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intSDM (version 2.1.1)

species_model: R6 class for creating a species_model object.

Description

An object containing the data, covariates and other relevant information to be used in the reproducible workflow. The function startWorkflow acts as a wrapper in creating one of these objects. This object has additional slot functions within, which allow for further specification and customization of the reproducible workflow.

Arguments

Methods

Method help()

Obtain documentation for a species_model object.

Usage

species_model$help(...)

Arguments

...

Not used

Method new()

initialize the species_model object.

Usage

species_model$new(
  Countries,
  Species,
  nameProject,
  Save,
  Richness,
  Directory,
  Projection,
  Quiet = TRUE
)

Arguments

Countries

Name of the countries to include in the workflow.

Species

Name of the species to include in the workflow.

nameProject

Name of the project for the workflow.

Save

Logical argument indicating if the model outputs should be saved.

Richness

Logical create a species richness model or not.

Directory

Directory where the model outputs should be saved.

Projection

The coordinate reference system used in the workflow.

Quiet

Logical variable indicating if the workflow should provide messages throughout the estimation procedure.

Method addArea()

Usage

species_model$addArea(Object = NULL, countryName = NULL, ...)

Arguments

Object

An sf object of the study area. If NULL then countryName needs to be provided.

countryName

Name of the countries to obtain a boundary for. This argument will then use the gisco_get_countries function from the giscoR package to obtain a boundary.

...

Additional arguments passed to gisco_get_countries.

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Sweden')
}

Method print()

Prints the datasets, their data type and the number of observations, as well as the marks and their respective families.

Usage

species_model$print(...)

Arguments

...

Not used.

Examples

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$print()

Method plot()

Makes a plot of the features used in the integrated model.

Usage

species_model$plot(
  Mesh = FALSE,
  Boundary = TRUE,
  Species = FALSE,
  Covariates = FALSE
)

Arguments

Mesh

Add the mesh to the plot.

Boundary

Add the boundary to the plot.

Species

Add the species location data to the plot.

Covariates

Add the spatial covariates to the plot.

Returns

A ggplot object.

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Germany')
workflow$plot(Boundary = TRUE)
}

Method workflowOutput()

Function to specify the workflow output from the model. This argument must be at least one of: 'Model', 'Prediction', 'Maps' 'Cross-validation', Bias and 'Summary'.

Usage

species_model$workflowOutput(Output)

Arguments

Output

The names of the outputs to give in the workflow. Must be at least one of: 'Model', 'Prediction', 'Maps', 'Bias', Summary and 'Cross-validation'.

Examples

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$workflowOutput('Predictions')

Method addStructured()

The function is used to convert structured datasets into a framework which is usable by the model. The three types of structured data allowed by this function are presence-absence (PA), presence-only (PO) and counts/abundance datasets, which are controlled using the datasetType argument. The other arguments of this function are used to specify the appropriate variable (such as response name, trial name, species name and coordinate name) names in these datasets.

Usage

species_model$addStructured(
  dataStructured,
  datasetType,
  responseName,
  trialsName,
  datasetName = NULL,
  speciesName,
  coordinateNames,
  generateAbsences = FALSE
)

Arguments

dataStructured

The dataset used in the model. Must be either a data.frame, sf or SpatialPoints* object, or a list containing multiples of these classes.

datasetType

A vector which gives the type of dataset. Must be either 'count', 'PO' or 'PA'.

responseName

Name of the response variable in the dataset. If dataType is 'PO', then this argument may be missing.

trialsName

Name of the trial name variable in the PA datasets.

datasetName

An optional argument to create a new name for the dataset. Must be the same length as dataStructured if that is provided as a list.

speciesName

Name of the species variable name in the datasets.

coordinateNames

Names of the coordinate vector in the dataset. Only required if the datasets added are data.frame objects.

generateAbsences

Generates absences for 'PA' data. This is done by combining all the sampling locations for all the species in a given dataset, and creating an absence where each of the species do not occur. Requires datasetType = 'PA'.

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Sweden')
#Generate random species
speciesData <- data.frame(X = runif(1000, 12, 24),
                          Y = runif(1000, 56, 68),
               Response = sample(c(0,1), 1000, replace = TRUE),
               Name = 'Fraxinus_excelsior')
workflow$addStructured(dataStructured = speciesData, datasetType = 'PA',
                       datasetName = 'xx', responseName = 'Response',
                       speciesName = 'Name', coordinateNames = c('X', 'Y'))
                       }

Method addMesh()

Function to add an fm_mesh_2d object to the workflow. The user may either add their own mesh to the workflow, or use the arguments of this function to help create one.

