The Nine-banded Armadillo (Dasypus novemcinctus) is a widespread burrowing species with an expanding geographic range across the southeastern and midwestern United States. Armadillos dig numerous, large burrows within their home ranges and these burrows are likely used by a diverse suite of wildlife species as has been reported for other burrowing ecosystem engineers such as Gopher Tortoises (Gopherus polyphemus), Desert Tortoises (Gopherus agassizi), and Black-tailed Prairie Dogs (Cynomys ludovicianus). We used motion-triggered game cameras at 35 armadillo burrows in 4 ecoregions of Arkansas and documented 19 species of mammals, 4 species of reptile, 1 species of amphibian, and 40 species of bird interacting with burrows. Bobcat (Lynx rufus), Coyote (Canis latrans), Eastern Cottontail (Sylvilagus floridanus), Gray Fox (Urocyon cinereoargenteus), Gray Squirrel (Sciurus carolinensis), Northern Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), and unidentified rodents (mice and rats) were documented using burrows in all four ecoregions. We documented wildlife hunting, seeking shelter, rearing young in, and taking over and modifying armadillo burrows. The rate of use was highest in the Mississippi Alluvial Valley, a landscape dominated by agriculture, where natural refugia may be limited and rodents are abundant. Armadillo burrows are clearly visited and used by numerous wildlife species to fulfill various life stage requirements, and this list will likely expand if more attention is devoted to understanding the role of armadillos burrows. Armadillos are important ecosystem engineers, and their ecological role warrants more investigation and attention as opposed to only being viewed and managed as agricultural and garden pests.
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\"}}]}","volume":"12","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"DeGregorio, Brett Alexander 0000-0002-5273-049X","orcid":"https://orcid.org/0000-0002-5273-049X","contributorId":243214,"corporation":false,"usgs":true,"family":"DeGregorio","given":"Brett","email":"","middleInitial":"Alexander","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Veon, John T.","contributorId":341550,"corporation":false,"usgs":false,"family":"Veon","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Massey, Andrhea","contributorId":341551,"corporation":false,"usgs":false,"family":"Massey","given":"Andrhea","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908597,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70267788,"text":"70267788 - 2022 - Attraction, entrance, and passage efficiency of Arctic Grayling, trout, and suckers at Denil fishways in the Big Hole River basin, Montana","interactions":[],"lastModifiedDate":"2025-06-02T15:31:35.247658","indexId":"70267788","displayToPublicDate":"2022-05-14T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Attraction, entrance, and passage efficiency of Arctic Grayling, trout, and suckers at Denil fishways in the Big Hole River basin, Montana","docAbstract":"The Big Hole River basin in southwestern Montana supports the only indigenous, self‐sustaining fluvial population of Arctic Grayling Thymallus arcticus in the conterminous United States, but the basin is fragmented by numerous low‐head irrigation diversion dams. Denil fishways at 63 diversion dams provide Arctic Grayling and other fishes opportunities for year‐round access to critical habitats; however, their efficiency has not been evaluated. We quantified attraction, entrance, and passage for hatchery‐reared Arctic Grayling, wild trout (Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta), and wild suckers (White Sucker Catostomus commersonii and Longnose Sucker C. catostomus) during 14 field trials conducted at six Denil fishways over a representative range of fishway slopes and hydraulic conditions using passive integrated transponder telemetry. Attraction (60.4–84.3%) and entrance (44.3–78.6%) efficiencies were variable across test conditions and reduced overall fishway efficiencies (19.1–55.8%). In contrast, upon entry, passage efficiencies were high (96.2–97.0%) for all taxa across all test conditions. Attraction of hatchery‐reared Arctic Grayling increased with upstream depth (a surrogate for fishway discharge) and attraction flow, but attraction of wild fish was less affected by these conditions. Entrance of Arctic Grayling, Brook Trout, and Brown Trout decreased with upstream depth and fishway slope, especially when plunging entrance conditions associated with shallow downstream depths were present. However, entrance of Arctic Grayling and both trout species increased with downstream depth, and submerged fishway entrances demonstrated promise for increasing entrance efficiency at fishways with high discharges and steep slopes. We demonstrate that comprehensive evaluations of fishway efficiency components can identify specific solutions that improve fishway efficiency; application of these engineering solutions at individual fishways (as needed) could improve their efficiency and further enhance aquatic connectivity for fishes in the Big Hole River basin and elsewhere.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/tafs.10362","usgsCitation":"Triano, B., Kappenman, K., McMahon, T., Blank, M., Heim, K., Parker, A., Zale, A.V., Platt, N., and Plymesser, K., 2022, Attraction, entrance, and passage efficiency of Arctic Grayling, trout, and suckers at Denil fishways in the Big Hole River basin, Montana: Transactions of the American Fisheries Society, v. 151, no. 4, p. 453-473, https://doi.org/10.1002/tafs.10362.","productDescription":"21 p.","startPage":"453","endPage":"473","ipdsId":"IP-132814","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":490656,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/tafs.10362","text":"Publisher Index Page"},{"id":489403,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Big Hole River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.60253591434008,\n 45.88362120565034\n ],\n [\n -112.60253591434008,\n 45.38912468392812\n ],\n [\n -112.20177404522745,\n 45.38912468392812\n ],\n [\n -112.20177404522745,\n 45.88362120565034\n ],\n [\n -112.60253591434008,\n 45.88362120565034\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Triano, Ben","contributorId":356217,"corporation":false,"usgs":false,"family":"Triano","given":"Ben","affiliations":[{"id":84938,"text":"Ecology Department","active":true,"usgs":false}],"preferred":false,"id":938890,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kappenman, Kevin M.","contributorId":356218,"corporation":false,"usgs":false,"family":"Kappenman","given":"Kevin M.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McMahon, Thomas E.","contributorId":356219,"corporation":false,"usgs":false,"family":"McMahon","given":"Thomas E.","affiliations":[{"id":84938,"text":"Ecology Department","active":true,"usgs":false}],"preferred":false,"id":938892,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blank, Matt","contributorId":356220,"corporation":false,"usgs":false,"family":"Blank","given":"Matt","affiliations":[{"id":84940,"text":"Western Transportation Institute","active":true,"usgs":false}],"preferred":false,"id":938893,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heim, Kurt C.","contributorId":356221,"corporation":false,"usgs":false,"family":"Heim","given":"Kurt C.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":938894,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parker, Albert E.","contributorId":356222,"corporation":false,"usgs":false,"family":"Parker","given":"Albert E.","affiliations":[{"id":80194,"text":"Center for Biofilm Engineering","active":true,"usgs":false}],"preferred":false,"id":938895,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zale, Alexander V. 0000-0003-1703-885X","orcid":"https://orcid.org/0000-0003-1703-885X","contributorId":244099,"corporation":false,"usgs":true,"family":"Zale","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":938889,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Platt, Nolan","contributorId":356223,"corporation":false,"usgs":false,"family":"Platt","given":"Nolan","affiliations":[{"id":36226,"text":"U.S. Department of Agriculture Forest Service","active":true,"usgs":false}],"preferred":false,"id":938896,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Plymesser, Katey","contributorId":356224,"corporation":false,"usgs":false,"family":"Plymesser","given":"Katey","affiliations":[{"id":84941,"text":"Department of Civil Engineering","active":true,"usgs":false}],"preferred":false,"id":938897,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70236468,"text":"70236468 - 2022 - Credit where credit is due","interactions":[],"lastModifiedDate":"2023-03-30T13:00:41.605746","indexId":"70236468","displayToPublicDate":"2022-05-13T16:55:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7458,"text":"Eos Science News","active":true,"publicationSubtype":{"id":10}},"title":"Credit where credit is due","docAbstract":"Credit is the currency of science. 