No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Hartpence, A., 2022, Landsat Update March 2022: Landsat Update, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-138688","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":399091,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399090,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/landsat-missions/news/landsat-update-march-2022"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hartpence, Anya 0000-0002-4510-3236","orcid":"https://orcid.org/0000-0002-4510-3236","contributorId":247379,"corporation":false,"usgs":false,"family":"Hartpence","given":"Anya","email":"","affiliations":[{"id":48475,"text":"KBR, Contractor to USGS EROS","active":true,"usgs":false}],"preferred":false,"id":838990,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70230040,"text":"70230040 - 2022 - A methodology to assess the historical environmental footprint of in-situ recovery (ISR) of uranium: A demonstration in the Goliad Sand in the Texas Coastal Plain, USA","interactions":[],"lastModifiedDate":"2022-03-28T14:30:38.45732","indexId":"70230040","displayToPublicDate":"2022-03-28T09:20:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"A methodology to assess the historical environmental footprint of in-situ recovery (ISR) of uranium: A demonstration in the Goliad Sand in the Texas Coastal Plain, USA","docAbstract":"In-situ recovery (ISR) has been the only technique used to extract uranium from sandstone-hosted uranium deposits in the Pliocene Goliad Sand in the Texas Coastal Plain. Water plays a crucial role throughout the ISR lifecycle of production and groundwater restoration yet neither the water use nor other environmental footprints have been well documented. The goal of this study is to examine historical records for all six ISR operations completed in the Goliad Sand to identify and quantify parameters that indicate the surface and aquifer disturbances, water use, and radon emissions. Overall, the average mine area was 0.00023 ± 0.00006 acres per pound (ac/lb) U3O8. The average mine pore volume was 48.9 ± 50 gal/lb U3O8 with a minimum affected aquifer volume of 0.51 ± 0.08 cubic feet per pound (cu ft/lb) U3O8. An average of 258 ± 40 gallons (gal) of fluid were disposed per pound (lb) U3O8, with an average of 169 ± 26 gal/lb U3O8 attributed to restoration and 89 ± 36 gal/lb U3O8 attributed to the uranium production phase. The average radon emitted was 1.06 × 10−3 ± 7.4 × 10−4 curies per pound (Ci/lb) U3O8. Goodness-of-fit (R2) values are ≥0.79 for linear regressions of the amount of uranium produced versus mine area, mine pore volumes, mine aquifer volumes, water pumped, and total water disposed. The R2 value for radon emitted was 0.68. However, the water disposed only during the uranium production phase is more strongly correlated to the number of production days (R2 = 0.96) than to uranium production (R2 = 0.84), whereas the volume of water disposed during restoration is more strongly correlated to the “pore volume” (R2 = 0.97) than to uranium production (R2 = 0.90). Pore volume is an industry term used to describe the amount of fluid circulated through the aquifer during the uranium production period and stipulated in bond agreements in order to satisfy groundwater restoration requirements. Models constructed in this study can be used to estimate probable water use and the extent of surface and aquifer disturbances associated with ISR-amenable undiscovered uranium resources in the Goliad Sand. The historical perspective offered by the data compiled and correlations may prove useful to both industry and regulators.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/min12030369","usgsCitation":"Gallegos, T., Scott, A., Stengel, V.G., and Teeple, A., 2022, A methodology to assess the historical environmental footprint of in-situ recovery (ISR) of uranium: A demonstration in the Goliad Sand in the Texas Coastal Plain, USA: Minerals, v. 12, no. 3, 369, 29 p., https://doi.org/10.3390/min12030369.","productDescription":"369, 29 p.","ipdsId":"IP-132933","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":448350,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12030369","text":"Publisher Index Page"},{"id":397704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Texas Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.69140625,\n 31.44741029142872\n ],\n [\n -96.328125,\n 31.22219703210317\n ],\n [\n -99.65698242187499,\n 27.61540601339959\n ],\n [\n -99.525146484375,\n 27.45953933271788\n ],\n [\n -99.60205078124999,\n 27.332735136859146\n ],\n [\n -99.503173828125,\n 27.225325836903373\n ],\n [\n -99.481201171875,\n 27.039556602163195\n ],\n [\n -99.2724609375,\n 26.686729520004036\n ],\n [\n -99.129638671875,\n 26.362342068998764\n ],\n [\n -98.843994140625,\n 26.303264239389534\n ],\n [\n -98.69018554687499,\n 26.165298896316042\n ],\n [\n -98.58032226562499,\n 26.194876675795218\n ],\n [\n -98.250732421875,\n 25.987674852384966\n ],\n [\n -97.85522460937499,\n 25.987674852384966\n ],\n [\n -97.3828125,\n 25.77021384896025\n ],\n [\n -97.174072265625,\n 26.007424156802212\n ],\n [\n -97.152099609375,\n 26.352497858154024\n ],\n [\n -97.31689453125,\n 26.755420897359123\n ],\n [\n -97.294921875,\n 27.01998400798257\n ],\n [\n -97.283935546875,\n 27.342494467201007\n ],\n [\n -97.03125,\n 27.819644755099446\n ],\n [\n -96.734619140625,\n 28.091366281406945\n ],\n [\n -95.965576171875,\n 28.565225490654658\n ],\n [\n -95.38330078125,\n 28.76765910569123\n ],\n [\n -95.108642578125,\n 29.084976575985912\n ],\n [\n -94.7021484375,\n 29.31514119318728\n ],\n [\n -93.98803710937499,\n 29.640320395351402\n ],\n [\n -93.812255859375,\n 29.640320395351402\n ],\n [\n -93.84521484375,\n 29.79298413547051\n ],\n [\n -93.702392578125,\n 29.964452850852005\n ],\n [\n -93.66943359374999,\n 30.07860131571654\n ],\n [\n -93.66943359374999,\n 30.287531589298727\n ],\n [\n -93.66943359374999,\n 30.552800413453546\n ],\n [\n -93.50463867187499,\n 30.911651004518244\n ],\n [\n -93.482666015625,\n 31.21280145833882\n ],\n [\n -93.69140625,\n 31.44741029142872\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":838857,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scott, Annie 0000-0001-7286-3698 annescott@usgs.gov","orcid":"https://orcid.org/0000-0001-7286-3698","contributorId":223421,"corporation":false,"usgs":true,"family":"Scott","given":"Annie","email":"annescott@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":838858,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stengel, Victoria G. 0000-0003-0481-3159 vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838859,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838976,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230665,"text":"70230665 - 2022 - Local diversity in phenological responses of migratory lake sturgeon to warm winters","interactions":[],"lastModifiedDate":"2022-06-01T15:20:29.68924","indexId":"70230665","displayToPublicDate":"2022-03-28T09:12:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2939,"text":"Oikos","active":true,"publicationSubtype":{"id":10}},"title":"Local diversity in phenological responses of migratory lake sturgeon to warm winters","docAbstract":"Rich intraspecific diversity in traits that shape responses to environmental conditions implies that effects of climate change will differ within species or even populations. Nevertheless, few studies investigate how different groups within species respond to climatic fluctuations, and most risk assessments rely upon species-wide generalizations. We studied effects of among-year variation in air temperature on the spring migratory phenology of a metapopulation of lake sturgeon Acipenser fulvescens within waters connecting Lake Huron and Lake Erie of the Laurentian Great Lakes. Sturgeon here express multiple migratory phenotypes that all spawn in either the St. Clair River or Detroit River but differ in their use after spawning of more than 86 000 km2 of accessible lake and river habitat. Acoustic tracking over nine years (2012–2020) revealed mixed phenological responses to late-winter air temperatures, with three migratory groups arriving at rivers earlier during warm years and one whose arrival was consistent across years regardless of temperature. Notably, two groups that spawn in the same river but overwinter in different lakes entered the river with greater synchrony during warm years because one advanced its phenology while the other did not. The results indicated warm weather could alter the dynamics of the metapopulation and broader community, and exemplify the complexity hidden beneath broadscale generalizations of species' response to climate change.
