Introduction to mizerEvolution

Overview

The mizerEvolution package is an extension of the mizer package (ref) and simulate evolutionary processes within a size-structured model. Below are described the X steps to use this package wich are described more in details further down.

1. Installing mizerEvolution.

2. Setting the model parameters.

3. Running a simulation.

4. Additional functions

5. Exploring the results.

Installing mizerEvolution

mizerEvolution is an R package available on GitHub so you first need to have R installed on your computer, need devtools package and then start an R session and type:

devtools::install_github("baldrech/mizerEvolution")

After installing mizerEvolution, you need to load it via library().

library(mizerEvolution)

mizerEvolution is compatible with R versions 2.0.4 and later. The source code for mizerEvolution is hosted on Github.

Setting the model parameters

As with Mizer, you first need to create an object of class ? MizerParams. At the moment, the wrapper function evoParams() helps you create this object. It is a tweaked version of newTraitParams which add the necessary parameters to support species evolution, therefore all the default parameters from Mizer are found back in this function. evoParams() adds the lineage parameter which is used to track the ancetry tree of species and the RDD parameter which is used to set the recruitment function. It’s default is extinctionRDD() which allow species to be removed from the ecosystem when they reach an abundance below 10^− 30ind.m^− 3. In mizerEvolution, evolution comes from allowing specific parameters to change through time. These parameters are defined in the trait argument of evoProject. During the simulation, species will be considered as pool of phenotypes, each with a slighty different trait value. These phenotypes are generated semi-randomly, using only a rate of apparition inputed by the user. The phenotypes compete between each others and are pooled together only during the reproduction phase since they are still part of a same species. Generating phenotypes and removing the less abundant proxy natural selection and trait adaptation.

params <- evoParams(no_sp = 5 )

Running a simulation

This is done by calling the evoProject() function (as in “project forward in time”) with the model parameters. Similar to Mizer’s project(), evoProject() projects the simulation in time while adding new phenotypes using the mutation parameter (rate of apparition of new phenotypes in the simulation). The trait parameter determines which species parameter is going to vary during the simulation and differentiate phenotypes between each other within each species. The default is set to w_mat, the maturation size.

sim <- evoProject(params = params, t_max = 200, mutation = 3, trait = "beta")
#> [1] "Data handling"

This produces an object of class MizerSim which contains the results of the simulation.

Additional functions

The parameter initPool in evoProject() allows to initiate species as pool of phenotypes. Instead of starting with species composed of one phenotypes each, having initPool = 5 will add 5 randomly generated phenotypes per species at the start of the simulation (for a total of 6 phenotypes per species). The parameter initCondition allows to input a mizer object instead of a mizer param. This allows to start simulations from previously saved simulations.

Exploring the results

After a simulation has been run, the results can be examined using a range of ?plotting_functions, ?summary_functions and ?indicator_functions. The plot() function combines several of these plots into one:

plot(sim)

In this default Mizer plot, added phenotypes are considered as new species.

Package functionalities

• New species are copy of existing ones albeit with a change to one trait.

• Each projections containing a new species is independent of the others. They are saved in a temporary folder before being binded at the end of the simulation

• New plot functions allow to explore the evolutionary results

Algorithm

Instead of projecting for the entire time one mizer class object, the evoProject() will only project for a shorter amount of time, until a new species needs to be added in the ecosystem. When adding a new species (time chosen randomly), the projection stops, a new species is added with addSpecies(). At the moment, to simulate mutations, the new species is a copy of an existing one albeit for a slight difference in one trait: the maturation size. Further update will include more than one trait (any parameter in the model can become a trait) and mixed reproduction as at the moment new species included this way reproduce independtly from their “parent” and therefore make 2 diffent species. Afte adding a new species, a new projection starts. It means that one simulation is in reality a string of small projection. The finalTouch function takes all theses projection and makes them into one MizerObject wich is usable by any vanilla Mizer functions.

