Specify targets based on area units

Specify targets expressed as area-based units. For example, this function can be used to express targets as hectares, acres, or km². To ensure feasibility, area-based targets are clamped based on the total abundance of features.

Usage

spec_area_targets(targets, area_units)

Arguments

targets

numeric vector denoting the area-based targets for the features. These values must be expressed in the same units as area_units. If a single numeric value is specified, then all features are assigned targets based on the area-based target.

area_units

character vector denoting the unit of measurement for each targets value (e.g., "km^2", "ha", and "acres"). If a single character value is specified, then all features are assigned targets assuming that targets are in the same units.

Value

An object (TargetMethod) for specifying targets that can be used with add_auto_targets() and add_group_targets() to add targets to a problem().

Mathematical formulation

This method provides an approach for setting target thresholds based on an area-based threshold. To express this mathematically, we will define the following terminology. Let \(f\) denote the total spatial extent of a feature (e.g., geographic range size expressed as km²), and \(a\) the specified area-based target (expressed as km², per targets and area_units). Given this terminology, the target threshold (\(t\)) for the feature is calculated as follows. $$ t = min(f, a) $$

Data calculations

This function involves calculating targets based on the spatial extent of the features in x. Although it can be readily applied to problem() objects that have the feature data provided as a terra::rast() object, you will need to specify the spatial units for the features when initializing the problem() objects if the feature data are provided in a different format. In particular, if the feature data are provided as a data.frame or character vector, then you will need to specify an argument to feature_units when using the problem() function. See the Examples section of the documentation for add_auto_targets() for a demonstration of specifying the spatial units for features.

See also

Other target setting methods: spec_absolute_targets(), spec_duran_targets(), spec_interp_absolute_targets(), spec_interp_area_targets(), spec_jung_targets(), spec_max_targets(), spec_min_targets(), spec_polak_targets(), spec_pop_size_targets(), spec_relative_targets(), spec_rl_ecosystem_targets(), spec_rl_species_targets(), spec_rodrigues_targets(), spec_rule_targets(), spec_ward_targets(), spec_watson_targets(), spec_wilson_targets()

Examples

# \dontrun{
# set seed for reproducibility
set.seed(500)

# load data
sim_complex_pu_raster <- get_sim_complex_pu_raster()
sim_complex_features <- get_sim_complex_features()

# create base problem
p0 <-
  problem(sim_complex_pu_raster, sim_complex_features) %>%
  add_min_set_objective() %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# create problem with targets of 50 km^2 for each feature
p1 <-
  p0 %>%
  add_auto_targets(
    method = spec_area_targets(targets = 50, area_units = "km^2")
  )

# solve problem
s1 <- solve(p1)

# plot solution
plot(s1, main = "solution based on constant targets", axes = FALSE)


# targets can also be specified for each feature separately.
# to demonstrate this, we will set a target value for each
# feature based on a random number between 5000 and 30000 hectares
target_values <- runif(terra::nlyr(sim_complex_features), 5000, 30000)

# create problem with targets defined separately for each feature
p2 <-
  p0 %>%
  add_auto_targets(
    method = spec_area_targets(targets = target_values, area_units = "ha")
  )

# solve problem
s2 <- solve(p2)

# plot solution
plot(s2, main = "solution based on varying targets", axes = FALSE)

# }