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Using CE, decision-makers can visualize decade-by-decade changes in climate conditions in their county and the magnitude of changes projected for the end of this century under two plausible emissions pathways. They can also check how projected changes relate to user-defined thresholds that represent points at which valued assets may become stressed, damaged, or destroyed. By providing easy access to authoritative information in an elegant interface, the Climate Explorer can help communities recognize—and prepare to avoid or respond to—emerging climate hazards. Another important step in the evolution of CE builds on the purposeful alignment of the CRT with the U.S. Global Change Research Program’s (USGCRP) National Climate Assessment (NCA). By closely linking these two authoritative resources, we envision that users can easily transition from static maps and graphs within NCA reports to dynamic, interactive versions of the same data within CE and other resources within the CRT, which they can explore at higher spatial scales or customize for their own purposes. The provision of consistent climate data and information—a result of collaboration among USGCRP’s federal agencies—will assist decision-making by other governmental entities, nongovernmental organizations, businesses, and individuals.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/BAMS-D-18-0298.1","collaboration":"","usgsCitation":"Lipschultz, F., Herring, D., Ray, A.J., Alder, J.R., Dahlman, L., DeGaetano, A., Fox, J.F., Gardiner, E., Herring, J., Hicks, J., Melton, F., Morefield, P.E., and Sweet, W., 2020, Climate explorer: Improved access to local climate projections, v. 101, no. 3, p. e265-e273, https://doi.org/10.1175/BAMS-D-18-0298.1.","productDescription":"9 p.","startPage":"e265","endPage":"e273","ipdsId":"IP-091613","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":457311,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Cornell University, Ithaca, NY","active":true,"usgs":false}],"preferred":false,"id":786617,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fox, James F.","contributorId":223922,"corporation":false,"usgs":false,"family":"Fox","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":40794,"text":"National Environmental Modeling and Analysis Center, University of North Carolina","active":true,"usgs":false}],"preferred":false,"id":786624,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gardiner, Edward","contributorId":223916,"corporation":false,"usgs":false,"family":"Gardiner","given":"Edward","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":786618,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Herring, Jamie","contributorId":223917,"corporation":false,"usgs":false,"family":"Herring","given":"Jamie","affiliations":[{"id":40792,"text":"Habitat Seven","active":true,"usgs":false}],"preferred":false,"id":786619,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hicks, Jeff","contributorId":223918,"corporation":false,"usgs":false,"family":"Hicks","given":"Jeff","email":"","affiliations":[{"id":40793,"text":"Fernleaf Interactive","active":true,"usgs":false}],"preferred":false,"id":786620,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Melton, Forrest","contributorId":223919,"corporation":false,"usgs":false,"family":"Melton","given":"Forrest","affiliations":[{"id":38788,"text":"NASA","active":true,"usgs":false}],"preferred":false,"id":786621,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Morefield, Philip E.","contributorId":223920,"corporation":false,"usgs":false,"family":"Morefield","given":"Philip","email":"","middleInitial":"E.","affiliations":[{"id":37230,"text":"EPA","active":true,"usgs":false}],"preferred":false,"id":786622,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Sweet, William ","contributorId":223921,"corporation":false,"usgs":false,"family":"Sweet","given":"William ","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":786623,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70209153,"text":"70209153 - 2020 - Implementation of a surface water extent model in Cambodia using cloud-based remote sensing","interactions":[],"lastModifiedDate":"2020-03-20T06:38:20","indexId":"70209153","displayToPublicDate":"2020-03-19T18:59:44","publicationYear":"2020","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":"Implementation of a surface water extent model in Cambodia using cloud-based remote sensing","docAbstract":"Mapping surface water over time provides the spatially explicit information essential for hydroclimatic research focused on droughts and flooding. Hazard risk assessments and water management planning also rely on accurate, long-term measurements describing hydrologic fluctuations. Stream gages are a common measurement tool used to better understand flow and inundation dynamics, but gage networks are incomplete or non-existent in many parts of the world. In such instances, satellite imagery may provide the only data available to monitor surface water changes over time. Here, we describe an effort to extend the applicability of the USGS Dynamic Surface Water Extent (DSWE) model to non-US regions. We leverage the multi-decadal archive of the Landsat satellite in the Google Earth Engine (GEE) cloud-based computing platform to produce and analyze 372 monthly composite maps and 31 annual maps (January 1988–December 2018) in Cambodia, a flood-prone country in Southeast Asia that lacks a comprehensive stream gage network. DSWE relies on a series of spectral water indices and elevation data to classify water into four categories of water inundation. We compared model outputs to existing surface water maps and independently assessed DSWE accuracy at discrete dates across the time series. Despite considerable cloud obstruction and missing imagery across the monthly time series, the overall accuracy exceeded 85% for all annual tests. The DSWE model consistently mapped open water with high accuracy, and areas classified as “high confidence” water correlate well to other available maps at the country scale. Results in Cambodia suggest that extending DSWE globally using a cloud computing framework may benefit scientists, managers, and planners in a wide array of applications across the globe.","language":"English","publisher":"MDPI","doi":"10.3390/rs12060984","usgsCitation":"Soulard, C.E., Walker, J.J., and Petrakis, R.E., 2020, Implementation of a surface water extent model in Cambodia using cloud-based remote sensing: Remote Sensing, v. 12, no. 6, 984, https://doi.org/10.3390/rs12060984.","productDescription":"984","ipdsId":"IP-115688","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":457313,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12060984","text":"Publisher Index Page"},{"id":437053,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LH9YYF","text":"USGS data release","linkHelpText":"Implementation of a Surface Water Extent Model using Cloud-Based Remote Sensing - Code and Maps"},{"id":373394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Cambodia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[103.49728,10.63256],[103.09069,11.15366],[102.58493,12.18659],[102.3481,13.39425],[102.98842,14.22572],[104.28142,14.41674],[105.21878,14.27321],[106.04395,13.88109],[106.49637,14.57058],[107.38273,14.20244],[107.61455,13.53553],[107.4914,12.33721],[105.81052,11.56761],[106.24967,10.96181],[105.19991,10.88931],[104.33433,10.48654],[103.49728,10.63256]]]},\"properties\":{\"name\":\"Cambodia\"}}]}","volume":"12","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":785150,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walker, Jessica J. 0000-0002-3225-0317 jjwalker@usgs.gov","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":169458,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica","email":"jjwalker@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":785151,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Petrakis, Roy E. 0000-0001-8932-077X","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":219707,"corporation":false,"usgs":false,"family":"Petrakis","given":"Roy","email":"","middleInitial":"E.","affiliations":[{"id":27608,"text":"Contractor to the USGS","active":true,"usgs":false}],"preferred":false,"id":785152,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211978,"text":"70211978 - 2020 - A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health","interactions":[],"lastModifiedDate":"2020-08-12T22:51:49.456224","indexId":"70211978","displayToPublicDate":"2020-03-19T17:41:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1566,"text":"Environmental Science: Processes and Impacts","active":true,"publicationSubtype":{"id":10}},"title":"A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health","docAbstract":"

Neonicotinoid insecticides are widely used in both urban and agricultural settings around the world. Historically, neonicotinoid insecticides have been viewed as ideal replacements for more toxic compounds, like organophosphates, due in part to their perceived limited potential to affect the environment and human health. This critical review investigates the environmental fate and toxicity of neonicotinoids and their metabolites and the potential risks associated with exposure. Neonicotinoids are found to be ubiquitous in the environment, drinking water, and food, with low-level exposure commonly documented below acceptable daily intake standards. Available toxicological data from animal studies indicate possible genotoxicity, cytotoxicity, impaired immune function, and reduced growth and reproductive success at low concentrations, while limited data from ecological or cross-sectional epidemiological studies have identified acute and chronic health effects ranging from acute respiratory, cardiovascular, and neurological symptoms to oxidative genetic damage and birth defects. Due to the heavy use of neonicotinoids and potential for cumulative chronic exposure, these insecticides represent novel risks and necessitate further study to fully understand their risks to humans.

","language":"English","publisher":"Royal Society of Chemistry","doi":"10.1039/C9EM00586B","usgsCitation":"Lehmler, H., Kolpin, D.W., Hladik, M., Vargo, J.D., Schilling, K.E., LeFevre, G.H., Peeples, T.L., Poch, M.C., LaDuca, L.E., Cwiertny, D.M., and Field, R.W., 2020, A critical review on the potential impacts of neonicotinoid insecticide use: Current knowledge of environmental fate, toxicity, and implications for human health: Environmental Science: Processes and Impacts, v. 22, p. 1315-1346, https://doi.org/10.1039/C9EM00586B.","productDescription":"32 p.","startPage":"1315","endPage":"1346","ipdsId":"IP-116942","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":487009,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11755762","text":"External Repository"},{"id":377457,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lehmler, Hans-Joachim","contributorId":238108,"corporation":false,"usgs":false,"family":"Lehmler","given":"Hans-Joachim","email":"","affiliations":[],"preferred":false,"id":796064,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":796065,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":201293,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":796066,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vargo, John D.","contributorId":238109,"corporation":false,"usgs":false,"family":"Vargo","given":"John","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":796067,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schilling, Keith E.","contributorId":106429,"corporation":false,"usgs":false,"family":"Schilling","given":"Keith","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796068,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"LeFevre, Gregory H.","contributorId":211880,"corporation":false,"usgs":false,"family":"LeFevre","given":"Gregory","email":"","middleInitial":"H.","affiliations":[{"id":6768,"text":"University of Iowa","active":true,"usgs":false}],"preferred":true,"id":796069,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peeples, Tonya L.","contributorId":238110,"corporation":false,"usgs":false,"family":"Peeples","given":"Tonya","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":796070,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Poch, Matthew C.","contributorId":238111,"corporation":false,"usgs":false,"family":"Poch","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":796071,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"LaDuca, Lauren E.","contributorId":238112,"corporation":false,"usgs":false,"family":"LaDuca","given":"Lauren","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":796072,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cwiertny, David M.","contributorId":190557,"corporation":false,"usgs":false,"family":"Cwiertny","given":"David","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":796073,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Field, R. William","contributorId":238113,"corporation":false,"usgs":false,"family":"Field","given":"R.","email":"","middleInitial":"William","affiliations":[],"preferred":false,"id":796074,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70208050,"text":"sim3447 - 2020 - Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","interactions":[],"lastModifiedDate":"2022-04-22T20:02:50.44033","indexId":"sim3447","displayToPublicDate":"2020-03-19T13:23:38","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3447","displayTitle":"Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico","title":"Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","docAbstract":"

This geologic map depicts and briefly describes geologic units underlying Petroglyph National Monument and immediately adjacent areas in Bernalillo County, New Mexico. The Monument is underlain dominantly by Quaternary basalts of the Albuquerque Volcanoes volcanic field, a series of basin-filling volcanic flows and associated vents from a monogenetic volcanic highland along the eastern margin of the Llano de Albuquerque. This compilation builds on data of previously published geologic maps and reports but includes new interpretive synthesis of volcanic stratigraphy and a unified representation of Quaternary surficial deposits overlying volcanic deposits within the Monument and areas immediately adjacent. This geologic map emphasizes the distribution of Quaternary volcanic vent areas and lava flow deposits which were incompletely mapped on previous publications. Surficial deposits are simplified, but uniformly mapped and described in contrast to varying map unit distributions, names and descriptions presented in the references above. Underlying deposits of the upper Santa Fe Group are exposed in the western part of the map area and described briefly.