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","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022EO220239","usgsCitation":"Parsons, M.A., Katz, D.S., Langseth, M., Ramapriyan, H., and Ramdeen, S., 2022, Credit where credit is due: Eos Science News, HTML Document, https://doi.org/10.1029/2022EO220239.","productDescription":"HTML Document","ipdsId":"IP-143013","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":447787,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022eo220239","text":"Publisher Index Page"},{"id":406349,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsons, Mark A. 0000-0002-7723-0950","orcid":"https://orcid.org/0000-0002-7723-0950","contributorId":296275,"corporation":false,"usgs":false,"family":"Parsons","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":36730,"text":"University of Alabama","active":true,"usgs":false}],"preferred":false,"id":851123,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katz, Daniel S. 0000-0001-5934-7525","orcid":"https://orcid.org/0000-0001-5934-7525","contributorId":296276,"corporation":false,"usgs":false,"family":"Katz","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":16984,"text":"University of Illinois at Urbana-Champaign","active":true,"usgs":false}],"preferred":false,"id":851124,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langseth, Madison 0000-0002-4472-9106 mlangseth@usgs.gov","orcid":"https://orcid.org/0000-0002-4472-9106","contributorId":191744,"corporation":false,"usgs":true,"family":"Langseth","given":"Madison","email":"mlangseth@usgs.gov","affiliations":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":851125,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramapriyan, Hampapuram 0000-0002-8425-8943","orcid":"https://orcid.org/0000-0002-8425-8943","contributorId":296277,"corporation":false,"usgs":false,"family":"Ramapriyan","given":"Hampapuram","email":"","affiliations":[{"id":7239,"text":"Science Systems and Applications, Inc.","active":true,"usgs":false}],"preferred":false,"id":851126,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ramdeen, Sarah 0000-0003-1135-5942","orcid":"https://orcid.org/0000-0003-1135-5942","contributorId":296278,"corporation":false,"usgs":false,"family":"Ramdeen","given":"Sarah","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":851127,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231492,"text":"sim3487 - 2022 - Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia","interactions":[],"lastModifiedDate":"2026-04-01T15:15:19.750245","indexId":"sim3487","displayToPublicDate":"2022-05-13T11:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3487","displayTitle":"Geologic Maps of the Stephenson and Winchester Quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall Quadrangles, Berkeley and Jefferson Counties, West Virginia","title":"Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia","docAbstract":"The study area consists of four contiguous 7.5-minute quadrangles and is located in Frederick and Clarke Counties, Virginia, and Berkeley and Jefferson Counties, West Virginia. The individual quadrangles are Stephenson, Winchester, Inwood, and White Hall. The study area lies within the Great Valley subprovince of the Valley and Ridge physiographic province where about 23,000 feet (ft) (7,000 meters [m]) of Middle Cambrian to Upper Devonian sedimentary rocks are exposed and are overlain by Holocene and older surficial deposits. The area of the four maps is divided into three geologic regions based on the following primary lithologies: (1) Cambrian and Ordovician carbonate rocks of the Great Valley southeast of the North Mountain fault zone and east and west of the core of the Massanutten synclinorium; (2) shale, graywacke, and calcareous shale of the Ordovician Martinsburg Formation of the Great Valley and Massanutten synclinorium; and (3) Ordovician through Devonian clastic rocks and minor limestone and dolostone northwest of and within the North Mountain fault zone. Rocks of all three regions were folded and faulted during the late Paleozoic Alleghanian orogeny (roughly 320 to 250 million years before present). The terrain of this portion of the Great Valley generally is gently to moderately rolling with low local relief with elevations in the study area ranging from about 425 ft (130 m) where Opequon Creek flows out of the eastern edge of the Inwood quadrangle to about 950 ft (290 m) adjacent to Round Hill in the western part of the Winchester quadrangle. Sinkholes and other karst features are common in the carbonate rocks of the Great Valley. The area west of the North Mountain fault zone is underlain by middle Paleozoic strata and consists of a series of ridges and valleys with higher local relief, with elevations ranging from about 785 ft (240 m) in the vicinity of Green Spring in the central part of the White Hall quadrangle to about 1,435 ft (437 m) at the summit of North Mountain in the northeastern part of the White Hall quadrangle.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3487","usgsCitation":"Weary, D.J., Doctor, D.H., and Orndorff, R.C., 2022, Geologic maps of the Stephenson and Winchester quadrangles, Frederick and Clarke Counties, Virginia, and Inwood and White Hall quadrangles, Berkeley and Jefferson Counties, West Virginia: U.S. Geological Survey Scientific Investigations Map 3487, 4 sheets, scale 1:24,000, 33-p. pamphlet, https://doi.org/10.3133/sim3487.","productDescription":"Pamphlet: viii, 33 p.; 4 Sheets: 28.00 x 42.00 inches or smaller; Base Map; Metadata; Database; Read Me","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-009285","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501931,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113056.htm","linkFileType":{"id":5,"text":"html"}},{"id":400509,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_openaccess.zip","text":"Open Access","size":"10.5 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400508,"rank":7,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_basemaps.zip","text":"Base Maps","size":"540 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400507,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_mapsheets.zip","text":"Map Sheets 1–4","size":"604 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400502,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3487/coverthb3.jpg"},{"id":400505,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_metadata.zip","text":"Metadata","size":"108 KB","linkFileType":{"id":6,"text":"zip"}},{"id":400506,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_readme.txt","text":"Read Me","size":"8.50 KB","linkFileType":{"id":2,"text":"txt"}},{"id":400504,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_database.zip","text":"Database","size":"36.2 MB","linkFileType":{"id":6,"text":"zip"}},{"id":400503,"rank":2,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sim/3487/sim3487_pamphlet.pdf","text":"Pamphlet","size":"9.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3487"}],"country":"United States","state":"Virginia, West Virginia","county":"Berkeley County, Clarke County, Frederick County, Jefferson County","otherGeospatial":"Inwood, Stephenson, White Hall and Winchester quadrangles","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.25,\n 39.375\n ],\n [\n -78,\n 39.375\n ],\n [\n -78,\n 39.125\n ],\n [\n -78.25,\n 39.125\n ],\n [\n -78.25,\n 39.375\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
U.S. Geological Survey
926A National Center
12201 Sunrise Valley Drive
Reston, VA 20192
To examine controls on the local stress field at Augustine Volcano, Alaska, before its 2006 eruption, we calculated fault plane solutions for volcano-tectonic earthquakes from 2002 to 2006. The P-axis orientation was first aligned to the regional maximum compression (NW) and then rotated by about 90° (perpendicular to the dike alignment) after the onset of surface deformation in mid-August 2005. Using 3D finite element models, we systematically evaluated the effects of tectonic stresses, volcanic edifice densities, and dike overpressures on the local stress field orientation. Combining data and models to generate “phase diagrams” of different stress controls by these competing effects, we argue that moderate tectonic stress of 2–3 MPa at 600 m above sea level slightly exceeded the edifice loading before the precursory deformation and was then overprinted by a local stress field from dike opening with an overpressure of ~15 MPa.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL097958","usgsCitation":"Zhan, Y., Roman, D., Le Mevel, H., and Power, J., 2022, Earthquakes indicated stress field change during the 2006 unrest of Augustine Volcano, Alaska: Geophysical Research Letters, v. 49, e2022GL097958, 9 p., https://doi.org/10.1029/2022GL097958.","productDescription":"e2022GL097958, 9 p.","ipdsId":"IP-137090","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":463353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Augustine Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -153.5968722856842,\n 59.431514142471286\n ],\n [\n -153.5968722856842,\n 59.29604332497132\n ],\n [\n -153.3209313297999,\n 59.29604332497132\n ],\n [\n -153.3209313297999,\n 59.431514142471286\n ],\n [\n -153.5968722856842,\n 59.431514142471286\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationDate":"2022-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhan, Yan","contributorId":345673,"corporation":false,"usgs":false,"family":"Zhan","given":"Yan","email":"","affiliations":[{"id":82691,"text":"Carnegie Institution for Science, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":917229,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":917230,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Le Mevel, Helene","contributorId":345674,"corporation":false,"usgs":false,"family":"Le Mevel","given":"Helene","affiliations":[{"id":82691,"text":"Carnegie Institution for Science, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":917231,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917232,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231610,"text":"70231610 - 2022 - OPERA Product Description Document","interactions":[],"lastModifiedDate":"2022-05-31T16:21:24.236411","indexId":"70231610","displayToPublicDate":"2022-05-13T11:00:22","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesNumber":"JPL D-107389 Rev. A","title":"OPERA Product Description Document","docAbstract":"No abstract available.
