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
)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.
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)
#>
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#----------------------------------------------------------------------------
# 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))
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# 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")
}
# }