Lake ecosystems are shifting due to many drivers including climate change and landscape-scale habitat disturbance, diminishing their potential to support some fisheries. Walleye Sander vitreus (Mitchill) populations, which support recreational and tribal fisheries across North America, have declined in some lakes. Climate change, harvest, invasive species and concurrent increases in warm-water fishes (e.g. Centrarchidae) may have contributed to declines. To test the utility of an intensive management action to resist walleye loss, an experimental removal of ~285,000 centrarchids from a 33-ha lake over 4 years was conducted while monitoring the fish community response. Centrarchid abundance declined and yellow perch Perca flavescens (Mitchill) increased, yet no evidence of walleye recruitment was observed. These findings explore the feasibility of intensive resistance as a management strategy in supporting walleye facing environmental change and provide a platform for management discussions to move beyond resist strategies in the Resist-Accept-Direct (RAD) framework to navigate ecosystem change.
The U.S. Geological Survey worked in cooperation with the New York State Department of Environmental Conservation to assess the potential sources of fecal contamination entering Patchogue and Bellport Bays, two embayments on the south shore of Suffolk County, Long Island, New York. Water samples are routinely collected by the New York State Department of Environmental Conservation in the bays and analyzed for fecal coliform bacteria, an indicator of fecal contamination, to determine the need for closure of shellfish beds for harvest and consumption. Fecal coliform and other bacteria are an indicator of the potential presence of pathogenic (disease-causing) bacteria. However, indicator bacteria alone cannot determine the biological or geographical sources of contamination; therefore, microbial source tracking was implemented to determine various biological sources of contamination. In addition, information such as the location, weather and season, and surrounding land use where a sample was collected help determine the geographical source and conveyance of land-based water to the embayment.
Analysis revealed that the most substantial source of fecal contamination to Patchogue and Bellport Bays was discharge from sites draining ponds and wetlands into the rivers and tributaries sampled, particularly during the summer months. Fecal coliform bacteria at sites where ponds and wetlands drain are increased by stormwater runoff, which is another substantial source of fecal contamination. Overall, canine- and waterfowl-associated bacterial contributions were prevalent in source samples in both bays. Human-associated markers were present in surface-water source samples and completely absent in receptor samples in Patchogue Bay. The Fireplace Neck receptor site in Bellport Bay had a human-associated marker present in the summer wet sample only. Human markers were detected at the sample site downstream from the Patchogue wastewater treatment plant but were associated with low fecal coliform concentrations, indicating that the wastewater treatment plant is not a likely source of fecal contamination to Patchogue Bay. The lack of human-associated marker detections within Patchogue and Bellport Bays in summer source samples coupled with low to no detections of fecal coliform, especially where small marinas are present in creeks and tributaries, suggest that boats do not substantially contribute fecal coliform bacteria to the bays. There was little evidence of groundwater-contributing fecal bacteria by direct discharge from the subsurface. Further, the sandy sediment alongside Patchogue and Bellport Bays is unlikely to contribute fecal coliform bacteria from the test host organisms when resuspended in the water column. A classification scheme was developed to convey the degree of fecal contamination to stakeholders and resource managers. Based on this classification scheme, the Corey Creek Near Middle Road, Patchogue River Near Division Street, and Swan River Mouth sampling sites were identified as locations that contribute substantial fecal contamination to Patchogue Bay. In Bellport Bay, the Culvert at Beaverdam Creek site was identified as the location contributing the most substantial fecal contamination.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225007","collaboration":"Prepared in cooperation with New York State Department of Environmental Conservation","usgsCitation":"Tagliaferri, T.N., Fisher, S.C., Kephart, C.M., Cheung, N., Reed, A.P., and Welk, R.J., 2022, Using microbial source tracking to identify fecal contamination sources in Patchogue and Bellport Bays on Long Island, New York: U.S. Geological Survey Scientific Investigations Report 2022-5007, 30 p., https://doi.org/10.3133/sir20225007.","productDescription":"Report: vii, 30 p.; Database","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-129363","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":397884,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225007/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":397854,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20215033","text":"Scientific Investigations Report 2021–5033","linkHelpText":"- Overview and Methodology for a Study To Identify Fecal Contamination Sources Using Microbial Source Tracking in Seven Embayments on Long Island, New York"},{"id":397783,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5007/images/"},{"id":397782,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5007/sir20225007.XML"},{"id":397781,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the nation"},{"id":397780,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5007/sir20225007.pdf","text":"Report","size":"1.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5007"},{"id":397779,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5007/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, Patchogue Bay, Bellport Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.0810546875,\n 40.706148461723764\n ],\n [\n -72.83592224121094,\n 40.706148461723764\n ],\n [\n -72.83592224121094,\n 40.795617968801466\n ],\n [\n -73.0810546875,\n 40.795617968801466\n ],\n [\n -73.0810546875,\n 40.706148461723764\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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The U.S. Geological Survey, in cooperation with the Ashokan Watershed Stream Management Program, investigated the feasibility of bedload monitoring in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, from 2017 to 2020. Traditional bedload samples were collected at two locations: Birch Creek at Big Indian, New York (station 013621955), and Stony Clove Creek at Jansen Road at Lanesville, New York (station 01362336), during two storms. Measured bedload-transport rates ranged from less than 1 to 37.2 short tons per day during the study period. Active and passive tracers were deployed in Stony Clove Creek at Jansen Road to measure bed-material displacement during storms. Accelerometers in the active tracers provided data on the initiation and duration of motion of bed material in the 128- to 190-millimeter size class (B-axis measurement of 175 millimeters). The active tracers were loosely placed on the streambed and were generally mobilized at streamflows of 130–375 cubic feet per second. Displacement of the passive tracers was measured five times and provided data on the variability of displacement of multiple-size classes of bed material by different streamflows. Passive-tracer data also indicated that particles larger than the opening of the Elwha sampler deployed for traditional sampling may have been in transport during sampling. Sediment-generated noise was not distinguishable from background stream noise in hydrophone recordings.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1151","collaboration":"Prepared in cooperation with the Ashokan Watershed Stream Management Program","usgsCitation":"Siemion, J., Antidormi, M.R., Bonville, D.B., Finkelstein, J., and Marineau, M., 2022, Bed-material transport in the upper Esopus Creek watershed, Ulster and Greene Counties, New York, 2017–20: U.S. Geological Survey Data Report 1151, 20 p., https://doi.org/10.3133/dr1151.","productDescription":"Report: vi, 20 p.; Data Release","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-130089","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":397770,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1151/coverthb.jpg"},{"id":397771,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1151/dr1151.pdf","text":"Report","size":"3.84 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U.S. Geological Survey
425 Jordan Road
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The U.S. Geological Survey (USGS) calculated discharge at 13 water control structures in Florida using theoretical equations and uncalibrated coefficients gathered from previous studies and typical textbook values for selected flow regimes and structure types. These discharges were compared to the real-time discharges calculated and published by the USGS from October 1, 2007, to September 30, 2019, using traditional methods and coefficients verified by direct discharge measurements. The theoretical and USGS-calculated daily mean discharges were compared at each structure for different flow regimes covering the entire range of discharges that occurred over the study period except those less than 10 cubic feet per second to avoid large percentage errors for small actual differences in discharge. The discharges were also not compared if (1) any alterations were made to the USGS discharge to account for factors such as debris or construction, (2) any values were missing throughout the day, (3) the flow regime changed during the day, or (4) the USGS discharge was estimated. The structures compared include a mixture of vertical lift and radial gates with free and submerged conditions for orifice and weir flow.
