{"pageNumber":"110","pageRowStart":"2725","pageSize":"25","recordCount":46638,"records":[{"id":70252810,"text":"70252810 - 2023 - Application of electricity and underwater acoustics to clear fish from a navigation lock during maintenance","interactions":[],"lastModifiedDate":"2024-04-08T23:55:25.754351","indexId":"70252810","displayToPublicDate":"2023-05-23T11:11:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Application of electricity and underwater acoustics to clear fish from a navigation lock during maintenance","docAbstract":"The presence of fish within navigation locks can introduce an environmental challenge for construction crews when maintenance is required. This study examined the effectiveness of a dual-deterrent fish herding technique using electricity and acoustic stimuli to reduce the abundance of fish within a navigation lock before a scheduled dewatering for maintenance. Fixed-location multi-beam imaging sonar was used to evaluate fish per minute (FPM) detections near the lock entrance before, during, and after the herding effort. Standardized mobile side-scan sonar surveys were also conducted before and after the herding to examine fish presence throughout the entire lock. Collectively, a 73% reduction in fish targets detected by side-scan sonar and a 43% reduction in FPM detected by imaging sonar were observed following the herding technique. Post-fish herding, 88% reduction in FPM >400mm and a 35% reduction in FPM <400mm were observed. Fish abundance within the lock chamber was reduced and did not result in construction delays, which was problematic and costly during previous dewatering events. Since data from this study are limited to a single lock dewatering and fish clearing event, caution should be taken with the extrapolation of these results. However, the applied methods show promise and may inform future fish clearing efforts to aid lock maintenance.","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre (REABIC)","doi":"10.3391/mbi.2023.14.3.08","usgsCitation":"Rivera, J., Cupp, A.R., Ridgway, J.L., Chapman, D., Hoster, B.E., Acre, M.R., Calfee, R.D., Fischer, J.R., and Duncker, J.J., 2023, Application of electricity and underwater acoustics to clear fish from a navigation lock during maintenance: Management of Biological Invasions, v. 14, no. 3, p. 493-502, https://doi.org/10.3391/mbi.2023.14.3.08.","productDescription":"10 p.","startPage":"493","endPage":"502","ipdsId":"IP-127408","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":443403,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.3.08","text":"Publisher Index Page"},{"id":435319,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94TS236","text":"USGS data release","linkHelpText":"Starved Rock navigation lock fish clearing hydroacoustic data, Illinois River (2020)"},{"id":427559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Starved Rock Lock","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.0202409171892,\n 41.34115935633514\n ],\n [\n -89.0202409171892,\n 41.31325412195346\n ],\n [\n -88.97587342581757,\n 41.31325412195346\n ],\n [\n -88.97587342581757,\n 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rcalfee@usgs.gov","orcid":"https://orcid.org/0000-0001-6056-7023","contributorId":1841,"corporation":false,"usgs":true,"family":"Calfee","given":"Robin","email":"rcalfee@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":898306,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fischer, Jesse Robert 0000-0002-9071-7931","orcid":"https://orcid.org/0000-0002-9071-7931","contributorId":329677,"corporation":false,"usgs":true,"family":"Fischer","given":"Jesse","email":"","middleInitial":"Robert","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":898307,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":898308,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243699,"text":"sim3503 - 2023 - Landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California","interactions":[],"lastModifiedDate":"2026-02-19T17:54:22.824025","indexId":"sim3503","displayToPublicDate":"2023-05-23T07:42:15","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3503","displayTitle":"Landslides Triggered by the 2016–2017 Storm Season, Eastern San Francisco Bay Region, California","title":"Landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California","docAbstract":"

Discussion

The winter rainy season of 2016–2017 brought abundant rainfall to the State of California and to the San Francisco Bay region. In January and February of 2017, intense rainfall from strong winter storms saturated soils in the region and triggered thousands of shallow landslides. The highest concentration of these landslides was in the eastern part of the bay region, where landslides in the hills east of the Cities of Richmond, Berkeley, Oakland, Hayward, and Fremont (see main map for locations) damaged homes, displaced a major electrical transmission-line tower, and blocked several heavily traveled roadways.

This map shows 8,928 landslides manually mapped from rectified high-resolution (0.25-meter [m]) satellite imagery (March 11, 2017, from Google Earth) in a three-dimensional geographic information system (GIS) framework. The map area encompasses approximately 1,050 square kilometers (km2), bounded by the Carquinez Strait and San Francisco Bay to the north and west, respectively, and extending to the Interstate Highway 680 corridor to the south and east. Individual landslides were mapped as polygons, but for ease of display, they are shown here as points denoting the highest elevation of each landslide headscarp. The greatest calculated landslide concentration (measured as the total number of landslides per unit area) exceeded 80 landslides per 0.25 km2 in the hills east of the City of Berkeley. This zone is illustrated on the map in red, a color which denotes areas of greater than or equal to 30 landslides per 0.25 km2. The source area and deposit polygon datasets are available as a data release (Corbett and Collins, 2023). Complementary field investigations at more than 150 landslides of those displayed here indicate that most mapped landslides are shallow (less than 1 m deep) debris slides and debris flows. Although most landslides occurred in undeveloped areas (for example, parks and open space), about 1,400 landslides, or 16 percent of the total number, affected structures or infrastructure (including paved and dirt roads) in some way. The inset figures (figs. 1–5) show photographs taken in January and February 2017 that depict some areas of damage that occurred during and after the storms; the locations of where these photographs were taken are shown on the map.

In the San Francisco Bay region, landslides usually take place each year during the winter rainy season, but widespread, intense rainfall events such as those whose effects are depicted here typically occur as a result of considerably above-average precipitation conditions. Although the landslides of early 2017 are not the most damaging to affect the region historically, they are still a potent reminder of the potential for landslide hazards present in the San Francisco Bay region.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3503","usgsCitation":"Corbett, S.C., and Collins, B.D., 2023, Landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California: U.S. Geological Survey Scientific Investigations Map 3503, scale 1:75,000, https://doi.org/10.3133/sim3503.","productDescription":"1 Plate: 37.12 x 44.88 inches; Data Release","ipdsId":"IP-126449","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":417161,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3503/sim3503_sheet.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3503/covrthb.jpg"},{"id":417163,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98MVEGI","text":"Mapped polygons of landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California","description":"Corbett, S.C., and Collins, B.D., 2023, Mapped polygons of landslides triggered by the 2016–2017 storm season, eastern San Francisco Bay region, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9XPA7UC."},{"id":500209,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114739.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"eastern San Francisco Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.86773652737136,\n 37.59196580130809\n ],\n [\n -121.95011125798024,\n 37.760393221950594\n ],\n [\n -122.01875686682075,\n 37.85530428686653\n ],\n [\n -122.03935054947307,\n 38.00420363734628\n ],\n [\n -122.06680879300936,\n 38.03935395235649\n ],\n [\n -122.23842281511067,\n 38.06908335518102\n ],\n [\n -122.35855263058198,\n 38.006908106324346\n ],\n [\n -122.43406280030648,\n 37.96362463343144\n ],\n [\n -122.37571403279212,\n 37.88781713463695\n ],\n [\n -122.23842281511067,\n 37.790235557897105\n ],\n [\n -122.07024107345114,\n 37.64905661408321\n ],\n [\n -121.95011125798024,\n 37.551159813602936\n ],\n [\n -121.86773652737136,\n 37.59196580130809\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035

","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-05-23","noUsgsAuthors":false,"publicationDate":"2023-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Corbett, Skye C. 0000-0003-3277-1021 scorbett@usgs.gov","orcid":"https://orcid.org/0000-0003-3277-1021","contributorId":200617,"corporation":false,"usgs":true,"family":"Corbett","given":"Skye","email":"scorbett@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":872950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":872951,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243856,"text":"70243856 - 2023 - Remotely sensed short-crested breaking waves in a laboratory directional wave basin","interactions":[],"lastModifiedDate":"2023-05-23T11:56:54.213257","indexId":"70243856","displayToPublicDate":"2023-05-23T06:53:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1262,"text":"Coastal Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Remotely sensed short-crested breaking waves in a laboratory directional wave basin","docAbstract":"Short-crested breaking waves that result from directionally spread wave conditions dissipate energy and generate turbulence within the surf zone, altering sediment transport processes, wave runup, and forces on structures. Additionally, vertical vorticity generated near crest ends during breaking, which depends on the gradient in wave height along a crest, may enhance nearshore dispersion of pollutants, nutrients, and larvae. Although directionally spread irregular wave fields are ubiquitous on ocean and large lake coastlines, the dependence of short-crested breaking wave characteristics (including the along-crest length and number of crest ends) on offshore wave conditions is not well established. To assess this relationship, laboratory experiments with alongshore-uniform barred bathymetry were performed in a large-scale directional wave basin. A three-dimensional scanning lidar, trinocular camera stereo processing methods, and in situ measurements were used to study short-crested wave field breaking characteristics in the laboratory, yielding a dataset with dense spatio-temporal coverage relative to prior laboratory or field measurements. Wave height estimates are similar for remotely sensed and in situ observations, except in the outer surf zone where plunging breaking occurred. Directional wave properties estimated with an array of in situ or remotely sensed sea-surface elevation estimates are similar and yield smaller directional spreads than single-point colocated pressure and velocity based in situ estimates when waves are less directionally spread. Using a breaking crest identification procedure combining visible imagery and stereo sea-surface elevation, we find that the average along-crest length of breaking waves decreases and the average number of crest ends increases with increasing directional spread. Relative to observations, a parameterized relationship between directional spread and crest characteristics based on theory for non-breaking, refracting waves generally over-estimates breaking crest lengths and is similar to or underestimates the total number of crest ends observed in the surf zone. The wave-field-dependent breaking-wave characteristics examined in the laboratory with remote sensing techniques can inform future investigations of depth-limited short-crested wave breaking and resulting surfzone eddy processes.","language":"English","publisher":"Elsevier","doi":"10.1016/j.coastaleng.2023.104327","usgsCitation":"Baker, C., Moulton, M., Palmsten, M.L., Brodie, K., Nuss, E., and Chickadel, C.C., 2023, Remotely sensed short-crested breaking waves in a laboratory directional wave basin: Coastal Engineering, v. 183, 104327, 26 p., https://doi.org/10.1016/j.coastaleng.2023.104327.","productDescription":"104327, 26 p.","ipdsId":"IP-137980","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443433,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coastaleng.2023.104327","text":"Publisher Index Page"},{"id":417326,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"183","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Baker, Christine","contributorId":305678,"corporation":false,"usgs":false,"family":"Baker","given":"Christine","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":873511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Moulton, Melissa","contributorId":305679,"corporation":false,"usgs":false,"family":"Moulton","given":"Melissa","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":873512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmsten, Margaret L. 0000-0002-6424-2338","orcid":"https://orcid.org/0000-0002-6424-2338","contributorId":239955,"corporation":false,"usgs":true,"family":"Palmsten","given":"Margaret","email":"","middleInitial":"L.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brodie, Katherine","contributorId":305680,"corporation":false,"usgs":false,"family":"Brodie","given":"Katherine","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":873514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nuss, Emma","contributorId":305681,"corporation":false,"usgs":false,"family":"Nuss","given":"Emma","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":873515,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chickadel, C. Christopher","contributorId":106337,"corporation":false,"usgs":true,"family":"Chickadel","given":"C.","email":"","middleInitial":"Christopher","affiliations":[],"preferred":false,"id":873519,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243807,"text":"70243807 - 2023 - Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","interactions":[],"lastModifiedDate":"2023-05-25T15:59:15.380077","indexId":"70243807","displayToPublicDate":"2023-05-22T08:29:31","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California","docAbstract":"