Plotting evolutionary results

base Mizer functions cannot handle species being formed of different “sub-species” so all plot functions have been updated to show species or their “phenotypes” using the lineage parameter

Checking the biomass through time. The default plot has a colorblind of 10 colors and won’t work with more than 10 species, if you want to personalise the plot, use returnData = TRUE.

plotDynamics

plotDynamics output the same results as plotBiomass (species’ biomass thorugh time) if the phenotype argument is set to FALSE

plotDynamics(sim, phenotype = F)

Showing the phenotypes as semi-transparent lines, with phenotype = TRUE (default)

plotDynamics(sim, phenotype = T)

The species argument displays all the phenotypes of one species only (take species identity as value)

plotDynamics(sim,species = 2)

The trait value of the phenotypes can be displayed per phenotypes as a continuous gradient, only available when only one species is selected

plotDynamics(sim, species = 2, trait = sim@params@species_params$beta)

The SpIdx argument selects for a subset of species

plotDynamics(sim,SpIdx = c(1,2,3))

plotSS

The size spectrum plot can display abundance density or biomass with the biomass argument

plotSS(sim,biomass = F)

plotSS(sim,biomass = T)

It can also display 3 levels of grouping: community, species, phenotypes

plotSS(sim, community = T)

plotSS(sim)

plotSS(sim, species = F)

Feeding level

plotevoFeeding(sim)

Growth

plotevoGrowth(sim)

Mortality

plotevoMortality(sim)

Trait evolution

plotevoTrait(sim, traitID = "beta", returnData = F)

Species invasion

Instead of generating phenotypic diversity within existing species, the model can instead introduce mutants in an existing ecosystem. One just needs to give a data frame to the mutation argument instead of a numeric.

params<- evoParams()

alien <- params@species_params[4:5,] # copy existing data frame
alien$h <- 50 # change some parameters
alien$alpha <- .6
alien$time <- c(50,75) # when are the invasive species coming in?
alien$lineage <- factor(c(12,13),levels = c(12,13)) # need to specify a lineage for these species otherwise they will be related to the endemic ones
alien$init_n_multiplier <- NULL # multiplier for the initial abundance
alien$species <- as.character(c(12,13))

