{"pageNumber":"176","pageRowStart":"4375","pageSize":"25","recordCount":46666,"records":[{"id":70207507,"text":"70207507 - 2022 - Circumpolar patterns of Arctic freshwater fish biodiversity: A baseline for monitoring","interactions":[],"lastModifiedDate":"2022-01-25T16:32:30.429897","indexId":"70207507","displayToPublicDate":"2019-10-01T16:08:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1696,"text":"Freshwater Biology","active":true,"publicationSubtype":{"id":10}},"title":"Circumpolar patterns of Arctic freshwater fish biodiversity: A baseline for monitoring","docAbstract":"<ol class=\"\"><li>Climate change, biological invasions, and anthropogenic disturbance pose a threat to the biodiversity and function of Arctic freshwater ecosystems. Understanding potential changes in fish species distribution and richness is necessary, given the great importance of fish to the function of freshwater ecosystems and as a resource to humans. However, information gaps limit large-scale studies and our ability to determine patterns and trends in space and time. This study takes the first step in determining circumpolar patterns of fish species richness and composition, which provides a baseline to improve both monitoring and conservation of Arctic freshwater biodiversity.</li><li>Information on species presence/absence was gathered from the Circumpolar Biodiversity Monitoring Program's Freshwater Database and used to examine patterns of freshwater fish γ-, α-, and β-diversity across 234° of longitude in the Arctic. The metrics of diversity provided information on species richness and composition across hydrobasins, ecoregions, and Arctic zones.</li><li>Circumpolar patterns of fish species biodiversity varied with latitude, isolation, and coarse ecoregion characteristics; patterns were consistent with historic and contemporary barriers to colonisation and environmental characteristics. Gamma-diversity was lower in the high Arctic compared to lower latitude zones, but α-diversity did not decrease with increasing latitude below 71°N, reflecting glacial history. Alpha-diversity was reduced to a single species, Arctic charr<span>&nbsp;</span><i>Salvelinus alpinus</i>, in ecoregions above 71°N, where γ-diversity was the lowest. Beta-diversity indicated little variation in the composition and richness of species across the High Arctic; at lower latitudes, ecoregions contained more species, although species composition turned over across large spatial extents.</li><li>In an analysis of five ecoregions in the circumpolar Arctic, physical isolation, and ecoregion area and topography were identified as strong drivers of γ-, α-, and β-diversity. Physical isolation reduced the γ- and α-diversity, and changes in β-diversity between adjacent locations were due mainly to losses in species richness, rather than due to differences in species composition. Heterogeneity of habitats, environmental gradients, and geographic distance probably contributed to patterns of fish dissimilarity within and across ecoregions.</li><li>This study presents the first analysis of large-scale patterns of freshwater fish biodiversity in the circumpolar Arctic. However, information gaps in space, time, and among taxonomic groups remain. Future inclusion of extensive archive and new data will allow future studies to test for changes and drivers of the observed patterns of biodiversity. This is important given the potential impacts of ongoing and accelerating climate change, land use, and biotic exchange on Arctic fish biodiversity.</li></ol>","language":"English","publisher":"Wiley","doi":"10.1111/fwb.13405","usgsCitation":"Laske, S.M., Amundsen, P., Christoffersen, K., Erkinaro, J., Gudbergsson, G., Hayden, B., Heino, J., Holmgren, K., Kahilainen, K.K., Lento, J., Orell, P., Ostergren, J., Power, M., Rafikov, R., Romakkaniemi, A., Svenning, M., Swanson, H.K., Whitman, M., and Zimmerman, C.E., 2022, Circumpolar patterns of Arctic freshwater fish biodiversity: A baseline for monitoring: Freshwater Biology, v. 67, no. 1, p. 176-193, https://doi.org/10.1111/fwb.13405.","productDescription":"18 p.","startPage":"176","endPage":"193","ipdsId":"IP-102863","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":449889,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://researchprofiles.ku.dk/da/publications/75fcd215-eb7c-481f-bae3-a44f60a32b9f","text":"Publisher Index Page"},{"id":370587,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Arctic","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -157.85156249999997,\n              69.28725695167886\n            ],\n            [\n              144.84375,\n              69.28725695167886\n            ],\n            [\n              144.84375,\n              81.56996820323275\n            ],\n            [\n              -157.85156249999997,\n              81.56996820323275\n            ],\n            [\n              -157.85156249999997,\n              69.28725695167886\n            ]\n          ]\n   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Norway","active":true,"usgs":false}],"preferred":false,"id":778278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christoffersen, Kirsten","contributorId":221446,"corporation":false,"usgs":false,"family":"Christoffersen","given":"Kirsten","email":"","affiliations":[{"id":40379,"text":"Department of Biology, University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":778279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erkinaro, Jaakko","contributorId":221447,"corporation":false,"usgs":false,"family":"Erkinaro","given":"Jaakko","email":"","affiliations":[{"id":40380,"text":"Natural Resources Institute Finland","active":true,"usgs":false}],"preferred":false,"id":778280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gudbergsson, Gudni","contributorId":221448,"corporation":false,"usgs":false,"family":"Gudbergsson","given":"Gudni","email":"","affiliations":[{"id":40381,"text":"Marine and Freshwater Research Institute, Iceland","active":true,"usgs":false}],"preferred":false,"id":778281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hayden, Brian","contributorId":190917,"corporation":false,"usgs":false,"family":"Hayden","given":"Brian","email":"","affiliations":[],"preferred":false,"id":778282,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heino, Jani","contributorId":221449,"corporation":false,"usgs":false,"family":"Heino","given":"Jani","email":"","affiliations":[{"id":40382,"text":"Finnish Environment Institute","active":true,"usgs":false}],"preferred":false,"id":778283,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Holmgren, Kerstin","contributorId":221450,"corporation":false,"usgs":false,"family":"Holmgren","given":"Kerstin","email":"","affiliations":[{"id":12666,"text":"Swedish University of Agricultural Sciences","active":true,"usgs":false}],"preferred":false,"id":778284,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kahilainen, Kimmo K.","contributorId":194585,"corporation":false,"usgs":false,"family":"Kahilainen","given":"Kimmo","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":778285,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Lento, Jennifer","contributorId":221451,"corporation":false,"usgs":false,"family":"Lento","given":"Jennifer","email":"","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":778286,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Orell, Panu","contributorId":221452,"corporation":false,"usgs":false,"family":"Orell","given":"Panu","email":"","affiliations":[{"id":40380,"text":"Natural Resources Institute Finland","active":true,"usgs":false}],"preferred":false,"id":778287,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ostergren, 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Atso","contributorId":221455,"corporation":false,"usgs":false,"family":"Romakkaniemi","given":"Atso","email":"","affiliations":[{"id":40380,"text":"Natural Resources Institute Finland","active":true,"usgs":false}],"preferred":false,"id":778291,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Svenning, Martin-A.","contributorId":221456,"corporation":false,"usgs":false,"family":"Svenning","given":"Martin-A.","email":"","affiliations":[{"id":40384,"text":"Norwegian Institute for Nature Research (NINA)","active":true,"usgs":false}],"preferred":false,"id":778292,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Swanson, Heidi K.","contributorId":203350,"corporation":false,"usgs":false,"family":"Swanson","given":"Heidi","email":"","middleInitial":"K.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":778293,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Whitman, Matthew","contributorId":19257,"corporation":false,"usgs":false,"family":"Whitman","given":"Matthew","affiliations":[],"preferred":false,"id":778294,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":778276,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}}
,{"id":70237033,"text":"70237033 - 2022 - Responses of the Carquinez, California suspension bridge during the MW6.0 South Napa earthquake of August 24, 2014","interactions":[],"lastModifiedDate":"2022-09-28T16:48:43.850791","indexId":"70237033","displayToPublicDate":"2017-01-01T11:29:16","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Responses of the Carquinez, California suspension bridge during the MW6.0 South Napa earthquake of August 24, 2014","docAbstract":"The behavior of the suspension bridge in Carquinez, CA, during the Mw6.0 24 August 2014 South Napa, CA earthquake is studied using data recorded by an extensive array of accelerometers. Modes, corresponding frequencies and damping are identified and compared with previous studies that used ambient data of the deck only plus mathematical models. Data are systematically analyzed for vertical, transverse and torsional motions of the deck, and transverse, longitudinal and torsional motions of the towers.  The transverse and vertical fundamental mode frequencies of the deck are the same (0.17Hz) due to coupling. Higher frequencies for transverse and vertical coupled modes are the same at 0.46Hz and 0.98Hz. Tower translational frequencies are 0.39Hz in the transverse direction and 0.46 Hz in the longitudinal direction, and are coupled with those of the deck. Coupling of torsional modes of the tower and deck are identified. A beating effect is observed, particularly for torsional motions.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the sixteenth World Conference on Earthquake Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"16th World Conference on Earthquake","conferenceDate":"January 9-13, 2017","conferenceLocation":"Santiago, Chile","language":"English","publisher":"National Information Centre of Earthquake Engineering","usgsCitation":"Celebi, M., Ghahari, S.F., and Taciroglu, E., 2022, Responses of the Carquinez, California suspension bridge during the MW6.0 South Napa earthquake of August 24, 2014, <i>in</i> Proceedings of the sixteenth World Conference on Earthquake Engineering, Santiago, Chile, January 9-13, 2017, 30, 12 p.","productDescription":"30, 12 p.","ipdsId":"IP-071784","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":407522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":407446,"type":{"id":15,"text":"Index Page"},"url":"https://www.wcee.nicee.org/wcee/sixteenth_conf_Santiago/"}],"country":"United States","state":"California","otherGeospatial":"Carquinez Suspension Bridge","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -122.23169803619385,\n              38.05038912975082\n            ],\n            [\n              -122.2309684753418,\n              38.049611852048756\n            ],\n            [\n              -122.22659111022949,\n              38.05228159747128\n            ],\n            [\n              -122.224702835083,\n              38.05420780901275\n            ],\n            [\n              -122.2243595123291,\n              38.05576225855872\n            ],\n            [\n              -122.22556114196776,\n              38.0677912298829\n            ],\n            [\n              -122.22633361816406,\n              38.069784604330586\n            ],\n            [\n              -122.22873687744139,\n              38.072825240387616\n            ],\n            [\n              -122.22980976104736,\n              38.0723860452116\n            ],\n            [\n              -122.22762107849121,\n              38.0677912298829\n            ],\n            [\n              -122.22710609436035,\n              38.05437677251913\n            ],\n            [\n              -122.23169803619385,\n              38.05038912975082\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Celebi, Mehmet 0000-0002-4769-7357 celebi@usgs.gov","orcid":"https://orcid.org/0000-0002-4769-7357","contributorId":200969,"corporation":false,"usgs":true,"family":"Celebi","given":"Mehmet","email":"celebi@usgs.gov","affiliations":[],"preferred":true,"id":853120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ghahari, S. F.","contributorId":147707,"corporation":false,"usgs":false,"family":"Ghahari","given":"S.","email":"","middleInitial":"F.","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":853199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Taciroglu, E.","contributorId":147710,"corporation":false,"usgs":false,"family":"Taciroglu","given":"E.","email":"","affiliations":[{"id":13399,"text":"UCLA","active":true,"usgs":false}],"preferred":false,"id":853200,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70250895,"text":"70250895 - 2021 - Three-dimensional electrical resistivity characterization of Mountain Pass, California and surrounding region","interactions":[],"lastModifiedDate":"2024-01-11T14:37:26.712196","indexId":"70250895","displayToPublicDate":"2024-01-11T08:27:41","publicationYear":"2021","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":"Three-dimensional electrical resistivity characterization of Mountain Pass, California and surrounding region","docAbstract":"<p><span>The Sulphide Queen carbonatite deposit at Mountain Pass in southeast California is a world class rare earth element (REE) resource. This study images electrical resistivity structure of the REE deposit and surrounding area to characterize resources under cover. An east-west elongated grid (35&nbsp;×&nbsp;15&nbsp;km) of 65 wideband magnetotelluric stations spanning from eastern Shadow Valley to eastern Ivanpah Valley were collected and modeled in three-dimensions (3-D). Gravity, aeromagnetic, and geologic data are used to inform interpretation of structures in the resistivity model, including the following observations. Shadow Valley is filled with conductive sediment that locally dips southward to a depth of 1&nbsp;km. The Kingston Range-Halloran Hills detachment fault dips westward at ∼15 degrees. The REE deposit is a moderate low resistivity zone dipping southwest to a possible depth of ∼1&nbsp;km, and is bounded by the North and South faults and bisected by the Middle fault. Ivanpah Dry Lake is underlain by a north striking southward dipping sedimentary basin. Two possible zones of mineralization are observed in Ivanpah Valley, one along the western edge of Ivanpah Dry Lake and one on the western edge of valley along a new inferred fault. The brittle-ductile transition is imaged at ∼10&nbsp;km below mean sea level. No deep electrically conductive structures are imaged to be related to the REE deposit likely due to the complex geologic history of the Mojave terrane. Future studies should regional target Proterozoic rocks and search within for geophysical signatures similar to Mountain Pass.