As diving foragers, sea ducks are vulnerable to underwater anthropogenic activity, including ships, underwater construction, seismic surveys and gillnet fisheries. Bycatch in gillnets is a contributing source of mortality for sea ducks, killing hundreds of thousands of individuals annually. We researched underwater hearing in sea duck species to increase knowledge of underwater avian acoustic sensitivity and to assist with possible development of gillnet bycatch mitigation strategies that include auditory deterrent devices. We used both psychoacoustic and electrophysiological techniques to investigate underwater duck hearing in several species including the long-tailed duck (Clangula hyemalis), surf scoter (Melanitta perspicillata) and common eider (Somateria mollissima). Psychoacoustic results demonstrated that all species tested share a common range of maximum auditory sensitivity of 1.0–3.0 kHz, with the long-tailed ducks and common eiders at the high end of that range (2.96 kHz), and surf scoters at the low end (1.0 kHz). In addition, our electrophysiological results from 4 surf scoters and 2 long-tailed ducks, while only tested at 0.5, 1 and 2 kHz, generally agree with the audiogram shape from our psychoacoustic testing. The results from this study are applicable to the development of effective acoustic deterrent devices or pingers in the 2–3 kHz range to deter sea ducks from anthropogenic threats.
River erosion affects the carbon cycle and thus climate by exporting terrigenous carbon to seafloor sediment and by nourishing CO2-consuming marine life. The Yukon River–Bering Sea system preserves rare source-to-sink records of these processes across profound changes in global climate during the past 5 million years (Ma). Here, we expand the terrestrial erosion record by dating terraces along the Charley River, Alaska, and explore linkages among previously published Yukon River tributary incision chronologies and Bering Sea sedimentation. Cosmogenic
The construction of a wall at the United States-Mexico border is known to impede and deter movement of terrestrial wildlife between the two countries. One such species is the jaguar, in its northernmost range in the borderlands of Arizona and Sonora. We developed an anisotropic cost distance model for jaguar in a binational crossing area of the Madrean Sky Islands at the United States-Mexico border in Southern Arizona as a case study by using previously collected GPS tracking data for jaguars, bioenergetic calculations for pumas, and a digital elevation model. This model describes projected energy expenditure for jaguar to reach key water sources north of the international border. These desert springs and the broader study region provide vital habitat for jaguar conservation and reintroduction efforts in the United States. An emerging impediment to jaguar conservation and reintroduction is border infrastructure including border wall. By comparing walled and un-walled border sections, and three remediation scenarios, we demonstrate that existing border infrastructure significantly increases energy expenditure by jaguars and that some partial remediation scenarios are more beneficial than others. Our results demonstrate opportunities for remediation. Improved understanding of how border infrastructure impacts physiological requirements and resulting impacts to jaguar and other terrestrial wildlife in the United States-Mexico borderlands may inform conservation management.
The U.S. Geological Survey has been working in collaboration with the Massachusetts Department of Environmental Protection on a project to collect water-quality data from the Merrimack River watershed since April 2020. Twelve locations in the Merrimack River watershed are being sampled for nutrients (such as nitrogen), metals (such as aluminum), Escherichia coli bacteria, and other measures.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip216","isbn":"978-1-4113-4484-6","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Laabs, K.L., Roth, N.L., and Yates, L.K., 2022, Water-quality monitoring of the Merrimack River watershed in Massachusetts: U.S. Geological Survey General Information Product 216, 2 p., https://doi.org/10.3133/gip216.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-137638","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":407973,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/216/gip216.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 216"},{"id":407972,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/216/coverthb.jpg"},{"id":407974,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/gip216/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"GIP 216"},{"id":407975,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/gip/216/gip216.XML"},{"id":407976,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/gip/216/images/"},{"id":407977,"rank":6,"type":{"id":18,"text":"Project Site"},"url":"https://www.usgs.gov/centers/new-england-water-science-center/science/water-quality-monitoring-merrimack-river-watershed","linkHelpText":"- Water Quality Monitoring of Merrimack River Watershed"},{"id":501518,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113785.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Massachusetts","otherGeospatial":"Merrimack River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -72.04408003426745,\n 42.714075621414736\n ],\n [\n -72.05232335631223,\n 42.22560913291625\n ],\n [\n -71.52749851945524,\n 42.23781638310115\n ],\n [\n -71.12082796524118,\n 42.49159699630479\n ],\n [\n -70.78285176140129,\n 42.796796190967825\n ],\n [\n -70.78834730943093,\n 42.87940632947269\n ],\n [\n -70.84879833776031,\n 42.85926769903671\n ],\n [\n -70.90100604404424,\n 42.88947318066107\n ],\n [\n -70.93672710623856,\n 42.88745994184143\n ],\n [\n -70.97794371646322,\n 42.869337835583906\n ],\n [\n -71.03289919676229,\n 42.86128185775681\n ],\n [\n -71.06862025895661,\n 42.81090817917965\n ],\n [\n -71.14006238334593,\n 42.82904743388025\n ],\n [\n -71.18952231561535,\n 42.802844580085036\n ],\n [\n -71.1867745416002,\n 42.74031604943306\n ],\n [\n -71.21974782977938,\n 42.75242327229461\n ],\n [\n -71.25272111795921,\n 42.74636995644474\n ],\n [\n -71.30767659825833,\n 42.69792214863861\n ],\n [\n -72.04408003426745,\n 42.714075621414736\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
A Decree of the Supreme Court of the United States, entered June 7, 1954, established the position of Delaware River Master within the U.S. Geological Survey. In addition, the Decree authorizes diversion of water from the Delaware River Basin and requires compensating releases from certain reservoirs, owned by New York City, to be made under the supervision and direction of the River Master. The Decree stipulates that the River Master will furnish reports to the Court, not less frequently than annually. This report is the 60th annual report of the River Master of the Delaware River. It covers the 2013 River Master report year, the period from December 1, 2012 to November 30, 2013.
During the report year, precipitation in the upper Delaware River Basin was 44.50 inches or 100 percent of the long-term average. Combined storage in the Pepacton, Cannonsville, and Neversink Reservoirs remained high until October 2013 when it decreased below 80 percent combined capacity. The lowest combined storage of the report year was 70.2 percent of combined capacity on November 26, 2013. Delaware River Master operations during the year were conducted as stipulated by the Decree and the Flexible Flow Management Program.
