{"pageNumber":"650","pageRowStart":"16225","pageSize":"25","recordCount":184884,"records":[{"id":70227806,"text":"70227806 - 2020 - Use of multiple temperature logger models can alter conclusions","interactions":[],"lastModifiedDate":"2022-02-01T20:38:40.959549","indexId":"70227806","displayToPublicDate":"2020-03-01T15:37:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3709,"text":"Water","active":true,"publicationSubtype":{"id":10}},"title":"Use of multiple temperature logger models can alter conclusions","docAbstract":"

Remote temperature loggers are often used to measure water temperatures for ecological studies and by regulatory agencies to determine whether water quality standards are being maintained. Equipment specifications are often given a cursory review in the methods; however, the effect of temperature logger model is rarely addressed in the discussion. In a laboratory environment, we compared measurements from three models of temperature loggers at 5 to 40 °C to better understand the utility of these devices. Mean water temperatures recorded by logger models differed statistically even for those with similar accuracy specifications, but were still within manufacturer accuracy specifications. Maximum mean temperature difference between models was 0.4 °C which could have regulatory and ecological implications, such as when a 0.3 °C temperature change triggers a water quality violation or increases species mortality rates. Additionally, precision should be reported as the overall precision (including a consideration of significant digits) for combined model types which in our experiment was 0.7 °C, not the ≤0.4 °C for individual models. Our results affirm that analyzing data collected by different logger models can result in potentially erroneous conclusions when <1 °C difference has regulatory compliance or ecological implications and that combining data from multiple logger models can reduce the overall precision of results.

","language":"English","publisher":"MDPI","doi":"10.3390/w12030668","usgsCitation":"Whittier, J.B., Westhoff, J.T., Paukert, C.P., and Rotman, R.M., 2020, Use of multiple temperature logger models can alter conclusions: Water, v. 12, no. 3, 9 p., https://doi.org/10.3390/w12030668.","productDescription":"9 p.","ipdsId":"IP-092924","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457535,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/w12030668","text":"Publisher Index Page"},{"id":395243,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Whittier, Joanna B.","contributorId":53151,"corporation":false,"usgs":false,"family":"Whittier","given":"Joanna","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":832344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Westhoff, Jacob T.","contributorId":58106,"corporation":false,"usgs":true,"family":"Westhoff","given":"Jacob","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":832345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paukert, Craig P. 0000-0002-9369-8545 cpaukert@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":879,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","email":"cpaukert@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":832346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rotman, Robin M.","contributorId":272858,"corporation":false,"usgs":false,"family":"Rotman","given":"Robin","email":"","middleInitial":"M.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":832347,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208830,"text":"70208830 - 2020 - The first occurrence of the Australian redclaw crayfish Cherax quadricarinatus (von Martens, 1868) in the contiguous United States","interactions":[],"lastModifiedDate":"2020-03-05T15:40:50","indexId":"70208830","displayToPublicDate":"2020-03-01T15:35:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":994,"text":"BioInvasions Records","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The first occurrence of the Australian redclaw crayfish Cherax quadricarinatus (von Martens, 1868) in the contiguous United States","title":"The first occurrence of the Australian redclaw crayfish Cherax quadricarinatus (von Martens, 1868) in the contiguous United States","docAbstract":"

The Australian redclaw crayfish, Cherax quadricarinatus, is a popular aquaculture crayfish that has been introduced around the world. Here we report the first occurrence of the species in the United States in Lake Balboa, Los Angeles, California. The impacts of this species are largely unknown, and further research is needed to determine the species’ effects on native ecosystems. Sampling is needed to evaluate the population status in Lake Balboa to determine to what extent the species has spread in the greater Los Angeles River basin.

","language":"English","publisher":"REABIC","doi":"10.3391/bir.2020.9.1.16","usgsCitation":"Morningstar, C., Daniel, W., Neilson, M., and Yazaryan, A.K., 2020, The first occurrence of the Australian redclaw crayfish Cherax quadricarinatus (von Martens, 1868) in the contiguous United States: BioInvasions Records, v. 9, no. 1, p. 120-126, https://doi.org/10.3391/bir.2020.9.1.16.","productDescription":"7 p.","startPage":"120","endPage":"126","ipdsId":"IP-112100","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":457539,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/bir.2020.9.1.16","text":"Publisher Index Page"},{"id":372960,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles","otherGeospatial":"Lake Balboa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.49759101867674,\n 34.178399942758894\n ],\n [\n -118.49274158477783,\n 34.178399942758894\n ],\n [\n -118.49274158477783,\n 34.18383180934353\n ],\n [\n -118.49759101867674,\n 34.18383180934353\n ],\n [\n -118.49759101867674,\n 34.178399942758894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"1","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Morningstar, Cayla 0000-0002-0078-9430","orcid":"https://orcid.org/0000-0002-0078-9430","contributorId":222918,"corporation":false,"usgs":true,"family":"Morningstar","given":"Cayla","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":222919,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783523,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Neilson, Matthew 0000-0002-5139-5677","orcid":"https://orcid.org/0000-0002-5139-5677","contributorId":222920,"corporation":false,"usgs":true,"family":"Neilson","given":"Matthew","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":783524,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yazaryan, Ara K.","contributorId":222921,"corporation":false,"usgs":false,"family":"Yazaryan","given":"Ara","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":783525,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208889,"text":"70208889 - 2020 - Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","interactions":[],"lastModifiedDate":"2020-03-04T15:12:43","indexId":"70208889","displayToPublicDate":"2020-03-01T15:07:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2456,"text":"Journal of Soil and Water Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses","docAbstract":"Hydrology and water quality (suspended sediment, total nitrogen, ammonia, total Kjeldahl nitrogen, nitrate plus nitrite, and total phosphorus (TP)) were monitored in two small agricultural drainages in northwestern Mississippi to document changes in water quality that coincided with the implementation of BMPs in upstream drainages. Using an event-based dataset and bootstrapping techniques, we tested for difference and equivalence in median event concentration and differences in concentration-discharge (C-Q) relationships between an early and late period at each site, where most of the major BMP implementation occurred during the early period. Results for one site were inconclusive. None of the constituents had statistically different or equivalent event concentrations between the periods, indicating a lack of evidence to tell whether water quality had changed or stayed the same, and only TP had a significantly higher C-Q slope during the late period. At the other site, more than half the constituents had a significantly different median, slope, or intercept between periods, indicating a 35% or more decrease in event concentration following a period of intense BMP implementation. These mixed results could be due to variety of differences between the sites including BMP implementation, production practices, and crops. We also used the monitoring data to generate synthetic data and perform a simulation-based power analysis to explore the ability to detect change under 25 scenarios of sampled event counts and hypothetical percent changes. The simulation-based power analysis indicated that high natural variability in event concentration and flow hindered our ability to detect change. Based on our monitoring, data analysis, and power analysis, we provide recommendations for future monitoring.","language":"English","publisher":"Soil and Water Conservation Society","doi":"10.2489/jswc.75.2.218","usgsCitation":"Murphy, J.C., Hicks, M.B., and Stocks, S.J., 2020, Assessing water-quality changes in agricultural drainages: Examples from oxbow lake tributaries in Mississippi, USA and simulation-based power analyses: Journal of Soil and Water Conservation, v. 75, no. 2, p. 218-230, https://doi.org/10.2489/jswc.75.2.218.","productDescription":"13 p.","startPage":"218","endPage":"230","ipdsId":"IP-091590","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":457542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2489/jswc.75.2.218","text":"Publisher Index Page"},{"id":437076,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7FJJ","text":"USGS data release","linkHelpText":"Hydrologic event-based water-quality and streamflow data for three oxbow tributaries in northwestern Mississippi, 2007-2016"},{"id":372917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Mississippi","otherGeospatial":"Bee Lake, Lake Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.889892578125,\n 32.35212281198644\n ],\n [\n -90.186767578125,\n 33.15594830078649\n ],\n [\n -90.098876953125,\n 33.93424531117312\n ],\n [\n -90.208740234375,\n 34.96699890670367\n ],\n [\n -90.54931640625,\n 34.67839374011646\n ],\n [\n -90.802001953125,\n 34.27083595165\n ],\n [\n -91.0546875,\n 33.925129700072\n ],\n [\n -91.1865234375,\n 33.63291573870479\n ],\n [\n -91.153564453125,\n 33.27543541298162\n ],\n [\n -91.131591796875,\n 32.80574473290688\n ],\n [\n -91.043701171875,\n 32.44488496716713\n ],\n [\n -90.889892578125,\n 32.35212281198644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"75","issue":"2","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, Jennifer C. 0000-0002-0881-0919 jmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-0881-0919","contributorId":167405,"corporation":false,"usgs":true,"family":"Murphy","given":"Jennifer","email":"jmurphy@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":783845,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hicks, Matthew B. 0000-0001-5516-0296 mhicks@usgs.gov","orcid":"https://orcid.org/0000-0001-5516-0296","contributorId":3778,"corporation":false,"usgs":true,"family":"Hicks","given":"Matthew","email":"mhicks@usgs.gov","middleInitial":"B.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783846,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stocks, Shane J. 0000-0003-1711-3071 sjstocks@usgs.gov","orcid":"https://orcid.org/0000-0003-1711-3071","contributorId":3811,"corporation":false,"usgs":true,"family":"Stocks","given":"Shane","email":"sjstocks@usgs.gov","middleInitial":"J.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":true,"id":783898,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211902,"text":"70211902 - 2020 - Estimating abiotic thresholds for sagebrush condition class in the western United States","interactions":[],"lastModifiedDate":"2024-05-17T15:45:40.859631","indexId":"70211902","displayToPublicDate":"2020-03-01T14:13:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6002,"text":"Rangeland Ecology & Management","active":true,"publicationSubtype":{"id":10}},"title":"Estimating abiotic thresholds for sagebrush condition class in the western United States","docAbstract":"

Sagebrush ecosystems of the western United States can transition from extended periods of relatively stable conditions to rapid ecological change if acute disturbances occur. Areas dominated by native sagebrush can transition from species-rich native systems to altered states where non-native annual grasses dominate, if resistance to annual grasses is low. The non-native annual grasses provide relatively little value to wildlife, livestock, and humans and function as fuel that increases fire frequency. The more land area covered by annual grasses, the higher the potential for fire, thus reducing the potential for native vegetation to reestablish, even when applying restoration treatments. Mapping areas of stability and areas of change using machine-learning algorithms allows both the identification of dominant abiotic variables that drive ecosystem dynamics and the variables’ important thresholds. We develop a decision-tree model with rulesets that estimate three classes of sagebrush condition (i.e., sagebrush recovery, tipping point [ecosystem degradation], and stable). We find rulesets that primarily drive development of the sagebrush recovery class indicate areas of midelevations (1 602 m), warm 30-yr July temperature maximums (tmax) (30.62°C), and 30-yr March precipitation (ppt) averages equal to 26.26 mm, about 10% of the 30-yr annual ppt values. Tipping point and stable classes occur at elevations that are lower (1 505 m) and higher (1 939 m), respectively, more mesic during March and annually, and experience lower 30-yr July tmax averages. These defined variable averages can be used to understand current dynamics of sagebrush condition and to predict where future transitions may occur under novel conditions.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2019.10.010","usgsCitation":"Boyte, S., Wylie, B.K., Gu, Y., and Major, D.J., 2020, Estimating abiotic thresholds for sagebrush condition class in the western United States: Rangeland Ecology & Management, v. 73, no. 2, p. 297-308, https://doi.org/10.1016/j.rama.2019.10.010.","productDescription":"12 p.","startPage":"297","endPage":"308","ipdsId":"IP-109577","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457545,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rama.2019.10.010","text":"Publisher Index Page"},{"id":377373,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.59667968749999,\n 35.71083783530009\n ],\n [\n -103.0078125,\n 35.71083783530009\n ],\n [\n -103.0078125,\n 47.517200697839414\n ],\n [\n -121.59667968749999,\n 47.517200697839414\n ],\n [\n -121.59667968749999,\n 35.71083783530009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"73","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Boyte, Stephen P. 0000-0002-5462-3225","orcid":"https://orcid.org/0000-0002-5462-3225","contributorId":205374,"corporation":false,"usgs":true,"family":"Boyte","given":"Stephen P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":795726,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":795727,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gu, Yingxin 0000-0002-3544-1856","orcid":"https://orcid.org/0000-0002-3544-1856","contributorId":209983,"corporation":false,"usgs":false,"family":"Gu","given":"Yingxin","affiliations":[],"preferred":false,"id":795728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Major, Donald J.","contributorId":83405,"corporation":false,"usgs":false,"family":"Major","given":"Donald","email":"","middleInitial":"J.","affiliations":[{"id":7217,"text":"Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":795729,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208171,"text":"70208171 - 2020 - Preserving meander bend geometry through scale","interactions":[],"lastModifiedDate":"2020-08-28T12:39:57.506553","indexId":"70208171","displayToPublicDate":"2020-03-01T13:01:09","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preserving meander bend geometry through scale","docAbstract":"

