Package 'bamm' reference manual

Title:	Species Distribution Models as a Function of Biotic, Abiotic and Movement Factors (BAM)
Description:	Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4> presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The 'bamm' package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) <doi:10.1111/jbi.14587>.
Authors:	Luis Osorio-Olvera [aut, cre] , Jorge Soberón [aut] , Rusby G. Contreras-Díaz [ctb]
Maintainer:	Luis Osorio-Olvera <[email protected]>
License:	GPL (>= 3)
Version:	0.5.0
Built:	2025-02-08 03:23:47 UTC
Source:	https://github.com/luismurao/bamm

adj_mat: Function to compute the adjacency matrix of an area.

Description

Creates an adjacency matrix of an area of interest. This could be the accessible area (M) of a species or any geographic region of interest.

Usage

adj_mat(modelsparse, ngbs = 1, eigen_sys = FALSE, which_eigs = 1)
adj_mat(modelsparse, ngbs = 1, eigen_sys = FALSE, which_eigs = 1)

Arguments

`modelsparse`	A setA object returned by the function `model2sparse`.
`ngbs`	Numeric. Number of neighbors (see details).
`eigen_sys`	Logical. If TRUE the eigen analyses of the adjacency matrix will be returned.
`which_eigs`	Numeric. Which eigen value and eigen vector will be returned.

Details

The model is a raster object of the area where the dispersal process will occur. The number of neighbors depends on the dispersal abilities of the species and the spatial resolution of the niche model; for example, a species's with big dispersal abilities will move throughout more than 1 km^2 per day, so the idea is to give an approximate number of moving neighbors (pixels) per unit of time. For more information about see adjacency matrices in the context of the theory of area of distribution (Soberon and Osorio-Olvera, 2022).

Value

Returns an object of class setM with 7 slots. The first contains the adjacency matrix. A n x n sparse matrix (n=number of non-NA cells of the niche model) where connected cells are represented by 1. The second slot has the adjacency list. It is a list of matrices with four columns (FromRasCell -from cell ID of the raster-, -to cell ID of the raster-, -from non-NA cell-, -to non-NA cell-). Other slots contain information about initial coordinates where dispersal occurs (initial_points), number of cells used to define the neighborhood (ngbs), non-NA coordinates (coordinates), and a matrix of eigen vectors (eigen_vec).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

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

x_coord <- c(-106.5699, -111.3737,-113.9332,
             -110.8913, -106.4262, -106.5699)
y_coord <- c(16.62661, 17.72373, 19.87618,
             22.50763, 21.37728, 16.62661)
xy <- cbind(x_coord, y_coord)
p <- sp::Polygon(xy)
ps <- sp::Polygons(list(p),1)
sps <- sp::SpatialPolygons(list(ps))
mx_grid <- bamm::shape2Grid(sps,resolution = 0.25,ones = TRUE)
mx_sparse <- bamm::model2sparse(model=mx_grid, threshold = 0.1)
adj_mx <- bamm::adj_mat(modelsparse=mx_sparse,
                        ngbs=1,eigen_sys=TRUE,which_eigs=1)
print(adj_mx)
mx_grid_eigen <- mx_grid
mx_grid_eigen[mx_sparse@cellIDs] <- adj_mx@eigen_vec
raster::plot(mx_grid_eigen)

x_coord <- c(-106.5699, -111.3737,-113.9332,
             -110.8913, -106.4262, -106.5699)
y_coord <- c(16.62661, 17.72373, 19.87618,
             22.50763, 21.37728, 16.62661)
xy <- cbind(x_coord, y_coord)
p <- sp::Polygon(xy)
ps <- sp::Polygons(list(p),1)
sps <- sp::SpatialPolygons(list(ps))
mx_grid <- bamm::shape2Grid(sps,resolution = 0.25,ones = TRUE)
mx_sparse <- bamm::model2sparse(model=mx_grid, threshold = 0.1)
adj_mx <- bamm::adj_mat(modelsparse=mx_sparse,
                        ngbs=1,eigen_sys=TRUE,which_eigs=1)
print(adj_mx)
mx_grid_eigen <- mx_grid
mx_grid_eigen[mx_sparse@cellIDs] <- adj_mx@eigen_vec
raster::plot(mx_grid_eigen)

bam_clusters: Function to estimate the connectivity of suitable areas

Description

Function to estimate the connectivity of suitable areas given an adjacency matrix.

Usage

bam_clusters(model, ngbs = 1, plot_model = FALSE)
bam_clusters(model, ngbs = 1, plot_model = FALSE)

Arguments

`model`	A niche model in raster format or a `setA` object (see `model2sparse`).
`ngbs`	Numeric. Number of neighbors (see details).
`plot_model`	Logical. Indicates whether to plot the niche model using a leaflet map, connected suitable cells shown in the same color.

Details

The main result of the function is the Connectivity-Suitability-Diagram (CSD). In this diagram connected suitable cells make clusters of pixels. For more details about the CSD see (Soberon and Osorio-Olvera, 2022).

Value

An object of class csd. It contains three slots. 1) connections: a data.frame with three columns where first and the second represent (x and y) centroid coordinates of the niche model and the third column with the cluster ID where they belong. 2) interactive_map: a leaflet map of connected suitable pixels shown in the same color. 3) A RasterLayer of connected suitable pixels.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples

set.seed(891)
model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
model <- model > 0.7
clusterin <- bamm::bam_clusters(model,ngbs=1,plot_model=TRUE)
raster::plot(clusterin@raster_map)

clusterin@interactive_map

set.seed(891)
model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
model <- model > 0.7
clusterin <- bamm::bam_clusters(model,ngbs=1,plot_model=TRUE)
raster::plot(clusterin@raster_map)

clusterin@interactive_map

bam_sim: Simulate dispersal dynamics using the set B of the BAM framework.

Description

bam_sim: Simulate dispersal dynamics using the set B of the BAM framework.

Usage

bam_sim(
  sp1,
  sp2,
  set_M,
  initial_points,
  periods_toxic,
  periods_suitable,
  nsteps,
  progress_bar = TRUE
)
bam_sim(
  sp1,
  sp2,
  set_M,
  initial_points,
  periods_toxic,
  periods_suitable,
  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
`nsteps`	Number of steps to run the simulation
`progress_bar`	Show progress bar

Details

The returned object inherits from setA, setM classes. Details about the dynamic model can be found in Soberon and Osorio-Olvera (2022). The model is cellular automata where the occupied area of a species at time $t+1$ is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B) (Soberon and Osorio-Olvera, 2022).

