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Simulate population dynamics following the steps approach

Source: R/simulate_population_steps.R
simulate_population_steps.Rd

This function adds a flag to a BiodiversityScenario object to indicate that species abundances are to be simulated based on the expected habitat suitability, as well as demography, density-dependence and dispersal information. The simulation is done using the steps package (Visintin et al. 2020) and conducted after a habitat suitability projection has been created. steps is a spatially explicit population models coded mostly in R.

For a detailed description of steps parameters, please see the respective reference and help files. Default assumptions underlying this wrapper are presented in the details

Usage

simulate_population_steps(
  mod,
  vital_rates,
  replicates = 1,
  carrying_capacity = NULL,
  initial = NULL,
  dispersal = NULL,
  density_dependence = NULL,
  include_suitability = TRUE
)

# S4 method for class 'BiodiversityScenario,matrix'
simulate_population_steps(
  mod,
  vital_rates,
  replicates = 1,
  carrying_capacity = NULL,
  initial = NULL,
  dispersal = NULL,
  density_dependence = NULL,
  include_suitability = TRUE
)

Arguments

mod

A BiodiversityScenario object with specified predictors.

vital_rates

A symmetrical demographic matrix. Should have column and row names equivalent to the vital stages that are to be estimated.

replicates

A numeric vector of the number of replicates (Default: 1).

carrying_capacity

Either SpatRaster or a numeric estimate of the maximum carrying capacity, e.g. how many adult individual are likely to occur per grid cell. If set to numeric, then carrying capacity is estimated up to a maximum set (Note: a more clever way would be to use a species-area relationship for scaling. This is not yet implemented).

initial

A SpatRaster giving the initial population size. If not provided, then initial populations are guessed (see details) from the projected suitability rasters (Default: NULL).

dispersal

A dispersal object defined by the steps package (Default: NULL).

density_dependence

Specification of density dependence defined by the steps package (Default: NULL).

include_suitability

A logical flag on whether the projected suitability estimates should be used (Default: TRUE) or only the initial conditions set to the first time step.

Value

Adds flag to a BiodiversityScenario object to indicate that further simulations are added during projection.

Details

In order for this function to work the steps package has to be installed separately. Instructions to do so can be found on github.

If initial population lifestages are not provided, then they are estimated assuming a linear scaling with suitability, a 50:50 split between sexes and a 1:3 ratio of adults to juveniles. The provision of different parameters is highly encouraged!

Note

The steps package has multiple options for simulating species population and not all possible options are represented in this wrapper.

Furthermore, the package still makes use of the raster package for much of its internal data processing. Since ibis.iSDM switched to terra a while ago, there can be efficiency problems as layers need to be translated between packages.

References

  • Visintin, C., Briscoe, N. J., Woolley, S. N., Lentini, P. E., Tingley, R., Wintle, B. A., & Golding, N. (2020). steps: Software for spatially and temporally explicit population simulations. Methods in Ecology and Evolution, 11(4), 596-603. https://doi.org/10.1111/2041-210X.13354

See also

Other constraint: add_constraint(), add_constraint_MigClim(), add_constraint_adaptability(), add_constraint_boundary(), add_constraint_connectivity(), add_constraint_dispersal(), add_constraint_minsize(), add_constraint_threshold()

Examples

if (FALSE) { # \dontrun{
# Define vital rates
vt <- matrix(c(0.0,0.5,0.75,
               0.5,0.2,0.0,
               0.0,0.5,0.9),
               nrow = 3, ncol = 3, byrow = TRUE)
colnames(vt) <- rownames(vt) <- c('juvenile','subadult','adult')

# Assumes that a trained 'model' object exists
 mod <- scenario(model) |>
  add_predictors(env = predictors, transform = 'scale',
                 derivates = "none") |>
  # Use Vital rates here, but note the other parameters!
  simulate_population_steps(vital_rates = vt) |>
  project()
} # }