Usage

species_model$addMesh(Object, ...)

Arguments

Object

An fm_mesh_2d object to add to the workflow.

...

Additional arguments to pass to fmesher's fm_mesh_2d_inla. Use ?fm_mesh_2d_inla to find out more about the different arguments.

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addMesh(cutoff = 20000,
                 max.edge=c(60000, 80000),
                 offset= 100000)
}

Method addGBIF()

Function to add species occurrence records from GBIF (using the rgbif package) to the reproducible workflow. The arguments for this function are used to either filter the GBIF records, or to specify the characteristics of the observation model.

Usage

species_model$addGBIF(
  Species = "All",
  datasetName = NULL,
  datasetType = "PO",
  removeDuplicates = FALSE,
  generateAbsences = FALSE,
  filterDistance = 0,
  ...
)

Arguments

Species

The names of the species to include in the workflow (initially specified using startWorkflow). Defaults to All, which will find occurrence records for all specie specified in startWorkflow.

datasetName

The name to give the dataset obtained from GBIF. Cannot be NULL.

datasetType

The data type of the dataset. Defaults to PO, but may also be PA or Counts.

removeDuplicates

Argument used to remove duplicate observations for a species across datasets. May take a long time if there are many observations obtained across multiple datasets. Defaults to FALSE.

generateAbsences

Generates absences for 'PA' data. This is done by combining all the sampling locations for all the species, and creating an absence where a given species does not occur.

filterDistance

Remove all points that are x kilometers away from the boundary polygon. Value must be provided in kilometers. Defaults to 0 km which removes no points.

...

Additional arguments to specify the occ_data function from rgbif. See ?occ_data for more details.

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')
workflow$addGBIF(datasetName = 'exampleGBIF',
                 datasetType = 'PA',
                 limit = 10000,
                 coordinateUncertaintyInMeters = '0,50')
}

Method addCovariates()

Function to add spatial covariates to the workflow. The covariates may either be specified by the user, or they may come from worldClim obtained with the geodata package.

Usage

species_model$addCovariates(
  Object = NULL,
  worldClim = NULL,
  landCover = NULL,
  res = 2.5,
  Months = "All",
  Function = "mean",
  ...
)

Arguments

Object

A object of class: spatRaster, SpatialPixelsDataFrame or raster containing covariate information across the area. Note that this function will check if the covariates span the boundary area, so it may be preferable to add your own boundary using `.$addArea` if this argument is specified.

worldClim

Name of the worldClim to include in the model. See ?worldclim_country from the geodata package for more information.

landCover

Name of the land cover covariates to include in the model. See ?landcover from the geodata package for more information.

res

Resolution of the worldclim variable. Valid options are: 10, 5, 2.5 or 0.5 (minutes of a degree).

Months

The months to include the covariate for. Defaults to All which includes covariate layers for all months.

Function

The function to aggregate the temporal data into one layer. Defaults to mean.

...

Not used.

Examples

\dontrun{
if (requireNamespace('INLA')) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addCovariates(worldClim = 'tavg', res = '10')
}
}

Method crossValidation()

Function to add a spatial cross validation method to the workflow.

Usage

species_model$crossValidation(
  Method,
  blockOptions = list(k = 5, rows_cols = c(4, 4), plot = FALSE, seed = 123),
  blockCVType = "DIC"
)

Arguments

Method

The spatial cross-validation methods to use in the workflow. May be at least one of spatialBlock or Loo (leave-one-out). See the PointedSDMs package for more details.

blockOptions

A list of options to specify the spatial block cross-validation. Must be a named list with arguments specified for: k, rows_cols, plot, seed. See blockCV::cv_spatial for more information.

blockCVType

The cross-validation method to complete if Method = 'spatialBlock'. May be one of 'DIC' (default) which will iteratively return the DIC scores for each block, or 'Predict'. This method return scores of marginal likelihood for each combination of dataset across all blocks, by fitting a model on all blocks but one, and predicting on the left out block. The prediction dataset is automatically chosen as the first PA dataset added to the model. See blockedCV for more information. Note that this may take a long time to estimate if there are many datasets included in the model.

Examples

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$crossValidation(Method = 'Loo')

Method modelOptions()

Function to specify model options for the INLA and PointedSDMs parts of the model.

Usage

species_model$modelOptions(ISDM = list(), Richness = list())

Arguments

ISDM

Arguments to specify in startISDM from the PointedSDMs function. This argument needs to be a named list of the following options:

  1. pointsIntercept: Logical: intercept terms for the dataset. Defaults to TRUE

  2. pointsSpatial: Choose how the spatial effects are included in the model. If 'copy' then the spatial effects are shared across the datasets, if 'individual' then the spatial effects are created for each dataset individually, and if 'correlate' then the spatial effects are correlated. If NULL, then spatial effects are turned off for the datasets.

