Estimate community dynamics using the bamm framework
Usage
community_sim(
en_models,
ngbs_vect,
init_coords,
nsteps,
threshold_vec = NULL,
stochastic_dispersal = FALSE,
disp_prop2_suitability = TRUE,
disper_prop = 0.5
)Arguments
- en_models
A stack or directory with the ecological niche models for each species in the community.
- ngbs_vect
A vector containing the number of neighbors for each adjacency matrix of each species in the community see
adj_mat.- init_coords
A data.frame with 3 columns: sp_name, x and y; x is the longitude and y is the latitude of initial dispersal points
- nsteps
Number of iteration steps for the simulation.
- threshold_vec
A vector of threshold values used to bnarize niche models.
- 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.
Value
An object of class community_sim. The object contains simulation results for each species in the community.
Details
Each element in community_sim is an object of class. For more
details about the simulation see sdm_sim.
bam.
References
Soberón J, Osorio-Olvera L (2023). “A dynamic theory of the area of distribution.” Journal of Biogeography6, 50, 1037-1048. doi:10.1111/jbi.14587 , https://onlinelibrary.wiley.com/doi/abs/10.1111/jbi.14587. .
Examples
# \donttest{
lagos_path <- system.file("extdata/conejos",
package = "bamm")
enm_path <- list.files(lagos_path,
pattern = ".tif",
full.names = TRUE)[seq(1,10)]
en_models <- raster::stack(enm_path)
ngbs_vect <- sample(1:2,replace = TRUE,
size = raster::nlayers(en_models))
init_coords <- read.csv(file.path(lagos_path,
"lagos_initit.csv"))[seq(1,10),]
nsteps <- 12
sdm_comm <- bamm::community_sim(en_models = en_models,
ngbs_vect = ngbs_vect,
init_coords = init_coords,
nsteps = nsteps)
#>
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com_pam <- bamm::csim2pam(sdm_comm,which_steps = seq(1,nsteps))
rich_pam <- pam2richness(com_pam,which_steps = c(1,5,10))
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raster::plot(rich_pam)
# }