# bam_ssim: Simulate dispersal dynamics using the set B of the BAM framework. ```r bam_ssim( sp1, sp2, set_M, initial_points, periods_toxic, periods_suitable, dispersal_prob = 0.85, palatable_matrices = FALSE, nsteps, progress_bar = TRUE ) ``` ## Arguments - `sp1`: Niche model of the focal species (the one that disperses). - `sp2`: Niche model of the species with whom sp1 interacts (currently no dispersal dynamics for this species). - `set_M`: A setM object containing the adjacency matrix for sp1. See `adj_mat` - `initial_points`: A sparse vector returned by the function `occs2sparse` - `periods_toxic`: Time periods that sps2 takes to develop defense mechanisms (i.e. toxic). - `periods_suitable`: This is the time that sp2 takes to become non-toxic - `dispersal_prob`: A numeric value indicating the probability to disperse to neighboring cells. This probability is assumed to be binomially distributed - `palatable_matrices`: Logical. If TRUE palatable matrices for each time will be returned. - `nsteps`: Number of steps to run the simulation - `progress_bar`: Show progress bar ## Returns An object of class bam. The object contains 12 slots of information (see details) from which simulation results are stored in sdm_sim object, a list of sparse matrices with results of each simulation step. Palatable matrices are returned as a list of sparse matrices with information about palatable pixels for each step of the simulation. ## Details The returned object inherits from `setA`, `setM` classes. Details about the dynamic model can be found in Soberon and Osorio-Olvera (2022). ## Examples ```r urap <- system.file("extdata/urania_omph/urania_guanahacabibes.tif", package = "bamm") ura <- raster::raster(urap) ompp <- system.file("extdata/urania_omph/omphalea_guanahacabibes.tif", package = "bamm") omp <- raster::raster(ompp) msparse <- bamm::model2sparse(ura) init_coordsdf <- data.frame(x=-84.38751, y= 22.02932) initial_points <- bamm::occs2sparse(modelsparse = msparse,init_coordsdf) set_M <- bamm::adj_mat(modelsparse = msparse,ngbs = 1) ura_ssim <- bamm::bam_ssim(sp1=ura, sp2=omp, set_M=set_M, dispersal_prob = 0.75, initial_points=initial_points, periods_toxic=5, periods_suitable=1, nsteps=40) ura_omp <- bamm::sim2Raster(ura_ssim) raster::plot(ura_omp[[c(1,2,5,10,15,20,30,35,40)]]) if(requireNamespace("animation")){ # Animation example anp <-tempfile(pattern = "simulation_results_",fileext = ".gif") #new_sim <- bamm::sim2Animation(sdm_simul = ura_ssim, # which_steps = seq_len(ura_ssim@sim_steps), # fmt = "GIF", # filename = anp) } ``` ## References \insertRef SoberonOsoriobamm. ## Author(s) Luis Osorio-Olvera & Jorge Soberón

bam_ssim function