</span></p>","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC010029","usgsCitation":"Peacock, J., Denton, K., and Ponce, D.A., 2021, Three-dimensional electrical resistivity characterization of Mountain Pass, California and surrounding region: Geochemistry, Geophysics, Geosystems, v. 22, no. 11, e2021GC010029, 16 p., https://doi.org/10.1029/2021GC010029.","productDescription":"e2021GC010029, 16 p.","ipdsId":"IP-132719","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":449891,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gc010029","text":"Publisher Index Page"},{"id":424329,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mountain Pass","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -115,\n              36\n            ],\n            [\n              -116,\n              36\n            ],\n            [\n              -116,\n              35\n            ],\n            [\n              -115,\n              35\n            ],\n            [\n              -115,\n              36\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"22","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":891967,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Denton, Kevin 0000-0001-9604-4021","orcid":"https://orcid.org/0000-0001-9604-4021","contributorId":207718,"corporation":false,"usgs":true,"family":"Denton","given":"Kevin","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":891968,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ponce, David A. 0000-0003-4785-7354 ponce@usgs.gov","orcid":"https://orcid.org/0000-0003-4785-7354","contributorId":1049,"corporation":false,"usgs":true,"family":"Ponce","given":"David","email":"ponce@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891969,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70218463,"text":"70218463 - 2021 - Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085","interactions":[],"lastModifiedDate":"2021-06-10T13:56:04.581613","indexId":"70218463","displayToPublicDate":"2024-01-01T10:12:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085","docAbstract":"<p><span>Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic influences are unknown. While many studies have projected the effects of climate change using several modeling approaches, none has evaluated the impacts to fractional component cover at a 30-m resolution across the full sagebrush (</span><i>Artemisia</i><span>&nbsp;spp.) biome. We used fractional component cover data for rangeland functional groups and weather data from the 1985 to 2018 reference period in conjunction with soils and topography data to develop empirical models describing the spatiotemporal variation in component cover. To investigate the ramifications of future change across the western United States, we extended models based on historical relationships over the reference period to model landscape effects based on future weather conditions from two emission scenarios and three time periods (2020s, 2050s, and 2080s). We tested both generalized additive models (GAMs) and regression tree models, finding that the former led to superior spatial and statistical results. Our results indicate more xeric vegetation across most of the study area, with an increasing dominance of non-sagebrush shrubs, annual herbaceous cover, and bare ground over herbaceous and sagebrush cover in both the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. In general, both scenarios yielded similar results, but RCP 8.5 tended to be more extreme, with greater change relative to the reference period. Results demonstrate that in cool sites some degree of warming to growing season maximum temperature or nongrowing season minimum temperature could be beneficial to sagebrush and shrub growth. However, warming nongrowing season maximum temperature was beneficial to shrub, but not to sagebrush growth. Our results inform rangeland managers of potential future vegetation composition, cover, and species distributions, which could improve prioritization of conservation and restoration efforts.</span></p>","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3538","usgsCitation":"Rigge, M.B., Shi, H., and Postma, K., 2021, Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085: Ecosphere, v. 12, no. 6, e03538, 25 p., https://doi.org/10.1002/ecs2.3538.","productDescription":"e03538, 25 p.","ipdsId":"IP-120420","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449892,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3538","text":"Publisher Index Page"},{"id":436071,"rank":0,"type":{"id":30,"text":"Data 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0000-0001-7013-1565","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":192768,"corporation":false,"usgs":false,"family":"Shi","given":"Hua","affiliations":[],"preferred":false,"id":817368,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Postma, Kory 0000-0001-8058-498X","orcid":"https://orcid.org/0000-0001-8058-498X","contributorId":252852,"corporation":false,"usgs":true,"family":"Postma","given":"Kory","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":817369,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70247507,"text":"70247507 - 2021 - Partial differential equation driven dynamic graph networks for predicting stream water temperature","interactions":[],"lastModifiedDate":"2023-08-10T12:22:01.499669","indexId":"70247507","displayToPublicDate":"2023-01-24T07:20:46","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Partial differential equation driven dynamic graph networks for predicting stream water temperature","docAbstract":"<div class=\"abstract-text row g-0\"><div class=\"col-12\"><div class=\"u-mb-1\"><div>This paper presents a physics-guided machine learning approach that incorporates partial differential equations (PDEs) in a graph neural network model to improve the prediction of water temperature in river networks. The standard graph neural network model often uses pre-defined edge weights based on distance or similarity measures. Such static graph structure can be limited in capturing multiple processes in a physical system that interact and evolve over time. The limitation to represent underlying physical processes can severely affect the performance of the predictive model, especially when we have access to limited training data. To better capture the dynamic interactions among multiple segments in a river network, we built a dynamic graph model, where the graph structure is driven by the PDE that describes underlying physical processes. We further combine the dynamic graph structure and the recurrent layers to model temporal dependencies and improve the prediction. We demonstrate the effectiveness of the proposed method in a subnetwork of the Delaware River Basin. In particular, we show that the proposed method outperforms existing physics-based and machine learning models in temperature prediction using sparse observation data for training. The proposed method has also been shown to produce better performance when generalized to different seasons.</div></div></div></div>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2021 IEEE International Conference on Data Mining (ICDM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IEEE International Conference on Data Mining (ICDM)","conferenceDate":"December 7-10, 2021","conferenceLocation":"Auckland, New Zealand","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/ICDM51629.2021.00011","usgsCitation":"Bao, T., Jia, X., Zwart, J.A., Sadler, J.M., Appling, A.P., Oliver, S.K., and Johnson, T.T., 2021, Partial differential equation driven dynamic graph networks for predicting stream water temperature, <i>in</i> 2021 IEEE International Conference on Data Mining (ICDM), Auckland, New Zealand, December 7-10, 2021, p. 11-20, https://doi.org/10.1109/ICDM51629.2021.00011.","productDescription":"10 p.","startPage":"11","endPage":"20","ipdsId":"IP-130754","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":419700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bao, Tianshu","contributorId":318090,"corporation":false,"usgs":false,"family":"Bao","given":"Tianshu","email":"","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":879901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jia, Xiaowei 0000-0001-8544-5233","orcid":"https://orcid.org/0000-0001-8544-5233","contributorId":237807,"corporation":false,"usgs":false,"family":"Jia","given":"Xiaowei","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":879902,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":879903,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":879904,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":879905,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":879906,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Taylor T. 0000-0001-8021-9923","orcid":"https://orcid.org/0000-0001-8021-9923","contributorId":318093,"corporation":false,"usgs":false,"family":"Johnson","given":"Taylor","email":"","middleInitial":"T.","affiliations":[{"id":36656,"text":"Vanderbilt University","active":true,"usgs":false}],"preferred":false,"id":879907,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70256778,"text":"70256778 - 2021 - Fine-scale weather patterns drive reproductive success in the Brown Pelican","interactions":[],"lastModifiedDate":"2024-09-06T16:03:47.59938","indexId":"70256778","displayToPublicDate":"2022-12-23T10:54:51","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3731,"text":"Waterbirds","onlineIssn":"19385390","printIssn":"15244695","active":true,"publicationSubtype":{"id":10}},"title":"Fine-scale weather patterns drive reproductive success in the Brown Pelican","docAbstract":"<p><span>In the northern Gulf of Mexico, island restoration and creation have been used to mitigate potential negative effects of anthropogenic and environmental stressors to breeding seabirds. The long-term success of such projects can be enhanced when data are available to elucidate how site-specific and larger-scale factors may contribute to reproductive success. Nest-specific daily survival rate (DSR) of Eastern Brown Pelicans (</span><i>Pelecanus occidentalis carolinensis</i><span>) during incubation (i.e., pre-hatch;&nbsp;</span><i>n</i><span>&nbsp;= 245) and brood-rearing (i.e., post-hatch;&nbsp;</span><i>n</i><span>&nbsp;= 185) were measured at two breeding islands in the northern Gulf of Mexico USA in 2017 and 2018 in relation to macro- and micro- scale habitat and environmental measurements. DSR of nests during incubation ranged from 91-99%, and the DSR during brood-rearing exceeded 99% each year. Regional weather variables occurred in top-performing models more often and with more significance compared to microhabitat variables. Results suggest that reproductive success of Brown Pelicans may respond at least in part to weather factors that occur outside of the scope of habitat structure as it is typically incorporated into the restoration or creation of breeding habitat, indicating that climate conditions are likely an important factor in the success of restoration efforts.</span></p>","language":"English","publisher":"The Waterbird Society","doi":"10.1675/063.044.0202","usgsCitation":"Streker, R., Lamb, J., Dindo, J., and Jodice, P.G., 2021, Fine-scale weather patterns drive reproductive success in the Brown Pelican: Waterbirds, v. 44, no. 2, p. 153-166, https://doi.org/10.1675/063.044.0202.","productDescription":"14 p.","startPage":"153","endPage":"166","ipdsId":"IP-112416","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449897,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1675/063.044.0202","text":"Publisher Index Page"},{"id":433565,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Cat Island, Gaillard Island","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.21140334799445,\n              30.323486762297065\n            ],\n            [\n              -88.21140334799445,\n              30.318872526999428\n            ],\n            [\n              -88.20869591533123,\n              30.318872526999428\n            ],\n            [\n              -88.20869591533123,\n              30.323486762297065\n            ],\n            [\n              -88.21140334799445,\n              30.323486762297065\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    },\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -88.02240138633157,\n              30.52620636279063\n            ],\n            [\n              -88.0537680271092,\n              30.52620636279063\n            ],\n            [\n              -88.0537680271092,\n              30.488155985064907\n            ],\n            [\n              -88.02240138633157,\n              30.488155985064907\n            ],\n            [\n              -88.02240138633157,\n              30.52620636279063\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"44","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Streker, R.A.","contributorId":279819,"corporation":false,"usgs":false,"family":"Streker","given":"R.A.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908929,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamb, J.S.","contributorId":279814,"corporation":false,"usgs":false,"family":"Lamb","given":"J.S.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":908930,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dindo, J.","contributorId":341823,"corporation":false,"usgs":false,"family":"Dindo","given":"J.","email":"","affiliations":[{"id":48711,"text":"Dauphin Island Sea Lab","active":true,"usgs":false}],"preferred":false,"id":908931,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908932,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70228336,"text":"70228336 - 2021 - Genetic and morphological characterization of the freshwater mussel clubshell species complex (Pleurobema clava and Pleurobema oviforme) to inform conservation planning","interactions":[],"lastModifiedDate":"2022-02-09T16:41:08.092853","indexId":"70228336","displayToPublicDate":"2022-10-20T10:25:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7470,"text":"Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Genetic and morphological characterization of the freshwater mussel clubshell species complex (<i>Pleurobema clava</i> and <i>Pleurobema oviforme</i>) to inform conservation planning","title":"Genetic and morphological characterization of the freshwater mussel clubshell species complex (Pleurobema clava and Pleurobema oviforme) to inform conservation planning","docAbstract":"<p><span>The shell morphologies of the freshwater mussel species&nbsp;</span><i>Pleurobema clava</i><span>&nbsp;(federally endangered) and&nbsp;</span><i>Pleurobema oviforme</i><span>&nbsp;(species of concern) are similar, causing considerable taxonomic confusion between the two species over the last 100&nbsp;years. While&nbsp;</span><i>P.&nbsp;clava</i><span>&nbsp;was historically widespread throughout the Ohio River basin and tributaries to the lower Laurentian Great Lakes,&nbsp;</span><i>P.&nbsp;oviforme</i><span>&nbsp;was confined to the Tennessee and the upper Cumberland River basins. We used two mitochondrial DNA (mtDNA) genes, 13 novel nuclear DNA microsatellite markers, and shell morphometrics to help resolve this taxonomic confusion. Evidence for a single species was apparent in phylogenetic analyses of each mtDNA gene, revealing monophyletic relationships with minimal differentiation and shared haplotypes. Analyses of microsatellites showed significant genetic structuring, with four main genetic clusters detected, respectively, in the upper Ohio River basin, the lower Ohio River and Great Lakes, and upper Tennessee River basin, and a fourth genetic cluster, which included geographically intermediate populations in the Ohio and Tennessee river basins. While principal components analysis (PCA) of morphometric variables (i.e., length, height, width, and weight) showed significant differences in shell shape, only 3% of the variance in shell shape was explained by nominal species. Using Linear Discriminant and Random Forest (RF) analyses, correct classification rates for the two species' shell forms were 65.5% and 83.2%, respectively. Random Forest classification rates for some populations were higher; for example, for North Fork Holston (HOLS), it was &gt;90%. While nuclear DNA and shell morphology indicate that the HOLS population is strongly differentiated, perhaps indicative of cryptic biodiversity, we consider the presence of a single widespread species the most likely biological scenario for many of the investigated populations based on our mtDNA dataset. However, additional sampling of&nbsp;</span><i>P.&nbsp;oviforme</i><span>&nbsp;populations at nuclear loci is needed to corroborate this finding.