Anticoagulant rodenticides (ARs) used to control mammalian pest populations cause secondary exposure of predatory species throughout much of the world. It is important to understand the drivers of non-target AR exposure patterns as context for assessing long-term effects and developing effective mitigation for these toxicants. In Australia, however, little is known about exposure and effects of ARs on predators. We detected AR residues in 74% of 50 opportunistically collected carcasses of the Tasmanian wedge-tailed eagle (Aquila audax fleayi), an endangered apex predator. In 22% of birds tested, or 31% of those exposed, liver concentrations of second generation ARs (SGARs) were >0.1 mg/kg ww. Eagles were exposed to flocoumafen, a toxicant only available from agricultural suppliers, at an exceptionally high rate (40% of birds tested). Liver SGAR concentrations were positively associated with the proportion of agricultural habitat and human population density in the area around where each eagle died. The high exposure rate in a species not known to regularly prey upon synanthropic rodents supports the hypothesis that apex predators are vulnerable to SGARs. Our results indicate that AR exposure constitutes a previously unrecognized threat to Tasmanian wedge-tailed eagles and highlight the importance of efforts to address non-target AR exposure in Australia.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2021.147673","usgsCitation":"Pay, J.M., Katzner, T., Hawkins, C.E., Barmuta, L.A., Brown, W.E., Koch, A.J., Mooney, N.J., and Cameron, E.Z., 2021, Endangered Australian top predator is frequently exposed to anticoagulant rodenticides: Science of the Total Environment, v. 788, 147673, 9 p., https://doi.org/10.1016/j.scitotenv.2021.147673.","productDescription":"147673, 9 p.","ipdsId":"IP-126326","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":452326,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":387408,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia","state":"Tasmania","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 143.26171875,\n -44.087585028245165\n ],\n [\n 149.23828125,\n -44.087585028245165\n ],\n [\n 149.23828125,\n -40.44694705960048\n ],\n [\n 143.26171875,\n -40.44694705960048\n ],\n [\n 143.26171875,\n -44.087585028245165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"788","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pay, James M.","contributorId":245078,"corporation":false,"usgs":false,"family":"Pay","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":819878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawkins, Clare E.","contributorId":245079,"corporation":false,"usgs":false,"family":"Hawkins","given":"Clare","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":819880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barmuta, Leon A.","contributorId":261351,"corporation":false,"usgs":false,"family":"Barmuta","given":"Leon","email":"","middleInitial":"A.","affiliations":[{"id":52830,"text":"School of Natural Sciences, University of Tasmania, Hobart, TAS, Australia","active":true,"usgs":false}],"preferred":false,"id":819881,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, William E. 0000-0003-1595-9655","orcid":"https://orcid.org/0000-0003-1595-9655","contributorId":245082,"corporation":false,"usgs":false,"family":"Brown","given":"William","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":819882,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koch, Amelia J.","contributorId":245080,"corporation":false,"usgs":false,"family":"Koch","given":"Amelia","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":819883,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mooney, Nick J.","contributorId":245083,"corporation":false,"usgs":false,"family":"Mooney","given":"Nick","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":819884,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cameron, Elissa Z.","contributorId":245084,"corporation":false,"usgs":false,"family":"Cameron","given":"Elissa","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":819885,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70220421,"text":"70220421 - 2021 - Virus shedding kinetics and unconventional virulence tradeoffs","interactions":[],"lastModifiedDate":"2021-05-13T12:02:23.891402","indexId":"70220421","displayToPublicDate":"2021-05-10T07:01:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2981,"text":"PLoS Pathogens","active":true,"publicationSubtype":{"id":10}},"title":"Virus shedding kinetics and unconventional virulence tradeoffs","docAbstract":"Tradeoff theory, which postulates that virulence provides both transmission costs and benefits for pathogens, has become widely adopted by the scientific community. Although theoretical literature exploring virulence-tradeoffs is vast, empirical studies validating various assumptions still remain sparse. In particular, truncation of transmission duration as a cost of virulence has been difficult to quantify with robust controlled in vivo studies. We sought to fill this knowledge gap by investigating how transmission rate and duration were associated with virulence for infectious hematopoietic necrosis virus (IHNV) in rainbow trout (Oncorhynchus mykiss). Using host mortality to quantify virulence and viral shedding to quantify transmission, we found that IHNV did not conform to classical tradeoff theory. More virulent genotypes of the virus were found to have longer transmission durations due to lower recovery rates of infected hosts, but the relationship was not saturating as assumed by tradeoff theory. Furthermore, the impact of host mortality on limiting transmission duration was minimal and greatly outweighed by recovery. Transmission rate differences between high and low virulence genotypes were also small and inconsistent. Ultimately, more virulent genotypes were found to have the overall fitness advantage, and there was no apparent constraint on the evolution of increased virulence for IHNV. However, using a mathematical model parameterized with experimental data, it was found that host culling resurrected the virulence tradeoff and provided low virulence genotypes with the advantage. Human-induced or natural culling, as well as host population fragmentation, may be some of the mechanisms by which virulence diversity is maintained in nature. This work highlights the importance of considering non-classical virulence tradeoffs.
Biological communities, including periphyton, are continuously affected by chemical, physical, and other biological factors, and the health of these communities can reflect the overall health of the aquatic system. A diverse community is more robust, and communities with lower richness and evenness often indicate a degraded community dominated by few taxa tolerant to the degraded conditions, which makes the community more susceptible to ecological changes. Water-quality nutrient samples were collected at sites in the Lower Grand River during 2010 through 2018 and periphyton sample collections began in 2011 to describe the periphyton community and overall ecological health. Nutrient sample concentrations were generally elevated at these sites, which can lead to eutrophication, excessive plant and algae growth, drinking-water taste and odor problems, low dissolved-oxygen concentrations, and harmful algal blooms. Concentrations of total nitrogen were greater than acceptable as described by the U.S. Environmental Protection Agency, and total phosphorus concentrations were greater than reference concentrations. Periphyton communities were dominated by taxa that are tolerant to or indicative of elevated nutrient concentrations; and nuisance algae, or harmful algal bloom producers, were identified at all sites, except one. The presence of these producers indicates that harmful algal blooms may have high potential during optimal conditions at these sites. Chlorophyll concentrations that exceed 100 milligrams per square meter are considered nuisance and were determined in 11 percent of the samples and at every site during September 2012. Samples were collected during low-flow conditions when nutrient concentrations are generally lower than during high-flow and runoff conditions. Elevated nutrient concentrations during low-flow conditions indicate nutrient concentrations are likely elevated throughout most of the year. Agriculture is the primary land use within the Lower Grand River and is likely a primary source of nutrients and sediments. Conservation practices intended to reduce nutrient loss from agriculture fields have increased because of the Mississippi River Basin Healthy Watersheds Initiative and will potentially increase the ecological, chemical, and physical health of these waterways.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215012","collaboration":"Prepared in cooperation with the Missouri Department of Natural Resources","usgsCitation":"Krempa, H.M., 2021, Periphyton biomass and community compositions as indicators of water quality in the Lower Grand River hydrologic unit, Missouri and Iowa, 2011–18: U.S. Geological Survey Scientific Investigations Report 2021–5012, 51 p., https://doi.org/10.3133/sir20215012.","productDescription":"Report: vi, 51 p.; Data Release; Dataset","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-117668","costCenters":[{"id":394,"text":"Mississippi Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":385478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5012/coverthb.jpg"},{"id":385479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5012/sir20215012.pdf","text":"Report","size":"2.