The decision to reintroduce a species can be difficult owing to conflicting opinions and objectives, as well as uncertainty of the outcome. Structured decision making addresses these considerations by identifying realistic fundamental objectives and building achievable management alternatives, within a quantitative modeling framework. The process is driven by participation of stakeholders that represent diverse objectives, policy mandates, and opinions regarding decision alternatives. We applied structured decision making to evaluate reintroduction of Bull Trout Salvelinus confluentus in the lower Pend Oreille River in northeastern Washington State. We engaged stakeholders from Tribal, municipal, county, state and federal agencies to specify fundamental objectives, formulate feasible reintroduction decisions, and conceptualize a modeling framework that includes biological information and stakeholder assumptions. Stakeholders requested iterative decision sets to determine the optimal recipient streams and release strategies. The optimal decision, based on the fundamental objective of maximizing adult abundance at year 10, was artificial propagation of 4500 juvenile Bull Trout coupled with translocation of 25 adult migrants to be reintroduced into a tributary and lake system that produced at least 18% more adult fish relative to alternatives. Sensitivity analyses were robust to the identity of the recipient stream (i.e., Sullivan Lake/Harvey Creek was always the optimal recipient stream) but suggested that maximizing the number of artificially produced juveniles released could produce a similar number of adult Bull Trout as the coupled release strategy. Results also suggested that ensuring fish passage at the Albeni Falls Dam in the mainstem Pend Oreille River could increase the abundance of adult fish. The process followed for this case study can be adapted to similar decisions regarding reintroduction or other translocations of fish in other systems.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10334","usgsCitation":"Benjamin, J.R., Brignon, W.R., and Dunham, J.B., 2019, Decision analysis for the reintroduction of Bull Trout into the lower Pend Oreille River, Washington: North American Journal of Fisheries Management, v. 39, no. 5, p. 1026-1045, https://doi.org/10.1002/nafm.10334.","productDescription":"20 p.","startPage":"1026","endPage":"1045","additionalOnlineFiles":"N","ipdsId":"IP-104456","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":366328,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Pend Oreille River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.48779296875,\n 48.07257353224749\n ],\n [\n -116.74072265625,\n 48.07257353224749\n ],\n [\n -116.74072265625,\n 48.99103162515999\n ],\n [\n -117.48779296875,\n 48.99103162515999\n ],\n [\n -117.48779296875,\n 48.07257353224749\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"39","issue":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Benjamin, Joseph R. 0000-0003-3733-6838 jbenjamin@usgs.gov","orcid":"https://orcid.org/0000-0003-3733-6838","contributorId":3999,"corporation":false,"usgs":true,"family":"Benjamin","given":"Joseph","email":"jbenjamin@usgs.gov","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":767827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brignon, William R.","contributorId":193087,"corporation":false,"usgs":false,"family":"Brignon","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":767828,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":767829,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223294,"text":"70223294 - 2019 - Spatial and temporal variation of ecosystem properties at macroscales","interactions":[],"lastModifiedDate":"2021-08-20T14:04:32.279269","indexId":"70223294","displayToPublicDate":"2019-07-25T08:59:06","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Spatial and temporal variation of ecosystem properties at macroscales","docAbstract":"Although spatial and temporal variation in ecological properties has been well-studied, crucial knowledge gaps remain for studies conducted at macroscales and for ecosystem properties related to material and energy. We test four propositions of spatial and temporal variation in ecosystem properties within a macroscale (1000 km's) extent. We fit Bayesian hierarchical models to thousands of observations from over two decades to quantify four components of variation – spatial (local and regional) and temporal (local and coherent); and to model their drivers. We found strong support for three propositions: (1) spatial variation at local and regional scales are large and roughly equal, (2) annual temporal variation is mostly local rather than coherent, and, (3) spatial variation exceeds temporal variation. Our findings imply that predicting ecosystem responses to environmental changes at macroscales requires consideration of the dominant spatial signals at both local and regional scales that may overwhelm temporal signals.
","language":"English","publisher":"Wiley","doi":"10.1111/ele.13346","usgsCitation":"Soranno, P.A., Wagner, T., Collins, S., Lapierre, J., and Oliver, S.K., 2019, Spatial and temporal variation of ecosystem properties at macroscales: Ecology Letters, v. 22, no. 10, p. 1587-1598, https://doi.org/10.1111/ele.13346.","productDescription":"12 p.","startPage":"1587","endPage":"1598","ipdsId":"IP-101556","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":388232,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.6142578125,\n 36.66841891894786\n ],\n [\n -90.04394531249999,\n 36.491973470593685\n ],\n [\n -89.7802734375,\n 36.20882309283712\n ],\n [\n -89.56054687499999,\n 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Leetown","active":true,"usgs":true}],"preferred":true,"id":821628,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collins, Sarah M.","contributorId":264519,"corporation":false,"usgs":false,"family":"Collins","given":"Sarah M.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":821630,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lapierre, Jean-Francois","contributorId":264522,"corporation":false,"usgs":false,"family":"Lapierre","given":"Jean-Francois","affiliations":[{"id":54487,"text":"University of Montreal","active":true,"usgs":false}],"preferred":false,"id":821631,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":821632,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70206209,"text":"70206209 - 2019 - Fluvial sedimentary history of Arlington Canyon, Channel Islands National Park, California","interactions":[],"lastModifiedDate":"2019-10-25T06:59:32","indexId":"70206209","displayToPublicDate":"2019-07-25T06:58:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2437,"text":"Journal of Quaternary Science","active":true,"publicationSubtype":{"id":10}},"title":"Fluvial sedimentary history of Arlington Canyon, Channel Islands National Park, California","docAbstract":"Arlington Canyon, in the northwest part of Santa Rosa Island, Channel Islands National Park, California, has been the setting for important scientific discoveries