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PointedSDMs

The goal of PointedSDMs is to simplify the construction of integrated species distribution models (ISDMs) for large collections of heterogeneous data. It does so by building wrapper functions around inlabru, which uses the INLA methodology to estimate a class of latent Gaussian models.

Installation

You can install the development version of PointedSDMs from GitHub with:

# install.packages("devtools")
devtools::install_github("PhilipMostert/PointedSDMs")

or directly through CRAN using:

install.packages('PointedSDMs')

Package functionality

PointedSDMs includes a selection of functions used to streamline the construction of ISDMs as well and perform model cross-validation. The core functions of the package are:

Function nameFunction description
startISDM()Initialize and specify the components used in the integrated model.
startSpecies()Initialize and specify the components used in the multi-species integrated model.
blockedCV()Perform spatial blocked cross-validation.
fitISDM()Estimate and preform inference on the integrated model.
datasetOut()Perform dataset-out cross-validation, which calculates the impact individual datasets have on the full model.

The function intModel() produces an R6 object, and as a result there are various slot functions available to further specify the components of the model. These slot functions include:

intModel() slot functionFunction description
`.$help()`Show documentation for each of the slot functions.
`.$plot()`Used to create a plot of the available data. The output of this function is an object of class gg.
`.$addBias()`Add an additional spatial field to a dataset to account for sampling bias in unstructured datasets.
`.$updateFormula()`Used to update a formula for a process. The idea is to start specify the full model with startISDM(), and then thin components per dataset with this function.
`.$updateComponents()`Change or add new components used by inlabru in the integrated model.
`.$priorsFixed()`Change the specification of the prior distribution for the fixed effects in the model.
`.$specifySpatial()`Specify the spatial field in the model using penalizing complexity (PC) priors.
`.$spatialBlock()`Used to specify how the points are spatially blocked. Spatial cross-validation is subsequently performed using blockedCV().
`.$addSamplers()`Function to add an integration domain for the PO datasets.
`.$specifyRandom()`Specify the priors for the random effects in the model.
`.$changeLink()`Change the link function of a process.

Example

This is a basic example which shows you how to specify and run an integrated model, using three disparate datasets containing locations of the solitary tinamou (Tinamus solitarius).


library(PointedSDMs)
library(ggplot2)
library(terra)

bru_options_set(inla.mode = "experimental")

#Load data in

data("SolitaryTinamou")

projection <- "+proj=longlat +ellps=WGS84"

species <- SolitaryTinamou$datasets

covariates <- terra::rast(system.file('extdata/SolitaryTinamouCovariates.tif', 
                                      package = "PointedSDMs"))

mesh <- SolitaryTinamou$mesh

Setting up the model is done easily with startISDM(), where we specify the required components of the model:


#Specify model -- here we run a model with one spatial covariate and a shared spatial field

model <- startISDM(species, spatialCovariates = covariates,
                 Projection = projection, Mesh = mesh, responsePA = 'Present')

We can also make a quick plot of where the species are located using `.$plot()`:


region <- SolitaryTinamou$region

model$plot(Boundary = FALSE) + 
  geom_sf(data = st_boundary(region))

To improve stability, we specify priors for the intercepts of the model using `.$priorsFixed()`


model$priorsFixed(Effect = 'Intercept',
                  mean.linear = 0, 
                  prec.linear = 1)

And PC priors for the spatial field using `.$specifySpatial()`:


model$specifySpatial(sharedSpatial = TRUE,
                     prior.range = c(0.2, 0.1),
                     prior.sigma = c(0.1, 0.1))

We can then estimate the parameters in the model using the fitISDM() function:


modelRun <- fitISDM(model, options = list(control.inla = 
                                            list(int.strategy = 'eb'), 
                                          safe = TRUE))
summary(modelRun)
#> Summary of 'modISDM' object:
#> 
#> inlabru version: 2.12.0
#> INLA version: 24.06.27
#> 
#> Types of data modelled:
#>                                     
#> eBird                   Present only
#> Parks                Present absence
#> Gbif                    Present only
#> Time used:
#>     Pre = 1.16, Running = 21.3, Post = 0.274, Total = 22.7 
#> Fixed effects:
#>                   mean    sd 0.025quant 0.5quant 0.975quant   mode kld
#> Forest           0.054 0.006      0.042    0.054      0.066  0.054   0
#> NPP              0.000 0.000      0.000    0.000      0.000  0.000   0
#> Altitude        -0.002 0.001     -0.003   -0.002     -0.001 -0.002   0
#> eBird_intercept -5.419 0.435     -6.271   -5.419     -4.567 -5.419   0
#> Parks_intercept -4.783 0.485     -5.734   -4.783     -3.832 -4.783   0
#> Gbif_intercept  -6.128 0.412     -6.936   -6.128     -5.320 -6.128   0
#> 
#> Random effects:
#>   Name     Model
#>     eBird_spatial SPDE2 model
#>    Parks_spatial Copy
#>    Gbif_spatial Copy
#> 
#> Model hyperparameters:
#>                          mean    sd 0.025quant 0.5quant 0.975quant mode
#> Range for eBird_spatial 3.615 0.912      2.460    3.421      5.946 2.92
#> Stdev for eBird_spatial 2.514 0.427      1.924    2.438      3.567 2.21
#> Beta for Parks_spatial  0.137 0.100     -0.043    0.133      0.348 0.11
#> Beta for Gbif_spatial   0.714 0.067      0.585    0.713      0.848 0.71
#> 
#> Deviance Information Criterion (DIC) ...............: 259.79
#> Deviance Information Criterion (DIC, saturated) ....: 253.61
#> Effective number of parameters .....................: -840.82
#> 
#> Watanabe-Akaike information criterion (WAIC) ...: 2441.50
#> Effective number of parameters .................: 686.08
#> 
#> Marginal log-Likelihood:  -1330.87 
#>  is computed 
#> Posterior summaries for the linear predictor and the fitted values are computed
#> (Posterior marginals needs also 'control.compute=list(return.marginals.predictor=TRUE)')