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5012"},{"id":385480,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BYF1EN","text":"USGS data release","description":"USGs Data Release","linkHelpText":"Periphyton community data within the Lower Grand River hydrologic unit code 10280103, Missouri and Iowa, 2011–2018"},{"id":385481,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Iowa, Missouri","otherGeospatial":"Lower Grand River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.4716796875,\n 40.97989806962013\n ],\n [\n -94.10888671875,\n 41.36031866306708\n ],\n [\n -94.72412109375,\n 40.83043687764923\n ],\n [\n -94.833984375,\n 40.027614437486655\n ],\n [\n -94.41650390625,\n 39.232253141714885\n ],\n [\n -93.6474609375,\n 38.94232097947902\n ],\n [\n -92.83447265624999,\n 39.16414104768742\n ],\n [\n -92.8125,\n 39.757879992021756\n ],\n [\n -92.94433593749999,\n 40.74725696280421\n ],\n [\n -93.4716796875,\n 40.97989806962013\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Pinyon and juniper expansion into sagebrush ecosystems is one of the major challenges facing land managers in the Great Basin. Effective pinyon and juniper treatment requires maps that accurately and precisely depict tree location and degree of woodland development so managers can target restoration efforts for early stages of pinyon and juniper expansion. However, available remotely sensed layers that cover a regional spatial extent lack the spatial resolution or accuracy to meet this need. Accuracy can be improved using object-based image analysis methods such as automated feature extraction, which has proven successful in accurately classifying land cover at the site-level but to date has yet to be applied to regional extents due to time and computational limitations. Using Feature Analyst™, we implement our framework with 1-m2 reference imagery provided by National Agricultural Imagery Program to classify conifers across Nevada and northeastern California. Our resulting binary conifer map has an overall accuracy of 86%. We discuss the advantages to accuracy and precision our framework provides compared to other classification methods
","language":"English","publisher":"Elsevier","doi":"10.1016/j.mex.2021.101379","usgsCitation":"Roth, C.L., Coates, P.S., Gustafson, K.B., Chenaille, M.P., Ricca, M.A., Sanchez-Chopitea, E., and Casazza, M.L., 2021, A customized framework for regional classification of conifers using automated feature extraction: MethodsX, v. 8, 101379, 16 p., https://doi.org/10.1016/j.mex.2021.101379.","productDescription":"101379, 16 p.","ipdsId":"IP-098781","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":452330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.mex.2021.101379","text":"Publisher Index Page"},{"id":386335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, California, Nevada, Utah","otherGeospatial":"the Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.3671875,\n 41.44272637767212\n ],\n [\n -118.21289062499999,\n 43.99281450048989\n ],\n [\n -120.62988281249999,\n 44.5278427984555\n ],\n [\n -122.34374999999999,\n 41.934976500546604\n ],\n [\n -120.62988281249999,\n 38.51378825951165\n ],\n [\n -116.5869140625,\n 35.42486791930558\n ],\n [\n -115.31249999999999,\n 37.09023980307208\n ],\n [\n -114.0380859375,\n 36.4566360115962\n ],\n [\n -114.08203125,\n 38.06539235133249\n ],\n [\n -111.22558593749999,\n 38.09998264736481\n ],\n [\n -111.09374999999999,\n 42.09822241118974\n ],\n [\n -116.3671875,\n 41.44272637767212\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Roth, Cali L. 0000-0001-9077-2765 croth@usgs.gov","orcid":"https://orcid.org/0000-0001-9077-2765","contributorId":174422,"corporation":false,"usgs":true,"family":"Roth","given":"Cali","email":"croth@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":817208,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817209,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gustafson, K. Benjamin 0000-0003-3530-0372 kgustafson@usgs.gov","orcid":"https://orcid.org/0000-0003-3530-0372","contributorId":166818,"corporation":false,"usgs":true,"family":"Gustafson","given":"K.","email":"kgustafson@usgs.gov","middleInitial":"Benjamin","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817210,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817211,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817212,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sanchez-Chopitea, Erika 0000-0003-2942-8417 esanchez-chopitea@usgs.gov","orcid":"https://orcid.org/0000-0003-2942-8417","contributorId":199468,"corporation":false,"usgs":true,"family":"Sanchez-Chopitea","given":"Erika","email":"esanchez-chopitea@usgs.gov","affiliations":[],"preferred":true,"id":817213,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":817214,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70227122,"text":"70227122 - 2021 - White-nose syndrome-related changes to Mid-Atlantic bat communities across an urban-to-rural gradient","interactions":[],"lastModifiedDate":"2022-01-03T15:34:37.44233","indexId":"70227122","displayToPublicDate":"2021-05-09T08:14:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9972,"text":"BMC Zoology","active":true,"publicationSubtype":{"id":10}},"title":"White-nose syndrome-related changes to Mid-Atlantic bat communities across an urban-to-rural gradient","docAbstract":"White-nose Syndrome (WNS) has reduced the abundance of many bat species within the United States’ Mid-Atlantic region. To determine changes within the National Park Service National Capital Region (NCR) bat communities, we surveyed the area with mist netting and active acoustic sampling (2016–2018) and compared findings to pre-WNS (2003–2004) data.
The results indicated the continued presence of the threatened Myotis septentrionalis (Northern Long-eared bat) and species of conservation concern, including Perimyotis subflavus (Tri-colored bat), Myotis leibii (Eastern Small-footed bat) and Myotis lucifugus (Little Brown bat). However, we documented a significant reduction in the abundance and distribution of M. lucifugus and P. subflavus, a decrease in the distribution of M. septentrionalis, and an increase in the abundance of Eptesicus fuscus (Big Brown bat).
Documented post-WNS M. septentrionalis recruitment suggests that portions of the NCR may be important bat conservation areas. Decreases in distribution and abundance of P. subflavus and M. lucifugus indicate probable extirpation from many previously occupied portions of the region.
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","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14226","usgsCitation":"Rosenberry, D., Engesgaard, P., and Hatch, C.E., 2021, Hydraulic conductivity can no longer be considered a fixed property when quantifying flow between groundwater and surface water: Hydrological Processes, v. 35, no. 6, e14226, 7 p., https://doi.org/10.1002/hyp.14226.","productDescription":"e14226, 7 p.","ipdsId":"IP-128235","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":386567,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosenberry, Donald O. 0000-0003-0681-5641","orcid":"https://orcid.org/0000-0003-0681-5641","contributorId":257638,"corporation":false,"usgs":true,"family":"Rosenberry","given":"Donald O.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":817782,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Engesgaard, Peter 0000-0002-5925-8757","orcid":"https://orcid.org/0000-0002-5925-8757","contributorId":260357,"corporation":false,"usgs":false,"family":"Engesgaard","given":"Peter","email":"","affiliations":[{"id":12672,"text":"University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":817783,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hatch, Christine E. 0000-0002-4996-1617","orcid":"https://orcid.org/0000-0002-4996-1617","contributorId":260358,"corporation":false,"usgs":false,"family":"Hatch","given":"Christine","email":"","middleInitial":"E.","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":817784,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70250184,"text":"70250184 - 2021 - The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure","interactions":[],"lastModifiedDate":"2023-11-28T18:00:43.540846","indexId":"70250184","displayToPublicDate":"2021-05-08T11:45:15","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The 4th paradigm in multiscale data representation: Modernizing the National Geospatial Data Infrastructure","docAbstract":"The need of citizens in any nation to access geospatial data in readily usable form is critical to societal well-being, and in the United States (US), demands for information by scientists, students, professionals and citizens continue to grow. Areas such as public health, urbanization, resource management, economic development and environmental management require a variety of data collected from many sources to identify problems, monitor trends and propose solutions. Such information needs and demands have driven the coordination of federal and regional government agencies with respective private sector participation to develop national geospatial data infrastructures in many countries.
