predict,bam-method | R Documentation |
predicts species' distribution under suitability changes
## 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 )
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 declered 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. Posible 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 |
## Not run: # Load R packages library(bam) library(raster) # rm(list = ls()) # Read raster model for Lepus californicus model_path <- system.file("extdata/Lepus_californicus_cont.tif", package = "bam") model <- raster::raster(model_path) # Convert model to sparse sparse_mod <- bam::model2sparse(model = model) # Compute adjacency matrix adj_mod <- bam::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 <- bam::occs2sparse(modelsparse = sparse_mod, occs = occs_lep_cal) # Run the bam (sdm) simultation for 100 time steps smd_lep_cal <- bam::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 = "bam") niche_mods_stack <- raster::stack(list.files(layers_path, pattern = ".tif$", full.names = TRUE)) > 0.1 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 new_preds <- predict(object = smd_lep_cal, niche_layers = niche_mods_stack, nsteps_vec = c(10,10), animate=TRUE, filename="/home/l916o895/Desktop/animacion_01.html", fmt="HTML") ## End(Not run)
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