Global seismographic networks (GSNs) emerged during the late nineteenth and early twentieth centuries, facilitated by seminal international developments in theory, technology, instrumentation, and data exchange. The mid- to late-twentieth century saw the creation of the World-Wide Standardized Seismographic Network (1961) and International Deployment of Accelerometers (1976), which advanced global geographic coverage as seismometer bandwidth increased greatly allowing for the recording of the Earth's principal seismic spectrum. The modern era of global observations and rapid data access began during the 1980s, and notably included the inception of the GEOSCOPE initiative (1982) and GSN (1988). Through continual improvements, GEOSCOPE and the GSN have realized near-real time recording of ground motion with state-of-art data quality, dynamic range, and timing precision to encompass 180 seismic stations, many in very remote locations. Data from GSNs are increasingly integrated with other geophysical data (e.g., space geodesy, infrasound and Interferometric Synthetic Aperture Radar). Globally distributed seismic data are critical to resolving crust, mantle, and core structure; illuminating features of the plate tectonic and mantle convection system; rapid characterization of earthquakes; identification of potential tsunamis; global nuclear test verification; and provide sensitive proxies for environmental changes. As the global geosciences community continues to advance our understanding of Earth structure and processes controlling elastic wave propagation, GSN infrastructure offers a springboard to realize increasingly multi-instrument geophysical observatories. Here, we review the historical, scientific, and monitoring heritage of GSNs, summarize key discoveries, and discuss future associated opportunities for Earth Science.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021RG000749","usgsCitation":"Ringler, A.T., Anthony, R.E., Aster, R., Ammon, C., Arrowsmith, S., Benz, H.M., Ebeling, C., Frassetto, A., Kim, W.Y., Koelemeijer, P., Lau, H.C., Lekic, V., Montagner, J.P., Richards, P., Schaff, D., Vallee, M., and Yeck, W.L., 2022, Achievements and prospects of global broadband seismographic networks after 30 years of continuous geophysical observations: Reviews of Geophysics, v. 60, no. 3, e2021RG000749, 98 p., https://doi.org/10.1029/2021RG000749.","productDescription":"e2021RG000749, 98 p.","ipdsId":"IP-132866","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447073,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021rg000749","text":"External Repository"},{"id":408320,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"60","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-09-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Ringler, Adam T. 0000-0002-9839-4188 aringler@usgs.gov","orcid":"https://orcid.org/0000-0002-9839-4188","contributorId":3946,"corporation":false,"usgs":true,"family":"Ringler","given":"Adam","email":"aringler@usgs.gov","middleInitial":"T.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854668,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert 0000-0001-7089-8846 reanthony@usgs.gov","orcid":"https://orcid.org/0000-0001-7089-8846","contributorId":202829,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert","email":"reanthony@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854669,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aster, R. 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P.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":854676,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Lekic, V.","contributorId":251944,"corporation":false,"usgs":false,"family":"Lekic","given":"V.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":854677,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Montagner, J. P.","contributorId":297943,"corporation":false,"usgs":false,"family":"Montagner","given":"J.","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":854697,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Richards, P. G.","contributorId":297937,"corporation":false,"usgs":false,"family":"Richards","given":"P. G.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854679,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schaff, D. P.","contributorId":297936,"corporation":false,"usgs":false,"family":"Schaff","given":"D. P.","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":854678,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Vallee, M.","contributorId":297938,"corporation":false,"usgs":false,"family":"Vallee","given":"M.","affiliations":[{"id":64458,"text":"Universite de Paris, CNRS","active":true,"usgs":false}],"preferred":false,"id":854680,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":854681,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70233213,"text":"70233213 - 2022 - Gull plumages are, and are not, what they appear to human vision","interactions":[],"lastModifiedDate":"2022-07-19T14:16:53.782233","indexId":"70233213","displayToPublicDate":"2022-07-19T09:13:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":783,"text":"Annales Zoologici Fennici","active":true,"publicationSubtype":{"id":10}},"title":"Gull plumages are, and are not, what they appear to human vision","docAbstract":"Clear correlations between human and bird visual assessments of color have been documented, and are often assumed, despite fundamental differences in human and avian visual physiology and morphology. Analyses of plumage colors with avian perceptual models have shown widespread hidden inter-sexual and inter-specific color variation among passerines perceived as monochromatic to humans, highlighting the uncertainty of human vision to predict potentially relevant variation in color. Herein, we use reflectance data from 13 Larus gull species as an exemplar data set to study concordance between human vision and avian visual modeling of feather colors near, or below, the human threshold for discrimination. We found little evidence among gulls for sexual dichromatism hidden from human vision, but did find inter-specific color variation among gulls that is not seen by humans. Neither of these results were predictable a priori, and we reassert that reflectance measurements of actual feather colors, analyzed with avian relevant visual models, represent best practice when studying bird coloration.