Stream meander geometry is a function of hydrologic, geologic, and anthropogenic forces. Meander morphometrics are used in geomorphic classification, ecological characterization, and tectonic and hydrologic change detection. Thus, detailed measurement and classification of meander geometry is imperative to multiscale representation of hydrographic features, which raises important questions. What meander geometries are important to preserve in multi-scale databases? How are geometries measured? How are they preserved? Is the choice between preservation of geometry or use of classification attributes? Questions related to multiscale measurement and representation of hydrographic features continue to emerge with increased spatial and temporal data collection.

A key metric for understanding meander bend geometry is sinuosity. The most common measure of sinuosity is the length of a feature divided by the distance between stream head and mouth. The measure relays deviation from a straight line but nothing about meander wavelength. There is not a clear consensus on methods for measuring meander geometry, much less efficiently, at scales made viable with increased data resolution. Here we propose a method for automated characterization of meander wavelength or bend radius. The method, termed Scale-Specific Sinuosity (S3), is a derivation from the Richardson plot. The Richardson (1961) plot is a classic means of calculating fractal dimension of natural line features and describes feature length (ℓ) given increasing vertex spacing, or step size (S), plotted on a log-log plot. The S3 metric is defined as negative one times the slope of a Richardson plot for a given stride length. This paper demonstrates utility of S3 for estimating changes in sinuosity with scale change.

","conferenceTitle":"Second Annual SPARC Workshop, Scale and Spatial Analytics","conferenceDate":"February 10-11, 2020","conferenceLocation":"Tempe, AZ","language":"English","publisher":"Arizona State University","usgsCitation":"Shavers, E.J., Stanislawski, L., Buttenfield, B.P., and Kronenfeld, B.J., 2020, Preserving meander bend geometry through scale, Second Annual SPARC Workshop, Scale and Spatial Analytics, Tempe, AZ, February 10-11, 2020, 3 p.","productDescription":"3 p.","ipdsId":"IP-113570","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":377952,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377950,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://sgsup.asu.edu/sparc/ScaleWorkshop"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shavers, Ethan J. 0000-0001-9470-5199 eshavers@usgs.gov","orcid":"https://orcid.org/0000-0001-9470-5199","contributorId":206890,"corporation":false,"usgs":true,"family":"Shavers","given":"Ethan","email":"eshavers@usgs.gov","middleInitial":"J.","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":780795,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stanislawski, Larry 0000-0002-9437-0576","orcid":"https://orcid.org/0000-0002-9437-0576","contributorId":217849,"corporation":false,"usgs":true,"family":"Stanislawski","given":"Larry","affiliations":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":780796,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buttenfield, Barbara P. 0000-0001-5961-5809","orcid":"https://orcid.org/0000-0001-5961-5809","contributorId":206887,"corporation":false,"usgs":false,"family":"Buttenfield","given":"Barbara","email":"","middleInitial":"P.","affiliations":[{"id":16144,"text":"University of Colorado-Boulder","active":true,"usgs":false}],"preferred":false,"id":780797,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kronenfeld, Barry J. 0000-0002-9518-2462","orcid":"https://orcid.org/0000-0002-9518-2462","contributorId":207104,"corporation":false,"usgs":false,"family":"Kronenfeld","given":"Barry","email":"","middleInitial":"J.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":780798,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227932,"text":"70227932 - 2020 - Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest","interactions":[],"lastModifiedDate":"2022-02-02T18:19:51.200058","indexId":"70227932","displayToPublicDate":"2020-03-01T11:56:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest","docAbstract":"

Climate-related changes in fish communities can present new challenges for fishery managers who must address declines in cool- and cold-water sportfish while dealing with increased abundance of warm-water sportfish. We used largemouth bass (Micropterus salmoides) in Wisconsin lakes as model populations to determine whether angler harvest provides a realistic method for reducing abundance of a popular warm-water sportfish that has become more prevalent and has prompted management concerns around the globe. Model results indicate largemouth bass will be resilient to increased fishing mortality. Furthermore, high rates of voluntary catch-and-release occurring in most largemouth bass fisheries likely preclude fishing mortality rates required to reduce bass abundance at meaningful levels (≥25% reductions). Increasing fishing mortality in these scenarios may require more “stimulus” than merely providing anglers with greater harvest opportunities via less stringent harvest regulations. Angler harvest could result in populations dominated by small fish, a scenario that may be undesirable to anglers, but could provide ecological benefits in certain situations.

","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2019-0035","usgsCitation":"Sullivan, C., Isermann, D.A., Whitlock, K.E., and Hansen, J.F., 2020, Assessing the potential to mitigate climate-related expansion of largemouth bass populations using angler harvest: Canadian Journal of Fisheries and Aquatic Sciences, v. 77, no. 3, p. 520-533, https://doi.org/10.1139/cjfas-2019-0035.","productDescription":"14 p.","startPage":"520","endPage":"533","ipdsId":"IP-094995","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":395288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconain","otherGeospatial":"Big Arbor Vitae Lake, Big Sissabagama Lake, Jungle Lake, Kawaguesaga Lake, Teal Lake, Waupaca Chain Lakes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": 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story","interactions":[],"lastModifiedDate":"2025-03-14T15:13:40.124095","indexId":"70220183","displayToPublicDate":"2020-03-01T11:16:00","publicationYear":"2020","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":8569,"text":"Boatman's Quarterly Review","active":true,"publicationSubtype":{"id":30}},"title":"A primer of fishery studies in Grand Canyon: The nonnative fish removal story","docAbstract":"Globally, rivers have become the most altered of ecosystems, chiefly due to pollution, water withdrawals, and dams that have modified their former function, and led to large and unforeseen impacts, particularly for fish populations. Extensive research is directed at studying impacts of dams because they sever migration routes and change the physical template (flow, temperature, and sediment and organic loads), and by extension, influence vital rates of fish populations such as growth, survival, movement and recruitment. Prior to introduction of nonnative fishes and network of dams, the humpback chub (Gila cypha, chub) was broadly distributed throughout the Colorado River (mainstem). Since then, chub have declined over their entire historical range and are now restricted to six populations, a factor that led to it being Federally listed as an endangered species. The largest of these chub populations is found in Grand Canyon and is isolated from other upstream populations by Glen Canyon Dam (Dam). Over 90% of this population resides within the Little Colorado River (LCR) and mainstem in regions adjacent to the confluence. The remainder is broadly distributed in small aggregations throughout the ecosystem. Cold water temperatures from the Dam has largely impeded the growth and spawning of chub in the mainstem. Luckily, chub spawn and rear young successfully in the seasonally warm and saline waters of the LCR, though survival of some juveniles (< 200 mm total length) that disperse into the mainstem varies among years.","language":"English","publisher":"Grand Canyon River Guides","usgsCitation":"Yard, M.D., 2020, A primer of fishery studies in Grand Canyon: The nonnative fish removal story: Boatman's Quarterly Review, v. 33, no. 1, p. 8-10.","productDescription":"3 p.","startPage":"8","endPage":"10","ipdsId":"IP-114117","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":399159,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcrg.org/bqr"},{"id":399160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.04907226562499,\n 35.49198366469642\n ],\n [\n -111.412353515625,\n 35.49198366469642\n ],\n [\n -111.412353515625,\n 36.97183825093165\n ],\n [\n -114.04907226562499,\n 36.97183825093165\n ],\n [\n -114.04907226562499,\n 35.49198366469642\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"33","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":814658,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70227680,"text":"70227680 - 2020 - Testing prediction accuracy in short-term ecological studies","interactions":[],"lastModifiedDate":"2022-01-26T17:27:52.033911","indexId":"70227680","displayToPublicDate":"2020-03-01T11:13:26","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":970,"text":"Basic and Applied Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Testing prediction accuracy in short-term ecological studies","docAbstract":"