North-trending, syn- and post-eruption faulting is well preserved in the volcanic field and reflected in the subsurface models of aeromagnetic data. These faults are dominated by dip-slip displacement and are interpreted as extensional faults of the central Albuquerque Basin of the northern Rio Grande rift. Elongate distribution of vents for most of the volcanic deposits are spatially associated with the easternmost of these faults and are interpreted to reflect eruptions from fissures paralleling the regional extensional fault trends of the rift.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3447","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Thompson, R.A., Chan, C.F., Gilmer, A.K., and Shroba, R.R., 2020, Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico: U.S. Geological Survey Scientific Investigations Map 3447, scale 1:24,000, https://doi.org/10.3133/sim3447.","productDescription":"2 Sheets: 50.50 inches x 40.00 inches; Data Release; ReadMe","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-102605","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":373216,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LW817K","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data Release for Geologic Map of Petroglyph National Monument and Vicinity, Bernalillo County, New Mexico"},{"id":373215,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447_georeferenced.pdf","text":"Sheet—Georeferenced geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"},{"id":399520,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109803.htm"},{"id":373213,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3447/coverthb.jpg"},{"id":373222,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3447/ReadMe.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3447 Read Me"},{"id":373214,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3447/sim3447.pdf","text":"Sheet—Geologic map of Petroglyph National Monument and vicinity, Bernalillo County, New Mexico","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3447"}],"scale":"24000","country":"United States","state":"New Mexico","county":"Bernalillo County","otherGeospatial":"Petroglyph National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.79946899414062,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.097439809364204\n ],\n [\n -106.68823242187499,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.188961188789925\n ],\n [\n -106.79946899414062,\n 35.097439809364204\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Center Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, Mail Stop 980
Denver, CO 80225

","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-03-19","noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Ren A. 0000-0002-3044-3043","orcid":"https://orcid.org/0000-0002-3044-3043","contributorId":207982,"corporation":false,"usgs":true,"family":"Thompson","given":"Ren A.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":780272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chan, Christine F. 0000-0002-4933-3258","orcid":"https://orcid.org/0000-0002-4933-3258","contributorId":221802,"corporation":false,"usgs":false,"family":"Chan","given":"Christine F.","affiliations":[{"id":6773,"text":"University of Kansas","active":true,"usgs":false}],"preferred":false,"id":780273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gilmer, Amy K. 0000-0001-5038-8136","orcid":"https://orcid.org/0000-0001-5038-8136","contributorId":218307,"corporation":false,"usgs":true,"family":"Gilmer","given":"Amy","email":"","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":780275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shroba, Ralph R. 0000-0002-2664-1813 rshroba@usgs.gov","orcid":"https://orcid.org/0000-0002-2664-1813","contributorId":1266,"corporation":false,"usgs":true,"family":"Shroba","given":"Ralph","email":"rshroba@usgs.gov","middleInitial":"R.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":780274,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261319,"text":"70261319 - 2020 - Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2019","interactions":[],"lastModifiedDate":"2024-12-06T14:18:20.448582","indexId":"70261319","displayToPublicDate":"2020-03-19T10:44:33","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2019","docAbstract":"

Lakewide acoustic (AT) and bottom trawl (BT) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m and include 70 fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species (i.e., alewife, bloater, rainbow smelt, deepwater sculpin, slimy sculpin, round goby, ninespine stickleback) as well as for age-0 yellow perch and large burbot. The AT survey has been conducted each late summer/early fall since 2004, and the 2019 survey consisted of 26 transects [513 km total (319 miles)] covering bottom depths ranging from 15 to 235 m and 30 midwater trawl tows covering bottom depths ranging 27 to 204 m; this survey estimates densities of three prey fish species (i.e., alewife, bloater, and rainbow smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks.

For the BT survey, total biomass density of prey fish equaled only 1.77 kg/ha, the 2nd lowest estimate of the time series and well below the long-term average total biomass of 35.7 kg/ha. For the AT survey, total biomass density of prey fish equaled 4.71 kg/ha, just above the long-term average total biomass of 4.25 kg/ha. Both surveys reported bloater to be the dominant species (by biomass) among prey fishes. Mean biomass of yearling and older (YAO) alewives in 2019 was 1.56 kg/ha in the AT survey and 0.07 kg/ha in the BT survey. Comparing the acoustic estimate to previous years, YAO alewife biomass was 76% lower than the 2018 estimate and less than the average from 2004-2019. Numeric density of age-0 alewife from the AT survey was only 35.1/ha in 2019, which is indicative of a poor year-class and only the fourth since 2004 with a density less than 100/ha. The alewife age distribution remained truncated, with age-2 fish dominating the population and only three alewife (out of 525 aged) that were older than age 3. Biomass density of YAO bloater was 3.08 kg/ha in the AT survey and 0.78 kg/ha in the BT survey- each at least an order of magnitude lower than what was estimated by the BT survey between 1981 and 1998. Numeric density of age-0 bloater was the lowest ever measured for each survey: 0/ha for the AT survey and 0.12/ha for the BT survey. Biomass density of YAO rainbow smelt was 0.03 kg/ha in the AT survey and 0.04 kg/ha in the BT survey, continuing the low rainbow smelt biomass that has been observed since 2001. Numeric density of age-0 rainbow smelt was 1.33/ha in the AT survey and 0.99 in the BT survey, indicating a weak year-class that follows three year-classes that exceeded 41/ha between 2016 and 2018. All four prey fish species sampled only by the BT survey indicated below average biomass densities. Deepwater sculpin was estimated at 0.47 kg/ha, which makes 9 of the past 10 years when biomass was <1 kg/ha. Slimy sculpin was estimated at 0.02 kg/ha, the second lowest density ever measured. Round goby was estimated at 0.39 kg/ha, which was below the average biomass of 0.96 kg/ha since 2008. Ninespine stickleback were only caught in one tow, and not surprisingly was estimated at a record low biomass. Burbot biomass remained near record low levels, and no age-0 yellow perch were caught, indicating a weak yellow perch year-class in 2019.

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0000-0003-4939-5368","orcid":"https://orcid.org/0000-0003-4939-5368","contributorId":217346,"corporation":false,"usgs":true,"family":"Warner","given":"David","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":920364,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":920365,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Turschak, Ben","contributorId":257454,"corporation":false,"usgs":false,"family":"Turschak","given":"Ben","email":"","affiliations":[],"preferred":false,"id":920366,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dieter, Patricia 0000-0003-1686-2679","orcid":"https://orcid.org/0000-0003-1686-2679","contributorId":217345,"corporation":false,"usgs":true,"family":"Dieter","given":"Patricia","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":920367,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Desorcie, Tim 0000-0002-9965-1668","orcid":"https://orcid.org/0000-0002-9965-1668","contributorId":346953,"corporation":false,"usgs":false,"family":"Desorcie","given":"Tim","affiliations":[],"preferred":false,"id":920368,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70210163,"text":"70210163 - 2020 - A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery","interactions":[],"lastModifiedDate":"2020-05-19T15:05:04.146927","indexId":"70210163","displayToPublicDate":"2020-03-19T09:58:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery","docAbstract":"Crop emergence is a critical stage for crop development and crop growth modeling. Mapping crop emergence using remote sensing data is challenging. Previous remote sensing phenology algorithms showed that crop stages could be detected around the V3-V4 (3 to 4 established leaves) vegetative stage. Traditional approaches have a strong assumption regarding the temporal evolution of plant growth and normally require a complete growth period of observations to define seasonal changes. Most approaches were not designed for the within-season mapping in the early growing season. In the current paper, we developed a new within-season emergence (WISE) approach to mapping crop green-up date using satellite observations during early growth stages. The approach was first optimized using high spatiotemporal resolution (10 m, 2 day revisit) imagery from the Vegetation and Environment monitoring New MicroSatellite (VENµS) research mission, and assessed using ground observations of early crop growth stages (emergence VE and one leaf V1 stages for corn, and emergence VE and unifoliolate VC stages for soybeans) collected over the Beltsville Agricultural Research Center (BARC) experimental fields in Beltsville, MD during the 2019 growing season. Results show that early crop growth stages can be reliably detected at sub-field scale about two weeks after crop emergence. The remote sensing green-up dates were about 4-5 days after crop emergence on average. Coefficients of determination (R2) between green-up dates and the mid-point dates of the early growth stages were above 0.90. The mean absolute differences, standard deviations, and root mean square errors comparing to the early growth stage mid-point dates were within six days. The maximum differences were within ±10 days across all fields. The WISE approach was assessed using operational Sentinel-2 data (10 m, 5 day revisit) in BARC. The detected green-up dates from Sentinel-2 were found close to VENµS results. Some fields were not detected due to the lack of observations during emergence dates. For independent evaluation, the WISE approach was applied over an agricultural watershed on the Maryland Eastern Shore using both VENµS and the harmonized Landsat and Sentinel-2 (HLS) data (30 m, 3-4 day revisit). The green-up dates were compared with crop progress reports of crop emergence dates from the National Agricultural Statistics Service (NASS) at the state-level. The WISE -detected green-up dates at the regional scale are within VE stage ranges but slightly earlier than NASS crop progress reports at the state-level. The WISE approach uses remote sensing observations during the early crop growth stages and has potential for operational application within the season using Sentinel-2 and HLS data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2020.111752","usgsCitation":"Gao, F., Anderson, M., Daughtry, C.S., Karnieli, A., Hively, W.D., and Kustas, W.P., 2020, A within-season approach for detecting early crop stage of corn and soybean using high temporal and spatial resolution imagery: Remote Sensing of Environment, v. 242, 111752, 19 p., https://doi.org/10.1016/j.rse.2020.111752.","productDescription":"111752, 19 p.","ipdsId":"IP-113523","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457324,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2020.111752","text":"Publisher Index Page"},{"id":374923,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","otherGeospatial":"Beltsville Agricultural Research Center (BARC), Choptank River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.94412231445312,\n 38.756225137839074\n ],\n [\n -76.38381958007812,\n 38.756225137839074\n ],\n [\n -76.38381958007812,\n 39.29392267616436\n ],\n [\n -76.94412231445312,\n 39.29392267616436\n ],\n [\n -76.94412231445312,\n 38.756225137839074\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"242","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gao, Feng","contributorId":197297,"corporation":false,"usgs":false,"family":"Gao","given":"Feng","affiliations":[],"preferred":false,"id":789358,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Martha","contributorId":210925,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[],"preferred":false,"id":789359,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Daughtry, Craig S. T.","contributorId":211093,"corporation":false,"usgs":false,"family":"Daughtry","given":"Craig","email":"","middleInitial":"S. T.","affiliations":[{"id":38179,"text":"USDA Agricultural Research Service, Hydrology and Remote Sensing Laboratory","active":true,"usgs":false}],"preferred":false,"id":789360,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karnieli, Arnon 0000-0001-8065-9793","orcid":"https://orcid.org/0000-0001-8065-9793","contributorId":224743,"corporation":false,"usgs":false,"family":"Karnieli","given":"Arnon","email":"","affiliations":[{"id":40930,"text":"Jacob Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Israel","active":true,"usgs":false}],"preferred":false,"id":789361,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hively, W. Dean 0000-0002-5383-8064","orcid":"https://orcid.org/0000-0002-5383-8064","contributorId":201565,"corporation":false,"usgs":true,"family":"Hively","given":"W.","email":"","middleInitial":"Dean","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":789362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kustas, William P.","contributorId":29962,"corporation":false,"usgs":false,"family":"Kustas","given":"William","email":"","middleInitial":"P.","affiliations":[{"id":6622,"text":"US Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":789363,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249570,"text":"70249570 - 2020 - Detecting commonality in multidimensional fish movement histories using sequence analysis","interactions":[],"lastModifiedDate":"2023-10-17T12:00:36.066173","indexId":"70249570","displayToPublicDate":"2020-03-19T06:57:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":773,"text":"Animal Biotelemetry","active":true,"publicationSubtype":{"id":10}},"title":"Detecting commonality in multidimensional fish movement histories using sequence analysis","docAbstract":"

Background

Acoustic telemetry, for tracking fish movement histories, is multidimensional capturing both spatial and temporal domains. Oftentimes, analyses of such data are limited to a single domain, one domain nested within the other, or ad hoc approaches that simultaneously consider both domains. Sequence analysis, on the other hand, offers a repeatable statistical framework that uses a sequence alignment algorithm to calculate pairwise dissimilarities among individual movement histories and then hierarchical agglomerative clustering to identify groups of fish with similar movement histories. The objective of this paper is to explore how acoustic telemetry data can be fit to this statistical framework and used to identify commonalities in the movement histories of acoustic-tagged sea lamprey during upstream migration through the St. Clair-Detroit River System.