","language":"English","publisher":"Finnish Zoological and Botanical Publishing Board","doi":"10.5735/086.059.0116","usgsCitation":"Eaton, M.D., Benites, P., Campillo, L., Wilson, R.E., and Sonsthagen, S.A., 2022, Gull plumages are, and are not, what they appear to human vision: Annales Zoologici Fennici, v. 59, no. 1, p. 187-203, https://doi.org/10.5735/086.059.0116.","productDescription":"7 p.","startPage":"187","endPage":"203","ipdsId":"IP-118461","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":404018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Eaton, Muir D","contributorId":293231,"corporation":false,"usgs":false,"family":"Eaton","given":"Muir","email":"","middleInitial":"D","affiliations":[{"id":63252,"text":"Drake University","active":true,"usgs":false}],"preferred":false,"id":846813,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benites, Pilar","contributorId":293232,"corporation":false,"usgs":false,"family":"Benites","given":"Pilar","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":846814,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Campillo, Luke","contributorId":293233,"corporation":false,"usgs":false,"family":"Campillo","given":"Luke","email":"","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":846815,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Robert E.","contributorId":293234,"corporation":false,"usgs":false,"family":"Wilson","given":"Robert","email":"","middleInitial":"E.","affiliations":[{"id":63255,"text":"Nebraska State Museum","active":true,"usgs":false}],"preferred":false,"id":846816,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":846817,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70233201,"text":"70233201 - 2022 - Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability","interactions":[],"lastModifiedDate":"2022-07-19T14:12:33.66365","indexId":"70233201","displayToPublicDate":"2022-07-19T09:08:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":605,"text":"AAPG Bulletin","printIssn":"0149-1423","active":true,"publicationSubtype":{"id":10}},"title":"Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability","docAbstract":"Quantifying the petrophysical and geomechanical properties of gas hydrate reservoirs is essential for understanding the natural hydrate system and predicting gas production behavior for future resource development. Pressure-core analysis tools were used to characterize methane hydrate–bearing sediments recovered from the Gulf of Mexico Green Canyon Block 955, under an international collaboration with The University of Texas and the National Institute of Advanced Industrial Science and Technology. Pressure-core samples were successfully transferred from Austin, Texas to Sapporo, Japan. Index property measurements (grain size, grain density, hydration number, gas composition, thermal conductivity), along with triaxial compression, consolidation, and permeability tests with a nuclear magnetic resonance (NMR) analyzer were conducted. Compression tests at different strain rates confirmed a strain rate dependence for hydrate-bearing sediment, and an equation for predicting strength as a function of hydrate saturation and strain rate is proposed. Compression and swelling indices were obtained from high-effective stress consolidation tests. Furthermore, secondary compression coefficients for hydrate-bearing sediments were obtained, suggesting that hydrate exhibits creeping behavior on timescales of minutes to hours. A relatively high initial permeability of a few millidarcys was confirmed. In addition, the first NMR signal measurement was performed on a hydrate-bearing pressure core to acquire the NMR transverse or spin-spin (T2) distribution. Results confirm that the Schlumberger Doll Research model and Timur-Coates model predictions underestimate permeability measured directly via fluid flow. Permeability estimated using specific surface values derived from NMR T2 distributions is in good agreement with flow test results. Finally, an extended Timur-Coates model was proposed and predicts intrinsic permeability with high accuracy.
","language":"English","publisher":"American Association of Petroleum Geologists","doi":"10.1306/04272120204","usgsCitation":"Yoneda, J., Jin, Y., Muraoka, M., Oshima, M., Suzuki, K., Waite, W., and Flemings, P., 2022, Comprehensive pressure core analysis for hydrate-bearing sediments from Gulf of Mexico Green Canyon Block 955, including assessments of geomechanical viscous behavior and nuclear magnetic resonance permeability: AAPG Bulletin, v. 106, no. 5, p. 1143-1177, https://doi.org/10.1306/04272120204.","productDescription":"35 p.","startPage":"1143","endPage":"1177","ipdsId":"IP-124941","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":404017,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Green Canyon, Green Canyon Block 955, Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.76904296874999,\n 26.194876675795218\n ],\n [\n -89.23095703125,\n 26.194876675795218\n ],\n [\n -89.23095703125,\n 27.6251403350933\n ],\n [\n -90.76904296874999,\n 27.6251403350933\n ],\n [\n -90.76904296874999,\n 26.194876675795218\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"106","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yoneda, Jun","contributorId":240073,"corporation":false,"usgs":false,"family":"Yoneda","given":"Jun","affiliations":[{"id":40273,"text":"National Institute of Advanced Industrial Science and Technology","active":true,"usgs":false}],"preferred":false,"id":846772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jin, Yusuke","contributorId":220832,"corporation":false,"usgs":false,"family":"Jin","given":"Yusuke","email":"","affiliations":[],"preferred":false,"id":846773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muraoka, Michihiro","contributorId":248423,"corporation":false,"usgs":false,"family":"Muraoka","given":"Michihiro","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oshima, Motoi","contributorId":248424,"corporation":false,"usgs":false,"family":"Oshima","given":"Motoi","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Suzuki, Kiyofumi","contributorId":248425,"corporation":false,"usgs":false,"family":"Suzuki","given":"Kiyofumi","affiliations":[{"id":49900,"text":"National Institute of Advanced Industrial Science and Technology (AIST)","active":true,"usgs":false}],"preferred":false,"id":846776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":846777,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flemings, Peter","contributorId":198205,"corporation":false,"usgs":false,"family":"Flemings","given":"Peter","affiliations":[{"id":13127,"text":"Jackson School of Geosciences, University of Texas, Austin","active":true,"usgs":false}],"preferred":false,"id":846778,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233462,"text":"70233462 - 2022 - Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems","interactions":[],"lastModifiedDate":"2022-07-21T13:59:42.32542","indexId":"70233462","displayToPublicDate":"2022-07-19T08:55:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems","docAbstract":"Sagebrush ecosystems of western North America are threatened by invasive annual grasses and