ellipsoid_model: Ellipsoid-based ecological niche models

In marlonecobos/ellipsenm: Ecological Niche's Characterizations Using Ellipsoids

Ellipsoid-based ecological niche models

Description

ellipsoid_model helps in finding the centroid and matrix that define an ellipsoid. It uses distinct methods with asumptions that differ from each other.

Usage

ellipsoid_model(data, species, longitude, latitude, raster_layers,
                method = "covmat", level = 95, truncate = TRUE, replicates = 1,
                replicate_type = "bootstrap", bootstrap_percentage = 75,
                projection_variables = NULL, prvariables_format = NULL,
                prediction = "suitability", return_numeric = TRUE,
                tolerance = 1e-60, format = "GTiff",
                overwrite = FALSE, color_palette = viridis::magma,
                output_directory = "ellipsenm_model")

Arguments

data	data.frame of occurrence records. Columns must be: species, longitude, and latitude. Optionally, if raster_layers is not defined, data must include more columns containing the values of at least two variables to be used for fitting ellipsoid* models.
species	(character) name of the column with the name of the species.
longitude	(character) name of the column with longitude data.
latitude	(character) name of the column with latitude data.
raster_layers	RasterStack of at least two environmental variables to be extracted using geographic coordinates present in data. If not provided data must include additional columns containing values of variables to fit ellipsoid* models.
method	(character) method to construct the ellipsoid that characterizes the species ecological niche. Available methods are: "covmat", "mve1", and "mve2". See details of ellipsoid_fit. Default = "covmat".
level	(numeric) the confidence level of a pairwise confidence region for the ellipsoid, expresed as percentage. Default = 95.
truncate	(logical) whether or not to truncate values of suitability based on ellipsoid limits. All values outside the ellipsoid will be zero. Default = TRUE.
replicates	(numeric) number of replicates to perform. Default = 1 produces a single model using all the data.
replicate_type	(character) type of replicates to perform. Options are: "bootstrap" and "jackknife"; default = "bootstrap". See details. Ignored if replicates = 1.
bootstrap_percentage	(numeric) percentage of data to be bootstrapped for each replicate. Default = 50. Valid if replicates > 1 and replicate_type = "bootstrap".
projection_variables	optional, (RasterStack, list, or character): if RasterStack, a stack of layers respresenting an only scenario for projection; if list, a named list of RasterStacks representing multiple scenarios for projection; if character, name of the folder (in the working directory) containing other folders (scenarios for projection) with raster layers to be used as variables. See details. Default = NULL.
prvariables_format	(character) if projection_variables is a list, raster type of variables (raster layers) to be used and located in subdirectories. Default = NULL. See writeFormats for details and options.
prediction	(character) type of prediction to be made, options are: "suitability", "mahalanobis", and "both". Default = "suitability".
return_numeric	(logical) whether or not to return values of mahalanobis distance and suitability as part of the results (it depends on the type of prediction selected). Default = FALSE.
tolerance	the tolerance for detecting linear dependencies. Default = 1e-60.
format	(charater) file type for raster outputs to be written in output_directory. Default = "GTiff". See writeFormats.
overwrite	(logical) whether or not to overwrite exitent results in output_directory. Default = FALSE.
color_palette	a color palette function to be used in plotting suitability values in an HTML report produced at the end of all analyses.
output_directory	name of the folder were all results will be written. This avoids saturation of the RAM.

Details

replicate_type

projection_variables

Value

An object of class ellipsoid_model_sim or ellipsoid_model_rep.

Examples

# reading data
occurrences <- read.csv(system.file("extdata", "occurrences.csv",
                                    package = "ellipsenm"))

# raster layers of environmental data
vars <- raster::stack(list.files(system.file("extdata", package = "ellipsenm"),
                                 pattern = "bio", full.names = TRUE))

# creating the model with no replicates
ell_model <- ellipsoid_model(data = occurrences, species = "species",
                             longitude = "longitude", latitude = "latitude",
                             raster_layers = vars, method = "covmat", level = 99,
                             replicates = 1, prediction = "suitability",
                             return_numeric = TRUE, format = "GTiff",
                             overwrite = FALSE, output_directory = "ellipsenm_model")

class(ell_model)
# check your directory, folder "ellipsenm_model"

# creating the model with replicates
ell_model1 <- ellipsoid_model(data = occurrences, species = "species",
                              longitude = "longitude", latitude = "latitude",
                              raster_layers = vars, method = "covmat", level = 99,
                              replicates = 5, prediction = "suitability",
                              return_numeric = TRUE, format = "GTiff",
                              overwrite = FALSE, output_directory = "ellipsenm_model1")

class(ell_model1)
# check your directory, folder "ellipsenm_model1"

# creating the model with projections
pr_vars <- raster::stack(system.file("extdata", "proj_variables.tif",
                                     package = "ellipsenm"))
names(pr_vars) <- names(vars)

ell_model2 <- ellipsoid_model(data = occurrences, species = "species",
                              longitude = "longitude", latitude = "latitude",
                              raster_layers = vars, method = "covmat", level = 99,
                              replicates = 3, replicate_type = "bootstrap",
                              bootstrap_percentage = 75, projection_variables = pr_vars,
                              prediction = "suitability", return_numeric = TRUE,
                              format = "GTiff", overwrite = FALSE,
                              output_directory = "ellipsenm_model2")

class(ell_model2)
# check your directory, folder "ellipsenm_model2"

marlonecobos/ellipsenm documentation built on Oct. 18, 2023, 8:09 a.m.