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":70209339,"text":"70209339 - 2020 - Contaminant subsidies to riparian food webs in Appalachian streams impacted by mountaintop removal coal mining","interactions":[],"lastModifiedDate":"2020-05-05T17:16:53.604546","indexId":"70209339","displayToPublicDate":"2020-03-19T15:27:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Contaminant subsidies to riparian food webs in Appalachian streams impacted by mountaintop removal coal mining","docAbstract":"Selenium is highly elevated in Appalachian streams and stream organisms that receive alkaline mine drainage from mountaintop removal coal mining compared to unimpacted streams in the region. Adult aquatic insects can be important vectors of waterborne contaminants to riparian food webs, yet pathways of Se transport and exposure of riparian organisms are poorly characterized. We investigated Se concentrations in stream and riparian organisms to determine whether mining extent increased Se uptake in stream biofilms and insects and if these insects were effective Se biovectors to riparian spiders. Biofilm Se concentration increased (p = 0.006) with mining extent, reaching a maximum value of 16.5 μg/g of dw. Insect and spider Se increased with biofilm Se (p = 0.004, p = 0.003), reaching 95 and 26 μg/g of dw, respectively, in mining-impacted streams. Adult insect biomass was not related to mining extent or Se concentrations in biofilm. Even though Se concentrations in aquatic insects were significantly and positively related to mining extent, aquatic insect Se flux was not associated with mining extent because the mass of emerging insects did not change appreciably over the mining gradient. Insect and spider Se concentrations were among the highest reported in the literature, regularly exceeding the bird Se dietary risk threshold of 5 μg/g of dw. Risks of Se exposure and toxicity related to mining are thus not constrained to aquatic systems but extend to terrestrial habitats and food webs.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.9b05907","usgsCitation":"Naslund, L.C., Gerson, J.R., Brooks, A.C., Walters, D., and Bernhardt, E.S., 2020, Contaminant subsidies to riparian food webs in Appalachian streams impacted by mountaintop removal coal mining: Environmental Science & Technology, v. 54, no. 7, p. 3951-3959, https://doi.org/10.1021/acs.est.9b05907.","productDescription":"9 p.","startPage":"3951","endPage":"3959","ipdsId":"IP-112482","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":457323,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.9b05907","text":"Publisher Index Page"},{"id":373727,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"7","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Naslund, Laura C.","contributorId":223770,"corporation":false,"usgs":false,"family":"Naslund","given":"Laura","email":"","middleInitial":"C.","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":786206,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gerson, Jacqueline R.","contributorId":198378,"corporation":false,"usgs":false,"family":"Gerson","given":"Jacqueline","email":"","middleInitial":"R.","affiliations":[{"id":5082,"text":"Syracuse University","active":true,"usgs":false},{"id":27331,"text":"Duke University, Durham, NC","active":true,"usgs":false}],"preferred":false,"id":786207,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brooks, Alexander C.","contributorId":223771,"corporation":false,"usgs":false,"family":"Brooks","given":"Alexander","email":"","middleInitial":"C.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":786208,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walters, David 0000-0002-4237-2158","orcid":"https://orcid.org/0000-0002-4237-2158","contributorId":205915,"corporation":false,"usgs":true,"family":"Walters","given":"David","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":786205,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bernhardt, Emily S.","contributorId":173736,"corporation":false,"usgs":false,"family":"Bernhardt","given":"Emily","email":"","middleInitial":"S.","affiliations":[{"id":27285,"text":"Duke Univerisity","active":true,"usgs":false}],"preferred":false,"id":786209,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"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
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":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water 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":70211314,"text":"70211314 - 2020 - Mechanics of near-field deformation during co- and post-seismic shallow fault slip","interactions":[],"lastModifiedDate":"2020-07-23T20:28:56.833088","indexId":"70211314","displayToPublicDate":"2020-03-19T09:33:17","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Mechanics of near-field deformation during co- and post-seismic shallow fault slip","docAbstract":"Poor knowledge of how faults slip and distribute deformation in the shallow crust hinders efforts to mitigate hazards where faults increasingly intersect with the expanding global population at Earth’s surface. Here we analyze two study sites along the 2014 M 6.0 South Napa, California, earthquake rupture, each dominated by either co- or post-seismic shallow fault slip. We combine