Type:	Package
Title:	Species Distribution Model Selection
Version:	1.3.2
Description:	User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.
License:	GPL-3
URL:	https://consbiol-unibern.github.io/SDMtune/
BugReports:	https://github.com/ConsBiol-unibern/SDMtune/issues
SystemRequirements:	Java (>= 8)
Depends:	R (≥ 4.1.0)
Imports:	cli (≥ 3.4.1), dismo (≥ 1.3-9), gbm (≥ 2.1.5), ggplot2 (≥ 3.4.0), jsonlite (≥ 1.6), maxnet (≥ 0.1.4), methods, nnet (≥ 7.3-12), randomForest (≥ 4.6-14), Rcpp (≥ 1.0.1), rlang (≥ 0.4.5), rstudioapi (≥ 0.10), stringr (≥ 1.4.0), terra (≥ 1.6-47), whisker (≥ 0.3-2)
Encoding:	UTF-8
LazyData:	true
LinkingTo:	Rcpp
RoxygenNote:	7.3.2
Suggests:	covr, htmltools (≥ 0.3.6), kableExtra (≥ 1.1.0), knitr (≥ 1.23), maps (≥ 3.3.0), pkgdown (≥ 2.0.7), plotROC (≥ 2.2.1), rasterVis (≥ 0.50), reshape2 (≥ 1.4.3), rJava (≥ 0.9-11), rmarkdown (≥ 2.16), scales (≥ 1.0.0), testthat (≥ 3.1.7), withr (≥ 2.5.0), zeallot (≥ 0.1.0)
VignetteBuilder:	knitr
Config/testthat/edition:	3
Collate:	'ANN-class.R' 'BRT-class.R' 'Maxent-class.R' 'Maxnet-class.R' 'RF-class.R' 'RcppExports.R' 'SWD-class.R' 'SDMmodel-class.R' 'SDMmodel2MaxEnt.R' 'SDMmodelCV-class.R' 'SDMtune-class.R' 'SDMtune-package.R' 'addSamplesToBg.R' 'aicc.R' 'auc.R' 'chart-utils.R' 'checkMaxentInstallation.R' 'combineCV.R' 'confMatrix.R' 'convertFolds.R' 'corVar.R' 'doJk.R' 'getTunableArgs.R' 'gridSearch.R' 'maxentTh.R' 'maxentVarImp.R' 'mergeSWD.R' 'modelReport.R' 'optimizeModel.R' 'plotCor.R' 'plotJk.R' 'plotPA.R' 'plotPred.R' 'plotROC.R' 'plotResponse.R' 'plotVarImp.R' 'predict-ANN.R' 'predict-BRT.R' 'predict-Maxent.R' 'predict-Maxnet.R' 'predict-RF.R' 'predict-SDMmodel.R' 'predict-SDMmodelCV.R' 'prepareSWD.R' 'randomFolds.R' 'randomSearch.R' 'reduceVar.R' 'swd2csv.R' 'thinData.R' 'thresholds.R' 'train.R' 'trainANN.R' 'trainBRT.R' 'trainMaxent.R' 'trainMaxnet.R' 'trainRF.R' 'trainValTest.R' 'tss.R' 'utils.R' 'varImp.R' 'varSel.R' 'virtualSp.R' 'zzz.R'
Language:	en-US
NeedsCompilation:	yes
Packaged:	2024-12-16 16:09:52 UTC; vignali
Author:	Sergio Vignali [aut, cre], Arnaud Barras [aut], Veronika Braunisch [aut], Conservation Biology - University of Bern [fnd]
Maintainer:	Sergio Vignali <sdmtune@gmail.com>
Repository:	CRAN
Date/Publication:	2024-12-16 16:50:06 UTC

SDMtune: Species Distribution Model Selection

Description

User-friendly framework that enables the training and the evaluation of species distribution models (SDMs). The package implements functions for data driven variable selection and model tuning and includes numerous utilities to display the results. All the functions used to select variables or to tune model hyperparameters have an interactive real-time chart displayed in the 'RStudio' viewer pane during their execution.

Author(s)

Maintainer: Sergio Vignali sdmtune@gmail.com (ORCID)

Authors:

• Arnaud Barras (ORCID)

• Veronika Braunisch (ORCID)

Other contributors:

• Conservation Biology - University of Bern [funder]

See Also

Useful links:

• https://consbiol-unibern.github.io/SDMtune/

• Report bugs at https://github.com/ConsBiol-unibern/SDMtune/issues

Artificial Neural Network

Description

This Class represents an Artificial Neural Network model object and hosts all the information related to the model.

Usage

## S4 method for signature 'ANN'
show(object)

Arguments

Details

See nnet for the meaning of the slots.

Slots

size

integer. Number of the units in the hidden layer.

decay

numeric. Weight decay.

rang

numeric. Initial random weights.

maxit

integer. Maximum number of iterations.

model

nnet. The nnet model object.

Author(s)

Sergio Vignali

Boosted Regression Tree

Description

This Class represents a Boosted Regression Tree model objects and hosts all the information related to the model.

Usage

## S4 method for signature 'BRT'
show(object)

Arguments

Details

See gbm for the meaning of the slots.

Slots

distribution

character. Name of the used distribution.

n.trees

integer. Maximum number of grown trees.

interaction.depth

integer. Maximum depth of each tree.

shrinkage

numeric. The shrinkage parameter.

bag.fraction

numeric. Random fraction of data used in the tree expansion.

model

gbm. The Boosted Regression Tree model object.

Author(s)

Sergio Vignali

Maxent

Description

This Class represents a MaxEnt model objects and hosts all the information related to the model.

Usage

## S4 method for signature 'Maxent'
show(object)

Arguments

Slots

results

matrix. The result that usually MaxEnt provide as a csv file.

reg

numeric. The value of the regularization multiplier used to train the model.

fc

character. The feature class combination used to train the model.

iter

numeric. The number of iterations used to train the model.

extra_args

character. Extra arguments used to run MaxEnt.

lambdas

vector. The lambdas parameters of the model.

coeff

data.frame. The lambda coefficients of the model.

formula

formula. The formula used to make prediction.

lpn

numeric. Linear Predictor Normalizer.

dn

numeric. Density Normalizer.

entropy

numeric. The entropy value.

min_max

data.frame. The minimum and maximum values of the continuous variables, used for clamping.

Author(s)

Sergio Vignali

Maxnet

Description

This Class represents a Maxnet model objects and hosts all the information related to the model.

Usage

## S4 method for signature 'Maxnet'
show(object)

Arguments

Slots

reg

numeric. The value of the regularization multiplier used to train the model.

fc

character. The feature class combination used to train the model.

model

maxnet. The maxnet model object.

Author(s)

Sergio Vignali

Random Forest

Description

This Class represents a Random Forest model objects and hosts all the information related to the model.