over the past half century, including the oldest human remains in North America, several vertebrate fossil sites, and purported evidence of a catastrophic extinction event at the end of the Pleistocene. The canyon is filled with alluvial sediments that date to between 16.4 and 1.1 ka (thousands of calibrated years before present), representing accumulation that occurred primarily in response to rising sea levels during the late Pleistocene and Holocene. The deposits are laterally discontinuous, exhibit a high degree of sedimentary complexity, and contain evidence of past climates and environments, including fossil bones, burned plant macrofossils, and invertebrate microfossils. Here, we show that it is critical to view the observations, data, and conclusions of scientific studies conducted in the canyon within this larger context so that localized facets of the spatially and temporally extensive alluvial deposits are not misinterpreted or misrepresented. By improving the baseline understanding of processes and drivers of sediment accumulation in Arlington Canyon, we hope to offer a solid foundation and better underpinning for future archeological, paleontological, and geochemical studies here and throughout the northern Channel Islands.","language":"English","publisher":"Wiley","doi":"10.1002/jqs.3123","usgsCitation":"Schumann, R.R., and Pigati, J.S., 2019, Fluvial sedimentary history of Arlington Canyon, Channel Islands National Park, California: Journal of Quaternary Science, v. 34, no. 7, p. 499-508, https://doi.org/10.1002/jqs.3123.","productDescription":"10 p.","startPage":"499","endPage":"508","ipdsId":"IP-102249","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":368588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Channel Islands National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.62988281249999,\n 33.80197351806589\n ],\n [\n -119.24560546875001,\n 33.80197351806589\n ],\n [\n -119.24560546875001,\n 34.18454183141725\n ],\n [\n -120.62988281249999,\n 34.18454183141725\n ],\n [\n -120.62988281249999,\n 33.80197351806589\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"7","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Schumann, R. Randall 0000-0001-8158-6960 rschumann@usgs.gov","orcid":"https://orcid.org/0000-0001-8158-6960","contributorId":1569,"corporation":false,"usgs":true,"family":"Schumann","given":"R.","email":"rschumann@usgs.gov","middleInitial":"Randall","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773858,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":773859,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203076,"text":"ofr20191045 - 2019 - Using UAS capabilities to help identify hummock-hollow formation and fragmentation in critical marsh habitat (Spartina patens) for mottled ducks in southeast Texas","interactions":[],"lastModifiedDate":"2019-07-25T10:44:25","indexId":"ofr20191045","displayToPublicDate":"2019-07-24T15:13:36","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1045","displayTitle":"Using UAS Capabilities To Help Identify Hummock-Hollow Formation and Fragmentation in Critical Marsh Habitat (Spartina patens) for Mottled Ducks in Southeast Texas","title":"Using UAS capabilities to help identify hummock-hollow formation and fragmentation in critical marsh habitat (Spartina patens) for mottled ducks in southeast Texas","docAbstract":"For many years, marshes in the coastal areas from Texas to Louisiana have served as critical habitat for Anas fulvigula, the mottled duck. Mottled ducks are a priority species in the Texas/Louisiana Gulf Coast area and have been affected by critical habitat reduction. In recent years, mottled duck habitats have been threatened by natural and anthropogenic changes including urbanization, flooding, saltwater intrusion, and hydrologic alterations. These impacts are affecting the quality of habitat that is essential for the mottled duck nesting, feeding, and livelihood. Cumulative and synergistic effects of contamination and invasive species encroachment have also caused mottled duck habitat to be considered as some of the most critically endangered habitats in the United States. To help understand the environmental conditions that characterize the coastal landscape, U.S. Geological Survey researchers used an unmanned aerial system (UAS) to acquire high-resolution imagery to document current land and water spatial configuration and wetland health at McFaddin National Wildlife Refuge, Texas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191045","usgsCitation":"Jones, W.R., Hartley, S.B., Stagg, C.L., and Osland, M.J., 2019, Using UAS capabilities to help identify hummock-hollow formation and fragmentation in critical marsh habitat (Spartina patens) for mottled ducks in southeast Texas: U.S. Geological Survey Open-File Report 2019–1045, 6 p., https://doi.org/10.3133/ofr20191045.","productDescription":"6 p.","numberOfPages":"15","onlineOnly":"Y","ipdsId":"IP-105924","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":365530,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1045/coverthb.jpg"},{"id":365531,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1045/ofr20191045.pdf","text":"Report","size":"4.98 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1045"}],"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 -99.66796875,\n 25.760319754713887\n ],\n [\n -93.6474609375,\n 25.760319754713887\n ],\n [\n -93.6474609375,\n 32.24997445586331\n ],\n [\n -99.66796875,\n 32.24997445586331\n ],\n [\n -99.66796875,\n 25.760319754713887\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
We surveyed the bee fauna at Napatree Point, a coastal barrier beach in southwestern Rhode Island, using bee-bowl and netting samples, and compared results to bee-bowl samples at 2 inland sites. We collected a total of 53 species and morphospecies at Napatree Point, including 5 likely Rhode Island state records and several coastal dune and sand-nesting species that were not found inland. The comparative bee-bowl samples (colored bowls with soapy water placed at the sites to collect visiting bees) captured 35 species at Napatree Point and 66 at the inland sites (which included 6 likely state records, 2 shared with Napatree). The Napatree fauna shared numerous species with the inland sites, but overall species composition differed substantially. Both Napatree and inland sites showed greatest bee activity and species richness in spring. During spring, the most common bees at Napatree were twig- and cavity-nesting species such as Ceratina dupla and Osmia simillima, and the wood-nesting Lasioglossum oblongum, while the most abundant bees inland were the soil-nesting Andrena nasonii and Augochlorella aurata. Netting samples differed from bee-bowl samples in that they captured larger species and species foraging at flowers distant from the bee-bowl transects, but they missed several diminutive species that were captured by bee bowls. Use of 2 sampling methods, therefore, provided a broader view of the bee fauna than would have been possible with a single collection method.
