Targets are used to specify the minimum amount or proportion of a feature's distribution that should (ideally) be covered (represented) by a solution.


Please note that most objectives require targets, and attempting to solve a problem that requires targets will throw an error.

The following functions can be used to specify targets for a conservation planning problem():


Set targets as a proportion (between 0 and 1) of the total amount of each feature in the the study area.


Set targets that denote the minimum amount of each feature required in the prioritization.


Set targets as a proportion (between 0 and 1) that are calculated using log-linear interpolation.


Set targets manually.

See also


# load data data(sim_pu_raster, sim_features) # create base problem p <- problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_binary_decisions() %>% add_default_solver(verbose = FALSE) # 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 loglinear targets p3 <- p %>% add_loglinear_targets(10, 0.9, 100, 0.2) # create problem with manual targets that equate to 10% relative targets p4 <- p %>% add_manual_targets(data.frame(feature = names(sim_features), target = 0.1, type = "relative")) # \dontrun{ # solve problem s <- stack(solve(p1), solve(p2), solve(p3), solve(p4)) # plot solution plot(s, axes = FALSE, box = FALSE, main = c("relative targets", "absolute targets", "loglinear targets", "manual targets"))
# }