Regional classification of streams is an early step in the Ecological Limits of Hydrologic Alteration framework. Many stream classifications are based on an inductive approach using hydrologic data from minimally disturbed basins, but this approach may underrepresent streams from heavily disturbed basins or sparsely gaged arid regions. An alternative is a deductive approach, using watershed climate, land use, and geomorphology to classify streams, but this approach may miss important hydrological characteristics of streams. We classified all stream reaches in California using both approaches. First, we used Bayesian and hierarchical clustering to classify reaches according to watershed characteristics. Streams were clustered into seven classes according to elevation, sedimentary rock, and winter precipitation. Permutation-based analysis of variance and random forest analyses were used to determine which hydrologic variables best separate streams into their respective classes. Stream typology (i.e., the class that a stream reach is assigned to) is shaped mainly by patterns of high and mean flow behavior within the stream's landscape context. Additionally, random forest was used to determine which hydrologic variables best separate minimally disturbed reference streams from non-reference streams in each of the seven classes. In contrast to stream typology, deviation from reference conditions is more difficult to detect and is largely defined by changes in low-flow variables, average daily flow, and duration of flow. Our combined deductive/inductive approach allows us to estimate flow under minimally disturbed conditions based on the deductive analysis and compare to measured flow based on the inductive analysis in order to estimate hydrologic change.
","language":"English","publisher":"Wiley","doi":"10.1002/eco.1802","usgsCitation":"Pyne, M.I., Carlisle, D.M., Konrad, C.P., and Stein, E.D., 2017, Classification of California streams using combined deductive and inductive approaches: Setting the foundation for analysis of hydrologic alteration: Ecohydrology, v. 10, no. 3, e1802, 14 p; Data Release, https://doi.org/10.1002/eco.1802.","productDescription":"e1802, 14 p; Data Release","ipdsId":"IP-073147","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":348846,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70R9MJ7","text":"USGS data release","description":"USGS data release","linkHelpText":"Select watershed attributes for California stream segments (NHDPlus V.1)"},{"id":348662,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Center","active":false,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":717395,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Konrad, Christopher P. 0000-0002-7354-547X cpkonrad@usgs.gov","orcid":"https://orcid.org/0000-0002-7354-547X","contributorId":1716,"corporation":false,"usgs":true,"family":"Konrad","given":"Christopher","email":"cpkonrad@usgs.gov","middleInitial":"P.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":717397,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stein, Eric D.","contributorId":198848,"corporation":false,"usgs":false,"family":"Stein","given":"Eric","email":"","middleInitial":"D.","affiliations":[{"id":12704,"text":"Southern California Coastal Water Research Project","active":true,"usgs":false}],"preferred":false,"id":717398,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70193709,"text":"70193709 - 2017 - Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation","interactions":[],"lastModifiedDate":"2017-11-29T16:10:58","indexId":"70193709","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2776,"text":"Molecular Ecology Resources","active":true,"publicationSubtype":{"id":10}},"title":"Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation","docAbstract":"Environmental DNA (eDNA) can be used as an assessment tool to detect populations of threatened species and provide fine-scale data required to make management decisions. The objectives of this project were to use quantitative PCR (qPCR) to: (i) detect spiked salamander DNA in soil, (ii) quantify eDNA degradation over time, (iii) determine detectability of salamander eDNA in a terrestrial environment using soil, faeces, and skin swabs, (iv) detect salamander eDNA in a mesocosm experiment. Salamander eDNA was positively detected in 100% of skin swabs and 66% of faecal samples and concentrations did not differ between the two sources. However, eDNA was not detected in soil samples collected from directly underneath wild-caught living salamanders. Salamander genomic DNA (gDNA) was detected in all qPCR reactions when spiked into soil at 10.0, 5.0, and 1.0 ng/g soil and spike concentration had a significant effect on detected concentrations. Only 33% of samples showed recoverable eDNA when spiked with 0.25 ng/g soil, which was the low end of eDNA detection. To determine the rate of eDNA degradation, gDNA (1 ng/g soil) was spiked into soil and quantified over seven days. Salamander eDNA concentrations decreased across days, but eDNA was still amplifiable at day 7. Salamander eDNA was detected in two of 182 mesocosm soil samples over 12 weeks (n = 52 control samples; n = 65 presence samples; n = 65 eviction samples). The discrepancy in detection success between experiments indicates the potential challenges for this method to be used as a monitoring technique for small-bodied wild terrestrial salamander populations.
","language":"English","publisher":"Wiley","doi":"10.1111/1755-0998.12667","usgsCitation":"Walker, D.M., Leys, J.E., Dunham, K.E., Oliver, J.C., Schiller, E.E., Stephenson, K.S., Kimrey, J.T., Wooten, J., and Rogers, M.W., 2017, Methodological considerations for detection of terrestrial small-body salamander eDNA and implications for biodiversity conservation: Molecular Ecology Resources, v. 17, no. 6, p. 1223-1230, https://doi.org/10.1111/1755-0998.12667.","productDescription":"8 p.","startPage":"1223","endPage":"1230","ipdsId":"IP-080810","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":348200,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-11","publicationStatus":"PW","scienceBaseUri":"5a003150e4b0531197b5a748","contributors":{"authors":[{"text":"Walker, Donald M.","contributorId":39132,"corporation":false,"usgs":false,"family":"Walker","given":"Donald","email":"","middleInitial":"M.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Leys, Jacob E.","contributorId":199800,"corporation":false,"usgs":false,"family":"Leys","given":"Jacob","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunham, Kelly E.","contributorId":169093,"corporation":false,"usgs":false,"family":"Dunham","given":"Kelly","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720391,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliver, Joshua C.","contributorId":199613,"corporation":false,"usgs":false,"family":"Oliver","given":"Joshua","email":"","middleInitial":"C.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720392,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schiller, Emily E.","contributorId":145533,"corporation":false,"usgs":false,"family":"Schiller","given":"Emily","email":"","middleInitial":"E.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720393,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, Kelsey S.","contributorId":100992,"corporation":false,"usgs":false,"family":"Stephenson","given":"Kelsey","email":"","middleInitial":"S.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720394,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kimrey, John T.","contributorId":199571,"corporation":false,"usgs":false,"family":"Kimrey","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":35244,"text":"Tennessee Technological University","active":true,"usgs":false}],"preferred":false,"id":720395,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wooten, Jessica","contributorId":190940,"corporation":false,"usgs":false,"family":"Wooten","given":"Jessica","email":"","affiliations":[{"id":35654,"text":"Centre College, Danville, KY, USA","active":true,"usgs":false}],"preferred":false,"id":720396,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Rogers, Mark W. 0000-0001-7205-5623 mwrogers@usgs.gov","orcid":"https://orcid.org/0000-0001-7205-5623","contributorId":4590,"corporation":false,"usgs":true,"family":"Rogers","given":"Mark","email":"mwrogers@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":720397,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70192160,"text":"70192160 - 2017 - Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","interactions":[],"lastModifiedDate":"2017-10-23T13:54:01","indexId":"70192160","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Automated cropland mapping of continental Africa using Google Earth Engine cloud computing","docAbstract":"The automation of agricultural mapping using satellite-derived remotely sensed data remains a challenge in Africa because of the heterogeneous and fragmental landscape, complex crop cycles, and limited access to local knowledge. Currently, consistent, continent-wide routine cropland mapping of Africa does not exist, with most studies focused either on certain portions of the continent or at most a one-time effort at mapping the continent at coarse resolution remote sensing. In this research, we addressed these limitations by applying an automated cropland mapping algorithm (ACMA) that captures extensive knowledge on the croplands of Africa available through: (a) ground-based training samples, (b) very high (sub-meter to five-meter) resolution imagery (VHRI), and (c) local knowledge captured during field visits and/or sourced from country reports and literature. The study used 16-day time-series of Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) composited data at 250-m resolution for the entire African continent. Based on these data, the study first produced accurate reference cropland layers or RCLs (cropland extent/areas, irrigation versus rainfed, cropping intensities, crop dominance, and croplands versus cropland fallows) for the year 2014 that provided an overall accuracy of around 90% for crop extent in different agro-ecological zones (AEZs). The RCLs for the year 2014 (RCL2014) were then used in the development of the ACMA algorithm to create ACMA-derived cropland layers for 2014 (ACL2014). ACL2014 when compared pixel-by-pixel with the RCL2014 had an overall similarity greater than 95%. Based on the ACL2014, the African continent had 296 Mha of net cropland areas (260 Mha cultivated plus 36 Mha fallows) and 330 Mha of gross cropland areas. Of the 260 Mha of net cropland areas cultivated during 2014, 90.6% (236 Mha) was rainfed and just 9.4% (24 Mha) was irrigated. Africa has about 15% of the world’s population, but only about 6% of world’s irrigation. Net cropland area distribution was 95 Mha during season 1, 117 Mha during season 2, and 84 Mha continuous. About 58% of the rainfed and 39% of the irrigated were single crops (net cropland area without cropland fallows) cropped during either season 1 (January-May) or season 2 (June-September). The ACMA algorithm was deployed on Google Earth Engine (GEE) cloud computing platform and applied on MODIS time-series data from 2003 through 2014 to obtain ACMA-derived cropland layers for these years (ACL2003 to ACL2014). The results indicated that over these twelve years, on average: (a) croplands increased by 1 Mha/yr, and (b) cropland fallows decreased by 1 Mha/year. Cropland areas computed from ACL2014 for the 55 African countries were largely underestimated when compared with an independent source of census-based cropland data, with a root-mean-square error (RMSE) of 3.5 Mha. ACMA demonstrated the ability to hind-cast (past years), now-cast (present year), and forecast (future years) cropland products using MODIS 250-m time-series data rapidly, but currently, insufficient reference data exist to rigorously report trends from these results.