wildfires that can remove fire-intolerant shrubs for decades. Fuel reduction treatments are used ostensibly to aid in fire suppression, conserve wildlife habitat, and restore historical fire regimes, but long-term ecological impacts of these treatments are not clear. In 2006, we initiated fuel reduction treatments (prescribed fire, mowing, and herbicide applications [tebuthiuron and imazapic]) in six Artemisia tridentata ssp. wyomingensis communities. We evaluated long-term effects of these fuel treatments on: (1) magnitude and longevity of fuel reduction; (2) Greater Sage-grouse habitat characteristics; and (3) ecological resilience and resistance to invasive annual grasses. Responses were analyzed using repeated-measures linear mixed models. Response variables included plant biomass, cover, density and height, distances between perennial plants, and exposed soil cover. Prescribed fire produced the greatest reduction in woody fuel over time. Mowing initially reduced woody biomass, which recovered by year 10. Tebuthiuron did not significantly reduce woody biomass compared to controls. All woody fuel treatments reduced sagebrush cover to below 15% (recommended minimum for Greater Sage-grouse habitat), but only prescribed fire reduced cover to below controls. Median mowed sagebrush height remained above the recommended 30 cm. Cheatgrass (Bromus tectorum) cover increased to above the recommended maximum of 10% across all treatments and controls. Ecological resilience to woody fuel treatments was lowest with fire and greatest with mowing. Low resilience over the 10 posttreatment years was identified by: (1) poor perennial plant recovery posttreatment with sustained reductions in cover and density of some perennial plant species; (2) sustained reductions in lichen and moss cover; and (3) increases in cheatgrass cover. Although 10 years is insufficient to conclusively describe final ecological responses to fuel treatments, mowing woody fuels has the greatest potential to reduce woody fuel, minimize shrub mortality and soil disturbance, maintain lichens and mosses, and minimize long-term negative impacts on Greater Sage-grouse habitat. However, maintaining ecological resilience and resistance to invasion may be threatened by increases in cheatgrass cover, which are occurring regionally.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4176","usgsCitation":"Pyke, D.A., Shaff, S.E., Chambers, J., Schupp, E.W., Newingham, B.A., Gray, M.L., and Ellsworth, L.M., 2022, Ten-year ecological responses to fuel treatments within semiarid Wyoming big sagebrush ecosystems: Ecosphere, v. 13, no. 7, e4176, 21 p., https://doi.org/10.1002/ecs2.4176.","productDescription":"e4176, 21 p.","ipdsId":"IP-128463","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447085,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4176","text":"Publisher Index Page"},{"id":404212,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nevada, Oregon, Utah, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.4541015625,\n 38.37611542403604\n ],\n [\n -110.8740234375,\n 38.37611542403604\n ],\n [\n -110.8740234375,\n 48.1367666796927\n ],\n [\n -120.4541015625,\n 48.1367666796927\n ],\n [\n -120.4541015625,\n 38.37611542403604\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847154,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaff, Scott E. 0000-0001-8978-9260","orcid":"https://orcid.org/0000-0001-8978-9260","contributorId":219813,"corporation":false,"usgs":true,"family":"Shaff","given":"Scott","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":847155,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chambers, Jeanne C.","contributorId":75889,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[],"preferred":false,"id":847156,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schupp, Eugene W.","contributorId":178262,"corporation":false,"usgs":false,"family":"Schupp","given":"Eugene","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":847157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Newingham, Beth A.","contributorId":195932,"corporation":false,"usgs":false,"family":"Newingham","given":"Beth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":847158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gray, Margaret L 0000-0002-4810-8876","orcid":"https://orcid.org/0000-0002-4810-8876","contributorId":221166,"corporation":false,"usgs":false,"family":"Gray","given":"Margaret","email":"","middleInitial":"L","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":847159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellsworth, Lisa M.","contributorId":255109,"corporation":false,"usgs":false,"family":"Ellsworth","given":"Lisa","email":"","middleInitial":"M.","affiliations":[{"id":51436,"text":"Fisheries and Wildlife Department, Oregon State University, Corvallis, Oregon 97331 USA","active":true,"usgs":false}],"preferred":false,"id":847160,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233220,"text":"70233220 - 2022 - Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico","interactions":[],"lastModifiedDate":"2022-07-19T13:56:10.749169","indexId":"70233220","displayToPublicDate":"2022-07-19T08:49:06","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1396,"text":"Diseases of Aquatic Organisms","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico","title":"Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico","docAbstract":"ABSTRACT: Fibropapillomatosis (FP) is a tumor disease that affects all sea turtle species but is mainly seen in green turtles Chelonia mydas. The pathology of FP has been described extensively, but its dynamics in populations over time have been less studied. We analyzed the dynamics of FP in a population of green turtles in Akumal Bay on the central coast of the Mexican Caribbean. A total of 475 green turtles were captured over 15 yr (2004-2018). The highest prevalence of FP was found in the largest turtles, and there was a positive relationship between FP prevalence and size of turtles. FP was first detected in 2008 at a prevalence of 1.6%, and annual prevalence increased markedly from 17.9% in 2015 to 54% by 2018. Likewise, severity of FP increased over time, with most turtles falling into moderately to severely diseased categories (tumor score 2). The average size of turtles with FP was significantly larger than the size of individuals without FP. Regression of tumors was seen in 21% of turtles, tumor score was higher in smaller individuals, and only tumor score 2 was present in the largest sea turtles. An increase in the prevalence and tumor score of FP coincided with the massive arrival of Sargassum in 2015, suggesting that altered environmental conditions may have played a role. The increased prevalence of FP in Akumal Bay prompts the need to explain what might be driving this phenomenon and how widespread it is in the Caribbean.