mobile laser scanning (MLS), active-source seismic tomography, and finite element modeling to investigate how deformation rate and mechanical properties of the shallow crust affect fault behavior. Despite four orders-of-magnitude difference in the rupture velocities, MLS-derived shear strain fields are remarkably similar at the two sites and suggest deceleration of the co-seismic rupture near Earth’s surface. Constrained by the MLS and seismic data, finite element models indicate shallow faulting is more sensitive to lithologic layering and plastic yielding than to the presence of fault compliant zones (i.e., regions surrounding faults with reduced stiffness). Although both elastic and elastoplastic models can reproduce the observed surface displacement fields within the uncertainty of MLS data, elastoplastic models likely provide the most reliable representations of subsurface fault behavior, as they produce geologically reasonable stress states and are consistent with field, geodetic, and seismological observations.","language":"English","publisher":"Springer Nature","doi":"10.1038/s41598-020-61400-9","usgsCitation":"Nevitt, J., Brooks, B.A., Catchings, R.D., Goldman, M., Ericksen, T., and Glennie, C.L., 2020, Mechanics of near-field deformation during co- and post-seismic shallow fault slip: Scientific Reports, v. 10, 5031, 13 p., https://doi.org/10.1038/s41598-020-61400-9.","productDescription":"5031, 13 p.","ipdsId":"IP-099149","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":457326,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-61400-9","text":"Publisher Index Page"},{"id":376665,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Napa Fault Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65274047851562,\n 38.22199865889175\n ],\n [\n -122.21328735351562,\n 38.22199865889175\n ],\n [\n -122.21328735351562,\n 38.6897975322717\n ],\n [\n -122.65274047851562,\n 38.6897975322717\n ],\n [\n -122.65274047851562,\n 38.22199865889175\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Nevitt, Johanna 0000-0003-3819-1773 jnevitt@usgs.gov","orcid":"https://orcid.org/0000-0003-3819-1773","contributorId":198144,"corporation":false,"usgs":true,"family":"Nevitt","given":"Johanna","email":"jnevitt@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793732,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brooks, Benjamin A. 0000-0001-7954-6281 bbrooks@usgs.gov","orcid":"https://orcid.org/0000-0001-7954-6281","contributorId":5237,"corporation":false,"usgs":true,"family":"Brooks","given":"Benjamin","email":"bbrooks@usgs.gov","middleInitial":"A.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793733,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Catchings, Rufus D. 0000-0002-5191-6102 catching@usgs.gov","orcid":"https://orcid.org/0000-0002-5191-6102","contributorId":1519,"corporation":false,"usgs":true,"family":"Catchings","given":"Rufus","email":"catching@usgs.gov","middleInitial":"D.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":793734,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldman, Mark 0000-0002-0802-829X","orcid":"https://orcid.org/0000-0002-0802-829X","contributorId":205863,"corporation":false,"usgs":true,"family":"Goldman","given":"Mark","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":793735,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ericksen, Todd 0000-0001-9340-575X tericksen@usgs.gov","orcid":"https://orcid.org/0000-0001-9340-575X","contributorId":198145,"corporation":false,"usgs":true,"family":"Ericksen","given":"Todd","email":"tericksen@usgs.gov","affiliations":[],"preferred":true,"id":793749,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Glennie, Craig L.","contributorId":198143,"corporation":false,"usgs":false,"family":"Glennie","given":"Craig","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":793737,"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":"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.
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.
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":70208812,"text":"ofr20201021 - 2020 - Geologic map of the Paeroa Fault block and surrounding area, Taupo Volcanic Zone, New Zealand","interactions":[],"lastModifiedDate":"2020-03-20T07:07:13","indexId":"ofr20201021","displayToPublicDate":"2020-03-18T12:42:34","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":"2020-1021","displayTitle":"Geologic Map of the Paeroa Fault Block and Surrounding Area, Taupo Volcanic Zone, New Zealand","title":"Geologic map of the Paeroa Fault block and surrounding area, Taupo Volcanic Zone, New Zealand","docAbstract":"The Taupo Volcanic Zone (TVZ), New Zealand, is the most productive area of explosive silicic volcanism in the world. Faulted early and middle Pleistocene volcanic products are generally concealed beneath voluminous, generally unfaulted, younger volcanic products. An exception is the southeast margin of the TVZ where the two parallel, northeast-trending Paeroa and Te Weta Fault blocks expose Quaternary volcanic products consisting predominantly of caldera-related, rhyolitic ignimbrites and lacustrine sediments. The Taupo-Reporoa Basin is situated along the eastern part of the map area, and its northernmost part underwent collapse to form Reporoa Caldera.