Usage

## S4 method for signature 'RF'
show(object)

Arguments

Details

See randomForest for the meaning of the slots.

Slots

mtry

integer. Number of variable randomly sampled.

ntree

integer. Number of grown trees.

nodesize

integer. Minimum size of terminal nodes.

model

randomForest. The randomForest model object.

Author(s)

Sergio Vignali

SDMmodel

Description

This Class represents an SDMmodel object and hosts all the information related to the model.

Usage

## S4 method for signature 'SDMmodel'
show(object)

Arguments

Slots

data

SWD object. The data used to train the model.

model

An object of class ANN, BRT, RF, Maxent or Maxnet.

Author(s)

Sergio Vignali

SDMmodel2MaxEnt

Description

Converts an SDMmodel object containing a MaxEnt model into a dismo MaxEnt object.

Usage

SDMmodel2MaxEnt(model)

Arguments

Value

The converted dismo MaxEnt object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a Maxent model
model <- train(method = "Maxent",
               data = data,
               fc = "l")

dismo_model <- SDMmodel2MaxEnt(model)
dismo_model

SDMmodelCV

Description

This Class represents an SDMmodel model object with replicates and hosts all the models trained during the cross validation.

Usage

## S4 method for signature 'SDMmodelCV'
show(object)

Arguments

Slots

models

list. A list containing all the models trained during the cross validation.

data

SWD object. Full dataset used to make the partitions.

folds

list with two matrices, the first for the training and the second for the testing dataset. Each column of one matrix represents a fold with TRUE for the locations included in and FALSE excluded from the partition.

Author(s)

Sergio Vignali

SDMtune class

Description

Class used to save the results of one of the following functions: gridSearch, randomSearch or optimizeModel.

Plot an SDMtune object. Use the interactive argument to create an interactive chart.

Usage

## S4 method for signature 'SDMtune'
show(object)

## S4 method for signature 'SDMtune,missing'
plot(x, title = "", interactive = FALSE)

Arguments

Value

If interactive = FALSE the function returns a ggplot object otherwise it returns an SDMtuneChart object that contains the path of the temporary folder where the necessary files to create the chart are saved. In both cases the objects are returned as invisible.

Slots

results

data.frame. Results with the evaluation of the models.

models

list. List of SDMmodel or SDMmodelCV objects.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd", full.names = TRUE)
predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
                   env = predictors, categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data, test = 0.2, only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet", data = train, fc = "l")

# Define the hyperparameters to test
h <- list(reg = 1:5, fc = c("lqp", "lqph"))

# Run the gridSearch function using as metric the AUC
output <- gridSearch(model, hypers = h, metric = "auc", test = test)
output

# Plot the output
plot(output, title = "My experiment")

# Plot the interactive chart
p <- plot(output, title = "My experiment", interactive = TRUE)
# Print the temporary folder that stores the files used to create the chart
str(p)

Sample With Data

Description

Object similar to the MaxEnt SWD format that hosts the species name, the coordinates of the locations and the value of the environmental variables at the location places.

Usage

## S4 method for signature 'SWD'
show(object)

Arguments

Details

The object can contains presence/absence, presence/background, presence only or absence/background only data. Use the prepareSWD function to create the object.

Slots

species

character. Name of the species.

coords

data.frame. Coordinates of the locations.

data

data.frame. Value of the environmental variables at location sites.

pa

numeric. Vector with 1 for presence and 0 for absence/background locations.

Author(s)

Sergio Vignali

Add Samples to Background

Description

The function add the presence locations to the background. This is equivalent to the Maxent argument addsamplestobackground=true.

Usage

addSamplesToBg(x, all = FALSE)

Arguments

Value

An object of class SWD.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")
data

# Add presence locations with values not included in the backgrounds to the
# background locations
new_data <- addSamplesToBg(data)
new_data

# Add all the presence locations to the background locations, even if they
# have values already included in the backgrounds
new_data <- addSamplesToBg(data,
                           all = TRUE)
new_data

AICc

Description

Compute the Akaike Information Criterion corrected for small samples size (Warren and Seifert, 2011).

Usage

aicc(model, env)

Arguments

Details

The function is available only for Maxent and Maxnet methods.

Value

The computed AICc

Author(s)

Sergio Vignali

References

Warren D.L., Seifert S.N., (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21(2), 335–342.

See Also

auc and tss.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                   full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a model
model <- train(method = "Maxnet",
               data = data,
               fc = "l")

# Compute the AICc
aicc(model,
     env = predictors)

AUC

Description

Compute the AUC using the Man-Whitney U Test formula.

Usage

auc(model, test = NULL)

Arguments

Details

For SDMmodelCV objects, the function computes the mean of the training AUC values of the k-folds. If test = TRUE it computes the mean of the testing AUC values for the k-folds. If test is an SWD object, it computes the mean AUC values for the provided testing dataset.

Value

The value of the AUC.

Author(s)

Sergio Vignali

References

Mason, S. J. and Graham, N. E. (2002), Areas beneath the relative operating characteristics (ROC) and relative operating levels (ROL) curves: Statistical significance and interpretation. Q.J.R. Meteorol. Soc., 128: 2145-2166.

See Also

aicc and tss.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Compute the training AUC
auc(model)

# Compute the testing AUC
auc(model,
    test = test)

# Same example but using cross validation instead of training and testing
# datasets
folds <- randomFolds(data,
                     k = 4,
                     only_presence = TRUE)

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               folds = folds)

# Compute the training AUC
auc(model)

# Compute the testing AUC
auc(model,
    test = TRUE)

# Compute the AUC for the held apart testing dataset
auc(model,
    test = test)

Check Maxent Installation

Description

The function checks if Maxent is correctly installed.

Usage

checkMaxentInstallation(verbose = TRUE)

Arguments

Details

In order to have Maxent correctly configured is necessary that:

• Java is installed;

• the package "rJava" is installed;

• the file "maxent.jar" is in the correct folder.

Value

TRUE if Maxent is correctly installed, FALSE otherwise.

Author(s)

Sergio Vignali

Examples

checkMaxentInstallation()

Combine Cross Validation models

Description

This function combines cross-validation models by retraining a new model with all presence and absence/background locations and the same hyperparameters.

Usage

combineCV(model)

Arguments

Details

This is an utility function to retrain a model with all data after, for example, the hyperparameters tuning (gridSearch, randomSearch or optimizeModel) to avoid manual setting of the hyperparameters in the train function.