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2017.01.019","usgsCitation":"Xiong, J., Thenkabail, P.S., Gumma, M., Teluguntla, P.G., Poehnelt, J., Congalton, R.G., Yadav, K., and Thau, D., 2017, Automated cropland mapping of continental Africa using Google Earth Engine cloud computing: ISPRS Journal of Photogrammetry and Remote Sensing, v. 126, p. 225-244, https://doi.org/10.1016/j.isprsjprs.2017.01.019.","productDescription":"20 p.","startPage":"225","endPage":"244","ipdsId":"IP-081308","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469973,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.isprsjprs.2017.01.019","text":"Publisher Index Page"},{"id":347130,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Africa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -18.80859375,\n -36.03133177633187\n ],\n [\n 52.03125,\n -36.03133177633187\n ],\n [\n 52.03125,\n 37.579412513438385\n ],\n [\n -18.80859375,\n 37.579412513438385\n ],\n [\n -18.80859375,\n -36.03133177633187\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"126","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59eeffa9e4b0220bbd988fa9","contributors":{"authors":[{"text":"Xiong, Jun 0000-0002-2320-0780 jxiong@usgs.gov","orcid":"https://orcid.org/0000-0002-2320-0780","contributorId":5276,"corporation":false,"usgs":true,"family":"Xiong","given":"Jun","email":"jxiong@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714479,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thenkabail, Prasad S. 0000-0002-2182-8822 pthenkabail@usgs.gov","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":570,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","email":"pthenkabail@usgs.gov","middleInitial":"S.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714480,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gumma, Murali Krishna","contributorId":50426,"corporation":false,"usgs":true,"family":"Gumma","given":"Murali Krishna","affiliations":[],"preferred":false,"id":714481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Teluguntla, Pardhasaradhi G. 0000-0001-8060-9841 pteluguntla@usgs.gov","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":5275,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","email":"pteluguntla@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":714482,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Poehnelt, Justin 0000-0001-5914-4269","orcid":"https://orcid.org/0000-0001-5914-4269","contributorId":192328,"corporation":false,"usgs":false,"family":"Poehnelt","given":"Justin","email":"","affiliations":[],"preferred":false,"id":714483,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Congalton, Russell G.","contributorId":138718,"corporation":false,"usgs":false,"family":"Congalton","given":"Russell","email":"","middleInitial":"G.","affiliations":[{"id":12507,"text":"Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA","active":true,"usgs":false}],"preferred":false,"id":714484,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yadav, Kamini","contributorId":138720,"corporation":false,"usgs":false,"family":"Yadav","given":"Kamini","affiliations":[{"id":12507,"text":"Department of Natural Resources and the Environment, University of New Hampshire, 56 College Road, Durham, NH 03824, USA","active":true,"usgs":false}],"preferred":false,"id":714485,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Thau, David","contributorId":103581,"corporation":false,"usgs":true,"family":"Thau","given":"David","email":"","affiliations":[],"preferred":false,"id":714878,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70191872,"text":"70191872 - 2017 - Urbanization may limit impacts of an invasive predator on native mammal diversity","interactions":[],"lastModifiedDate":"2017-10-18T14:45:36","indexId":"70191872","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Urbanization may limit impacts of an invasive predator on native mammal diversity","docAbstract":"Aim
Our understanding of the effects of invasive species on faunal diversity is limited in part because invasions often occur in modified landscapes where other drivers of community diversity can exacerbate or reduce the net impacts of an invader. Furthermore, rigorous assessments of the effects of invasive species on native communities that account for variation in sampling, species-specific detection and occurrence of rare species are lacking. Invasive Burmese pythons (Python molurus bivittatus) may be causing declines in medium- to large-sized mammals throughout the Greater Everglades Ecosystem (GEE); however, other factors such as urbanization, habitat changes and drastic alteration in water flow may also be influential in structuring mammal communities. The aim of this study was to gain an understanding of how mammal communities simultaneously facing invasive predators and intensively human-altered landscapes are influenced by these drivers and their interactions.
Location
Florida, USA.
Methods
We used data from trail cameras and scat searches with a hierarchical community model that accounts for undetected species to determine the relative influence of introduced Burmese pythons, urbanization, local hydrology, habitat types and interactive effects between pythons and urbanization on mammal species occurrence, site-level species richness, and turnover.
Results
Python density had significant negative effects on all species except coyotes. Despite these negative effects, occurrence of some generalist species increased significantly near urban areas. At the community level, pythons had the greatest impact on species richness, while turnover was greatest along the urbanization gradient where communities were increasingly similar as distance to urbanization decreased.
Main conclusions
We found evidence for an antagonistic interaction between pythons and urbanization where the impacts of pythons were reduced near urban development. Python-induced changes to mammal communities may be mediated near urban development, but elsewhere in the GEE, pythons are likely causing a fundamental restructuring of the food web, declines in ecosystem function, and creating complex and unpredictable cascading effects.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.12531","usgsCitation":"Reichert, B., Sovie, A.R., Udell, B.J., Hart, K.M., Borkhataria, R.R., Bonneau, M., Reed, R., and McCleery, R.A., 2017, Urbanization may limit impacts of an invasive predator on native mammal diversity: Diversity and Distributions, v. 23, no. 4, p. 355-367, https://doi.org/10.1111/ddi.12531.","productDescription":"13 p.","startPage":"355","endPage":"367","ipdsId":"IP-077761","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469970,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.12531","text":"Publisher Index Page"},{"id":346891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.529296875,\n 25.085598897064752\n ],\n [\n -80.0189208984375,\n 25.085598897064752\n ],\n [\n -80.0189208984375,\n 27.235094607795503\n ],\n [\n -82.529296875,\n 27.235094607795503\n ],\n [\n -82.529296875,\n 25.085598897064752\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-26","publicationStatus":"PW","scienceBaseUri":"59e86836e4b05fe04cd4d1ff","contributors":{"authors":[{"text":"Reichert, Brian E.","contributorId":197423,"corporation":false,"usgs":false,"family":"Reichert","given":"Brian E.","affiliations":[],"preferred":false,"id":713475,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sovie, Adia R.","contributorId":197424,"corporation":false,"usgs":false,"family":"Sovie","given":"Adia","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":713477,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Udell, Brad J.","contributorId":197490,"corporation":false,"usgs":false,"family":"Udell","given":"Brad","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":713606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hart, Kristen M. 0000-0002-5257-7974 kristen_hart@usgs.gov","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":1966,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","email":"kristen_hart@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":713478,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Borkhataria, Rena R.","contributorId":197425,"corporation":false,"usgs":false,"family":"Borkhataria","given":"Rena","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":713479,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bonneau, Mathieu","contributorId":150041,"corporation":false,"usgs":false,"family":"Bonneau","given":"Mathieu","email":"","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":713480,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":713474,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McCleery, Robert A.","contributorId":139849,"corporation":false,"usgs":false,"family":"McCleery","given":"Robert","email":"","middleInitial":"A.","affiliations":[{"id":12557,"text":"University of Florida, FLREC","active":true,"usgs":false}],"preferred":false,"id":713476,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70192128,"text":"70192128 - 2017 - From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments","interactions":[],"lastModifiedDate":"2017-10-23T14:56:58","indexId":"70192128","displayToPublicDate":"2017-04-01T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5053,"text":"Earth's Future","active":true,"publicationSubtype":{"id":10}},"title":"From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments","docAbstract":"Our different kinds of minds and types of thinking affect the ways we decide, take action, and cooperate (or not). Derived from these types of minds, innate biases, beliefs, heuristics, and values (BBHV) influence behaviors, often beneficially, when individuals or small groups face immediate, local, acute situations that they and their ancestors faced repeatedly in the past. BBHV, though, need to be recognized and possibly countered or used when facing new, complex issues or situations especially if they need to be managed for the benefit of a wider community, for the longer-term and the larger-scale. Taking BBHV into account, we explain and provide a cyclic science-infused adaptive framework for (1) gaining knowledge of complex systems and (2) improving their management. We explore how this process and framework could improve the governance of science and policy for different types of systems and issues, providing examples in the area of natural resources, hazards, and the environment. Lastly, we suggest that an “Open Traceable Accountable Policy” initiative that followed our suggested adaptive framework could beneficially complement recent Open Data/Model science initiatives.