","language":"English","publisher":"Wiley","doi":"10.1111/oik.08977","usgsCitation":"Buchinger, T.J., Hondorp, D.W., and Krueger, C.C., 2022, Local diversity in phenological responses of migratory lake sturgeon to warm winters: Oikos, v. 2022, no. 6, e08977, 6 p., https://doi.org/10.1111/oik.08977.","productDescription":"e08977, 6 p.","ipdsId":"IP-137745","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":448353,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/oik.08977","text":"Publisher Index Page"},{"id":399398,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Michigan, Ontario","otherGeospatial":"Detroit River, Lake St. Clair, St. Clair River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.353271484375,\n 42.04113400940807\n ],\n [\n -82.2216796875,\n 42.04113400940807\n ],\n [\n -82.2216796875,\n 43.02472955416351\n ],\n [\n -83.353271484375,\n 43.02472955416351\n ],\n [\n -83.353271484375,\n 42.04113400940807\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"2022","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchinger, Tyler J. 0000-0002-4590-341X","orcid":"https://orcid.org/0000-0002-4590-341X","contributorId":290501,"corporation":false,"usgs":false,"family":"Buchinger","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":841114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":841115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krueger, Charles C. 0000-0002-6735-5012","orcid":"https://orcid.org/0000-0002-6735-5012","contributorId":274493,"corporation":false,"usgs":false,"family":"Krueger","given":"Charles","email":"","middleInitial":"C.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":841116,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230042,"text":"70230042 - 2022 - The reuse of avian samples: Opportunities, pitfalls and a solution","interactions":[],"lastModifiedDate":"2022-03-28T14:15:21.898949","indexId":"70230042","displayToPublicDate":"2022-03-28T09:02:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"The reuse of avian samples: Opportunities, pitfalls and a solution","docAbstract":"Tissue samples are frequently collected to study various aspects of avian biology, but in many cases these samples are not used in their entirety and are stored by the collector. The already collected samples provide a largely overlooked opportunity because they can be used by different researchers in different biological fields. Broad reuse of samples could result in multispecies or large-scale studies, interdisciplinary collaborations, and the generation of new ideas, thereby increasing the quality and impact of research. Sample reuse could also reduce the number of new samples needed for a study, which is especially pertinent to endangered species where sample collection is necessarily limited. Importantly, reusing samples may be mutually beneficial for both the researchers providing samples and those reusing them. Here, we identify the benefits of sample reuse, describe currently available sources of already collected samples and their limitations, and highlight the wide range of potential applications in a single research field – avian isotopic ecology. To facilitate the reuse of avian samples worldwide and across research fields, we introduce the AviSample Network metadata repository. The main aims of this metadata repository are to collate and provide access to descriptions of available avian tissue samples. We contend that the creation of the AviSample Network metadata repository will provide the opportunity for new collaborations and studies. Moreover, we believe that this will help create research connections between ornithologists across the globe and encourage sample reuse in other fields.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12997","usgsCitation":"Brlik, V., Pipek, P., Brandis, K., Chernetsov, N., Costa, F.J., Herrera M., L.G., Kiat, Y., Lanctot, R., Marra, P.P., Norris, D.R., Nwaogu, C.J., Quillfeldt, P., Saalfeld, S.T., Stricker, C.A., Thomson, R.L., Zhao, T., and Procházka, P., 2022, The reuse of avian samples: Opportunities, pitfalls and a solution: Ibis, v. 164, p. 343-349, https://doi.org/10.1111/ibi.12997.","productDescription":"7 p.","startPage":"343","endPage":"349","ipdsId":"IP-124863","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":448356,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ibi.12997","text":"Publisher Index Page"},{"id":397701,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Earth","volume":"164","noUsgsAuthors":false,"publicationDate":"2021-07-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Brlik, Vojtech","contributorId":213771,"corporation":false,"usgs":false,"family":"Brlik","given":"Vojtech","email":"","affiliations":[{"id":38851,"text":"Ustav Biologie Obratlovcu Akademie ved Ceske Republiky","active":true,"usgs":false}],"preferred":false,"id":838860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pipek, Pavel","contributorId":289273,"corporation":false,"usgs":false,"family":"Pipek","given":"Pavel","email":"","affiliations":[{"id":37178,"text":"Charles University","active":true,"usgs":false}],"preferred":false,"id":838861,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandis, Kate","contributorId":289275,"corporation":false,"usgs":false,"family":"Brandis","given":"Kate","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":838862,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chernetsov, Nikita","contributorId":289277,"corporation":false,"usgs":false,"family":"Chernetsov","given":"Nikita","email":"","affiliations":[{"id":62091,"text":"Zoological Institute of Russian Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":838863,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Costa, Fabio J. V.","contributorId":289278,"corporation":false,"usgs":false,"family":"Costa","given":"Fabio","email":"","middleInitial":"J. V.","affiliations":[{"id":62093,"text":"Policia Federal","active":true,"usgs":false}],"preferred":false,"id":838864,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Herrera M., L. Gerardo","contributorId":289279,"corporation":false,"usgs":false,"family":"Herrera M.","given":"L.","email":"","middleInitial":"Gerardo","affiliations":[{"id":18923,"text":"Universidad Nacional Autonoma de Mexico","active":true,"usgs":false}],"preferred":false,"id":838865,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kiat, Yosef","contributorId":289280,"corporation":false,"usgs":false,"family":"Kiat","given":"Yosef","email":"","affiliations":[{"id":62094,"text":"Society for the Protection of Nature in Israel","active":true,"usgs":false}],"preferred":false,"id":838866,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lanctot, Richard B.","contributorId":77879,"corporation":false,"usgs":false,"family":"Lanctot","given":"Richard B.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":838867,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marra, Peter P.","contributorId":190140,"corporation":false,"usgs":false,"family":"Marra","given":"Peter","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":838868,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Norris, D. Ryan","contributorId":289281,"corporation":false,"usgs":false,"family":"Norris","given":"D.","email":"","middleInitial":"Ryan","affiliations":[{"id":12660,"text":"University of Guelph","active":true,"usgs":false}],"preferred":false,"id":838869,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nwaogu, Chima J.","contributorId":289282,"corporation":false,"usgs":false,"family":"Nwaogu","given":"Chima","email":"","middleInitial":"J.","affiliations":[{"id":12665,"text":"University of Cape Town","active":true,"usgs":false}],"preferred":false,"id":838870,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Quillfeldt, Petra","contributorId":243493,"corporation":false,"usgs":false,"family":"Quillfeldt","given":"Petra","affiliations":[{"id":38764,"text":"Justus Liebig University Giessen","active":true,"usgs":false}],"preferred":false,"id":838871,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Saalfeld, Sarah T.","contributorId":208223,"corporation":false,"usgs":false,"family":"Saalfeld","given":"Sarah","email":"","middleInitial":"T.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":838872,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Stricker, Craig A. 0000-0002-5031-9437 cstricker@usgs.gov","orcid":"https://orcid.org/0000-0002-5031-9437","contributorId":1097,"corporation":false,"usgs":true,"family":"Stricker","given":"Craig","email":"cstricker@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":838873,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Thomson, Robert L.","contributorId":289283,"corporation":false,"usgs":false,"family":"Thomson","given":"Robert","email":"","middleInitial":"L.","affiliations":[{"id":12665,"text":"University of Cape Town","active":true,"usgs":false}],"preferred":false,"id":838874,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zhao, Tianhao","contributorId":289284,"corporation":false,"usgs":false,"family":"Zhao","given":"Tianhao","email":"","affiliations":[{"id":62095,"text":"University of Groningen","active":true,"usgs":false}],"preferred":false,"id":838875,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Procházka, Petr","contributorId":289285,"corporation":false,"usgs":false,"family":"Procházka","given":"Petr","affiliations":[{"id":17790,"text":"Czech Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":838876,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70230057,"text":"70230057 - 2022 - First juvenile Chum Salmon confirms successful reproduction for Pacific salmon in the North American Arctic","interactions":[],"lastModifiedDate":"2022-05-13T15:00:49.834278","indexId":"70230057","displayToPublicDate":"2022-03-28T08:35:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"First juvenile Chum Salmon confirms successful reproduction for Pacific salmon in the North American Arctic","docAbstract":"The distributional extent of Pacific salmon Oncorhynchus spp. in the North American Arctic is unresolved. While adult Pacific salmon have a recurring presence across the Alaskan North Slope and into the Canadian Arctic, it is uncertain if these fish are part of established Arctic populations, vagrants from outside sources reproducing unsuccessfully, or both. Here we present the first confirmed record of a juvenile Chum Salmon O. keta captured in the nearshore marine ecosystem in the North American Arctic. This provides the first scientific evidence of successful spawning and early marine survival of Pacific salmon in the North American Arctic. It was caught near Kaktovik, Alaska in August 2017 with a group of similarly sized age-0 Mackenzie River Arctic Cisco Coregonus autumnalis. Stable isotope and otolith microchemistry analyses are consistent with use of the nearshore estuarine corridor from the Mackenzie River west along the northern coast. This contributes critical information needed to identify, manage, and conserve biodiversity at the northern range edge, and will help to clarify the status of Pacific salmon as potentially emerging fisheries develop in the North American Arctic due to climate warming.