The painted turtle (Chrysemys picta) is widely distributed from coast to coast in North America with each of four subspecies generally occupying different regions. In the southwestern USA and northern Mexico, where C. p. bellii is the expected native race, populations are small and widelyscattered. Introduced populations of other painted turtle subspecies are reported from various locations in the USA. We discovered a small but dense introduced population of C. p. marginata on the Colorado Plateau in northern Arizona, a region with few, if any, turtles due to aridity and an elevated topography with little surface water. The turtles were in a remote pond constructed to provide cattle with water. Chrysemys p. marginata occur naturally east of the Mississippi River, over 2,000 km away. The nearest native population of C. p. bellii in Arizona is over 160 km away. We observed nesting females, juveniles, and the presence of shelled eggs in females via Xradiography confirming a self-sustaining population. The body sizes and nesting season we observed were consistent with data for those variables from native populations of the taxon. It is unknown exactly how the turtles came to be established in such a remote location, but it is unlikely that they will spread due to the scarcity of perennial water sources in the semi-arid region. Due to increasing drought frequency and duration in the region, small populations like this one, introduced into a novel environment, may be bellwethers for monitoring the effects of climate change.
","language":"English","publisher":"The Herpetological Society of Japan","doi":"10.5358/hsj.38.91","usgsCitation":"Lovich, J.E., Christman, B.L., Cummings, K.L., Norris, J., Puffer, S., and Jones, C., 2019, An introduced breeding population of Chrysemys picta marginata in the Kaibab National Forest, northern Arizona: Current Herpetology, v. 38, no. 1, p. 91-98, https://doi.org/10.5358/hsj.38.91.","productDescription":"8 p.","startPage":"91","endPage":"98","ipdsId":"IP-101843","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":361868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Kaibab National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.63458251953125,\n 34.951241964789645\n ],\n [\n -111.73095703125,\n 34.951241964789645\n ],\n [\n -111.73095703125,\n 35.655064568953875\n ],\n [\n -112.63458251953125,\n 35.655064568953875\n ],\n [\n -112.63458251953125,\n 34.951241964789645\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lovich, Jeffrey E. 0000-0002-7789-2831 jeffrey_lovich@usgs.gov","orcid":"https://orcid.org/0000-0002-7789-2831","contributorId":458,"corporation":false,"usgs":true,"family":"Lovich","given":"Jeffrey","email":"jeffrey_lovich@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":759042,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Christman, Bruce L.","contributorId":207392,"corporation":false,"usgs":false,"family":"Christman","given":"Bruce","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":759043,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cummings, Kristy L. 0000-0002-8316-5059","orcid":"https://orcid.org/0000-0002-8316-5059","contributorId":202061,"corporation":false,"usgs":true,"family":"Cummings","given":"Kristy","email":"","middleInitial":"L.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":759044,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Norris, Jenna 0000-0003-1312-4478","orcid":"https://orcid.org/0000-0003-1312-4478","contributorId":214059,"corporation":false,"usgs":false,"family":"Norris","given":"Jenna","email":"","affiliations":[{"id":38973,"text":"Formerly USGS SBSC Flagstaff, AZ now at NAU","active":true,"usgs":false}],"preferred":false,"id":759045,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Puffer, Shellie R. 0000-0003-4957-0963","orcid":"https://orcid.org/0000-0003-4957-0963","contributorId":193099,"corporation":false,"usgs":true,"family":"Puffer","given":"Shellie R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":759046,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jones, Christina","contributorId":214060,"corporation":false,"usgs":false,"family":"Jones","given":"Christina","affiliations":[{"id":38974,"text":"Arizona Game and Fish Department, Terrestrial Wildlife Branch, 5000 W. Carefree Highway, Phoenix, AZ 85086-5000","active":true,"usgs":false}],"preferred":false,"id":759047,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202005,"text":"sir20185169 - 2019 - Flood-inundation maps for Lake Champlain in Vermont and New York","interactions":[{"subject":{"id":70170965,"text":"sir20165060 - 2016 - Flood-inundation maps for Lake Champlain in Vermont and in northern Clinton County, New York","indexId":"sir20165060","publicationYear":"2016","noYear":false,"title":"Flood-inundation maps for Lake Champlain in Vermont and in northern Clinton County, New York"},"predicate":"SUPERSEDED_BY","object":{"id":70202005,"text":"sir20185169 - 2019 - Flood-inundation maps for Lake Champlain in Vermont and New York","indexId":"sir20185169","publicationYear":"2019","noYear":false,"title":"Flood-inundation maps for Lake Champlain in Vermont and New York"},"id":1}],"lastModifiedDate":"2019-03-11T13:07:35","indexId":"sir20185169","displayToPublicDate":"2019-03-07T16:15:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5169","displayTitle":"Flood-Inundation Maps for Lake Champlain in Vermont and New York","title":"Flood-inundation maps for Lake Champlain in Vermont and New York","docAbstract":"In 2016, digital flood-inundation maps along the shoreline of Lake Champlain in Addison, Chittenden, Franklin, and Grand Isle Counties in Vermont and northern Clinton County in New York were created by the U.S. Geological Survey (USGS) in cooperation with the International Joint Commission (IJC). This report discusses the creation of updated static digital flood-inundation mapping, in 2018, to include the entire shoreline of Lake Champlain in the United States. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science website at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent of flooding corresponding to selected water-surface elevations (stages) at the USGS lake gages on Lake Champlain.