","language":"English","publisher":"AGU","doi":"10.1002/2016EF000487","usgsCitation":"Glynn, P.D., Voinov, A.A., Shapiro, C.D., and White, P.A., 2017, From data to decisions: Processing information, biases, and beliefs for improved management of natural resources and environments: Earth's Future, v. 5, no. 4, p. 356-378, https://doi.org/10.1002/2016EF000487.","productDescription":"33 p.","startPage":"356","endPage":"378","ipdsId":"IP-083142","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469968,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016ef000487","text":"Publisher Index Page"},{"id":347146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-24","publicationStatus":"PW","scienceBaseUri":"59eeffa9e4b0220bbd988fac","contributors":{"authors":[{"text":"Glynn, Pierre D. 0000-0001-8804-7003 pglynn@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":2141,"corporation":false,"usgs":true,"family":"Glynn","given":"Pierre","email":"pglynn@usgs.gov","middleInitial":"D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":714336,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voinov, Alexey A.","contributorId":197796,"corporation":false,"usgs":false,"family":"Voinov","given":"Alexey","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shapiro, Carl D. 0000-0002-1598-6808 cshapiro@usgs.gov","orcid":"https://orcid.org/0000-0002-1598-6808","contributorId":3048,"corporation":false,"usgs":true,"family":"Shapiro","given":"Carl","email":"cshapiro@usgs.gov","middleInitial":"D.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":true,"id":714338,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"White, Paul A.","contributorId":197797,"corporation":false,"usgs":false,"family":"White","given":"Paul","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":714339,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70183250,"text":"sir20175016 - 2017 - Occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir, Kansas, 2001-16","interactions":[],"lastModifiedDate":"2025-07-24T13:03:34.594363","indexId":"sir20175016","displayToPublicDate":"2017-03-31T11:15:00","publicationYear":"2017","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":"2017-5016","title":"Occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir, Kansas, 2001-16","docAbstract":"Cheney Reservoir, located in south-central Kansas, is one of the primary drinking-water supplies for the city of Wichita and an important recreational resource. Since 1990, cyanobacterial blooms have been present occasionally in Cheney Reservoir, resulting in increased treatment costs and decreased recreational use. Cyanobacteria, the cyanotoxin microcystin, and the taste-and-odor compounds geosmin and 2-methylisoborneol have been measured in Cheney Reservoir by the U.S. Geological Survey, in cooperation with the city of Wichita, for about 16 years. The purpose of this report is to describe the occurrence of cyanobacteria, microcystin, and taste-and-odor compounds in Cheney Reservoir during May 2001 through June 2016 and to update previously published logistic regression models that used continuous water-quality data to estimate the probability of microcystin and geosmin occurrence above relevant thresholds.
Cyanobacteria, microcystin, and geosmin were detected in about 84, 52, and 31 percent of samples collected in Cheney Reservoir during May 2001 through June 2016, respectively. 2-methylisoborneol was less common, detected in only 3 percent of samples. Microcystin and geosmin concentrations exceeded advisory values of concern more frequently than cyanobacterial abundance; therefore, cyanobacteria are not a good indicator of the presence of these taste-and-odor compounds in Cheney Reservoir. Broad seasonal patterns in cyanobacteria and microcystin were evident, though abundance and concentration varied by orders of magnitude across years. Cyanobacterial abundances generally peaked in late summer or early fall (August through October), and smaller peaks were observed in winter (January through February). In a typical year, microcystin was first detected in June or July, increased to its seasonal maxima in the summer (July through September), and then decreased. Seasonal patterns in geosmin were less consistent than cyanobacteria and microcystin, but geosmin typically had a small peak during winter (January through March) during most years and a large peak during summer (July through September) during some years. Though the relation between cyanobacterial abundance and microcystin and geosmin concentrations was positive, overall correlations were weak, likely because production is strain-specific and cyanobacterial strain composition may vary substantially over time. Microcystin often was present without taste-and-odor compounds. By comparison, where taste-and-odor compounds were present, microcystin frequently was detected. Taste-and-odor compounds, therefore, may be used as indicators that microcystin may be present; however, microcystin was present without taste-and-odor compounds, so taste or odor alone does not provide sufficient warning to ensure human-health protection.
Logistic regression models that estimate the probability of microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter and geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter were developed. Models were developed using the complete dataset (January 2003 through June 2016 for microcystin [14-year dataset]; May 2001 through June 2016 for geosmin [16-year dataset]) and an abbreviated 4-year dataset (January 2013 through June 2016 for microcystin and geosmin). Performance of the newly developed models was compared with previously published models that were developed using data collected during May 2001 through December 2009. A seasonal component and chlorophyll fluorescence (a surrogate for algal biomass) were the explanatory variables for microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in all models. All models were relatively robust, though the previously published and 14-year models performed better over time; however, as a tool to estimate microcystin occurrence at concentrations greater than or equal to 0.1 micrograms per liter in a real-time notification system near the Cheney Dam, the 4-year model is most representative of recent (2013 through 2016) conditions. All models for geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter had different explanatory variables and model forms. The previously published and 16-year models were not robust over time, likely because of changing environmental conditions and seasonal patterns in geosmin occurrence. By comparison, the abbreviated 4-year model may be a useful tool to estimate geosmin occurrence at concentrations greater than or equal to 5 nanograms per liter in a real-time notification system near the Cheney Dam. The better performance of the abbreviated 4-year geosmin model during 2013 through 2016 relative to the previously published and 16-year models demonstrates the need for continuous reevaluation of models estimating the probability of occurrence.
Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
This report summarizes the results of three-dimensional (3-D) resistivity inversion simulations that were performed to account for local 3-D distortion of the electric field in the presence of 3-D regional structure, without any a priori information on the actual 3-D distribution of the known subsurface geology. The methodology used a 3-D geologic model to create a 3-D resistivity forward (“known”) model that depicted the subsurface resistivity structure expected for the input geologic configuration. The calculated magnetotelluric response of the modeled resistivity structure was assumed to represent observed magnetotelluric data and was subsequently used as input into a 3-D resistivity inverse model that used an iterative 3-D algorithm to estimate 3-D distortions without any a priori geologic information. A publicly available inversion code, WSINV3DMT, was used for all of the simulated inversions, initially using the default parameters, and subsequently using adjusted inversion parameters. A semiautomatic approach of accounting for the static shift using various selections of the highest frequencies and initial models was also tested. The resulting 3-D resistivity inversion simulation was compared to the “known” model and the results evaluated. The inversion approach that produced the lowest misfit to the various local 3-D distortions was an inversion that employed an initial model volume resistivity that was nearest to the maximum resistivities in the near-surface layer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171007","usgsCitation":"Rodriguez, B.D., 2017, Semiautomatic approaches to account for 3-D distortion of the electric field from local, near-surface structures in 3-D resistivity inversions of 3-D regional magnetotelluric data: U.S. Geological Survey Open-File Report 2017–1007, 25 p., https://doi.org/10.3133/ofr20171007.","productDescription":"iii, 25 p.","numberOfPages":"29","onlineOnly":"Y","ipdsId":"IP-068212","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":338862,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1007/coverthb.jpg"},{"id":338863,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1007/ofr20171007.pdf","text":"Report","size":"20.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1007"}],"contact":"Director, Crustal Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS 964
Denver, CO 80225
A partnership established between the European Space Agency (ESA) and the U.S. Geological Survey (USGS) allows for USGS storage and redistribution of images acquired by the MultiSpectral Instrument (MSI) on the European Union's Sentinel-2 satellite mission. The MSI data are acquired from a pair of satellites, Sentinel-2A and Sentinel-2B, which are part of a larger set of ESA missions focusing on different aspects of Earth observation. The primary purpose of the Sentinel-2 series is to collect multispectral imagery over the Earth’s land surfaces, large islands, and inland and coastal waters. Sentinel-2A was launched in 2015 and Sentinel-2B launched in 2017.
The collaborative effort between ESA and USGS provides for public access and redistribution of global acquisitions of Sentinel-2 data at no cost, which allows users to download the MSI imagery from USGS access systems such as Earth- Explorer, in addition to the ESA Sentinels Scientific Data Hub. The MSI sensor acquires 13 spectral bands that are highly complementary to data acquired by the USGS Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+). The product options from USGS include a Full-Resolution Browse (FRB) image product generated by USGS, along with a 100-kilometer (km) by 100-km tile-based Level-1C top-of-atmosphere (TOA) reflectance product that is very similar (but not identical) to the currently (2017) distributed ESA Level 1C product.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173026","usgsCitation":"Pieschke, R.L., 2017, U.S. Geological Survey distribution of European Space Agency's Sentinel-2 data: U.S. Geological Survey Fact Sheet 2017–3026, 2 p., https://doi.org/10.3133/fs20173026.\n","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-082585","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":338860,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3026/fs20173026.pdf","text":"Fact Sheet","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017–3026"},{"id":338859,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3026/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science (EROS) Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198–0001
This paper presents a method using Monte Carlo simulations for assessing uncertainty of moving-boat acoustic Doppler current profiler (ADCP) discharge measurements using a software tool known as QUant, which was developed for this purpose. Analysis was performed on 10 data sets from four Water Survey of Canada gauging stations in order to evaluate the relative contribution of a range of error sources to the total estimated uncertainty. The factors that differed among data sets included the fraction of unmeasured discharge relative to the total discharge, flow nonuniformity, and operator decisions about instrument programming and measurement cross section. As anticipated, it was found that the estimated uncertainty is dominated by uncertainty of the discharge in the unmeasured areas, highlighting the importance of appropriate selection of the site, the instrument, and the user inputs required to estimate the unmeasured discharge. The main contributor to uncertainty was invalid data, but spatial inhomogeneity in water velocity and bottom-track velocity also contributed, as did variation in the edge velocity, uncertainty in the edge distances, edge coefficients, and the top and bottom extrapolation methods. To a lesser extent, spatial inhomogeneity in the bottom depth also contributed to the total uncertainty, as did uncertainty in the ADCP draft at shallow sites. The estimated uncertainties from QUant can be used to assess the adequacy of standard operating procedures. They also provide quantitative feedback to the ADCP operators about the quality of their measurements, indicating which parameters are contributing most to uncertainty, and perhaps even highlighting ways in which uncertainty can be reduced. Additionally, QUant can be used to account for self-dependent error sources such as heading errors, which are a function of heading. The results demonstrate the importance of a Monte Carlo method tool such as QUant for quantifying random and bias errors when evaluating the uncertainty of moving-boat ADCP measurements.