In this study, we compare the Southern California Earthquake Center CyberShake platform against the Next Generation Attenuation‐West2 empirical datasets. Because the CyberShake and empirical datasets cover very different magnitude ranges and site conditions, we develop ground‐motion models (GMMs) for CyberShake datasets to compare trends with empirical GMMs and decompose the residuals for further analysis. We apply mixed effects regression to four CyberShake datasets in southern, central, and northern California at 2, 3, 5, and 10 s periods, and compare the results with the empirical datasets using the same approach. CyberShake captures the total variability of ground motions in the empirical datasets but tends to predict larger median ground motions relative to the empirical GMMs. We then calculate and compare the repeatable source‐specific location, site, and path effects between CyberShake and empirical datasets. We find that the correlations of site effects between the CyberShake and empirical datasets are generally satisfactory, but the variability of site effects is slightly smaller for CyberShake datasets. There is no apparent correlation of source‐specific location effects between the CyberShake and empirical datasets. Comparison of path effects shows a wide range of correlation coefficients. Finally, we investigate the source of observed differences between the CyberShake and empirical datasets. We attribute the larger median ground‐motion levels in CyberShake to a combination of the homogeneous slip patterns of the earthquake ruptures, the low resolution of near‐surface materials in the velocity models, and strong reflections at high‐contrast boundaries in the velocity models. These factors also impact the correlations of site and path effects between the CyberShake and empirical datasets. Moreover, the leakage from location effects into site and path terms further weakens the correlations. In summary, we find that CyberShake could be improved, but it is still very useful to supplement empirical datasets for ground‐motion studies, especially to inform their nonergodic components.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220049","usgsCitation":"Meng, X., Goulet, C., Milner, K.R., Graves, R., and Callaghan, S., 2023, Comparison of nonergodic ground-motion components from CyberShake and NGA-West2 datasets in California: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1152-1175, https://doi.org/10.1785/0120220049.","productDescription":"24 p.","startPage":"1152","endPage":"1175","ipdsId":"IP-140662","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417292,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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US","interactions":[],"lastModifiedDate":"2023-05-22T13:08:24.434575","indexId":"70243816","displayToPublicDate":"2023-05-22T07:57:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1551,"text":"Environmental Modelling and Software","active":true,"publicationSubtype":{"id":10}},"title":"Generating a reference flow network with improved connectivity to support durable data integration and reproducibility in the coterminous US","docAbstract":"

This report presents a reference flow network for the conterminous United States that is built from the best available information from the U.S. Geological Survey, the National Oceanic and Atmospheric Administration National Weather Service, and the U.S. Environmental Protection Agency. The work is intended to support durable data integration and reproducibility. Originating from the National Hydrography Dataset Plus (NHDPlus) V2.1, the reference flow network incorporates network connectivity enhancements from federal agency efforts. After incorporating these network improvements, many original NHDPlus attributes were regenerated to enable network navigation and related operations. After introducing the motivation and background for this work, this report describes the attribute generation workflow and data quality checks that were performed in preparation of the dataset. The reference flow network follows the NHDPlus data model and is described using terms defined in the Mainstem and Drainage Basin logical model and WaterML2 Part3: Surface Hydrology Features conceptual model.

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Center","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":873360,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, J. Michael","contributorId":304963,"corporation":false,"usgs":false,"family":"Johnson","given":"J. Michael","affiliations":[{"id":66193,"text":"NOAA-NWS-OWP","active":true,"usgs":false}],"preferred":false,"id":873361,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bock, Andrew R. 0000-0001-7222-6613 abock@usgs.gov","orcid":"https://orcid.org/0000-0001-7222-6613","contributorId":4580,"corporation":false,"usgs":true,"family":"Bock","given":"Andrew","email":"abock@usgs.gov","middleInitial":"R.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873362,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243804,"text":"70243804 - 2023 - User engagement testing with a pilot decision support tool aimed to support species managers","interactions":[],"lastModifiedDate":"2023-05-22T12:55:06.432596","indexId":"70243804","displayToPublicDate":"2023-05-22T07:46:34","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5829,"text":"Weather, Climate, and Society","printIssn":"1948-8327","active":true,"publicationSubtype":{"id":10}},"title":"User engagement testing with a pilot decision support tool aimed to support species managers","docAbstract":"Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/WCAS-D-22-0010.1","usgsCitation":"Cashwell, H.J., McNeal, K.S., Dello, K., Boyles, R., and Davis, C., 2023, User engagement testing with a pilot decision support tool aimed to support species managers: Weather, Climate, and Society, v. 15, no. 2, p. 327-338, https://doi.org/10.1175/WCAS-D-22-0010.1.","productDescription":"12 p.","startPage":"327","endPage":"338","ipdsId":"IP-137275","costCenters":[{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":443439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1175/wcas-d-22-0010.1","text":"Publisher Index Page"},{"id":417289,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cashwell, Haven J.","contributorId":305618,"corporation":false,"usgs":false,"family":"Cashwell","given":"Haven","email":"","middleInitial":"J.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":873329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McNeal, Karen S.","contributorId":305619,"corporation":false,"usgs":false,"family":"McNeal","given":"Karen","email":"","middleInitial":"S.","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":873330,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dello, Kathie","contributorId":224723,"corporation":false,"usgs":false,"family":"Dello","given":"Kathie","email":"","affiliations":[{"id":25510,"text":"NC State University","active":true,"usgs":false}],"preferred":false,"id":873331,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Ryan 0000-0001-9272-867X","orcid":"https://orcid.org/0000-0001-9272-867X","contributorId":221983,"corporation":false,"usgs":true,"family":"Boyles","given":"Ryan","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":873332,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Corey","contributorId":221987,"corporation":false,"usgs":false,"family":"Davis","given":"Corey","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":873333,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247515,"text":"70247515 - 2023 - High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands","interactions":[],"lastModifiedDate":"2023-08-10T11:38:17.536908","indexId":"70247515","displayToPublicDate":"2023-05-22T06:36:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands","docAbstract":"

Translocation, often a management solution reserved for at-risk species, is a highly time-sensitive intervention in the face of a rapidly changing climate. The definition of abiotic and biotic habitat requirements is essential to the selection of appropriate release sites in novel environments. However, field-based approaches to gathering this information are often too time intensive, especially in areas of complex topography where common, coarse-scale climate models lack essential details. We apply a fine-scale remote sensing-based approach to study the 'akikiki (Oreomystis bairdi) and 'akeke'e (Loxops caeruleirostris), Hawaiian honeycreepers endemic to Kaua'i that are experiencing large-scale population declines due to warming-induced spread of invasive disease. We use habitat suitability modeling based on fine-scale light detection and ranging (lidar)-derived habitat structure metrics to refine coarse climate ranges for these species in candidate translocation areas on Maui. We found that canopy density was consistently the most important variable in defining habitat suitability for the two Kaua'i species. Our models also corroborated known habitat preferences and behavioral information for these species that are essential for informing translocation. We estimated a nesting habitat that will persist under future climate conditions on east Maui of 23.43 km2 for 'akikiki, compared to the current Kaua'i range of 13.09 km2. In contrast, the novel nesting range for 'akeke'e in east Maui was smaller than its current range on Kaua'i (26.29 vs. 38.48 km2, respectively). We were also able to assess detailed novel competitive interactions at a fine scale using models of three endemic Maui species of conservation concern: 'ākohekohe (Palmeria dolei), Maui 'alauahio (Paroreomyza montana), and kiwikiu (Pseudonestor xanthophrys). Weighted overlap areas between the species from both islands were moderate (<12 km2), and correlations between Maui and Kaua'i bird habitat were generally low, indicating limited potential for competition. Results indicate that translocation to east Maui could be a viable option for 'akikiki but would be more uncertain for 'akeke'e. Our novel multifaceted approach allows for the timely analysis of both climate and vegetation structure at informative scales for the effective selection of appropriate translocation sites for at-risk species.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2889","usgsCitation":"Gallerani, E.M., Burgett, J., Vaughn, N.R., Fortini, L., Fricker, G.A., Mounce, H., Gillespie, T.W., Crampton, L.H., Knapp, D., Hite, J.M., and Gilb, R., 2023, High resolution lidar data shed light on inter-island translocation of endangered bird species in the Hawaiian Islands: Ecological Applications, v. 33, no. 5, e2889, 17 p., https://doi.org/10.1002/eap.2889.","productDescription":"e2889, 17 p.","ipdsId":"IP-146020","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":443451,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2889","text":"Publisher Index Page"},{"id":419690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type","interactions":[],"lastModifiedDate":"2023-05-24T15:56:03.751044","indexId":"70243886","displayToPublicDate":"2023-05-20T10:50:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type","docAbstract":"