Diversions from the Delaware River Basin by New York City and New Jersey were in full compliance with the Decree. Reservoir releases were made as directed by the River Master at rates designed to meet the Montague flow objective for the Delaware River at the Montague, New Jersey streamgage on 71 days during the report year. Interim Excess Release Quantity and conservation releases, designed to relieve thermal stress and protect the fishery and aquatic habitat in the tailwaters of the reservoirs, were also made during the report year. An agreement was signed on July 16, 2013 to temporarily increase releases to provide thermal protection below Cannonsville Reservoir.
The quality of water in the Delaware River estuary between streamgages at Trenton, New Jersey, and Reedy Island Jetty, Delaware, was monitored at several locations. Data on water temperature, specific conductance, dissolved oxygen, and pH were collected continuously by electronic instruments at four sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221068","isbn":"978-1-4113-4486-0","usgsCitation":"DiFrenna, V.J., Andrews, W.J., Russell, K.L., Norris, J.M., and Mason, R.R., Jr., 2022, Report of the River Master of the Delaware River for the period December 1, 2012–November 30, 2013: U.S. Geological Survey Open-File Report 2022–1068, 99 p., https://doi.org/10.3133/ofr20221068.","productDescription":"xii, 99 p.","numberOfPages":"99","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-123834","costCenters":[{"id":509,"text":"Office of the Associate Director for Water","active":true,"usgs":true}],"links":[{"id":501821,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113786.htm","linkFileType":{"id":5,"text":"html"}},{"id":408577,"rank":4,"type":{"id":31,"text":"Publication 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Office of the Delaware River Master
U.S. Geological Survey
120 Route 209 South
Milford, PA 18337
The Chesapeake Bay, along the mid-Atlantic coast of North America, is the largest estuary in the United States and provides critical habitat for wildlife. In contrast to point and non-point source release of pesticides, metals, and industrial, personal care and household use chemicals on biota in this watershed, there has only been scant attention to potential exposure and effects of algal toxins on wildlife in the Chesapeake Bay region. As background, we first review the scientific literature on algal toxins and harmful algal bloom (HAB) events in various regions of the world that principally affected birds, and to a lesser degree other wildlife. To examine the situation for the Chesapeake, we compiled information from government reports and databases summarizing wildlife mortality events for 2000 through 2020 that were associated with potentially toxic algae and HAB events. Summary findings indicate that there have been few wildlife mortality incidents definitively linked to HABs, other mortality events that were suspected to be related to HABs, and more instances in which HABs may have indirectly contributed to or occurred coincident with wildlife mortality. The dominant toxins found in the Chesapeake Bay drainage that could potentially affect wildlife are microcystins, with concentrations in water approaching or exceeding human-based thresholds for ceasing recreational use and drinking water at a number of locations. As an increasing trend in HAB events in the U.S. and in the Chesapeake Bay have been reported, additional information on HAB toxin exposure routes, comparative sensitivity among species, consequences of sublethal exposure, and better diagnostic and risk criteria would greatly assist in predicting algal toxin hazard and risks to wildlife.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hal.2022.102319","usgsCitation":"Rattner, B., Wazniak, C.E., Lankton, J.S., McGowan, P.C., Drovetski, S.V., and Egerton, T.A., 2022, Review of harmful algal blooms effects on birds with implications for avian wildlife in the Chesapeake Bay region: Harmful Algae, v. 120, 102319, 20 p.; Data Release, https://doi.org/10.1016/j.hal.2022.102319.","productDescription":"102319, 20 p.; Data Release","ipdsId":"IP-138638","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":446019,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hal.2022.102319","text":"Publisher Index Page"},{"id":408805,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418367,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ULAM7G","text":"Wildlife mortality events in counties surrounding the Chesapeake Bay recorded in the Wildlife Health Information Sharing Partnership Event Reporting System (WHISPers) from 2000-2020"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -77.18954322170708,\n 37.53524166994633\n ],\n [\n -77.44783397911205,\n 37.364931581898986\n ],\n [\n -76.73341273522277,\n 36.72453067388703\n ],\n [\n -76.21133567238093,\n 36.76416355102856\n ],\n [\n -75.98601820315514,\n 37.06311061060744\n ],\n [\n -75.93655827088573,\n 37.308289271077044\n ],\n [\n -75.41997675607354,\n 37.95675381093146\n ],\n [\n -75.69475415756958,\n 38.48348716740804\n ],\n [\n -75.77718737801817,\n 39.59326365864615\n ],\n [\n -76.0464692314839,\n 39.66521791944069\n ],\n [\n -77.45332952714315,\n 38.82253108298832\n ],\n [\n -77.40386959487374,\n 38.276702908455235\n ],\n [\n -77.18954322170708,\n 37.53524166994633\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"120","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":298580,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett A.","email":"brattner@usgs.gov","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":855925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wazniak, Catherine E.","contributorId":298582,"corporation":false,"usgs":false,"family":"Wazniak","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":33964,"text":"Maryland Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":855989,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":855990,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":855991,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Drovetski, Sergei V. 0000-0002-1832-5597","orcid":"https://orcid.org/0000-0002-1832-5597","contributorId":229520,"corporation":false,"usgs":true,"family":"Drovetski","given":"Sergei","middleInitial":"V.