.f.choosy(generations = 10000, # Number of generations init.pop = 100, # Number of mature diploids in initial pop gnum.base = c(100, 100, 100), # Number of gametes per individual per cytotype d = c(0.0001, 0.0009, 0.0001), # Strength of density dependency density.type = \"all\", # Type of density dependence b = 0.02, # Proportion of unreduced gamete formation cc = 0.052, # Proportion of 3n gamete formation s = 0.1, # Selfing rate mc = 0.1, # Strength of mating choice mate.lazy = FALSE, # Prevents selfing from occurring during outcrossing env.ci = 0.1, # Proportion of environmental variance aii.vec = c(0.0005, 0.005, 0.0005), # Probability of survival of immature individuals as.msurv = c(0.60, 0.06, 0.60), # Probability of survival of mature individuals as.matur = c(0.4, 0.02, 0.4) # Maturation rate )"},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"parallel-example","dir":"Articles","previous_headings":"","what":"Parallel Example","title":"Introduction to AutoPop","text":"Note, example designed run SLURM-based cluster.","code":"# Load Packages library(foreach) library(doParallel) library(pryr) # Detect CPIs available ## Eegister the cluster for using foreach n.cpus <- as.numeric(Sys.getenv(\"SLURM_CPUS_PER_TASK\")) print(n.cpus) cl <- makeCluster(n.cpus) registerDoParallel(cl) # Example Simulation startparallel <- Sys.time() temp <- foreach(iter = 1:500)%dopar%{ suppressWarnings(gen.iter.f.choosy(generations = 10000, # Number of generations init.pop = 100, # Number of mature diploids in initial pop gnum.base = c(100, 100, 100), # Number of gametes per individual per cytotype d = c(0.0001, 0.0009, 0.0001), # Strength of density dependency density.type = \"all\", # Type of density dependence b = 0.02, # Proportion of unreduced gamete formation cc = 0.052, # Proportion of 3n gamete formation s = 0.1, # Selfing rate mc = 0.1, # Strength of mating choice mate.lazy = FALSE, # Prevents selfing from occurring during outcrossing env.ci = 0.1, # Proportion of environmental variance aii.vec = c(0.0005, 0.005, 0.0005), # Probability of survival of immature individuals as.msurv = c(0.60, 0.06, 0.60), # Probability of survival of mature individuals as.matur = c(0.4, 0.02, 0.4) # Maturation rate )) } save(temp, file = \"temp.RData\") ## Benchmark totalparallel <- Sys.time() - startparallel print(totalparallel) # How much time did this simulation take? print(pryr::mem_used()) # How much memory did this simulation use?"},{"path":[]},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"number-of-gametes-produced","dir":"Articles","previous_headings":"Gamete Formation","what":"Number of Gametes Produced","title":"Reproduction","text":"number gametes per individual cytotype time step (\\(X_{}(t)\\)) determined based base number gametes per individual (\\(gnum.base\\)) strength density dependency (\\(d\\)). \\[X_{}(t) \\sim {\\rm Poisson} \\left(gnum*\\exp\\left\\{-d_{}\\sum_{=2}^4\\sum_{j=1}^{2}c_{,j}(t)\\right\\} \\right)\\] \\(c_{,j}\\) indicates total number individuals cytotype (= {2, 3, 4}) life stage (j = {1,2}). inspect number gametes expected populations 10 100,000 individuals. see minimum population size 0 gametes sampled (indicated colored lines) among different \\(gnum.base\\) values decreases increased \\(d\\) values. also observe larger values \\(d\\) decrease distance among minimum population size 0 gametes sampled different \\(gnum.base\\) values. Due dynamics revealed Figure 1, decided vary strength density dependence, rather base number gametes produced (see Figure 2). Figure 1: Expected number gametes sampled d values (- E) color indicating gnum.base (number gametes produced individual). color lines indicate population size 0 gametes sampled first seen set gamete value. Figure 2: Number gametes sampled diploids (blue), triploids (orange), tetraploids (purple) d value (strength density dependence) specified cytotype, gnum (number gametes produced individual) set 100 .","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"types-of-gametes-produced","dir":"Articles","previous_headings":"Gamete Formation","what":"Types of Gametes Produced","title":"Reproduction","text":"current model allows cytotypes produce reduced unreduced gametes rates set \\(b\\) \\(cc\\) (\\(v\\)). gametes produced, viable. Diploids produce 1n 2n gametes, viable. Triploids produce viable gametes 3n, nonviable 1n 2n gametes. Autotetraploids produce viable 2n gametes. Figure 3: Gametes produced cytotype. gam.vec indicates \\(X_{}\\) time step, b rate unreduced gamete formation, v proportion triploid gametes viable (cc), \\(c_{,j}\\) total number ith cytotype reproductively mature (j=2) time t. figure Figure A1 Gaynor et al. 2023.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"gamete-union","dir":"Articles","previous_headings":"","what":"Gamete Union","title":"Reproduction","text":"remaining parameters associated selfing rate (\\(s\\)) mating choice (\\(mc\\)). Figure 4: Figure A1 Gaynor et al. 2023. visual display reproduction including gamete formation (\\(gam.vec\\), \\(b\\), \\(v\\)) union gametes via selfing outcrossing (\\(s\\) \\(mc\\)). number gametes type calculated based number mature individuals cytotype (\\(c_{2,2}, c_{3,2}, c_{4,2}\\)), number gametes produced individual (\\(gam.vec\\)), frequency unreduced gamete formation (\\(b\\)), proportion triploid gametes viable (\\(v\\), \\(cc\\)). gamete formation, selfing occur based defined selfing rate (\\(s\\)), remaining gametes outcross. outcrossing gametes, proportion pair gametes produced like-cytotypes indicated mating choice (\\(mc\\)), remainder freely pair outcrossing pool.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"lazy-mating","dir":"Articles","previous_headings":"Gamete Union","what":"Lazy Mating","title":"Reproduction","text":"Note, included parameter called ‘mate.lazy’ can equal TRUE FALSE. FALSE, prevents selfing occurring outcrossing. Since preventing union gametes produced individual increases computational time 31x, provide function without feature. recommend lazy mating.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Survival.html","id":"immature-survival","dir":"Articles","previous_headings":"","what":"Immature survival","title":"Survival","text":"Immature probability survival defined followed: \\[S_{, 1}(t) = e^{-aii[j]*\\sum_{j=1}^{2}c_{,j}(t)}\\] \\(c_{, 1}(t + 1)\\) defined binominal distribution \\(c_{,1}(t)\\) trials probability success equal \\(S_{,1}(t)\\). aii increases, number individuals expected survive decreases (Figure 1). Figure 1: Expected number individuals survive given different immature survival probabilities.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Survival.html","id":"mature-survival","dir":"Articles","previous_headings":"","what":"Mature survival","title":"Survival","text":"Mature probability survival defined using beta distribution sample mature survival probability generation based mean mature survival probability (\\(\\mu\\)) proportion variance (\\(env.ci\\)). \\(c_{, 2}(t + 1)\\) defined based binominal distribution \\(c_{,2}(t)\\) trials probability success equal \\(S_{,2}(t)\\). survival probability, \\(S_{,2}(t)\\), varies time equal \\(Beta(\\alpha, \\beta)\\). can define \\(\\alpha\\) \\(\\beta\\) based \\(\\mu\\) \\(env.ci\\) first defining variance \\(\\sigma^{2} = ci*\\mu(1-\\mu)\\), \\(\\alpha = \\mu * \\frac{\\mu*(1-\\mu)}{(\\sigma^{2} - 1)}\\) \\(\\beta = (1-\\mu)*\\frac{\\mu*(1-\\mu)}{(\\sigma^{2} - 1)}\\). Note, \\(0 \\le env.ci < 1\\), see Gaynor et al. 2023 details. env.ci increases, see variance probability survival also increase (Figure 2). Additionally, mature survival increase, sampled probability survival. Figure 2: Expected survival probability sample beta distribution based mean mature survival probability (\\(\\mu\\)) (x-axis) proportion variance (\\(env.ci\\)) (plot title).","code":""},{"path":"http://mlgaynor.com/AutoPop/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michelle L Gaynor. Author, maintainer. Nicholas Kortessis. Contributor. Douglas E. Soltis. Contributor. Pamela S. Soltis. Contributor. José Miguel Ponciano. Author.","code":""},{"path":"http://mlgaynor.com/AutoPop/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gaynor ML, Ponciano JM (2023). AutoPop: Autopolyploid population simulations. http://mlgaynor.com/AutoPop/, https://github.com/mgaynor1/AutoPop.","code":"@Manual{, title = {AutoPop: Autopolyploid population simulations}, author = {Michelle L Gaynor and José Miguel Ponciano}, year = {2023}, note = {http://mlgaynor.com/AutoPop/, https://github.com/mgaynor1/AutoPop}, }"},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"autopop","dir":"","previous_headings":"","what":"Autopolyploid population simulations","title":"Autopolyploid population simulations","text":"Michelle L. Gaynor, Nicholas Kortessis, Douglas E. Soltis, Pamela S. Soltis, José Miguel Ponciano","code":""},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Autopolyploid population simulations","text":"R-based joint-dynamic population simulation diploids, triploids, autotetraploids. details methods, see Gaynor et al. 2023. summarize, package contains stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants overlapping generations two life-stages (reproductively immature reproductively mature). Stay-tune v2.0.0, include population genetic simulations.","code":""},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Autopolyploid population simulations","text":"","code":"install.packages(\"devtools\"\") devtools::install_github(\"mgaynor1/AutoPop\")"},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Autopolyploid population simulations","text":"Gaynor ML, Kortessis N, Soltis DE, Soltis PS, Ponciano JM. 2023. Dynamics mixed-ploidy populations demographic environmental stochasticities. Review. Preprint Available Soon Gaynor ML, Soltis DE, Soltis PS, Ponciano JM. Modeling dynamics multiple origins gene flow autopolyploids. prep.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"Calculates alpha beta parameters based mu, mean probability mature survival, calculated variance.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"","code":"alphabeta.calc(mu, var)"},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"mu Mean probability mature survival. Must single integer 0 1. var Derived variance based var.option. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"List parameters alpha beta.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":null,"dir":"Reference","previous_headings":"","what":"Reproduction function. — cytotype_repro_mate","title":"Reproduction function. — cytotype_repro_mate","text":"function generate number offspring produced per generation. Assumes mixed mating system selfing outcrossing occur. Also allows group-based assortative mating (see Otto et al. 2008). function prevents selfing occurring outcrossing.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reproduction function. — cytotype_repro_mate","text":"","code":"cytotype_repro_mate(cgen, b, cc, gnum.vec, s, mc)"},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reproduction function. — cytotype_repro_mate","text":"cgen Number mature individuals cytotype current generation. Must list three numeric values. example, cgen = c(100, 100, 100). b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. gnum.vec mean number gametes produced individual cytotype. Must list three numeric values. example, gnum.vec = c(100, 100, 100). s Selfing rate. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reproduction function. — cytotype_repro_mate","text":"Output generation log including number 2x offspring, 3x offspring, 4x offspring, gametes sampled per 2x individual, gametes sampled per 3x individual, gametes sampled per 4x individual.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reproduction function. — cytotype_repro_mate","text":"Otto, S. P., Servedio, M. R., Nuismer, S. L. (2008). Frequency-dependent selection evolution assortative mating. Genetics, 179(4) 2091 - 2112. doi: 10.1534/genetics.107.084418","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":null,"dir":"Reference","previous_headings":"","what":"Format multiple generation simulation. — form.autopop","title":"Format multiple generation simulation. — form.autopop","text":"function formats output gen-iter-f-choosy.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format multiple generation simulation. — form.autopop","text":"","code":"form.autopop(popvect, generations)"},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format multiple generation simulation. — form.autopop","text":"popvect Output gen-iter-f-choosy function. generations Number generations included.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format multiple generation simulation. — form.autopop","text":"Output file data frame row generation. columns follows, V1: number immature diploids. V2: number immature triploids. V3: number immature tetraploids. V4: number mature diploids. V5: number mature triploids. V6: number mature tetraploids. V7: number diploid offspring produced t - 1. V8: number triploid offspring produced t - 1. V9: number tetraploid offspring produced t - 1. V10: number gametes per diploid individual t - 1. V11: number gametes per triploid individual t - 1. V12: number gametes per tetraploid individual t - 1. gen: generation. sum: total number individuals. sum2x-sum4x: total number cytotype generation. V1a:V6a: relative abundance V1:V6 generation. C2: relative abundance diploids (ie. sum2x/sum). C3: relative abundance triploids (ie. sum3x/sum). C4: relative abundance tetraploids (ie. sum4x/sum)","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate multiple generations. — gen.iter.f.choosy","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"Defined detail Gaynor et al. 2023. summarize, function simulates stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants overlapping generations two life-stages (reproductively immature reproductively mature). Population composition time t + 1 defined reproduction, survival, maturation. function loops set number generations. Note, lists three always values representing c(diploids, triploid, autotetraploids).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"","code":"gen.iter.f.choosy( generations, init.pop, env.ci, aii.vec, as.matur, as.msurv, d, gnum.base, b, cc, s, mc, density.type = \"all\", mate.lazy = FALSE )"},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"generations Number generations simulate. Must numeric value. init.pop number mature diploids initial founding population. Must numeric value greater 0. env.ci Proportion environmental variance used define mature survival rate per generation. Must integer greater equal 0 less 1. aii.vec survival probability immature individual cytotype. Must list three integers 0 1. example, aii.vec = c(0.5, 0.3, 0.5). .matur probability maturation immature stage mature stage cytotype. Must list three integers 0 1. example, .matur = c(0.5, 0.3, 0.5). .msurv mean survival probability mature individual cytotype. Must list three integers 0 1. example, .msurv = c(0.5, 0.3, 0.5). d Strength density dependency gamete production cytotype. Must list three integers 0 1. example, d = c(0.001, 0.009, 0.001). gnum.base Mean number gametes per individual per cytotype. Must list three numeric values. example, gnum.base = c(100, 100, 100). b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. s Selfing rate. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1. density.type Default = \"\", sets density time t individuals time t. Alternatively, \"like-cytotype\" sets density time t cytotype based total immature mature individuals cytotype time t. mate.lazy Default = FALSE, prevents selfing occurring outcrossing. However, increases computational time 31x!","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"single data frame defined form.autopop(). row generation. columns follows, V1: number immature diploids. V2: number immature triploids. V3: number immature tetraploids. V4: number mature diploids. V5: number mature triploids. V6: number mature tetraploids. V7: number diploid offspring produced t - 1. V8: number triploid offspring produced t - 1. V9: number tetraploid offspring produced t - 1. V10: number gametes per diploid individual t - 1. V11: number gametes per triploid individual t - 1. V12: number gametes per tetraploid individual t - 1. gen: generation. sum: total number individuals. sum2x: sum immature mature diploids. sum3x: sum immature mature triploids. sum4x: sum immature mature tetraploids. V1a: relative abundance immature diploids (V1). V2a: relative abundance immature triploids (V2). V3a: relative abundance immature tetraploids (V3). V4a: relative abundance mature diploids (V4). V5a: relative abundance mature triploids (V5). V6a: relative abundance mature tetraploids (V6). C2: relative abundance diploids (sum2x/sum). C3: relative abundance triploids (sum3x/sum). C4: relative abundance tetraploids (sum4x/sum)","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":null,"dir":"Reference","previous_headings":"","what":"MAR(1) Conditional least squares parameter estimates. — mars.cls","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"described Ives et al. 2003, Multivariate Autoregressive Model, also known MAR(1) model, discrete-time model multispecies stochastic community subject environmental noise. Markov process. Given multispecies time series (log) abundance data without observation error, parameter estimation model parameters can quickly done via Conditional Least Squares.