Despite significant investigation of fly ash spills and mineralogical controls on the release of potentially toxic elements (PTEs) from fly ash, interactions with the surficial environment remain relatively poorly understood. We conducted 90-day batch leaching studies with paired analysis of supernatant and solid-phase mineralogy to assess the elemental release and transformation of fly ash upon reaction with aquatic media (18 MΩ cm−1 water and simulated rainwater). The fly ash in this study, collected from the University of Alaska Fairbanks stoker-boiler power plant, is high in unburned carbon (~20% LOI) and highly enriched in several PTEs relative to the upper continental crust. Supernatant concentrations of oxyanion-forming elements (e.g., As, Se, Mo, Sb) remained relatively low and constant, suggesting equilibrium with the solid phase, possibly ettringite [Ca6Al2(SO4)3(OH)12•26H2O], which is known to incorporate and sorb oxyanion-forming PTEs and was identified by X-ray diffraction. Synthetic precipitation leaching procedure (SPLP) results failed to capture important temporal trends. Lead and Ba supernatant concentrations consistently exceeded drinking water standards, as well as others upon exposure to simulated physiological solutions. Seven-day experiments with dissolved organic matter-isolate solutions indicated that for certain elements, liberation was influenced by carbon concentration and/or the identity of the isolate. Overall, this paired approach can serve as a model for future studies, bridging existing gaps between batch leaching and single-element mineralogical, sorption, or speciation studies.
The coastal waters of Cuba are home to a small, endangered population of West Indian manatee, which would benefit from a comprehensive characterization of the population’s genetic variation. We conducted the first genetic assessment of Cuban manatees to determine the extent of the population's genetic structure and characterize the neutral genetic diversity among regions within the archipelago. We genotyped 49 manatees at 18 microsatellite loci, a subset of 27 samples on 1703 single nucleotide polymorphisms (SNPs), and sequenced 59 manatees at the mitochondrial control region. The Cuba manatee population had low nuclear (microsatellites HE = 0.44, and SNP HE = 0.29) and mitochondrial genetic diversity (h = 0.068 and π = 0.00025), and displayed moderate departures from random mating (microsatellite FIS = 0.12, SNP FIS = 0.10). Our results suggest that the western portion of the archipelago undergoes periodic exchange of alleles based on the evidence of shared ancestry and low but significant differentiation. The southeast Guantanamo Bay region and the western portion of the archipelago were more differentiated than southwest and northwest manatees. The genetic distinctiveness observed in the southeast supports its recognition as a demographically independent unit for natural resource management regardless of whether it is due to historical isolation or isolation by distance. Estimates of the regional effective population sizes, with the microsatellite and SNP datasets, were small (all Ne < 60). Subsequent analyses using additional samples could better examine how the observed structure is masking simple isolation by distance patterns or whether ecological or biogeographic forces shape genetic patterns.
Seasonal snow melt dominates the hydrologic budget across a large portion of the globe. Snow accumulation and melt vary over a broad range of spatial scales, preventing accurate extrapolation of sparse in situ observations to watershed scales. The lidar onboard the Ice, Cloud, and land Elevation, Satellite (ICESat-2) was designed for precise mapping of ice sheets and sea ice, and here we assess the feasibility of snow depth-mapping using ICESat-2 data in more complex and rugged mountain landscapes. We explore the utility of ATL08 Land and Vegetation Height and ATL06 Land Ice Height differencing from reference elevation datasets in two end member study sites. We analyze ∼3 years of data for Reynolds Creek Experimental Watershed in Idaho's Owyhee Mountains and Wolverine Glacier in southcentral Alaska's Kenai Mountains. Our analysis reveals decimeter-scale uncertainties in derived snow depth and glacier mass balance at the watershed scale. Both accuracy and precision decrease as slope increases: the magnitudes of the median and median of the absolute deviation of elevation errors (MAD) vary from ∼0.2 m for slopes <5° to >1 m for slopes >20°. For glacierized regions, failure to account for intra- and inter-annual evolution of glacier surface elevations can strongly bias ATL06 elevations, resulting in under-estimation of the mass balance gradient with elevation. Based on these results, we conclude that ATL08 and ATL06 observations are best suited for characterization of watershed-scale snow depth and mass balance gradients over relatively shallow slopes with thick snowpacks. In these regions, ICESat-2 elevation residual-derived snow depth and mass balance transects can provide valuable watershed scale constraints on terrain parameter- and model-derived estimates of snow accumulation and melt.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2022.113307","usgsCitation":"Enderlin, E., Elkin, C., Gendreau, M., Marshall, H., O'Neel, S., McNeil, C., Florentine, C., and Sass, L., 2022, Uncertainty of ICESat-2 ATL06- and ATL08-derived snow depths for glacierized and vegetated mountain regions: Remote Sensing of Environment, v. 283, 113307, 17 p., https://doi.org/10.1016/j.rse.2022.113307.","productDescription":"113307, 17 p.","ipdsId":"IP-141547","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":446058,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2022.113307","text":"Publisher Index Page"},{"id":486323,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76Q1WHK","text":"USGS data release","linkHelpText":"Point Raw Glaciological Data: Ablation Stake, Snow Pit, and Probed Snow Depth Data on USGS Benchmark Glaciers"},{"id":408693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska, Idaho","otherGeospatial":"Reynolds Creek Experimental Watershed, Wolverine Glacier","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.9169563546656,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.1201924666604\n ],\n [\n -116.63496346150279,\n 43.36296558422342\n ],\n [\n -116.9169563546656,\n 43.36296558422342\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -148.84207609134586,\n 60.42699332401864\n ],\n [\n -148.8772978314237,\n 60.46763185035496\n ],\n [\n -148.92384084509808,\n 60.44126913255184\n ],\n [\n -148.95214402908923,\n 60.43009729404224\n ],\n [\n -148.9219539661653,\n 60.37666770702921\n ],\n [\n -148.9112616522131,\n 60.37542411458642\n ],\n [\n -148.84207609134586,\n 60.42699332401864\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"283","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Enderlin, Ellyn","contributorId":187445,"corporation":false,"usgs":false,"family":"Enderlin","given":"Ellyn","email":"","affiliations":[],"preferred":false,"id":855759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Elkin, Colten","contributorId":298508,"corporation":false,"usgs":false,"family":"Elkin","given":"Colten","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gendreau, Madeline","contributorId":298509,"corporation":false,"usgs":false,"family":"Gendreau","given":"Madeline","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Marshall, H. 