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2022-0006","usgsCitation":"Dunmall, K.M., McNicholl, D.G., Zimmerman, C.E., Gilk-Baumer, S.E., Burril, S.E., and von Biela, V.R., 2022, First juvenile Chum Salmon confirms successful reproduction for Pacific salmon in the North American Arctic: Canadian Journal of Fisheries and Aquatic Sciences, v. 79, no. 5, p. 703-707, https://doi.org/10.1139/cjfas-2022-0006.","productDescription":"5 p.","startPage":"703","endPage":"707","ipdsId":"IP-135249","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"links":[{"id":448359,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1139/cjfas-2022-0006","text":"Publisher Index Page"},{"id":435905,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9120X5B","text":"USGS data release","linkHelpText":"Fish Communities of the Nearshore Beaufort Sea, Alaska, Across Three Decades, 1988-2019"},{"id":397698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Kaktovik","otherGeospatial":"Beaufort Sea, Qaaktugvik","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -143.6743927001953,\n 70.08114699794389\n ],\n [\n -143.6840057373047,\n 70.07131950686258\n ],\n [\n -143.64864349365234,\n 70.07050033933828\n ],\n [\n -143.63800048828125,\n 70.07447885045708\n ],\n [\n -143.6235809326172,\n 70.0755318582064\n ],\n [\n -143.56143951416013,\n 70.08056215839737\n ],\n [\n -143.536376953125,\n 70.07401082987566\n ],\n [\n -143.51165771484375,\n 70.08582512129848\n ],\n [\n -143.51062774658203,\n 70.10955035225408\n ],\n [\n -143.44539642333984,\n 70.12169464177494\n ],\n [\n -143.44058990478516,\n 70.12542991464234\n ],\n [\n -143.53946685791016,\n 70.1460784799744\n ],\n [\n -143.57379913330078,\n 70.1488766999914\n ],\n [\n -143.6184310913086,\n 70.14502904988304\n ],\n [\n -143.6575698852539,\n 70.12414599056665\n ],\n [\n -143.67507934570312,\n 70.10487757635919\n ],\n [\n -143.6743927001953,\n 70.08114699794389\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"79","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dunmall, Karen M.","contributorId":189272,"corporation":false,"usgs":false,"family":"Dunmall","given":"Karen","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":838915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McNicholl, Darcy G.","contributorId":289325,"corporation":false,"usgs":false,"family":"McNicholl","given":"Darcy","email":"","middleInitial":"G.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":838916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Christian E. 0000-0002-3646-0688 czimmerman@usgs.gov","orcid":"https://orcid.org/0000-0002-3646-0688","contributorId":410,"corporation":false,"usgs":true,"family":"Zimmerman","given":"Christian","email":"czimmerman@usgs.gov","middleInitial":"E.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":838917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilk-Baumer, Sara E.","contributorId":289326,"corporation":false,"usgs":false,"family":"Gilk-Baumer","given":"Sara","email":"","middleInitial":"E.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":838918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burril, Sean E.","contributorId":215441,"corporation":false,"usgs":false,"family":"Burril","given":"Sean","email":"","middleInitial":"E.","affiliations":[{"id":39248,"text":"College of Fisheries and Ocean Sciences, University of Alaska Fairbanks","active":true,"usgs":false}],"preferred":false,"id":838919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"von Biela, Vanessa R. 0000-0002-7139-5981 vvonbiela@usgs.gov","orcid":"https://orcid.org/0000-0002-7139-5981","contributorId":3104,"corporation":false,"usgs":true,"family":"von Biela","given":"Vanessa","email":"vvonbiela@usgs.gov","middleInitial":"R.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":838920,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230055,"text":"70230055 - 2022 - Landscape-scale forest restoration decreases vulnerability to drought mortality under climate change in southwest USA ponderosa forest","interactions":[],"lastModifiedDate":"2022-03-29T10:54:55.451953","indexId":"70230055","displayToPublicDate":"2022-03-28T08:09:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale forest restoration decreases vulnerability to drought mortality under climate change in southwest USA ponderosa forest","docAbstract":"Drought-induced tree mortality is predicted to increase in dry forests across the western USA as future projections show hotter, drier climates potentially resulting in large-scale tree die-offs, changes in species composition, and loss of forest ecosystem services, including carbon storage. While some studies have found that forest stands with greater basal areas (BA) have higher drought mortality, many have not evaluated the extent to which forest structure, either overly dense forests due to fire suppression or forests restored to lower densities, interacts with drought mortality. The southwestern USA is particularly susceptible to tree mortality due to the predicted increases in temperature, drier soils, and forests with high density. Our objective was to evaluate how ponderosa pine mortality is expected to be influenced by the Four Forests Restoration Initiative, a large-scale forest restoration effort ongoing in northern Arizona, USA, that will reduce stand BA by approximately 40%. Specifically, we modeled drought mortality in three time periods, one contemporary (1970-2010), and two future (2020-2059 and 2060-2099) under three restoration scenarios: no thinning, 4FRI thinning, and a BA reduction beyond the 4FRI plan (4FRI-intensive). We estimated mortality using 11 climate models under two emissions scenarios. Without thinning, our model predicted that by mid-century (2020-2059), changes in climate could increase annual ponderosa pine mortality rates by 45-57% over contemporary rates. However, with thinning, mid-century mortality was predicted to remain near or below contemporary rates and these rates are 31-35% (4FRI) and 46-51% (4FRI-intensive) less than the mid-century scenarios without thinning. Our study shows that while climate change is likely to increase tree mortality rates, large-scale forest restoration projects, such as 4FRI, have the potential to ameliorate the effects of climate change and keep mortality rates near contemporary levels for decades.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2022.120088","usgsCitation":"McCauley, L., Bradford, J., Robles, M.D., Shriver, R.K., Woolley, T.J., and Andrews, C.M., 2022, Landscape-scale forest restoration decreases vulnerability to drought mortality under climate change in southwest USA ponderosa forest: Forest Ecology and Management, v. 509, 120088, 11 p., https://doi.org/10.1016/j.foreco.2022.120088.","productDescription":"120088, 11 p.","ipdsId":"IP-135260","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2022.120088","text":"Publisher Index Page"},{"id":397690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Coconino National Forest, Kaibab National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.66754150390625,\n 34.24586516842103\n ],\n [\n -110.5389404296875,\n 34.24586516842103\n ],\n [\n -110.5389404296875,\n 35.61488368245436\n ],\n [\n -112.66754150390625,\n 35.61488368245436\n ],\n [\n -112.66754150390625,\n 34.24586516842103\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"509","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCauley, Lisa A","contributorId":268774,"corporation":false,"usgs":false,"family":"McCauley","given":"Lisa A","affiliations":[{"id":55658,"text":"The Nature Conservancy, Center for Science and Public Policy, 1510 E Ft Lowell Road, Tucson, AZ","active":true,"usgs":false}],"preferred":false,"id":838909,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":219257,"corporation":false,"usgs":true,"family":"Bradford","given":"John B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":838910,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robles, Marcos D.","contributorId":244893,"corporation":false,"usgs":false,"family":"Robles","given":"Marcos","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":838911,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shriver, Robert K 0000-0002-4590-4834","orcid":"https://orcid.org/0000-0002-4590-4834","contributorId":222834,"corporation":false,"usgs":false,"family":"Shriver","given":"Robert","email":"","middleInitial":"K","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":838912,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Woolley, Travis J.","contributorId":229070,"corporation":false,"usgs":false,"family":"Woolley","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":41578,"text":"The Nature Conservancy, 114 N., San Francisco Street #205, Flagstaff, Arizona, 86001, USA","active":true,"usgs":false}],"preferred":false,"id":838913,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Andrews, Caitlin M. 0000-0003-4593-1071 candrews@usgs.gov","orcid":"https://orcid.org/0000-0003-4593-1071","contributorId":192985,"corporation":false,"usgs":true,"family":"Andrews","given":"Caitlin","email":"candrews@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":838914,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230440,"text":"70230440 - 2022 - Sedimentary record of annual-decadal timescale reservoir dynamics: Anthropogenic stratigraphy of Lake Powell, Utah, U.S.A.","interactions":[],"lastModifiedDate":"2022-04-13T11:57:33.660983","indexId":"70230440","displayToPublicDate":"2022-03-28T06:54:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9134,"text":"The Sedimentary Record","active":true,"publicationSubtype":{"id":10}},"title":"Sedimentary record of annual-decadal timescale reservoir dynamics: Anthropogenic stratigraphy of Lake Powell, Utah, U.S.A.","docAbstract":"The tributaries of Lake Powell were impounded following construction of Glen Canyon Dam, resulting in deposition of reservoir sediment over a ∼650 km2 area since 1963. These units have been exposed through erosion as water storage in Lake Powell has decreased since 2000. This anthropogenic sedimentary record reflects the complex interplay among wet and dry periods of Colorado River runoff and the reservoir operating rules of Lake Powell. The relevant sedimentary exposures are mapped at reconnaissance level over 300 river-km above Glen Canyon Dam in canyons of the Colorado, San Juan, Escalante, and Dirty Devil Rivers. A detailed reference section measured in Calf Canyon, a tributary to the Colorado River, preserves more than 12 m of lacustrine, mainstem Colorado River, and local tributary sediment in an up-river location and elevation that is determined to have been inundated only during the highest reservoir level periods. At Calf Canyon, exposed reservoir sediment is comprised of cyclic sand-mud interbeds that record periods of deposition when reservoir level was at or above full pool. Six depositional cycles are identified in Calf Canyon, and each of these is interpreted to represent rapid sand deposition during Colorado River flood events (likely related to spring snowmelt runoff) followed by deposition of lacustrine mud while reservoir levels were high. The lacustrine mud units display significant pedogenic modification, indicating exposure and colonization dominated by tamarisk plants, prior to deposition of the next sand unit. High-precision elevation surveys of the 6 main lacustrine marker beds in Calf Canyon are correlated to multiple lake level highstands between 1975 and 2000. Preliminary observations suggest that age-equivalent