intSDM
Reproducible Integrated Species Distribution Models Across Norway using 'INLA'
Integration of disparate datasets is needed in order to make efficient use of all available data and thereby address the issues currently threatening biodiversity. Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset. We therefore introduce the package, 'intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online. An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.
README
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# *intSDM*
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The goal of *intSDM* is to assist users in creating a reproducible
workflow for large-scale integrated species distribution models (ISDM).
The package does this by providing the tools and functions to obtain
species’ occurrence data from [GBIF](https://www.gbif.org) and
environmental covariates from [WorldClim](https://www.worldclim.org),
specify the components of an ISDM, estimate the model, and produce
useful outputs. The package estimates the ISDM using a Bayesian
framework with the [integrated nested Laplace
approximation](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1467-9868.2008.00700.x)
method, which is computationally efficient in comparison to MCMC
methods. Furthermore, the package builds wrapper functions around
[inlabru](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13168)
and
[PointedSDMs](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14091),
two *R* packages designed to simplify modelling spatial processes and
integrated species distribution models respectively. As a result, good
knowledge of both these packages is required before using *intSDM*.
The outputs of the model are objects which are typically useful in
species distribution modelling analyses. These include: models,
predictions, maps of predictions and cross-validation scores. The
default of *intSDM* is to produce single-species models and outputs;
however the package does also allow multi-species models to assist the
user in obtaining estimates of species richness.
## Installation
You can install the development version of this package from
[GitHub](https://github.com/) with:
``` r
#install.packages('devtools')
devtools::install_github("PhilipMostert/intSDM")
```
or directly through CRAN:
``` r
install.packages('intSDM')
```
## Functionality
The package contains two main functions: `startWorkflow` which
initializes the workflow, and `sdmWorkflow`, which estimates one of the
specified outcomes of the workflow. `startWorkflow` produces an
[*R6*](https://r6.r-lib.org), which has a multitude of different *slot*
functions to help customize the workflow. These include:
| Function name | Function use |
|-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `.$plot()` | Plot data and other objects required for the model. |
| `.$addStructured()` | Add data not available on GBIF. |
| `.$addMesh()` | Create an *inla.mesh* object. |
| `.$addGBIF()` | Add data from GBIF. |
| `.$addArea()` | Specify sampling domain. |
| `.$addCovariates()` | Add spatial covariates. |
| `.$crossValidation()` | Specify the cross-validation method. |
| `.$modelOptions()` | Add [*R-INLA*](https://www.r-inla.org), [*inlabru*](https://inlabru-org.github.io/inlabru/) and [*PointedSDMs*](https://github.com/PhilipMostert/PointedSDMs) options. |
| `.$specifySpatial()` | Add penalizing complexity priors to the spatial effects. |
| `.$biasFields()` | Specify an additional spatial effect for a dataset. |
| `.$workflowOutput()` | Specify the output of the workflow. |
| `.$specifyPriors()` | Specify the priors of the model. |
| `.$modelFormula` | Add a formula for the covariates and bias of the model. |
| `.$obtainMeta()` | Obtain metadata for the occurrence records. |
Documentation and examples for each of these slot functions may easily
be obtained using `.$help()`.
Examples of the package being used is provided as vignettes within the
package.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
2.1.2 |
rolling linux/jammy R-4.5 | intSDM_2.1.2.tar.gz |
86.8 KiB |
2.1.2 |
rolling linux/noble R-4.5 | intSDM_2.1.2.tar.gz |
239.5 KiB |
2.1.2 |
rolling source/ R- | intSDM_2.1.2.tar.gz |
86.8 KiB |
2.1.2 |
latest linux/jammy R-4.5 | intSDM_2.1.2.tar.gz |
86.8 KiB |
2.1.2 |
latest linux/noble R-4.5 | intSDM_2.1.2.tar.gz |
239.5 KiB |
2.1.2 |
latest source/ R- | intSDM_2.1.2.tar.gz |
86.8 KiB |
2.1.2 |
2026-04-26 source/ R- | intSDM_2.1.2.tar.gz |
86.8 KiB |
2.1.2 |
2026-04-23 source/ R- | intSDM_2.1.2.tar.gz |
86.8 KiB |
2.1.2 |
2026-04-09 windows/windows R-4.5 | intSDM_2.1.2.zip |
248.7 KiB |
2.1.1 |
2025-04-20 source/ R- | intSDM_2.1.1.tar.gz |
86.1 KiB |