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P.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":855762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"O'Neel, Shad 0000-0002-9185-0144","orcid":"https://orcid.org/0000-0002-9185-0144","contributorId":289666,"corporation":false,"usgs":false,"family":"O'Neel","given":"Shad","affiliations":[{"id":62222,"text":"Cold Regions Research Laboratory","active":true,"usgs":false}],"preferred":false,"id":855763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McNeil, Christopher J. 0000-0003-4170-0428 cmcneil@usgs.gov","orcid":"https://orcid.org/0000-0003-4170-0428","contributorId":5803,"corporation":false,"usgs":true,"family":"McNeil","given":"Christopher J.","email":"cmcneil@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"preferred":true,"id":855764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Florentine, Caitlyn 0000-0002-7028-0963","orcid":"https://orcid.org/0000-0002-7028-0963","contributorId":205964,"corporation":false,"usgs":true,"family":"Florentine","given":"Caitlyn","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":855766,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sass, Louis C. 0000-0003-4677-029X lsass@usgs.gov","orcid":"https://orcid.org/0000-0003-4677-029X","contributorId":3555,"corporation":false,"usgs":true,"family":"Sass","given":"Louis C.","email":"lsass@usgs.gov","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":855765,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238583,"text":"70238583 - 2022 - Higher temperature sensitivity of ecosystem respiration in low marsh compared to high elevation marsh ecosystems","interactions":[],"lastModifiedDate":"2022-11-30T12:47:02.173161","indexId":"70238583","displayToPublicDate":"2022-10-22T06:42:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8116,"text":"Journal of Geophysical Research-Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Higher temperature sensitivity of ecosystem respiration in low marsh compared to high elevation marsh ecosystems","docAbstract":"Salt marsh habitats contain some of the highest quantities of soil organic carbon (C) per unit area, but increasing anthropogenic stressors threaten their ability to maintain themselves as large C reservoirs in some regions. We quantify rates of C gas exchange (methane [CH4] and carbon dioxide [CO2]) monthly across a 16-month period from a low nitrogen “reference” salt marsh on Cape Cod in New England using static chambers. While the summer period is the most dynamic period of marsh C gas exchange, we observed substantial fluxes in the early summer through late fall, highlighting the importance of including shoulder seasons in studies of marsh C exchange. We estimate annual ecosystem respiration between 108 and 252 g C m−2 yr−1, which varied based on temperature and elevation. This flux is lower than in other nearby marshes, which we attribute to the frequently inundated, microtidal nature of the site, resulting in the majority of respired CO2 being exported via lateral, not vertical, fluxes from this marsh. We observed significantly higher temperature sensitivity from the low elevation of the marsh compared to the high marsh. Recent acceleration in the rate of sea level rise is leading to a well-documented expansion of low marsh into high marsh vegetation zones in this marsh system and others in the region. While rates of C burial are higher in the low marsh compared to the high marsh, the higher temperature sensitivity of respiration in the low marsh may diminish the longevity of marsh C stocks with climate warming.
No abstract available.
","language":"English","publisher":"Great Lakes Phragmites Collaborative","usgsCitation":"Tucker, T., 2022, Freaky phrag phenomenon: Witches' broom: GLPC Newsletter, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-145983","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":408632,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.greatlakesphragmites.net/blog/freaky-phrag-phenomenon-witches-broom/"},{"id":425734,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tucker, Taaja 0000-0003-1534-4677","orcid":"https://orcid.org/0000-0003-1534-4677","contributorId":217908,"corporation":false,"usgs":true,"family":"Tucker","given":"Taaja","email":"","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":855660,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70237805,"text":"70237805 - 2022 - Quality of groundwater used for public supply in the continental United States: A comprehensive assessment","interactions":[],"lastModifiedDate":"2022-12-15T15:08:18.716393","indexId":"70237805","displayToPublicDate":"2022-10-21T10:00:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12792,"text":"Environmental Science & Technology - Water","active":true,"publicationSubtype":{"id":10}},"title":"Quality of groundwater used for public supply in the continental United States: A comprehensive assessment","docAbstract":"The presence of contaminants in a source water can constrain its suitability for drinking. The quality of groundwater used for public supply was assessed in 25 principal aquifers (PAs) that account for 84% of groundwater pumped for public supply in the U.S. (89.6 million people on a proportional basis). Each PA was sampled across its lateral extent using an equal-area grid, typically with 60 wells per PA. Samples were analyzed for 502 constituents, of which 374 had either a regulatory or nonregulatory human health benchmark (HHB). Nationally, elevated concentrations (relative to HHBs) of geogenic constituents have a larger effect than anthropogenic constituents, as indicated by three metrics: detection frequency, 35% versus 8.1%; prevalence (based on area), 41% versus 6.4%; and population potentially affected, 31.2 million versus 7.1 million. Prevalence of any constituent at elevated concentrations was high─40 to 75%─in PAs comprising unconsolidated sediment (eight PAs) and sandstone or interbedded sandstones and carbonates (four PAs) in the West and Central Interior. Prevalence was lower─15 to 35%─in PAs comprising sediment and sedimentary rocks along the Gulf and Atlantic Coasts (four PAs), carbonates distributed across the continental U.S. (seven PAs), and hard rock (two PAs).