strata are widespread within the reservoir-affected zones of all major tributaries including the Colorado and San Juan River arms as well as the Escalante and Dirty Devil Rivers. We predict that future map- ping in other Lake Powell side canyons will demonstrate strong local control on sediment provenance, dictated by side canyon lithology, as well as time-transgressive deposition (and erosion) moving up and down the main canyons.","language":"English","publisher":"Society for Sedimentary Geology","doi":"10.2110/sedred.2022.1.3","usgsCitation":"Johnson, C., Root, J.C., Hynek, S., and Schmidt, J., 2022, Sedimentary record of annual-decadal timescale reservoir dynamics: Anthropogenic stratigraphy of Lake Powell, Utah, U.S.A.: The Sedimentary Record, v. 20, no. 1, p. 15-29, https://doi.org/10.2110/sedred.2022.1.3.","productDescription":"15 p.","startPage":"15","endPage":"29","ipdsId":"IP-133199","costCenters":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":448362,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2110/sedred.2022.1.3","text":"Publisher Index Page"},{"id":398629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Lake Powell","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.73095703125,\n 37.01132594307015\n ],\n [\n -109.720458984375,\n 37.01132594307015\n ],\n [\n -109.720458984375,\n 38.16911413556086\n ],\n [\n -111.73095703125,\n 38.16911413556086\n ],\n [\n -111.73095703125,\n 37.01132594307015\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Cari","contributorId":290196,"corporation":false,"usgs":false,"family":"Johnson","given":"Cari","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":840434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Root, Jonathan Casey 0000-0003-0537-4418","orcid":"https://orcid.org/0000-0003-0537-4418","contributorId":223107,"corporation":false,"usgs":true,"family":"Root","given":"Jonathan","email":"","middleInitial":"Casey","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":840435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hynek, Scott 0000-0002-6885-0445","orcid":"https://orcid.org/0000-0002-6885-0445","contributorId":216634,"corporation":false,"usgs":true,"family":"Hynek","given":"Scott","email":"","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":840436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schmidt, John (Jack) C.","contributorId":290197,"corporation":false,"usgs":false,"family":"Schmidt","given":"John (Jack) C.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":840437,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230415,"text":"70230415 - 2022 - Mapping aquifer salinity gradients and effects of oil field produced water disposal using geophysical logs: Elk Hills, Buena Vista and Coles Levee Oil Fields, San Joaquin Valley, California","interactions":[],"lastModifiedDate":"2022-04-12T11:46:20.251679","indexId":"70230415","displayToPublicDate":"2022-03-28T06:39:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Mapping aquifer salinity gradients and effects of oil field produced water disposal using geophysical logs: Elk Hills, Buena Vista and Coles Levee Oil Fields, San Joaquin Valley, California","docAbstract":"The effects of oil and gas production on adjacent groundwater quality are becoming a concern in many areas of the United States. As a result, it has become increasingly important to identify which aquifers require monitoring and protection. In this study, we map the extent of groundwater with less than 10,000 mg/L TDS both laterally and vertically near the Elk Hills, Buena Vista and Coles Levee Oil Fields in the San Joaquin Valley, California and note evidence of effects of produced water disposal on salinity within the Tulare aquifer. Subsurface maps showing the depth at which groundwater salinity is less than 10,000 mg/L (or Base 10K) in the Tulare aquifer are generated using geophysical logs and verified by comparison to water sample analyses. The depth to Base 10K ranges from 240 m (800 ft) in Elk Hills to 800 m (2650 ft) in the adjacent Buena Vista syncline and is 670 m (2,200 ft) deep in the Coles Levee area to the east. Log-calculated salinities show a relatively smooth increase with depth prior to disposal activities whereas salinities calculated from logs collected near and after disposal activities show a more variable salinity profile with depth. The effect of produced water injection is represented by log resistivity profiles that change from low resistivity at the base of the sand to higher resistivity near the top due to density differences between the saline produced water and the brackish groundwater within each sand. Continued post-disposal logging in new wells in the 18G disposal area on the south flank of Elk Hills shows that injected water has migrated approximately 1,200 m (4,000 ft) downdip (south) over a period of 20 years since the inception of disposal activity.
Based on field investigations, interpretations of high-resolution UAV images, and analyses of available InSAR data, we mapped the fault geometry and surface ruptures of the 2021 Mw 7.4 Maduo earthquake that occurred on a low-activity strike-slip fault within the Tibetan Plateau. The results indicate that (a) the earthquake activated a fault that is ∼161 km long and has complicated structural geometry; (b) the surface rupture occurs over a distance of 148 km, but is separated into three distinct segments by two large gaps (38 and 20 km, respectively); (c) within the surface-rupture segments, the horizontal and vertical displacements are typically 0.2–2.6 m (much lower than the InSAR-based slip maximum of 2–6 m at depth) and ≤0.4 m, respectively. The two large gaps of the Maduo surface rupture represent the two largest surface-rupture discontinuities of strike-slip earthquakes ever documented, and coincide with structurally complicated fault portions and near-surface soft sediments.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GL096874","usgsCitation":"Yuan, Z., Li, T., Su, P., Sun, H., Ha, G., Guo, P., Chen, G., and Jobe, J.A., 2022, Large surface-rupture gaps and low surface fault slip of the 2021 Mw 7.4 Maduo earthquake along a low-activity strike-slip fault, Tibetan Plateau: Geophysical Research Letters, v. 49, no. 6, e2021GL096874, 10 p., https://doi.org/10.1029/2021GL096874.","productDescription":"e2021GL096874, 10 p.","ipdsId":"IP-136428","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":492249,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"China","otherGeospatial":"Tibetan Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 79.93234344766347,\n 35.575939914700754\n ],\n [\n 78.3705489233405,\n 34.05958555585446\n ],\n [\n 78.58705356226119,\n 31.97131709519209\n ],\n [\n 81.47010014962359,\n 29.39655531835615\n ],\n [\n 87.00214684836624,\n 28.084505917777577\n ],\n [\n 99.31512001410783,\n 28.000996533493065\n ],\n [\n 99.27319956834918,\n 30.130779740906647\n ],\n [\n 98.68230833106509,\n 32.40275119051756\n ],\n [\n 91.05587796892081,\n 33.44813606172204\n ],\n [\n 90.21348545176429,\n 35.861369285610074\n ],\n [\n 88.99351787460091,\n 36.93775549578366\n ],\n [\n 79.93234344766347,\n 35.575939914700754\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Yuan, Zhaode","contributorId":358037,"corporation":false,"usgs":false,"family":"Yuan","given":"Zhaode","affiliations":[{"id":85575,"text":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":943118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Tao","contributorId":215164,"corporation":false,"usgs":false,"family":"Li","given":"Tao","email":"","affiliations":[],"preferred":false,"id":943119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Su, Peng","contributorId":358038,"corporation":false,"usgs":false,"family":"Su","given":"Peng","affiliations":[{"id":85575,"text":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":943120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sun, Haoyue","contributorId":358039,"corporation":false,"usgs":false,"family":"Sun","given":"Haoyue","affiliations":[{"id":85575,"text":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":943121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ha, Guanghao","contributorId":358040,"corporation":false,"usgs":false,"family":"Ha","given":"Guanghao","affiliations":[{"id":85575,"text":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":943122,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Guo, Peng","contributorId":358041,"corporation":false,"usgs":false,"family":"Guo","given":"Peng","affiliations":[{"id":85575,"text":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":943123,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chen, Guihua","contributorId":358042,"corporation":false,"usgs":false,"family":"Chen","given":"Guihua","affiliations":[{"id":85575,"text":"State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing, China","active":true,"usgs":false}],"preferred":false,"id":943124,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jobe, Jessica Ann Thompson 0000-0001-5574-4523","orcid":"https://orcid.org/0000-0001-5574-4523","contributorId":295377,"corporation":false,"usgs":true,"family":"Jobe","given":"Jessica","email":"","middleInitial":"Ann Thompson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":943125,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70248721,"text":"70248721 - 2022 - Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density","interactions":[],"lastModifiedDate":"2023-09-18T14:05:04.028786","indexId":"70248721","displayToPublicDate":"2022-03-27T08:53:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density","docAbstract":"Near-term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit 13 Bayesian state-space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin-producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near-term (1- to 4-week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the 4-week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week's densities in 1-week-ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long-term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2590","usgsCitation":"Lofton, M., Brentrup, J.A., Beck, W.S., Zwart, J.A., Bhattacharya, R., Brighenti, L.S., Burnett, S.H., McCullough, I.M., Steele, B., Carey, C.C., Cottingham, K., Dietze, M., Ewing, H.A., Weathers, K.C., and LaDeau, S.L., 2022, Using near-term forecasts and uncertainty partitioning to inform prediction of oligotrophic lake cyanobacterial density: Ecological Applications, v. 32, e2590, 24 p., https://doi.org/10.1002/eap.2590.","productDescription":"e2590, 24 p.","ipdsId":"IP-119852","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448368,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2590","text":"Publisher Index Page"},{"id":420889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire","otherGeospatial":"Lake Sunapee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.10639790180213,\n 43.45916503807919\n ],\n [\n -72.10639790180213,\n 43.3077735355308\n ],\n [\n -72.01805843117764,\n 43.3077735355308\n ],\n [\n -72.01805843117764,\n 43.45916503807919\n ],\n [\n -72.10639790180213,\n 43.45916503807919\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2022-05-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Lofton, Mary","contributorId":329783,"corporation":false,"usgs":false,"family":"Lofton","given":"Mary","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":883298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brentrup, Jennifer A.","contributorId":194457,"corporation":false,"usgs":false,"family":"Brentrup","given":"Jennifer","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":883299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beck, Whitney S.","contributorId":268335,"corporation":false,"usgs":false,"family":"Beck","given":"Whitney","email":"","middleInitial":"S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":883300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":883301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bhattacharya, Ruchi","contributorId":297412,"corporation":false,"usgs":false,"family":"Bhattacharya","given":"Ruchi","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":883302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brighenti, Ludmila S","contributorId":317713,"corporation":false,"usgs":false,"family":"Brighenti","given":"Ludmila","email":"","middleInitial":"S","affiliations":[{"id":69135,"text":"Universidade do Estado de Minas Gerais","active":true,"usgs":false}],"preferred":false,"id":883303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burnett, Sarah H.","contributorId":288140,"corporation":false,"usgs":false,"family":"Burnett","given":"Sarah","email":"","middleInitial":"H.