This function includes scenarios in the sdm_area object.
Usage
add_scenarios(sa, scen = NULL, scenarios_names = NULL, pred_as_scen = TRUE,
variables_selected = NULL, stationary = NULL, crop_area = NULL)
set_scenarios_names(i, scenarios_names = NULL)
scenarios_names(i)
get_scenarios_data(i)
select_scenarios(i, scenarios_names = NULL)Arguments
- sa
A
sdm_areaorinput_sdmobject.- scen
RasterStack,SpatRasterorstarsobject. IfNULLadds predictors as a scenario.- scenarios_names
Character vector with names of scenarios.
- pred_as_scen
Logical. If
TRUEadds the current predictors as a scenario.- variables_selected
Character vector with variables names in
scento be used as variables. IfNULLadds all variables.- stationary
Names of variables from
sathat should be used in scenarios as stationary variables.- crop_area
A
sfobject to crop thescenobject if necessary.- i
A
sdm_areaorinput_sdmobject.
Value
add_scenarios returns the input sdm_area or input_sdm object with a
new slot called scenarios with scen data as a list, where each slot of the
list holds a scenario and each scenario is a sf object.
set_scenarios_names sets new names for scenarios in sdm_area/input_sdm
object.
scenarios_names returns scenarios' names.
get_scenarios_data retrieves scenarios data as a list of sf objects.
select_scenarios selects scenarios from sdm_area/input_sdm object.
Details
The function add_scenarios adds scenarios to the sdm_area or input_sdm
object. If scen has variables that are not present as predictors the function will use
only variables present in both objects. stationary variables are those that don't change
through the scenarios. It is useful for hidrological variables in fish habitat modeling, for
example (see examples below). When adding multiple scenarios in multiple runs, the function will
always add a new "current" scenario. To avoid that, set pred_as_scen = FALSE.
Examples
# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, crs = 6933)
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
# Include predictors:
sa <- add_predictors(sa, bioc)
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
# Include scenarios:
sa <- add_scenarios(sa, scen[1:2]) |> select_predictors(c("bio1", "bio12"))
#> Warning: Some variables in `variables_selected` are not present in `scen`.
#> ℹ Using only variables present in `scen`: bio1, bio4, and bio12
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
# Set scenarios names:
sa <- set_scenarios_names(sa, scenarios_names = c("future_1", "future_2",
"current"))
scenarios_names(sa)
#> [1] "future_1" "future_2" "current"
# Get scenarios data:
scenarios_grid <- get_scenarios_data(sa)
scenarios_grid
#> $future_1
#> Simple feature collection with 25 features and 3 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -5371070 ymin: -3380515 xmax: -4571070 ymax: -2680515
#> Projected CRS: WGS 84 / NSIDC EASE-Grid 2.0 Global
#> First 10 features:
#> cell_id bio1 bio12 geometry
#> 1 11 27.20656 267.6684 POLYGON ((-5171070 -2780515...
#> 2 12 26.67131 268.5434 POLYGON ((-5071070 -2780515...
#> 3 13 26.86012 271.4922 POLYGON ((-4971070 -2780515...
#> 4 14 25.76905 279.0779 POLYGON ((-4871070 -2780515...
#> 5 18 26.66205 303.4123 POLYGON ((-5271070 -2880515...
#> 6 19 25.84875 288.1490 POLYGON ((-5171070 -2880515...
#> 7 20 24.58088 285.8529 POLYGON ((-5071070 -2880515...
#> 8 21 24.34115 270.2369 POLYGON ((-4971070 -2880515...
#> 9 22 24.76392 279.5493 POLYGON ((-4871070 -2880515...
#> 10 26 25.65019 326.4529 POLYGON ((-5271070 -2980515...
#>
#> $future_2
#> Simple feature collection with 25 features and 3 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -5371070 ymin: -3380515 xmax: -4571070 ymax: -2680515
#> Projected CRS: WGS 84 / NSIDC EASE-Grid 2.0 Global
#> First 10 features:
#> cell_id bio1 bio12 geometry
#> 1 11 32.55341 267.4699 POLYGON ((-5171070 -2780515...
#> 2 12 31.94110 269.0596 POLYGON ((-5071070 -2780515...
#> 3 13 32.06373 271.1092 POLYGON ((-4971070 -2780515...
#> 4 14 30.82671 276.9349 POLYGON ((-4871070 -2780515...
#> 5 18 32.06298 300.8890 POLYGON ((-5271070 -2880515...
#> 6 19 31.18231 286.0259 POLYGON ((-5171070 -2880515...
#> 7 20 29.84121 282.7132 POLYGON ((-5071070 -2880515...
#> 8 21 29.51664 269.0459 POLYGON ((-4971070 -2880515...
#> 9 22 29.76377 278.5903 POLYGON ((-4871070 -2880515...
#> 10 26 30.84594 323.3235 POLYGON ((-5271070 -2980515...
#>
#> $current
#> Simple feature collection with 27 features and 3 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: -5301744 ymin: -3295037 xmax: -4701744 ymax: -2795037
#> Projected CRS: WGS 84 / NSIDC EASE-Grid 2.0 Global
#> First 10 features:
#> cell_id bio1 bio12 geometry
#> 1 2 21.98257 270.4756 POLYGON ((-5201744 -2795037...
#> 2 3 21.23959 267.7715 POLYGON ((-5101744 -2795037...
#> 3 4 21.46965 264.9304 POLYGON ((-5001744 -2795037...
#> 4 5 21.07145 275.6752 POLYGON ((-4901744 -2795037...
#> 5 8 21.50790 308.3119 POLYGON ((-5301744 -2895037...
#> 6 9 20.93666 295.2626 POLYGON ((-5201744 -2895037...
#> 7 10 19.79140 287.9215 POLYGON ((-5101744 -2895037...
#> 8 11 19.24563 278.8340 POLYGON ((-5001744 -2895037...
#> 9 12 19.49505 278.0062 POLYGON ((-4901744 -2895037...
#> 10 13 19.62922 273.3334 POLYGON ((-4801744 -2895037...
#>
# Select scenarios:
sa <- select_scenarios(sa, scenarios_names = c("future_1"))
# Setting stationary variables in scenarios:
sa <- sdm_area(rivs[c(1:200),], cell_size = 100000, crs = 6933, lines_as_sdm_area = TRUE) |>
add_predictors(bioc) |>
add_scenarios(scen, stationary = c("LENGTH_KM", "DIST_DN_KM"))
#> ! Making grid over study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUE
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.
#> ! Making grid over the study area is an expensive task. Please, be patient!
#> ℹ Using GDAL to make the grid and resample the variables.