#important: for now need to add new species following the number of existing species (e.g. generated ecosystem contains 11 species so first invading species is 12). Does not work otherwise
sim <- evoProject(params = params, alien = alien, mutation = 0)
#>    species w_min       w_inf       w_mat w_min_idx  h    gamma ks  f0   fc beta
#> 1        1 0.001    10.00000    2.511886         1 40 2754.672  4 0.6 0.25  100
#> 2        2 0.001    19.95262    5.011872         1 40 2754.672  4 0.6 0.25  100
#> 3        3 0.001    39.81072   10.000000         1 40 2754.672  4 0.6 0.25  100
#> 4        4 0.001    79.43282   19.952623         1 40 2754.672  4 0.6 0.25  100
#> 5        5 0.001   158.48932   39.810717         1 40 2754.672  4 0.6 0.25  100
#> 6        6 0.001   316.22777   79.432823         1 40 2754.672  4 0.6 0.25  100
#> 7        7 0.001   630.95734  158.489319         1 40 2754.672  4 0.6 0.25  100
#> 8        8 0.001  1258.92541  316.227766         1 40 2754.672  4 0.6 0.25  100
#> 9        9 0.001  2511.88643  630.957344         1 40 2754.672  4 0.6 0.25  100
#> 10      10 0.001  5011.87234 1258.925412         1 40 2754.672  4 0.6 0.25  100
#> 11      11 0.001 10000.00000 2511.886432         1 40 2754.672  4 0.6 0.25  100
#> 12      12 0.001    79.43282   19.952623         1 50 2754.672  4 0.6 0.25  100
#>    sigma z0 alpha     erepro interaction_resource         n         p         q
#> 1    1.3  0   0.4 0.11952402                    1 0.6666667 0.6666667 0.7166667
#> 2    1.3  0   0.4 0.09480694                    1 0.6666667 0.6666667 0.7166667
#> 3    1.3  0   0.4 0.07513533                    1 0.6666667 0.6666667 0.7166667
#> 4    1.3  0   0.4 0.05979226                    1 0.6666667 0.6666667 0.7166667
#> 5    1.3  0   0.4 0.04809642                    1 0.6666667 0.6666667 0.7166667
#> 6    1.3  0   0.4 0.03924217                    1 0.6666667 0.6666667 0.7166667
#> 7    1.3  0   0.4 0.03232946                    1 0.6666667 0.6666667 0.7166667
#> 8    1.3  0   0.4 0.02658807                    1 0.6666667 0.6666667 0.7166667
#> 9    1.3  0   0.4 0.02163458                    1 0.6666667 0.6666667 0.7166667
#> 10   1.3  0   0.4 0.01743907                    1 0.6666667 0.6666667 0.7166667
#> 11   1.3  0   0.4 0.01403004                    1 0.6666667 0.6666667 0.7166667
#> 12   1.3  0   0.6 0.05979226                    1 0.6666667 0.6666667 0.7166667
#>    pred_kernel_type k     w_mat25 m       R_max lineage ea_int ca_int zeta pop
#> 1         lognormal 0    2.250546 1 1.032270637       1      0      0  0.2   1
#> 2         lognormal 0    4.490429 1 0.635115196       2      0      0  0.2   1
#> 3         lognormal 0    8.959585 1 0.390761200       3      0      0  0.2   1
#> 4         lognormal 0   17.876722 1 0.240419874       4      0      0  0.2   1
#> 5         lognormal 0   35.668749 1 0.147920816       5      0      0  0.2   1
#> 6         lognormal 0   71.168510 1 0.091009814       6      0      0  0.2   1
#> 7         lognormal 0  141.999846 1 0.055994730       7      0      0  0.2   1
#> 8         lognormal 0  283.326942 1 0.034451337       8      0      0  0.2   1
#> 9         lognormal 0  565.311571 1 0.021196542       9      0      0  0.2   1
#> 10        lognormal 0 1127.944873 1 0.013041392      10      0      0  0.2   1
#> 11        lognormal 0 2250.545898 1 0.008023851      11      0      0  0.2   1
#> 12        lognormal 0   17.876722 1 0.240419874      12      0      0  0.2   1
#>      ext time
#> 1  FALSE   NA
#> 2  FALSE   NA
#> 3  FALSE   NA
#> 4  FALSE   NA
#> 5  FALSE   NA
#> 6  FALSE   NA
#> 7  FALSE   NA
#> 8  FALSE   NA
#> 9  FALSE   NA
#> 10 FALSE   NA
#> 11 FALSE   NA
#> 12 FALSE   50
