3D prioritization algorithm

Usage

prioritize_3D(split_features, depth_raster, breaks, biodiv_df,
priority_weights = NULL, budget_percents = seq(0,1,0.1), budget_weights = "equal",
penalty = 0, edge_factor = 0.5, gap = 0.1, threads = 1L, sep_priority_weights = ",",
portfolio = "gap", portfolio_opts = list(number_solutions = 10, pool_gap = 0.1),
sep_biodiv_df = ",", locked_in_raster = NULL, locked_out_raster = NULL, verbose = FALSE)

Arguments

split_features: list of SpatRaster objects representing depth zones, where each element corresponds to a different depth level, ranging from surface to the bottom of the sea. The function uses the output of the split_rast function, but other multilevel (3D) distribution data that conform to this structure is also acceptable. First list element corresponds to the shallowest distribution and the last list element to the deepest one.
depth_raster: SpatRaster object or file path with elevation/bathymetric map.
breaks: Numeric vector defining the range of depth layers to use.
biodiv_df: data.frame or a file path (CSV, TXT, XLS, or XLSX) containing additional information about biodiversity features.
priority_weights: data.frame object or file path (CSV, TXT, XLS, or XLSX) containing group names of biodiversity features in the first column and corresponding group weights in the second column. This data.frame attributes distinct prioritization weights to different biodiversity features or groups of features.
budget_percents: Numeric value \([0,1]\) or vector containing budget percentages to use. The default is seq(0,1,0.1).
budget_weights: Numeric weight vector for budget_percents allocation among depth levels. Otherwise it can be a string with one of the choices "equal", "area" or "richness". Alternatively, it can be a numerical vector with custom weights corresponding to each depth layer, where the first value corresponds to the surface and last one corresponds to the bottom of the sea. The weights are normalized if their sum exceeds 1. If not specified, an equal distribution of budget among depth levels is used, as the default.
penalty: A single numeric penalty applied to each depth zone, as defined in the
prioritizr::add_boundary_penalties.
edge_factor: A single numeric edge factor applied to each depth zone, as defined in the
prioritizr::add_boundary_penalties.
gap: The optimality gap for the solver, as defined in the prioritizr package. The default gap is 0.1.
threads: The number of solver threads to be used. The default is 1.
sep_priority_weights: Separator used in priority_weights file, if priority_weights is in path format.
portfolio: The portfolio to be used, choosing between "extra", "gap", "cuts" and "shuffle" portfolios. The default is "gap". portfolio="" indicates that no portfolio is used. For more about portfolios see prioritizr.
portfolio_opts: The prioritizr portfolio options to be used.
sep_biodiv_df: Separator used in biodiv_df file, if biodiv_df is in path format.
locked_in_raster: An optional locked_in_raster SpatRaster to be used. Note that these areas are considered as zero-cost.
locked_out_raster: An optional locked_out_raster SpatRaster to be used. Note that these areas are excluded from the solution.
verbose: If verbose = TRUE, then solver messages are printed as well. The default is FALSE.

Details

This function is used to generate prioritization maps. Single budget settings (ex. total_budget = 0.3) produce standard maps, as typical Marxan outputs. Multiple budgets, by using a vector (ex. c(0.1,0.3,0.5), result in cumulative (frequency) maps, illustrating areas selected by various budget levels. Although this output follows a different approach, it resembles to typical Zonation output maps.

The main reason for biodiv_df here is defining prioritization weights for features. In this package weights are defined per group of features (if needed). biodiv_df has mandatory column names (and any other names are ignored):

"species_name": Mandatory column with the feature names, which must be the same with split_rast.
"group": Mandatory column with the group weights names.

Except from biodiv_df, an additional data.frame object can also be used, named priority_weights. If used, this data.frame object must have two columns:

"group": Mandatory column with the group weights names.
"weight": Mandatory column with the group weights.