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":855992,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Egerton, Todd A.","contributorId":298583,"corporation":false,"usgs":false,"family":"Egerton","given":"Todd","email":"","middleInitial":"A.","affiliations":[{"id":64620,"text":"Virginia Department of Health","active":true,"usgs":false}],"preferred":false,"id":855993,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70249889,"text":"70249889 - 2022 - Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation","interactions":[],"lastModifiedDate":"2023-11-04T13:24:14.694271","indexId":"70249889","displayToPublicDate":"2022-10-27T08:15:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation","docAbstract":"Two 130+ meter diameter impact craters formed on Mars during the later half of 2021. These are the two largest fresh impact craters discovered by the Mars Reconnaissance Orbiter since operations started 16 years ago. The impacts created two of the largest seismic events (magnitudes greater than 4) recorded by InSight during its three year mission. The combination of orbital imagery and seismic ground motion enables the investigation of subsurface and atmospheric energy partitioning of the impact process on a planet with a thin atmosphere and the first direct test of Martian deep-interior seismic models with known event distances.The impact at 35°N excavated blocks of water ice, which is the lowest latitude ice has been directly observed on Mars.","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/science.abq7704","usgsCitation":"Posiolova, L., Lognonne, P., Banerdt, W., Clinton, J., Collins, G., Kawamura, T., Ceylan, S., Daubar, I.J., Fernando, B., Froment, M., Giardini, D., Malin, M.C., Miljkovic, K., Stahler, S.C., Xue, Z.G., Banks, M.E., Beucler, E., Cantor, B., Charalambous, C., Dahmen, N., Davis, P.W., Duran, C.M., Drilleau, M., Dundas, C., Euchner, F., Garcia, R., Golombek, M.P., Horleston, A., Keegan, C., Khan, A.S., Kim, D., Larmat, C., Lorenz, R.D., Margerin, L., Menina, S., Panning, M., Pardo, C., Perrin, C., Pike, W., Plasman, M., Rajsic, A., Rolland, L., Rougier, E., Speth, G., Spiga, A., Stott, A.E., Susko, D., Teanby, N., Valeh, A., Werynski, A., Wojcicka, N., and Zenhausern, G., 2022, Largest recent impact craters on Mars: Orbital imaging and surface seismic co-investigation: Science, v. 378, no. 6618, p. 412-417, https://doi.org/10.1126/science.abq7704.","productDescription":"6 p.","startPage":"412","endPage":"417","ipdsId":"IP-141384","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":446025,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://research-information.bris.ac.uk/en/publications/588e1d41-1d7e-4bb5-83dd-c431c832594c","text":"External 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snags","interactions":[],"lastModifiedDate":"2022-11-04T12:20:49.605004","indexId":"70238026","displayToPublicDate":"2022-10-27T07:19:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Level and pattern of overstory retention shape the abundance and long-term dynamics of natural and created snags","docAbstract":"Standing dead trees, or snags, serve myriad functions in natural forests, but are often scarce in forests managed for timber production. Variable retention (VR), the retention of live and dead trees through harvest, has been adopted globally as a less intensive form of regeneration harvest. In this study, we explore how two key elements of VR systems — level (amount) and spatial pattern of live-tree retention — affect the carryover and post-harvest dynamics of natural and artificially created snags. We present nearly two decades of data from the DEMO Study, a regional-scale experiment in VR harvests of Douglas-fir-dominated forests in the Pacific Northwest. Snag losses to harvest were greater at 15 than at 40% retention (67 vs. 47% declines in density) and greater in dispersed than in aggregated treatments (64 vs. 50% declines). Densities of hard and tall (≥5 m) snags were particularly sensitive to low-level dispersed retention, declining by 76 and 81%, respectively. Despite these losses, post-harvest densities correlated with pre-harvest densities for most snag size and decay classes. In contrast to initial harvest effects, snag densities changed minimally over the post-harvest period (years 1 to 18 or 19), with low rates of recruitment offsetting low rates of loss. Post-harvest survival of snags was greater at 15 than at 40% retention (79 vs. 69%), as were rates of decay (68 vs. 52% of hard snags transitioned to soft). However, pattern had no effect on either process. Snag recruitment did not vary with retention level or pattern at the scale of the 13-ha harvest unit, but was several-fold greater in the 1-ha aggregates (14.3–27.8 snags ha−1) than in the corresponding dispersed treatments (4.2–5.3 snags ha−1). Snag size (diameter) distributions showed greater change in dispersed than in aggregated treatments, reflecting greater loss of smaller snags and recruitment biased toward larger snags. Created snags showed uniformly high survival (97%), irrespective of treatment, but rates of decay were greater at lower retention. If a goal of VR is to sustain snag abundance and diversity through harvest, emphasis should be placed on minimizing initial losses, either by reducing the intensity of felling in areas of dispersed retention or locating forest aggregates in areas of greater initial snag density, diversity, or incipient decay.
Space use by mammals can differ among age-classes, sexes, or seasons, and these processes are recognized as adaptive behavioral strategies. Semi-fossorial ground squirrels, in particular, have shown age- and sex-specific patterns in their aboveground movement behaviors. We studied space use of Mohave ground squirrels (Xerospermophilus mohavensis) at the Freeman Gulch study site in the central region of their range in the Mojave Desert, California. We documented the timing of their full annual cycle, investigated correlates of size of home ranges of adults and distance of long-distance movements by juveniles, and evaluated whether juvenile body masses and movements were related to interannual climatic variation. Adult males emerged from burrows and entered hibernation sooner than did adult females. Home ranges were larger for males (
Tularemia is an infectious zoonotic disease caused by one of several subspecies of Francisella tularensis bacteria. Infections by F. tularensis are common throughout the northern hemisphere and have been detected in more than 250 wildlife species. In Alaska, US, where the pathogen was first identified in 1938, studies have identified F. tularensis antibodies in a diverse suite of taxa, including insects, birds, and mammals. However, few such investigations have been conducted recently and knowledge about the current distribution and disease ecology of F. tularensis is limited, particularly in Arctic Alaska, an area undergoing rapid environmental changes from climate warming. To help address these information gaps and provide insights about patterns of exposure among wildlife, we assessed the seroprevalence of F. tularensis antibodies in mammals and tundra-nesting geese from the Arctic Coastal Plain of Alaska, 2014–17. With a commercially available slide agglutination test, we detected antibodies in 14.7% of all individuals sampled (n=722), with titers ranging from 1:20 to 1:320. We detected significant differences in seroprevalence between family groups, with Canidae (foxes, Vulpes spp.) and Sciuridae (Arctic ground squirrel, Spermophilus parryii) having the highest seroprevalence at 21.5% and 33.3%, respectively. Mean seroprevalence for Ursidae (polar bears, Ursus maritimus) was 13.3%, whereas Cervidae (caribou, Rangifer tarandus) had comparatively low seroprevalence at 6.5%. Antibodies were detected in all Anatidae species sampled, with Black Brant (Branta bernicla nigricans) having the highest seroprevalence at 13.6%. The detection of F. tularensis antibodies across multiple taxa from the Arctic Coastal Plain and its nearshore marine region provides evidence of exposure to this pathogen throughout the region and highlights the need for renewed surveillance in Alaska.