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Plasma-lipid metabolites (i.e. triglyceride [TRIG], β-hydroxybutyrate [BOHB]) have been used to index changes in lipid stores and, thus, have utility for assessing foraging habitat quality during migration. However, such an index may be affected by energetic maintenance costs, diet, and other factors, and further validation under experimental conditions is needed to understand potential sources of variation and verify existing indices. We evaluated a plasma-lipid metabolite index using 30 female and 28 male wild Lesser Scaup (Aythya affinis; hereafter scaup) held in short-term captivity (~24 hr) during spring migration. Similar to previous observational studies, BOHB was negatively associated and TRIG was positively associated with mass change (R2 = 0.68). BOHB estimates were nearly identical to those published on free-living scaup, but TRIG estimates differed from free-living scaup and varied by sex, with females having a greater rate of predicted mass change than captive and free-living males. Our results suggest TRIG may be a better measure of energy income than deposition because lipid deposition likely varies with energetic maintenance costs, stress, and underlying physiological processes while TRIG relates primarily to energy income. In contrast, BOHB was a reliable predictor of negative mass change across sexes. The sex-based differences in apparent lipid deposition rates warrant further research before a generalizable model is advisable for comparing mass change predictions across studies. However, if predictions are standardized, this technique is generally robust to variations in energy income vs. lipid deposition across sexes. 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","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithology/ukab029","usgsCitation":"Smith, E.J., Anteau, M.J., Hagy, H.M., and Jacques, C.N., 2021, Plasma metabolite indices are robust to extrinsic variation and useful indicators of foraging habitat quality in Lesser Scaup: Ornithology, v. 138, no. 3, ukab029, 11 p., https://doi.org/10.1093/ornithology/ukab029.","productDescription":"ukab029, 11 p.","ipdsId":"IP-101470","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":396096,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"Mississippi River, Pool 19","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.43131256103514,\n 40.39336516184327\n ],\n [\n -91.32110595703125,\n 40.39336516184327\n ],\n [\n -91.32110595703125,\n 40.541199704952014\n ],\n [\n -91.43131256103514,\n 40.541199704952014\n ],\n [\n -91.43131256103514,\n 40.39336516184327\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"138","issue":"3","noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Eric J.","contributorId":333129,"corporation":false,"usgs":false,"family":"Smith","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":49637,"text":"Western Illinois University","active":true,"usgs":false}],"preferred":false,"id":892052,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835229,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hagy, Heath M.","contributorId":172326,"corporation":false,"usgs":false,"family":"Hagy","given":"Heath","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":835230,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacques, Christopher N.","contributorId":274285,"corporation":false,"usgs":false,"family":"Jacques","given":"Christopher","email":"","middleInitial":"N.","affiliations":[{"id":49637,"text":"Western Illinois University","active":true,"usgs":false}],"preferred":false,"id":835231,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222489,"text":"70222489 - 2021 - The timing and magnitude of changes to Hortonian overland flow at the watershed scale during the post-fire recovery process","interactions":[],"lastModifiedDate":"2021-07-30T13:19:06.765468","indexId":"70222489","displayToPublicDate":"2021-05-08T08:16:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"The timing and magnitude of changes to Hortonian overland flow at the watershed scale during the post-fire recovery process","docAbstract":"Extreme hydrologic responses following wildfires can lead to floods and debris flows with costly economic and societal impacts. Process-based hydrologic and geomorphic models used to predict the downstream impacts of wildfire must account for temporal changes in hydrologic parameters related to the generation and subsequent routing of infiltration-excess overland flow across the landscape. However, we lack quantitative relationships showing how parameters change with time-since-burning, particularly at the watershed scale. To assess variations in best-fit hydrologic parameters with time, we used the KINEROS2 hydrological model to explore temporal changes in hillslope saturated hydraulic conductivity (Ksh) and channel hydraulic roughness (nc) following a wildfire in the upper Arroyo Seco watershed (41.5 km2), which burned during the 2009 Station fire in the San Gabriel Mountains, California, USA. This study explored runoff-producing storms between 2008 and 2014 to infer watershed hydraulic properties by calibrating the model to observations at the watershed outlet. Modelling indicates Ksh is lowest in the first year following the fire and then increases at an average rate of approximately 4.2 mm/h/year during the first 5 years of recovery. The estimated values for Ksh in the first year following the fire are similar to those obtained in previous studies on smaller watersheds (<1.5 km2) following the Station fire, suggesting hydrologic changes detected here can be applied to lower-order watersheds. Hydraulic roughness, nc, was lowest in the first year following the fire, but increased by a factor of 2 after 1 year of recovery. Post-fire observations suggest changes in nc are due to changes in grain roughness and vegetation in channels. These results provide quantitative constraints on the magnitude of fire-induced hydrologic changes following severe wildfires in chaparral-dominated ecosystems as well as the timing of hydrologic recovery.
Any large, multifaceted data collection that is challenging to handle with traditional management practices can be branded ‘Big Data.’ Any big data containing geo-referenced attributes can be considered big geospatial data. The increased proliferation of big geospatial data is currently reforming the geospatial industry into a data-driven enterprise. Challenges in the big spatial data domain can be summarized as the ‘Big Vs’ – variety, volume, velocity, veracity and value. Big spatial data sources can be considered in two broad classes, active and passive, as each is impacted to varying degrees. Some of these challenges may be alleviated by reducing unprocessed, or minimally processed, (raw) data to features, which we refer to as the extraction of Elements of Interest (EOI). In fact, many applications require EOI extraction from raw data to enable their basic employment. This chapter presents current state-of-the-art methods to create EOI from some types of georeferenced big data. We classify the data types into two realms: active and passive. Active data are those collected specifically for the purpose to which they are applied. Passive data are those collected for purposes other than those for which they are utilized, included those ‘collected’ for no particular purpose at all. The chapter then presents use cases from both the active and passive spatial realms, including the active applications of terrain feature extraction from digital elevation models and vegetation mapping from remotely-sensed imagery and passive applications like building identification from VGI and point-of-interest data mining from social networks for land use classification. Finally, the chapter concludes with future research needs.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Handbook of big geospatial data","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-55462-0_5","usgsCitation":"Arundel, S., and Usery, E., 2021, Spatial data reduction through element -of-interest (EOI) extraction, chap. of Handbook of big geospatial data, p. 119-134, https://doi.org/10.1007/978-3-030-55462-0_5.","productDescription":"16 p.","startPage":"119","endPage":"134","ipdsId":"IP-113380","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":385704,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Arundel, Samantha T. 0000-0002-4863-0138 sarundel@usgs.gov","orcid":"https://orcid.org/0000-0002-4863-0138","contributorId":192598,"corporation":false,"usgs":true,"family":"Arundel","given":"Samantha","email":"sarundel@usgs.gov","middleInitial":"T.","affiliations":[{"id":404,"text":"NGTOC Rolla","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":815856,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Usery, E. Lynn 0000-0002-2766-2173","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":204684,"corporation":false,"usgs":true,"family":"Usery","given":"E. Lynn","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":815857,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70220569,"text":"70220569 - 2021 - Understanding constraints on submersed vegetation distribution in a large, floodplain river: The role of water level fluctuations, water clarity and river geomorphology","interactions":[],"lastModifiedDate":"2021-05-21T13:33:40.508668","indexId":"70220569","displayToPublicDate":"2021-05-08T07:26:32","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Understanding constraints on submersed vegetation distribution in a large, floodplain river: The role of water level fluctuations, water clarity and river geomorphology","docAbstract":"Aquatic vegetation is a key component of large floodplain river ecosystems. In the Upper Mississippi River System (UMRS), there is a long-standing interest in restoring aquatic vegetation in areas where it has declined or disappeared. To better understand what constrains vegetation distribution in large river ecosystems and inform ongoing efforts to restore submersed aquatic vegetation (SAV), we delineated areas in ~1200 river km of the UMRS where the combined effects of water clarity, water level fluctuation, and bathymetry appeared suitable for establishment and persistence of SAV based on a 22-year dataset for total suspended solids (TSS), water surface elevation, and aquatic vegetation distribution. We found a large increase in suitable area downstream from a large natural riverine lake near the northern end of the UMRS (river km 1230) that functions as a sink for suspended material. Downstream from river km 895, there was much less suitable area due to decreased water clarity from tributary input of suspended material, changes in river geomorphology, and increased water level fluctuation. A hypothetical scenario of 75% reduction in TSS resulted in only minor increases in suitable area in the southern portion of the UMRS, indicating limitations by water level fluctuation and/or bathymetry (i.e., limited shallow area). These results improve our understanding of the structure and function of large river systems by illustrating how water clarity, fluctuations in water level, and river geomorphology interact to create complex spatial patterns in habitat suitability for aquatic species and may help to identify locations most and least likely to benefit from management and restoration efforts.