","language":"English","publisher":"BioOne","doi":"10.1656/045.026.0301","usgsCitation":"Rothwell, A., and Ginsberg, H., 2019, The bee fauna of coastal Napatree Point and two inland sites in southern Rhode Island: Northeastern Naturalist, v. 26, no. 3, p. 446-464, https://doi.org/10.1656/045.026.0301.","productDescription":"19 p.","startPage":"446","endPage":"464","ipdsId":"IP-100044","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488834,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://digitalcommons.uri.edu/pls_facpubs/138","text":"External Repository"},{"id":368032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Rhode Island","otherGeospatial":"Napatree Point","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.90277099609375,\n 41.30257109430557\n ],\n [\n -71.46469116210936,\n 41.352072144512924\n ],\n [\n -71.51309967041016,\n 41.50832019744722\n ],\n [\n -71.63223266601562,\n 41.48311944560493\n ],\n [\n -71.90277099609375,\n 41.30257109430557\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"3","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rothwell, Aya","contributorId":219515,"corporation":false,"usgs":false,"family":"Rothwell","given":"Aya","email":"","affiliations":[{"id":6922,"text":"University of Rhode Island","active":true,"usgs":false}],"preferred":false,"id":772438,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":772437,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70204133,"text":"tm7C23 - 2019 - Resource Assessment Economic Filter (RAEF)—A graphical user interface supporting implementation of simple engineering mine cost analyses of quantitative mineral resource assessment simulations","interactions":[],"lastModifiedDate":"2019-07-25T10:29:02","indexId":"tm7C23","displayToPublicDate":"2019-07-24T09:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C23","displayTitle":"Resource Assessment Economic Filter (RAEF)—A Graphical User Interface Supporting Implementation of Simple Engineering Mine Cost Analyses of Quantitative Mineral Resource Assessment Simulations","title":"Resource Assessment Economic Filter (RAEF)—A graphical user interface supporting implementation of simple engineering mine cost analyses of quantitative mineral resource assessment simulations","docAbstract":"Economic evaluations of undiscovered mineral resources provide important context in which to consider the results of quantitative mineral resource assessments. The U.S. Geological Survey economic analysis method uses a simple engineering cost model approach developed by the U.S. Bureau of Mines that applies mine and mill engineering cost equations to simulated undiscovered deposits. The important characteristics of these deposits are derived from Monte Carlo simulations that combine probabilistic estimates of undiscovered deposits that might occur in a study area and a grade-tonnage model defined for a specific deposit type. This report describes the Resource Assessment Economic Filter (RAEF), a graphical user interface (GUI) tool that applies a set of mine cost equations to the deposits under consideration. RAEF, which is written in the open-source statistical programming language R, is an easy-to-use tool to apply user-defined mine, mill, and study area parameters to simulated deposits. For a given deposit type, it estimates the undiscovered resources that might be economic to extract. In addition, RAEF provides a series of graphical, tabular, and statistical summaries that document the results of the economic filter analysis.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C23","usgsCitation":"Shapiro, J.L., and Robinson, G.R., Jr., 2019, Resource Assessment Economic Filter (RAEF)—A graphical user interface supporting implementation of simple engineering mine cost analyses of quantitative mineral resource assessment simulations: U.S. Geological Survey Techniques and Methods, book 7, chap. C23, 18 p., https://doi.org/10.3133/tm7C23.","productDescription":"18 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-104850","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":365868,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c23/tm7c23.pdf","text":"Report","size":"1.69 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-C23"},{"id":365867,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c23/coverthb.jpg"},{"id":365869,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/tm/07/c23/tm7c23_package.zip","text":"Resource Assessment Economic Filter Package","size":"125.87 MB","linkFileType":{"id":6,"text":"zip"}}],"contact":"Eastern Mineral and Environmental Resources Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive
954 Mail Stop
Reston, VA 20192
Satellite-derived spectral indices such as the relativized burn ratio (RBR) allow fire severity maps to be produced in a relatively straightforward manner across multiple fires and broad spatial extents. These indices often have strong relationships with field-based measurements of fire severity, thereby justifying their widespread use in management and science. However, satellite-derived spectral indices have been criticized because their non-standardized units render them difficult to interpret relative to on-the-ground fire effects. In this study, we built a Random Forest model describing a field-based measure of fire severity, the composite burn index (CBI), as a function of multiple spectral indices, a variable representing spatial variability in climate, and latitude. CBI data primarily representing forested vegetation from 263 fires (8075 plots) across the United States and Canada were used to build the model. Overall, the model performed well, with a cross-validated R2 of 0.72, though there was spatial variability in model performance. The model we produced allows for the direct mapping of CBI, which is more interpretable compared to spectral indices. Moreover, because the model and all spectral explanatory variables were produced in Google Earth Engine, predicting and mapping of CBI can realistically be undertaken on hundreds to thousands of fires. We provide all necessary code to execute the model and produce maps of CBI in Earth Engine. This study and its products will be extremely useful to managers and scientists in North America who wish to map fire effects over large landscapes or regions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11141735","usgsCitation":"Parks, S., Holsinger, L.M., Koontz, M.J., Collins, L.S., Whitman, E., Parisien, M., Loehman, R.A., Barnes, J.L., Bourdon, J., Boucher, J., Boucher, Y., Caprio, A.C., Collingwood, A., Hall, R., Park, J., Saperstein, L., Smetanka, C., Smith, R.J., and Soverel, N., 2019, Giving ecological meaning to satellite-derived fire severity metrics across North American forests: Remote Sensing, v. 11, 1735, 19 p., https://doi.org/10.3390/rs11141735.","productDescription":"1735, 19 p.","ipdsId":"IP-109412","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":460326,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11141735","text":"Publisher Index Page"},{"id":367416,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United 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National Park, PO Box 168, 22 Stable Street, Yellowstone National Park, WY, 82190, USA","active":true,"usgs":false}],"preferred":false,"id":770808,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Soverel, Nick","contributorId":218977,"corporation":false,"usgs":false,"family":"Soverel","given":"Nick","email":"","affiliations":[{"id":39948,"text":"Self-employed","active":true,"usgs":false}],"preferred":false,"id":770809,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70203800,"text":"ofr20191062 - 2019 - Monitoring breeding and survival of ring-necked pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, northern California—Five-year summary, 2013–17","interactions":[],"lastModifiedDate":"2019-07-25T10:24:32","indexId":"ofr20191062","displayToPublicDate":"2019-07-23T14:26:27","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1062","displayTitle":"Monitoring Breeding and Survival of Ring-Necked Pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, Northern California—Five-Year Summary, 2013–17","title":"Monitoring breeding and survival of ring-necked pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, northern California—Five-year summary, 2013–17","docAbstract":"The U.S. Geological Survey Western Ecological Research Center, Pheasants Forever, Mandeville Island Duck Club, and the California Department of Fish and Wildlife collaborated in a reconnaissance study to monitor populations of ring-necked pheasant (Phasianus colchicus) using radio-telemetry in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin of northern California. The purpose of this study was to provide agencies and private landowners with a framework of decision-support tools to help manage pheasant populations in California. During winter, spring, and autumn of 2013–17, we radio- or Global Positioning System-marked 227 female