We present a shoreline change model for coastal hazard assessment and management planning. The model, CoSMoS-COAST (Coastal One-line Assimilated Simulation Tool), is a transect-based, one-line model that predicts short-term and long-term shoreline response to climate change in the 21st century. The proposed model represents a novel, modular synthesis of process-based models of coastline evolution due to longshore and cross-shore transport by waves and sea-level rise. Additionally, the model uses an extended Kalman filter for data assimilation of historical shoreline positions to improve estimates of model parameters and thereby improve confidence in long-term predictions. We apply CoSMoS-COAST to simulate sandy shoreline evolution along 500 km of coastline in Southern California, which hosts complex mixtures of beach settings variably backed by dunes, bluffs, cliffs, estuaries, river mouths, and urban infrastructure, providing applicability of the model to virtually any coastal setting. Aided by data assimilation, the model is able to reproduce the observed signal of seasonal shoreline change for the hindcast period of 1995-2010, showing excellent agreement between modeled and observed beach states. The skill of the model during the hindcast period improves confidence in the model's predictive capability when applied to the forecast period (2010-2100) driven by GCM-projected wave and sea-level conditions. Predictions of shoreline change with limited human intervention indicate that 31% to 67% of Southern California beaches may become completely eroded by 2100 under sea-level rise scenarios of 0.93 to 2.0 m.
","language":"English","publisher":"Wiley","doi":"10.1002/2016JF004065","usgsCitation":"Vitousek, S., Barnard, P., Limber, P.W., Erikson, L.H., and Cole, B., 2017, A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change: Journal of Geophysical Research F: Earth Surface, v. 122, no. 4, p. 782-806, https://doi.org/10.1002/2016JF004065.","productDescription":"25 p.","startPage":"782","endPage":"806","ipdsId":"IP-079262","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":338922,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","volume":"122","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-06","publicationStatus":"PW","scienceBaseUri":"58df6abfe4b02ff32c6aea27","contributors":{"authors":[{"text":"Vitousek, Sean","contributorId":190192,"corporation":false,"usgs":false,"family":"Vitousek","given":"Sean","affiliations":[],"preferred":false,"id":687761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barnard, Patrick L. 0000-0003-1414-6476 pbarnard@usgs.gov","orcid":"https://orcid.org/0000-0003-1414-6476","contributorId":147147,"corporation":false,"usgs":true,"family":"Barnard","given":"Patrick L.","email":"pbarnard@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":687760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Limber, Patrick W. 0000-0002-8207-3750 plimber@usgs.gov","orcid":"https://orcid.org/0000-0002-8207-3750","contributorId":5773,"corporation":false,"usgs":true,"family":"Limber","given":"Patrick","email":"plimber@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":687762,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erikson, Li H. 0000-0002-8607-7695 lerikson@usgs.gov","orcid":"https://orcid.org/0000-0002-8607-7695","contributorId":149963,"corporation":false,"usgs":true,"family":"Erikson","given":"Li","email":"lerikson@usgs.gov","middleInitial":"H.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":687763,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cole, Blake","contributorId":190193,"corporation":false,"usgs":false,"family":"Cole","given":"Blake","email":"","affiliations":[],"preferred":false,"id":687764,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70186025,"text":"70186025 - 2017 - Full annual cycle climate change vulnerability assessment for migratory birds","interactions":[],"lastModifiedDate":"2017-03-30T12:19:21","indexId":"70186025","displayToPublicDate":"2017-03-30T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Full annual cycle climate change vulnerability assessment for migratory birds","docAbstract":"Climate change is a serious challenge faced by all plant and animal species. Climate change vulnerability assessments (CCVAs) are one method to assess risk and are increasingly used as a tool to inform management plans. Migratory animals move across regions and continents during their annual cycles where they are exposed to diverse climatic conditions. Climate change during any period and in any region of the annual cycle could influence survival, reproduction, or the cues used to optimize timing of migration. Therefore, CCVAs for migratory animals best estimate risk when they include climate exposure during the entire annual cycle. We developed a CCVA incorporating the full annual cycle and applied this method to 46 species of migratory birds breeding in the Upper Midwest and Great Lakes (UMGL) region of the United States. Our methodology included background risk, climate change exposure × climate sensitivity, adaptive capacity to climate change, and indirect effects of climate change. We compiled information about migratory connectivity between breeding and stationary non-breeding areas using literature searches and U.S. Geological Survey banding and re-encounter data. Climate change exposure (temperature and moisture) was assessed using UMGL breeding season climate and winter climate from non-breeding regions for each species. Where possible, we focused on non-breeding regions known to be linked through migratory connectivity. We ranked 10 species as highly vulnerable to climate change and two as having low vulnerability. The remaining 34 species were ranked as moderately vulnerable. In general, including non-breeding data provided more robust results that were highly individualistic by species. Two species were found to be highly vulnerable throughout their annual cycle. Projected drying will have the greatest effect during the non-breeding season for species overwintering in Mexico and the Caribbean. Projected temperature increases will have the greatest effect during the breeding season in UMGL as well as during the non-breeding season for species overwintering in South America. We provide a model for adaptive management of migratory animals in the face of projected climate change, including identification of priority species, research needs, and regions within non-breeding ranges for potential conservation partnerships.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.1565","usgsCitation":"Culp, L.A., Cohen, E.B., Scarpignato, A.L., Thogmartin, W.E., and Marra, P.P., 2017, Full annual cycle climate change vulnerability assessment for migratory birds: Ecological Applications, v. 8, no. 3, e01565; 22 p., https://doi.org/10.1002/ecs2.1565.","productDescription":"e01565; 22 p.","ipdsId":"IP-078803","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":461685,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1565","text":"Publisher Index Page"},{"id":338822,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"8","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-06","publicationStatus":"PW","scienceBaseUri":"58de194ce4b02ff32c699c83","contributors":{"authors":[{"text":"Culp, Leah A.","contributorId":190138,"corporation":false,"usgs":false,"family":"Culp","given":"Leah","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":687378,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cohen, Emily B.","contributorId":57774,"corporation":false,"usgs":false,"family":"Cohen","given":"Emily","email":"","middleInitial":"B.","affiliations":[{"id":7035,"text":"Smithsonian Conservation Biology Institute, National Zoological Park","active":true,"usgs":false}],"preferred":false,"id":687379,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Scarpignato, Amy L.","contributorId":190139,"corporation":false,"usgs":false,"family":"Scarpignato","given":"Amy","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":687380,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":687377,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Marra, Peter P.","contributorId":190140,"corporation":false,"usgs":false,"family":"Marra","given":"Peter","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":687381,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70185601,"text":"ofr20171034 - 2017 - Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production","interactions":[],"lastModifiedDate":"2017-03-30T12:15:26","indexId":"ofr20171034","displayToPublicDate":"2017-03-29T17:45:00","publicationYear":"2017","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":"2017-1034","title":"Landsat and agriculture—Case studies on the uses and benefits of Landsat imagery in agricultural monitoring and production","docAbstract":"The use of Landsat satellite imagery for global agricultural monitoring began almost immediately after the launch of Landsat 1 in 1972, making agricultural monitoring one of the longest-standing operational applications for the Landsat program. More recently, Landsat imagery has been used in domestic agricultural applications as an input for field-level production management. The enactment of the U.S. Geological Survey’s free and open data policy in 2008 and the launch of Landsat 8 in 2013 have both influenced agricultural applications. This report presents two primary sets of case studies on the applications and benefits of Landsat imagery use in agriculture. The first set examines several operational applications within the U.S. Department of Agriculture (USDA) and the second focuses on private sector applications for agronomic management.