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-021-01454-1","usgsCitation":"Carhart, A., Kalas, J., Rogala, J.T., Rohweder, J.J., Drake, D.C., and Houser, J.N., 2021, Understanding constraints on submersed vegetation distribution in a large, floodplain river: The role of water level fluctuations, water clarity and river geomorphology: Wetlands, v. 41, 57, 15 p., https://doi.org/10.1007/s13157-021-01454-1.","productDescription":"57, 15 p.","ipdsId":"IP-119793","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":436377,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TWZXVZ","text":"USGS data release","linkHelpText":"Predicted total number of years and percentage of years from 1993-2014 with conditions suitable for submersed aquatic vegetation based on light availability and water level fluctuations for the Upper Mississippi River System data"},{"id":385752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"Upper Mississippi River System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.033203125,\n 37.09023980307208\n ],\n [\n -89.12109375,\n 39.436192999314095\n ],\n [\n -88.9453125,\n 42.032974332441405\n ],\n [\n -88.857421875,\n 43.54854811091286\n ],\n [\n -89.3408203125,\n 45.1510532655634\n ],\n [\n -91.3623046875,\n 46.13417004624326\n ],\n [\n -94.04296874999999,\n 46.13417004624326\n ],\n [\n -95.537109375,\n 45.42929873257377\n ],\n [\n -95.4052734375,\n 42.71473218539458\n ],\n [\n -94.306640625,\n 40.245991504199026\n ],\n [\n -92.59277343749999,\n 38.20365531807149\n ],\n [\n -90.4833984375,\n 36.98500309285596\n ],\n [\n -89.47265625,\n 36.80928470205937\n ],\n [\n -89.033203125,\n 37.09023980307208\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Carhart, Alicia 0000-0002-9977-8124","orcid":"https://orcid.org/0000-0002-9977-8124","contributorId":223884,"corporation":false,"usgs":false,"family":"Carhart","given":"Alicia","email":"","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":816042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kalas, John","contributorId":223883,"corporation":false,"usgs":false,"family":"Kalas","given":"John","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":816043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rogala, James T. 0000-0002-1954-4097 jrogala@usgs.gov","orcid":"https://orcid.org/0000-0002-1954-4097","contributorId":2651,"corporation":false,"usgs":true,"family":"Rogala","given":"James","email":"jrogala@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":816044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":816045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drake, Deanne C.","contributorId":207846,"corporation":false,"usgs":false,"family":"Drake","given":"Deanne","email":"","middleInitial":"C.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":816046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Houser, Jeffrey N. 0000-0003-3295-3132 jhouser@usgs.gov","orcid":"https://orcid.org/0000-0003-3295-3132","contributorId":2769,"corporation":false,"usgs":true,"family":"Houser","given":"Jeffrey","email":"jhouser@usgs.gov","middleInitial":"N.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":816047,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220388,"text":"70220388 - 2021 - Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA","interactions":[],"lastModifiedDate":"2024-05-16T15:27:57.216807","indexId":"70220388","displayToPublicDate":"2021-05-08T06:59:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA","docAbstract":"Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.
","language":"English","publisher":"MDPI","doi":"10.3390/fire4020026","usgsCitation":"Teske, C., Vanderhoof, M.K., Hawbaker, T., Noble, J., and Hires, J.K., 2021, Using the Landsat Burned Area products to derive fire history relevant for fire management and conservation in the state of Florida, southeastern USA: Fire, v. 4, no. 2, 26, 21 p., https://doi.org/10.3390/fire4020026.","productDescription":"26, 21 p.","ipdsId":"IP-126697","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":452347,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/fire4020026","text":"Publisher Index Page"},{"id":385562,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Florida Panhandle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.64892578125,\n 29.554345125748267\n ],\n [\n -83.43017578125,\n 29.554345125748267\n ],\n [\n -83.43017578125,\n 30.939924331023445\n ],\n [\n -87.64892578125,\n 30.939924331023445\n ],\n [\n -87.64892578125,\n 29.554345125748267\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-05-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Teske, Casey","contributorId":224732,"corporation":false,"usgs":false,"family":"Teske","given":"Casey","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":815369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":815372,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hawbaker, Todd 0000-0003-0930-9154 tjhawbaker@usgs.gov","orcid":"https://orcid.org/0000-0003-0930-9154","contributorId":568,"corporation":false,"usgs":true,"family":"Hawbaker","given":"Todd","email":"tjhawbaker@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":547,"text":"Rocky Mountain Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":815371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noble, Joe","contributorId":257938,"corporation":false,"usgs":false,"family":"Noble","given":"Joe","email":"","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":815370,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hires, J. Kevin","contributorId":257941,"corporation":false,"usgs":false,"family":"Hires","given":"J.","email":"","middleInitial":"Kevin","affiliations":[{"id":36874,"text":"Tall Timbers Research Station","active":true,"usgs":false}],"preferred":false,"id":815373,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221481,"text":"70221481 - 2021 - Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study","interactions":[],"lastModifiedDate":"2021-06-17T11:49:03.530012","indexId":"70221481","displayToPublicDate":"2021-05-08T06:44:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1828,"text":"Geothermics","active":true,"publicationSubtype":{"id":10}},"title":"Stochastic inversion of gravity, magnetic, tracer, lithology, and fault data for geologically realistic structural models: Patua Geothermal Field case study","docAbstract":"Financial risk due to geological uncertainty is a major barrier for geothermal development. Production from a geothermal well depends on the unknown location of subsurface geological structures, such as faults that contain hydrothermal fluids. Traditionally, geoscientists collect many different datasets, interpret the datasets manually, and create a single model estimating faults' locations. This method, however, does not provide information about the uncertainty regarding the location of faults and often does not fully respect all observed datasets. Previous researchers investigated the use of stochastic inversion schemes for addressing geological uncertainty, but often at the expense of geologic realism. In this paper, we present algorithms and open-source code to stochastically invert five typical datasets for creating geologically realistic structural models. Using a case study with real data from the Patua Geothermal Field, we show that these inversion algorithms are successful in finding an ensemble of structural models that are geologically realistic and match the observed data sufficiently. Geoscientists can use this ensemble of models to optimize reservoir management decisions given structural uncertainty.
Distilling my experience in having field mapped in detail the volcanic fields at Laguna del Maule and Long Valley and having worked out their time-volume-composition magmatic histories, I compare and contrast the postglacial rhyolites of the former with six multi-vent eruptive sequences of rhyolite in California. Compilations and discussions are made of volcanic-field areas and longevities, their compositions, vent distributions, individual batch and total volumes, eruptive episodicities, and tectonic influences. Growth of long-lived pluton-scale reservoirs of granitic crystal mush, from which the rhyolite melts separated, are interpreted in terms of conceptual models I published previously—(1) fundamentally basaltic transcrustal magmatism, 1981; (2) the deep-crustal MASH zone model, 1988; and (3) the rhyolite-melt crystal-mush model, 2001. Inferences and speculations are advanced concerning processes and timescales of rhyolite-melt separation from granitic mush and of prompt or long-delayed subsequent eruption.
Anthropogenic activities can lead to the loss, fragmentation, and alteration of wildlife habitats. I reviewed the recent literature (2014–2019) focused on the responses of avian, mammalian, and herpetofaunal species to oil and natural gas development, a widespread and still-expanding land use worldwide. My primary goals were to identify any generalities in species’ responses to development and summarize remaining gaps in knowledge. To do so, I evaluated the directionality of a wide variety of responses in relation to taxon, location, development type, development metric, habitat type, and spatiotemporal aspects.
Studies (n = 70) were restricted to the USA and Canada, and taxonomically biased towards birds and mammals. Longer studies, but not those incorporating multiple spatial scales, were more likely to detect significant responses. Negative responses of all types were present in relatively low frequencies across all taxa, locations, development types, and development metrics but were context-dependent. The directionality of responses by the same species often varied across studies or development metrics.