The total concentration of dissolved constituents in water is routinely quantified by measurements of salinity or total dissolved solids (TDS). However, salinity and TDS are operationally defined by their analytical methods and are not equivalent for most waters. Furthermore, multiple methods are available to determine salinity and TDS, and these methods have inherent differences. TDS is defined as the mass of anhydrous residue remaining in a sample vessel after evaporation and subsequent oven drying at a defined temperature. Salinity is a measure of the mass of dissolved salts in a given mass of solution. In addition, there are approaches that quantify the total solute (TS) concentration, including gases. The purpose of this study is to develop a proxy method using specific conductance and major-ion water type to reliably predict salinity, TDS, and/or TS. Thus, we compared several methods to calculate salinity, TDS, and TS for 6391 surface water samples and conclude the following: TDS measurements are best suited for studies of anhydrous residue (e.g., evaporites); salinity determined by summing the speciated ion concentrations, termed S∑spec, is the most comprehensive method to represent the concentration of dissolved constituents in natural waters; and TS determinations are useful if dissolved CO2 and other gases are of interest. Thus, we utilized S∑spec to compare differences between the various salinity, TDS, and TS methods, and to develop a new proxy method to predict salinity based on specific conductance (SC) and major ion water type, termed SSC_WT. For the surface waters used in this study, the median difference between TDS and S∑Spec was between −19% and −24%, depending on the method. The median difference between SSC_WT and S∑Spec was −2.4% for the samples in this study. The SSC_WT approach is cost effective, rapid, and capable of providing reliable real-time salinity determinations at surface water sites where SC data are available and water type is known.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2023.105684","usgsCitation":"McCleskey, R., Cravotta, C., Miller, M., Tillman, F.D., Stackelberg, P.E., Knierim, K.J., and Wise, D., 2023, Salinity and total dissolved solids measurements for natural waters: An overview and a new salinity method based on specific conductance and water type: Applied Geochemistry, v. 154, 105684, 13 p., https://doi.org/10.1016/j.apgeochem.2023.105684.","productDescription":"105684, 13 p.","ipdsId":"IP-148865","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":443454,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2023.105684","text":"Publisher Index Page"},{"id":417398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"154","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. Blaine","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":873614,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cravotta, Charles A. III 0000-0003-3116-4684","orcid":"https://orcid.org/0000-0003-3116-4684","contributorId":207249,"corporation":false,"usgs":true,"family":"Cravotta","given":"Charles A.","suffix":"III","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":220622,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873616,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873617,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":873618,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Knierim, Katherine J. 0000-0002-5361-4132 kknierim@usgs.gov","orcid":"https://orcid.org/0000-0002-5361-4132","contributorId":191788,"corporation":false,"usgs":true,"family":"Knierim","given":"Katherine","email":"kknierim@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873619,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wise, Daniel 0000-0002-1215-9612","orcid":"https://orcid.org/0000-0002-1215-9612","contributorId":217259,"corporation":false,"usgs":true,"family":"Wise","given":"Daniel","email":"","affiliations":[],"preferred":true,"id":873620,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70255217,"text":"70255217 - 2023 - Bayesian forecasting of disease spread with little or no local data","interactions":[],"lastModifiedDate":"2024-06-14T00:09:20.34351","indexId":"70255217","displayToPublicDate":"2023-05-19T19:07:09","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Bayesian forecasting of disease spread with little or no local data","docAbstract":"

Rapid and targeted management actions are a prerequisite to efficiently mitigate disease outbreaks. Targeted actions, however, require accurate spatial information on disease occurrence and spread. Frequently, targeted management actions are guided by non-statistical approaches that define the affected area by a pre-determined distance surrounding a small number of disease detections. As an alternative, we present a long-recognized but underutilized Bayesian technique that uses limited local data and informative priors to make statistically valid predictions and forecasts about disease occurrence and spread. As a case study, we use limited local data that were available after the detection of chronic wasting disease in Michigan, U.S. along with information rich priors obtained from a previous study in a neighboring state. Using these limited local data and informative priors, we generate statistically valid predictions of disease occurrence and spread for the Michigan study area. This Bayesian technique is conceptually and computationally simple, relies on little to no local data, and is competitive with non-statistical distance-based metrics in all performance evaluations. Bayesian modeling has added benefits because it allows practitioners to generate immediate forecasts of future disease conditions and provides a principled framework to incorporate new data as they accumulate. We contend that the Bayesian technique offers broad-scale benefits and opportunities to make statistical inference across a diversity of data-deficient systems, not limited to disease.

","language":"English","publisher":"Nature","doi":"10.1038/s41598-023-35177-6","usgsCitation":"Cook, J., Williams, D.M., Walsh, D.P., and Hefley, T.J., 2023, Bayesian forecasting of disease spread with little or no local data: Scientific Reports, v. 13, 8137, 8 p., https://doi.org/10.1038/s41598-023-35177-6.","productDescription":"8137, 8 p.","ipdsId":"IP-138124","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":443457,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-35177-6","text":"Publisher Index Page"},{"id":430174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","noUsgsAuthors":false,"publicationDate":"2023-05-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cook, Jonathan D","contributorId":256954,"corporation":false,"usgs":false,"family":"Cook","given":"Jonathan D","affiliations":[{"id":24700,"text":"Student contractor","active":true,"usgs":false}],"preferred":false,"id":903759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Williams, David M.","contributorId":205354,"corporation":false,"usgs":false,"family":"Williams","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":37088,"text":"Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824, USA","active":true,"usgs":false}],"preferred":false,"id":903760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hefley, Trevor J.","contributorId":147146,"corporation":false,"usgs":false,"family":"Hefley","given":"Trevor","email":"","middleInitial":"J.","affiliations":[{"id":16796,"text":"Dept Fish, Wildlife & Cons Biol, Colorado St Univ, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":903762,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243759,"text":"70243759 - 2023 - Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","interactions":[],"lastModifiedDate":"2023-05-19T12:54:11.017578","indexId":"70243759","displayToPublicDate":"2023-05-19T07:29:23","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system","docAbstract":"

Objective: The American Eel Anguilla rostrata historically was one of the most common fish species in Atlantic coast watersheds, but extensive dam construction and other factors caused a widespread population decline. One of the watersheds where American Eels have declined considerably is the Mohawk River in eastern and central New York. Recent attempts to characterize the distribution and abundance of American Eels in this watershed have been ineffective, and the extent to which a series of locks and dams on the Hudson River and lower Mohawk River limits use of the watershed is unclear.

Methods: We developed a model between environmental DNA (eDNA) quantity and American Eel abundance in the Hudson River watershed in which the DNA concentration in water samples explained up to 65% of the variability in eel density and 56% of the variability in eel biomass. We then used this relationship to interpret eDNA data collected twice from 36 sites across the Mohawk River watershed in 2021 and make inferences about the distribution and abundance of American Eels.

Result: American Eel DNA was detected almost exclusively in the downstream-most 4 km of the Mohawk River within a series of barriers. The concentration of DNA was reduced by approximately 80% across each successive upstream barrier before becoming too low to detect consistently. Our data suggest that eel population density was high in the Hudson River estuary and declined rapidly in the lower Mohawk River, and the species was nearly absent or undetectable in the Mohawk River and its tributaries upstream of the Crescent Dam and the Waterford Flight of Locks.

Conclusion: Barriers appear to be largely restricting American Eels from using over 99% of the Mohawk River watershed. Therefore, improvements in fish passage at dams and hydroelectric facilities in the region could help the American Eel to regain access to this part of its native range.

","language":"English","publisher":"Wiley","doi":"10.1002/tafs.10404","usgsCitation":"George, S.D., Baldigo, B., Rees, C., Bartron, M.L., Wiley, J.J., Stich, D.S., Wells, S.M., and Winterhalter, D., 2023, Use of environmental DNA to assess American Eel distribution, abundance, and barriers in a river-canal system: Transactions of the American Fisheries Society, v. 152, no. 3, p. 310-326, https://doi.org/10.1002/tafs.10404.","productDescription":"17 p.","startPage":"310","endPage":"326","ipdsId":"IP-143996","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":443462,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10404","text":"Publisher Index Page"},{"id":417238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Mohawk River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.46545959281143,\n 43.193210843337965\n ],\n [\n -75.36068780837059,\n 43.1807103892566\n ],\n [\n -75.25020119932465,\n 43.095917619682325\n ],\n [\n -75.11304540878423,\n 43.06391605114166\n ],\n [\n -75.0406576304439,\n 43.00264908923049\n ],\n [\n -74.84063876923905,\n 43.01240019708166\n ],\n [\n -74.66347920645833,\n 42.963629178932905\n ],\n [\n -74.58156672044112,\n 42.881327587792015\n ],\n [\n -74.45774552064795,\n 42.879931698082856\n ],\n [\n -74.25772665944312,\n 42.84083396491155\n ],\n [\n -74.11866592736799,\n 42.86317855831385\n ],\n [\n -74.03865838288596,\n 42.83524655344701\n ],\n [\n -74.02532379213918,\n 42.75417231574653\n ],\n [\n -74.00627437678641,\n 42.707996466290325\n ],\n [\n -73.81976366766865,\n 42.62025113498041\n ],\n [\n -73.79690436924501,\n 42.550123385716574\n ],\n [\n -73.8254784922747,\n 42.384309188935845\n ],\n [\n -73.81404884306264,\n 42.31351753996279\n ],\n [\n -73.93977498439143,\n 42.2035468271101\n ],\n [\n -73.97025404895584,\n 42.10469190424271\n ],\n [\n -74.00454299659106,\n 41.89801206244189\n ],\n [\n -73.91310580289745,\n 41.86681033353875\n ],\n [\n -73.88262673833307,\n 42.11317123319691\n ],\n [\n -73.72642153243986,\n 42.28251957771869\n ],\n [\n -73.74547094779265,\n 42.37828214920688\n ],\n [\n -73.71880176629864,\n 42.619854148073244\n ],\n [\n -73.61212504032306,\n 42.82697116015191\n ],\n [\n -73.66165352024026,\n 42.88841312774437\n ],\n [\n -73.94442157442768,\n 42.88448522793766\n ],\n [\n -74.22444798011406,\n 42.97932677531503\n ],\n [\n -74.37874824447162,\n 42.982114021563405\n ],\n [\n -74.51971391808242,\n 42.93192425763374\n ],\n [\n -74.65305982555196,\n 43.01415827143066\n ],\n [\n -74.86679009610465,\n 43.05871377504309\n ],\n [\n -75.00966071125109,\n 43.05314610631808\n ],\n [\n -75.22491910473742,\n 43.151897073297846\n ],\n [\n -75.36207489527783,\n 43.21717980329879\n ],\n [\n -75.47065656278859,\n 43.221344415722996\n ],\n [\n -75.46545959281143,\n 43.193210843337965\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"152","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-05-03","publicationStatus":"PW","contributors":{"authors":[{"text":"George, Scott D. 0000-0002-8197-1866 sgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-8197-1866","contributorId":3014,"corporation":false,"usgs":true,"family":"George","given":"Scott","email":"sgeorge@usgs.gov","middleInitial":"D.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Baldigo, Barry P. 0000-0002-9862-9119","orcid":"https://orcid.org/0000-0002-9862-9119","contributorId":25174,"corporation":false,"usgs":true,"family":"Baldigo","given":"Barry P.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rees, Christopher B.","contributorId":196308,"corporation":false,"usgs":false,"family":"Rees","given":"Christopher B.","affiliations":[],"preferred":false,"id":873175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false},{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":873176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wiley, John J. Jr.","contributorId":305554,"corporation":false,"usgs":false,"family":"Wiley","given":"John","suffix":"Jr.","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":873177,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stich, Daniel S.","contributorId":280276,"corporation":false,"usgs":false,"family":"Stich","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":33660,"text":"SUNY Oneonta","active":true,"usgs":false}],"preferred":false,"id":873178,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wells, Scott M.","contributorId":305556,"corporation":false,"usgs":false,"family":"Wells","given":"Scott","email":"","middleInitial":"M.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":873179,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winterhalter, Dylan R. 0000-0003-1774-8034","orcid":"https://orcid.org/0000-0003-1774-8034","contributorId":251765,"corporation":false,"usgs":true,"family":"Winterhalter","given":"Dylan R.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873180,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243768,"text":"70243768 - 2023 - Watershed carbon yield derived from gauge observations and river network connectivity in the United States","interactions":[],"lastModifiedDate":"2023-05-19T12:28:27.653939","indexId":"70243768","displayToPublicDate":"2023-05-19T07:04:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Watershed carbon yield derived from gauge observations and river network connectivity in the United States","docAbstract":"River networks play a critical role in the global carbon cycle. Although global/continental scale riverine carbon cycle studies demonstrate the significance of rivers and streams for linking land and coastal regions, the lack of spatially distributed riverine carbon load data represents a gap for quantifying riverine carbon net gain or net loss in different regions, understanding mechanisms and factors that influence the riverine carbon cycle, and testing simulations of aquatic carbon cycle models at fine scales. Here, we (1) derive the riverine load of particulate organic carbon (POC) and dissolved organic carbon (DOC) for over 1,000 hydrologic stations across the Conterminous United States (CONUS) and (2) use the river network connectivity information for over 80,000 catchment units within the National Hydrography Dataset Plus (NHDPlus) to estimate riverine POC and DOC net gain or net loss for watersheds controlled between upstream-downstream hydrologic stations. 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The U.S. Geological Survey Precipitation Chemistry Quality Assurance project (PCQA) operated four distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network (NTN) and Mercury Deposition Network (MDN) during 2019–20. The NTN programs included (1) a field audit program to evaluate sample contamination and stability, and (2) an interlaboratory comparison program to evaluate analytical laboratory performance. The MDN programs included the (3) system blank program to evaluate sample contamination and stability, and (4) an interlaboratory comparison program. The results indicated increased levels of sample contamination compared to previous years for NTN samples and decreased contamination in MDN samples. Strong analytical laboratory performance with low overall variability and bias in concentration data were indicated for the NTN’s Central Analytical Laboratory. A positive bias in the hydrogen ion concentrations in NTN samples during 2019 was eliminated by correction of a pH calibration protocol during 2020. The MDN’s Mercury Analytical Laboratory performance declined in 2020 compared to 2019 as indicated by increased variability in analytical results and a negative bias of approximately -1 nanogram per liter in the concentrations of total mercury. Slight perturbations in contamination levels in NTN samples and in analytical performance for MDN are considered small. The PCQA results indicate that NADP data continue to be of sufficient quality for applications in independent research and NADP data products, including spatial interpolations and time trends for chemical constituents in wet deposition. Small shifts in data quality indicated by the 2019–20 PCQA results are intended to be used for interpretation of the NADP data products.

