We sampled juvenile wild Steelhead Oncorhynchus mykiss in headwater streams of the Wind River, WA, to characterize populations and investigate life-history metrics, particularly migratory patterns. We used Passive Integrated Transponder (PIT)-tagging and a series of instream PIT-tag interrogation systems (PTISs) to track juveniles. The Wind River subbasin is considered a wild Steelhead refuge by Washington Department of Fish and Wildlife (WDFW). No hatchery Steelhead have been planted in the Wind River subbasin since 1997, and hatchery adults are estimated to be less than one percent of spawners in most years (pers comm. Thomas Buehrens, Washington Department of Fish and Wildlife). Our repeated headwater sampling of consistent sites in the Wind River subbasin has also allowed us to track relative abundance of Brook Trout, a non-native species to the Wind River. Our work is contributing to understanding of Steelhead population response to numerous restoration actions in the subbasin, including removal of Hemlock Dam from Trout Creek in 2009, where our PTISs are helping to quantify adult response.
Data from our study, and companion work by Washington Department of Fish and Wildlife, are contributing to Bonneville Power Administration’s (BPA) Research Monitoring and Evaluation (RM&E) Program Strategy of Fish Population Status Monitoring (www.cbfish.org/ProgramStrategy.mvc/ViewProgramStrategySummary/1). Specifically this work addresses the sub-strategies of: 1) Assessing the Status and Trends of Diversity of Natural Origin Fish Populations and to Uncertainties Research regarding differing life histories of a wild Steelhead population, 2) Assessing the Status and Trend of Adult Natural Origin Fish Populations, and 3) Monitoring and Evaluating the Effectiveness of Tributary Habitat Actions Relative to Environmental, Physical, or Biological Performance Objectives. Our headwaters parr PIT tagging, WDFW parr, smolt, and adult tagging and our instream PTISs are providing data on movements and life histories of parr, smolt, and adult Steelhead.
During summer 2018, we PIT-tagged 1,592 age-0 and age-1 Steelhead parr in headwater areas of the Wind River subbasin to characterize population traits and investigate life-history diversity, including growth and pre-smolt downstream movement. Repeat headwater sampling and smolt trap operations provide opportunities for recapture, and instream PTISs and Columbia River infrastructure provide opportunity for detection of PIT-tagged fish. Throughout the year, we maintained a series of six instream PTISs to monitor movement of tagged Steelhead parr, smolts, and adults.
Detections at the instream PTISs have demonstrated trends of age-0 and age-1 parr emigration from natal areas during summer and fall, in addition to the expected movement of parr and smolts in spring. Substantial numbers of parr make downstream movements as age-1 fish. We have estimated that from 15 to 33 percent of parr tagged as age-0 fish make downstream migrations at age-1 for additional rearing. We have estimated that from 1 to 27 percent of parr tagged as age-1 fish make downstream migrations during fall. These findings raise many questions about parr rearing strategies, habitat use, and success of these migrants and suggest a need for broader monitoring of juvenile Steelhead in some river systems to fully document juvenile production. Long-term monitoring of PIT-tagged fish is providing information on contribution of various life-history strategies to smolt production and adult returns.
Movements of PIT-tagged adult Steelhead were recorded at instream PTISs. These data have allowed assessment of adult returns to tributary watersheds within the Wind River subbasin. Detection efficiency of adult PIT-tagged Steelhead at our primary adult-monitoring PTIS in Trout Creek has been greater than 92 percent during 6 of the past 7 years. This is providing excellent data to estimate adult returns to this watershed. Determination of adult use of tributary watersheds is providing data to help evaluate the efficacy of the removal of Hemlock Dam on Trout Creek. Hemlock Dam, located at rkm 2.0 of Trout Creek, was removed in summer 2009. The dam contributed to hydrologic impairment of Trout Creek and had potential negative effects on Steelhead. The improvements made to the upper Wind River PTIS (site code WRU at rkm 28.3; better site characteristics and grid power) during 2016 and 2017, and a planned new site in the Mine Reach of the upper Wind River, will allow estimates of subbasin adult escapement like those in Trout Creek.
