eval_rare_richness_importance: Evaluate solution importance using rarity weighted richness...

In prioritizr: Systematic Conservation Prioritization in R

Evaluate solution importance using rarity weighted richness scores

Description

Calculate importance scores for planning units selected in a solution using rarity weighted richness scores (based on Williams et al. 1996).

Usage

eval_rare_richness_importance(x, solution, rescale = TRUE)

Arguments

x	problem() object.
solution	numeric, matrix, data.frame, terra::rast(), or sf::sf() object. Note that solution must have the same format as the planning unit data in x. See the Solution format section for more information.
rescale	logical flag indicating if replacement cost values – excepting infinite (Inf) and zero values – should be rescaled to range between 0.01 and 1. Defaults to TRUE.

Details

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. Given these terms, rarity weighted richness for planning unit k is calculated as follows:

\mathit{RWR}_{k} = \sum_{j}^{J} \frac{ \frac{r_{ik}}{m_j} }{\sum_{i}^{I}r_{ij}}

This method is only recommended for large-scaled conservation planning exercises (i.e., more than 100,000 planning units) where importance scores cannot be calculated using other methods 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 units — unlike the replacement cost metric which does not suffer any of these limitations.

Value

A numeric, matrix, data.frame, terra::rast(), or sf::sf() object containing the importance scores for each planning unit in the solution. Specifically, the returned object is in the same format as the planning unit data in x.

Solution format

Broadly speaking, solution must be in the same format as the planning unit data in x. Further details on the correct format are listed separately for each of the different planning unit data formats.

x has numeric planning units

Here solution must be a numeric vector with each element corresponding to a different planning unit. It should have the same number of planning units as those in x. Additionally, any planning units with missing cost (NA) values should also have missing (NA) values in the solution.

x has matrix planning units

Here solution must be a matrix vector with each row corresponding to a different planning unit, and each column correspond to a different management zone. It should have the same number of planning units and zones as those in x. Additionally, any planning units with missing cost (NA) values for a particular zone should also have a missing (NA) values in solution.

x has terra::rast() planning units

Here solution be a terra::rast() object where different cells correspond to different planning units and layers correspond to a different management zones. It should have the same dimensionality (rows, columns, layers), resolution, extent, and coordinate reference system as the planning units in x. Additionally, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

x has data.frame planning units

Here solution must be a data.frame with each column corresponding to a different zone, each row corresponding to a different planning unit, and cell values corresponding to the solution value. This means that if a data.frame object containing the solution also contains additional columns, then these columns will need to be subsetted prior to using this function (see below for example with sf::sf() data). Additionally, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

x has sf::sf() planning units

Here solution must be a sf::sf() object with each column corresponding to a different zone, each row corresponding to a different planning unit, and cell values corresponding to the solution value. This means that if the sf::sf() object containing the solution also contains additional columns, then these columns will need to be subsetted prior to using this function (see below for example). Additionally, solution must also have the same coordinate reference system as the planning unit data. Furthermore, any planning units with missing cost (NA) values for a particular zone should also have missing (NA) values in solution.

References

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: 155–174.

See Also

See importance for an overview of all functions for evaluating the importance of planning units selected in a solution.

Other functions for evaluating solution importance: eval_ferrier_importance(), eval_rank_importance(), eval_replacement_importance()

Examples

# set seed for reproducibility
set.seed(600)

# load data
sim_pu_raster <- get_sim_pu_raster()
sim_pu_polygons <- get_sim_pu_polygons()
sim_features <- get_sim_features()

# create minimal problem with raster planning units
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)

# solve problem
s1 <- solve(p1)

# print solution
print(s1)

# plot solution
plot(s1, main = "solution", axes = FALSE)

# calculate importance scores
rwr1 <- eval_rare_richness_importance(p1, s1)

# print importance scores
print(rwr1)

# plot importance scores
plot(rwr1, main = "rarity weighted richness", axes = FALSE)

# create minimal problem with polygon planning units
p2 <-
  problem(sim_pu_polygons, sim_features, cost_column = "cost") %>%
  add_min_set_objective() %>%
  add_relative_targets(0.05) %>%
  add_binary_decisions() %>%
  add_default_solver(gap = 0, verbose = FALSE)

# solve problem
s2 <- solve(p2)

# print solution
print(s2)

# plot solution
plot(s2[, "solution_1"], main = "solution")

# calculate importance scores
rwr2 <- eval_rare_richness_importance(p2, s2[, "solution_1"])

# plot importance scores
plot(rwr2, main = "rarity weighted richness")

prioritizr documentation built on Sept. 24, 2026, 5:07 p.m.