As a result of the record setting floods of May 2011 in Lake Champlain and the Richelieu River, the U.S. and Canadian governments requested that the IJC issue a reference for a study to identify how flood forecasting, preparedness, and mitigation could be improved in the Lake Champlain–Richelieu River Basin. The IJC submitted the Lake Champlain–Richelieu River Plan of Study to the governments of Canada and the United States in 2013. The flood-inundation maps in this study are one aspect of the task work outlined in the IJC 2013 Plan of Study.
Wind and seiche effects (standing oscillating wave with a long wavelength) that can influence flooding along the Lake Champlain shoreline were not represented. The flood-inundation maps reflect 11 stages for Lake Champlain that are static for the entire area of the lake. Near-real-time stages at the USGS gages on Lake Champlain may be obtained from the USGS National Water Information System website at http://waterdata.usgs.gov/ (https://doi.org/10.5066/F7P55KJN) or from the National Weather Service Advanced Hydrologic Prediction Service at http://water.weather.gov/ahps/.
Updated static flood-inundation boundary extents were created for Lake Champlain in Franklin, Chittenden, Addison, Rutland, and Grand Isle Counties in Vermont and Clinton, Essex, and Washington Counties in New York by using recently acquired (2009, 2012, 2014, and 2015) light detection and ranging (lidar) data. The corresponding flood-inundation maps may be referenced to any of the four active USGS lake gages on Lake Champlain. Of these four active lake gages, USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y.; USGS lake gage 04294500, Lake Champlain at Burlington, Vt.; USGS lake gage 04279085 Lake Champlain north of Whitehall, N.Y.; and USGS lake gage 04294413, Lake Champlain at Port Henry, N.Y., only the Richelieu River (Lake Champlain) at Rouses Point, N.Y., gage also serves as a National Weather Service prediction location. Lake Champlain static flood-inundation map boundary extents corresponding to the May 2011 peak flood stage (103.20 feet [ft], National Geodetic Vertical Datum of 1929 [NGVD 29], as recorded at the USGS Rouses Point lake gage, were compared to the flood-inundation area extents determined from satellite imagery for the May 2011 flood (which incorporated documented high-water marks from the flood of May 2011) and were found to be in good agreement. The May 2011 flood is the highest recorded lake water level (stage) at the Rouses Point, N.Y., and Burlington, Vt., lake gages. Flood stages greater than 101.5 ft (NGVD 29) exceed the “major flood stage” as defined by the National Weather Service for USGS lake gage 04295000.
Updated digital elevation models (DEMs) were created from the recent lidar data for Lake Champlain in Vermont and New York. These DEMs were used in determining the flood-inundation boundary and associated depth grids for 11 flood stages at 0.5-ft or 1-ft intervals from 100.0 to 106.0 ft (NGVD 29) as referenced to the USGS lake gages. In addition, the May 2011 flood-inundation area for elevation 103.20 ft (NGVD 29) (102.77 ft, North American Vertical Datum of 1988) was determined from these updated DEMs.
The availability of these maps, along with online information regarding current stages at the USGS lake gages and forecasted high-flow stages from the National Weather Service at USGS lake gage 04295000, Richelieu River (Lake Champlain) at Rouses Point, N.Y., will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for post-flood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185169","collaboration":"Prepared in cooperation with the International Joint Commission","usgsCitation":"Flynn, R.H., and Hayes, L., 2019, Flood-inundation maps for Lake Champlain in Vermont and New York: U.S. Geological Survey Scientific Investigations Report 2018–5169, 14 p., https://doi.org/10.3133/sir20185169. [Supersedes USGS Scientific Investigations Report 2016–5060.]","productDescription":"Report: v, 14 p.; Application Site; Data release","numberOfPages":"24","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-101452","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":437545,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S","text":"USGS data release","linkHelpText":"Flood-Inundation Shapefiles and Grids for Lake Champlain in Vermont and New York"},{"id":361774,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://wimcloud.usgs.gov/apps/FIM/FloodInundationMapper.html ","linkHelpText":"- Flood Inundation Mapper"},{"id":361771,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5169/coverthb.jpg"},{"id":361772,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5169/sir20185169.pdf","text":"Report","size":"1.30 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5169"},{"id":361773,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZBDF6S ","text":"USGS data release","description":"USGS data release","linkHelpText":"Flood-inundation shapefiles and grids for Lake Champlain in Vermont and New York"}],"country":"United States","state":"New York, Vermont","county":"Addison, Chittenden, Clinton, Franklin, Grand Isle ","otherGeospatial":"Lake Champlain","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -73.7081,43.5785 ], [ -73.7081,45.0891 ], [ -72.8948,45.0891 ], [ -72.8948,43.5785 ], [ -73.7081,43.5785 ] ] ] } } ] }","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
A decline in submerged aquatic vegetation (SAV) within Florida’s spring-fed thermal refuges raises questions about how these systems support winter foraging of Florida manatees Trichechus manatus latirostris. We analyzed telemetry data for 12 manatees over 7 yr to assess their use of Kings Bay, a winter refuge with diminished SAV. After accounting for the effect of water temperature, we hypothesized that the number of trips out of Kings Bay would increase and the time wintering manatees spent in Kings Bay would decrease. Trips out of and into Kings Bay were also compared to assess potential influences on exiting or entering. There were no detectable differences in the number of trips out of the bay or overall time manatees spent in Kings Bay across winters. The percentage of time water temperatures were below 20°C was the single best predictor of increased time spent in Kings Bay. Trips out of Kings Bay were more likely than trips into the bay to occur after 12:00 h and during a high but ebbing tide. Nine manatees tracked for longer than 75 d in winter spent 7 to 57% of their time in the Gulf of Mexico, and 3 of these manatees spent 7 to 65% of the winter >80 km from the mouth of Kings Bay. Results suggest the low amount of SAV in Kings Bay does not obviate its use by manatees, though there are likely tradeoffs for manatees regularly foraging elsewhere. Accounting for movements of Florida manatees through a network of habitats may improve management strategies and facilitate desirable conservation outcomes.