#>    species w_min       w_inf       w_mat w_min_idx  h    gamma ks  f0   fc beta
#> 1        1 0.001    10.00000    2.511886         1 40 2754.672  4 0.6 0.25  100
#> 2        2 0.001    19.95262    5.011872         1 40 2754.672  4 0.6 0.25  100
#> 3        3 0.001    39.81072   10.000000         1 40 2754.672  4 0.6 0.25  100
#> 4        4 0.001    79.43282   19.952623         1 40 2754.672  4 0.6 0.25  100
#> 5        5 0.001   158.48932   39.810717         1 40 2754.672  4 0.6 0.25  100
#> 6        6 0.001   316.22777   79.432823         1 40 2754.672  4 0.6 0.25  100
#> 7        7 0.001   630.95734  158.489319         1 40 2754.672  4 0.6 0.25  100
#> 8        8 0.001  1258.92541  316.227766         1 40 2754.672  4 0.6 0.25  100
#> 9        9 0.001  2511.88643  630.957344         1 40 2754.672  4 0.6 0.25  100
#> 10      10 0.001  5011.87234 1258.925412         1 40 2754.672  4 0.6 0.25  100
#> 11      11 0.001 10000.00000 2511.886432         1 40 2754.672  4 0.6 0.25  100
#> 12      12 0.001    79.43282   19.952623         1 50 2754.672  4 0.6 0.25  100
#> 13      13 0.001   158.48932   39.810717         1 50 2754.672  4 0.6 0.25  100
#>    sigma z0 alpha     erepro interaction_resource         n         p         q
#> 1    1.3  0   0.4 0.11952402                    1 0.6666667 0.6666667 0.7166667
#> 2    1.3  0   0.4 0.09480694                    1 0.6666667 0.6666667 0.7166667
#> 3    1.3  0   0.4 0.07513533                    1 0.6666667 0.6666667 0.7166667
#> 4    1.3  0   0.4 0.05979226                    1 0.6666667 0.6666667 0.7166667
#> 5    1.3  0   0.4 0.04809642                    1 0.6666667 0.6666667 0.7166667
#> 6    1.3  0   0.4 0.03924217                    1 0.6666667 0.6666667 0.7166667
#> 7    1.3  0   0.4 0.03232946                    1 0.6666667 0.6666667 0.7166667
#> 8    1.3  0   0.4 0.02658807                    1 0.6666667 0.6666667 0.7166667
#> 9    1.3  0   0.4 0.02163458                    1 0.6666667 0.6666667 0.7166667
#> 10   1.3  0   0.4 0.01743907                    1 0.6666667 0.6666667 0.7166667
#> 11   1.3  0   0.4 0.01403004                    1 0.6666667 0.6666667 0.7166667
#> 12   1.3  0   0.6 0.05979226                    1 0.6666667 0.6666667 0.7166667
#> 13   1.3  0   0.6 0.04809642                    1 0.6666667 0.6666667 0.7166667
#>    pred_kernel_type k     w_mat25 m       R_max lineage ea_int ca_int zeta pop
#> 1         lognormal 0    2.250546 1 1.032270637       1      0      0  0.2   1
#> 2         lognormal 0    4.490429 1 0.635115196       2      0      0  0.2   1
#> 3         lognormal 0    8.959585 1 0.390761200       3      0      0  0.2   1
#> 4         lognormal 0   17.876722 1 0.240419874       4      0      0  0.2   1
#> 5         lognormal 0   35.668749 1 0.147920816       5      0      0  0.2   1
#> 6         lognormal 0   71.168510 1 0.091009814       6      0      0  0.2   1
#> 7         lognormal 0  141.999846 1 0.055994730       7      0      0  0.2   1
#> 8         lognormal 0  283.326942 1 0.034451337       8      0      0  0.2   1
#> 9         lognormal 0  565.311571 1 0.021196542       9      0      0  0.2   1
#> 10        lognormal 0 1127.944873 1 0.013041392      10      0      0  0.2   1
#> 11        lognormal 0 2250.545898 1 0.008023851      11      0      0  0.2   1
#> 12        lognormal 0   17.876722 1 0.240419874      12      0      0  0.2   1
#> 13        lognormal 0   35.668749 1 0.147920816      13      0      0  0.2   1
#>      ext time
#> 1  FALSE   NA
#> 2  FALSE   NA
#> 3  FALSE   NA
#> 4  FALSE   NA
#> 5  FALSE   NA
#> 6  FALSE   NA
#> 7  FALSE   NA
#> 8  FALSE   NA
#> 9  FALSE   NA
#> 10 FALSE   NA
#> 11 FALSE   NA
#> 12 FALSE   50
#> 13 FALSE   75
#> [1] "Data handling"
plotDynamics(sim)

