U.S. Geological Survey decision analysts and technical experts worked with representatives from the U.S. Bureau of Reclamation, Utah Division of Wildlife Resources, and Central Utah Water Conservancy District to predict the consequences of alternative actions following environmental DNA detections of dreissenid mussel eDNA in Jordanelle Reservor, Utah. This dataset provides the consequence and model inputs for each of the alternative actions under different levels of uncertainty.

Environmental DNA results for water samples collected at USGS streamgages in the Columbia and Colorado river basins. Water samples were collected by USGS hydrologic technicians May - September 2018 and analyzed for presence of dreissenid mussel DNA. In this dataset, we report quantitative PCR results for each streamgage locations.

Positive and negative dreissenid mussel DNA quantitiative PCR results from environmental DNA water samples collected in Montana, Wisconsin and Minnesota to assess if environmental DNA can extend the seasonal window for dreissenid mussel early detection.

Positive and negative dreissenid mussel DNA quantitiative PCR results from environmental DNA water samples collected in Montana, Wisconsin and Minnesota to assess if environmental DNA can extend the seasonal window for dreissenid mussel early detection.

This data product develops a composite relative risk index (CRR) for watersheds within the Missouri River Basin, a region in the U.S. on the front line of dreissenid spread. The CRR is developed using datasets in order to account for the direct and indirect damages from a potential infestation along with the risk of an infestation occurring with a HUC10 watershed. Included are indices calculated on a Missouri River Basin-wide scale, and indices calculated on an individual state basis. This dataset includes the code used to build the CRR dataset and generated geospatial output.

The Minnesota Department of Natural Resources (MNDNR) has been quantitatively sampling a mussel bed in West Newton Chute (a side channel in Navigation Pool 5 of the Upper Mississippi River, UMR) annually since 2008. Briefly, ~200 systematically-placed 0.25 m2 quads are sampled annually; divers excavate substrates to a depth of ~15 cm and place material into a 6 mm mesh bag. Mussels are identified to species, aged via external annuli, measured for shell length, and sexed. From 2008-2016, this mussel bed contained 12-16 live species, had densities that ranged from 4-10/m2, and juveniles (≤ 5 years old) comprised 3-18% of the assemblage. Because this assemblage was well characterized, it represented an excellent location to estimate vital rates (i.e., survival and growth) in mussels. Our objectives were to estimate patterns in survival and growth across four species of mussels and over time within a mussel bed, and to assess if these patterns changed across patches with varying mussel densities. The counts of live mussels in quadrats sampled by the MNDNR during surveys from 2008-2011 was compiled and interpolated using an inverse distance weighted (IDW) algorithm in ArcGIS. The IDW surface of mussel density was classified by quartiles and the highest quartile was delineated as the core areas of the bed and the lowest quartile was delineated as the peripheral areas of the bed. This resulted in four polygons—two with relatively high mussel densities (core, labelled A1-A5 and B1-B5) and two with relatively low mussel densities (periphery, labelled C1-C5 and D1-D5). Five study plots (5 m x 5 m) were randomly selected within each polygon. Plot C5 was inaccessible, so we used plot C5a. Plots were aligned with the direction of river flow and demarcated into four quarters by driving nine pieces of PVC pipe into the substrate in a 3 x 3 array. To obtain mussels to PIT tag, we haphazardly searched West Newton Chute in August 2012 and obtained 578 mussels, including both common (Amblema plicata, Obliquaria reflexa) and less common (Cyclonaias pustulosa, Pleurobema sintoxia) species. Shells were scrubbed to remove existing zebra mussels. A 20- or 23-mm PIT tag was attached near the umbo of each mussel with cyanoacrylate glue to enable recovery of individual mussels in subsequent years. One end of a 36-cm piece of buoyant fishing line (color coded by species) was glued near the posterior edge of each shell to facilitate recovery. We randomly allocated 9-10 A. plicata and O. reflexa and 4-5 C. pustulosa and P. sintoxia into each plot. Mussels were placed into a randomly chosen quarter of each plot. The age, shell length, and PIT tag identification number of each mussel was recorded prior to placement within a plot. We returned to WNC to recover tagged mussels in August 2013, August 2014, July 2015, and July 2016. Once each plot was found, a diver placed a 2.5 m x 2.5 m PVC frame over each plot quarter to facilitate a thorough search. The diver systematically searched each plot quarter using an 18-cm loop antenna that was connected, via a 15.2 m cord, to a PIT-tag reader located in an attending boat. Surface to diver communication was used to notify the diver when a marked mussel had been found. During recovery efforts, divers searched within each plot and then searched the periphery of each plot (~1-2 m outside each plot) for any marked mussels that might have moved out of the plot. All recovered mussels were identified by PIT tag, recorded as alive or dead, measured for age and shell length, and any attached zebra mussels were removed and counted. If the PIT tag was damaged or missing, we replaced it with a new one and recorded the new PIT tag ID number.