Early life stages of pallid sturgeon Scaphirhynchus albus are rarely collected, and thus information on their biology and ecology is extremely limited. We sampled 75 larval pallid sturgeon (25-110 mm) and 148 larval shovelnose sturgeon S. platorynchus (15-95 mm) by trawl from the upper Missouri River (USA) in 2019. Stomach contents were identified to compare food use and diet overlap between the 2 sturgeon species at the order, family, and genus levels of taxonomic prey identification. Analyses were conducted with sites pooled and with sites separated by the confluence of the Yellowstone River (upper and lower). Abundance of dominant prey in the gut (Diptera larvae) increased with fish length for both species, and regression slopes were similar. Diet overlap at pooled sites decreased from 0.94 to 0.49 when prey were identified to order and genus, respectively, and decreases in diet overlap at individual sites were more pronounced. Larval pallid sturgeon consumed a maximum of 11 unique taxa, whereas shovelnose sturgeon consumed 6 taxa that were not consumed by pallid sturgeon. These results indicate that larval diets are similar between species when evaluated at coarse taxonomic scales, but at fine taxonomic scales, notable differences exist. As information about the diets of larval pallid sturgeon captured from a riverine environment are scarce and the use of shovelnose sturgeon as an indicator of available suitable food and habitat and as a dietary surrogate for pallid sturgeon has been under consideration, our results suggest that caution be exercised in modeling efforts or management actions relating to surrogacy.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01205","usgsCitation":"Holley, C.T., Braaten, P., Poulton, B., Heist, E.J., Umland, L., and Haddix, T., 2022, Diet composition and overlap of larval pallid sturgeon and shovelnose sturgeon from the upper Missouri River, USA: Endangered Species Research, v. 49, p. 103-114, https://doi.org/10.3354/esr01205.","productDescription":"12 p.","startPage":"103","endPage":"114","ipdsId":"IP-137091","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":446034,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01205","text":"Publisher Index Page"},{"id":435644,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B6I9A2","text":"USGS data release","linkHelpText":"Invertebrates enumerated from the diets of larval pallid sturgeon (Scaphirhynchus albus) and shovelnose sturgeon (S. platorynchus) in the Upper Missouri River, Montana and North Dakota in 2019"},{"id":412491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota","otherGeospatial":"Upper Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -106.41387315510876,\n 48.772256767913774\n ],\n [\n -106.41387315510876,\n 47.094926014436766\n ],\n [\n -101.86746408165837,\n 47.094926014436766\n ],\n [\n -101.86746408165837,\n 48.772256767913774\n ],\n [\n -106.41387315510876,\n 48.772256767913774\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"49","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holley, Colt Taylor 0000-0003-4172-4331","orcid":"https://orcid.org/0000-0003-4172-4331","contributorId":272272,"corporation":false,"usgs":true,"family":"Holley","given":"Colt","email":"","middleInitial":"Taylor","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":862820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":862821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Poulton, Barry","contributorId":301852,"corporation":false,"usgs":false,"family":"Poulton","given":"Barry","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":862823,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heist, Edward J.","contributorId":221082,"corporation":false,"usgs":false,"family":"Heist","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":40317,"text":"Southern Illinois University, Fisheries and Illinois Aquaculture Center","active":true,"usgs":false}],"preferred":false,"id":862824,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Umland, Levi","contributorId":301851,"corporation":false,"usgs":false,"family":"Umland","given":"Levi","email":"","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":862822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Haddix, Tyler M.","contributorId":268184,"corporation":false,"usgs":false,"family":"Haddix","given":"Tyler M.","affiliations":[{"id":55585,"text":"Montana Fish, Wildlife and Parks, P.O. Box 165, Fort Peck, Montana","active":true,"usgs":false}],"preferred":false,"id":862851,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70267759,"text":"70267759 - 2022 - Effects of 17α-ethinylestradiol and density on juvenile fathead minnow survival and body size","interactions":[],"lastModifiedDate":"2025-05-30T16:28:32.514912","indexId":"70267759","displayToPublicDate":"2022-10-26T09:16:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":21800,"text":"Journal of Aquatic Pollution and Toxicology","onlineIssn":"2581-804X","active":true,"publicationSubtype":{"id":10}},"title":"Effects of 17α-ethinylestradiol and density on juvenile fathead minnow survival and body size","docAbstract":"Anthropogenic changes have led to the increased use of wastewater treatment plants in stream systems near urbanized areas. Synthetic oral contraceptives, observed in wastewater treatment effluents, can cause negative effects on fish life history metrics. Previous exposures of 17α-ethinylestradiol (EE2) have been shown to affect survival and reproduction of fathead minnows (Pimephales promelas). However, density effects were not considered, and additional research is needed to examine the role of density among fish exposed to EE2. Multiple hypotheses indicate the interaction of density with contaminant exposure may ameliorate or exacerbate mortality. We examined how nominal EE2 concentrations of 0 ng/L, 5 ng/L, and 10 ng/L affect body size and mortality at various densities. Fish body size was influenced by density but not EE2 exposure. When density was high, we did not detect an effect of EE2 exposure on mortality. However, when density was low, EE2 exposures increased mortality. Thus, toxic effects of EE2 exposures were observable at low density but at high density, density-dependence in body size and mortality overwhelmed the effect of EE2. The results from our study provide insight into the relationship between density and EE2 exposures on fish survival and can be used to adjust population dynamic parameters for more accurate population dynamic estimates.