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/dao03669","usgsCitation":"Munoz Teneria, F.A., Labrada-Martagon, V., Herrera-Pavon, R., Work, T.M., Gonzalez Ballesteros, E., Negrete-Philippe, A., and Maldonado-Saldana, G., 2022, Fibropapillomatosis dynamics in green sea turtles Chelonia mydas over 15 years of monitoring in Akumal Bay, Quintana Roo, Mexico: Diseases of Aquatic Organisms, v. 149, p. 133-143, https://doi.org/10.3354/dao03669.","productDescription":"11 p.","startPage":"133","endPage":"143","ipdsId":"IP-137508","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":447087,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/dao03669","text":"Publisher Index Page"},{"id":404014,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","state":"Quintana Roo","otherGeospatial":"Akumal Bay, Marine Life Refuge of Akumal Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.32285499572754,\n 20.376411864521312\n ],\n [\n -87.32173919677734,\n 20.37496358008667\n ],\n [\n -87.30628967285156,\n 20.399341222168914\n ],\n [\n -87.31521606445311,\n 20.406661807347298\n ],\n [\n -87.3288631439209,\n 20.38292897619788\n ],\n [\n -87.32714653015137,\n 20.381239381095543\n ],\n [\n -87.32285499572754,\n 20.376411864521312\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Munoz Teneria, Fernando A.","contributorId":191521,"corporation":false,"usgs":false,"family":"Munoz Teneria","given":"Fernando","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":846832,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Labrada-Martagon, Vanessa","contributorId":191523,"corporation":false,"usgs":false,"family":"Labrada-Martagon","given":"Vanessa","email":"","affiliations":[],"preferred":false,"id":846833,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrera-Pavon, Roberto","contributorId":191522,"corporation":false,"usgs":false,"family":"Herrera-Pavon","given":"Roberto","email":"","affiliations":[],"preferred":false,"id":846834,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":846835,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gonzalez Ballesteros, Erik","contributorId":293241,"corporation":false,"usgs":false,"family":"Gonzalez Ballesteros","given":"Erik","email":"","affiliations":[{"id":25354,"text":"Universidad Nacional Autónoma de México","active":true,"usgs":false}],"preferred":false,"id":846836,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Negrete-Philippe, Ana","contributorId":191526,"corporation":false,"usgs":false,"family":"Negrete-Philippe","given":"Ana","email":"","affiliations":[],"preferred":false,"id":846837,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maldonado-Saldana, Gisela","contributorId":293243,"corporation":false,"usgs":false,"family":"Maldonado-Saldana","given":"Gisela","email":"","affiliations":[{"id":63261,"text":"Kanantik Servicios y Soluciones Ambientales","active":true,"usgs":false}],"preferred":false,"id":846838,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236308,"text":"70236308 - 2022 - Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","interactions":[],"lastModifiedDate":"2022-09-01T12:08:58.021882","indexId":"70236308","displayToPublicDate":"2022-07-19T07:06:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not","docAbstract":"The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE is optimal for Laplacian errors. When errors deviate from these distributions, other metrics are superior.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-15-5481-2022","usgsCitation":"Hodson, T.O., 2022, Root-mean-square error (RMSE) or mean absolute error (MAE): When to use them or not: Geoscientific Model Development, v. 15, p. 5481-5487, https://doi.org/10.5194/gmd-15-5481-2022.","productDescription":"7 p.","startPage":"5481","endPage":"5487","ipdsId":"IP-136463","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447095,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-15-5481-2022","text":"Publisher Index Page"},{"id":406059,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationDate":"2022-07-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Hodson, Timothy O. 0000-0003-0962-5130","orcid":"https://orcid.org/0000-0003-0962-5130","contributorId":78634,"corporation":false,"usgs":true,"family":"Hodson","given":"Timothy","email":"","middleInitial":"O.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850544,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70239310,"text":"70239310 - 2022 - Thermophysical and compositional properties of paleobedforms on Mars","interactions":[],"lastModifiedDate":"2023-01-09T13:00:41.226167","indexId":"70239310","displayToPublicDate":"2022-07-19T06:59:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7353,"text":"Journal of Geophysical Research - Planets","active":true,"publicationSubtype":{"id":10}},"title":"Thermophysical and compositional properties of paleobedforms on Mars","docAbstract":"Bedforms on Earth and Mars are often preserved in the rock record in the form of sedimentary rock with distinct cross-bedding. On rare occasions, the full-surface geometry of a bedform can be preserved through burial and lithification. These features, known as paleobedforms, are found in a variety of geographic locations on Mars. Evidence in the morphology of paleobedforms, such as the retention of impact craters and steep erosional scarps, suggests that these features are well-lithified and capable of withstanding prolonged weathering and erosion. Here, we present results from thermophysical and compositional analyses on a subset of the best preserved paleobedform candidate fields on Mars. Thermophysical modeling elucidates the changes these bedforms underwent from their unconsolidated, particulate nature to their currently observed properties. Certain paleobedforms have elevated thermal inertias (e.g., ∼300–500 J·m−2·s−1/2·K−1) when compared with modern bedforms (∼250 J·m−2·s−1/2·K−1), and modeling indicates that they have cement volumes of 0.8%–1.5% even as high as 30%. However, most paleobedform candidates have unexpectedly low thermal inertia when compared with modern dunes. Additionally, compositional analyses reveal a range of spectral characteristics within paleobedforms (e.g., primary and secondary alteration products). These features add to the already existing class of Martian surfaces in which thermal inertia does not seem to correspond to erodibility, cohesion, or mechanical strength. Studying paleobedforms with both raised and nonraised thermal inertia has provided new insights into lithification on Mars and constrained the environmental conditions leading to the formation of these enigmatic features.
1. Technological advances in the field of animal tracking have greatly expanded the potential to remotely monitor animals, opening the door to exploring how animals shift their behavior over time or respond to external stimuli. A wide variety of animal-borne sensors can provide information on an animal’s location, movement characteristics, external environmental conditions, and internal physiological status.
2. Here, we demonstrate how piecewise regression can be used to identify the presence and timing of potential shifts in a variety of biological responses using GPS telemetry and other biologging data streams. Different biological latent states can be inferred by partitioning a time-series into multiple segments based on changes in modeled responses (e.g., their mean, variance, trend, degree of autocorrelation) and specifying a unique model structure for each interval.