Applied ecology is based on an assumption that a management action will result in a predicted outcome. Testing the prediction accuracy of ecological models is the most powerful way of evaluating the knowledge implicit in this cause-effect relationship, however, the prevalence of predictive modeling and prediction testing are spreading slowly in ecology. The challenge of prediction testing is particularly acute for small-scale studies, because withholding data for prediction testing (e.g., via k-fold cross validation) can reduce model precision. However, by necessity small-scale studies are common. We use one such study that explored small mammal abundance along an elevational gradient to test prediction accuracy of models with varying degrees of information content. For each of three small mammal species, we conducted 5000 iterations of the following process: (1) randomly selected 75 % of the data to develop generalized linear models of species abundance that used detailed site measurements as covariates, (2) used an information theoretic approach to compare the top model with detailed covariates to habitat type-only and null models constructed with the same data, (3) tested those models’ ability to predict the 25 % of the randomly withheld data, and (4) evaluated prediction accuracy with a quadratic loss function. Detailed models fit the model-evaluation data best but had greater expected prediction error when predicting out-of-sample data relative to the habitat type models. Relationships between species and detailed site variables may be evident only within the framework of explicitly hierarchical analyses. We show that even with a small but relatively typical dataset (n = 28 sampling locations across 125 km over two years), researchers can effectively compare models with different information content and measure models’ predictive power, thus evaluating their own ecological understanding and defining the limits of their inferences. Identifying the appropriate scope of inference through prediction testing is ecologically valuable and is attainable even with small datasets.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.baae.2020.01.003","usgsCitation":"Wood, C.M., Loman, Z., McKinney, S.T., and Loftin, C., 2020, Testing prediction accuracy in short-term ecological studies: Basic and Applied Ecology, v. 43, p. 77-85, https://doi.org/10.1016/j.baae.2020.01.003.","productDescription":"9 p.","startPage":"77","endPage":"85","ipdsId":"IP-073394","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":457548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.baae.2020.01.003","text":"Publisher Index Page"},{"id":394885,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine, New Hampshire","otherGeospatial":"Appalachian Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.08129882812499,\n 44.190082025040525\n ],\n [\n -72.00439453125,\n 43.739352079154706\n ],\n [\n -71.52099609375,\n 43.58834891179792\n ],\n [\n -69.66430664062499,\n 45.127804527473224\n ],\n [\n -70.125732421875,\n 45.598665689820635\n ],\n [\n -70.86181640625,\n 45.22848059584359\n ],\n [\n -71.817626953125,\n 44.72332018895825\n ],\n [\n -72.08129882812499,\n 44.190082025040525\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, Connor M.","contributorId":167785,"corporation":false,"usgs":false,"family":"Wood","given":"Connor","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":831705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loman, Zachary G.","contributorId":145932,"corporation":false,"usgs":false,"family":"Loman","given":"Zachary G.","affiliations":[],"preferred":false,"id":831788,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKinney, Shawn T. smckinney@usgs.gov","contributorId":5175,"corporation":false,"usgs":true,"family":"McKinney","given":"Shawn","email":"smckinney@usgs.gov","middleInitial":"T.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":831706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Cynthia S. 0000-0001-9104-3724 cyndy_loftin@usgs.gov","orcid":"https://orcid.org/0000-0001-9104-3724","contributorId":2167,"corporation":false,"usgs":true,"family":"Loftin","given":"Cynthia S.","email":"cyndy_loftin@usgs.gov","affiliations":[],"preferred":true,"id":831707,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70204123,"text":"70204123 - 2020 - Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","interactions":[],"lastModifiedDate":"2024-05-17T15:49:38.223294","indexId":"70204123","displayToPublicDate":"2020-03-01T11:07:27","publicationYear":"2020","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":"Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program","docAbstract":"The U.S. Geological Survey Land Change Monitoring, Assessment and Projection (USGS LCMAP) initiative is working toward a comprehensive capability to characterize land cover and land cover change using dense Landsat time series data. A suite of products including annual land cover maps and annual land cover change maps will be produced using the Landsat 4-8 data record. LCMAP products will initially be created for the conterminous United States (CONUS) and then extended to include Alaska and Hawaii. A critical component of LCMAP is the collection of reference data using the TimeSync tool, a web-based interface for manually interpreting and recording land cover from Landsat data supplemented with fine resolution imagery and other ancillary data. These reference data will be used for area estimation and validation of the LCMAP annual land cover products. Nearly 12,000 LCMAP reference sample pixels have been interpreted and a simple random subsample of these pixels has been interpreted independently by a second analyst (hereafter referred to as \"duplicate interpretations\"). The annual land cover reference class labels for the 1984-2016 monitoring period obtained from these duplicate interpretations are used to address the following questions: 1) How consistent are the reference class labels among interpreters overall and per class? 2) Does consistency vary by geographic region? 3) Does consistency vary as interpreters gain experience over time; and 4) Does interpreter consistency change with improving availability and quality of imagery from 1984 to 2016? Overall agreement between interpreters was 88%. Class-specific agreement ranged from 46% for Disturbed to 94% for Water, with more prevalent classes (Tree Cover, Grass/Shrub and Cropland) generally having greater agreement than rare classes (Developed, Barren and Wetland). Agreement between interpreters remained approximately the same over the 12-month period during which these interpretations were completed. Increasing availability of Landsat and Google Earth fine resolution data over the 1984 to 2016 monitoring period coincided with increased interpreter consistency for the post-2000 data record. The reference data interpretation and quality assurance protocols implemented for LCMAP demonstrate the technical and practical feasibility of using the Landsat archive and intensive human interpretation to produce national, annual reference land cover data over a 30 year period. Protocols to quantify and enhance interpreter consistency are critical elements to document and ensure quality of these reference data.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111261","usgsCitation":"Pengra, B., Stehman, S.V., Horton, J., Dockter, D., Schroeder, T.A., Yang, Z., Cohen, W.B., Healey, S.P., and Loveland, T., 2020, Quality control and assessment of interpreter consistency of annual land cover reference data in an operational national monitoring program: Remote Sensing of Environment, v. 238, 111261, 10 p., https://doi.org/10.1016/j.rse.2019.111261.","productDescription":"111261, 10 p.","ipdsId":"IP-101422","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457550,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111261","text":"Publisher Index Page"},{"id":437077,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QA5Q25","text":"USGS data release","linkHelpText":"LCMAP CONUS Intensification Reference Data Product 1984&ndash;2019 land cover, land use and change process attributes"},{"id":414788,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pengra, Bruce 0000-0003-2497-8284 bpengra@usgs.gov","orcid":"https://orcid.org/0000-0003-2497-8284","contributorId":5132,"corporation":false,"usgs":true,"family":"Pengra","given":"Bruce","email":"bpengra@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":765622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stehman, Stephen V. 0000-0001-5234-2027","orcid":"https://orcid.org/0000-0001-5234-2027","contributorId":216812,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen","email":"","middleInitial":"V.","affiliations":[{"id":39524,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse, NY 13210, USA","active":true,"usgs":false}],"preferred":false,"id":765623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Josephine 0000-0001-8436-4095","orcid":"https://orcid.org/0000-0001-8436-4095","contributorId":216813,"corporation":false,"usgs":true,"family":"Horton","given":"Josephine","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dockter, Daryn 0000-0003-1914-8657","orcid":"https://orcid.org/0000-0003-1914-8657","contributorId":216814,"corporation":false,"usgs":true,"family":"Dockter","given":"Daryn","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":765625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schroeder, Todd A. taschroeder@fs.fed.us","contributorId":190802,"corporation":false,"usgs":false,"family":"Schroeder","given":"Todd","email":"taschroeder@fs.fed.us","middleInitial":"A.","affiliations":[],"preferred":false,"id":765626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yang, Zhiqiang","contributorId":189584,"corporation":false,"usgs":false,"family":"Yang","given":"Zhiqiang","email":"","affiliations":[],"preferred":false,"id":765627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cohen, Warren B 0000-0003-3144-9532","orcid":"https://orcid.org/0000-0003-3144-9532","contributorId":216815,"corporation":false,"usgs":false,"family":"Cohen","given":"Warren","email":"","middleInitial":"B","affiliations":[{"id":39525,"text":"USDA Forest Service, Pacific Northwest Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331","active":true,"usgs":false}],"preferred":false,"id":765628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Healey, Sean P.","contributorId":216816,"corporation":false,"usgs":false,"family":"Healey","given":"Sean","email":"","middleInitial":"P.","affiliations":[{"id":39526,"text":"USDA Forest Service, Rocky Mountain Research Station, 507 25th Street, Ogden, UT 84401","active":true,"usgs":false}],"preferred":false,"id":765629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Loveland, Thomas 0000-0003-3114-6646","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":202518,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":765630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70210752,"text":"70210752 - 2020 - Clinical presentation and serological responses to natural outbreaks of rabies in a captive colony of common vampire bats","interactions":[],"lastModifiedDate":"2020-06-23T15:43:21.117496","indexId":"70210752","displayToPublicDate":"2020-03-01T10:40:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5968,"text":"Tropical Medicine and Infectious Disease","active":true,"publicationSubtype":{"id":10}},"title":"Clinical presentation and serological responses to natural outbreaks of rabies in a captive colony of common vampire bats","docAbstract":"
We report mortality events in a group of 123 common vampire bats (Desmodus rotundus) captured in México and housed for a rabies vaccine efficacy study in Madison, Wisconsin. Bat mortalities occurred in México and Wisconsin, but rabies cases reported herein are only those that occurred after arrival in Madison (n = 15). Bats were confirmed positive for rabies virus (RABV) by the direct fluorescent antibody test. In accordance with previous reports, we observed long incubation periods (more than 100 days), variability in clinical signs prior to death, excretion of virus in saliva, and changes in rabies neutralizing antibody (rVNA) titers post-infection. We observed that the furious form of rabies (aggression, hyper-salivation, and hyper-excitability) manifested in three bats, which has not been reported in vampire bat studies since 1936. RABV was detected in saliva of 5/9 bats, 2–5 days prior to death, but was not detected in four of those bats that had been vaccinated shortly after exposure. Bats from different capture sites were involved in two separate outbreaks, and phylogenetic analysis revealed differences in the glycoprotein gene sequences of RABV isolated from each event, indicating that two different lineages were circulating separately during capture at each site.
","language":"English","publisher":"MDPI","doi":"10.3390/tropicalmed5010034","usgsCitation":"Cardenas-Canales, E.M., Gigante, C.M., Greenberg, L.A., Velasco-Villa, A., Ellison, J.A., Satheshkumar, P.S., Medina-Magües, L., Griesser, R., Falendysz, E., Amezcua, I., Osorio, J., and Rocke, T.E., 2020, Clinical presentation and serological responses to natural outbreaks of rabies in a captive colony of common vampire bats: Tropical Medicine and Infectious Disease, v. 5, no. 1, 34, 13 p., https://doi.org/10.3390/tropicalmed5010034.","productDescription":"34, 13 p.","ipdsId":"IP-115623","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":457552,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/tropicalmed5010034","text":"Publisher Index Page"},{"id":375819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-97.14001,25.87],[-97.52807,24.99214],[-97.70295,24.27234],[-97.77604,22.93258],[-97.87237,22.44421],[-97.69904,21.89869],[-97.38896,21.41102],[-97.18933,20.63543],[-96.52558,19.89093],[-96.29213,19.32037],[-95.90088,18.82802],[-94.83906,18.56272],[-94.42573,18.14437],[-93.54865,18.42384],[-92.78611,18.52484],[-92.03735,18.70457],[-91.4079,18.87608],[-90.77187,19.28412],[-90.53359,19.86742],[-90.45148,20.70752],[-90.27862,20.99986],[-89.60132,21.26173],[-88.54387,21.49368],[-87.65842,21.45885],[-87.05189,21.54354],[-86.81198,21.33151],[-86.84591,20.84986],[-87.38329,20.2554],[-87.62105,19.64655],[-87.43675,19.4724],[-87.58656,19.04013],[-87.83719,18.25982],[-88.09066,18.51665],[-88.30003,18.49998],[-88.49012,18.48683],[-88.84834,17.8832],[-89.02986,18.00151],[-89.15091,17.95547],[-89.14308,17.80832],[-90.06793,17.81933],[-91.00152,17.81759],[-91.00227,17.25466],[-91.45392,17.25218],[-91.08167,16.91848],[-90.71182,16.68748],[-90.60085,16.47078],[-90.43887,16.41011],[-90.46447,16.06956],[-91.74796,16.06656],[-92.22925,15.25145],[-92.08722,15.06458],[-92.20323,14.8301],[-92.22775,14.53883],[-93.35946,15.61543],[-93.87517,15.94016],[-94.69166,16.20098],[-95.25023,16.12832],[-96.05338,15.75209],[-96.55743,15.65352],[-97.26359,15.91706],[-98.01303,16.10731],[-98.94768,16.56604],[-99.6974,16.70616],[-100.8295,17.17107],[-101.66609,17.64903],[-101.91853,17.91609],[-102.47813,17.97575],[-103.50099,18.29229],[-103.91753,18.74857],[-104.99201,19.31613],[-105.49304,19.94677],[-105.7314,20.4341],[-105.39777,20.53172],[-105.50066,20.8169],[-105.27075,21.07628],[-105.26582,21.4221],[-105.60316,21.87115],[-105.69341,22.26908],[-106.02872,22.77375],[-106.90998,23.76777],[-107.91545,24.54892],[-108.4019,25.17231],[-109.2602,25.58061],[-109.44409,25.82488],[-109.29164,26.44293],[-109.80146,26.67618],[-110.39173,27.16211],[-110.64102,27.85988],[-111.17892,27.94124],[-111.75961,28.46795],[-112.22823,28.95441],[-112.27182,29.26684],[-112.80959,30.02111],[-113.16381,30.78688],[-113.14867,31.17097],[-113.87188,31.56761],[-114.20574,31.52405],[-114.77645,31.79953],[-114.9367,31.39348],[-114.77123,30.91362],[-114.6739,30.16268],[-114.33097,29.75043],[-113.58888,29.06161],[-113.42405,28.82617],[-113.27197,28.75478],[-113.14004,28.41129],[-112.9623,28.42519],[-112.76159,27.78022],[-112.45791,27.52581],[-112.24495,27.17173],[-111.61649,26.66282],[-111.28467,25.73259],[-110.98782,25.29461],[-110.71001,24.826],[-110.65505,24.29859],[-110.17286,24.26555],[-109.77185,23.81118],[-109.4091,23.36467],[-109.43339,23.18559],[-109.85422,22.81827],[-110.03139,22.82308],[-110.29507,23.43097],[-110.9495,24.00096],[-111.67057,24.48442],[-112.18204,24.73841],[-112.14899,25.47013],[-112.30071,26.012],[-112.7773,26.32196],[-113.46467,26.76819],[-113.59673,26.63946],[-113.84894,26.90006],[-114.46575,27.14209],[-115.05514,27.72273],[-114.98225,27.7982],[-114.57037,27.74149],[-114.19933,28.115],[-114.16202,28.56611],[-114.93184,29.27948],[-115.51865,29.55636],[-115.88737,30.18079],[-116.25835,30.83646],[-116.72153,31.63574],[-117.12776,32.53534],[-115.99135,32.61239],[-114.72139,32.72083],[-114.815,32.52528],[-113.30498,32.03914],[-111.02361,31.33472],[-109.035,31.34194],[-108.24194,31.34222],[-108.24,31.75485],[-106.50759,31.75452],[-106.1429,31.39995],[-105.63159,31.08383],[-105.03737,30.64402],[-104.70575,30.12173],[-104.45697,29.57196],[-103.94,29.27],[-103.11,28.97],[-102.48,29.76],[-101.6624,29.7793],[-100.9576,29.38071],[-100.45584,28.69612],[-100.11,28.11],[-99.52,27.54],[-99.3,26.84],[-99.02,26.37],[-98.24,26.06],[-97.53,25.84],[-97.14001,25.87]]]},\"properties\":{\"name\":\"Mexico\"}}]}","volume":"5","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-03-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Cardenas-Canales, Elsa M.","contributorId":192489,"corporation":false,"usgs":false,"family":"Cardenas-Canales","given":"Elsa","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":791241,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gigante, Crystal M.","contributorId":225448,"corporation":false,"usgs":false,"family":"Gigante","given":"Crystal","email":"","middleInitial":"M.","affiliations":[{"id":41114,"text":"Poxvirus and Rabies Branch, Division of High Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.","active":true,"usgs":false}],"preferred":false,"id":791242,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Greenberg, Lauren A.","contributorId":225449,"corporation":false,"usgs":false,"family":"Greenberg","given":"Lauren","email":"","middleInitial":"A.","affiliations":[{"id":41114,"text":"Poxvirus and Rabies Branch, Division of High Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.","active":true,"usgs":false}],"preferred":false,"id":791243,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Velasco-Villa, Andres","contributorId":174760,"corporation":false,"usgs":false,"family":"Velasco-Villa","given":"Andres","email":"","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":false,"id":791244,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellison, James A.","contributorId":197066,"corporation":false,"usgs":false,"family":"Ellison","given":"James","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":791245,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Satheshkumar, Panayampalli S.","contributorId":225450,"corporation":false,"usgs":false,"family":"Satheshkumar","given":"Panayampalli","email":"","middleInitial":"S.","affiliations":[{"id":41114,"text":"Poxvirus and Rabies Branch, Division of High Consequence Pathogens and Pathology, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, Atlanta, GA, USA.","active":true,"usgs":false}],"preferred":false,"id":791246,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Medina-Magües, Lex G","contributorId":225451,"corporation":false,"usgs":false,"family":"Medina-Magües","given":"Lex G","affiliations":[{"id":41115,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin-Madison. Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":791247,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Griesser, Richard","contributorId":225452,"corporation":false,"usgs":false,"family":"Griesser","given":"Richard","email":"","affiliations":[{"id":41116,"text":"Wisconsin State Laboratory of Hygiene, Madison, WI, USA","active":true,"usgs":false}],"preferred":false,"id":791248,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Falendysz, Elizabeth 0000-0003-2895-8918 efalendysz@usgs.gov","orcid":"https://orcid.org/0000-0003-2895-8918","contributorId":127751,"corporation":false,"usgs":true,"family":"Falendysz","given":"Elizabeth","email":"efalendysz@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":791249,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Amezcua, Ignacio","contributorId":225453,"corporation":false,"usgs":false,"family":"Amezcua","given":"Ignacio","email":"","affiliations":[{"id":41117,"text":"Comité Estatal para el Fomento y Protección Pecuaria de San Luis Potosí, San Luis Potosí, México","active":true,"usgs":false}],"preferred":false,"id":791250,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Osorio, Jorge E.","contributorId":50392,"corporation":false,"usgs":false,"family":"Osorio","given":"Jorge E.","affiliations":[{"id":13052,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":791251,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Rocke, Tonie E. 0000-0003-3933-1563 trocke@usgs.gov","orcid":"https://orcid.org/0000-0003-3933-1563","contributorId":2665,"corporation":false,"usgs":true,"family":"Rocke","given":"Tonie","email":"trocke@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":791252,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70249358,"text":"70249358 - 2020 - Transitioning from change detection to monitoring with remote sensing: A paradigm shift","interactions":[],"lastModifiedDate":"2023-10-04T23:41:21.337275","indexId":"70249358","displayToPublicDate":"2020-03-01T09:55:47","publicationYear":"2020","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":"Transitioning from change detection to monitoring with remote sensing: A paradigm shift","docAbstract":"The use of time series analysis with moderate resolution satellite imagery is increasingly common, particularly since the advent of freely available Landsat data. Dense time series analysis is providing new information on the timing of landscape changes, as well as improving the quality and accuracy of information being derived from remote sensing. Perhaps most importantly, time series analysis is expanding the kinds of land surface change that can be monitored using remote sensing. In particular, more subtle changes in ecosystem health and condition and related to land use dynamics are being monitored. The result is a paradigm shift away from change detection, typically using two points in time, to monitoring, or an attempt to track change continuously in time. This trend holds many benefits, including the promise of near real-time monitoring. Anticipated future trends include more use of multiple sensors in monitoring activities, increased focus on the temporal accuracy of results, applications over larger areas and operational usage of time series analysis.","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2019.111558","usgsCitation":"Woodcock, C.E., Loveland, T., Herold, M., and Bauer, M.E., 2020, Transitioning from change detection to monitoring with remote sensing: A paradigm shift: Remote Sensing of Environment, v. 238, 111558, 5 p., https://doi.org/10.1016/j.rse.2019.111558.","productDescription":"111558, 5 p.","ipdsId":"IP-113612","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457553,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2019.111558","text":"Publisher Index Page"},{"id":421598,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"238","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Woodcock, Curtis E.","contributorId":294423,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","email":"","middleInitial":"E.","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":885300,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loveland, Thomas 0000-0003-3114-6646 loveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3114-6646","contributorId":140611,"corporation":false,"usgs":true,"family":"Loveland","given":"Thomas","email":"loveland@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":885301,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herold, Martin","contributorId":330558,"corporation":false,"usgs":false,"family":"Herold","given":"Martin","email":"","affiliations":[{"id":37803,"text":"Wageningen University","active":true,"usgs":false}],"preferred":false,"id":885302,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bauer, Marvin E.","contributorId":330559,"corporation":false,"usgs":false,"family":"Bauer","given":"Marvin","email":"","middleInitial":"E.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":885303,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215305,"text":"70215305 - 2020 - Planetary sensor models interoperability using the community sensor model specification","interactions":[],"lastModifiedDate":"2020-10-15T14:38:30.665861","indexId":"70215305","displayToPublicDate":"2020-03-01T09:35:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5026,"text":"Earth and Space Science","active":true,"publicationSubtype":{"id":10}},"title":"Planetary sensor models interoperability using the community sensor model specification","docAbstract":"

