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Prepare Data Required To Perform A Simulation

Source: R/initialise.R
initialise.Rd

This function generates a sim_data object containing all the necessary information required to run a simulation by the sim function. The input maps (n1_map and K_map) can be in the Cartesian or longitude/latitude coordinate system.

Usage

initialise(
  n1_map,
  K_map,
  K_sd = 0,
  r,
  r_sd = 0,
  growth = "gompertz",
  A = NA,
  dens_dep = c("K2N", "K", "none"),
  border = c("reprising", "absorbing"),
  kernel_fun = "rexp",
  ...,
  max_dist = NA,
  calculate_dist = TRUE,
  dlist = NULL,
  progress_bar = TRUE,
  quiet = FALSE
)

Arguments

n1_map

SpatRaster object with one layer; population numbers in every grid cell at the first time step

K_map

SpatRaster object with one layer; carrying capacity map (if K is constant across time) or maps (if K is time-varying)

K_sd

numeric vector of length 1 with value >= 0 (default 0); this parameter can be used if additional environmental stochasticity is required; if K_sd > 0, random numbers are generated from a log-normal distribution with the mean K_map and standard deviation K_sd

r

numeric vector of length 1; intrinsic population growth rate

r_sd

numeric vector of length 1 with value >= 0 (default 0); if additional demographic stochasticity is required, r_sd > 0 is the standard deviation for a normal distribution around r (defined for each time step)

growth

character vector of length 1; the name of a population growth function, either defined in growth or provided by the user (case-sensitive, default "gompertz")

A

numeric vector of length 1; strength of the Allee effect (see the growth function)

dens_dep

character vector of length 1 specifying if the probability of settling in a target grid cell is (case-sensitive, default "K2N"):

  • "none" - fully random,

  • "K" - proportional to the carrying capacity of a target cell,

  • "K2N" - density-dependent, i.e. proportional to the ratio of carrying capacity of a target cell to the number of individuals already present in a target cell

border

character vector of length 1 defining how to deal with borders (case-sensitive, default "absorbing"):

  • "reprising" - cells outside the study area are not allowed as targets for dispersal

  • "absorbing" - individuals that disperse outside the study area are removed from the population

kernel_fun

character vector of length 1; name of a random number generation function defining a dispersal kernel (case-sensitive, default "rexp")

...

any parameters required by kernel_fun

max_dist

numeric vector of length 1; maximum distance of dispersal to pre-calculate target cells

calculate_dist

logical vector of length 1; determines if target cells will be precalculated

dlist

list; target cells at a specified distance calculated for every cell within the study area

progress_bar

logical vector of length 1; determines if progress bar for calculating distances should be displayed

quiet

logical vector of length 1; determines if messages should be displayed

Value

Object of class sim_data which inherits from list. This object contains all necessary information to perform a simulation using sim function.

Details

The most time-consuming part of computations performed by the sim function is the simulation of dispersal. To speed it up, a list containing indexes of target cells at a specified distance from a focal cell is calculated in advance and stored in a dlist slot. The max_dist parameter sets the maximum distance at which this pre-calculation is performed. If max_dist is NULL, it is set to 0.99 quantile from the kernel_fun. All distance calculations are always based on metres if the input maps are latitude/longitude. For planar input maps, distances are calculated in map units, which are typically metres, but check the crs() if in doubt.

If the input maps are in the Cartesian coordinate system and the grid cells are squares, then the distances between cells are calculated using the distance function from the terra package. These distances are later divided by the resolution of the input maps.

For input maps with grid cells in shapes other than squares (e.g. with rectangular cells or longitude/latitude coordinate system), the distance resolution is calculated by finding the shortest distance between each "queen" type neighbor. All distances calculated by the distance function are further divided by this distance resolution. To avoid discontinuities in the distances at which the target cells are located, an additional parameter dist_bin is calculated as half of the maximum distance between each "queen" type neighbour. It is used to expand the distances at which target cells are located from a single number to a range.

NA in the input maps represents cells outside the study area.

The K_get_interpolation function can be used to prepare K_map that changes over time. This may be useful, when simulating environmental change or exploring the effects of ecological disturbances.

References

Hijmans R (2024). terra: Spatial Data Analysis. R package version 1.7-81, https://rspatial.github.io/terra/, https://rspatial.org/

Solymos P, Zawadzki Z (2023). pbapply: Adding Progress Bar to '*apply' Functions. R package version 1.7-2, https://CRAN.R-project.org/package=pbapply.

See also

update

Examples

if (FALSE) { # \dontrun{

# input maps
library(terra)

n1_small <- rast(system.file("input_maps/n1_small.tif", package = "rangr"))
K_small <- rast(system.file("input_maps/K_small.tif", package = "rangr"))
K_small_changing <- rast(system.file("input_maps/K_small_changing.tif",
                         package = "rangr"))
n1_small_lon_lat <- rast(system.file("input_maps/n1_small_lon_lat.tif", package = "rangr"))
K_small_lon_lat <- rast(system.file("input_maps/K_small_lon_lat.tif", package = "rangr"))

# basic example
sim_data_1 <- initialise(
  n1_map = n1_small,
  K_map = K_small,
  r = log(2),
  rate = 1 / 1e3
)

# example with changing environment
K_interpolated <- K_get_interpolation(
  K_small_changing,
  K_time_points = c(1, 25, 50)
)

sim_data_2 <- initialise(
  n1_map = n1_small,
  K_map = K_interpolated,
  r = log(2),
  rate = 1 / 1e3
)

# example with lon/lat rasters
sim_data_3 <- initialise(
  n1_map = n1_small_lon_lat,
  K_map = K_small_lon_lat,
  r = log(2),
  rate = 1 / 1e3
)

# example without progress bar and messages
sim_data_4 <- initialise(
  n1_map = n1_small, K_map = K_small, K_sd = 0.1, r = log(5),
  r_sd = 4, growth = "ricker", rate = 1 / 200,
  max_dist = 5000, dens_dep = "K2N", progress_bar = FALSE, quiet = TRUE
)
} # }