3. We provide six example applications highlighting a variety of taxonomic species, data streams, timescales and biological phenomena. These examples include a short-term behavioural response (flee and return) by a trumpeter swan Cygnus buccinator following a GPS collar deployment; remote identification of parturition based on movements by a pregnant moose Alces alces; a physiological response (spike in heart-rate) in a black bear Ursus americanus to a stressful stimulus(presence of a drone); a mortality event of a trumpeter swan signalled by changes in collar temperature and overall dynamic body acceleration; an unsupervised method for identifying the onset, return, duration and staging use of sandhill crane Antigone canadensis migration; and estimation of the transition between incubation and brood-rearing (i.e. hatching) for a breeding trumpeter swan.
4. We implement analyses using the MCP package in R, which provides functionality for specifying and fitting a wide variety of user-defined model structures in a Bayesian framework and methods for assessing and comparing models using information criteria and cross-validation measures.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13779","usgsCitation":"Wolfson, D., Andersen, D.E., and Fieberg, J., 2022, Using piecewise regression to identify biological phenomena in biotelemetry datasets: Journal of Animal Ecology, v. 91, no. 9, p. 1755-1769, https://doi.org/10.1111/1365-2656.13779.","productDescription":"15 p.","startPage":"1755","endPage":"1769","ipdsId":"IP-134564","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467175,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.13779","text":"Publisher Index Page"},{"id":466005,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"91","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfson, David W.","contributorId":348002,"corporation":false,"usgs":false,"family":"Wolfson","given":"David W.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":922812,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Andersen, David E. 0000-0001-9535-3404 dea@usgs.gov","orcid":"https://orcid.org/0000-0001-9535-3404","contributorId":199408,"corporation":false,"usgs":true,"family":"Andersen","given":"David","email":"dea@usgs.gov","middleInitial":"E.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fieberg, John R.","contributorId":348003,"corporation":false,"usgs":false,"family":"Fieberg","given":"John R.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":922813,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236309,"text":"70236309 - 2022 - Tephrochronology of the Miocene Monterey and Modelo Formations, California","interactions":[],"lastModifiedDate":"2022-09-01T12:17:20.986718","indexId":"70236309","displayToPublicDate":"2022-07-17T07:15:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Tephrochronology of the Miocene Monterey and Modelo Formations, California","docAbstract":"Tuff beds have been known in the Miocene Monterey and Modelo Formations since the initial descriptions; however, age control and correlation is predominantly biostratigraphy. Here we combine tephrochronology and biostratigraphy in order to provide numerical age control for eight sedimentary sequences of the Monterey and Modelo Formations from Monterey, California to Orange County, California. We correlate 38 tuffs and tephra beds in the Monterey and Modelo Formations to 26 different dated tuffs found mainly in non-marine sequences in Nevada, Idaho and New Mexico. We also include geochemical data for an additional 19 tuffs in the Monterey and Modelo Formations for which there are no known correlative tuffs and geochemical data for 11 additional tuffs in other units that will add to the Miocene tephrostratigraphy. The identified tuffs range in age from 16 to 7 Ma with 31 tuffs erupted from volcanic centers of the Snake River Plain of northern Nevada to eastern Idaho. Twelve other tuffs erupted from the Southern Nevada Volcanic Field, one from the Sonoma Volcanic Field, north of San Francisco, and the eruptive source of 12 other tuffs is uncertain. These tuffs provide useful correlations of marine sequences deposited at varying depths along offshore Miocene California and possible insight into the distribution of air-fall tephra from so-called super eruptions","language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.2556(08)","usgsCitation":"Knott, J.R., Sarna-Wojcicki, A., Barron, J.A., Wan, E., Heizler, N., and Martinez, P., 2022, Tephrochronology of the Miocene Monterey and Modelo Formations, California: GSA Special Papers, https://doi.org/10.1130/2022.2556(08).","ipdsId":"IP-122368","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.2556(08)","text":"Publisher Index Page"},{"id":406061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knott, Jeffrey R. 0000-0002-4600-5961","orcid":"https://orcid.org/0000-0002-4600-5961","contributorId":218427,"corporation":false,"usgs":false,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":39844,"text":"CSU Fullerton, Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":850545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sarna-Wojcicki, Andrei M. 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":296073,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":850547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":296074,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":850548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heizler, Nancy","contributorId":296075,"corporation":false,"usgs":false,"family":"Heizler","given":"Nancy","email":"","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":850549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martinez, Priscilla","contributorId":296076,"corporation":false,"usgs":false,"family":"Martinez","given":"Priscilla","email":"","affiliations":[{"id":63349,"text":"California State University Fullerton","active":true,"usgs":false}],"preferred":false,"id":850550,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.
Secondary forests regenerating from human disturbance are increasingly becoming a predominant forest type in many regions, and they play a significant role in forest community dynamics. Understanding the factors that underlie the variation in species responses during secondary succession is important for understanding community assembly and biodiversity monitoring and management. Because species vary in ecology and behaviour, responses to ecosystem change should vary among species. Here, we show that habitat type (riparian, upland), phylogeny, and species traits mediate anuran and lizard probability of occurrence and species richness in pasture and secondary forest.
Sarapiquí and Osa Peninsula, Costa Rica.
We used phylogenetic occupancy models to estimate assemblage-level and species-specific responses to forest succession in 30 chronosequence sites that include pasture, secondary forest regenerating from pasture, and mature forest sites.