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acsestwater.2c00390","usgsCitation":"Belitz, K., Fram, M.S., Lindsey, B.D., Stackelberg, P.E., Bexfield, L.M., Johnson, T., Jurgens, B., Kingsbury, J.A., McMahon, P.B., and Dubrovsky, N.M., 2022, Quality of groundwater used for public supply in the continental United States: A comprehensive assessment: Environmental Science & Technology - Water, v. 2, no. 12, p. 2645-2656, https://doi.org/10.1021/acsestwater.2c00390.","productDescription":"12 p.","startPage":"2645","endPage":"2656","ipdsId":"IP-142535","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":446063,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acsestwater.2c00390","text":"Publisher Index Page"},{"id":408645,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"continental United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n 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]\n}","volume":"2","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Belitz, Kenneth 0000-0003-4481-2345","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":201889,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fram, Miranda S. 0000-0002-6337-059X 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Program","active":true,"usgs":true}],"preferred":true,"id":855704,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stackelberg, Paul E. 0000-0002-1818-355X","orcid":"https://orcid.org/0000-0002-1818-355X","contributorId":204864,"corporation":false,"usgs":true,"family":"Stackelberg","given":"Paul","middleInitial":"E.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":855705,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855706,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855708,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203409,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855709,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kingsbury, James A. 0000-0003-4985-275X jakingsb@usgs.gov","orcid":"https://orcid.org/0000-0003-4985-275X","contributorId":883,"corporation":false,"usgs":true,"family":"Kingsbury","given":"James","email":"jakingsb@usgs.gov","middleInitial":"A.","affiliations":[{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":855707,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McMahon, Peter B. 0000-0001-7452-2379 pmcmahon@usgs.gov","orcid":"https://orcid.org/0000-0001-7452-2379","contributorId":724,"corporation":false,"usgs":true,"family":"McMahon","given":"Peter","email":"pmcmahon@usgs.gov","middleInitial":"B.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855710,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Dubrovsky, Neil M. 0000-0001-7786-1149 nmdubrov@usgs.gov","orcid":"https://orcid.org/0000-0001-7786-1149","contributorId":1799,"corporation":false,"usgs":true,"family":"Dubrovsky","given":"Neil","email":"nmdubrov@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":855711,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70238476,"text":"70238476 - 2022 - Tectonics, fault zones, and topography in the Alaska-Canada Cordillera with a focus on the Alaska Range and Denali fault zone","interactions":[],"lastModifiedDate":"2022-11-28T14:29:36.972497","indexId":"70238476","displayToPublicDate":"2022-10-21T08:15:10","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"13","title":"Tectonics, fault zones, and topography in the Alaska-Canada Cordillera with a focus on the Alaska Range and Denali fault zone","docAbstract":"Synergistic interactions between geologic structures and topography have long been recognized to reflect numerous Earth processes and rock properties over time. It was not until the advent of plate tectonics in the midtwentieth century that researchers began to view the nature of the northern Cordillera orogen as a quilt of foreign pieces of crust or “suspect terranes”. The Alaska Range shows complexity in topographic, geometric, and exhumational age asymmetry along and across the strike of the Denali fault zone attributable to several factors. Although direct exposures of the Denali fault zone in bedrock are exceptionally rare, regional to outcrop scale observations show the common internal structure consisting of some degree of strain localization in one or more, and presumably relatively weak, fault cores and an associated, commonly hydrothermally altered, damage zone.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Atlas of structural geological and geomorphological interpretation of remote sensing images","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Wiley","doi":"10.1002/9781119813392.ch13","usgsCitation":"Caine, J., and Benowitz, J.A., 2022, Tectonics, fault zones, and topography in the Alaska-Canada Cordillera with a focus on the Alaska Range and Denali fault zone, chap. 13 of Atlas of structural geological and geomorphological interpretation of remote sensing images, p. 135-145, https://doi.org/10.1002/9781119813392.ch13.","productDescription":"11 p.","startPage":"135","endPage":"145","ipdsId":"IP-129709","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":409692,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Alaska Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -141,\n 64.23134398587158\n ],\n [\n -156.36743326683552,\n 64.23134398587158\n ],\n [\n -156.36743326683552,\n 60.48117613047023\n ],\n [\n -141,\n 60.48117613047023\n ],\n [\n -141,\n 64.23134398587158\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2022-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":857582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benowitz, Jeff A. 0000-0003-2294-9172","orcid":"https://orcid.org/0000-0003-2294-9172","contributorId":229570,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":41671,"text":"Geophysical Institute and Geochronology Laboratory, University of Alaska–Fairbanks","active":true,"usgs":false}],"preferred":false,"id":857583,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70259706,"text":"70259706 - 2022 - Exploring declustering methodology for addressing geothermal exploration bias","interactions":[],"lastModifiedDate":"2024-10-21T12:29:48.729606","indexId":"70259706","displayToPublicDate":"2022-10-21T07:28:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Exploring declustering methodology for addressing geothermal exploration bias","docAbstract":"Geothermal resources assessments use data that are unevenly distributed in space, with more data collected in areas with known thermal features. To meet the assumptions for geostatistical modeling (e.g., variography and kriging) such as having a random sample representative of the population, declustering may be needed to correct for spatial sample bias. Several declustering methods exist and to understand how best to use these methods, we apply these to real data and samples of that data. The work described herein summarizes the application of cell-based declustering to shallow temperature data (~20 cm) collected in a survey across a thermal feature in the Lower Geyser Basin, Yellowstone National Park, Wyoming. The sample dataset is a regular grid (3-m spacing) of temperatures across a 72-m square area, providing a shallow, subsurface temperature dataset collected with minimal spatial bias (a few grid locations near a hot spring could