","language":"English","publisher":"NASA","usgsCitation":"Bato, G., Bekaert, D., Brancato, V., Fattahi, H., Hansen, M., Jones, J., Logan, T., Lu, Z., Marshak, C., Meyer, F., Pikens, A., and Shiroma, G., 2022, OPERA Product Description Document, 33 p.","productDescription":"33 p.","ipdsId":"IP-137221","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":401372,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401371,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.jpl.nasa.gov/go/opera/products"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bato, Grace","contributorId":292158,"corporation":false,"usgs":false,"family":"Bato","given":"Grace","email":"","affiliations":[],"preferred":false,"id":843941,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bekaert, David 0000-0002-1062-6221","orcid":"https://orcid.org/0000-0002-1062-6221","contributorId":270718,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":843942,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brancato, Virginiia","contributorId":292159,"corporation":false,"usgs":false,"family":"Brancato","given":"Virginiia","email":"","affiliations":[],"preferred":false,"id":843943,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fattahi, Heresh","contributorId":292160,"corporation":false,"usgs":false,"family":"Fattahi","given":"Heresh","email":"","affiliations":[],"preferred":false,"id":843944,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Matt","contributorId":61330,"corporation":false,"usgs":true,"family":"Hansen","given":"Matt","email":"","affiliations":[],"preferred":false,"id":843945,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":843108,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Logan, Thomas","contributorId":292161,"corporation":false,"usgs":false,"family":"Logan","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":843946,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":843947,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Marshak, Charles","contributorId":292162,"corporation":false,"usgs":false,"family":"Marshak","given":"Charles","email":"","affiliations":[],"preferred":false,"id":843948,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Meyer, Franz","contributorId":219958,"corporation":false,"usgs":false,"family":"Meyer","given":"Franz","affiliations":[{"id":40098,"text":"Geophysical Institute, 2156 Koyukuk Drive, University of Alaska Fairbanks, Fairbanks, AK 99775","active":true,"usgs":false}],"preferred":false,"id":843949,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pikens, Amy","contributorId":292163,"corporation":false,"usgs":false,"family":"Pikens","given":"Amy","email":"","affiliations":[],"preferred":false,"id":843950,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shiroma, Gustavo","contributorId":292164,"corporation":false,"usgs":false,"family":"Shiroma","given":"Gustavo","email":"","affiliations":[],"preferred":false,"id":843951,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70231597,"text":"70231597 - 2022 - Value of information: Exploring behavioral and social factors","interactions":[],"lastModifiedDate":"2022-05-17T13:31:49.83722","indexId":"70231597","displayToPublicDate":"2022-05-13T08:28:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Value of information: Exploring behavioral and social factors","docAbstract":"There is growing interest within and beyond the economics community in assessing the value of information (VOI) used in decision making. VOI assessments often do not consider the complex behavioral and social factors that affect the perception, valuation, and use of information by individuals and groups. Additionally, VOI assessments frequently do not examine the full suite of interactions and outcomes affecting different groups or individuals. The behavioral and social factors that we mention are often (but not always) innately-derived, less-than-conscious influences that reflect human and societal adaptations to the past. We first discuss these concepts in the context of the recognition and use of information for decision making. We then find fifteen different aspects of value and information pertinent to VOI assessments. We examine methodologies and issues related to current VOI estimation practices in economics. Building on this examination, we explore the perceptions, social factors, and behavioral factors affecting information sharing, prioritization, valuation, and discounting. Information and valuation issues are then considered in the context of information production, information trading and controls, and information communication pathologies. Lastly, we describe issues relating to information useability and actionability. Our examples mention the value and use of geospatial information, and more generally concern societal issues relating to the management of natural resources, environments, and natural and anthropogenic hazards. Our paper aims to be instrumentally relevant to anyone interested in the use and value of science.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fenvs.2022.805245","usgsCitation":"Glynn, P.D., Chiavacci, S.J., Rhodes, C., Helgeson, J., Shapiro, C.D., and Straub, C.L., 2022, Value of information: Exploring behavioral and social factors: Frontiers in Environmental Science, v. 10, 805245, 21 p., https://doi.org/10.3389/fenvs.2022.805245.","productDescription":"805245, 21 p.","ipdsId":"IP-137172","costCenters":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"links":[{"id":447790,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fenvs.2022.805245","text":"Publisher Index Page"},{"id":400693,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2022-05-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Glynn, Pierre D. 0000-0001-8804-7003 pglynn@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":2141,"corporation":false,"usgs":true,"family":"Glynn","given":"Pierre","email":"pglynn@usgs.gov","middleInitial":"D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":843095,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chiavacci, Scott J. 0000-0003-3579-8377","orcid":"https://orcid.org/0000-0003-3579-8377","contributorId":206161,"corporation":false,"usgs":true,"family":"Chiavacci","given":"Scott","email":"","middleInitial":"J.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":843096,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rhodes, Charles 0000-0002-9040-3684","orcid":"https://orcid.org/0000-0002-9040-3684","contributorId":245881,"corporation":false,"usgs":true,"family":"Rhodes","given":"Charles","email":"","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":843097,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helgeson, Jennifer 0000-0002-3692-7874","orcid":"https://orcid.org/0000-0002-3692-7874","contributorId":291799,"corporation":false,"usgs":false,"family":"Helgeson","given":"Jennifer","email":"","affiliations":[{"id":25356,"text":"National Institute of Standards and Technology","active":true,"usgs":false}],"preferred":false,"id":843098,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shapiro, Carl D. 0000-0002-1598-6808 cshapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-1598-6808","contributorId":3048,"corporation":false,"usgs":true,"family":"Shapiro","given":"Carl","email":"cshapiro@usgs.gov","middleInitial":"D.