Value

An SDMmodel object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Create 4 random folds splitting only the presence data
folds <- randomFolds(data,
                     k = 4,
                     only_presence = TRUE)

model <- train(method = "Maxnet",
               data = data,
               folds = folds)

# Define the hyperparameters to test
h <- list(reg = 1:2,
          fc = c("lqp", "lqph"))

# Run the function using the AUC as metric
output <- gridSearch(model,
                     hypers = h,
                     metric = "auc")
output@results
output@models

# Order results by highest test AUC
output@results[order(-output@results$test_AUC), ]

# Combine cross validation models for output with highest test AUC
idx <- which.max(output@results$test_AUC)
combined_model <- combineCV(output@models[[idx]])
combined_model

Confusion Matrix

Description

Computes Confusion Matrixes for threshold values varying from 0 to 1.

Usage

confMatrix(model, test = NULL, th = NULL, type = NULL)

Arguments

Details

• For models trained with the Maxent method the argument type can be: "raw", "logistic" and "cloglog".

• For models trained with the Maxnet method the argument type can be: "link", "exponential", "logistic" and "cloglog", see maxnet for more details.

Value

The Confusion Matrix for all the used thresholds.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a model
model <- train(method = "Maxnet",
               data = data,
               fc = "l")

# Get the confusion matrix for thresholds ranging from 0 to 1
cm <- confMatrix(model,
                 type = "cloglog")
head(cm)
tail(cm)

# Get the confusion matrix for a specific threshold
confMatrix(model,
           type = "logistic",
           th = 0.6)

Print Correlated Variables

Description

Utility that prints the name of correlated variables and the relative correlation coefficient value.

Usage

corVar(
  bg,
  method = "spearman",
  cor_th = NULL,
  order = TRUE,
  remove_diagonal = TRUE
)

Arguments

Value

A data.frame with the variables and their correlation.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare background locations
bg_coords <- terra::spatSample(predictors,
                               size = 10000,
                               method = "random",
                               na.rm = TRUE,
                               xy = TRUE,
                               values = FALSE)

# Create SWD object
bg <- prepareSWD(species = "Virtual species",
                 a = bg_coords,
                 env = predictors,
                 categorical = "biome")

# Get the correlation among all the environmental variables
corVar(bg,
       method = "spearman")

# Get the environmental variables that have a correlation greater or equal to
# the given threshold
corVar(bg,
       method = "pearson",
       cor_th = 0.8)

Jackknife Test

Description

Run the Jackknife test for variable importance removing one variable at time.

Usage

doJk(
  model,
  metric,
  variables = NULL,
  test = NULL,
  with_only = TRUE,
  env = NULL,
  return_models = FALSE,
  progress = TRUE
)

Arguments

Value

A data frame with the test results. If return_model = TRUE it returns a list containing the test results together with the models.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "lq")

# Execute the Jackknife test only for the environmental variables "bio1" and
# "bio12", using the metric AUC
doJk(model,
     metric = "auc",
    variables = c("bio1", "bio12"),
    test = test)

# The same without testing dataset
doJk(model,
     metric = "auc",
     variables = c("bio1", "bio12"))

# Execute the Jackknife test only for the environmental variables "bio1" and
# "bio12", using the metric TSS but without running the test for one single
# variable
doJk(model,
     metric = "tss",
     variables = c("bio1", "bio12"),
     test = test,
     with_only = FALSE)

# Execute the Jackknife test only for the environmental variables "bio1" and
# "bio12", using the metric AICc but without running the test for one single
# variable
doJk(model,
     metric = "aicc",
     variables = c("bio1", "bio12"),
     with_only = FALSE,
     env = predictors)

# Execute the Jackknife test for all the environmental variables using the
# metric AUC and returning all the trained models
jk <- doJk(model,
           metric = "auc",
           test = test,
           return_models = TRUE)

jk$results
jk$models_without
jk$models_withonly

Get Tunable Arguments

Description

Returns the name of all function arguments that can be tuned for a given model.

Usage

getTunableArgs(model)

Arguments

Value

character vector.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a Maxnet model and get tunable hyperparameters
model <- train(method = "Maxnet",
               data = data,
               fc = "l")

getTunableArgs(model)

Grid Search

Description

Given a set of possible hyperparameter values, the function trains models with all the possible combinations of hyperparameters.

Usage

gridSearch(
  model,
  hypers,
  metric,
  test = NULL,
  env = NULL,
  save_models = TRUE,
  interactive = TRUE,
  progress = TRUE
)

Arguments

Details

To know which hyperparameters can be tuned you can use the output of the function getTunableArgs. Hyperparameters not included in the hypers argument take the value that they have in the passed model.

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

Value

SDMtune object.

Author(s)

Sergio Vignali

See Also

randomSearch and optimizeModel.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Define the hyperparameters to test
h <- list(reg = 1:2,
          fc = c("lqp", "lqph"))

# Run the function using the AUC as metric
output <- gridSearch(model,
                     hypers = h,
                     metric = "auc",
                     test = test)
output@results
output@models

# Order results by highest test AUC
output@results[order(-output@results$test_AUC), ]

# Run the function using the AICc as metric and without saving the trained
# models, helpful when numerous hyperparameters are tested to avoid memory
# problems
output <- gridSearch(model,
                     hypers = h,
                     metric = "aicc",
                     env = predictors,
                     save_models = FALSE)
output@results

MaxEnt Thresholds

Description

Returns the value of the thresholds generated by the MaxEnt software.

Usage

maxentTh(model)

Arguments

Value

data.frame with the thresholds.

Author(s)

Sergio Vignali

See Also

maxentVarImp.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a Maxent model
model <- train(method = "Maxent",
               data = data,
               fc = "l")

maxentTh(model)

Maxent Variable Importance

Description

Shows the percent contribution and permutation importance of the environmental variables used to train the model.

Usage

maxentVarImp(model)

Arguments

Details

When an SDMmodelCV object is passed to the function, the output is the average of the variable importance of each model trained during the cross validation.

Value

A data frame with the variable importance.

Author(s)

Sergio Vignali

See Also

maxentTh.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a Maxent model
# The next line checks if Maxent is correctly configured but you don't need
# to run it in your script
model <- train(method = "Maxent",
               data = data,
               fc = "l")

maxentVarImp(model)

Merge SWD Objects

Description

Merge two SWD objects.

Usage

mergeSWD(swd1, swd2, only_presence = FALSE)

Arguments

Details

• In case the two SWD objects have different columns, only the common columns are used in the merged object.

• The SWD object is created in a way that the presence locations are always before than the absence/background locations.

Value

The merged SWD object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split only presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Merge the training and the testing datasets together
merged <- mergeSWD(train,
                   test,
                   only_presence = TRUE)

# Split presence and absence locations in training (80%) and testing (20%)
datasets
datasets <- trainValTest(data,
                         test = 0.2)
train <- datasets[[1]]
test <- datasets[[2]]

# Merge the training and the testing datasets together
merged <- mergeSWD(train, test)

Model Report

Description

Make a report that shows the main results.