This paper presents the photogrammetric foundations upon which the Community Sensor Model specification depends, describes common coordinate system and reference frame transformations that support conversion between image sensor (charge‐coupled device) coordinates to some arbitrary body coordinate, and describes the U.S. Geological Survey Astrogeology Community Sensor Model implementation (https://github.com/USGS-Astrogeology/usgscsm). We present a new image support data specification that provides the position, pointing, timing, and metadata information necessary to properly locate a pixel or observations location on a body and describe a system architecture designed to explicitly identify the responsibilities of software components within a larger pipeline or analytical environment. This paper concludes with a set of experiments that illustrate positional and pointing error in the sensor location and the impact on the computed surface location.

","language":"English","publisher":"Wiley","doi":"10.1029/2019EA000713","usgsCitation":"Laura, J., Mapel, J., and Hare, T.M., 2020, Planetary sensor models interoperability using the community sensor model specification: Earth and Space Science, v. 7, no. 6, e2019EA000713, 17 p., https://doi.org/10.1029/2019EA000713.","productDescription":"e2019EA000713, 17 p.","ipdsId":"IP-108414","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":457556,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019ea000713","text":"Publisher Index Page"},{"id":379404,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-06-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Laura, Jason 0000-0002-1377-8159","orcid":"https://orcid.org/0000-0002-1377-8159","contributorId":222124,"corporation":false,"usgs":true,"family":"Laura","given":"Jason","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801664,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mapel, Jesse 0000-0001-5756-0373","orcid":"https://orcid.org/0000-0001-5756-0373","contributorId":206344,"corporation":false,"usgs":true,"family":"Mapel","given":"Jesse","email":"","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801665,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hare, Trent M. 0000-0001-8842-389X thare@usgs.gov","orcid":"https://orcid.org/0000-0001-8842-389X","contributorId":3188,"corporation":false,"usgs":true,"family":"Hare","given":"Trent","email":"thare@usgs.gov","middleInitial":"M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":801666,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70227142,"text":"70227142 - 2020 - Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","interactions":[],"lastModifiedDate":"2022-01-03T16:02:02.227914","indexId":"70227142","displayToPublicDate":"2020-03-01T08:28:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics","docAbstract":"

Eurasian zebra and quagga mussels were likely introduced to the Laurentian Great Lakes via ballast water release in the 1980s, and their range has since expanded across the US, including some of their southernmost occurrences in Texas. Their spread into the state has resulted in a need to revise previous delimitations of suitable dreissenid habitat. We therefore assessed invasion risk in Texas by 1) predicting distribution of suitable habitat of zebra and quagga mussels using Maxent species distribution models based upon global occurrence and climate data; and 2) refining lake-specific predictions via collection and analysis of physicochemical data. Maxent models predicted a lack of suitable habitat for quagga mussels within Texas. However, models did predict the presence of suitable zebra mussel habitat, with hotspots of suitable habitat occurring along the Red and Sabine Rivers of north and east Texas, as well as patches of suitable habitat in central Texas between the Colorado and Brazos Rivers and extending inland along the Gulf Coast. Although predicted suitable habitat extended further west than in previous models, most of the Texas panhandle, west Texas extending toward El Paso, and the Rio Grande valley were predicted to provide poor zebra mussel habitat suitability. Collection of physicochemical data (i.e., dissolved oxygen, pH, specific conductance, and temperature on-site as well as laboratory analysis for Ca, N, and P) from zebra mussel-invaded lakes and a subset of uninvaded but high-risk lakes of North and Central Texas, did not refine model predictions because there was no apparent distinction between invaded and uninvaded lakes. Overall, we demonstrated that while quagga mussels do not appear to represent an invasive threat in Texas, abundant suitable habitat for continuing zebra mussel invasion exists within the state. The threat of continued expansion of this poster-child for negative invasive species impacts warrants further prevention efforts, management, and research.