During 2018, we also completed planning and permitting with U.S. Forest Service for a new PTIS site at rkm 36 of the Wind River (the Mine Reach, mentioned above). This site will replace two sites (one in Paradise Creek and one at rkm 41 of the Wind River), which had operational challenges due to lack of adequate solar power and winter difficulties. The new Mine Reach PTIS site at rkm 36, will have better solar exposure, fewer winter operations difficulties, and provide opportunity to detect fish from juvenile sampling sites that were downstream of the previous two PTISs. The more consistent operation of the new Mine Reach PTIS site will increase our ability to estimate migrant abundance as all the juveniles tagged upstream of it will be subject to the same potential detection history, instead of three different potential detection histories as before. Additionally, with the new Mine Reach PTIS site lower in the watershed, it will subject more PIT-tagged adult Steelhead to detection and provide ability to generate a nonbiased adult-detection efficiency estimate for the WRU PTIS at rkm 28.3 of the Wind River. This will provide the opportunity to estimate yearly adult Steelhead abundance to the upper Wind watershed area. Permitting is complete and some supplies have been purchased to build and install this new site in 2019.
Repeat sampling at consistent locations in the subbasin has allowed investigation into juvenile Steelhead growth patterns. Growth rates (relative change in weight) of age-0 PIT-tagged parr during summer are similar across the subbasin, but lower for age-1 parr in the Trout Creek watershed than the upper Wind River watershed. Yearly growth for parr tagged at age-0 is similar across the subbasin. Yearly growth for parr tagged at age-1 is lowest in Martha Creek, but similar elsewhere.
Non-native Brook Trout are present in portions of the subbasin, chiefly the Trout Creek watershed, and repeat sampling has allowed us to index their prevalence. Percentage of catch that is Brook Trout at each of four sample sites in Trout Creek have declined from the period 1998 – 2003 to the period 2011 – 2018. There was a pattern of decline in percent of catch and number of Brook Trout at the Trout Creek sites from 2011 through 2016, though a slight upward trend during 2017 and 2018 has been evident.
Evaluating and planning restoration efforts are of interest to many managers and agencies to ensure efficient use of resources. The evaluation of various life-histories of Steelhead within the Wind River subbasin will provide information to better track populations, and to direct habitat restoration and water allocation planning. Movement of Steelhead parr raises many questions regarding estimating juvenile abundance, origin, and habitat use within watersheds. Improved PTISs and focused PIT tagging of age-0 and age-1 Steelhead parr are increasingly allowing us to investigate such questions. Increasingly detailed Viable Salmonid Population information, such as that provided by PIT-tagging and instream PTISs networks like those in the Wind River subbasin, provide data to inform policy and management, as life-history strategies and production bottlenecks are identified and understood.
","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Jezorek, I.G., 2020, Wind River subbasin restoration: Annual report of US..Geological Survey activities, January 2018 through December 2018, 74 p.","productDescription":"74 p.","ipdsId":"IP-115314","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":373279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373226,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org/Document.mvc/Viewer/P170098"}],"country":"United States","state":"Washington","otherGeospatial":"Wind River subbasin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.34374999999999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 45.69083283645816\n ],\n [\n -120.62988281249999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 46.649436163350245\n ],\n [\n -122.34374999999999,\n 45.69083283645816\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jezorek, Ian G. 0000-0002-3842-3485 ijezorek@usgs.gov","orcid":"https://orcid.org/0000-0002-3842-3485","contributorId":3572,"corporation":false,"usgs":true,"family":"Jezorek","given":"Ian","email":"ijezorek@usgs.gov","middleInitial":"G.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":784716,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70205095,"text":"sir20195080 - 2020 - Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18","interactions":[],"lastModifiedDate":"2022-04-22T21:26:12.93031","indexId":"sir20195080","displayToPublicDate":"2020-03-02T10:35:00","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":"2019-5080","displayTitle":"Assessment of Bridge Scour Countermeasures at Selected Bridges in the United States, 2014–18","title":"Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18","docAbstract":"Erosion of the streambed, known also as scour, around pier 3 of the New York State Thruway bridge over Schoharie Creek caused the pier to fail, which ultimately resulted in bridge failure during the flooding event of April 5, 1987. The Federal Highway Administration (FHWA) responded to the need for better guidance on the evaluation of bridge scour and the selection and installation of scour countermeasures with the release of several Hydraulic Engineering Circulars. Although this information has been available, used, and updated over the years, an evaluation of the current conditions of scour countermeasures has not been performed. Therefore, the U.S. Geological Survey, in cooperation with the FHWA, began a study in 2013 to assess the current conditions of bridge scour countermeasures at selected sites around the country. The bridge scour countermeasure site assessments included reviewing countermeasure design plans, field inspections, traditional surveys, motion-compensated terrestrial light detection and ranging technology (lidar), high-resolution multi-beam bathymetry scanning, underwater video imaging, and a review of the peak and daily streamflow history for the associated river or stream. A total of 34 bridge scour countermeasure sites were selected in 11 states for this study. The types of countermeasures installed at the bridge scour study sites ranged from riprap, the most common countermeasure in the study, to A-Jacks and cabled-concrete mattresses.
