Accurate estimates of flood frequency and magnitude on rivers and streams in Maine are a key component of effective flood risk management, flood mitigation, and flood recovery programs for the State. Flood-frequency estimates are published here for 148 streamgages in and adjacent to Maine. Equations are provided for users to compute flood-frequency estimates at any location on a stream that does not have a streamgage. Estimates and equations are presented for peak flows with annual exceedance probabilities (AEPs) of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent. AEPs correspond to flood recurrence intervals of 2, 5, 10, 25, 50, 100, 200, and 500 years, respectively. New estimates use a regional skew coefficient of 0.02 with a standard error of prediction of 0.30 developed specifically for Maine as a part of this work.
Equations are designed for use at ungaged sites without substantial flow regulation or urbanization in Maine, with drainage areas between 0.26 and 5,680 square miles. The equations were developed from streamflows and basin characteristics at 124 unregulated streamgages using generalized least-squares regression techniques. Explanatory variables used in the equations for computing peak flows are drainage area, percentage of area in the basin that contains wetlands, and basin mean 24-hour rainfall intensities. The average standard error of prediction (ASEP) for these equations ranges from −31.5 to 45.9 percent for the 50-percent AEP and from −34.2 to 52.0 percent for the 0.2-percent AEP. Equations that use only drainage area are provided for use in cases where lower accuracy is acceptable. The ASEP for estimating peak flows with these simpler equations ranges from −40 to 66 percent for the 50-percent AEP and from −44 to 79 percent for the 0.2-percent AEP.
Final peak flows at unregulated streamgages are computed as weighted averages between the at-station peak flows and peak flows computed at those same sites using the regression equations. Peak flow estimates and equations presented here are accessible in the U.S. Geological Survey StreamStats application. StreamStats is a web application that computes selected basin characteristics and estimates of peak flows and other available streamflow statistics for user-selected streams in Maine.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205092","collaboration":"Prepared in cooperation with the Maine Department of Transportation","usgsCitation":"Lombard, P.J., and Hodgkins, G.A., 2020, Estimating flood magnitude and frequency on gaged and ungaged streams in Maine: U.S. Geological Survey Scientific Investigations Report 2020–5092, 56 p., https://doi.org/10.3133/sir20205092.","productDescription":"Report: vii, 56 p.; 2 Tables; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-109858","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":379515,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5092/sir20205092_table1.4.csv","text":"Table 1.4","size":"49.3 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Peak flows for selected annual exceedance probabilities for selected 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In 2004, the U.S. Geological Survey (USGS) began sampling 14 wadable streams in urban or urbanizing watersheds near Milwaukee, Wisconsin. The overall goal of the study is to assess the health of the aquatic communities in the Milwaukee Metropolitan Sewerage District planning area to inform current and future watershed management. In addition to collection of biological data on aquatic communities, physical and chemical data were also collected to evaluate effects of potential environmental stressors on the aquatic communities. This fact sheet summarizes the primary results of the study from 2004 to 2013. Detailed information is described in Scudder Eikenberry and others (2020a), and all data are available in Scudder Eikenberry and others (2020b; https://doi.org/10.5066/P9FWMODL).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203051","issn":"2327-6932","usgsCitation":"Scudder Eikenberry, B., Nott, M.A., Stewart, J.S., Sullivan, D.J., Alvarez, D.A., Bell, A.H., and Fitzpatrick, F.A., 2020, Physical and chemical stressors on algal, invertebrate, and fish communities in 14 Milwaukee area streams, 2004–2013: U.S. Geological Survey Fact Sheet 2020-3051, 6 p., https://doi.org/10.3133/fs20203051.","productDescription":"Report: 6 p.; Data Release","onlineOnly":"Y","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":379614,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FWMODL","text":"USGS data release","description":"USGS data release","linkHelpText":"Aquatic community and environmental data for 14 rivers and streams in the Milwaukee Metropolitan Sewerage District Planning Area, 2004-13"},{"id":379615,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20205035","text":"SIR 2020-5035—","description":"SIR 2020-5035","linkHelpText":"Ecological Status of Aquatic Communities in Selected Streams in the Milwaukee Metropolitan Sewerage District Planning Area of Wisconsin, 2004–13"},{"id":379612,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3051/coverthb.jpg"},{"id":379613,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3051/fs20203051.pdf","text":"Report","size":"8.97 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020-3051"}],"country":"United States","state":"Wisconsin","city":"Milwaukee","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.1982421875,\n 42.88401467044253\n ],\n [\n -87.84530639648436,\n 42.88401467044253\n ],\n [\n -87.84530639648436,\n 43.313188139196406\n ],\n [\n -88.1982421875,\n 43.313188139196406\n ],\n [\n -88.1982421875,\n 42.88401467044253\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
8505 Research Way
Middleton, WI 53562
This study developed a spatially explicit framework to support the conservation of Western Brook Lamprey Lampetra richardsoni and Pacific Lamprey Entosphenus tridentatus in the Umpqua River basin, Oregon. This framework identified locations within the river network likely to support “potential burrowing habitat” for lamprey larvae based on geomorphic conditions and evaluated the overlap of potential burrowing habitat with water temperatures suitable for the nonnative, piscivorous Smallmouth Bass Micropterus dolomieu. The study also documented reach‐scale factors that create heterogeneity in potential burrowing habitat to guide on‐the‐ground habitat restoration. Based on criteria for mean annual suspended sediment loads and channel slope, 18% of the Umpqua River network was classified as potential burrowing habitat. Existing mean August water temperatures of ≥20°C were suitable for Smallmouth Bass for 32% of the potential burrowing habitat. This percentage increased to 41% of the potential burrowing habitat using projected mean August water temperatures for year 2040, suggesting that water temperatures in the future will facilitate upstream expansion of Smallmouth Bass into the potential burrowing habitat. At finer spatial scales, potential burrowing habitat was influenced by channel features, such as large wood, pools, and local channel slope and width. These results provide an initial template for identifying locations in river networks likely to have potential burrowing habitat, considering the overlap between threats and lamprey habitats, and planning conservation actions to support native lampreys.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10487","usgsCitation":"Jones, K., Dunham, J.B., O'Connor, J., Keith, M.K., Mangano, J.F., Coates, K., and Mackie, T., 2020, River network and reach‐scale controls on habitat for lamprey larvae in the Umpqua River Basin, Oregon: North American Journal of Fisheries Management, v. 40, no. 6, p. 1400-1416, https://doi.org/10.1002/nafm.10487.","productDescription":"17 p.","startPage":"1400","endPage":"1416","ipdsId":"IP-109592","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":454987,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/nafm.10487","text":"Publisher Index Page"},{"id":436747,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CXSCK4","text":"USGS data release","linkHelpText":"Geomorphic and larval lamprey surveys in tributaries of the Umpqua River, Oregon"},{"id":379761,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umpqua River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.1290283203125,\n 42.40317854182803\n ],\n [\n -121.3275146484375,\n 42.40317854182803\n ],\n [\n -121.3275146484375,\n 44.08758502824516\n ],\n [\n -124.1290283203125,\n 44.08758502824516\n ],\n [\n -124.1290283203125,\n 42.40317854182803\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-10-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Krista 0000-0002-0301-4497","orcid":"https://orcid.org/0000-0002-0301-4497","contributorId":205206,"corporation":false,"usgs":true,"family":"Jones","given":"Krista","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802963,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":802964,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Connor, Jim E. 0000-0002-7928-5883 oconnor@usgs.gov","orcid":"https://orcid.org/0000-0002-7928-5883","contributorId":140771,"corporation":false,"usgs":true,"family":"O'Connor","given":"Jim E.","email":"oconnor@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":false,"id":802965,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keith, Mackenzie K. 0000-0002-7239-0576 mkeith@usgs.gov","orcid":"https://orcid.org/0000-0002-7239-0576","contributorId":196963,"corporation":false,"usgs":true,"family":"Keith","given":"Mackenzie","email":"mkeith@usgs.gov","middleInitial":"K.