Results

Five significant clusters were identified among individual fish. Clusters represented differences in timing of movements (short vs long duration in the Detroit R. and Lake St. Clair); extent of upstream migration (ceased migration in Lake St. Clair, lower St. Clair R., or upper St. Clair R.), and occurrence of fallback (return to Lake St. Clair after ceasing migration in the St. Clair R.). Inferences about sea lamprey distribution and behavior from these results were similar to those reached in a previous analysis using ad-hoc analysis methods.

Conclusions

The repeatable statistical framework outlined here can be used to group sea lamprey movement histories based on shared sequence characteristics (i.e., chronological order of “states” occupied). Further, this framework is flexible and allows researchers to define a priori the movement aspect (e.g., order, timing, duration) that is important for identifying both common or previously undetected movement histories. As such, we do not view sequence analysis as a panacea but as a useful complement to other modelling approaches (i.e., exploratory tool for informing hypothesis development) or a stand-alone semi-quantitative method for generating a simplified, temporally and spatially structured view of complex acoustic telemetry data and hypothesis testing when observed patterns warrant further investigation.

","language":"English","publisher":"Springer Nature","doi":"10.1186/s40317-020-00195-y","usgsCitation":"Lowe, M.R., Holbrook, C., and Hondorp, D.W., 2020, Detecting commonality in multidimensional fish movement histories using sequence analysis: Animal Biotelemetry, v. 8, 10, 14 p., https://doi.org/10.1186/s40317-020-00195-y.","productDescription":"10, 14 p.","ipdsId":"IP-114379","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457331,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-020-00195-y","text":"Publisher Index Page"},{"id":421938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan","otherGeospatial":"St. Clair River Detroit River system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -83.33021246125514,\n 42.011299379305854\n ],\n [\n -82.17664800813024,\n 42.011299379305854\n ],\n [\n -82.17664800813024,\n 43.03151009761868\n ],\n [\n -83.33021246125514,\n 43.03151009761868\n ],\n [\n -83.33021246125514,\n 42.011299379305854\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Lowe, Michael R. 0000-0002-4645-9429","orcid":"https://orcid.org/0000-0002-4645-9429","contributorId":10539,"corporation":false,"usgs":true,"family":"Lowe","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":886255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886256,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":886257,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208116,"text":"fs20203002 - 2020 - Water resources of Union Parish, Louisiana","interactions":[],"lastModifiedDate":"2022-04-20T18:06:15.077165","indexId":"fs20203002","displayToPublicDate":"2020-03-18T12:38:43","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3002","displayTitle":"Water Resources of Union Parish, Louisiana","title":"Water resources of Union Parish, Louisiana","docAbstract":"

Information concerning the availability, use, and quality of water in Union Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 4.88 million gallons per day (Mgal/d) of water were withdrawn in Union Parish: 4.70 Mgal/d from groundwater sources and 0.18 Mgal/d from surface-water sources. Withdrawals for public-supply use accounted for about 89 percent (4.36 Mgal/d) of the total water withdrawn. Other categories of use included industrial, rural domestic, livestock, rice irrigation, and general irrigation. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2000 at about 8.89 Mgal/d.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203002","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Robinson, A.L., 2020, Water resources of Union Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3002, 6 p., https://doi.org/10.3133/fs20203002.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103356","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":399197,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109796.htm"},{"id":373339,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":373338,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3002/fs20203002.pdf","text":"Report","size":"835 kB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3002"},{"id":373337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3002/coverthb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Union Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.0647,33.0089],[-92.0641,33.0021],[-92.0717,32.997],[-92.0711,32.9875],[-92.0771,32.9861],[-92.082,32.9815],[-92.0754,32.9738],[-92.0759,32.9697],[-92.083,32.9692],[-92.0857,32.9651],[-92.0835,32.9619],[-92.0851,32.9569],[-92.0791,32.9565],[-92.0807,32.951],[-92.0779,32.9465],[-92.0767,32.9342],[-92.0728,32.9251],[-92.0739,32.916],[-92.0798,32.9091],[-92.0776,32.8968],[-92.0786,32.8877],[-92.084,32.8777],[-92.0751,32.8604],[-92.0663,32.85],[-92.0564,32.8446],[-92.0558,32.8378],[-92.0645,32.8341],[-92.0651,32.8313],[-92.0601,32.8277],[-92.0601,32.8241],[-92.0661,32.8213],[-92.0633,32.814],[-92.0572,32.805],[-92.0545,32.7981],[-92.0533,32.7941],[-92.0548,32.7722],[-92.0554,32.7699],[-92.0602,32.7663],[-92.0613,32.7617],[-92.0624,32.7576],[-92.0629,32.7558],[-92.0623,32.7508],[-92.0568,32.7481],[-92.0519,32.7435],[-92.054,32.7371],[-92.0589,32.7348],[-92.0605,32.7303],[-92.0654,32.723],[-92.0724,32.7161],[-92.0756,32.7088],[-92.0822,32.7042],[-92.0902,32.6878],[-92.0989,32.6836],[-92.1092,32.6827],[-92.1201,32.6735],[-92.1277,32.6762],[-92.1391,32.6757],[-92.1418,32.6702],[-92.1385,32.6611],[-92.19,32.6294],[-92.1964,32.6252],[-92.2187,32.6114],[-92.2214,32.611],[-92.2241,32.6073],[-92.224,32.6005],[-92.2266,32.5941],[-92.2288,32.5909],[-92.2342,32.5895],[-92.2402,32.5908],[-92.2451,32.5917],[-92.2506,32.5898],[-92.2566,32.5911],[-92.2625,32.5906],[-92.2647,32.587],[-92.2712,32.5828],[-92.2766,32.5814],[-92.2793,32.5837],[-92.2815,32.586],[-92.2843,32.585],[-92.2886,32.5836],[-92.2935,32.5841],[-92.2968,32.5845],[-92.3049,32.5831],[-92.3512,32.5832],[-92.4132,32.5845],[-92.4153,32.672],[-92.4736,32.6715],[-92.5188,32.6725],[-92.5195,32.7239],[-92.5233,32.723],[-92.5271,32.7202],[-92.5342,32.7224],[-92.5374,32.7206],[-92.5418,32.7187],[-92.5472,32.7205],[-92.5517,32.7268],[-92.5572,32.7331],[-92.5672,32.7453],[-92.5722,32.7489],[-92.5787,32.748],[-92.5852,32.7488],[-92.5913,32.7528],[-92.5968,32.7551],[-92.6044,32.7555],[-92.6099,32.7549],[-92.6147,32.7526],[-92.6191,32.7548],[-92.6279,32.7575],[-92.6312,32.7593],[-92.6361,32.7597],[-92.7256,32.7597],[-92.7252,32.8039],[-92.7339,32.8033],[-92.7341,32.8179],[-92.7254,32.818],[-92.7257,32.8758],[-92.7253,32.9209],[-92.7253,32.9222],[-92.7251,32.9445],[-92.725,33.0083],[-92.7246,33.0147],[-92.6255,33.0136],[-92.3864,33.0123],[-92.1457,33.0093],[-92.1194,33.0092],[-92.0647,33.0089]]]},\"properties\":{\"name\":\"Union\",\"state\":\"LA\"}}]}","contact":"

Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-03-18","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Robinson, Angela L. 0000-0001-5845-4847","orcid":"https://orcid.org/0000-0001-5845-4847","contributorId":206329,"corporation":false,"usgs":true,"family":"Robinson","given":"Angela","email":"","middleInitial":"L.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":780543,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70209081,"text":"sir20195077 - 2020 - Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona","interactions":[],"lastModifiedDate":"2022-04-22T21:15:48.594847","indexId":"sir20195077","displayToPublicDate":"2020-03-18T12:15:00","publicationYear":"2020","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":"2019-5077","displayTitle":"Geochemical and Mineralogical Study of the Red Mountain Porphyry Copper-Molybdenum Deposit and Vicinity, Santa Cruz County, Arizona","title":"Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona","docAbstract":"

The Red Mountain porphyry copper-molybdenum deposit (Cu-Mo deposit or PCD) is located in the northern part of the Patagonia Mountains, Santa Cruz County, Arizona. Extensive core drilling has delineated a large, deep-seated, structurally intact mineral system that extends from the present surface to depths of more than 1,765 meters. This system is hosted in a thick complex of predominantly felsic to andesitic volcanic rocks of the Cretaceous Period. This complex was intruded by scattered bodies of the Tertiary Period that are predominantly quartz monzonite porphyry; no major associated source intrusion has yet been found at depth.

A total of 818 samples of core were analyzed for as many as 44 elements. The abundances and distributions at depth of at least 17 of these elements (silver [Ag], arsenic [As], gold [Au], boron [B], bismuth [Bi], copper [Cu], mercury [Hg], potassium [K], molybdenum [Mo], lead [Pb], sulfur [S], antimony [Sb], tin [Sn], tellurium [Te], thallium [Tl], tungsten [W], and zinc [Zn]) are related mostly to events that generated the Red Mountain system. Many of these same samples were also analyzed by X-ray diffraction for a suite of minerals. The multielement and mineralogical analyses of the core samples provide important information about the concentrations, associations, and distributions of select elements and minerals, including zoning patterns that may not be apparent from visual examination of core samples. The distributions of selected elements and minerals in these samples reveal an unusually complete mineral system that extends from a typical PCD with potassic alteration at depth to peripheral zones of phyllic and advanced argillic alteration as well as a copper-rich supergene enriched zone and the remnants of a leached cap.

R-mode factor analysis was run with 34 elements for a set of samples from the deep part of the hypogene Cu-Mo deposit and another set from the part of the supergene zone with the highest copper enrichment. For the hypogene zone dataset, five factors are related to the PCD: (1) Ag, Cu, Mo, S, and Te; (2) As, B, Hg, and Sb; (3) Au and sodium (Na); (4) manganese (Mn), Pb, and Zn; and (5) K and Tl. For the supergene dataset, the deposit-related factors include (1) Cu, Mo, S, and Te; (2) Ag, As, Hg, Pb, Sb, and Tl; (3) Au and Na; and (4) K and rubidium (Rb). The changes in element associations between the two datasets indicate that some of these new associations are a result of formation of several suites of hypogene minerals in the deep part of the deposit and different hypogene mineral suites in the peripheral part of the deposit. Some changes may be because of the effects of supergene processes.

Zones containing deposit-related elements and minerals common to many PCDs are present at Red Mountain. These zones include a crude, inverted cup-shaped shell containing anomalous copper accompanied by high concentrations of Ag, Au, K, Mo, total S, sulfate S, Sb, Te, and Tl, as well as local concentrations of As, B, Hg, Pb, and Zn. Hydrothermal minerals spatially associated with the deep hypogene Cu-Mo deposit include chalcopyrite, molybdenite, pyrite, plagioclase, orthoclase, biotite, magnetite, calcite, quartz, and anhydrite.

Many of the hydrothermally deposited elements that are spatially related to the deposit are also concentrated in zones above the deep part of the deposit, including Ag, As, K, Pb, Sb, Te, Tl, and Zn. These elements are concentrated either (1) in generally wide, flat zones present in the upper part of the system or (2) in crudely arcuate peripheral zones found mainly in the middle part of the system and surrounding the deep part of the deposit. Near-surface, restricted hypogene anomalies are present for bismuth, mercury, tin, and tungsten.

The upper part of the deposit has been subjected to supergene enrichment and weathering. Deposit-related elements that remain anomalous in this area include Ag, As, Au, B, Bi, cobalt (Co), Cu, Hg, Mo, Pb, S, Sb, Sn, Te, Tl, uranium (U), and W. These positive concentrations indicate that, with the exception of copper and possibly mercury and uranium, these elements had relatively low chemical mobilities in the supergene enrichment and later weathering environments at Red Mountain. Most may have been deposited during one or more hypogene events and then redistributed locally during later events. Zinc is the only deposit-related element that has clearly been depleted as a result of supergene and (or) weathering events. Minerals that are common in the unweathered upper part of the system include chalcocite, pyrite, quartz, sericite, alunite, and pyrophyllite, as well as less common covellite, enargite, tennantite, tourmaline, barite, anglesite, and other sulfide or sulfate minerals.