PointedSDMs also includes generic predict and plot functions:


predictions <- predict(modelRun, mesh = mesh,
                       mask = region, 
                       spatial = TRUE,
                       fun = 'linear')

plot(predictions, variable = c('mean', 'sd'))

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Version

Install

install.packages('PointedSDMs')

Monthly Downloads

577

Version

2.1.5

License

GPL (>= 3)

Issues

3

Pull Requests

0

Stars

27

Forks

6

Maintainer

Philip Mostert

Last Published

February 18th, 2026

Functions in PointedSDMs (2.1.5)

predict.bruSDM

Generic predict function for bru_SDM objects.
nearestValue

nearestValue: Match species location data to environmental raster layers
print.blockedCV

Print function for blockedCV.
print.blockedCVpred

Print function for blockedCV.
print.bruSDM

Generic print function for bruSDM.
Gbif

data.frame object containing solitary tinamou observations from Gbif
modISDM_predict-class

Export class predict.modISDM
modMarks-class

Export modMarks class
fitISDM

fitISDM: function used to run the integrated model.
data2ENV

data2ENV: function used to move objects from one environment to another.
dataOrganize

R6 class to assist in reformatting the data to be used in dataSDM.
plot.bruSDM_predict

Generic plot function for predict_bru_sdm.
print.bruSDM_predict

Generic print function for bru_sdm_predict.
print.datasetOut

Generic print function for datasetOut.
bruSDM_predict-class

Export class predict_bru_sdm
BBSColinusVirginianus

Dataset of Colinus Virginianus obtained from the North American Breeding Bird survey across Alabama state.
SolitaryTinamou

List of all data objects used for the solitary tinamou vignette.
elev_raster

Raster object containing the elevation across Pennsylvania state.
removeFormula

removeFormula: Function to remove term from a formula.
nameChanger

nameChanger: function to change a variable name.
region

sf object containing the boundary region for solitary tinamouc
summary.bruSDM

Generic summary function for bruSDM.
runModel

runModel: function used to run the integrated model. Note that this function is depreciated, and will be removed in a later version of the package.
specifyISDM

R6 class for creating a startISDM object.
startSpecies

startSpecies: Function used to initialize a multi-species integrated species distribution model.
specifyMarks

R6 class for creating a specifyMarks object.
changeCoords

changeCoords: function used to change coordinate names.
modMarks_predict-class

Export class predict_modMarks
startISDM

startISDM: Function used to initialize the integrated species distribution model.
modSpecies-class

Export modSpecies class
print.modISDM

Generic print function for modISDM.
print.modMarks

Generic print function for modMarks.
startMarks

startMarks: Function used to initialize a marked-point process model.
specifySpecies

R6 class for creating a startSpecies object.
print.modSpecies

Generic print function for modSpecies.
datasetOut

datasetOut: function that removes a dataset out of the main model, and calculates some cross-validation score.
bruSDM-class

Export bru_sdm class
reduceComps

reduceComps: Reduce the components of the model.
eBird

data.frame object containing solitary tinamou observations from eBird
BBS

Dataset of setophaga caerulescens obtained from the North American Breeding Bird survey across Pennsylvania state.
checkVar

checkVar: Function used to check variable names.
checkCoords

checkCoords: function used to check coordinate names.
blockedCV

blockedCV: run spatial blocked cross-validation on the integrated model.
blockedCV-class

Export class blockedCV
blockedCVpred-class

Export class blockedCVpred
makeLhoods

makeLhoods: function to make likelihoods.
SetophagaData

List of all data objects used for the Setophaga vignette.
Parks

data.frame object containing solitary tinamou observations from Parks
Koala

Dataset of Eucalyptus globulus (common name: blue gum) sightings collected across the Koala conservation reserve on Phillip island (Australia) between 1993 and 2004. Two marks are considered from this dataset: "koala" which describes the number of koala visits to each tree, and "food" which is some index of the palatability of the leaves.
SolTinCovariates

spatRaster object containing covariate values
BBA

Dataset of setophaga caerulescens obtained from the Pennsylvania Atlas of Breeding Birds.
NLCD_canopy_raster

Raster object containing the canopy cover across Pennsylvania state.
modISDM-class

Export modISDM class
intModel

intModel: Function used to initialize the integrated species distribution model.
makeFormulaComps

makeFormulaComps: function to make components for the covariate and bias Formulas.
modSpecies_predict-class

Export class predict_modSpecies
dataSet

Internal function used to standardize datasets, as well as assign metadata.
datasetOut-class

Export class bru_sdm_leave_one_out