Randomly introducing alien species. By giving an integer to the alien argument, this will become a rate per thousand of invation rate. The alien_n_init controls the initial level of abundance when introducing an invasive species. It is a multiplier of the inital abundance calculated to create a size spectrum. Default is set to NULL, making it kappa/1000.

params <- evoParams()

sim <- evoProject(params = params, mutation = 0, alien = 3, alien_init_n = NULL)
#> [1] "alien trying to invade ecosystem"
#>    species w_min       w_inf       w_mat w_min_idx      h        gamma       ks
#> 1        1 0.001    10.00000    2.511886         1  40.00 2.754672e+03 4.000000
#> 2        2 0.001    19.95262    5.011872         1  40.00 2.754672e+03 4.000000
#> 3        3 0.001    39.81072   10.000000         1  40.00 2.754672e+03 4.000000
#> 4        4 0.001    79.43282   19.952623         1  40.00 2.754672e+03 4.000000
#> 5        5 0.001   158.48932   39.810717         1  40.00 2.754672e+03 4.000000
#> 6        6 0.001   316.22777   79.432823         1  40.00 2.754672e+03 4.000000
#> 7        7 0.001   630.95734  158.489319         1  40.00 2.754672e+03 4.000000
#> 8        8 0.001  1258.92541  316.227766         1  40.00 2.754672e+03 4.000000
#> 9        9 0.001  2511.88643  630.957344         1  40.00 2.754672e+03 4.000000
#> 10      10 0.001  5011.87234 1258.925412         1  40.00 2.754672e+03 4.000000
#> 11      11 0.001 10000.00000 2511.886432         1  40.00 2.754672e+03 4.000000
#> 12      12 0.001  3940.80059  339.489082         1 126.02 1.637959e-10 3.773004
#>     f0   fc     beta    sigma         z0 alpha     erepro interaction_resource
#> 1  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.11952402                    1
#> 2  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.09480694                    1
#> 3  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.07513533                    1
#> 4  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.05979226                    1
#> 5  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.04809642                    1
#> 6  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.03924217                    1
#> 7  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.03232946                    1
#> 8  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.02658807                    1
#> 9  0.6 0.25    100.0 1.300000 0.00000000   0.4 0.02163458                    1
#> 10 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.01743907                    1
#> 11 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.01403004                    1
#> 12 0.6 0.25 396272.8 2.265746 0.03798596   0.6 1.00000000                    1
#>            n         p         q pred_kernel_type k     w_mat25 m        R_max
#> 1  0.6666667 0.6666667 0.7166667        lognormal 0    2.250546 1  1.032270637
#> 2  0.6666667 0.6666667 0.7166667        lognormal 0    4.490429 1  0.635115196
#> 3  0.6666667 0.6666667 0.7166667        lognormal 0    8.959585 1  0.390761200
#> 4  0.6666667 0.6666667 0.7166667        lognormal 0   17.876722 1  0.240419874
#> 5  0.6666667 0.6666667 0.7166667        lognormal 0   35.668749 1  0.147920816
#> 6  0.6666667 0.6666667 0.7166667        lognormal 0   71.168510 1  0.091009814
#> 7  0.6666667 0.6666667 0.7166667        lognormal 0  141.999846 1  0.055994730
#> 8  0.6666667 0.6666667 0.7166667        lognormal 0  283.326942 1  0.034451337
#> 9  0.6666667 0.6666667 0.7166667        lognormal 0  565.311571 1  0.021196542
#> 10 0.6666667 0.6666667 0.7166667        lognormal 0 1127.944873 1  0.013041392
#> 11 0.6666667 0.6666667 0.7166667        lognormal 0 2250.545898 1  0.008023851
#> 12 0.6666667 0.7000000 0.7166667        lognormal 0  304.168115 1 10.000000000
#>    lineage ea_int ca_int zeta pop   ext      k_vb
#> 1        1      0      0  0.2   1 FALSE        NA
#> 2        2      0      0  0.2   1 FALSE        NA
#> 3        3      0      0  0.2   1 FALSE        NA
#> 4        4      0      0  0.2   1 FALSE        NA
#> 5        5      0      0  0.2   1 FALSE        NA
#> 6        6      0      0  0.2   1 FALSE        NA
#> 7        7      0      0  0.2   1 FALSE        NA
#> 8        8      0      0  0.2   1 FALSE        NA
#> 9        9      0      0  0.2   1 FALSE        NA
#> 10      10      0      0  0.2   1 FALSE        NA
#> 11      11      0      0  0.2   1 FALSE        NA
#> 12      12      0      0  0.2  19 FALSE 0.7476512
#> [1] "alien trying to invade ecosystem"
#>    species w_min       w_inf       w_mat w_min_idx         h        gamma
#> 1        1 0.001    10.00000    2.511886         1  40.00000 2.754672e+03
#> 2        2 0.001    19.95262    5.011872         1  40.00000 2.754672e+03
#> 3        3 0.001    39.81072   10.000000         1  40.00000 2.754672e+03
#> 4        4 0.001    79.43282   19.952623         1  40.00000 2.754672e+03
#> 5        5 0.001   158.48932   39.810717         1  40.00000 2.754672e+03
#> 6        6 0.001   316.22777   79.432823         1  40.00000 2.754672e+03
#> 7        7 0.001   630.95734  158.489319         1  40.00000 2.754672e+03
#> 8        8 0.001  1258.92541  316.227766         1  40.00000 2.754672e+03
#> 9        9 0.001  2511.88643  630.957344         1  40.00000 2.754672e+03
#> 10      10 0.001  5011.87234 1258.925412         1  40.00000 2.754672e+03
#> 11      11 0.001 10000.00000 2511.886432         1  40.00000 2.754672e+03
#> 12      12 0.001  3940.80059  339.489082         1 126.01997 1.637959e-10
#> 13      13 0.001   277.17742   46.981701         1  41.45574 6.588478e-11
#>          ks  f0   fc     beta    sigma         z0 alpha     erepro
#> 1  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.11952402
#> 2  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.09480694
#> 3  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.07513533
#> 4  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.05979226
#> 5  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.04809642
#> 6  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.03924217
#> 7  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.03232946
#> 8  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.02658807
#> 9  4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.02163458
#> 10 4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.01743907
#> 11 4.000000 0.6 0.25    100.0 1.300000 0.00000000   0.4 0.01403004
#> 12 3.773004 0.6 0.25 396272.8 2.265746 0.03798596   0.6 1.00000000
#> 13 2.074027 0.6 0.25 207208.2 1.917540 0.09202348   0.6 1.00000000
#>    interaction_resource         n         p         q pred_kernel_type k