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr00933","usgsCitation":"Littles, C.J., Bonde, R.K., Butler, S.M., Jacoby, C.A., Notestein, S.K., Reid, J.P., Slone, D.H., and Frazer, T.K., 2019, Coastal habitat change and marine megafauna behavior: Florida manatees encountering reduced food provisions in a prominent winter refuge: Endangered Species Research, v. 38, p. 29-43, https://doi.org/10.3354/esr00933.","productDescription":"15 p.","startPage":"29","endPage":"43","ipdsId":"IP-088011","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":467834,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr00933","text":"Publisher Index Page"},{"id":361825,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.80258178710938,\n 28.5941685062326\n ],\n [\n -82.56912231445312,\n 28.5941685062326\n ],\n [\n -82.56912231445312,\n 29.039361975917828\n ],\n [\n -82.80258178710938,\n 29.039361975917828\n ],\n [\n -82.80258178710938,\n 28.5941685062326\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Littles, Chanda J.","contributorId":214014,"corporation":false,"usgs":false,"family":"Littles","given":"Chanda","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":758925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bonde, Robert K. 0000-0001-9179-4376 rbonde@usgs.gov","orcid":"https://orcid.org/0000-0001-9179-4376","contributorId":2675,"corporation":false,"usgs":true,"family":"Bonde","given":"Robert","email":"rbonde@usgs.gov","middleInitial":"K.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":758924,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butler, Susan M. 0000-0003-3676-9332 sbutler@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-9332","contributorId":195796,"corporation":false,"usgs":true,"family":"Butler","given":"Susan","email":"sbutler@usgs.gov","middleInitial":"M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":758926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacoby, Charles A.","contributorId":214015,"corporation":false,"usgs":false,"family":"Jacoby","given":"Charles","email":"","middleInitial":"A.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":758927,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Notestein, Sky K.","contributorId":214017,"corporation":false,"usgs":false,"family":"Notestein","given":"Sky","email":"","middleInitial":"K.","affiliations":[{"id":35620,"text":"Southwest Florida Water Management District","active":true,"usgs":false}],"preferred":false,"id":758931,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reid, James P. 0000-0002-8497-1132 jreid@usgs.gov","orcid":"https://orcid.org/0000-0002-8497-1132","contributorId":3460,"corporation":false,"usgs":true,"family":"Reid","given":"James","email":"jreid@usgs.gov","middleInitial":"P.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":758928,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Slone, Daniel H. 0000-0002-9903-9727 dslone@usgs.gov","orcid":"https://orcid.org/0000-0002-9903-9727","contributorId":205617,"corporation":false,"usgs":true,"family":"Slone","given":"Daniel","email":"dslone@usgs.gov","middleInitial":"H.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":758929,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Frazer, Thomas K.","contributorId":214016,"corporation":false,"usgs":false,"family":"Frazer","given":"Thomas","email":"","middleInitial":"K.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":758930,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70200357,"text":"sir20185141 - 2019 - Spatial distribution of nutrients, chloride, and suspended sediment concentrations and loads determined by using different sampling methods in a cross section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015","interactions":[],"lastModifiedDate":"2019-03-08T10:17:20","indexId":"sir20185141","displayToPublicDate":"2019-03-07T10:00:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5141","displayTitle":"Spatial Distribution of Nutrients, Chloride, and Suspended Sediment Concentrations and Loads Determined by Using Different Sampling Methods in a Cross Section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015","title":"Spatial distribution of nutrients, chloride, and suspended sediment concentrations and loads determined by using different sampling methods in a cross section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015","docAbstract":"The Detroit River separates the United States and Canada as it flows from Lake St. Clair to Lake Erie. The Trenton Channel is a 13-kilometer-long branch of the Detroit River that flows to the west of Grosse Ile before rejoining the Detroit River near its mouth, just before the Detroit River flows into Lake Erie. The U.S. Environmental Protection Agency has listed both the Trenton Channel and Detroit River as Areas of Concern because of a list of Beneficial Use Impairments such as interrupted drinking-water services, loss of aquatic life, and reduced recreational use. Phosphorus loading from tributaries such as the Trenton Channel is one of the primary drivers of eutrophication in Lake Erie. The complex flow patterns and variable distribution of chemical constituents in the Trenton Channel make it difficult to accurately characterize the concentrations and loads of nutrients and other constituents conveyed through the channel to Lake Erie.
In order to better understand the Trenton Channel’s contributions of nutrients (total phosphorus, orthophosphate, total nitrogen, and ammonia), chloride, and suspended sediment to Lake Erie and evaluate differences in results obtained by using different sample methodologies, the U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and Environment Canada, completed 12 sampling campaigns on the Trenton Channel in Detroit, Michigan, from November 2014 through November 2015.