","language":"English","publisher":"Prime Scholars","doi":"10.21767/2581-804X.22.6.60","usgsCitation":"Riepe, T., Avila, B., and Winkelman, D.L., 2022, Effects of 17α-ethinylestradiol and density on juvenile fathead minnow survival and body size: Journal of Aquatic Pollution and Toxicology, v. 6, no. 6, 60, 5 p., https://doi.org/10.21767/2581-804X.22.6.60.","productDescription":"60, 5 p.","ipdsId":"IP-145341","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489295,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.primescholars.com/articles/effects-of-17ethinylestradiol-and-density-on-juvenile-fathead-minnow-survival-and-body-size-116277.html","linkFileType":{"id":5,"text":"html"}},{"id":489296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Riepe, Tawni B.","contributorId":288699,"corporation":false,"usgs":false,"family":"Riepe","given":"Tawni B.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":938751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Avila, Brian W.","contributorId":338191,"corporation":false,"usgs":false,"family":"Avila","given":"Brian W.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":938752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkelman, Dana L. 0000-0002-5247-0114 danaw@usgs.gov","orcid":"https://orcid.org/0000-0002-5247-0114","contributorId":4141,"corporation":false,"usgs":true,"family":"Winkelman","given":"Dana","email":"danaw@usgs.gov","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":938753,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70252246,"text":"70252246 - 2022 - Getting ahead of flash drought: From early warning to early action","interactions":[],"lastModifiedDate":"2024-03-21T12:00:39.095827","indexId":"70252246","displayToPublicDate":"2022-10-26T06:58:39","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12798,"text":"Bulletin of American Meteorological Society (BAMS)","active":true,"publicationSubtype":{"id":10}},"title":"Getting ahead of flash drought: From early warning to early action","docAbstract":"Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.
The Salt Lake Valley, UT, USA, is proximal to the desiccating Great Salt Lake (GSL). Prior work has found that this lakebed/playa contributes metals-laden dust to snow in the Wasatch and Uinta Mountains. Dust and industrial particulate pollution are also delivered to communities along the Wasatch Front, but their sources, compositions, and fluxes are poorly characterized. In this study, we analyzed the dust deposited in 18 passive samplers positioned near the GSL, in cities in and near the Salt Lake Valley for total dust flux, the <63 µm dust fraction, 87Sr/86Sr, and trace element geochemistry. We compared spatial patterns in metal flux and abundance with community-level socioeconomic metrics. We observed the highest dust fluxes at sites near the GSL playa. Within the urban corridor, 87Sr/86Sr and trace element relative abundances suggest that most of the dust to which people are regularly exposed may be fugitive dust from local soil materials. The trace metal content of dust deposited along the Wasatch Front exceeded Environmental Protection Agency screening levels and exhibited enrichment relative to both the upper continental crust and the dust collected adjacent to GSL. Sources of metals to dust deposited along the Wasatch Front may include industrial activities like mining, oil refining, as well as past historical pesticide and herbicide applications. Arsenic and vanadium indicated a statistically significant positive correlation with income, whereas lead, thallium, and nickel exhibited higher concentrations in the least wealthy and least white neighborhoods.
Fault damage zones provide a window into the non-elastic processes of an earthquake. Geological and seismic tomography methods have been unable to measure damage zones at depth with sufficient spatial sampling to evaluate the relative influence of depth, distance, and lithological variations. Here, we identify and analyze the damage zone of the Palos Verdes Fault offshore southern California using two 3D seismic reflection datasets. We apply a novel algorithm to identify discontinuities attributed to faults and fractures in large seismic volumes and examine the spatial distribution of fault damage in sedimentary rock surrounding the Palos Verdes Fault. Our results show that damage through fracturing is most concentrated around mapped faults and decays exponentially to a distance of ∼2 km, where fracturing reaches a clearly defined and relatively undamaged background for all examined depths and lithologies (450 m to 2.2 km). This decrease in fracturing with distance from the central fault strand exhibits similar functional form to outcrop studies. However, here we extend analysis to distances seldom accessible (∼10 km lateral distance). Separating the data by geologic units we find that the damage decay and background level differs for each unit, with the older and deeper units having higher levels of background fracturing and shallower exponential decays of fracturing with distance from the fault. Surprisingly, these differences in damage decay and background level trade-off result in a consistent damage zone width regardless of lithology or depth. We find that the damage zone has similar decay trends on both sides of the fault. When examining the damage zone at shorter (4 km vs 17 km) along strike distances, the damage zone has a more complex decay trend and at least two strands are resolvable.
White-tailed deer (WTD; Odocoileus virginianus) are a critical species for ecosystem function and wildlife management. As such, studies of cause-specific mortality among WTD have long been used to understand population dynamics. However, detailed pathological information is rarely documented for free-ranging WTD, especially in regions with a high prevalence of chronic wasting disease (CWD). This leaves a significant gap in understanding how CWD is associated with disease processes or comorbidities which may subsequently alter broader population dynamics. In this study, we investigated unknown mortalities among collared WTD in southwestern Wisconsin, an area of high CWD prevalence. We tested for associations between CWD and other disease processes and used a network approach to test for co-occurring disease processes. Predation and infectious disease were top suspected causes of death, with high prevalence of CWD (42.4%; of 245 evaluated) and pneumonia (51.2%; of 168 evaluated) in our sample. CWD prevalence increased with age, before decreasing among older individuals, with more older females than males in our sample. Females were more likely to be CWD positive, and while this was not statistically significant when accounting for age, females were significantly more likely to die with end-stage CWD than were males and may consequently be an underrecognized source of CWD transmission. Presence of CWD was associated with emaciation, atrophy of marrow fat and hematopoietic cells, and ectoparasitism (i.e., lice, ticks). Occurrences of severe infectious disease pathologies clustered together (e.g., pneumonia, CWD), as compared to non-infectious or low severity processes (e.g., sarcocystosis). However, pneumonia cases were not fully explained by CWD status. With the prevalence of CWD increasing across North America, our results highlight the critical importance of understanding the potential role of CWD in favoring or maintaining disease processes of importance for deer population health and dynamics.