The Paeroa Fault block is the largest exposed fault block within the TVZ, and it encompasses early and middle Pleistocene ignimbrites and sedimentary deposits that are buried throughout the Taupo-Reporoa Basin to the east. This map displays the volcanic and sedimentary geology of ~430 km2 of the Paeroa Fault block and the adjacent Te Weta Fault block at a scale of 1:50,000. Volcanic and sedimentary rocks are divided into the Reporoa Group, Whakamaru Group, and Huka Group (from oldest to youngest), which are overlain by relatively unfaulted late Pleistocene and Holocene surficial volcanic and sedimentary deposits.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201021","usgsCitation":"Downs, D.T., Leonard, G.S., Wilson, C.J.N., and Rowland, J.V., 2020, Geologic map of the Paeroa Fault block and surrounding area, Taupo Volcanic Zone, New Zealand: U.S. Geological Survey Open-File Report 2020–1021, scale 1:50,000, https://doi.org/10.3133/ofr20201021.","productDescription":"1 Map: 50.22 x 34.79 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-107861","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":373326,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DYBBGX","text":"USGS data release ","description":"USGS Data Release","linkHelpText":"Database for the geologic map of the Paeroa fault block and surrounding area, Taupo Volcanic Zone, New Zealand"},{"id":373324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1021/coverthb.jpg"},{"id":373325,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1021/ofr20201021.pdf","text":"Sheet","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1021"}],"country":"New Zealand ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 165.948486328125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -46.72103466129568\n ],\n [\n 170.7550048828125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -44.15462243076732\n ],\n [\n 165.948486328125,\n -46.72103466129568\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory staff
Alaska Volcano Observatory
4210 University Drive
Anchorage, AK 99508
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
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
Due to extensive land conversion over the last century, much of the native prairie pothole ecosystem has been converted to agricultural or other human uses. The prairie pothole ecosystem is found in the northern plains of Iowa, Minnesota, South Dakota, North Dakota, and Montana. Because most of the land in this region is privately owned and used for agricultural production, most impacts to wildlife habitat are the result of decisions by individual landowners. Landowner trust in natural resource management agencies is important for agencies to effectively accomplish their mission. We measured the nature (competence and fairness) and level of trust that western Minnesota landowners have in the Minnesota Department of Natural Resources (MnDNR) and landowners’ wildlife value orientations (WVO). Landowners rated MnDNR slightly higher in competence than fairness; however, these two dimensions were strongly correlated. We developed a MnDNR trust scale (six items) and a three-cluster model dividing landowners along the MnDNR trust scale, which we named Negative (28%), Neutral (43%), and Positive (29%). We provide evidence supporting the salient values similarity (SVS) model that states people have trust in agencies holding similar values; landowners reporting greater importance for wildlife consideration when making land-use decisions also reported greater trust in the MnDNR. In addition, mutualist landowners had the highest trust in the MnDNR and utilitarian landowners the lowest level of trust, which is opposite of the trust relationship reported for the general public with state wildlife agencies. Based on the SVS model, our results suggest that mutualist landowners perceive greater congruence with MnDNR goals related to wildlife habitat compared to utilitarian landowners.
","language":"English","publisher":"Springer","doi":"10.1007/s10669-020-09766-z","usgsCitation":"Gigliotti, L.M., Sweikert, L., Cornicelli, L., and Fulton, D.C., 2020, Minnesota landowners’ trust in their department of natural resources, salient values similarity and wildlife value orientations: Environment Systems and Decisions, v. 40, p. 577-587, https://doi.org/10.1007/s10669-020-09766-z.","productDescription":"11 p.","startPage":"577","endPage":"587","ipdsId":"IP-103189","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388554,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"40","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Gigliotti, Larry M. 0000-0002-1693-5113 lgigliotti@usgs.gov","orcid":"https://orcid.org/0000-0002-1693-5113","contributorId":3906,"corporation":false,"usgs":true,"family":"Gigliotti","given":"Larry","email":"lgigliotti@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweikert, Lily A.","contributorId":264825,"corporation":false,"usgs":false,"family":"Sweikert","given":"Lily A.","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":822032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornicelli, Louis","contributorId":264826,"corporation":false,"usgs":false,"family":"Cornicelli","given":"Louis","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":822033,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fulton, David C. 0000-0001-5763-7887 dcf@usgs.gov","orcid":"https://orcid.org/0000-0001-5763-7887","contributorId":2208,"corporation":false,"usgs":true,"family":"Fulton","given":"David","email":"dcf@usgs.gov","middleInitial":"C.