Advances in Understanding Landscape Influences on Freshwater Habitats and Biological Assemblages

Lotic Fish Assemblage Clusters across the Conterminous United States and Their Associations with Environmental Variables

Alan T. Herlihy, Jean C. Sifneos, Robert M. Hughes, David V. Peck, and Richard M. Mitchell


Abstract.—Between 2008 and 2014, the first two phases of the National Rivers and Streams Assessment sampled fish assemblages in 2,554 stream and river sites across the conterminous United States. Associated physical habitat, water chemistry, and landscape data were also collected. We used cluster analysis to derive fish assemblage clusters. Assemblage clusters were then related to the local and catchment scale environmental data to assess the primary drivers of fish assemblage structure and to predict cluster membership. The results from our study show that whereas variability in fish assemblages is large over the range of stream/river sizes across the conterminous United States, it is possible to divide sites into six clusters that could be defined by specific indicator species and predictable from environmental data using both classification tree analysis and discriminant function analysis. Ordination identified three environmental gradients as the primary drivers of the biological clusters: stream size, temperature, and stream water ionic strength. The biological classification was very reproducible within a sample year based on repeat sample visits to the same sites but was lower based on samples taken 4–6 years apart. Biological monitoring is essential for the complete assessment of aquatic ecosystem condition. As the natural variability in aquatic biota is quite high, reporting and analyzing the results of biomonitoring requires a classification framework that minimizes this variability so that expectations of least-disturbed condition and the effects of anthropogenic disturbance can be more clearly defined. Our results showed that biologically derived classes of sites have higher classification strength than either basins or ecoregions. Thus, for the purposes of biomonitoring, it could be advantageous to replace physical regions with a classification based on aquatic biota.