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"","code":"mars.cls(comm.mat, covariate = NULL)"},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"comm.mat Data frame log-scale abundance time step following burn-. expect one column per species one row per time-step. covariate Matrix covariates (ex. precipitation per time step) column representing variable row representing time step. Defaults \"NULL\" matrix supplied.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"Resulting data frame includes: , vector length p (p = number species) containing estimates intrinsic rate natural increase species. B, matrix p x p estimates diagonal elements represent intra-specific, density-dependent effects. elements \\(b_{ij}\\) gives effect abundance species j per capita growth rate species . sigma, matrix p x p, represents environmental noise variance-covariance matrix. C, vector estimated coefficients every covariate representing effects every covariate every species. E, vector representing stochastic environmental variability. Yhat, \\(\\hat{Y}\\) estimator predicted abundance. R2, \\(R^2\\), proportion explained variation log scale population abundance species. R2_D, conditional \\(R^2\\), proportion variation change one unit time log scale population abundance explained model species. AIC, Akaike information criterion. BIC, Bayesian information criterion. lnlike, maximized log likelihood MAR(1) model.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"Ives, . R., Dennis, B., Cottingham, K. L., Carpenter, S. R. (2003). Estimating community stability ecological interactions time-series data. Ecological Monographs, 72(2): 301 - 330","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":null,"dir":"Reference","previous_headings":"","what":"Lazy reproduction function. — matrix_cytotype_repro_mate","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"function generate number offspring produced per generation. Assumes mixed mating system selfing outcrossing occur. Also allows group-based assortative mating (see Otto et al. 2008).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"","code":"matrix_cytotype_repro_mate(cgen, b, cc, gnum.vec, s, mc)"},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"cgen Number mature individuals cytotype current generation. Must list three numeric values. example, cgen = c(100, 100, 100). b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. gnum.vec mean number gametes produced individual cytotype. Must list three numeric values. example, gnum.vec = c(100, 100, 100). s Selfing rate. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"Output generation log including number 2x offspring, 3x offspring, 4x offspring, gametes sampled per 2x individual, gametes sampled per 3x individual, gametes sampled per 4x individual.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"Otto, S. P., Servedio, M. R., Nuismer, S. L. (2008). Frequency-dependent selection evolution assortative mating. Genetics, 179(4) 2091 - 2112. doi: 10.1534/genetics.107.084418","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"Calculates probability mature survival based beta binomial distribution.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"","code":"mature.surv.calc(env.ci, as.msurv)"},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"env.ci Proportion environmental variance used define mature survival rate per generation. Must integer greater equal 0 less 1. .msurv Mean probability mature survival. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"Probability mature survival.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"function can used convert alpha beta back mu var.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"","code":"muvar.calc(alpha, beta)"},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"alpha See alphabeta.calc. beta See alphabeta.calc.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"List parameters mu var.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a single generation. — one.iter.f.choosy","title":"Simulate a single generation. — one.iter.f.choosy","text":"Defined detail Gaynor et al. 2023. summarize, function single time step stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants two life-stages (reproductively immature reproductively mature). Population composition time t + 1 defined reproduction, survival, maturation. Note, lists three always values representing c(diploids, triploid, autotetraploids).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a single generation. — one.iter.f.choosy","text":"","code":"one.iter.f.choosy( ct.vec, aii.vec, as.matur, as.msurv.e.set, d, gnum.base, s, b, cc, mc, density.type = \"all\", mate.lazy = FALSE )"},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate a single generation. — one.iter.f.choosy","text":"ct.vec Population composition time t. Indicates sum type time t, ct.vec = c(2x_immature, 3x_immature, 4x_immature, 2x_mature, 3x_mature, 4x_mature) aii.vec survival probability immature individual cytotype. Must list three integers 0 1. example, aii.vec = c(0.5, 0.3, 0.5). .matur probability maturation immature stage mature stage cytotype. Must list three integers 0 1. example, .matur = c(0.5, 0.3, 0.5). .msurv.e.set survival probability mature individual cytotype. Must list three integers 0 1. example, .msurv.e.set = c(0.5, 0.3, 0.5). list defined within gen.iter.f.choosy() function. d Strength density dependency gamete production cytotype. Must list three integers 0 1. example, d = c(0.001, 0.009, 0.001). gnum.base Mean number gametes per individual per cytotype. Must list three numeric values. example, gnum.base = c(100, 100, 100). s Selfing rate. Must single integer 0 1. b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1. density.type Default = \"\", sets density time t individuals time t. Alternatively, \"like-cytotype\" sets density time t cytotype based total immature mature individuals cytotype time t. mate.lazy Default = FALSE, prevents selfing occurring outcrossing. However, increases computational time 31x!","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate a single generation. — one.iter.f.choosy","text":"List 9, 1:6 representing number individuals cytotype stages time t + 1. Items 7:9 number gametes sampled 2x, 3x, 4x individuals time t.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":null,"dir":"Reference","previous_headings":"","what":"Ives et al. 2003 stability metrics calculator. — stability","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"Based Ives et al. 2003, calculate four stability metrics based estimates matrix ecological interactions B estimated variance-covariance matrix environmental noise 'Sigma'. two quantities can read output function mars.cls().","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"","code":"stability(B, sigma)"},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"B Matrix p x p (p = number species), \\(b_{ij}\\) gives effect abundance species j per capita growth rate species. can calculated mars.cls() function. sigma Matrix p x p, environmental noise variance-covariance matrix. can calculated mars.cls() function.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"Resulting list includes: var.prop, stationarity var.prop variance proportion attributable environmental noise. smaller values, stable dynamics. mean.return.time & var.return.time, rate transition distribution converges back stationary distribution. less time takes return stationary distribution, stable population. reactivity, measures reaction perturbations distance away stationary system moves response disturbance. , smaller better terms stability. sp.contribs, squared eigenvalue representing characteristic return rate variance transition distribution estimated MARS(1) Markov Process.