not be sampled). To test the influence of sample clustering on geothermal estimates, this dense dataset is sub-sampled irregularly to evaluate bias on temperature estimation. Three sampling strategies were tested: a simple random sample, a stratified random sample, and a stratified biased random sample. The naive mean (before declustering) values for each dataset were compared to the post-declustering mean to evaluate the effectiveness of declustering on correcting the mean for spatial bias. For the limited number of sample datasets evaluated, we found that although cell-based declustering did partially correct the mean, some bias remained (i.e., the estimate was improved, but not fully corrected). It is possible that the procedure documented herein (applied here to only a few random samples) could be applied to many random samples, so that robust conclusions might be drawn (e.g., Is there always some remaining bias in declustered estimates? Does it depend on the number of sample points?). In particular, bias could be evaluated for persistency, and uncertainty could be evaluated.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Lindsey, C.R., Price, A.N., and Burns, E.R., 2022, Exploring declustering methodology for addressing geothermal exploration bias: Geothermal Resources Council Transactions, v. 46, p. 1109-1119.","productDescription":"11 p.","startPage":"1109","endPage":"1119","ipdsId":"IP-141054","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":463063,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034661"},{"id":463064,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lindsey, Cary Ruth","contributorId":345373,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":916395,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Adam N. 0000-0002-7211-4758","orcid":"https://orcid.org/0000-0002-7211-4758","contributorId":295971,"corporation":false,"usgs":false,"family":"Price","given":"Adam","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":916396,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burns, Erick R. 0000-0002-1747-0506 eburns@usgs.gov","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":192154,"corporation":false,"usgs":true,"family":"Burns","given":"Erick","email":"eburns@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":916397,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241136,"text":"70241136 - 2022 - Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","interactions":[],"lastModifiedDate":"2023-03-13T12:07:56.782111","indexId":"70241136","displayToPublicDate":"2022-10-21T07:05:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Climate disequilibrium dominates uncertainty in long-term projections of primary productivity","docAbstract":"Rapid climate change may exceed ecosystems' capacities to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, consequences of the resulting climate disequilibria for ecosystem functioning are rarely considered in projections of climate change impacts. Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous net primary productivity with an ensemble of climate models, we demonstrate that assumptions concerning the magnitude of climate disequilibrium are a dominant source of uncertainty: models assuming maximum disequilibrium project widespread decreases in productivity in the western US by 2100, while models assuming minimal disequilibrium project productivity increases. Uncertainty related to climate disequilibrium is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of processes that regulate climate disequilibria is essential for improving long-term projections of ecological responses and informing management to maintain ecosystem functioning at historical baselines.
Previous research suggests that predation by piscivorous colonial waterbirds may negatively influence the survival of Lost River Suckers (LRS) Deltistes luxatus and Shortnose Suckers (SNS) Chasmistes brevirostris in the Upper Klamath Basin (UKB), USA. However, estimates of predation from past studies, which were based on suckers with PIT tags, represent minimum estimates of sucker mortality because analyses did not account for the proportion of tags that were consumed by birds and deposited beyond their breeding colony. To address this uncertainty, we fed PIT-tagged suckers to American white pelicans Pelecanus erythrorhynchos to estimate deposition probabilities. A hierarchical Bayesian model was then used to estimate predation rates (percentage of available tagged fish that were consumed) on juvenile suckers that were released as part of the Sucker Assisted Rearing Program (SARP) and on wild juvenile and adult LRS and SNS during 2009–2020. Pelican deposition probabilities were estimated to be 0.47 (95% credible interval = 0.36–0.60), indicating that for every 100 tags consumed, 47 tags on average were deposited on breeding colonies by birds. Deposition-corrected estimates of predation rates were approximately two times greater than those previously reported and ranged annually from 4.3% (95% credible interval = 2.9–6.7%) to 8.5% (6.3–12.7%) on SARP juvenile suckers, from 4.3% (0.9–13.2%) to 10.5% (3.8–24.5%) on wild juvenile suckers, and from 0.1% (<0.1–0.3%) to 7.2% (2.8–16.4%) on adult suckers, depending on species and location. Results suggest that predation by colonial waterbirds, although not the original cause of sucker declines, was a substantial source of sucker mortality in some years. Future studies should consider models that jointly estimate both predation and survival and models that include environmental factors that potentially influence sucker susceptibility to avian predators in the UKB.
Observations of recent earthquake surface ruptures show that ground deformations include a localized component occurring on faults, and an off-fault component affecting the surrounding medium. This second component is also referred to as off-fault deformation (OFD). The localized component generally occurs on complex networks of faults that connect at depth onto a unique fault plane, whereas OFD consists of distributed fracturing and diffuse deformation of the bulk volume, and occurs over scales of hundreds of meters to kilometers around the faults. High-resolution optical image correlation presents a unique potential to characterize the complexity of the surface displacements, including on-fault displacements and OFDs. In this study, we used sub-pixel correlation of 0.5-m resolution optical images to measure the surface displacement field with a <20 cm accuracy for a 30-km long section of the 2013 Mw7.7 Baluchistan, Pakistan, rupture. Our results document significant variability in the fault displacements, associated with large proportions of OFD in regions of fault geometrical complexity. Conversely, in regions where the fault geometry is simple, surface deformation is entirely accommodated by the primary faults with 0% OFD. When combining the localized deformation on faults with the OFD, we show that the total surface displacement budget is constant along the strike of the rupture, despite strong variations observed in the rupture geometry. Based on this analysis, we propose an idealized scenario of earthquake surface deformation as a function of the rupture geometrical variations.