","language":"English","publisher":"REABIC","usgsCitation":"Barnes, M., and Patino, R., 2020, Predicting suitable habitat for dreissenid mussel invasion in Texas based on climatic and lake physical characteristics: Management of Biological Invasions, v. 11, no. 1, p. 63-79.","productDescription":"17 p.","startPage":"63","endPage":"79","ipdsId":"IP-107295","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":393733,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":393746,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.reabic.net/journals/mbi/2020/Issue1.aspx"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.30541992187499,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 29.334298230315675\n ],\n [\n -95.361328125,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 33.925129700072\n ],\n [\n -99.30541992187499,\n 29.334298230315675\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnes, M. A.","contributorId":270689,"corporation":false,"usgs":false,"family":"Barnes","given":"M. A.","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":829770,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Patino, Reynaldo 0000-0002-4831-8400 r.patino@usgs.gov","orcid":"https://orcid.org/0000-0002-4831-8400","contributorId":2311,"corporation":false,"usgs":true,"family":"Patino","given":"Reynaldo","email":"r.patino@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":829769,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70255818,"text":"70255818 - 2020 - How do we stop fungal disease from devastating North American salamanders","interactions":[],"lastModifiedDate":"2024-07-08T13:30:18.415061","indexId":"70255818","displayToPublicDate":"2020-03-01T08:25:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17992,"text":"Wildlife Professional","active":true,"publicationSubtype":{"id":10}},"title":"How do we stop fungal disease from devastating North American salamanders","docAbstract":"

No abstract available.

","language":"English","publisher":"The Wildlife Society","usgsCitation":"Periera, K., Gray, M.L., Kerby, J., Campbell Grant, E.H., and Voyles, J., 2020, How do we stop fungal disease from devastating North American salamanders: Wildlife Professional, v. 14, no. 2, p. 41-46.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-112740","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":430798,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Periera, K","contributorId":339937,"corporation":false,"usgs":false,"family":"Periera","given":"K","email":"","affiliations":[{"id":30221,"text":"Duquesne University","active":true,"usgs":false}],"preferred":false,"id":905669,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Margaret L 0000-0002-4810-8876","orcid":"https://orcid.org/0000-0002-4810-8876","contributorId":221166,"corporation":false,"usgs":false,"family":"Gray","given":"Margaret","email":"","middleInitial":"L","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":905670,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kerby, Jacob","contributorId":244593,"corporation":false,"usgs":false,"family":"Kerby","given":"Jacob","affiliations":[{"id":16684,"text":"University of South Dakota","active":true,"usgs":false}],"preferred":false,"id":905671,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":905672,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Voyles, Jamie","contributorId":127709,"corporation":false,"usgs":false,"family":"Voyles","given":"Jamie","email":"","affiliations":[{"id":7026,"text":"New Mexico Tech","active":true,"usgs":false}],"preferred":false,"id":905673,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70209032,"text":"70209032 - 2020 - The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes","interactions":[],"lastModifiedDate":"2020-06-04T16:59:39.703625","indexId":"70209032","displayToPublicDate":"2020-03-01T07:42:45","publicationYear":"2020","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":"The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes","docAbstract":"(Munson) The challenges of restoration in dryland ecosystems are growing due to a rise in anthropogenic disturbance and increasing aridity. Plant functional traits are often used to predict plant performance and can offer a window into the potential outcomes of restoration efforts across environmental gradients. We tracked 15 years of seeding outcomes across 150 sites on the Colorado Plateau, a cold desert ecoregion in the western United States, and analyzed the independent and interactive effects of functional traits (seed mass, height, and specific leaf area) and local biologically relevant climate variables on seeding success. We predicted that the best models would include an interaction between plant traits and climate, indicating a need to match the right trait value to the right climate conditions in order to maximize seeding success. Indeed, we found that both plant height and seed size significantly interacted with temperature seasonality, with larger seeds and taller plants performing better in more seasonal environments. We also determined that these trait-environment patterns are not driven by the use of native vs. non-native species. Our results lend insight to using plant traits to inform the selection of seed mixes for restoring areas with specific climatic conditions, while also demonstrating the strong influence of temperature seasonality on seeding success in the Colorado Plateau region.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2110","usgsCitation":"Balazs, K.R., Kramer, A.T., Munson, S.M., Talkington, N., Still, S., and Butterfield, B.J., 2020, The right trait in the right place at the right time: Matching traits to environment improves restoration outcomes: Ecological Applications, v. 30, no. 4, e02110, 7 p., https://doi.org/10.1002/eap.2110.","productDescription":"e02110, 7 p.","ipdsId":"IP-104892","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":457560,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2110","text":"Publisher Index Page"},{"id":373164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Colorado Plateau","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.42138671875,\n 39.57182223734374\n ],\n [\n -118.23486328125,\n 36.65079252503471\n ],\n [\n -111.7529296875,\n 33.76088200086917\n ],\n [\n -107.1826171875,\n 33.137551192346145\n ],\n [\n -104.0185546875,\n 33.284619968887675\n ],\n [\n -104.7216796875,\n 39.027718840211605\n ],\n [\n -107.70996093749999,\n 40.111688665595956\n ],\n [\n -111.4013671875,\n 41.77131167976407\n ],\n [\n -114.5654296875,\n 42.52069952914966\n ],\n [\n -117.2900390625,\n 42.06560675405716\n ],\n [\n -118.87207031250001,\n 40.84706035607122\n ],\n [\n -119.42138671875,\n 39.57182223734374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-04-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Balazs, Kathleen R.","contributorId":223214,"corporation":false,"usgs":false,"family":"Balazs","given":"Kathleen","email":"","middleInitial":"R.","affiliations":[{"id":24810,"text":"Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA","active":true,"usgs":false}],"preferred":false,"id":784588,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, Andrea T.","contributorId":207328,"corporation":false,"usgs":false,"family":"Kramer","given":"Andrea","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":784589,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Munson, Seth M. 0000-0002-2736-6374 smunson@usgs.gov","orcid":"https://orcid.org/0000-0002-2736-6374","contributorId":1334,"corporation":false,"usgs":true,"family":"Munson","given":"Seth","email":"smunson@usgs.gov","middleInitial":"M.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":784590,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Talkington, Nora","contributorId":223215,"corporation":false,"usgs":false,"family":"Talkington","given":"Nora","email":"","affiliations":[{"id":40685,"text":"Botanic Gardens Conservation International US, Chicago Botanic Garden, 1000 Lake Cook Road, Glencoe, IL 60022, USA","active":true,"usgs":false}],"preferred":false,"id":784591,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Still, Shannon","contributorId":149504,"corporation":false,"usgs":false,"family":"Still","given":"Shannon","email":"","affiliations":[{"id":17752,"text":"Chicago Botanic Garden","active":true,"usgs":false}],"preferred":false,"id":784592,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butterfield, Bradley J. 0000-0003-0974-9811","orcid":"https://orcid.org/0000-0003-0974-9811","contributorId":167009,"corporation":false,"usgs":false,"family":"Butterfield","given":"Bradley","email":"","middleInitial":"J.","affiliations":[{"id":24591,"text":"Merriam-Powell Center for Environmental Research and Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, USA","active":true,"usgs":false}],"preferred":false,"id":784593,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208943,"text":"70208943 - 2020 - Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach","interactions":[],"lastModifiedDate":"2024-05-17T15:47:05.397919","indexId":"70208943","displayToPublicDate":"2020-03-01T06:33:50","publicationYear":"2020","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":"Lessons learned implementing an operational continuous United States national land change monitoring capability: The Land Change Monitoring, Assessment, and Projection (LCMAP) approach","docAbstract":"

Growing demands for temporally specific information on land surface change are fueling a new generation of maps and statistics that can contribute to understanding geographic and temporal patterns of change across large regions, provide input into a wide range of environmental modeling studies, clarify the drivers of change, and provide more timely information for land managers. To meet these needs, the U.S. Geological Survey has implemented a capability to monitor land surface change called the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative. This paper describes the methodological foundations and lessons learned during development and testing of the LCMAP approach. Testing and evaluation of a suite of 10 annual land cover and land surface change data sets over six diverse study areas across the United States revealed good agreement with other published maps (overall agreement ranged from 73% to 87%) as well as several challenges that needed to be addressed to meet the goals of robust, repeatable, and geographically consistent monitoring results from the Continuous Change Detection and Classification (CCDC) algorithm. First, the high spatial and temporal variability of observational frequency led to differences in the number of changes identified, so CCDC was modified such that change detection is dependent on observational frequency. Second, the CCDC classification methodology was modified to improve its ability to characterize gradual land surface changes. Third, modifications were made to the classification element of CCDC to improve the representativeness of training data, which necessitated replacing the random forest algorithm with a boosted decision tree. Following these modifications, assessment of prototype Version 1 LCMAP results showed improvements in overall agreement (ranging from 85% to 90%).