The state of knowledge about wildlife responses to oil and natural gas development has developed considerably, though many biases and gaps remain. Studies outside of North America and that focus on herpetofauna are lacking. Tests of mechanistic hypotheses for effects, long-term studies, assessment of response thresholds, and experimental designs that isolate the effects of different stimuli associated with development, remain critical. Moreover, tests of the efficacy of habitat mitigation efforts have been rare. Finally, investigations of the demographic effects of development across the full annual cycle were absent for non-game species and are critical for the estimation of population-level effects.
Rabbit hemorrhagic disease, a notifiable foreign animal disease in the US, was reported for the first time in wild native North American lagomorphs in April 2020 in the southwestern US. Affected species included the desert cottontail (Sylvilagus audubonii), mountain cottontail (Sylvilagus nuttallii), black-tailed jackrabbit (Lepus californicus), and antelope jackrabbit (Lepus alleni). Desert cottontails (n=7) and black-tailed jackrabbits (n=7) collected in April and May 2020 were necropsied at the US Geological Survey National Wildlife Health Center and tested positive for Lagovirus europaeus GI.2, also known as rabbit hemorrhagic disease virus 2 (GI.2/RHDV2/b), by real-time PCR at the US Department of Agriculture's Foreign Animal Disease Diagnostic Laboratory. Gross and microscopic lesions were similar to those reported in European rabbits (Oryctolagus cuniculus) and other hare (Lepus) species with GI.2/RHDV2/b infection; they included epistaxis (12/13; 92%); massive hepatocellular dissociation (14/14; 100%) and necrosis or apoptosis (11/11; 100%); pulmonary congestion (12/12; 100%), edema (12/13; 92%), and hemorrhage (11/12; 92%); and acute renal tubular injury (3/8; 38%). As in previous reports, massive hepatocellular dissociation and necrosis or apoptosis were the most diagnostically distinct finding. As North American Sylvilagus and Lepus species appear to be susceptible to fatal GI.2/RHDV2/b infection, additional work is needed to understand the host range, pathogenicity, and potential population effects of GI.2/RHDV2/b in North America.
","language":"English","publisher":"Wildlife Disease Association","doi":"10.7589/JWD-D-20-00207","usgsCitation":"Lankton, J.S., Knowles, S., Keller, S., Shearn-Bochsler, V.I., and Ip, H., 2021, Pathology of Lagovirus europaeus GI.2/RHDV2/b (rabbit hemorrhagic disease virus 2) in native North American lagomorphs: Journal of Wildlife Diseases, v. 57, no. 3, p. 694-700, https://doi.org/10.7589/JWD-D-20-00207.","productDescription":"7 p.","startPage":"694","endPage":"700","ipdsId":"IP-123143","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":436379,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZINO2P","text":"USGS data release","linkHelpText":"Data from pathology of Lagovirus europaeus GI.2/RHDV2/b (rabbit hemorrhagic disease virus 2) in native North American lagomorphs"},{"id":386100,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, New Mexico, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.5986328125,\n 29.57345707301757\n ],\n [\n -98.87695312499999,\n 29.57345707301757\n ],\n [\n -98.87695312499999,\n 36.70365959719456\n ],\n [\n -113.5986328125,\n 36.70365959719456\n ],\n [\n -113.5986328125,\n 29.57345707301757\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"57","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lankton, Julia S. 0000-0002-6843-4388 jlankton@usgs.gov","orcid":"https://orcid.org/0000-0002-6843-4388","contributorId":5888,"corporation":false,"usgs":true,"family":"Lankton","given":"Julia","email":"jlankton@usgs.gov","middleInitial":"S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815542,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knowles, Susan 0000-0002-0254-6491 sknowles@usgs.gov","orcid":"https://orcid.org/0000-0002-0254-6491","contributorId":5254,"corporation":false,"usgs":true,"family":"Knowles","given":"Susan","email":"sknowles@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815544,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Saskia","contributorId":255627,"corporation":false,"usgs":false,"family":"Keller","given":"Saskia","affiliations":[{"id":16925,"text":"University of Wisconsin-Madison","active":true,"usgs":false}],"preferred":false,"id":815543,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shearn-Bochsler, Valerie I. 0000-0002-5590-6518 vbochsler@usgs.gov","orcid":"https://orcid.org/0000-0002-5590-6518","contributorId":3234,"corporation":false,"usgs":true,"family":"Shearn-Bochsler","given":"Valerie","email":"vbochsler@usgs.gov","middleInitial":"I.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815545,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ip, Hon S. 0000-0003-4844-7533","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":126815,"corporation":false,"usgs":true,"family":"Ip","given":"Hon S.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":815546,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70219506,"text":"ofr20211045 - 2021 - Methodology and technical input for the 2021 review and revision of the U.S. Critical Minerals List","interactions":[],"lastModifiedDate":"2021-05-07T19:28:21.952536","indexId":"ofr20211045","displayToPublicDate":"2021-05-07T13:35:00","publicationYear":"2021","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":"2021-1045","displayTitle":"Methodology and Technical Input for the 2021 Review and Revision of the U.S. Critical Minerals List","title":"Methodology and technical input for the 2021 review and revision of the U.S. Critical Minerals List","docAbstract":"Pursuant to Section 7002 (“Mineral Security”) of Title VII (“Critical Minerals”) of the Energy Act of 2020 (Public Law 116–260, December 27, 2020, 116th Cong.), the Secretary of the Interior, acting through the Director of the U.S. Geological Survey, is tasked with reviewing and revising the methodology used to evaluate mineral criticality and the U.S. Critical Minerals List no less than every 3 years. The initial Critical Minerals List was published in the Federal Register on May 18, 2018, in response to Executive Order No. 13817, A Federal Strategy to Ensure Secure and Reliable Supplies of Critical Minerals (3 CFR, 2017 Comp, p. 397–399). This report documents the updated evaluation methodology and the resultant updated draft list of minerals recommended for inclusion in the Critical Minerals List.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211045","usgsCitation":"Nassar, N.T., and Fortier, S.M., 2021, Methodology and technical input for the 2021 review and revision of the U.S. Critical Minerals List: U.S. Geological Survey Open-File Report 2021–1045, 31 p., https://doi.org/10.3133/ofr20211045.","productDescription":"viii, 31 p.","numberOfPages":"31","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-127319","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":384978,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1045/ofr20211045.pdf","text":"Report","size":"1.45 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1045"},{"id":384977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1045/coverthb.jpg"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