Subsequent to formation of the Red Mountain Cu-Mo deposit and supergene enrichment, chemical weathering produced an area of pervasive hematite and other iron oxides in the near-surface part of the deposit to form a leached cap. These iron-rich minerals formed primarily as a result of the oxidation of pyrite. This event was accompanied by losses of cobalt, mercury, magnesium, and zinc, as well as destruction of sericite, plagioclase, pyrite, clay minerals, and pyrophyllite.

A total of 122 rock samples, 119 soil samples, and samples of three plant species (57 mesquite, 108 oak, and 68 juniper) were collected over and around Red Mountain. For the rock and soil samples, the distributions of anomalous Ag, As, Bi, Cu, Fe, Mo, Pb, Sb, Te, and Tl best delineated the exposed part of the deposit. The highest concentrations of many of these elements are centered on one or both of two main areas with exposures of quartz monzonite porphyry. The high concentrations of arsenic in the deposit area (as much as 390 parts per million (ppm) in rock and 1,500 ppm in soil) and of lead (as much as 2,370 ppm in rock and 1,490 ppm in soil) are particularly noteworthy.

The concentrations of various elements in the plant ash vary widely among the three species and are species dependent. Many of the deposit-related elements are either nonessential for plant growth or are considered toxic at certain concentration ranges. In spite of this, the distributions of potentially toxic Ag, As, Bi, Cd, Cu, Mo, Pb, Sb, selenium (Se), and Zn produce deposit-related anomalies for one or more of the three species.

Vegetation sampling offered no advantage over rock or soil sampling as an exploration tool. From an environmental standpoint, however, the plant analyses provide baseline data for both essential and nonessential elements that might be useful, for example, for selecting native plant species for revegetating mine waste areas.

The exposed part of the Red Mountain deposit has not been greatly disturbed as a result of mining and other activities. However, some of the rock, soil, and plant samples that were collected near the Harshaw Creek and Alum Gulch drainages, which are peripheral to Red Mountain, are also anomalous for various deposit-related elements. These anomalies are probably the result of dispersion of stream sediments contaminated with material from past mining.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195077","usgsCitation":"Chaffee, M.A., 2020, Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona: U.S. Geological Survey Scientific Investigations Report 2019–5077, 164 p., https://doi.org/10.3133/sir20195077.","productDescription":"Report: x, 164 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-085267","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":373304,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BS56JZ","text":"USGS data release","linkHelpText":"Data to accompany U.S. Geological Survey Scientific Investigations Report 2019-5077: Geochemical and mineralogical study of the Red Mountain porphyry copper-molybdenum deposit and vicinity, Santa Cruz County, Arizona"},{"id":399536,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109795.htm"},{"id":373303,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5077/sir20195077.pdf","text":"Report","size":"22.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5077"},{"id":373302,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5077/coverthb.jpg"}],"country":"United States","state":"Arizona","county":"Santa Cruz County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-111.364,31.4234],[-111.3654,31.5211],[-111.2983,31.5216],[-111.2634,31.5218],[-111.1608,31.522],[-111.1595,31.5403],[-111.1616,31.5508],[-111.1612,31.6389],[-111.1614,31.7242],[-111.0036,31.7247],[-110.9557,31.7247],[-110.8906,31.7255],[-110.8712,31.7257],[-110.8518,31.7255],[-110.8523,31.731],[-110.7941,31.7309],[-110.7042,31.7308],[-110.6902,31.7306],[-110.6838,31.7305],[-110.6692,31.7308],[-110.6644,31.7303],[-110.617,31.7306],[-110.5341,31.7309],[-110.4485,31.7307],[-110.4485,31.702],[-110.4482,31.6883],[-110.4483,31.6536],[-110.448,31.6157],[-110.4561,31.6154],[-110.4558,31.6017],[-110.4555,31.5871],[-110.4562,31.4684],[-110.4561,31.3328],[-110.4611,31.3328],[-110.4888,31.3328],[-110.5574,31.3324],[-110.6259,31.3323],[-110.6645,31.3321],[-110.7229,31.3318],[-110.7915,31.3315],[-110.8238,31.3313],[-110.8261,31.3312],[-110.8351,31.3312],[-110.8659,31.3309],[-110.8787,31.3308],[-110.9721,31.3301],[-111.0496,31.3294],[-111.0664,31.3292],[-111.0728,31.3292],[-111.1604,31.3577],[-111.1676,31.3601],[-111.1705,31.361],[-111.1725,31.3617],[-111.1746,31.3624],[-111.2218,31.3778],[-111.2843,31.3978],[-111.364,31.4234]]]},\"properties\":{\"name\":\"Santa Cruz\",\"state\":\"AZ\"}}]}","contact":"

Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-03-18","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Chaffee, Maurice 0000-0001-7391-1401 mchaffee@usgs.gov","orcid":"https://orcid.org/0000-0001-7391-1401","contributorId":223366,"corporation":false,"usgs":true,"family":"Chaffee","given":"Maurice","email":"mchaffee@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":784855,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70216012,"text":"70216012 - 2020 - Relocated aftershocks and background seismicity in eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences","interactions":[],"lastModifiedDate":"2020-11-03T13:29:28.447413","indexId":"70216012","displayToPublicDate":"2020-03-18T07:22:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Relocated aftershocks and background seismicity in eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences","docAbstract":"

High seismicity rates in eastern Indonesia occur due to the complex interaction of several tectonic plates which resulted in two deadly, destructive earthquake sequences that occurred in Lombok Island and the city of Palu, Sulawesi in 2018. The first sequence began in July with an Mw 6.4 event near Lombok, culminating in an Mw 7.0 event 8 d later. This was then followed by a nearby Mw 6.9 event 12 d later. Approximately 1000 km to the northeast, a separate sequence began several weeks later near Palu where an Mw 7.5 event occurred that triggered a tsunami. In this study, we present hypocentre relocations for both earthquake sequences as well as all other regional earthquakes in eastern Indonesia. The relocations were performed using a teleseismic double-difference relocation method and arrival times for P and S waves from stations at local, regional, and teleseismic distances. The catalogue and phase data were taken from the Agency for Meteorology, Climatology and Geophysics (BMKG) of Indonesia and the International Seismological Centre (ISC) for the period of April 2009 through November 2018. The relocated catalogue provides an improved view of seismicity in eastern Indonesia over the study period, sharpening locations and interpretations of seismogenic features throughout the region. In the Lombok area, the relocated earthquakes clearly show a backarc thrust to the north of the Sunda-Banda Arc transition zone. The relocated aftershocks show that the destructive Mw 7.0 and Mw 6.9 earthquakes of the Lombok sequence ruptured two different regions: The Mw 7.0 earthquake propagated westward, whereas the Mw 6.9 earthquake propagated eastward. The entire sequence of Lombok earthquakes was most likely started by the Mw 6.4 event as the initial event or foreshock, which then triggered backarc thrusts on both sides. Several weeks later and far to the northeast, the Mw 7.5 Palu earthquake occurred along the Palu-Koro Fault, filling a seismic gap that had not ruptured in an Mw 6.0 event or larger since at least 1900. The distribution of aftershocks indicates that the northern part of the Palu-Koro Fault has lower relative seismicity rates than the southern part at shallow depths, and that off fault aftershocks are mostly located to the east of the Palu-Koro Fault.

","language":"English","publisher":"Royal Astronomical Society","doi":"10.1093/gji/ggaa118","usgsCitation":"Supendi, P., Nugraha, A.D., Widiyantoro, S., Pesicek, J.D., Thurber, C., Abdullah, C., Daryono, D., Wiyono, S., Shiddiqi, H., and Rosalia, S., 2020, Relocated aftershocks and background seismicity in eastern Indonesia shed light on the 2018 Lombok and Palu earthquake sequences: Geophysical Journal International, v. 221, no. 3, p. 1845-1855, https://doi.org/10.1093/gji/ggaa118.","productDescription":"11 p.","startPage":"1845","endPage":"1855","ipdsId":"IP-114885","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":457340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggaa118","text":"Publisher Index Page"},{"id":380066,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Lombok, Palu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 119.14672851562499,\n -1.4720060101903352\n ],\n [\n 120.574951171875,\n -1.4720060101903352\n ],\n [\n 120.574951171875,\n -0.15380840901698828\n ],\n [\n 119.14672851562499,\n -0.15380840901698828\n ],\n [\n 119.14672851562499,\n -1.4720060101903352\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 115.7904052734375,\n -9.091248585779377\n ],\n [\n 116.77917480468749,\n -9.091248585779377\n ],\n [\n 116.77917480468749,\n -8.07554603328031\n ],\n [\n 115.7904052734375,\n -8.07554603328031\n ],\n [\n 115.7904052734375,\n -9.091248585779377\n ]\n ]\n ]\n }\n }\n 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Bandung","active":true,"usgs":false}],"preferred":false,"id":803752,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":803753,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thurber, C.H.","contributorId":244318,"corporation":false,"usgs":false,"family":"Thurber","given":"C.H.","affiliations":[{"id":13451,"text":"Univ. of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":803754,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Abdullah, C.I.","contributorId":244319,"corporation":false,"usgs":false,"family":"Abdullah","given":"C.I.","email":"","affiliations":[{"id":48884,"text":"Bandung Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803755,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Daryono, D.","contributorId":244320,"corporation":false,"usgs":false,"family":"Daryono","given":"D.","email":"","affiliations":[{"id":48887,"text":"BMKG, Indonesia","active":true,"usgs":false}],"preferred":false,"id":803756,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wiyono, S.H.","contributorId":244321,"corporation":false,"usgs":false,"family":"Wiyono","given":"S.H.","affiliations":[{"id":48887,"text":"BMKG, Indonesia","active":true,"usgs":false}],"preferred":false,"id":803757,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shiddiqi, H.A.","contributorId":244322,"corporation":false,"usgs":false,"family":"Shiddiqi","given":"H.A.","affiliations":[{"id":48888,"text":"Univ. of Bergen","active":true,"usgs":false}],"preferred":false,"id":803758,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Rosalia, S.","contributorId":244323,"corporation":false,"usgs":false,"family":"Rosalia","given":"S.","email":"","affiliations":[{"id":48884,"text":"Bandung Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803759,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70211840,"text":"70211840 - 2020 - Consequences of ignoring group association in spatial capture-recapture analysis","interactions":[],"lastModifiedDate":"2020-10-28T15:45:48.200351","indexId":"70211840","displayToPublicDate":"2020-03-17T15:32:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3766,"text":"Wildlife Biology","active":true,"publicationSubtype":{"id":10}},"title":"Consequences of ignoring group association in spatial capture-recapture analysis","docAbstract":"

Many models in population ecology, including spatial capture–recapture (SCR) models, assume that individuals are distributed and detected independently of one another. In reality, this is rarely the case – both antagonistic and gregarious relationships lead to non-independent spatial configurations, with territorial exclusion at one end of the spectrum and group-living at the other. Previous simulation studies suggest that grouping has limited impact on the outcome of SCR analyses. However, group associations entail not only spatial clustering of activity centers but also coordinated space use by group members, potentially impacting both ecological and observation processes underlying SCR analysis. We simulated SCR scenarios with different strengths of aggregation (clustering of individuals into groups with shared activity centers) and cohesion (synchronization of detection patterns of members of a group). We then fit SCR models to the simulated data sets and evaluated the effect of aggregation and cohesion on parameter estimates. Low to moderate aggregation and cohesion did not impact the bias and precision of estimates of density and the scale parameter of the detection function. However, non-independence between individuals led to high levels of overdispersion. Overdispersion strongly decreased the coverage of confidence intervals around parameter estimates, thereby increasing the probability of erroneous predictions. Our results indicate that SCR models are robust to moderate levels of aggregation and cohesion. Nonetheless, spatial dependence between individuals can lead to false inference. We recommend that practitioners 1) test for the presence of overdispersion in SCR data caused by aggregation and cohesion, and, if necessary, 2) correct their variance estimates using the overdispersion factor ĉ . Approaches for doing both are described in this paper. We also urge the development of SCR models that incorporate spatial associations between individuals not only to account for overdispersion but also to obtain quantitative information about social aspects of study populations.