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Medicine","active":true,"usgs":false}],"preferred":false,"id":922800,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meaux, Nicolette M.","contributorId":347995,"corporation":false,"usgs":false,"family":"Meaux","given":"Nicolette M.","affiliations":[{"id":83274,"text":"University of Wisconsin–Madison","active":true,"usgs":false}],"preferred":false,"id":922801,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunsaker, Matthew","contributorId":347996,"corporation":false,"usgs":false,"family":"Hunsaker","given":"Matthew","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922802,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jarosinski, Dana","contributorId":347997,"corporation":false,"usgs":false,"family":"Jarosinski","given":"Dana","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":922803,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ellarson, Wesley","contributorId":347998,"corporation":false,"usgs":false,"family":"Ellarson","given":"Wesley","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922804,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Walsh, Daniel P. 0000-0002-7772-2445","orcid":"https://orcid.org/0000-0002-7772-2445","contributorId":219539,"corporation":false,"usgs":true,"family":"Walsh","given":"Daniel","email":"","middleInitial":"P.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":922805,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Storm, Daniel J.","contributorId":347999,"corporation":false,"usgs":false,"family":"Storm","given":"Daniel J.","affiliations":[{"id":6913,"text":"Wisconsin Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":922806,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Turner, Wendy Christine 0000-0002-0302-1646","orcid":"https://orcid.org/0000-0002-0302-1646","contributorId":287053,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy","email":"","middleInitial":"Christine","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922807,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70262041,"text":"70262041 - 2022 - Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus)","interactions":[],"lastModifiedDate":"2025-01-10T15:07:46.307314","indexId":"70262041","displayToPublicDate":"2022-10-26T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2792,"text":"Movement Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus)","docAbstract":"Background
Dispersal is a fundamental process to animal population dynamics and gene flow. In white-tailed deer (WTD; Odocoileus virginianus), dispersal also presents an increasingly relevant risk for the spread of infectious diseases. Across their wide range, WTD dispersal is believed to be driven by a suite of landscape and host behavioral factors, but these can vary by region, season, and sex. Our objectives were to (1) identify dispersal events in Wisconsin WTD and determine drivers of dispersal rates and distances, and (2) determine how landscape features (e.g., rivers, roads) structure deer dispersal paths.
Methods
We developed an algorithmic approach to detect dispersal events from GPS collar data for 590 juvenile, yearling, and adult WTD. We used statistical models to identify host and landscape drivers of dispersal rates and distances, including the role of agricultural land use, the traversability of the landscape, and potential interactions between deer. We then performed a step selection analysis to determine how landscape features such as agricultural land use, elevation, rivers, and roads affected deer dispersal paths.
Results
Dispersal predominantly occurred in juvenile males, of which 64.2% dispersed, with dispersal events uncommon in other sex and age classes. Juvenile male dispersal probability was positively associated with the proportion of the natal range that was classified as agricultural land use, but only during the spring. Dispersal distances were typically short (median 5.77 km, range: 1.3–68.3 km), especially in the fall. Further, dispersal distances were positively associated with agricultural land use in potential dispersal paths but negatively associated with the number of proximate deer in the natal range. Lastly, we found that, during dispersal, juvenile males typically avoided agricultural land use but selected for areas near rivers and streams.
Conclusion
Land use—particularly agricultural—was a key driver of dispersal rates, distances, and paths in Wisconsin WTD. In addition, our results support the importance of deer social environments in shaping dispersal behavior. Our findings reinforce knowledge of dispersal ecology in WTD and how landscape factors—including major rivers, roads, and land-use patterns—structure host gene flow and potential pathogen transmission.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40462-022-00342-5","usgsCitation":"Gilbertson, M., Ketz, A., Hunsaker, M., Jarosinski, D., Ellarson, W., Walsh, D.P., Storm, D., and Turner, W.C., 2022, Agricultural land use shapes dispersal in white-tailed deer (Odocoileus virginianus): Movement Ecology, v. 10, 43, 18 p., https://doi.org/10.1186/s40462-022-00342-5.","productDescription":"43, 18 p.","ipdsId":"IP-139079","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467154,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40462-022-00342-5","text":"Publisher Index Page"},{"id":465983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"southwestern Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -90.9914420863848,\n 43.07956941638602\n ],\n [\n -90.9914420863848,\n 42.580032684433775\n ],\n [\n -89.45335614888498,\n 42.580032684433775\n ],\n [\n -89.45335614888498,\n 43.07956941638602\n ],\n [\n -90.9914420863848,\n 43.07956941638602\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Gilbertson, Marie L. 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The USACE authorized the development of an off-channel harbor (hereinafter referred to as the “proposed slack water harbor”) along the McClellan-Kerr Arkansas River Navigation System at river mile 202.6, and an initial evaluation of shoreline stability and adjacent land near the proposed harbor was considered essential in establishing a baseline for potential effects and future monitoring. In October 2021, the U.S. Geological Survey, in cooperation with the USACE, completed high-resolution bathymetric (underwater elevation) and topographic surveys of the Arkansas River and a quarry at the location of the proposed slack water harbor near Dardanelle, Arkansas, using a combination of multibeam sound navigation and ranging (sonar) and high-resolution, low-altitude aerial light detection and ranging (lidar) data to provide data and analysis needed for as-built information and future monitoring of river shoreline and floodplain management and maintenance.
Bathymetric data were collected using a high-resolution multibeam mapping system, which consists of a multibeam echosounder and an inertial navigation system mounted on a marine survey vessel. Data were collected as the vessel traversed the river and quarry along overlapping survey lines distributed throughout the areas.