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Director, Observing Systems Division
Hydrologic Instrumentation Facility
U.S. Geological Survey
Buildings 2101 2204 HIF
Stennis Space Center
Bay St Louis, MS 39529

","tableOfContents":"","publishedDate":"2023-05-18","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wetherbee, Gregory A. 0000-0002-6720-2294","orcid":"https://orcid.org/0000-0002-6720-2294","contributorId":215100,"corporation":false,"usgs":true,"family":"Wetherbee","given":"Gregory","email":"","middleInitial":"A.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":872996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, RoseAnn 0000-0002-2611-8395 ramartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2611-8395","contributorId":202920,"corporation":false,"usgs":true,"family":"Martin","given":"RoseAnn","email":"ramartin@usgs.gov","affiliations":[{"id":143,"text":"Branch of Quality Systems","active":true,"usgs":true}],"preferred":true,"id":872997,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liethen, Alexander 0000-0002-7301-7255","orcid":"https://orcid.org/0000-0002-7301-7255","contributorId":305495,"corporation":false,"usgs":false,"family":"Liethen","given":"Alexander","email":"","affiliations":[{"id":24700,"text":"Student contractor","active":true,"usgs":false}],"preferred":false,"id":872998,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70243696,"text":"sir20235027 - 2023 - Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","interactions":[],"lastModifiedDate":"2026-03-06T20:51:31.369117","indexId":"sir20235027","displayToPublicDate":"2023-05-18T10:56:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-5027","displayTitle":"Evaluating Drivers of Hydrology, Water Quality, and Benthic Macroinvertebrates in Streams of Fairfax County, Virginia, 2007–18","title":"Evaluating drivers of hydrology, water quality, and benthic macroinvertebrates in streams of Fairfax County, Virginia, 2007–18","docAbstract":"

In 2007, the U.S. Geological Survey partnered with Fairfax County, Virginia, to establish a long-term water-resources monitoring program to evaluate the hydrology, water quality, and ecology of Fairfax County streams and the watershed-scale effects of management practices. Fairfax County uses a variety of management practices, policies, and programs to protect and restore its water resources, but the effects of such strategies are not well understood. This report used streamflow, water-quality, and ecological monitoring data collected from 20 Fairfax County watersheds from 2007 through 2018 to assess the effects of management practices, landscape factors, and climatic conditions on observed nutrient, sediment, salinity, and benthic-macroinvertebrate community responses.

Urbanization, climatic variability, and an increase in management practices occurred within Fairfax County during the study period. Impervious cover, housing units, wastewater infrastructure, and (or) stormwater infrastructure increased in most study watersheds. Climatic conditions varied among study years; countywide estimates of average-annual air temperature differed by about 3 degrees Celsius, and total precipitation ranged from about 34 to 63 inches per year. The effects of the management practices, implemented to reduce nitrogen, phosphorus, and (or) sediment loads, are considered in this study. These management practices primarily consist of stormwater retrofits and stream restorations; however, stream restorations account for most of the financial investment and expected load reductions. Management practices were implemented in half of the study watersheds, and most practices were installed and reductions credited late in the study period.

Changes in hydrologic response during storm events were evaluated over the study period because many management practices that were implemented were designed to achieve nutrient and sediment reductions by slowing or intercepting runoff. The average number and length of storm events was mostly unchanged throughout the monitoring network. Four watersheds with 10 years of streamflow data showed a mixture of trends in stormflow peak, volume, and rate-of-change. Event-mean nutrient and sediment concentrations from these watersheds were evaluated during storm events and generally showed increases in total phosphorus (TP) and suspended sediment and reductions or no changes in total nitrogen (TN).

Landscape inputs of nitrogen and phosphorus and the percentage of inputs delivered to streams were estimated for the study watersheds. Estimated phosphorus from fertilizer and nitrogen from atmospheric deposition represented large nutrient inputs in most watersheds; amounts of other nonpoint sources varied based on land use. Estimated nitrogen inputs declined throughout Fairfax County and in most study watersheds from 2008 through 2018; in comparison, phosphorus input changes were relatively small. Most nonpoint-nutrient inputs were retained on the landscape and did not reach streams, with slightly more nitrogen retention than phosphorus, on average. Retention rates were lower for years with more precipitation and streamflow. After adjusting for streamflow, TN and TP loads were generally higher for years with more nutrient inputs. Calculated as a function of flow-adjusted loads, TP retention declined at most stations from 2009 through 2018, in comparison, TN retention was relatively unchanged.

Landscape and climatic conditions affected spatial differences and changes in Fairfax County stream conditions from 2009 through 2018. TN concentrations were higher and increases over time were larger in watersheds with elevated septic-system density. TP concentrations were higher in watersheds with more turfgrass; concentrations were lower, but had larger increases over time, in watersheds with deeper soils. Suspended-sediment concentrations were higher in watersheds with greater stream densities. Specific conductance was higher in watersheds with more developed land use and shallower soils. Benthic-macroinvertebrate index of biotic integrity (IBI) scores were lower in watersheds with high road density and had larger increases over time in bigger, more developed watersheds. Annual variability in TN and TP concentrations and benthic-macroinvertebrate IBI scores was affected by precipitation; annual variability in suspended sediment concentrations and specific conductance was affected by air temperature.

After accounting for influences from landscape and climatic conditions, expected management-practice effects were not consistently observed in monitored stream responses. These effects were assessed by comparing expected management-practice load reductions with the timing, direction, and magnitude of changes in storm-event hydrology, nutrient and sediment loads, median-annual water-quality conditions, and benthic-macroinvertebrate IBI scores. An important consideration for future investigations of management-practice effects is how to control for water-quality and ecological variability caused by geologic properties, the urban environment, precipitation, and (or) air temperature. The interpretation of management-practice effects in this report was likely influenced by a combination of factors, including (1) the amount, timing, and location of management-practice implementation; (2) unmeasured landscape and climatic factors; (3) uncertain management-practice expectations; (4) hydrologic variability; and (5) analytical assumptions. Through continued data-collection efforts, particularly after management practices have been completed, many of these factors may become less influential in the future.

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U.S. Geological Survey
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","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-05-18","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Webber, James S. 0000-0001-6636-1368","orcid":"https://orcid.org/0000-0001-6636-1368","contributorId":222000,"corporation":false,"usgs":true,"family":"Webber","given":"James","email":"","middleInitial":"S.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872938,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chanat, Jeffrey G. 0000-0002-3629-7307 jchanat@usgs.gov","orcid":"https://orcid.org/0000-0002-3629-7307","contributorId":5062,"corporation":false,"usgs":true,"family":"Chanat","given":"Jeffrey","email":"jchanat@usgs.gov","middleInitial":"G.","affiliations":[{"id":614,"text":"Virginia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872939,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porter, Aaron J. 0000-0002-0781-3309","orcid":"https://orcid.org/0000-0002-0781-3309","contributorId":239980,"corporation":false,"usgs":true,"family":"Porter","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872940,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jastram, John D. 0000-0002-9416-3358 jdjastra@usgs.gov","orcid":"https://orcid.org/0000-0002-9416-3358","contributorId":3531,"corporation":false,"usgs":true,"family":"Jastram","given":"John","email":"jdjastra@usgs.gov","middleInitial":"D.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872941,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70243612,"text":"sir20225047 - 2023 - Simulation of flow and eutrophication in the central Salem River, New Jersey","interactions":[],"lastModifiedDate":"2026-02-23T19:12:37.435815","indexId":"sir20225047","displayToPublicDate":"2023-05-18T10:55:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-5047","displayTitle":"Simulation of Flow and Eutrophication in the Central Salem River, New Jersey","title":"Simulation of flow and eutrophication in the central Salem River, New Jersey","docAbstract":"

The central Salem River in New Jersey is subject to periods of water-quality impairment, marked by elevated concentrations of phosphorus and chlorophyll-a, and low concentrations of and large diurnal swings in concentrations of dissolved oxygen. These seasonal eutrophic conditions are controlling factors for water quality in lower reaches, where the river is more lacustrine than in upper reaches, as a result of downstream damming. This biological productivity is supported by nutrient wash-off from agricultural areas in the surrounding watershed. To investigate this impairment, flow measurement and water-quality sampling were conducted during 2007–08 in support of development of a one-dimensional surface-water-quality model that simulates nutrient cycling and transformation processes.