The study totals showed that the average absolute difference for all structures was 18.7 percent. Average percent differences ranged from −26.5 to 28.6 percent, and 4 of the 13 structures had average differences within 10 percent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221011","usgsCitation":"Ryan, P.J., and Hazelbaker, C.L., 2022, Comparison of computed flow through manually operated water control structures in Florida using theoretical versus calibrated coefficients: U.S. Geological Survey Open-File Report 2022–1011, 25 p., https://doi.org/10.3133/ofr20221011.","productDescription":"Report: vii, 25 p.; Data Release; Dataset","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-129610","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":501757,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112754.htm","linkFileType":{"id":5,"text":"html"}},{"id":397688,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1011/coverthb.jpg"},{"id":397689,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1011/ofr20221011.pdf","text":"Report","size":"3.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1011"},{"id":397691,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1011/ofr20221011.XML"},{"id":397692,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1011/images"},{"id":397693,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MHF0GD","text":"USGS data release","linkHelpText":"Data for the comparison of computed flow through manually operated water control structures in Florida using theoretical versus calibrated coefficients"},{"id":397695,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397885,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221011/full","text":"Report","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.9248046875,\n 28.70986084394286\n ],\n [\n -83.1884765625,\n 28.110748760633534\n ],\n [\n -82.33154296875,\n 26.03704188651584\n ],\n [\n -81.23291015625,\n 25.443274612305746\n ],\n [\n -80.13427734374999,\n 25.383735254706842\n ],\n [\n -79.73876953125,\n 26.725986812271756\n ],\n [\n -80.22216796875,\n 28.323724553546015\n ],\n [\n -81.03515625,\n 29.496987596535742\n ],\n [\n -83.38623046875,\n 29.49698759653577\n ],\n [\n -82.9248046875,\n 28.70986084394286\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
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We propose a flexible model for a partially migrating species, which we demonstrate using yearly paths for golden eagles (Aquila chrysaetos). Our model relies on a smoothly time-varying potential surface defined by a number of attractors. We compare our proposed approach using varying coefficients to a latent-state model, which we define differently for migrating, dispersing, and local individuals. While latent-state models are more common in the existing animal movement literature, varying coefficient models have various benefits including the ability to fit a wide range of movement strategies without the need for major model adjustments. We compare simulations from the models for three individuals to illustrate the ability of our model to better describe movement behavior for specific movement strategies. We also demonstrate the flexibility of our model by fitting several individuals whose movement behavior is less stereotypical.
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2022 - Pesticide exposure of wild bees and honey bees foraging from field border flowers in intensively managed agriculture areas","interactions":[],"lastModifiedDate":"2022-04-12T14:17:47.27571","indexId":"70230125","displayToPublicDate":"2022-03-30T10:38:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Pesticide exposure of wild bees and honey bees foraging from field border flowers in intensively managed agriculture areas","docAbstract":"Bees are critical for food crop pollination, yet their populations are declining as agricultural practices intensify. Pollinator-attractive field border plantings (e.g. hedgerows and forb strips) can increase bee diversity and abundance in agricultural areas, however recent studies suggest these plants may contain pesticides. Pesticide exposure for wild bees in agricultural areas remains largely unknown, however this information is needed to inform agricultural practices and pesticide regulations meant to protect bees. It is important to determine whether border plantings that attract and support pollinators by providing floral resources may also deliver pesticides to them. In this study, we collected various samples for pesticide residue analysis including: multiple species of wild bees, honey bees, flowers from four types of bee-attractive field border plants, and soil. Silicone bands were also utilized as passive aerial samplers of pesticide residues. The five pesticides detected most frequently across all samples were the insecticide bifenthrin, the herbicides thiobencarb, metolaclor, and propanil, and the fungicide fluopyram. We detected the greatest number of parent pesticides in passive aerial samplers (24), followed by soil (21). Pesticides were also detected in field border plant flowers (16), which do not receive direct pesticides applications and many of which were not applied to adjacent field crops. Pesticide concentrations were lower in bees than in flowers but higher in bees than in soils. Pesticide residue per bee (ng/bee) increased with increasing wild bee size, though pesticide concentration (ng/g) did not increase. While honey bees and wild bees contained a similar number and concentration of pesticides overall, pesticide mixtures varied by bee type, and included some mixtures known to cause sublethal effects. The results from this study highlight the benefits of measuring more sample types to capture the total exposome of bees, including a greater range of bee species, as well as the need to consider exposure to pesticides at the landscape level. ","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.154697","usgsCitation":"Ward, L.T., Hladik, M.L., Guzman, A., Winsemius, S., Bautista, A., Kremen, C., and Mills, N., 2022, Pesticide exposure of wild bees and honey bees foraging from field border flowers in intensively managed agriculture areas: Science of the Total Environment, v. 831, 154697, 11 p., https://doi.org/10.1016/j.scitotenv.2022.154697.","productDescription":"154697, 11 p.","ipdsId":"IP-134964","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":448312,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.scitotenv.2022.154697","text":"Publisher Index Page"},{"id":435901,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S8TRLX","text":"USGS data release","linkHelpText":"Concentrations of pesticides in multiple matrices to measure exposure of wild bees visiting pollinator hedgerows in northern California"},{"id":397862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Colusa County, Yolo 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Laura T.","contributorId":289488,"corporation":false,"usgs":false,"family":"Ward","given":"Laura","email":"","middleInitial":"T.","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":839188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":221087,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":839189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Guzman, Aidee","contributorId":289489,"corporation":false,"usgs":false,"family":"Guzman","given":"Aidee","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":839190,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Winsemius, Sara","contributorId":289491,"corporation":false,"usgs":false,"family":"Winsemius","given":"Sara","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":839191,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bautista, Ariana","contributorId":289494,"corporation":false,"usgs":false,"family":"Bautista","given":"Ariana","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":839192,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kremen, Claire","contributorId":289497,"corporation":false,"usgs":false,"family":"Kremen","given":"Claire","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":839193,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mills, Nicholas","contributorId":289500,"corporation":false,"usgs":false,"family":"Mills","given":"Nicholas","email":"","affiliations":[{"id":36942,"text":"University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":839194,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230131,"text":"70230131 - 2022 - Evaluation of MPA designs that protect highly mobile megafauna now and under climate change scenarios","interactions":[],"lastModifiedDate":"2022-03-30T15:36:53.064256","indexId":"70230131","displayToPublicDate":"2022-03-30T10:16:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of MPA designs that protect highly mobile megafauna now and under climate change scenarios","docAbstract":"Marine protected area (MPA) designs, including large-scale MPAs (LSMPAs; >150,000 km2), mobile MPAs (fluid spatiotemporal boundaries), and MPA networks, may offer different benefits to species and could enhance protection by encompassing spatiotemporal scales of animal movement. We sought to understand how well LSMPAs could benefit nine highly-mobile marine species in the tropics now and into the future by: 1) evaluating current range overlap within a LSMPA; 2) evaluating range overlap under climate change projections; and 3) evaluating how well theoretical MPA designs benefit these nine species. We focused on Palmyra Atoll and Kingman Reef, a 2000 km2 area within the 1.2 million km2 U.S. Pacific Remote Islands Marine National Monument (PRIMNM) that contains marine megafauna (reef and pelagic fishes; sea turtles; seabirds; cetaceans) reflecting different behaviors and habitat use. Our approach is useful for evaluating the effectiveness of the Palmyra-Kingman MPA and PRIMNM in protecting these species, and tropical LSMPAs in general, and for informing future MPA design. Stationary MPAs provided protection at varying scales. Reef manta rays (Mobula alfredi), grey reef sharks (Carcharhinus amblyrhynchos), green sea turtles (Chelonia mydas), and bottlenose dolphins (Tursiops truncatus) had overall small ranges (<100 km from Palmyra-Kingman) and could benefit from stationary MPAs that contained heterogenous reef habitats. Yellowfin tuna (Thunnus albacares), sooty terns (Onychoprion fuscatus), red-footed boobies (Sula sula), great frigatebirds (Fregata minor), and melon-headed whales (Peponocephala electra) navigated complex oceanographic processes and may benefit most from mobile MPAs that shift with features including thermal fronts, cyclic regions of elevated productivity, and eddies, if relationships with these features are established and predictable. All species had capacity to travel to nearby reef systems, illustrating potential benefits of MPA networks and protected corridors. Suitable habitats will likely contract for all species as warm water expands under climate change scenarios (species habitats were predicted to decrease by 4–49% at Palmyra-Kingman) and MPAs may not protect suitable habitats into the future. Species habitat requirements and movement ecologies are critical aspects of marine spatial planning, especially with respect to dynamic ocean processes and a changing climate.
