The cdyns package can be installed with the following script:
#install.packages("remotes")
remotes::install_github("aterui/cdyns")
library(cdyns)The R package cdyns is a collection of functions to perform community
dynamics simulations with stock enhancement. The current version of the
package includes the following functions:
cdynsim: Community dynamics simulation with stock enhancement
The key arguments are n_timestep (the number of time step to be
saved), n_warmup (warm-up period during which seeding happens),
n_burnin (burn-in period for eliminating initial condition effects),
and the number of species in a community (n_species). The community
dynamics are simulated using either a Ricker equation
(model = "ricker") or a Beverton-Holt equation (model = "bh").
Sample script:
library(cdyns)
sim <- cdynsim(n_timestep = 1000,
n_warmup = 200,
n_burnin = 200,
n_species = 10)This script returns the following:
-
df_dyn: dataframe for dynamics. Columns include time step (timestep), species id (species), and density (density) -
df_community: dataframe for the whole community. Columns include a temporal mean (mean_density) and sd (sd_density) of the whole community density. -
df_species: dataframe for species density and traits. Columns include species id (species), temporal mean (mean_density) and sd of species density (sd_density), carrying capacity (k), intrinsic population growth rate (r), and interspecific competition coefficient with the stocked species (alpha_j1). -
interaction_matrix: matrix of intra- and interspecific interactions.
print(sim)## $df_dyn
## # A tibble: 10,000 × 3
## timestep species density
## <dbl> <dbl> <dbl>
## 1 1 1 16.0
## 2 1 2 21.8
## 3 1 3 20.0
## 4 1 4 24.5
## 5 1 5 14.1
## 6 1 6 20.2
## 7 1 7 20.8
## 8 1 8 16.2
## 9 1 9 19.9
## 10 1 10 14.6
## # … with 9,990 more rows
##
## $df_community
## # A tibble: 1 × 2
## mean_density sd_density
## <dbl> <dbl>
## 1 182. 6.76
##
## $df_species
## # A tibble: 10 × 6
## species mean_density sd_density k r alpha_j1
## <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 18.3 3.41 100 1.5 1
## 2 2 17.9 3.61 100 1.5 0.5
## 3 3 18.3 3.49 100 1.5 0.5
## 4 4 18.0 3.38 100 1.5 0.5
## 5 5 18.2 3.43 100 1.5 0.5
## 6 6 18.1 3.43 100 1.5 0.5
## 7 7 17.9 3.33 100 1.5 0.5
## 8 8 18.6 3.49 100 1.5 0.5
## 9 9 18.5 3.28 100 1.5 0.5
## 10 10 18.1 3.33 100 1.5 0.5
##
## $interaction_matrix
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [2,] 0.5 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [3,] 0.5 0.5 1.0 0.5 0.5 0.5 0.5 0.5 0.5 0.5
## [4,] 0.5 0.5 0.5 1.0 0.5 0.5 0.5 0.5 0.5 0.5
## [5,] 0.5 0.5 0.5 0.5 1.0 0.5 0.5 0.5 0.5 0.5
## [6,] 0.5 0.5 0.5 0.5 0.5 1.0 0.5 0.5 0.5 0.5
## [7,] 0.5 0.5 0.5 0.5 0.5 0.5 1.0 0.5 0.5 0.5
## [8,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.0 0.5 0.5
## [9,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.0 0.5
## [10,] 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 1.0
##
## $vcov_matrix
## species1 species2 species3 species4 species5 species6
## species1 11.64802866 -1.7038982 -1.4740907 -1.1130980 -0.6672343 -0.04444368
## species2 -1.70389816 13.0265790 -1.4947944 -1.2865694 -0.8272281 -1.45325147
## species3 -1.47409073 -1.4947944 12.2086851 -1.0842118 0.1159128 0.62925744
## species4 -1.11309801 -1.2865694 -1.0842118 11.4234922 -0.9715645 -0.75601922
## species5 -0.66723428 -0.8272281 0.1159128 -0.9715645 11.7657396 -1.64855365
## species6 -0.04444368 -1.4532515 0.6292574 -0.7560192 -1.6485537 11.76768074
## species7 -1.20254580 0.1270377 -0.2007672 -0.7199614 -1.6027865 -0.73613316
## species8 -0.46812400 -1.5757900 -0.1001612 -0.4401668 0.3222239 -1.93580936
## species9 -1.69436531 0.5526940 -1.1378852 -1.1959470 -1.6485844 -0.58164199
## species10 0.88830657 -0.7458556 -2.3854508 -0.1383661 -0.4176972 -0.17558458
## species7 species8 species9 species10
## species1 -1.2025458 -0.4681240 -1.6943653 0.8883066
## species2 0.1270377 -1.5757900 0.5526940 -0.7458556
## species3 -0.2007672 -0.1001612 -1.1378852 -2.3854508
## species4 -0.7199614 -0.4401668 -1.1959470 -0.1383661
## species5 -1.6027865 0.3222239 -1.6485844 -0.4176972
## species6 -0.7361332 -1.9358094 -0.5816420 -0.1755846
## species7 11.0910564 -1.5351145 0.7550421 -2.2499270
## species8 -1.5351145 12.1517215 -0.8478887 -1.1512771
## species9 0.7550421 -0.8478887 10.7730985 0.3769068
## species10 -2.2499270 -1.1512771 0.3769068 11.1192567
Stock enhancement can be added using stock argument. For example,
stock = 100 adds 100 individuals to species 1 every time step:
sim <- cdynsim(n_timestep = 1000,
n_warmup = 200,
n_burnin = 200,
n_species = 10,
stock = 100)