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"Ives, . R., Dennis, B., Cottingham, K. L., Carpenter, S. R. (2003). Estimating community stability ecological interactions frm time-series data. Ecological Monographs, 72(2): 301 - 330.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculate variance from env.ci and mu. — var.option","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"Calculates variance based env.ci, proportion env. variance, mu, mean probability mature survival.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"","code":"var.option(env.ci, mu)"},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"env.ci Proportion environmental variance used define mature survival rate per generation beta distribution. number must 0 1, equal 0 1. mu Mean probability mature survival (list).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"Calculated variance.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"","code":"variance <- var.option(env.ci = 0.9, mu = 0.6) #> Error in var.option(env.ci = 0.9, mu = 0.6): could not find function \"var.option\""},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":null,"dir":"Reference","previous_headings":"","what":"Processing function to subset a single replicate from a model set. — write.one.rep","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"Subset single replicate every model set.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"","code":"write.one.rep(set, setname)"},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"set List containing paths simulations wth replicates. can made function list.files(path = \"\", full.names = TRUE). setname Name output folder.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"write.one.rep() creates folder \"output/one_rep/setname\" csv file replicate made.","code":""}] +[{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright © 2007 Free Software Foundation, Inc.  Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program–make sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Definitions","title":"GNU General Public License","text":"“License” refers version 3 GNU General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. 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Protecting Users’ Legal Rights From Anti-Circumvention Law","title":"GNU General Public License","text":"covered work shall deemed part effective technological measure applicable law fulfilling obligations article 11 WIPO copyright treaty adopted 20 December 1996, similar laws prohibiting restricting circumvention measures. convey covered work, waive legal power forbid circumvention technological measures extent circumvention effected exercising rights License respect covered work, disclaim intention limit operation modification work means enforcing, work’s users, third parties’ legal rights forbid circumvention technological measures.","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"id_4-conveying-verbatim-copies","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"4. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License “later version” applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy’s public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM “” WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION.","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"id_16-limitation-of-liability","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"16. Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"http://mlgaynor.com/AutoPop/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least “copyright” line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program’s commands might different; GUI interface, use “box”. also get employer (work programmer) school, , sign “copyright disclaimer” program, necessary. information , apply follow GNU GPL, see . GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read .","code":" Copyright (C) This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . Copyright (C) This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"summary","dir":"Articles","previous_headings":"","what":"Summary","title":"Introduction to AutoPop","text":"AutoPop R-based joint-dynamic population simulation diploids, triploids, autotetraploids. details methods, see Gaynor et al. 2023. summarize, package contains stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants overlapping generations. Specifically, model includes three cytotypes (diploids, triploids, autotetraploids), well two life stages, regards reproductive ability (immature mature), total six stages represented \\(c_{,j}\\) = {2, 3, 4} j = {1,2}. time step includes three processes: \\(\\color{green}{F_{,k}(t)}\\) number immature individuals ploidal level \\(\\) produced mature individuals ploidal level \\(k\\). \\(\\color{red}{S_{,j}(t)}\\) survival probability individuals ploidal level \\(\\) life stage \\(j\\). \\(\\color{blue}{M_{}}\\) maturation probability immature individuals ploidal level \\(\\). \\[ = \\left(\\begin{array}{cc} c_{2,1}(t) \\\\ c_{3,1}(t) \\\\ c_{4,1}(t) \\\\ c_{2,2}(t) \\\\ c_{3,2}(t) \\\\ c_{4,2}(t) \\\\ \\end{array}\\right) \\left(\\begin{array}{cc} \\color{red}{S_{2,1}(t)} & 0 & 0 & \\color{green}{F_{2,2}(t)} & \\color{green}{F_{2,3}(t)} & \\color{green}{F_{2,4}(t)}\\\\ 0 & \\color{red}{S_{3,1}(t)} & 0 & \\color{green}{F_{3,2}(t)} & \\color{green}{F_{3,3}(t)} & \\color{green}{F_{3,4}(t)}\\\\ 0 & 0 & \\color{red}{S_{4,1}(t)} & \\color{green}{F_{4,2}(t)} & \\color{green}{F_{4,3}(t)} & \\color{green}{F_{4,4}(t)}\\\\ \\color{blue}{M_{2}}& 0 & 0 & \\color{red}{S_{2,2}(t)} & 0 & 0 \\\\ 0 & \\color{blue}{M_{3}} & 0 & 0 & \\color{red}{S_{3,2}(t)} & 0\\\\ 0 & 0 & \\color{blue}{M_{4}} & 0 & 0 & \\color{red}{S_{4,2}(t)} \\\\ \\end{array}\\right) \\] matrix notation: \\[ \\begin{equation} \\mathbf{C}(t+1) = \\mathbf{}(t)\\mathbf{C}(t), \\end{equation} \\]","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"model-parameters","dir":"Articles","previous_headings":"","what":"Model Parameters","title":"Introduction to AutoPop","text":"Table 1: Summary model parameters definitions. Color indicates processes: reproduction (green), survival (red), maturation (blue).","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"simulation-example","dir":"Articles","previous_headings":"","what":"Simulation Example","title":"Introduction to AutoPop","text":"simulate multiple generations, developed function gen.iter.f.choosy(). exploring many parameter sets, recommend running model replicates parallel using foreach doParallel.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"basic-example","dir":"Articles","previous_headings":"Simulation Example","what":"Basic Example","title":"Introduction to AutoPop","text":"","code":"# Load Packages library(AutoPop) # Example Simulation example.sim <- gen.iter.f.choosy(generations = 10000, # Number of generations init.pop = 100, # Number of mature diploids in initial pop gnum.base = c(100, 100, 100), # Number of gametes per individual per cytotype d = c(0.0001, 0.0009, 0.0001), # Strength of density dependency density.type = \"all\", # Type of density dependence b = 0.02, # Proportion of unreduced gamete formation cc = 0.052, # Proportion of 3n gamete formation s = 0.1, # Selfing rate mc = 0.1, # Strength of mating choice mate.lazy = FALSE, # Prevents selfing from occurring during outcrossing env.ci = 0.1, # Proportion of environmental variance aii.vec = c(0.0005, 0.005, 0.0005), # Probability of survival of immature individuals as.msurv = c(0.60, 0.06, 0.60), # Probability of survival of mature individuals as.matur = c(0.4, 0.02, 0.4) # Maturation rate )"},{"path":"http://mlgaynor.com/AutoPop/articles/Introduction.html","id":"parallel-example","dir":"Articles","previous_headings":"","what":"Parallel Example","title":"Introduction to AutoPop","text":"Note, example designed run SLURM-based cluster.","code":"# Load Packages library(foreach) library(doParallel) library(pryr) # Detect CPIs available ## Eegister the cluster for using foreach n.cpus <- as.numeric(Sys.getenv(\"SLURM_CPUS_PER_TASK\")) print(n.cpus) cl <- makeCluster(n.cpus) registerDoParallel(cl) # Example Simulation startparallel <- Sys.time() temp <- foreach(iter = 1:500)%dopar%{ suppressWarnings(gen.iter.f.choosy(generations = 10000, # Number of generations init.pop = 100, # Number of mature diploids in initial pop gnum.base = c(100, 100, 100), # Number of gametes per individual per cytotype d = c(0.0001, 0.0009, 0.0001), # Strength of density dependency density.type = \"all\", # Type of density dependence b = 0.02, # Proportion of unreduced gamete formation cc = 0.052, # Proportion of 3n gamete formation s = 0.1, # Selfing rate mc = 0.1, # Strength of mating choice mate.lazy = FALSE, # Prevents selfing from occurring during outcrossing env.ci = 0.1, # Proportion of environmental variance aii.vec = c(0.0005, 0.005, 0.0005), # Probability of survival of immature individuals as.msurv = c(0.60, 0.06, 0.60), # Probability of survival of mature individuals as.matur = c(0.4, 0.02, 0.4) # Maturation rate )) } save(temp, file = \"temp.RData\") ## Benchmark totalparallel <- Sys.time() - startparallel print(totalparallel) # How much time did this simulation take? print(pryr::mem_used()) # How much memory did this simulation use?"},{"path":[]},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"number-of-gametes-produced","dir":"Articles","previous_headings":"Gamete Formation","what":"Number of Gametes Produced","title":"Reproduction","text":"number gametes per individual cytotype time step (\\(X_{}(t)\\)) determined based base number gametes per individual (\\(gnum.base\\)) strength density dependency (\\(d\\)). \\[X_{}(t) \\sim {\\rm Poisson} \\left(gnum*\\exp\\left\\{-d_{}\\sum_{=2}^4\\sum_{j=1}^{2}c_{,j}(t)\\right\\} \\right)\\] \\(c_{,j}\\) indicates total number individuals cytotype (= {2, 3, 4}) life stage (j = {1,2}). inspect number gametes expected populations 10 100,000 individuals. see minimum population size 0 gametes sampled (indicated colored lines) among different \\(gnum.base\\) values decreases increased \\(d\\) values. also observe larger values \\(d\\) decrease distance among minimum population size 0 gametes sampled different \\(gnum.base\\) values. Due dynamics revealed Figure 1, decided vary strength density dependence, rather base number gametes produced (see Figure 2). Figure 1: Expected number gametes sampled d values (- E) color indicating gnum.base (number gametes produced individual). color lines indicate population size 0 gametes sampled first seen set gamete value. Figure 2: Number gametes sampled diploids (blue), triploids (orange), tetraploids (purple) d value (strength density dependence) specified cytotype, gnum (number gametes produced individual) set 100 .","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"types-of-gametes-produced","dir":"Articles","previous_headings":"Gamete Formation","what":"Types of Gametes Produced","title":"Reproduction","text":"current model allows cytotypes produce reduced unreduced gametes rates set \\(b\\) \\(cc\\) (\\(v\\)). gametes produced, viable. Diploids produce 1n 2n gametes, viable. Triploids produce viable gametes 3n, nonviable 1n 2n gametes. Autotetraploids produce viable 2n gametes. Figure 3: Gametes produced cytotype. gam.vec indicates \\(X_{}\\) time step, b rate unreduced gamete formation, v proportion triploid gametes viable (cc), \\(c_{,j}\\) total number ith cytotype reproductively mature (j=2) time t. figure Figure A1 Gaynor et al. 2023.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"gamete-union","dir":"Articles","previous_headings":"","what":"Gamete Union","title":"Reproduction","text":"remaining parameters associated selfing rate (\\(s\\)) mating choice (\\(mc\\)). Figure 4: Figure A1 Gaynor et al. 2023. visual display reproduction including gamete formation (\\(gam.vec\\), \\(b\\), \\(v\\)) union gametes via selfing outcrossing (\\(s\\) \\(mc\\)). number gametes type calculated based number mature individuals cytotype (\\(c_{2,2}, c_{3,2}, c_{4,2}\\)), number gametes produced individual (\\(gam.vec\\)), frequency unreduced gamete formation (\\(b\\)), proportion triploid gametes viable (\\(v\\), \\(cc\\)). gamete formation, selfing occur based defined selfing rate (\\(s\\)), remaining gametes outcross. outcrossing gametes, proportion pair gametes produced like-cytotypes indicated mating choice (\\(mc\\)), remainder freely pair outcrossing pool.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Reproduction.html","id":"lazy-mating","dir":"Articles","previous_headings":"Gamete Union","what":"Lazy Mating","title":"Reproduction","text":"Note, included parameter called ‘mate.lazy’ can equal TRUE FALSE. FALSE, prevents selfing occurring outcrossing. Since preventing union gametes produced individual increases computational time 31x, provide function without feature. recommend lazy mating.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Survival.html","id":"immature-survival","dir":"Articles","previous_headings":"","what":"Immature survival","title":"Survival","text":"Immature probability survival defined followed: \\[S_{, 1}(t) = e^{-aii[j]*\\sum_{j=1}^{2}c_{,j}(t)}\\] \\(c_{, 1}(t + 1)\\) defined binominal distribution \\(c_{,1}(t)\\) trials probability success equal \\(S_{,1}(t)\\). aii increases, number individuals expected survive decreases (Figure 1). Figure 1: Expected number individuals survive given different immature survival probabilities.","code":""},{"path":"http://mlgaynor.com/AutoPop/articles/Survival.html","id":"mature-survival","dir":"Articles","previous_headings":"","what":"Mature survival","title":"Survival","text":"Mature probability survival defined using beta distribution sample mature survival probability generation based mean mature survival probability (\\(\\mu\\)) proportion variance (\\(env.ci\\)). \\(c_{, 2}(t + 1)\\) defined based binominal distribution \\(c_{,2}(t)\\) trials probability success equal \\(S_{,2}(t)\\). survival probability, \\(S_{,2}(t)\\), varies time equal \\(Beta(\\alpha, \\beta)\\). can define \\(\\alpha\\) \\(\\beta\\) based \\(\\mu\\) \\(env.ci\\) first defining variance \\(\\sigma^{2} = ci*\\mu(1-\\mu)\\), \\(\\alpha = \\mu * \\frac{\\mu*(1-\\mu)}{(\\sigma^{2} - 1)}\\) \\(\\beta = (1-\\mu)*\\frac{\\mu*(1-\\mu)}{(\\sigma^{2} - 1)}\\). Note, \\(0 \\le env.ci < 1\\), see Gaynor et al. 2023 details. env.ci increases, see variance probability survival also increase (Figure 2). Additionally, mature survival increase, sampled probability survival. Figure 2: Expected survival probability sample beta distribution based mean mature survival probability (\\(\\mu\\)) (x-axis) proportion variance (\\(env.ci\\)) (plot title).","code":""},{"path":"http://mlgaynor.com/AutoPop/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Michelle L Gaynor. Author, maintainer. Nicholas Kortessis. Contributor. Douglas E. Soltis. Contributor. Pamela S. Soltis. Contributor. José Miguel Ponciano. Author.","code":""},{"path":"http://mlgaynor.com/AutoPop/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gaynor ML, Ponciano JM (2023). AutoPop: Autopolyploid population simulations. http://mlgaynor.com/AutoPop/, https://github.com/mgaynor1/AutoPop.","code":"@Manual{, title = {AutoPop: Autopolyploid population simulations}, author = {Michelle L Gaynor and José Miguel Ponciano}, year = {2023}, note = {http://mlgaynor.com/AutoPop/, https://github.com/mgaynor1/AutoPop}, }"},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"autopop","dir":"","previous_headings":"","what":"Autopolyploid population simulations","title":"Autopolyploid population simulations","text":"Michelle L. Gaynor, Nicholas Kortessis, Douglas E. Soltis, Pamela S. Soltis, José Miguel Ponciano","code":""},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Autopolyploid population simulations","text":"R-based joint-dynamic population simulation diploids, triploids, autotetraploids. details methods, see Gaynor et al. 2023. summarize, package contains stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants overlapping generations two life-stages (reproductively immature reproductively mature). Stay-tune v2.0.0, include population genetic simulations.","code":""},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Autopolyploid population simulations","text":"","code":"install.packages(\"devtools\"\") devtools::install_github(\"mgaynor1/AutoPop\")"},{"path":"http://mlgaynor.com/AutoPop/index.html","id":"references","dir":"","previous_headings":"","what":"References","title":"Autopolyploid population simulations","text":"Gaynor ML, Kortessis N, Soltis DE, Soltis PS, Ponciano JM. 2023. Dynamics mixed-ploidy populations demographic environmental stochasticities. Review. Preprint Available Gaynor ML, Soltis DE, Soltis PS, Ponciano JM. Modeling dynamics multiple origins gene flow autopolyploids. prep.