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The role of national parks in conservation, however, often goes far beyond limiting human influence. The U.S. National Park Service and its system of land units contribute substantively to conservation by providing protected lands where researchers can document trends in species distributions and abundances, examine characteristics important for generating these trends, and identify and implement conservation strategies to preserve biodiversity. We reviewed the contribution of U.S. national parks to amphibian research and conservation and highlight important challenges and findings in several key areas. First, U.S. national parks were instrumental in providing strong support that amphibian declines were real and unlikely to be simply a consequence of habitat loss. Second, research in U.S. national parks provided evidence against certain hypothesized causes of decline, like UV-B radiation, and evidence for others, such as introduced species and disease. However, describing declines and identifying causes contributes to conservation only if it leads to management; importantly, U.S. national parks have implemented many conservation strategies and evaluated their effectiveness in recovering robust amphibian populations. Among these, removal of invasive species, especially fishes; conservation translocations; and habitat creation and enhancement stand out as examples of successful conservation strategies with broad applicability. Successful management for amphibians is additionally complicated by competing mandates and stakeholder interests; for example, past emphasis on increasing visitor enjoyment by introducing fish to formerly fishless lakes had devastating consequences for many amphibians. Other potential conflicts with amphibian conservation include increasing development, increased risk of introductions of disease and exotic species with increased visitation, and road mortality. Decision science and leveraging partnerships have proven to be key components of effective conservation under conflicting mandates in national parks. As resource managers grapple with large-scale drivers that are outside local control, public-private partnerships and adaptive strategies are increasing in importance. U.S. national parks have played an important role in many aspects of identifying and ameliorating the amphibian decline crisis and will continue to be essential for the conservation of amphibians in the future.","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108631","usgsCitation":"Halstead, B., Ray, A.M., Muths, E., Campbell Grant, E.H., Grasso, R., Adams, M.J., Delaney, K., Carlson, J., and Hossack, B., 2022, Looking ahead, guided by the past: The role of U.S. national parks in amphibian research and conservation: Ecological Indicators, v. 136, 108631, 14 p., https://doi.org/10.1016/j.ecolind.2022.108631.","productDescription":"108631, 14 p.","ipdsId":"IP-132918","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448319,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108631","text":"Publisher Index 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production","interactions":[],"lastModifiedDate":"2022-04-21T13:39:21.769355","indexId":"70230691","displayToPublicDate":"2022-03-30T08:35:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3908,"text":"Royal Society Open Science","active":true,"publicationSubtype":{"id":10}},"title":"Vulnerability of avian populations to renewable energy production","docAbstract":"Renewable energy production can kill individual birds, but little is known about how it affects avian populations. We assessed the vulnerability of populations for 23 priority bird species killed at wind and solar facilities in California, USA. Bayesian hierarchical models suggested that 48% of these species were vulnerable to population-level effects from added fatalities caused by renewables and other sources. Effects of renewables extended far beyond the location of energy production to impact bird populations in distant regions across continental migration networks. Populations of species associated with grasslands where turbines were located were most vulnerable to wind. Populations of nocturnal migrant species were most vulnerable to solar, despite not typically being associated with deserts where the solar facilities we evaluated were located. Our findings indicate that addressing declines of North American bird populations requires consideration of the effects of renewables and other anthropogenic threats on both nearby and distant populations of vulnerable species.
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The 2019 Mw 7.1 Ridgecrest California earthquake rupture passed within 4 km of the Trona Pinnacles, a large group of tufa rock pillars. Reconnaissance following the Ridgecrest mainshock documented fresh damage to several of the Pinnacles. Repeated aerial photogrammetric surveys also documented damage during subsequent aftershocks. Here, we describe the photogrammetric data with emphasis on a specific rock spire that toppled during an Mw5.5 aftershock. We calculate the volumes of the intact spire and of its subsequent debris. To explore the utility of the pinnacles as fragile geologic features for constraining past earthquake shaking intensity, we calculate the quasi static, horizontal acceleration required to break the spire at its base. We also examine the response of this feature to observed shaking using a dynamic model of the spire. In this case, we find that the quasi‐static estimate provides a conservative maximum constraint on fragility. The dynamic model of the spire suggests that shaking during the Mw 7.1 mainshock likely generated tensile stresses in excess of the spire’s bulk strength, thereby making it vulnerable to collapse in subsequent aftershocks.