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802966,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mangano, Joseph F. 0000-0003-4213-8406 jmangano@usgs.gov","orcid":"https://orcid.org/0000-0003-4213-8406","contributorId":4722,"corporation":false,"usgs":true,"family":"Mangano","given":"Joseph","email":"jmangano@usgs.gov","middleInitial":"F.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802967,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coates, Kelly","contributorId":244008,"corporation":false,"usgs":false,"family":"Coates","given":"Kelly","affiliations":[],"preferred":false,"id":803016,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mackie, Travis","contributorId":244009,"corporation":false,"usgs":false,"family":"Mackie","given":"Travis","email":"","affiliations":[],"preferred":false,"id":803017,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70215132,"text":"70215132 - 2020 - Spectral wave-driven bedload transport across a coral reef flat/lagoon complex","interactions":[],"lastModifiedDate":"2020-10-29T15:04:55.965749","indexId":"70215132","displayToPublicDate":"2020-10-22T10:04:11","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Spectral wave-driven bedload transport across a coral reef flat/lagoon complex","docAbstract":"Coral reefs are an important source of sediment for reef-lined coasts by helping to maintain beaches while also providing protection in the form of wave energy dissipation. Understanding the mechanisms by which sediment is delivered to the coast as well as better constraining the total volumes generated are critical for projecting future coastal change. A month-long hydrodynamics and sediment transport study on a fringing reef/lagoon complex in Western Australia indicates that lower frequency constituents of wave energy are important to the total bedload transport of sediment across the reef flat and lagoon to the shoreline. The reef flat and the lagoon are characterized by distinctly different transport regimes, resulting in an offset in the timing of bedform migration between the two. Short-term storage of sediment is noted on the reef flat, which is subsequently washed out into the lagoon when offshore wave heights increase and strong currents due to wave breaking at the reef crest develop. This sudden influx of sediment is a significant control on bedform migration rates in the lagoon. Infragravity wave energy on the reef flat and lagoon make an important contribution to the migration of bedforms and resultant bedload transport. Given the complexity of the hydrodynamics of fringing reefs, the transfer of energy to lower frequency bands, as well as accurate estimates of sources and sinks of sediment, must but considered in order to correctly model the transport of sediment from the reef to the coast.
Quantifying the distribution of animals and identifying underlying characteristics that define suitable habitat are essential for effective conservation of free-ranging species. Prioritizing areas for conservation is important in managing a geographic extent that has a high level of disturbance and limited conservation resources. We examined the potential use of a species distribution model ensemble for multi-species conservation in marine habitats. Using satellite telemetry locations during foraging as input data, and ensemble ecological niche models, we predicted foraging areas for 2 nesting marine turtle species within the Gulf of Mexico (GoM): Kemp’s ridley Lepidochelys kempii (n = 63) and loggerhead Caretta caretta (n = 63). We considered 7 geophysical, biological, and climatic variables and compared contributing factors for each species’ foraging habitat selection. For both species, predicted suitable foraging habitats encompassed large areas along the GoM coast, but only intersected with each other in relatively small areas. Highly parameterized models resulted in overall greater fits, suggesting that multiple factors influence habitat selection by these species. Model validation results were mixed: cross-validation resulted in high prediction accuracy for both species, but an evaluation against independent data resulted in a low omission rate (5%) for Kemp’s ridleys and a high omission rate (72%) for loggerheads. The relatively small intersection of model-predicted foraging areas for these 2 species within the study area may indicate possible niche differentiations. The high omission rate for loggerheads indicates our samples likely underrepresent the population and illustrates the challenges in predicting suitable foraging extents for species that make dynamic movements and have greater individual variability.
","language":"English","publisher":"Inter Research","doi":"10.3354/esr01059","usgsCitation":"Fujisaki, I., Hart, K., Bucklin, D.N., Iverson, A., Rubio, C., Lamont, M.M., Miron, R.D., Burchfield, P., Pena, J., and Shaver, D.J., 2020, Predicting multi-species foraging hotspots for marine turtles in the Gulf of Mexico: Endangered Species Research, v. 43, p. 253-266, https://doi.org/10.3354/esr01059.","productDescription":"14 p.","startPage":"253","endPage":"266","ipdsId":"IP-120330","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":454990,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01059","text":"Publisher Index Page"},{"id":380022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Mexico","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.701171875,\n 21.779905342529645\n ],\n [\n -96.0205078125,\n 18.22935133838668\n ],\n [\n -93.2958984375,\n 17.518344187852218\n ],\n [\n -91.0546875,\n 18.104087015773956\n ],\n [\n -80.5517578125,\n 24.966140159912975\n ],\n [\n -82.6171875,\n 30.977609093348686\n ],\n [\n -97.822265625,\n 30.486550842588485\n ],\n [\n -98.701171875,\n 21.779905342529645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fujisaki, Ikuko","contributorId":38359,"corporation":false,"usgs":false,"family":"Fujisaki","given":"Ikuko","affiliations":[],"preferred":false,"id":803648,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hart, Kristen 0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":206816,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803649,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bucklin, David N.","contributorId":175273,"corporation":false,"usgs":false,"family":"Bucklin","given":"David","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":803650,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Iverson, Autumn R. 0000-0002-8353-6745","orcid":"https://orcid.org/0000-0002-8353-6745","contributorId":173555,"corporation":false,"usgs":false,"family":"Iverson","given":"Autumn R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":803651,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rubio, Cynthia","contributorId":244274,"corporation":false,"usgs":false,"family":"Rubio","given":"Cynthia","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":803652,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lamont, Margaret M. 0000-0001-7520-6669","orcid":"https://orcid.org/0000-0001-7520-6669","contributorId":218323,"corporation":false,"usgs":true,"family":"Lamont","given":"Margaret","email":"","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":803653,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Miron, Raul de Jesus G.D.","contributorId":244275,"corporation":false,"usgs":false,"family":"Miron","given":"Raul","email":"","middleInitial":"de Jesus G.D.","affiliations":[{"id":48880,"text":"Acuario de Veracruz A.C., Veracruz, Veracruz Mexico","active":true,"usgs":false}],"preferred":false,"id":803654,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Burchfield, Patrick M.","contributorId":244276,"corporation":false,"usgs":false,"family":"Burchfield","given":"Patrick M.","affiliations":[{"id":48881,"text":"Gladys Porter Zoo","active":true,"usgs":false}],"preferred":false,"id":803655,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pena, Jaime","contributorId":168392,"corporation":false,"usgs":false,"family":"Pena","given":"Jaime","email":"","affiliations":[],"preferred":false,"id":803656,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Shaver, Donna J.","contributorId":191186,"corporation":false,"usgs":false,"family":"Shaver","given":"Donna","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":803657,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70224320,"text":"70224320 - 2020 - U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) program overview and status as of October 22, 2020","interactions":[],"lastModifiedDate":"2022-09-05T13:15:35.616524","indexId":"70224320","displayToPublicDate":"2020-10-22T08:10:08","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"displayTitle":"U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) Program Overview and Status as of October 22, 2020","title":"U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) program overview and status as of October 22, 2020","docAbstract":"The USGS Water-Use Data and Research Program (WUDR) is an appropriated program that began in Federal fiscal year 2015 and is authorized under the SECURE Water Act (Sec. 9508 (c)). WUDR provides financial assistance through cooperative agreements to State water resource agencies.