targets: Targets

In prioritizr/prioritizrutils: Utilities for prioritizr

Description

Targets are used to specify the minimum amount or proportion of a feature"s distribution that needs to be protected. Below is a list of different targets that can be added to a conservation planning problem.

Usage

add_default_targets(x)

add_relative_targets(x, targets, ...) # x=ConservationProblem, targets=numeric

add_relative_targets(x, targets, ...) # x=ConservationProblem, targets=character

add_absolute_targets(x, targets, ...) # x=ConservationProblem, targets=numeric

add_absolute_targets(x, targets, ...) # x=ConservationProblem, targets=character

add_loglinear_targets(x, lower_bound_amount, lower_bound_target,
  upper_bound_amount, upper_bound_target)

Arguments

x	ConservationProblem-class object.
lower_bound_amount	numeric lower bound for the total amount of the features.
lower_bound_target	numeric relative target that should be applied to features with a total amount that is less than or equal to lower_bound_amount.
upper_bound_amount	numeric upper bound for the total amount of features.
upper_bound_target	numeric relative target that should be applied to features with a total amount that is greater than or equal to upper_bound_amount.
targets	numeric targets for features. If all features should have the same target, targets can be a single number. Otherwise, targets should be a numeric vector specifying a target for each feature. Alternatively, if the features in x were specified using a data.frame object, then argument to targets may refer to a column name.
...	not used.

Details

default_targets

The default targets are used when targets have not explicitly been set using the above functions. The creators of this package do not believe that there can be any sensible default targets. Thus relying on the default target will yield an error.

relative_targets

Targets are expressed as a proportion (between 0 and 1) of the maximum level of representation in the study area. The argument to x should have a single value if all features have the same target. Otherwise, the vector should have a value for each feature. In this case, targets are assigned to features based on the their position in the argument to x and the feature when specifying the problem.

absolute_targets

Targets are expressed as the actual value that needs to be represented in the prioritisation. The argument to x is treated the same as for relative_targets.

loglinear_targets

Targets are expressed as a proportion (between 0 and 1) which is calculated using a log-linear equation and four tuning parameters (as used in XXX et al. XXX). The first tuning parameter specifies the first cut-off range size, and the second the second cut-off range size, the third argument specifies the target required for species with a range size equal to or less than the first cut-off range size, and the fourth argument specifies the target required for species with a range size equal to or greater than the required range size.

Value

ConservationProblem-class object with the target added to it.

See Also

constraints, objectives, problem, add_feature_weights.

Examples

# load data
data(sim_pu_raster, sim_features)

# create basic problem
p <- problem(sim_pu_raster, sim_features) %>%
  add_min_set_objective()

# create problem with added relative targets
p1 <- p %>% add_relative_targets(0.1)

# create problem with added absolute targets
p2 <- p %>% add_absolute_targets(3)

# create problem with added log-linear target
p3 <- p %>% add_loglinear_targets(10, 0.9, 100, 0.2)


# solve solutions
s <- stack(solve(p1), solve(p2), solve(p3))

# plot solutions
plot(s, main=c("relative targets", "absolute targets",
               "log-linear targets"))

prioritizr/prioritizrutils documentation built on May 25, 2019, 12:20 p.m.