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Coral narrative rules","interactions":[],"lastModifiedDate":"2022-04-04T16:49:10.741224","indexId":"70230196","displayToPublicDate":"2022-03-29T11:42:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules","docAbstract":"As coral reef condition and sustainability continue to decline worldwide, losses of critical habitat and their ecosystem services have generated an urgency to understand and communicate reef response to management actions, environmental contamination, and natural disasters. Increasingly, coral reef protection and restoration programs emphasize the need for robust assessment tools for protecting high-quality waters and establishing conservation goals. Of equal importance is the need to communicate assessment results to stakeholders, beneficiaries, and the public so that environmental consequences of decisions are understood. The Biological Condition (BCG) model provides a structure to evaluate the condition of a coral reef in increments of change along a gradient of human disturbance. Communication of incremental change, regardless of direction, is important for decision makers and the public to better understand what is gained or lost depending on what actions are taken. We developed a narrative (qualitative) Biological Condition Gradient (BCG) from the consensus of a diverse expert panel to provide a framework for coral reefs in US Caribbean Territories. The model uses narrative descriptions of biological attributes for benthic organisms to evaluate reefs relative to undisturbed or minimally disturbed conditions. Using expert elicitation, narrative decision rules were proposed and deliberated to discriminate among six levels of change along a gradient of increasing anthropogenic stress. Narrative rules for each of the BCG levels are presented to facilitate the evaluation of benthic communities in coral reefs and provide specific narrative features to detect changes in coral reef condition and biological integrity. The BCG model can be used in the absence of numeric, or quantitative metrics, to evaluate actions that may encroach on coral reef ecosystems, manage endangered species habitat, and develop and implement management plans for marine protected areas, watersheds, and coastal zones. The narrative BCG model is a defensible model and communication tool that translates scientific results so the nontechnical person can understand and support both regulatory and non-regulatory water quality and natural resource programs.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108805","usgsCitation":"Santavy, D.L., Jackson, S.K., Jessup, B., Gerritsen, J., Rogers, C., Fisher, W.S., Weil, E., Szmant, A., Cuevas-Miranda, D., Walker, B.K., Jeffrey, C., Bradley, P., Ballantine, D., Roberson, L., Ruiz-Torres, H., Todd, B., Smith, T.B., Clark, R., Diaz, E.L., Bauza-Ortega, J., Horstmann, C., and Raimondo, S., 2022, A biological condition gradient for coral reefs in the US Caribbean Territories: Part I. Coral narrative rules: Ecological Indicators, v. 138, 108805, 13 p., https://doi.org/10.1016/j.ecolind.2022.108805.","productDescription":"108805, 13 p.","ipdsId":"IP-132623","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":448332,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108805","text":"Publisher Index Page"},{"id":398022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Puerto Rico, U.S. Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.236083984375,\n 17.38209494787749\n ],\n [\n -64.544677734375,\n 17.38209494787749\n ],\n [\n -64.544677734375,\n 18.646245142670608\n ],\n [\n 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However, there is a recognized need to update grazing management practices to be responsive and adaptive to future climate change. As a step toward developing a process to address this need, this project worked with Dinosaur National Monument to consider climate change in its grazing management planning process. In this project, we convened researchers, managers, subject-matter experts, and climate change adaptation specialists through a participatory climate change scenario planning workshop to develop and apply a small set of challenging, plausible, relevant, and divergent scenarios that qualitatively assessed how grazing resources and management may be affected under climate change.","language":"English","publisher":"North Central Climate Adaptation Center","usgsCitation":"Miller, B.W., Hardy, A., Spencer, E., Spaak, J., Eckert, G., Schuurman, G., Childress, A., Rangwala, I., Thoma, D., Frid, L., Sherrill, K.R., Zeigenfuss, L., and Bockus, D., 2022, Integrating climate considerations into grazing management programs in national parks: Final Project Report, 9 p.","productDescription":"9 p.","ipdsId":"IP-140869","costCenters":[{"id":40927,"text":"North Central Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":400374,"type":{"id":15,"text":"Index 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Danguole","contributorId":292170,"corporation":false,"usgs":false,"family":"Bockus","given":"Danguole","email":"","affiliations":[],"preferred":false,"id":843963,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70230121,"text":"70230121 - 2022 - Interlaboratory comparison of three sediment bioaccumulation tests","interactions":[],"lastModifiedDate":"2022-04-26T12:15:02.338916","indexId":"70230121","displayToPublicDate":"2022-03-29T11:12:45","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Interlaboratory comparison of three sediment bioaccumulation tests","docAbstract":"Standard bioaccumulation tests are commonly conducted using Macoma nasuta (clam), and Alitta virens (polychaete) for marine tests, and Lumbriculus variegatus (an oligochaete) for freshwater tests. Because the interlaboratory variability associated with these tests is unknown, four experienced laboratories conducted standard 28-day bioaccumulation tests with the above species using sediments contaminated with polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs). Chemical analysis of tissue samples was performed by a single laboratory. The intralaboratory variance among replicates was relatively low for PCB tissue concentrations, with coefficients of variation (CVs) ranging from 9% to 28% for all laboratories and species, with the exception of one laboratory reporting higher variability for L. variegatus (CV = 51%). Intralaboratory variance for PCB tissue concentrations was higher than interlaboratory variance for A. virens and L. variegatus, and the magnitude of difference (MOD) for laboratory means ranged from 1.4 to 2.0 across species. Intralaboratory variability was also low for lipid content, and lipid normalization of PCB and PAH body residues generally had little impact on variability. In addition to variability across bioassay laboratories, analytical variability was evaluated by different laboratories measuring the concentration of PCBs and total lipids in a subsample of tissue homogenate of sediment-exposed test organisms. Variability associated with tissue analysis was higher than bioassay laboratory variability only in tests with L. variegatus. Statistical differences between samples may be observed due to the low intralaboratory variability; however, the biological significance of these differences may be limited because the MOD is low. Considering the MOD when comparing bioaccumulation across treatments accounts for uncertainty related to inherent variability of the test in the interpretation of statistically significant results.
","language":"English","publisher":"Society of Environmental Chemistry and Toxicology (SETAC)","doi":"10.1002/etc.5300","usgsCitation":"Lotufo, G., Biedenbach, J.M., Farrar, J., Chanov, M.K., Hester, B.W., Warbritton, C.R., Steevens, J.A., Netchaev, J.M., Bednar, A., and Moore, D.W., 2022, Interlaboratory comparison of three sediment bioaccumulation tests: Environmental Toxicology and Chemistry, v. 41, no. 5, p. 1260-1275, https://doi.org/10.1002/etc.5300.","productDescription":"16 p.","startPage":"1260","endPage":"1275","ipdsId":"IP-130532","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":448334,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5300","text":"Publisher Index Page"},{"id":397867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","otherGeospatial":"New Bedford Harbor","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.92206954956055,\n 41.62237216807198\n ],\n [\n -70.90198516845703,\n 41.62237216807198\n ],\n [\n -70.90198516845703,\n 41.646107652521614\n ],\n [\n -70.92206954956055,\n 41.646107652521614\n ],\n [\n -70.92206954956055,\n 41.62237216807198\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-05-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Lotufo, Guilherme R.","contributorId":289468,"corporation":false,"usgs":false,"family":"Lotufo","given":"Guilherme R.","affiliations":[{"id":40033,"text":"US Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":839169,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biedenbach, James M.","contributorId":289469,"corporation":false,"usgs":false,"family":"Biedenbach","given":"James","email":"","middleInitial":"M.","affiliations":[{"id":40033,"text":"US Army Engineer Research and Development Center","active":true,"usgs":false}],"preferred":false,"id":839170,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Farrar, J. 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The toxicity of chloride is poorly understood in the high hardness waters characteristic of the region. We evaluated the toxicity of chloride to two endemic species, Daphnia magna (water flea) and Lemna gibba (duckweed), exposed in field-collected waters (hardness ~ 3000 mg/L as CaCO3) and reconstituted waters (hardness 370 mg/L as CaCO3) intended to mimic PPR background waters. We also investigated the role of chloride in the toxicity of water reconstituted to mimic legacy brine-contaminated wetlands, using two populations of native Pseudacris maculata (Boreal Chorus Frog). Chloride toxicity was similar in field-collected and reconstituted waters for both D. magna (LC50s 3070–3788 mg Cl−1/L) and L. gibba (IC50s 2441–2887). Although hardness can ameliorate chloride toxicity at low to high hardness, we did not observe additional protection as hardness increased from 370 to ~ 3000 mg/L. In P. maculata exposures, chloride did not fully explain toxicity. Chloride sensitivity also differed between populations, with mortality at 2000 mg Cl−/L in one population but not the other, and population-specific growth responses. Overall, these results (1) document toxicity to native species at chloride concentrations occurring in the PPR, (2) indicate that very high hardness in the region’s waters may not provide additional protection against chloride and (3) highlight challenges of brine investigations, including whether surrogate study populations are representative of local populations.