$\mathbf{G}_j(t+1) =\mathbf{B}_j(t)\mathbf{A}_j(t)\mathbf{M}_j \mathbf{G}_j(t)$

Value

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples

# Compute dispersal dynamics of Urania boisduvalii as a function of
# palatable Omphalea
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_sim <- bamm::bam_sim(sp1=ura, sp2=omp, set_M=set_M,
                         initial_points=initial_points,
                         periods_toxic=5,
                         periods_suitable=1,
                         nsteps=40)
ura_omp <- bamm::sim2Raster(ura_sim)
raster::plot(ura_omp[[c(1,5,10,15,20,30,35,40)]])

if(requireNamespace("animation")){
# Animation example
anp <-tempfile(pattern = "simulation_results_",fileext = ".gif")
# new_sim <- bamm::sim2Animation(sdm_simul = ura_sim,
#                                which_steps = seq_len(ura_sim@sim_steps),
#                                fmt = "GIF",
#                               filename = anp)
}

# Compute dispersal dynamics of Urania boisduvalii as a function of
# palatable Omphalea
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_sim <- bamm::bam_sim(sp1=ura, sp2=omp, set_M=set_M,
                         initial_points=initial_points,
                         periods_toxic=5,
                         periods_suitable=1,
                         nsteps=40)
ura_omp <- bamm::sim2Raster(ura_sim)
raster::plot(ura_omp[[c(1,5,10,15,20,30,35,40)]])

if(requireNamespace("animation")){
# Animation example
anp <-tempfile(pattern = "simulation_results_",fileext = ".gif")
# new_sim <- bamm::sim2Animation(sdm_simul = ura_sim,
#                                which_steps = seq_len(ura_sim@sim_steps),
#                                fmt = "GIF",
#                               filename = anp)
}

bam_ssim: Simulate dispersal dynamics using the set B of the BAM framework.

Description

bam_ssim: Simulate dispersal dynamics using the set B of the BAM framework.

Usage

bam_ssim(
  sp1,
  sp2,
  set_M,
  initial_points,
  periods_toxic,
  periods_suitable,
  dispersal_prob = 0.85,
  palatable_matrices = FALSE,
  nsteps,
  progress_bar = TRUE
)
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

Details

The returned object inherits from setA, setM classes. Details about the dynamic model can be found in Soberon and Osorio-Olvera (2022).

Value

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.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples


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)
}

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)
}

Class `bam` digram

Description

Class bam digram

Value

An object of class bam

Slots

sdm_sim: A list of sparse vectors representing the area occupied
palatable_matrices: A list of sparse vectors representing palatable sites.
sim_steps: Number of simulation steps by the species

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Class `bioindex_sparse`

Description

Class bioindex_sparse

Value

An object of class bioindex_sparse

Slots

alpha: A sparse matrix with the richness of species per site
omega: A sparse matrix with the range size of every species
wBeta: A numeric value with Whittaker’s multiplicative beta index
laBeta: A numeric value with Lande’s additive beta index
leBeta: A numeric value with Legendre’s beta index
nestedness: A numeric value with Wright and Reeves' nestedness
dispersion_field: A sparse matrix with the set of ranges of all species that occur in at each locality
richness_field: A sparse matrix with the number of shared species in each site

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Class `bioindex`

Description

Class bioindex

Value

An object of class bioindex

Slots

alpha: A matrix with the richness of species per site
omega: A matrix with the range size of every species
wBeta: A numeric value with Whittaker’s multiplicative beta index
laBeta: A numeric value with Lande’s additive beta index
leBeta: A numeric value with Legendre’s beta index
nestedness: A numeric value with Wright and Reeves' nestedness
dispersion_field: A matrix with the set of ranges of all species that occur in at each locality
richness_field: A matrix with the number of shared species in each site

Author(s)

Luis Osorio-Olvera & Jorge Soberón

community_bam: Community `bamm`

Description

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
)
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.

Details

Each element in community_sim is an object of class. For more details about the simulation see sdm_sim. bam.

Value

An object of class community_sim. The object contains simulation results for each species in the community.

Author(s)

Luis Osorio-Olvera & Jorge Soberon

References

Examples


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)

com_pam <- bamm::csim2pam(sdm_comm,which_steps = seq(1,nsteps))
rich_pam <- pam2richness(com_pam,which_steps = c(1,5,10))
raster::plot(rich_pam)

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)

com_pam <- bamm::csim2pam(sdm_comm,which_steps = seq(1,nsteps))
rich_pam <- pam2richness(com_pam,which_steps = c(1,5,10))
raster::plot(rich_pam)

Class `community_sim` digram

Description

Class community_sim digram

Value

An object of class community_sim

Slots

community_sim: A list of sparse vectors representing the area occupied by the species

Author(s)

Luis Osorio-Olvera & Jorge Soberón

csd_estimate: Estimate the connectivity suitability and dispersal plot

Description

csd_plot gives an estimate of the number of geographic clusters given a set of dispersal hypothesis and a suitability raster

Usage

csd_estimate(model, dispersal_steps = c(2, 4, 8, 16, 32, 64))
csd_estimate(model, dispersal_steps = c(2, 4, 8, 16, 32, 64))

Arguments

`model`	A raster model or a setA object representing the suitability model
`dispersal_steps`	A numeric vector with elements representing the dispersal hypothesis to test.

Details

For more information about the Connectivity-Suitability-Diagram see bam_clusters

Value

A list of length three. The first element contains the Connectivity- Suitability-Diagram information estimated for each element in the vector of dispersal_steps. The second is tbl_df object with a summary of the number of cluster of each dispersal step and the mean number of connected clusters. The last element is base plot showing the information contained in the tbl_df object.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
model <- model > 0.7
csd_plot <- bamm::csd_estimate(model,
                         dispersal_steps=c(2,4,8))
csd_plot$plot

model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
model <- model > 0.7
csd_plot <- bamm::csd_estimate(model,
                         dispersal_steps=c(2,4,8))
csd_plot$plot

Class `csd`

Description

Class csd

Value

An object of class csd

Slots

connections: A data.frame with four columns: x, y, clusterID and cluster_size
interactive_map: A leaflet map with markers showing the geographical clusters
raster_map: A raster map with cluster IDs as values.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

csim2pam: Converts community simulation to a Presence Absence Matrix (PAM)

Description

Converts community simulation object into a Presence Absence Matrices (PAM) for a given simulation steps.

