Specify targets following Watson et al. (2010)

Specify targets based on the methodology outlined by Watson et al. (2010). Briefly, this method involves setting targets thresholds as a percentage based on whether or not a feature is considered rare. To help prevent widespread features from obscuring priorities, targets are capped following Butchart et al. (2015). This method was designed for species protection at national-scales. Note that this function is designed to be used with add_auto_targets() and add_group_targets().

Usage

spec_watson_targets(
  rare_area_threshold = 10000,
  rare_relative_target = 1,
  rare_area_target = 1000,
  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 10000 (i.e., 10000 km^2).
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%).
rare_area_target: numeric value indicating the area-based target for features with a spatial distribution that is smaller than rare_area_threshold. Defaults to 1000 (i.e., 1000 km^2).
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).

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().

Details

This target setting method was designed to protect species in a national-scale prioritization (Watson et al. 2010). Although similar methods have also been successfully to national-scale prioritizations (e.g., Kark et al. 2009), it may fail to identify meaningful priorities for prioritizations conducted at smaller geographic scales (e.g., local or county-level scales). For example, if this target setting 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 and cap_area_threshold) were originally developed based on criteria for national-scales. As such, if you working at the sub-national 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.

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 10,000 km² (per rare_area_threshold and area_units) and common features as those with a larger spatial distribution. Given this, rare features are assigned target threshold of 100% (per rare_relative_target) or 1,000 km² (per rare_area_threshold) (whichever of these two values is smaller), and common features are assigned a target threshold of 10% (per common_relative_target). 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.

Kark S, Levin N, Grantham HS, Possingham HP (2009) Between-country collaboration and consideration of costs increase conservation planning efficiency in the Mediterranean Basin. Proceedings of the National Academy of Sciences, 106: 15368–15373.

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

Watson JEM, Evans MC, Carwardine J, Fuller RA, Joseph LN, Segan DB, Taylor MFJ, Fensham RJ, Possingham HP (2010) The capacity of Australia's protected-area system to represent threatened species. Conservation Biology,25: 324–332.

Examples

# \dontrun{
# 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 Watson et al. (2010) targets
p1 <-
  problem(sim_complex_pu_raster, sim_complex_features) %>%
  add_min_set_objective() %>%
  add_auto_targets(method = spec_watson_targets()) %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# solve problem
s1 <- solve(p1)

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

# }