| predict,bam-method {bamm} | R Documentation |
Predict method of the package bamm.
Description
predicts species' distribution under suitability changes
Usage
## S4 method for signature '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
)
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 |
Value
A RasterStack of predictions of dispersal dynamics as a function of environmental change scenarios.
Author(s)
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
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")
}