Acute selective serotonin-reuptake inhibition elevates basal ventilation, attenuates the rebreathing ventilatory response, independent of cerebral perfusion.

Serotonin (5-HT) is integral to signalling in areas of the brainstem controlling ventilation and is involved in central chemoreception. Selective serotonin reuptake inhibitors (SSRIs), used to effectively increase 5-HT concentrations, are commonly prescribed for depression. The effects of SSRIs on the control of breathing and the potential influence of cerebral blood flow (CBF) have not been directly assessed.

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the -spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN package but leverages modern -spatial tools like sf (Pebesma, 2018).

Spatial and Temporal Variability in Stream Thermal Regime Drivers for Three River Networks During the Summer Growing Season.

Many cold-water dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins.

Indexing and partitioning the spatial linear model for large data sets.

We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) block-kriging (predicting the average value over a region). Each of these goals can present different challenges when analyzing large spatial data sets. Current research uses a variety of methods, including spatial basis functions (reduced rank), covariance tapering, etc, to achieve these goals.

Riparian vegetation shade restoration and loss effects on recent and future stream temperatures.

River temperatures are expected to increase this century harming species requiring cold-water habitat unless restoration activities protect or improve habitat availability. Local shading by riparian vegetation can cool water temperatures, but uncertainty exists over the scaling of this local effect to larger spatial extents. We evaluate this issue using a regional spatial stream network temperature model with covariates representing shade effects to predict mean August stream temperatures across 78,195 km of tributaries flowing into the Columbia River in the northwestern US.