The installed countermeasures were generally exposed to hydraulic forces from floods that equaled or exceeded the 1-percent, and even the 0.2-percent, annual exceedance probability at some of the study sites, but not all. The field inspections and countermeasure evaluations identified areas of shifting, slumping, and some scour holes and damage or washouts to the countermeasures, but generally most remained in place. The high-resolution laser scanner data, photo imaging and traditional survey data, and field notes were provided to the FHWA for expert evaluation of the bridge scour countermeasure performance.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195080","collaboration":"Prepared in cooperation with the Federal Highway Administration","usgsCitation":"Suro, T.P., Huizinga, R.J., Fosness, R.L., and Dudunake, T.J., 2020, Assessment of bridge scour countermeasures at selected bridges in the United States, 2014–18: U.S. Geological Survey Scientific Investigations Report 2019–5080, 29 p., https://doi.org/10.3133/sir20195080.","productDescription":"Report: ix, 29 p.; 2 Data Releases","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-108279","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science 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\"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ 08648
One of the objectives of the U.S. Geological Survey National Water-Quality Assessment (NAWQA) Project is to assess groundwater quality in aquifers that are important sources of drinking water such as the coastal lowlands aquifer system, which is often referred to in Texas as the “Gulf Coast aquifer system.” The Gulf Coast aquifer system extends from Louisiana to Mexico and is a source of groundwater for several cities including Houston, Tex. The NAWQA groundwater studies in Texas in 2013–15 that assessed the Gulf Coast aquifer system included Principal Aquifer Surveys (PAS), Major Aquifer Studies (MAS), and Land Use Studies (LUS). These three study types are based on sampling networks of wells distributed in an area of interest. The PAS networks typically consist of public-supply wells that are relatively deep, the MAS networks typically consist of domestic-supply wells that are intermediate in depth, and the LUS networks typically consist of monitoring wells that are relatively shallow.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203010","collaboration":"U.S. Geological Survey National Water-Quality Assessment","usgsCitation":"Ging, P.B., 2020, Water-quality comparison of the Gulf Coast aquifer system at various scales in Texas from National Water-Quality Assessment groundwater studies, 2013–15: U.S. Geological Survey Fact Sheet 2020–3010, 4 p., https://doi.org/10.3133/fs20203010.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-111987","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":399200,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109733.htm"},{"id":372712,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3010/fs20203010.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3010"},{"id":372711,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3010/coverthb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Gulf Coast aquifer system","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.5,\n 25.8378\n ],\n [\n -93.5069,\n 25.8378\n ],\n [\n -93.5069,\n 31.333\n ],\n [\n -98.5,\n 31.333\n ],\n [\n -98.5,\n 25.8378\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Predicting the decadal evolution of barrier island systems is important for coastal managers who propose restoration or preservation alternatives aimed at increasing the resiliency of the island and its associated habitats or communities. Existing numerical models for simulating morphologic changes typically include either long-term (for example, longshore transport under quiescent conditions) or short-term (for example, storm-driven waves) processes, with limited capacity to predict the decadal time-scale that is often most relevant in coastal planning. As part of the Alabama Barrier Island Restoration Assessment, a methodology was developed to predict barrier island evolution on decadal time scales. The developed modeling scheme uses multiple models including (1) Delft3D; (2) the empirical dune growth model (EDGR); and (3) XBeach that run sequentially to simulate evolution of barrier island geomorphology. The model framework was developed and applied to hindcast the evolution of Dauphin Island, Alabama, between 2004 and 2015, and was assessed using lidar data over the same period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191139","usgsCitation":"Mickey, R.C., Long, J.W., Dalyander, P.S., Jenkins, R.L., III, Thompson, D.M., Passeri, D.L., and Plant, N.G., 2019, Development of a modeling framework for predicting decadal barrier island evolution: U.S. Geological Survey Open-File Report 2019–1139, 46 p., https://doi.org/10.3133/ofr20191139.","productDescription":"Report: vi, 46 p.; Data Release","ipdsId":"IP-111247","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399428,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109735.htm"},{"id":372441,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91ALL6C","text":"USGS data release","linkHelpText":"Dauphin Island decadal hindcast model inputs and results"},{"id":372678,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2019/1139/ofr20191139.