</span></p>","language":"English","publisher":"Wiley","doi":"10.1002/ece3.8219","usgsCitation":"Morrison, C., Johnson, N., Jones, J.W., Eackles, M.S., Aunins, A.W., Fitzgerald, D.B., Hallerman, E.M., and King, T.L., 2021, Genetic and morphological characterization of the freshwater mussel clubshell species complex (Pleurobema clava and Pleurobema oviforme) to inform conservation planning: Ecology & Evolution, v. 11, no. 21, p. 15325-15350, https://doi.org/10.1002/ece3.8219.","productDescription":"26 p.","startPage":"15325","endPage":"15350","ipdsId":"IP-124957","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449898,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.8219","text":"External Repository"},{"id":436072,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P928BVHR","text":"USGS data release","linkHelpText":"Novel genetic resources for Clubshell freshwater mussels (Pleurobema clava, P. oviforme) for enhanced conservation"},{"id":395678,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana, Kentucky, Ohio, Pennsylvania, Tennessee, Virginia, West Virginia","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -89.5166015625,\n              34.994003757575776\n            ],\n            [\n              -80.2880859375,\n              34.994003757575776\n            ],\n         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0000-0001-5167-1988","orcid":"https://orcid.org/0000-0001-5167-1988","contributorId":218986,"corporation":false,"usgs":true,"family":"Johnson","given":"Nathan A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":833820,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Jess W","contributorId":238525,"corporation":false,"usgs":false,"family":"Jones","given":"Jess","email":"","middleInitial":"W","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":833821,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":833822,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":833823,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fitzgerald, Daniel Bruce 0000-0002-3254-7428","orcid":"https://orcid.org/0000-0002-3254-7428","contributorId":245718,"corporation":false,"usgs":true,"family":"Fitzgerald","given":"Daniel","email":"","middleInitial":"Bruce","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":833824,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hallerman, Eric M.","contributorId":202528,"corporation":false,"usgs":false,"family":"Hallerman","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":833825,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"King, Timothy L.","contributorId":199023,"corporation":false,"usgs":false,"family":"King","given":"Timothy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":833826,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70224463,"text":"ofr20211093 - 2021 - Hydrogeologic framework, water levels, and selected contaminant concentrations at Valmont TCE Superfund Site, Luzerne County, Pennsylvania, 2020","interactions":[],"lastModifiedDate":"2026-03-25T17:39:09.333201","indexId":"ofr20211093","displayToPublicDate":"2022-08-09T07:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1093","displayTitle":"Hydrogeologic Framework, Water Levels, and Selected Contaminant Concentrations at Valmont TCE Superfund Site, Luzerne County, Pennsylvania, 2020","title":"Hydrogeologic framework, water levels, and selected contaminant concentrations at Valmont TCE Superfund Site, Luzerne County, Pennsylvania, 2020","docAbstract":"<p>The Valmont TCE Superfund Site, Luzerne County, Pennsylvania is underlain by fractured and folded sandstones and shales of the Pottsville and Mauch Chunk Formations, which form a fractured-rock aquifer recharged locally by precipitation. Industrial activities at the former Chromatex Plant resulted in trichloroethene (TCE) contamination of groundwater at and near the facility, which was identified in 1987 and led to listing as a Superfund site by the U.S. Environmental Protection Agency (EPA) in 1989. To address the problem of TCE concentrations in nearby residential wells that exceed the maximum contaminant level (MCL) of 5 micrograms per liter (μg/L), alternate water supplies were provided. A 2015 review of initial characterization and subsequent remediation by the EPA identified the need for an updated understanding of the complex hydrogeology and the conceptual site model. Additional contaminants present in groundwater at the site include some other volatile organic compounds (VOCs) and per- and polyfluoroalkyl substances (PFAS), predominantly consisting of perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS) present in concentrations that exceeded the EPA Health Advisory (HA) level of 5 nanograms per liter (ng/L) for combined PFOA and PFOS.</p><p>In response to a request from the EPA in 2019, the U.S. Geological Survey (USGS) prepared cross sections and maps to provide more information about the hydrogeologic framework at and near the site and assist in improving the conceptual site model using water level and contaminant data collected by the EPA in 2020. The cross sections present lithologic correlations from available geophysical logs collected in wells from 2002 to 2014; they show alternating intervals of relatively elevated and reduced natural gamma activity that correspond to changes in lithology, with water-bearing zones and well screens commonly located at lithologic contacts, sometimes near thin coal seams. Water-bearing zones commonly are associated with fractures at or near lithologic contacts but also may be associated with fractures at or near apparent faulting. Recent (March 2020) water-level data shown on cross sections and maps indicate large downward vertical gradients and apparent radial gradients laterally to the northeast, northwest, and southwest that generally following topography. Recent (February to March 2020) data for TCE groundwater concentration shown on cross sections and maps indicate the highest TCE concentrations (greater than 3,000 μg/L and as much as 75,000 μg/L) and combined PFOA and PFOS concentrations (greater than 1,000 ng/L and up to at least 2,350 ng/L) are from shallow (less than 60 feet [ft] below land surface [bls]) and intermediate depth (60 to 100 ft bls) wells near the center of the former Chromatex Plant. TCE and PFAS (as combined PFOA and PFOS) contamination is present at greater depths, as much as 304 ft bls, as evidenced by samples collected from one well (a reconstructed former production well) near the plant, that contained concentrations of about 240 μg/L and 508 ng/L, respectively. The 2020 data also indicate that TCE and PFAS concentrations which exceed drinking-water MCL or HA levels are present in groundwater depths of less than 200 ft in an area that extends predominantly in a northeast direction from the former Chromatex Plant, and is apparently influenced by hydraulic gradients, lithology, and geologic structure.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Senior, L.A., Fiore, A.R., and Bird, P.H., 2021, Hydrogeologic framework, water levels, and selected contaminant concentrations at Valmont TCE Superfund Site, Luzerne County, Pennsylvania, 2020 (ver. 1.1, August 2022): U.S. Geological Survey Open-File Report 2021–1093, 80 p., https://doi.org/10.3133/ofr20211093.","productDescription":"Report: xii, 80 p.; 17 Plates: 17.00 x 11.00 inches or 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1.0: September 30, 2021; Version 1.1: August 9, 2022","contact":"<p><a href=\"mailto:dc_pa@usgs.gov\" data-mce-href=\"mailto:dc_pa@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/pa-water\" data-mce-href=\"https://www.usgs.gov/centers/pa-water\">Pennsylvania Water Science Center</a><br>U.S. Geological Survey<br>215 Limekiln Road<br>New Cumberland, PA 17070-2424</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Hydrogeologic Setting</li><li>Hydrogeologic Framework, Water Levels, and Selected Contaminant Concentrations</li><li>Limitations and Data Gaps</li><li>Summary</li><li>References Cited</li><li>Appendix 1 Supplementary Geophysical Log Figures</li><li>Appendix 2 Supplementary Hydrologic Data</li></ul>","publishingServiceCenter":{"id":10,"text":"Baltimore 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The New York State Department of Environmental Conservation has been working with the agricultural industry to raise awareness of karst aquifer contamination susceptibility and how to reduce effects on surface water and groundwater resources, especially in karst areas. There is also a need to make industries, State and local regulators, planners, and the public aware of New York’s karst resources to properly protect and manage these resources and the quality of surface water and groundwater that flows through the karst aquifer.</p><p>Publicly available geospatial data were identified, collated, and analyzed for a region of karst terrain extending from Albany to Buffalo. The region was divided into 10 subareas. A series of geospatial datasets were assembled to determine the location and extent of karstic rock; bedrock geology and depth to bedrock; average water-table configuration; surficial geology; soil type, thickness, and hydraulic conductivity; land cover; and closed depressions in the land surface.</p><p>Repeated glaciation and recession across New York have left the landscape pockmarked with closed depressions, which may or may not be related to the underlying bedrock. Closed depressions in areas where carbonate or evaporite karst are present are of primary concern to this study because of the increased potential of karst aquifer contamination from focused recharge. Closed depressions present in areas not associated with karst bedrock can also be evaluated to better understand their ability to transmit surface water to the groundwater system. Information on closed depressions can be used to develop land-management plans to protect local and regional water resources.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215094","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Sporleder, B.A., Fisher, B.N., Keto, D.S., Kappel, W.M., Reddy, J.E., and DeMott, L.M., 2021, Methods of data collection and analysis for an assessment of karst aquifer systems between Albany and Buffalo, New York (ver. 2.0, July 2022): U.S. Geological Survey Scientific Investigations Report 2021–5094, 8 p., https://doi.org/10.3133/sir20215094","productDescription":"Report: vi, 8 p.; Data Release","numberOfPages":"8","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120497","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":389881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AYMP94","text":"USGS data release","linkHelpText":"Geospatial data to assess karst aquifer systems between Albany and Buffalo, New York"},{"id":389878,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5094/coverthb.jpg"},{"id":389879,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5094/sir20215094.pdf","text":"Report","size":"2.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5094"},{"id":389883,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5094/images/"},{"id":389882,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5094/sir20215094.XML"},{"id":390035,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20215094/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":502113,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111818.htm","linkFileType":{"id":5,"text":"html"}},{"id":404517,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5094/versionHist.txt","text":"Version History","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"New York","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -73.71826171874999,\n              42.76314586689492\n            ],\n            [\n              -74.53125,\n              42.97250158602597\n            ],\n            [\n              -76.4208984375,\n              42.94033923363181\n            ],\n            [\n              -77.9150390625,\n              42.90816007196054\n            ],\n            [\n              -78.85986328125,\n              42.98857645832184\n            ],\n            [\n              -78.9697265625,\n              42.71473218539458\n            ],\n            [\n              -78.75,\n              42.52069952914966\n            ],\n            [\n              -77.80517578125,\n              42.52069952914966\n            ],\n            [\n              -76.39892578125,\n              42.58544425738491\n            ],\n            [\n              -74.77294921875,\n              42.66628070564928\n            ],\n            [\n              -73.67431640625,\n              42.65012181368022\n            ],\n            [\n              -73.71826171874999,\n              42.76314586689492\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","edition":"Originally posted October 18, 2021; Revised July 29, 2022","contact":"<p><a href=\"mailto:dc_ny@usgs.gov\" data-mce-href=\"mailto:dc_ny@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/ny-water\" data-mce-href=\"https://www.usgs.gov/centers/ny-water\">New York Water Science Center</a><br>U.S. Geological Survey<br>425 Jordan Road<br>Troy, NY 12180–8349</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Geology of Karst Between Albany and Buffalo, New York</li><li>Methods of Data Collection and Analysis</li><li>Geologic Mapping of Karst Regions</li><li>Closed-Depression Identification Methods</li><li>Limitations of the Assessment</li><li>Summary</li><li>References Cited</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-10-18","revisedDate":"2022-07-29","noUsgsAuthors":false,"publicationDate":"2021-10-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Sporleder, Bradley A. 0000-0001-6579-4030","orcid":"https://orcid.org/0000-0001-6579-4030","contributorId":266021,"corporation":false,"usgs":true,"family":"Sporleder","given":"Bradley","email":"","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fisher, Benjamin N. 0000-0003-1308-1906","orcid":"https://orcid.org/0000-0003-1308-1906","contributorId":220916,"corporation":false,"usgs":true,"family":"Fisher","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keto, Douglas S. 0000-0002-9131-9361","orcid":"https://orcid.org/0000-0002-9131-9361","contributorId":266022,"corporation":false,"usgs":true,"family":"Keto","given":"Douglas","email":"","middleInitial":"S.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kappel, William M. 0000-0002-2382-9757 wkappel@usgs.gov","orcid":"https://orcid.org/0000-0002-2382-9757","contributorId":1074,"corporation":false,"usgs":true,"family":"Kappel","given":"William","email":"wkappel@usgs.gov","middleInitial":"M.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824117,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reddy, James E. 0000-0002-6998-7267 jreddy@usgs.gov","orcid":"https://orcid.org/0000-0002-6998-7267","contributorId":1080,"corporation":false,"usgs":true,"family":"Reddy","given":"James","email":"jreddy@usgs.gov","middleInitial":"E.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":824118,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"DeMott, Laura M. 0000-0003-1277-3484 ldemott@usgs.gov","orcid":"https://orcid.org/0000-0003-1277-3484","contributorId":293790,"corporation":false,"usgs":true,"family":"DeMott","given":"Laura","email":"ldemott@usgs.gov","middleInitial":"M.","affiliations":[{"id":48782,"text":"USGS New York Water Science Center","active":true,"usgs":false}],"preferred":true,"id":847630,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70230585,"text":"70230585 - 2021 - DLR Earth Sensing Imaging Spectrometer (DESIS) level 1 product evaluation using RadCalNet measurements","interactions":[],"lastModifiedDate":"2022-04-18T11:49:18.20331","indexId":"70230585","displayToPublicDate":"2022-06-21T06:47:57","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"DLR Earth Sensing Imaging Spectrometer (DESIS) level 1 product evaluation using RadCalNet measurements","docAbstract":"<div class=\"art-abstract in-tab hypothesis_container\">The DLR Earth Sensing Imaging Spectrometer (DESIS) is the first hyperspectral imaging spectrometer installed in the Multi-User System for Earth Sensing (MUSES) on the International Space Station (ISS) for acquiring routine science grade images from orbit. It was launched on 29 June 2018 and integrated into MUSES. DESIS measures energy in the spectral range of 400 to 1000 nm with high spatial and spectral resolution: 30 m and 2.55 nm, respectively. DESIS data should be sufficiently quantitative and accurate to use it for different applications and research. This work performs a radiometric evaluation of DESIS Level 1 product (Top of Atmosphere (TOA) reflectance) by comparing it with coincident Radiometric Calibration Network (RadCalNet) measurements at Railroad Valley Playa (RVUS), Gobabeb (GONA), and La Crau (LCFR). RVUS, GONA, and LCFR offer 4, 15, and 5 coincident datasets between DESIS and RadCalNet measurements, respectively. The results show an agreement between DESIS and RadCalNet TOA reflectance within ~5% for most spectral regions. However, there is an additional ~5% disagreement across the wavelengths affected by water vapor absorption and atmospheric scattering. Among the three RadCalNet sites, RVUS and GONA show a similar measurement disagreement with DESIS of ~5%, while LCFR differs by ~10%. Agreement between DESIS and RadCalNet measurements is variable across all three sites, likely due to surface type differences. DESIS and RadCalNet agreement show a precision of ~2.5%, 4%, and 7% at RVUS, GONA, and LCFR, respectively. RVUS and GONA, which have a similar surface type, sand, have a similar level of radiometric accuracy and precision, whereas LCFR, which consists of sparse vegetation, has lower accuracy and precision. The observed precision of DESIS Level 1 products from all the sites, especially LCFR, can be improved with a better Bidirectional Reflection Distribution Function (BRDF) characterization of the RadCalNet sites.