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The study area includes ~85 active geothermal systems as potential training sites and >12 geologic, geophysical, and geochemical features. The main goal is to develop an algorithmic approach to identify new geothermal systems in the Great Basin region. Major objectives include: 1) integrate ML techniques into the geothermal community; 2) develop open community datasets, whereby all play fairway and ML datasets and algorithms are publicly released and available for modification by various user groups; 3) identify data acquisition targets with high value for future work; 4) identify new signatures to detect blind geothermal systems; and 5) foster new capabilities for characterizing subsurface temperature and permeability. Initially, ML techniques are being applied to the same play fairway datasets and workflow. ML will then be applied to both enhanced and additional datasets, with modification of the PFA workflow to incorporate the new datasets. 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Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","interactions":[],"lastModifiedDate":"2020-08-12T15:04:28.21349","indexId":"70211876","displayToPublicDate":"2020-02-29T10:39:46","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Play fairway analysis in geothermal exploration: The Snake River plain volcanic province","docAbstract":"The Snake River volcanic province (SRP) has long been considered a target for geothermal development. It overlies a thermal anomaly that extends deep into the mantle and represents one of the highest heat flow provinces in North America, but systematic exploration been hindered by lack of a conceptual model. Play Fairway Analysis (PFA) is a methodology adapted from the petroleum industry that integrates data at the regional or basin scale to define favorable plays for exploration in a systematic fashion. The success of play fairway analysis in geothermal exploration depends critically on defining a systematic methodology that is grounded in theory and adapted to the geologic and hydrologic framework of real geothermal systems. \nThis study focused on identifying three critical resource parameters for exploitable hydrothermal systems in the Snake River Plain: heat source, reservoir and recharge permeability, and cap or seal. Data included in the compilation for Heat were heat flow, the distribution and ages of volcanic vents, groundwater temperatures, thermal springs and wells, helium isotope anomalies, and reservoir temperatures estimated using geothermometry. Permeability was derived from stress orientations and magnitudes, post-Miocene faults, and subsurface structural lineaments based on magnetic and gravity data. Data for Seal included the distribution of impermeable lake sediments and clay-seal associated with hydrothermal alteration below the regional aquifer. These data were used to compile Common Risk Segment (CRS) maps for Heat, Permeability and Seal, which were combined to create a Composite Common Risk Segment (CCRS) map for all of southern Idaho that reflects the risk associated with geothermal resource exploration and helps to identify favorable resource tracks. \nOur data suggests that important undiscovered geothermal resources may be located in several areas of the SRP, including the western SRP (associated with buried lineaments capped by lacustrine sediment), at lineament intersections in the central SRP, and along the margins of the eastern SRP. These blind resources are associated with temperatures sufficient to support electricity production, and may be exploitable with existing deep drilling technology. We are testing our methodology by drilling a geothermal test well in Camas Prairie, ID, confirm our predictions of permeability and reservoir temperature.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings: 45th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"45th Workshop on Geothermal Reservoir Engineering 2020","conferenceDate":"February 10-12, 2020","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford Geothermal Program","usgsCitation":"Shervais, J., Glen, J.M., Siler, D.L., Liberty, L., Nielson, D., Garg, S., Dobson, P., Gasperikova, E., Sonnenthal, E., Newell, D., Evans, J.E., DeAngelo, J., Peacock, J., Earney, T.E., Schermerhorn, W.D., and Neupane, G., 2020, Play fairway analysis in geothermal exploration: The Snake River plain volcanic province, in Proceedings: 45th workshop on geothermal reservoir engineering, Stanford, CA, February 10-12, 2020, p. 186-194.","productDescription":"9 p.","startPage":"186","endPage":"194","ipdsId":"IP-115891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":377335,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":377334,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.proceedings.com/53283.html"}],"country":"United States","state":"Idaho","otherGeospatial":"Snake River Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5936279296875,\n 43.36512572875844\n ],\n [\n -111.544189453125,\n 44.15462243076731\n ],\n [\n -112.587890625,\n 44.33956524809713\n ],\n [\n -113.192138671875,\n 43.47285413777968\n ],\n [\n -114.60937499999999,\n 43.32517767999296\n ],\n [\n -115.7464599609375,\n 43.32517767999296\n ],\n [\n -116.72973632812499,\n 44.06390660801779\n ],\n [\n -116.92199707031249,\n 43.34914966389313\n ],\n [\n -116.1474609375,\n 42.59757641618889\n ],\n [\n -114.42260742187499,\n 42.293564192170095\n ],\n [\n -112.994384765625,\n 42.36666166373274\n ],\n [\n -111.9342041015625,\n 42.94033923363181\n ],\n [\n -111.5936279296875,\n 43.36512572875844\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shervais, John W.","contributorId":237914,"corporation":false,"usgs":false,"family":"Shervais","given":"John W.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glen, Jonathan M.G. 0000-0002-3502-3355 jglen@usgs.gov","orcid":"https://orcid.org/0000-0002-3502-3355","contributorId":176530,"corporation":false,"usgs":true,"family":"Glen","given":"Jonathan","email":"jglen@usgs.gov","middleInitial":"M.G.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Liberty, Lee","contributorId":189113,"corporation":false,"usgs":false,"family":"Liberty","given":"Lee","affiliations":[],"preferred":false,"id":795550,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielson, Dennis","contributorId":237918,"corporation":false,"usgs":false,"family":"Nielson","given":"Dennis","affiliations":[{"id":47642,"text":"DOSECC Exploration Services","active":true,"usgs":false}],"preferred":false,"id":795551,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Garg, Sabodh","contributorId":193564,"corporation":false,"usgs":false,"family":"Garg","given":"Sabodh","email":"","affiliations":[],"preferred":false,"id":795552,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dobson, Patrick","contributorId":193558,"corporation":false,"usgs":false,"family":"Dobson","given":"Patrick","email":"","affiliations":[],"preferred":false,"id":795553,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gasperikova, Erika","contributorId":193561,"corporation":false,"usgs":false,"family":"Gasperikova","given":"Erika","affiliations":[],"preferred":false,"id":795554,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sonnenthal, Eric","contributorId":146807,"corporation":false,"usgs":false,"family":"Sonnenthal","given":"Eric","affiliations":[],"preferred":false,"id":795555,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Newell, Dennis","contributorId":237921,"corporation":false,"usgs":false,"family":"Newell","given":"Dennis","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":795556,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Evans, James E.","contributorId":194435,"corporation":false,"usgs":false,"family":"Evans","given":"James","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":795557,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795558,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795559,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Earney, Tait E. 0000-0002-1504-0457","orcid":"https://orcid.org/0000-0002-1504-0457","contributorId":210080,"corporation":false,"usgs":true,"family":"Earney","given":"Tait","email":"","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795560,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schermerhorn, William D. 0000-0002-0167-378X","orcid":"https://orcid.org/0000-0002-0167-378X","contributorId":210081,"corporation":false,"usgs":true,"family":"Schermerhorn","given":"William","email":"","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":795561,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Neupane, Ghanashyam","contributorId":237924,"corporation":false,"usgs":false,"family":"Neupane","given":"Ghanashyam","email":"","affiliations":[{"id":27243,"text":"Idaho National Laboratory","active":true,"usgs":false}],"preferred":false,"id":795562,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70209967,"text":"70209967 - 2020 - Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures","interactions":[],"lastModifiedDate":"2020-05-07T12:38:54.176536","indexId":"70209967","displayToPublicDate":"2020-02-29T07:32:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures","docAbstract":"Elevated nitrate (NO3) and sulfate (SO4) in surface water are of global concern, and studies are needed to generate toxicity data to develop environmental guideline values for NO3 and SO4. The present study was designed to fill existing gaps in toxicity databases by determining the acute and/or chronic toxicity of NO3 (tested as NaNO3) to a unionid mussel (Lampsilis siliquoidea), a midge (Chironomus dilutus), a fish (rainbow trout, Oncorhynchus mykiss), and 2 amphibians (Hyla versicolor and Lithobates sylvaticus), and to determine the acute and/or chronic toxicity of SO4 (tested as Na2SO4) to 2 unionid mussels (L. siliquoidea and Villosa iris), an amphipod (Hyalella azteca), and 2 fish species (fathead minnow, Pimephales promelas and O. mykiss). Among the different test species, acute NO3 median effect concentrations (EC50s) ranged from 189 to >883 mg NO3‐N/L, and chronic NO3 20% effect concentrations (EC20s) based on the most sensitive endpoint ranged from 9.6 to 47 mg NO3‐N/L. The midge was the most sensitive species, and the trout was the least sensitive species in both acute and chronic NO3 exposures. Acute SO4 EC50s for the 2 mussel species (2071 and 2064 mg SO4/L) were similar to the EC50 for the amphipod (2689 mg SO4/L), whereas chronic EC20s for the 2 mussels (438 and 384 mg SO4/L) were >2‐fold lower than the EC20 of the amphipod (1111 mg SO4/L), indicating the high sensitivity of mussels in chronic SO4 exposures. However, the fathead minnow, with an EC20 of 374 mg SO4/L, was the most sensitive species in chronic SO4 exposures whereas the rainbow trout was the least sensitive species (EC20 > 3240 mg SO4/L). The high sensitivity of fathead minnow was consistent with the finding in a previous chronic Na2SO4 study. However, the EC20 values from the present study conducted in test water containing a higher potassium concentration (3 mg K/L) were >2‐fold greater than those in the previous study at a lower potassium concentration (1 mg K/L), which confirmed the influence of potassium on chronic Na2SO4 toxicity to the minnow.","language":"English","publisher":"SETAC","doi":"10.1002/etc.4701","collaboration":"","usgsCitation":"Wang, N., Dorman, R.A., Ivey, C.D., Soucek, D.J., Dickinson, A., Kunz, B.K., Steevens, J.A., Hammer, E.J., and Bauer, C.R., 2020, Acute and chronic toxicity of sodium nitrate and sodium sulfate to several freshwater organisms in water-only exposures: Environmental Toxicology and Chemistry, v. 39, no. 5, p. 1071-1085, https://doi.org/10.1002/etc.4701.","productDescription":"15 p.","startPage":"1071","endPage":"1085","ipdsId":"IP-114888","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":437081,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V2O84R","text":"USGS data release","linkHelpText":"Chemical and biological data from acute and chronic exposure to sodium nitrate and sodium sulfate for several freshwater organisms in water-only bioassays"},{"id":374531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"39","issue":"5","noUsgsAuthors":false,"publicationDate":"2020-02-29","publicationStatus":"PW","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":788623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dorman, Rebecca A. 0000-0002-5748-7046","orcid":"https://orcid.org/0000-0002-5748-7046","contributorId":28522,"corporation":false,"usgs":true,"family":"Dorman","given":"Rebecca","email":"","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":788625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soucek, David J. 0000-0002-7741-0193","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":224591,"corporation":false,"usgs":false,"family":"Soucek","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":788626,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickinson, Amy","contributorId":224592,"corporation":false,"usgs":false,"family":"Dickinson","given":"Amy","email":"","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":788627,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kunz, Bethany K. 0000-0002-7193-9336 bkunz@usgs.gov","orcid":"https://orcid.org/0000-0002-7193-9336","contributorId":3798,"corporation":false,"usgs":true,"family":"Kunz","given":"Bethany","email":"bkunz@usgs.gov","middleInitial":"K.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788628,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":788629,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":788630,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":788631,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70209184,"text":"70209184 - 2020 - A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states","interactions":[],"lastModifiedDate":"2020-07-09T14:43:19.756855","indexId":"70209184","displayToPublicDate":"2020-02-29T07:07:04","publicationYear":"2020","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":"A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states","docAbstract":"Bayesian population models can be exceedingly slow due, in part, to the choice to simulate discrete latent states. Here, we discuss an alternative approach to discrete latent states, marginalization, that forms the basis of maximum likelihood population models and is much faster. Our manuscript has two goals: 1) to introduce readers unfamiliar with marginalization to the concept and provide worked examples, and 2) to address topics associated with marginalization that have not been previously synthesized and are relevant to both Bayesian and maximum likelihood models. We begin by explaining marginalization using a Cormack-Jolly-Seber model. Next, we apply marginalization to multistate capture-recapture, community occupancy, and integrated population models and briefly discuss random effects, priors, and pseudo-R2. Then, we focus on recovery of discrete latent states, defining different types of conditional probabilities and showing how quantities such as population abundance or species richness can be estimated in marginalized code. Lastly, we show that occupancy and site abundance models with auto-covariates can be fit with marginalized code with minimal impact on parameter estimates.\n\nMarginalized code was anywhere from five to >1000 times faster than discrete code. Differences in inferences were minimal using marginalized code. Discrete latent states and fully conditional approaches provide the best estimates of conditional probabilities for a given site or individual. However, estimates for parameters and derived quantities such as species richness and abundance were minimally affected by marginalization and use of imperfect estimates of conditional probabilities. The results applied even when auto-covariates based on imperfect estimates of conditional probabilities were used. Understanding how marginalization works shrinks the divide between Bayesian and maximum likelihood approaches to population models. Some models that have only been presented in a Bayesian framework can easily be fit in maximum likelihood. On the other hand, factors such as informative priors, random effects, or pseudo-R2 values may motivate a Bayesian approach in some applications. An understanding of marginalization allows users to minimize the speed that is sacrificed when switching from a maximum likelihood approach. Widespread application of marginalization in Bayesian population models will facilitate more thorough simulation studies, comparisons of alternative model structures, and faster learning.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2112","usgsCitation":"Yackulic, C.B., Dodrill, M.J., Dzul, M.C., Sanderlin, J.S., and Reid, J.A., 2020, A need for speed in Bayesian population models: A practical guide to marginalizing and recovering discrete latent states: Ecological Applications, v. 30, no. 5, e02112, 19 p., https://doi.org/10.1002/eap.2112.","productDescription":"e02112, 19 p.","ipdsId":"IP-108648","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":437083,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9JN5C0L","text":"USGS data release","linkHelpText":"Marginalizing Bayesian population models - data for examples in the Grand Canyon region, southeastern Arizona, western Oregon USA - 1990-2015"},{"id":373429,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"5","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785280,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dodrill, Michael J. 0000-0002-7038-7170 mdodrill@usgs.gov","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":5468,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","email":"mdodrill@usgs.gov","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785281,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dzul, Maria C. 0000-0002-4798-5930 mdzul@usgs.gov","orcid":"https://orcid.org/0000-0002-4798-5930","contributorId":5469,"corporation":false,"usgs":true,"family":"Dzul","given":"Maria","email":"mdzul@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785282,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sanderlin, Jamie S.","contributorId":223514,"corporation":false,"usgs":false,"family":"Sanderlin","given":"Jamie","email":"","middleInitial":"S.","affiliations":[{"id":40727,"text":"USDA Forest Service, Rocky Mountain Research Station, Flagstaff, AZ 86001 USA","active":true,"usgs":false}],"preferred":false,"id":785283,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reid, Janice A.","contributorId":223515,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","email":"","middleInitial":"A.","affiliations":[{"id":40726,"text":"USDA Forest Service, Pacific Northwest Research Station, Roseburg Field Station, Roseburg, OR USA","active":true,"usgs":false}],"preferred":false,"id":785284,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70205260,"text":"fs20193051 - 2020 - The 3D Elevation Program and energy for the Nation","interactions":[],"lastModifiedDate":"2020-03-02T06:19:52","indexId":"fs20193051","displayToPublicDate":"2020-02-28T16:25:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-3051","displayTitle":"The 3D Elevation Program and Energy for the Nation","title":"The 3D Elevation Program and energy for the Nation","docAbstract":"