No abstract available.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/fsh.10604","usgsCitation":"Zydlewski, J.D., 2021, Book Review: “From catastrophe to recovery: Stories of fisheries management successes” Krueger, C. C., Taylor, W. M., and Youn, S. J., editors: Fisheries Magazine, v. 46, no. 7, https://doi.org/10.1002/fsh.10604.","productDescription":"1 p.","startPage":"340","ipdsId":"IP-128179","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":397260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"46","issue":"7","noUsgsAuthors":false,"publicationDate":"2021-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Zydlewski, Joseph D. 0000-0002-2255-2303 jzydlewski@usgs.gov","orcid":"https://orcid.org/0000-0002-2255-2303","contributorId":2004,"corporation":false,"usgs":true,"family":"Zydlewski","given":"Joseph","email":"jzydlewski@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":838213,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70223096,"text":"70223096 - 2021 - Legacy contaminant-stable isotope-age relationships in Lake Ontario year-class Alewife (Alosa pseudoharengus)","interactions":[],"lastModifiedDate":"2021-08-11T16:13:50.940846","indexId":"70223096","displayToPublicDate":"2021-05-07T11:05:02","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Legacy contaminant-stable isotope-age relationships in Lake Ontario year-class Alewife (Alosa pseudoharengus)","title":"Legacy contaminant-stable isotope-age relationships in Lake Ontario year-class Alewife (Alosa pseudoharengus)","docAbstract":"Alewife (Alosa pseudoharengus) are the preferred prey species of the top piscivore predators in the Lake Ontario food web and are an essential constituent in the bioaccumulation of persistent organic contaminants. Year-class samples collected in 2016 represent the alewife age ranges of 2015 (Age-01) sequentially dating back to 2008 (Age-08). The most abundant contaminant measured in Lake Ontario alewife (151.5 ng/g) were total polychlorinated biphenyls (PCBs), increasing at a rate of 11.8 ng/g per year on an age-averaged concentration basis. Total mercury demonstrated the largest percent increase (240%) accumulated over alewife ages of 1–8 years. Average total concentrations of the most abundant polychlorinated dibenzo-p-dioxin isomer (2378-tetrachlorinated dibenzo-p-dioxin (TCDD), 1.3 pg/g) and polychlorinated dibenzofuran isomer (2378-tetrachlorinated dibenzofuran (2378-TCDF), 6.6 pg/g) comprised most of the overall total dioxin (2.5 pg/g) and total furan concentrations (8.7 pg/g). The vast majority (69%) of alewife total toxic equivalence (TEQ) was comprised of the non-ortho coplanar PCBs. Both mammal and avian wildlife protection values based on total TEQ were uniformly exceeded for the dioxin-like compounds measured in Lake Ontario alewife. Ontogenetic dietary influences expressed a significant impact on Age-01 alewife age-contaminant relationships and age-stable isotope concentrations and trends for legacy contaminants. Total Hg and all dioxin-like contaminants did not demonstrate the Age-01 ontogenetic dietary effects found in legacy contaminants. A prominent polychlorinated naphthalene (PCN) concentration peak measured in year-class Age-04 alewife was followed by a corresponding lake trout peak 3–4 years later illustrating a unique example of trophic-level contaminant uptake and concomitant integration delay.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2021.04.016","usgsCitation":"Pagano, J.J., Garner, J.J., Weidel, B., McGoldrick, D.J., Walsh, M., and Holsen, T.M., 2021, Legacy contaminant-stable isotope-age relationships in Lake Ontario year-class Alewife (Alosa pseudoharengus): Journal of Great Lakes Research, v. 47, no. 4, p. 1086-1096, https://doi.org/10.1016/j.jglr.2021.04.016.","productDescription":"11 p.","startPage":"1086","endPage":"1096","ipdsId":"IP-126832","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":387862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Ohio","city":"Hamilton, Olcott","otherGeospatial":"Lake Ontario, Nine Mile Point, Oak Orchard","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.8211669921875,\n 43.229195113965005\n ],\n [\n -79.595947265625,\n 43.229195113965005\n ],\n [\n -79.595947265625,\n 43.337164854911094\n ],\n [\n -79.8211669921875,\n 43.337164854911094\n ],\n [\n -79.8211669921875,\n 43.229195113965005\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.34056091308594,\n 43.54854811091286\n ],\n [\n -76.21490478515625,\n 43.54854811091286\n ],\n [\n -76.21490478515625,\n 43.61047672368098\n ],\n [\n -76.34056091308594,\n 43.61047672368098\n ],\n [\n -76.34056091308594,\n 43.54854811091286\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.77609252929688,\n 43.34140974534528\n ],\n [\n -78.67378234863281,\n 43.34140974534528\n ],\n [\n -78.67378234863281,\n 43.42948658186479\n ],\n [\n -78.77609252929688,\n 43.42948658186479\n ],\n [\n -78.77609252929688,\n 43.34140974534528\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.26179504394531,\n 43.37236349833033\n ],\n [\n -78.09871673583984,\n 43.37236349833033\n ],\n [\n -78.09871673583984,\n 43.46612672776958\n ],\n [\n -78.26179504394531,\n 43.46612672776958\n ],\n [\n -78.26179504394531,\n 43.37236349833033\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pagano, James J.","contributorId":264131,"corporation":false,"usgs":false,"family":"Pagano","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":820934,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garner, James J.","contributorId":264132,"corporation":false,"usgs":false,"family":"Garner","given":"James","email":"","middleInitial":"J.","affiliations":[{"id":48660,"text":"SUNY Oswego","active":true,"usgs":false}],"preferred":false,"id":820935,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":820936,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGoldrick, Daryl J.","contributorId":56517,"corporation":false,"usgs":false,"family":"McGoldrick","given":"Daryl","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":820937,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walsh, Maureen G.","contributorId":264133,"corporation":false,"usgs":false,"family":"Walsh","given":"Maureen G.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":820938,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holsen, Thomas M.","contributorId":150058,"corporation":false,"usgs":false,"family":"Holsen","given":"Thomas","email":"","middleInitial":"M.","affiliations":[{"id":17897,"text":"Department of Civil and Environmental Engineering, Clarkson University, Potsdam, New York","active":true,"usgs":false}],"preferred":false,"id":820939,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228932,"text":"70228932 - 2021 - Mapping out a future for ungulate migrations","interactions":[],"lastModifiedDate":"2022-02-25T20:45:22.28195","indexId":"70228932","displayToPublicDate":"2021-05-07T10:49:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Mapping out a future for ungulate migrations","docAbstract":"Migration of ungulates (hooved mammals) is a fundamental ecological process that promotes abundant herds, whose effects cascade up and down terrestrial food webs. Migratory ungulates provide the prey base that maintains large carnivore and scavenger populations and underpins terrestrial biodiversity (fig. S1). When ungulates move in large aggregations, their hooves, feces, and urine create conditions that facilitate distinct biotic communities. The migrations of ungulates have sustained humans for thousands of years, forming tight cultural links among Indigenous people and local communities. Yet ungulate migrations are disappearing at an alarming rate (1). Efforts by wildlife managers and conservationists are thwarted by a singular challenge: Most ungulate migrations have never been mapped in sufficient detail to guide effective conservation. Without a strategic and collaborative effort, many of the world's great migrations will continue to be truncated, severed, or lost in the coming decades. Fortunately, a combination of animal tracking datasets, historical records, and local and Indigenous knowledge can form the basis for a global atlas of migrations, designed to support conservation action and policy at local, national, and international levels.