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1139"},{"id":372308,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20201001","text":"Open-File Report 2020-1001","linkHelpText":"- Application of Decadal Modeling Approach to Forecast Barrier Island Evolution, Dauphin Island, Alabama"},{"id":372306,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2019/1139/coverthb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.37677001953125,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.18905718468536\n ],\n [\n -87.99156188964844,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.34088005484784\n ],\n [\n -88.37677001953125,\n 30.18905718468536\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Forecasting barrier island evolution provides coastal managers and stakeholders the ability to assess the resiliency of these important coastal environments that are home to both established communities and existing natural habitats. This study uses an established coupled model framework to assess how Dauphin Island, Alabama, responds to various storm and sea-level change scenarios, along with a suite of restoration measures, over the course of a decade. The coupled model framework uses validated models for long-term alongshore sediment transport (Delft 3D), short-term storm induced impacts (XBeach), as well as dune building and recovery (empirical dune growth model). This model framework was simulated with the various storm and sea-level change scenarios on a non-restored Dauphin Island, then a subset of the storm and sea-level change scenarios were applied to a suite of seven different restoration measures to determine how they would influence the morphologic evolution over a decadal period. Topographic and bathymetric changes captured in post-simulation digital elevation models were then passed on to partners for various simulations to determine the effects on habitat evolution and water quality as it relates to oyster reef and submerged aquatic vegetation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201001","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, The Water Institute of the Gulf, and the University of North Carolina at Wilmington","usgsCitation":"Mickey, R.C., Godsey, E., Dalyander, P.S., Gonzalez, V., Jenkins, R.L., III, Long, J.W., Thompson, D.M., and Plant, N.G., 2020, Application of decadal modeling approach to forecast barrier island evolution, Dauphin Island, Alabama: U.S. Geological Survey Open-File Report 2020–1001, 45 p., https://doi.org/10.3133/ofr20201001.","productDescription":"Report: viii, 45 p.; Data Release","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113301","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":399440,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109734.htm"},{"id":372467,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ofr/2020/1001/ofr20201001.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1001"},{"id":372442,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PDM1OJ","text":"USGS data release","linkHelpText":"Dauphin Island decadal forecast evolution model inputs and results"},{"id":372303,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ofr20191139","text":"Open-File Report 2019-1139","linkHelpText":"- Development of a Modeling Framework for Predicting Decadal Barrier Island Evolution"},{"id":372301,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ofr/2020/1001/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 -88.3355712890625,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.180747605060766\n ],\n [\n -88.05541992187499,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.288717426233095\n ],\n [\n -88.3355712890625,\n 30.180747605060766\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Shoreline sand harbors high concentrations of fecal indicator bacteria (FIB) that may be resuspended into the water column through washing and resuspension. Studies have explored coastal processes that influence this sand-water flux for FIB, but little is known about how microbial markers of contamination or the bacterial community interact in the sand-water interface. In this study, we take a three-tiered approach to explore the relationship between bacteria in sand, sediment, and overlying water at three shoreline sites and two associated rivers along an extended freshwater shoreline. Samples were collected over two years and analyzed for FIB, two microbial source tracking (MST) markers (Catellicoccus marimammalium, Gull2; Bacteroides HF183), and targeted metagenomic 16S rRNA gene analysis. FIB was much higher in sand than in water at all three sites. Gull2 marker was abundant in shoreline sand and water while HF183 marker was mostly present in rivers. Overall bacterial communities were dissimilar between sand/sediment and water, indicating little interaction. Sediment composition was generally unfavorable to bacterial resuspension. Results show that FIB and MST markers were effective estimates of short-term conditions at these locations, and bacterial communities in sand and sediment reflected longer-term conditions. Findings are useful for locating contamination sources and targeting restoration by evaluating scope of shoreline degradation.