Predict the potential distribution of species based on environmental conditions
Source:R/00_methods.R
predict.RdPredict the potential distribution of species based on environmental conditions
Usage
# S4 method for class '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.
Value
A SpatRaster object representing predicted suitability values or Mahalanobis distances to niche center.
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.
Examples
# \donttest{
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$env_data)],
method = "spearman",
threshold = 0.8,
verbose = FALSE)
#> Warning: the standard deviation is zero
mod_sel <- tenm::tenm_selection(this_species = abbg,
omr_criteria =0.1,
ellipsoid_level=0.975,
vars2fit = varcorrs$descriptors,
nvars_to_fit=c(3,4),
proc = TRUE,
RandomPercent = 50,
NoOfIteration=1000,
parallel=TRUE,
n_cores=2)
#> -------------------------------------------------------------------
#> **** Starting model selection process ****
#> -------------------------------------------------------------------
#>
#> A total number of 84 models will be created for combinations of 9 variables taken by 3
#>
#> A total number of 126 models will be created for combinations of 9 variables taken by 4
#>
#> -------------------------------------------------------------------
#> **A total number of 210 models will be tested **
#>
#> -------------------------------------------------------------------
#> Doing calibration from model 1 to 100 in process 1
#>
#> Doing calibration from model 101 to 200 in process 2
#>
#> Doing calibration from model 201 to 210 in process 3
#>
#> Finishing calibration of models 1 to 100
#>
#> Finishing calibration of models 101 to 200
#>
#> Finishing calibration of models 201 to 210
#>
#> Finishing...
#>
#> -------------------------------------------------------------------
#> 124 models passed omr_criteria for train data
#> 20 models passed omr_criteria for test data
#> 20 models passed omr_criteria for train and test data
# 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$mods_table)
suit_1970_2000 <- predict(mod_sel,
model_variables = NULL,
layers_path = layers_70_00_dir,
layers_ext = ".tif$")
#> No selected variables. Using the first model in mods_table
#>
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# 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$")
#>
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# Pass a list containing the paths of the modeling layers
layers_1939_2016 <- file.path(tempora_layers_dir,c("1939","2016"))
suit_1939_2016 <- predict(mod_sel,model_variables = NULL,
layers_path = layers_1939_2016,
layers_ext = ".tif$")
#> No selected variables. Using the first model in mods_table
#>
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# 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$")
#>
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# }