","language":"English","publisher":"BioOne","doi":"10.2981/wlb.00649","usgsCitation":"Bischof, R., Dupont, P., Milleret, C., Chipperfield, J., and Royle, J.A., 2020, Consequences of ignoring group association in spatial capture-recapture analysis: Wildlife Biology, v. 2020, no. 1, wlb.00649, 11 p., https://doi.org/10.2981/wlb.00649.","productDescription":"wlb.00649, 11 p.","ipdsId":"IP-113777","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457343,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2981/wlb.00649","text":"Publisher Index Page"},{"id":377200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"2020","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bischof, Richard","contributorId":237793,"corporation":false,"usgs":false,"family":"Bischof","given":"Richard","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795324,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dupont, Pierre","contributorId":237794,"corporation":false,"usgs":false,"family":"Dupont","given":"Pierre","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milleret, Cyril","contributorId":237795,"corporation":false,"usgs":false,"family":"Milleret","given":"Cyril","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795326,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chipperfield, Joseph","contributorId":237796,"corporation":false,"usgs":false,"family":"Chipperfield","given":"Joseph","email":"","affiliations":[{"id":40295,"text":"Norwegian University of Life Sciences","active":true,"usgs":false}],"preferred":false,"id":795327,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Royle, J. 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ShakeCast is a freely available, postearthquake situational awareness software application that automatically retrieves earthquake shaking data from ShakeMap to compare ground shaking intensity measures against users’ facilities (Lin and Wald 2008). ShakeCast then generates potential damage assessment and inspection priority notifications, maps, and web-based products for critical users, emergency managers, and those on a need-to-know basis.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of solid earth geophysics, 2nd edition","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-10475-7_255-1","collaboration":"","usgsCitation":"Lin, K., Wald, D.J., and Slosky, D., 2020, Earthquakes, ShakeCast, chap. of Encyclopedia of solid earth geophysics, 2nd edition, HTML document, https://doi.org/10.1007/978-3-030-10475-7_255-1.","productDescription":"HTML document","ipdsId":"IP-109506","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Lin, Kuo-wan 0000-0002-7520-8151 klin@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-8151","contributorId":1539,"corporation":false,"usgs":true,"family":"Lin","given":"Kuo-wan","email":"klin@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":786318,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":786320,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Slosky, Daniel 0000-0001-7407-3606 dslosky@usgs.gov","orcid":"https://orcid.org/0000-0001-7407-3606","contributorId":194954,"corporation":false,"usgs":true,"family":"Slosky","given":"Daniel","email":"dslosky@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":786319,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208385,"text":"fs20203007 - 2020 - A historical look at changing water quality in the Delaware River basin","interactions":[],"lastModifiedDate":"2022-04-20T18:22:35.40781","indexId":"fs20203007","displayToPublicDate":"2020-03-17T08:08:47","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3007","displayTitle":"A Historical Look at Changing Water Quality in the Delaware River Basin","title":"A historical look at changing water quality in the Delaware River basin","docAbstract":"

In 2019 the U.S. Geological Survey (USGS) launched a pilot regional Integrated Water Availability Assessment (IWAA) in the Delaware River Basin (fig. 1). IWAA is intended to explore, test, and refine systems and processes for assessing water availability for human and ecological uses and understanding their underlying controls. Water quality plays an important role in supporting ecological health and determining the suitability of water for human consumption, recreation, agriculture, and industry. Understanding how water quality has changed over time in response to natural and human-induced changes in landscape and climate identifies potential challenges in safeguarding water for all uses. The USGS has evaluated water-quality trends across the Nation, and 22 of the evaluated sites are in the Delaware River Basin. These 22 sites are in the Appalachian Plateau, Valley and Ridge, Piedmont, and Coastal Plain Physiographic Provinces. Data from these sites indicate decadal to multidecadal changes in water quality and provide an initial look at how nutrient concentrations, such as total phosphorous, total nitrogen, and nitrate, and salinity indicators, such as specific conductance, sulfate, and chloride, have varied over time in the basin. The time period of the evaluation ranged from 1972 to 2012.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203007","collaboration":"Integrated Water Availability Assessments Program","usgsCitation":"Murphy, J.C., and Shoda, M.E., 2020, A historical look at changing water quality in the Delaware River basin: U.S. Geological Survey Fact Sheet 2020–3007, 2 p., https://doi.org/10.3133/fs20203007.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-113624","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373184,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TQ5ZS3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality trends and trend component estimates for the Nation's rivers and streams using Weighted Regressions on Time, Discharge, and Season (WRTDS) models and generalized flow normalization, 1972–2012"},{"id":373182,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3007/coverthb2.jpg"},{"id":373183,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3007/fs20203007.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3007"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5,\n 38.625\n ],\n [\n -74.5,\n 38.625\n ],\n [\n -74.5,\n 43\n ],\n [\n -76.5,\n 43\n ],\n [\n -76.5,\n 38.625\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Program Coordinator, Water Availability and Use Science Program
U.S. Geological Survey
Water Resources Mission Area

Email: wausp-info@usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-03-17","noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":781679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shoda, Megan E. 0000-0002-5343-9717 meshoda@usgs.gov","orcid":"https://orcid.org/0000-0002-5343-9717","contributorId":4352,"corporation":false,"usgs":true,"family":"Shoda","given":"Megan","email":"meshoda@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781680,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70209623,"text":"70209623 - 2020 - Dynamics, variability, and change in seasonal precipitation reconstructions for North America","interactions":[],"lastModifiedDate":"2022-04-13T20:34:18.100665","indexId":"70209623","displayToPublicDate":"2020-03-17T07:12:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2216,"text":"Journal of Climate","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics, variability, and change in seasonal precipitation reconstructions for North America","docAbstract":"Cool and warm season precipitation totals have been reconstructed on a gridded basis for North America using 439 tree-ring chronologies correlated with December-April totals and 547 different chronologies correlated with May-July totals. 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The reconstructions are included in the North American Seasonal Precipitation Atlas (NASPA) and provide new insight into decadal droughts and pluvials. They indicate that the 16th century megadrought, the most severe and sustained North American drought of the past 500-years, was the combined result of three distinct seasonal droughts each bearing unique spatial patterns potentially associated with seasonal forcing from ENSO, the Arctic Oscillation, and the Atlantic Multidecadal Oscillation. Significant 200- to 500-year long trends toward increased precipitation have been detected in the cool and warm season reconstructions for eastern North America. These seasonal precipitation changes appear to be part of the positive moisture trend measured in other paleoclimate proxies for the East that began due to natural forcing before the industrial revolution and may have recently been enhanced by anthropogenic climate change.","language":"English","publisher":"American Meteorological Society","doi":"10.1175/JCLI-D-19-0270.1","usgsCitation":"Stahle, D.W., Cook, E.R., Burnette, D.J., Torbenson, M.C., Howard, I.M., Griffin, D., Villanueva Diaz, J., Cook, B.I., Williams, P.A., Watson, E., Sauchyn, D.J., Pederson, N., Woodhouse, C.A., Pederson, G.T., Meko, D.M., Coulthard, B., and Crawford, C., 2020, Dynamics, variability, and change in seasonal precipitation reconstructions for North America: Journal of Climate, v. 8, no. 33, p. 3173-3195, https://doi.org/10.1175/JCLI-D-19-0270.1.","productDescription":"23 p.","startPage":"3173","endPage":"3195","ipdsId":"IP-107203","costCenters":[{"id":222,"text":"Earth Resources Observation and Science 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Puerto Rico","interactions":[],"lastModifiedDate":"2025-05-16T17:02:41.74285","indexId":"ofr20201022","displayToPublicDate":"2020-03-16T14:40:00","publicationYear":"2020","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":"2020-1022","displayTitle":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico","title":"Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico","docAbstract":"

Landslides in Puerto Rico range from nuisances to deadly events. Centuries of agricultural and urban modification of the landscape have perturbed many already unstable hillsides on the tropical island. One of the main triggers of mass wasting on the island is the high-intensity rainfall that is associated with tropical atmospheric systems. Puerto Rico’s geographic position and rugged topography render millions of residents vulnerable to widespread landslide events. In this study, a high-resolution (5 meters), high-intensity rainfall-induced landslide susceptibility model was produced using the frequency-ratio method. Datasets utilized in the model included a complete-island landslide inventory created from imagery obtained after Hurricanes Irma and María impacted the island during September 2017, slope inclination, land-surface curvature, soil type, geologic terrane, mean annual precipitation, land use, soil moisture, and distance to roadways and streams. The final data product (plate 1) is a statistically viable representation of where landslides are likely to initiate during or soon after intense rainfall, with a robust receiver operating characteristic area-under-curve value of 0.87. The model output raster pixel values were binned into 100 equal-area quantiles and then classified into Low, Moderate, High, Very High, and Extremely High classes of susceptibility. The Extremely High susceptibility classification represents the most vulnerable 1 percent of the island, whereas Very High, High, Moderate, and Low classifications cover 9, 20, 30, and 40 percent of the island, respectively. The susceptibility map is intended to assist in planning future development, mitigation measures, and post-event emergency response; however, it is not a substitute for site-specific, slope-stability assessments performed by licensed geologists and engineers. Additionally, the map does not portray locations where landslide material may travel after mobilization, and which may be at extreme risk; nor does it necessarily portray where landslides may occur during earthquakes or mass wasting triggered by prolonged, relatively low-intensity rainfall.

","language":"English, Spanish","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20201022","collaboration":"Prepared in cooperation with the University of Puerto Rico at Mayagüez","usgsCitation":"Hughes, K.S., and Schulz, W.H., 2020, Map depicting susceptibility to landslides triggered by intense rainfall, Puerto Rico: U.S. Geological Survey Open-File Report 2020–1022, 91 p., 1 plate, scale 1:150,000, https://doi.org/10.3133/ofr20201022.","productDescription":"Report: viii, 91 pages; 2 Sheets: 49.11 x 33.86 inches; Application Sites; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-116377","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":373245,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_pamphlet.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1022 pamphlet","linkHelpText":"English 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Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced"},{"id":373249,"rank":12,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_georeferenced.tif","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico, Geo-referenced (Geotiff)","description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico (Geotiff)"},{"id":373260,"rank":13,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2020-1022 readme file"},{"id":376484,"rank":17,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico, georreferenciado"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3736572265625,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 17.832374329567518\n ],\n [\n -65.577392578125,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 18.58377568837094\n ],\n [\n -67.3736572265625,\n 17.832374329567518\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046

