Data from: Statistical stream temperature modelling with SSN and INLA: an introduction for conservation practitioners

Statistical stream temperature models can predict the fine-scale
spatial distribution of water temperatures and guide species recovery and
habitat restoration efforts. However, stream temperature modelling is
complicated by spatial autocorrelation arising from non-independence data
collected within dendritic networks. We used data from miniature sensors
deployed in Canadian Rocky Mountain streams to develop and validate two
statistical stream temperature modelling techniques that account for
spatial autocorrelation. The first was based on spatial steam network
models (SSNs) specifically developed to account for spatial
autocorrelation in dendritic stream networks. The second used integrated
nested Laplace approximation (INLA) that accounts for spatial
autocorrelation but was not designed to address anisotropic stream network
data. We evaluated the best-fitted SSN and INLA models using leave-one-out
cross validation from the data collected along the stream network. Both
modelling techniques had similar RMSE and MAE (near 1oC) and r2 (>
0.6) values, and proved flexible with respect to implementation; however,
the SSN models required more preprocessing steps before incorporating
spatially correlated random errors. We provide practical advice and
open-access data and r-script to help non-experts develop statistical
stream temperature models of their own.