River Size and Fish Assemblages in Southwestern South Dakota

We studied relations between river size, fish species diversity, and fish species composition along four major rivers in the Great Plains of southwestern South Dakota to assess patterns of species diversity and composition. We expected diversity to increase with river size and fish composition to change via species addition downstream. Previous surveys of 52 sampling stations provided fish assemblage data, and we used the Geographic Information System (GIS) to determine watershed area by station. Watershed area did not predict species richness or species diversity (Fisher's a), so species richness of 12 ± 3.5 SD species and Fisher's a of 2.3 ± 0.87 SD characterized species diversity in the study area. Cluster analysis of faunal similarity (Sorensen's Index) among the 52 sampling stations identified two geographically distinct faunal divisions, so species composition was variable within the study area, but changed via species replacements among faunas rather than species additions downstream. Nonnative species were a minor component of all faunas. Uniform species diversity may be a recent phenomenon caused by impacts of Missouri River dams on native large-river fishes and the unsuitability of rivers in the Great Plains for nonnative species. Variation in faunal composition may also be recent because it was affected by dams.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.