pheasant across six study sites. Data collection was focused on investigating nest-site and brood-rearing habitat selection, examining avian predator composition, and estimating population vital rates to improve our understanding of pheasant population dynamics and to identify factors that may contribute to decreases in pheasant populations in California. The cumulative annual adult survival probability across all sites during 2013–17 was 27.6 percent (95-percent confidence interval [CI], 21.9–33.6), and the cumulative nest and brood survival probabilities were 34.5 percent (95-percent CI, 27.0–42.2) and 54.2 percent (95-percent CI, 43.7–63.5), respectively. Evidence from microhabitat surveys completed at nest-sites, brood locations, and random locations suggested that marked female pheasant tended to select increasing vertical cover and residual vegetation cover and tended to avoid areas of increasing bare ground cover regardless of life-history stage. However, females at nest-sites selected increasing grass cover and height, whereas brood-rearing females tended to select increasing forb cover and height. Only perennial grass cover and perennial grass height were shown to have a positive influence on nest survival, which suggests that increasing perennial grass cover in areas occupied by pheasant may increase nest survival. Analysis of environmental factors linked to vital rate information are ongoing and will continue with investigations at increased spatial scales (that is, macro-habitat) to develop integrated population models that can incorporate abundance estimates from crow count data with vital rates from telemetry data. This report includes results from 5 years of data collection and should be interpreted with caution, as these findings are preliminary.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191062","collaboration":"Prepared in cooperation with the California Department of Fish and Wildlife and Pheasants Forever","usgsCitation":"=Dwight, I.A., Coates, P.S., Vogt, J.H., Atkinson, J.L., Fleskes, J.P., Connelly, D.P., Meshriy, M.C., Gardner, S.C., Stoute, S.T., and Pitesky M.E., 2019, Monitoring breeding and survival of ring-necked pheasant (Phasianus colchicus) in the Sacramento Valley, Sacramento-San Joaquin River Delta, and Klamath Basin, northern California—Five-year summary, 2013–17: U.S. Geological Survey Open-File Report 2019–1062, 90 p., https://doi.org/10.3133/ofr20191062.","productDescription":"90 p.","onlineOnly":"Y","ipdsId":"IP-099248","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":365826,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1062/ofr20191062.pdf","text":"Report","size":"44.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1062"},{"id":365825,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1062/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.9644775390625,\n 37.55328764595765\n ],\n [\n -119.5147705078125,\n 37.55328764595765\n ],\n [\n -119.5147705078125,\n 39.89709437260048\n ],\n [\n -122.9644775390625,\n 39.89709437260048\n ],\n [\n -122.9644775390625,\n 37.55328764595765\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive
Modoc Hall, Room 4004
Sacramento, California 95819
This scientific investigations report describes an effort by the U.S. Geological Survey (USGS) that used research, monitoring data, and modeling to develop a methodology to assess both the current and future population-level consequences of wind energy development on species of birds and bats that are present in the United States during any part of their life cycle. The methodology is currently applicable to birds and bats, focuses primarily on the effects of collisions with turbines, and can be applied to any species that breeds in, migrates through, or otherwise uses any part of the United States. The methodology assesses species at the national and regional scales and identifies those species potentially in need of more detailed study, as well as those species that are likely at low risk from wind energy development. This approach is fundamentally different from existing methods focusing on impacts at individual facilities.
This report supersedes USGS Scientific Investigations Report 2015–5066 by the same authors, which described a preliminary version of the methodology. Following reviews of the preliminary methodology by a panel of external experts, public comments, and additional internal review, the methodology was revised and finalized.
The three components of the refined methodology described in this new report rely on publicly available fatality information, population estimates, species range maps, turbine location data, biological characteristics of species, and population models. First, three metrics are combined to determine direct and indirect relative effects from wind energy facilities to generate a list of species scores. Second, a generic population model estimates the expected change in population trend caused by the additive mortality from collisions with wind turbines. Third, the methodology combines an estimate of observed fatalities and an estimate of potential biological removal to assess the possibility of a decrease in population size. The latter two components are quantitative. In a test case, the methodology was used to analyze data for six bird species and three bat species.
Components of the methodology are based on simplifying assumptions and require information that, for many species, may be sparse or unreliable or may require further study. These assumptions should be carefully considered when using outputs from the methodology. Increases in the quality of data for fatalities from collisions with wind turbines, species distributions, abundance, and demography will likely improve results for uses of the methodology.
The methodology’s design identifies and prioritizes a subset of the bird and bat species that may experience population-level impacts from collisions with wind turbines, both currently and from future wind energy development in the United States. Results of an assessment using this methodology could focus future research to improve our understanding of those impacts and to guide avoidance and minimization strategies. In addition, this methodology can be used to identify species for more intensive demographic modeling or to highlight those species that may not require any additional research because effects of wind energy development on their populations are projected to be small. The effects of wind energy facilities on nine unidentified species used in the test case described in this report have not been assessed. Their data were simply used to show the application of the methodology to real-world data and the types of outputs it would produce.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185157","usgsCitation":"Diffendorfer, J.E., Beston, J.A., Merrill, M.D., Stanton, J.C., Corum, M.D., Loss, S.R., Thogmartin, W.E., Johnson, D.H., Erickson, R.A., and Heist, K.W., 2019, A methodology to assess the national and regional impacts of U.S. wind energy development on birds and bats: U.S. Geological Survey Scientific Investigations Report 2018–5157, 45 p., https://doi.org/10.3133/sir20185157. [Supersedes USGS Scientific Investigations Report 2015–5066.]","productDescription":"ix, 45 p.","onlineOnly":"Y","ipdsId":"IP-079749","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":365690,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5157/coverthb.jpg"},{"id":365691,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5157/sir20185157.pdf","text":"Report","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5157"}],"publicComments":"Scientific Investigations Report 2018-5157 supersedes Scientific Investigations Report 2015-5066.","contact":"Director, Eastern Energy Resources Science Center
U.S. Geological Survey
Mail Stop 956
12201 Sunrise Valley Drive
Reston, VA 20192
Energy Resources Program
Wind Energy
In support of nutrient reduction efforts, nitrate (as nitrate plus nitrite) and phosphorus loads and yields were computed for selected streams in Iowa based on continuously monitored sensor data for 2008–17 and 2014–17, respectively. Sample data were used to assess nitrate sensor bias and to create phosphorus-turbidity surrogate models. Where needed, nitrate loads were corrected for site-specific sensor bias, which was determined to be as high as 9.25 percent. Nitrate loads presented in this report using continuous (generally 15-minute interval) data were on average 4 percent less, but as much as 38 percent less, than annual loads computed from daily mean nitrate concentrations not corrected for sensor bias. Streamflow-based phosphorus models had poorer fit (adjusted coefficient of determination values less than 0.75) than turbidity-based models (adjusted coefficient of determination approximately 0.9). However, alternate models based on streamflow were used to obtain a more complete annual phosphorus load despite seasonal and fragmentary sensor data.