Usage

csim2pam(community_sim, which_steps)
csim2pam(community_sim, which_steps)

Arguments

`community_sim`	An object of class `community_bam`.
`which_steps`	Steps in the simulation object to be converted into a PAM

Details

For details about the object community_sim see community_sim

Value

An object of class pam; it contains five slots. 1) pams: a list of sparse matrices with Presence-Absence information (PAMs). 2) which_steps: time steps corresponding to each PAM. 3) sp_names: a vector of species names. 4) the grid area used in the simulation. 5) Non NA cell (pixel) IDs.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples


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 <- 10
sdm_comm <- bamm::community_sim(en_models = en_models,
                               ngbs_vect = ngbs_vect,
                               init_coords = init_coords,
                               nsteps = nsteps,
                               threshold = 0.1)

pamt10 <- bamm::csim2pam(community_sim = sdm_comm ,
                        which_steps = 10)
pams <- bamm::csim2pam(community_sim = sdm_comm ,
                       which_steps = seq_len(10))
rich_pam <- bamm::pam2richness(pams,which_steps = c(1,5))
print(rich_pam)


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 <- 10
sdm_comm <- bamm::community_sim(en_models = en_models,
                               ngbs_vect = ngbs_vect,
                               init_coords = init_coords,
                               nsteps = nsteps,
                               threshold = 0.1)

pamt10 <- bamm::csim2pam(community_sim = sdm_comm ,
                        which_steps = 10)
pams <- bamm::csim2pam(community_sim = sdm_comm ,
                       which_steps = seq_len(10))
rich_pam <- bamm::pam2richness(pams,which_steps = c(1,5))
print(rich_pam)

range_diversity_analysis: diversity analysis

Description

diversity_range_analysis biodiversity indices related to diversity-range plots

Usage

diversity_range_analysis(
  pam,
  xy_mat = NULL,
  lower_interval = 0.05,
  upper_interval = 0.95,
  raster_templete = NULL,
  niter = 100,
  return_null_dfield = FALSE,
  randal = "indep_swap",
  parallel = TRUE,
  n_cores = 2
)
diversity_range_analysis(
  pam,
  xy_mat = NULL,
  lower_interval = 0.05,
  upper_interval = 0.95,
  raster_templete = NULL,
  niter = 100,
  return_null_dfield = FALSE,
  randal = "indep_swap",
  parallel = TRUE,
  n_cores = 2
)

Arguments

`pam`	A Presence-Absence-Matrix of matrix class or sparse matrix.
`xy_mat`	A two dimensional matrix with longitude and latitude data.
`lower_interval`	Lower interval.
`upper_interval`	Upper interval.
`raster_templete`	A raster template.
`niter`	Number of iterations to obtain the distribution.
`return_null_dfield`	If TRUE the null distribution of dispersal field will be returned.
`randal`	Randomization algorithm applied to the PAM. Possible choices "curveball","fastball" and "indep_swap".
`parallel`	If TRUE the computations will be performed in parallel.
`n_cores`	Number of cores for the parallel computation.

Details

For more information about the biodiversity indices see Soberon and Cavner (2015). For detail about the diversity range analysis see Soberon et al. (2022). To plot diversity range results use plot method for objects of class diversity_range.

For details about randomization algorithms applied to the PAM see null_dispersion_field_distribution.

Value

An object of class diversity_range. The main result is the diversity range analysis which shows jointly two indices describing the community composition of every cell in the grid: (1) the relative number of species, and (2) the mean dispersion field (see plot method for plot (Soberon et al. 2022). The contains 12 slots with different measurements of biodiversity such as alpha diversity (species richness in each site or pixel), omega (size of the area of distribution of each species), dispersion field (the standardized size of the area of distribution of all species occurring in each pixel).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Soberón J, Cobos ME, Nuñez-Penichet C (2021). “Visualizing species richness and site similarity from presence-absence matrices.” Biodiversity Informatics, 16(1), 20–27. doi:10.17161/bi.v16i1.14782, https://journals.ku.edu/jbi/article/view/14782..

Soberon J, Cavner J (2015). “Indices of Biodiversity Pattern Based on Presence-Absence Matrices: A GIS Implementation.” Biodiversity Informatics, 10, 22–34..

Examples

set.seed(111)
pam <- matrix(rbinom(10000,1,0.5),nrow = 100,ncol = 1000)
rdivan <- bamm::diversity_range_analysis(pam=pam,
                                         parallel = FALSE,
                                         niter = 10,
                                         return_null_dfield=TRUE)
bamm::plot(rdivan,plot_type="diversity_range")
# Lagomorphos

lagos_path <- system.file("extdata/conejos",
                          package = "bamm")
enm_path <- list.files(lagos_path,
                       pattern = ".tif",
                       full.names = TRUE)
en_models <- raster::stack(enm_path) >0.01
nonas <- which(!is.na(en_models[[1]][]))
xy_mat <- sp::coordinates(en_models[[1]])[ nonas,]
pam <- bamm::models2pam(en_models,sparse=FALSE)

rdivan <- bamm::diversity_range_analysis(pam=pam,
                                         xy_mat=xy_mat,
                                         raster_templete = en_models[[1]],
                                         parallel=TRUE,
                                         n_cores=2,
                                         return_null_dfield=TRUE)
bamm::plot(rdivan,plot_type="diversity_range")
bamm::plot(rdivan,plot_type="diversity_range_map")
if(require(plotly) && require(crosstalk)){
#bamm::plot(rdivan,plot_type="diversity_range_interactive")
}

set.seed(111)
pam <- matrix(rbinom(10000,1,0.5),nrow = 100,ncol = 1000)
rdivan <- bamm::diversity_range_analysis(pam=pam,
                                         parallel = FALSE,
                                         niter = 10,
                                         return_null_dfield=TRUE)
bamm::plot(rdivan,plot_type="diversity_range")
# Lagomorphos

lagos_path <- system.file("extdata/conejos",
                          package = "bamm")
enm_path <- list.files(lagos_path,
                       pattern = ".tif",
                       full.names = TRUE)
en_models <- raster::stack(enm_path) >0.01
nonas <- which(!is.na(en_models[[1]][]))
xy_mat <- sp::coordinates(en_models[[1]])[ nonas,]
pam <- bamm::models2pam(en_models,sparse=FALSE)

rdivan <- bamm::diversity_range_analysis(pam=pam,
                                         xy_mat=xy_mat,
                                         raster_templete = en_models[[1]],
                                         parallel=TRUE,
                                         n_cores=2,
                                         return_null_dfield=TRUE)
bamm::plot(rdivan,plot_type="diversity_range")
bamm::plot(rdivan,plot_type="diversity_range_map")
if(require(plotly) && require(crosstalk)){
#bamm::plot(rdivan,plot_type="diversity_range_interactive")
}