<span id=\"_mce_caret\" data-mce-bogus=\"1\" data-mce-type=\"format-caret\"><span></span></span></div>","language":"English","publisher":"MDPI","doi":"10.3390/rs13122420","usgsCitation":"Shrestha, M., Helder, D., and Christopherson, J., 2021, DLR Earth Sensing Imaging Spectrometer (DESIS) level 1 product evaluation using RadCalNet measurements: Remote Sensing, v. 13, no. 12, 2420, 25 p., https://doi.org/10.3390/rs13122420.","productDescription":"2420, 25 p.","ipdsId":"IP-129286","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":449901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13122420","text":"Publisher Index Page"},{"id":398910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-06-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Shrestha, Mahesh 0000-0002-8368-6399 mshrestha@contractor.usgs.gov","orcid":"https://orcid.org/0000-0002-8368-6399","contributorId":259303,"corporation":false,"usgs":false,"family":"Shrestha","given":"Mahesh","email":"mshrestha@contractor.usgs.gov","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":true,"id":840798,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":195522,"corporation":false,"usgs":false,"family":"Helder","given":"Dennis","affiliations":[],"preferred":false,"id":840799,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christopherson, Jon 0000-0002-2472-0059","orcid":"https://orcid.org/0000-0002-2472-0059","contributorId":290324,"corporation":false,"usgs":false,"family":"Christopherson","given":"Jon","affiliations":[{"id":54490,"text":"KBR, Inc., under contract to USGS","active":true,"usgs":false}],"preferred":false,"id":840800,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70256702,"text":"70256702 - 2021 - Using automated telemetry to identify population connectivity and migration phenology of Snowy Plovers breeding in the Southern Great Plains","interactions":[],"lastModifiedDate":"2024-09-03T12:02:54.843164","indexId":"70256702","displayToPublicDate":"2022-06-20T06:59:37","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Using automated telemetry to identify population connectivity and migration phenology of Snowy Plovers breeding in the Southern Great Plains","docAbstract":"<div class=\"abstract-group \"><div class=\"article-section__content en main\"><p>Within-breeding season movements have not been quantified for Snowy Plovers (<i>Charadrius nivosus</i>) breeding on the Southern Great Plains (SGP), where suitable breeding habitat can range from less than 10 km to more than 600 km apart. This mosaic distribution of discrete patches of breeding habitat, combined with weather stochasticity and low densities of Snowy Plovers in Texas and New Mexico, increases the risk of local and regional extirpation. Further, little is known about SGP Snowy Plover migration phenology or winter habitat. We used the Motus Wildlife Tracking System to examine population connectivity, migration phenology, and winter habitat locations of adult Snowy Plovers in the SGP. Movements of Snowy Plovers during the 2017 and 2018 breeding seasons suggest little to no connectivity between the Salt Plains National Wildlife Refuge population in Oklahoma and populations in Texas and New Mexico. However, several Snowy Plovers in Texas moved to a lake formed by freshwater springs that may have provided higher-quality breeding and foraging habitat. Migrating primarily at night, we found that Snowy Plovers from a breeding area in Oklahoma made migratory movements to Texas and the Louisiana Gulf Coast. These data may be important to long-term conservation and planning efforts relative to understanding regional persistence and connectivity among breeding populations of Snowy Plovers in the SGP. Our results also highlight the need for future studies of wintering habitats used by SGP Snowy Plovers.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1111/jofo.12393","usgsCitation":"Boal, C.W., Heath-Acre, K.M., Collins, D.P., and Johnson, W.P., 2021, Using automated telemetry to identify population connectivity and migration phenology of Snowy Plovers breeding in the Southern Great Plains: Journal of Field Ornithology, v. 92, no. 4, p. 461-474, https://doi.org/10.1111/jofo.12393.","productDescription":"14 p.","startPage":"461","endPage":"474","ipdsId":"IP-133208","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":449904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jofo.12393","text":"Publisher Index Page"},{"id":433401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"92","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Boal, Clint W. 0000-0001-6008-8911 cboal@usgs.gov","orcid":"https://orcid.org/0000-0001-6008-8911","contributorId":1909,"corporation":false,"usgs":true,"family":"Boal","given":"Clint","email":"cboal@usgs.gov","middleInitial":"W.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Heath-Acre, Kristen M.","contributorId":341612,"corporation":false,"usgs":false,"family":"Heath-Acre","given":"Kristen","email":"","middleInitial":"M.","affiliations":[{"id":36845,"text":"School of Natural Resources, University of Missouri","active":true,"usgs":false}],"preferred":false,"id":908704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Daniel P.","contributorId":341613,"corporation":false,"usgs":false,"family":"Collins","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908705,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, W. P.","contributorId":341616,"corporation":false,"usgs":false,"family":"Johnson","given":"W.","email":"","middleInitial":"P.","affiliations":[{"id":81759,"text":"National Wildlife Refuge System","active":true,"usgs":false}],"preferred":false,"id":908706,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70229477,"text":"70229477 - 2021 - Development and evaluation of habitat suitability criteria for native fishes in three Arizona streams","interactions":[],"lastModifiedDate":"2022-03-09T15:24:32.987147","indexId":"70229477","displayToPublicDate":"2022-03-09T09:16:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Development and evaluation of habitat suitability criteria for native fishes in three Arizona streams","docAbstract":"<p><span>Habitat loss is a main contributor to fish fauna declines in the southwestern USA. Several studies have defined stream-specific habitat conditions that support the growth and survival of native fish in Arizona to inform stream restoration efforts, yet general habitat use of most individual species across the region is not established. Therefore, we evaluated habitat use of four native fishes, Speckled Dace&nbsp;</span><i>Rhinichthys osculus</i><span>, Sonora Sucker&nbsp;</span><i>Catostomus insignis</i><span>, Desert Sucker&nbsp;</span><i>Catostomus clarkii</i><span>, and Longfin Dace&nbsp;</span><i>Agosia chrysogaster</i><span>, across three Arizona streams through the development of habitat suitability criteria (HSC). We developed both stream-specific and generalized HSC for each species. Generalized HSC were calculated as the combination of stream-specific HSC for each species. We then assessed the utility of generalized HSC through transferability among study streams. Also, past HSC studies have not considered the occurrence of nonnative species, so we tested whether the presence of nonnative fishes influenced native fish habitat use through logistic regression models. Fish and habitat data were collected along the Mogollon Rim in Arizona during the 2017 summer field season at base flow conditions. We established minimum microhabitat use for four native Arizona fish species through developing HSC. Most generalized criteria did not transfer among study streams due to variation in habitat availability and fish community structure. Logistic regression analysis showed that the presence of nonnative fishes was inversely related to the presence of two native fish species, which could have influenced habitat use of both species. The lack of transferability across streams as demonstrated in this study confirms that only HSC developed in the stream of interest or in similar undegraded streams with comparable fish communities should be used for restoration efforts. For projects to restore native fishes in streams where nonnative competitors will not dominate, the least degraded similar streams without coexisting nonnative fishes can guide restoration efforts.</span></p>","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10575","usgsCitation":"Nemec, Z.C., Lee, L.N., and Bonar, S.A., 2021, Development and evaluation of habitat suitability criteria for native fishes in three Arizona streams: North American Journal of Fisheries Management, v. 41, no. 3, p. 661-677, https://doi.org/10.1002/nafm.10575.","productDescription":"17 p.","startPage":"661","endPage":"677","ipdsId":"IP-125478","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396914,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Blue River, Eagle Creek, Tonto Creek, Verde River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -113.26904296874999,\n              32.82421110161336\n            ],\n            [\n              -109.072265625,\n              32.82421110161336\n            ],\n            [\n              -109.072265625,\n              35.40696093270201\n            ],\n            [\n              -113.26904296874999,\n              35.40696093270201\n            ],\n            [\n              -113.26904296874999,\n              32.82421110161336\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"41","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-03-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Nemec, Zach C.","contributorId":288222,"corporation":false,"usgs":false,"family":"Nemec","given":"Zach","email":"","middleInitial":"C.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":837576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lee, Larissa N.","contributorId":288223,"corporation":false,"usgs":false,"family":"Lee","given":"Larissa","email":"","middleInitial":"N.","affiliations":[{"id":56363,"text":"uaz","active":true,"usgs":false}],"preferred":false,"id":837577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bonar, Scott A. 0000-0003-3532-4067 sbonar@usgs.gov","orcid":"https://orcid.org/0000-0003-3532-4067","contributorId":3712,"corporation":false,"usgs":true,"family":"Bonar","given":"Scott","email":"sbonar@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837575,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70229054,"text":"70229054 - 2021 - Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands","interactions":[],"lastModifiedDate":"2022-02-28T15:54:29.873881","indexId":"70229054","displayToPublicDate":"2022-02-28T09:41:44","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands","docAbstract":"Forests are often affected by management that could influence demographics of breeding and post-breeding birds that reside within. Numerous studies have focused on immediate effects from management on wildlife soon after forestry treatment (e.g., 0–5 years), however, fewer studies have examined changes in focal species abundance over longer durations as a forest regenerates after disturbance. We examined how forest management influenced 18 conifer-associated birds during breeding and post-breeding over the forest regeneration period in a landscape dominated by forestry. To achieve this, we paired avian detection data from point count surveys in lowland conifer and mixed-wood forests with Bayesian distance-removal models and an information-theoretic framework. We estimated abundance and associations with seven common forestry treatment categories applied at the stand scale, years-since-harvest (YSH; 5–120+), and seven vegetation variables measured within stands. Forestry treatment categories and YSH were poor predictors of abundance, and none of the 14 species with good-fitting models had associations with these covariates. Twelve of 13 species with good-fitting models had important associations between abundance and vegetation variables. All vegetation variables were associated with abundance of some species, irrespective of the forestry treatment in which the site occurred, including spruce-fir tree composition (seven species), tree basal area (six species), midstory cover (five species), live crown ratio (three species), shrub cover (three species), tree diameter at breast height (two species), and shrub composition (one species). In a companion study, several species assemblages were associated with vegetation variables (i.e., spruce-fir tree composition, tree basal area, and tree diameter at breast height) that varied with YSH and forestry treatments, suggesting that some forestry treatments may indirectly influence avian abundance when certain vegetation outcomes are achieved. Our results suggest that managers should target species-specific vegetation outcomes rather than more broadly categorized forestry treatment types when managing for individual focal species because of large variations in vegetative outcomes across stands within a forest treatment category. Our study informs management and conservation of biodiversity in regions such as the Atlantic Northern Forest where commercial forestry is the dominant human land use.","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119708","usgsCitation":"Rolek, B.W., Harrison, D.J., Linden, D.W., Loftin, C., and Wood, P.B., 2021, Habitat associations of breeding conifer-associated birds in managed and regenerating forested stands: Forest Ecology and Management, v. 502, p. 1-15, https://doi.org/10.1016/j.foreco.2021.119708.","productDescription":"119708, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-123856","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":449917,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2021.119708","text":"Publisher Index 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,{"id":70228800,"text":"70228800 - 2021 - Diatoms.org: Supporting taxonomists, connecting communities","interactions":[],"lastModifiedDate":"2022-03-18T15:16:10.161986","indexId":"70228800","displayToPublicDate":"2022-02-21T08:27:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1388,"text":"Diatom Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"<i>Diatoms.org</i>: Supporting taxonomists, connecting communities","title":"Diatoms.org: Supporting taxonomists, connecting communities","docAbstract":"<p>Consistent identification of diatoms is a prerequisite for studying their ecology, biogeography, and successful application as environmental indicators. However, taxonomic consistency among observers has been difficult to achieve because taxonomic information is scattered across numerous literature sources, presenting challenges to the diatomist. Firstly, literature is often inaccessible because of cost or its location in journals that are not widely circulated. Secondly, taxonomic revisions of diatoms are taking place faster than floras can be updated. Finally, taxonomic information is often contradictory across literature sources. These issues can be addressed by developing a content creation community dedicated to making taxonomic, ecological, and image-based data freely available for diatom researchers.