Mean annual nitrate yields for 18 selected sites (96 site-years) ranged from 1.68 to 164 pounds per square mile per day (lb/mi2/d), compared to 19.4 lb/mi2/d average statewide yield needed to achieve the nitrate-reduction goal. Mean annual phosphorus yields for selected sites on the Maquoketa River, South Raccoon River, and West Nishnabotna River range from 1.57 to 7.19 lb/mi2/d, compared to 1.06 lb/mi2/d average statewide yield needed to achieve the phosphorus-reduction goal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195054","collaboration":"Prepared in cooperation with the Iowa Department of Natural Resources","usgsCitation":"Garrett, J.D., 2019, The use of continuous water-quality time-series data to compute nutrient loadings for selected Iowa streams, 2008–17: U.S. Geological Survey Scientific Investigations Report 2019–5054, 31 p., https://doi.org/10.3133/sir20195054.","productDescription":"Report: viii, 31 p.; Appendixes: 2","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098143","costCenters":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":365814,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5054/sir20195054.pdf","text":"Report","size":"1.97 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Bayesian statistics is currently undergoing something of a renaissance. At its heart is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. It is an approach that is ideally suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian approach corrects or replaces the assumptions and alters its decision-making accordingly to generate a new set of probabilities. As new data/evidence becomes available the probability for a particular hypothesis can therefore be steadily refined and revised. It is very well-suited to the scientific method in general and is widely used across the social, biological, medical, and physical sciences. Key to this book's novel and informal perspective is its unique pedagogy, a question and answer approach that utilizes accessible language, humor, plentiful illustrations, and frequent reference to on-line resources.
Bayesian Statistics for Beginners is an introductory textbook suitable for senior undergraduate and graduate students, professional researchers, and practitioners seeking to improve their understanding of the Bayesian statistical techniques they routinely use for data analysis in the life and medical sciences, psychology, public health, business, and other fields.
The diversity of life in the sea is critical to the health of ocean ecosystems that support living resources and therefore essential to the economic, nutritional, recreational, and health needs of billions of people. Yet there is evidence that the biodiversity of many marine habitats is being altered in response to a changing climate and human activity. Understanding this change, and forecasting where changes are likely to occur, requires monitoring of organism diversity, distribution, abundance, and health. It requires a minimum of measurements including productivity and ecosystem function, species composition, allelic diversity, and genetic expression. These observations need to be complemented with metrics of environmental change and socio-economic drivers. However, existing global ocean observing infrastructure and programs often do not explicitly consider observations of marine biodiversity and associated processes. Much effort has focused on physical, chemical and some biogeochemical measurements. Broad partnerships, shared approaches, and best practices are now being organized to implement an integrated observing system that serves information to resource managers and decision-makers, scientists and educators, from local to global scales. This integrated observing system of ocean life is now possible due to recent developments among satellite, airborne, and in situ sensors in conjunction with increases in information system capability and capacity, along with an improved understanding of marine processes represented in new physical, biogeochemical, and biological models.
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We determined SHRIMP-RG U-Pb zircon ages for the following units: (1) Larrea complex (~253 Ma and ~149–146 Ma); (2) Daisy granodiorite (~151 Ma); (3) Jack Spring quartz monzonite (~85–82 Ma); (4) unnamed porphyritic dikes and stocks (~80–73 Ma); and (5) Lane Mountain volcanics (~22 Ma). These results confirm and refine previous age determinations for the Larrea complex, Jack Spring quartz monzonite, and Lane Mountain volcanics, and provide the first reliable ages for the Daisy granodiorite and the unnamed porphyritic rocks. Sri of the plutonic rocks increases from ~0.704 to ~0.705–0.706 to ~0.708–0.709 for samples dated as ~250, ~150, and ~85–70 Ma, respectively, which indicates progressively greater involvement of Precambrian continental lithosphere in magma generation through time. The Sri and geochemical data suggest that the most significant change in magmatic source occurred between ~150 and 85 Ma.
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Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
Building 19, 350 N. Akron Rd.