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2020.115671","usgsCitation":"Nevers, M., Byappanahalli, M., Nakatsu, C., Kinzelman, J.L., Phanikumar, M.S., Shively, D., and Spoljaric, A., 2020, Interaction of bacterial communities and indicators of water quality in shoreline sand, sediment, and water of Lake Michigan: Water Research, v. 178, 115671, 11 p., https://doi.org/10.1016/j.watres.2020.115671.","productDescription":"115671, 11 p.","ipdsId":"IP-112926","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":457529,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2020.115671","text":"Publisher Index Page"},{"id":437074,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NFKBEB","text":"USGS data release","linkHelpText":"Microbial communities and bacterial indicators for shoreline sand, sediment, and water in Racine, Wisconsin; Chicago, Illinois; and East Chicago, Indiana; 2016-2017"},{"id":377002,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3740234375,\n 41.47566020027821\n ],\n [\n -84.6826171875,\n 41.47566020027821\n ],\n [\n -84.6826171875,\n 46.28622391806706\n ],\n [\n -88.3740234375,\n 46.28622391806706\n ],\n [\n -88.3740234375,\n 41.47566020027821\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"178","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nevers, Meredith B. 0000-0001-6963-6734","orcid":"https://orcid.org/0000-0001-6963-6734","contributorId":201531,"corporation":false,"usgs":true,"family":"Nevers","given":"Meredith B.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794759,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Byappanahalli, Muruleedhara 0000-0001-5376-597X byappan@usgs.gov","orcid":"https://orcid.org/0000-0001-5376-597X","contributorId":147923,"corporation":false,"usgs":true,"family":"Byappanahalli","given":"Muruleedhara","email":"byappan@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794760,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nakatsu, Cindy H.","contributorId":236943,"corporation":false,"usgs":false,"family":"Nakatsu","given":"Cindy H.","affiliations":[{"id":13186,"text":"Purdue University","active":true,"usgs":false}],"preferred":false,"id":794761,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinzelman, Julie L.","contributorId":236944,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","email":"","middleInitial":"L.","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":794762,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Phanikumar, Mantha S.","contributorId":208872,"corporation":false,"usgs":false,"family":"Phanikumar","given":"Mantha","email":"","middleInitial":"S.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":794763,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shively, Dawn 0000-0002-6119-924X dshively@usgs.gov","orcid":"https://orcid.org/0000-0002-6119-924X","contributorId":201533,"corporation":false,"usgs":true,"family":"Shively","given":"Dawn","email":"dshively@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794764,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spoljaric, Ashley 0000-0001-6262-030X","orcid":"https://orcid.org/0000-0001-6262-030X","contributorId":202887,"corporation":false,"usgs":true,"family":"Spoljaric","given":"Ashley","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794765,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70209123,"text":"70209123 - 2020 - Analysis of nearshore placement of sediments at Ogden Dunes, Indiana","interactions":[],"lastModifiedDate":"2020-03-18T07:36:56","indexId":"70209123","displayToPublicDate":"2020-03-02T07:33:27","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"ERDC/CHL TR-20-4","title":"Analysis of nearshore placement of sediments at Ogden Dunes, Indiana","docAbstract":"The harbor structures/shoreline armoring on the southern Lake Michigan shoreline interrupt sand migration. Ogden Dunes, Indiana, and the nearby Indiana Dunes National Lakeshore observed shoreline erosion due to engineered structures associated with Burns Waterway Harbor, east of Ogden Dunes, impeding natural east to west sediment migration. To remedy this, USACE placed over 450,000 cubic meters, or m³, of dredged material post 2006 in the nearshore of Ogden Dunes. However, the effectiveness of nearshore placements for shoreline protection and littoral nourishment is not fully established. To improve nearshore placement effectiveness, USACE monitored the June/July 2016 placement and subsequent movement of 107,000 m³ of dredged material in the nearshore region at Ogden Dunes. This involved an extensive monitoring scheme of three bathymetry surveys, and two acoustic Doppler current profiler deployments, a Coastal Modeling System numerical model of the changes following placement, and