Class `diversity_range`

Description

Class diversity_range

Value

An object of class diversity_range

Slots

alpha: A column vector with species richness per site
omega: A column vector with the size of the area of distribution per species.
alpha_raster: Species richness in raster format.
dispersion_field: A matrix with the set of ranges of all species that occur in at each locality.
dispersion_field_raster: Raster object with the observed values of dispersion field.
diversity_range_raster: Raster object of diversity range.
diversity_range_colors: Colors to plot endemism levels.
null_dispersion_field_dist: A matrix with dispersion field null distribution.
xy_coordinates: A matrix of geographical coordinates
n_iterations: Number of iterations used to estimate the dispersion field null distribution.
nsps: Number of species in the PAM.
nsites: Number of sites in the PAM.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

eigen_bam: Compute the Eigen system of two bam objects

Description

Calculates the Eigen values and Eigen vectors of bam objects

Usage

eigen_bam(A = NULL, M = NULL, AM = TRUE, which_eigen = 1, rmap = TRUE)
eigen_bam(A = NULL, M = NULL, AM = TRUE, which_eigen = 1, rmap = TRUE)

Arguments

`A`	A bam object of class setA.
`M`	A bam object of class setM.
`AM`	A logical value to specify whether to use the product AM or MA. If true the AM will be returned else the product MA will be returned.
`which_eigen`	An integer representing the which eigen value and eigen vector will be computed.
`rmap`	Logical. If TRUE the function will return a map of the eigen vector of the product AM.

Details

The eigenvector associated with the dominant eigenvalue of an adjacency matrix provides information about the number of forms in which a cell can be visited from other cells. Details about the eigen analysis in the context of the area of distribution can be found in Soberon and Osorio-Olvera (2022).

Value

A list with four objects. 1) eigen_values (these are indicated in which_eigen parameter of the function), 2) eigen_vectors (the corresponding eigen vectors of each eigen value), 3) Standardized eigen vectors (0 to 1), 4) A RasterLayer depicting the information of the first eigen vector of the system.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
sparse_mod <- bamm::model2sparse(model = model,0.75)
plot(sparse_mod@niche_model)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs = 1,eigen_sys = TRUE)
# Product AM
eig_bam_am <- bamm::eigen_bam(A=sparse_mod,M=adj_mod,AM=TRUE)
raster::plot(eig_bam_am$map)
# Product MA
eig_bam_ma <- bamm::eigen_bam(A=sparse_mod,M=adj_mod,AM=FALSE)
raster::plot(eig_bam_ma$map)

model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
sparse_mod <- bamm::model2sparse(model = model,0.75)
plot(sparse_mod@niche_model)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs = 1,eigen_sys = TRUE)
# Product AM
eig_bam_am <- bamm::eigen_bam(A=sparse_mod,M=adj_mod,AM=TRUE)
raster::plot(eig_bam_am$map)
# Product MA
eig_bam_ma <- bamm::eigen_bam(A=sparse_mod,M=adj_mod,AM=FALSE)
raster::plot(eig_bam_ma$map)

S4 classes to organize data and results of `bamm` objects

Description

S4 classes to organize data and results of bamm objects

Value

An object of class g_area

Slots

coordinates: A two column matrix with coordinates
eigen_vec: Eigen vector of adjacency matrix
eigen_val: Eigen value of adjacency matrix slot g_model A raster representing the geographic area slot g_sparse A sparse matrix of the geographic area

Author(s)

Luis Osorio-Olvera & Jorge Soberón

jaccard: Estimates the Jaccard index for comparing two binary maps

Description

Estimates the Jaccard index for comparing two binary maps

Usage

jaccard(m1, m2)
jaccard(m1, m2)

Arguments

`m1`	A binary raster A or an object of class setA returned by the function `model2sparse`.
`m2`	A binary raster A or an object of class setA returned by the function `model2sparse`.

Details

The Jaccard index is computed as follows

$J(A,B)={{|A\cap B|}\over{|A\cup B|}}={{|A\cap B|}\over{|A|+|B|-|A\cap B|}}.$

Value

Returns a data.frame with three values: 1) jaccard (Jaccard index), 2) percentage_m1 (the percentage of m1 that the intersection $|A \cap B|$ represents), and 3) percentage_m2

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples

m1_path <- system.file("extdata/conejos/Lepus_othus_cont.tif",
                       package = "bamm")
m2_path <- system.file("extdata/conejos/Brachylagus_idahoensis_cont.tif",
                       package = "bamm")
m1 <- raster::raster(m1_path) > 0.01
m2 <- raster::raster(m2_path) >0.01
jcc <- bamm::jaccard(m1,m2)
print(jcc)
m1_path <- system.file("extdata/conejos/Lepus_othus_cont.tif",
                       package = "bamm")
m2_path <- system.file("extdata/conejos/Brachylagus_idahoensis_cont.tif",
                       package = "bamm")
m1 <- raster::raster(m1_path) > 0.01
m2 <- raster::raster(m2_path) >0.01
jcc <- bamm::jaccard(m1,m2)
print(jcc)

Class `leaflet` leaflet

Description

Class leaflet leaflet

Value

An object of class leaflet

Author(s)

Luis Osorio-Olvera & Jorge Soberón

model2sparse: Converts a niche model into a diagonal sparse matrix

Description

model2sparse: Converts a niche model into a diagonal sparse matrix

Usage

model2sparse(model, threshold = NULL)
model2sparse(model, threshold = NULL)

Arguments

`model`	A raster object representing the geographic projection of a niche model.
`threshold`	A threshold to convert a continuous model into a binary model.

Details

threshold parameter represents the suitability value used to convert continuous model into a binary model.

Value

An object of class setA. The niche model is stored as diagonal sparse matrix (slot sparse_model).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples

model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)

sparse_mod <- bamm::model2sparse(model, threshold=0.75)
print(sparse_mod)
raster::plot(sparse_mod@niche_model)
model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)

sparse_mod <- bamm::model2sparse(model, threshold=0.75)
print(sparse_mod)
raster::plot(sparse_mod@niche_model)

models2pam: Converts binary rasters to a PAM

Description

Function to convert binary raster models to a Presence Absences Matrix.

Usage

models2pam(
  mods_stack,
  return_coords = FALSE,
  sparse = TRUE,
  parallel = FALSE,
  ncores = 2
)
models2pam(
  mods_stack,
  return_coords = FALSE,
  sparse = TRUE,
  parallel = FALSE,
  ncores = 2
)

Arguments

`mods_stack`	A raster stack containing binary models of each species in the community.
`return_coords`	Logical. If TRUE the pam will be returned with coordinates in the first two columns.
`sparse`	Logical. If TRUE the PAM will be returned as a sparse matrix.
`parallel`	Logical. If TRUE computations will be done in parallel
`ncores`	Integer. Number of cores to run the parallel process.

