Organize data for multiple features for multiple management zones. Specifically, the data should describe the expected amount of each feature within each planning unit given each management zone. For example, the data could describe the occupancy (e.g., presence/absence), probability of occurrence, or abundance expected for each feature when each planning unit is allocated to a different zone.

zones(..., zone_names = NULL, feature_names = NULL)

## Arguments

...

raster::raster() or character objects that pertain to the biodiversity data. See Details for more information.

zone_names

character names of the management zones. Defaults to NULL which results in sequential integers.

feature_names

character names of the features zones. Defaults to NULL which results in sequential integers.

## Value

Zones object.

## Details

This function is used to store and organize data for use in a conservation planning problem() that has multiple management zones. In all cases, the data for each zone is input as a separate argument. The correct arguments depends on the type of planning unit data used when building the conservation planning problem().

planning unit data are a Raster or Spatial object

Raster object can be supplied to specify the expected amount of each feature within each planning unit under each management zone. Data for each zone should be specified as separate arguments, and the data for each feature in a given zone are specified in separate layers in a raster::stack() object. Note that all layers for a given zone must have NA values in exactly the same cells.

planning unit data are a Spatial or data.frame object

character vector containing column names can be supplied to specify the expected amount of each feature under each zone. Note that these columns must not contain any NA values.

planning unit data are a Spatial, data.frame, or matrix object

data.frame object can be supplied to specify the expected amount of each feature under each zone. Following conventions used in Marxan, the data.frame object should contain the following columns.

pu

integer planning unit identifier.

species

integer feature identifier.

amount

numeric amount of the feature in the planning unit for a given zone.

Note that data for each zone are specified in a separate argument, and the data contained in a single data.frame object should correspond to a single zone. Also, note that data are not required for all combinations of planning units, features, and zones. The expected amount of features in planning units under management zones that are missing from the table are assumed to be zero.

problem().

## Examples

# \dontrun{
data(sim_pu_raster)

# (note this requires the RandomFields package to be installed)
zone_1 <- simulate_species(sim_pu_raster, 3)
#> ...
zone_2 <- simulate_species(sim_pu_raster, 3)
#> ...

# create zones using two raster stack objects
# each object corresponds to a different zone and each layer corresponds to
# a different species
z <- zones(zone_1, zone_2, zone_names = c("zone_1", "zone_2"),
feature_names = c("feature_1", "feature_2", "feature_3"))
print(z)
#> Zones
#>   zones: zone_1, zone_2 (2 zones)
#>   features: feature_1, feature_2, feature_3 (3 features)
#>   data type: RasterStack

# note that the do.call function can also be used to create a Zones object
# this method for creating a Zones object can be helpful when there are many
# management zones
l <- list(zone_1, zone_2, zone_names = c("zone_1", "zone_2"),
feature_names = c("feature_1", "feature_2", "feature_3"))
z <- do.call(zones, l)
print(z)
#> Zones
#>   zones: zone_1, zone_2 (2 zones)
#>   features: feature_1, feature_2, feature_3 (3 features)
#>   data type: RasterStack

# create zones using character vectors that represent the names of
# fields (columns) in a data.frame or Spatial object that contain the amount
# of each species expected different management zones
z <- zones(c("spp1_zone1", "spp2_zone1"),
c("spp1_zone2", "spp2_zone2"),
c("spp1_zone3", "spp2_zone3"),
zone_names = c("zone1", "zone2", "zone3"),
feature_names = c("spp1", "spp2"))
print(z)
#> Zones
#>   zones: zone1, zone2, zone3 (3 zones)
#>   features: spp1, spp2 (2 features)
#>   data type: character
# }