","language":"English","publisher":"Springer","doi":"10.1007/s00244-022-00927-6","usgsCitation":"Harper, D., Puglis, H.J., Kunz, B.K., and Farag, A., 2022, Chloride toxicity to native freshwater species in natural and reconstituted prairie pothole waters: Archives of Environmental Contamination and Toxicology, v. 82, no. 3, p. 416-428, https://doi.org/10.1007/s00244-022-00927-6.","productDescription":"13 p.","startPage":"416","endPage":"428","ipdsId":"IP-134067","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":435903,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BQ6YRZ","text":"USGS data release","linkHelpText":"Biological and chemical data from chloride bioassays with native wetland species in natural and reconstituted Prairie Pothole waters"},{"id":412680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri, Montana, North Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95.85320647480594,\n 48.99581509747179\n ],\n [\n -97.77592768425632,\n 48.99581509747179\n ],\n [\n -97.77592768425632,\n 48.223855848161435\n ],\n [\n -95.85320647480594,\n 48.223855848161435\n ],\n [\n -95.85320647480594,\n 48.99581509747179\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.64129262095554,\n 43.90595317724575\n ],\n [\n -110.64129262095554,\n 43.79956999926114\n ],\n [\n -110.49449847140379,\n 43.79956999926114\n ],\n [\n -110.49449847140379,\n 43.90595317724575\n ],\n [\n -110.64129262095554,\n 43.90595317724575\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -92.8164783846994,\n 40.361202906330504\n ],\n [\n -92.93357933722913,\n 40.361202906330504\n ],\n [\n -92.93357933722913,\n 40.26768330837069\n ],\n [\n -92.8164783846994,\n 40.26768330837069\n ],\n [\n -92.8164783846994,\n 40.361202906330504\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"82","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Harper, David 0000-0001-7061-8461 david_harper@usgs.gov","orcid":"https://orcid.org/0000-0001-7061-8461","contributorId":169848,"corporation":false,"usgs":true,"family":"Harper","given":"David","email":"david_harper@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863225,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Puglis, Holly J. 0000-0002-3090-6597 hpuglis@usgs.gov","orcid":"https://orcid.org/0000-0002-3090-6597","contributorId":4686,"corporation":false,"usgs":true,"family":"Puglis","given":"Holly","email":"hpuglis@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863226,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863227,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Farag, Aida 0000-0003-4247-6763 aida_farag@usgs.gov","orcid":"https://orcid.org/0000-0003-4247-6763","contributorId":200690,"corporation":false,"usgs":true,"family":"Farag","given":"Aida","email":"aida_farag@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":863228,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230103,"text":"70230103 - 2022 - Poplar Island: Understanding the development of a beneficial use restoration site","interactions":[],"lastModifiedDate":"2022-03-30T14:39:14.395853","indexId":"70230103","displayToPublicDate":"2022-03-29T09:26:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1462,"text":"Ecological Restoration","active":true,"publicationSubtype":{"id":10}},"title":"Poplar Island: Understanding the development of a beneficial use restoration site","docAbstract":"Poplar Island, like many other islands throughout the Chesapeake Bay, eroded from 460 hectares in 1847 to only 1.5 hectares by the 1990’s. However, the U.S. Army Corps of Engineers, Maryland Department of Transportation, and numerous other state and federal agencies selected this site as the location of a beneficial use project aimed at restoring remote island habitat in the Chesapeake Bay using clean dredge material. While monitoring efforts since the beginning of restoration efforts have documented extensive use of Poplar Island by numerous species across multiple taxa, these efforts have previously lacked context regarding underlying habitat patterns. However, such information is especially important on a site like Poplar Island where habitat distribution and availability can change dramatically between years due to ongoing construction efforts. To address this information gap we digitized annual aerial imagery of Poplar Island from 2006-2017 into 20 habitat classes. The resulting data layers demonstrate the transition of cells along the eastern side of the island from undeveloped cells to planted marsh cells, which matches trends seen in avian monitoring data. Similarly, our data display changes in the distribution of specific resources such as sand across the island, and how individual locations of interest such as constructed habitat islands have evolved over time. We believe that these data will provide critical insight into the factors influencing wildlife distribution patterns on Poplar Island, and will allow for the identification of management actions that may either be targeted or avoided in the planning of future beneficial use projects.","language":"English","publisher":"University of Wisconsin Press","doi":"10.3368/er.40.1.17","usgsCitation":"Prosser, D., Sullivan, J.D., Wall, J.L., Buck, E., Taylor, J.F., Callahan, C.R., and McGowan, P.C., 2022, Poplar Island: Understanding the development of a beneficial use restoration site: Ecological Restoration, v. 40, p. 17-24, https://doi.org/10.3368/er.40.1.17.","productDescription":"8 p.","startPage":"17","endPage":"24","ipdsId":"IP-128662","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":397857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland","county":"Talbot","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.39360427856445,\n 38.74250272111668\n ],\n [\n -76.3553237915039,\n 38.74250272111668\n ],\n [\n -76.3553237915039,\n 38.78339443129763\n ],\n [\n -76.39360427856445,\n 38.78339443129763\n ],\n [\n -76.39360427856445,\n 38.74250272111668\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","noUsgsAuthors":false,"publicationDate":"2022-03-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":839037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullivan, Jeffery D. 0000-0002-9242-2432","orcid":"https://orcid.org/0000-0002-9242-2432","contributorId":265822,"corporation":false,"usgs":true,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":839038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wall, Jennifer L.","contributorId":205845,"corporation":false,"usgs":false,"family":"Wall","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":839039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buck, Evan J","contributorId":265821,"corporation":false,"usgs":false,"family":"Buck","given":"Evan J","affiliations":[{"id":12716,"text":"University of Tennessee","active":true,"usgs":false}],"preferred":false,"id":839040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Taylor, John F.","contributorId":80890,"corporation":false,"usgs":false,"family":"Taylor","given":"John","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":839041,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Callahan, Carl R.","contributorId":205289,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl","email":"","middleInitial":"R.","affiliations":[{"id":37073,"text":"USFWS, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":839042,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":839043,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70230841,"text":"70230841 - 2022 - Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","interactions":[],"lastModifiedDate":"2022-04-26T14:00:19.627318","indexId":"70230841","displayToPublicDate":"2022-03-29T08:51:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5207,"text":"Minerals","active":true,"publicationSubtype":{"id":10}},"title":"Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain","docAbstract":"A previously completed mineral resources assessment of the Texas Coastal Plain indicated the potential for the future discovery of uranium resources. Geoenvironmental assessments that include the hydrogeologic framework can be used as a tool to understand the potential effects of mining operations. The hydrogeologic framework for this study focused on the composite hydrogeologic unit of the tract permissive for the occurrence of uranium consisting of the upper part of the Miocene-age Fleming Formation/Lagarto Clay, Pliocene-age Goliad and Pleistocene-age Willis Sands, Pleistocene-age Lissie and Beaumont Formations, and Holocene-age alluvial sediments (fluvial alluvium and eolian sand deposits). This composite hydrogeologic unit, which contains the Chicot and Evangeline aquifers of the Gulf Coast aquifer system, is intended for inclusion in a regional-scale geoenvironmental assessment of as yet undiscovered uranium resources. This article provides (1) a brief literature review describing the geologic and hydrogeologic settings, (2) the methodology used to develop a composite hydrogeologic framework, and (3) descriptions and maps of the land-surface altitude, composite hydrogeologic unit base and midpoint depth, water-level altitude, depth of water, unsaturated and saturated zone thickness, and transmissivity and hydraulic conductivity. A composite hydrogeologic unit, created by combining geologic and hydrogeologic data and maps for individual geologic and hydrogeologic units, is intended for use as a tool in a geoenvironmental assessment to evaluate potential contaminant migration through various avenues. Potential applications include using the hydrogeologic framework as an input into a geoenvironmental assessment to help estimate the potential for (1) runoff of contaminants into surface water, (2) infiltration of contaminants into the groundwater (aquifers), or (3) movement of contaminants from the mining area through wind, groundwater-flow, or streamflow in a given permissive tract. The procedures outlined in this paper also provide a method for developing hydrogeologic frameworks that can be applied in other areas where mining may occur.
