Add scenarios to sdm_area — add_scenarios • caretSDM

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_area or input_sdm object.
scen: RasterStack, SpatRaster or stars object. If NULL adds predictors as a scenario.
scenarios_names: Character vector with names of scenarios.
pred_as_scen: Logical. If TRUE adds the current predictors as a scenario.
variables_selected: Character vector with variables names in scen to be used as variables. If NULL adds all variables.
stationary: Names of variables from sa that should be used in scenarios as stationary variables.
crop_area: A sf object to crop the scen object if necessary.
i: A sdm_area or input_sdm object.

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.

Author

Luíz Fernando Esser (luizesser@gmail.com) https://luizfesser.wordpress.com

Examples

# Create sdm_area object:
sa <- sdm_area(parana, cell_size = 100000, output_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 27.23990 1105.015 POLYGON ((-5171070 -2780515...
#> 2       12 26.67789 1089.278 POLYGON ((-5071070 -2780515...
#> 3       13 26.77645 1135.707 POLYGON ((-4971070 -2780515...
#> 4       14 25.75506 1100.358 POLYGON ((-4871070 -2780515...
#> 5       18 26.64249 1287.040 POLYGON ((-5271070 -2880515...
#> 6       19 25.78418 1226.114 POLYGON ((-5171070 -2880515...
#> 7       20 24.56363 1232.960 POLYGON ((-5071070 -2880515...
#> 8       21 24.13556 1306.557 POLYGON ((-4971070 -2880515...
#> 9       22 24.81822 1146.873 POLYGON ((-4871070 -2880515...
#> 10      26 25.63413 1407.331 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 32.58110  978.0547 POLYGON ((-5171070 -2780515...
#> 2       12 31.94988  963.9417 POLYGON ((-5071070 -2780515...
#> 3       13 31.98031 1000.3107 POLYGON ((-4971070 -2780515...
#> 4       14 30.81193  952.3783 POLYGON ((-4871070 -2780515...
#> 5       18 32.04770 1134.1117 POLYGON ((-5271070 -2880515...
#> 6       19 31.13231 1083.4467 POLYGON ((-5171070 -2880515...
#> 7       20 29.82842 1086.9093 POLYGON ((-5071070 -2880515...
#> 8       21 29.31869 1147.7869 POLYGON ((-4971070 -2880515...
#> 9       22 29.82839  983.9998 POLYGON ((-4871070 -2880515...
#> 10      26 30.82495 1249.6981 POLYGON ((-5271070 -2980515...
#> 
#> $current
#> Simple feature collection with 31 features and 3 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: -5301744 ymin: -3295037 xmax: -4601744 ymax: -2795037
#> Projected CRS: WGS 84 / NSIDC EASE-Grid 2.0 Global
#> First 10 features:
#>    cell_id     bio1 bio12                       geometry
#> 1        2 21.92975  1343 POLYGON ((-5201744 -2795037...
#> 2        3 21.92975  1343 POLYGON ((-5101744 -2795037...
#> 3        4 21.11269  1272 POLYGON ((-5001744 -2795037...
#> 4        5 21.36936  1329 POLYGON ((-4901744 -2795037...
#> 5        6 21.02648  1272 POLYGON ((-4801744 -2795037...
#> 6        8 21.47524  1592 POLYGON ((-5301744 -2895037...
#> 7        9 21.92975  1343 POLYGON ((-5201744 -2895037...
#> 8       10 21.92975  1343 POLYGON ((-5101744 -2895037...
#> 9       11 21.11269  1272 POLYGON ((-5001744 -2895037...
#> 10      12 21.36936  1329 POLYGON ((-4901744 -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, output_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.

Add scenarios to `sdm_area`