","language":"English","publisher":"AAAS","doi":"10.1126/science.abf0998","usgsCitation":"Kauffman, M., Cagnacci, F., Chamaille-Jammes, S., Hebblewhite, M., Hopcraft, J., Merkle, J., Mueller, T., Mysterud, A., Peters, W., Roettger, C., Steingisser, A., Meacham, J., Abera, K., Adamczewski, J., Aikens, E., Bartlam-Brooks, H., Bennitt, E., Berger, J., Boyd, C., Cote, S.D., Debeffe, L., Dekrout, A.S., Dejid, N., Donadio, E., Dziba, L., Fagan, W., Fischer, C., Focardi, S., Fryxell, J.M., Fynn, R.W., Geremia, C., Gonzalez, B.A., Gunn, A., Gurarie, E., Heurich, M., Hilty, J.A., Hurley, M., Johnson, A., Joly, K., Kaczensky, P., Kendall, C.J., Kochkarev, P., Kolpaschikov, L., Kowalczyk, R., Langeveld, F.V., Binbin, V.L., Lobora, A.L., Loison, A., Madiri, T.H., Mallon, D.P., Marchland, P., Medellin, R., Meisingset, E., Merrill, E., Middleton, A.D., Monteith, K., Morjan, M., Morrison, T., Mumme, S., Naidoo, R., Novaro, A., Ogutu, J.O., Olson, K.A., Oteng-Yeboah, A., Ramiro J.A., O., Owen-Smith, N., Paasivaara, A., Packer, C., Panchenko, D., Pedrotti, L., Plumptre, A.J., Rolandsen, C.M., Said, S., Salemgareyev, A., Savchenko, P., Hall Sawyer, Selebatso, M., Skroch, M., Solberg, E.J., Stabach, J.A., Strand, O., Suitor, M.J., Tachiki, Y., Trainor, A., Tshipa, A., Virani, M., Vynne, C., Ward, S., Wittemyer, G., Xu, W., and Zuther, S., 2021, Mapping out a future for ungulate migrations: Science, v. 372, no. 6542, p. 566-569, https://doi.org/10.1126/science.abf0998.","productDescription":"4 p.","startPage":"566","endPage":"569","ipdsId":"IP-123411","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":452359,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-03425923","text":"External Repository"},{"id":396522,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ethiopia, France, Mongolia, South Sudan, United States","volume":"372","issue":"6542","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":835954,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cagnacci, Francesca","contributorId":205070,"corporation":false,"usgs":false,"family":"Cagnacci","given":"Francesca","email":"","affiliations":[],"preferred":false,"id":836198,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chamaille-Jammes, Simon","contributorId":205072,"corporation":false,"usgs":false,"family":"Chamaille-Jammes","given":"Simon","email":"","affiliations":[],"preferred":false,"id":836199,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hebblewhite, Mark","contributorId":69455,"corporation":false,"usgs":true,"family":"Hebblewhite","given":"Mark","affiliations":[],"preferred":false,"id":836200,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hopcraft, J. Grant C.","contributorId":272240,"corporation":false,"usgs":false,"family":"Hopcraft","given":"J. Grant C.","affiliations":[{"id":56374,"text":"ug","active":true,"usgs":false}],"preferred":false,"id":836201,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Merkle, Jerod A.","contributorId":264421,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod A.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":836202,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mueller, Thomas","contributorId":274278,"corporation":false,"usgs":false,"family":"Mueller","given":"Thomas","affiliations":[{"id":56593,"text":"Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":836203,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mysterud, Atle","contributorId":205104,"corporation":false,"usgs":false,"family":"Mysterud","given":"Atle","email":"","affiliations":[],"preferred":false,"id":836204,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Peters, 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onshore extent of megathrust earthquake rupture in the Cascadia subduction zone represents a key uncertainty in earthquake hazard for the Pacific Northwest that is governed by the physical state and mechanical properties of the plate interface. The Cascadia plate interface is segmented into an interseismically locked zone located primarily offshore that is expected to rupture in large earthquakes, a region of aseismic slow slip at greater depth, and an intervening transition zone of uncertain rupture potential. Here we image the evolution of the ratio of seismic compressional to shear wave velocities from the locked zone to the transition zone, which is related to changes in fluid content of the plate boundary zone, using a dense onshore–offshore seismic dataset from southernmost Cascadia. The locked zone shows evidence of high fluid content implying a high porosity, yet the downdip transition zone shows an order of magnitude lower porosity. This strong variation is consistent with models that contain a ductile region between the earthquake rupture and slow slip zones that would inhibit onshore propagation of future large earthquake ruptures and hence reduce seismic hazard.
","language":"English","publisher":"Nature Publishing Group","doi":"10.1038/s41561-021-00740-1","usgsCitation":"Guo, H., McGuire, J., and Zhang, H., 2021, Correlation of porosity variations and rheological transitions on the southern Cascadia megathrust: Nature Geoscience, v. 14, no. 5, p. 341-348, https://doi.org/10.1038/s41561-021-00740-1.","productDescription":"8 p.","startPage":"341","endPage":"348","ipdsId":"IP-106445","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":387387,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Cascadia subduction zone, Mendocino triple junction","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.925048828125,\n 39.85915479295669\n ],\n [\n -123.28857421875,\n 39.85915479295669\n ],\n [\n -123.28857421875,\n 40.96330795307353\n ],\n [\n -126.925048828125,\n 40.96330795307353\n ],\n [\n -126.925048828125,\n 39.85915479295669\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Guo, Hao","contributorId":261277,"corporation":false,"usgs":false,"family":"Guo","given":"Hao","email":"","affiliations":[{"id":52789,"text":"Univ. of Science and Technology of China","active":true,"usgs":false}],"preferred":false,"id":819659,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":819661,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhang, Haijiang","contributorId":174443,"corporation":false,"usgs":false,"family":"Zhang","given":"Haijiang","email":"","affiliations":[{"id":36359,"text":"University of Science and Technology of China, Anhui, China","active":true,"usgs":false}],"preferred":false,"id":819663,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222510,"text":"70222510 - 2021 - TrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends","interactions":[],"lastModifiedDate":"2021-08-02T14:27:22.853972","indexId":"70222510","displayToPublicDate":"2021-05-07T09:25:09","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"TrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends","docAbstract":"A simulation-based power analysis can be used to estimate the sample sizes needed for a successful monitoring program, but requires technical expertise and sometimes extensive computing resources. We developed a web-based lookup app, called TrendPowerTool (https://www.usgs.gov/apps/TrendPowerTool/), to provide guidance for ecological monitoring programs when resources are not available for a simulation-based power analysis. TrendPowerTool is implemented through the shiny package in R, but is accessible through a webpage without the need for users to install any software. By drawing on results of 1.4 million scenarios that we simulated on a supercomputer, TrendPowerTool quickly and easily provides an estimate of the statistical power to detect a population trend of a particular magnitude with a planned monitoring program, based on user-specified parameters for the monitoring design and population of interest. TrendPowerTool provides a user-friendly interface that retrieves results instantaneously, facilitating the important step of conducting a power analysis when designing monitoring programs.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.445","usgsCitation":"Weiser, E.L., Diffendorfer, J., Lopez-Hoffman, L., Semmens, D., and Thogmartin, W.E., 2021, TrendPowerTool: A lookup tool for estimating the statistical power of a monitoring program to detect population trends: Conservation Science and Practice, v. 3, e445, 7 p., https://doi.org/10.1111/csp2.445.","productDescription":"e445, 7 p.","ipdsId":"IP-119582","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":452363,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.445","text":"Publisher Index Page"},{"id":387627,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","noUsgsAuthors":false,"publicationDate":"2021-05-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":206605,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":820365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":820366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez-Hoffman, Laura","contributorId":231064,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":28236,"text":"Univ of Arizona","active":true,"usgs":false}],"preferred":false,"id":820367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semmens, Darius J. 0000-0001-7924-6529","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":64201,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":820368,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820369,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220370,"text":"ofr20211036 - 2021 - Survival and growth of suckers in mesocosms at three locations within Upper Klamath Lake, Oregon, 2018","interactions":[],"lastModifiedDate":"2021-05-07T19:39:47.723836","indexId":"ofr20211036","displayToPublicDate":"2021-05-07T08:23:02","publicationYear":"2021","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":"2021-1036","displayTitle":"Survival and Growth of Suckers in Mesocosms at Three Locations Within Upper Klamath Lake, Oregon, 2018","title":"Survival and growth of suckers in mesocosms at three locations within Upper Klamath Lake, Oregon, 2018","docAbstract":"Due to high mortality in the first year or two of life, Lost River (Deltistes luxatus sp.) and Shortnose suckers (Chasmistes brevirostris sp.) in Upper Klamath Lake, Oregon rarely reach maturity. In 2015, the U.S. Fish and Wildlife Service began the Sucker Assisted Rearing Program (SARP) to improve early life survival before releasing the fish back into Upper Klamath Lake. Survival and growth rates were