","tableOfContents":"","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"publishedDate":"2020-03-16","noUsgsAuthors":false,"publicationDate":"2020-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Hughes, K. Stephen","contributorId":221160,"corporation":false,"usgs":false,"family":"Hughes","given":"K.","email":"","middleInitial":"Stephen","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":784666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schulz, William H. 0000-0001-9980-3580 wschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-9980-3580","contributorId":942,"corporation":false,"usgs":true,"family":"Schulz","given":"William","email":"wschulz@usgs.gov","middleInitial":"H.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":784627,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70210825,"text":"70210825 - 2020 - Ecology of influenza A viruses in wild birds and wetlands of Alaska","interactions":[],"lastModifiedDate":"2020-06-29T13:21:04.561218","indexId":"70210825","displayToPublicDate":"2020-03-16T09:41:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Ecology of influenza A viruses in wild birds and wetlands of Alaska","docAbstract":"Alaska represents a globally important region for the ecology of avian-origin influenza A viruses (IAVs) given expansive wetlands in this region which serve as habitat for numerous hosts of IAVs that disperse among four continents during the annual cycle. Extensive sampling of wild birds for IAVs in Alaska since 1991 has greatly extended inference regarding intercontinental viral exchange between North America and East Asia and the importance of Beringian endemic species to IAV ecology within this region. Data on IAVs in aquatic birds inhabiting Alaska has also been useful for helping to establish global patterns of prevalence in wild birds and viral dispersal across the landscape. In this review, we summarize main findings from investigations of IAVs in wild birds and wetlands of Alaska with the aim of providing readers with an understanding of viral ecology within this region. More specifically, we review viral detections, evidence of IAV exposure, and genetic characterization of isolates derived from wild bird samples collected in Alaska by host taxonomy. Additionally, we provide a short overview of wetland complexes within Alaska that may be important to IAV ecology at the continental scale.","language":"English","publisher":"BioOne","doi":"10.1637/0005-2086-64.2.109","usgsCitation":"Ramey, A.M., and Reeves, A.B., 2020, Ecology of influenza A viruses in wild birds and wetlands of Alaska: Avian Diseases, v. 64, no. 2, p. 109-122, https://doi.org/10.1637/0005-2086-64.2.109.","productDescription":"14 p.","startPage":"109","endPage":"122","ipdsId":"IP-114972","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":375950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -147.568359375,\n 65.05360170595502\n ],\n [\n -164.1796875,\n 64.16810689799152\n ],\n [\n -167.4755859375,\n 60.1524422143808\n ],\n [\n -164.794921875,\n 54.41892996865827\n ],\n [\n -154.0283203125,\n 56.559482483762245\n ],\n [\n -143.8330078125,\n 60.108670463036\n ],\n [\n -147.568359375,\n 65.05360170595502\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"64","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":791604,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reeves, Andrew B. 0000-0002-7526-0726 areeves@usgs.gov","orcid":"https://orcid.org/0000-0002-7526-0726","contributorId":167362,"corporation":false,"usgs":true,"family":"Reeves","given":"Andrew","email":"areeves@usgs.gov","middleInitial":"B.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":791605,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70228573,"text":"70228573 - 2020 - Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","interactions":[],"lastModifiedDate":"2022-02-14T15:33:01.949286","indexId":"70228573","displayToPublicDate":"2020-03-15T09:19:41","publicationYear":"2020","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}},"displayTitle":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","title":"Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires","docAbstract":"

The combined effects of long-term fire suppression, logging, and overgrazing have negatively impacted many southwestern U.S. forests, resulting in decreased habitat quality for wildlife, and more frequent and severe wildfires. In response, land management agencies are implementing large-scale forest restoration treatments, but data on how wildlife respond to restoration treatments and wildfires are often limited. We investigated bed and den site selection of American black bears (Ursus americanus) using GPS location data and a use/available study design to assess the influence of habitat characteristics, including wildfires, prescribed burns, and thinning treatments on bed and den site selection in the Jemez Mountains, New Mexico. The most supported models suggested that black bears were more likely to select bed sites with a combination of low horizontal visibility (β = −0.007, SE = 0.002; P = 0.002) and high stand basal area (β = 0.013, SE = 0.005; P = 0.004). The highest-ranking model for den site selection indicated that black bears were more likely to select den sites with low horizontal visibility (β = −0.0102, SE = 0.004; P = 0.006). Black bears used all disturbed sites to varying degrees (45% of study area), although 48% of bed sites were located in undisturbed habitat (55% of study area) while only 11% and 2% of bed sites were located in thinned and prescribed burn sites, respectively. Thirty-nine percent of bed sites were located in previous wildfire locations; however, 67% of these sites were in areas with low burn severity. Thirty-eight percent of den sites were located in previously disturbed habitat, 8 of these sites were burned by wildfires. In order to develop effective management plans for black bears, it is essential to understand responses to landscape-scale habitat disturbances due to wildfires and restoration activities, all of which are becoming more prevalent and widespread across southwestern forests. Accounting for the timing, size, and proximity of future restoration efforts would aid in mitigating potential short-term negative effects on black bears.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2020.117904","usgsCitation":"Bard, S.M., and Cain, J.W., 2020, Investigation of bed and den site selection by American black bears (Ursus americanus) in a landscape impacted by forest restoration treatments and wildfires: Forest Ecology and Management, v. 460, p. 1-11, https://doi.org/10.1016/j.foreco.2020.117904.","productDescription":"117904, 11 p.","startPage":"1","endPage":"11","ipdsId":"IP-112372","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":457367,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1595500","text":"Publisher Index Page"},{"id":395886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Collaborative Forest Landscape Restoration Program area, Jemez Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.67587280273438,\n 35.65004306288284\n ],\n [\n -106.67587280273438,\n 35.622698214535184\n ],\n [\n -106.62506103515625,\n 35.623256366178964\n ],\n [\n -106.42936706542969,\n 35.85455268869835\n ],\n [\n -106.39503479003906,\n 35.85343961959182\n ],\n [\n -106.39022827148438,\n 36.00800626603582\n ],\n [\n -106.62368774414062,\n 36.00911716117325\n ],\n [\n -106.68960571289062,\n 35.884043325566886\n ],\n [\n -106.86882019042969,\n 35.879592612012026\n ],\n [\n -106.86744689941405,\n 35.821153818963175\n ],\n [\n -106.85714721679688,\n 35.8217105820067\n ],\n [\n -106.85302734374999,\n 35.649485098277204\n ],\n [\n -106.67587280273438,\n 35.65004306288284\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"460","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bard, Susan M.","contributorId":264967,"corporation":false,"usgs":false,"family":"Bard","given":"Susan","email":"","middleInitial":"M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":834645,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":834644,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208470,"text":"sir20205010 - 2020 - Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018","interactions":[],"lastModifiedDate":"2022-04-25T21:35:14.628865","indexId":"sir20205010","displayToPublicDate":"2020-03-13T09:15:00","publicationYear":"2020","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":"2020-5010","displayTitle":"Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018","title":"Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018","docAbstract":"

Morris Lake, also known as Newton Reservoir, has been the source of drinking water for the Town of Newton, New Jersey, since the early 1900s. Although Morris Lake has been used as a source of drinking water for many years, its capacity was previously uncertain. In April 2018, the U.S. Geological Survey and the New Jersey Department of Environmental Protection conducted a bathymetric survey of Morris Lake using a multibeam echosounder to map the reservoir. The points measured with the multibeam echosounder were combined with light detection and ranging data above the water surface and processed to create a 3.3-foot (1 meter) raster grid of the bathymetric surface, bathymetric contours at 2-foot intervals of depth and elevation, and an elevation-area-capacity table.

The results of the bathymetric survey show that Morris Lake has a maximum depth of just over 119 feet with an average depth of 42 feet. Like the surrounding topography, parts of the reservoir are extremely steep. The capacity of the reservoir at full spillway level is 1,980 million gallons, with a corresponding surface area of 145 acres. The accuracy of the mapped multibeam echosounder bathymetric data was evaluated using a quality assurance dataset collected with a single-beam echosounder; 9,386 quality assurance points were spatially joined with the mapped raster surface to compute measurement errors. The calculated median point error for Morris Lake was 0.23 foot, the median absolute error was 0.35 foot, and the 95-percent accuracy was 2.68 feet. The largest errors occurred in the steepest areas of the reservoir and in unmeasured areas. Geospatial files of the bathymetry data, including the mapped bathymetric surface, contours, and capacity tables, quality assurance points, and associated metadata are available for download as part of an accompanying U.S. Geological Survey data release.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205010","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Nystrom, E.A., and Collenburg, J.V., 2020, Bathymetry of Morris Lake (Newton Reservoir), New Jersey, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5010, 14 p., https://doi.org/10.3133/sir20205010.","productDescription":"Report: vii, 14 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-103879","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":399631,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109786.htm"},{"id":373089,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P977GO3J","text":"USGS data release","linkHelpText":"Geospatial Bathymetry Dataset and Elevation-Area-Capacity Table for Morris Lake (Newton Reservoir), New Jersey"},{"id":373091,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5010/sir20205010.pdf","text":"Report","size":"4.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5010"},{"id":373090,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5010/coverthb.jpg"}],"country":"United States","state":"New Jersey","otherGeospatial":"Morris Lake (Newton Reservoir)","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.62128639221191,\n 41.0387074972886\n ],\n [\n -74.59296226501463,\n 41.0387074972886\n ],\n [\n -74.59296226501463,\n 41.05366055046841\n ],\n [\n -74.62128639221191,\n 41.05366055046841\n ],\n [\n -74.62128639221191,\n 41.0387074972886\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349

","tableOfContents":"","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"publishedDate":"2020-03-13","noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Nystrom, Elizabeth A. 0000-0002-0886-3439 nystrom@usgs.gov","orcid":"https://orcid.org/0000-0002-0886-3439","contributorId":1072,"corporation":false,"usgs":true,"family":"Nystrom","given":"Elizabeth","email":"nystrom@usgs.gov","middleInitial":"A.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collenburg, Jerilyn V. 0000-0002-3513-3116","orcid":"https://orcid.org/0000-0002-3513-3116","contributorId":222391,"corporation":false,"usgs":true,"family":"Collenburg","given":"Jerilyn","email":"","middleInitial":"V.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782037,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238974,"text":"70238974 - 2020 - Building a landslide hazard indicator with machine learning and land surface models","interactions":[],"lastModifiedDate":"2022-12-20T13:20:18.345312","indexId":"70238974","displayToPublicDate":"2020-03-13T07:14:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Building a landslide hazard indicator with machine learning and land surface models","docAbstract":"