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":843099,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Straub, Crista L. 0000-0001-7828-3328","orcid":"https://orcid.org/0000-0001-7828-3328","contributorId":219353,"corporation":false,"usgs":true,"family":"Straub","given":"Crista","email":"","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":843100,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237692,"text":"70237692 - 2022 - Evaluating aromatization of solid bitumen generated in the presence and absence of water: Implications for solid bitumen reflectance as a thermal proxy","interactions":[],"lastModifiedDate":"2022-10-19T13:22:32.108904","indexId":"70237692","displayToPublicDate":"2022-05-13T08:20:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating aromatization of solid bitumen generated in the presence and absence of water: Implications for solid bitumen reflectance as a thermal proxy","docAbstract":"Geological models for petroleum generation suggest thermal conversion of oil-prone sedimentary organic matter in the presence of water promotes increased liquid saturate yield, whereas absence of water causes formation of an aromatic, cross-linked solid bitumen residue. To test the influence of hydrogen from water, organic-rich (22 wt% total organic carbon, TOC) mudrock samples from the Eocene lacustrine Green River Formation Mahogany zone oil shale were pyrolyzed under hydrous and anhydrous conditions in closed system batch reactors at temperatures between 300 and 370 °C for 72 h. Pre- and post-pyrolysis samples were characterized using petrographic approaches including optical microscopy, reflectance, Raman spectroscopy, and scanning electron and transmission electron microscopy to quantify differences in relative appearance, abundance, and composition of solid bitumen newly generated during the pyrolysis experiments. Petrographic analyses were supplemented by geochemical screening measurements (TOC content and programmed temperature pyrolysis). Results show post-hydrous pyrolysis residues contain lower TOC, are comprised of solid bitumen with greater aromaticity, and have textures indicative of lower viscosities, relative to anhydrous residues from the same temperature pyrolysis conditions. These observations suggest solid bitumen forming from thermal conversion of oil-prone sedimentary organic matter under anhydrous conditions may be less aromatic, although more cross-linked, than solid bitumen forming under hydrous conditions at the same time-temperature combination. To explain these results, we suggest that a radical disproportionation mechanism is favored in the presence of hydrogen donated from water, and that this disproportionation promotes aromatization in the solid residue with concomitant expulsion of saturated hydrocarbons.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104016","usgsCitation":"Hackley, P.C., Jubb, A., Smith, P.L., McAleer, R.J., Valentine, B.J., Hatcherian, J.J., Botterell, P.J., and Birdwell, J.E., 2022, Evaluating aromatization of solid bitumen generated in the presence and absence of water: Implications for solid bitumen reflectance as a thermal proxy: International Journal of Coal Geology, v. 258, 104016, 16 p., https://doi.org/10.1016/j.coal.2022.104016.","productDescription":"104016, 16 p.","ipdsId":"IP-136449","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447793,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.coal.2022.104016","text":"Publisher Index Page"},{"id":408537,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"258","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Patrick L.","contributorId":298071,"corporation":false,"usgs":false,"family":"Smith","given":"Patrick","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":855032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":855033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Valentine, Brett J. 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":3846,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":855034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Botterell, Palma J. 0000-0001-7140-0915 pjarboe@usgs.gov","orcid":"https://orcid.org/0000-0001-7140-0915","contributorId":5805,"corporation":false,"usgs":true,"family":"Botterell","given":"Palma","email":"pjarboe@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855036,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":855037,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231655,"text":"70231655 - 2022 - Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: An example using freshwater mussels in México","interactions":[],"lastModifiedDate":"2022-05-19T12:22:18.441838","indexId":"70231655","displayToPublicDate":"2022-05-13T07:20:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Using a multi-model ensemble approach to determine biodiversity hotspots with limited occurrence data in understudied areas: An example using freshwater mussels in México","docAbstract":"Species distribution models (SDMs) are an increasingly important tool for conservation particularly for difficult-to-study locations and with understudied fauna. Our aims were to (1) use SDMs and ensemble SDMs to predict the distribution of freshwater mussels in the Pánuco River Basin in Central México; (2) determine habitat factors shaping freshwater mussel occurrence; and (3) use predicted occupancy across a range of taxa to identify freshwater mussel biodiversity hotspots to guide conservation and management. In the Pánuco River Basin, we modeled the distributions of 11 freshwater mussel species using an ensemble approach, wherein multiple SDM methodologies were combined to create a single ensemble map of predicted occupancy. A total of 621 species-specific observations at 87 sites were used to create species-specific ensembles. These predictive species ensembles were then combined to create local diversity hotspot maps. Precipitation during the warmest quarter, elevation, and mean temperature were consistently the most important discriminatory environmental variables among species, whereas land use had limited influence across all taxa. To the best of our knowledge, our study is the first freshwater mussel-focused research to use an ensemble approach to determine species distribution and predict biodiversity hotspots. Our study can be used to guide not only current conservation efforts but also prioritize areas for future conservation and study.
Prescribed fire in dry coniferous forests of the western U.S. is used to reduce fire hazards. How large, old trees respond to these treatments is an important management consideration. Growth is a key indicator of residual tree condition, which can be predictive of mortality and response to future disturbance. Using a combination of long-term plot records and dendrochronological samples, we analyzed the effects of prescribed fire treatments from the early 1990 s on forest structure and individual tree growth in mixed-conifer forests of Lassen Volcanic National Park in northern California. Prescribed fire reduced stand live tree basal area and stem density at our sites up to 10 years following fire. Within two prescribed fire burn units and two adjacent unburned stands, we analyzed tree cores from 136 large (mean stem diameter > 70 cm) yellow pine (Pinus jeffreyi and P. ponderosa) and 136 large (mean stem diameter > 50 cm) white fir (Abies concolor). After accounting for annual precipitation, basal area increment for individual trees initially declined up to < 3 years post-fire for white fir and > 10 years post-fire for yellow pine, presumably in response to tree injuries. Growth improved for both species at a site that was burned twice, particularly for white fir. Recent average basal area increment was positively related to crown ratio and negatively associated with an index of local competition. Our findings suggest that forest management, such as prescribed fire and mechanical thinning, may be beneficial in terms of maintaining or improving tree growth among large residual trees. However, managers may want to balance the benefits of these treatments against inadvertent injury and mortality of large trees.
Green Lake, a deep mesotrophic lake located in a primarily agricultural watershed in central Wisconsin, USA, has experienced annual metalimnetic oxygen minima since the early 20th century. However, the severity of the phenomenon has increased over time, and late-summer dissolved oxygen (DO) concentrations have typically been <3 mg L−1 in recent years. In situ, high-frequency observations of oxygen depletion at multiple depths reveal that while DO consumption during stratification occurs most rapidly in the metalimnion, there is synchrony between DO time series extending into the hypolimnion. A biochemical oxygen demand-based modeling approach suggests that much of the relationship between water depth and respiration rates can be explained by differences in water temperature. The amount of labile organic matter present throughout the water column at the onset of stratification seems to be a primary determinant of the severity of the annual metalimnetic DO minimum in late summer. Productivity has increased in the lake as a result of increased nutrient loading and is the likely driver of the decrease in minimum DO concentrations. In addition, the onset and duration of stratification is an important factor in determining the severity of the DO minimum.
