Calculate irreplaceability scores for planning units selected in a solution
using rarity weighted richness (based on Williams et al. 1996). Please
note that this method is only recommended for largescaled conservation
planning exercises (i.e. more than 100,000 planning units) where
irreplaceability scores cannot be calculated using the replacement cost
method (replacement_cost()
) in a feasible period of
time. This is because rarity weighted richness scores cannot (i) account for
the cost of different planning units, (ii) account for multiple management
zones, and (iii) identify truly irreplaceable planning unitsunlike the
replacement cost metric which does not suffer any of these limitations.
rarity_weighted_richness(x, solution, ...) # S4 method for ConservationProblem,numeric rarity_weighted_richness(x, solution, rescale, ...) # S4 method for ConservationProblem,matrix rarity_weighted_richness(x, solution, rescale, ...) # S4 method for ConservationProblem,data.frame rarity_weighted_richness(x, solution, rescale, ...) # S4 method for ConservationProblem,Spatial rarity_weighted_richness(x, solution, rescale, ...) # S4 method for ConservationProblem,sf rarity_weighted_richness(x, solution, rescale, ...) # S4 method for ConservationProblem,Raster rarity_weighted_richness(x, solution, rescale, ...)
x 


solution 

...  not used. 
rescale 

A numeric
, matrix
,
RasterLayer
, Spatial
,
or sf::sf()
object containing the rarity weighted richness
scores for each planning unit in the solution.
Rarity weighted richness scores are calculated using the following terms . Let \(I\) denote the set of planning units (indexed by \(i\)), let \(J\) denote the set of conservation features (indexed by \(j\)), let \(r_{ij}\) denote the amount of feature \(j\) associated with planning unit \(i\), and let \(M_j\) denote the maximum value of feature \(j\) in \(r_{ij}\) in all planning units \(i \in I\). To calculate the rarity weighted richness (RWR) for planning unit \(k\):
$$ \mathit{RWR}_{k} = \sum_{j}^{J} \frac{ \frac{r_{kj}}{M_j} }{ \sum_{i}^{I} r_{ij}} $$
The argument to solution
must correspond
to the planning unit data in the argument to x
in terms
of data representation, dimensionality, and spatial attributes (if
applicable). This means that if the planning unit data in x
is a numeric
vector then the argument to solution
must be a
numeric
vector with the same number of elements; if the planning
unit data in x
is a RasterLayer
then the
argument to solution
must also be a
RasterLayer
with the same number of rows and
columns and the same resolution, extent, and coordinate reference system;
if the planning unit data in x
is a Spatial
object then the argument to solution
must also be a
Spatial
object and have the same number of spatial
features (e.g. polygons) and have the same coordinate reference system;
if the planning unit data in x
is a sf::sf()
object then the argument to solution
must also be a
sf::sf()
object and have the same number of spatial
features (e.g. polygons) and have the same coordinate reference system;
if the planning units in x
are a data.frame
then the
argument to solution
must also be a data.frame
with each
column correspond to a different zone and each row correspond to
a different planning unit, and values correspond to the allocations
(e.g. values of zero or one).
Solutions must have planning unit statuses set to missing (NA
)
values for planning units that have missing (NA
) cost data. For
problems with multiple zones, this means that planning units must have
missing (NA
) allocation values in zones where they have missing
(NA
) cost data. In other words, planning units that have missing
(NA
) cost values in x
should always have a missing
(NA
) value the argument to solution
. If an argument is
supplied to
solution
where this is not the case, then an error will be thrown.
Williams P, Gibbons D, Margules C, Rebelo A, Humphries C, and Pressey RL (1996) A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds. Conservation Biology, 10: 155174.
# seed seed for reproducibility set.seed(600) # load data data(sim_pu_raster, sim_features) # create minimal problem with binary decisions p1 < problem(sim_pu_raster, sim_features) %>% add_min_set_objective() %>% add_relative_targets(0.1) %>% add_binary_decisions() %>% add_default_solver(gap = 0, verbose = FALSE) # \dontrun{ # solve problem s1 < solve(p1) # print solution print(s1)#> class : RasterLayer #> dimensions : 10, 10, 100 (nrow, ncol, ncell) #> resolution : 0.1, 0.1 (x, y) #> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax) #> crs : NA #> source : memory #> names : layer #> values : 0, 1 (min, max) #># calculate irreplaceability scores rwr1 < rarity_weighted_richness(p1, s1) # print irreplaceability scores print(rwr1)#> class : RasterLayer #> dimensions : 10, 10, 100 (nrow, ncol, ncell) #> resolution : 0.1, 0.1 (x, y) #> extent : 0, 1, 0, 1 (xmin, xmax, ymin, ymax) #> crs : NA #> source : memory #> names : layer #> values : 0, 1 (min, max) #># plot irreplaceability scores plot(rwr1, main = "rarity weighted richness", axes = FALSE, box = FALSE)# }