\nThe WUDR Program has two main goals:\nTo improve the availability, quality, compatibility, and delivery of water-use data that are collected and/or estimated by States to support national water-use assessments; and\nTo integrate the water-use data into USGS databases in electronic or machine-readable formats.","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"Shaffer, K., 2020, U.S. Geological Survey (USGS) Water-Use Data and Research (WUDR) program overview and status as of October 22, 2020, 9 p.","productDescription":"9 p.","ipdsId":"IP-124105","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":389592,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":389591,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://water.usgs.gov/wausp/wudr-files/WUDR-Overview-20201022.pdf"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shaffer, Kimberly 0000-0001-9386-7671 kshaffer@usgs.gov","orcid":"https://orcid.org/0000-0001-9386-7671","contributorId":206648,"corporation":false,"usgs":true,"family":"Shaffer","given":"Kimberly","email":"kshaffer@usgs.gov","affiliations":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":823746,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70224302,"text":"70224302 - 2020 - Spatial fingerprint of younger dryas cooling and warming in eastern North America","interactions":[],"lastModifiedDate":"2021-09-21T13:03:33.362473","indexId":"70224302","displayToPublicDate":"2020-10-22T08:00:04","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spatial fingerprint of younger dryas cooling and warming in eastern North America","docAbstract":"The Younger Dryas (YD, 12.9–11.7 ka) is the most recent, near-global interval of abrupt climate change with rates similar to modern global warming. Understanding the causes and biodiversity effects of YD climate changes requires determining the spatial fingerprints of past temperature changes. Here we build pollen-based and branched glycerol dialkyl glycerol tetraether-based temperature reconstructions in eastern North America (ENA) to better understand deglacial temperature evolution. YD cooling was pronounced in the northeastern United States and muted in the north central United States. Florida sites warmed during the YD, while other southeastern sites maintained a relatively stable climate. This fingerprint is consistent with an intensified subtropical high during the YD and demonstrates that interhemispheric responses were more complex spatially in ENA than predicted by the bipolar seesaw model. Reduced-amplitude or antiphased millennial-scale temperature variability in the southeastern United States may support regional hotspots of biodiversity and endemism.
Spectral analysis is widely used to estimate and refine earthquake source parameters such as source radius, seismic moment, and stress drop. This study aims to quantify the precision of the single spectra and empirical Green's function spectral ratio approach using the Large‐n Seismic Survey in Oklahoma (LASSO) array. The dense station coverage in an area of local saltwater disposal offers a unique opportunity to observe and quantify radiation pattern effects and subsequent precision of spectral estimates of small earthquakes (M < 3). The results suggest that the precision of source properties estimated from direct phase arrivals for arrays with less than 20 stations should be assumed to be not less than 30% and could be as high as 150% if less than five stations are used. Furthermore, we do not see clear evidence for, or against, a scaling of stress drop with magnitude of small earthquakes (M < 3) as observed by other studies.
Chlorinated volatile organic compounds (CVOCs) have migrated to groundwater beneath a former 9-acre landfill at Operable Unit 1 (OU-1) on Naval Base Kitsap, which was active from the 1930s through 1973 on the Keyport Peninsula, in Kitsap County, Washington. Biodegradation of CVOCs at OU-1 limits the mass of dissolved-phase CVOCs in groundwater that discharges to surface water, but contaminant concentrations up to 630 milligrams per liter persist in localized areas, likely from the dissolution of residual, non-aqueous phase liquids. Variable-density groundwater-flow and contaminant-transport models were developed using the SEAWAT-Version 4 computer program to simulate the direction and rate of groundwater flow in a 5.9 square-mile (mi2) - area surrounding the Keyport Peninsula, to estimate the CVOC mass in groundwater and the rate of mass loading, and to assess possible remedial activities at OU-1.
The study area is underlain by Quaternary deposits consisting of alternating glacial and interglacial sediments ranging from 500 to 1,500 feet (ft) thick. A hydrogeologic model delineated a sequence of 10 units including a relatively thin package (less than 100 ft) of recent sediments (Vashon Stade and younger) beneath the Keyport Peninsula that are underlain by the much thicker (more than 300 ft) Clover Park Aquitard, which overlies a confined, sea-level aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205066","collaboration":"Prepared in cooperation with the Department of the Navy, Naval Facilities Engineering Command, Northwest","usgsCitation":"Yager, R.M., Welch, W.B., Headman, A., and Dinicola, R.S., 2020, Variable-density groundwater flow and contaminant transport, Operable Unit 1, Naval Base Kitsap, Keyport, Washington: U.S. Geological Survey Scientific Investigations Report 2020–5066, 58 p., https://doi.org/10.3133/sir20205066.","productDescription":"x, 62 p.","onlineOnly":"Y","ipdsId":"IP-112628","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":379666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95WQ7TM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil water balance (SWB) model of Keyport, Washington"},{"id":379617,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5066/sir20205066.pdf","text":"Report","size":"10.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5066"},{"id":379667,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YNPPNL","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005, MODFLOW-NWT, and SEAWAT V.4 models used to simulate variable-density groundwater flow and contaminant transport at Naval Base Kitsap, Keyport, Washington"},{"id":379616,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5066/coverthb2.jpg"}],"country":"United States","state":"Washington","city":"Keyport","otherGeospatial":"Naval Base Kitsap","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.65,\n 47.6666\n ],\n [\n -122.60833,\n 47.6666\n ],\n [\n -122.60833,\n 47.71666\n ],\n [\n -122.65,\n 47.71666\n ],\n [\n -122.65,\n 47.6666\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
Declining water levels and reduced natural discharge at springs, seeps, and phreatophyte areas primarily are the result of decades of groundwater development in the Death Valley regional flow system, in Nevada and California. A calibrated groundwater-flow model was used to simulate potential future effects of groundwater pumping on water levels and natural groundwater discharge in the study area. Effects of climate change on future groundwater pumping were not considered and were beyond the scope of the study. Four groundwater-pumping scenarios were developed by stakeholders to predict and compare (1) the extent of regional water-level declines; (2) drawdown at Devils Hole; and (3) reductions in natural discharge at select discharge areas, including the Amargosa Wild and Scenic River, the Ash Meadows discharge area, the Furnace Creek area, and Stump Spring. Scenarios were simulated from 1913 to 2120, with historical pumping occurring from 1913 to 2010, historical 2010 pumping rates projected from 2010 to 2020, and scenario pumping beginning in 2020. Pumping scenarios included a base case and scenarios A, B, and C. The base case projected 2010 pumping rates from 2010 to 2120, and scenarios A, B, and C projected base case pumping plus additional pumping at various locations from 2020 to 2120. By 2020, historical (1913–2020) pumping resulted in the propagation of simulated drawdown of 1 foot (ft) or more westward from Pahrump Valley to areas north of Shoshone in the Pahrump to Death Valley South (PDVS) groundwater basin and the merging of simulated 1-ft drawdown contours between the Alkali Flat–Furnace Creek Ranch (AFFCR) and Ash Meadows groundwater basins. In the base case scenario, extent and magnitude of simulated drawdown continued to increase in the Ash Meadows and AFFCR groundwater basins from 2020 to 2120. In the base case, the magnitude of simulated drawdown continued to increase in western Pahrump Valley from 2020 to 2120, whereas simulated water levels rose in eastern Pahrump Valley from 2020 to 2070 and then stabilized from 2070 to 2120. Scenarios A and B primarily affected the PDVS and AFFCR groundwater basins by increasing the magnitude of drawdown in 2120, compared to the base case. In scenario C, drawdown propagated throughout a high-transmissivity part of the carbonate aquifer known as the megachannel, greatly affecting water levels in the Ash Meadows discharge area. Scenario C resulted in an additional 10–100 ft of drawdown (compared to the base case) throughout the southeastern part of the Ash Meadows groundwater basin by 2120. Simulated drawdowns in Devils Hole in 2120 were 3.2, 3.4, 3.8, and 25.4 ft for the base case and scenarios A, B, and C, respectively. The federally mandated minimum water level for Devils Hole is 2.7 ft below a reference point. In 2020, the simulated water level in Devils Hole was above the minimum water level, at 1.7 ft below the reference. Simulated water levels in Devils Hole fell below the federally mandated water level by 2078, 2073, 2058, and 2025 for the base case and scenarios A, B, and C, respectively, assuming a hypothetical recharge scenario of constant natural recharge. Simulated reductions in predevelopment (natural) discharge at select discharge areas ranged from 3 to 38 percent by 2120 for all scenarios. Amargosa Wild and Scenic River was the least affected discharge area with simulated capture rates ranging from 3 to 4 percent of predevelopment discharge by 2120. Ash Meadows discharge area was greatly affected by groundwater pumping in scenario C with a simulated capture rate of 38 percent, compared to simulated capture rates of 8, 8, and 9 percent for the base case, scenario A, and scenario B, respectively, in 2120. Simulated capture rates in the Furnace Creek area ranged from 10 to 11 percent for all scenarios in 2120. Simulated capture rates at Stump Spring ranged from 32 to 36 percent for all scenarios in 2120.