a prediction of sediment transport direction using the Sediment Mobility Tool. The SMT predicted sediment migration direction was compared to observations. Observations indicated that between 10/11/2016 and 11/15/2016 the centroid of the sediment above the pre-placement survey moved 17 m onshore. These observations agreed with SMT predictions onshore migration under storm and typical wave conditions. CMS accurately reproduced the hydrodynamic features.","language":"English","publisher":"U.S. Coastal and Hydraulics Laboratory, U.S. Engineer Research and Development Center ","doi":"10.21079/11681/35853","collaboration":"USACE ERDC-CHL\nUSACE Chicago District","usgsCitation":"Young, D.L., Brutsche, K.E., Li, H., McFall, B.C., Maloney, E., McClain, K.E., Bucaro, D.F., LeRoy, J.Z., Duncker, J.J., Johnson, K.K., and Jackson, P.R., 2020, Analysis of nearshore placement of sediments at Ogden Dunes, Indiana, ix, 85 p., https://doi.org/10.21079/11681/35853.","productDescription":"ix, 85 p.","ipdsId":"IP-104560","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":457533,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.21079/11681/35853","text":"Publisher Index Page"},{"id":373334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","otherGeospatial":"Ogden Dunes","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.28363037109374,\n 41.63494664852403\n ],\n [\n -87.26577758789062,\n 41.59182393372352\n ],\n [\n -87.22457885742188,\n 41.6010669423553\n ],\n [\n -87.11746215820312,\n 41.58771550500517\n ],\n [\n -86.98699951171874,\n 41.612362155265984\n ],\n [\n -86.86614990234375,\n 41.68316883525891\n ],\n [\n -86.88125610351562,\n 41.72623044860004\n ],\n [\n -86.90048217773438,\n 41.7508241355329\n ],\n [\n -86.9677734375,\n 41.71905551584262\n ],\n [\n -87.28363037109374,\n 41.63494664852403\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Young, David L","contributorId":223414,"corporation":false,"usgs":false,"family":"Young","given":"David","email":"","middleInitial":"L","affiliations":[{"id":18947,"text":"USACE ERDC","active":true,"usgs":false}],"preferred":false,"id":784998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brutsche, Katherine E","contributorId":223415,"corporation":false,"usgs":false,"family":"Brutsche","given":"Katherine","email":"","middleInitial":"E","affiliations":[{"id":18947,"text":"USACE ERDC","active":true,"usgs":false}],"preferred":false,"id":784999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Li, Honghai","contributorId":223416,"corporation":false,"usgs":false,"family":"Li","given":"Honghai","email":"","affiliations":[{"id":18947,"text":"USACE ERDC","active":true,"usgs":false}],"preferred":false,"id":785000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McFall, Brian C","contributorId":223417,"corporation":false,"usgs":false,"family":"McFall","given":"Brian","email":"","middleInitial":"C","affiliations":[{"id":18947,"text":"USACE ERDC","active":true,"usgs":false}],"preferred":false,"id":785001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maloney, Erin C","contributorId":223418,"corporation":false,"usgs":false,"family":"Maloney","given":"Erin C","affiliations":[{"id":40713,"text":"USACE Chicago District","active":true,"usgs":false}],"preferred":false,"id":785002,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McClain, Kaitlyn E","contributorId":223419,"corporation":false,"usgs":false,"family":"McClain","given":"Kaitlyn","email":"","middleInitial":"E","affiliations":[{"id":40713,"text":"USACE Chicago District","active":true,"usgs":false}],"preferred":false,"id":785003,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bucaro, David F.","contributorId":223420,"corporation":false,"usgs":false,"family":"Bucaro","given":"David","email":"","middleInitial":"F.","affiliations":[{"id":40713,"text":"USACE Chicago District","active":true,"usgs":false}],"preferred":false,"id":785004,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeRoy, Jessica Z. 0000-0003-4035-6872 jzinger@usgs.gov","orcid":"https://orcid.org/0000-0003-4035-6872","contributorId":174534,"corporation":false,"usgs":true,"family":"LeRoy","given":"Jessica","email":"jzinger@usgs.gov","middleInitial":"Z.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":784997,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785005,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Johnson, Kevin K. 0000-0003-2703-5994 johnsonk@usgs.gov","orcid":"https://orcid.org/0000-0003-2703-5994","contributorId":4220,"corporation":false,"usgs":true,"family":"Johnson","given":"Kevin","email":"johnsonk@usgs.gov","middleInitial":"K.