For the majority of species, we found increasing probability of occurrence in upland habitats as forest regenerated from pasture to secondary forest and similar probability of occurrence in riparian habitats across pasture, secondary forest, and mature forest sites. Species' responses to forest stage were phylogenetically correlated, and the trend was especially strong for anuran response to pasture sites. Anurans with lentic larval habitat had a positive occupancy response to pasture upland habitat, and anurans with lotic larval habitat had a variable response to different forest stages compared to mature forest.
Our study, which focuses on sites that are minimally isolated from mature forest reference sites, indicated that anuran and lizard occupancy rapidly recovered to a level similar to mature forest in a relatively short time span (approximately 20 years). Riparian habitats are key ecosystem features in our system and provide refugia for organisms in early successional stages. Maintenance of vegetation along streams shows that we can mitigate forest conversion by maintaining riparian buffers.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13601","usgsCitation":"Thompson, M.E., Halstead, B., and Donnelly, M., 2022, Riparian buffers provide refugia during secondary forest succession: Diversity and Distributions, v. 28, no. 9, p. 2008-2019, https://doi.org/10.1111/ddi.13601.","productDescription":"12 p.","startPage":"2008","endPage":"2019","ipdsId":"IP-132963","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13601","text":"Publisher Index Page"},{"id":404477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Osa Peninsula, Sarapiquí","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9,\n 10.3\n ],\n [\n -83.9,\n 10.5\n ],\n [\n -84.1,\n 10.5\n ],\n [\n -84.1,\n 10.3\n ],\n [\n -83.9,\n 10.3\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.4,\n 8.32\n ],\n [\n -83.3,\n 8.32\n ],\n [\n -83.3,\n 8.52\n ],\n [\n -83.4,\n 8.52\n ],\n [\n -83.4,\n 8.32\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Michelle E.","contributorId":210341,"corporation":false,"usgs":false,"family":"Thompson","given":"Michelle","email":"","middleInitial":"E.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":847546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":847547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Maureen A.","contributorId":293649,"corporation":false,"usgs":false,"family":"Donnelly","given":"Maureen A.","affiliations":[{"id":63353,"text":"Department of Biological Sciences, Florida International University, Miami, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":847548,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232990,"text":"70232990 - 2022 - The formation mechanisms for mid-latitude ice scarps on Mars","interactions":[],"lastModifiedDate":"2022-07-15T13:30:06.924916","indexId":"70232990","displayToPublicDate":"2022-07-15T08:25:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"The formation mechanisms for mid-latitude ice scarps on Mars","docAbstract":"Mid-latitude exposed ice scarps have recently been identified on Mars (Dundas et al., 2018; 2021). The presence of such surface ice exposures at relatively low latitudes was itself a mystery, and the formation dynamics of such scarps have also not been explained. In this work we model the ice ablation rates of several identified mid-latitude scarps. 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An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.
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Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 973
Denver, CO 80225
Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02222","usgsCitation":"Stevens, B., Conway, C.J., Luke, K., Weldon, A., Hand, C., Schwarzer, A., Smith, F., Watson, C., and Watts, B.D., 2022, Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems: Global Ecology and Conservation, v. 38, e02222, 12 p., https://doi.org/10.1016/j.gecco.2022.e02222.","productDescription":"e02222, 12 p.","ipdsId":"IP-136723","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02222","text":"Publisher Index Page"},{"id":430022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":903426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luke, Kirsten","contributorId":338653,"corporation":false,"usgs":false,"family":"Luke","given":"Kirsten","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weldon, Aimee","contributorId":338654,"corporation":false,"usgs":false,"family":"Weldon","given":"Aimee","email":"","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hand, Christy","contributorId":338655,"corporation":false,"usgs":false,"family":"Hand","given":"Christy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarzer, Amy","contributorId":338656,"corporation":false,"usgs":false,"family":"Schwarzer","given":"Amy","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":903431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Fletcher","contributorId":338657,"corporation":false,"usgs":false,"family":"Smith","given":"Fletcher","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Watson, Craig","contributorId":338659,"corporation":false,"usgs":false,"family":"Watson","given":"Craig","email":"","affiliations":[{"id":81184,"text":"Atlanti Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watts, Bryan D.","contributorId":338660,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":37406,"text":"College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":903434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70232270,"text":"ofr20191023D - 2022 - Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico — Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium","interactions":[],"lastModifiedDate":"2026-03-25T16:57:34.886331","indexId":"ofr20191023D","displayToPublicDate":"2022-07-14T10:33:00","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":"2019-1023","chapter":"D","displayTitle":"Focus Areas for Data Acquisition for Potential Domestic Resources of 13 Critical Minerals in the Conterminous United States and Puerto Rico—Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Helium, Magnesium, Manganese, Potash, Uranium, Vanadium, and Zirconium","title":"Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico — Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium","docAbstract":"The Earth Mapping Resources Initiative (Earth MRI) is conducted in phases to identify areas for acquiring new geologic framework data to identify potential domestic resources of the 35 mineral materials designated as critical minerals for the United States. This report describes the data sources and summary results for 13 critical minerals evaluated in the conterminous United States and Puerto Rico during phase 3 of the study (antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium). Phases 1 and 2 of the Earth MRI addressed aluminum, cobalt, graphite, lithium, niobium, platinum-group elements (PGEs), rare earth elements (REEs), tantalum, tin, titanium, and tungsten. Critical minerals in Alaska are covered in a separate report. No focus areas for phase 3 critical minerals are delineated for Hawaii.
The geologic, geochemical, topographic, and geophysical mapping provided by the Earth MRI documents geologic features that reflect the extent of individual mineral systems and provides information about critical mineral deposits that may not have been previously considered. The mineral-systems approach links critical mineral commodities to deposit types that represent the manifestations of large mineral systems.
