bamm (version 0.5.0)

predict,bam-method: Predict method of the package bamm.

Description

predicts species' distribution under suitability changes

Usage

# S4 method for bam
predict(
  object,
  niche_layers,
  nbgs_vec = NULL,
  nsteps_vec,
  stochastic_dispersal = FALSE,
  disp_prop2_suitability = TRUE,
  disper_prop = 0.5,
  animate = FALSE,
  period_names = NULL,
  fmt = "GIF",
  filename,
  bg_color = "#F6F2E5",
  suit_color = "#0076BE",
  occupied_color = "#03C33F",
  png_keyword = "sdm_sim",
  ani.width = 1200,
  ani.height = 1200,
  ani.res = 300
)

Value

A RasterStack of predictions of dispersal dynamics as a function of environmental change scenarios.

Arguments

object

a of class bam.

niche_layers

A raster or RasterStack with the niche models for each time period

nbgs_vec

A vector with the number of neighbors for the adjacency matrices

nsteps_vec

Number of simulation steps for each time period.

stochastic_dispersal

Logical. If dispersal depends on a probability of visiting neighbor cells (Moore neighborhood).

disp_prop2_suitability

Logical. If probability of dispersal is proportional to the suitability of reachable cells. The proportional value must be declared in the parameter `disper_prop`.

disper_prop

Probability of dispersal to reachable cells.

animate

Logical. If TRUE a dispersal animation on climate change scenarios will be created

period_names

Character vector with the names of periods that will be animated. Default NULL.

fmt

Animation format. Possible values are GIF and HTML

filename

File name.

bg_color

Color for unsuitable pixels. Default "#F6F2E5".

suit_color

Color for suitable pixels. Default "#0076BE".

occupied_color

Color for occupied pixels. Default "#03C33F".

png_keyword

A keyword name for the png images generated by the function

ani.width

Animation width unit in px

ani.height

Animation height unit in px

ani.res

Animation resolution unit in px

Author

Luis Osorio-Olvera & Jorge Soberón

Examples

# rm(list = ls())
# Read raster model for Lepus californicus
model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
# Convert model to sparse
sparse_mod <- bamm::model2sparse(model = model,threshold=0.1)
# Compute adjacency matrix
adj_mod <- bamm::adj_mat(sparse_mod,ngbs=1)

# Initial points to start dispersal process

occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))
# Convert to sparse the initial points
occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)

# Run the bam (sdm) simultation for 100 time steps
smd_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                             set_M = adj_mod,
                             initial_points = occs_sparse,
                             nsteps = 10)
#----------------------------------------------------------------------------
# Predict species' distribution under suitability change
# scenarios (could be climate chage scenarios).
#----------------------------------------------------------------------------

# Read suitability layers (two suitability change scenarios)
layers_path <- system.file("extdata/suit_change",
                           package = "bamm")
niche_mods_stack <- raster::stack(list.files(layers_path,
                                             pattern = ".tif$",
                                             full.names = TRUE))
raster::plot(niche_mods_stack)
# Predict
new_preds <- predict(object = smd_lep_cal,
                     niche_layers = niche_mods_stack,
                     nsteps_vec = c(50,100))

# Generate the dispersal animation for time period 1 and 2
# \donttest{
if(requireNamespace("animation")){
ani_prd <- tempfile(pattern = "prediction_",fileext = ".gif")
#new_preds <- predict(object = smd_lep_cal,
#                      niche_layers = niche_mods_stack,
#                      nsteps_vec = c(10,10),
#                      animate=TRUE,
#                      filename=ani_prd,
#                    fmt="GIF")

}
# }