The U.S. Pacific Northwest has a history of frequent and occasionally deadly landslides caused by various factors. Using a multivariate, machine-learning approach, we combined a Pacific Northwest Landslide Inventory with a 36-year gridded hydrologic dataset from the National Climate Assessment – Land Data Assimilation System to produce a landslide hazard indicator (LHI) on a daily 0.125-degree grid. The LHI identified where and when landslides were most probable over the years 1979–2016, addressing issues of bias and completeness that muddy the analysis of multi-decadal landslide inventories. The seasonal cycle was strong along the west coast, with a peak in the winter, but weaker east of the Cascade Range. This lagging indicator can fill gaps in the observational record to identify the seasonality of landslides over a large spatiotemporal domain and show how landslide hazard has responded to a changing climate.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2020.104692","usgsCitation":"Stanley, T.A., Kirschbaum, D.B., Sobieszczyk, S., Jasinski, M.F., Borak, J.S., and Slaughter, S.L., 2020, Building a landslide hazard indicator with machine learning and land surface models: Environmental Modelling & Software, v. 129, 104692, 15 p., https://doi.org/10.1016/j.envsoft.2020.104692.","productDescription":"104692, 15 p.","ipdsId":"IP-114297","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":457392,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2020.104692","text":"Publisher Index Page"},{"id":410787,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, 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F.","contributorId":300152,"corporation":false,"usgs":false,"family":"Jasinski","given":"M.","email":"","middleInitial":"F.","affiliations":[{"id":40052,"text":"NASA Goddard","active":true,"usgs":false}],"preferred":false,"id":859495,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borak, J. S.","contributorId":300155,"corporation":false,"usgs":false,"family":"Borak","given":"J.","email":"","middleInitial":"S.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":859496,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Slaughter, Stephen L. 0000-0002-4322-3330","orcid":"https://orcid.org/0000-0002-4322-3330","contributorId":224686,"corporation":false,"usgs":true,"family":"Slaughter","given":"Stephen","email":"","middleInitial":"L.","affiliations":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":859497,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209825,"text":"70209825 - 2020 - A post-eruption study of gases and thermal waters at Okmok Volcano, Alaska","interactions":[],"lastModifiedDate":"2020-04-30T12:12:19.796285","indexId":"70209825","displayToPublicDate":"2020-03-13T07:05:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"A post-eruption study of gases and thermal waters at Okmok Volcano, Alaska","docAbstract":"We report here on the first focused study of gas discharges and thermal spring waters at Okmok Volcano since the 2008 phreatomagmatic eruptions. Results include the first compositional gas data from Okmok with minimal air contamination and the first data on magmatic carbon in Okmok spring waters. Chemical and isotopic analyses of the waters and gases are used to assess the character of Okmok fluids eight years after the eruptions ceased. \n\nGases from vents on intracaldera Cone C have high concentrations of H2 and contain H2S rather than SO2, demonstrating the influence of a hydrothermal system, while isotope values of carbon ( 10.2 to 8.9‰) and helium (~8 RA) confirm the presence of magma-derived volatiles. Estimates of equilibrium temperatures for the Cone C gas are ~230 ± 30 ºC. A much cooler reservoir with a maximum temperature of ~55 ºC feeds the intracaldera warm springs. Based on discharge measurements of creeks draining the caldera, the total heat output of the warm springs is estimated to be about 32 MW.\n\nGas data from a single location of steaming ground at the Geyser Bight geothermal area southwest of the Okmok Caldera are given. The gas is typical of geothermal gases with high concentrations of H2S and an air-corrected helium isotope ratio of 7.15 RA.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.106853","collaboration":"","usgsCitation":"Bergfeld, D., Evans, W.C., Hunt, A., Lopez, T., and Schaefer, J., 2020, A post-eruption study of gases and thermal waters at Okmok Volcano, Alaska: Journal of Volcanology and Geothermal Research, v. 396, https://doi.org/10.1016/j.jvolgeores.2020.106853.","productDescription":"106853, 16 p.","startPage":"","ipdsId":"IP-115008","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":457400,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2020.106853","text":"Publisher Index Page"},{"id":374393,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Okmok Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -168.5687255859375,\n 53.212612189941574\n ],\n [\n -167.6898193359375,\n 53.212612189941574\n ],\n [\n -167.6898193359375,\n 53.589244357588655\n ],\n [\n -168.5687255859375,\n 53.589244357588655\n ],\n [\n -168.5687255859375,\n 53.212612189941574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"396","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bergfeld, Deborah 0000-0003-4570-7627 dbergfel@usgs.gov","orcid":"https://orcid.org/0000-0003-4570-7627","contributorId":152531,"corporation":false,"usgs":true,"family":"Bergfeld","given":"Deborah","email":"dbergfel@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":788182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, William C. 0000-0001-5942-3102 wcevans@usgs.gov","orcid":"https://orcid.org/0000-0001-5942-3102","contributorId":2353,"corporation":false,"usgs":true,"family":"Evans","given":"William","email":"wcevans@usgs.gov","middleInitial":"C.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":788183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hunt, Andrew G. 0000-0002-3810-8610","orcid":"https://orcid.org/0000-0002-3810-8610","contributorId":206197,"corporation":false,"usgs":true,"family":"Hunt","given":"Andrew G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":788186,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lopez, Taryn","contributorId":146828,"corporation":false,"usgs":false,"family":"Lopez","given":"Taryn","affiliations":[{"id":16753,"text":"University of Alaska Geophysical Institute","active":true,"usgs":false}],"preferred":false,"id":788184,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schaefer, Janet","contributorId":199547,"corporation":false,"usgs":false,"family":"Schaefer","given":"Janet","affiliations":[],"preferred":false,"id":788185,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219483,"text":"70219483 - 2020 - Small-scale water deficits after wildfires create long-lasting ecological impacts","interactions":[],"lastModifiedDate":"2021-04-12T11:58:12.438476","indexId":"70219483","displayToPublicDate":"2020-03-13T07:01:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Small-scale water deficits after wildfires create long-lasting ecological impacts","docAbstract":"

Ecological droughts are deficits in soil–water availability that induce threshold-like ecosystem responses, such as causing altered or degraded plant-community conditions, which can be exceedingly difficult to reverse. However, 'ecological drought' can be difficult to define, let alone to quantify, especially at spatial and temporal scales relevant to land managers. This is despite a growing need to integrate drought-related factors into management decisions as climate changes result in precipitation instability in many semi-arid ecosystems. We asked whether success in restoration seedings of the foundational species big sagebrush (Artemisia tridentata) was related to estimated water deficit, using the SoilWat2 model and data from >600 plots located in previously burned areas in the western United States. Water deficit was characterized by: (1) the standardized precipitation-evapotranspiration index (SPEI), a coarse-scale drought index, and (2) the number of days with wet and warm conditions in the near-surface soil, where seeds and seedlings germinate and emerge (i.e. days with 0–5 cm deep soil water potential >−2.5 MPa and temperature above 0 °C). SPEI, a widely used drought index, was not predictive of whether sagebrush had reestablished. In contrast, wet-warm days elicited a critical drought threshold response, with successfully reestablished sites having experienced seven more wet-warm days than unsuccessful sites during the first March following summer wildfire and restoration. Thus, seemingly small-scale and short-term changes in water availability and temperature can contribute to major ecosystem shifts, as many of these sites remained shrubless two decades later. These findings help clarify the definition of ecological drought for a foundational species and its imperiled semi-arid ecosystem. Drought is well known to affect the occurrence of wildfires, but drought in the year(s) after fire can determine whether fire causes long-lasting, negative impacts on ecosystems.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/ab79e4","usgsCitation":"O’Connor, R., Germino, M., Barnard, D.M., Andrews, C.M., Bradford, J., Pilliod, D., Arkle, R.S., and Shriver, R.K., 2020, Small-scale water deficits after wildfires create long-lasting ecological impacts: Environmental Research Letters, v. 15, no. 4, 044001, 11 p., https://doi.org/10.1088/1748-9326/ab79e4.","productDescription":"044001, 11 p.","ipdsId":"IP-114720","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457404,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ab79e4","text":"Publisher Index Page"},{"id":437058,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LDKQE2","text":"USGS data release","linkHelpText":"Ecological drought for sagebrush seedings in the Great Basin"},{"id":384960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Idaho, Nevada, Utah","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.0810546875,\n 40.84706035607122\n ],\n [\n -113.0712890625,\n 40.84706035607122\n ],\n [\n -113.0712890625,\n 43.32517767999296\n ],\n [\n -118.0810546875,\n 43.32517767999296\n ],\n [\n -118.0810546875,\n 40.84706035607122\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-03-13","publicationStatus":"PW","contributors":{"authors":[{"text":"O’Connor, Rory 0000-0002-6473-0032","orcid":"https://orcid.org/0000-0002-6473-0032","contributorId":222832,"corporation":false,"usgs":true,"family":"O’Connor","given":"Rory","email":"","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnard, David M 0000-0003-1877-3151","orcid":"https://orcid.org/0000-0003-1877-3151","contributorId":222833,"corporation":false,"usgs":false,"family":"Barnard","given":"David","email":"","middleInitial":"M","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":813766,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813767,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":813768,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":229349,"corporation":false,"usgs":true,"family":"Pilliod","given":"David S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813769,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Arkle, Robert S. 0000-0003-3021-1389","orcid":"https://orcid.org/0000-0003-3021-1389","contributorId":218006,"corporation":false,"usgs":true,"family":"Arkle","given":"Robert","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":813770,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":813771,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70208384,"text":"fs20203006 - 2020 - Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin","interactions":[],"lastModifiedDate":"2022-04-20T18:14:13.866211","indexId":"fs20203006","displayToPublicDate":"2020-03-12T16:33:50","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-3006","displayTitle":"Pooling Resources Across Organizations — Multisource Water-Quality Data for the Delaware River Basin","title":"Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin","docAbstract":"

The U.S. Geological Survey (USGS) recently launched a pilot Integrated Water Availability Assessment (IWAA) in the Delaware River Basin to explore, test, and refine systems and processes for assessing water availability for human and ecological uses based on water monitoring data. Water-quality monitoring provides citizens, managers, and scientists with the information needed to evaluate the health of aquatic ecosystems and the safety and availability of water for drinking, agriculture, recreation, and other uses. Many organizations collect water-quality data at various sites and sampling frequencies to meet their assessment needs. The result is multiple individual datasets suitable for the specific organization’s needs that also hold great potential if pooled into a much larger dataset sourced from multiple organizations (multisource data). A multisource dataset increases the value and power of multiple single datasets and expands the breadth and depth of available water-quality data to ultimately increase the number and types of questions that can be answered. This fact sheet describes the process of “harmonizing” water-quality data from multiple organizations and presents a recently developed dataset for surface-water quality in the Delaware River Basin. This harmonized multisource surface-water-quality dataset will serve as a resource for analysis and modeling of surface-water quality to support IWAA efforts in the basin. Furthermore, this harmonization process can be expanded and applied to other regional IWAA basins or applied nationally.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203006","collaboration":"Integrated Water Availability Assessments Program","usgsCitation":"Murphy, J.C., and Shoda, M.E., 2020, Pooling resources across organizations — Multisource water-quality data for the Delaware River Basin: U.S. Geological Survey Fact Sheet 2020–3006, 2 p., https://doi.org/10.3133/fs20203006.","productDescription":"Report: 2 p.; Data Release","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-113620","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":373170,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PX8LZO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Multisource surface-water-quality data and U.S. Geological Survey streamgage match for the Delaware River Basin"},{"id":373169,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3006/fs20203006.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3006"},{"id":373168,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3006/coverthb.jpg"},{"id":399198,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109784.htm"}],"country":"United States","state":"Delaware, Maryland, New York, New Jersey, Pennsylvania","otherGeospatial":"Delaware River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.5,\n 38.6\n ],\n [\n -74.333,\n 38.6\n ],\n [\n -74.333,\n 42.5\n ],\n [\n -76.5,\n 42.5\n ],\n [\n -76.5,\n 38.6\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Program Coordinator, Water Availability and Use Science Program
U.S. Geological Survey
Water Resources Mission Area

Email: wausp-info@usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2020-03-12","noUsgsAuthors":false,"publicationDate":"2020-03-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":781677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shoda, Megan E. 0000-0002-5343-9717 meshoda@usgs.gov","orcid":"https://orcid.org/0000-0002-5343-9717","contributorId":4352,"corporation":false,"usgs":true,"family":"Shoda","given":"Megan","email":"meshoda@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":346,"text":"Indiana Water Science Center","active":true,"usgs":true},{"id":27231,"text":"Indiana-Kentucky Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":781678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70208635,"text":"ds1123 - 2020 - Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2019 breeding season","interactions":[],"lastModifiedDate":"2020-03-16T13:50:45","indexId":"ds1123","displayToPublicDate":"2020-03-12T15:04:48","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1123","displayTitle":"Abundance and Productivity of Marbled Murrelets (Brachyramphus marmoratus) Off Central California During the 2019 Breeding Season","title":"Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2019 breeding season","docAbstract":"

Marbled murrelets (Brachyramphus marmoratus) have been listed as “endangered” by the State of California and “threatened” by the U.S. Fish and Wildlife Service since 1992 in California, Oregon, and Washington. Information regarding marbled murrelet abundance, distribution, population trends, and habitat associations is critical for risk assessment, effective management, evaluation of conservation efficacy, and ultimately, to meet Federal and State recovery efforts for this species. During June–August 2019, the U.S. Geological Survey Western Ecological Research Center continued previously established, long-term (1996–2019), at-sea surveys to estimate abundance and productivity of marbled murrelets in U.S. Fish and Wildlife Service Conservation Zone 6 (San Francisco Bay to Point Sur in central California). Using conventional distance sampling methods, we estimated marbled murrelet abundance using 125 detections of 216 murrelets (mean group size, 1.72) observed on 8 surveys. The abundance estimated for the entire study area using all surveys in 2019 was 404 birds (95-percent confidence interval, 272–601 birds). Estimated abundance from 2019 is comparable to most prior years of study. In 2019, we estimated reproductive productivity (calculated as the hatch-year [HY] to after-hatch-year [AHY] ratio) using three detections of three HY murrelets observed on six surveys. After date-correcting HY and AHY counts to account for birds expected to be absent from the water while inland at nests, the date-corrected juvenile ratio was 0.025±0.020 standard error. We discuss changes in methodologies during 1996–2019 that could be addressed in re-analysis of this long-term dataset. We updated a synthesized database of all Zone 6 marbled murrelet survey data since 1999 with 2019 data to allow scientists and managers to evaluate established survey methods and assess trends in abundance and productivity estimates.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1123","usgsCitation":"Felis, J.J., Kelsey, E.C., Adams, J., Horton, C., and White, L., 2020, Abundance and productivity of marbled murrelets (Brachyramphus marmoratus) off central California during the 2019 breeding season: U.S. Geological Survey Data Series 1123, 13 p., https://doi.org/10.3133/ds 1123.","productDescription":"Report: vi, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-114914","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":373097,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1123/coverthb.jpg"},{"id":373098,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1123/ds1123.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1123"},{"id":373220,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75B01RW","linkHelpText":"Annual Marbled Murrelet Abundance and Productivity Surveys Off Central California (Zone 6), 1999-2018 (ver. 2.0, March 2019)"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.991943359375,\n 36.89719446989036\n ],\n [\n -121.92626953124999,\n 36.89719446989036\n ],\n [\n -121.92626953124999,\n 37.68382032669382\n ],\n [\n -122.991943359375,\n 37.68382032669382\n ],\n [\n -122.991943359375,\n 36.89719446989036\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

