Package index • DeltaMultipleBenefits

All functions

calc_change_SDM(): Calculate the difference between predictions of species presence for a baseline and scenario landscape
classify_landcover(): Classify landcover
classify_landcover(<SpatRaster>): Classify landcover for SpatRaster object
classify_landcover(<sf>): Classify landcover data for simple features object
create_directory(): create_directory
create_predictor_stack(): Create raster stack representing SDM predictors
estimate_tima_patchsize(): Estimate tidal marsh patch size
fit_SDM(): Apply species distribution models to new landscapes.
key: Land cover classification scheme for the DeltaMultipleBenefits framework
metrics: Metrics by land cover class and benefits category
predictors_riparian: Predictors used in distribution models for riparian focal species
predictors_tima: Predictors used in distribution models for tidal marsh bird focal species
predictors_waterbird_fall: Predictors used in distribution models for waterbird groups during the fall season
predictors_waterbird_win: Predictors used in distribution models for waterbird groups during the winter season
python_dist(): Calculate Euclidean distance via Python
python_focal_prep(): Prepare landscape rasters for focal statistics via Python
python_focal_stats(): Run focal statistics via Python
rasterize_stream_channels(): Rasterize stream channels
roosts_original: Traditional nighttime crane roost locations
sum_change(): Summarize net change between scenario and baseline landscapes
sum_habitat(): Summarize total habitat scores
sum_landcover(): Summarize total area of land cover classes
sum_metrics(): Summarize total metric scores
transform_SDM(): Transform predictions from species distribution models to binary
update_covertype(): Update waterbird and tidal marsh bird predictors: covertype and LANDCOVER
update_pwater(): Update waterbird predictors: pwater & pfld
update_roosts(): Update waterbird predictors: crane roost locations