In case that no feature weights are desired, then biodiv_df and priority_weights can be both kept to NULL.

breaks must be in correspondence to depth_raster file. For example, if depth_raster has range \([10, -3000]\), then a breaks vector of c(0,-40,-200,-2000,-Inf) will create depth levels \([0,-40],\\(-40,-200], (-200, -2000], (-2000, -\infty)\) and set to NA cells with values greater than \(10\).

budget_percents: Budget reflects the desired level of protection to be modeled. It ranges from 0 to 1, with 0 indicating no resources available for protection, while 1 signifies resources sufficient to protect the entire study area. Typically, setting a budget of 0.3 corresponds to the 30% conservation target (i.e. 30% of the total area set aside for conservation). Users also have the flexibility to define multiple budget levels using a vector, allowing for the exploration of various protection scenarios. For instance, a vector like c(0.1, 0.3, 0.5) represents three scenarios where 10%, 30%, and 50% of the study area are designated for protection.

budget_weights: The prioritize_3D function allows users to specify how the budget is distributed among depth levels. Three allocation methods are available:

Equal Distribution: Allocates an equal share of the budget to each depth level
(budget_weights = "equal").
Proportional to Area: Allocates budget based on the spatial extent of each depth
level (budget_weights = "area").
Proportional to Species Richness: Prioritizes budget allocation to depth levels with higher species diversity (number of species) (budget_weights = "richness").

Otherwise, it can be a numeric vector with length equal to the number of depth levels, where each number indicates the budget share per depth level.

The solver used for solving the prioritization problems is the best available on the computer, following the solver hierarchy of prioritizr.

Value

A list containing the following objects (non-referenced are identical to the input ones):

solution3D: list with 3D solution per budget percentage
absolute_held3D: absolute_held for 3D solutions (see evaluate_3D)
overall_held3D: overall_held for 3D solutions (see evaluate_3D)
relative_helds3D: relative_held for 3D solutions (see evaluate_3D)
mean_overall_helds3D: base::mean of overall_held for 3D solution (see evaluate_3D) per budget
sd_overall_helds3D: base::mean of overall_held for 3D solution (see evaluate_3D) per budget
depth_overall_available3D: depth_overall_available for 3D solutions (see evaluate_3D)

References

Hanson, Jeffrey O, Richard Schuster, Nina Morrell, Matthew Strimas-Mackey, Brandon P M Edwards, Matthew E Watts, Peter Arcese, Joseph Bennett, and Hugh P Possingham. 2024. prioritizr: Systematic Conservation Prioritization in R. https://prioritizr.net.

Lehtomäki, Joona (2016). Comparing prioritization methods, 21 June.
Available at: https://rpubs.com/jlehtoma/priocomp (Accessed 1 June 2024).

Examples

if (FALSE) { # \dontrun{
## This example requires commercial solver from 'gurobi' package for
## portfolio = "gap". Else replace it with e.g. portfolio = "shuffle" for using
## a free solver like the one from 'highs' package.

biodiv_raster <- get_biodiv_raster()
depth_raster <- get_depth_raster()
data(biodiv_df)

# You can split features' 2D distributions into 3D ones and then run only 3D analysis
split_features <- split_rast(biodiv_raster,
                             depth_raster,
                             breaks = c(0, -40, -200, -2000, -Inf),
                             biodiv_df)

out_3D <- prioritize_3D(split_features = split_features,
                        depth_raster = depth_raster,
                        breaks = c(0, -40, -200, -2000, -Inf),
                        biodiv_df = biodiv_df,
                        budget_percents = seq(0, 1, 0.1),
                        budget_weights = "richness",
                        threads = parallel::detectCores(),
                        portfolio = "gap",
                        portfolio_opts = list(number_solutions = 10))

plot_3D(out_3D, to_plot="all", add_lines=TRUE)

# Arbitrary random weights
priority_weights <- data.frame(c("A", "B", "C"), c(0.001, 1000, 1))
names(priority_weights) <- c("group", "weight")
biodiv_df$group <- rep(c("A", "B", "C"), length.out=20)
out_3D <- prioritize_3D(split_features = split_features,
                        depth_raster = depth_raster,
                        biodiv_df = biodiv_df,
                        priority_weights = priority_weights,
                        breaks = c(0, -40, -200, -2000, -Inf),
                        budget_percents = seq(0, 1, 0.1),
                        budget_weights = "richness",
                        threads = parallel::detectCores(),
                        portfolio = "gap",
                        portfolio_opts = list(number_solutions = 10))

plot_3D(out_3D, to_plot="all", add_lines=TRUE)
} # }