Details

For more information about PAM see Soberon and Cavner (2015).

Value

A presence-absence matrix (PAM).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Soberon J, Cavner J (2015). “Indices of Biodiversity Pattern Based on Presence-Absence Matrices: A GIS Implementation.” Biodiversity Informatics, 10, 22–34..

Examples


lagos_path <- system.file("extdata/conejos",
                          package = "bamm")
enm_path <- list.files(lagos_path,
                       pattern = ".tif",
                       full.names = TRUE)[1:10]
en_models <- raster::stack(enm_path) >0.01
pam <- bamm::models2pam(en_models,
                        return_coords=TRUE,
                        sparse=FALSE,
                        parallel=FALSE,ncores=2)
head(pam)

lagos_path <- system.file("extdata/conejos",
                          package = "bamm")
enm_path <- list.files(lagos_path,
                       pattern = ".tif",
                       full.names = TRUE)[1:10]
en_models <- raster::stack(enm_path) >0.01
pam <- bamm::models2pam(en_models,
                        return_coords=TRUE,
                        sparse=FALSE,
                        parallel=FALSE,ncores=2)
head(pam)

null_dispersion_field_distribution: Null distribution of the dispersion field

Description

null_dispersion_field_distribution estimates a random distribution of the dispersion field values.

Usage

null_dispersion_field_distribution(
  pam,
  n_iter = 10,
  randal = "indep_swap",
  parallel = TRUE,
  n_cores = 2
)
null_dispersion_field_distribution(
  pam,
  n_iter = 10,
  randal = "indep_swap",
  parallel = TRUE,
  n_cores = 2
)

Arguments

`pam`	A Presence-Absence-Matrix of matrix class or sparse matrix.
`n_iter`	Number of iterations to obtain the distribution.
`randal`	Randomization algorithm applied to the PAM. Possible choices "curveball", "fastball", and "indep_swap".
`parallel`	If TRUE the computations will be performed in parallel.
`n_cores`	Number of cores for the parallel computation.

Details

Estimates a random distribution of the dispersion field values. To obtain random values it uses the function permute_pam at each step of the iterations. Randomization of the PAM can be performed using the "fastball" (Godard and Neal, 2022) and the "curveball" (Strona et al., 2014), and and the independent swap (Kembel et al. 2010) algorithms. The implementation of the "fastball" in C++ is provided in https://github.com/zpneal/fastball/blob/main/fastball.cpp

Value

A data matrix of size nrow(pam) X n_iter with dispersion field values.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Soberon J, Cavner J (2015). “Indices of Biodiversity Pattern Based on Presence-Absence Matrices: A GIS Implementation.” Biodiversity Informatics, 10, 22–34.

Strona G, Nappo D, Boccacci F, Fattorini S, San-Miguel-Ayanz J (2014). “A fast and unbiased procedure to randomize ecological binary matrices with fixed row and column totals.” Nature Communications, 5(1), 1–9. ISSN 20411723, doi:10.1038/ncomms5114, https://www.r-project.org.

Godard K, Neal ZP (2022). “fastball: a fast algorithm to randomly sample bipartite graphs with fixed degree sequences.” Journal of Complex Networks, 10(6), cnac049. ISSN 2051-1329, doi:10.1093/comnet/cnac049, https://academic.oup.com/comnet/article-pdf/10/6/cnac049/47758701/cnac049.pdf.

Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blomberg SP, Webb CO (2010). “Picante: R tools for integrating phylogenies and ecology.” Bioinformatics, 26, 1463–1464.

Examples

set.seed(111)
pam <- matrix(rbinom(100,1,0.3),nrow = 10,ncol = 10)
dfield_rand <- bamm::null_dispersion_field_distribution(pam,n_iter=10,
                                                       parallel=FALSE,
                                                       randal="indep_swap",
                                                       n_cores = 2)
head(dfield_rand)
set.seed(111)
pam <- matrix(rbinom(100,1,0.3),nrow = 10,ncol = 10)
dfield_rand <- bamm::null_dispersion_field_distribution(pam,n_iter=10,
                                                       parallel=FALSE,
                                                       randal="indep_swap",
                                                       n_cores = 2)
head(dfield_rand)

occs2sparse: Converts occurrence data into a sparse matrix object

Description

occs2sparse: Converts occurrence data into a sparse matrix object

Usage

occs2sparse(modelsparse, occs)
occs2sparse(modelsparse, occs)

Arguments

`modelsparse`	A setA object returned by the function `model2sparse`
`occs`	A matrix or a data.frame containing two columns. The first one is the longitude and the second is the latitude.

Details

Rows of this column vector represent non NA pixels of the niche model.

Value

A sparse vector of zeros (presences) and ones (absences).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)

sparse_mod <- bamm::model2sparse(model,threshold=0.05)

occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))

occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)

head(occs_sparse)
model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)

sparse_mod <- bamm::model2sparse(model,threshold=0.05)

occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))

occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)

head(occs_sparse)

Class `pam` Presence-Absence Matrix

Description

Class pam Presence-Absence Matrix

Value

An object of class pam

Slots

pams: A list of sparse matrices representing Presence-Absence Matrix for each simulation time
which_steps: Simulation steps
sp_names: Names of species in the PAM
grid: Raster grid of the studied area
cellIDs: Cells with ids of the PAM sites

Author(s)

Luis Osorio-Olvera & Jorge Soberón

pam2bioindex: PAM to biodiversity index

Description

pam2bioindex estimates various biodiversity indices for a certain PAM.

Usage

pam2bioindex(pam, biodiv_index = "dispersion_field", as_sparse = FALSE)
pam2bioindex(pam, biodiv_index = "dispersion_field", as_sparse = FALSE)

Arguments

`pam`	A Presence-Absence-Matrix of matrix class or sparse matrix.
`biodiv_index`	Possible values are alpha, omega, wbeta (Whittaker’s multiplicative beta index), laBeta (Lande’s additive beta index) dispersion_field, all.
`as_sparse`	Return indices as sparse objects

Details

The biodiversity indices can be found in Soberón and Cavner (2015).

Value

An object of class bioindex with three slots each represents a matrix of diversity indices: alpha, omega, and dispersion field, richness_field.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Soberon J, Cavner J (2015). “Indices of Biodiversity Pattern Based on Presence-Absence Matrices: A GIS Implementation.” Biodiversity Informatics, 10, 22–34.