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"Calculates alpha beta parameters based mu, mean probability mature survival, calculated variance.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"","code":"alphabeta.calc(mu, var)"},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"mu Mean probability mature survival. Must single integer 0 1. var Derived variance based var.option. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/alphabeta.calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculates alpha and beta from mean and variance. — alphabeta.calc","text":"List parameters alpha beta.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":null,"dir":"Reference","previous_headings":"","what":"Reproduction function. — cytotype_repro_mate","title":"Reproduction function. — cytotype_repro_mate","text":"function generate number offspring produced per generation. Assumes mixed mating system selfing outcrossing occur. Also allows group-based assortative mating (see Otto et al. 2008). function prevents selfing occurring outcrossing.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reproduction function. — cytotype_repro_mate","text":"","code":"cytotype_repro_mate(cgen, b, cc, gnum.vec, s, mc)"},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reproduction function. — cytotype_repro_mate","text":"cgen Number mature individuals cytotype current generation. Must list three numeric values. example, cgen = c(100, 100, 100). b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. gnum.vec mean number gametes produced individual cytotype. Must list three numeric values. example, gnum.vec = c(100, 100, 100). s Selfing rate. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reproduction function. — cytotype_repro_mate","text":"Output generation log including number 2x offspring, 3x offspring, 4x offspring, gametes sampled per 2x individual, gametes sampled per 3x individual, gametes sampled per 4x individual.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/cytotype_repro_mate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Reproduction function. — cytotype_repro_mate","text":"Otto, S. P., Servedio, M. R., Nuismer, S. L. (2008). Frequency-dependent selection evolution assortative mating. Genetics, 179(4) 2091 - 2112. doi: 10.1534/genetics.107.084418","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":null,"dir":"Reference","previous_headings":"","what":"Format multiple generation simulation. — form.autopop","title":"Format multiple generation simulation. — form.autopop","text":"function formats output gen-iter-f-choosy.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Format multiple generation simulation. — form.autopop","text":"","code":"form.autopop(popvect, generations)"},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Format multiple generation simulation. — form.autopop","text":"popvect Output gen-iter-f-choosy function. generations Number generations included.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/form.autopop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Format multiple generation simulation. — form.autopop","text":"Output file data frame row generation. columns follows, V1: number immature diploids. V2: number immature triploids. V3: number immature tetraploids. V4: number mature diploids. V5: number mature triploids. V6: number mature tetraploids. V7: number diploid offspring produced t - 1. V8: number triploid offspring produced t - 1. V9: number tetraploid offspring produced t - 1. V10: number gametes per diploid individual t - 1. V11: number gametes per triploid individual t - 1. V12: number gametes per tetraploid individual t - 1. gen: generation. sum: total number individuals. sum2x-sum4x: total number cytotype generation. V1a:V6a: relative abundance V1:V6 generation. C2: relative abundance diploids (ie. sum2x/sum). C3: relative abundance triploids (ie. sum3x/sum). C4: relative abundance tetraploids (ie. sum4x/sum)","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate multiple generations. — gen.iter.f.choosy","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"Defined detail Gaynor et al. 2023. summarize, function simulates stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants overlapping generations two life-stages (reproductively immature reproductively mature). Population composition time t + 1 defined reproduction, survival, maturation. function loops set number generations. Note, lists three always values representing c(diploids, triploid, autotetraploids).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"","code":"gen.iter.f.choosy( generations, init.pop, env.ci, aii.vec, as.matur, as.msurv, d, gnum.base, b, cc, s, mc, density.type = \"all\", mate.lazy = FALSE )"},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"generations Number generations simulate. Must numeric value. init.pop number mature diploids initial founding population. Must numeric value greater 0. env.ci Proportion environmental variance used define mature survival rate per generation. Must integer greater equal 0 less 1. aii.vec survival probability immature individual cytotype. Must list three integers 0 1. example, aii.vec = c(0.5, 0.3, 0.5). .matur probability maturation immature stage mature stage cytotype. Must list three integers 0 1. example, .matur = c(0.5, 0.3, 0.5). .msurv mean survival probability mature individual cytotype. Must list three integers 0 1. example, .msurv = c(0.5, 0.3, 0.5). d Strength density dependency gamete production cytotype. Must list three integers 0 1. example, d = c(0.001, 0.009, 0.001). gnum.base Mean number gametes per individual per cytotype. Must list three numeric values. example, gnum.base = c(100, 100, 100). b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. s Selfing rate. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1. density.type Default = \"\", sets density time t individuals time t. Alternatively, \"like-cytotype\" sets density time t cytotype based total immature mature individuals cytotype time t. mate.lazy Default = FALSE, prevents selfing occurring outcrossing. However, increases computational time 31x!","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/gen.iter.f.choosy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate multiple generations. — gen.iter.f.choosy","text":"single data frame defined form.autopop(). row generation. columns follows, V1: number immature diploids. V2: number immature triploids. V3: number immature tetraploids. V4: number mature diploids. V5: number mature triploids. V6: number mature tetraploids. V7: number diploid offspring produced t - 1. V8: number triploid offspring produced t - 1. V9: number tetraploid offspring produced t - 1. V10: number gametes per diploid individual t - 1. V11: number gametes per triploid individual t - 1. V12: number gametes per tetraploid individual t - 1. gen: generation. sum: total number individuals. sum2x: sum immature mature diploids. sum3x: sum immature mature triploids. sum4x: sum immature mature tetraploids. V1a: relative abundance immature diploids (V1). V2a: relative abundance immature triploids (V2). V3a: relative abundance immature tetraploids (V3). V4a: relative abundance mature diploids (V4). V5a: relative abundance mature triploids (V5). V6a: relative abundance mature tetraploids (V6). C2: relative abundance diploids (sum2x/sum). C3: relative abundance triploids (sum3x/sum). C4: relative abundance tetraploids (sum4x/sum)","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":null,"dir":"Reference","previous_headings":"","what":"MAR(1) Conditional least squares parameter estimates. — mars.cls","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"described Ives et al. 2003, Multivariate Autoregressive Model, also known MAR(1) model, discrete-time model multispecies stochastic community subject environmental noise. Markov process. Given multispecies time series (log) abundance data without observation error, parameter estimation model parameters can quickly done via Conditional Least Squares.