Topographic data were collected as a lidar point cloud using an unmanned aircraft system (UAS) with a YellowScan Vx20–100 lidar payload, which consists of the lidar scanner and an inertial navigation system. The lidar point cloud data were collected as the UAS followed two sets of parallel transect lines, oriented perpendicular to each other (nominally north to south and east to west) on separate flights. The bathymetric and UAS topographic datasets were combined with topographic data extracted from publicly available aerial lidar data collected in 2014 to create a multisource point cloud classified as “ground” (code 2) according to the American Society for Photogrammetry and Remote Sensing standard lidar point classes in the proposed harbor area and surroundings, from which topographic contours were derived.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3494","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Southwestern Division, Little Rock District","usgsCitation":"Huizinga, R.J., Richards, J.M., and Rivers, B.C., 2022, Use of high-resolution topobathymetry to assess shoreline topography and potential future development of a slack water harbor near Dardanelle, Arkansas, October 2021: U.S. Geological Survey Scientific Investigations Map 3494, 1 sheet, https://doi.org/10.3133/sim3494.","productDescription":"Sheet: 36.00 x 39.50 inches; Data Release","numberOfPages":"1","onlineOnly":"Y","ipdsId":"IP-137686","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":408700,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3494/sim3494.pdf","text":"Report","size":"2.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3494"},{"id":408699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3494/coverthb.jpg"},{"id":408701,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3494/sim3494.XML"},{"id":408702,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3494/images"},{"id":408722,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3494/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":408703,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KW1D2D","text":"USGS data release","linkHelpText":"Use of high-resolution topobathymetry to assess shoreline topography and future development of a slack water harbor near Dardanelle, Arkansas, October 2021"},{"id":501937,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113784.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arkansas","county":"Dardanelle","otherGeospatial":"Slack Water Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.1753984002736,\n 34.7148432917307\n ],\n [\n -92.1753984002736,\n 34.70185497290542\n ],\n [\n -92.15152711351902,\n 34.70185497290542\n ],\n [\n -92.15152711351902,\n 34.7148432917307\n ],\n [\n -92.1753984002736,\n 34.7148432917307\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
The presence of year-round surface water in streams (i.e., streamflow permanence) is an important factor for identifying aquatic habitat availability, determining the regulatory status of streams, managing land use change, allocating water resources, and designing scientific studies. However, accurate, high resolution, and dynamic prediction of streamflow permanence that accounts for year-to-year variability at a regional extent is a major gap in modeling capability. Herein, we expand and adapt the U.S. Geological Survey (USGS) PRObability of Streamflow PERmanence (PROSPER) model from its original implementation in the Pacific Northwest (PROSPERPNW) to the upper Missouri River basin (PROSPERUM), a geographical region that includes mountain and prairie ecosystems of the northern United States. PROSPERUM is an empirical model used to estimate the probability that a stream channel has year-round flow in response to climatic conditions (monthly and annual) and static physiographic predictor variables of the upstream basin. The structure and approach of PROSPERUM are generally consistent with the PROSPERPNW model but include improved spatial resolution (10 m) and a longer modeling period. Average model accuracy was 81 %. Drainage area, upstream proportion as wetlands, and upstream proportion as developed land cover were the most important predictor variables. The PROSPERUM model identifies decreases in streamflow permanence during climatically drier years, although there is variability in the magnitude across basins highlighting geographically varying sensitivity to drought. Variability in the response of perennial streams to drought conditions among basins in the study area was also observed.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.hydroa.2022.100138","usgsCitation":"Sando, R., Jaeger, K.L., Farmer, W., Barnhart, T., McShane, R., Welborn, T.L., Kaiser, K.E., Hafen, K., Blasch, K.W., York, B.C., and Shallcross, A., 2022, Predictions and drivers of sub-reach-scale annual streamflow permanence for the upper Missouri River basin: 1989-2018: Journal of Hydrology X, v. 17, 100138, 22 p., https://doi.org/10.1016/j.hydroa.2022.100138.","productDescription":"100138, 22 p.","ipdsId":"IP-137870","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":446045,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.hydroa.2022.100138","text":"Publisher Index Page"},{"id":413911,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, North Dakota, South Dakota, Wyoming","otherGeospatial":"upper Missouri River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.06578880812711,\n 48.98607018582902\n ],\n [\n -114.41405308204301,\n 46.6259860103564\n ],\n [\n -114.45505714000817,\n 45.56604735512229\n ],\n [\n -114.1280129319525,\n 45.6810748968652\n ],\n [\n -113.43692249781836,\n 44.85861341197983\n ],\n [\n -112.98504047311934,\n 44.442264035596594\n ],\n [\n -111.79851411576917,\n 44.50526246095063\n ],\n [\n -111.2396387557593,\n 44.90171571207168\n ],\n [\n -110.61806405817302,\n 42.14074973473086\n ],\n [\n -105.77175988800175,\n 41.952647712608155\n ],\n [\n -104.56426824820389,\n 42.95942508508247\n ],\n [\n -103.25615692243574,\n 43.83191953044022\n ],\n [\n -101.00144324324455,\n 44.44211500891038\n ],\n [\n -100.09948505313812,\n 44.838575527202494\n ],\n [\n -99.6320641494822,\n 46.96241959544966\n ],\n [\n -99.99744313272754,\n 48.133167378584716\n ],\n [\n -102.2714095903581,\n 48.758327670163794\n ],\n [\n -107.84693201879426,\n 48.8300878096519\n ],\n [\n -115.06578880812711,\n 48.98607018582902\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Sando, Roy 0000-0003-0704-6258","orcid":"https://orcid.org/0000-0003-0704-6258","contributorId":3874,"corporation":false,"usgs":true,"family":"Sando","given":"Roy","email":"","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":865992,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":865993,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farmer, William H. 0000-0002-2865-2196","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":223181,"corporation":false,"usgs":true,"family":"Farmer","given":"William H.","affiliations":[{"id":37778,"text":"WMA - 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2022 - Autumn precipitation: The competition with Santa Ana winds in determining fire outcomes in southern California","interactions":[],"lastModifiedDate":"2023-02-06T13:15:01.365483","indexId":"70240346","displayToPublicDate":"2022-10-25T07:12:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2083,"text":"International Journal of Wildland Fire","active":true,"publicationSubtype":{"id":10}},"title":"Autumn precipitation: The competition with Santa Ana winds in determining fire outcomes in southern California","docAbstract":"Background: California’s South Coast has experienced peak burned area in autumn. Following typically dry, warm summers, precipitation events and Santa Ana winds (SAWs) each occur with increasing frequency from autumn to winter and may affect fire outcomes.
Aims: We investigate historical records to understand how these counteracting influences have affected fires.
Methods: We defined autumn precipitation onset as the first 3 days when precipitation ≥8.5 mm, and assessed how onset timing and SAWs were associated with frequency of ≥100 ha fires and area burned during 1948–2018.
Key results: Timing of autumn precipitation onset had negligible trend but varied considerably from year to year. A total of 90% of area burned in autumn through winter occurred from fires started before onset. Early onset autumns experienced considerably fewer fires and area burned than late onset autumns. SAWs were involved in many of the large fires before onset and nearly all of the lesser number after onset.
Conclusions: Risk of large fires is reduced after autumn precipitation onset, but may resurge during SAWs, which provide high risk weather required to generate a large fire.
Implications: During autumn before onset, and particularly during late onset autumns, high levels of preparation and vigilance are needed to avoid great fire impacts.
