samc (version 1.3.0)

dispersal: Calculate dispersal metrics

Description

Calculates the probability of individuals visiting locations

Usage

dispersal(samc, occ, origin, dest, time)
# S4 method for samc,missing,missing,location,numeric
dispersal(samc, dest, time)
# S4 method for samc,RasterLayer,missing,location,numeric
dispersal(samc, occ, dest, time)
# S4 method for samc,matrix,missing,location,numeric
dispersal(samc, occ, dest, time)
# S4 method for samc,missing,missing,missing,missing
dispersal(samc)
# S4 method for samc,missing,location,missing,missing
dispersal(samc, origin)
# S4 method for samc,missing,missing,location,missing
dispersal(samc, dest)
# S4 method for samc,missing,location,location,missing
dispersal(samc, origin, dest)
# S4 method for samc,RasterLayer,missing,missing,missing
dispersal(samc, occ)
# S4 method for samc,matrix,missing,missing,missing
dispersal(samc, occ)
# S4 method for samc,RasterLayer,missing,location,missing
dispersal(samc, occ, dest)
# S4 method for samc,matrix,missing,location,missing
dispersal(samc, occ, dest)

Arguments

samc

A samc-class object. This should be output from the samc function.

occ

A RasterLayer-class or matrix. The input type must match the input type used to create the samc-class object, and must have the same properties as the rest of the landscape data. See the check function for more details.

origin

A positive integer or named location representing a cell in the landscape. Corresponds to row i of matrix P in the samc-class object. When paired with the dest parameter, multiple values may be provided as a vector.

dest

A positive integer or named location representing a cell in the landscape. Corresponds to column j of matrix P in the samc-class object. When paired with the origin parameter, multiple values may be provided as a vector.

time

A positive integer or a vector of positive integers representing time steps. Vectors must be ordered and contain no duplicates. Vectors may not be used for metrics that return dense matrices. The maximum time step value is capped at 10,000.

Value

A matrix, a vector, a list of vectors, or a numeric

Performance

Any relevant performance information about this function can be found in the performance vignette: vignette("performance", package = "samc")

Details

\(\tilde{D}_{jt}=(\sum_{n=0}^{t-1}\tilde{Q}^n)\tilde{q}_j\)

• dispersal(samc, dest, time)

  The result is a vector (single time step) or a list of vectors (multiple time steps) where each element corresponds to a cell in the landscape, and can be mapped back to the landscape using the map function. Element k is the probability of ever visiting a given destination, if starting at any other location, within t or fewer time steps.

\(\psi^T\tilde{D}_{jt}\)

• dispersal(samc, occ, dest, time)

  The result is a numeric (single time step) or a list of numerics (multiple time steps) that is the unconditional probability of visiting a given destination within t or fewer time steps.

\(D=(F-I)diag(F)^{-1}\)

• dispersal(samc)

  The result is a matrix where element (i,j) is the probability that location j is visited when starting in location i.

  The returned matrix will always be dense and cannot be optimized. Must enable override to use.

• dispersal(samc, origin)

  This function has not been optimized yet, and will not run.

• dispersal(samc, dest)

  The result is a vector where each element corresponds to a cell in the landscape, and can be mapped back to the landscape using the map function. Element i is the probability that the destination is visited when starting in location i.

• dispersal(samc, origin, dest)

  The result is a numeric value that is the probability that an individual starting at the origin visits the destination.

\(\psi^TD\)

• dispersal(samc, occ)

  The result is a vector where each element corresponds to a cell in the landscape, and can be mapped back to the landscape using the map function. Element j is the unconditional probability distribution of ever visiting location j, regardless of the initial location.

• dispersal(samc, occ, dest)

  The result is a numeric value that is the unconditional probability distribution of ever visiting a given destination, regardless of the initial location.

Examples

# NOT RUN {
# "Load" the data. In this case we are using data built into the package.
# In practice, users will likely load raster data using the raster() function
# from the raster package.
res_data <- samc::ex_res_data
abs_data <- samc::ex_abs_data
occ_data <- samc::ex_occ_data


# Make sure our data meets the basic input requirements of the package using
# the check() function.
check(res_data, abs_data)
check(res_data, occ_data)


# Create a `samc-class` object with the resistance and absorption data using
# the samc() function. We use the recipricol of the arithmetic mean for
# calculating the transition matrix. Note, the input data here are matrices,
# not RasterLayers. If using RasterLayers, the latlon parameter must be set.
samc_obj <- samc(res_data, abs_data, tr_fun = function(x) 1/mean(x))


# Convert the occupancy data to probability of occurrence
occ_prob_data <- occ_data / sum(occ_data, na.rm = TRUE)


# Calculate short- and long-term metrics using the analytical functions
short_mort <- mortality(samc_obj, occ_prob_data, time = 50)
short_dist <- distribution(samc_obj, origin = 3, time = 50)
long_disp <- dispersal(samc_obj, occ_prob_data)
visit <- visitation(samc_obj, dest = 4)
surv <- survival(samc_obj)


# Use the map() function to turn vector results into RasterLayer objects.
short_mort_map <- map(samc_obj, short_mort)
short_dist_map <- map(samc_obj, short_dist)
long_disp_map <- map(samc_obj, long_disp)
visit_map <- map(samc_obj, visit)
surv_map <- map(samc_obj, surv)
# }