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

","tableOfContents":"

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However, it is unknown whether global plant trait relationships extend to climatic extremes, and if these interspecific relationships are confounded by trait variation within species. We test whether trait relationships extend to the cold extremes of life on Earth using the largest database of tundra plant traits yet compiled. We show that tundra plants demonstrate remarkably similar resource economic traits, but not size traits, compared to global distributions, and exhibit the same two dimensions of trait variation. Three quarters of trait variation occurs among species, mirroring global estimates of interspecific trait variation. 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Coal mining has been the dominant industry and land use in West Virginia’s southern coal fields since the mid-1800s. Mortality rates for a variety of serious chronic conditions, such as diabetes, heart disease, and some forms of cancer in Appalachian coal mining regions, are higher than in areas lacking substantial coal mining activity within the Appalachian Region or elsewhere in the United States. Causes of the increased mortality and morbidity are not clear, but poor diet, high rates of smoking, socioeconomic factors, and the quality of groundwater used by area residents are all possible contributing factors. This study was conducted by the U.S. Geological Survey in cooperation with the West Virginia Department of Health and Human Resources and the West Virginia Department of Environmental Protection, with grant support from the Centers for Disease Control and Prevention (CDC) to assess the quality of groundwater in southern West Virginia. The data from this assessment of groundwater quality may be used by the CDC and other agencies to potentially investigate the role or lack thereof of groundwater quality with respect to mortality and morbidity rates in the region. The study was conducted in a region where a high density of current or past coal mining combined with a lack of advanced sewage treatment could affect concentrations of commonly occurring constituents plus contaminants, including nitrate, trace metals, major ions, indicator bacteria, radon, hydrogen sulfide, and dissolved hydrocarbons.

Because rural residential wells and mine outfalls are considered private sources of water in the region, and are therefore unregulated and unmonitored, water-quality data are sparse. To fill the data gap and assess the groundwater quality in the region, water-quality samples were collected from 60 sites in a 10-county area. The 60 sites sampled included 46 rural residential homeowner wells and 14 mine outfall discharges used for residential supply. For this study, all samples were collected prior to any filtration or other treatments, typically at the pressure tank, and are indicative of total and dissolved constituents in the untreated water.

Generally, data for the 60 sites indicate that most waters sampled do not exceed thresholds for most U.S. Environmental Protection Agency (EPA) drinking-water standards and U.S. Geological Survey (USGS) drinking-water screening criteria. However, there were several notable exceptions. Turbidity exceeded the 5-Nephelometric Turbidity Unit (NTU) EPA treatment technique (TT) drinking-water standard in 14 of 60 (23 percent) sites sampled and exceeded the 1-NTU TT standard in 51 of 60 (85 percent) sites sampled. Turbidity is common in many wells in southern West Virginia and may be attributed to iron oxyhydroxide precipitates, sediment carried into the aquifers from the shallow soil zone due to improperly constructed or cased wells or transported to the aquifer in shallow stress-relief fracture zones or through permeable bedding-plane partings. For the sites sampled, 31 of 60 (52 percent) had pH values at, above, or below the upper and lower range of the EPA Secondary Maximum Contaminant Level (SMCL, 6.5–8.5 standard units). Of those 31 sites, 28 (90 percent) were indicative of acidic corrosive water and 3 (10 percent) were indicative of alkaline water.

The Langelier Saturation Index (LSI), which is a measure of the corrosivity of the water, was computed for all sites sampled for the study. Eighty-two percent of the sites sampled had waters that were classified as corrosive, based on a LSI less than −0.5. Corrosive water has the potential to leach lead, copper, and other metals from lead, copper, galvanized, or lead-tin soldered connections in water lines. The chloride to sulfate mass ratio also was assessed with the alkalinity to indicate the potential to promote galvanic corrosion (PPGC) of water lines and plumbing fixtures. Only one of the sites (1.7 percent) classified as a corrosive water site, had a PPGC considered high; the remaining sites were classified as having either a moderate (53.3 percent) or low (45 percent) PPGC. Therefore, the type of plumbing systems sampled for this study may be affected by corrosive water, but the potential for leaching trace metals and other constituents from residential plumbing systems containing older galvanized pipes or lead-tin soldered copper pipes is moderate to low.

The indicator bacteria total coliform and Escherichia coli (E. coli) also were detected in groundwater samples to varying degrees. Total coliforms, which are a broad class of indicator bacteria, are common in groundwater in southern West Virginia and were detected in 39 of the 60 sites (65 percent) sampled. The presence of total coliform bacteria is a potential indicator of surface contamination, due to improperly constructed or cased wells, or infiltration of soil or other surface contaminants into the aquifer or well bore. E. coli bacteria, however, are much more indicative of fecal contamination of groundwater from either human or animal sources, and 14 of the 60 (23 percent) sites sampled had detections of E. coli. Although only a few strains of E. coli are known pathogens, their presence in groundwater may be an indicator of other related pathogens such as viruses and should be regarded as a serious potential issue. Water treatment such as chlorination, ozonation, or ultraviolet light may be appropriate to kill potential pathogenic bacteria or viruses in the source water.

Manganese and iron were prevalent contaminants in the groundwater samples collected for this study, with 30 of 60 (50 percent) sites analyzed for manganese and 25 of 60 (42 percent) sites analyzed for iron exceeding the proposed 50- and 300-micrograms per liter (µg/L) SMCL drinking-water standards, respectively, for aesthetic criteria such as taste, odor, or staining of plumbing fixtures. Fourteen of the 60 sites sampled (23 percent) had concentrations of manganese that exceeded the 300-µg/L USGS health-based screening level, and 1 site exceeded the 1,600-µg/L EPA drinking-water equivalent level, which is based on a lifetime exposure level. Sodium is another common constituent in groundwater within the study area. Sodium has an EPA health-based value (HBV) of 20 milligrams per liter (mg/L) for individuals who are on a sodium-restricted diet for blood pressure or other health reasons. Sodium concentrations exceeded the 20-mg/L EPA HBV in 27 of 60 (45 percent) samples.

Radon, a naturally occurring carcinogenic radioactive gas known to cause lung cancer, was detected at concentrations at or exceeding the proposed 300-picocuries per liter (pCi/L) EPA Maximum Contaminant Level (MCL) in 12 of the 60 (20 percent) sites sampled. Sites with radon gas concentrations exceeding the 300-pCi/L proposed MCL have the potential for airborne concentrations of radon to exceed the 4-pCi/L indoor air standard. Inhalation of radon can cause lung cancer, and the 4-pCi/L indoor air standard is based on an inhalation standard. Therefore, homeowners whose wells have radon gas concentrations exceeding 300 pCi/L may be advised to have their indoor air tested to determine if indoor air concentrations exceed the 4-pCi/L indoor air standard established by the EPA.

Various factors were analyzed statistically and graphically to determine whether they have an influence on groundwater quality within the study area, including topographic setting, well depth, type of mining (surface or underground), type of site (well or mine outfall), and geologic formation. Only geologic formation and the type of site sampled had strong statistical correlations with one or more of the constituents of concern for this study. The overall chemistry of outfalls (mine outfalls) and wells was significantly different, with a much higher dissolved oxygen content in outfalls than in wells. The dissolved oxygen content is the primary component driving the oxidation and reduction of minerals, and the precipitation of minerals that are saturated or super saturated with respect to various cations and anions. Median dissolved oxygen concentrations for the outfalls sampled was 8.75 mg/L, and only 0.4 mg/L for the wells sampled.

Median concentrations of sulfate and selenium were much higher in waters from the outfalls sampled, with median concentrations of 73.75 mg/L and 2.35 µg/L, respectively, compared to the wells sampled, which had median concentrations of 18.3 mg/L and less than (<) the 0.05-µg/L method detection limit, respectively. The maximum selenium concentration was for a well, with a concentration of 16.6 µg/L. The geochemical processes that control sulfate and selenium concentrations in groundwater are similar and are the result of the oxidation of sulfide minerals such as pyrite and ferroselite. Iron and manganese concentrations were elevated in most of the wells sampled, with median concentrations of 269.5 and 124.5 µg/L, respectively, but were rarely detected in the outfalls sampled, with median concentrations of < 4.0 and < 0.4 µg/L, respectively. The difference in iron and manganese between wells and outfalls is indicative of the role of dissolved oxygen on processes controlling groundwater chemistry in the region.

Three principal geologic formations were assessed for the study, and the overall chemistry for the Pocahontas, New River, and Kanawha Formations varied substantially with respect to several constituents. Concentrations of calcium, magnesium, and total dissolved solids were highest for sites sampled in the Pocahontas Formation, with median concentrations of 41.9, 18.6, and 312 mg/L, respectively. For constituents that are commonly associated with mining activity, the highest concentrations were for sites sampled in the New River Formation, with median concentrations of iron and manganese of 2,450 µg/L and 482 µg/L, respectively, and a median pH of 6.35 standard units. Concentrations of barium also were elevated in samples collected from sites in the New River Formation, with a median barium concentration of 184 µg/L. The source of the barium is not fully known but may be associated with commingling of shallow groundwater with deeper brines or dissolution of the mineral barite. The highest median sulfate concentrations were from sites sampled in the Pocahontas Formation, with a median concentration of 64.0 mg/L. Of the 12 sites at or exceeding the 300-pCi/L proposed drinking-water standard for radon, 8 (67 percent of MCL exceedances) were for sites deriving water from the Kanawha Formation, 3 (25 percent of MCL exceedances) were for sites deriving water from the New River Formation, and only 1 site was for water from the Pocahontas Formation (8 percent of proposed MCL exceedances).

Dissolved hydrocarbons, including methane, ethane, propane, propene, n- and i-butane, 1-butene, n- and i-pentane, pentane, 2- and 3-ethyl pentane, hexane, and benzene were analyzed in samples collected from 59 of the 60 sites to assess the potential occurrence and sources of these trace gases in groundwater within the study area. Results of the analysis indicate that most of the gas is of shallow biogenic origin, possibly associated with coal-bed methane, but a subset of samples has a gas signature and a chloride to bromide ratio indicative of potential mixing with deeper thermogenic gases. Only 2 of the 59 (3.3 percent) sites sampled had concentrations of methane gas, which is a highly combustible and explosive gas, exceeding the 10 milligrams per kilogram level of concern established by the U.S. Office of Surface Mining Reclamation and Enforcement.

Principal components analysis was used to assess the primary geochemical processes occurring in the aquifers sampled. The first principal component had significant positive loadings for bromide, chloride, silica, ammonia, barium, iron, manganese, and arsenic, and significant negative loadings for dissolved oxygen, potassium, nitrate, and uranium, and reflects reduction and oxidation (redox) processes occurring in deeper anoxic groundwater or shallow oxic groundwater. The strong positive loadings for iron, manganese, barium, and arsenic are correlated with reducing conditions often found deeper in the aquifer. More oxic water is correlated with oxidation of nitrogen species to nitrate and environmental mobilization of uranium and sulfate in shallow wells and mine outfalls.

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Director, Virginia/West Virginia Science Center
U.S. Geological Survey
11 Dunbar Street
Charleston, WV 25301

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