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205103","collaboration":"Prepared in cooperation with the Bureau of Land Management; National Park Service; Nevada Division of Wildlife; Nye County, Nevada; and U.S. Fish and Wildlife Service","usgsCitation":"Nelson, N.C., and Jackson, T.R., 2020, Simulated effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120: U.S. Geological Survey Scientific Investigations Report 2020–5103, 30 p., https://doi.org/10.3133/sir20205103.","productDescription":"Report: vii, 30 p.; Data Releases","onlineOnly":"Y","ipdsId":"IP-112177","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":379438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5103/coverthb.jpg"},{"id":379439,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5103/sir20205103.pdf","text":"Report","size":"6.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5103"},{"id":379440,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OBUPXU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 models used to simulate effects of pumping in the Death Valley Regional Groundwater Flow System, Nevada and California—Selected management scenarios projected to 2120"},{"id":379476,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FH3","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Update to the groundwater withdrawals database for the Death Valley regional groundwater flow system, Nevada and California, 1913 -2010"},{"id":379477,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley Regional Groundwater Flow System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.3779296875,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 33.62376800118811\n ],\n [\n -114.08203125,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 38.62545397209084\n ],\n [\n -117.3779296875,\n 33.62376800118811\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
The complete mitogenome of the northern long-eared bat (Myotis septentrionalis) was determined to be 17,362 bp and contained 22 tRNA genes, 2 rRNA genes and one control region. The whole genome base composition was 33.8% GC. Phylogenetic analysis suggests that M. septentrionalis be positioned next to M. auriculus in the Nearctic subclade of the Myotis genus. This complete mitochondrial genome provides essential molecular markers for resolving phylogeny and future conservation efforts.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/23802359.2020.1830726","usgsCitation":"Gaughan, S.J., Pope, K.L., White, J.A., Lemen, C.A., and Freeman, P.W., 2020, Mitogenome of northern long-eared bat: Mitochondrial DNA Part B, v. 5, no. 3, p. 3592-3593, https://doi.org/10.1080/23802359.2020.1830726.","productDescription":"2 p.","startPage":"3592","endPage":"3593","ipdsId":"IP-115702","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454996,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/23802359.2020.1830726","text":"Publisher Index Page"},{"id":395783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Gaughan, S. J.","contributorId":275637,"corporation":false,"usgs":false,"family":"Gaughan","given":"S.","email":"","middleInitial":"J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":834185,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":834186,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, J. A.","contributorId":275639,"corporation":false,"usgs":false,"family":"White","given":"J.","email":"","middleInitial":"A.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":834187,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lemen, C. A.","contributorId":275640,"corporation":false,"usgs":false,"family":"Lemen","given":"C.","email":"","middleInitial":"A.","affiliations":[{"id":36206,"text":"Retired","active":true,"usgs":false}],"preferred":false,"id":834188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Freeman, P. W.","contributorId":275642,"corporation":false,"usgs":false,"family":"Freeman","given":"P.","email":"","middleInitial":"W.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":834189,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216947,"text":"70216947 - 2020 - Advancements towards selective barrier passage by automatic species identification: Applications of deep convolutional neural networks on images of dewatered fish","interactions":[],"lastModifiedDate":"2021-01-25T16:51:53.379463","indexId":"70216947","displayToPublicDate":"2020-10-21T10:47:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1936,"text":"ICES Journal of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Advancements towards selective barrier passage by automatic species identification: Applications of deep convolutional neural networks on images of dewatered fish","docAbstract":"Invasive species negatively affect enterprises such as fisheries, agriculture, and international trade. In the Laurentian Great Lakes Basin, threats include invasive sea lamprey (Petromyzon marinus) and the four major Chinese carps. Barriers have proven to be an effective mechanism for managing invasive species but are detrimental in that they also limit the migration of desirable, native species. Fish passage technologies that selectively pass desirable species while blocking undesirable species are needed. Key to an automated selective barrier passage system is a high precision fish classifier to assign fish to be passed or blocked. Presented is an evaluation of two classifiers developed using images of partially dewatered fish captured from a commercial, high-speed camera array. For a lamprey vs. non-lamprey classification task, an ensemble prediction approach achieved near perfect accuracy on both a validation and test dataset. For a species classification task for 13 species found in the Great Lakes region, an ensemble prediction approach achieved accuracies of 96% and 97% on a validation and test dataset, respectively. Both prediction approaches were based on deep convolutional neural networks constructed using transfer learning and image augmentation. The study provides an important proof-of-concept for the viability in fully automated, selective fish passage systems.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/icesjms/fsaa150","usgsCitation":"Eickholt, J., Kelly, D., Bryan, J., Miehls, S.M., and Zielinski, D., 2020, Advancements towards selective barrier passage by automatic species identification: Applications of deep convolutional neural networks on images of dewatered fish: ICES Journal of Marine Science, v. 77, no. 7-8, p. 2804-2813, https://doi.org/10.1093/icesjms/fsaa150.","productDescription":"10 p.","startPage":"2804","endPage":"2813","ipdsId":"IP-114452","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":454998,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/icesjms/fsaa150","text":"Publisher Index Page"},{"id":382555,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Michigan,Ohio","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.59374999999999,\n 40.68063802521456\n ],\n [\n -82.1337890625,\n 40.68063802521456\n ],\n [\n -82.1337890625,\n 46.5286346952717\n ],\n [\n -88.59374999999999,\n 46.5286346952717\n ],\n [\n -88.59374999999999,\n 40.68063802521456\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"7-8","noUsgsAuthors":false,"publicationDate":"2020-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Eickholt, Jesse","contributorId":245809,"corporation":false,"usgs":false,"family":"Eickholt","given":"Jesse","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807048,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, Dylan","contributorId":245810,"corporation":false,"usgs":false,"family":"Kelly","given":"Dylan","email":"","affiliations":[{"id":13588,"text":"Central Michigan University","active":true,"usgs":false}],"preferred":false,"id":807049,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bryan, Janine","contributorId":245811,"corporation":false,"usgs":false,"family":"Bryan","given":"Janine","email":"","affiliations":[{"id":49332,"text":"Whooshh