The U.S. Geological Survey, in cooperation with the New Jersey Department of Environmental Protection, used the U.S. Environmental Protection Agency Water Quality Analysis Simulation Program (WASP) to develop a receiving-water-quality model of the central Salem River between Woodstown and Deepwater, New Jersey, from April 2007 to October 2008. The main-stem river and largest tributary were simulated. In the flow model, kinematic wave flow is used to simulate flow in upper reaches and ponded weir flow is used to simulate flow in lower reaches. The water-quality model makes use of a mass-balance equation to simulate the fate and transport of nutrients, phytoplankton chlorophyll-a, dissolved oxygen, and oxygen demands (an indicator rather than a substance) in the river. Model input included channel characteristics, boundary conditions for flow and water quality, environmental parameters, vertical dispersion coefficients, settling rates, and kinetic constants. Inputs were estimated where field data were lacking, notably for tributary flows and nutrient loads.

The model was calibrated to observed flow variables and concentrations of dissolved oxygen, chlorophyll-a, and nutrients at sampling locations, with emphasis on growing-season conditions. Calibration was achieved through graphical and statistical comparison of simulated results to observed data. Sensitivity analyses were performed, and model limitations and applicability were evaluated. Simulated results closely matched observed data in most cases, although some were overpredicted slightly. The most important causes of overprediction were estimated tributary flows for the flow model and estimated tributary watershed loads for the water-quality model. Calibration of dissolved-oxygen concentrations was closer, and predicted diurnal variations were consistent with high algal photosynthesis/respiration, although lack of continuous dissolved-oxygen data precluded verifying these predictions. A similar caveat applies to predicted diurnal variations in chlorophyll-a. Simulated limitations on algal growth were consistent with those based on observed data and indicated phosphorus was the main limiting nutrient, except during certain periods when nitrogen was limiting.

Two water-quality management scenarios were simulated with the model to assess the effect of point- and nonpoint-source nutrient reductions on water-quality conditions in the river. Scenarios involved (1) a return of watershed land use to predevelopment natural conditions and (2) an extreme reduction in nutrient input. Although the extreme-nutrient-reduction scenario yielded improvements in water quality, the natural-conditions scenario yielded the largest improvements as indicated by minimal violations of surface-water-quality standards or thresholds. However, years may be needed to attain the full benefit of these management scenarios as a result of accumulation of phosphorus and organic carbon in riverbed sediments in lacustrine reaches. The results of this study indicate that the quality of water in the central Salem River will improve if management policies that mitigate the effects of nutrient-loading practices in the watershed, particularly those related to agriculture, are implemented.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225047","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Spitz, F.J., and DePaul, V.T., 2023, Simulation of flow and eutrophication in the central Salem River, New Jersey: U.S. Geological Survey Scientific Investigations Report 2022–5047, 72 p., https://doi.org/10.3133/sir20225047.","productDescription":"Report: x, 72 p.; Data Release","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109225","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":500449,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114735.htm","linkFileType":{"id":5,"text":"html"}},{"id":417027,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78G8JPJ","text":"USGS data release","linkHelpText":"WASP model used to simulate flow and eutrophication in the central Salem River, New Jersey"},{"id":417026,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5047/images/"},{"id":417025,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.XML"},{"id":417024,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225047/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5047"},{"id":417023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5047/sir20225047.pdf","text":"Report","size":"12.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5047"},{"id":417022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5047/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Central Salem River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.51261142665348,\n 39.66506027345514\n ],\n [\n -75.13011677160785,\n 39.493754929673486\n ],\n [\n -75.01123329774249,\n 39.637202213256444\n ],\n [\n -75.41569555121957,\n 39.76545497451639\n ],\n [\n -75.51261142665348,\n 39.66506027345514\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2023-05-18","noUsgsAuthors":false,"publicationDate":"2023-05-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Spitz, Frederick J. 0000-0002-1391-2127 fspitz@usgs.gov","orcid":"https://orcid.org/0000-0002-1391-2127","contributorId":2777,"corporation":false,"usgs":true,"family":"Spitz","given":"Frederick","email":"fspitz@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":872613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DePaul, Vincent T. 0000-0002-7977-5217 vdepaul@usgs.gov","orcid":"https://orcid.org/0000-0002-7977-5217","contributorId":2778,"corporation":false,"usgs":true,"family":"DePaul","given":"Vincent","email":"vdepaul@usgs.gov","middleInitial":"T.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872614,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243717,"text":"70243717 - 2023 - Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial","interactions":[],"lastModifiedDate":"2023-05-18T14:09:36.87445","indexId":"70243717","displayToPublicDate":"2023-05-18T09:05:02","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":14427,"text":"BMJ Open","active":true,"publicationSubtype":{"id":10}},"title":"Study design and methods of the Wells and Enteric disease Transmission (WET) Trial, a randomised controlled trial","docAbstract":"

Introduction: The burden of disease attributed to drinking water from private wells is not well characterised. The Wells and Enteric disease Transmission trial is the first randomised controlled trial to estimate the burden of disease that can be attributed to the consumption of untreated private well water. To estimate the attributable incidence of gastrointestinal illness (GI) associated with private well water, we will test if the household treatment of well water by ultraviolet light (active UV device) versus sham (inactive UV device) decreases the incidence of GI in children under 5 years of age.

Methods and analysis: The trial will enrol (on a rolling basis) 908 families in Pennsylvania, USA, that rely on private wells and have a child 3 years old or younger. Participating families are randomised to either an active whole-house UV device or a sham device. During follow-up, families will respond to weekly text messages to report the presence of signs and symptoms of gastrointestinal or respiratory illness and will be directed to an illness questionnaire when signs/symptoms are present. These data will be used to compare the incidence of waterborne illness between the two study groups. A randomly selected subcohort submits untreated well water samples and biological specimens (stool and saliva) from the participating child in both the presence and absence of signs/symptoms. Samples are analysed for the presence of common waterborne pathogens (stool and water) or immunoconversion to these pathogens (saliva).

Ethics: Approval has been obtained from Temple University’s Institutional Review Board (Protocol 25665). The results of the trial will be published in peer-reviewed journals.

Trial registration number: NCT04826991.

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Jingwei","contributorId":222925,"corporation":false,"usgs":false,"family":"Wu","given":"Jingwei","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":873041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":873042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":151033,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":6684,"text":"USDA Forest Service, Southern Research Station, Aiken, SC","active":true,"usgs":false}],"preferred":false,"id":873043,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Murphy, Heather","contributorId":203052,"corporation":false,"usgs":false,"family":"Murphy","given":"Heather","email":"","affiliations":[{"id":12547,"text":"Temple University","active":true,"usgs":false}],"preferred":false,"id":873044,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243719,"text":"70243719 - 2023 - Mapping landslide susceptibility over large regions with limited data","interactions":[],"lastModifiedDate":"2023-05-18T13:12:16.140007","indexId":"70243719","displayToPublicDate":"2023-05-18T08:04:32","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Mapping landslide susceptibility over large regions with limited data","docAbstract":"

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006810","usgsCitation":"Woodard, J.B., Mirus, B., Crawford, M., Or, D., Leshchinsky, B., Allstadt, K.E., and Wood, N.J., 2023, Mapping landslide susceptibility over large regions with limited data: Journal of Geophysical Research: Earth Surface, v. 128, no. 5, e2022JF006810, 21 p., https://doi.org/10.1029/2022JF006810.","productDescription":"e2022JF006810, 21 p.","ipdsId":"IP-142367","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443478,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006810","text":"Publisher Index Page"},{"id":435324,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P959G9JN","text":"USGS data 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]\n}","volume":"128","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873053,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Matthew","contributorId":224687,"corporation":false,"usgs":false,"family":"Crawford","given":"Matthew","email":"","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":873054,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Or, Dani","contributorId":267915,"corporation":false,"usgs":false,"family":"Or","given":"Dani","affiliations":[{"id":55530,"text":"ETH / DRI","active":true,"usgs":false}],"preferred":false,"id":873055,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leshchinsky, Ben","contributorId":267910,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":873056,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":873057,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":873058,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70244197,"text":"70244197 - 2023 - Environmental antimicrobial resistance gene detection from wild bird habitats using two methods: A commercially available culture-independent qPCR assay and culture of indicator bacteria followed by whole-genome sequencing","interactions":[],"lastModifiedDate":"2023-06-07T12:05:21.407844","indexId":"70244197","displayToPublicDate":"2023-05-18T06:58:45","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7759,"text":"Journal of Global Antimicrobial Resistance","active":true,"publicationSubtype":{"id":10}},"title":"Environmental antimicrobial resistance gene detection from wild bird habitats using two methods: A commercially available culture-independent qPCR assay and culture of indicator bacteria followed by whole-genome sequencing","docAbstract":"

Objectives

A variety of methods have been developed to detect antimicrobial resistance (AMR) in different environments to better understand the evolution and dissemination of this public health threat. Comparisons of results generated using different AMR detection methods, such as quantitative PCR (qPCR) and whole-genome sequencing (WGS), are often imperfect, and few studies have analysed samples in parallel to evaluate differences. In this study, we compared bacterial culture and WGS to a culture-independent commercially available qPCR assay to evaluate the concordance between methods and the utility of each in answering research questions regarding the presence and epidemiology of AMR in wild bird habitats.

Methods

We first assessed AMR gene detection using qPCR in 45 bacterial isolates from which we had existing WGS data. We then analysed 52 wild bird faecal samples and 9 spatiotemporally collected water samples using culture-independent qPCR and WGS of phenotypically resistant indicator bacterial isolates.

Results

Overall concordance was strong between qPCR and WGS of bacterial isolates, although concordance differed among antibiotic classes. Analysis of wild bird faecal and water samples revealed that more samples were determined to be positive for AMR via qPCR than via culture and WGS of bacterial isolates, although qPCR did not detect AMR genes in two samples from which phenotypically resistant isolates were found.