Here we describe the methods and results for biological characterization of the benthos on a previously unexplored area of central California, USA seafloor. We conducted 40 remotely operated vehicle dives from 371 to 1173 m water depth. Seafloor habitats and megafauna (fish and invertebrates) were documented from 46.8 km of seafloor video footage. Our expanded development and analysis of biotopes from quantitative data allowed us to describe detailed biological communities, along with the physical characteristics and habitat associations within the study area. This method provides a framework for potential monitoring, detection of future environmental change (natural and anthropogenic) and comprehensive comparison to other deep water regions. From 185 h of observational video at 25 sites, nearly 120,000 annotations of organisms, habitat characters, biological detritus and anthropogenic debris were recorded and analyzed. We identified a total of 228 taxa, with 173 of them present on linear quantitative transects. Species richness for transects ranged from 0.04 to 0.28 m-2 (8–55 taxa), with densities ranging from 0.07 to 5.20 ind. m−2. Both were highest on hard substrate with greatest surface area. Densities decreased with depth. Within soft substratum zones was a large field of pockmarks, which are seafloor depressions averaging 175 m in diameter and 5 m in depth. Pockmarks have sometimes been associated with seafloor gas seepage, but here we found no biological evidence of chemosynthetic organisms. No significant differences were found in either density nor species richness at pockmark sites vs. non-pockmark sites. Mud draped greenish-black coarse sand occurred only in low oxygen areas, while hummocky, rugose mud supported somewhat different species than flat mud plains. Seventy percent of the transects occurred inside the oxygen minimum zone. We conclude that high rugosity, slope, and the presence of hard substratum were better predictors of species richness and density than oxygen concentration in this specific study. Abundant biological detritus, in the form of dead and dying pelagic pyrosomes and salps, created a large, presumably ephemeral flux of carbon to the seafloor during the study period.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.dsr.2022.103872","usgsCitation":"Kuhnz, L.A., Gilbane, L., Cochrane, G.R., and Paull, C.K., 2022, Multi-factor biotopes as a method for detailed site characterization in diverse benthic megafaunal communities and habitats in deep-water off Morro Bay, California: Deep Sea Research Part I: Oceanographic Research Papers, v. 190, 103872, 19 p., https://doi.org/10.1016/j.dsr.2022.103872.","productDescription":"103872, 19 p.","ipdsId":"IP-137867","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":446078,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.dsr.2022.103872","text":"Publisher Index Page"},{"id":408743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Morro Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.66975787964981,\n 35.804510935101575\n ],\n [\n -121.66975787964981,\n 34.864126610922014\n ],\n [\n -120.43438488666736,\n 34.864126610922014\n ],\n [\n -120.43438488666736,\n 35.804510935101575\n ],\n [\n -121.66975787964981,\n 35.804510935101575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kuhnz, Linda A. 0000-0002-8359-3803","orcid":"https://orcid.org/0000-0002-8359-3803","contributorId":289638,"corporation":false,"usgs":false,"family":"Kuhnz","given":"Linda","email":"","middleInitial":"A.","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":true,"id":855821,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilbane, Lisa 0000-0001-9170-5388","orcid":"https://orcid.org/0000-0001-9170-5388","contributorId":289639,"corporation":false,"usgs":false,"family":"Gilbane","given":"Lisa","email":"","affiliations":[{"id":20318,"text":"Bureau of Ocean Energy Management","active":true,"usgs":false}],"preferred":true,"id":855822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cochrane, Guy R. 0000-0002-8094-4583 gcochrane@usgs.gov","orcid":"https://orcid.org/0000-0002-8094-4583","contributorId":2870,"corporation":false,"usgs":true,"family":"Cochrane","given":"Guy","email":"gcochrane@usgs.gov","middleInitial":"R.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":855823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":855824,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70237677,"text":"ofr20221092 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 2022","interactions":[],"lastModifiedDate":"2022-10-20T10:57:08.281875","indexId":"ofr20221092","displayToPublicDate":"2022-10-19T14:35:42","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":"2022-1092","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 2, 2022","title":"ECCOE Landsat Quarterly Calibration and Validation report—Quarter 2, 2022","docAbstract":"The U.S. Geological Survey Earth Resources Observation and Science 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 2 (April–June), 2022. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.
One specific activity that the ECCOE Landsat Cal/Val Team closely monitored was the lowering of the Landsat 7 orbit. On April 6, 2022, the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor was placed into standby mode, and a series of spacecraft burns was completed throughout the month of April to lower the satellite’s orbit by 8 kilometers. Imaging resumed at the lower orbit of 697 kilometers on May 5, 2022, extending the science mission to allow for essential data to be acquired during the 2022 Northern Hemisphere fire and growing season. Additional information about the Landsat 7 orbit lowering is here:
https://www.usgs.gov/centers/eros/news/landsat-7-lowered-standard-landsat-orbit#:~:text=The%20satellite's%20primary%20science%20mission%20has%20ended&text=On%20April%206%2C%202022%2C%20the,satellite's%20orbit%20by%208%20kilometers.
Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Water, bed sediment, and invertebrate tissue were sampled in streams from Butte to near Missoula, Montana, as part of a monitoring program in the Clark Fork Basin. The sampling program was completed by the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency, to characterize aquatic resources in the Clark Fork Basin and monitor trace elements associated with historical mining and smelting activities. Sampling sites were on the Clark Fork River and a subset of its tributaries. Water samples were collected periodically at 22 sites from October 2019 through September 2020. Bed-sediment and tissue samples were collected once at 12 sites during July 2020.