  3. Offset: The name of the offset variable.

See ?PointedSDMs::startISDM for more details on these choices.

Richness

Options to specify the richness model. This argument needs to be a named list of the following options:

  1. samplingSize: The sample area size for the dataset provided in predictionIntercept. The units should be the same as specified in startWorkflow

  2. speciesSpatial: Specify the species spatial model. If 'replicate' then create a spatial effect for each species with shared hyperparameters, if 'copy' create a spatial effect for each species. If NULL then the spatial effects for the species will be turned off.

  3. speciesIntercept: If TRUE (default) incorporate a random intercept for the species, if FALSE use a fixed intercept and if NULL include no intercept for the species.

Examples

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

Method specifySpatial()

Function to specify pc priors for the shared random field in the model. See ?INLA::inla.spde2.pcmatern for more details.

Usage

species_model$specifySpatial(...)

Arguments

...

Arguments passed on to inla.spde2.pcmatern.

Examples

\dontrun{
if (requireNamespace('INLA')) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addMesh(cutoff = 20000,
                 max.edge=c(60000, 80000),
                 offset= 100000)
workflow$specifySpatial(prior.range = c(200000, 0.05),
                        prior.sigma = c(5, 0.1))
}
}

Method specifyPriors()

Function to specify priors for the fixed effects in the model. The priors of the fixed effects are assumed to be Gaussian; this function alows the user to specify the parameters of this distribution.

Usage

species_model$specifyPriors(
  effectNames,
  Mean = 0,
  Precision = 0.01,
  copyModel = list(beta = list(fixed = FALSE)),
  priorIntercept = list(prior = "loggamma", param = c(1, 5e-05)),
  priorGroup = list(model = "iid", hyper = list(prec = list(prior = "loggamma", param =
    c(1, 5e-05))))
)

Arguments

effectNames

The name of the effects to specify the prior for. Must be the name of any of the covariates incldued in the model, or 'Intercept' to specify the priors for the intercept terms.

Mean

The mean of the prior distribution. Defaults to 0.

Precision

The precision (inverse variance) of the prior distribution. Defaults to 0.01.

copyModel

List of model specifications given to the hyper parameters for the "copy" model. Defaults to list(beta = list(fixed = FALSE)).

priorIntercept

Prior for the precision parameter for the random intercept in the species richness model. Needs Output = "Richness". Defaults to the default INLA prior.

priorGroup

Prior for the precision for the iid effect in the species spatial effect in the richness model. Needs Output = "Richness" and speciesSpatial = "replicate" in the richness options. Defualts to the default INLA prior.

Examples

\dontrun{
if (requireNamespace('INLA')) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addMesh(cutoff = 20000,
                 max.edge=c(60000, 80000),
                 offset= 100000)
workflow$specifyPriors(effectName = 'Intercept', mean = 0, Precision = 0.1)
}
}

Method biasFields()

Function to add bias fields to the model.

Usage

species_model$biasFields(
  datasetName,
  copyModel = FALSE,
  shareModel = FALSE,
  ...
)

Arguments

datasetName

Name of the dataset to add a bias field to.

copyModel

Create copies of the biasField across the different datasets. Defaults to FALSE.

shareModel

Share a bias field across the datasets specified with datasetNames. Defaults to FALSE.

...

Additional arguments passed on to inla.spde2.pcmatern to customize the priors for the pc matern for the bias fields.

Examples

\dontrun{
if(requireNamespace('INLA')) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')
workflow$addGBIF(datasetName = 'exampleGBIF',
                 datasetType = 'PA',
                 limit = 10000,
                 coordinateUncertaintyInMeters = '0,50')
workflow$biasFields(datasetName = 'exampleGBIF')
}
}

Method modelFormula()

Add a formula to the model

Usage

species_model$modelFormula(covariateFormula, biasFormula)

Arguments

covariateFormula

Change the covariate formula of the model.

biasFormula

Change the bias formula of the model

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')
workflow$addCovariate(rasterStack)
workflow$addFormula(covariateFormula = ~ covariate)
workflow$addFormula(biasFormula = ~ biasFormula)

}

Method obtainMeta()

Obtain metadata from the workflow.

Usage

species_model$obtainMeta(Number = TRUE, Citations = TRUE)

Arguments

Number

Print the number of observations per dataset. Defaults to TRUE.

Citations

Print the citations for the GBIF obtained datasets. Defaults to TRUE.