P.O. Box 158
Moffett Field, CA 94035
Plant roots are critical weathering agents in deep soils, yet the impact of resulting mineral transformations on the vast deep soil carbon (C) reservoir are largely unknown. Root-driven weathering of primary minerals may cause the formation of reactive secondary minerals, which protect mineral-organic associations (MOAs) for centuries or millennia. Conversely, root-driven weathering may also transform secondary minerals, potentially enhancing the bioavailability of C previously protected in MOAs. Here we examined the impact of root-driven weathering on MOAs and their capacity to store C over pedogenic time scales. To accomplish this, we examined deep horizons (100-160 cm) that experienced root-driven weathering in four soils of increasing ages (65-226 kyr) of the Santa Cruz Marine Terrace chronosequence. Specifically, we compared discrete rhizosphere zones subject to root-driven weathering, with adjacent zones that experienced no root growth. Using a combination of radiocarbon, mass spectrometry, 57Fe Mössbauer spectroscopy, high-resolution mass spectrometry, and X-ray spectromicroscopy approaches, we characterized transformations of MOAs in relation to changes in C content, Δ14C values, and chemistry across the chronosequence. We found that the onset of root-driven weathering (65-90kyr) increased the amount of C associated with poorly crystalline iron (Fe) and aluminum (Al) phases, particularly highly disordered nano-particulate goethite (np-goethite). This increase coincided with greater C concentrations, lower Δ14C values, and greater abundance of what is likely microbially-derived C. Continued root-driven weathering (137-226kyr) did not significantly change the amount of C associated with crystalline Fe and Al phases, but resulted in a decline in the amount of C associated with poorly crystalline Fe and Al phases. This decline coincided with a decrease in C concentrations, an increase in 14C values, and a shift toward plant-derived C. In contrast, soil not affected by root-driven weathering showed comparatively low amounts of C bound to poorly crystalline Fe and Al phases regardless of soil age and, correspondingly, lower C concentrations. Our results demonstrate that root-driven formation and disruption of MOAs are direct controls on both C accrual and loss in deep soil. This finding suggests that root impacts on soil C storage are dependent on soil weathering stage, a consideration that is critical for future predictions of the vulnerability of deep soil C to global change.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gca.2019.07.030","usgsCitation":"Arredondo, M.G., Lawrence, C., Schulz, M., Tfaily, M.M., Kukkadapu, R.K., Jones, M.E., Boye, K., and Keiluweit, M., 2019, Root-driven weathering impacts on mineral-organic associations in deep soils over pedogenic time scales: Geochimica et Cosmochimica Acta, v. 263, p. 68-84, https://doi.org/10.1016/j.gca.2019.07.030.","productDescription":"17 p.","startPage":"68","endPage":"84","ipdsId":"IP-101586","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":467431,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.osti.gov/biblio/1558176","text":"Publisher Index Page"},{"id":367764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Santa Cruz","otherGeospatial":"Santa Cruz Marine Terrace","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.20504760742186,\n 36.90707799098376\n ],\n [\n -121.95682525634766,\n 36.90707799098376\n ],\n [\n -121.95682525634766,\n 36.99432681110192\n ],\n [\n -122.20504760742186,\n 36.99432681110192\n ],\n [\n -122.20504760742186,\n 36.90707799098376\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"263","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Arredondo, Mariela Garcia","contributorId":219252,"corporation":false,"usgs":false,"family":"Arredondo","given":"Mariela","email":"","middleInitial":"Garcia","affiliations":[{"id":39975,"text":"U of Mass Amherst","active":true,"usgs":false}],"preferred":false,"id":771799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lawrence, Corey 0000-0001-6143-7781","orcid":"https://orcid.org/0000-0001-6143-7781","contributorId":219251,"corporation":false,"usgs":true,"family":"Lawrence","given":"Corey","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":771798,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulz, Marjorie S. 0000-0001-5597-6447 mschulz@usgs.gov","orcid":"https://orcid.org/0000-0001-5597-6447","contributorId":3720,"corporation":false,"usgs":true,"family":"Schulz","given":"Marjorie S.","email":"mschulz@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - 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2019 - Monitoring storm tide, flooding, and precipitation from Hurricane Maria in Puerto Rico and the U.S. Virgin Islands, September 2017","interactions":[],"lastModifiedDate":"2019-07-23T14:27:01","indexId":"ofr20191065","displayToPublicDate":"2019-07-23T08:40:30","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1065","displayTitle":"Monitoring Storm Tide, Flooding, and Precipitation From Hurricane Maria in Puerto Rico and the U.S. Virgin Islands, September 2017","title":"Monitoring storm tide, flooding, and precipitation from Hurricane Maria in Puerto Rico and the U.S. Virgin Islands, September 2017","docAbstract":"Hurricane Maria made landfall south of Yabucoa Harbor, Puerto Rico, as a category 4 hurricane with maximum sustained winds of 155 miles per hour on September 20, 2017. The hurricane devastated much of the U.S. Virgin Islands and Puerto Rico. The U.S. Geological Survey (USGS), in cooperation with Federal Emergency Management Agency, installed a temporary monitoring network of 13 water-level and barometric pressure sensors along the coast of Puerto Rico prior to the storm. In addition to the temporary sensors, the USGS maintains 99 permanent real-time streamgages and 36 real-time precipitation stations in Puerto Rico. The real-time data, updated hourly, during and after the hurricane are displayed in the USGS Flood Event Viewer (https://stn.wim.usgs.gov/FEV/#MariaSeptember2017) and in the USGS National Water Information System.
The USGS measured 181 coastal and riverine high-water marks throughout Puerto Rico after the storm. Water elevations are referenced to the Puerto Rico Vertical Datum of 2002 (PRVD02) and local datums in Puerto Rico and to the U.S. Virgin Islands Vertical Datum of 2009 (VIVD09) in the U.S. Virgin Islands. Data from the Hurricane Maria storm-tide network are available in tab-delimited, American Standard Code for Information Interchange (ASCII) format and Network Common Data Form (NetCDF) format by site for each sensor through the USGS Flood Event Viewer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191065","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Byrne, M.J., Sr., 2019, Monitoring storm tide, flooding, and precipitation from Hurricane Maria in Puerto Rico and the U.S. Virgin Islands, September 2017: U.S. Geological Survey Open-File Report 2019–1065,16 p., https://doi.org/10.3133/ofr20191065.","productDescription":"vi, 16 p.","numberOfPages":"26","onlineOnly":"N","ipdsId":"IP-097015","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":365764,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1065/coverthb.jpg"},{"id":365765,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1065/ofr20191065.pdf","text":"Report","size":"4.84 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, FL 33559
Invasive predators are responsible for almost 60% of all vertebrate extinctions worldwide with the most vulnerable faunas occurring on islands. The brown treesnake (Boiga irregularis) is a notorious invasive predator that caused the extirpation or extinction of most native forest birds on Guam. The success of avian reintroduction efforts on Guam will depend on whether snake-control techniques sufficiently reduce contact rates between brown treesnakes and reintroduced birds. Mouse-lure traps can successfully reduce brown treesnake populations at local scales. Over a 22-week period both with and without active snake removal, we evaluated snake-trap contact rates for mouse- and bird-lure traps. Bird-lure traps served as a proxy for reintroduced nesting birds. Overall, mouse-lure traps caught more snakes per trap night than did bird-lure traps. However, cameras revealed that bird-lure traps had a snake contact rate almost 15 times greater than the number of successfully captured snakes. Snakes that entered bird-lure traps tended to be larger and in better body condition and were mostly captured in bird-lure traps, despite numerous adjacent mouse-lure traps. Traps placed along grid edges caught more snakes than interior traps, suggesting continuous immigration into the trapping grid within which bird-lure traps were located. Contact between snakes and bird-lure traps was equivalent before and after snake removal, suggesting mouse-lure traps did not adequately reduce the density of snakes that posed a risk to birds, at least at the timescale of this project. This study provides evidence that some snakes exhibit prey selectivity for live birds over live mouse lures. Reliance on a single control tool and lure may be inadequate for support of avian reintroductions and could lead to unintended harvest-driven trait changes of this invasive predator.
