2022/02/15 by Edgar Santos–Fernández, Jay M. Ver Hoef, Santos-Fernandez, Edgar +9
Environmental Science · #Computation (stat.CO) #FOS: Computer and information sciences #Hydrology and Watershed Management Studies #Methodology (stat.ME) #Soil and Water Nutrient Dynamics #Species Distribution and Climate Change
paper · pdf · doi:10.48550/arxiv.2202.07166
openalex publication_date 2022/02/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, most covariance functions describe spatial relationships based on Euclidean distance only. 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 Idaho, USA.