Examples

\dontrun{
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')
workflow$addGBIF(datasetName = 'exampleGBIF',
                 datasetType = 'PA',
                 limit = 10000,
                 coordinateUncertaintyInMeters = '0,50')
workflow$obtainMeta()
}

Method clone()

The objects of this class are cloneable with this method.

Usage

species_model$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

Run this code

## ------------------------------------------------
## Method `species_model$addArea`
## ------------------------------------------------

if (FALSE) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Sweden')
}

## ------------------------------------------------
## Method `species_model$print`
## ------------------------------------------------

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

workflow$print()

## ------------------------------------------------
## Method `species_model$plot`
## ------------------------------------------------

if (FALSE) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Germany')
workflow$plot(Boundary = TRUE)
}

## ------------------------------------------------
## Method `species_model$workflowOutput`
## ------------------------------------------------

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$workflowOutput('Predictions')

## ------------------------------------------------
## Method `species_model$addStructured`
## ------------------------------------------------

if (FALSE) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Sweden')

#Generate random species
speciesData <- data.frame(X = runif(1000, 12, 24),
                          Y = runif(1000, 56, 68),
               Response = sample(c(0,1), 1000, replace = TRUE),
               Name = 'Fraxinus_excelsior')
workflow$addStructured(dataStructured = speciesData, datasetType = 'PA',
                       datasetName = 'xx', responseName = 'Response',
                       speciesName = 'Name', coordinateNames = c('X', 'Y'))
                       }

## ------------------------------------------------
## Method `species_model$addMesh`
## ------------------------------------------------

if (FALSE) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addMesh(cutoff = 20000,
                 max.edge=c(60000, 80000),
                 offset= 100000)

}

## ------------------------------------------------
## Method `species_model$addGBIF`
## ------------------------------------------------

if (FALSE) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')

workflow$addGBIF(datasetName = 'exampleGBIF',
                 datasetType = 'PA',
                 limit = 10000,
                 coordinateUncertaintyInMeters = '0,50')
}

## ------------------------------------------------
## Method `species_model$addCovariates`
## ------------------------------------------------

if (FALSE) {
if (requireNamespace('INLA')) {

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addCovariates(worldClim = 'tavg', res = '10')

}
}

## ------------------------------------------------
## Method `species_model$crossValidation`
## ------------------------------------------------

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

workflow$crossValidation(Method = 'Loo')

## ------------------------------------------------
## Method `species_model$modelOptions`
## ------------------------------------------------

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))


## ------------------------------------------------
## Method `species_model$specifySpatial`
## ------------------------------------------------

if (FALSE) {
if (requireNamespace('INLA')) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addMesh(cutoff = 20000,
                 max.edge=c(60000, 80000),
                 offset= 100000)
workflow$specifySpatial(prior.range = c(200000, 0.05),
                        prior.sigma = c(5, 0.1))
}
}

## ------------------------------------------------
## Method `species_model$specifyPriors`
## ------------------------------------------------

if (FALSE) {
if (requireNamespace('INLA')) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))

#Add boundary
workflow$addArea(countryName = 'Sweden')
workflow$addMesh(cutoff = 20000,
                 max.edge=c(60000, 80000),
                 offset= 100000)
workflow$specifyPriors(effectName = 'Intercept', mean = 0, Precision = 0.1)
}
}

## ------------------------------------------------
## Method `species_model$biasFields`
## ------------------------------------------------

if (FALSE) {
if(requireNamespace('INLA')) {

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')

workflow$addGBIF(datasetName = 'exampleGBIF',
                 datasetType = 'PA',
                 limit = 10000,
                 coordinateUncertaintyInMeters = '0,50')
workflow$biasFields(datasetName = 'exampleGBIF')
}
}

## ------------------------------------------------
## Method `species_model$modelFormula`
## ------------------------------------------------

if (FALSE) {

workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')

workflow$addCovariate(rasterStack)

workflow$addFormula(covariateFormula = ~ covariate)
workflow$addFormula(biasFormula = ~ biasFormula)


}

## ------------------------------------------------
## Method `species_model$obtainMeta`
## ------------------------------------------------

if (FALSE) {
workflow <- startWorkflow(Species = 'Fraxinus excelsior',
                          Projection = '+proj=longlat +ellps=WGS84',
                          Save = FALSE,
                          saveOptions = list(projectName = 'example'))
workflow$addArea(countryName = 'Sweden')

workflow$addGBIF(datasetName = 'exampleGBIF',
                 datasetType = 'PA',
                 limit = 10000,
                 coordinateUncertaintyInMeters = '0,50')
workflow$obtainMeta()
}

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