Usage

modelReport(
  model,
  folder,
  test = NULL,
  type = NULL,
  response_curves = FALSE,
  only_presence = FALSE,
  jk = FALSE,
  env = NULL,
  clamp = TRUE,
  permut = 10,
  verbose = TRUE
)

Arguments

Details

The function produces a report similar to the one created by MaxEnt software. See terra documentation to see how to pass factors.

Author(s)

Sergio Vignali

Examples

# If you run the following examples with the function example(),
# you may want to set the argument ask like following: example("modelReport",
# ask = FALSE)
# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "lq")

# Create the report
## Not run: 
modelReport(model,
            type = "cloglog",
            folder = "my_folder",
            test = test,
            response_curves = TRUE,
            only_presence = TRUE,
            jk = TRUE,
            env = predictors,
            permut = 2)
## End(Not run)

Optimize Model

Description

The function uses a Genetic Algorithm implementation to optimize the model hyperparameter configuration according to the chosen metric.

Usage

optimizeModel(
  model,
  hypers,
  metric,
  test = NULL,
  pop = 20,
  gen = 5,
  env = NULL,
  keep_best = 0.4,
  keep_random = 0.2,
  mutation_chance = 0.4,
  interactive = TRUE,
  progress = TRUE,
  seed = NULL
)

Arguments

Details

To know which hyperparameters can be tuned you can use the output of the function getTunableArgs. Hyperparameters not included in the hypers argument take the value that they have in the passed model.

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

Part of the code is inspired by this post.

Value

SDMtune object.

Author(s)

Sergio Vignali

See Also

gridSearch and randomSearch.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         val = 0.2,
                         test = 0.2,
                         only_presence = TRUE,
                         seed = 61516)
train <- datasets[[1]]
val <- datasets[[2]]

# Train a model
model <- train("Maxnet",
               data = train)

# Define the hyperparameters to test
h <- list(reg = seq(0.2, 5, 0.2),
          fc = c("l", "lq", "lh", "lp", "lqp", "lqph"))

# Run the function using as metric the AUC
## Not run: 
output <- optimizeModel(model,
                        hypers = h,
                        metric = "auc",
                        test = val,
                        pop = 15,
                        gen = 2,
                        seed = 798)
output@results
output@models
output@models[[1]]  # Best model
## End(Not run)

Plot Correlation

Description

Plot a correlation matrix heat map with the value of the correlation coefficients according with the given method. If cor_th is passed then it prints only the coefficients that are higher or lower than the given threshold.

Usage

plotCor(bg, method = "spearman", cor_th = NULL, text_size = 3)

Arguments

Value

A ggplot object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare background locations
bg_coords <- terra::spatSample(predictors,
                               size = 9000,
                               method = "random",
                               na.rm = TRUE,
                               xy = TRUE,
                               values = FALSE)

# Create SWD object
bg <- prepareSWD(species = "Virtual species",
                 a = bg_coords,
                 env = predictors,
                 categorical = "biome")

# Plot heat map
plotCor(bg,
        method = "spearman")

# Plot heat map showing only values higher than given threshold and change
# text size
plotCor(bg,
        method = "spearman",
        cor_th = 0.8,
        text_size = 4)

Plot Jackknife Test

Description

Plot the Jackknife Test for variable importance.

Usage

plotJk(jk, type = c("train", "test"), ref = NULL)

Arguments

Value

A ggplot object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "lq")

# Execute the Jackknife test for all the environmental variables using the
# metric AUC
jk <- doJk(model,
           metric = "auc",
           test = test)

# Plot Jackknife test result for training
plotJk(jk,
       type = "train",
       ref = auc(model))

#' # Plot Jackknife test result for testing
plotJk(jk,
       type = "test",
       ref = auc(model, test = test))

Plot Presence Absence Map

Description

Plot a presence absence map using the given threshold.

Usage

plotPA(
  map,
  th,
  colors = NULL,
  hr = FALSE,
  filename = "",
  overwrite = FALSE,
  wopt = list(),
  ...
)

Arguments

Value

A ggplot object.

Author(s)

Sergio Vignali

See Also

plotPred.

Examples

map <- terra::rast(matrix(runif(400, 0, 1),
                                    nrow = 20,
                                    ncol = 20))
plotPA(map,
       th = 0.8)

# Custom colors
plotPA(map,
       th = 0.5,
       colors = c("#d8b365", "#018571"))

## Not run: 
# Save the file. The following command will save the map in the working
# directory. Note that `filename` must include the extension.
plotPA(map,
       th = 0.7,
       filename = "my_map.tif")
## End(Not run)

Plot Prediction

Description

Plot Prediction output.

Usage

plotPred(map, lt = "", colorramp = NULL, hr = FALSE)

Arguments

Value

A ggplot object.

Author(s)

Sergio Vignali

See Also

plotPA.

Examples

map <- terra::rast(matrix(runif(400, 0, 1),
                                    nrow = 20,
                                    ncol= 20))

plotPred(map,
         lt = "Habitat suitability \ncloglog")

# Custom colors
plotPred(map,
         lt = "Habitat suitability",
         colorramp = c("#2c7bb6", "#ffffbf", "#d7191c"))

Plot ROC curve

Description

Plot the ROC curve of the given model and print the AUC value.

Usage

plotROC(model, test = NULL)

Arguments

Value

A ggplot object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Plot the training ROC curve
plotROC(model)

# Plot the training and testing  ROC curves
plotROC(model,
        test = test)

Plot Response Curve

Description

Plot the Response Curve of the given environmental variable.

Usage

plotResponse(
  model,
  var,
  type = NULL,
  only_presence = FALSE,
  marginal = FALSE,
  fun = mean,
  rug = FALSE,
  color = "red"
)

Arguments

Details

• Note that fun is not a character argument, you must use mean and not "mean".

• If you want to modify the plot, first you have to assign the output of the function to a variable, and then you have two options:

  • Modify the ggplot object by editing the theme or adding additional elements

  • Get the data with ggplot2::ggplot_build() and then build your own plot (see examples)

Value

A ggplot object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a model
model <- train(method = "Maxnet",
               data = data,
               fc = "lq")

# Plot cloglog response curve for a continuous environmental variable (bio1)
plotResponse(model,
             var = "bio1",
             type = "cloglog")

# Plot marginal cloglog response curve for a continuous environmental
# variable (bio1)
plotResponse(model,
             var = "bio1",
             type = "cloglog",
             marginal = TRUE)

# Plot logistic response curve for a continuous environmental variable
# (bio12) adding the rugs and giving a custom color
plotResponse(model,
             var = "bio12",
             type = "logistic",
             rug = TRUE,
             color = "blue")

# Plot response curve for a categorical environmental variable (biome) giving
# a custom color
plotResponse(model,
             var = "biome",
             type = "logistic",
             color = "green")

# Modify plot
# Change y axes limits
my_plot <- plotResponse(model,
                        var = "bio1",
                        type = "cloglog")
my_plot +
  ggplot2::scale_y_continuous(limits = c(0, 1))

# Get the data and create your own plot:
df <- ggplot2::ggplot_build(my_plot)$data[[1]]
plot(df$x, df$y,
     type = "l",
     lwd = 3,
     col = "blue",
     xlab = "bio1",
     ylab = "cloglog output")

# Train a model with cross validation
folds <- randomFolds(data,
                     k = 4,
                     only_presence = TRUE)

model <- train(method = "Maxnet",
               data = data,
               fc = "lq",
               folds = folds)

# Plot cloglog response curve for a continuous environmental variable (bio17)
plotResponse(model,
             var = "bio1",
             type = "cloglog")

# Plot logistic response curve for a categorical environmental variable
# (biome) giving a custom color
plotResponse(model,
             var = "biome",
             type = "logistic",
             color = "green")

Plot Variable Importance

Description

Plot the variable importance as a bar plot.

