Low-head dams induce biotic homogenization/differentiation of fish assemblages in subtropical streams

Extensive distribution of widespread species and the loss of native species driven by anthropogenic disturbances modify community similarity, resulting in a decrease or increase in community distinctiveness. Data from four basins in the Wannan Mountains, China, were used to evaluate the effects of low-head dams on patterns of fish faunal homogenization and differentiation based on abundance data. We aimed to examine the spatial changes in taxonomic and functional similarities of fish assemblages driven by low-head dams and to examine whether the changes in the similarity of fish assemblages differed between taxonomic and functional components. We found that low-head dams significantly decreased the mean taxonomic similarity but increased the mean functional similarity of fish assemblages in impoundments using abundance-based approaches, suggesting that taxonomic differentiation accompanied functional homogenization in stream fish assemblages. These results show the importance of population abundance in structuring fish faunal homogenization and differentiation at small scales, especially when the major differences among assemblages are in species abundance ranks rather than species identities. Additionally, we also found only a weak positive correlation between changes in mean taxonomic and functional similarities, and partial pairs exhibited considerable variation in patterns of fish faunal homogenization and differentiation for taxonomic and functional components. In conclusion, this study highlighted that the observed taxonomic differentiation of current fish assemblages (short-term phenomenon) is probably an early warning sign of further homogenization in regions where native species are completely predominated and that changes in taxonomic similarity cannot be used to predict changes in functional similarity.

Mapping biodiversity hotspots of fish communities in subtropical streams through environmental DNA

Large tropical and subtropical rivers are among the most biodiverse ecosystems worldwide, but also suffer from high anthropogenic pressures. These rivers are hitherto subject to little or no routine biomonitoring, which would be essential for identification of conservation areas of high importance. Here, we use a single environmental DNA multi-site sampling campaign across the 200,000 km² Chao Phraya river basin, Thailand, to provide key information on fish diversity. We found a total of 108 fish taxa and identified key biodiversity patterns within the river network. By using hierarchical clustering, we grouped the fish communities of all sites across the catchment into distinct clusters. The clusters not only accurately matched the topology of the river network, but also revealed distinct groups of sites enabling informed conservation measures. Our study reveals novel opportunities of large-scale monitoring via eDNA to identify relevant areas within whole river catchments for conservation and habitat protection.

Incorporating Network Connectivity into Stream Classification Frameworks

Stream classification frameworks are important tools for conserving aquatic resources. Yet despite their utility, most classification frameworks have not incorporated network connectivity. We developed and compared three biologically informed stream classification frameworks considering the effects of variables indexing local habitat and/or connectivity on stream fish communities. The first framework classified streams according to local environmental variables largely following the precedent set by previous stream classifications. The second framework classified streams according solely to network connectivity variables, while the third framework considered both local and connectivity variables. Using fish community data from 291 wadeable streams in South Carolina, USA, we used conditional inference tree analyses to identify either seven or eight discrete types of wadeable streams within each framework. Classifications were evaluated on their ability to describe community composition at a subset of sites not used in model training, and canonical correspondence analysis suggested that each framework performed similarly in describing overall community variation, with about 19% of variation explained. After accounting for the effects of biogeography and land use in our analytical approach, each classification explained a substantially higher amount of community variation with 46% of variation explained by our connectivity-informed classification and 42% explained by our locally informed classification. Classifications differed in their ability to describe elements of community structure; a classification incorporating connectivity predicted species richness better than the one that did not. This study ultimately addresses an important knowledge gap in the classification literature while providing broader implications for the conservation of aquatic organisms and their habitats.

PiSCES: Pi(scine) stream community estimation system

The Piscine Stream Community Estimation System (PiSCES) provides users with a hypothesized fish community for any stream reach in the conterminous United States using information obtained from Nature Serve, the US Geological Survey (USGS), StreamCat, and the Peterson Field Guide to Freshwater Fishes of North America for over 1000 native and non-native freshwater fish species. PiSCES can filter HUC8-based fish assemblages based on species-specific occurrence models; create a community abundance/biomass distribution by relating relative abundance to mean body weight of each species; and allow users to query its database to see ancillary characteristics of each species (e.g., habitat preferences and maximum size). Future efforts will aim to improve the accuracy of the species distribution database and refine/augment increase the occurrence models. The PiSCES tool is accessible at the EPA's Quantitative Environmental Domain (QED) website at https://qed.epacdx.net/pisces/.