README.md

In aterui/cdyns: Community Dynamics Simulation with Stock Enhancement

README

Installation

The cdyns package can be installed with the following script:

#install.packages("remotes")
remotes::install_github("aterui/cdyns")
library(cdyns)

Overview

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

Instruction

Basic usage

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

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)

aterui/cdyns documentation built on Nov. 29, 2024, 9:32 a.m.