predict: Predict method of the package 'bam'.

In luismurao/bam: Species Distribution Models in the light of the BAM theory

Predict method of the package bam.

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 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

Examples

## 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)

luismurao/bam documentation built on Nov. 28, 2022, 3:02 p.m.