Impacts of dreissenid mussels (Dreissena spp.) on Great Lakes ecosystems are well documented, and a better understanding of mechanisms that cause variation in mussel abundance is needed. An outstanding question is how much mussel biomass is consumed by fish predation. A significant difficulty for investigating mussel consumption by fish is that mussels in stomachs are often a mix of crushed shell and flesh. Here, we provide information on the relationship between shell-and-flesh dry weight measurements and flesh-only dry weight of two species of dreissenid mussel, quagga (Dreissena rostiformis bugensis) and zebra (Dreissena polymorpha), to be used in formulating conservative estimates of flesh-only dry weight in fish diets. Dry weight analyses were conducted to simulate stomach contents ranging from small (individual mussels) to large (aggregate of mussels). All measurements were taken at the USGS Lake Erie Biological Station in Sandusky, Ohio using quagga and zebra mussels collected from Lake Erie in May, 2014.

Zebra mussels are representative of nonindigenous aquatic species (NAS) with devastating economic, recreational, and environmental impacts that are already under watch as a problematic species across the U.S. and in Texas. Targeting dispersal pathways is likely the most efficient means of controlling their spread further west. The movement of recreational watercraft is one of the main vectors for the spread of zebra mussels and other NAS among lakes. Thus, we created a risk assessment to guide monitoring efforts to detect and report new sightings of zebra mussels and other NAS. Lake risk scores were based on the potential establishment and spread of zebra mussels. Lake establishment risk was determined by applying a habitat suitability index (HSI) of water physicochemical parameters compiled from preexisting sources and supplemental field collection (Child Item 1: "Water physicochemical parameters of twenty Texas and New Mexico lakes 2022-2023"). The risk of spreading zebra mussels from a lake was determined by centrality measures of network analysis to identify lakes acting as hubs (degree score), stepping stones (betweenness score), and cutpoints. We applied network analysis at three different maximum roadway distances based on the 95th , 75th , and 50th percentiles of boater movement. The combination of HSI and centrality scores (Child Item 2: "Habitat suitability scores, network scores, and infestation status for 225 lakes in Texas and New Mexico") were used to identify potential high risk lakes.

This dataset contains the accumulated stream survey data collected to identify climate impacts to fish communities and assess stream restoration as a potential climate-change mitigation action across the Great Plains and High Desert of Wyoming and Montana (2021-2024). We also provide data of incidental observations of amphibians, reptiles, crayfishes, and mussels seen while conducting fisheries work, as well as structured surveys for amphibians and crayfishes. Habitat data for many sites is also provided.

This layer represents fundamentally suitable and unsuitable habitat for freshwater mussels in the Meramec Basin as modeled by these authors on May 17, 2017 based on spatial data ranging from 1990 to 2014. Identification of habitat characteristics associated with the presence of freshwater mussels is challenging but crucial for the conservation of this declining fauna. Most mussel species are found in multi-species assemblages suggesting that physical factors influence presence similarly across species. In lotic environments, geomorphic and hydraulic characteristics appear to be important factors for predicting mussel presence. We used maximum entropy (MaxEnt) modeling to evaluate hydrogeomorphic variables associated with mussel presence at a riverscape-scale along 530 river km of the Meramec River basin, USA. Mussel locations were obtained from an existing multi-year dataset, and hydrogeographic variables were derived using high-resolution, open-source datasets of aerial imagery and topography. The following hydrogeomorphic variables were associated with mussel presence: lateral channel stability, low-flow surface water availability, presence of gravel bars, and stream power, but presence of gravel bars appeared to be the most important variable. Identification of suitable habitat was strongly influenced by the distance to gravel bars, suggesting mussels are found near gravel bars. A subset of the data not used in model development was used to validate the final model. The validation locations fell almost exclusively and disproportionately in habitats that the model identified as suitable, suggesting that we identified common habitat requirements for multiple mussel species. These findings can inform how resource managers allocate survey, monitoring, and conservation efforts.