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785006,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Jackson, P. Ryan 0000-0002-3154-6108 pjackson@usgs.gov","orcid":"https://orcid.org/0000-0002-3154-6108","contributorId":194529,"corporation":false,"usgs":true,"family":"Jackson","given":"P.","email":"pjackson@usgs.gov","middleInitial":"Ryan","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":785007,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70217319,"text":"70217319 - 2020 - Testing the interactive effects of flooding and salinity on tidal marsh plant productivity","interactions":[],"lastModifiedDate":"2021-01-27T22:01:59.199443","indexId":"70217319","displayToPublicDate":"2020-03-02T07:15:38","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":861,"text":"Aquatic Botany","active":true,"publicationSubtype":{"id":10}},"title":"Testing the interactive effects of flooding and salinity on tidal marsh plant productivity","docAbstract":"Tidal wetlands support plant communities that facilitate carbon storage, accrete soil, and provide habitat for terrestrial and aquatic species. Climate change is likely to alter estuaries through sea-level rise and changing precipitation patterns, although the ecological responses are uncertain. We were interested in plant responses to physiological stress induced by elevated water salinity and flooding conditions, which may be more prevalent under climate change. . We used a greenhouse experiment and factorial flooding (1, 12, 24, and 48 % time) and salinity (0, 5, 15, 30 PSU) treatments to evaluate the productivity responses of three emergent herbaceous species (Carex lyngbyei, Triglochin maritima, and Argentina pacifica) common to tidal marshes of the Pacific Northwest, USA. We measured weekly changes in plant height and final above and belowground biomass for all species after 10 weeks. Increased salinity reduced final above and belowground biomass significantly in all three species, with A. pacifica responding the most, followed by C. lyngbyei and T. maritima. Increased flooding also reduced total biomass in A. pacifica and T. maritima. There was a significant response in C. lyngbyei aboveground biomass and A. pacifica height to the flooding-salinity interaction. These results indicate emergent plant community composition may change in response to novel climate conditions in estuaries, driven by distinct physiological tolerances to salinity and flooding, and highlight the importance of considering multiple climate drivers when projecting ecosystem change. This may be especially true for estuaries that currently have prolonged freshwater phases like those in the Pacific Northwest.
No abstract available.
","language":"English","publisher":"AWRA","usgsCitation":"Blodgett, D.L., and Read, E., 2020, USGS-Water Resources Mission Area progress toward an internet of water, v. 22, no. 2, p. 11-12.","productDescription":"2 p.","startPage":"11","endPage":"12","ipdsId":"IP-116911","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":373087,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373078,"type":{"id":15,"text":"Index Page"},"url":"https://www.awra.org/Members/Publications/IMPACT.aspx"}],"volume":"22","issue":"2","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Blodgett, David L. 0000-0001-9489-1710 dblodgett@usgs.gov","orcid":"https://orcid.org/0000-0001-9489-1710","contributorId":3868,"corporation":false,"usgs":true,"family":"Blodgett","given":"David","email":"dblodgett@usgs.gov","middleInitial":"L.","affiliations":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":784460,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Read, Emily 0000-0002-9617-9433 eread@usgs.gov","orcid":"https://orcid.org/0000-0002-9617-9433","contributorId":190110,"corporation":false,"usgs":true,"family":"Read","given":"Emily","email":"eread@usgs.gov","affiliations":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true},{"id":5054,"text":"Office of Water Information","active":true,"usgs":true}],"preferred":true,"id":784461,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"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.
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","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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Leetown","active":true,"usgs":true}],"preferred":true,"id":832600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Whitlock, Kaitlin E.","contributorId":273695,"corporation":false,"usgs":false,"family":"Whitlock","given":"Kaitlin","email":"","middleInitial":"E.","affiliations":[{"id":17613,"text":"University of Wisconsin - Stevens Point","active":true,"usgs":false}],"preferred":false,"id":832750,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hansen, Jonathan F.","contributorId":171519,"corporation":false,"usgs":false,"family":"Hansen","given":"Jonathan","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":832602,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220183,"text":"70220183 - 2020 - A primer of fishery studies in Grand Canyon: The nonnative fish removal 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.
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":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":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological 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 - 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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. 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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. 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