Greedy heuristic prioritization — greedy_heuristic

Generate a prioritization for protected area establishment.

Usage

greedy_heuristic_prioritization(
  site_data,
  feature_data,
  site_probability_columns,
  site_management_cost_column,
  feature_target_column,
  total_budget,
  site_management_locked_in_column = NULL,
  site_management_locked_out_column = NULL
)

Arguments

site_data: sf::sf() object with site data.
feature_data: base::data.frame() object with feature data.
site_probability_columns: character names of numeric columns in the argument to site_data that contain modeled probabilities of occupancy for each feature in each site. Each column should correspond to a different feature, and contain probability data (values between zero and one). No missing (NA) values are permitted in these columns.
site_management_cost_column: character name of column in the argument to site_data that contains costs for managing each site for conservation. This column should have numeric values that are equal to or greater than zero. No missing (NA) values are permitted in this column.
feature_target_column: character name of the column in the argument to feature_data that contains the \(target\) values used to parametrize the conservation benefit of managing of each feature. This column should have numeric values that are equal to or greater than zero. No missing (NA) values are permitted in this column.
total_budget: numeric maximum expenditure permitted for conducting surveys and managing sites for conservation.
site_management_locked_in_column: character name of the column in the argument to site_data that contains logical (TRUE / FALSE) values indicating which sites should be locked in for (TRUE) being managed for conservation or (FALSE) not. No missing (NA) values are permitted in this column. This is useful if some sites have already been earmarked for conservation, or if some sites are already being managed for conservation. Defaults to NULL such that no sites are locked in.
site_management_locked_out_column: character name of the column in the argument to site_data that contains logical (TRUE / FALSE) values indicating which sites should be locked out for (TRUE) being managed for conservation or (FALSE) not. No missing (NA) values are permitted in this column. This is useful if some sites could potentially be surveyed to improve model predictions even if they cannot be managed for conservation. Defaults to NULL such that no sites are locked out.

Value

A list containing the following elements:

x: logical vector indicating if each site is selected for protection or not.
objval: numeric value denoting the objective value for the prioritization.

Details

The prioritization is generated using a greedy heuristic algorithm. The objective function for this algorithm is calculated by: (i) estimating the probability that each species meets its target, and (ii) calculating the sum of these probabilities. Note that this function underpins the value of information calculations because it is used to assess a potential management decision given updated information on the presence of particular species in particular sites.

Examples

# set seeds for reproducibility
set.seed(123)

# load example site data
data(sim_sites)
print(sim_sites)
#> Simple feature collection with 6 features and 13 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: 0.02541313 ymin: 0.07851093 xmax: 0.9888107 ymax: 0.717068
#> CRS:           NA
#> # A tibble: 6 × 14
#>   survey_cost management_cost    f1    f2    f3    n1    n2    n3     e1     e2
#>         <dbl>           <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
#> 1          19              99     0     0  0        0     0     0  1.13   0.535
#> 2          22              87     0     1  0.25     4     4     4 -1.37  -1.45 
#> 3          13              94     1     1  0        1     1     1  0.155 -0.867
#> 4          19              61     0     0  0        0     0     0 -0.792  1.32 
#> 5           9             105     0     0  0        0     0     0 -0.194  0.238
#> 6          12             136     0     0  0        0     0     0  1.07   0.220
#> # ℹ 4 more variables: p1 <dbl>, p2 <dbl>, p3 <dbl>, geometry <POINT>

# load example feature data
data(sim_features)
print(sim_features)
#> # A tibble: 3 × 7
#>   name  survey survey_sensitivity survey_specificity model_sensitivity
#>   <chr> <lgl>               <dbl>              <dbl>             <dbl>
#> 1 f1    TRUE                0.951              0.854             0.711
#> 2 f2    TRUE                0.990              0.832             0.722
#> 3 f3    TRUE                0.986              0.808             0.772
#> # ℹ 2 more variables: model_specificity <dbl>, target <dbl>

# set total budget for managing sites for conservation
 # (i.e. 50% of the cost of managing all sites)
total_budget <- sum(sim_sites$management_cost) * 0.5

# generate reserve selection prioritization
results <- greedy_heuristic_prioritization(
  sim_sites, sim_features,
  c("p1", "p2", "p3"),
  "management_cost",
  "target",
  total_budget
)

# print results
print(results)
#> $x
#> [1]  TRUE FALSE FALSE  TRUE  TRUE FALSE
#> 
#> $objval
#> [1] 0.982308
#>