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":883304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCullough, Ian M.","contributorId":149952,"corporation":false,"usgs":false,"family":"McCullough","given":"Ian","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":883305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Steele, Bethel 0000-0003-4365-4103","orcid":"https://orcid.org/0000-0003-4365-4103","contributorId":329785,"corporation":false,"usgs":false,"family":"Steele","given":"Bethel","email":"","affiliations":[{"id":36248,"text":"Cary Institute of Ecosystem Studies","active":true,"usgs":false}],"preferred":false,"id":883306,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Carey, Cayelan C.","contributorId":130969,"corporation":false,"usgs":false,"family":"Carey","given":"Cayelan","email":"","middleInitial":"C.","affiliations":[{"id":7185,"text":"Department of Biological Sciences, Virginia Tech, Blacksburg, VA, USA","active":true,"usgs":false}],"preferred":false,"id":883307,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Cottingham, Kathryn L","contributorId":329786,"corporation":false,"usgs":false,"family":"Cottingham","given":"Kathryn L","affiliations":[{"id":39657,"text":"Dartmouth College","active":true,"usgs":false}],"preferred":false,"id":883308,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Dietze, Michael","contributorId":248349,"corporation":false,"usgs":false,"family":"Dietze","given":"Michael","affiliations":[],"preferred":false,"id":883309,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Ewing, Holly A.","contributorId":191962,"corporation":false,"usgs":false,"family":"Ewing","given":"Holly","email":"","middleInitial":"A.","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":883310,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Weathers, Kathleen C.","contributorId":202417,"corporation":false,"usgs":false,"family":"Weathers","given":"Kathleen","email":"","middleInitial":"C.","affiliations":[{"id":36424,"text":"Cary Institute of Ecosystems Studies","active":true,"usgs":false}],"preferred":false,"id":883311,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"LaDeau, Shannon L.","contributorId":172640,"corporation":false,"usgs":false,"family":"LaDeau","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":883312,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70230163,"text":"70230163 - 2022 - The wildland-urban interface in the United States based on 125 million building locations","interactions":[],"lastModifiedDate":"2022-07-07T16:44:38.536733","indexId":"70230163","displayToPublicDate":"2022-03-27T08:40:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The wildland-urban interface in the United States based on 125 million building locations","docAbstract":"The wildland-urban interface (WUI) is the focus of many important land management issues, such as wildfire, habitat fragmentation, invasive species, and human-wildlife conflicts. Wildfire is an especially critical issue, because housing growth in the WUI increases wildfire ignitions and the number of homes at risk. Identifying the WUI is important for assessing and mitigating impacts of development on wildlands and for protecting homes from natural hazards, but data on housing development for large areas are often coarse. We created new WUI maps for the conterminous U.S. based on 125 million individual building locations, offering higher spatial precision compared to existing maps based on U.S. census housing data. Building point locations were based on a building footprint dataset from Microsoft®. We classified WUI across the conterminous U.S. at 30-m resolution using a circular neighborhood mapping algorithm with a variable radius to determine thresholds of housing density and vegetation cover. We used our maps to (1) determine the total area of the WUI and number of buildings included, (2) assess the sensitivity of WUI area included and spatial pattern of WUI maps to choice of neighborhood size, (3) assess regional differences between building-based WUI maps and census-based WUI maps, and (4) determine how building location accuracy affected WUI map accuracy. Our building-based WUI maps identified 5.6% – 18.8% of the conterminous U.S. as being in the WUI, with larger neighborhoods increasing WUI area but excluding isolated building clusters. Building-based maps identified more WUI area relative to census-based maps for all but the smallest neighborhoods, particularly in the north-central states, and large differences were attributable to high numbers of non-housing structures in rural areas. Overall WUI classification accuracy was 98.0%. For wildfire risk mapping and for general purposes, WUI maps based on the 500-m neighborhood represent the original Federal Register definition of the WUI; these maps include clusters of buildings in and adjacent to wildlands and exclude remote, isolated buildings. Our approach for mapping the WUI offers flexibility and high spatial detail, and can be widely applied to take advantage of the growing availability of high-resolution building footprint datasets and classification methods.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2597","usgsCitation":"Carlson, A., Helmers, D., Hawbaker, T., Mockrin, M.H., and Radeloff, V.C., 2022, The wildland-urban interface in the United States based on 125 million building locations: Ecological Applications, v. 32, no. 5, e2597, 18 p., https://doi.org/10.1002/eap.2597.","productDescription":"e2597, 18 p.","ipdsId":"IP-129426","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":435908,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H094S2","text":"USGS data release","linkHelpText":"Lake trout hatch rates using adults collected in 2019 from Northern Refuge, Lake Michigan"},{"id":435907,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94BT6Q7","text":"USGS data release","linkHelpText":"Wildland-urban interface maps for the conterminous U.S. based on 125 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0000-0002-0450-2636","orcid":"https://orcid.org/0000-0002-0450-2636","contributorId":195661,"corporation":false,"usgs":false,"family":"Carlson","given":"Amanda R.","affiliations":[],"preferred":false,"id":839345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helmers, David P.","contributorId":177497,"corporation":false,"usgs":false,"family":"Helmers","given":"David P.","affiliations":[],"preferred":false,"id":839346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839347,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mockrin, Miranda H.","contributorId":211622,"corporation":false,"usgs":false,"family":"Mockrin","given":"Miranda","email":"","middleInitial":"H.","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":839348,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Radeloff, Volker C.","contributorId":141124,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker","email":"","middleInitial":"C.","affiliations":[{"id":13679,"text":"SILVIS Lab, Department of Forest and Wildlife Ecology, University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":839349,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70239265,"text":"70239265 - 2022 - Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data","interactions":[],"lastModifiedDate":"2023-01-06T12:56:15.663693","indexId":"70239265","displayToPublicDate":"2022-03-27T06:51:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Eyes on the herd: Quantifying ungulate density from satellite, unmanned aerial systems, and GPScollar data","docAbstract":"Novel approaches to quantifying density and distributions could help biologists adaptively manage wildlife populations, particularly if methods are accurate, consistent, cost-effective, rapid, and sensitive to change. Such approaches may also improve research on interactions between density and processes of interest, such as disease transmission across multiple populations. We assess how satellite imagery, unmanned aerial system (UAS) imagery, and Global Positioning System (GPS) collar data vary in characterizing elk density, distribution, and count patterns across times with and without supplemental feeding at the National Elk Refuge (NER) in the US state of Wyoming. We also present the first comparison of satellite imagery data with traditional counts for ungulates in a temperate system. We further evaluate seven different aggregation metrics to identify the most consistent and sensitive metrics for comparing density and distribution across time and populations. All three data sources detected higher densities and aggregation locations of elk during supplemental feeding than non-feeding at the NER. Kernel density estimates (KDEs), KDE polygon areas, and the first quantile of interelk distances detected differences with the highest sensitivity and were most highly correlated across data sources. Both UAS and satellite imagery provide snapshots of density and distribution patterns of most animals in the area at lower cost than GPS collars. While satellite-based counts were lower than traditional counts, aggregation metrics matched those from UAS and GPS data sources when animals appeared in high contrast to the landscape, including brown elk against new snow in open areas. UAS counts of elk were similar to traditional ground-based counts on feed grounds and are the best data source for assessing changes in small spatial extents. Satellite, UAS, or GPS data can provide appropriate data for assessing density and changes in density from adaptive management actions. For the NER, where high elk densities are beneath controlled airspace, GPS collar data will be most useful for evaluating how management actions, including changes in the dates of supplemental feeding, influence elk density and aggregation across large spatial extents. Using consistent and sensitive measures of density may improve research on the drivers and effects of density within and across a wide range of species.