High-resolution light detection and ranging (lidar) data are used in energy infrastructure siting, design, permitting, construction, and monitoring to promote public safety through the reduction of risks. For example, lidar data are used to identify safe locations for energy infrastructure by analyzing terrain parameters and identifying and evaluating geologic hazards (for example, landslide and fault locations) and their potential public safety effects on the location or design of infrastructure. Increasingly, engineering companies and regulatory agencies are using lidar and other remote sensing techniques as an efficient method to collect accurate, comprehensive data while reducing risks to field personnel.

The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) is collecting lidar data nationwide (interferometric synthetic aperture radar [IfSAR] data in Alaska) to support a wide range of applications, including projects related to energy infrastructure construction and safety. Renewable energy resources, resource mining, and oil and gas resources were identified by the National Enhanced Elevation Assessment as business uses requiring three-dimensional (3D) elevation data.

Elevation data are critical in assessing potential sites for energy infrastructure, such as pipelines, refineries and other facilities, to mitigate risks from natural hazards. For example, the Federal Energy Regulatory Commission (FERC), an independent agency that regulates the interstate transmission of electricity, natural gas, and oil, uses enhanced elevation data to conduct National Environmental Policy Act (NEPA) compliance assessments. The acquisition of high-resolution lidar data by the USGS 3DEP initiative helps the FERC and NEPA permit applicants by providing accurate and consistent data for hazards analysis. The use of these data accelerates the application and review process and avoids the much higher costs of acquiring elevation data along proposed energy facility locations and pipeline corridors.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193051","usgsCitation":"Thatcher, C.A., Lukas, Vicki, and Stoker, J.M., 2020, The 3D Elevation Program and energy for the Nation: U.S. Geological Survey Fact Sheet 2019–3051, 2 p., https://doi.org/10.3133/fs20193051.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-107266","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":372745,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3051/coverthb.jpg"},{"id":372746,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2019/3051/fs20193051.pdf","text":"Report","size":"566 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2019-3051"}],"contact":"

Director, National Geospatial Program
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 511
Reston, VA 20192

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-02-28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Thatcher, Cindy A. 0000-0003-0331-071X","orcid":"https://orcid.org/0000-0003-0331-071X","contributorId":218872,"corporation":false,"usgs":true,"family":"Thatcher","given":"Cindy","email":"","middleInitial":"A.","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":770590,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lukas, Vicki 0000-0002-3151-6689 vlukas@usgs.gov","orcid":"https://orcid.org/0000-0002-3151-6689","contributorId":2890,"corporation":false,"usgs":true,"family":"Lukas","given":"Vicki","email":"vlukas@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":770591,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":770592,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208312,"text":"ofr20201011 - 2020 - Development of a process-based littoral sediment transport model for Dauphin Island, Alabama","interactions":[],"lastModifiedDate":"2022-04-21T20:39:46.098727","indexId":"ofr20201011","displayToPublicDate":"2020-02-28T14:45:00","publicationYear":"2020","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":"2020-1011","displayTitle":"Development of a Process-Based Littoral Sediment Transport Model for Dauphin Island, Alabama","title":"Development of a process-based littoral sediment transport model for Dauphin Island, Alabama","docAbstract":"

Dauphin Island, Alabama, located in the Northern Gulf of Mexico just outside of Mobile Bay, is Alabama’s only barrier island and provides an array of historical, natural, and economic resources. The dynamic island shoreline of Dauphin Island evolved across time scales while constantly acted upon by waves and currents during both storms and calm periods. Reductions in the vulnerability and enhancements to the resiliency of Dauphin Island—through offshore sand placement, breach closure, berm construction, and other means—have been used to protect the island and its vital resources. Planning for a resilient Dauphin Island requires predicting the long-term evolution of the barrier island system and the dominant, temporally varying processes that influence it, including littoral alongshore sediment transport under typical wave conditions, beach and dune erosion, the island overwash and breaching that occur rapidly during storm events, and the recovery of primary sand dunes through Aeolian transport over decadal time scales. Littoral sediment transport within the Dauphin Island decadal-scale framework was simulated using the Delft-3D modeling software suite. The influences of wind, waves, water levels, and sediment transport are incorporated into the model. Model skill in the prediction of waves, water levels, currents, volumetric flow rates through inlets, and shoreline position was assessed by using a set of deterministic and statistical hindcast simulations. The Delft-3D modeling application described here can be coupled with validated models of storm-response and dune recovery to predict the evolution of Dauphin Island on decadal time scales.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201011","usgsCitation":"Jenkins, R.L., III, Long, J.W., Dalyander, P.S., Thompson, D.M., and Mickey, R.C., 2020, Development of a process-based littoral sediment transport model for Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1011, 43 p., https://doi.org/10.3133/ofr20201011.","productDescription":"vii, 43 p.","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-109477","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399455,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109731.htm"},{"id":372743,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1011/ofr20201011.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1011"},{"id":372742,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1011/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.36715698242186,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.210421455819937\n ],\n [\n -88.06640625,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.26974231529823\n ],\n [\n -88.36715698242186,\n 30.210421455819937\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2020-02-28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jenkins, Robert L. 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Soupy ","affiliations":[{"id":13499,"text":"The Water Institute of the Gulf","active":true,"usgs":false}],"preferred":false,"id":781370,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thompson, David M. 0000-0002-7103-5740 dthompson@usgs.gov","orcid":"https://orcid.org/0000-0002-7103-5740","contributorId":3502,"corporation":false,"usgs":true,"family":"Thompson","given":"David","email":"dthompson@usgs.gov","middleInitial":"M.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781371,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mickey, Rangley C. 0000-0001-5989-1432 rmickey@usgs.gov","orcid":"https://orcid.org/0000-0001-5989-1432","contributorId":141016,"corporation":false,"usgs":true,"family":"Mickey","given":"Rangley","email":"rmickey@usgs.gov","middleInitial":"C.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":781372,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208827,"text":"70208827 - 2020 - Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA","interactions":[],"lastModifiedDate":"2020-03-03T09:05:19","indexId":"70208827","displayToPublicDate":"2020-02-28T09:04:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2603,"text":"Landscape and Urban Planning","active":true,"publicationSubtype":{"id":10}},"title":"Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA","docAbstract":"Recreational activities can negatively affect protected area landscapes and resources and soil erosion is frequently cited as the most significant long-term impact to recreational trails. Comprehensive modeling of soil loss on trails can identify influential factors that managers can manipulate to design and manage more sustainable trails. Field measurements assessed soil loss as the mean vertical depth along 135 trail transects across the Appalachian Trail sampled along three 5km trail segments in the White Mountains National Forest of New Hampshire. Using LiDAR data to accurately measure terrain characteristics that influence trail erosion can improve predictive models of trail system soil loss. Borrowing from geomorphic and agricultural soil erosion models, this study evaluated a variety of terrain and hydrology characteristics to model trail soil loss at three spatial scales: transect, trail corridor, and watershed. The model for each spatial scale and a combined model are presented. The adjusted R2 explaining variation in soil loss is 0.57 using variables from all spatial scales, a substantial improvement on previous trail erosion models. Environmental and trail design factors such as slope and watershed flow length were found to be significantly correlated to soil loss and have implications for sustainable trail design and management.","language":"English","publisher":"Elsevier","doi":"10.1016/j.landurbplan.2020.103765","usgsCitation":"Eagleston, H., and Marion, J.L., 2020, Application of airborne LiDAR and GIS in modeling trail erosion along the Appalachian Trail, New Hampshire, USA: Landscape and Urban Planning, v. 198, 103765, 9 p., https://doi.org/10.1016/j.landurbplan.2020.103765.","productDescription":"103765, 9 p.","ipdsId":"IP-088107","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":457565,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10919/98678","text":"External 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loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17","interactions":[],"lastModifiedDate":"2022-04-22T21:35:38.301278","indexId":"sir20195092","displayToPublicDate":"2020-02-28T08:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5092","displayTitle":"Sediment and Chemical Contaminant Loads in Tributaries to the Anacostia River, Washington, District of Columbia, 2016–17","title":"Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17","docAbstract":"

A study was conducted by the U.S. Geological Survey (USGS) in cooperation with the Washington, D.C., Department of Energy & Environment to estimate the loads of suspended-sediment-bound chemical compounds in five gaged tributaries and four ungaged tributaries of the Anacostia River (known locally as “Lower Anacostia River”) in Washington, D.C. Tributaries whose discharge is measured by the USGS are the Northeast and Northwest Branches of the Anacostia River, referred to in this report as “Northeast Branch” (NEB) and “Northwest Branch” (NWB), respectively; Watts Branch (WB); and Hickey Run (HR). A USGS streamflow-gaging station was established in 2016 on Beaverdam Creek (known locally as “Lower Beaverdam Creek” [LBDC]) to support this study. The ungaged streams studied include Nash Run; Pope Branch; an unnamed stream at Fort DuPont, referred to in this report as “Fort DuPont Creek”; and an unnamed stream at Fort Stanton, referred to in this report as “Fort Stanton Creek.” The gaged streams were sampled during four to five storms and two low-flow events during January, March, May, and July 2017. The ungaged streams were sampled during one storm and one low-flow event during July 2017. Storm sampling involved collecting large-volume (60- to 70-liter) composite samples, then removing sediment by filtration in the laboratory. Low-flow samples were obtained by filtering streamwater directly in the field. Continuously recording data sondes were deployed throughout the study to measure turbidity and other water-quality characteristics. During sampling, multiple discrete samples of streamwater were collected to determine suspended-sediment concentration (SSC) and particulate organic carbon (POC) concentration. Shortly after each storm, bed sediment was collected for chemical analysis.

