R6 class representing a dispersal friction.

R6 class functionality for modeling sea, ice and other frictional barriers to dispersal within a spatially-explicit population model. The dispersal friction model utilizes the gdistance package functionality to calculate distance multipliers to modify distance-based dispersal rates for simulated migrations in a spatio-temporal frictional landscape. The frictional landscape is defined via conductance/permeability values, the inverse of friction, which ranges from zero (barrier) to one (no friction) with values in-between representing some friction. For example, a conductance value of 1/5 = 0.2 represents a landscape in which simulated animals move 5 times slower than a non-friction landscape. In this example the resultant distance multiplier would be 5, thus reducing the effective dispersal range.

Super classes

poems::GenericClass -> poems::GenericModel -> poems::SpatialModel -> DispersalFriction

Public fields

attached

A list of dynamically attached attributes (name-value pairs).

Active bindings

model_attributes

A vector of model attribute names.

region

A Region (or inherited class) object specifying the study region.

coordinates

Data frame (or matrix) of X-Y population (WGS84) coordinates in longitude (degrees West) and latitude (degrees North) (get and set), or distance-based coordinates dynamically returned by region raster (get only).

parallel_cores

Number of cores for running the simulations in parallel.

write_to_dir

Directory path for storing distance multipliers when memory performance is an issue.

transition_directions

Number of transition directions or neighbors in which cells are connected: usually 4, 8 (default), or 16 (see gdistance::transition).

conductance

Matrix/raster of conductance (inverse friction) values (range: 0 = barrier; 0 < some friction < 1; 1 = no friction) for each grid cell (rows/cells) at each simulation time step (columns/layers).

attribute_aliases

A list of alternative alias names for model attributes (form: alias = "attribute") to be used with the set and get attributes methods.

error_messages

A vector of error messages encountered when setting model attributes.

warning_messages

A vector of warning messages encountered when setting model attributes.

Methods

Public methods

• DispersalFriction$calculate_distance_multipliers()

• DispersalFriction$clone()

Inherited methods

• poems::GenericModel$get_attribute()
• poems::GenericModel$get_attribute_aliases()
• poems::GenericModel$get_attribute_names()
• poems::GenericModel$get_attributes()
• poems::GenericModel$set_attributes()
• poems::SpatialModel$initialize()
• poems::SpatialModel$new_clone()

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Method calculate_distance_multipliers()

Calculates and returns spatio-temporal dispersal distance multipliers for each in-range migration.

Usage

DispersalFriction$calculate_distance_multipliers(dispersal_indices, ...)

Arguments

dispersal_indices

Two-column integer matrix, data.frame, or array representing the target and source coordinate index for each in-range migration.

...

Parameters passed via a params list or individually.

Returns

Temporal list of dispersal distance multiplier arrays with values for each in-range migration.

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Method clone()

The objects of this class are cloneable with this method.

Usage

DispersalFriction$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # interactive()
#' U Island example region
coordinates <- data.frame(
  x = rep(seq(177.01, 177.05, 0.01), 5),
  y = rep(seq(-18.01, -18.05, -0.01), each = 5)
)
template_raster <- Region$new(coordinates = coordinates)$region_raster #' full extent
template_raster[][-c(7, 9, 12, 14, 17:19)] <- NA #' make U Island
region <- Region$new(template_raster = template_raster)
raster::plot(region$region_raster,
  main = "Example region (indices)",
  xlab = "Longitude (degrees)", ylab = "Latitude (degrees)",
  colNA = "blue"
)

#' Dispersal distances
dispersal_gen <- DispersalGenerator$new(region = region)
dispersal_gen$set_attributes(params = list(p = 0.5, b = 700, r = 3000))
distances <- round(dispersal_gen$calculate_distance_matrix()) #' in m
dispersal_gen$calculate_distance_data()
dispersal_indices <- as.matrix(dispersal_gen$distance_data$base[, 1:2])

#' Distance multipliers with friction in cell 4
dispersal_friction <- DispersalFriction$new(
  region = region,
  conductance = c(1, 1, 1, 0.5, 1, 1, 1)
)
multipliers <- dispersal_friction$calculate_distance_multipliers(dispersal_indices)
cbind(dispersal_indices,
  distance = distances[dispersal_indices],
  multiplier = multipliers[[1]]
)

#' Note that crossing the water is avoided.
}