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":822034,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70210858,"text":"70210858 - 2020 - Deglacial temperature controls on no-analog community establishment in the Great Lakes Region","interactions":[],"lastModifiedDate":"2020-06-30T13:37:00.272631","indexId":"70210858","displayToPublicDate":"2020-03-18T08:30:28","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Deglacial temperature controls on no-analog community establishment in the Great Lakes Region","docAbstract":"Understanding the drivers of vegetation dynamics and no-analog communities in eastern North America is hampered by a scarcity of independent temperature indicators. We present a new branched glycerol dialkyl glycerol tetraether (brGDGT) temperature record from Bonnet Lake, Ohio (18 to 8 ka) and report uncertainty estimates based on Bayesian linear regression and bootstrapping. We also reanalyze a previously published brGDGT record from Silver Lake, Ohio, using improved chromatographic methods. All pollen- and brGDGT-based temperature reconstructions showed qualitatively similar deglacial trends but varying magnitudes. Separating 5- and 6- methyl brGDGTs resulted in substantially lower estimates of deglacial temperature variations (6.4 °C) than inferred from earlier brGDGT methods and pollen (11.8 °C, 12.0 °C respectively). Similar trends among proxies suggest good fidelity of brGDGTs to temperature, despite calibration uncertainties. At both sites, the rise and decline of no-analog communities closely track brGDGT-inferred temperatures, with a lag of 0 to 150 years. The timing of temperature and ecological events varies between Bonnet and Silver Lakes, likely due to age model uncertainties. Climate sensitivity analyses indicate a linear sensitivity of vegetation composition to temperature variations, albeit noisy and significant only with a 500-year bin. The formation of no-analog plant communities in the upper Midwest is closely linked to late-glacial warming, but other factors, such as temperature seasonality or end-Pleistocene megafaunal extinctions, remain viable.","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2020.106245","usgsCitation":"Fastovich, D., Russell, J.M., Jackson, S., and Williams, J.W., 2020, Deglacial temperature controls on no-analog community establishment in the Great Lakes Region: Quaternary Science Reviews, v. 234, 106245, 16 p., https://doi.org/10.1016/j.quascirev.2020.106245.","productDescription":"106245, 16 p.","ipdsId":"IP-107598","costCenters":[{"id":41166,"text":"Southwest Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":457336,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2020.106245","text":"Publisher Index Page"},{"id":376014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Lakes Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.869140625,\n 37.71859032558816\n ],\n [\n -79.7607421875,\n 37.71859032558816\n ],\n [\n -79.7607421875,\n 41.902277040963696\n ],\n [\n -85.869140625,\n 41.902277040963696\n ],\n [\n -85.869140625,\n 37.71859032558816\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"234","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fastovich, David","contributorId":225614,"corporation":false,"usgs":false,"family":"Fastovich","given":"David","email":"","affiliations":[],"preferred":false,"id":791886,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, James M.","contributorId":174740,"corporation":false,"usgs":false,"family":"Russell","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":27506,"text":"Department of Earth, Environmental and Planetary Sciences, Brown University, Providence RI 02912 USA","active":true,"usgs":false}],"preferred":false,"id":791887,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Stephen 0000-0002-1487-4652","orcid":"https://orcid.org/0000-0002-1487-4652","contributorId":219995,"corporation":false,"usgs":true,"family":"Jackson","given":"Stephen","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":791749,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John W.","contributorId":16761,"corporation":false,"usgs":true,"family":"Williams","given":"John","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":791888,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"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 ]\n}","volume":"221","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Supendi, Pepen","contributorId":244317,"corporation":false,"usgs":false,"family":"Supendi","given":"Pepen","email":"","affiliations":[{"id":48884,"text":"Bandung Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":803750,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nugraha, Andri Dian","contributorId":202043,"corporation":false,"usgs":false,"family":"Nugraha","given":"Andri","email":"","middleInitial":"Dian","affiliations":[{"id":36333,"text":"Institut Teknologi Bandung","active":true,"usgs":false}],"preferred":false,"id":803751,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Widiyantoro, Sri","contributorId":202045,"corporation":false,"usgs":false,"family":"Widiyantoro","given":"Sri","email":"","affiliations":[{"id":36333,"text":"Institut Teknologi 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":70259110,"text":"70259110 - 2020 - Linking landscape-scale conservation to regional and continental outcomes for a migratory species","interactions":[],"lastModifiedDate":"2024-10-03T16:17:29.344521","indexId":"70259110","displayToPublicDate":"2020-03-18T07:03:03","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Linking landscape-scale conservation to regional and continental outcomes for a migratory species","docAbstract":"Land-use intensification on arable land is expanding and posing a threat to biodiversity and ecosystem services worldwide. We develop methods to link funding for avian breeding habitat conservation and management at landscape scales to equilibrium abundance of a migratory species at the continental scale. We apply this novel approach to a harvested bird valued by birders and hunters in North America, the northern pintail duck (Anas acuta), a species well below its population goal. Based on empirical observations from 2007–2016, habitat conservation investments for waterfowl cost $313 M and affected less than 2% of the pintail’s primary breeding area in the Prairie Pothole Region of Canada. Realistic scenarios for harvest and habitat conservation costing an estimated $588 M (2016 USD) led to predicted pintail population sizes less than 3 M when assuming average parameter values. Accounting for parameter uncertainty, converting 70–100% of these croplands to idle grassland (cost: $35.7B–50B) is required to achieve the continental population goal of 4 M individuals under the current harvest policy. Using our work as a starting point, we propose continued development of modeling approaches that link conservation funding, habitat delivery, and population response to better integrate conservation efforts and harvest management of economically important migratory species.
