Expected value of the decision given current information

Calculate the expected value of the management decision given current information. This metric describes the value of the management decision that is expected when the decision maker is limited to existing biodiversity data (i.e. survey data and environmental niche models).

Usage

evdci(
  site_data,
  feature_data,
  site_detection_columns,
  site_n_surveys_columns,
  site_probability_columns,
  site_management_cost_column,
  feature_survey_sensitivity_column,
  feature_survey_specificity_column,
  feature_model_sensitivity_column,
  feature_model_specificity_column,
  feature_target_column,
  total_budget,
  site_management_locked_in_column = NULL,
  site_management_locked_out_column = NULL,
  prior_matrix = NULL
)

Arguments

site_data: sf::sf() object with site data.
feature_data: base::data.frame() object with feature data.
site_detection_columns: character names of numeric columns in the argument to site_data that contain the proportion of surveys conducted within each site that detected each feature. Each column should correspond to a different feature, and contain a proportion value (between zero and one). If a site has not previously been surveyed, a value of zero should be used.
site_n_surveys_columns: character names of numeric columns in the argument to site_data that contain the total number of surveys conducted for each each feature within each site. Each column should correspond to a different feature, and contain a non-negative integer number (e.g. 0, 1, 2, 3). If a site has not previously been surveyed, a value of zero should be used.
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_survey_sensitivity_column: character name of the column in the argument to feature_data that contains probability of future surveys correctly detecting a presence of each feature in a given site (i.e. the sensitivity of the survey methodology). This column should have numeric values that are between zero and one. No missing (NA) values are permitted in this column.
feature_survey_specificity_column: character name of the column in the argument to feature_data that contains probability of future surveys correctly detecting an absence of each feature in a given site (i.e. the specificity of the survey methodology). This column should have numeric values that are between zero and one. No missing (NA) values are permitted in this column.
feature_model_sensitivity_column: character name of the column in the argument to feature_data that contains probability of the initial models correctly predicting a presence of each feature in a given site (i.e. the sensitivity of the models). This column should have numeric values that are between zero and one. No missing (NA) values are permitted in this column. This should ideally be calculated using fit_xgb_occupancy_models() or fit_hglm_occupancy_models().
feature_model_specificity_column: character name of the column in the argument to feature_data that contains probability of the initial models correctly predicting an absence of each feature in a given site (i.e. the specificity of the models). This column should have numeric values that are between zero and one. No missing (NA) values are permitted in this column. This should ideally be calculated using fit_xgb_occupancy_models() or fit_hglm_occupancy_models().
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.
prior_matrix: numeric matrix containing the prior probability of each feature occupying each site. Rows correspond to features, and columns correspond to sites. Defaults to NULL such that prior data is calculated automatically using prior_probability_matrix().

Value

A numeric value.

Details

This function calculates the expected value and does not use approximation methods. As such, this function can only be applied to very small problems.

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

# calculate expected value of management decision given current information
# using exact method
ev_current <- evdci(
  sim_sites, sim_features,
  c("f1", "f2", "f3"), c("n1", "n2", "n3"), c("p1", "p2", "p3"),
  "management_cost", "survey_sensitivity", "survey_specificity",
  "model_sensitivity", "model_specificity",
  "target", total_budget)

# print exact value
print(ev_current)
#> [1] 1.7165

Expected value of the decision given current information

Usage

Arguments

Value

Details

See also

Examples