compared for fish in mesocosms among three potential release or in-lake rearing sites, and in a pond at the SARP rearing facility. Fish used in this study included a mix of Lost River, Shortnose, and Klamath largescale suckers reared at either U.S. Fish and Wildlife Service or Klamath Tribes fish rearing facilities. These sites were Shoalwater Bay (SWB), Rattlesnake Point (RPT), and Cove Point (CPT). Ninety-nine to 103 suckers tagged with passive integrated transponders (PIT) were placed into each mesocosm for up to 80 days and up to 103 days in the SARP pond. Cessation of movement, as determined by passive detection of tagged fish on remote antennas, indicated mortality. Dissolved-oxygen saturation, temperature, and pH were tracked hourly in each mesocosm. All the suckers placed into the SWB mesocosm died during an extreme hypoxia event. These fish were replaced with another 120 PIT-tagged and 2 untagged hatchery-reared Lost River suckers from the Klamath Tribes Fish Research Facility (KTFRF), of which, all but two died during a second extreme hypoxia event. It was determined that SWB was an unsuitable site for summertime release or rearing of juvenile suckers in 2018. The summer survival rate was ≥86 percent at CPT, RPT, and the SARP pond. Suckers in the SARP pond grew slightly slower and gained less weight relative to increases in length than suckers held at RPT and CPT. All suckers sampled at the start of the study from both the SARP facility and the KTFRF, when water temperatures averaged approximately 18–22 degrees Celsius (°C), were infected with low levels of the gill parasite Ichthyobodo sp. Ichthyobodo sp. was detected on only 1 of 16 suckers sampled from CPT, RPT, and the SARP pond in late September or early October when water temperatures were approximately 16–19 °C, indicating fish were able to shed the parasite in cooler temperatures. Water quality conditions at RPT and CPT were adequate for in-lake rearing of SARP suckers in 2018. Due to interannual differences in water quality conditions, these sites may not be suitable in all years. Future research focused on the suitability of RPT, CPT and other potential sites under in years with varying conditions would be beneficial for improving sucker in-lake rearing practices. Additional research could help to elucidate how size at entry into the mesocosms affects sucker survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211036","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Burdick, S.M., Conway, C.M., Ostberg, C.O., Bart, R.J., and Elliott, D.G., 2021, Survival and growth of suckers in mesocosms at three locations within Upper Klamath Lake, Oregon, 2018: U.S. Geological Survey Open-File Report 2021–1036, 18 p., https://doi.org/10.3133/ofr20211036.","productDescription":"v, 18 p.","onlineOnly":"Y","ipdsId":"IP-119761","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":385517,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1036/coverthb.jpg"},{"id":385518,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1036/ofr20211036.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1036"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.79031372070312,\n 42.24478535602799\n ],\n [\n -121.79855346679686,\n 42.39810802339276\n ],\n [\n -121.95098876953125,\n 42.6147595985433\n ],\n [\n -122.12265014648438,\n 42.48627657532139\n ],\n [\n -121.96884155273436,\n 42.34129022434778\n ],\n [\n -121.9207763671875,\n 42.261049162113856\n ],\n [\n -121.81365966796874,\n 42.22139878761366\n ],\n [\n -121.79031372070312,\n 42.24478535602799\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
Understanding groundwater flow and pumping effects near pending mining operations requires accurate subsurface hydraulic characterization. To improve conceptual models of groundwater flow and development in the complex hydrogeologic system near Long Canyon Mine, in northwestern Goshute Valley, northeastern Nevada, the U.S. Geological Survey characterized the hydraulic properties of carbonate rocks and basin-fill aquifers using an integrated analysis of steady-state and stressed aquifer conditions informed by water chemistry and aquifer-test data. Hydraulic gradients and groundwater-age data in northern Goshute Valley indicate carbonate rocks in the Pequop Mountains just west and south of the Long Canyon Mine project area constitute a more permeable and active flow system than saturated rocks in the northern Pequop Mountains, western Toano Range, and basin fill. Permeable carbonate rocks in the northern Pequop Mountains, in part, discharge to the Johnson Springs wetland complex (JSWC), where mean groundwater ages range from 500 to 2,400 years and samples all contain a small fraction of modern waters, relative to mean ages of 8,600 to more than 22,000 years for most groundwater sampled to the north and east. Recharge to the JSWC occurs from a roughly 27-square-mile area in the upgradient Pequop Mountains to the west, composed mostly of permeable carbonate rock and fractured quartzite, and bounded by low-permeability shales and marbleized and siliclastic rocks.
Single-well aquifer-test analyses provided transmissivity estimates at pumped wells. Transmissivity estimates ranged from 7,000 to 400,000 feet squared per day (ft2/d) in carbonate rocks and from 2,000 to 80,000 ft2/d in basin fill near the Long Canyon Mine. Water-level drawdown from multiple-well aquifer testing and rise from unintentional leakage into the overlying basin-fill aquifer were estimated and distinguished from natural fluctuations in 93 pumping and monitoring sites using analytical water-level models. Leakage of disposed aquifer-test pumpage occurred south of the aquifer test area through an unlined irrigation ditch. Drawdown was detected at distances of as much as 3 miles (mi) from pumping wells at all but one carbonate-rock site, at basin-fill sites on the alluvial fan immediately downgradient from pumping wells, and in Big Spring and spring NS-05. Similar drawdowns in carbonate rocks within the drawdown detection area suggest all wells penetrate a highly transmissive zone (HTZ) that is bounded by low-permeability rocks. Drawdown was not detected in carbonate rocks to the west of Canyon fault, in any basin-fill sites on the valley floor east of the Hardy fault, or at volcanic sites to the north, indicating that these major fault structures and (or) permeability contrasts between hydrogeologic units impeded groundwater flow or obscured pumping signals. Alternatively, unintentional leakage might have obscured drawdown at basin-fill sites on the valley floor, where water-level rise was detected at nine sites over 3 mi.
Consistent hydraulic properties were estimated by simultaneously interpreting steady-state flow during predevelopment conditions and changes in groundwater levels and springflows from the 2016 carbonate-rock aquifer test with an integrated groundwater-flow model. Hydraulic properties were distributed across carbonate rocks, basin fill, volcanic rocks, and siliciclastic rocks with a hydrogeologic framework developed from geologic mapping and hydraulic testing. Estimated transmissivity distributions spanned at least three orders of magnitude in each rock unit. In the HTZ, simulated transmissivities ranged from 10,000 to 23,000,000 ft2/d, with the most transmissive areas occurring around Big Spring. Comparatively low carbonate-rock transmissivities of less than 10,000 ft2/d were estimated in the northern Pequop Mountains and poorly defined values of less than 1,000 ft2/d were estimated in the western Toano Range. Transmissivities in basin fill ranged from less than 10 to 80,000 ft2/d and were minimally constrained by the 2016 carbonate-rock aquifer test because poorly quantified leakage affected water levels more so than pumping. The most transmissive areas were informed by single-well aquifer tests along the eastern edge of the Pequop Mountains near Long Canyon Mine and could be indicative of a hydraulic connection between basin fill and more transmissive underlying carbonate rocks. Simulated transmissivities of volcanic and low-permeability rocks mostly are less than 1,000 ft2/d. The estimated hydraulic-property distributions and informed interpretation of hydraulic connections among hydrogeologic units improved the characterization and representation of groundwater flow near the Long Canyon Mine.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215021","collaboration":"Prepared in cooperation with the Nevada Division of Water Resources","usgsCitation":"Garcia, C.A., Halford, K.J., Gardner, P.M., and Smith, D.W., 2021, Hydraulic characterization of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada: U.S. Geological Survey Scientific Investigations Report 2021–5021, 99 p., https://doi.org/10.3133/sir20215021.","productDescription":"Report: xii, 99 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-094004","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":397361,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5021/sir20215021.XML"},{"id":397360,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5021/images"},{"id":385454,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P1P7QV","text":"USGS data release","description":"USGS data release","linkHelpText":"Appendixes and supplemental data—Hydraulic characterization of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada, 2011–16."},{"id":385453,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JI8NQF","text":"USGS data release","description":"USGS data release","linkHelpText":"MODFLOW-2005 and PEST models used to simulate the 2016 carbonate-rock aquifer test and characterize hydraulic properties of carbonate-rock and basin-fill aquifers near Long Canyon, Goshute Valley, northeastern Nevada."},{"id":385451,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5021/coverthb.jpg"},{"id":385452,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5021/sir20215021.pdf","text":"Report","size":"9.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5021"}],"country":"United States","state":"Nevada","otherGeospatial":"Goshute Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.98840332031249,\n 40.55554790286311\n ],\n [\n -114.2633056640625,\n 40.55554790286311\n ],\n [\n -114.2633056640625,\n 41.693424216151314\n ],\n [\n -114.98840332031249,\n 41.693424216151314\n ],\n [\n -114.98840332031249,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201