Acoustic Doppler current profiler (ADCP) techniques were used to characterize the distribution of velocity components within a cross section corresponding to a transect of the Trenton Channel at U.S. Geological Survey station 041686401 Trenton Channel of Detroit River at Grosse Ile, Mich. Three methods of collecting water-quality data at the same transect of the Trenton Channel were used: multiple-vertical depth-integrated (MVDI), fixed-point, and discrete samples. Horizontal and vertical variations in concentrations of nutrients, chloride, and suspended sediment were analyzed from discrete samples to better understand distributions of these constituents throughout the channel. Constituent loads were calculated by using individual sample concentrations and ADCP measurements for discharge made on the same day that the water-quality samples were collected. Constituent loads calculated from MVDI and fixed-point sampling methods were compared. The relation between MVDI and fixed-point samples helped quantify the differences between the sampling methods. Linear regression equations depicting the relation between concentrations measured by using MVDI and fixed-point samples were prepared.
ADCP data indicates that velocities throughout the sampled transect remain uniform except for one location around 200 meters from the west bank of the channel. Secondary flow vectors suggest the presence of counter-rotating helical flow cells, and these helical flow cells could affect the mixing of constituents in transport by preventing cross-channel mixing. Flow discharges throughout the sampling campaign showed small variations, although lower flow rates were observed in the early winter months than in the summer months. Discrete sampling methods results displayed both heterogeneity throughout the channel horizontally, representing limited horizontal mixing in the channel, and displayed homogeneity throughout vertical transects, indicating mixing vertically. Comparisons between MVDI and fixed-point methods found consistently higher concentrations were measured in MVDI samples compared to concentrations measured in fixed-point samples. To correct for this bias between MVDI and fixed-point sample results, simple linear-regression equations were developed for all major constituents to help estimate constituent concentrations from fixed-point samples equivalent to those measured by using MVDI sampling techniques. Instantaneous constituent loads were developed by using velocity and discharge data obtained from ADCPs and constituent concentrations obtained from MVDI and fixed-point samples.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185141","collaboration":"Prepared in cooperation with the United States Environmental Protection Agency and Environment and Climate Change Canada","usgsCitation":"Totten, A.R., and Duris, J.W., 2019, Spatial distribution of nutrients, chloride, and suspended sediment concentrations and loads determined by using different sampling methods in a cross section of the Trenton Channel of the Detroit River, Michigan, November 2014–November 2015: U.S. Geological Survey Scientific Investigations Report 2018–5141, 25 p., https://doi.org/10.3133/sir20185141.","productDescription":"viii, 25 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091065","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361790,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5141/sir20185141.pdf","text":"Report","size":"4.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5141"},{"id":361789,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5141/coverthb.jpg"}],"country":"Canada, United States","state":"Michigan","otherGeospatial":"Detroit River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.26263427734375,\n 41.97174336327968\n ],\n [\n -82.78884887695312,\n 41.97174336327968\n ],\n [\n -82.78884887695312,\n 42.40622065620649\n ],\n [\n -83.26263427734375,\n 42.40622065620649\n ],\n [\n -83.26263427734375,\n 41.97174336327968\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way, Suite 5
Lansing, MI 48911
The U.S. Geological Survey, in cooperation with the Kansas Department of Health and Environment (KDHE), completed a study to quantify the spatial and temporal variability of cyanobacterial blooms in Milford Lake, Kansas, over a range of environmental conditions at various time scales (hours to months). A better understanding of the spatial and temporal variability of cyanobacteria and microcystin will inform sampling and management strategies for Milford Lake and for other lakes with cyanobacterial harmful algal bloom (CyanoHAB) issues throughout the Nation. Spatial and temporal variability were assessed in the upstream one-third of Milford Lake (designated as “Zone C” by KDHE) during May through November 2016 using a combination of time-lapse photography, continuous water-quality monitors, discrete phytoplankton, chlorophyll, and microcystin samples, and spatially dense near-surface data. Combined, these data were used to characterize variability of cyanobacterial abundance, algal biomass, and microcystin concentrations in Zone C of Milford Lake before, during, and after cyanobacterial blooms in 2016.
Temporal patterns were evaluated during May through November 2016 using time-lapse photography at six locations in Zone C and at a single point location (the Wakefield site) using a combination of discrete and continuously measured water-quality data (including the cyanobacterial pigment phycocyanin). Based on time-lapse photography, CyanoHABs developed in Zone C of Milford Lake in early July and persisted through the end of November. Bloom accumulations at individual sites were dependent on wind direction. After a change in wind direction, it would take about 1 day for accumulations to become visible at different locations. During periods with low wind, accumulations were widespread and visible at all sites. Cyanobacteria were absent from the algal community at the Wakefield site in late May and were a minor component of the community in June; however, by mid-July the cyanobacteria were dominant and remained dominant until early November.
Chlorophyll and microcystin concentrations at the Wakefield site were estimated using sensor-measured phycocyanin based on regression models developed for Zone C. Regression-estimated concentrations likely are more indicative of seasonal patterns in algal biomass (as indicated by chlorophyll concentrations) and microcystin than discretely collected samples because regression-estimated data have a much higher temporal resolution. Based on regression estimates, algal biomass and microcystin concentrations at the Wakefield site steadily increased from May through August. After August, concentrations decreased but remained relatively high compared to May and June. Daily chlorophyll maxima were as much as 400 times higher than daily minima, and daily microcystin maxima were as many as several orders of magnitude higher than daily minima. The extreme variability in algal biomass and microcystin concentrations at the Wakefield site reflects the development and dissipation of blooms, as indicated by the time-lapse cameras.