Many tiger beetles (Family Cicindelidae) are critically imperiled due to their dependence on small patches of suitable habitat that are frequently threatened by natural and anthropogenic disturbances. In the eastern United States, conservation of three tiger beetles - Habroscelimorpha dorsalis dorsalis, H. dorsalis media, and Ellipsoptera puritana - has been inhibited by the absence of population genetic information that is needed for effective recovery planning and potential reintroductions. Using microsatellite panels, we performed population genetic analyses and compared patterns in diversity and differentiation within and between taxa. Nearly all collections of the three taxa had less observed heterozygosity than expected under Hardy-Weinberg Equilibrium, and there was a strong latitudinal gradient in genetic diversity in H. d. dorsalis distributed along the eastern and western shores of the Chesapeake Bay. We also found clear spatial patterns of genetic differentiation which reflected strong isolation-by-distance within all three taxa and between collections of H. d. dorsalis and H. d. media. However, there was evidence of admixture in current (mouth of the Chesapeake Bay) and former (coastal New Jersey) contact zones of H. d. dorsalis and H. d. media. Taken together, our study suggests that relatively few adult tiger beetles may maintain many populations, and that gene flow among nearby habitat patches is common in all three taxa – a characteristic that may help tiger beetles persist in dynamic coastal environments. Results of our analyses can be used to support conservation and management by identifying the spatial scale of metapopulation connectivity and locating populations at the greatest risk of extirpation.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10592-022-01440-y","usgsCitation":"Kazyak, D., Aunins, A.W., White, S.L., Eackles, M.S., and Knisley, C.B., 2022, Population genetics of three at-risk tiger beetles Habroscelimorpha dorsalis dorsalis, H. d. media, and Ellipsoptera puritana: Conservation Genetics, v. 23, p. 623-638, https://doi.org/10.1007/s10592-022-01440-y.","productDescription":"16 p.","startPage":"623","endPage":"638","ipdsId":"IP-128201","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":399082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay, Eastern Virginia Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.2119140625,\n 36.59788913307022\n ],\n [\n -75.41015624999999,\n 36.59788913307022\n ],\n [\n -75.41015624999999,\n 39.740986355883564\n ],\n [\n -77.2119140625,\n 39.740986355883564\n ],\n [\n -77.2119140625,\n 36.59788913307022\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","noUsgsAuthors":false,"publicationDate":"2022-03-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":202481,"corporation":false,"usgs":true,"family":"Kazyak","given":"David C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840978,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aunins, Aaron W. 0000-0001-5240-1453 aaunins@usgs.gov","orcid":"https://orcid.org/0000-0001-5240-1453","contributorId":5863,"corporation":false,"usgs":true,"family":"Aunins","given":"Aaron","email":"aaunins@usgs.gov","middleInitial":"W.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840979,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840980,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eackles, Michael S. 0000-0001-5624-5769 meackles@usgs.gov","orcid":"https://orcid.org/0000-0001-5624-5769","contributorId":218936,"corporation":false,"usgs":true,"family":"Eackles","given":"Michael","email":"meackles@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":840981,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Knisley, C. Barry","contributorId":290423,"corporation":false,"usgs":false,"family":"Knisley","given":"C.","email":"","middleInitial":"Barry","affiliations":[{"id":62424,"text":"Randolph-Macon (emeritus)","active":true,"usgs":false}],"preferred":false,"id":840982,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230098,"text":"70230098 - 2022 - Arsenic in private well water and birth outcomes in the United States","interactions":[],"lastModifiedDate":"2022-03-29T11:50:35.732821","indexId":"70230098","displayToPublicDate":"2022-03-26T06:46:01","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1523,"text":"Environment International","active":true,"publicationSubtype":{"id":10}},"title":"Arsenic in private well water and birth outcomes in the United States","docAbstract":"Prenatal exposure to drinking water with arsenic concentrations >50 μg/L is associated with adverse birth outcomes, with inconclusive evidence for concentrations ≤50 μg/L. In a collaborative effort by public health experts, hydrologists, and geologists, we used published machine learning model estimates to characterize arsenic concentrations in private wells—federally unregulated for drinking water contaminants—and evaluated associations with birth outcomes throughout the conterminous U.S.
Using several machine learning models, including boosted regression trees (BRT) and random forest classification (RFC), developed from measured groundwater arsenic concentrations of ∼20,000 private wells, we characterized the probability that arsenic concentrations occurred within specific ranges in groundwater. Probabilistic model estimates and private well usage data were linked by county to all live birth certificates from 2016 (n = 3.6 million). We evaluated associations with gestational age and term birth weight using mixed-effects models, adjusted for potential confounders and incorporated random intercepts for spatial clustering.
We generally observed inverse associations with term birth weight. For instance, when using BRT estimates, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 5 μg/L was associated with a −1.83 g (95% CI: −3.30, −0.38) lower term birth weight after adjusting for covariates. Similarly, a 10-percentage point increase in the probability that private well arsenic concentrations exceeded 10 μg/L was associated with a −2.79 g (95% CI: −4.99, −0.58) lower term birth weight. Associations with gestational age were null.
In this largest epidemiologic study of arsenic and birth outcomes to date, we did not observe associations of modeled arsenic estimates in private wells with gestational age and found modest inverse associations with term birth weight. Study limitations may have obscured true associations, including measurement error stemming from a lack of individual-level information on primary water sources, water arsenic concentrations, and water consumption patterns.
Yellowstone Cutthroat Trout (YCT) Oncorhynchus clarkii bouvieri is a species with significant ecological and recreational value. In many YCT fisheries, managers are tasked with balancing angler expectations and fish conservation. Henrys Lake supports a popular trophy trout fishery, but the increase of nonnative Utah Chub Gila atraria has caused concern for YCT. We summarized long-term trends in abundance, length structure, body condition, and growth of YCT to evaluate the effect of Utah Chub. Additionally, we investigated abiotic and biotic factors influencing YCT. We examined archived hard structures to provide a comprehensive evaluation of changes in age and growth of YCT in the system. We used stocking records and catch rates of Utah Chub and trout in Henrys Lake as covariates to explain changes in YCT catch rates and growth. Catch rates varied from 1.5 to 15.4 YCT per net night during the 2002 to 2020 sampling period, but we did not identify consistent patterns. Length structure was consistently dominated by stock- to quality-length fish, and we captured few fish >600 mm in total length. Relative weight of YCT was decreased from a mean ± standard deviation (SD) of 115.9 ± 16.5 in 2004 to 93.2 ± 8.2 in 2020. The age of YCT varied between 1 and 11 years; fish that we captured during 2010 to 2020 were the oldest. The majority of fish that we sampled were age 4 and younger. Total annual mortality of age-2 and older YCT was higher than other Cutthroat Trout populations (i.e., 0.70 during 2002 to 2010 and 0.60 during 2011 to 2020). Based on regression models, we identified positive relationships between catch rates of YCT, Brook Trout Salvelinus fontinalis, and Rainbow Trout Oncorhynchus mykiss × YCT hybrid trout. We observed negative relationships between growth of YCT and abundance of Utah Chub and Brook Trout. Although we identified negative relationships, YCT growth in recent decades is as fast as or faster than earlier time periods. Results from this research suggest that major changes in YCT population dynamics are not evident over the last 20 years. This study provides insight into the factors influencing an adfluvial trout population. In particular, results from this research may be useful for managers of systems where Utah Chub have been introduced.