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"","code":"mars.cls(comm.mat, covariate = NULL)"},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"comm.mat Data frame log-scale abundance time step following burn-. expect one column per species one row per time-step. covariate Matrix covariates (ex. precipitation per time step) column representing variable row representing time step. Defaults \"NULL\" matrix supplied.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"Resulting data frame includes: , vector length p (p = number species) containing estimates intrinsic rate natural increase species. B, matrix p x p estimates diagonal elements represent intra-specific, density-dependent effects. elements \\(b_{ij}\\) gives effect abundance species j per capita growth rate species . sigma, matrix p x p, represents environmental noise variance-covariance matrix. C, vector estimated coefficients every covariate representing effects every covariate every species. E, vector representing stochastic environmental variability. Yhat, \\(\\hat{Y}\\) estimator predicted abundance. R2, \\(R^2\\), proportion explained variation log scale population abundance species. R2_D, conditional \\(R^2\\), proportion variation change one unit time log scale population abundance explained model species. AIC, Akaike information criterion. BIC, Bayesian information criterion. lnlike, maximized log likelihood MAR(1) model.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mars.cls.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"MAR(1) Conditional least squares parameter estimates. — mars.cls","text":"Ives, . R., Dennis, B., Cottingham, K. L., Carpenter, S. R. (2003). Estimating community stability ecological interactions time-series data. Ecological Monographs, 72(2): 301 - 330","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":null,"dir":"Reference","previous_headings":"","what":"Lazy reproduction function. — matrix_cytotype_repro_mate","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"function generate number offspring produced per generation. Assumes mixed mating system selfing outcrossing occur. Also allows group-based assortative mating (see Otto et al. 2008).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"","code":"matrix_cytotype_repro_mate(cgen, b, cc, gnum.vec, s, mc)"},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"cgen Number mature individuals cytotype current generation. Must list three numeric values. example, cgen = c(100, 100, 100). b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. gnum.vec mean number gametes produced individual cytotype. Must list three numeric values. example, gnum.vec = c(100, 100, 100). s Selfing rate. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"Output generation log including number 2x offspring, 3x offspring, 4x offspring, gametes sampled per 2x individual, gametes sampled per 3x individual, gametes sampled per 4x individual.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/matrix_cytotype_repro_mate.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Lazy reproduction function. — matrix_cytotype_repro_mate","text":"Otto, S. P., Servedio, M. R., Nuismer, S. L. (2008). Frequency-dependent selection evolution assortative mating. Genetics, 179(4) 2091 - 2112. doi: 10.1534/genetics.107.084418","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"Calculates probability mature survival based beta binomial distribution.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"","code":"mature.surv.calc(env.ci, as.msurv)"},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"env.ci Proportion environmental variance used define mature survival rate per generation. Must integer greater equal 0 less 1. .msurv Mean probability mature survival. Must single integer 0 1.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/mature.surv.calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculates probability of mature survival. — mature.surv.calc","text":"Probability mature survival.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"function can used convert alpha beta back mu var.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"","code":"muvar.calc(alpha, beta)"},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"alpha See alphabeta.calc. beta See alphabeta.calc.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/muvar.calc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculates mean and variance from alpha and beta. — muvar.calc","text":"List parameters mu var.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a single generation. — one.iter.f.choosy","title":"Simulate a single generation. — one.iter.f.choosy","text":"Defined detail Gaynor et al. 2023. summarize, function single time step stochastic stage-structured matrix population dynamics model diploid, triploid, autotetraploid perennial plants two life-stages (reproductively immature reproductively mature). Population composition time t + 1 defined reproduction, survival, maturation. Note, lists three always values representing c(diploids, triploid, autotetraploids).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a single generation. — one.iter.f.choosy","text":"","code":"one.iter.f.choosy( ct.vec, aii.vec, as.matur, as.msurv.e.set, d, gnum.base, s, b, cc, mc, density.type = \"all\", mate.lazy = FALSE )"},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate a single generation. — one.iter.f.choosy","text":"ct.vec Population composition time t. Indicates sum type time t, ct.vec = c(2x_immature, 3x_immature, 4x_immature, 2x_mature, 3x_mature, 4x_mature) aii.vec survival probability immature individual cytotype. Must list three integers 0 1. example, aii.vec = c(0.5, 0.3, 0.5). .matur probability maturation immature stage mature stage cytotype. Must list three integers 0 1. example, .matur = c(0.5, 0.3, 0.5). .msurv.e.set survival probability mature individual cytotype. Must list three integers 0 1. example, .msurv.e.set = c(0.5, 0.3, 0.5). list defined within gen.iter.f.choosy() function. d Strength density dependency gamete production cytotype. Must list three integers 0 1. example, d = c(0.001, 0.009, 0.001). gnum.base Mean number gametes per individual per cytotype. Must list three numeric values. example, gnum.base = c(100, 100, 100). s Selfing rate. Must single integer 0 1. b Proportion unreduced gamete formed diploid tetraploid individual. Must single integer 0 1. cc Proportion 3n gamete formation triploid individual. Must single integer 0 1. mc Strength mating choice. Must single integer 0 1. density.type Default = \"\", sets density time t individuals time t. Alternatively, \"like-cytotype\" sets density time t cytotype based total immature mature individuals cytotype time t. mate.lazy Default = FALSE, prevents selfing occurring outcrossing. However, increases computational time 31x!","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/one.iter.f.choosy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate a single generation. — one.iter.f.choosy","text":"List 9, 1:6 representing number individuals cytotype stages time t + 1. Items 7:9 number gametes sampled 2x, 3x, 4x individuals time t.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":null,"dir":"Reference","previous_headings":"","what":"Ives et al. 2003 stability metrics calculator. — stability","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"Based Ives et al. 2003, calculate four stability metrics based estimates matrix ecological interactions B estimated variance-covariance matrix environmental noise 'Sigma'. two quantities can read output function mars.cls().","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"","code":"stability(B, sigma)"},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"B Matrix p x p (p = number species), \\(b_{ij}\\) gives effect abundance species j per capita growth rate species. can calculated mars.cls() function. sigma Matrix p x p, environmental noise variance-covariance matrix. can calculated mars.cls() function.