#> 1                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 2                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 3                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 4                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 5                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 6                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 7                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 8                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 9                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 10                    1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 11                    1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 12                    1 0.6666667 0.7000000 0.7166667        lognormal 0
#> 13                    1 0.6666667 0.7000000 0.7166667        lognormal 0
#>        w_mat25 m        R_max lineage ea_int ca_int zeta pop   ext      k_vb
#> 1     2.250546 1  1.032270637       1      0      0  0.2   1 FALSE        NA
#> 2     4.490429 1  0.635115196       2      0      0  0.2   1 FALSE        NA
#> 3     8.959585 1  0.390761200       3      0      0  0.2   1 FALSE        NA
#> 4    17.876722 1  0.240419874       4      0      0  0.2   1 FALSE        NA
#> 5    35.668749 1  0.147920816       5      0      0  0.2   1 FALSE        NA
#> 6    71.168510 1  0.091009814       6      0      0  0.2   1 FALSE        NA
#> 7   141.999846 1  0.055994730       7      0      0  0.2   1 FALSE        NA
#> 8   283.326942 1  0.034451337       8      0      0  0.2   1 FALSE        NA
#> 9   565.311571 1  0.021196542       9      0      0  0.2   1 FALSE        NA
#> 10 1127.944873 1  0.013041392      10      0      0  0.2   1 FALSE        NA
#> 11 2250.545898 1  0.008023851      11      0      0  0.2   1 FALSE        NA
#> 12  304.168115 1 10.000000000      12      0      0  0.2  19 FALSE 0.7476512
#> 13   42.093652 1 10.000000000      13      0      0  0.2  78 FALSE 0.6667926
#> [1] "alien trying to invade ecosystem"
#>    species w_min       w_inf       w_mat w_min_idx         h        gamma
#> 1        1 0.001    10.00000    2.511886         1  40.00000 2.754672e+03
#> 2        2 0.001    19.95262    5.011872         1  40.00000 2.754672e+03
#> 3        3 0.001    39.81072   10.000000         1  40.00000 2.754672e+03
#> 4        4 0.001    79.43282   19.952623         1  40.00000 2.754672e+03
#> 5        5 0.001   158.48932   39.810717         1  40.00000 2.754672e+03
#> 6        6 0.001   316.22777   79.432823         1  40.00000 2.754672e+03
#> 7        7 0.001   630.95734  158.489319         1  40.00000 2.754672e+03
#> 8        8 0.001  1258.92541  316.227766         1  40.00000 2.754672e+03
#> 9        9 0.001  2511.88643  630.957344         1  40.00000 2.754672e+03
#> 10      10 0.001  5011.87234 1258.925412         1  40.00000 2.754672e+03
#> 11      11 0.001 10000.00000 2511.886432         1  40.00000 2.754672e+03
#> 12      12 0.001  3940.80059  339.489082         1 126.01997 1.637959e-10
#> 13      13 0.001   277.17742   46.981701         1  41.45574 6.588478e-11
#> 14      14 0.001   831.22889  160.883313         1  91.66642 5.071192e-10
#>          ks  f0   fc     beta     sigma         z0 alpha     erepro
#> 1  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.11952402
#> 2  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.09480694
#> 3  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.07513533
#> 4  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.05979226
#> 5  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.04809642
#> 6  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.03924217
#> 7  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.03232946
#> 8  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.02658807
#> 9  4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.02163458
#> 10 4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.01743907
#> 11 4.000000 0.6 0.25    100.0 1.3000000 0.00000000   0.4 0.01403004
#> 12 3.773004 0.6 0.25 396272.8 2.2657458 0.03798596   0.6 1.00000000
#> 13 2.074027 0.6 0.25 207208.2 1.9175405 0.09202348   0.6 1.00000000
#> 14 7.172202 0.6 0.25 480634.8 0.5304179 0.06381328   0.6 1.00000000
#>    interaction_resource         n         p         q pred_kernel_type k
#> 1                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 2                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 3                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 4                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 5                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 6                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 7                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 8                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 9                     1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 10                    1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 11                    1 0.6666667 0.6666667 0.7166667        lognormal 0
#> 12                    1 0.6666667 0.7000000 0.7166667        lognormal 0
#> 13                    1 0.6666667 0.7000000 0.7166667        lognormal 0
#> 14                    1 0.6666667 0.7000000 0.7166667        lognormal 0
#>        w_mat25 m        R_max lineage ea_int ca_int zeta pop   ext      k_vb
#> 1     2.250546 1  1.032270637       1      0      0  0.2   1 FALSE        NA
#> 2     4.490429 1  0.635115196       2      0      0  0.2   1 FALSE        NA
#> 3     8.959585 1  0.390761200       3      0      0  0.2   1 FALSE        NA
#> 4    17.876722 1  0.240419874       4      0      0  0.2   1 FALSE        NA
#> 5    35.668749 1  0.147920816       5      0      0  0.2   1 FALSE        NA
#> 6    71.168510 1  0.091009814       6      0      0  0.2   1 FALSE        NA
#> 7   141.999846 1  0.055994730       7      0      0  0.2   1 FALSE        NA
#> 8   283.326942 1  0.034451337       8      0      0  0.2   1 FALSE        NA
#> 9   565.311571 1  0.021196542       9      0      0  0.2   1 FALSE        NA
#> 10 1127.944873 1  0.013041392      10      0      0  0.2   1 FALSE        NA
#> 11 2250.545898 1  0.008023851      11      0      0  0.2   1 FALSE        NA
#> 12  304.168115 1 10.000000000      12      0      0  0.2  19 FALSE 0.7476512
#> 13   42.093652 1 10.000000000      13      0      0  0.2  78 FALSE 0.6667926
#> 14  144.144765 1 10.000000000      14      0      0  0.2  95 FALSE 1.0382023
#> [1] "Data handling"