Incorporating user experience (UX) testing when creating research cyberinfrastructure is often overlooked, but if left too late, the cost of retrofitting is considerable, and the very clients the cyberinfrastructure was built to serve may be lost. Successfully integrating UX testing into the product development cycle can be difficult but rewarding. This paper describes how UX evaluations were incorporated over ten years of operation of DataONE (www.dataone.org), a multi-sector science research cyberinfrastructure project created to support the discovery, access, and sustainability of data about life on Earth and the environment that sustains it. The diverse stakeholders in DataONE include data creators and users such as researchers and government workers across the broad scope of the earth and environmental sciences as well as those who hold and manage data such as libraries and data repositories. Between 2009 and 2019 DataONE members designed and constructed data management tools and services to fulfill the DataONE objectives. To assist in achieving its goals, a participatory design approach was used by establishing several largely volunteer and stakeholder-representative working groups, including the Usability and Assessment Working Group. This Working Group conducted over forty UX evaluations to assess the usability of DataONE products and websites at various stages of the development process. In addition to improving the usability of DataONE products, the UX evaluations fostered community involvement by building trust and engagement with the products being developed. The DataONE UX experience yields several important lessons which will improve the success of other projects. It is our conclusion that UX testing should be a mandatory part of the design of any cyberinfrastructure project.
Effective conservation requires understanding species' abundance patterns and demographic rates across space and time. Ideally, such knowledge should be available for whole communities, as variation in species' dynamics can elucidate factors leading to biodiversity losses. However, collecting data to simultaneously estimate abundance and demographic rates is often prohibitively time-intensive and expensive for communities of species. We developed a “multi-species dynamic N-occupancy model” to estimate unbiased, community-wide relative abundance and demographic rates. Our model uses detection-nondetection data (e.g., repeated presence-absence surveys) to estimate both species- and community-level parameters as well as the effects of environmental factors. We conducted a simulation study that validated our modeling framework, demonstrating how and when such an approach can be valuable. Using data from a network of camera traps across tropical equatorial Africa, we then used our model to evaluate the statuses and trends of a forest-dwelling antelope community. We estimated relative abundance, rates of recruitment (i.e., reproduction and immigration), and apparent survival probabilities for each species' local population. Our analysis indicated that the antelope community was fairly stable in this region (although 17% of populations [species-park combinations] declined over the study period), with variation in apparent survival linked more closely to differences among national parks rather than individual species' life histories. The multi-species dynamic N-occupancy model requires only detection-nondetection data to evaluate the population dynamics of multiple sympatric species and can thus be a valuable tool for conservation efforts seeking to understand the reasons behind recent biodiversity loss.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.13934","usgsCitation":"Farr, M.T., O’Brien, T.O., Yackulic, C., and Zipkin, E.F., 2022, Quantifying the conservation status and abundance trends of wildlife communities with detection-nondetection data: Conservation Biology, v. 36, no. 6, e13934, 11 p., https://doi.org/10.1111/cobi.13934.","productDescription":"e13934, 11 p.","ipdsId":"IP-131250","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447800,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.13934","text":"Publisher Index Page"},{"id":402056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-08-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Farr, Matthew T","contributorId":292414,"corporation":false,"usgs":false,"family":"Farr","given":"Matthew","email":"","middleInitial":"T","affiliations":[{"id":62897,"text":"Dept. of Integrative Biology, Michigan State University, East Lansing, MI 48824; Ecology, Evolution, and Behavior Program, Michigan State University, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":844497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Timothy O","contributorId":292415,"corporation":false,"usgs":false,"family":"O’Brien","given":"Timothy","email":"","middleInitial":"O","affiliations":[{"id":62898,"text":"Wildlife Conservation Society, Global Conservation Program, Bronx, NY","active":true,"usgs":false}],"preferred":false,"id":844498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":844499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zipkin, Elise F. 0000-0003-4155-6139","orcid":"https://orcid.org/0000-0003-4155-6139","contributorId":192755,"corporation":false,"usgs":false,"family":"Zipkin","given":"Elise","email":"","middleInitial":"F.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":844500,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252112,"text":"70252112 - 2022 - Evaluation for internal consistency in the thermodynamic network involving fluorite, cryolite and villiaumite solubilities and aqueous species at 25°C and 1 bar","interactions":[],"lastModifiedDate":"2024-03-14T11:45:36.5056","indexId":"70252112","displayToPublicDate":"2022-05-13T06:43:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2748,"text":"Mineralogical Magazine","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation for internal consistency in the thermodynamic network involving fluorite, cryolite and villiaumite solubilities and aqueous species at 25°C and 1 bar","docAbstract":"Thermodynamic data are constrained by the interrelated thermodynamic equations in addition to the observational measurements and their uncertainties. The consequence is a network of thermodynamic properties that can be evaluated for their internal consistency. In this study, three fluoride minerals that can cause high fluoride concentrations in groundwaters are evaluated for their solubilities and their internal thermodynamic consistency with calorimetric, isopiestic and electrochemical measurements: fluorite, CaF2, cryolite, Na3AlF6, and villiaumite, NaF. This evaluation involves the three solids and 13 aqueous species, the free ions of Ca2+, Na+, Al3+ and F–, and the hydroxido and fluorido complexes of Al3+, and the CaF+ ion pair. For the fluorite–cryolite–villiaumite–aqueous species network, the number of components is minimal, and the solubility studies are mostly of high quality. Re-evaluations of original data using PHREEQC helps to broaden the quantitative evaluation of thermodynamic properties and to resolve apparent discrepancies. A check on this thermodynamic network shows that through a careful appraisal of the literature, a highly consistent set of values can be derived. The resultant infinite-dilution solubility-product constants at 25°C and 1 bar are: for fluorite solubility, logKsp = –10.57 ± 0.08; for cryolite solubility, logKsp = –33.9 ± 0.2; and for villiaumite solubility, logKsp = –0.4981 ± 0.003.
The role of hybridization in diversification is complex and may result in many possible outcomes. Not only can hybridization produce new lineages, but those lineages may contain unique combinations of adaptive genetic variation derived from parental taxa that allow hybrid-origin lineages to occupy unique environmental space relative to one (or both) parents. We document such a case of hybridization between two sedge species, Carex nova and Carex nelsonii (Cyperaceae), that occupy partially overlapping environmental space in the southern Rocky Mountains, USA. In the region hypothesized to be the origin of the hybrid lineage, one parental taxon (C. nelsonii) is at the edge of its environmental tolerance. Hybrid-origin individuals display mixed ancestry between the parental taxa – of nearly 7,000 unlinked loci sampled, almost 30% showed evidence of excess ancestry from one parental lineage – approximately half displayed a genomic background skewed towards one parent, and half skewed towards the other. To test whether excess ancestry loci may have conferred an adaptive advantage to the hybrid-origin lineage, we conducted genotype-environment association analyses on different combinations of loci – with and without excess ancestry – and with multiple contrasts between the hybrids and parental taxa. Loci with skewed ancestry showed significant environmental associations distinguishing the hybrid lineage from one parent (C. nelsonii), whereas loci with relatively equal representation of parental ancestries showed no such environmental associations. Moreover, the overwhelming majority of candidate adaptive loci with respect to environmental gradients also had excess ancestry from a parental lineage, implying these loci have facilitated the persistence of the hybrid lineage in an environment unsuitable to at least one parent.