Each of the 13 critical mineral commodities for phase 3 of the Earth MRI is discussed in terms of its importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types, and is accompanied by maps and tables listing examples of focus areas in the conterminous United States and Puerto Rico. Examples of important mineral systems for this group of 13 critical minerals include basin brine path systems for barite and fluorspar, Carlin-type systems and Coeur d’Alene systems for antimony, chemical weathering and volcanogenic seafloor systems for manganese, Climax-type systems for beryllium, mafic magmatic systems for chromium, marine evaporite systems for potash and magnesium, meteoric recharge systems for uranium, petroleum systems for helium, and placer systems for zirconium and hafnium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023D","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Dicken, C.L., Woodruff, L.G., Andersen, A.K., Brennan, S., Day, W.C., Drenth, B.J., Foley, N.K., Hall, S., Hofstra, A.H., McCafferty, A.E., Shah, A.K., and Ponce, D.A., 2022, Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico—Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium, chap. D of U.S. Geological Survey, Focus areas for data acquisition for potential domestic sources of critical minerals: U.S. Geological Survey Open-File Report 2019–1023, 65 p., https://doi.org/10.3133/ofr20191023D.","productDescription":"Report: xv, 66 p.; Data Release","numberOfPages":"66","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130167","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":435771,"rank":13,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DIZ9N8","text":"USGS data release","linkHelpText":"GIS, supplemental data table, and references for focus areas of potential domestic resources of critical minerals and related commodities in the United States and Puerto Rico (ver. 2.0, April 2024)"},{"id":435770,"rank":14,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95CHIL0","text":"USGS data release","linkHelpText":"GIS and Data Tables for Focus Areas for 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Tungsten"},{"id":402397,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023C","text":"Open-File Report 2019-1023-C","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in Alaska—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":402684,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P95CHIL0","text":"USGS data release","linkHelpText":"- GIS and data tables for focus areas for potential domestic nonfuel sources of rare earth elements"},{"id":402685,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.5066/P9U6SODG","text":"USGS data release","linkHelpText":"- GIS for focus areas of potential domestic resources of 11 critical minerals—aluminum, cobalt, graphite, lithium, niobium, platinum group elements, rare earth elements, tantalum, tin, titanium, and tungsten 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Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
We have identified and provided ecological interpretations of 30 taxa recovered at two shellmounds at the Dominican University of California archaeology site in Marin County, California (CA-MRN-254). A Q-mode cluster analysis was used to group the samples according to their faunal similarity. The clusters ranged from a diverse grouping of 100 samples with 27 taxa (Cluster A) to those with a more restricted assemblage (4–9 taxa in Clusters B to E). The Q-mode clusters were then used to interpret the variability in food resources utilized through the 1800 years of site occupation. During the Intermediate Middle Period (A.D.100-300), the inhabitants appeared to be selective in the marine taxa they used, evident by the presence of Cluster B and E assemblages. A diverse (Cluster A) assemblage was then utilized at the site at one or both of the shellmounds through the remainder of the occupancy period, including the Middle/Late Period Transition (A.D. 700–900) and Late Period Phase 1C (A.D. 900–1300), coincident with the extensive drought conditions of the Medieval Climatic Anomaly (MCA) in the San Francisco Bay area. These findings suggest the marine invertebrate resources utilized by the site occupants were not significantly affected by the persistent aridity associated with the MCA.
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2088651","usgsCitation":"Gumma, M., Thenkabail, P., Panjala, P., Teluguntla, P., Yamano, T., and Mohammad, I., 2022, Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security: GIScience & Remote Sensing, v. 59, no. 1, p. 1048-1077, https://doi.org/10.1080/15481603.2022.2088651.","productDescription":"30 p.","startPage":"1048","endPage":"1077","ipdsId":"IP-135578","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":447129,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/15481603.2022.2088651","text":"Publisher Index 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(ICRISAT)","active":true,"usgs":false}],"preferred":false,"id":848827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi","contributorId":294758,"corporation":false,"usgs":false,"family":"Teluguntla","given":"Pardhasaradhi","affiliations":[{"id":63639,"text":"Bay Area Environmental Research Institute (BAERI) @ USGS","active":true,"usgs":false}],"preferred":false,"id":848828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yamano, Takashi","contributorId":294759,"corporation":false,"usgs":false,"family":"Yamano","given":"Takashi","email":"","affiliations":[{"id":63641,"text":"Asian Development Bank (ADB)","active":true,"usgs":false}],"preferred":false,"id":848829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mohammad, Ismail","contributorId":294760,"corporation":false,"usgs":false,"family":"Mohammad","given":"Ismail","email":"","affiliations":[{"id":7069,"text":"International Crops Research Institute for the Semi Arid 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Leading hypotheses include formation by snowmelt in a past climate, or formation via currently active CO2 frost processes. This paper presents an expanded catalog of >300 recent flows in gullies. The results indicate that sediment transport in current gully flows moves the full range of materials needed for gully formation. New flows are more likely to transport boulders in gullies that have pre-existing boulder-covered aprons, indicating that current flows are transporting the same materials required for gully formation overall. The distribution of gully activity frequencies can be described by a power law and indicates that the recurrence intervals for flows in individual gullies are commonly tens to hundreds of Mars years. Over the last ~300 kyr, climate variations have been modest but individual gullies have had tens to thousands of flow events. This could be sufficient to account for the entirety of gully formation in some cases, although the same processes are likely to have occurred further in the past. For any gullies that may have initiated under higher-obliquity conditions, this level of recent activity indicates that the observable morphology has been shaped by CO2-driven flows. These observations of sediment transport and the tempo of gully activity are consistent with gully formation entirely by CO2 frost processes, likely with spatial and temporal variability, but with no role required for liquid water.