Conclusions

Both qPCR and culture followed by sequencing may be effective approaches for characterising AMR genes harboured by wild birds, although data streams produced using these different tools may have advantages and disadvantages that should be considered given the application and sample matrix.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jgar.2023.03.009","usgsCitation":"Ahlstrom, C., Scott, L.C., Woksepp, H., Bonnedahl, J., and Ramey, A.M., 2023, Environmental antimicrobial resistance gene detection from wild bird habitats using two methods: A commercially available culture-independent qPCR assay and culture of indicator bacteria followed by whole-genome sequencing: Journal of Global Antimicrobial Resistance, v. 33, p. 186-193, https://doi.org/10.1016/j.jgar.2023.03.009.","productDescription":"8 p.","startPage":"186","endPage":"193","ipdsId":"IP-144384","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443490,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jgar.2023.03.009","text":"Publisher Index Page"},{"id":435328,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZS79OL","text":"USGS data release","linkHelpText":"Antimicrobial Resistance Testing and qPCR Detection of Antimicrobial Resistance Genes in Surface Water and Gull (Larus spp.) Feces on the Kenai Peninsula, Alaska, 2021"},{"id":417904,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":874849,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, Laura Celeste 0000-0003-0303-5340","orcid":"https://orcid.org/0000-0003-0303-5340","contributorId":306143,"corporation":false,"usgs":true,"family":"Scott","given":"Laura","email":"","middleInitial":"Celeste","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":874850,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woksepp, Hanna","contributorId":207263,"corporation":false,"usgs":false,"family":"Woksepp","given":"Hanna","email":"","affiliations":[],"preferred":false,"id":874851,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":874852,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":874853,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247335,"text":"70247335 - 2023 - Exchange of carbapenem-resistant Escherichia coli Sequence Type 38 intercontinentally and among wild bird, human, and environmental niches","interactions":[],"lastModifiedDate":"2023-07-27T16:00:17.726145","indexId":"70247335","displayToPublicDate":"2023-05-17T10:56:04","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":850,"text":"Applied and Environmental Microbiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Exchange of carbapenem-resistant Escherichia coli Sequence Type 38 intercontinentally and among wild bird, human, and environmental niches","title":"Exchange of carbapenem-resistant Escherichia coli Sequence Type 38 intercontinentally and among wild bird, human, and environmental niches","docAbstract":"

Carbapenem-resistant Enterobacteriaceae (CRE) are a global threat to human health and are increasingly being isolated from nonclinical settings. OXA-48-producing Escherichia coli sequence type 38 (ST38) is the most frequently reported CRE type in wild birds and has been detected in gulls or storks in North America, Europe, Asia, and Africa. The epidemiology and evolution of CRE in wildlife and human niches, however, remains unclear. We compared wild bird origin E. coli ST38 genome sequences generated by our research group and publicly available genomic data derived from other hosts and environments to (i) understand the frequency of intercontinental dispersal of E. coli ST38 clones isolated from wild birds, (ii) more thoroughly measure the genomic relatedness of carbapenem-resistant isolates from gulls sampled in Turkey and Alaska, USA, using long-read whole-genome sequencing and assess the spatial dissemination of this clone among different hosts, and (iii) determine whether ST38 isolates from humans, environmental water, and wild birds have different core or accessory genomes (e.g., antimicrobial resistance genes, virulence genes, plasmids) which might elucidate bacterial or gene exchange among niches. Our results suggest that E. coli ST38 strains, including those resistant to carbapenems, are exchanged between humans and wild birds, rather than separately maintained populations within each niche. Furthermore, despite close genetic similarity among OXA-48-producing E. coli ST38 clones from gulls in Alaska and Turkey, intercontinental dispersal of ST38 clones among wild birds is uncommon. Interventions to mitigate the dissemination of antimicrobial resistance throughout the environment (e.g., as exemplified by the acquisition of carbapenem resistance by birds) may be warranted.

","language":"English","publisher":"ASM Journals","doi":"10.1128/aem.00319-23","usgsCitation":"Ahlstrom, C., Woksepp, H., Sandegren, L., Ramey, A.M., and Bonnedahl, J., 2023, Exchange of carbapenem-resistant Escherichia coli Sequence Type 38 intercontinentally and among wild bird, human, and environmental niches: Applied and Environmental Microbiology, v. 89, no. 6, e0031923, https://doi.org/10.1128/aem.00319-23.","productDescription":"e0031923","ipdsId":"IP-149692","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443503,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/10304903","text":"External Repository"},{"id":419396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"89","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ahlstrom, Christina 0000-0001-5414-8076","orcid":"https://orcid.org/0000-0001-5414-8076","contributorId":214540,"corporation":false,"usgs":true,"family":"Ahlstrom","given":"Christina","email":"","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":879254,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woksepp, Hanna","contributorId":207263,"corporation":false,"usgs":false,"family":"Woksepp","given":"Hanna","email":"","affiliations":[],"preferred":false,"id":879255,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sandegren, Linus","contributorId":279688,"corporation":false,"usgs":false,"family":"Sandegren","given":"Linus","email":"","affiliations":[{"id":57339,"text":"Department of Medical Biochemistry and Microbiology, Infection biology, antimicrobial resistance and immunology, Uppsala University","active":true,"usgs":false}],"preferred":false,"id":879256,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":879257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bonnedahl, Jonas","contributorId":181800,"corporation":false,"usgs":false,"family":"Bonnedahl","given":"Jonas","email":"","affiliations":[],"preferred":false,"id":879258,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243627,"text":"dr1175 - 2023 - Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2022","interactions":[],"lastModifiedDate":"2023-05-18T13:32:13.544725","indexId":"dr1175","displayToPublicDate":"2023-05-17T10:15:58","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1175","displayTitle":"Range-wide Population Trend Analysis for Greater Sage-Grouse (Centrocercus urophasianus)—Updated 1960–2022","title":"Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2022","docAbstract":"

Greater sage-grouse (Centrocercus urophasianus) are at the center of state and national land-use policies largely because of their unique life-history traits as an ecological indicator for health of sagebrush ecosystems. This updated population trend analysis provides state and federal land and wildlife managers with best-available science to help guide current management and conservation plans aimed at benefitting sage-grouse populations. This analysis relied on previously published population trend modeling methodology from Coates and others (2021, 2022a) and incorporated population lek count data through 2022. Bayesian state-space models estimated 2.9 percent average annual decline in sage-grouse populations across their geographical range, which varied among subpopulations at the largest scale of analysis, termed climate clusters (2.2–4.7). Cumulative declines were 40.9, 65.0, and 79.6 percent range-wide across short (19 years), medium (35 years), and long (55 years) temporal periods, respectively. These results indicate that the most recent nadir for range-wide populations occurred during 2021. However, growth during 2022 was modest, making 2021 a tentative final nadir at this point.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1175","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the Bureau of Land Management","programNote":"Species Management Research Program","usgsCitation":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Range-wide population trend analysis for greater sage-grouse (Centrocercus urophasianus)—Updated 1960–2022: U.S. Geological Survey Data Report 1175, 17 p., https://doi.org/10.3133/dr1175.","productDescription":"Report: viii, 17 p.; Data Release","numberOfPages":"17","onlineOnly":"Y","ipdsId":"IP-151795","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":417065,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1175/full"},{"id":417060,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OQWGIV","text":"Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 2.0, May 2023)","description":"Coates, P.S., Prochazka, B.G., Aldridge, C.L., O'Donnell, M.S., Edmunds, D.R., Monroe, A.P., Hanser, S.E., Wiechman, L.A., and Chenaille, M.P., 2023, Trends and a targeted annual warning system for greater sage-grouse in the western United States (ver. 2.0, May 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9OQWGIV."},{"id":417061,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1175/covrthb.jpg"},{"id":417062,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1175/dr1175.pdf","text":"Report","size":"13 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417063,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1175/dr1175.xml"},{"id":417064,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1175/images"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103,\n 49\n ],\n [\n -122,\n 49\n ],\n [\n -122,\n 36\n ],\n [\n -103,\n 36\n ],\n [\n -103,\n 49\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2023-05-17","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":872658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"O’Donnell, Michael S. 0000-0002-3488-003X odonnellm@usgs.gov","orcid":"https://orcid.org/0000-0002-3488-003X","contributorId":3351,"corporation":false,"usgs":true,"family":"O’Donnell","given":"Michael","email":"odonnellm@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":872659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edmunds, David R. 0000-0002-5212-8271 dedmunds@usgs.gov","orcid":"https://orcid.org/0000-0002-5212-8271","contributorId":152210,"corporation":false,"usgs":true,"family":"Edmunds","given":"David","email":"dedmunds@usgs.gov","middleInitial":"R.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":872660,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":872661,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hanser, Steve E. 0000-0002-4430-2073 shanser@usgs.gov","orcid":"https://orcid.org/0000-0002-4430-2073","contributorId":152523,"corporation":false,"usgs":true,"family":"Hanser","given":"Steve","email":"shanser@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":872662,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wiechman, Lief A. 0000-0002-3804-4426","orcid":"https://orcid.org/0000-0002-3804-4426","contributorId":184047,"corporation":false,"usgs":true,"family":"Wiechman","given":"Lief","email":"","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":872663,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":872664,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70244211,"text":"70244211 - 2023 - Supervised versus unsupervised approaches to classification of accelerometry data","interactions":[],"lastModifiedDate":"2023-06-07T13:50:04.432165","indexId":"70244211","displayToPublicDate":"2023-05-17T08:46:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Supervised versus unsupervised approaches to classification of accelerometry data","docAbstract":"

Sophisticated animal-borne sensor systems are increasingly providing novel insight into how animals behave and move. Despite their widespread use in ecology, the diversity and expanding quality and quantity of data they produce have created a need for robust analytical methods for biological interpretation. Machine learning tools are often used to meet this need. However, their relative effectiveness is not well known and, in the case of unsupervised tools, given that they do not use validation data, their accuracy can be difficult to assess. We evaluated the effectiveness of supervised (n = 6), semi-supervised (n = 1), and unsupervised (n = 2) approaches to analyzing accelerometry data collected from critically endangered California condors (Gymnogyps californianus). Unsupervised K-means and EM (expectation–maximization) clustering approaches performed poorly, with adequate classification accuracies of <0.8 but very low values for kappa statistics (range: −0.02 to 0.06). The semi-supervised nearest mean classifier was moderately effective at classification, with an overall classification accuracy of 0.61 but effective classification only of two of the four behavioral classes. Supervised random forest (RF) and k-nearest neighbor (kNN) machine learning models were most effective at classification across all behavior types, with overall accuracies >0.81. Kappa statistics were also highest for RF and kNN, in most cases substantially greater than for other modeling approaches. Unsupervised modeling, which is commonly used for the classification of a priori-defined behaviors in telemetry data, can provide useful information but likely is instead better suited to post hoc definition of generalized behavioral states. This work also shows the potential for substantial variation in classification accuracy among different machine learning approaches and among different metrics of accuracy. As such, when analyzing biotelemetry data, best practices appear to call for the evaluation of several machine learning techniques and several measures of accuracy for each dataset under consideration.