","language":"English","publisher":"MDPI","doi":"10.3390/min12040420","usgsCitation":"Teeple, A., Becher, K.D., Walton-Day, K., Humberson, D.G., and Gallegos, T., 2022, Development and description of a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain: Minerals, v. 12, no. 4, 420, 29 p., https://doi.org/10.3390/min12040420.","productDescription":"420, 29 p.","ipdsId":"IP-136336","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":448338,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12040420","text":"Publisher Index Page"},{"id":435904,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AZMEU0","text":"USGS data release","linkHelpText":"Data used for developing a composite hydrogeologic framework for inclusion in a geoenvironmental assessment of undiscovered uranium resources in Pliocene- to Pleistocene-age geologic units of the Texas Coastal Plain"},{"id":399663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.84521484375,\n 29.707139348134145\n ],\n [\n -93.69140625,\n 30.29701788337205\n ],\n [\n -93.55957031249999,\n 30.996445897426373\n ],\n [\n -93.84521484375,\n 31.74685416292141\n ],\n [\n -94.0869140625,\n 32.287132632616384\n ],\n [\n -94.19677734375,\n 32.97180377635759\n ],\n [\n -95.537109375,\n 32.62087018318113\n ],\n [\n -96.7236328125,\n 30.86451022625836\n ],\n [\n -97.9541015625,\n 29.34387539941801\n ],\n [\n -99.11865234374999,\n 27.994401411046148\n ],\n [\n -99.36035156249999,\n 27.332735136859146\n ],\n [\n -99.29443359375,\n 26.745610382199022\n ],\n [\n -98.67919921875,\n 26.254009699865737\n ],\n [\n -98.06396484375,\n 26.03704188651584\n ],\n [\n -97.42675781249999,\n 25.958044673317843\n ],\n [\n -97.00927734375,\n 25.760319754713887\n ],\n [\n -97.1630859375,\n 26.54922257769204\n ],\n [\n -97.1630859375,\n 27.547241546253268\n ],\n [\n -95.8447265625,\n 28.5941685062326\n ],\n [\n -94.41650390625,\n 29.305561325527698\n ],\n [\n -93.84521484375,\n 29.707139348134145\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Becher, Kent D 0000-0002-3947-0793","orcid":"https://orcid.org/0000-0002-3947-0793","contributorId":290642,"corporation":false,"usgs":false,"family":"Becher","given":"Kent","email":"","middleInitial":"D","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":841446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Humberson, Delbert G 0000-0001-6789-9135","orcid":"https://orcid.org/0000-0001-6789-9135","contributorId":240891,"corporation":false,"usgs":false,"family":"Humberson","given":"Delbert","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":841448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gallegos, Tanya J. 0000-0003-3350-6473","orcid":"https://orcid.org/0000-0003-3350-6473","contributorId":206859,"corporation":false,"usgs":true,"family":"Gallegos","given":"Tanya J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":841449,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70230714,"text":"70230714 - 2022 - Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?","interactions":[],"lastModifiedDate":"2022-05-13T15:19:01.582298","indexId":"70230714","displayToPublicDate":"2022-03-29T06:42:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?","docAbstract":"The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML for emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality.