Water-quality data included concentrations of major ions, dissolved organic carbon, nitrogen (nitrate plus nitrite), trace elements, and suspended sediment. Daily values of turbidity were determined at four sites. Bed-sediment data included trace-element concentrations in the fine-grained (less than 0.063 millimeter) fraction. Biological data included trace-element concentrations in whole-body tissue of selected aquatic benthic invertebrates. Statistical summaries of water-quality, bed-sediment, and invertebrate tissue trace-element data for sites in the Clark Fork Basin were provided for the period of record: March 1985–September 2020.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221090","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Clark, G.D., Hornberger, M.I., Hepler, E.J., and Heinert, T.L., 2022, Water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019–September 2020: U.S. Geological Survey Open-File Report 2022–1090, 17 p., https://doi.org/10.3133/ofr20221090.","productDescription":"Report: vii, 17 p.; Data Release; Dataset","numberOfPages":"30","onlineOnly":"Y","ipdsId":"IP-138065","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":501834,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113781.htm","linkFileType":{"id":5,"text":"html"}},{"id":408546,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221090/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408387,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":408386,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93BP9P8","text":"USGS data release","linkHelpText":"Results of water-quality, bed-sediment, and invertebrate tissue trace-element concentrations for tributaries in the Clark Fork Basin, Montana, October 2019– September 2020"},{"id":408385,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1090/images"},{"id":408384,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.XML"},{"id":408383,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1090/ofr20221090.pdf","text":"Report","size":"0.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1090"},{"id":408381,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1090/coverthb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Clark Fork Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.67714251796224,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 45.00795879114483\n ],\n [\n -111.56647259158387,\n 47.58172589143089\n ],\n [\n -115.67714251796224,\n 47.58172589143089\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Recreational hunting has been the dominant game management and conservation mechanism in the United States for the past century. However, there are numerous modern-day issues that reduce the viability and efficacy of hunting-based management, such as fewer hunters, overabundant wildlife populations, limited access, and emerging infectious diseases in wildlife. Quantifying the drivers of recreational harvest by hunters could inform potential management actions to address these issues, but this is seldom comprehensively accomplished because data collection practices limit some analytical applications (e.g., differing spatial scales of harvest regulations and harvest data). Additionally, managing large-scale issues, such as infectious diseases, requires collaborations across management agencies, which is challenging or impossible if data are not standardized. Here we discuss modern issues with the prevailing wildlife management framework in the United States from an analytical point of view with a case study of white-tailed deer (Odocoileus virginianus) in the Midwest. We have four aims: (1) describe the interrelated processes that comprise hunting and suggest improvements to current data collections systems, (2) summarize data collection systems employed by state wildlife management agencies in the Midwestern United States and discuss potential for large-scale data standardization, (3) assess how aims 1 and 2 influence managing infectious diseases in hunted wildlife, and (4) suggest actionable steps to help guide data collection standards and management practices. To achieve these goals, Wisconsin Department of Natural Resources disseminated a questionnaire to state wildlife agencies (Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, Wisconsin), and we report and compare their harvest management structures, data collection practices, and responses to chronic wasting disease. We hope our “call to action” encourages re-evaluation, coordination, and improvement of harvest and management data collection practices with the goal of improving the analytical potential of these data. A deeper understanding of the strengths and deficiencies of our current management systems in relation to harvest and management data collection methods could benefit the future development of comprehensive and collaborative management and research initiatives (e.g., adaptive management) for wildlife and their diseases.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.943411","usgsCitation":"Brandell, E., Storm, D., Van Deelen, T., Walsh, D.P., and Turner, W.C., 2022, A call to action: Standardizing white-tailed deer harvest data in the Midwestern United States and implications for quantitative analysis and disease management: Frontiers in Ecology and Evolution, v. 19, 943411, 19 p., https://doi.org/10.3389/fevo.2022.943411.","productDescription":"943411, 19 p.","ipdsId":"IP-139769","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":467155,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.943411","text":"Publisher Index Page"},{"id":466006,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Ohio, 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\"}}]}","volume":"19","noUsgsAuthors":false,"publicationDate":"2022-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Brandell, Ellen E.","contributorId":347965,"corporation":false,"usgs":false,"family":"Brandell","given":"Ellen E.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Storm, Daniel J.","contributorId":347966,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Deelen, Timothy R.","contributorId":347967,"corporation":false,"usgs":false,"family":"Van Deelen","given":"Timothy R.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":922781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922782,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237709,"text":"gip218 - 2022 - Social Scientist GS–0101","interactions":[],"lastModifiedDate":"2022-10-19T16:00:59.230549","indexId":"gip218","displayToPublicDate":"2022-10-19T10:21:45","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":315,"text":"General Information Product","code":"GIP","onlineIssn":"2332-354X","printIssn":"2332-3531","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"218","displayTitle":"Social Scientist GS–0101","title":"Social Scientist GS–0101","docAbstract":"This broad study field focuses on understanding values, perceptions, attitudes, and knowledge of humans and society as they relate to one another and the world around them. Several Social Science branches use various methodologies to conduct research on natural resources and hazards, climate and land-use change, and other related topics and interactions. Social Science discipline examples include anthropology, political science, psychology, sociology, and human geographers.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip218","usgsCitation":"Restrepo-Osorio, D.L., 2022, Social Scientist GS–0101: U.S. Geological Survey General Information Product 218, 2 p., https://doi.org/10.3133/gip218.","productDescription":"Postcard: 6.00 x 4.00 inches","numberOfPages":"2","onlineOnly":"N","ipdsId":"IP-146141","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":408534,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/218/coverthb.jpg"},{"id":408535,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/218/gip218.pdf","text":"Report","size":"240 kB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 218"}],"contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
