spec_rodrigues_targets: Specify targets following Rodrigues _et al._ (2004)

In prioritizr: Systematic Conservation Prioritization in R

Specify targets following Rodrigues et al. (2004)

Description

Specify targets based on the methodology outlined by Rodrigues et al. (2004). Briefly, this method involves setting targets based on log-linear interpolation methods. To help prevent widespread features from obscuring priorities, targets are capped following Butchart et al. (2015). This method was designed for global-scale prioritizations. Note that this function is designed to be used with add_auto_targets() and add_group_targets().

Usage

spec_rodrigues_targets(
  rare_area_threshold = 1000,
  rare_relative_target = 1,
  common_area_threshold = 250000,
  common_relative_target = 0.1,
  cap_area_target = 1e+06,
  area_units = "km^2"
)

Arguments

rare_area_threshold	numeric value indicating the threshold area for rare identifying rare features. Defaults to 1000 (i.e., 1000 km2).
rare_relative_target	numeric value denoting the relative target for features with a spatial distribution that is smaller than rare_area_threshold. Note that this value must be a proportion between 0 and 1. Defaults to 1 (i.e., 100%).
common_area_threshold	numeric value indicating the threshold area for identifying common features. Defaults to 250000 (i.e., 250,000 km2).
common_relative_target	numeric value denoting the relative target for features with a spatial distribution that is greater than common_area_threshold. Defaults to 0.1 (i.e., 10%). Since this default value is based on historical levels of global protected area coverage, it may be appropriate to set this value based on current levels of protected area coverage (e.g., 17.6% for terrestrial and 8.4% for marine systems globally; UNEP-WCMC and IUCN 2025).
cap_area_target	numeric value denoting the area-based target cap. To avoid setting a target cap, a missing (NA) value can be specified. Defaults to 1000000 (i.e., 1,000,000 km2).
area_units	character value denoting the unit of measurement for the area-based arguments. Defaults to "km^2" (i.e., km2).

Details

This target setting method was designed to protect species in global-scale prioritizations (Rodrigues et al. 2004). Although it has also been successfully applied to global-scales (e.g., Butchart et al. 2015; Hanson et al. 2020; Venter et al. 2014), it may fail to identify meaningful priorities for prioritizations conducted at smaller geographic scales (e.g., national, state-level or county scales). For example, if this method is applied to such geographic scales, then the resulting prioritizations may select an overly large percentage of the study area, or be biased towards over-representing common and widespread species. This is because the thresholds (i.e., rare_area_threshold, common_area_threshold, and cap_area_threshold) were originally developed based on rationale for promoting the long-term persistence of entire species. As such, if you are working at a sub-global scale, it is recommended to set thresholds based on that criteria are appropriate to the spatial extent of the planning region. Please note that this function is provided as convenient method to set targets for problems with a single management zone, and cannot be used for those with multiple management zones.

Value

An object (TargetMethod) for specifying targets that can be used with add_auto_targets() and add_group_targets() to add targets to a problem().

Mathematical formulation

This method involves setting target thresholds based on the spatial extent of the features. By default, this method identifies rare features as those with a spatial distribution smaller than 1,000 km² (per rare_area_threshold and area_units) and common features as those with a spatial distribution larger than 250,000 km² (per common_area_threshold and area_units). Given this, rare features are assigned a target threshold of 100% (per rare_relative_target), common features are assigned a target threshold of 10% (per common_relative_target), and features with a spatial distribution that is between the area-based thresholds used to identify rare and common features are assigned a target threshold through log-linear interpolation. Additionally, following Butchart et al. (2015), a cap of 1,000,000 km² is applied to target thresholds (per cap_area_threshold and area_units).

Data calculations

This function involves calculating targets based on the spatial extent of the features in x. Although it can be readily applied to problem() objects that have the feature data provided as a terra::rast() object, you will need to specify the spatial units for the features when initializing the problem() objects if the feature data are provided in a different format. In particular, if the feature data are provided as a data.frame or character vector, then you will need to specify an argument to feature_units when using the problem() function. See the Examples section of the documentation for add_auto_targets() for a demonstration of specifying the spatial units for features.

References

Butchart SHM, Clarke M, Smith RJ, Sykes RE, Scharlemann JPW, Harfoot M, Buchanan GM, Angulo A, Balmford A, Bertzky B, Brooks TM, Carpenter KE, Comeros‐Raynal MT, Cornell J, Ficetola GF, Fishpool LDC, Fuller RA, Geldmann J, Harwell H, Hilton‐Taylor C, Hoffmann M, Joolia A, Joppa L, Kingston N, May I, Milam A, Polidoro B, Ralph G, Richman N, Rondinini C, Segan DB, Skolnik B, Spalding MD, Stuart SN, Symes A, Taylor J, Visconti P, Watson JEM, Wood L, Burgess ND (2015) Shortfalls and solutions for meeting national and global conservation area targets. Conservation Letters, 8: 329–337.

Hanson JO, Rhodes JR, Butchart SHM, Buchanan GM, Rondinini C, Ficetola GF, Fuller RA (2020) Global conservation of species' niches. Global conservation of species' niches. Nature, 580: 232–234

Rodrigues ASL, Akçakaya HR, Andelman SJ, Bakarr MI, Boitani L, Brooks TM, Chanson JS, Fishpool LDC, Da Fonseca GAB, Gaston KJ, Hoffmann M, Marquet PA, Pilgrim JD, Pressey RL, Schipper J, Sechrest W, Stuart SN, Underhill LG, Waller RW, Watts MEJ, Yan X (2004) Global gap analysis: priority regions for expanding the global protected-area network. BioScience, 54: 1092–1100.

UNEP-WCMC and IUCN (2025) Protected Planet Report 2024. Cambridge, UK: UNEP-WCMC and IUCN. Available at <www.protectedplanet.net>.

Venter O, Fuller RA, Segan DB, Carwardine J, Brooks T, Butchart SHM, Di Marco M, Iwamura T, Joseph L, O'Grady D, Possingham HP, Rondinini C, Smith RJ, Venter M, Watson JEM (2014) Targeting global protected area expansion for imperiled biodiversity. PLoS Biology, 12: e1001891.

See Also

Other target setting methods: spec_absolute_targets(), spec_area_targets(), spec_duran_targets(), spec_interp_absolute_targets(), spec_interp_area_targets(), spec_jung_targets(), spec_max_targets(), spec_min_targets(), spec_polak_targets(), spec_pop_size_targets(), spec_relative_targets(), spec_rl_ecosystem_targets(), spec_rl_species_targets(), spec_rule_targets(), spec_ward_targets(), spec_watson_targets(), spec_wilson_targets()

Examples

## Not run: 
# set seed for reproducibility
set.seed(500)

# load data
sim_complex_pu_raster <- get_sim_complex_pu_raster()
sim_complex_features <- get_sim_complex_features()

# create problem with Rodrigues et al. (2004) targets
p1 <-
  problem(sim_complex_pu_raster, sim_complex_features) %>%
  add_min_set_objective() %>%
  add_auto_targets(method = spec_rodrigues_targets()) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s1 <- solve(p1)

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

## End(Not run)

prioritizr documentation built on Nov. 10, 2025, 5:07 p.m.