The U.S. Geological Survey (USGS) Central Midwest Water Science Center (CMWSC) includes three States—Illinois, Iowa, and Missouri. USGS water science centers across the Nation provide information on water resources including streamflow, water use, water availability, and the quality of surface water and groundwater (https://www.usgs.gov/mission-areas/water-resources).
The USGS CMWSC Harmful Algal Blooms (HABs) team is dedicated to studying the complexity of HABs and is currently (2021) researching ways to better predict the timing, magnitude, and toxicity of HABs. Updated information about the HABs team including current projects, data releases, and publications are available on the CMWSC website (https://www.usgs.gov/centers/cm-water/science-topics/harmful-algal-blooms).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223011","usgsCitation":"Summers, K.M., Krempa, H.M., and Garrett, J.D., 2022, Central Midwest Water Science Center— Harmful Algal Blooms team: U.S. Geological Survey Fact Sheet, 2022–3011, 4 p., https://doi.org/10.3133/fs20223011.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-132581","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":400625,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223011/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":400591,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3011/images"},{"id":400590,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3011/fs20223011.XML"},{"id":400589,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3011/fs20223011.pdf","text":"Report","size":"7.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3011"},{"id":400588,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3011/coverthb.jpg"}],"contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin
Urbana, IL 61801
The alteration of thermal regimes, including increased temperatures and shifts in seasonality, is a key challenge to the health and survival of federally protected cold-water salmonids in streams of the Willamette River basin in northwestern Oregon. To better support threatened fish species, the U.S. Army Corps of Engineers (USACE) and other water managers seek to improve the thermal regime in the Willamette River and key tributaries downstream of USACE dams by utilizing strategically timed flow releases from USACE dams. To inform flow management decisions, regression relations were developed for 12 Willamette River basin locations below USACE dams relating stream temperature with streamflow and air temperature utilizing publicly available datasets spanning 2000–18. The resulting relations provide simple tools to investigate stream temperature responses to changes in streamflow and climatic conditions in the Willamette River system.
Regression relations on the Willamette River and key tributaries show that, at locations sufficiently distant from the direct temperature influence of upstream dam releases, air temperature and streamflow are reasonable proxies to predict the 7-day average of the daily mean (7dADMean) and 7-day average of the daily maximum (7dADMax) water temperature with errors generally ≤1 degrees Celsius (°C). To account for seasonal variations in the relation between air temperature, streamflow, and stream temperature, a transition-smoothed, seasonal regression approach was used. Stream temperature is inversely correlated with streamflow in all seasons except “winter” (January–March), when it is relatively independent. Stream temperature is positively correlated with air temperature in all seasons, but the slope decreases at very low or very high air temperatures. Generally, fit is best for seasonal models “winter” (January–March), “spring” (April–May), “summer” (June–August), and “early autumn” (September–October). Error in “autumn” (November–December) is larger, probably due to variation in the onset timing of winter storms.
Simulated results from a climatological analysis of predicted stream temperature suggest that, excluding extremes and accounting for some seasonal variability, the 7dADMean and 7dADMax stream temperature sensitivity to air temperature and streamflow varies by location on the river. To investigate the potential range of stream temperature variability based on historical air temperature and streamflow conditions, stream temperature predictions were calculated using synthetic time series comprised of daily temperature values representing the 0.10, 0.33, 0.50, 0.67, and 0.90 quantile of air temperature and streamflow from 1954 (the year meaningful streamflow augmentation began) to 2018. Results show that from a “very hot” (0.90 quantile) and “very dry” (0.10 quantile) year to a “very cool” (0.10 quantile) and “very wet” (0.90; all quantiles from 1954 to 2018) year, the stream temperature sensitivity to air temperature and streamflow is about 3 °C at Harrisburg (river mile 161.0) and increases to about 5 °C at Keizer (river mile 82.2). While the number of days exceeding regulatory criteria are fewer in cooler, wetter years than in warmer, dryer years, the models suggest that the Willamette River will likely continue to exceed the State of Oregon maximum water-temperature criterion of 18 °C for sustained periods from late spring to early autumn and that the flow management practices evaluated in this study, while effective at influencing stream temperature, likely cannot prevent many or all such exceedances.
As modeled for 2018, a representative very hot year with normal to below-normal streamflow, stream temperature sensitivity to changes in streamflow of ±100 to ±1000 cubic feet per second produced mean monthly temperature changes from 0.0 to 1.4 °C at Keizer, Albany, and Harrisburg during summer. For a specified change in flow, temperature sensitivity is greater at upstream locations where streamflow is less than that at downstream locations because the change in streamflow is a greater percentage of total streamflow at upstream locations. Similarly, temperature response to a set change in flow is greater in the summer and early autumn low-flow season than in spring when flows are higher. The regression models developed in this study thus indicate that flow management is likely to have a greater effect on stream temperature at upstream locations (such as Harrisburg or Albany) and during the low-flow season than at downstream locations (such as Keizer) or during periods of higher streamflow.
","largerWorkType":{"id":18,"text":"Report"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215022","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Portland District","usgsCitation":"Stratton Garvin, L.E., Rounds, S.A., and Buccola, N.L., 2022, Estimating stream temperature in the Willamette River Basin, northwestern Oregon—A regression-based approach: U.S. Geological Survey Scientific Investigations Report 2021–5022, 40 p., https://doi.org/10.3133/sir20215022.","productDescription":"Report: viii, 40 p.; Data Release","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-119336","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":501948,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113055.htm","linkFileType":{"id":5,"text":"html"}},{"id":400563,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PALKQZ","text":"USGS Data Release","description":"Stratton Garvin, L.E., 2022, Stream temperature predic tions for the Willamette River Basin, northwestern Oregon estimated from regression equations (1954–2018): U.S. Geological Survey data release, https://doi.org/10.5066/P9PALKQZ.","linkHelpText":"Stream temperature predictions for the Willamette River Basin, northwestern Oregon estimated from regression equations (1954–2018)"},{"id":400560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5022/sir20215022.pdf","text":"Report","size":"8.5 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":400559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5022/covrthb.jpg"},{"id":400561,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5022/sir20215022.xml"},{"id":400562,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5022/images"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64013671874999,\n 43.54854811091286\n ],\n [\n -122.18994140624999,\n 43.54854811091286\n ],\n [\n -122.18994140624999,\n 45.99696161820381\n ],\n [\n -123.64013671874999,\n 45.99696161820381\n ],\n [\n -123.64013671874999,\n 43.54854811091286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
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The 15 January 2022 climactic eruption of Hunga volcano, Tonga, produced an explosion in the atmosphere of a size that has not been documented in the modern geophysical record. The event generated a broad range of atmospheric waves observed globally by various ground-based and spaceborne instrumentation networks. Most prominent was the surface-guided Lamb wave (≲0.01 hertz), which we observed propagating for four (plus three antipodal) passages around Earth over 6 days. As measured by the Lamb wave amplitudes, the climactic Hunga explosion was comparable in size to that of the 1883 Krakatau eruption. The Hunga eruption produced remarkable globally detected infrasound (0.01 to 20 hertz), long-range (~10,000 kilometers) audible sound, and ionospheric perturbations. Seismometers worldwide recorded pure seismic and air-to-ground coupled waves. Air-to-sea coupling likely contributed to fast-arriving tsunamis. Here, we highlight exceptional observations of the atmospheric waves.