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. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795328,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209364,"text":"70209364 - 2020 - Earthquakes, ShakeCast","interactions":[],"lastModifiedDate":"2020-04-03T14:37:10.136529","indexId":"70209364","displayToPublicDate":"2020-03-17T09:32:45","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Earthquakes, ShakeCast","docAbstract":"ShakeCast® – short for ShakeMap Broadcast – is a fully automated software system for delivering specific ShakeMap products to critical users and for triggering established post-earthquake response protocols. 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
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Water Resources Mission Area
Email: wausp-info@usgs.gov
","tableOfContents":"The evolution of male signals and female preferences remains a central question in the study of animal communication. The sensory trap model suggests males evolve signals that mimic cues used in nonsexual contexts and thus manipulate female behavior to generate mating opportunities. Much evidence supports the sensory trap model, but how females glean reliable information from both mimetic signals and their model cues remains unknown. We discovered a mechanism whereby a manipulative male signal guides reliable communication in sea lamprey (Petromyzon marinus). Migratory sea lamprey follow a larval cue into spawning streams; once sexually mature, males release a pheromone that mimics the larval cue and attracts females. Females conceivably benefit from the mimetic pheromone during mate search but must discriminate against the model cue to avoid orienting toward larvae in nearby nursery habitats. We tested the hypothesis that spawning females respond to petromyzonol sulfate (PZS) as a behavioral antagonist to avoid attraction to the larval cue while tracking the male pheromone despite each containing attractive 3-keto petromyzonol sulfate (3kPZS). We found 1) PZS inhibited electrophysiological responses to 3kPZS and abated preferences for 3kPZS when mixed at the same or greater concentrations, 2) larvae released more PZS than 3kPZS whereas males released more 3kPZS than PZS, and 3) mixtures of 3kPZS and PZS applied at ratios measured in larval and male odorants resulted in the discrimination observed between the natural odors. Our study elucidates how communication systems that arise via deception can facilitate reliable communication.
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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Rico"},{"id":373241,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_LandslideGuide_2020.pdf","text":"Landslide Guide for Residents of Puerto Rico"},{"id":373242,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://hazards.colorado.edu/uploads/documents/PuertoRico_GuiaDerrumbe_2020.pdf","text":"Guía sobre deslizamientos de tierra para residentes de Puerto Rico"},{"id":373256,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_kmz.zip","text":"Landslide susceptibility map as a Google Earth file","linkFileType":{"id":6,"text":"zip"},"description":"OFR 2020-1022 kmz"},{"id":373246,"rank":9,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/SI_raster_for_SMAP.zip","text":"SI raster for SMAP","linkFileType":{"id":6,"text":"zip"},"description":"SI raster for SMAP"},{"id":373247,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet.pdf","text":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Map Depicting Susceptibility to Landslides Triggered by Intense Rainfall, Puerto Rico"},{"id":376481,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp.pdf","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico","linkFileType":{"id":1,"text":"pdf"},"description":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por Precipitación Intensa en Puerto Rico"},{"id":376485,"rank":18,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2020/1022/ofr20201022_sheet_esp_georeferenced.tif","text":"Mapa de Susceptibilidad a Deslizamientos de Tierra Desencadenados por 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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