<span>&nbsp;</span><i>Diatoms.org</i><span>&nbsp;</span>represents such a content curation community, providing open, online access to a vast amount of recent and historical information on North American diatom taxonomy and ecology. The content curation community aggregates existing taxonomic information, creates new content, and provides feedback in the form of corrections and notices of literature with nomenclatural changes. The website not only addresses the needs of experienced diatom scientists for consistent identification but is also designed to meet users at their level of expertise, including engaging the lay public in the importance of diatom science. The website now contains over 1000 species pages contributed by over 100 content contributors, from students to established scientists. The project began with the intent to provide accurate information on diatom identification, ecology, and distribution using an approach that incorporates engaging design, user feedback, and advanced data access technology. In retrospect, the project that began as an ‘extended electronic book’ has emerged not only as a means to support taxonomists, but for practitioners to communicate and collaborate, expanding the size of and benefits to the content curation community. In this paper, we outline the development of<span>&nbsp;</span><i>diatoms.org</i>, document key elements of the project, examine ongoing challenges and consider the unexpected emergent properties, including the value of<span>&nbsp;</span><i>diatoms.org</i><span>&nbsp;</span>as a source of data. Ultimately, if the field of diatom taxonomy, ecology, and biodiversity is to be relevant, a new generation of taxonomists needs to be trained and employed using new tools. We propose that<span>&nbsp;</span><i>diatoms.org</i><span>&nbsp;</span>is in a key position to serve as a hub of training and continuity for the study of diatom biodiversity and aquatic conditions.</p>","language":"English","publisher":"Taylor & Francis","doi":"10.1080/0269249X.2021.2006790","usgsCitation":"Spaulding, S., Potapova, M., Bishop, I., Lee, S.S., Gasperak, T., Jovanoska, E., Furey, P.C., and Edlund, M.B., 2021, Diatoms.org: Supporting taxonomists, connecting communities: Diatom Research, v. 36, no. 4, p. 291-304, https://doi.org/10.1080/0269249X.2021.2006790.","productDescription":"14 p.","startPage":"291","endPage":"304","ipdsId":"IP-126410","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":449919,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/0269249x.2021.2006790","text":"Publisher Index Page"},{"id":396219,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Spaulding, Sarah A. 0000-0002-9787-7743","orcid":"https://orcid.org/0000-0002-9787-7743","contributorId":223186,"corporation":false,"usgs":true,"family":"Spaulding","given":"Sarah","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":835508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Potapova, Marina","contributorId":279822,"corporation":false,"usgs":false,"family":"Potapova","given":"Marina","affiliations":[{"id":57366,"text":"Academy of Natural Sciences of Drexel University","active":true,"usgs":false}],"preferred":false,"id":835509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bishop, Ian W.","contributorId":207505,"corporation":false,"usgs":false,"family":"Bishop","given":"Ian W.","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":835510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lee, Sylvia S.","contributorId":41746,"corporation":false,"usgs":true,"family":"Lee","given":"Sylvia","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":835511,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gasperak, Tim","contributorId":279824,"corporation":false,"usgs":false,"family":"Gasperak","given":"Tim","email":"","affiliations":[{"id":57369,"text":"Strange Attractor LLC","active":true,"usgs":false}],"preferred":false,"id":835513,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jovanoska, Elena","contributorId":279823,"corporation":false,"usgs":false,"family":"Jovanoska","given":"Elena","email":"","affiliations":[{"id":57367,"text":"Senckenberg Research Institute","active":true,"usgs":false}],"preferred":false,"id":835512,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Furey, Paula C.","contributorId":279825,"corporation":false,"usgs":false,"family":"Furey","given":"Paula","email":"","middleInitial":"C.","affiliations":[{"id":57370,"text":"St. Catherine University","active":true,"usgs":false}],"preferred":false,"id":835514,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Edlund, Mark B.","contributorId":104335,"corporation":false,"usgs":true,"family":"Edlund","given":"Mark","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":835515,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70227786,"text":"70227786 - 2021 - Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization","interactions":[],"lastModifiedDate":"2022-01-31T15:46:01.075703","indexId":"70227786","displayToPublicDate":"2022-01-31T09:32:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10069,"text":"Frontiers in Soil Science","active":true,"publicationSubtype":{"id":10}},"title":"Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization","docAbstract":"Wetland soil stocks are important global repositories of carbon (C) but are difficult to quantify and model due to varying sampling protocols, and geomorphic/spatio-temporal discontinuity. Merging scales of soil-survey spatial extents with wetland-specific point-based data offers an explicit, empirical and updatable improvement for regional and continental scale soil C stock assessments. Agency-collected (U.S. Department of Agriculture, U.S. Environmental Protection Agency) and community-contributed soil datasets were compared for representativeness and bias, with the goal of producing a harmonized national map of wetland soil C stocks with error quantification for wetland areas of the conterminous United States (CONUS) identified by the USGS National Landcover Change Dataset (NLCD). This allowed application of an empirical predictive model of SOC density to be applied across the entire CONUS using relational %OC distribution alone. A broken-stick quantile-regression model identified %OC with its relatively high analytical confidence as a key predictor of SOC density in soil segments; soils less than 6%OC (hereafter, mineral wetland soils, 85% of the dataset) had a strong linear relationship of %OC to SOC density (RMSE = 0.0059, ~4% mean RMSE) and soils greater than 6%OC (organic wetland soils, 15% of the dataset) had virtually no predictive relationship of %OC to SOC density (RMSE = 0.0348 g C cm-3, ~56% mean RMSE). Disaggregation by vegetation type (woody v. emergent herbaceous), or region did not alter the breakpoint significantly (6% OC) nor improve model accuracies for inland and tidal wetlands. Similarly, SOC stocks in tidal wetlands were related to %OC, but without a mappable product for disaggregation to improve accuracy by soil class, region or depth. Our layered, harmonized CONUS wetland soil maps have now revised wetland SOC stock estimates downward by 24% (9.5 vs. 12.5Pg C) with the overestimation being entirely an issue of inland, organic wetland soils, (35% lower than SSURGO-derived SOC stocks).  Further, SSURGO underestimated soil carbon stocks at depth, as modeled wetland SOC stocks for organic-rich soils showed significant preservation downcore in the NWCA dataset (<3% loss between 0-30 cm and 30-100 cm depths) in contrast to mineral-rich soils (37% downcore stock loss). Future CONUS wetland soil C assessments will benefit from focused attention on improved organic wetland soil measurements, land history, and spatial representativeness.","language":"English","publisher":"Frontiers Media","doi":"10.3389/fsoil.2021.706701","usgsCitation":"Uhran, B.R., Windham-Myers, L., Bliss, N.B., Nahlik, A.M., Sundquist, E.T., and Stagg, C.L., 2021, Improved wetland soil organic carbon stocks of the conterminous U.S. through data harmonization: Frontiers in Soil Science, v. 1, p. 1-16, https://doi.org/10.3389/fsoil.2021.706701.","productDescription":"706701, 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-123603","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience 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       -75.37747,\n                38.01551\n              ],\n              [\n                -75.94023,\n                37.21689\n              ],\n              [\n                -76.03127,\n                37.2566\n              ],\n              [\n                -75.72205,\n                37.93705\n              ],\n              [\n                -76.23287,\n                38.31921\n              ],\n              [\n                -76.35,\n                39.15\n              ],\n              [\n                -76.54272,\n                38.71762\n              ],\n              [\n                -76.32933,\n                38.08326\n              ],\n              [\n                -76.99,\n                38.23999\n              ],\n              [\n                -76.30162,\n                37.91794\n              ],\n              [\n                -76.25874,\n                36.9664\n              ],\n              [\n                -75.9718,\n                36.89726\n              ],\n              [\n                -75.86804,\n                36.55125\n              ],\n              [\n                -75.72749,\n                35.55074\n              ],\n              [\n                -76.36318,\n                34.80854\n              ],\n              [\n                -77.39763,\n                34.51201\n              ],\n              [\n                -78.05496,\n                33.92547\n              ],\n              [\n                -78.55435,\n                33.86133\n              ],\n              [\n                -79.06067,\n                33.49395\n              ],\n              [\n                -79.20357,\n                33.15839\n              ],\n              [\n                -80.30132,\n                32.50935\n              ],\n              [\n                -80.86498,\n                32.0333\n              ],\n              [\n                -81.33629,\n                31.44049\n              ],\n              [\n                -81.49042,\n                30.72999\n              ],\n              [\n                -81.31371,\n                30.03552\n              ],\n              [\n                -80.98,\n                29.18\n              ],\n              [\n                -80.53558,\n                28.47213\n              ],\n              [\n                -80.53,\n                28.04\n              ],\n              [\n                -80.05654,\n                26.88\n              ],\n              [\n                -80.08801,\n                26.20576\n              ],\n              [\n                -80.13156,\n                25.81677\n              ],\n              [\n                -80.38103,\n                25.20616\n              ],\n              [\n                -80.68,\n                25.08\n              ],\n              [\n                -81.17213,\n                25.20126\n              ],\n              [\n                -81.33,\n                25.64\n              ],\n              [\n                -81.71,\n                25.87\n              ],\n              [\n                -82.24,\n                26.73\n              ],\n              [\n                -82.70515,\n                27.49504\n              ],\n              [\n                -82.85526,\n                27.88624\n              ],\n              [\n                -82.65,\n                28.55\n              ],\n              [\n                -82.93,\n                29.1\n              ],\n              [\n                -83.70959,\n                29.93656\n              ],\n              [\n                -84.1,\n                30.09\n              ],\n              [\n                -85.10882,\n                29.63615\n              ],\n              [\n                -85.28784,\n                29.68612\n              ],\n              [\n                -85.7731,\n                30.15261\n              ],\n              [\n                -86.4,\n                30.4\n              ],\n              [\n                -87.53036,\n                30.27433\n              ],\n              [\n                -88.41782,\n                30.3849\n              ],\n              [\n                -89.18049,\n                30.31598\n              ],\n              [\n                -89.59383,\n                30.15999\n              ],\n              [\n                -89.41373,\n                29.89419\n              ],\n              [\n                -89.43,\n                29.48864\n              ],\n              [\n                -89.21767,\n                29.29108\n              ],\n              [\n                -89.40823,\n                29.15961\n              ],\n              [\n                -89.77928,\n                29.30714\n              ],\n              [\n                -90.15463,\n                29.11743\n              ],\n              [\n                -90.88022,\n                29.14854\n              ],\n              [\n                -91.62678,\n                29.677\n              ],\n              [\n                -92.49906,\n                29.5523\n              ],\n              [\n                -93.22637,\n                29.78375\n              ],\n              [\n                -93.84842,\n                29.71363\n              ],\n              [\n                -94.69,\n                29.48\n              ],\n              [\n                -95.60026,\n                28.73863\n              ],\n              [\n                -96.59404,\n                28.30748\n              ],\n              [\n                -97.14,\n                27.83\n              ],\n              [\n                -97.37,\n                27.38\n              ],\n              [\n                -97.38,\n                26.69\n              ],\n              [\n                -97.33,\n                26.21\n              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28.97\n              ],\n              [\n                -103.94,\n                29.27\n              ],\n              [\n                -104.45697,\n                29.57196\n              ],\n              [\n                -104.70575,\n                30.12173\n              ],\n              [\n                -105.03737,\n                30.64402\n              ],\n              [\n                -105.63159,\n                31.08383\n              ],\n              [\n                -106.1429,\n                31.39995\n              ],\n              [\n                -106.50759,\n                31.75452\n              ],\n              [\n                -108.24,\n                31.75485\n              ],\n              [\n                -108.24194,\n                31.34222\n              ],\n              [\n                -109.035,\n                31.34194\n              ],\n              [\n                -111.02361,\n                31.33472\n              ],\n              [\n                -113.30498,\n                32.03914\n              ],\n              [\n                -114.815,\n                32.52528\n              ],\n              [\n                -114.72139,\n                32.72083\n              ],\n              [\n                -115.99135,\n                32.61239\n              ],\n              [\n                -117.12776,\n                32.53534\n              ],\n              [\n                -117.29594,\n                33.04622\n              ],\n              [\n                -117.944,\n                33.62124\n              ],\n              [\n                -118.4106,\n                33.74091\n              ],\n              [\n                -118.51989,\n                34.02778\n              ],\n              [\n                -119.081,\n                34.078\n              ],\n              [\n                -119.43884,\n                34.34848\n              ],\n              [\n                -120.36778,\n                34.44711\n              ],\n              [\n                -120.62286,\n                34.60855\n              ],\n              [\n                -120.74433,\n                35.15686\n              ],\n              [\n                -121.71457,\n                36.16153\n              ],\n              [\n                -122.54747,\n                37.55176\n              ],\n              [\n                -122.51201,\n                37.78339\n              ],\n              [\n                -122.95319,\n                38.11371\n              ],\n              [\n                -123.7272,\n                38.95166\n              ],\n              [\n                -123.86517,\n                39.76699\n              ],\n              [\n                -124.39807,\n                40.3132\n              ],\n              [\n                -124.17886,\n                41.14202\n              ],\n              [\n                -124.2137,\n                41.99964\n              ],\n              [\n                -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"1","noUsgsAuthors":false,"publicationDate":"2021-10-12","publicationStatus":"PW","contributors":{"editors":[{"text":"Sulman, Benjamin N. 0000-0002-3265-6691","orcid":"https://orcid.org/0000-0002-3265-6691","contributorId":209890,"corporation":false,"usgs":false,"family":"Sulman","given":"Benjamin","email":"","middleInitial":"N.","affiliations":[{"id":7108,"text":"Princeton Univ.","active":true,"usgs":false}],"preferred":false,"id":832308,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Uhran, Bergit Rose 0000-0003-2074-5248","orcid":"https://orcid.org/0000-0003-2074-5248","contributorId":272619,"corporation":false,"usgs":true,"family":"Uhran","given":"Bergit","email":"","middleInitial":"Rose","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":832241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":832242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bliss, Norman B. 0000-0003-2409-5211 bliss@usgs.gov","orcid":"https://orcid.org/0000-0003-2409-5211","contributorId":1921,"corporation":false,"usgs":true,"family":"Bliss","given":"Norman","email":"bliss@usgs.gov","middleInitial":"B.