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"Resulting list includes: var.prop, stationarity var.prop variance proportion attributable environmental noise. smaller values, stable dynamics. mean.return.time & var.return.time, rate transition distribution converges back stationary distribution. less time takes return stationary distribution, stable population. reactivity, measures reaction perturbations distance away stationary system moves response disturbance. , smaller better terms stability. sp.contribs, squared eigenvalue representing characteristic return rate variance transition distribution estimated MARS(1) Markov Process.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/stability.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Ives et al. 2003 stability metrics calculator. — stability","text":"Ives, . R., Dennis, B., Cottingham, K. L., Carpenter, S. R. (2003). Estimating community stability ecological interactions frm time-series data. Ecological Monographs, 72(2): 301 - 330.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":null,"dir":"Reference","previous_headings":"","what":"Mature survival - Calculate variance from env.ci and mu. — var.option","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"Calculates variance based env.ci, proportion env. variance, mu, mean probability mature survival.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"","code":"var.option(env.ci, mu)"},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"env.ci Proportion environmental variance used define mature survival rate per generation beta distribution. number must 0 1, equal 0 1. mu Mean probability mature survival (list).","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"Calculated variance.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/var.option.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Mature survival - Calculate variance from env.ci and mu. — var.option","text":"","code":"variance <- var.option(env.ci = 0.9, mu = 0.6) #> Error in var.option(env.ci = 0.9, mu = 0.6): could not find function \"var.option\""},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":null,"dir":"Reference","previous_headings":"","what":"Processing function to subset a single replicate from a model set. — write.one.rep","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"Subset single replicate every model set.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"","code":"write.one.rep(set, setname)"},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"set List containing paths simulations wth replicates. can made function list.files(path = \"\", full.names = TRUE). setname Name output folder.","code":""},{"path":"http://mlgaynor.com/AutoPop/reference/write.one.rep.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Processing function to subset a single replicate from a model set. — write.one.rep","text":"write.one.rep() creates folder \"output/one_rep/setname\" csv file replicate made.","code":""}] diff --git a/vignettes/Introduction.Rmd b/vignettes/Introduction.Rmd index 7c75302..38497d8 100644 --- a/vignettes/Introduction.Rmd +++ b/vignettes/Introduction.Rmd @@ -17,7 +17,7 @@ library(knitr) ## Summary -AutoPop is an R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see Gaynor et al. 2023. To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations. Specifically, this model includes three cytotypes (diploids, triploids, and autotetraploids), as well as two life stages, in regards to reproductive ability (immature and mature), for a total of six stages each represented by $c_{i,j}$ where i = \{2, 3, 4\} and j = \{1,2\}. +AutoPop is an R-based joint-dynamic population simulation for diploids, triploids, and autotetraploids. For details on these methods, see [Gaynor et al. 2023](https://doi.org/10.1101/2023.03.29.534764). To summarize, this package contains a stochastic stage-structured matrix population dynamics model for diploid, triploid, and autotetraploid perennial plants with overlapping generations. Specifically, this model includes three cytotypes (diploids, triploids, and autotetraploids), as well as two life stages, in regards to reproductive ability (immature and mature), for a total of six stages each represented by $c_{i,j}$ where i = \{2, 3, 4\} and j = \{1,2\}. Each time step includes three processes: diff --git a/vignettes/Reproduction.Rmd b/vignettes/Reproduction.Rmd index d17c59d..f64a559 100644 --- a/vignettes/Reproduction.Rmd +++ b/vignettes/Reproduction.Rmd @@ -290,7 +290,7 @@ The current model allows all cytotypes to produce reduced and unreduced gametes ```{r echo = FALSE, fig.align='center'} knitr::include_graphics("img/Reproduction01.jpg", dpi = 200) ``` -**Figure 3**: Gametes produced by each cytotype. Here gam.vec indicates the $X_{i}$ at a time step, b is the rate of unreduced gamete formation, v is the proportion triploid gametes that will be viable (cc), and $c_{i,j}$ is the total number of the ith cytotype that is reproductively mature (j=2) at time t. This figure is Figure A1 in Gaynor et al. 2023. +**Figure 3**: Gametes produced by each cytotype. Here gam.vec indicates the $X_{i}$ at a time step, b is the rate of unreduced gamete formation, v is the proportion triploid gametes that will be viable (cc), and $c_{i,j}$ is the total number of the ith cytotype that is reproductively mature (j=2) at time t. This figure is Figure A1 in [Gaynor et al. 2023](https://doi.org/10.1101/2023.03.29.534764). @@ -301,7 +301,7 @@ The remaining parameters are associated with selfing rate ($s$) and mating choic ```{r echo = FALSE, fig.align='center'} knitr::include_graphics("img/FigureS1.jpg", dpi = 200) ``` -**Figure 4**: Figure A1 in Gaynor et al. 2023. This is a visual display of reproduction including gamete formation ($gam.vec$, $b$, and $v$) and the union of gametes via selfing or outcrossing ($s$ and $mc$). The number of gametes of each type is calculated based on the number of mature individuals of each cytotype ($c_{2,2}, c_{3,2}, c_{4,2}$), the number of gametes produced by each individual ($gam.vec$), the frequency of unreduced gamete formation ($b$), and the proportion triploid gametes that will be viable ($v$, or $cc$). After gamete formation, selfing will occur based on a defined selfing rate ($s$), the remaining gametes will outcross. Of these outcrossing gametes, a proportion will only pair with gametes produced by like-cytotypes indicated by mating choice ($mc$), the remainder will freely pair in a outcrossing pool. +**Figure 4**: Figure A1 in [Gaynor et al. 2023](https://doi.org/10.1101/2023.03.29.534764). This is a visual display of reproduction including gamete formation ($gam.vec$, $b$, and $v$) and the union of gametes via selfing or outcrossing ($s$ and $mc$). The number of gametes of each type is calculated based on the number of mature individuals of each cytotype ($c_{2,2}, c_{3,2}, c_{4,2}$), the number of gametes produced by each individual ($gam.vec$), the frequency of unreduced gamete formation ($b$), and the proportion triploid gametes that will be viable ($v$, or $cc$). After gamete formation, selfing will occur based on a defined selfing rate ($s$), the remaining gametes will outcross. Of these outcrossing gametes, a proportion will only pair with gametes produced by like-cytotypes indicated by mating choice ($mc$), the remainder will freely pair in a outcrossing pool. diff --git a/vignettes/Survival.Rmd b/vignettes/Survival.Rmd index 1631106..c4780fb 100644 --- a/vignettes/Survival.Rmd +++ b/vignettes/Survival.Rmd @@ -78,7 +78,7 @@ Mature probability of survival was defined using a beta distribution to sample m We can define $\alpha$ and $\beta$ based on $\mu$ and $env.ci$ by first defining variance as $\sigma^{2} = ci*\mu(1-\mu)$, then $\alpha = \mu * \frac{\mu*(1-\mu)}{(\sigma^{2} - 1)}$ and $\beta = (1-\mu)*\frac{\mu*(1-\mu)}{(\sigma^{2} - 1)}$. -Note, $0 \le env.ci < 1$, see Gaynor et al. 2023 for details. As env.ci increases, we see the variance in probability of survival also increase (**Figure 2**). Additionally, as the mature survival increase, so does the sampled probability of survival. +Note, $0 \le env.ci < 1$, see [Gaynor et al. 2023](https://doi.org/10.1101/2023.03.29.534764) for details. As env.ci increases, we see the variance in probability of survival also increase (**Figure 2**). Additionally, as the mature survival increase, so does the sampled probability of survival. ```{r mature, echo=FALSE, fig.align='center', fig.height = 8, fig.width = 8}