No abstract available.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2022.1050548","usgsCitation":"Fabrizio, M., Henderson, M., Rose, K., and Petitgas, P., 2022, Editorial: Habitat and distribution models of marine and estuarine species: Advances for a sustainable future: Frontiers in Marine Science, v. 9, 1050548., 4 p., https://doi.org/10.3389/fmars.2022.1050548.","productDescription":"1050548., 4 p.","ipdsId":"IP-145479","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489758,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2022.1050548","text":"Publisher Index Page"},{"id":481462,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Fabrizio, Mary C.","contributorId":350223,"corporation":false,"usgs":false,"family":"Fabrizio","given":"Mary C.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925506,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henderson, Mark J. 0000-0002-2861-8668 mhenderson@usgs.gov","orcid":"https://orcid.org/0000-0002-2861-8668","contributorId":198609,"corporation":false,"usgs":true,"family":"Henderson","given":"Mark J.","email":"mhenderson@usgs.gov","affiliations":[],"preferred":false,"id":925507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rose, Kenneth","contributorId":350225,"corporation":false,"usgs":false,"family":"Rose","given":"Kenneth","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925508,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Petitgas, Pierre","contributorId":350227,"corporation":false,"usgs":false,"family":"Petitgas","given":"Pierre","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":925509,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70259709,"text":"70259709 - 2022 - A modern multicentennial record of radiocarbon variability from an exactly dated bivalve chronology at the Tree Nob site (Alaska Coastal Current)","interactions":[],"lastModifiedDate":"2024-10-19T13:12:35.431924","indexId":"70259709","displayToPublicDate":"2022-10-19T08:09:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3225,"text":"Radiocarbon","active":true,"publicationSubtype":{"id":10}},"title":"A modern multicentennial record of radiocarbon variability from an exactly dated bivalve chronology at the Tree Nob site (Alaska Coastal Current)","docAbstract":"Quantifying the marine radiocarbon reservoir effect, offsets (ΔR), and ΔR variability over time is critical to improving dating estimates of marine samples while also providing a proxy of water mass dynamics. In the northeastern Pacific, where no high-resolution time series of ΔR has yet been established, we sampled radiocarbon (14C) from exactly dated growth increments in a multicentennial chronology of the long-lived bivalve, Pacific geoduck (Paneopea generosa) at the Tree Nob site, coastal British Columbia, Canada. Samples were taken at approximately decadal time intervals from 1725 CE to 1920 CE and indicate average ΔR values of 256 ± 22 years (1σ) consistent with existing discrete estimates. Temporal variability in ΔR is small relative to analogous Atlantic records except for an unusually old-water event, 1802–1812. The correlation between ΔR and sea surface temperature (SST) reconstructed from geoduck increment width is weakly significant (r2 = .29, p = .03), indicating warm water is generally old, when the 1802–1812 interval is excluded. This interval contains the oldest (–2.1σ) anomaly, and that is coincident with the coldest (–2.7σ) anomalies of the temperature reconstruction. An additional 32 14C values spanning 1952–1980 were detrended using a northeastern Pacific bomb pulse curve. Significant positive correlations were identified between the detrended 14C data and annual El Niño Southern Oscillation (ENSO) and summer SST such that cooler conditions are associated with older water. Thus, 14C is generally relatively stable with weak, potentially inconsistent associations to climate variables, but capable of infrequent excursions as illustrated by the unusually cold, old-water 1802–1812 interval.
Hydrologic and geophysical components interact to produce streambed scour. This study investigates methods for improving the utility of estimates of hydrologic flow in streams and rivers used when evaluating potential pier scour over the design-life of highway bridges in Virginia. Recent studies of streambed composition identify potential bridge design cost savings when attributes of cohesive soil and weathered rock unique to certain streambeds are considered within the bridge planning design. To achieve potential cost savings, however, attributes and effects of scour forces caused by water movement across the streambed surface must be accurately described and estimated.
This study explores the potential for improving estimates of the hydrologic component, namely hydrologic flow, afforded by empirically based deterministic, probabilistic, and statistical modeling of flows using streamgage data from 10 selected sites in Virginia. Methods are described and tools are provided that may assist with estimating hydrological components of flow duration and potential cumulative stream power for bridge designs in specific settings, and calculation of comprehensive projections of anticipated individual bridge pier scour rates. Examples of hydrologic properties needed to determine the rates of streambed scour are described for sites spanning a range of basin sizes and locations in Virginia. Deterministic, probabilistic, and statistical modeling methods are demonstrated for estimating hydrological components of streambed scour over a bridge design lifespan. Eight tools provide examples of streamflow analysis using daily and instantaneous streamflow data collected at 10 study sites in Virginia. Tool 1 provides a generalized system dynamics model of streamflow and sediment motion that may be used to estimate hydrologic flow over time. Tool 2 illustrates at-a-station hydraulic geometry using methods pioneered by Leopold and others. Tool 3 provides a system dynamics model developed to test the use of Monte-Carlo sampling of instantaneous streamflow measurements to augment and increase precision of site-specific period-of-record daily-flow values useful for driving stream-power and streambed scour estimates. Tool 4 integrates deterministic modeling, maximum likelihood logistic regression, and Monte-Carlo sampling to identify probable hydrologic flows. Tool 5 provides instantaneous flow hydrologic envelope profiles, using measured instantaneous flow data integrated with measured daily-flow value data. Tool 6 provides precise estimates of hydrologic flow over entire data time-series suitable for driving scour simulation models. Tool 7 provides a threshold of flow and probability of time-under-load interactive calculator that allows selection of a desired bridge design lifespan, ranging from 1 to 250 years, and identification of a flow interval of interest. Tool 8 provides a flow-random sampling interactive tool, developed to facilitate easy access to large datasets of randomly sampled flow data measurements from unique locations for purposes of computing and testing future models of bridge pier scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225059","collaboration":"Prepared in cooperation with the Virginia Department of Transportation","usgsCitation":"Austin, S.H., 2022, Virginia Bridge Scour Pilot Study—Hydrological Tools: U.S. Geological Survey Scientific Investigations Report 2022–5059, 46 p., https://doi.org/10.3133/sir20225059.","productDescription":"Report: vii, 46 p.; Data Release; Dataset","numberOfPages":"46","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137495","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":408481,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5059/coverthb.jpg"},{"id":408486,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957ABZN","text":"USGS data release","linkHelpText":"Virginia bridge scour pilot study streamflow data"},{"id":408485,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.XML"},{"id":408487,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":503375,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113768.htm","linkFileType":{"id":5,"text":"html"}},{"id":408484,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5059/images/"},{"id":408483,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225059/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5059"},{"id":408482,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5059/sir20225059.pdf","text":"Report","size":"9.59 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5059"}],"country":"United States","state":"Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.28857421875,\n 39.554883059924016\n ],\n [\n -80.39794921875,\n 38.18638677411551\n ],\n [\n -80.4638671875,\n 37.52715361723378\n ],\n [\n -77.49755859375,\n 37.59682400108367\n ],\n [\n -77.32177734375,\n 39.53793974517628\n ],\n [\n -78.28857421875,\n 39.554883059924016\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
Planning stormwater projects requires estimates of current and future extreme precipitation depths for events with specified return periods and durations. In this study, precipitation data from four downscaled climate datasets are used to determine changes in precipitation depth-duration-frequency curves from the period 1966–2005 to the period 2050–89 primarily on the basis of Representative Concentration Pathways 4.5 and 8.5 emission scenarios from the Coupled Model Intercomparison Project Phase 5. The four downscaled climate datasets are (1) the Coordinated Regional Downscaling Experiment (CORDEX) dataset, (2) the Localized Constructed Analogs (LOCA) dataset, (3) the Multivariate Adaptive Constructed Analogs (MACA) dataset, and (4) the Jupiter Intelligence Weather Research and Forecasting Model (JupiterWRF) dataset. Change factors—multiplicative changes in expected extreme precipitation magnitude from current to future period—were computed for grid cells from the downscaled climate datasets containing National Oceanic and Atmospheric Administration Atlas 14 stations in central and south Florida. Change factors for specific durations and return periods may be used to scale the National Oceanic and Atmospheric Administration Atlas 14 historical depth-duration-frequency values to the period 2050–89 on the basis of changes in extreme precipitation derived from downscaled climate datasets. Model culling was implemented to select downscaled climate models that best captured observed historical patterns of precipitation extremes in central and south Florida.