Examples

set.seed(111)
pam <- matrix(rbinom(100,1,0.3),nrow = 10,ncol = 10)
bioindices <- bamm::pam2bioindex(pam=pam,biodiv_index="all")
# Return results as sparse models
bioindices <- bamm::pam2bioindex(pam=pam,biodiv_index="all",as_sparse=TRUE)
bioindices@alpha
bioindices@omega
bioindices@dispersion_field
set.seed(111)
pam <- matrix(rbinom(100,1,0.3),nrow = 10,ncol = 10)
bioindices <- bamm::pam2bioindex(pam=pam,biodiv_index="all")
# Return results as sparse models
bioindices <- bamm::pam2bioindex(pam=pam,biodiv_index="all",as_sparse=TRUE)
bioindices@alpha
bioindices@omega
bioindices@dispersion_field

pam2richness: Converts Presence Absence Matrix (pam object) to richness raster

Description

Converts Presence Absence Matrix (pam object) to richness raster

Usage

pam2richness(pamobj, which_steps)
pam2richness(pamobj, which_steps)

Arguments

`pamobj`	An object of class pam see `csim2pam`
`which_steps`	Time steps in the pam to convert

Value

A RasterStack richness for each simulation step

Author(s)

Luis Osorio-Olvera & Jorge Soberón.

Examples


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(2,replace = TRUE,
                    size = raster::nlayers(en_models))
init_coords <- read.csv(file.path(lagos_path,
                                  "lagos_initit.csv"))[seq(1,10),]
nsteps <- 10
sdm_comm <- bamm::community_sim(en_models = en_models,
                                ngbs_vect = ngbs_vect,
                                init_coords = init_coords,
                                nsteps = nsteps,
                                threshold = 0.1)

pams <-bamm::csim2pam(community_sim = sdm_comm ,
                      which_steps = seq_len(nsteps))
richness_stack <- bamm::pam2richness(pamobj = pams,
                                     which_steps = pams@which_steps)
raster::plot(richness_stack)

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(2,replace = TRUE,
                    size = raster::nlayers(en_models))
init_coords <- read.csv(file.path(lagos_path,
                                  "lagos_initit.csv"))[seq(1,10),]
nsteps <- 10
sdm_comm <- bamm::community_sim(en_models = en_models,
                                ngbs_vect = ngbs_vect,
                                init_coords = init_coords,
                                nsteps = nsteps,
                                threshold = 0.1)

pams <-bamm::csim2pam(community_sim = sdm_comm ,
                      which_steps = seq_len(nsteps))
richness_stack <- bamm::pam2richness(pamobj = pams,
                                     which_steps = pams@which_steps)
raster::plot(richness_stack)

permute_pam: Function to permute a Presence-Absence-Matrix.

Description

permute_pam: Function to permute a Presence-Absence-Matrix.

Usage

permute_pam(m, niter = NULL, as_sparse = FALSE, randal = "indep_swap")
permute_pam(m, niter = NULL, as_sparse = FALSE, randal = "indep_swap")

Arguments

`m`	Presence-Absence-Matrix (PAM) or a binary matrix with columns representing species and rows sites.
`niter`	Number of iterations to permute the PAM.
`as_sparse`	If TRUE the PAM will be returned as a sparse matrix
`randal`	Randomization algorithm applied to the PAM. Possible choices "curveball", "fastball", and "indep_swap".

Details

This function can use the "curveball" (Strona et al., 2014), the fastball (Godard and Neal, 2022), and the independent swap algorithms. The implementation of the "fastball" in C++ is provided in https://github.com/zpneal/fastball/blob/main/fastball.cpp. Please when using the "fastball" algorithm for publications cite Godard and Neal (2022). When using the "curveball" cite Strona et al. (2014). When using independent swap ("indep_swap") cite Kembel et al. (2010)

Value

Returns a permuted matrix of the same dimensions of m (same number of rows and columns). Note that the sum of each row and column of this permuted matrix is equal to that of m. species.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD, Blomberg SP, Webb CO (2010). “Picante: R tools for integrating phylogenies and ecology.” Bioinformatics, 26, 1463–1464.

Examples

set.seed(111)
pam <- matrix(rbinom(100,1,0.3),nrow = 10,ncol = 10)
ppam <- bamm::permute_pam(m = pam,niter = NULL,as_sparse = FALSE)
# Check if matrices are different
all(pam == ppam)
# Check if row totals are the same
all(Matrix::rowSums(pam) == Matrix::rowSums(ppam))
# Check if column total are the same
all(Matrix::colSums(pam) == Matrix::colSums(ppam))
set.seed(111)
pam <- matrix(rbinom(100,1,0.3),nrow = 10,ncol = 10)
ppam <- bamm::permute_pam(m = pam,niter = NULL,as_sparse = FALSE)
# Check if matrices are different
all(pam == ppam)
# Check if row totals are the same
all(Matrix::rowSums(pam) == Matrix::rowSums(ppam))
# Check if column total are the same
all(Matrix::colSums(pam) == Matrix::colSums(ppam))

Plot method for objects of class diversity_range bamm.

Description

Plot method for objects of class diversity_range bamm.

Usage

## S4 method for signature 'diversity_range,ANY'
plot(
  x,
  xlab = NULL,
  plot_type = "diversity_range",
  legend = TRUE,
  legend_position = "bottomright",
  ylab = NULL,
  col = NULL,
  pch = NULL,
  pch_legend = 19,
  radius = 0.5,
  ...
)
## S4 method for signature 'diversity_range,ANY'
plot(
  x,
  xlab = NULL,
  plot_type = "diversity_range",
  legend = TRUE,
  legend_position = "bottomright",
  ylab = NULL,
  col = NULL,
  pch = NULL,
  pch_legend = 19,
  radius = 0.5,
  ...
)

Arguments

`x`	An object of class diversity_range
`xlab`	x label
`plot_type`	Plot type: possible options: "diversity_range" (range-diversity plot), "diversity_range_map" (a raster map with diversity_range categories), "alpha" (a raster map with alpha diversity values), "dispersion_field" (a raster with dispersion field)
`legend`	Logical. If TRUE the legend of the categorical diversity range values will appear.
`legend_position`	Legend position.
`ylab`	y label
`col`	Plot colors.
`pch`	Patch type.
`pch_legend`	Patch type for legends.
`radius`	Size of the patch for the interactive map.
`...`	Graphical parameters. Any argument that can be passed to 1) base::plot, such as axes=FALSE, main='title', ylab='latitude' 2) leaflet::leaflet or 3) leaflet::addCircleMarkers.