Pyrethroids are a class of widely-used insecticides that can be transported from terrestrial applications to aquatic systems via runoff and tend to sorb to organic carbon in sediments. Pyrethroid occurrence is detrimental to stream ecosystems due to toxicity to sediment-dwelling invertebrates which are particularly at risk of pyrethroid exposure in urban streams. In this work, 49 streams located in watersheds in the northeastern United States were surveyed for nine current-use pyrethroids using two extraction methods. Total sediment concentrations were determined by exhaustive chemical extraction, while bioaccessible concentrations were determined by single-point Tenax extraction. Total and bioaccessible pyrethroid concentrations were detected in 76% and 67% of the sites, and the average sum of pyrethroids was 232 ng/g organic carbon (OC) for total and 43.8 ng/g OC for bioaccessible pyrethroids. Bifenthrin was the most commonly detected pyrethroid in streambed sediments. Sediment toxicity was assessed using 10-d Hyalella azteca bioassays, and 28% and 15% of sediments caused a decrease in H. azteca biomass and survival, respectively. A temperature-based focused toxicity identification evaluation was used to assess pyrethroids as the causal factor for toxicity. The concentrations of pyrethroids was only weakly correlated with the degree of urban land use. Sediment toxicity was predicted by total and bioaccessible pyrethroid concentrations expressed as toxic units. This work suggests that bioaccessibility-based methods, such as Tenax extraction, can be a valuable tool in assessing sediment toxicity.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2019.07.099","usgsCitation":"Huff-Hartz, K.E., Nutile, S.A., Fung, C.Y., Sinche, F.L., Moran, P.W., Van Metre, P.C., Nowell, L.H., and Lydy, M.J., 2019, Survey of bioaccessible pyrethroid insecticides and sediment toxicity in urban streams of the northeast United States: Environmental Pollution, v. 254, no. 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2019 - Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016","interactions":[],"lastModifiedDate":"2019-12-30T11:37:00","indexId":"sir20195049","displayToPublicDate":"2019-07-22T11:20:00","publicationYear":"2019","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":"2019-5049","displayTitle":"Water-Budget Analysis of the Upper Big Sandy Designated Groundwater Basin Alluvial Aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016","title":"Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016","docAbstract":"The U.S. Geological Survey in cooperation with the Colorado Water Conservation Board and the Upper Big Sandy Groundwater Management District carried out a study in 2016 to evaluate potential groundwater storage changes within the Upper Big Sandy Designated Groundwater Basin (UBSDGB) alluvial aquifer, including groundwater flow between the UBSDGB alluvial aquifer and the Denver Basin bedrock aquifers. The UBSDGB alluvial aquifer is located along the ephemeral Big Sandy Creek on the east-central edge of the Denver Basin aquifer system and covers an area of about 66,560 acres within the UBSDGB. The UBSDGB alluvial aquifer consists of unconsolidated Quaternary sand and gravel deposits that contain an unconfined (water table) groundwater system. The western three-fourths of the UBSDGB alluvial aquifer overlies the Tertiary and Cretaceous bedrock formations that compose the Denver Basin aquifer system. The updated water budget for the UBSDGB alluvial aquifer, including annual change in groundwater storage in 2016, was determined by combining water-budget information from an existing Denver Basin model for about three-fourths of the study area with best estimates for the major water-budget components for the area outside the Denver Basin aquifer system. The western part of the UBSDGB was included in the Denver Basin model (modeled area), whereas the eastern part of the UBSDGB was not included in the Denver Basin model (unmodeled area). The water-budget components were first estimated for the modeled area using outputs from the Denver Basin model, which uses the modular finite-difference groundwater flow computer model MODFLOW-2000 with 1-mile grid cells. For this study, the Denver Basin model was updated with additional data from 2004 through 2016 to generate current (2016) estimates of water consumption in the UBSDGB alluvial aquifer. A basin-specific water budget for the UBSDGB alluvial aquifer from the Denver Basin model was computed using a modeling tool called ZONEBUDGET. The modeled area groundwater budget, along with previous studies, was used to estimate a groundwater budget for the unmodeled area, and results for the modeled and unmodeled areas were combined for an overall water-budget estimate for the entire UBSDGB alluvial aquifer.
The net groundwater flow into the basin from adjacent alluvial aquifers was positive with flow entering the UBSDGB alluvial aquifer. Combining the total inflow from adjacent alluvial and the total outflow to adjacent alluvial aquifers resulted in a net flow from adjacent alluvial aquifers to UBSDGB alluvial aquifer of 5,125 acre-feet (ac-ft) in 2016. The net flow between the underlying bedrock aquifers and the UBSDGB alluvial aquifer was positive with flow entering the UBSDGB alluvial aquifer from the bedrock aquifers. The net flow from the bedrock aquifers to the UBSDGB alluvial aquifer was 347 ac-ft in 2016. Net recharge (precipitation and irrigation return flows minus evaporation) into the UBSDGB alluvial aquifer was negative with groundwater being removed from the UBSDGB alluvial aquifer over the total area of the basin. Combining the total inflow from recharge to the UBSDGB alluvial aquifer of 11,153 ac-ft in 2016 and the total evapo-transpiration of −11,656 ac-ft from the UBSDGB alluvial aquifer in 2016 resulted in a net recharge from UBSDGB alluvial aquifer of −503 ac-ft in 2016. Combining the modeled and unmodeled well pumping resulted in a total well pumping volume of −3,735 ac-ft in 2016 from the UBSDGB alluvial aquifer. The net groundwater flow to the stream network in the basin was negative with flow discharging from the UBSDGB alluvial aquifer into streams. Combining the total inflow from streams and the total outflow to streams for the UBSDGB alluvial aquifer resulted in −1,032 ac-ft in 2016 that was lost to the stream network in the UBSDGB. The net groundwater flow out of the UBSDGB was negative with flow leaving the UBSDGB alluvial aquifer. Combining the total area inflow to the basin from upgradient areas and the total area outflow from the basin for the UBSDGB alluvial aquifer resulted in a net flow out of the basin of −2,300 ac-ft. In the annual groundwater budget for 2016, groundwater storage in the UBSDGB alluvial aquifer system was removed because annual groundwater outflows from storage exceeded groundwater inflows to storage; in other words, water was removed from storage to balance the annual water budget. Combining the net flow from storage for the modeled area of 73 ac-ft and the inflow from storage for the unmodeled area of 2,025 ac-ft resulted in a net positive flow from storage of the UBSDGB alluvial aquifer of 2,098 ac-ft.