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":832243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nahlik, Amanda M. 0000-0003-0591-375X","orcid":"https://orcid.org/0000-0003-0591-375X","contributorId":272622,"corporation":false,"usgs":false,"family":"Nahlik","given":"Amanda","email":"","middleInitial":"M.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":832244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sundquist, Eric T. 0000-0002-1449-8802 esundqui@usgs.gov","orcid":"https://orcid.org/0000-0002-1449-8802","contributorId":1922,"corporation":false,"usgs":true,"family":"Sundquist","given":"Eric","email":"esundqui@usgs.gov","middleInitial":"T.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":832245,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stagg, Camille L. 0000-0002-1125-7253 staggc@usgs.gov","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":4111,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","email":"staggc@usgs.gov","middleInitial":"L.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":832246,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70227429,"text":"70227429 - 2021 - An assessment of uranium in groundwater in the Grand Canyon region","interactions":[],"lastModifiedDate":"2022-01-14T15:29:36.464993","indexId":"70227429","displayToPublicDate":"2022-01-14T09:20:34","publicationYear":"2021","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":"An assessment of uranium in groundwater in the Grand Canyon region","docAbstract":"The Grand Canyon region in northern Arizona is a home or sacred place of origin for many Native Americans and is visited by over 6 million tourists each year.  Most communities in the area depend upon groundwater for all water uses.  Some of the highest-grade uranium ore in the United States also is found in the Grand Canyon region.  A withdrawal of over 1 million acres of Federal land in the Grand Canyon region from new uranium mining activities for 20 years was instituted in 2012, owing in part to a lack of scientific data on potential effects from uranium mining on water resources in the area.  The USGS collects groundwater chemistry samples in the Grand Canyon region to understand the current state of groundwater quality, to monitor for changes in groundwater quality that may be the result of mining activities, and to identify \"hot spots\" with elevated metal concentrations and investigate the causes.  This manuscript presents results for the assessment of uranium in groundwater in the Grand Canyon region.  Analytical results for uranium in groundwater in the Grand Canyon region were available for 573 samples collected from 180 spring sites and 26 wells from September 1, 1981 to October 7, 2020.  Samples were collected from springs issuing from stratigraphic units above, within, and below the Permian strata that hosts uranium ore in breccia pipes in the area.  Maximum uranium concentrations at groundwater sites in the region ranged from less than 1 µg/L at 23 sites (11%) to 100 µg/L or more at 4 sites (2%).  Of the 206 groundwater sites sampled, 195 sites (95%) had maximum observed uranium concentrations less than the USEPA Maximum Contaminant Level of 30 µg/L and 177 sites (86%) had uranium concentrations less than the 15 µg/L Canadian benchmark for protection of aquatic life in freshwater.  The establishment of baseline groundwater quality is an important first step in monitoring for change in water chemistry throughout mining lifecycles and beyond to ensure the health of these critical groundwater resources.","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41598-021-01621-8","usgsCitation":"Tillman, F.D., Beisner, K.R., Anderson, J.R., and Unema, J., 2021, An assessment of uranium in groundwater in the Grand Canyon region: Scientific Reports, v. 11, p. 1-15, https://doi.org/10.1038/s41598-021-01621-8.","productDescription":"22157, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-129976","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":449930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-021-01621-8","text":"Publisher Index Page"},{"id":394379,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -114.554443359375,\n              35.209721645221386\n            ],\n            [\n              -111.016845703125,\n              35.209721645221386\n            ],\n            [\n              -111.016845703125,\n              37.65773212628272\n            ],\n            [\n              -114.554443359375,\n              37.65773212628272\n            ],\n            [\n              -114.554443359375,\n              35.209721645221386\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2021-11-16","publicationStatus":"PW","contributors":{"authors":[{"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":830870,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830871,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Jessica R. 0000-0002-3286-7552 jranderson@usgs.gov","orcid":"https://orcid.org/0000-0002-3286-7552","contributorId":193158,"corporation":false,"usgs":true,"family":"Anderson","given":"Jessica","email":"jranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830872,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Unema, Joel A. 0000-0002-7428-219X","orcid":"https://orcid.org/0000-0002-7428-219X","contributorId":211449,"corporation":false,"usgs":true,"family":"Unema","given":"Joel A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":830873,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227184,"text":"70227184 - 2021 - Concentrations, loads, and associated trends of nutrients entering the Sacramento-San Joaquin Delta, California","interactions":[],"lastModifiedDate":"2022-01-04T15:54:08.327037","indexId":"70227184","displayToPublicDate":"2022-01-04T09:44:45","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Concentrations, loads, and associated trends of nutrients entering the Sacramento-San Joaquin Delta, California","docAbstract":"<p>Statistical modeling of water-quality data collected at the Sacramento River at Freeport and San Joaquin River near Vernalis, California, USA, was used to examine trends in concentrations and loads of various forms of dissolved and particulate nitrogen and phosphorus that entered the Sacramento–San Joaquin River Delta (Delta) from upstream sources between 1970 and 2019. Ammonium concentrations and loads decreased at the Sacramento River site from the mid-1970s through 1990 because of the consolidation of wastewater treatment and continuously reduced from the mid-1970s to 2019 at the San Joaquin River site. Current ammonium concentrations are mostly below 4 µM&nbsp;(0.056 mg N L<sup>–1</sup>) at both sites, a concentration above which reductions in phytoplankton productivity or changes in algal species composition may occur. The Sacramento River at Freeport site is located upstream of the Sacramento Regional County Sanitation District’s treatment facility’s discharge point; nutrient water quality there is representative of upstream sources. Inorganic nitrogen (nitrate plus ammonium) concentrations and loading differed at both sites. At the Sacramento River location, concentrations decrease in the summer agricultural season, reducing the molar ratios of nitrogen to phosphorus.</p><p>In contrast, inorganic nitrogen concentrations increase in the San Joaquin River during the agricultural season as a result of irrigation runoff, increasing the molar ratio of nitrogen to phosphorus. This increase suggests a possible nitrogen limitation in the northern Delta and a phosphorus limitation in the southern Delta, as indicated by the molar ratios of bioavailable nitrogen to bioavailable phosphorus. Planned upgrades to the Sacramento Regional Wastewater Treatment Plant (SRWTP) will reduce inorganic nitrogen inputs to the northern Delta. Consequently, the supply of bioavailable nitrogen throughout the upper estuary should diminish. Source modeling of nitrogen and phosphorus identifies agriculture, atmospheric deposition, and wastewater effluent as sources of total nitrogen in the Central Valley. In contrast, geologic sources, agriculture, and wastewater discharge are the primary sources of phosphorus.</p>","language":"English","publisher":"University of California","doi":"10.15447/sfews.2021v19iss4art6","usgsCitation":"Saleh, D., and Domagalski, J.L., 2021, Concentrations, loads, and associated trends of nutrients entering the Sacramento-San Joaquin Delta, California: San Francisco Estuary and Watershed Science, v. 19, no. 4, p. 1-25, https://doi.org/10.15447/sfews.2021v19iss4art6.","productDescription":"6, 25 p.","startPage":"1","endPage":"25","ipdsId":"IP-114557","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":449932,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2021v19iss4art6","text":"Publisher Index Page"},{"id":393859,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Freeport, Vernalis","otherGeospatial":"Sacramento-San Joaquin Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -123.629150390625,\n              37.23470197166817\n            ],\n            [\n              -119.0643310546875,\n              37.23470197166817\n            ],\n            [\n              -119.0643310546875,\n              39.11727568585598\n            ],\n            [\n              -123.629150390625,\n              39.11727568585598\n            ],\n            [\n              -123.629150390625,\n              37.23470197166817\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"19","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Saleh, Dina 0000-0002-1406-9303 dsaleh@usgs.gov","orcid":"https://orcid.org/0000-0002-1406-9303","contributorId":939,"corporation":false,"usgs":true,"family":"Saleh","given":"Dina","email":"dsaleh@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829996,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Domagalski, Joseph L. 0000-0002-6032-757X joed@usgs.gov","orcid":"https://orcid.org/0000-0002-6032-757X","contributorId":1330,"corporation":false,"usgs":true,"family":"Domagalski","given":"Joseph","email":"joed@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829997,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70227203,"text":"70227203 - 2021 - Dominant Sonoran Desert plant species have divergent phenological responses to climate change","interactions":[],"lastModifiedDate":"2022-01-04T14:31:38.457341","indexId":"70227203","displayToPublicDate":"2022-01-04T08:19:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9976,"text":"Madroño - A West American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Dominant Sonoran Desert plant species have divergent phenological responses to climate change","docAbstract":"<div class=\"div0\"><div class=\"row ArticleContentRow\"><p id=\"ID0EF\" class=\"first\">The southwestern U.S. is a global hotspot of climate change. Models project that temperatures will continue to rise through the end of the 21st century, accompanied by significant changes to the hydrological cycle. Within the Sonoran Desert, a limited number of studies have documented climate change impacts on the phenology of native plant species. Much of this phenological work to understand climate change impacts to phenology builds on research conducted nearly three decades ago to define flowering triggers and developmental requirements for native keystone Sonoran Desert woody species. Here we expand on the drivers and explore recent phenological trends for six species using a unique 36-year observational data set. We use statistical models to determine which aspects of climate influence the probability of flowering, and how flowering time may respond to climate change. We move beyond traditional models of phenology by incorporating different metrics of moisture availability in addition to temperature, weather, and climate at several time scales, including daily, weekly, seasonal, and antecedent conditions. Our results provide evidence of a trend towards earlier flowering (on the order of 1–4 days per decade) for five of the six species analyzed, and no trend for one species. The species we evaluated had contrasting phenological responses to different aspects of climate, suggesting individualistic changes in phenology and the potential of divergent plant community flowering patterns under future climate change. Understanding recent changes in flowering phenology and their climatic triggers is important to anticipating whether plant species can attract pollinators, reproduce, and persist within the community under continued climate change.