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This chapter introduces key elements and core skills for successful cross-sectoral and transdisciplinary leadership that will help wildlife health practitioners effectively lead change toward integrated, mutually beneficial health outcomes for all sectors. Leading change benefits from having good individual leadership skills, including emotional intelligence or the capacity to be aware of, control, and express one’s emotions, and to handle interpersonal relationships judiciously and empathetically. Driving multi-sectoral change is facilitated by wildlife health professionals becoming champions for change and being empowered to form and lead teams and participate effectively on governance structures such as interagency committees or working groups. Finally, the four main elements in leading change are envisioning the future state, engaging key stakeholders and coalition building, identifying barriers and breaking down resistance to change, and institutionalizing change. Effective leadership can be enabled by being open to new ideas, constantly questioning the effectiveness of one’s approach, and a commitment to lifelong learning. Application of these leadership skills and approaches can help effect change, resulting in improved wildlife population health.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wildlife population health","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer Link","doi":"10.1007/978-3-030-90510-1_22","usgsCitation":"Machalaba, C., and Sleeman, J.M., 2022, Leading change with diverse stakeholders, chap. 22 of Wildlife population health, p. 227-237, https://doi.org/10.1007/978-3-030-90510-1_22.","productDescription":"11 p.","startPage":"227","endPage":"237","ipdsId":"IP-131023","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":400882,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Machalaba, Catherine","contributorId":291948,"corporation":false,"usgs":false,"family":"Machalaba","given":"Catherine","email":"","affiliations":[{"id":62789,"text":"EcoHealth Alliance, New York, New York","active":true,"usgs":false}],"preferred":false,"id":843458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":843459,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232161,"text":"70232161 - 2022 - Flight characteristics forecast entry by eagles into rotor-swept zones of wind turbines","interactions":[],"lastModifiedDate":"2022-09-27T16:44:47.037239","indexId":"70232161","displayToPublicDate":"2022-05-12T08:27:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Flight characteristics forecast entry by eagles into rotor-swept zones of wind turbines","docAbstract":"Operators of wind power facilities can mitigate wildlife mortality by slowing or stopping wind turbines (hereafter ‘curtail’) when birds are at an increased risk of collision. Some facility operators curtail when individual birds have flight characteristics (e.g. altitude, distance or relative bearing of a bird's flight path) that exceed some threshold value, but thresholds currently in use have not been empirically evaluated. Overly restrictive thresholds can cause turbine curtailment for birds that never enter rotor-swept zones, thereby resulting in excess power loss. We evaluated the probability that birds, specifically eagles, entered the rotor-swept zone (hereafter ‘entry probability’) in response to their flight characteristics. We used an automated monitoring system to classify individuals as eagles or non-eagles and record flight paths of purported eagles at a wind facility in Wyoming, USA. We used logistic regression with occupancy dynamics and a distance-dependent colonization process to model entry probability. As a result, this model allowed entry probability to decrease with horizontal distance to the nearest turbine. The probability of entry varied with distance to the nearest turbine and approached zero when that distance was more than 202 m. Entry probability peaked when eagles flew 89 m above ground, corresponding to hub heights of turbines (80 m), and decreased to near-zero at altitudes of 189 m or more. Entry probabilities were greatest when flight paths were near the rotor-swept zone and when eagles flew slowly toward the nearest turbine. Compass bearing of a flight path was not associated with entry probability. Our model accurately forecasted entry probability in Wyoming (area under the curve (AUC) = 0.96) and was transferable to another facility in California, USA (AUC = 0.97); therefore, our results may be applicable across a variety of settings. Curtailment criteria can be based on flight path characteristics to forecast entry into rotor-swept zones. The use of distance and altitude thresholds when making curtailment decisions is justified. However, this analysis suggests alteration of the time to collision threshold, with curtailment initiated at greater distances as the speed of the bird decreases. Our novel modelling method and our results can inform curtailment criteria in any situation where curtailment decisions are made in real-time.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.13076","usgsCitation":"Rolek, B.W., Braham, M., Miller, T.A., Duerr, A.E., Katzner, T., McCabe, J.D., Dunn, L., and McClure, C.J., 2022, Flight characteristics forecast entry by eagles into rotor-swept zones of wind turbines: Ibis, v. 164, no. 4, p. 968-980, https://doi.org/10.1111/ibi.13076.","productDescription":"13 p.","startPage":"968","endPage":"980","ipdsId":"IP-136356","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447812,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ibi.13076","text":"Publisher Index Page"},{"id":401974,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","county":"Converse 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Brian W.","contributorId":200318,"corporation":false,"usgs":false,"family":"Rolek","given":"Brian","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":844398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braham, Melissa A.","contributorId":140127,"corporation":false,"usgs":false,"family":"Braham","given":"Melissa A.","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":844399,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Tricia A.","contributorId":190591,"corporation":false,"usgs":false,"family":"Miller","given":"Tricia","email":"","middleInitial":"A.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":844400,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duerr, Adam E.","contributorId":190590,"corporation":false,"usgs":false,"family":"Duerr","given":"Adam","email":"","middleInitial":"E.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia University","active":true,"usgs":false}],"preferred":false,"id":844401,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":844402,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McCabe, Jennifer D.","contributorId":264224,"corporation":false,"usgs":false,"family":"McCabe","given":"Jennifer","email":"","middleInitial":"D.","affiliations":[{"id":54406,"text":"The Peregrine Fund, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":844403,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dunn, Leah","contributorId":217944,"corporation":false,"usgs":false,"family":"Dunn","given":"Leah","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844404,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McClure, Christopher J.W.","contributorId":264223,"corporation":false,"usgs":false,"family":"McClure","given":"Christopher","email":"","middleInitial":"J.W.","affiliations":[{"id":54406,"text":"The Peregrine Fund, Boise, Idaho","active":true,"usgs":false}],"preferred":false,"id":844405,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231931,"text":"70231931 - 2022 - Pollution and wildlife health","interactions":[],"lastModifiedDate":"2022-06-03T13:28:05.573706","indexId":"70231931","displayToPublicDate":"2022-05-12T08:26:55","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Pollution and wildlife health","docAbstract":"Pollution is a pervasive and growing threat to wildlife health. This chapter discusses two broad groups of pollution, those whose abatement could have immediate beneficial effects including light, air, and noise pollution, and those that will take relatively longer to address due to their environmental persistence or their continuing discharge. Whilst we are very good at detecting the presence of pollutants in tissues or the environment, making a convincing link between the presence of these compounds and mortality events in the field or population effects will remain a challenge for the foreseeable future. Creative new approaches are also being considered to mitigate the effects of pollution on wildlife and ecosystems. Depending on the source of pollution, the beneficial outcomes of mitigation measures, if properly implemented, could have immediate effects. Given the plethora of potential adverse pollution effects, frameworks to prioritize which threats are most likely to cause adverse effects and develop means to address or manage them are an imperative. In the interim, focusing on preserving existing habitats and reducing our footprint by adjusting human activities to minimize the release of pollutants into the environment will go a long way toward promoting healthy wildlife and ecosystems.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Wildlife population health","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-90510-1_16","usgsCitation":"Work, T.M., 2022, Pollution and wildlife health, chap. of Wildlife population health, p. 177-186, https://doi.org/10.1007/978-3-030-90510-1_16.","productDescription":"10","startPage":"177","endPage":"186","ipdsId":"IP-129951","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":401675,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2022-05-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Work, Thierry M. 0000-0002-4426-9090 thierry_work@usgs.gov","orcid":"https://orcid.org/0000-0002-4426-9090","contributorId":1187,"corporation":false,"usgs":true,"family":"Work","given":"Thierry","email":"thierry_work@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":844141,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70231653,"text":"70231653 - 2022 - Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity","interactions":[],"lastModifiedDate":"2022-08-15T13:51:56.290092","indexId":"70231653","displayToPublicDate":"2022-05-12T07:23:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity","docAbstract":"1. Autonomous Recording Units (ARUs) are now widely used to survey communities of species. These surveys generate spatially and temporally replicated counts of unmarked animals, but such data typically include false negatives and misclassified detections, both of which may vary across sites in proportion to abundance. These data challenges can bias estimates of occupancy, and the typical approach of verifying individual detections is expensive.