Based on regression-estimated microcystin concentrations, the KDHE watch and warning thresholds for microcystin were exceeded during mid-June through late November. Exceedance of KDHE advisory thresholds often changed from no advisory to watch or warning over the course of the day because of the variability in algal biomass and microcystin concentrations caused by bloom development and dissipation. Continuous water-quality monitors may be useful in informing public-health decisions in lakes with variable CyanoHAB conditions; however, site-specific models need to be developed, and best practices for using continuous water-quality monitors to inform CyanoHAB management strategies need to be established.
Spatial data were collected on May 26, July 21, and September 15, 2016, using a combination of a boat-mounted array and discrete water-quality samples analyzed for phytoplankton community composition and chlorophyll and microcystin concentrations. Spatial patterns were described using regression-estimated chlorophyll and microcystin concentrations. During the May 26, 2016, spatial surveys, cyanobacterial abundances were relatively low throughout Zone C and did not exceed KDHE guidance values compared to spatial surveys on July 21 and September 15. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass uplake in Zone C, and decreases in the downlake direction towards Zone B. Regression-estimated chlorophyll concentrations also were more variable uplake than downlake. Based on regression estimates, microcystin concentrations did not exceed KDHE guidance values anywhere in Zone C on May 26. Spatial patterns in microcystin throughout Zone C did not match patterns in regression-estimated chlorophyll concentrations, likely because the algal community was not dominated by cyanobacteria at most locations in May.
During the July 21, 2016, spatial surveys, cyanobacterial abundances in Zone C exceeded KDHE guidance values in 50 percent of samples. The algal community in Zone C was dominated by cyanobacteria at all locations except two, where cyanobacteria codominated with diatoms. Both locations where cyanobacteria and diatoms codominated were north of the causeway. Regression-estimated chlorophyll concentrations were indicative of higher algal biomass north of the causeway and on the eastern shore of Zone C. On July 21, algal biomass did not always decrease in the downlake direction. There was a decrease just south of the causeway but an increase shortly after with higher concentrations into Zone B. Spatial maps indicated changes in algal distribution at a 0.5-meter depth, with algae moving to the central part of the lake north of the causeway and along the eastern shore south of the causeway. Most regression-estimated microcystin concentrations on July 21 exceeded KDHE guidance values, reflecting the pervasive bloom conditions in Zone C during this period. Spatial patterns in regression-estimated microcystin concentrations throughout Zone C were similar to patterns seen in discrete samples and regression-estimated chlorophyll concentrations, with higher concentrations north of the causeway and on the east shore of Zone C.
During the September 15, 2016, spatial surveys, cyanobacterial abundances did not exceed KDHE guidance values. The algal community north of the causeway was dominated by diatoms. The algal community throughout the rest of Zone C was dominated by cyanobacteria. Of regression-estimated microcystin concentrations on September 15, 80 percent did not exceed KDHE guidance values. Spatial patterns indicated northward movement of the cyanobacterial bloom consistent with a wind shift noted the previous day. On September 14, winds were generally from the north to northwest, shifting to the south by September 15. There was a northward progression of chlorophyll and microcystin during the spatial surveys. These data, along with the camera data and spatial and wind data from May and July, indicate that wind can be a major driver of the spatial and temporal variability of cyanobacterial blooms in Milford Lake and likely plays a role in the extent and duration of near-shore accumulations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185166","collaboration":"Prepared in cooperation with the Kansas Department of Health and Environment","usgsCitation":"Foster, G.M., Graham, J.L., and King, L.R., 2019, Spatial and temporal variability of harmful algal blooms in Milford Lake, Kansas, May through November 2016: U.S. Geological Survey Scientific Investigations Report 2018–5166, 36 p., https://doi.org/10.3133/sir20185166.","productDescription":"Report: vi, 36 p.; Appendixes: 28 p.; Data Releases: 4","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-093516","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":361764,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78S4P4M","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water-quality data from two sites on Milford Lake, Kansas, May 25–26, June 8–10, July 20–21, and September 14–15, 2016"},{"id":361765,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7JH3KCV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Time-lapse photography of Milford Lake, Kansas, June through November 2016"},{"id":361760,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5166/coverthb.jpg"},{"id":361763,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DJ5DVX","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Milford Lake, Kansas spatial water-quality data, May 26, June 9, July 14, July 21, and September 15, 2016"},{"id":361761,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166.PDF","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166"},{"id":361762,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5166/sir20185166_appendixes.pdf","text":"Appendix 1 and 2","size":"571 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5166 Appendixes 1 and 2"},{"id":361766,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7513XFN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Phytoplankton data for Milford Lake, Kansas, May through November 2016"}],"country":"United States","state":"Kansas","otherGeospatial":"Milford Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.1630859375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 39.38526381099774\n ],\n [\n -96.49017333984375,\n 38.982897808179985\n ],\n [\n -97.1630859375,\n 38.982897808179985\n ]\n ]\n ]\n }\n }\n ]\n}\n\n\n\n","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
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The chaparral-dominated national forests of southern California were in part established to provide water provision services to the surrounding urban populations and irrigation for agriculture. However, water provision in the form of groundwater recharge and surface runoff depends on the climatological conditions of any given year and also landscape-scale disturbances such as fire. Fire is increasing in frequency in southern California and understanding its impacts both immediately postfire and as vegetation recovers, and the interactions between fire and hydrology, are key components to managing federal lands effectively. In this study we focus on nine fires in a study area that encompasses the four southern California national forests (Los Padres, Angeles, San Bernardino, and Cleveland) and use a water balance model to investigate the effects of water provision services post-fire at a regional scale. We found that runoff and recharge increased post-fire, with increases in recharge being greater with recovery times ranging from 2 to 4 y post-fire. Vegetation recovery occurred 2 y post-fire for all basins as indicated by remotely sensed imagery measuring vegetation greenness having returned to or exceeded pre-fire values for the basin. We found that runoff and recharge were more sensitive to the effects of climate than to length of time post-fire. Findings from these modeling tools allow users to anticipate the impact of fire on water provision services in the region and develop management strategies that help reduce the impacts of wildfire.
