RISDM is an R package that provides functionality for estimating, diagnosing, predicting and interpreting Integrated Species Distribution Models. These models take data from disparate sources and utilises their best attributes whilst minimising their worst. See Foster et al. (2024) for more details about the package.
At its very base, the package implements the models described in
(Fletcher Jr. et al. 2019, Miller et al. 2019, Isaac et al. 2020). These
models exploit multiple data types into a single species distribution
model. Such models coherently capture uncertainty throughout the entire
estimation and prediction process, unlike most approaches that consist
of multiple analysis stages. The structure of code for the core function
in RISDM, namely isdm(), evolved from that specified in Dambly et al.
(2019).
Worked examples are presented in the package’s vignette, as well as in (Foster et al. 2024).
The RISDM package can be installed using devtools R package.
install.packages('devtools')
library( devtools)
devtools::install_github( repo="Scott-Foster/RISDM", build_vignettes=FALSE)RISDM bases its inference on the INLA package. As such, an
installation of INLA is required. This can be performed using the
following code.
library( devtools)
install.packages("INLA",repos=c(getOption("repos"),
INLA="https://inla.r-inla-download.org/R/stable"), dep=TRUE)This work was part of The National Vertebrate Pests and Weeds Distribution project, which was partially funded by the Australian Government Department of Agriculture, Fisheries and Forestry’s Established Pest Animals and Weeds Management Pipeline Program and Supporting Communities Manage Pests and Weeds Program.
Please note that the ppmData project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Fork the RISDM repository
Clone your fork using the command:
git clone https://github.com/<username>/RISDM.git
Contribute to your forked repository.
Create a pull request.
If your code passes the necessary checks and is documented, your changes
and/or additions will be merged in the main RISDM repository.
If you notice an issue with this repository, please report it using Github Issues. When reporting an implementation bug, include a small example that helps to reproduce the error (a non-working minimal example). The issue will be addressed as quickly as possible.
If you have questions or need additional support, please open a Github Issues or send a direct email to scott.foster@data61.csiro.au.
Dambly, L., O’Hara, B. and Golding, N. 2019. oharar/IM_warbler: Integrated analysis of black- throated blue warbler data from PA, USA.
Fletcher Jr., R. J., Hefley, T. J., Robertson, E. P., Zuckerberg, B., McCleery, R. A. and Dorazio, R. M. 2019. A practical guide for combining data to model species distributions. - Ecology 100: e02710.
Foster, S. D., Peel, D., Hosack, G. R., Hoskins, A., Mitchell, D. J., Proft, K., Yang, W.-H., Uribe-Rivera, D. E. and Froese, J. G. 2024. ‘RISDM‘: Species distribution modelling from multiple data sources in r. - Ecography n/a: e06964.
Isaac, N. J. B., Jarzyna, M. A., Keil, P., Dambly, L. I., Boersch-Supan, P. H., Browning, E., Freeman, S. N., Golding, N., Guillera-Arroita, G., Henrys, P. A., Jarvis, S., Lahoz-Monfort, J., Pagel, J., Pescott, O. L., Schmucki, R., Simmonds, E. G. and O’Hara, R. B. 2020. Data integration for large-scale models of species distributions. - Trends in Ecology & Evolution 35: 56–67.
Miller, D. A. W., Pacifici, K., Sanderlin, J. S. and Reich, B. J. 2019. The recent past and promising future for data integration methods to estimate species’ distributions. - Methods in Ecology and Evolution 10: 22–37.