","language":"English","publisher":"Wiley","doi":"10.1002/ece3.10035","usgsCitation":"Sur, M., Hall, J.C., Brandt, J., Astell, M., Poessel, S.A., and Katzner, T., 2023, Supervised versus unsupervised approaches to classification of accelerometry data: Ecology and Evolution, v. 13, no. 5, e10035, 11 p., https://doi.org/10.1002/ece3.10035.","productDescription":"e10035, 11 p.","ipdsId":"IP-144075","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":443508,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.10035","text":"Publisher Index Page"},{"id":435332,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PAVUEZ","text":"USGS data release","linkHelpText":"Tri-axial acceleration data from California condors (Gymnogyps californianus), California, USA"},{"id":417910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Sur, Maitreyi","contributorId":191354,"corporation":false,"usgs":false,"family":"Sur","given":"Maitreyi","email":"","affiliations":[],"preferred":false,"id":874874,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hall, Jonathan C.","contributorId":202606,"corporation":false,"usgs":false,"family":"Hall","given":"Jonathan","email":"","middleInitial":"C.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":874875,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, Joseph","contributorId":127742,"corporation":false,"usgs":false,"family":"Brandt","given":"Joseph","affiliations":[{"id":7133,"text":"California Condor Recovery Program, US Fish and Wildlife Service, Ventura, CA","active":true,"usgs":false}],"preferred":false,"id":874876,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Astell, Molly","contributorId":199753,"corporation":false,"usgs":false,"family":"Astell","given":"Molly","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":874877,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poessel, Sharon A. 0000-0002-0283-627X spoessel@usgs.gov","orcid":"https://orcid.org/0000-0002-0283-627X","contributorId":168465,"corporation":false,"usgs":true,"family":"Poessel","given":"Sharon","email":"spoessel@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":874878,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":874879,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70243677,"text":"70243677 - 2023 - Measuring basin-scale aquifer storage change and mapping specific yield in Albuquerque, New Mexico, USA, with repeat microgravity data","interactions":[],"lastModifiedDate":"2023-05-17T13:46:57.844673","indexId":"70243677","displayToPublicDate":"2023-05-17T08:37:17","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3823,"text":"Journal of Hydrology: Regional Studies","active":true,"publicationSubtype":{"id":10}},"title":"Measuring basin-scale aquifer storage change and mapping specific yield in Albuquerque, New Mexico, USA, with repeat microgravity data","docAbstract":"

Study Region

The groundwater basin underlying the City of Albuquerque, New Mexico, USA.

Study Focus

The study focuses on changes in groundwater storage and how those changes relate to groundwater-level changes. Groundwater storage change was measured using repeat microgravity at 35 stations from 2016 to 2021. Usually, storage is monitored by converting groundwater-level changes to storage changes using the aquifer storage coefficient, a difficult property to measure. With gravity, storage change can be measured directly. The storage coefficient, or specific yield in an unconfined aquifer, was estimated using the gravity method at individual sites, and a map created showing how this property varies over the region.

New Hydrological Insights for the Region

For the first time, a map of specific yield was produced based on interpolated maps of gravity-derived storage change and groundwater-level change, allowing inference of this property over a broad region even without collocated monitoring wells and gravity stations. Gravity data indicate aquifer drawdown and recovery mainly occurs in the central part of the basin where pumping is greatest. This spatial distribution is not captured by the more widely spaced monitoring well network. Because the aquifer is recovering from historically greater-magnitude pumping, net-neutral storage change (inflow=outflow) occurs when pumping from the central part of the well field is about 1.54 × 107 cubic meters (12,500 acre-feet) per year.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ejrh.2023.101413","usgsCitation":"Kennedy, J.R., and Bell, M., 2023, Measuring basin-scale aquifer storage change and mapping specific yield in Albuquerque, New Mexico, USA, with repeat microgravity data: Journal of Hydrology: Regional Studies, v. 47, 101413, 15 p., https://doi.org/10.1016/j.ejrh.2023.101413.","productDescription":"101413, 15 p.","ipdsId":"IP-142695","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":443510,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ejrh.2023.101413","text":"Publisher Index Page"},{"id":417129,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.40748889587313,\n 35.31586215046366\n ],\n [\n -106.79511678846217,\n 35.31586215046366\n ],\n [\n -106.79511678846217,\n 34.98223495299035\n ],\n [\n -106.40748889587313,\n 34.98223495299035\n ],\n [\n -106.40748889587313,\n 35.31586215046366\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kennedy, Jeffrey R. 0000-0002-3365-6589 jkennedy@usgs.gov","orcid":"https://orcid.org/0000-0002-3365-6589","contributorId":176478,"corporation":false,"usgs":true,"family":"Kennedy","given":"Jeffrey","email":"jkennedy@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":872893,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bell, Meghan T. 0000-0003-4993-1642","orcid":"https://orcid.org/0000-0003-4993-1642","contributorId":209712,"corporation":false,"usgs":true,"family":"Bell","given":"Meghan T.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":872894,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243691,"text":"70243691 - 2023 - Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast","interactions":[],"lastModifiedDate":"2023-06-27T16:55:28.533795","indexId":"70243691","displayToPublicDate":"2023-05-17T08:23:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2822,"text":"Natural Hazards","active":true,"publicationSubtype":{"id":10}},"title":"Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast","docAbstract":"A 38-year hindcast water level product is developed for the U.S. Southeast Atlantic coastline from the entrance of Chesapeake Bay to the southeast tip of Florida. The water level modelling framework utilized in this study combines a global-scale hydrodynamic model (Global Tide and Surge Model, GTSM-ERA5), a novel ensemble-based tide model, a parameterized wave setup model, and statistical corrections applied to improve modelled water level components. Corrected water level data are found to be skillful, with an RMSE of 13 cm, when compared to observed water level measurement at tide gauge locations. The largest errors in the hindcast are location-based and typically found in the tidal component of the model. Extreme water levels across the region are driven by compound events, in this case referring to combined surge, tide, and wave forcing. However, the relative importance of water level components varies spatially, such that tides are found to be more important in the center of the study region, non-tidal residual water levels to the north, and wave setup in the north and south. Hurricanes drive the most extreme water level events within the study area, but non-hurricane events define the low to mid-level recurrence interval water level events. This study presents a robust analysis of the complex oceanographic factors that drive coastal flood events. This dataset will support a variety of critical coastal research goals including research related to coastal hazards, landscape change, and community risk assessments.","language":"English","publisher":"Springer","doi":"10.1007/s11069-023-05939-6","usgsCitation":"Parker, K.A., Erikson, L.H., Thomas, J.A., Nederhoff, C.M., Barnard, P.L., and Muis, S., 2023, Relative contributions of water-level components to extreme water levels along the US Southeast Atlantic Coast from a regional-scale water-level hindcast: Natural Hazards, v. 117, p. 2219-2248, https://doi.org/10.1007/s11069-023-05939-6.","productDescription":"30 p.","startPage":"2219","endPage":"2248","ipdsId":"IP-145520","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443513,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11069-023-05939-6","text":"Publisher Index 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]\n}","volume":"117","noUsgsAuthors":false,"publicationDate":"2023-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Parker, Kai Alexander 0000-0002-0268-3891","orcid":"https://orcid.org/0000-0002-0268-3891","contributorId":292869,"corporation":false,"usgs":true,"family":"Parker","given":"Kai","email":"","middleInitial":"Alexander","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872924,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thomas, Jennifer Anne 0000-0002-8338-0146","orcid":"https://orcid.org/0000-0002-8338-0146","contributorId":297988,"corporation":false,"usgs":true,"family":"Thomas","given":"Jennifer","email":"","middleInitial":"Anne","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nederhoff, Cornelis M. 0000-0003-0552-3428","orcid":"https://orcid.org/0000-0003-0552-3428","contributorId":265889,"corporation":false,"usgs":false,"family":"Nederhoff","given":"Cornelis","email":"","middleInitial":"M.","affiliations":[{"id":33886,"text":"Deltares USA","active":true,"usgs":false}],"preferred":true,"id":872927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":140982,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick","email":"pbarnard@usgs.gov","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":872928,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muis, Sanne 0000-0002-8145-0171","orcid":"https://orcid.org/0000-0002-8145-0171","contributorId":305488,"corporation":false,"usgs":false,"family":"Muis","given":"Sanne","email":"","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":872929,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70246287,"text":"70246287 - 2023 - Reference genome of an iconic lizard in western North America, Blainville’s horned lizard Phrynosoma blainvillii","interactions":[],"lastModifiedDate":"2023-06-30T11:58:09.623078","indexId":"70246287","displayToPublicDate":"2023-05-17T06:55:51","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2333,"text":"Journal of Heredity","active":true,"publicationSubtype":{"id":10}},"title":"Reference genome of an iconic lizard in western North America, Blainville’s horned lizard Phrynosoma blainvillii","docAbstract":"