In the U.S., about 44 million people rely on self-supplied groundwater for drinking water. Because most self-supplied homeowners do not treat their water to control corrosion, drinking water can be susceptible to lead (Pb) contamination from metal plumbing. To assess the types and locations of susceptible groundwater, a geochemical reaction model that included pure Pb minerals and solid solutions of calcite (CaxPb1–xCO3) and apatite [CaxPb5-x(PO4)3(OH; Cl; F)] was developed to estimate the lead solubility potential (LSP) for over 8300 untreated groundwater samples collected from domestic and public-supply sites between 2000 and 2016 in the U.S. The LSP is the calculated amount of Pb metal that could dissolve at 25 °C before a Pb-bearing mineral precipitates. About 33% of untreated groundwater samples had LSP greater than 15 μg/L—the USEPA action level for dissolved plus particulate forms of Pb. Five percent of samples had high LSP (above 300 μg/L) and tended to occur in the eastern and southeastern U.S. Measured Pb concentrations above 15 μg/L were rarely detected (<1%) but always coincided with high LSP values. Future work will provide a better understanding of the relation between water chemistry, Pb-mineral formation, and dissolved Pb concentrations in tap water.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.8b04475","usgsCitation":"Jurgens, B., Parkhurst, D.L., and Belitz, K., 2019, Assessing the lead solubility potential of untreated groundwater of the United States: Environmental Science & Technology, v. 53, no. 6, p. 3095-3103, https://doi.org/10.1021/acs.est.8b04475.","productDescription":"Article: 9 p.; Data Release ","startPage":"3095","endPage":"3103","ipdsId":"IP-083634","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":467842,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.8b04475","text":"Publisher Index 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]\n}","volume":"53","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jurgens, Bryant 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770836,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parkhurst, David L. 0000-0003-3348-1544 dlpark@usgs.gov","orcid":"https://orcid.org/0000-0003-3348-1544","contributorId":1088,"corporation":false,"usgs":true,"family":"Parkhurst","given":"David","email":"dlpark@usgs.gov","middleInitial":"L.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":770837,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 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differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions","docAbstract":"Peat cores are valuable archives of past environmental change because they accumulate plant organic matter over millennia. While studies have primarily focused on physical, ecological, and some biogeochemical proxies, cores from peatlands have increasingly been used to interpret hydroclimatic change using stable isotope analyses of cellulose preserved in plant remains. Previous studies indicate that the stable oxygen isotope compositions (δ18O) preserved in alpha cellulose extracted from specific plant macrofossils reflect the δ18O values of past peatland water and thereby provide information on long-term changes in hydrology in response to climate. Oxygen isotope analyses of peat cellulose (δ18Ocellulose) have been successfully developed from peat cores that accumulate the same species for millennia. However, to fully exploit the potential of this proxy in species-diverse fens, studies are needed that account for the isotopic variations caused by changes in dominant species composition. This study assesses variation in δ18O values among peatland plant species and how they relate to environmental waters in two fens informally named Horse Trail and Goldfin, located on the leeward (dry) and windward (wet) side, respectively, of the climatic gradient across the Kenai Peninsula, Alaska. Environmental water δ18O values at both fens reflect unmodified δ18O values of mean annual precipitation, although at Goldfin standing pools were slightly influenced by evaporation. Modern plant [mosses and Carex spp. (sedges)] δ18Ocellulose values indicate that all Carex spp. are higher (~2.5‰) than those of mosses, likely driven by their vascular structure and ecophysiological difference from non-vascular mosses. Moss δ18Ocellulose values within each peatland are similar among the species, and differences appear related to evaporation effects on environmental waters within hummocks and hollows. The plant taxa-environmental water δ18O differences are applied to the previously determined Horse Trail Fen untreated bulk δ18O record. Results include significant changes to inferred millennial-to-centennial scale hydroclimatic trends where dominant taxa shift from moss to Carex spp., indicating that modern calibration datasets are necessary for interpreting stable isotopes from fens, containing a mix of vascular and nonvascular plants. Accounting for isotopic offsets through macrofossil analysis and modern plant-water isotope measurements opens new opportunities for hydroclimatic reconstructions from fen peatlands.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2019.00025","usgsCitation":"Jones, M., Anderson, L., Keller, K., Nash, B., Littell, V., Wooller, M.J., and Jolley, C., 2019, An assessment of plant species differences on cellulose oxygen isotopes from two Kenai Peninsula, Alaska peatlands: Implications for hydroclimatic reconstructions: Frontiers in Earth Science, v. 7, 25, 16 p., https://doi.org/10.3389/feart.2019.00025.","productDescription":"25, 16 p.","ipdsId":"IP-102651","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":467843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2019.00025","text":"Publisher Index Page"},{"id":368887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arc Lake, Bear Lake, Bear Mountain Lake, Browse Lake, Headquarters Lake, Horse Trail clearing, Kenai Lake, Lower Ohmer Lake, Portage Lake, Skilak Lake, Summit Lake, Tern Lake, Upper Ohmer Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -151.578369140625,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 59.9274956808828\n ],\n [\n -149.04052734375,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 60.919754532399686\n ],\n [\n -151.578369140625,\n 59.9274956808828\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -173.485107421875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.10319489936693\n ],\n [\n -171.826171875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.925093815014655\n ],\n [\n -173.485107421875,\n 