Innovations","active":true,"usgs":false}],"preferred":false,"id":807050,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":807051,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zielinski, Daniel","contributorId":245812,"corporation":false,"usgs":false,"family":"Zielinski","given":"Daniel","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":807052,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215538,"text":"70215538 - 2020 - Land subsidence contributions to relative sea level rise at tide gauge Galveston Pier 21, Texas","interactions":[],"lastModifiedDate":"2020-11-10T19:06:57.624092","indexId":"70215538","displayToPublicDate":"2020-10-21T09:43:05","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Land subsidence contributions to relative sea level rise at tide gauge Galveston Pier 21, Texas","docAbstract":"Relative sea level rise at tide gauge Galveston Pier 21, Texas, is the combination of absolute sea level rise and land subsidence. We estimate subsidence rates of 3.53 mm/a during 1909–1937, 6.08 mm/a during 1937–1983, and 3.51 mm/a since 1983. Subsidence attributed to aquifer-system compaction accompanying groundwater extraction contributed as much as 85% of the 0.7 m relative sea level rise since 1909, and an additional 1.9 m is projected by 2100, with contributions from land subsidence declining from 30 to 10% over the projection interval. We estimate a uniform absolute sea level rise rate of 1.10 mm ± 0.19/a in the Gulf of Mexico during 1909–1992 and its acceleration of 0.270 mm/a2 at Galveston Pier 21 since 1992. This acceleration is 87% of the value for the highest scenario of global mean sea level rise. Results indicate that evaluating this extreme scenario would be valid for resource-management and flood-hazard-mitigation strategies for coastal communities in the Gulf of Mexico, especially those affected by subsidence.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-74696-4","usgsCitation":"Liu, Y., Li, J., Fasullo, J., and Galloway, D., 2020, Land subsidence contributions to relative sea level rise at tide gauge Galveston Pier 21, Texas: Scientific Reports, v. 10, 17905, 11 p., https://doi.org/10.1038/s41598-020-74696-4.","productDescription":"17905, 11 p.","ipdsId":"IP-116040","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":455000,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-74696-4","text":"Publisher Index Page"},{"id":379653,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","city":"Galveston","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.90951538085938,\n 29.235481227452947\n ],\n [\n -94.72206115722656,\n 29.235481227452947\n ],\n [\n -94.72206115722656,\n 29.367215978710348\n ],\n [\n -94.90951538085938,\n 29.367215978710348\n ],\n [\n -94.90951538085938,\n 29.235481227452947\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2020-10-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Liu, Yi","contributorId":244757,"corporation":false,"usgs":false,"family":"Liu","given":"Yi","affiliations":[],"preferred":false,"id":804523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Li, Jiang","contributorId":167428,"corporation":false,"usgs":false,"family":"Li","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":802619,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fasullo, John","contributorId":243581,"corporation":false,"usgs":false,"family":"Fasullo","given":"John","email":"","affiliations":[{"id":48738,"text":"National Center for Atmospheric Research, Climate and Global Dynamics Lab, Boulder, CO 80305","active":true,"usgs":false}],"preferred":false,"id":802620,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Galloway, Devin 0000-0003-0904-5355","orcid":"https://orcid.org/0000-0003-0904-5355","contributorId":215888,"corporation":false,"usgs":true,"family":"Galloway","given":"Devin","email":"","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"preferred":true,"id":802621,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215548,"text":"70215548 - 2020 - Simulated estuary-wide response of seagrass (Zostera marina) to future scenarios of temperature and sea level","interactions":[],"lastModifiedDate":"2020-10-22T14:19:07.016285","indexId":"70215548","displayToPublicDate":"2020-10-21T09:10:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"Simulated estuary-wide response of seagrass (Zostera marina) to future scenarios of temperature and sea level","docAbstract":"Seagrass communities are a vital component of estuarine ecosystems, but are threatened by projected sea level rise (SLR) and temperature increases with climate change. To understand these potential effects, we developed a spatially explicit model that represents seagrass (Zostera marina) habitat and estuary-wide productivity for Barnegat Bay-Little Egg Harbor (BB-LEH) in New Jersey, United States. Our modeling approach included an offline coupling of a numerical seagrass biomass model with the spatially variable environmental conditions from a hydrodynamic model to calculate above and belowground biomass at each grid cell of the hydrodynamic model domain. Once calibrated to represent present day seagrass habitat and estuary-wide annual productivity, we applied combinations of increasing air temperature and sea level following regionally specific climate change projections, enabling analysis of the individual and combined impacts of these variables on seagrass biomass and spatial coverage. Under the SLR scenarios, the current model domain boundaries were maintained, as the land surrounding BB-LEH is unlikely to shift significantly in the future. SLR caused habitat extent to decrease dramatically, pushing seagrass beds toward the coastline with increasing depth, with a 100% loss of habitat by the maximum SLR scenario. The dramatic loss of seagrass habitat under SLR was in part due to the assumption that surrounding land would not be inundated, as the model did not allow for habitat expansion outside the current boundaries of the bay. Temperature increases slightly elevated the rate of summer die-off and decreased habitat area only under the highest temperature increase scenarios. In combined scenarios, the effects of SLR far outweighed the effects of temperature increase. Sensitivity analysis of the model revealed the greatest sensitivity to changes in parameters affecting light limitation and seagrass mortality, but no sensitivity to changes in nutrient limitation constants. The high vulnerability of seagrass in the bay to SLR exceeded that demonstrated for other systems, highlighting the importance of site- and region-specific assessments of estuaries under climate change.
Biological assemblages are commonly used for assessing stream health, but there is increased interest among the freshwater research community in incorporating measures of stream function, such as metabolism, to strengthen stream-health assessments. Presently, there is limited information about the relationships between stream metabolism and biological assemblages, along with the measurement period required to relate metabolism with stream biota. Our study assessed which environmental factors explained stream metabolism and to what degree stream metabolism and minimum dissolved oxygen (DOmin) were related to invertebrate and fish metrics in streams distributed across several regions of the United States. Furthermore, we evaluated the number of metabolism monitoring days required for maximizing the ability to detect relationships between stream metabolism and biological assemblage metrics. We sampled 17 sites distributed among reference, agricultural, and urban areas for stream metabolism, nutrients, habitat, and biological assemblages (invertebrates and fishes). Overall, sites were heterotrophic with gross primary production (GPP) and ecosystem respiration (ER) related primarily to days since last high flow, canopy cover, maximum water temperature, and total phosphorus. DOmin was related to days since last high flow, canopy cover, and maximum water temperature. We were unable to determine a clear statistical relationship between invertebrate metrics (invertebrate richness; Ephemeroptera, Plecoptera, and Trichoptera richness; and scraper-taxa richness) and GPP, ER, or DOmin. In contrast, we found that 2 fish-assemblage metrics were associated with stream metabolism and DOmin. A fish multimetric index (FMMI) was negatively correlated with GPP (r = −0.5, p = 0.048) and positively correlated with DOmin (r = 0.47, p = 0.06). Percentage of omnivorous fish taxa was positively correlated with GPP (r = 0.72, p = 0.001) and ER (r = 0.55, p = 0.02) and negatively correlated with DOmin (r = −0.67, p = 0.003). The lack of detected relationships for most of the biological-assemblage metrics with stream metabolism may be partially due to 1 or more factors, including high variability, low sample size, limited range in metabolism values, assemblage metrics used, and geographic distribution of sites. Comparing stream-metabolism measurement periods (in days) to biological-assemblage metrics indicated that optimum correlations occurred at 2 d for DOmin, 3 d for GPP, and 14 d for ER. Although our study found limited relationships of stream metabolism and DOmin with biological assemblages, future studies should consider a larger sample size (≥30), 14-d or longer metabolism measurement period, and assessment of other taxa-specific or assemblage metrics.