Increased pumping since 1958 in the Denver and upper Arapahoe aquifers, not necessarily in the UBSDGB, has caused a change in flow from bedrock units, which were minor or non-contributors of inflow to the UBSDGB alluvial aquifer, to receiving outflow from the UBSDGB alluvial aquifer. Since 2000, aquifer storage has been an inflow component of the water budget, which means that outflow from the modeled area exceeded inflow for the UBSDGB alluvial aquifer. Increased recharge from wetter than average years could replenish the UBSDGB alluvial aquifer. From 2003 through 2016, 13 of the 25 observation wells completed in the UBSDGB alluvial aquifer had a decline in the groundwater-level elevation with an average decline of −2.21 feet, and 12 of the 25 observation wells had an increase in the groundwater-level elevation with an average increase of 1.54 feet. In general, wells at the eastern and western edges of the UBSDGB showed an increase in groundwater-level elevation that appears related to areas of groundwater discharge from the lower Dawson and Laramie-Fox Hills bedrock aquifers to the UBSDGB alluvial aquifer. The remaining wells exhibited water-level declines. Future work could include the development of a basin-specific model to serve as a basin management tool for modeling changes in groundwater levels and storage under various future groundwater recharge and withdrawal scenarios.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195049","collaboration":"Prepared in cooperation with the Colorado Water Conservation Board and the Upper Big Sandy Groundwater Management District","usgsCitation":"Kohn, M.S., Oden, J.H., and Arnold, L.R., 2019, Water-budget analysis of the Upper Big Sandy Designated Ground-water Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016: U.S. Geological Survey Scientific Investigations Report 2019-5049, 25 p., https://dx.doi.org/10.3133/sir20195049.","productDescription":"Report: vi, 25 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-091541","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":365584,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5049/sir20195049.pdf","text":"Report","size":"7.12 M","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5049"},{"id":365581,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5049/coverthb.jpg"},{"id":365748,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DEOYGZ","text":"USGS data release","linkHelpText":"MODFLOW2000 model and ZONEBUDGET computer program used to simulate the Upper Big Sandy Designated Groundwater Basin alluvial aquifer, Elbert, El Paso, and Lincoln Counties, Colorado, 2016"}],"country":"United States","state":"Colorado","county":"Elbert County, El Paso County, Lincoln County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-104.054,38.523],[-104.1629,38.5215],[-104.2759,38.5204],[-104.2794,38.5205],[-104.2836,38.5201],[-104.3759,38.52],[-104.4971,38.5192],[-104.6071,38.5187],[-104.7171,38.5186],[-104.736,38.5183],[-104.8295,38.5183],[-104.943,38.5175],[-104.9432,38.5479],[-104.943,38.5624],[-104.9429,38.6041],[-104.9427,38.6186],[-104.9429,38.6467],[-104.9429,38.6503],[-104.9427,38.6621],[-104.9427,38.6648],[-104.9428,38.6938],[-104.9399,38.6938],[-104.9386,38.7808],[-104.939,38.7949],[-105.0671,38.7946],[-105.0674,38.8666],[-105.0502,38.8665],[-105.0296,38.8668],[-105.026,39.0413],[-105.032,39.1311],[-104.9371,39.1312],[-104.9175,39.131],[-104.8303,39.1311],[-104.6642,39.1308],[-104.6638,39.2165],[-104.664,39.3026],[-104.663,39.3892],[-104.6626,39.4762],[-104.6627,39.5665],[-104.6054,39.5663],[-104.5374,39.5655],[-104.4927,39.5636],[-104.4891,39.5636],[-104.4742,39.5629],[-104.3841,39.5627],[-104.3763,39.5631],[-104.2695,39.5639],[-104.2647,39.5638],[-104.1602,39.5646],[-104.1543,39.565],[-104.0468,39.5652],[-104.0427,39.5651],[-103.9305,39.5646],[-103.9293,39.5646],[-103.8189,39.5646],[-103.8129,39.5649],[-103.7126,39.5649],[-103.7066,39.5648],[-103.6004,39.5646],[-103.595,39.5645],[-103.4882,39.5647],[-103.4804,39.5645],[-103.3748,39.5651],[-103.3658,39.5654],[-103.2631,39.5659],[-103.253,39.5657],[-103.1533,39.5657],[-103.1539,39.475],[-103.1537,39.3879],[-103.1542,39.3009],[-103.154,39.2147],[-103.1527,39.1258],[-103.161,39.1255],[-103.1615,39.0376],[-103.1626,38.9492],[-103.163,38.863],[-103.1634,38.7765],[-103.1638,38.6912],[-103.1709,38.6909],[-103.1705,38.6837],[-103.1731,38.6796],[-103.1716,38.6111],[-103.1714,38.5236],[-103.2809,38.5224],[-103.3897,38.5239],[-103.5086,38.5236],[-103.5089,38.5159],[-103.6118,38.5171],[-103.6116,38.5225],[-103.7228,38.5223],[-103.8328,38.523],[-103.9411,38.523],[-104.054,38.523]]]},\"properties\":{\"name\":\"Elbert\",\"state\":\"CO\"}}]}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225