Usage

plotVarImp(df, color = "grey")

Arguments

Value

A ggplot object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Train a model
model <- train(method = "Maxnet",
               data = data,
               fc = "l")

# Compute variable importance
vi <- varImp(model,
             permut = 1)

# Plot variable importance
plotVarImp(vi)

# Plot variable importance with custom color
plotVarImp(vi,
           color = "red")

Predict ANN

Description

Predict the output for a new dataset from a trained ANN model.

Usage

## S4 method for signature 'ANN'
predict(object, data, type, clamp)

Arguments

Details

Used by the predict,SDMmodel-method, not exported.

Value

A vector with the predicted values.

Author(s)

Sergio Vignali

Predict BRT

Description

Predict the output for a new dataset from a trained BRT model.

Usage

## S4 method for signature 'BRT'
predict(object, data, type, clamp)

Arguments

Details

Used by the predict,SDMmodel-method, not exported.

The function uses the number of tree defined to train the model and the "response" type output.

Value

A vector with the predicted values.

Author(s)

Sergio Vignali

Predict Maxent

Description

Predict the output for a new dataset from a trained Maxent model.

Usage

## S4 method for signature 'Maxent'
predict(object, data, type = c("cloglog", "logistic", "raw"), clamp = TRUE)

Arguments

Details

Used by the predict,SDMmodel-method, not exported.

The function performs the prediction in R without calling the MaxEnt Java software. This results in a faster computation for large datasets and might result in a slightly different output compared to the Java software.

Value

A vector with the prediction

Author(s)

Sergio Vignali

References

Wilson P.D., (2009). Guidelines for computing MaxEnt model output values from a lambdas file.

Predict Maxnet

Description

Predict the output for a new dataset from a trained Maxnet model.

Usage

## S4 method for signature 'Maxnet'
predict(
  object,
  data,
  type = c("link", "exponential", "cloglog", "logistic"),
  clamp = TRUE
)

Arguments

Details

Used by the predict,SDMmodel-method, not exported.

Value

A vector with the predicted values.

Author(s)

Sergio Vignali

Predict RF

Description

Predict the output for a new dataset from a trained RF model.

Usage

## S4 method for signature 'RF'
predict(object, data, type, clamp)

Arguments

Details

Used by the predict,SDMmodel-method, not exported.

Value

A vector with the predicted probabilities of class 1.

Author(s)

Sergio Vignali

Predict

Description

Predict the output for a new dataset given a trained SDMmodel model.

Usage

## S4 method for signature 'SDMmodel'
predict(
  object,
  data,
  type = NULL,
  clamp = TRUE,
  filename = "",
  overwrite = FALSE,
  wopt = list(),
  extent = NULL,
  ...
)

Arguments

Details

• filename, and extent are arguments used only when the prediction is run for a rast object.

• For models trained with the Maxent method the argument type can be: "raw", "logistic" and "cloglog". The function performs the prediction in R without calling the MaxEnt Java software. This results in a faster computation for large datasets and might result in a slightly different output compared to the Java software.

• For models trained with the Maxnet method the argument type can be: "link", "exponential", "logistic" and "cloglog", see maxnet for more details.

• For models trained with the ANN method the function uses the "raw" output type.

• For models trained with the RF method the output is the probability of class 1.

• For models trained with the BRT method the function uses the number of trees defined to train the model and the "response" output type.

Value

A vector with the prediction or a rast object if data is a raster rast.

Author(s)

Sergio Vignali

References

Wilson P.D., (2009). Guidelines for computing MaxEnt model output values from a lambdas file.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Make cloglog prediction for the test dataset
predict(model,
        data = test,
        type = "cloglog")

# Make logistic prediction for the whole study area
predict(model,
        data = predictors,
        type = "logistic")

## Not run: 
# Make logistic prediction for the whole study area and save it in a file.
# Note that the filename must include the extension. The function saves the
# file in your working directory
predict(model,
        data = predictors,
        type = "logistic",
        filename = "my_map.tif")
## End(Not run)

Predict for Cross Validation

Description

Predict the output for a new dataset given a trained SDMmodelCV model. The output is given as the provided function applied to the prediction of the k models.

Usage

## S4 method for signature 'SDMmodelCV'
predict(
  object,
  data,
  fun = "mean",
  type = NULL,
  clamp = TRUE,
  filename = "",
  overwrite = FALSE,
  wopt = list(),
  extent = NULL,
  progress = TRUE,
  ...
)

Arguments

Details

• filename, and extent are arguments used only when the prediction is run for a rast object.

• When a character vector is passed to the fun argument, than all the given functions are applied and a named list is returned, see examples.

• When filename is provided and the fun argument contains more than one function name, the saved files are named as filename_fun, see example.

• For models trained with the Maxent method the argument type can be: "raw", "logistic" and "cloglog". The function performs the prediction in R without calling the MaxEnt Java software. This results in a faster computation for large datasets and might result in a slightly different output compared to the Java software.

• For models trained with the Maxnet method the argument type can be: "link", "exponential", "logistic" and "cloglog", see maxnet for more details.

• For models trained with the ANN method the function uses the "raw" output type.

• For models trained with the RF method the output is the probability of class 1.

• For models trained with the BRT method the function uses the number of trees defined to train the model and the "response" output type.

Value

A vector with the prediction or a rast object if data is a rast or a list in the case of multiple functions.