</p></div></div>","language":"English","publisher":"California Botanical Society","doi":"10.3120/0024-9637-68.4.473","usgsCitation":"Zachmann, L.J., Wiens, J.F., Franklin, K., Crausbay, S.D., Landau, V.A., and Munson, S.M., 2021, Dominant Sonoran Desert plant species have divergent phenological responses to climate change: Madroño - A West American Journal of Botany, v. 68, no. 4, p. 473-486, https://doi.org/10.3120/0024-9637-68.4.473.","productDescription":"14 p.","startPage":"473","endPage":"486","ipdsId":"IP-126703","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":449939,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3120/0024-9637-68.4.473","text":"Publisher Index Page"},{"id":393845,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Arizona-Sonora Desert Museum, King Canyon, Saguaro National Park, Sonoran Desert, Tucson Mountain Park, Tucson Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -111.2197494506836,\n              32.204086355917944\n            ],\n            [\n              -111.06250762939452,\n              32.204086355917944\n            ],\n            [\n              -111.06250762939452,\n              32.283794824838274\n            ],\n            [\n              -111.2197494506836,\n              32.283794824838274\n            ],\n            [\n              -111.2197494506836,\n              32.204086355917944\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","volume":"68","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zachmann, Luke J 0000-0003-2313-1460","orcid":"https://orcid.org/0000-0003-2313-1460","contributorId":265938,"corporation":false,"usgs":false,"family":"Zachmann","given":"Luke","email":"","middleInitial":"J","affiliations":[{"id":54831,"text":"Conservation Science Partners, Inc","active":true,"usgs":false}],"preferred":false,"id":830071,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wiens, John F.","contributorId":270798,"corporation":false,"usgs":false,"family":"Wiens","given":"John","email":"","middleInitial":"F.","affiliations":[{"id":56218,"text":"Arizona-Sonora Desert Museum, Tucson, AZ 85743","active":true,"usgs":false}],"preferred":false,"id":830072,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Franklin, Kim","contributorId":270799,"corporation":false,"usgs":false,"family":"Franklin","given":"Kim","affiliations":[{"id":56218,"text":"Arizona-Sonora Desert Museum, Tucson, AZ 85743","active":true,"usgs":false}],"preferred":false,"id":830073,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crausbay, Shelley D.","contributorId":197220,"corporation":false,"usgs":false,"family":"Crausbay","given":"Shelley","email":"","middleInitial":"D.","affiliations":[{"id":54831,"text":"Conservation Science Partners, Inc","active":true,"usgs":false}],"preferred":false,"id":830074,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Landau, Vincent A. 0000-0001-9290-9438","orcid":"https://orcid.org/0000-0001-9290-9438","contributorId":265939,"corporation":false,"usgs":false,"family":"Landau","given":"Vincent","email":"","middleInitial":"A.","affiliations":[{"id":54831,"text":"Conservation Science Partners, Inc","active":true,"usgs":false}],"preferred":false,"id":830075,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":830076,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70226898,"text":"sir20215130 - 2021 - Evaluating the effects of replacing septic systems with municipal sewers on groundwater quality in a densely developed coastal neighborhood, Falmouth, Massachusetts, 2016–19","interactions":[],"lastModifiedDate":"2022-01-04T01:28:42.314083","indexId":"sir20215130","displayToPublicDate":"2022-01-03T20:30:00","publicationYear":"2021","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":"2021-5130","displayTitle":"Evaluating the Effects of Replacing Septic Systems With Municipal Sewers on Groundwater Quality in a Densely Developed Coastal Neighborhood, Falmouth, Massachusetts, 2016–19","title":"Evaluating the effects of replacing septic systems with municipal sewers on groundwater quality in a densely developed coastal neighborhood, Falmouth, Massachusetts, 2016–19","docAbstract":"<p>Land disposal of sewage wastewater through septic systems and cesspools is a major cause of elevated concentrations of nitrogen in the shallow coastal aquifers of southern New England. The discharge of nitrogen from these sources at the coast is affecting the environmental health of coastal saltwater bodies. In response, local, State, and Federal agencies are considering expensive actions to mitigate these effects, including installing municipal sewer systems. To increase the understanding of the effects of municipal sewering on groundwater quality discharging to coastal surface waters, a network of multilevel monitoring wells was established in a densely developed coastal neighborhood on the Maravista peninsula, Falmouth, Massachusetts, which was undergoing conversion from onsite septic disposal to municipal sewering.</p><p>The geohydrology of the study area on the peninsula is generally characterized as consisting of fine to coarse, well-sorted sands containing 2.9 to 9.3 meters of fresh groundwater and a flow system characterized by a groundwater divide slightly west of the center of the peninsula. The magnitude of hydraulic gradients at the water table is gently sloping, ranging from 0.000032 to 0.00059, and affected by daily and bimonthly tidal fluctuations from adjacent coastal ponds. On the western side of the divide, upgradient from Little Pond, average linear groundwater velocities and traveltimes along shallow flow paths, estimated from observed hydraulic gradients and estimated aquifer hydraulic conductivity and effective porosity, range from 0.076 to 0.094 meters per day and 7.8 to 9.7 years, respectively.</p><p>The groundwater monitoring network consists of 14 profile sites on the peninsula that each include a multilevel sampler for water-quality data collection and a shallow monitoring well for groundwater-level measurements. The study area encompasses about 230 residences that transitioned from onsite septic disposal to municipal sewering between spring 2017 and summer 2019. An additional multilevel sampler that was in a residential coastal setting but not undergoing sewering also was sampled periodically as a reference site.</p><p>Elevated nitrogen, as compared to typical uncontaminated, fresh groundwater in the Cape Cod aquifer, predominately as nitrate, was measured in 15 water-quality profiles at nitrate concentrations as great as 26.2 milligrams per liter as nitrogen (<i>n</i>=749; mean and median values were 5.1 and 4.1 milligrams per liter as nitrogen, respectively). At all 14 profile sites and the reference profile site on a nearby peninsula, wastewater effects were denoted by increased nitrate, boron, and specific conductance, and by decreased pH and dissolved oxygen. The highest concentrations of nitrate typically occurred in the deepest one-half of the freshwater zone and in intervals of suboxic and oxic groundwater.</p><p>Thickness-weighted mean and maximum nitrate concentrations, and total nitrate mass from four sampling rounds, provided a metric to evaluate expected changes at the 14 profile sites on the peninsula. Nitrate concentrations varied moderately by site between sampling rounds through both the presewering (June 2016 and April 2017) and transitional periods (April 2018 and June 2019). Nitrate concentrations greater than the U.S. Environmental Protection Agency maximum contaminant level for nitrate in drinking water (10 milligrams per liter as nitrogen), were detected at 9 of the 14 profile sites and at the reference site. The average of the mean thickness-weighted nitrate concentrations for the four full sampling rounds was greater than 5.0 milligrams per liter as nitrogen at 8 sites (7 profile sites and the reference site) and greater than 8 milligrams per liter as nitrogen at 3 profile sites. The total nitrate mass per square meter of land area at each profile site ranged from 1,830 to 36,800 milligrams per square meter. Nitrate mass flux, across a 500-meter-long section upgradient from Little Pond and covering about 15 percent of the total pond shoreline length, ranged from 124.3 to 192.6 kilograms per year for the four full sampling rounds under three groundwater-flow conditions.</p><p>The expected improvements in groundwater quality in the freshwater zone should be characterized by decreases in concentrations of dissolved total and inorganic nitrogen and common ions such as boron, chloride, and fluoride. A statistical analysis using the Regional Kendall test for sampling points grouped in specific depth ranges confirmed that water-quality changes were statistically significant in at least one depth group during the 3-year sampling period (nitrate: −0.76 milligram per liter per year; specific conductance: −12.1 microsiemens per centimeter at 25 degrees Celsius per year; dissolved oxygen: 0.82 milligram per liter per year); however, the rate at which the water-quality improvements will result in decreases in nitrate mass loads to the coastal ponds primarily depends on groundwater traveltimes and the rate of flushing of wastewater constituents from the aquifer.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215130","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency’s Southeast New England Program","usgsCitation":"McCobb, T.D., Barbaro, J.R., LeBlanc, D.R., and Belaval, M., 2021, Evaluating the effects of replacing septic systems with municipal sewers on groundwater quality in a densely developed coastal neighborhood, Falmouth, Massachusetts, 2016–19: U.S. Geological Survey Scientific Investigations Report 2021–5130, 39 p., https://doi.org/10.3133/sir20215130.","productDescription":"Report viii, 39 p.; Data Release; Dataset","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126300","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":393105,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5130/images/"},{"id":393103,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393102,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GEMMN6","text":"USGS data release","linkHelpText":"Baseline groundwater-quality data from a densely developed coastal neighborhood, Falmouth, Massachusetts (2016–2020) (ver. 3.0, April 2021)"},{"id":393101,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5130/sir20215130.pdf","text":"Report","size":"8.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5130"},{"id":393100,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5130/coverthb.jpg"},{"id":393104,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5130/sir20215130.XML"}],"country":"United States","state":"Massachusetts","city":"Falmouth","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"type\": \"Polygon\",\n        \"coordinates\": [\n          [\n            [\n              -70.65788269042969,\n              41.52245918082221\n            ],\n            [\n              -70.39627075195312,\n              41.52245918082221\n            ],\n            [\n              -70.39627075195312,\n              41.725205507257016\n            ],\n            [\n              -70.65788269042969,\n              41.725205507257016\n            ],\n            [\n              -70.65788269042969,\n              41.52245918082221\n            ]\n          ]\n        ]\n      }\n    }\n  ]\n}","contact":"<p><a href=\"mailto:dc_nweng@usgs.gov\" data-mce-href=\"mailto:dc_nweng@usgs.gov\">Director</a>, <a href=\"https://www.usgs.gov/centers/new-england-water\" data-mce-href=\"https://www.usgs.gov/centers/new-england-water\">New England Water Science Center</a><br>U.S. Geological Survey<br>10 Bearfoot Road<br>Northborough, MA 01532</p>","tableOfContents":"<ul><li>Acknowledgments</li><li>Abstract</li><li>Introduction</li><li>Geographic, Geologic, and Hydrologic Setting</li><li>Hydrogeologic Observations</li><li>Groundwater-Quality Results</li><li>Expected Trends in Water Quality</li><li>Summary and Conclusions</li><li>References Cited</li><li>Appendix 1. Methods of Data Collection, Laboratory Analysis, and Trend Evaluation</li></ul>","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2021-12-20","noUsgsAuthors":false,"publicationDate":"2021-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"McCobb, Timothy D. 0000-0003-1533-847X tmccobb@usgs.gov","orcid":"https://orcid.org/0000-0003-1533-847X","contributorId":2012,"corporation":false,"usgs":true,"family":"McCobb","given":"Timothy","email":"tmccobb@usgs.gov","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barbaro, Jeffrey R. 0000-0002-6107-2142 jrbarbar@usgs.gov","orcid":"https://orcid.org/0000-0002-6107-2142","contributorId":1626,"corporation":false,"usgs":true,"family":"Barbaro","given":"Jeffrey","email":"jrbarbar@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828714,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"LeBlanc, Denis R. 0000-0002-4646-2628 dleblanc@usgs.gov","orcid":"https://orcid.org/0000-0002-4646-2628","contributorId":1696,"corporation":false,"usgs":true,"family":"LeBlanc","given":"Denis","email":"dleblanc@usgs.gov","middleInitial":"R.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828715,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belaval, Marcel","contributorId":21636,"corporation":false,"usgs":true,"family":"Belaval","given":"Marcel","affiliations":[],"preferred":false,"id":828716,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227684,"text":"70227684 - 2021 - Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico","interactions":[],"lastModifiedDate":"2022-09-12T17:03:23.740912","indexId":"70227684","displayToPublicDate":"2021-12-31T11:51:41","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"4","title":"Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico","docAbstract":"Reliable information about the frequency of annual peak streamflow is needed for floodplain management, objective assessment of flood risk, and cost-effective design of dams, levees, other flood-control structures, and roads, bridges, and culverts. Generalized skew coefficients are among the data needed for log-Pearson type III peak-streamflow frequency analyses of annual peak streamflows. A technique is presented to estimate generalized skew coefficients used for log-Pearson type III peak-streamflow frequency analyses of annual peak streamflow from natural watersheds (minimal regulation and minimal impervious cover). The estimation of generalized skew coefficients was based on annual and historical peak streamflow data from an initial set of 444 selected USGS streamgaging stations (streamgages) with at least 30 years of recorded annual peak streamflows from natural watersheds in Texas, Oklahoma, and the part of New Mexico east of the Great Continental Divide. The primary focus was to obtain information that could be used to update previously published generalized skew coefficients in Texas.\n\nOf the 444 candidate streamgages, 341 were used in the final construction of statistical models. Two generalized additive models (GAMs) were used to predict generalized skew based on a 2-dimensional smooth on projected Albers equal area coordinates of either (1) the locations of the centroids of the gaged watersheds or (2) the streamgage locations. To create maps of generalized skew coefficients, predictions were made on a 1-kilometer grid and contour lines were superimposed. The centroid-location map, with a mean-squared error (MSE) of 0.216, is preferred. Generalized skew coefficients from the centroid-location map, along with the MSE, are useful for computing weighted-skew values when conducting frequency analyses of annual peak streamflow following the guidelines set forth in Bulletin 17C. Based on the results of the study, text revision of the TxDOT Hydraulic Design Manual could be made.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Generalized skew update and regional study of distribution shape for Texas flood frequency analyses","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Texas Tech University Center for Multidisciplinary Research in Transportation","doi":"10.18738/T8/SVLCOQ","collaboration":"Texas Department of Transportation","usgsCitation":"Asquith, W.H., Yesildirek, M.V., Landers, R.N., Cleveland, T.G., Fang, Z.N., and Zhang, J., 2021, Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico, chap. 4 <i>of</i> Generalized skew update and regional study of distribution shape for Texas flood frequency analyses, p. 31-58, https://doi.org/10.18738/T8/SVLCOQ.","productDescription":"28 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0000-0001-9871-8405","orcid":"https://orcid.org/0000-0001-9871-8405","contributorId":272210,"corporation":false,"usgs":false,"family":"Fang","given":"Zheng","email":"","middleInitial":"N.","affiliations":[{"id":12734,"text":"University of Texas at Arlington","active":true,"usgs":false}],"preferred":false,"id":831737,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Jiaqi","contributorId":202467,"corporation":false,"usgs":false,"family":"Zhang","given":"Jiaqi","email":"","affiliations":[{"id":36453,"text":"University of Texas, Arlington, TX, USA","active":true,"usgs":false}],"preferred":false,"id":831738,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70248897,"text":"70248897 - 2021 - Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery","interactions":[],"lastModifiedDate":"2024-02-28T17:54:21.980295","indexId":"70248897","displayToPublicDate":"2021-12-31T11:46:02","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery","docAbstract":"<p>Earthquake-induced secondary ground failure hazards, such as liquefaction and landslides, result in catastrophic building and infrastructure damage as well as human fatalities. To facilitate emergency responses and mitigate losses, the U.S. Geological Survey provides a rapid hazard estimation system for earthquake-triggered landslides and liquefaction using geospatial susceptibility proxies and ShakeMap ground motion estimates. However, the resolution and accuracy of these models are often limited by coarse-granularity and large uncertainties of available geospatial features provided at a regional scale. Recently, with the advancement of remote sensing technologies, synthetic aperture radar (SAR) images are captured and analyzed to obtain a rapid estimate of earthquake-induced correlation changes between pre- and post-event images. These correlation changes indicate ground failures and building damage t, showing the potential to provide supplementary information for rapid hazard and loss estimation. However, the exact causes of changes in satellite