Details

To show interactive diversity_range plots install the 'plotly' R package.

Value

Plot of the results of the diversity_range analysis

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Predict method of the package bamm.

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
)
## 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 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.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

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)
#----------------------------------------------------------------------------
# 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))

# Generate the dispersal animation for time period 1 and 2

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")

}

# 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)
#----------------------------------------------------------------------------
# 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))

# Generate the dispersal animation for time period 1 and 2

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")

}

sdm_sim: Simulate single species dispersal dynamics using the BAM framework.

Description

sdm_sim: Simulate single species dispersal dynamics using the BAM framework.

Usage

sdm_sim(
  set_A,
  set_M,
  initial_points,
  nsteps,
  stochastic_dispersal = TRUE,
  disp_prop2_suitability = TRUE,
  disper_prop = 0.5,
  progress_bar = TRUE
)
sdm_sim(
  set_A,
  set_M,
  initial_points,
  nsteps,
  stochastic_dispersal = TRUE,
  disp_prop2_suitability = TRUE,
  disper_prop = 0.5,
  progress_bar = TRUE
)

Arguments

`set_A`	A setA object returned by the function `model2sparse`
`set_M`	A setM object containing the adjacency matrix of the study area. See `adj_mat`
`initial_points`	A sparse vector returned by the function `occs2sparse`
`nsteps`	Number of steps to run the simulation
`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.
`progress_bar`	Show progress bar

Details

The model is cellular automata where the occupied area of a species at time $t+1$ is estimated by the multiplication of two binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A) (Soberon and Osorio-Olvera, 2022).

$\mathbf{G}_j(t+1) =\mathbf{A}_j(t)\mathbf{M}_j \mathbf{G}_j(t)$

The equation describes a very simple process: To find the occupied patches in $t+1$ start with those occupied at time $t$ denoted by $\mathbf{G}_j(t)$ , allow the individuals to disperse among adjacent patches, as defined by $\mathbf{M}_j$ , then remove individuals from patches that are unsuitable, as defined by $\mathbf{A}_j(t)$ .

Value

An object of class bam with simulation results. The simulation are stored in the sdm_sim slot (a list of sparse matrices).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Examples


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)

sparse_mod <- bamm::model2sparse(model,threshold=0.05)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs=1)
occs_lep_cal <- data.frame(longitude = c(-110.08880,
                                         -98.89638),
                           latitude = c(30.43455,
                                        25.19919))

occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)
sdm_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                             set_M = adj_mod,
                             initial_points = occs_sparse,
                             nsteps = 10,
                             stochastic_dispersal = TRUE,
                             disp_prop2_suitability=TRUE,
                             disper_prop=0.5,
                             progress_bar=TRUE)

sim_res <- bamm::sim2Raster(sdm_lep_cal)
raster::plot(sim_res)


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)

sparse_mod <- bamm::model2sparse(model,threshold=0.05)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs=1)
occs_lep_cal <- data.frame(longitude = c(-110.08880,
                                         -98.89638),
                           latitude = c(30.43455,
                                        25.19919))

occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)
sdm_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                             set_M = adj_mod,
                             initial_points = occs_sparse,
                             nsteps = 10,
                             stochastic_dispersal = TRUE,
                             disp_prop2_suitability=TRUE,
                             disper_prop=0.5,
                             progress_bar=TRUE)

sim_res <- bamm::sim2Raster(sdm_lep_cal)
raster::plot(sim_res)

Class for the A set of the BAM diagram

Description

A class for the A set of the BAM diagram. It contains raster models and IDs of pixels with values different than NA.

Value

An object of class setA showClass("setA")

Slots

niche_model: A niche model in raster format. It can be a binary model or continuous. If the model is in a continuous format.
suit_threshold: Suitability value used to binarize continuous model
cellIDs: A numeric vector with the IDs of the cells with prediction values
suit_values: A numeric vector with suitability value of the continuous map
sparse_model: A niche model in sparse matrix format

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Class for the M set of the `bamm` diagram

Description

Class for the M set of the bamm diagram

Value

An object of class setM

Slots

adj_matix: An adjacency matrix
adj_list: An adjacency list
initial_points: A presence-absence vector with species' occurrences
n_initial_points: Number of initial points used to start the dispersal process
ngbs: Number of neighbors

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples

showClass("setM")
showClass("setM")

shape2Grid: Function to create a grid given a spatial polygon

Description

shape2Grid creates a raster grid given a spatial polygon and a grid resolution.

Usage

shape2Grid(shpolygon, resolution, ones = TRUE)
shape2Grid(shpolygon, resolution, ones = TRUE)

Arguments

`shpolygon`	A SpatialPolygon, SpatialPolygonDataFrame representing the desired shape of the grid.
`resolution`	Numeric. Spatial resolution of the grid.
`ones`	Logical. Fill with ones the values of the raster. If not the values will be written as cellID values.

Value

Returns a raster object with the shape of 'shpolygon' of a given resolution.

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples

x_coord <- c(-106.5699, -111.3737,-113.9332, -110.8913, -106.4262, -106.5699)
y_coord <- c(16.62661, 17.72373, 19.87618, 22.50763, 21.37728, 16.62661)
xy <- cbind(x_coord, y_coord)
p <- sp::Polygon(xy)
ps <- sp::Polygons(list(p),1)
sps <- sp::SpatialPolygons(list(ps))
r1 <- bamm::shape2Grid(sps,resolution = 0.1,ones = FALSE)
plot(r1)
sp::plot(sps,add=TRUE)

x_coord <- c(-106.5699, -111.3737,-113.9332, -110.8913, -106.4262, -106.5699)
y_coord <- c(16.62661, 17.72373, 19.87618, 22.50763, 21.37728, 16.62661)
xy <- cbind(x_coord, y_coord)
p <- sp::Polygon(xy)
ps <- sp::Polygons(list(p),1)
sps <- sp::SpatialPolygons(list(ps))
r1 <- bamm::shape2Grid(sps,resolution = 0.1,ones = FALSE)
plot(r1)
sp::plot(sps,add=TRUE)

Show information in setA class bamm.

Description

Show information in setA class bamm.

Show information in csd class bamm.

Show information in pam class bamm.

Show information in setA class bamm.

Show information in diversity_range class bamm.