Information on the USDA applications is provided in the U.S. Department of Agriculture Uses of Landsat Imagery for Global and Domestic Agricultural Monitoring section of the report in the following subsections:
Private sector applications using Landsat imagery for agricultural management are discussed in the Landsat Imagery Use and Benefits in Field-Level Agricultural Production Management section of the report in the following subsections:
Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
To indicate that atomic weights of many elements are not constants of nature, in 2009 and 2011 the Commission on Isotopic Abundances and Atomic Weights (CIAAW) of the International Union of Pure and Applied Chemistry (IUPAC) replaced single-value standard atomic weight values with atomic weight intervals for 12 elements (hydrogen, lithium, boron, carbon, nitrogen, oxygen, magnesium, silicon, sulfur, chlorine, bromine, and thallium); for example, the standard atomic weight of nitrogen became the interval [14.00643, 14.00728]. CIAAW recognized that some users of atomic weight data only need representative values for these 12 elements, such as for trade and commerce. For this purpose, CIAAW provided conventional atomic weight values, such as 14.007 for nitrogen, and these values can serve in education when a single representative value is needed, such as for molecular weight calculations. Because atomic weight values abridged to four figures are preferred by many educational users and are no longer provided by CIAAW as of 2015, we provide a table containing both standard atomic weight values and conventional atomic weight values abridged to four figures for the chemical elements. A retrospective review of changes in four-digit atomic weights since 1961 indicates that changes in these values are due to more accurate measurements over time or to the recognition of the impact of natural isotopic fractionation in normal terrestrial materials upon atomic weight values of many elements. Use of the unit “u” (unified atomic mass unit on the carbon mass scale) with atomic weight is incorrect because the quantity atomic weight is dimensionless, and the unit “amu” (atomic mass unit on the oxygen scale) is an obsolete term: Both should be avoided.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.jchemed.6b00510","usgsCitation":"Coplen, T.B., Meyers, F., and Holden, N.E., 2017, Clarifying atomic weights: A 2016 four-figure table of standard and conventional atomic weights: Journal of Chemical Education, v. 94, no. 3, p. 311-319, https://doi.org/10.1021/acs.jchemed.6b00510.","productDescription":"9 p.","startPage":"311","endPage":"319","ipdsId":"IP-079688","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":438402,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7668B9R","text":"USGS data release","linkHelpText":"Four-place table of standard atomic weight values of hydrogen through uranium compared since 1961"},{"id":438401,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79Z9315","text":"USGS data release","linkHelpText":"Standard and conventional atomic weights 2016 abridged to four significant digits"},{"id":338533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"3","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-01-30","publicationStatus":"PW","scienceBaseUri":"58dcc7d3e4b02ff32c685661","chorus":{"doi":"10.1021/acs.jchemed.6b00510","url":"http://dx.doi.org/10.1021/acs.jchemed.6b00510","publisher":"American Chemical Society (ACS)","authors":"Coplen Tyler B., Meyers Fabienne, Holden Norman E.","journalName":"Journal of Chemical Education","publicationDate":"1/30/2017"},"contributors":{"authors":[{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":686617,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Meyers, Fabienne","contributorId":189963,"corporation":false,"usgs":false,"family":"Meyers","given":"Fabienne","email":"","affiliations":[],"preferred":false,"id":686618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holden, Norman E.","contributorId":189167,"corporation":false,"usgs":false,"family":"Holden","given":"Norman","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":686619,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70185756,"text":"70185756 - 2017 - Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)","interactions":[],"lastModifiedDate":"2022-04-22T16:13:23.878901","indexId":"70185756","displayToPublicDate":"2017-03-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)","docAbstract":"Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.12.026","usgsCitation":"Wickham, J., Stehman, S.V., Gass, L., Dewitz, J., Sorenson, D.G., Granneman, B.J., Poss, R.V., and Baer, L.A., 2017, Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD): Remote Sensing of Environment, v. 191, p. 328-341, https://doi.org/10.1016/j.rse.2016.12.026.","productDescription":"14 p.","startPage":"328","endPage":"341","ipdsId":"IP-079186","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":469987,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6657805","text":"Publisher Index Page"},{"id":338528,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"191","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58dcc7d2e4b02ff32c68565f","contributors":{"authors":[{"text":"Wickham, James","contributorId":140259,"corporation":false,"usgs":false,"family":"Wickham","given":"James","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":686666,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V.","contributorId":77283,"corporation":false,"usgs":true,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":686667,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":686665,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dewitz, Jon 0000-0002-0458-212X dewitz@usgs.gov","orcid":"https://orcid.org/0000-0002-0458-212X","contributorId":2401,"corporation":false,"usgs":true,"family":"Dewitz","given":"Jon","email":"dewitz@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":686668,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sorenson, Daniel G. 0000-0003-0365-9444 dsorenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0365-9444","contributorId":2898,"corporation":false,"usgs":true,"family":"Sorenson","given":"Daniel","email":"dsorenson@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":686669,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Granneman, Brian J. 0000-0002-1910-0955 grann@usgs.gov","orcid":"https://orcid.org/0000-0002-1910-0955","contributorId":4209,"corporation":false,"usgs":true,"family":"Granneman","given":"Brian","email":"grann@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":686670,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Poss, Richard V.","contributorId":189982,"corporation":false,"usgs":false,"family":"Poss","given":"Richard","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":686671,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Baer, Lori Anne 0000-0003-1908-979X labaer@usgs.gov","orcid":"https://orcid.org/0000-0003-1908-979X","contributorId":4429,"corporation":false,"usgs":true,"family":"Baer","given":"Lori","email":"labaer@usgs.gov","middleInitial":"Anne","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":686672,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70185702,"text":"70185702 - 2017 - Neonicotinoid insecticide removal by prairie strips in row-cropped watersheds with historical seed coating use","interactions":[],"lastModifiedDate":"2017-03-29T10:07:00","indexId":"70185702","displayToPublicDate":"2017-03-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":682,"text":"Agriculture, Ecosystems and Environment","active":true,"publicationSubtype":{"id":10}},"title":"Neonicotinoid insecticide removal by prairie strips in row-cropped watersheds with historical seed coating use","docAbstract":"Neonicotinoids are a widely used class of insecticides that are commonly applied as seed coatings for agricultural crops. Such neonicotinoid use may pose a risk to non-target insects, including pollinators and natural enemies of crop pests, and ecosystems. This study assessed neonicotinoid residues in groundwater, surface runoff water, soil, and native plants adjacent to corn and soybean crop fields with a history of being planted with neonicotinoid-treated seeds from 2008-2013. Data from six sites with the same crop management history, three with and three without in-field prairie strips, were collected in 2015-2016, 2-3 years after neonicotinoid (clothianidin and imidacloprid) seed treatments were last used. Three of the six neonicotinoids analyzed were detected in at least one environmental matrix: the two applied as seed coatings on the fields (clothianidin and imidacloprid) and another widely used neonicotinoid (thiamethoxam). Sites with prairie strips generally had lower concentrations of neonicotinoids: groundwater and footslope soil neonicotinoid concentrations were significantly lower in the sites with prairie strips than those without; mean concentrations for groundwater were 11 and 20 ng/L (p = 0.048) and <1 and 6 ng/g (p = 0.0004) for soil, respectively. Surface runoff water concentrations were not significantly (p = 0.38) different for control sites (44 ng/L) or sites with prairie strips (140 ng/L). Consistent with the decreased inputs of neonicotinoids, concentrations tended to decrease over the sampling timeframe. Two sites recorded concentration increases, however, potentially due to disturbance of previous applications or influence from nearby fields where use of seed treatments continued. There were no detections (limit of detection: 1 ng/g) of neonicotinoids in the foliage or roots of plants comprising prairie strips, indicating a low likelihood of exposure to pollinators and other insects visiting these plants following the cessation of seed coating use. Offsite transport of neonicotinoids to aquatic systems through the groundwater and surface water were furthermore reduced with prairie strips. This study demonstrates the potential for prairie strips comprising 10% of an agricultural catchment to mitigate the non-target impacts of neonicotinoids.","language":"English","publisher":"Elsevier","doi":"10.1016/j.agee.2017.03.015","usgsCitation":"Hladik, M., Bradbury, S., Schulte, L.A., Helmers, M., Witte, C., Kolpin, D.W., Garrett, J.D., and Harris, M., 2017, Neonicotinoid insecticide removal by prairie strips in row-cropped watersheds with historical seed coating use: Agriculture, Ecosystems and Environment, v. 241, p. 160-167, https://doi.org/10.1016/j.agee.2017.03.015.","productDescription":"8 p.","startPage":"160","endPage":"167","ipdsId":"IP-083340","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":488600,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://lib.dr.iastate.edu/nrem_pubs/209","text":"External Repository"},{"id":338539,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"241","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58dcc7d4e4b02ff32c68566d","contributors":{"authors":[{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":189904,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":686445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradbury, Steven","contributorId":177603,"corporation":false,"usgs":false,"family":"Bradbury","given":"Steven","email":"","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":686446,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulte, Lisa A.","contributorId":177987,"corporation":false,"usgs":false,"family":"Schulte","given":"Lisa","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":686447,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Helmers, Matthew","contributorId":189905,"corporation":false,"usgs":false,"family":"Helmers","given":"Matthew","email":"","affiliations":[],"preferred":false,"id":686448,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Witte, Christopher","contributorId":189906,"corporation":false,"usgs":false,"family":"Witte","given":"Christopher","email":"","affiliations":[],"preferred":false,"id":686449,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505 dwkolpin@usgs.gov","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":1239,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana","email":"dwkolpin@usgs.gov","middleInitial":"W.","affiliations":[{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":686450,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Garrett, Jessica D. 0000-0002-4466-3709 jgarrett@usgs.gov","orcid":"https://orcid.org/0000-0002-4466-3709","contributorId":4229,"corporation":false,"usgs":true,"family":"Garrett","given":"Jessica","email":"jgarrett@usgs.gov","middleInitial":"D.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":686452,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harris, Mary","contributorId":189907,"corporation":false,"usgs":false,"family":"Harris","given":"Mary","email":"","affiliations":[],"preferred":false,"id":686451,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70185740,"text":"70185740 - 2017 - Weather radar data correlate to hail-induced mortality in grassland birds","interactions":[],"lastModifiedDate":"2017-07-03T09:44:29","indexId":"70185740","displayToPublicDate":"2017-03-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5347,"text":"Remote Sensing in Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Weather radar data correlate to hail-induced mortality in grassland birds","docAbstract":"Small-bodied terrestrial animals such as songbirds (Order Passeriformes) are especially vulnerable to hail-induced mortality; yet, hail events are challenging to predict, and they often occur in locations where populations are not being studied. Focusing on nesting grassland songbirds, we demonstrate a novel approach to estimate hail-induced mortality. We quantify the relationship between the probability of nests destroyed by hail and measured Level-III Next Generation Radar (NEXRAD) data, including atmospheric base reflectivity, maximum estimated size of hail and maximum estimated azimuthal wind shear. On 22 June 2014, a hailstorm in northern Colorado destroyed 102 out of 203 known nests within our research site. Lark bunting (Calamospiza melanocorys) nests comprised most of the sample (n = 186). Destroyed nests were more likely to be found in areas of higher storm intensity, and distributions of NEXRAD variables differed between failed and surviving nests. For 133 ground nests where nest-site vegetation was measured, we examined the ameliorative influence of woody vegetation, nest cover and vegetation density by comparing results for 13 different logistic regression models incorporating the independent and additive effects of weather and vegetation variables. The most parsimonious model used only the interactive effect of hail size and wind shear to predict the probability of nest survival, and the data provided no support for any of the models without this predictor. We conclude that vegetation structure may not mitigate mortality from severe hailstorms and that weather radar products can be used remotely to estimate potential for hail mortality of nesting grassland birds. These insights will improve the efficacy of grassland bird population models under predicted climate change scenarios.