Avian haemosporidian parasites are a closely related group of apicomplexan parasites with important similarities in their life cycles, development, physiology, and reproduction. Current phylogenies based on mitochondrial and nuclear genes reflect more traditional attempts to classify these organisms based on life history characteristics and morphology, but limited sampling from poorly characterized taxa such as the Garniidae from tropical and subtropical regions continues to limit our understanding of their phylogeny and evolution. Recent advances in molecular diagnostics and the ability to barcode these parasites using mitochondrial cytochrome b sequences have revolutionized the field, but traditional methodology based on microscopy of Giemsa-stained blood smears remains essential for diagnostics and understanding life history characteristics and biodiversity of these organisms. The relative strengths and weaknesses of current methods in wildlife haemosporidian research are discussed. We call for a combination of microscopic, PCR-based, and serological diagnostic methodologies for better estimates of true distribution and other aspects of biology of haemosporidians, particularly in studies on virulence, prevalence, and biodiversity.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Avian Malaria and Related Parasites in the Tropics","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-030-51633-8","usgsCitation":"Valkiunas, G., and Atkinson, C.T., 2020, Introduction to life cycles, taxonomy, distribution and basic research techniques, chap. of Avian Malaria and Related Parasites in the Tropics, p. 45-80, https://doi.org/10.1007/978-3-030-51633-8.","productDescription":"36 p.","startPage":"45","endPage":"80","ipdsId":"IP-109471","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":383823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Valkiunas, Gediminas","contributorId":205399,"corporation":false,"usgs":false,"family":"Valkiunas","given":"Gediminas","email":"","affiliations":[{"id":37095,"text":"Nature Research Centre,Vilnius, Lithuania","active":true,"usgs":false}],"preferred":false,"id":811315,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Atkinson, Carter T. 0000-0002-4232-5335 catkinson@usgs.gov","orcid":"https://orcid.org/0000-0002-4232-5335","contributorId":1124,"corporation":false,"usgs":true,"family":"Atkinson","given":"Carter","email":"catkinson@usgs.gov","middleInitial":"T.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":811316,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70215759,"text":"70215759 - 2020 - An interactive data visualization framework for exploring geospatial environmental datasets and model predictions","interactions":[],"lastModifiedDate":"2020-10-29T13:11:24.16641","indexId":"70215759","displayToPublicDate":"2020-10-20T08:03:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"An interactive data visualization framework for exploring geospatial environmental datasets and model predictions","docAbstract":"Margaritifera monodonta, or the Spectaclecase Mussel, is a federally endangered freshwater mussel species that has experienced a 55% reduction in range and is currently concentrated in 3 rivers in the Midwest region of the United States (Gasconade and Meramec rivers, Missouri, and St Croix River, Wisconsin). The detection of new populations by traditional survey methods has been limited because these mussels tend to occur under large rocks and boulders. Environmental DNA (eDNA) technology has been used to detect invasive and rare species, but its use for detection of rare, benthic-dwelling species in large flowing systems has been limited. Here, we propose using eDNA to assess known populations of M. monodonta. We designed a M. monodonta-specific quantitative polymerase chain reaction (qPCR) assay and tested it using water samples from multiple M. monodonta housing tanks, water samples from 2 known mussel beds on the St Croix River, and water samples from 3 known mussel beds on the Mississippi River. We observed higher overall eDNA detection rates on the St Croix River (30.2%) compared to the upper Mississippi River (0.60%). We also observed higher eDNA detection rates (73.3–93.1%) in 2018 for samples collected during the larval release period in May compared to samples collected in August after the reproductive period had ended (55.6–70.8%) on the St Croix River. We tested samples collected at 3 distances downstream from the 2 mussel beds found in the St Croix River, but we did not observe a substantial effect of distance on our detection rates. However, we did observe greater detection rates for samples collected near the bottom compared to at the surface. Our results indicate that this novel qPCR assay can successfully detect M. monodonta eDNA and could be used to rapidly screen locations to guide intensive physical searches for populations in riverine systems.
","language":"English","publisher":"The University of Chicago Press-Society for Freshwater Science","doi":"10.1086/711673","usgsCitation":"Lor, Y., Schreier, T.M., Waller, D.L., and Merkes, C.M., 2020, Using environmental DNA (eDNA) to detect the endangered Spectaclecase Mussel (Margaritifera monodonta): Freshwater Science, v. 39, no. 4, p. 837-847, https://doi.org/10.1086/711673.","productDescription":"11 p.","startPage":"837","endPage":"847","ipdsId":"IP-111712","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":455012,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1086/711673","text":"Publisher Index Page"},{"id":436750,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F0COLN","text":"USGS data release","linkHelpText":"Transformation methods for glochidia of the Spectaclecase mussel Cumberlandia monodonta: Data"},{"id":381160,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin, Missouri","otherGeospatial":"Gasconade River, Meramec River, St. Croix River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.6534423828125,\n 38.68122173079789\n ],\n [\n -92.021484375,\n 38.33734763569314\n ],\n [\n -92.098388671875,\n 37.87051721701939\n ],\n [\n -91.9281005859375,\n 37.88352498087131\n ],\n [\n -91.505126953125,\n 38.61687046392973\n ],\n [\n -91.6534423828125,\n 38.68122173079789\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.4669189453125,\n 38.56212645363985\n ],\n [\n -90.63514709472656,\n 38.54923944473397\n ],\n [\n -90.74569702148436,\n 38.487994609214795\n ],\n [\n -90.73127746582031,\n 38.44014444555175\n ],\n [\n -90.61454772949219,\n 38.44821130413263\n ],\n [\n -90.46005249023438,\n 38.52775596312173\n ],\n [\n -90.4669189453125,\n 38.56212645363985\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.054443359375,\n 46.28622391806706\n ],\n [\n -92.98828125,\n 45.82879925192134\n ],\n [\n -92.8564453125,\n 45.251688256117646\n ],\n [\n -92.867431640625,\n 44.5435052132082\n ],\n [\n -92.28515625,\n 44.42593442145313\n ],\n [\n -92.054443359375,\n 46.28622391806706\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lor, Yer 0000-0002-5738-2412","orcid":"https://orcid.org/0000-0002-5738-2412","contributorId":210011,"corporation":false,"usgs":true,"family":"Lor","given":"Yer","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806610,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schreier, Theresa M. 0000-0001-7722-6292 tschreier@usgs.gov","orcid":"https://orcid.org/0000-0001-7722-6292","contributorId":3344,"corporation":false,"usgs":true,"family":"Schreier","given":"Theresa","email":"tschreier@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806611,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Diane L. 0000-0002-6104-810X dwaller@usgs.gov","orcid":"https://orcid.org/0000-0002-6104-810X","contributorId":5272,"corporation":false,"usgs":true,"family":"Waller","given":"Diane","email":"dwaller@usgs.gov","middleInitial":"L.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806612,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":806613,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216551,"text":"70216551 - 2020 - Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits","interactions":[],"lastModifiedDate":"2020-11-25T17:18:23.314417","indexId":"70216551","displayToPublicDate":"2020-10-20T07:33:47","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits","docAbstract":"Climate warming is expected to stimulate plant growth in high‐elevation and high‐latitude ecosystems, significantly increasing aboveground net primary production (ANPP). However, the effects of simultaneous changes in temperature, snowmelt timing, and summer water availability on total net primary production (NPP)—and elucidation of both above‐ and belowground responses—remain an important area in need of further study. In particular, measures of belowground net primary productivity (BNPP) are required to understand whether ANPP changes reflect changes in allocation or are indicative of a whole plant NPP response. Further, plant functional traits provide a key way to scale from the individual plant to the community level and provide insight into drivers of NPP responses to environmental change. We used infrared heaters to warm an alpine plant community at Niwot Ridge, Colorado, and applied supplemental water to compensate for soil water loss induced by warming. We measured ANPP, BNPP, and leaf and root functional traits across treatments after 5 yr of continuous warming. Community‐level ANPP and total NPP (ANPP + BNPP) did not respond to heating or watering, but BNPP increased in response to heating. Heating decreased community‐level leaf dry matter content and increased total root length, indicating a shift in strategy from resource conservation to acquisition in response to warming. Water use efficiency (WUE) decreased with heating, suggesting alleviation of moisture constraints that may have enabled the plant community to increase productivity. Heating may have decreased WUE by melting snow earlier and creating more days early in the growing season with adequate soil moisture, but stimulated dry mass investment in roots as soils dried down later in the growing season. Overall, this study highlights how ANPP and BNPP responses to climate change can diverge, and encourages a closer examination of belowground processes, especially in alpine systems, where the majority of NPP occurs belowground.