Genome assemblies are increasingly being used to identify adaptive genetic variation that can help prioritize the population management of protected species. This approach may be particularly relevant to species like Blainville’s horned lizard, Phrynosoma blainvillii, due to its specialized diet on noxious harvester ants, numerous adaptative traits for avoiding predation (e.g. cranial horns, dorsoventrally compressed body, cryptic coloration, and blood squirting from the orbital sinuses), and status as Species of Special Concern in California. Rangewide decline since the early 20th century, the basis of its conservation status, has been driven mainly by habitat conversion, over-collecting, and invasion of a non-native ant that displaces its native ant prey base. Here, we report on a scaffold-level genome assembly for P. blainvillii as part of the California Conservation Genomics Project (CCGP), produced using Pacific Biosciences HiFi long reads and Hi-C chromatin-proximity sequencing technology. The de novo assembly has 78 scaffolds, a total length of ~2.21 Gb, a scaffold N50 length of ~352 Mb, and BUSCO score of 97.4%. This is the second species of Phrynosoma for which a reference genome has been assembled and represents a considerable improvement in terms of contiguity and completeness. Combined with the landscape genomics data being compiled by the CCGP, this assembly will help strategize efforts to maintain and/or restore local genetic diversity, where interventions like genetic rescue, translocation, and strategic land preservation may be the only means by which P. blainvillii and other low-vagility species can survive in the fragmented habitats of California.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/jhered/esad032","usgsCitation":"Richmond, J.Q., McGuire, J.A., Escalona, M., Marimuthu, M.P., Nguyen, O., Sacco, S., Beraut, E., Toffelmier, E., Fisher, R., Wang, I.J., and Shaffer, H., 2023, Reference genome of an iconic lizard in western North America, Blainville’s horned lizard Phrynosoma blainvillii: Journal of Heredity, v. 114, no. 4, p. 410-417, https://doi.org/10.1093/jhered/esad032.","productDescription":"8 p.","startPage":"410","endPage":"417","ipdsId":"IP-153689","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":443517,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jhered/esad032","text":"Publisher Index Page"},{"id":418652,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n -122.88566838766037,\n 38.33784509001799\n ],\n [\n -123.10535225470667,\n 39.3982851260254\n ],\n [\n -122.88566838766037,\n 40.576466166638994\n ],\n [\n -122.22661678652125,\n 41.008890264692894\n ],\n [\n -121.523628411973,\n 40.006744959278336\n ],\n [\n -120.95245035765231,\n 38.88714445295605\n ],\n [\n -118.7995484605982,\n 36.48843092115578\n ],\n [\n -117.12995107104577,\n 35.707412434279306\n ],\n [\n -115.85578464217711,\n 34.04948965995452\n ],\n [\n -115.54822722831209,\n 32.91349595613495\n ],\n [\n -116.60270979013472,\n 32.728881202232856\n ],\n [\n -117.26176139127361,\n 32.54388334243451\n ],\n [\n -118.71167491377948,\n 33.72122159857152\n ],\n [\n -120.73276649060601,\n 34.303934264242145\n ],\n [\n -122.18268001311188,\n 35.921186851580856\n ],\n [\n -122.75385806743256,\n 36.98138772543193\n ],\n [\n -122.62204774720473,\n 37.819065373689384\n ]\n ],\n \"type\": \"LineString\"\n }\n }\n ]\n}","volume":"114","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Richmond, Jonathan Q. 0000-0001-9398-4894 jrichmond@usgs.gov","orcid":"https://orcid.org/0000-0001-9398-4894","contributorId":5400,"corporation":false,"usgs":true,"family":"Richmond","given":"Jonathan","email":"jrichmond@usgs.gov","middleInitial":"Q.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":876658,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jimmy A.","contributorId":216474,"corporation":false,"usgs":false,"family":"McGuire","given":"Jimmy","email":"","middleInitial":"A.","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":876659,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Escalona, Merly","contributorId":299346,"corporation":false,"usgs":false,"family":"Escalona","given":"Merly","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":876660,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marimuthu, Mohan P. A.","contributorId":299347,"corporation":false,"usgs":false,"family":"Marimuthu","given":"Mohan","email":"","middleInitial":"P. A.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":876661,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nguyen, Oanh","contributorId":299348,"corporation":false,"usgs":false,"family":"Nguyen","given":"Oanh","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":876662,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sacco, Samuel","contributorId":299349,"corporation":false,"usgs":false,"family":"Sacco","given":"Samuel","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":876663,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beraut, Eric","contributorId":299352,"corporation":false,"usgs":false,"family":"Beraut","given":"Eric","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":876664,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Toffelmier, Erin","contributorId":299356,"corporation":false,"usgs":false,"family":"Toffelmier","given":"Erin","email":"","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":876665,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":876666,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wang, Ian J.","contributorId":315485,"corporation":false,"usgs":false,"family":"Wang","given":"Ian","email":"","middleInitial":"J.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":876667,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shaffer, H.B.","contributorId":239488,"corporation":false,"usgs":false,"family":"Shaffer","given":"H.B.","affiliations":[{"id":47888,"text":"Department of Ecology and Evolutionary Biology and La Kretz Center for California Conservation Science, University of California, Los Angeles, CA, USA","active":true,"usgs":false}],"preferred":false,"id":876668,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70243629,"text":"sim3495 - 2023 - Logs and data from the Starthistle trench across a scarp within the Wallula Fault Zone, southeastern Washington","interactions":[],"lastModifiedDate":"2026-02-19T17:34:17.455519","indexId":"sim3495","displayToPublicDate":"2023-05-16T09:17:05","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3495","displayTitle":"Logs and Data from the Starthistle Trench Across a Scarp within the Wallula Fault Zone, Southeastern Washington","title":"Logs and data from the Starthistle trench across a scarp within the Wallula Fault Zone, southeastern Washington","docAbstract":"

Introduction

The Wallula Fault Zone is composed of a series of northwest-trending faults and folds that coincide with a prominent magnetic anomaly that extends uninterrupted for approximately 120 kilometers within the Cascadia back arc of southeastern Washington and northeastern Oregon. It is part of the geologic structures associated with the topographic lineament known as the Olympic-Wallowa lineament and represents a relatively narrow zone of active faulting and seismicity that trend along the northern flank of the Horse Heaven Hills. Prior paleoseismic study of the Wallula Fault at an exposure at Finley Quarry indicates multiple Quaternary ruptures, including a Holocene liquefaction event, demonstrating that this fault zone poses a seismic hazard to the Tri-Cities region (Richland, Pasco, Kennewick) in southeastern Washington.

Recent airborne light detection and ranging (lidar) data coverage east of the Columbia River reveals an approximately 0.5-meter-high south-facing scarp east of the Columbia River that extends, almost continuously, for about 25 km to the east along the base of the Horse Heaven Hills. As part of an effort to assess and characterize the seismic hazard posed by the Wallula Fault Zone, we excavated and studied a trench exposure across the scarp to understand its origin and the potential history of rupture along the fault zone. We present preliminary mapping and trench site information from a paleoseismic investigation. These field and laboratory data may support development of a history of the latest Pleistocene and Holocene surface rupture within the Wallula Fault Zone.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3495","usgsCitation":"Angster, S.J., Sherrod, B.L., and Lasher, J., 2023, Logs and data from the Starthistle trench across a scarp within the Wallula Fault Zone, southeastern Washington: U.S. Geological Survey Scientific Investigations Map 3495, pamphlet 3 p., 1 sheet, https://doi.org/10.3133/sim3495.","productDescription":"Report: iv, 3 p.; 1 Sheet: 49.32 × 32.44 inches","numberOfPages":"3","additionalOnlineFiles":"Y","ipdsId":"IP-126146","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":417068,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3495/sim3495_sheet.pdf","text":"Sheet","size":"25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Logs and Data from the Starthistle Trench Across a Scarp within the Wallula Fault Zone, Southeastern Washington"},{"id":417067,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3495/sim3495_pamphlet.pdf","text":"Pamphlet","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":417066,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3495/covrthb.jpg"},{"id":500198,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114733.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Washington","otherGeospatial":"Wallula Fault Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.64099161023873,\n 46.4241743263303\n ],\n [\n -119.64099161023873,\n 45.947340820158956\n ],\n [\n -118.30440547571823,\n 45.947340820158956\n ],\n [\n -118.30440547571823,\n 46.4241743263303\n ],\n [\n -119.64099161023873,\n 46.4241743263303\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-05-16","noUsgsAuthors":false,"publicationDate":"2023-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Angster, Stephen J. 0000-0001-9250-8415 sangster@usgs.gov","orcid":"https://orcid.org/0000-0001-9250-8415","contributorId":3885,"corporation":false,"usgs":true,"family":"Angster","given":"Stephen","email":"sangster@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":872665,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sherrod, Brian L. 0000-0002-4492-8631 bsherrod@usgs.gov","orcid":"https://orcid.org/0000-0002-4492-8631","contributorId":2834,"corporation":false,"usgs":true,"family":"Sherrod","given":"Brian","email":"bsherrod@usgs.gov","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":872666,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lasher, John","contributorId":271205,"corporation":false,"usgs":false,"family":"Lasher","given":"John","affiliations":[{"id":56315,"text":"Ellensburg, WA","active":true,"usgs":false}],"preferred":false,"id":872667,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70246260,"text":"70246260 - 2023 - Rift basins and intraplate earthquakes: New high-resolution aeromagnetic data provide insights into buried structures of the Charleston, South Carolina seismic zone","interactions":[],"lastModifiedDate":"2023-06-28T13:26:15.350566","indexId":"70246260","displayToPublicDate":"2023-05-16T08:19:10","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Rift basins and intraplate earthquakes: New high-resolution aeromagnetic data provide insights into buried structures of the Charleston, South Carolina seismic zone","docAbstract":"

The delineation of faults that pose seismic risk in intraplate seismic zones and the mapping of features associated with failed rift basins can help our understanding of links between the two. We use new high-resolution aeromagnetic data, previous borehole sample information, and reprocessed seismic reflection profiles to image subsurface structures and evaluate recent fault activity within the Charleston seismic zone, the associated Mesozoic South Georgia rift basin, and surrounds. The new aeromagnetic data provide an unprecedented view of buried basement structures. NE- and NW-trending lineaments of various lengths throughout the survey area are interpreted as Paleozoic orogenic structures and Mesozoic dikes, respectively. Within the rift basin, 15- to 20-km long ESE-trending lineaments are associated with faults in pre-Cretaceous strata of the reflection data and are interpreted as Mesozoic rift structures. Various intersections and terminations of interpreted faults suggest rift-related reactivation of Paleozoic faults and corresponding inheritance for Mesozoic structures. The reflection data show that several Paleozoic and Mesozoic faults are associated with deformation in Cretaceous and younger sediments, suggesting reactivation in the more recent passive margin setting. Two of these faults, one NE-striking and one ESE-striking, are coincident with surficial landforms, suggesting Quaternary slip; the ESE-striking fault is also well-aligned with a plan-view offset in modern seismicity. A favorable orientation for reverse motion on ESE-striking Mesozoic faults, a possible sub-basin, and potentially weakened lithosphere are failed rift basin features that may influence intraplate seismicity within the Charleston seismic zone.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GC010803","usgsCitation":"Shah, A.K., Pratt, T.L., and Horton,, J., 2023, Rift basins and intraplate earthquakes: New high-resolution aeromagnetic data provide insights into buried structures of the Charleston, South Carolina seismic zone: Geochemistry, Geophysics, Geosystems, v. 24, e2022GC010803, 25 p., https://doi.org/10.1029/2022GC010803.","productDescription":"e2022GC010803, 25 p.","ipdsId":"IP-144502","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":443523,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gc010803","text":"Publisher Index Page"},{"id":418582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Carolina","otherGeospatial":"Charleston seismic zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -81.62681453773914,\n 34.845813308656375\n ],\n [\n -81.62681453773914,\n 32.343066119325954\n ],\n [\n -79.1425481149703,\n 32.343066119325954\n ],\n [\n -79.1425481149703,\n 34.845813308656375\n ],\n [\n -81.62681453773914,\n 34.845813308656375\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"24","noUsgsAuthors":false,"publicationDate":"2023-05-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":876471,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pratt, Thomas L. 0000-0003-3131-3141 tpratt@usgs.gov","orcid":"https://orcid.org/0000-0003-3131-3141","contributorId":3279,"corporation":false,"usgs":true,"family":"Pratt","given":"Thomas","email":"tpratt@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":876472,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton,, J. Wright Jr. 0000-0001-6756-6365","orcid":"https://orcid.org/0000-0001-6756-6365","contributorId":219824,"corporation":false,"usgs":true,"family":"Horton,","given":"J. Wright","suffix":"Jr.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":876473,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}