images are not directly ascertained by the DPM alone. For example, changes could be due to building damage, landslides, liquefaction, noise or any combination thereof. More importantly, the occurrence and intensity of landslides, liquefaction, and building damages are spatially correlated, which makes it yet more challenging to distinguish the sources of any such changes. </p><p>In this study, we develop a generalized causal graph-based Bayesian Network that models the physical interdependencies between geospatial features, seismic ground failures and building damage, as well as DPMs. Geospatial features provide physical insights for estimating ground failure occurrence while DPMs contain event-specific surface change observations. This physics-informed causal graph incorporate these variables with complex physical relationships in one holistic Bayesian updating scheme to effectively fuse information from both geospatial models and remote sensing data. This framework is scalable and flexible enough to deal with highly complex multi-hazard combinations. We then develop a stochastic variational inference algorithm to jointly update the intractable posterior probabilities of unobserved landslides, liquefaction, and building damage at different locations efficiently. In addition, a local graphical model pruning algorithm is presented to reduce the computational cost of large-scale seismic ground failure estimation. We apply this framework to September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January 2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of our algorithm</p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 17th World Conference on Earthquake Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"The 17th World Conference on Earthquake Engineering","conferenceDate":"September 27-October 2, 2021","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H., 2021, Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery, <i>in</i> Proceedings of the 17th World Conference on Earthquake Engineering, Sendai, Japan, September 27-October 2, 2021, 12 p.","productDescription":"12 p.","ipdsId":"IP-127995","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":426079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421116,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, S.","contributorId":330153,"corporation":false,"usgs":false,"family":"Xu","given":"S.","affiliations":[{"id":78827,"text":"State University of New York at Stony Brook","active":true,"usgs":false}],"preferred":false,"id":884126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, J.","contributorId":330154,"corporation":false,"usgs":false,"family":"Dimasaka","given":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, H.","contributorId":330155,"corporation":false,"usgs":false,"family":"Noh","given":"H.","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884129,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70227728,"text":"70227728 - 2021 - Delivering real-time water hazard information through human-centered design","interactions":[],"lastModifiedDate":"2022-04-08T15:13:57.940317","indexId":"70227728","displayToPublicDate":"2021-12-31T10:07:21","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9366,"text":"CCAST Case Study on Actionable Science","active":true,"publicationSubtype":{"id":1}},"title":"Delivering real-time water hazard information through human-centered design","docAbstract":"<p><span>On Memorial Day, 2015, catastrophic flooding throughout central Texas resulted in the loss of 13 lives and caused&nbsp;</span><span>millions of dollars in damages</span><span>&nbsp;(Furl 2018). The flooding exposed the need for water resource managers, first responders, and the public to have better real-time access to streamflow gaging stations and weather information. In 2016, the U.S. Geological Survey (USGS) developed the Texas Water Dashboard (Dashboard) to combine datasets produced by multiple agencies and display them in a single web-mapping application.</span></p>","language":"English","publisher":"CCAST","usgsCitation":"Pasley, N.K., 2021, Delivering real-time water hazard information through human-centered design: CCAST Case Study on Actionable Science, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-123650","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":398388,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":394959,"type":{"id":15,"text":"Index Page"},"url":"https://arcg.is/1PP4by0"}],"country":"United 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,{"id":70240352,"text":"70240352 - 2021 - A desert tortoise-common raven viable conflict threshold","interactions":[],"lastModifiedDate":"2023-02-06T16:05:37.397759","indexId":"70240352","displayToPublicDate":"2021-12-31T10:03:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"A desert tortoise-common raven viable conflict threshold","docAbstract":"<p><span>Since 1966, common raven (</span><i>Corvus corax</i><span>; raven) abundance has increased throughout much of this species’ Holarctic distribution, fueled by an ever-expanding supply of anthropogenic resource subsidies (e.g., water, food, shelter, and nesting substrate) to ecoregion specific raven population carrying capacities. Consequently, ravens are implicated in declines of both avian and reptilian species of conservation concern, including the California (USA) endangered and federally threatened Mojave desert tortoise (</span><i>Gopherus agassizii</i><span>; desert tortoise). While ravens are a natural predator of desert tortoises, the inter-generational stability of desert tortoise populations is expected to be compromised as annual juvenile survival is suppressed below 0.77 through a combination of raven depredation and other sources of mortality. To estimate the extent to which raven depredation suppresses desert tortoise recruitment within the Mojave Desert of California, we collected data from 274 variable-radius point counts, 78 desert tortoise decoy stations, and 8 control stations during the spring of 2020. Additionally, we complied a geodatabase of previously active raven nests, observed between 2013 and 2020. Raven density estimates from 4 monitoring areas ranged between 0.63 (eastern most) and 2.44 (western most) raven km</span><sup>-2</sup><span>&nbsp;(95% CI: 0.35–1.14 and 1.33–4.48, respectively). We used a Bayesian shared frailty model to estimate the effects of raven density and distance to the nearest previously active raven nest on the annual “survival” of juvenile desert tortoise decoys (75-mm Midline Carapace Length), which we then converted into survival estimates for 0- to 10-year-old desert tortoises by adjusting exposure to reflect natural activity patterns. At the 1.72-km median distance from the nearest previously active raven nest, the estimated annual survival of desert tortoises decreased as raven density increased, ranging among conservation areas from 0.774 (eastern most) to 0.733 (western most). Accordingly, our model predicts that desert tortoise populations exposed to raven densities in excess of 0.89 raven km</span><sup>-2</sup><span>, at a distance</span></p>","language":"English","publisher":"Berryman Institute","doi":"10.26077/eeca-1eec","usgsCitation":"Holcomb, K.L., Coates, P.S., Prochazka, B.G., Shields, T., and Boarman, W., 2021, A desert tortoise-common raven viable conflict threshold: Human–Wildlife Interactions, v. 15, no. 3, p. 405-421, https://doi.org/10.26077/eeca-1eec.","productDescription":"17 p.","startPage":"405","endPage":"421","ipdsId":"IP-130973","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Basin & Range","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -117.97303916756042,\n              35.71726205140463\n            ],\n            [\n              -117.97303916756042,\n              34.34636579137755\n            ],\n            [\n              -114.99857556861961,\n              34.34636579137755\n            ],\n            [\n              -114.99857556861961,\n              35.71726205140463\n            ],\n            [\n              -117.97303916756042,\n              35.71726205140463\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holcomb, Kerry L.","contributorId":296962,"corporation":false,"usgs":false,"family":"Holcomb","given":"Kerry","email":"","middleInitial":"L.","affiliations":[{"id":64256,"text":"U.S. Fish and Wildlife Service, Carlsbad Fish and Wildlife Office, 777 East Tahquitz Canyon Way, Suite 208, Palm Springs, California, 92262, USA","active":true,"usgs":false}],"preferred":false,"id":863528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":863529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":863530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shields, Timothy","contributorId":296963,"corporation":false,"usgs":false,"family":"Shields","given":"Timothy","affiliations":[{"id":64257,"text":"Hardshell Labs, Inc., P.O. Box 362, Haines, Alaska, 99827, USA","active":true,"usgs":false}],"preferred":false,"id":863531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boarman, William I.","contributorId":302114,"corporation":false,"usgs":false,"family":"Boarman","given":"William I.","affiliations":[{"id":65416,"text":"Hardshell Labs","active":true,"usgs":false}],"preferred":false,"id":863532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70240335,"text":"70240335 - 2021 - A rapid assessment function to estimate common raven population densities: Implications for targeted management","interactions":[],"lastModifiedDate":"2023-02-06T15:59:35.728824","indexId":"70240335","displayToPublicDate":"2021-12-31T09:58:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"A rapid assessment function to estimate common raven population densities: Implications for targeted management","docAbstract":"<p><span>Common raven (</span><i>Corvus corax</i><span>; raven) populations have increased over the past 5 decades within the western United States. Raven population increases have been largely attributed to growing resource subsidies from expansion of human enterprise. Concomitantly, managers are becoming increasingly concerned about elevated adverse effects on multiple sensitive prey species, damage to livestock and agriculture, and human safety. Managers could benefit from a rapid but reliable method to estimate raven densities across spatiotemporal scales to monitor raven populations more efficiently and inform targeted and adaptive management frameworks. However, obtaining estimates of raven density is data- and resource-intensive, which renders monitoring within an adaptive framework unrealistic. To address this need, we developed a rapid survey protocol for resource managers to estimate site-level density based on the average number of ravens per survey. Specifically, we first estimated raven densities at numerous field sites with robust distance sampling procedures and then used regression to investigate the relationship between those density estimates and the number of ravens per survey, which revealed a strong correlation (</span><i>R</i><sup>2</sup><span>&nbsp;= 0.86). For management application, we provide access to R function software through a web-based interface to estimate density using number of ravens per survey, which we refer to as a Rapid Assessment Function (RAF). Then, using a simulation analysis of data from sites with abundant surveys and the RAF, we estimated raven density based on different numbers of surveys to help inform how many surveys are needed to achieve reliable estimates within this rapid assessment. While more robust procedures of distance sampling are the preferred methods for estimating raven densities from count surveys, the RAF tool presented herein provides a reliable approximation for informing management decisions when managers are faced with resource and small sample size constraints.</span></p>","language":"English","publisher":"Berryman Institute","doi":"10.26077/1svg-ej32","usgsCitation":"Brussee, B.E., Coates, P.S., O’Neil, S.T., Dettenmaier, S.J., Jackson, P.J., Howe, K., and Delehanty, D.J., 2021, A rapid assessment function to estimate common raven population densities: Implications for targeted management: Human–Wildlife Interactions, v. 15, no. 3, p. 433-446, https://doi.org/10.26077/1svg-ej32.","productDescription":"14 p.","startPage":"433","endPage":"446","ipdsId":"IP-130930","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412741,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","otherGeospatial":"Great Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -114.64724871350592,\n              35.13961157171347\n            ],\n            [\n              -114.11719290812712,\n              36.263508018383206\n            ],\n            [\n              -113.31573256140439,\n              43.69803466511934\n            ],\n            [\n              -120.81998484033755,\n              43.75896375090363\n            ],\n            [\n              -121.47400838048571,\n              39.959291447247125\n            ],\n            [\n              -118.79678730789294,\n              37.043166083806\n            ],\n            [\n              -114.64699890679083,\n              35.109972173503834\n            ],\n            [\n              -114.64724871350592,\n              35.13961157171347\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":863450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":863453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howe, Kristy B.","contributorId":192078,"corporation":false,"usgs":false,"family":"Howe","given":"Kristy B.","affiliations":[],"preferred":false,"id":863454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":863455,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70224525,"text":"70224525 - 2021 - Near real-time updating of pager loss estimates","interactions":[],"lastModifiedDate":"2024-02-21T15:52:50.673441","indexId":"70224525","displayToPublicDate":"2021-12-31T09:51:40","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Near real-time updating of pager loss estimates","docAbstract":"Initial alerts by PAGER (Prompt Assessment of Global Earthquakes for Response) within minutes following an earthquake include several uncertainties, mainly due to potential inaccuracies in location, depth, fault delineation, and shaking estimates. We enhance an updating framework by incorporating early reports of fatalities within the first 24 hours, or so, of an earthquake to update PAGER’s overall fatality estimates and its resulting alert level. Though initial loss reports by officials or the media are uncertain and often undercount the eventual reported impacts, their temporal evolution provides predictive constraints for the PAGER model. The proposed framework helps capture these in a systematic way to minimize potential large fluctuations in PAGER alerts as ShakeMap  (the USGS product which estimates how an area is affected by an earthquake) gets updated in the early hours after an earthquake. The new framework also accounts for uncertainties associated with early fatality reports as well as PAGER model-related uncertainties in order to improve the overall impact forecast. This updating framework improves the loss estimate and alert level to the correct level within the first 24 hours even when the initial estimation from PAGER is assumed to be off by two levels of alert, which is plausible due to potential over- or under-estimation of the PAGER model. While test results are very encouraging, our future work aims at implementation of operational PAGER model updating, which entails additional challenges in acquiring useful data, estimating their credibility, and developing rigorously tested operational code and protocols","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Engler, D., Jaiswal, K.S., Noh, H.Y., and Wald, D.J., 2021, Near real-time updating of pager loss estimates, 17th World Conference on Earthquake Engineering, 17WCEE, Sendai, Japan, September 13-18, 2020, 10 p.","productDescription":"10 p.","ipdsId":"IP-116417","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425817,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Engler, Davis 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265963,"corporation":false,"usgs":false,"family":"Engler","given":"Davis","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":823867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":823869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
]}