Author(s)

Sergio Vignali

References

Wilson P.D., (2009). Guidelines for computing MaxEnt model output values from a lambdas file.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Create 4 random folds splitting only the presence data
folds <- randomFolds(data,
                     k = 4,
                     only_presence = TRUE)

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               folds = folds)

# Make cloglog prediction for the whole study area and get the result as
# average of the k models
predict(model,
        data = predictors,
        fun = "mean",
        type = "cloglog")

# Make cloglog prediction for the whole study area, get the average, standard
# deviation, and maximum values of the k models, and save the output in three
# files.
# The following commands save the output in the working directory. Note that
# the `filename` must include the extension
## Not run: 
maps <- predict(model,
                data = predictors,
                fun = c("mean", "sd", "max"),
                type = "cloglog",
                filename = "prediction.tif")

# In this case three files are created: prediction_mean.tif,
# prediction_sd.tif and prediction_max.tif
plotPred(maps$mean)
plotPred(maps$sd)
plotPred(maps$max)

# Make logistic prediction for the whole study area, given as standard
# deviation of the k models, and save it in a file
predict(model,
        data = predictors,
        fun = "sd",
        type = "logistic",
        filename = "my_map.tif")
## End(Not run)

Prepare an SWD object

Description

Given the coordinates, the species' name and the environmental variables, the function creates an SWD object (sample with data).

Usage

prepareSWD(
  species,
  env,
  p = NULL,
  a = NULL,
  categorical = NULL,
  verbose = TRUE
)

Arguments

Details

The SWD object is created in a way that the presence locations are always before than the absence/background locations.

Value

An SWD object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create the SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")
data

Create Random Folds

Description

Create random folds for cross validation.

Usage

randomFolds(data, k, only_presence = FALSE, seed = NULL)

Arguments

Details

When only_presence = FALSE, the proportion of presence and absence is preserved.

Value

list with two matrices, the first for the training and the second for the testing dataset. Each column of one matrix represents a fold with TRUE for the locations included in and FALSE excluded from the partition.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd", full.names = TRUE)
predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
                   env = predictors, categorical = "biome")

# Create 4 random folds splitting presence and absence locations
folds <- randomFolds(data, k = 4)

# Create 4 random folds splitting only the presence locations
folds <- randomFolds(data, k = 4, only_presence = TRUE)

Random Search

Description

The function performs a random search in the hyperparameters space, creating a population of random models each one with a random combination of the provided hyperparameters values.

Usage

randomSearch(
  model,
  hypers,
  metric,
  test = NULL,
  pop = 20,
  env = NULL,
  interactive = TRUE,
  progress = TRUE,
  seed = NULL
)

Arguments

Details

To know which hyperparameters can be tuned you can use the output of the function getTunableArgs. Hyperparameters not included in the hypers argument take the value that they have in the passed model.

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

Value

SDMtune object.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Define the hyperparameters to test
h <- list(reg = seq(0.2, 3, 0.2),
          fc = c("lqp", "lqph", "lh"))

# Run the function using as metric the AUC
output <- randomSearch(model,
                       hypers = h,
                       metric = "auc",
                       test = test,
                       pop = 10,
                       seed = 25)
output@results
output@models

# Order results by highest test AUC
output@results[order(-output@results$test_AUC), ]

Reduce Variables

Description

Remove variables whose importance is less than the given threshold. The function removes one variable at time and after trains a new model to get the new variable contribution rank. If use_jk is TRUE the function checks if after removing the variable the model performance decreases (according to the given metric and based on the starting model). In this case the function stops removing the variable even if the contribution is lower than the given threshold.

Usage

reduceVar(
  model,
  th,
  metric,
  test = NULL,
  env = NULL,
  use_jk = FALSE,
  permut = 10,
  use_pc = FALSE,
  interactive = TRUE,
  verbose = TRUE
)

Arguments

Details

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

Value

The model trained using the selected variables.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a Maxnet model
model <- train(method = "Maxnet",
               data = train,
               fc = "lq")

# Remove all variables with permuation importance lower than 2%
output <- reduceVar(model,
                    th = 2,
                    metric = "auc",
                    test = test,
                    permut = 1)

# Remove variables with permuation importance lower than 3% only if testing
# TSS doesn't decrease
## Not run: 
output <- reduceVar(model,
                    th = 3,
                    metric = "tss",
                    test = test,
                    permut = 1,
                    use_jk = TRUE)

# Remove variables with permuation importance lower than 2% only if AICc
# doesn't increase
output <- reduceVar(model,
                    th = 2,
                    metric = "aicc",
                    permut = 1,
                    use_jk = TRUE,
                    env = predictors)

# Train a Maxent model
model <- train(method = "Maxent",
               data = train,
               fc = "lq")

# Remove all variables with percent contribution lower than 2%
output <- reduceVar(model,
                    th = 2,
                    metric = "auc",
                    test = test,
                    use_pc = TRUE)
## End(Not run)

SWD to csv

Description

Save an SWD object as csv file.

Usage

swd2csv(swd, file_name)

Arguments

Details

• The file_name argument should include the extension (i.e. my_file.csv).

• If file_name is a single name the function saves the presence absence/background locations in a single file, adding the column pa with 1s for presence and 0s for absence/background locations. If file_name is a vector with two names, it saves the object in two files: the first name is used for the presence locations and the second for the absence/background locations.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")
## Not run: 
# The following commands save the output in the working directory
# Save the SWD object as a single csv file
swd2csv(data,
        file_name = "train_data.csv")

# Save the SWD object in two separate csv files
swd2csv(data,
        file_name = c("presence.csv", "absence.csv"))
## End(Not run)

Thin Data

Description

Remove all but one location per raster cell. The function removes NAs and if more than one location falls within the same raster cell it selects randomly one.

Usage

thinData(coords, env, x = "x", y = "y", verbose = TRUE, progress = TRUE)

Arguments

Details

• coords and env must have the same coordinate reference system.

• The coords argument can contain several columns. This is useful if the user has information related to the coordinates that doesn't want to loose with the thinning procedure. The function expects to have the x coordinates in a column named "x", and the y coordinates in a column named "y". If this is not the case, the name of the columns containing the coordinates can be specified using the arguments x and y.

Value

a matrix or a data frame with the thinned locations.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare background locations, by sampling  also on areas with NA values
bg_coords <- terra::spatSample(predictors,
                               size = 9000,
                               method = "random",
                               xy = TRUE,
                               values = FALSE)
nrow(bg_coords)

# Thin the locations
# The function will remove the coordinates that have NA values for some
# predictors. Note that the function expects to have the coordinates in two
# columns named "x" and "y"

colnames(bg_coords)
thinned_bg <- thinData(bg_coords,
                       env = predictors)
nrow(thinned_bg)

# Here we sample only on areas without NA values and then we double the
# coordinates
bg_coords <- terra::spatSample(predictors,
                               size = 9000,
                               method = "random",
                               na.rm = TRUE,
                               xy = TRUE,
                               values = FALSE)

thinned_bg <- thinData(rbind(bg_coords, bg_coords),
                       env = predictors)

nrow(thinned_bg)

# In case of a dataframe containing more than two columns (e.g. a dataframe
# with the coordinates plus an additional column with the age of the species)
# and custom column names, use the function in this way
age <- sample(c(1, 2),
              size = nrow(bg_coords),
              replace = TRUE)

data <- cbind(age, bg_coords)
colnames(data) <- c("age", "X", "Y")

thinned_bg <- thinData(data,
                       env = predictors,
                       x = "X",
                       y = "Y")
head(data)

Thresholds

Description

Compute three threshold values: minimum training presence, equal training sensitivity and specificity and maximum training sensitivity plus specificity together with fractional predicted area and the omission rate. If a test dataset is provided it returns also the equal test sensitivity and specificity and maximum test sensitivity plus specificity thresholds and the p-values of the one-tailed binomial exact test.