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3270","usgsCitation":"Yang, Y., Klein, J.A., Winkler, D.E., Peng, A., Lazarus, B., Germino, M., Suding, K., Smith, J., and Kueppers, L.M., 2020, Warming of alpine tundra enhances belowground production and shifts community towards resource acquisition traits: Ecosphere, v. 11, no. 10, e03270, 15 p., https://doi.org/10.1002/ecs2.3270.","productDescription":"e03270, 15 p.","ipdsId":"IP-114058","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":455014,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3270","text":"Publisher Index Page"},{"id":380791,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Niwot Ridge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.62324523925781,\n 40.04732864506094\n ],\n [\n -105.56548118591309,\n 40.04732864506094\n ],\n [\n -105.56548118591309,\n 40.07189770843059\n ],\n [\n -105.62324523925781,\n 40.07189770843059\n ],\n [\n -105.62324523925781,\n 40.04732864506094\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"10","noUsgsAuthors":false,"publicationDate":"2020-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Yang, Yan","contributorId":245243,"corporation":false,"usgs":false,"family":"Yang","given":"Yan","affiliations":[],"preferred":false,"id":805674,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Klein, Julia A.","contributorId":76873,"corporation":false,"usgs":true,"family":"Klein","given":"Julia","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":805675,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":805589,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Peng, Ahui","contributorId":245244,"corporation":false,"usgs":false,"family":"Peng","given":"Ahui","email":"","affiliations":[],"preferred":false,"id":805676,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lazarus, Brynne E. 0000-0002-6352-486X","orcid":"https://orcid.org/0000-0002-6352-486X","contributorId":242732,"corporation":false,"usgs":true,"family":"Lazarus","given":"Brynne E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":805591,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Germino, Matthew 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":218007,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":805592,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Suding, Katherine","contributorId":167086,"corporation":false,"usgs":false,"family":"Suding","given":"Katherine","email":"","affiliations":[{"id":6709,"text":"University of Colorado, Denver","active":true,"usgs":false}],"preferred":false,"id":805677,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Smith, Jane G.","contributorId":245245,"corporation":false,"usgs":false,"family":"Smith","given":"Jane G.","affiliations":[],"preferred":false,"id":805678,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kueppers, Lara M.","contributorId":89778,"corporation":false,"usgs":false,"family":"Kueppers","given":"Lara","email":"","middleInitial":"M.","affiliations":[{"id":16805,"text":"University of California, Merced","active":true,"usgs":false},{"id":6670,"text":"Lawrence Berkeley National Laboratory, Berkeley, CA","active":true,"usgs":false}],"preferred":false,"id":805679,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215717,"text":"70215717 - 2020 - Modeling false positives","interactions":[],"lastModifiedDate":"2020-10-29T11:45:30.1053","indexId":"70215717","displayToPublicDate":"2020-10-19T08:48:46","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"7","title":"Modeling false positives","docAbstract":"Many of the models we are concerned with included explicit descriptions of false negative errors. However, false positive errors can also be commin in practice, especially in citizen science applications where observer skill is highly variable. In addition, new methods which determine detection based on statistical classification or machine learning methods are also prone to false positive errors which must be accounted for. \n An early treatment of the false positive detection problem by Royle & Link (2006) recognized that false positive errors can be accommodated by a mixture model for detection probability: one value of detection at occupied sites and another non-zero value at unoccupied sites. This model has been extended greatly in recent years to include more informative data about false positives including validation or confirmation data (Miller et al. 2011) and multiple detection methods, among others. \n A new frontier for the application of false positives models lies in the use of modern technologies such as bioacoustics for efficient automated monitoring. For these technologies to realize their promise there must be improvements in automated processing of the vast quantities of output produced. Statistical classification methods (machine learning) are fallible and necessarily produce false positive detections. Therefore models which account for this process are necessary (Chambert et al. 2017). It stands to reason that false positives will need to be accounted for in other new technologies that rely on automated digital processing, including eDNA, genetic barcoding, and automated detection in remote camera studies.\n We devise a new occupancy model that integrates data from bioacoustics sampling with an occupancy model. This integrated model allows occupancy probability to inform species classification of samples and vice versa bioacoustics detection data inform occupancy. We provide a proof of concept for this new model in this chapter. \n As the core hierarchical model for the false positives models covered in this chapter are just ordinary occupancy models, extension of the ideas to open systems poses no technical challenges. We provide a suite of illustrations of these extensions. \n Perhaps the most prominent mechanism that leads to false positive errors it he mis-classification of species detections, or the confusion of one species for another. Very little work has been done on developing models based on this mechanistic understanding although Chambert et al. (2018) develop this idea as a 2-species occupancy model with error. We believe one important area of future research is to extend these ideas to truly multi-species systems.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Academic Press","usgsCitation":"Kery, M., and Royle, A., 2020, Modeling false positives, chap. 7 of Applied hierarchical modeling in ecology: Analysis of distribution, abundance and species richness in R and BUGS, v. 2, p. 401-454.","productDescription":"54 p.","startPage":"401","endPage":"454","ipdsId":"IP-104271","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":379868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":379846,"type":{"id":15,"text":"Index Page"},"url":"https://www.elsevier.com/books/applied-hierarchical-modeling-in-ecology-analysis-of-distribution-abundance-and-species-richness-in-r-and-bugs/kery/978-0-12-809585-0"}],"volume":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kery, Marc","contributorId":168361,"corporation":false,"usgs":false,"family":"Kery","given":"Marc","affiliations":[{"id":12551,"text":"Swiss Ornithological Institute, Sempach, Switzerland","active":true,"usgs":false}],"preferred":false,"id":803278,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":803191,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216466,"text":"70216466 - 2020 - Diurnal timing of nonmigratory movement by birds: The importance of foraging spatial scales","interactions":[],"lastModifiedDate":"2020-12-29T21:55:39.324174","indexId":"70216466","displayToPublicDate":"2020-10-19T08:27:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2190,"text":"Journal of Avian Biology","active":true,"publicationSubtype":{"id":10}},"title":"Diurnal timing of nonmigratory movement by birds: The importance of foraging spatial scales","docAbstract":"Timing of activity can reveal an organism's efforts to optimize foraging either by minimizing energy loss through passive movement or by maximizing energetic gain through foraging. Here, we assess whether signals of either of these strategies are detectable in the timing of activity of daily, local movements by birds. We compare the similarities of timing of movement activity among species using six temporal variables: start of activity relative to sunrise, end of activity relative to sunset, relative speed at midday, number of movement bouts, bout duration, and proportion of active daytime hours. We test for the influence of flight mode and foraging habitat on the timing of movement activity across avian guilds. We used 64570 days of GPS movement data collected between 2002 and 2019 for local (non‐migratory) movements of 991 birds from 49 species, representing 14 orders. Dissimilarity among daily activity patterns was best explained by flight mode. Terrestrial soaring birds began activity later and stopped activity earlier than pelagic soaring or flapping birds. Broad‐scale foraging habitat explained less of the clustering patterns because of divergent timing of active periods of pelagic surface and diving foragers. Among pelagic birds, surface foragers were active throughout the day while diving foragers matched their active hours more closely to daylight hours. Pelagic surface foragers also had the greatest daily foraging distances, which was consistent with their daytime activity patterns. This study demonstrates that flight mode and foraging habitat influence temporal patterns of daily movement activity of birds.
