predict,sp.temporal.selection-method {tenm}

Predict the potential distribution of species based on environmental conditions

Description

Predict the potential distribution of species based on environmental conditions

Usage

## S4 method for signature 'sp.temporal.selection'
predict(
  object,
  model_variables = NULL,
  layers = NULL,
  layers_path = NULL,
  layers_ext = NULL,
  mve = TRUE,
  level = 0.975,
  output = "suitability",
  ...
)

Arguments

object

An object of class sp.temporal.selection

model_variables

A character vector specifying the variable names used to build the model.

layers

A SpatRaster object or a list where each element is a SpatRaster.

layers_path

Path to the directory containing raster layers.

layers_ext

File extension of the raster layers.

mve

Logical indicating whether to use the minimum volume ellipsoid algorithm.

level

Proportion of data to include inside the ellipsoid if mve is TRUE.

output

Character indicating if the model outputs "suitability" values or "mahalanobis" distances.

...

Additional parameters passed to ellipsoid_projection.

Details

This function predicts the potential distribution of a species based on environmental conditions represented by raster layers. The prediction is based on the model statistics and environmental variables specified in 'model_variables'. If 'mve' is TRUE, the minimum volume ellipsoid algorithm is used to model the niche space. The output can be either "suitability", or "mahalanobis", indicating distance to the niche center. Note that each SpatRaster in the 'layers' parameter should have the same number of elements (layers) as 'model_variables'. The predict method assumes that variables in each SpatRaster correspond to those in 'model_variables'. If layers in the 'layers' parameter are given as a list of objects of class SpatRaster, then the number of prediction layers will have the same number of elements in the list.

Value

A SpatRaster object representing predicted suitability values or Mahalanobis distances to niche center.

Examples

library(tenm)
data("abronia")
tempora_layers_dir <- system.file("extdata/bio",package = "tenm")
abt <- tenm::sp_temporal_data(occs = abronia,
                              longitude = "decimalLongitude",
                              latitude = "decimalLatitude",
                              sp_date_var = "year",
                              occ_date_format="y",
                              layers_date_format= "y",
                              layers_by_date_dir = tempora_layers_dir,
                              layers_ext="*.tif$")
abtc <- tenm::clean_dup_by_date(abt,threshold = 10/60)
future::plan("multisession",workers=2)
abex <- tenm::ex_by_date(this_species = abtc,train_prop=0.7)
abbg <- tenm::bg_by_date(this_species = abex,
                         buffer_ngbs=NULL,n_bg=50000)
abbg <- tenm::bg_by_date(this_species = abex,
                         buffer_ngbs=10,n_bg=50000)
future::plan("sequential")
varcorrs <- tenm::correlation_finder(environmental_data =
                                       abex$env_data[,-ncol(abex$env_data)],
                                     method = "spearman",
                                     threshold = 0.8,
                                     verbose = FALSE)
mod_sel <- tenm::tenm_selection(this_species = abbg,
                                omr_criteria =0.1,
                                ellipsoid_level=0.975,
                                vars2fit = varcorrs$descriptors,
                                nvars_to_fit=c(3,4),
                                proc = TRUE,
                                RandomPercent = 50,
                                NoOfIteration=1000,
                                parallel=TRUE,
                                n_cores=2)
# Prediction using variables path
layers_70_00_dir <- system.file("extdata/bio_1970_2000",package = "tenm")
# The if the 'model_variables' parameter is set to NULL, the method uses
# the first model in the results table (mod_sel$mods_table)
suit_1970_2000 <- predict(mod_sel,
                          model_variables = NULL,
                          layers_path = layers_70_00_dir,
                          layers_ext = ".tif$")
# You can select the modeling variables used to project the model
suit_1970_2000 <- predict(mod_sel,
                          model_variables = c("bio_01","bio_04",
                                              "bio_07","bio_12"),
                          layers_path = layers_70_00_dir,
                          layers_ext = ".tif$")

# Pass a list containing the paths of the modeling layers
layers_1939_2016 <- file.path(tempora_layers_dir,c("1939","2016"))
suit_1939_2016 <- predict(mod_sel,model_variables = NULL,
                          layers_path = layers_1939_2016,
                          layers_ext = ".tif$")
# Pass a list of raster layers
layers_1939 <- terra::rast(list.files(layers_1939_2016[1],
                                      pattern = ".tif$",full.names = TRUE))
layers_2016 <- terra::rast(list.files(layers_1939_2016[2],
                                      pattern = ".tif$",full.names = TRUE))
layers_1939 <- layers_1939[[c("bio_01","bio_04","bio_07")]]
layers_2016 <- layers_2016[[c("bio_01","bio_04","bio_07")]]
layers_list <- list(layers_1939,layers_2016)
suit_1939_2016 <- predict(object = mod_sel,
                          model_variables = c("bio_01","bio_04","bio_07"),
                          layers_path = NULL,
                          layers = layers_list,
                          layers_ext = ".tif$")