","language":"English","publisher":"Allen Press","doi":"10.3996/JFWM-21-074","usgsCitation":"McCarrick, D.K., Dillon, J., High, B., and Quist, M.C., 2022, Population dynamics of Yellowstone Cutthroat Trout in Henrys Lake, Idaho: Journal of Fish and Wildlife Management, v. 13, no. 1, p. 169-181, https://doi.org/10.3996/JFWM-21-074.","productDescription":"13 p.","startPage":"169","endPage":"181","ipdsId":"IP-131713","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":448374,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/jfwm-21-074","text":"Publisher Index Page"},{"id":430042,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Henrys Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.45365706101977,\n 44.679676467620226\n ],\n [\n -111.45365706101977,\n 44.60608365836251\n ],\n [\n -111.3617721462157,\n 44.60608365836251\n ],\n [\n -111.3617721462157,\n 44.679676467620226\n ],\n [\n -111.45365706101977,\n 44.679676467620226\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-03-25","publicationStatus":"PW","contributors":{"authors":[{"text":"McCarrick, Darcy K.","contributorId":269700,"corporation":false,"usgs":false,"family":"McCarrick","given":"Darcy","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":903389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dillon, Jeffrey","contributorId":338604,"corporation":false,"usgs":false,"family":"Dillon","given":"Jeffrey","email":"","affiliations":[{"id":36224,"text":"Idaho Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":903390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"High, Brett","contributorId":274499,"corporation":false,"usgs":false,"family":"High","given":"Brett","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":903391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael C. 0000-0001-8268-1839","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":207142,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903392,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230039,"text":"ofr20221025 - 2022 - ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021","interactions":[],"lastModifiedDate":"2022-04-14T15:52:36.403875","indexId":"ofr20221025","displayToPublicDate":"2022-03-25T11:52:25","publicationYear":"2022","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":"2022-1025","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 3, 2021","title":"ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021","docAbstract":"The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.
This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 3 (July–September), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221025","usgsCitation":"Micijevic, E., Rengarajan, R., Haque, M.O., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, N., Denevan, A., Franks, S., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat quarterly Calibration and Validation report—Quarter 3, 2021: U.S. Geological Survey Open-File Report 2022–1025, 38 p., https://doi.org/10.3133/ofr20221025.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-134677","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":397609,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221025/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397606,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov","text":"U.S. Geological Survey database","linkHelpText":"—EarthExplorer"},{"id":397605,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1025/images"},{"id":397604,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1025/ofr20221025.XML"},{"id":397603,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1025/ofr20221025.pdf","text":"Report","size":"2.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1025"},{"id":397602,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1025/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Spectroscopic data are rich in information and are commonly used in planetary research. Many mission teams, research labs, and individual research scientists derive thematic products from multi- and hyperspectral data sets and apply spectroscopic analysis techniques to derive new understanding. The PyHAT is a powerful and versatile, free, and open-source Python library designed to support exploratory spectral data analysis, the derivation of mission generated thematic products, and the application of statistical learning methods to spectral data. We present a general overview of the software architecture and identify the classes of users we seek to support. Four case studies demonstrate the use for both orbital and in-situ (landed) hyperspectral data.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Machine Learning for Planetary Science","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-818721-0.00012-4","usgsCitation":"Laura, J., Gaddis, L., Anderson, R.B., and Aneece, I.P., 2022, Introduction to the Python Hyperspectral Analysis Tool (PyHAT), chap. 4 of Machine Learning for Planetary Science, p. 55-90, https://doi.org/10.1016/B978-0-12-818721-0.00012-4.","productDescription":"36 p.","startPage":"55","endPage":"90","ipdsId":"IP-122322","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":421821,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":885549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaddis, Lisa R. 0000-0001-9953-5483","orcid":"https://orcid.org/0000-0001-9953-5483","contributorId":93178,"corporation":false,"usgs":true,"family":"Gaddis","given":"Lisa R.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":885550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Anderson, Ryan B. 0000-0003-4465-2871 rbanderson@usgs.gov","orcid":"https://orcid.org/0000-0003-4465-2871","contributorId":170054,"corporation":false,"usgs":true,"family":"Anderson","given":"Ryan","email":"rbanderson@usgs.gov","middleInitial":"B.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":885551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":885552,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230171,"text":"70230171 - 2022 - Fingerprinting historical tributary contributions to floodplain sediment using bulk geochemistry","interactions":[],"lastModifiedDate":"2022-04-01T21:47:47.81119","indexId":"70230171","displayToPublicDate":"2022-03-25T09:44:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1198,"text":"Catena","active":true,"publicationSubtype":{"id":10}},"title":"Fingerprinting historical tributary contributions to floodplain sediment using bulk geochemistry","docAbstract":"Sediment deposition on floodplains is essential for the development and maintenance of riparian ecosystems. Upstream erosion is known to influence downstream floodplain construction, but linking these disparate processes is challenging, especially over large spatial and temporal scales. Sediment fingerprinting is thus a robust tool to establish process linkages between downstream floodplain development and sediment production in distal headwater basins. Here we use sediment geochemistry to connect historical erosion in several tributaries of the Yampa River in Colorado and Wyoming, USA, to the construction of downstream floodplains on which extensive cottonwood forests established. Using a combination of conventional techniques and the relatively novel machine-learning random forest algorithm, we build multiple fingerprints of diagnostic geochemical tracers that are then input into a Bayesian mixing model to apportion provenance of floodplain sediment. Sediment samples for provenance analysis were collected from an excavated floodplain in Deerlodge Park on the Yampa River at the rooting surface of the surrounding cottonwood forest and dominantly comprised of very fine (4Φ) sand. Fingerprinting analysis of the 4Φ fraction of collected floodplain sink (n = 38) and tributary source (n = 218) samples revealed floodplain sediment to be dominantly sourced from the tributaries of Muddy Creek (45 ± 4%) and Sand Wash (42 ± 6%). Dendrochronology results moreover indicate the Deerlodge floodplain sediment was deposited in ∼1912, which falls squarely within the time (1880–1940) these tributaries were actively eroding. Taken together, study results indicate a demonstrable link between historical tributary erosion and downstream floodplain construction and concomitant forest establishment. Our findings suggest processes operating in tributary watersheds play an important role in the dynamics of large rivers and emphasize both the need for holistic, collaborative management of sediment as an essential resource and the potential to utilize sediment fingerprinting to inform and direct river ecosystem management.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2022.106231","usgsCitation":"Kemper, J.T., Rathburn, S.L., Friedman, J.M., Nelson, J.M., Mueller, E., and Vincent, K.R., 2022, Fingerprinting historical tributary contributions to floodplain sediment using bulk geochemistry: Catena, v. 214, 106231, 16 p., https://doi.org/10.1016/j.catena.2022.106231.","productDescription":"106231, 16 p.","ipdsId":"IP-136718","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":397975,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Wyoming","otherGeospatial":"Little Snake River, Yampa River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.49822998046875,\n 40.42813291388417\n ],\n [\n -108.36639404296875,\n 40.42813291388417\n ],\n [\n -108.36639404296875,\n 40.48873742102282\n ],\n [\n -108.49822998046875,\n 40.48873742102282\n ],\n [\n -108.49822998046875,\n 40.42813291388417\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"214","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kemper, John T.","contributorId":270040,"corporation":false,"usgs":false,"family":"Kemper","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":839361,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rathburn, Sara L.","contributorId":140606,"corporation":false,"usgs":false,"family":"Rathburn","given":"Sara","email":"","middleInitial":"L.","affiliations":[{"id":13539,"text":"Department of Geosciences, Colorado State University, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":839362,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":839363,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nelson, John M.","contributorId":83578,"corporation":false,"usgs":true,"family":"Nelson","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":839364,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mueller, Erich R. 0000-0001-8202-154X","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":207750,"corporation":false,"usgs":false,"family":"Mueller","given":"Erich R.","affiliations":[{"id":37626,"text":"Department of Geography, University of Wyoming, Laramie, WY, USA","active":true,"usgs":false}],"preferred":false,"id":839365,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vincent, Kirk R","contributorId":289578,"corporation":false,"usgs":false,"family":"Vincent","given":"Kirk","email":"","middleInitial":"R","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":839366,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230160,"text":"70230160 - 2022 - Spatial social value distributions for multiple user groups in a coastal national park","interactions":[],"lastModifiedDate":"2022-03-31T13:52:27.689105","indexId":"70230160","displayToPublicDate":"2022-03-25T08:45:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2926,"text":"Ocean and Coastal Management","active":true,"publicationSubtype":{"id":10}},"title":"Spatial social value distributions for multiple user groups in a coastal national park","docAbstract":"Managing public lands to maximize societal benefits requires spatially explicit understanding of societal valuation, and public participation geographic information systems (PPGIS) are increasingly used in coastal settings to accomplish this task. Social Values for Ecosystem Services (SolVES), a PPGIS tool that systematizes the mapping and modeling of social values and cultural ecosystem services, is promising for use in coastal settings but has seen relatively limited applications relative to other PPGIS approaches; it has also, to our knowledge, not yet been applied in a barrier island setting. In this study, we surveyed two visitor groups and residents living near Cape Lookout National Seashore (North Carolina, USA) to understand their social values in the context of the park's management needs. We developed social-value models to evaluate differences between three user groups (fall visitors, summer visitors, and residents) and to evaluate how respondents' experiences, attitudes, and recreational activities influence the locations they value and their most strongly held value types, which included aesthetic, recreation, biodiversity, future, therapeutic, and historic values. We found that accessibility, user types and the seasonality of major recreational activities, and the linear configuration of the barrier island system at Cape Lookout are important influences on the social values held by visitors and residents. The modeling results provide information relevant to management at Cape Lookout and can inform the design of future PPGIS studies in coastal and marine settings.