The Upper Geyser Basin at Yellowstone National Park (Wyoming, USA) harbors the greatest concentration of geysers worldwide. Research suggests that individual geysers are not isolated but rather are hydraulically connected in the subsurface with other geysers and thermal springs. To quantify such connections, we combined techniques from machine learning, causal inference, and dynamical systems to characterize the collective eruptive behavior of a set of 10 geysers over 18 months (April 2007 – September 2008) focusing on geyser-geyser interactions. Model predictions were up to 15 times more accurate when we sought to predict a geyser's eruption time series based on outflow channel temperatures from the network than based on its own time series alone, suggesting the existence of a complex interconnected subsurface groundwater system. On average, cone-type geysers had larger impacts on other geysers than did fountain-type geysers. Similarly, cone-type geysers were on average more insulated from other geysers. However, substantial unexplained variation remained after considering the cone versus fountain dichotomy. Distance between geysers also affected interactions: nearby geysers had stronger effects on focal geysers than did geysers located farther away. Collectively, results support the hypothesis of geyser interdependence at timescales of 5 min–10 days. Our analyses highlight the existence of quantifiable geyser-to-geyser interactions that can be resolved through pairwise and system-level analyses. These findings emphasize the subsurface interconnectedness of thermal features, provide information relevant to visitor experiences in Yellowstone National Park, and suggest strategies for exploring patterns of interdependence that may exist among other episodic geological phenomena.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JB023749","usgsCitation":"Fagan, W., Swain, A., Banerjee, A., Ranade, H., Thompson, P., Staniczenko, P.P., Flynn, B., Hungerford, J., and Hurwitz, S., 2022, Quantifying interdependencies in geyser eruptions at the Upper Geyser Basin, Yellowstone National Park: Journal of Geophysical Research, v. 127, no. 8, e2021JB023749, 23 p., https://doi.org/10.1029/2021JB023749.","productDescription":"e2021JB023749, 23 p.","ipdsId":"IP-137582","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":405590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Upper Geyser Basin, Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.839,\n 44.459\n ],\n [\n -110.823,\n 44.459\n ],\n [\n -110.823,\n 44.467\n ],\n [\n -110.839,\n 44.467\n ],\n [\n -110.839,\n 44.459\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Fagan, William F.","contributorId":108239,"corporation":false,"usgs":true,"family":"Fagan","given":"William F.","affiliations":[],"preferred":false,"id":849649,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Swain, Anshuman","contributorId":295531,"corporation":false,"usgs":false,"family":"Swain","given":"Anshuman","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849650,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Banerjee, Amitava","contributorId":295532,"corporation":false,"usgs":false,"family":"Banerjee","given":"Amitava","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849651,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ranade, Hamir","contributorId":295533,"corporation":false,"usgs":false,"family":"Ranade","given":"Hamir","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849652,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thompson, Peter","contributorId":295535,"corporation":false,"usgs":false,"family":"Thompson","given":"Peter","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":849653,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Staniczenko, Phillip P. 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Both local and trans-Pacific tsunamis were generated during this devastating megathrust event, but the local tsunami source region and the causative faults are poorly understood. We provide an updated view of the tsunami and earthquake hazard for the Kodiak Islands region through tsunami modeling and geophysical data analysis. Using seismic and bathymetric data, we characterize a regionally extensive seafloor lineament related to the Kodiak shelf fault zone, with focused uplift along a 50-km-long portion of the newly named Ugak fault as the most likely source of the local Kodiak Islands tsunami in 1964. We present evidence of Holocene motion along the Albatross Banks fault zone, but we suggest that this fault did not produce a tsunami in 1964. We relate major structural boundaries to active forearc splay faults, where tectonic uplift is collocated with gravity lineations. Differences in interseismic locking, seismicity rates, and potential field signatures argue for different stress conditions at depth near presumed segment boundaries. We find that the Kodiak segment boundaries have a clear geophysical expression and are linked to upper-plate structure and splay faulting. The tsunamigenic fault hazard is higher for the Kodiak shelf fault zone when compared to the nearby Albatross Banks fault zone, suggesting short wave travel paths and little tsunami warning time for nearby communities.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02486.1","usgsCitation":"Ramos, M.D., Liberty, L.M., Haeussler, P., and Humphreys, R.J., 2022, Upper-plate structure and tsunamigenic faults near the Kodiak Islands, Alaska, USA: Geosphere, v. 18, no. 5, p. 1474-1491, https://doi.org/10.1130/GES02486.1.","productDescription":"18 p.","startPage":"1474","endPage":"1491","ipdsId":"IP-135286","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":447140,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02486.1","text":"Publisher Index Page"},{"id":408601,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -147.4037941171657,\n 60.562811262269065\n ],\n [\n -151.83516963619837,\n 61.55980516417185\n ],\n [\n -156.47488362730599,\n 57.79635099382884\n ],\n [\n -154.29366188512998,\n 55.202746146556194\n ],\n [\n -147.4037941171657,\n 60.562811262269065\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Ramos, Marlon D. 0000-0003-4449-8624","orcid":"https://orcid.org/0000-0003-4449-8624","contributorId":293255,"corporation":false,"usgs":false,"family":"Ramos","given":"Marlon","email":"","middleInitial":"D.","affiliations":[{"id":63266,"text":"Air Force Research Lab","active":true,"usgs":false}],"preferred":false,"id":855359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Liberty, Lee M","contributorId":194078,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":855360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humphreys, Robert John 0000-0002-6733-6399","orcid":"https://orcid.org/0000-0002-6733-6399","contributorId":298308,"corporation":false,"usgs":true,"family":"Humphreys","given":"Robert","email":"","middleInitial":"John","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855362,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}