R/generate-objective.r
Generate an objective function to perform the metapopulation capacity-based conservation prioritization developed by Strimas-Mackey and Brodie (2018). This objective function maximizes the average metapopulation capacity across species for a fixed budget. There is an optional additional term, similar to the boundary term in Marxan, that can be used to favour more compact solutions.
generate_objective(pu, f, budget, delta, blm = 0, scale = rep(1, length(f)), benefit = function(y) y, cost = "cost", units = c("km", "m"), parallel = FALSE)
| pu | raster::RasterStack, sp::SpatialPolygonsDataFrame, or sf::sf object; planning units and representation levels of features as layers (raster inputs) or columns (vector inputs). |
|---|---|
| f | named list of dispersal survival functions with names matching the
layer/column names in |
| budget | numeric; the target budget for the prioritization exercise. |
| delta | numeric; a scale factor to apply to the budget term in the objective function that determines. To use simulated annealing for the optimization, the budget constraint must be incorporated directly into the objective function. This argument determines the relative importance to be placed on maximizing the metapopulation capacity compared to meeting the budget constraint. |
| blm | numeric; a scale factor to apply to the perimeter term in the objective function that determines the relative importance to be placed on maximizing the metapopulation capacity compared to producing more compact solutions. |
| scale | numeric; vales to rescale the metapopulation capacity by, e.g. to normalize them between 0-1 one can scale by the maximum, species-specific metapopulation capacity given by selecting all planning units. This should be a vector of length 1 or equal in length to the number of features. |
| benefit | function; a function to apply to the scaled metapopulation capacity before averaging across species. Often a saturating function is used to ensure that less value is given to changes in metapopulation capacity for species that are already near their maximum. |
| cost | name of the cost column or layer in the planning unit object,
|
| units | character; metapopulation capacity depends on the units used for the areas and distances, this argument determines whether these are measured in meters or kilometers. |
| parallel | logical; whether to parallelize the metapopulation capacity
calculations over the species. Parallelization is accomplished using
|
A function that, given a logical vector specifying which planning
units are selected, returns the corresponding objective function value. If
the objective function is called with components = TRUE, then a list is
returned with the three components of the object function:
mc: the metapoplation capacity of each species.
cost: the cost of the reserve network.
perimeter: the total perimeter length, in the specified units, of the
reserve network.
# generate features r <- raster::raster(nrows = 10, ncols = 10, crs = "+proj=aea", vals = sample(0:1, 100, replace = TRUE)) s <- raster::stack(r, r, r) s[[2]][] <- sample(0:1, 100, replace = TRUE, prob = c(0.6, 0.4)) s[[3]][] <- sample(0:1, 100, replace = TRUE, prob = c(0.8, 0.2)) names(s) <- c("a", "b", "c") features <- raster::rasterToPolygons(s) features <- sf::st_as_sf(features) # cost features$cost <- runif(nrow(features)) # dispersal functions disp_f <- list(a = dispersal_negexp(1 / 0.01), b = dispersal_negexp(1 / 0.005), c = dispersal_negexp(1 / 0.02)) # calculate scale factors scale_mc <- mc_reserve(features, rep(TRUE, nrow(features)), disp_f) # set budget at 50% of total budget <- 0.5 * sum(features$cost) # build an objective function objective <- generate_objective(features, disp_f, budget, delta = 0.001, blm = 0.001) # calculate objective selected <- sample(c(FALSE, TRUE), 100, replace = TRUE, prob = c(0.7, 0.3)) objective(selected, components = TRUE)#> $mc #> a b c #> 1.48870e-04 8.57889e-05 5.75590e-05 #> #> $cost #> [1] 16.64237 #> #> $perimeter #> [1] 2.412 #>objective(selected)#> [1] -0.002119782