","language":"English","publisher":"Wiley","doi":"10.1002/rse2.41","usgsCitation":"Carver, A., Ross, J.D., Augustine, D., Skagen, S.K., Dwyer, A.M., Tomback, D.F., and Wunder, M., 2017, Weather radar data correlate to hail-induced mortality in grassland birds: Remote Sensing in Ecology and Conservation, v. 3, no. 2, p. 90-101, https://doi.org/10.1002/rse2.41.","productDescription":"12 p.","startPage":"90","endPage":"101","ipdsId":"IP-073446","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469986,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.41","text":"Publisher Index Page"},{"id":338545,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"3","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-24","publicationStatus":"PW","scienceBaseUri":"58dcc7d3e4b02ff32c685665","contributors":{"authors":[{"text":"Carver, Amber","contributorId":189956,"corporation":false,"usgs":false,"family":"Carver","given":"Amber","email":"","affiliations":[],"preferred":false,"id":686605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ross, Jeremy D.","contributorId":189958,"corporation":false,"usgs":false,"family":"Ross","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":686608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Augustine, David J.","contributorId":36849,"corporation":false,"usgs":true,"family":"Augustine","given":"David J.","affiliations":[],"preferred":false,"id":686606,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Skagen, Susan K. 0000-0002-6744-1244 skagens@usgs.gov","orcid":"https://orcid.org/0000-0002-6744-1244","contributorId":2009,"corporation":false,"usgs":true,"family":"Skagen","given":"Susan","email":"skagens@usgs.gov","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":686604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dwyer, Angela M.","contributorId":189959,"corporation":false,"usgs":false,"family":"Dwyer","given":"Angela","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":686609,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tomback, Diana F.","contributorId":189960,"corporation":false,"usgs":false,"family":"Tomback","given":"Diana","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":686610,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wunder, Michael B.","contributorId":80599,"corporation":false,"usgs":false,"family":"Wunder","given":"Michael B.","affiliations":[{"id":6674,"text":"Department of Integrative Biology, University of Colorado Denver","active":true,"usgs":false}],"preferred":false,"id":686607,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70188401,"text":"70188401 - 2017 - Characterizing local variability in long‐period horizontal tilt noise","interactions":[],"lastModifiedDate":"2017-06-08T11:54:22","indexId":"70188401","displayToPublicDate":"2017-03-29T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Characterizing local variability in long‐period horizontal tilt noise","docAbstract":"Horizontal seismic data are dominated by atmospherically induced tilt noise at long periods (i.e., 30 s and greater). Tilt noise limits our ability to use horizontal data for sensitive seismological studies such as observing free earth modes. To better understand the local spatial variability of long‐period horizontal noise, we observe horizontal noise during quiet time periods in the Albuquerque Seismological Laboratory (ASL) underground vault using four small‐aperture array configurations. Each array comprises eight Streckeisen STS‐2 broadband seismometers. We analyze the spectral content of the data using power spectral density and magnitude‐squared coherence (γ2‐coherence). Our results show a high degree of spatial variability and frequency dependence in the long‐period horizontal wavefield. The variable nature of long‐period horizontal noise in the ASL vault suggests that it might be highly local in nature and not easily characterized by simple physical models when overall noise levels are low, making it difficult to identify locations in the vault with lower horizontal noise. This variability could be limiting our ability to apply coherence analysis for estimating horizontal sensor self‐noise and could also complicate various indirect methods for removing long‐period horizontal noise (e.g., collocated rotational sensor or microbarograph).","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220160193","usgsCitation":"Rohde, M., Ringler, A.T., Hutt, C.R., Wilson, D.C., Holland, A., Sandoval, L., and Storm, T., 2017, Characterizing local variability in long‐period horizontal tilt noise: Seismological Research Letters, v. 88, no. 3, p. 822-830, https://doi.org/10.1785/0220160193.","productDescription":"9 p. 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2017 - Geophysical logging and thermal imaging near the Hemphill Road TCE National Priorities List Superfund site near Gastonia, North Carolina","interactions":[],"lastModifiedDate":"2017-03-31T11:03:39","indexId":"ofr20171017","displayToPublicDate":"2017-03-27T16:30:00","publicationYear":"2017","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":"2017-1017","title":"Geophysical logging and thermal imaging near the Hemphill Road TCE National Priorities List Superfund site near Gastonia, North Carolina","docAbstract":"Borehole geophysical logs and thermal imaging data were collected by the U.S. Geological Survey near the Hemphill Road TCE (trichloroethylene) National Priorities List Superfund site near Gastonia, North Carolina, during August 2014 through February 2015. In an effort to assist the U.S. Environmental Protection Agency in the development of a conceptual groundwater model for the assessment of current contaminant distribution and future migration of contaminants, surface geological mapping and borehole geophysical log and thermal imaging data collection, which included the delineation of more than 600 subsurface features (primarily fracture orientations), was completed in five open borehole wells and two private supply bedrock wells. In addition, areas of possible groundwater discharge within a nearby creek downgradient of the study site were determined based on temperature differences between the stream and bank seepage using thermal imagery.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171017","issn":"2331-1258","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Region 4 Superfund Section","usgsCitation":"Antolino, D.J., and Chapman, M.J., 2017, Geophysical logging and thermal imaging near the Hemphill Road TCE National Priorities List Superfund site near Gastonia, North Carolina (ver. 1.1, March 2017): U.S. Geological Survey Open-File Report 2017–1017, 47 p., https://doi.org/10.3133/ofr20171017.","productDescription":"Report: v, 47 p.; Data Release","numberOfPages":"57","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-079978","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":337458,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1017/coverthb2.jpg"},{"id":337459,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1017/ofr20171017.pdf","text":"Report","size":"19.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1017"},{"id":337460,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F71R6NPM","text":"USGS data release ","description":"USGS data release","linkHelpText":"Geophysical logging and thermal imaging at the Hemphill Road TCE NPL Superfund site near Gastonia, North Carolina"},{"id":338827,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1017/versionHist.txt","linkFileType":{"id":2,"text":"txt"}}],"country":"United States","state":"North Carolina","county":"Gaston County","city":"Gastonia","otherGeospatial":"Hemphill Road trichloroethylene National Priorities List Superfund site","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-81.4535,35.4201],[-81.2581,35.4132],[-81.0069,35.4038],[-80.9549,35.4006],[-80.9554,35.3925],[-80.9632,35.3901],[-80.9761,35.3828],[-80.9806,35.3823],[-80.9846,35.3822],[-80.9868,35.38],[-80.9844,35.3695],[-80.9776,35.3646],[-80.9742,35.3642],[-80.9697,35.3669],[-80.9669,35.3688],[-80.9647,35.3738],[-80.9625,35.3756],[-80.9597,35.3756],[-80.9563,35.3738],[-80.9505,35.3675],[-80.9432,35.3658],[-80.9296,35.3636],[-80.9268,35.3627],[-80.9285,35.3614],[-80.9374,35.3572],[-80.9442,35.3521],[-80.9537,35.3521],[-80.9593,35.3489],[-80.9656,35.3506],[-80.9706,35.3501],[-80.9818,35.3446],[-80.984,35.3373],[-80.9823,35.3341],[-80.9805,35.3287],[-80.9844,35.3237],[-80.9894,35.3205],[-80.9938,35.3132],[-80.9961,35.3113],[-81.0022,35.3045],[-81.0033,35.3017],[-81.0105,35.2944],[-81.0133,35.293],[-81.0143,35.2876],[-81.0152,35.2685],[-81.0139,35.2585],[-81.0082,35.2509],[-81.012,35.2349],[-81.0113,35.2309],[-81.0129,35.2231],[-81.0071,35.2109],[-81.0054,35.2055],[-81.0064,35.1973],[-81.0063,35.1923],[-81.0046,35.1864],[-81.0045,35.1814],[-81.0049,35.1728],[-81.0088,35.165],[-81.0076,35.1569],[-81.0109,35.1532],[-81.0176,35.1536],[-81.0238,35.1486],[-81.0448,35.1494],[-81.0682,35.1507],[-81.1814,35.1568],[-81.2141,35.1586],[-81.3277,35.1637],[-81.3163,35.1906],[-81.3209,35.2609],[-81.355,35.2796],[-81.3548,35.2946],[-81.3594,35.3022],[-81.3675,35.314],[-81.3659,35.3181],[-81.3565,35.3309],[-81.3986,35.3531],[-81.4535,35.4201]]]},\"properties\":{\"name\":\"Gaston\",\"state\":\"NC\"}}]}","edition":"Version 1.0: Originally posted March 27, 2017; Version 1.1: March 30, 2017","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
https://www2.usgs.gov/water/southatlantic/
The U.S. Geological Survey Central Plains Water Science Center, serving the states of Kansas and Nebraska, has collected and interpreted hydrologic information for more than a century. Data collected include streamflow and gage height, reservoir content, water quality and water quantity, suspended sediment, and groundwater levels. Interpretative hydrologic studies are completed on national, regional, statewide, and local levels and cooperatively funded through partnerships with these agencies. The U.S. Geological Survey provides impartial scientific information to describe and understand the health of our ecosystems and environment; minimize loss of life and property from natural disasters; manage water, biological, energy, and mineral resources; and enhance and protect our quality of life. These collected data are in the National Water Information System (Kansas: https://dashboard.waterdata.usgs.gov/app/nwd/en/ and Nebraska: https://dashboard.waterdata.usgs.gov/app/nwd/en/), and all results are documented in reports that also are online (Kansas: https://www.usgs.gov/centers/kswsc and Nebraska: https://www.usgs.gov/centers/nebraska-water-science-center/publications).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/gip173","usgsCitation":"U.S. Geological Survey, 2017, Central Plains Water Science Center bookmark (ver. 1.2, July 2025): U.S. Geological Survey General Information Product 173, 2 p., https://doi.org/10.3133/gip173.","productDescription":"Bookmark: 2.25 x 7.50 inches","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-085657","costCenters":[{"id":84311,"text":"Central Plains Water Science Center","active":true,"usgs":true}],"links":[{"id":492407,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/gip/0173/coverthb4.jpg"},{"id":492363,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/gip/0173/gip173.pdf","text":"Report","size":"1.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"GIP 173, ver. 1.2"},{"id":492422,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/gip/0173/versionHist.txt","text":"Version History","size":"1KB txt"}],"edition":"Version 1.0: March 27, 2017; Version 1.1: September 11, 2018; Version 1.2: July 17, 2025","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Dr