","language":"English","publisher":"Wiley","doi":"10.1111/jav.02612","usgsCitation":"Mallon, J.M., Tucker, M.A., Beard, A., Bierregaard, R.O., Bildstein, K.L., Böhning-Gaese, K., Brzorad, J.N., Buechley, E., Bustamante, J., Carrapato, C., Castillo-Guerrero, J.A., Clingham, E., Desholm, M., DeSorbo, C.R., Domenech, R., Douglas, H., Duriez, O., Enggist, P., Farwig, N., Fiedler, W., Gagliardo, A., García‐Ripollés, C., Gil Gallus, J.A., Gilmour, M., Harel, R., Harrison, A., Henry, L., Katzner, T., Kays, R., Kleyheeg, E., Limiñana, R., Lopez-Lopez, P., Lucia, G., Maccarone, A., Mallia, E., Mellone, U., Mojica, E., Nathan, R., Newman, S., Oppel, S., Orchan, Y., Prosser, D.J., Riley, H., Rösner, S., Schabo, D.G., Schulz, H., Shaffer, S.A., Shreading, A., Silva, J., Sim, J., Skov, H., Spiegel, O., Stuber, M.J., Takekawa, J.Y., Urios, V., Vidal-Mateo, J., Warner, K., Watts, B.D., Weber, N., Weber, S., Wikelski, M., Zydelis, R., Mueller, T., and Fagan, W., 2020, Diurnal timing of nonmigratory movement by birds: The importance of foraging spatial scales: Journal of Avian Biology, v. 51, no. 12, e02612, 11 p., https://doi.org/10.1111/jav.02612.","productDescription":"e02612, 11 p.","ipdsId":"IP-115942","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":455018,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/jav.02612","text":"External Repository"},{"id":380648,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"51","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Mallon, Julie M.","contributorId":150853,"corporation":false,"usgs":false,"family":"Mallon","given":"Julie","email":"","middleInitial":"M.","affiliations":[{"id":16210,"text":"Division of Forestry and Natural Resources, West Virginia 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Although the existence and geometrical properties of SAG have been well documented, an understanding of the hydrodynamics over them is limited. Here, the three-dimensional flow patterns over SAG formations, and a sensitivity of those patterns to waves, currents, and SAG geometry were characterized using the physics-based Delft3D-FLOW and SWAN models. Shore-normal shoaling waves over SAG formations were shown to drive two circulation cells: a cell on the lower fore reef with offshore flow over the spurs and onshore flow over the grooves, except near the seabed where velocities were always onshore, and a cell on the upper fore reef with offshore surface velocities and onshore bottom currents, which result in depth-averaged onshore and offshore flow over the spurs and grooves, respectively. The mechanism driving this flow results from the net of the radiation stress gradients and pressure gradient, which is balanced by the Reynolds stress gradients and bottom friction that differ over the spur and over the groove. Waves were the primary driver of variations in modelled flow over SAG, with the flow strength increasing for increasing wave heights and periods. Spur height, SAG wavelength, and the water depth at peak spur height were the dominant influences on the hydrodynamics, with spur heights directly proportional to the strength of SAG circulation cells. SAG formations with shorter SAG wavelengths only presented one circulation cell on the shallower portion of the reef, as opposed to the two circulation cells for longer SAG wavelengths. SAG formations with peak spur heights occurring in shallower water had stronger circulation than those with peak spur heights occurring in deeper water. These hydrodynamic patterns also likely affect coral and reef development through sediment and nutrient fluxes.
Induced earthquakes from waste disposal operations in otherwise tectonically stable regions significantly increases seismic hazard. It remains unclear why injections induce large earthquakes on non‐optimally oriented faults kilometers below the injection horizon, particularly since fluids are not injected under pressure, but rather poured, into the well as observed in the Milan, Kansas area. Here we propose a mechanism for induced earthquakes whereby the karstic lower Arbuckle provides the short‐circuit that establishes a tens of MPa stepwise fluid pressure increase within the basement upon arrival of the hydraulic connection to the free surface and ultimately induce slip on the deeper fault. We investigate this scenario through modeling and mechanical analysis and show that earthquakes near Milan are likely induced by large (and sudden) fluid pressure changes when the karst network links two previously isolated hydrological systems.
Large-domain hydrological models are increasingly needed to support water-resource assessment and management in large river basins. Here, we describe results for the first Brazilian application of the SPAtially Referenced Regression On Watershed attributes (SPARROW) model using a new open-source modeling and interactive decision support system tool (RSPARROW) to quantify the origin, flux, and fate of total nitrogen (TN) in two sub-basins of the Grande River Basin (GRB; 43,000 km2). Land under cultivation for sugar cane, urban land, and point source inputs from wastewater treatment plants was estimated to each contribute approximately 30% of the TN load at the outlet, with pasture land contributing about 10% of the load. Hypothetical assessments of wastewater treatment plant upgrades and the building of new facilities that could treat currently untreated urban runoff suggest that these management actions could potentially reduce loading at the outlet by as much as 20–25%. This study highlights the ability of SPARROW and the RSPARROW mapping tool to assist with the development and evaluation of management actions aimed at reducing nutrient pollution and eutrophication. The freely available RSPARROW modeling tool provides new opportunities to improve understanding of the sources, delivery, and transport of water-quality contaminants in watersheds throughout the world.
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