Specify targets based on the IUCN Red List of Ecosystems

Specify targets based on criteria from the International Union for the Conservation of Nature (IUCN) Red List of Ecosystems (IUCN 2024). Briefly, this method can be used to set targets based on criteria pertaining to geographic distribution size (criterion B) and reductions in geographic distribution size (criterion A). To help prevent widespread features from obscuring priorities for rare features, targets are capped following Butchart et al. (2015). This method may be suitable for ecosystem protection at global and and national scales. Note that this function is designed to be used with add_auto_targets() and add_group_targets().

Usage

spec_rl_ecosystem_targets(
  status,
  criterion_a,
  criterion_b,
  prop_uplift = 0,
  method = "max",
  cap_area_target = 1e+06,
  area_units = "km^2"
)

Arguments

status: character value denoting the IUCN Red List threat status used for target setting. Available options include "CR" (Critically Endangered), "EN" (Endangered), and "VU" (Vulnerable).
criterion_a: character value indicating which subcriterion should be considered based on geographic distribution reduction. Available options include subcriterion based on "A1" (reductions over the past 50 years), "A2a" (reductions over the next 50 years), "A2b" (reductions over any 50 year period), or "A3" (reductions during historical time periods). For convenience, these option can also be specified with lower case letters. See Mathematical formulation below for details.
criterion_b: character value indicating which subcriterion should be considered based on geographic distribution size. Available options include subcriterion based on "B1" (extent of occupancy), and "B2" (area of occupancy). For convenience, these options can also be specified with lower case letters. See Mathematical formulation below for details.
prop_uplift: numeric value denoting the percentage uplift as a proportion. Defaults to 0 (i.e., 0%).
method: character indicating how the target thresholds should be calculated based on values derived from Criterion A and and Criterion B. Available options include "min" and "max". For example, method = "max" means that the target threshold should be calculated as a maximum of the values from Criterion A and Criterion B. Defaults to "max" as a precaution.
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

Targets based on criteria from the IUCN Red List of Ecosystems may be appropriate for global and national scale prioritizations. Despite this, prioritizations based on these criteria may fail to identify meaningful priorities for prioritizations conducted at smaller geographic scales (e.g, local or county scales). For example, if this method is applied to smaller 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 ecosystems. This is because the target thresholds were developed based on criteria for promoting the long-term persistence of entire ecosystems. As such, if you are working at smaller scales, 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 assessment criteria from the International Union for the Conservation of Nature (IUCN) Red List of Ecosystems (IUCN 2024). To express this mathematically, we will define the following terminology. Let $f$ denote the total abundance of a feature (e.g., geographic range size), $a$ the threshold value from Criterion A based on the specified threat status (per status, see below for details), $b$ the threshold value from Criterion B based on the specified threat status (per status, see below for details), $p$ the percentage uplift as a proportion (per prop_uplift), $c$ the target cap (per cap_area_target and area_units), and $m()$ denote either $max()$ or $min()$ (per method). Given this terminology, the target threshold ($t$) for the feature is calculated as follows. $$ t = min(m(b \times (1 + p), f \times ((1 + p) \times (1 - a))), c, f) $$

Here $a$ and $b$ are equal to one of the following values depending on status, criterion_a, and criterion_b. Note that if criterion_a has a value of "A2a" or "A2b", then $a$ is assigned the same value as if it were "A1".

If status = "CR" and criterion_a = "A1", then $a =$ 80%.
If status = "EN" and criterion_a = "A1", then $a =$ 50%.
If status = "VU" and criterion_a = "A1", then $a =$ 30%.
If status = "CR" and criterion_a = "A3", then $a =$ 90%.
If status = "EN" and criterion_a = "A3", then $a =$ 70%.
If status = "VU" and criterion_a = "A3", then $a =$ 30%.
If status = "CR" and criterion_b = "B1", then $b =$ 2,000 km².
If status = "EN" and criterion_b = "B1", then $b =$ 20,000 km².
If status = "VU" and criterion_b = "B1", then $b =$ 50,000 km².
If status = "CR" and criterion_b = "B2", then $b =$ 200 km².
If status = "EN" and criterion_b = "B2", then $b =$ 2,000 km².
If status = "VU" and criterion_b = "B2", then $b =$ 5,000 km².

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.

IUCN (2024) Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 2.0. Keith DA, Ferrer-Paris JR, Ghoraba SMM, Henriksen S, Monyeki M, Murray NJ, Nicholson E, Rowland J, Skowno A, Slingsby JA, Storeng AB, Valderrábano M, Zager I (Eds.). Gland, Switzerland: IUCN.

Examples

# \dontrun{
# set seed for reproducibility
set.seed(500)

# load data with features that are ecosystem types
tas_pu <- prioritizrdata::get_tas_pu()
tas_features <- prioritizrdata::get_tas_features()

# create base problem
p0 <-
  problem(tas_pu, tas_features, cost_column = "cost") %>%
  add_min_set_objective() %>%
  add_binary_decisions() %>%
  add_default_solver(verbose = FALSE)

# note that the following targets will be specified based on subcriterion
# A2 under the assumption that protected areas will be effectively managed,
# and B2 because the feature data (per tas_features) characterize
# area of occupancy

# create problem with targets based on criteria from the IUCN Red List of
# Ecosystems for the Endangered threat status with a 0% uplift
p1 <-
  p0 %>%
  add_auto_targets(
    method = spec_rl_ecosystem_targets(
      status = "EN",
      criterion_a = "A1",
      criterion_b = "B2",
      prop_uplift = 0
    )
  )

# create problem with targets based on criteria from the IUCN Red List of
# Ecosystems for the Endangered threat status with a 20% uplift
p2 <-
  p0 %>%
  add_auto_targets(
    method = spec_rl_ecosystem_targets(
      status = "EN",
      criterion_a = "A1",
      criterion_b = "B2",
      prop_uplift = 0.2
    )
  )

# create problem with targets based on criteria from the IUCN Red List of
# Ecosystems for the Vulnerable threat status with a 20% uplift
p3 <-
  p0 %>%
  add_auto_targets(
    method = spec_rl_ecosystem_targets(
      status = "VU",
      criterion_a = "A1",
      criterion_b = "B2",
      prop_uplift = 0.2
    )
  )

# solve problems
s <- tas_pu
s$s1 <- solve(p1)$solution_1
s$s2 <- solve(p2)$solution_1
s$s3 <- solve(p3)$solution_1
s <- s[, c("s1", "s2", "s3"), drop = FALSE]

# plot solutions
plot(s, axes = FALSE)

# }