","language":"English","publisher":"CSIRO","doi":"10.1071/WF22065","usgsCitation":"Cayan, D., DeHaan, L., Gershunov, A., Guzman-Morales, J., Keeley, J., Mumford, J., and Syphard, A., 2022, Autumn precipitation: The competition with Santa Ana winds in determining fire outcomes in southern California: International Journal of Wildland Fire, v. 31, no. 11, p. 1056-1067, https://doi.org/10.1071/WF22065.","productDescription":"12 p.","startPage":"1056","endPage":"1067","ipdsId":"IP-140732","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":446048,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wf22065","text":"Publisher Index Page"},{"id":412730,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.33457645560733,\n 32.526622846447296\n ],\n [\n -116.36818998588839,\n 32.74856608271622\n ],\n [\n -116.32426332817393,\n 33.37440344272031\n ],\n [\n -116.71960324760451,\n 34.86530362853355\n ],\n [\n -119.75054262990477,\n 35.332508897235726\n ],\n [\n -120.49729581105147,\n 35.04531474661148\n ],\n [\n -120.67300244190929,\n 34.286618961423045\n ],\n [\n -119.13556942190192,\n 33.95934343577578\n ],\n [\n -118.03740297903961,\n 33.26428523601193\n ],\n [\n -117.33457645560733,\n 32.526622846447296\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-10-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Cayan, Daniel R.","contributorId":219347,"corporation":false,"usgs":false,"family":"Cayan","given":"Daniel R.","affiliations":[{"id":38264,"text":"Scripps Institution of Oceanography","active":true,"usgs":false}],"preferred":false,"id":863511,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeHaan, Laurel","contributorId":302108,"corporation":false,"usgs":false,"family":"DeHaan","given":"Laurel","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":863512,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gershunov, Alexander","contributorId":261326,"corporation":false,"usgs":false,"family":"Gershunov","given":"Alexander","affiliations":[{"id":52819,"text":"Climate, Atmospheric Science and Physical Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA 92093, USA","active":true,"usgs":false}],"preferred":false,"id":863513,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Guzman-Morales, Janin","contributorId":261325,"corporation":false,"usgs":false,"family":"Guzman-Morales","given":"Janin","affiliations":[{"id":52819,"text":"Climate, Atmospheric Science and Physical Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, San Diego, CA 92093, USA","active":true,"usgs":false}],"preferred":false,"id":863514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keeley, Jon 0000-0002-4564-6521","orcid":"https://orcid.org/0000-0002-4564-6521","contributorId":216485,"corporation":false,"usgs":true,"family":"Keeley","given":"Jon","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863515,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mumford, Joshua","contributorId":302109,"corporation":false,"usgs":false,"family":"Mumford","given":"Joshua","email":"","affiliations":[{"id":15303,"text":"University of California, San Diego","active":true,"usgs":false}],"preferred":false,"id":863516,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Syphard, Alexandra D.","contributorId":298289,"corporation":false,"usgs":false,"family":"Syphard","given":"Alexandra D.","affiliations":[{"id":38279,"text":"Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":863517,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70237850,"text":"70237850 - 2022 - Actionable social science can guide community level wildfire solutions. An illustration from North Central Washington, US","interactions":[],"lastModifiedDate":"2022-10-26T11:43:38.700153","indexId":"70237850","displayToPublicDate":"2022-10-25T06:38:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2036,"text":"International Journal of Disaster Risk Reduction","active":true,"publicationSubtype":{"id":10}},"title":"Actionable social science can guide community level wildfire solutions. An illustration from North Central Washington, US","docAbstract":"In this study we illustrate the value of social data compiled at the community scale to guide a local wildfire mitigation and education effort. The four contiguous fire-prone study communities in North Central Washington, US, fall within the same jurisdictional fire service boundary and within one US census block group. Across the four communities, similar attitudes toward wildfire were observed. However, significant differences were found on the measures critical to tailoring wildfire preparation and mitigation programs to the local context such as risk mitigation behaviors, reported barriers to mitigation, and communication preferences across the four communities.
Groundwater-stream connectivity across mountain watersheds is critical for supporting streamflow during dry times and keeping streams cool during warm times, yet U.S. Geological Survey (USGS) stream measurements are often sparse in headwaters. Starting in 2019, the USGS Next Generation Water Observing System Program developed a multiscale methods and technology testbed approach to monitoring groundwater discharge to streams in the Neversink Reservoir watershed in the Catskill Mountains of New York. Groundwater discharge dynamics are complex across space and time because of geographic variability, topography, and preferential groundwater flow patterns, and the monitoring of discharge processes necessitates an innovative approach that includes emerging water tracing methods and enhanced local geologic mapping. This fact sheet describes the multiscale monitoring approach applied in the Neversink Reservoir watershed and specifically how the varied data types are complimentary in understanding groundwater-stream connectivity, with elements transferable to other mountain watersheds.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223077","usgsCitation":"Briggs, M.A., Gazoorian, C.L., Doctor, D.H., and Burns, D.A., 2022, A multiscale approach for monitoring groundwater discharge to headwater streams by the U.S. Geological Survey Next Generation Water Observing System Program—An example from the Neversink Reservoir watershed, New York: U.S. Geological Survey Fact Sheet 2022–3077, 6 p., https://doi.org/10.3133/fs20223077.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140810","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":501511,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113787.htm","linkFileType":{"id":5,"text":"html"}},{"id":435646,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9R3TYOZ","text":"USGS data release","linkHelpText":"Stream Temperature, Dissolved Radon, and Stable Water Isotope Data Collected along Headwater Streams in the Upper Neversink River Watershed, NY, USA (ver. 2.0, April 2023)"},{"id":408663,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3077/images/"},{"id":408662,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.XML"},{"id":408661,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223077/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3077"},{"id":408660,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3077/fs20223077.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3077"},{"id":408659,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3077/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Neversink Reservoir Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -74.20847061475345,\n 42.188348269216135\n ],\n [\n -74.59712682347332,\n 42.248700527889696\n ],\n [\n -75.01085762630467,\n 41.923046154032335\n ],\n [\n -74.86354438590236,\n 41.71281459504877\n ],\n [\n -74.42317182682854,\n 41.88105505768368\n ],\n [\n -74.19906764196153,\n 42.012767732647546\n ],\n [\n -74.20847061475345,\n 42.188348269216135\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Program Manager, Next Generation Water Observing System
Water Resources Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192