SSNbayes: An R Package for Bayesian Spatio-Temporal Modelling on Stream Networks

Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, a limited number of computational tools are available for modeling river network datasets in space and time. In this paper, we introduce the R package SSNbayes for fitting Bayesian spatio-temporal models and making predictions on branching stream networks. SSNbayes provides a linear regression framework with multiple options for incorporating spatial and temporal autocorrelation. Spatial dependence is captured using stream distance and flow connectivity while temporal autocorrelation is modelled using vector autoregression approaches. SSNbayes provides the functionality to make predictions across the whole network, compute exceedance probabilities, and other probabilistic estimates, such as the proportion of suitable habitat. We illustrate the functionality of the package using a stream temperature dataset collected in the Clearwater River Basin, USA.

Edgar Santos-Fernandez (School of Mathematical Sciences, Queensland University of Technology) , Jay M. Ver Hoef (Marine Mammal Laboratory, NOAA-NMFS Alaska Fisheries Science Center.) , James McGree (School of Mathematical Sciences, Queensland University of Technology) , Daniel J. Isaak (Rocky Mountain Research Station, US Forest Service) , Kerrie Mengersen (School of Mathematical Sciences, Queensland University of Technology) , Erin E. Peterson (EP Consulting)
2023-12-18

0.1 Supplementary materials

Supplementary materials are available in addition to this article. It can be downloaded at RJ-2023-061.zip

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Santos-Fernandez, et al., "SSNbayes: An R Package for Bayesian Spatio-Temporal Modelling on Stream Networks", The R Journal, 2023

BibTeX citation

@article{RJ-2023-061,
  author = {Santos-Fernandez, Edgar and Hoef, Jay M. Ver and McGree, James and Isaak, Daniel J. and Mengersen, Kerrie and Peterson, Erin E.},
  title = {SSNbayes: An R Package for Bayesian Spatio-Temporal Modelling on Stream Networks},
  journal = {The R Journal},
  year = {2023},
  note = {https://doi.org/10.32614/RJ-2023-061},
  doi = {10.32614/RJ-2023-061},
  volume = {15},
  issue = {3},
  issn = {2073-4859},
  pages = {26-58}
}

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