2. We developed a Bayesian implementation of a two-species, false-positive N-mixture model for estimating occupancy from ARU data or other counts of unmarked animals that does not require manual verification. The model accounts for species misclassification and abundance-induced detection heterogeneity, as well as false negatives. To evaluate this model, we simulated 200 data sets for each of 29 scenarios, including scenarios in which misclassifications outnumbered correct classifications for rare species. We also applied the model to acoustic surveys of bats conducted on Fort Carson Army Post and Piñon Canyon Maneuver Site, Colorado, USA.
3. In the simulation study, bias, coverage, and root mean square error for occupancy estimates obtained from the two-species false-positive N-mixture model were superior to metrics obtained from two competing two-species false-positive occupancy models. Across 29 scenarios, absolute bias was consistently low (range: -0.03–0.07), while coverage averaged 93% (range: 74%–98%). For alternative occupancy models, absolute bias was often high (range: -0.36–0.39), and coverage averaged from 47%–65%. Although our model included an abundance parameter, abundance estimates were not reliable. For two species of Myotis bats, we estimated that 1%–5% of field-recorded detections were misclassified. Estimated occupancy (0.91 and 0.76) was lower than naïve estimates (1.00 and 0.94). Competing occupancy models implausibly estimated local occupancy of 0.00 at sites with numerous detections.
4. Our two-species, false-positive N-mixture model is significant because it accounts for detection heterogeneity and improves occupancy estimates without expensive manual verification of detections. Our field application indicated that misclassifications were not common, yet affected occupancy inferences. Given that ARUs are increasingly used to survey a broad range of taxa, such an occupancy model could be widely useful.
","language":"English","publisher":"Wiley","doi":"10.1111/2041-210X.13895","usgsCitation":"Clement, M., Royle, A., and Mixan, R., 2022, Estimating occupancy from autonomous recording unit data in the presence of misclassifications and detection heterogeneity: Methods in Ecology and Evolution, v. 13, no. 8, p. 1719-1729, https://doi.org/10.1111/2041-210X.13895.","productDescription":"11 p.","startPage":"1719","endPage":"1729","ipdsId":"IP-139383","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447816,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13895","text":"Publisher Index Page"},{"id":400804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Clement, Matt","contributorId":291855,"corporation":false,"usgs":false,"family":"Clement","given":"Matt","email":"","affiliations":[{"id":62776,"text":"AZ fish and game","active":true,"usgs":false}],"preferred":false,"id":843247,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":843250,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mixan, Ronald","contributorId":291857,"corporation":false,"usgs":false,"family":"Mixan","given":"Ronald","email":"","affiliations":[{"id":62778,"text":"AZ Game and Fish Dept","active":true,"usgs":false}],"preferred":false,"id":843251,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231577,"text":"70231577 - 2022 - A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes","interactions":[],"lastModifiedDate":"2022-05-16T11:44:17.159917","indexId":"70231577","displayToPublicDate":"2022-05-12T06:20:12","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1878,"text":"Harmful Algae","active":true,"publicationSubtype":{"id":10}},"title":"A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes","docAbstract":"Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CIcyano). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CIcyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CIcyano was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.
The ability of young fish to find and consume food during early life history is an important factor affecting survival and recruitment. While dietary assessments for age-0 Scaphirhynchus sturgeon (shovelnose sturgeon and pallid sturgeon) in the Missouri River, USA have received increased attention over the last 15 years, there is currently limited information available to evaluate long-term trends in prey consumption and body condition. To better understand interannual factors during early-life history, we examined foraging and body condition of age-0 (<12.0 cm) Scaphirhynchus in relation to discharge and the availability of hypothesized foraging and food-producing habitats at a single reach (Lexington) of the Missouri River over a span of five years (2014–2018). Relatively high discharge in 2015 led to concomitant maxima in foraging and lipid levels for age-0 sturgeon. However, lower water levels in 2014 did not see declines in lipid content as would be expected given the low level of foraging observed that year. Additionally, the availability of hypothesized foraging and food-producing habitats had little effect on age-0 sturgeon prey consumption and condition in the Lexington Reach. Our results suggest that other factors in addition to river discharge may affect age-0 sturgeon foraging and condition and more research focused on understanding the flow-habitat relationships would be critical.
Increased fire size and frequency coupled with annual grass invasion pose major challenges to sagebrush (Artemisia spp.) ecosystem conservation, which is currently focused on protecting sagebrush community composition and structure. A common strategy for mitigating potential fire is to use fuel treatments that alter the structure and amount of burnable material, thus reducing fire behavior and creating access points for fire suppression resources. While there is some recent information on the impacts of fuel treatments on ecological communities, we have little information on fuel treatment effectiveness at modifying fire behavior in sagebrush ecosystems. We present 10 years of data on fuel accumulation and the resultant modeled fire behavior in prescribed fire, mowed, herbicide (tebuthiuron or imazapic), and untreated control plots in the Sagebrush Treatment Evaluation Project (SageSTEP) network in the Great Basin, USA. Fuel data (i.e., aboveground burnable live and dead biomass) were collected in each treatment plot at Years 0 (pretreatment), 1, 2, 3, 6, and 10 posttreatment. We used the Fuel and Fire Tool fire behavior modeling program to test whether treatments impacted potential fire behavior. Prescribed fire initially removed 49% of the total fuel load and 75% of shrubs, and fuel loads remained reduced through Year 10. Mowing shifted fuels from the shrub canopy to the ground surface but did not change the total fuel amount. Prescribed fire and mowing increased herbaceous fuel by the second posttreatment year and that trend persisted through Year 10. Tebuthiuron treatments were ineffective at altering fuel loads. Imazapic suppressed herbaceous vegetation by 30% in Years 2 and 3 following treatment. The modified fuel beds in fire and mow treatments resulted in modeled flame lengths that were significantly lower than untreated control plots for the duration of the study, with shorter term reductions in reaction intensity and rate of spread. Understanding fuel treatment effectiveness will allow natural resource managers to evaluate trade-offs between protecting wildlife habitat and reducing the potential for high-intensity wildfire.