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ocecoaman.2022.106126","usgsCitation":"Ancona, Z.H., Bagstad, K.J., Le, L., Semmens, D., Sherrouse, B.C., Murray, G., Cook, P.S., and DiDonato, E., 2022, Spatial social value distributions for multiple user groups in a coastal national park: Ocean and Coastal Management, v. 222, 106126, 17 p., https://doi.org/10.1016/j.ocecoaman.2022.106126.","productDescription":"106126, 17 p.","ipdsId":"IP-128127","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":435911,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TVMHYT","text":"USGS data release","linkHelpText":"Spatial social value distributions for multiple user groups in a coastal national park"},{"id":397932,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Cape Lookout National Seashore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.70379638671874,\n 34.56538299699511\n ],\n [\n -76.2506103515625,\n 34.56538299699511\n ],\n [\n -76.2506103515625,\n 34.84536693184101\n ],\n [\n -76.70379638671874,\n 34.84536693184101\n ],\n [\n -76.70379638671874,\n 34.56538299699511\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"222","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ancona, Zachary H. 0000-0001-5430-0218 zancona@usgs.gov","orcid":"https://orcid.org/0000-0001-5430-0218","contributorId":5578,"corporation":false,"usgs":true,"family":"Ancona","given":"Zachary","email":"zancona@usgs.gov","middleInitial":"H.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Le, Lena","contributorId":192544,"corporation":false,"usgs":false,"family":"Le","given":"Lena","email":"","affiliations":[],"preferred":false,"id":839339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sherrouse, Benson C. 0000-0002-5102-5895 bcsherrouse@usgs.gov","orcid":"https://orcid.org/0000-0002-5102-5895","contributorId":2445,"corporation":false,"usgs":true,"family":"Sherrouse","given":"Benson","email":"bcsherrouse@usgs.gov","middleInitial":"C.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":839341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Murray, Grant","contributorId":289567,"corporation":false,"usgs":false,"family":"Murray","given":"Grant","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":839342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cook, Philip S.","contributorId":149906,"corporation":false,"usgs":false,"family":"Cook","given":"Philip","email":"","middleInitial":"S.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":839343,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DiDonato, Eva","contributorId":149907,"corporation":false,"usgs":false,"family":"DiDonato","given":"Eva","email":"","affiliations":[{"id":6924,"text":"National Park Service, Upper Columbia Basin Network","active":true,"usgs":false}],"preferred":false,"id":839344,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70230060,"text":"70230060 - 2022 - Murky waters: Divergent ways scientists, practitioners, and landowners evaluate beaver mimicry","interactions":[],"lastModifiedDate":"2022-03-28T11:31:27.159926","indexId":"70230060","displayToPublicDate":"2022-03-25T06:26:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Murky waters: Divergent ways scientists, practitioners, and landowners evaluate beaver mimicry","docAbstract":"From its rolling pastures to its forested Appalachian peaks, Kentucky’s scenery offers beauty along with contrast. Rivers, including the Mississippi and the Ohio, border much of the State, and more rivers and hundreds of lakes are inside its borders. Kentucky is also home to the world’s longest known cave system, Mammoth Cave National Park, and its residents maintain long-held traditions of coal mining, farming, horse racing, and bourbon making.
Although residents and visitors have a lot to explore up close, viewing Kentucky through a long lens—one extending into space—can offer even more information about its environmental and natural resources. Landsat satellite data can help State and Federal governments monitor the quality and health of Kentucky’s lands and waters.
Here is a closer look at some of the many ways that Landsat benefits Kentucky.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223017","usgsCitation":"U.S. Geological Survey, 2022, Kentucky and Landsat (ver. 1.1, January 2023): U.S. Geological Survey Fact Sheet 2022–3017, 2 p., https://doi.org/10.3133/fs20223017.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-127727","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":412029,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223017/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":411899,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3017/images"},{"id":411898,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3017/fs20223017.XML"},{"id":411897,"rank":3,"type":{"id":25,"text":"Version 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\"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 18, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
In 2000, the U.S. Geological Survey published a distinctive map, entitled “A Tapestry of Time and Terrain,” which showed a generalized depiction of the geology in the conterminous United States, draped over shaded-relief topography. In 2003, that map concept was extended geographically, and the resulting new map was published at 1:8,000,000 scale as “The North America Tapestry of Time and Terrain” (NATTT).
The NATTT map showed the wide range of ages of the bedrock that underlies North America, as well as the distribution of the principal rock types— sedimentary, igneous (as two subtypes, volcanic and plutonic), and metamorphic. Regional processes active at the land surface, as well as continental-scale tectonic events, are exposed on the maps, in the three dimensions of space and in the fourth dimension, geologic time.
This new publication contains the geographic information system (GIS) data from the NATTT map, which, in turn, includes map data from the earlier Tapestry map. The GIS files contain over 20,000 polygons that represent geologic age and rock class. In addition, layer files are provided to facilitate a symbolized display of the map that is similar to that of the original NATTT map publication. The data are intended to be used at the scale of the published source map (1:8,000,000).
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Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS-954
Reston, VA 20192
Massachusetts is the seventh smallest U.S. State in land area, but its size is surpassed by its contributions to U.S. history and the economy, its academic and medical expertise, and its natural features. The Atlantic Ocean to the east gives the “Bay State” more than 1,500 miles of coastline that were important in past fishing and maritime trade industries and in the tourism industry of today for destinations such as Boston, Cape Cod, Nantucket, and Martha’s Vineyard. Forests cover roughly 60 percent of the State, which owns 315,000 acres of forests in parklands, reserves, and woodlands.
Massachusetts celebrates many strengths, but its strengths can be vulnerable to environmental change. A robust population of 7 million translates to a density of more than 800 people per square mile; in Boston, the density rises to more than 18,000 people per square mile. Urban temperature hotspots can increase health risks to residents.
Although the whims of wind and water have long reshaped coastlines, climate change-induced sea-level rise and severe storms can amplify coastal effects. In forests, changes in temperature or precipitation can provide more favorable conditions for invasive species.
State and local governments have been taking steps to address climate change. Here are several ways Landsat has benefited the residents of Massachusetts.
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\"}}]}","edition":"Version 1.0: March 24, 2022; Version 1.1: January 23, 2023","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Trees are bioindicators of global climate change and regional urbanization, but available monitoring tools are ineffective for fine-scale observation of many species. Using six accelerometers mounted on two urban ash trees (Fraxinus americana), we looked at high-frequency tree vibrations, or change in periodicity of tree sway as a proxy for mass changes, to infer seasonal patterns of flowering and foliage (phenophases). We compared accelerometer-estimated phenophases to those derived from digital repeat photography using Green Chromatic Coordinates (GCC) and visual observation of phenophases defined by the USA National Phenology Network (NPN). We also drew comparisons between two commercial accelerometers and assessed how placement height influenced the ability to extract seasonal transition dates. Most notably, tree sway data showed a greenness signal in an urban environment and produced a clear flowering time-series and peak flowering signal (PF), marking the first observations of a flower phenophase using accelerometer data. Estimated start of spring (SOS) from accelerometers and time-lapse GCC were more similar than start of autumn (SOA); accelerometers lagged behind the time-lapse camera dates by three and four days for SOS and 13 and 14 days for SOA for each tree. Estimates for SOS and SOA from accelerometers and time-lapse cameras aligned closely with different NPN phenophases. The two commercial accelerometers produced similar season onset: a difference of 2.4 to 3.8 days for SOS, 2.1 days for SOA, and 0.5 to 2.0 days for PF. Accelerometers placed at the main crown branch point versus higher in the canopy showed a difference of 0.2 to 4.9 days for SOS and -1.5 to 1.7 days for PF. Our results suggest accelerometers present a novel opportunity to objectively monitor reproductive tree biology and fill gaps in phenology observations. Furthermore, widely available accelerometers show promise for scaling up from individual trees to the landscape level to aid forest management and assessing climate change impacts to tree phenology.
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OPAs can be transported in suspension or deposited to the bed. Modeling the fate and transport of OPAs can provide useful information for making mitigation decisions. A novel open-source tool, FluOil, is developed to predict where OPAs may deposit and when they arrive in affected river/lake reaches by implementing the random walk particle tracking algorithm to represent the advection, diffusion, deposition, and resuspension of OPAs. The usability of FluOil is demonstrated with the 2010 Kalamazoo River oil spill case study. An unsteady hydrodynamic model simulates the river hydraulics and provides hydraulic data for use in FluOil. Settling velocity and critical shear stress for resuspension are the most important OPA properties concerning the transport and deposition of OPAs. 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