Sediment samples were analyzed for 209 polychlorinated biphenyl (PCB) congeners; 35 polyaromatic hydrocarbon (PAH) compounds, including 20 nonalkylated and 15 alkylated species; and 20 organochlorine pesticide (OP) compounds. Sediment from one storm was analyzed for 23 metals.

Relations were developed among turbidity, discharge, and measured SSC by using multiple linear regression of log-transformed data. These relations were used to estimate SSC from continuous records of discharge and turbidity and were subsequently used to estimate sediment loads for the 2017 calendar year. USGS continuous records of turbidity in NEB, NWB, Watts Branch, and Hickey Run were available for 2013–17, which allowed sediment loads to be calculated for these years. Sediment loads for the ungaged streams were estimated by using loads measured in Watts Branch adjusted on the basis of stream-basin areas.

Sediment loads for 2017 total 3.10×107 kilograms (kg), with 1.02×107 kg (33 percent of total) from the NEB, 1.55×107 kg (50 percent) from the NWB, 4.45×106 kg (14 percent) from LBDC, 5.62×105 kg (2 percent) from Watts Branch, and 2.82×105 kg (1 percent) from Hickey Run. Sediment yields were highest from NWB and LBDC (3.13×105 kilograms per year per square mile [kg/yr/mi2] and 3.01 kg/yr/mi2, respectively). As a result of gaps in turbidity and discharge data, the load for LBDC reported here was calculated from measurements representing only 88 percent of the year (2017), and thus underestimates the actual load. All other gaged tributaries had datasets covering 100 percent of the year and are considered to fully represent actual loads. Estimated sediment loads for the ungaged streams during 2017 total 3.5×105 kg, with 1.2×105 kg from Nash Run, 6.2×104 kg from Pope Branch, 1.1×105 kg from Fort DuPont Creek, and 5.6×104 kg from Fort Stanton Creek.

Concentrations of PCBs, PAHs, and chlorinated pesticides in streamwater are presented for stormflow and low-flow conditions. Average concentrations (in stormflow and low-flow samples) of total PCBs (sum of all congeners, including coelutions) are 5.9 micrograms per kilogram (µg/kg) for NEB, 6.6 µg/kg for NWB, 130 µg/kg for LBDC, 34 µg/kg for Watts Branch, and 69 µg/kg for Hickey Run. Average concentrations of total PAHs (tPAH) (total of nonalkylated and alkylated species) are 2,000 µg/kg for NEB, 3,300 µg/kg for NWB, 2,200 µg/kg for LBDC, 2,400 µg/kg for Watts Branch, and 18,000 µg/kg for Hickey Run. tPAH concentrations among the ungaged streams were highest in Nash Run (5,500 µg/kg); concentrations in the other ungaged streams were less than (<) 700 µg/kg.

The general magnitude of tPCB and tPAH concentrations in streamwater samples was low-flow samples greater than (>) stormflow samples greater than or equal to (≥) bed-sediment samples. PCB congener profiles in the three types of samples were nearly identical in each stream and were similar in all streams except for LBDC, where the dominant PCBs shifted to the lighter di- through tetra- homologs. LBDC showed higher tPCB concentrations and a distinct congener profile from the other streams. The similarity in congener makeup supported that averaging PCB concentrations in stormflow and low-flow samples was appropriate for calculating chemical loads.

Loads of tPCB, tPAH (total of alkylated and nonalkylated forms), and pesticides were estimated for each stream by multiplying average contaminant concentrations by the respective sediment loads. Total PCB loads for 2017 were estimated to be 820 grams (g) with 8 percent (60 g) from NEB, 12 percent (95 g) from NWB, 75 percent (590 g) from LBDC, 3 percent (25 g) from Watts Branch, and 2.5 percent (19 g) from Hickey Run. PCB toxicity totaled 3.8×10−3 µg/kg, with the largest contribution (47 percent) derived from LBDC. Total PAH loads (sum of alkylated and nonalkylated forms) for 2017 were estimated to be 89,000 g, with 23 percent (20,000 g) from NEB, 59 percent (52,000 g) from NWB, 11 percent (9,800 g) from LBDC, 2 percent (1,400 g) from Watts Branch, and 6 percent (5,200 g) from Hickey Run. These results indicate that the largest contributor (75 percent) of PCBs to the Anacostia River is LBDC, although it contributes only 15 percent of the sediment and its basin area represents only 10 percent of the area of the Anacostia River watershed. The majority of the PAH load originates from NWB (59 percent of total) and NEB (22 percent). The ungaged tributaries contribute extremely small loads of PCBs and PAHs, totaling 8.1 g and 765 kg, respectively. More than 94 percent of the total load from the ungaged tributaries is derived from the Nash Run Basin.

Various organochlorine pesticides were present in suspended and bed sediment from all gaged and ungaged tributaries; however, elevated detection levels associated with the analytical methods resulted in numerous unquantifiable concentrations in the suspended-sediment samples. Only the pesticide chlordane was found in measurable concentrations in all gaged tributaries. As a result, in this report, a combination of analytical data from suspended-sediment and bed-sediment samples was used to estimate the maximum pesticide loading for each tributary. Chlordane was the principal compound present in the gaged tributaries; the highest average concentration (average of stormflow and low-flow samples from each stream) was 62 µg/kg in sediment from Watts Branch. Chlordane loads for 2017 totaled 1,100 g, of which 7 percent (430 g) was from NEB, 28 percent (320 g) was from NWB, 28 percent (310 g) was from LBDC, 5 percent (56 g) was from Watts Branch, and 1 percent (11 g) was from Hickey Run. Chlordane was not present in suspended or bed sediment from any of the ungaged tributaries. Loads of the other pesticides were estimated by using the highest concentration measured in the combined suspended-sediment and bed-sediment data for each stream. Notable loads include dieldrin (860 g from NWB), methoxychlor (205 g from LBDC), endrin aldehyde (150 g from NWB), and 4,4-DDT (79 g from Watts Branch). Compared with pesticide loads from the gaged streams, those from the ungaged streams were minimal, with only the Pope Branch contribution exceeding 1 gram per year for 4,4-DDE (1.05 g) and 4,4’-DDT (1.3 g).

The results of this study show that the dominant source of PCBs and chlordane is LBDC, despite its relatively small basin area. PAHs are ubiquitous throughout the study area, with the largest sources being NEB and NWB; this finding is a result of the large sediment load originating from these basins. The small, ungaged streams supply only minimal PCB and PAH loads, with Nash Run being the largest contributor.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195092","collaboration":"Prepared in cooperation with the Washington, D.C., Department of Energy & Environment","usgsCitation":"Wilson, T.P., 2019, Sediment and chemical contaminant loads in tributaries to the Anacostia River, Washington, District of Columbia, 2016–17: U.S. Geological Survey Scientific Investigations Report 2019–5092, 146 p., https://doi.org/10.3133/sir20195092.","productDescription":"Report: x, 146 p.; Data Release","numberOfPages":"160","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-099743","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":399540,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109730.htm"},{"id":372690,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RUZSMV","text":"USGS data release","linkHelpText":"Discharge and sediment data for selected tributaries to the Anacostia River, Washington, District of Columbia, 2003–18"},{"id":372692,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5092/sir20195092.pdf","text":"Report","size":"5.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5092"},{"id":372691,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5092/coverthb.jpg"}],"country":"United States","state":"District of Columbia","county":"Washington","otherGeospatial":"Anacostia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.0797,\n 38.8447\n ],\n [\n -76.7689,\n 38.8447\n ],\n [\n -76.7689,\n 39.1611\n ],\n [\n -77.0797,\n 39.1611\n ],\n [\n -77.0797,\n 38.8447\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228

","tableOfContents":"","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"publishedDate":"2020-02-28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wilson, Timothy P. 0000-0003-1914-6344","orcid":"https://orcid.org/0000-0003-1914-6344","contributorId":219174,"corporation":false,"usgs":true,"family":"Wilson","given":"Timothy P.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771489,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70221103,"text":"70221103 - 2020 - Towards reproducible environmental modeling for decision support: A worked example","interactions":[],"lastModifiedDate":"2021-06-03T12:05:09.55066","indexId":"70221103","displayToPublicDate":"2020-02-28T07:20:13","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":8916,"text":"Frontiers in Earth Science, Hydrosphere","active":true,"publicationSubtype":{"id":10}},"title":"Towards reproducible environmental modeling for decision support: A worked example","docAbstract":"

A fully worked example of decision-support-scale uncertainty quantification (UQ) and parameter estimation (PE) is presented. The analyses are implemented for an existing groundwater flow model of the Edwards aquifer, Texas, USA, and are completed in a script-based workflow that strives to be transparent and reproducible. High-dimensional PE is used to history-match simulated outputs to corresponding state observations of spring flow and groundwater level. Then a hindcast of a historical drought is made. Using available state observations recorded during drought conditions, the combined UQ and PE analyses are shown to yield an ensemble of model results that bracket the observed hydrologic responses. All files and scripts used for the analyses are placed in the public domain to serve as a template for other practitioners who are interested in undertaking these types of analyses.

","language":"English","publisher":"Frontiers","doi":"10.3389/feart.2020.00050","usgsCitation":"White, J.T., Foster, L.K., Fienen, M., Knowling, M.J., Hemmings, B., and Winterle, J.R., 2020, Towards reproducible environmental modeling for decision support: A worked example: Frontiers in Earth Science, Hydrosphere, v. 28, 50, 11 p., https://doi.org/10.3389/feart.2020.00050.","productDescription":"50, 11 p.","ipdsId":"IP-115342","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":457567,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2020.00050","text":"Publisher Index Page"},{"id":437084,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AUZMI7","text":"USGS data release","linkHelpText":"Towards reproducible environmental modeling for decision support: a worked example"},{"id":386114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"southern-central Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.96435546875,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 28.76765910569123\n ],\n [\n -96.83349609375,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 30.14512718337613\n ],\n [\n -100.96435546875,\n 28.76765910569123\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2020-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"White, Jeremy T. 0000-0002-4950-1469 jwhite@usgs.gov","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":167708,"corporation":false,"usgs":true,"family":"White","given":"Jeremy","email":"jwhite@usgs.gov","middleInitial":"T.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816772,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Linzy K. 0000-0002-7373-7017","orcid":"https://orcid.org/0000-0002-7373-7017","contributorId":259186,"corporation":false,"usgs":true,"family":"Foster","given":"Linzy","email":"","middleInitial":"K.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816773,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":816774,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knowling, Matthew J.","contributorId":251909,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816775,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hemmings, Brioch","contributorId":259187,"corporation":false,"usgs":false,"family":"Hemmings","given":"Brioch","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":816776,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Winterle, James R.","contributorId":259189,"corporation":false,"usgs":false,"family":"Winterle","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":52328,"text":"Edwards Aquifer Authority","active":true,"usgs":false}],"preferred":false,"id":816777,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}