","language":"English","publisher":"Wiley","doi":"10.1002/hyp.14565","usgsCitation":"Varadharajan, C., Appling, A.P., Arora, B., Christianson, D., Hendrix, V., Kumar, V., Lima, A.R., Mueller, J., Oliver, S.K., Ombadi, M., Perciano, T., Sadler, J.M., Weierbach, H., Willard, J., Xu, Z., and Zwart, J.A., 2022, Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?: Hydrological Processes, v. 36, e14565, 22 p., https://doi.org/10.1002/hyp.14565.","productDescription":"e14565, 22 p.","ipdsId":"IP-133065","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":448340,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/hyp.14565","text":"Publisher Index Page"},{"id":399487,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","noUsgsAuthors":false,"publicationDate":"2022-04-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Varadharajan, Charuleka","contributorId":242712,"corporation":false,"usgs":false,"family":"Varadharajan","given":"Charuleka","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Appling, Alison P. 0000-0003-3638-8572 aappling@usgs.gov","orcid":"https://orcid.org/0000-0003-3638-8572","contributorId":150595,"corporation":false,"usgs":true,"family":"Appling","given":"Alison","email":"aappling@usgs.gov","middleInitial":"P.","affiliations":[{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":841218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arora, Bhavna 0000-0001-7841-886X","orcid":"https://orcid.org/0000-0001-7841-886X","contributorId":290532,"corporation":false,"usgs":false,"family":"Arora","given":"Bhavna","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Christianson, Danielle","contributorId":265829,"corporation":false,"usgs":false,"family":"Christianson","given":"Danielle","email":"","affiliations":[{"id":39617,"text":"Lawrence Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":841220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hendrix, Valerie 0000-0001-9061-8952","orcid":"https://orcid.org/0000-0001-9061-8952","contributorId":290533,"corporation":false,"usgs":false,"family":"Hendrix","given":"Valerie","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kumar, Vipin","contributorId":237812,"corporation":false,"usgs":false,"family":"Kumar","given":"Vipin","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":841222,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lima, Aranildo R.","contributorId":290536,"corporation":false,"usgs":false,"family":"Lima","given":"Aranildo","email":"","middleInitial":"R.","affiliations":[{"id":25337,"text":"Aquatic Informatics","active":true,"usgs":false}],"preferred":false,"id":841223,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Juliane 0000-0001-8627-1992","orcid":"https://orcid.org/0000-0001-8627-1992","contributorId":290539,"corporation":false,"usgs":false,"family":"Mueller","given":"Juliane","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841224,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Oliver, Samantha K. 0000-0001-5668-1165","orcid":"https://orcid.org/0000-0001-5668-1165","contributorId":211886,"corporation":false,"usgs":true,"family":"Oliver","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":841225,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ombadi, Mohammed","contributorId":290542,"corporation":false,"usgs":false,"family":"Ombadi","given":"Mohammed","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841226,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Perciano, Talita 0000-0002-2388-1803","orcid":"https://orcid.org/0000-0002-2388-1803","contributorId":290546,"corporation":false,"usgs":false,"family":"Perciano","given":"Talita","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841227,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sadler, Jeffrey Michael 0000-0001-8776-4844","orcid":"https://orcid.org/0000-0001-8776-4844","contributorId":260092,"corporation":false,"usgs":true,"family":"Sadler","given":"Jeffrey","email":"","middleInitial":"Michael","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":841228,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weierbach, Helen","contributorId":290549,"corporation":false,"usgs":false,"family":"Weierbach","given":"Helen","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841229,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Willard, Jared","contributorId":237808,"corporation":false,"usgs":false,"family":"Willard","given":"Jared","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":841230,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Xu, Zexuan","contributorId":290552,"corporation":false,"usgs":false,"family":"Xu","given":"Zexuan","email":"","affiliations":[{"id":38900,"text":"Lawrence Berkeley National Laboratory","active":true,"usgs":false}],"preferred":false,"id":841231,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Zwart, Jacob Aaron 0000-0002-3870-405X","orcid":"https://orcid.org/0000-0002-3870-405X","contributorId":237809,"corporation":false,"usgs":true,"family":"Zwart","given":"Jacob","email":"","middleInitial":"Aaron","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"preferred":true,"id":841232,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70237210,"text":"70237210 - 2022 - Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: Implications for people and plovers","interactions":[],"lastModifiedDate":"2022-10-04T11:43:54.452949","indexId":"70237210","displayToPublicDate":"2022-03-29T06:38:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"Predicted sea-level rise-driven biogeomorphological changes on Fire Island, New York: Implications for people and plovers","docAbstract":"Forecasting biogeomorphological conditions for barrier islands is critical for informing sea-level rise (SLR) planning, including management of coastal development and ecosystems. We combined five probabilistic models to predict SLR-driven changes and their implications on Fire Island, New York, by 2050. We predicted barrier island biogeomorphological conditions, dynamic landcover response, piping plover (Charadrius melodus) habitat availability, and probability of storm overwash under three scenarios of shoreline change (SLC) and compared results to observed 2014/2015 conditions. Scenarios assumed increasing rates of mean SLC from 0 to 4.71 m erosion per year. We observed uncertainty in several morphological predictions (e.g., beach width, dune height), suggesting decreasing confidence that Fire Island will evolve in response to SLR as it has in the past. Where most likely conditions could be determined, models predicted that Fire Island would become flatter, narrower, and more overwash-prone with increasing rates of SLC. Beach ecosystems were predicted to respond dynamically to SLR and migrate with the shoreline, while marshes lost the most area of any landcover type compared to 2014/2015 conditions. Such morphological changes may lead to increased flooding or breaching with coastal storms. However—although modest declines in piping plover habitat were observed with SLC—the dynamic response of beaches, flatter topography, and increased likelihood of overwash suggest storms could promote suitable conditions for nesting piping plovers above what our geomorphology models predict. Therefore, Fire Island may offer a conservation opportunity for coastal species that rely on early successional beach environments if natural overwash processes are encouraged.
The upper Mississippi River (UMR) contains diverse, dense, and reproducing assemblages of native freshwater mussels. In the case of an injury to mussels and their habitats, such as a hazardous material spill, train derailment, or barge grounding, resource managers have few restoration strategies. Resource managers need a means to document, quantify, and mitigate adverse effects on mussels resulting from injury. Habitat equivalency analysis (HEA), developed for use with a wide variety of habitat types, is a restoration scaling technique that compares ecological services lost from injury to ecological services gained through restoration actions. The U.S. Fish and Wildlife Service and Iowa Department of Natural Resources modified the HEA for use with native mussels. The mussel HEA has been applied within the UMR to estimate the quantity of restoration needed to compensate the public for injuries to mussels due to contaminant spills and construction projects. Our objective was to describe the UMR HEA for a general audience and assess if the four biological input variables used in the mussel HEA were reasonable based on literature values. We also evaluated the performance of HEA under a range of input scenarios. Although the input estimates used in HEA were within ranges reported in the peer-reviewed literature or were supported by professional judgment in the absence of peer-reviewed literature, outcomes of the mussel HEA were highly variable and would benefit from additional research to reduce uncertainty in the biological inputs. The application of HEA to mussels provides resource managers with a tool to quantify mussel-related ecological services lost from injury and to guide restoration efforts in the UMR.