Lawrence, KS 66049
Freshwater mussels, one of the most imperiled groups of animals in the world, are generally underrepresented in toxicity databases used for the development of ambient water quality criteria and other environmental guidance values. Acute 96-h toxicity tests were conducted to evaluate the sensitivity of 5 species of juvenile mussels from 2 families and 4 tribes to 10 chemicals (ammonia, metals, major ions, and organic compounds) and to screen 10 additional chemicals (mainly organic compounds) with a commonly tested mussel species, fatmucket (Lampsilis siliquoidea). In the multi-species study, median effect concentrations (EC50s) among the 5 species differed by a factor of ≤2 for chloride, potassium, sulfate, and zinc; a factor of ≤5 for ammonia, chromium, copper, and nickel; and factors of 6 and 12 for metolachlor and alachlor, respectively, indicating that mussels representing different families or tribes had similar sensitivity to most of the tested chemicals, regardless of modes of action. There was a strong linear relationship between EC50s for fatmucket and the other 4 mussel species across the 10 chemicals (r2 = 0.97, slope close to 1.0), indicating that fatmucket was similar to other mussel species; thus, this commonly tested species can be a good surrogate for protecting other mussels in acute exposures. The sensitivity of juvenile fatmucket among different populations or cultured from larvae of wild adults and captive-cultured adults was also similar in acute exposures to copper or chloride, indicating captive-cultured adult mussels can reliably be used to reproduce juveniles for toxicity testing. In compiled databases for all freshwater species, 1 or more mussel species were among the 4 most sensitive species for alachlor, ammonia, chloride, potassium, sulfate, copper, nickel, and zinc; therefore, the development of water quality criteria and other environmental guidance values for these chemicals should reflect the sensitivity of mussels. In contrast, the EC50s of fatmucket tested in the single-species study were in the high percentiles (>75th) of species sensitivity distributions for 6 of 7 organic chemicals, indicating mussels might be relatively insensitive to organic chemicals in acute exposures.
","language":"English","publisher":"SETAC Press","doi":"10.1002/etc.3642","usgsCitation":"Wang, N., Ivey, C.D., Ingersoll, C.G., Brumbaugh, W.G., Alvarez, D., Hammer, E.J., Bauer, C.R., Augspurger, T., Raimondo, S., and Barnhart, M., 2017, Acute sensitivity of a broad range of freshwater mussels to chemicals with different modes of toxic action: Environmental Toxicology and Chemistry, v. 36, no. 3, p. 786-796, https://doi.org/10.1002/etc.3642.","productDescription":"11 p.","startPage":"786","endPage":"796","ipdsId":"IP-077267","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":469990,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8220997","text":"External Repository"},{"id":338421,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-04","publicationStatus":"PW","scienceBaseUri":"58da2517e4b0543bf7fda7ec","contributors":{"authors":[{"text":"Wang, Ning 0000-0002-2846-3352 nwang@usgs.gov","orcid":"https://orcid.org/0000-0002-2846-3352","contributorId":2818,"corporation":false,"usgs":true,"family":"Wang","given":"Ning","email":"nwang@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":686402,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":686403,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ingersoll, Christopher G. 0000-0003-4531-5949 cingersoll@usgs.gov","orcid":"https://orcid.org/0000-0003-4531-5949","contributorId":2071,"corporation":false,"usgs":true,"family":"Ingersoll","given":"Christopher","email":"cingersoll@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":686404,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brumbaugh, William G. 0000-0003-0081-375X bbrumbaugh@usgs.gov","orcid":"https://orcid.org/0000-0003-0081-375X","contributorId":493,"corporation":false,"usgs":true,"family":"Brumbaugh","given":"William","email":"bbrumbaugh@usgs.gov","middleInitial":"G.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":686405,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alvarez, David 0000-0002-6918-2709 dalvarez@usgs.gov","orcid":"https://orcid.org/0000-0002-6918-2709","contributorId":150499,"corporation":false,"usgs":true,"family":"Alvarez","given":"David","email":"dalvarez@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":686406,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hammer, Edward J.","contributorId":150723,"corporation":false,"usgs":false,"family":"Hammer","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":686407,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bauer, Candice R.","contributorId":150724,"corporation":false,"usgs":false,"family":"Bauer","given":"Candice","email":"","middleInitial":"R.","affiliations":[{"id":18077,"text":"U. S. Environmental Protection Agency, Region 5, Water Quality Branch, Chicago, Illinois","active":true,"usgs":false}],"preferred":false,"id":686408,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Augspurger, Tom","contributorId":189894,"corporation":false,"usgs":false,"family":"Augspurger","given":"Tom","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":686409,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Raimondo, Sandy","contributorId":150748,"corporation":false,"usgs":false,"family":"Raimondo","given":"Sandy","email":"","affiliations":[{"id":18090,"text":"U.S. Environmental Protection Agency, Gulf Ecology Division, Gulf Breeze, FL","active":true,"usgs":false}],"preferred":false,"id":686410,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Barnhart, M.Christopher","contributorId":189895,"corporation":false,"usgs":false,"family":"Barnhart","given":"M.Christopher","affiliations":[],"preferred":false,"id":686411,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70181998,"text":"ds1034 - 2017 - Bathymetry data collected in October 2014 from Fire Island, New York—The wilderness breach, shoreface, and bay","interactions":[],"lastModifiedDate":"2017-03-27T09:54:31","indexId":"ds1034","displayToPublicDate":"2017-03-24T17:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1034","title":"Bathymetry data collected in October 2014 from Fire Island, New York—The wilderness breach, shoreface, and bay","docAbstract":"Scientists from the U.S. Geological Survey St. Petersburg Coastal and Marine Science Center in St. Petersburg, Florida, conducted a bathymetric survey of Fire Island, New York, from October 5 to 10, 2014. The U.S. Geological Survey is involved in a post-Hurricane Sandy effort to map and monitor the morphologic evolution of the wilderness breach, which formed in October 2012 during Hurricane Sandy, as part of the Hurricane Sandy Supplemental Project GS2-2B. During this study, bathymetry data were collected, using single-beam echo sounders and global positioning systems mounted to personal watercraft, along the Fire Island shoreface and within the wilderness breach, Fire Island Inlet, Narrow Bay, and Great South Bay east of Nicoll Bay. Additional bathymetry and elevation data were collected using backpack and wheel-mounted global positioning systems along the subaerial beach (foreshore and backshore), flood shoals, and shallow channels within the wilderness breach and adjacent shoreface.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1034","usgsCitation":"Nelson, T.R., Miselis, J.L., Hapke, C.J., Brenner, O.T., Henderson, R.E., Reynolds, B.J., and Wilson, K.E., 2017, Bathymetry data collected in October 2014 from Fire Island, New York—The wilderness breach, shoreface, and bay: U.S. Geological Survey Data Series 1034, https://doi.org/10.3133/ds1034.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071668","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":337454,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1034/index.html","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1034"},{"id":337453,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1034/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Fire Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.3172607421875,\n 40.60821853973967\n ],\n [\n -72.77412414550781,\n 40.60821853973967\n ],\n [\n -72.77412414550781,\n 40.77586181063573\n ],\n [\n -73.3172607421875,\n 40.77586181063573\n ],\n [\n -73.3172607421875,\n 40.60821853973967\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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600 4th Street South
St. Petersburg, FL 33701
https://coastal.er.usgs.gov/
The thermal maturity of shale is often measured by vitrinite reflectance (VRo). VRo measurements for the Devonian–Mississippian black shale source rocks evaluated herein predicted thermal immaturity in areas where associated reservoir rocks are oil-producing. This limitation of the VRo method led to the current evaluation of Raman spectroscopy as a suitable alternative for developing correlations between thermal maturity and Raman spectra. In this study, Raman spectra of Devonian–Mississippian black shale source rocks were regressed against measured VRo or sample-depth. Attempts were made to develop quantitative correlations of thermal maturity. Using sample-depth as a proxy for thermal maturity is not without limitations as thermal maturity as a function of depth depends on thermal gradient, which can vary through time, subsidence rate, uplift, lack of uplift, and faulting. Correlations between Raman data and vitrinite reflectance or sample-depth were quantified by peak-fitting the spectra. Various peak-fitting procedures were evaluated to determine the effects of the number of peaks and maximum peak widths on correlations between spectral metrics and thermal maturity. Correlations between D-frequency, G-band full width at half maximum (FWHM), and band separation between the G- and D-peaks and thermal maturity provided some degree of linearity throughout most peak-fitting assessments; however, these correlations and those calculated from the G-frequency, D/G FWHM ratio, and D/G peak area ratio also revealed a strong dependence on peak-fitting processes. This dependency on spectral analysis techniques raises questions about the validity of peak-fitting, particularly given the amount of subjective analyst involvement necessary to reconstruct spectra. This research shows how user interpretation and extrapolation affected the comparability of different samples, the accuracy of generated trends, and therefore, the potential of the Raman spectral method to become an industry benchmark as a thermal maturity probe. A Raman method devoid of extensive operator interaction and data manipulation is quintessential for creating a standard method.