plotDynamics(sim)

# plotSS(sim)

One can supply a data frame of trait values which should be used to create invasive species using trait_range.

The default data frame is

trait	distribution	mean	sd
w_inf	lnorm	6.764846e+00	2.252483e+00
betaS	norm	1.862222e+02	1.765973e+02
betaL	norm	2.434883e+05	1.443748e+05
sigma	norm	1.716667e+00	5.742144e-01
k_vb	norm	4.440833e-01	2.729348e-01
ks	norm	6.289310e+00	2.573763e+00
eta	norm	1.207153e-01	1.148086e-01

These values are taken from the North Sea parameters.

The current assumptions are:

• w_inf follows a logarithmic distribution

• beta follows a u-shaped distribution caracterised by fitting two normal distributions over small betas (piscivore) and large betas (planktivore) species

• sigma, k_vb, ks and eta follow normal distribution

• w_mat is generated through w_inf x eta

Next update: switch beta, sigma, k_vb and ks to uniform distribution

Updating existing params

The argument updateParams in evoParams updates an existing mizerParams object into a mizerEvolution compatible object.

newP <- evoParams(updateParams = NS_params)

sim <- evoProject(params = newP, mutation = 1, trait = "beta")
#> [1] "Data handling"

plotDynamics(sim)

There are no differences if mutation is set to 0

old_sim <- project(NS_params)

sim <- evoProject(params = newP, mutation = 0)

plotDynamics(sim)

plotBiomass(old_sim)

Invasions work too

sim_inv <- evoProject(params = newP, mutation = 0, alien =2)

plotDynamics(sim_inv)