Usage

thresholds(model, type = NULL, test = NULL)

Arguments

Details

The equal training sensitivity and specificity minimizes the difference between sensitivity and specificity. The one-tailed binomial test checks that test points are predicted no better than by a random prediction with the same fractional predicted area.

Value

data.frame with the thresholds.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Get the cloglog thresholds
thresholds(model,
           type = "cloglog")

# Get the logistic thresholds passing the test dataset
thresholds(model,
           type = "logistic",
           test = test)

Train

Description

Train a model using one of the following methods: Artificial Neural Networks, Boosted Regression Trees, Maxent, Maxnet or Random Forest.

Usage

train(method, data, folds = NULL, progress = TRUE, ...)

Arguments

Details

• For the ANN method possible arguments are (for more details see nnet):

  • size: integer. Number of the units in the hidden layer.

  • decay numeric. Weight decay, default is 0.

  • rang numeric. Initial random weights, default is 0.7.

  • maxit integer. Maximum number of iterations, default is 100.

• For the BRT method possible arguments are (for more details see gbm):

  • distribution: character. Name of the distribution to use, default is "bernoulli".

  • n.trees: integer. Maximum number of tree to grow, default is 100.

  • interaction.depth: integer. Maximum depth of each tree, default is 1.

  • shrinkage: numeric. The shrinkage parameter, default is 0.1.

  • bag.fraction: numeric. Random fraction of data used in the tree expansion, default is 0.5.

• For the RF method the model is trained as classification. Possible arguments are (for more details see randomForest):

  • mtry: integer. Number of variable randomly sampled at each split, default is ⁠floor(sqrt(number of variables))⁠.

  • ntree: integer. Number of tree to grow, default is 500.

  • nodesize: integer. Minimum size of terminal nodes, default is 1.

• Maxent models are trained using the arguments "removeduplicates=false" and "addsamplestobackground=false". Use the function thinData to remove duplicates and the function addSamplesToBg to add presence locations to background locations. For the Maxent method, possible arguments are:

  • reg: numeric. The value of the regularization multiplier, default is 1.

  • fc: character. The value of the feature classes, possible values are combinations of "l", "q", "p", "h" and "t", default is "lqph".

  • iter: numeric. Number of iterations used by the MaxEnt algorithm, default is 500.

• Maxnet models are trained using the argument "addsamplestobackground = FALSE", use the function addSamplesToBg to add presence locations to background locations. For the Maxnet method, possible arguments are (for more details see maxnet):

  • reg: numeric. The value of the regularization intensity, default is 1.

  • fc: character. The value of the feature classes, possible values are combinations of "l", "q", "p", "h" and "t", default is "lqph".

The folds argument accepts also objects created with other packages: ENMeval or blockCV. In this case the function converts internally the folds into a format valid for SDMtune.

When multiple methods are given as method argument, the function returns a named list of model object, with the name corresponding to the used method, see examples.

Value

An SDMmodel or SDMmodelCV or a list of model objects.

Author(s)

Sergio Vignali

References

Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0.

Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers (2019). gbm: Generalized Boosted Regression Models. https://CRAN.R-project.org/package=gbm.

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18–22.

Hijmans, Robert J., Steven Phillips, John Leathwick, and Jane Elith. 2017. dismo: Species Distribution Modeling. https://cran.r-project.org/package=dismo.

Steven Phillips (2017). maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'. https://CRAN.R-project.org/package=maxnet.

Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J., Uriarte, M. and R.P. Anderson (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods in Ecology and Evolution.

Roozbeh Valavi, Jane Elith, José Lahoz-Monfort and Gurutzeta Guillera-Arroita (2018). blockCV: Spatial and environmental blocking for k-fold cross-validation. https://github.com/rvalavi/blockCV.

See Also

randomFolds.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

## Train a Maxent model
model <- train(method = "Maxent",
               data = data,
               fc = "l",
               reg = 1.5,
               iter = 700)

# Add samples to background. This should be done preparing the data before
# training the model without using
data <- addSamplesToBg(data)
model <- train("Maxent",
               data = data)

## Train a Maxnet model
model <- train(method = "Maxnet",
               data = data,
               fc = "lq",
               reg = 1.5)

## Cross Validation
# Create 4 random folds splitting only the presence data
folds <- randomFolds(data,
                     k = 4,
                     only_presence = TRUE)

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               reg = 0.8,
               folds = folds)

## Not run: 
# Run only if you have the package ENMeval installed
## Block partition using the ENMeval package
require(ENMeval)
block_folds <- get.block(occ = data@coords[data@pa == 1, ],
                         bg.coords = data@coords[data@pa == 0, ])

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               reg = 0.8,
               folds = block_folds)

## Checkerboard1 partition using the ENMeval package
cb_folds <- get.checkerboard1(occ = data@coords[data@pa == 1, ],
                              env = predictors,
                              bg.coords = data@coords[data@pa == 0, ],
                              aggregation.factor = 4)

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               reg = 0.8,
               folds = cb_folds)

## Environmental block using the blockCV package
# Run only if you have the package blockCV
require(blockCV)
# Create sf object
sf_df <- sf::st_as_sf(cbind(data@coords, pa = data@pa),
                      coords = c("X", "Y"),
                      crs = terra::crs(predictors,
                                       proj = TRUE))

# Spatial blocks
spatial_folds <- cv_spatial(x = sf_df,
                            column = "pa",
                            rows_cols = c(8, 10),
                            k = 5,
                            hexagon = FALSE,
                            selection = "systematic")

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               reg = 0.8,
               folds = spatial_folds)
## End(Not run)

## Train presence absence models
# Prepare presence and absence locations
p_coords <- virtualSp$presence
a_coords <- virtualSp$absence
# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = a_coords,
                   env = predictors[[1:5]])

## Train an Artificial Neural Network model
model <- train("ANN",
               data = data,
               size = 10)

## Train a Random Forest model
model <- train("RF",
               data = data,
               ntree = 300)

## Train a Boosted Regression Tree model
model <- train("BRT",
               data = data,
               n.trees = 300,
               shrinkage = 0.001)

## Multiple methods trained together with default arguments
output <- train(method = c("ANN", "BRT", "RF"),
                data = data,
                size = 10)
output$ANN
output$BRT
output$RF

## Multiple methods trained together passing extra arguments
output <- train(method = c("ANN", "BRT", "RF"),
                data = data,
                size = 10,
                ntree = 300,
                n.trees = 300,
                shrinkage = 0.001)
output

Train, Validation and Test datasets

Description

Split a dataset randomly in training and testing datasets or training, validation and testing datasets.

