An objective is used to specify the overall goal of a conservation planning problem(). All conservation planning problems involve minimizing or maximizing some kind of objective. For instance, the planner may require a solution that conserves enough habitat for each species while minimizing the overall cost of the reserve network. Alternatively, the planner may require a solution that maximizes the number of conserved species while ensuring that the cost of the reserve network does not exceed the budget.

Details

Please note that all conservation planning problems formulated using the prioritizr package require an objective function---failing to do so will return an error message when attempting to solve problem.

The following objectives can be added to a conservation planning problem():

add_min_set_objective()

Minimize the cost of the solution whilst ensuring that all targets are met. This objective is similar to that used in Marxan.

add_max_cover_objective()

Represent at least one instance of as many features as possible within a given budget.

add_max_features_objective()

Fulfill as many targets as possible while ensuring that the cost of the solution does not exceed a budget.

add_min_shortfall_objective()

Minimize the overall (weighted sum) shortfall for as many targets as possible while ensuring that the cost of the solution does not exceed a budget.

add_min_largest_shortfall_objective()

Minimize the largest (maximum) shortfall among all targets while ensuring that the cost of the solution does not exceed a budget.

add_max_phylo_div_objective()

Maximize the phylogenetic diversity of the features represented in the solution subject to a budget.

add_max_phylo_end_objective()

Maximize the phylogenetic endemism of the features represented in the solution subject to a budget.

add_max_utility_objective()

Secure as much of the features as possible without exceeding a budget.

Examples

# load data
data(sim_pu_raster, sim_features, sim_phylogeny)

# create base problem
p <- problem(sim_pu_raster, sim_features) %>%
     add_relative_targets(0.1) %>%
     add_binary_decisions() %>%
     add_default_solver(verbose = FALSE)

 # create problem with added minimum set objective
p1 <- p %>% add_min_set_objective()

# create problem with added maximum coverage objective
# note that this objective does not use targets
p2 <- p %>% add_max_cover_objective(500)

# create problem with added maximum feature representation objective
p3 <- p %>% add_max_features_objective(1900)

# create problem with added minimum shortfall objective
p4 <- p %>% add_min_shortfall_objective(1900)

# create problem with added minimum largest shortfall objective
p5 <- p %>% add_min_largest_shortfall_objective(1900)

# create problem with added maximum phylogenetic diversity objective
p6 <- p %>% add_max_phylo_div_objective(1900, sim_phylogeny)

# create problem with added maximum phylogenetic diversity objective
p7 <- p %>% add_max_phylo_end_objective(1900, sim_phylogeny)

# create problem with added maximum utility objective
# note that this objective does not use targets
p8 <- p %>% add_max_utility_objective(1900)

# \dontrun{
# solve problems
s <- stack(solve(p1), solve(p2), solve(p3), solve(p4), solve(p5), solve(p6),
           solve(p7), solve(p8))
#> Warning: ignoring targets since the specified objective function doesn't use targets
#> Warning: ignoring targets since the specified objective function doesn't use targets
#> Warning: ignoring targets since the specified objective function doesn't use targets
#> Warning: ignoring targets since the specified objective function doesn't use targets

# plot solutions
plot(s, axes = FALSE, box = FALSE,
     main = c("min set", "max coverage", "max features",
              "min shortfall", "min largest shortfall",
              "max phylogenetic diversity",
              "max phylogenetic endemism", "max utility"))

# }