Overall, a large variation in change factors across downscaled climate datasets was found, with change factors generally greater than one and increasing with return period. In general, median change factors were higher for the south-central Florida climate region (1.05–1.55 depending on downscaled climate dataset, duration, and return period) than for the south Florida climate region (1–1.4 depending on downscaled climate dataset, duration, and return period) when considering best performing models for both areas, indicating a projected overall increase in future extreme precipitation events.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225093","collaboration":"Prepared in cooperation with the South Florida Water Management District","usgsCitation":"Irizarry-Ortiz, M.M., Stamm, J.F., Maran, C., and Obeysekera, J., 2022, Development of projected depth-duration frequency curves (2050–89) for south Florida: U.S. Geological Survey Scientific Investigations Report 2022–5093, 114 p., https://doi.org/10.3133/sir20225093.","productDescription":"Report: xii, 114 p.; 1 Table; Data Release","numberOfPages":"130","onlineOnly":"Y","ipdsId":"IP-134493","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":408474,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P935WRTG","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 174 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in central and south Florida"},{"id":435653,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LEIL","text":"USGS data release","linkHelpText":"Change factors to derive projected future precipitation depth-duration-frequency (DDF) curves at 242 National Oceanic and Atmospheric Administration (NOAA) Atlas 14 stations in Florida (ver 2.0, May 2024)"},{"id":408853,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225093/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408472,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.xlsx","text":"Table 1.1","size":"50.0 kB","linkFileType":{"id":3,"text":"xlsx"}},{"id":408471,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5093/images"},{"id":408470,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.XML"},{"id":408469,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093.pdf","text":"Report","size":"23.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5093"},{"id":408468,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5093/coverthb.jpg"},{"id":408473,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5093/sir20225093_table1.1.csv","text":"Table 1.1","size":"18.6 kB","linkFileType":{"id":7,"text":"csv"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.111572265625,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 24.327076540018634\n ],\n [\n -79.43115234375,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 28.98892237190413\n ],\n [\n -83.111572265625,\n 24.327076540018634\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
In this report, we apply the stream salmonid simulator (S3) to coho salmon (Oncorhynchus kisutch) in the Klamath River Basin by extending the original model to account for life history and disease dynamics specific to coho salmon. This version of S3 includes tracking of three separate life-history strategies representing the different time periods and ages at which fish leave natal tributaries such as the Scott and Shasta Rivers (age-0 spring, age-0 fall, or age-1 smolt). Once fish leave their natal tributaries and enter the Klamath River, the deterministic life-stage-structured population model simulates daily growth, movement, and survival. We extend the model to include non-natal tributary dynamics, where spring age-0 fish entry to non-natal tributaries is simulated based on environmental conditions in the main-stem Klamath River. Fish that use non-natal tributaries then reenter the Klamath River during the winter or spring as smolts and actively migrate downstream. We also consider the life history strategy where fish rear in natal tributaries and enter the Klamath River as age-1 smolts. In addition to simulating different life history pathways that coho salmon may take, we model disease dynamics, incorporating new information on Ceratonova shasta related infection and mortality. We incorporate competitive interactions between juvenile coho and Chinook salmon (Oncorhynchus tshawytscha) by simulating density-dependent movement dynamics in response to Chinook salmon abundance.
Model simulations suggest that total abundance and survival to the ocean differed between life-history strategies. In general, spring age-0 fish that leave their natal tributaries in their first spring had lower survival compared with fish that remained in natal tributaries and out-migrated later. Spring age-0 fish also had higher disease related mortality, owing to their residence in the main-stem Klamath River overlapping with periods of elevated C. shasta spore concentrations. Age-0 fish leaving their natal tributaries in the fall had near-zero disease related mortality. Most non-natal tributary use occurred at upstream tributary locations and was variable between the brood years depending on passage timing and environmental conditions. The inclusion of Chinook salmon in simulations resulted in decreased abundance and survival of Coho salmon reaching the ocean. In addition, we developed an R package to facilitate use of and continued development of S3 as a tool to guide management of juvenile salmonid populations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221071","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service and the Bureau of Reclamation","usgsCitation":"Dodrill, M.J., Perry, R.W., Som, N.A., Manhard, C.V., and Alexander, J.D., 2022, Extending the Stream Salmonid Simulator to accommodate the life history of coho salmon (Oncorhynchus kisutch) in the Klamath River Basin, Northern California: U.S. Geological Survey Open-File Report 2022–1071, 70 p., https://doi.org/10.3133/ofr20221071.","productDescription":"viii, 70 p.","onlineOnly":"Y","ipdsId":"IP-129401","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":408507,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.XML"},{"id":408506,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1071/images"},{"id":408505,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221071/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1071"},{"id":408503,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1071/coverthb.jpg"},{"id":408504,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1071/ofr20221071.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1071"}],"country":"United States","state":"California","otherGeospatial":"Klamath River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.16748046874999,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 41.071069130806414\n ],\n [\n -121.915283203125,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 42.037054301883806\n ],\n [\n -124.16748046874999,\n 41.071069130806414\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016