When making decisions about how to restore habitat for at-risk species, land managers must balance multiple competing objectives in the face of uncertainty about the impacts of management actions on at-risk populations. The Oregon silverspot (Speyeria = Argynnis zerene hippolyta) is a federally threatened butterfly, and uncertainty exists about the effects of proposed management. We used structured decision making (SDM) to guide restoration for Oregon silverspots. Managers identified persistence as their primary objective and further identified effects of herbicides as a key uncertainty. We developed a model of population dynamics incorporating management actions and parameterized the model based on experiments with a surrogate subspecies, the Zerene silverspot (S. z. zerene). Herbicide application may directly harm Oregon silverspots by increasing larval mortality, while indirectly benefiting silverspots by reducing the competition faced by their host plant. In this system, potential herbicide treatments include grass-specific fluazifop-P-butyl or forb-specific clopyralid applied with Agri-Dex® or Nu-Film®-IR adjuvants. We found that Oregon silverspot population growth rate is greatest with clopyralid and fluazifop-P-butyl when combined with Nu-Film®-IR than other alternatives (including no treatment) considering only direct effects or in combination with indirect effects. An adaptive management program would benefit Oregon silverspots by promoting exploration of management alternatives while allowing managers to act quickly to arrest steep declines. Uncertainty remains regarding how well our results will generalize in field conditions.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10841-022-00379-2","usgsCitation":"Doll, C., Converse, S.J., Edwards, C., and Schultz, C., 2022, Using structured decision making to guide habitat restoration for butterflies: A case study of Oregon silverspots: Journal of Insect Conservation, v. 26, p. 219-230, https://doi.org/10.1007/s10841-022-00379-2.","productDescription":"12 p.","startPage":"219","endPage":"230","ipdsId":"IP-133483","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":489386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","noUsgsAuthors":false,"publicationDate":"2022-03-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Doll, Cassandra F.","contributorId":356208,"corporation":false,"usgs":false,"family":"Doll","given":"Cassandra F.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":938878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":938877,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edwards, Collin B.","contributorId":356209,"corporation":false,"usgs":false,"family":"Edwards","given":"Collin B.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":938879,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schultz, Cheryl B.","contributorId":356210,"corporation":false,"usgs":false,"family":"Schultz","given":"Cheryl B.","affiliations":[{"id":37380,"text":"Washington State University","active":true,"usgs":false}],"preferred":false,"id":938880,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70230016,"text":"sir20215123 - 2022 - Synthesizing ground magnetic disturbance using dipole-aligned loop elementary currents and Biot-Savart relationship","interactions":[],"lastModifiedDate":"2022-03-29T10:43:37.616302","indexId":"sir20215123","displayToPublicDate":"2022-03-28T15:50:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5123","displayTitle":"Synthesizing Ground Magnetic Disturbance Using Dipole-Aligned Loop Elementary Currents and Biot-Savart Relationship","title":"Synthesizing ground magnetic disturbance using dipole-aligned loop elementary currents and Biot-Savart relationship","docAbstract":"This report presents a method for constructing a simplified numerical description of the electric current distributions in the ionosphere and gap region based on dipole-aligned loop elementary currents (DALECs). A theoretical basis for DALECs is presented, along with a prototypical algorithm for constructing an elementary numerical DALEC. The algorithm is verified and validated by combining DALECs with an efficient Biot-Savart solver in order to estimate magnetic disturbance on the Earth’s surface. We examine (1) simple scenarios with known solutions and (2) hemispherical magnetic disturbance fields obtained from a state-of-the-art global geospace circulation model.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20215123","collaboration":"Prepared in cooperation with National Center for Atmospheric Research High Altitude Observatory","usgsCitation":"Rigler, E.J., and Wiltberger, M., 2022, Synthesizing ground magnetic disturbance using dipole-aligned loop elementary currents and Biot-Savart relationship: U.S. Geological Survey Scientific Investigations Report 2021–5123, 17 p., https://doi.org/10.3133/sir20215123.","productDescription":"Report: vi, 17 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-126434","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":397506,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5123/sir50215123.pdf","text":"Report","size":"4.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5123"},{"id":397505,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5123/coverthb.jpg"},{"id":397507,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2021/5123/sir50215123_appendix.pdf","text":"Appendix 1. Algorithms","size":"296 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5123 Appendix 1. Algorithms"},{"id":397508,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5123/images"},{"id":397509,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5123/sir50215123.xml"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225
Monitoring discharge in the Chicago Sanitary and Ship Canal is critical for the accounting done by the U.S. Army Corps of Engineers of the diversion of water from Lake Michigan to the Mississippi River Basin by the State of Illinois. The primary streamgage used for this discharge monitoring, the Chicago Sanitary and Ship Canal near Lemont, Illinois (U.S. Geological Survey station 05536890), is operated by the U.S. Geological Survey as an index-velocity station and at the time of this study (water years 2006–16) had two continuous velocity meters (an acoustic Doppler velocity meter and an acoustic velocity meter) and a water-level sensor, among other instruments. Discharge is computed at the streamgage using an index-velocity rating developed by linear regression of the velocity meter values fitted to discharges intermittently measured with an acoustic Doppler current profiler. In this study, the uncertainties of the velocity meters and stage sensors were estimated using a type B (judgment-based) approach, and measured discharge uncertainties were taken from those provided by a common acoustic Doppler current profiler data processing software tool, QRev. The velocity meter uncertainties, expressed as standard deviations, were estimated to be about 2.5 percent of velocity except near zero, where they exceeded that fraction, whereas for the acoustic Doppler current profiler uncertainties, when converted to mean channel velocity, 2.5 percent of velocity was determined to be a lower bound. The estimated velocity meter and measured discharge uncertainties were compared to index-velocity ratings developed from regression analyses of two types: (1) those that allow specification of measurement uncertainties and (2) ordinary least squares (OLS) regression, which does not. Based on the linearity of the index-velocity rating and the approximate agreement of the distributions of the fitting and prediction velocities, the assumptions required for unbiased prediction by OLS regression were determined to be approximately satisfied. From the regression residuals, it was determined that the estimated measurement uncertainties are too small, too similar between acoustic velocity meter and acoustic Doppler velocity meter velocities, and possibly too strongly dependent on velocity. Large, non-Gaussian OLS regression residuals also were observed. The uncertainty of annual mean discharge computed using the different regressions also was considered and was determined to be strongly dependent on the assumed measurement uncertainty. Because the assumptions required for OLS regression to give unbiased and variance-maintaining predictions were determined to be approximately satisfied, the results of discharge computation using the index-velocity rating based on OLS regression were deemed to be reliable. These results indicate about 0.8-percent uncertainty in the computed discharge as measured by the coefficient of variation at the annual time scale when using the acoustic Doppler velocity meter and 1.2-percent uncertainty with the acoustic velocity meter. It may be possible to improve the accuracy of the computed discharge and its uncertainty by further examining the measurement uncertainties and addressing differences in the distributions of the velocities used in fitting the index-velocity ratings and those used in prediction. Although the index-velocity ratings and computed discharges presented in this study are similar to those used in computing the published discharge at the study streamgage, the values presented in this report are not intended to replace the published discharge.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221007","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Chicago District","usgsCitation":"Over, T.M., Muste, M., Duncker, J.J., Tsai, H., Jackson, P.R., Johnson, K.K., Engel, F.L., and Prater, C.D., 2022, Uncertainty analysis of index-velocity meters and discharge computations at the Chicago Sanitary and Ship Canal near Lemont, Illinois, water years 2006–16: U.S. Geological Survey Open-File Report 2022–1007, 35 p., https://doi.org/10.3133/ofr20221007.","productDescription":"Report: viii, 35 p.; Appendix; Data Release; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-125889","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501754,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112755.htm","linkFileType":{"id":5,"text":"html"}},{"id":397713,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7X63K41","text":"USGS data release","linkHelpText":"Discharge measurements at U.S. Geological Survey streamgage 05536890 Chicago Sanitary and Ship Canal near Lemont, Illinois, 2005–2013"},{"id":397709,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007_appendix2.pdf","text":"Appendix 2","size":"7.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1007 appendix 2","linkHelpText":"—Slides"},{"id":397714,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":397708,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007.pdf","text":"Report","size":"13.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1007"},{"id":397712,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1007/images"},{"id":397711,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1007/ofr20221007.XML"},{"id":397707,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1007/coverthb.jpg"}],"country":"United States","state":"Illinois","city":"Lemont","otherGeospatial":"Chicago Sanitary and Ship Canal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06846618652344,\n 41.66573093599398\n ],\n [\n -88.04718017578125,\n 41.64469659784919\n ],\n [\n -87.86796569824217,\n 41.699063978799174\n ],\n [\n -87.75672912597656,\n 41.789744876718984\n ],\n [\n -87.7979278564453,\n 41.83068856472101\n ],\n [\n -87.92701721191406,\n 41.75645886225854\n ],\n [\n -88.06709289550781,\n 41.6908605241911\n ],\n [\n -88.06846618652344,\n 41.66573093599398\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
405 North Goodwin Ave.
Urbana, IL 61801