We examine how historic flooding in 2011 affected the geomorphic adjustments created by dam regulation along the approximately 120 km free flowing reach of the Upper Missouri River bounded upstream by the Garrison Dam (1953) and downstream by Lake Oahe Reservoir (1959) near the City of Bismarck, ND, USA. The largest flood since dam regulation occurred in 2011. Flood releases from the Garrison Dam began in May 2011 and lasted until October, peaking with a flow of more than 4200 m3 s−1. Channel cross-section data and aerial imagery before and after the flood were compared with historic rates of channel change to assess the relative impact of the flood on the river morphology. Results indicate that the 2011 flood maintained trends in island area with the loss of islands in the reach just below the dam and an increase in island area downstream. Channel capacity changes varied along the Garrison Segment as a result of the flood. The thalweg, which has been stable since the mid-1970s, did not migrate. And channel morphology, as defined by a newly developed shoaling metric, which quantifies the degree of channel braiding, indicates significant longitudinal variability in response to the flood. These results show that the 2011 flood exacerbates some geomorphic trends caused by the dam while reversing others. We conclude that the presence of dams has created an alternate geomorphic and related ecological stable state, which does not revert towards pre-dam conditions in response to the flood of record. This suggests that management of sediment transport dynamics as well as flow modification is necessary to restore the Garrison Segment of the Upper Missouri River towards pre-dam conditions and help create or maintain habitat for endangered species. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.
","language":"English","publisher":"Wiley","publisherLocation":"New York, NY","doi":"10.1002/rra.3084","usgsCitation":"Skalak, K., Benthem, A.J., Hupp, C.R., Schenk, E.R., Galloway, J.M., and Nustad, R.A., 2017, Flood effects provide evidence of an alternate stable state from dam management on the Upper Missouri River: River Research and Applications, v. 33, no. 6, p. 889-902, https://doi.org/10.1002/rra.3084.","productDescription":"14 p.","startPage":"889","endPage":"902","ipdsId":"IP-078296","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":338264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Upper Missouri River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.25,\n 44.25\n ],\n [\n -100,\n 44.25\n ],\n [\n -100,\n 48.5\n ],\n [\n -104.25,\n 48.5\n ],\n [\n -104.25,\n 44.25\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","issue":"6","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-07","publicationStatus":"PW","scienceBaseUri":"58d63033e4b05ec7991310d1","contributors":{"authors":[{"text":"Skalak, Katherine 0000-0003-4122-1240 kskalak@usgs.gov","orcid":"https://orcid.org/0000-0003-4122-1240","contributorId":3990,"corporation":false,"usgs":true,"family":"Skalak","given":"Katherine","email":"kskalak@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":685979,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Benthem, Adam J. 0000-0003-2372-0281 abenthem@usgs.gov","orcid":"https://orcid.org/0000-0003-2372-0281","contributorId":2740,"corporation":false,"usgs":true,"family":"Benthem","given":"Adam","email":"abenthem@usgs.gov","middleInitial":"J.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":685980,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hupp, Cliff R. 0000-0003-1853-9197 crhupp@usgs.gov","orcid":"https://orcid.org/0000-0003-1853-9197","contributorId":2344,"corporation":false,"usgs":true,"family":"Hupp","given":"Cliff","email":"crhupp@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":685981,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schenk, Edward R. 0000-0001-6886-5754 eschenk@usgs.gov","orcid":"https://orcid.org/0000-0001-6886-5754","contributorId":2183,"corporation":false,"usgs":true,"family":"Schenk","given":"Edward","email":"eschenk@usgs.gov","middleInitial":"R.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":685982,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true},{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":685983,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nustad, Rochelle A. 0000-0002-4713-5944 ranustad@usgs.gov","orcid":"https://orcid.org/0000-0002-4713-5944","contributorId":1811,"corporation":false,"usgs":true,"family":"Nustad","given":"Rochelle","email":"ranustad@usgs.gov","middleInitial":"A.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":685984,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70185593,"text":"70185593 - 2017 - Assessment of a strain 19 brucellosis vaccination program in elk","interactions":[],"lastModifiedDate":"2017-03-29T14:55:36","indexId":"70185593","displayToPublicDate":"2017-03-24T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3779,"text":"Wildlife Society Bulletin","onlineIssn":"1938-5463","printIssn":"0091-7648","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of a strain 19 brucellosis vaccination program in elk","docAbstract":"Zoonotic diseases in wildlife present substantial challenges and risks to host populations, susceptible domestic livestock populations, and affected stakeholders. Brucellosis, a disease caused by the bacterium Brucella abortus, is endemic among elk (Cervus canadensis) attending winter feedgrounds and adjacent areas of western Wyoming, USA. To minimize transmission of brucellosis from elk to elk and elk to livestock, managers initiated a B. abortus strain 19 ballistic vaccination program in 1985. We used brucellosis prevalence (1971–2015) and reproductive outcome (2006–2015) data collected from female elk attending feedgrounds to assess efficacy of the strain 19 program while controlling for potentially confounding factors such as site and age. From our generalized linear models, we found that seroprevalence of brucellosis was 1) not lower following inception of vaccination; 2) not inversely associated with proportion of juveniles vaccinated over time; 3) not inversely associated with additional yearlings and adults vaccinated over time; and 4) associated more with feeding end-date than proportion of juveniles vaccinated. Using vaginal implant transmitters in adult females that were seropositive for brucellosis, we found little effect of vaccination coverage at reducing reproductive failures (i.e., abortion or stillbirth). Because we found limited support for efficacy of the strain 19 program, we support research to develop an oral vaccine and suggest that continuing other spatio-temporal management actions will be most effective to minimize transmission of brucellosis and reduce dependency of elk on supplemental winter feeding.
","language":"English","publisher":"The Wildlife Society","publisherLocation":"Washington, D.C.","doi":"10.1002/wsb.734","usgsCitation":"Maichak, E., Scurlock, B.M., Cross, P.C., Rogerson, J., Edwards, W.H., Wise, B., Smith, S.G., and Kreeger, T.J., 2017, Assessment of a strain 19 brucellosis vaccination program in elk: Wildlife Society Bulletin, v. 41, no. 1, p. 70-79, https://doi.org/10.1002/wsb.734.","productDescription":"10 p.","startPage":"70","endPage":"79","ipdsId":"IP-071247","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":499973,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/5c011601357f4269ab8e87d4a503fb36","text":"External Repository"},{"id":338279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.0662841796875,\n 42.57937729240967\n ],\n [\n -109.22607421875,\n 42.57937729240967\n ],\n [\n -109.22607421875,\n 43.54257572246922\n ],\n [\n -111.0662841796875,\n 43.54257572246922\n ],\n [\n -111.0662841796875,\n 42.57937729240967\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-07","publicationStatus":"PW","scienceBaseUri":"58d63032e4b05ec7991310cd","contributors":{"authors":[{"text":"Maichak, Eric","contributorId":36826,"corporation":false,"usgs":true,"family":"Maichak","given":"Eric","email":"","affiliations":[],"preferred":false,"id":686056,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Scurlock, Brandon M.","contributorId":93788,"corporation":false,"usgs":false,"family":"Scurlock","given":"Brandon","email":"","middleInitial":"M.","affiliations":[{"id":6917,"text":"Wyoming Game and Fish Department, Laramie, USA","active":true,"usgs":false}],"preferred":false,"id":686057,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":686055,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rogerson, Jared D.","contributorId":106401,"corporation":false,"usgs":true,"family":"Rogerson","given":"Jared D.","affiliations":[],"preferred":false,"id":686058,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Edwards, William H.","contributorId":9144,"corporation":false,"usgs":true,"family":"Edwards","given":"William","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":686059,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wise, Benjamin","contributorId":189800,"corporation":false,"usgs":false,"family":"Wise","given":"Benjamin","affiliations":[],"preferred":false,"id":686065,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Scott G.","contributorId":189801,"corporation":false,"usgs":false,"family":"Smith","given":"Scott","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":686066,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kreeger, Terry J.","contributorId":189227,"corporation":false,"usgs":false,"family":"Kreeger","given":"Terry","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":686062,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ]}