Usage

## S4 method for signature 'setA'
show(object)

## S4 method for signature 'csd'
show(object)

## S4 method for signature 'pam'
show(object)

## S4 method for signature 'bioindex_sparse'
show(object)

## S4 method for signature 'setM'
show(object)

## S4 method for signature 'diversity_range'
show(object)
## S4 method for signature 'setA'
show(object)

## S4 method for signature 'csd'
show(object)

## S4 method for signature 'pam'
show(object)

## S4 method for signature 'bioindex_sparse'
show(object)

## S4 method for signature 'setM'
show(object)

## S4 method for signature 'diversity_range'
show(object)

Arguments

object

An object of class diversity_range

Value

Display information about the setA object

Display information about the csd object

Display information about the pam object

Display information about the bioindex_spars object

Display information about the setM object

Display information about the diversity_range object

Author(s)

Luis Osorio-Olvera & Jorge Soberón

sim2Animation: Animate BAM simulation object.

Description

Animates BAM simulation object.

Usage

sim2Animation(
  sdm_simul,
  which_steps,
  fmt = "GIF",
  filename,
  png_keyword = "sdm_sim",
  extra_legend = NULL,
  bg_color = "#F6F2E5",
  suit_color = "#0076BE",
  occupied_color = "#03C33F",
  gif_vel = 0.8,
  ani.width = 1200,
  ani.height = 1200,
  ani.res = 300
)
sim2Animation(
  sdm_simul,
  which_steps,
  fmt = "GIF",
  filename,
  png_keyword = "sdm_sim",
  extra_legend = NULL,
  bg_color = "#F6F2E5",
  suit_color = "#0076BE",
  occupied_color = "#03C33F",
  gif_vel = 0.8,
  ani.width = 1200,
  ani.height = 1200,
  ani.res = 300
)

Arguments

`sdm_simul`	A bam object. See `sdm_sim`
`which_steps`	A numeric vector indicating the simulation steps that are going to be converted into raster layers.
`fmt`	Animation format. Possible values are GIF and HTML
`filename`	File name.
`png_keyword`	A keyword name for the png images generated by the function
`extra_legend`	A legend to add to the animation.
`bg_color`	Color for unsuitable pixels. Default "#F6F2E5".
`suit_color`	Color for suitable pixels. Default "#0076BE".
`occupied_color`	Color for occupied pixels. Default "#03C33F".
`gif_vel`	A value that regulates the velocity of frame transitions. The bigger it is the transition will be slower default 0.8
`ani.width`	Animation width unit in px
`ani.height`	Animation height unit in px
`ani.res`	Animation resolution unit in px

Details

The animation can be saved in a GIF or HTML format. Note that the generation of the GIF can be time consuming for large simulation (simulations with more than 60 time steps).

Value

A RasterStack of species' distribution at each simulation step

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples

model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
sparse_mod <- bamm::model2sparse(model,0.1)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs=2)
occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))
occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)
sdm_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                            set_M = adj_mod,
                            initial_points = occs_sparse,
                            nsteps = 50)

if(requireNamespace("animation")){
ani_name <- tempfile(pattern = "simulation_",fileext = ".html")
#sdm_lep_cal_st <- bamm::sim2Animation(sdm_simul = sdm_lep_cal,
#                                      which_steps = seq(1,50,by=1),
#                                      fmt = "HTML",ani.width = 1200,
#                                      ani.height = 1200,
#                                      filename = ani_name)
}

model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
sparse_mod <- bamm::model2sparse(model,0.1)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs=2)
occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))
occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)
sdm_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                            set_M = adj_mod,
                            initial_points = occs_sparse,
                            nsteps = 50)

if(requireNamespace("animation")){
ani_name <- tempfile(pattern = "simulation_",fileext = ".html")
#sdm_lep_cal_st <- bamm::sim2Animation(sdm_simul = sdm_lep_cal,
#                                      which_steps = seq(1,50,by=1),
#                                      fmt = "HTML",ani.width = 1200,
#                                      ani.height = 1200,
#                                      filename = ani_name)
}

sim2Raster: Convert a BAM simulation object to RasterStack

Description

Convert a BAM simulation object to RasterStack.

Usage

sim2Raster(sdm_simul, which_steps = NULL)
sim2Raster(sdm_simul, which_steps = NULL)

Arguments

`sdm_simul`	A bam object. See `sdm_sim`
`which_steps`	A numeric vector indicating the simulation steps that are going to be converted into raster layers.

Value

A RasterStack of species' distribution at each simulation step

Author(s)

Luis Osorio-Olvera & Jorge Soberón

Examples


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
sparse_mod <- bamm::model2sparse(model,threshold=0.1)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs = 1)
occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))
occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)
sdm_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                            set_M = adj_mod,
                            initial_points = occs_sparse,
                            nsteps = 10)
sdm_lep_cal_st <- bamm::sim2Raster(sdm_simul = sdm_lep_cal,
                                  which_steps = seq(1,10,by=1))

raster::plot(sdm_lep_cal_st)


model_path <- system.file("extdata/Lepus_californicus_cont.tif",
                          package = "bamm")
model <- raster::raster(model_path)
sparse_mod <- bamm::model2sparse(model,threshold=0.1)
adj_mod <- bamm::adj_mat(sparse_mod,ngbs = 1)
occs_lep_cal <- data.frame(longitude = c(-115.10417,
                                         -104.90417),
                           latitude = c(29.61846,
                                        29.81846))
occs_sparse <- bamm::occs2sparse(modelsparse = sparse_mod,
                                occs = occs_lep_cal)
sdm_lep_cal <- bamm::sdm_sim(set_A = sparse_mod,
                            set_M = adj_mod,
                            initial_points = occs_sparse,
                            nsteps = 10)
sdm_lep_cal_st <- bamm::sim2Raster(sdm_simul = sdm_lep_cal,
                                  which_steps = seq(1,10,by=1))

raster::plot(sdm_lep_cal_st)

Package 'bamm'

Help Index

adj_mat: Function to compute the adjacency matrix of an area.

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

bam_clusters: Function to estimate the connectivity of suitable areas

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

bam_sim: Simulate dispersal dynamics using the set B of the BAM framework.

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

bam_ssim: Simulate dispersal dynamics using the set B of the BAM framework.

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Class bam digram

Description

Value

Slots

Author(s)

Class bioindex_sparse

Description

Value

Slots

Author(s)

Class bioindex

Description

Value

Slots

Author(s)

community_bam: Community bamm

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Class community_sim digram

Description

Value

Slots

Author(s)

csd_estimate: Estimate the connectivity suitability and dispersal plot

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

Class csd

Description

Value

Slots

Class `bam` digram

Class `bioindex_sparse`

Class `bioindex`

community_bam: Community `bamm`

Class `community_sim` digram

Class `csd`

Class `diversity_range`

S4 classes to organize data and results of `bamm` objects

Class `leaflet` leaflet