Usage

trainValTest(x, test, val = 0, only_presence = FALSE, seed = NULL)

Arguments

Details

When only_presence = FALSE, the proportion of presence and absence is preserved.

Value

A list with the training, validation and testing or training and testing SWD objects accordingly.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
# and splitting only the presence locations
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Split presence locations in training (60%), validation (20%) and testing
# (20%) datasets and splitting the presence and the absence locations
datasets <- trainValTest(data,
                         val = 0.2,
                         test = 0.2)
train <- datasets[[1]]
val <- datasets[[2]]
test <- datasets[[3]]

True Skill Statistics

Description

Compute the max TSS of a given model.

Usage

tss(model, test = NULL)

Arguments

Details

For SDMmodelCV objects, the function computes the mean of the training TSS values of the k-folds. If test = TRUE it computes the mean of the testing TSS values for the k-folds. If test is an SWD object, it computes the mean TSS values for the provided testing dataset.

Value

The value of the TSS of the given model.

Author(s)

Sergio Vignali

References

Allouche O., Tsoar A., Kadmon R., (2006). Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). Journal of Applied Ecology, 43(6), 1223–1232.

See Also

aicc and auc.

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Compute the training TSS
tss(model)

# Compute the testing TSS
tss(model,
    test = test)

# Same example but using cross validation instead of training and
# testing datasets. Create 4 random folds splitting only the presence
# locations
folds = randomFolds(train,
                    k = 4,
                    only_presence = TRUE)

model <- train(method = "Maxnet",
               data = train,
               fc = "l",
               folds = folds)

# Compute the training TSS
tss(model)

# Compute the testing TSS
tss(model,
    test = TRUE)

# Compute the TSS for the held apart testing dataset
tss(model,
    test = test)

Variable Importance

Description

The function randomly permutes one variable at time (using training and absence/background datasets) and computes the decrease in training AUC. The result is normalized to percentages. Same implementation of MaxEnt java software but with the additional possibility of running several permutations to obtain a better estimate of the permutation importance. In case of more than one permutation (default is 10) the average of the decrease in training AUC is computed.

Usage

varImp(model, permut = 10, progress = TRUE)

Arguments

Details

Note that it could return values slightly different from MaxEnt Java software due to a different random permutation.

For SDMmodelCV objects the function returns the average and the standard deviation of the permutation importances of the single models.

Value

data.frame with the ordered permutation importance.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Compute variable importance
vi <- varImp(model,
             permut = 5)
vi

# Same example but using cross validation instead of training and testing
# datasets
# Create 4 random folds splitting only the presence locations
folds = randomFolds(data,
                    k = 4,
                    only_presence = TRUE)

model <- train(method = "Maxnet",
               data = data,
               fc = "l",
               folds = folds)

# Compute variable importance
vi <- varImp(model,
             permut = 5)
vi

Variable Selection

Description

The function performs a data-driven variable selection. Starting from the provided model it iterates through all the variables starting from the one with the highest contribution (permutation importance or maxent percent contribution). If the variable is correlated with other variables (according to the given method and threshold) it performs a Jackknife test and among the correlated variables it removes the one that results in the best performing model when removed (according to the given metric for the training dataset). The process is repeated until the remaining variables are not highly correlated anymore.

Usage

varSel(
  model,
  metric,
  bg4cor,
  test = NULL,
  env = NULL,
  method = "spearman",
  cor_th = 0.7,
  permut = 10,
  use_pc = FALSE,
  interactive = TRUE,
  progress = TRUE,
  verbose = TRUE
)

Arguments

Details

An interactive chart showing in real-time the steps performed by the algorithm is displayed in the Viewer pane.

To find highly correlated variables the following formula is used:

| coeff | \le cor_th

Value

The SDMmodel or SDMmodelCV object trained using the selected variables.

Author(s)

Sergio Vignali

Examples

# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd",
                    full.names = TRUE)

predictors <- terra::rast(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species",
                   p = p_coords,
                   a = bg_coords,
                   env = predictors,
                   categorical = "biome")

# Split presence locations in training (80%) and testing (20%) datasets
datasets <- trainValTest(data,
                         test = 0.2,
                         only_presence = TRUE)
train <- datasets[[1]]
test <- datasets[[2]]

# Train a model
model <- train(method = "Maxnet",
               data = train,
               fc = "l")

# Prepare background locations to test autocorrelation, this usually gives a
# warning message given that less than 10000 points can be randomly sampled
bg_coords <- terra::spatSample(predictors,
                               size = 9000,
                               method = "random",
                               na.rm = TRUE,
                               xy = TRUE,
                               values = FALSE)

bg <- prepareSWD(species = "Virtual species",
                 a = bg_coords,
                 env = predictors,
                 categorical = "biome")

## Not run: 
# Remove variables with correlation higher than 0.7 accounting for the AUC,
# in the following example the variable importance is computed as permutation
# importance
vs <- varSel(model,
             metric = "auc",
             bg4cor = bg,
             test = test,
             cor_th = 0.7,
             permut = 1)
vs

# Remove variables with correlation higher than 0.7 accounting for the TSS,
# in the following example the variable importance is the MaxEnt percent
# contribution
# Train a model
model <- train(method = "Maxent",
               data = train,
               fc = "l")

vs <- varSel(model,
             metric = "tss",
             bg4cor = bg,
             test = test,
             cor_th = 0.7,
             use_pc = TRUE)
vs

# Remove variables with correlation higher than 0.7 accounting for the aicc,
# in the following example the variable importance is the MaxEnt percent
# contribution
vs <- varSel(model,
             metric = "aicc",
             bg4cor = bg,
             cor_th = 0.7,
             use_pc = TRUE,
             env = predictors)
vs
## End(Not run)

Virtual Species

Description

Dataset containing a random generated virtual species. The purpose of this dataset is to demonstrate the use of the functions included in the package.

Usage

virtualSp

Format

A list with five elements:

presence

400 random generated coordinates for the presence locations.

absence

300 random generated coordinates for the absence locations.

background

5000 random generated coordinates for the background locations.

pa_map

Presence absence map used to extract the presence and absence locations.

prob_map

Probability map of the random generated virtual species.

Details

The random species has been generated using the package virtualspecies.

References

Leroy, B. , Meynard, C. N., Bellard, C. and Courchamp, F. (2016), virtualspecies, an R package to generate virtual species distributions. Ecography, 39: 599-607. doi:10.1111/ecog.01388