60.10319489936693\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Jones, Miriam 0000-0002-6650-7619","orcid":"https://orcid.org/0000-0002-6650-7619","contributorId":201994,"corporation":false,"usgs":true,"family":"Jones","given":"Miriam","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":false,"id":774422,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Lesleigh 0000-0002-5264-089X land@usgs.gov","orcid":"https://orcid.org/0000-0002-5264-089X","contributorId":436,"corporation":false,"usgs":true,"family":"Anderson","given":"Lesleigh","email":"land@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":774423,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keller, Katherine 0000-0001-6915-5455","orcid":"https://orcid.org/0000-0001-6915-5455","contributorId":218048,"corporation":false,"usgs":false,"family":"Keller","given":"Katherine","email":"","affiliations":[{"id":39732,"text":"Natural Systems Analysts, Harvard University","active":true,"usgs":false}],"preferred":false,"id":774424,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nash, Bailey 0000-0001-6423-2773 bnash@usgs.gov","orcid":"https://orcid.org/0000-0001-6423-2773","contributorId":220192,"corporation":false,"usgs":true,"family":"Nash","given":"Bailey","email":"bnash@usgs.gov","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":40146,"text":"Iowa State University, Ames, IA","active":true,"usgs":false}],"preferred":true,"id":774425,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Littell, Virginia","contributorId":220193,"corporation":false,"usgs":false,"family":"Littell","given":"Virginia","email":"","affiliations":[{"id":40147,"text":"University of Washington, Seattle, WA","active":true,"usgs":false}],"preferred":false,"id":774426,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wooller, Matthew J.","contributorId":192799,"corporation":false,"usgs":false,"family":"Wooller","given":"Matthew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":774427,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jolley, Chelsea","contributorId":220194,"corporation":false,"usgs":false,"family":"Jolley","given":"Chelsea","email":"","affiliations":[{"id":26916,"text":"Brigham Young University, Provo, UT","active":true,"usgs":false}],"preferred":false,"id":774428,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70205300,"text":"70205300 - 2019 - Hormones and pharmaceuticals in groundwater used as a source of drinking water across the United States","interactions":[],"lastModifiedDate":"2019-09-13T15:11:37","indexId":"70205300","displayToPublicDate":"2019-03-05T10:44:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Hormones and pharmaceuticals in groundwater used as a source of drinking water across the United States","docAbstract":"This is the first large-scale, systematic assessment of hormone and pharmaceutical occurrence in groundwater used for drinking across the United States. Samples from 1091 sites in Principal Aquifers representing 60% of the volume pumped for drinking-water supply had final data for 21 hormones and 103 pharmaceuticals. At least one compound was detected at 5.9% of 844 sites representing the resource used for public supply across the entirety of 15 Principal Aquifers, and at 11.3% of 247 sites representing the resource used for domestic supply over subareas of nine Principal Aquifers. Of 34 compounds detected, one plastics component (bisphenol A), three pharmaceuticals (carbamazepine, sulfamethoxazole, and meprobamate), and the caffeine degradate 1,7-dimethylxanthine were detected in more than 0.5% of samples. Hydrocortisone had a concentration greater than a human-health benchmark at 1 site. Compounds with high solubility and low Koc were most likely to be detected. Detections were most common in shallow wells with a component of recent recharge, particularly in crystalline-rock and mixed land-use settings. Results indicate vulnerability of groundwater used for drinking water in the U.S. to contamination by these compounds is generally limited, and exposure to these compounds at detected concentrations is unlikely to have adverse effects on human health.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.8b05592","usgsCitation":"Bexfield, L.M., Toccalino, P., Belitz, K., Foreman, W.T., and Furlong, E., 2019, Hormones and pharmaceuticals in groundwater used as a source of drinking water across the United States: Environmental Science & Technology, v. 53, no. 6, p. 2950-2960, https://doi.org/10.1021/acs.est.8b05592.","productDescription":"Article: 11 p.; 3 Data Releases ","startPage":"2950","endPage":"2960","ipdsId":"IP-076014","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"links":[{"id":460449,"rank":5,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.8b05592","text":"Publisher Index 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]\n}","volume":"53","issue":"6","noUsgsAuthors":false,"publicationDate":"2019-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bexfield, Laura M. 0000-0002-1789-654X bexfield@usgs.gov","orcid":"https://orcid.org/0000-0002-1789-654X","contributorId":1273,"corporation":false,"usgs":true,"family":"Bexfield","given":"Laura","email":"bexfield@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":770810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Toccalino, Patricia 0000-0003-1066-1702","orcid":"https://orcid.org/0000-0003-1066-1702","contributorId":213736,"corporation":false,"usgs":true,"family":"Toccalino","given":"Patricia","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":770811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":770812,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Foreman, William T. 0000-0002-2530-3310 wforeman@usgs.gov","orcid":"https://orcid.org/0000-0002-2530-3310","contributorId":190786,"corporation":false,"usgs":true,"family":"Foreman","given":"William","email":"wforeman@usgs.gov","middleInitial":"T.","affiliations":[{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":770813,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Furlong, Edward 0000-0002-7305-4603","orcid":"https://orcid.org/0000-0002-7305-4603","contributorId":213730,"corporation":false,"usgs":true,"family":"Furlong","given":"Edward","affiliations":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true},{"id":5046,"text":"Branch of Analytical Serv (NWQL)","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":770814,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215498,"text":"70215498 - 2019 - Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","interactions":[],"lastModifiedDate":"2020-10-21T15:39:49.079734","indexId":"70215498","displayToPublicDate":"2019-03-05T10:36:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7168,"text":"Journal of the American Water Resources Association (JAWRA)","active":true,"publicationSubtype":{"id":10}},"title":"Modeling connectivity of non‐floodplain wetlands: Insights, approaches, and recommendations","docAbstract":"Representing hydrologic connectivity of non‐floodplain wetlands (NFWs) to downstream waters in process‐based models is an emerging challenge relevant to many research, regulatory, and management activities. We review four case studies that utilize process‐based models developed to simulate NFW hydrology. Models range from a simple, lumped parameter model to a highly complex, fully distributed model. Across case studies, we highlight appropriate application of each model, emphasizing spatial scale, computational demands, process representation, and model limitations. We end with a synthesis of recommended “best modeling practices” to guide model application. These recommendations include: (1) clearly articulate modeling objectives, and revisit and adjust those objectives regularly; (2) develop a conceptualization of NFW connectivity using qualitative observations, empirical data, and process‐based modeling; (3) select a model to represent NFW connectivity by balancing both modeling objectives and available resources; (4) use innovative techniques and data sources to validate and calibrate NFW connectivity simulations; and (5) clearly articulate the limits of the resulting NFW connectivity representation. Our review and synthesis of these case studies highlights modeling approaches that incorporate NFW connectivity, demonstrates tradeoffs in model selection, and ultimately provides actionable guidance for future model application and development.
Background
Spatio-temporal patterns of movement can characterize relationships between organisms and their surroundings, and address gaps in our understanding of species ecology, activity budgets, bioenergetics, and habitat resource management. Highly mobile waterfowl, which can exploit resources over large spatial extents, are excellent models to understand relationships between movements and resource usage, landscape interactions and specific habitat needs.
Methods
We tracked 3 species of dabbling ducks with GPS-GSM transmitters in 2015–17 to examine fine-scale movement patterns over 24 h periods (30 min interval), dividing movement pathways into temporally continuous segments and spatially contiguous patches. We quantified distances moved, area used and time allocated across the day, using linear and generalized linear mixed models. We investigated behavior through relationships between these variables.
Results
Movements and space-use were small, and varied by species, sex and season. Gadwall (Mareca strepera) generally moved least (FFDs: 0.5–0.7 km), but their larger foraging patches resulted from longer within-area movements. Pintails (Anas acuta) moved most, were more likely to conduct flights > 300 m, had FFDs of 0.8–1.1 km, used more segments and patches per day that they revisited more frequently, resulting in the longest daily total movements. Females and males differed only during the post-hunt season when females moved more. 23.6% of track segments were short duration (1–2 locations), approximately 1/3 more than would be expected if they occurred randomly, and were more dispersed in the landscape than longer segments. Distance moved in 30 min shortened as segment duration increased, likely reflecting phases of non-movement captured within segments.
Conclusions
Pacific Flyway ducks spend the majority of time using smaller foraging and resting areas than expected or previously reported, implying that foraging areas may be highly localized, and nutrients obtainable from smaller areas. Additionally, movement reductions over time demonstrates behavioral adjustments that represent divergent energetic demands, the detection of which is a key advantage of higher frequency data. Ducks likely use less energy for movement than currently predicted and management, including distribution and configuration of essential habitat, may require reconsideration. Our study illustrates how fine-scale movement data from tracking help understand and inform various other fields of research.
Catchment models are useful tools to help describe and quantify the sources, transport, and fate of sediment, nutrients, and other constituents in a landscape. Results from catchment models are used to quantify and understand existing conditions and used in restoration efforts by defining areas with highest contributions (hotspots, where actions would be most beneficial) and describing the relative importance of various sources (what types of actions would be most beneficial). In practice, a continuum of models exists from simple empirical models to complex process-driven models, each requiring different types and amounts of information. Each of these models has its strengths and weaknesses, which should be considered when deciding which model to apply to a specific area. In many applications, a combination of models can be either coupled or run in series to help describe how nutrients and sediment are transported from the field to downstream receiving water bodies. In this chapter, we describe the continuum of catchment models that exist and provide information for choosing specific models for various management applications. We then provide examples of catchment models used to address a wide range of scientific and policy driven issues: two models commonly applied in New Zealand (CLUES and GLEAMS) and one model (SPARROW) applied to a large river basin in the United States (Mississippi River Basin).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Lake restoration handbook","language":"English","publisher":"Springer","doi":"10.1007/978-3-319-93043-5_2","usgsCitation":"Parshotam, A., and Robertson, D.M., 2019, Modelling for catchment management, chap. of Lake restoration handbook, p. 25-65, https://doi.org/10.1007/978-3-319-93043-5_2.","productDescription":"41 p.","startPage":"25","endPage":"65","ipdsId":"IP-074204","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361732,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-30","publicationStatus":"PW","contributors":{"editors":[{"text":"Hamilton, David P.","contributorId":166840,"corporation":false,"usgs":false,"family":"Hamilton","given":"David P.","affiliations":[{"id":24543,"text":"Environmental Research Institute, University of Waikato, Private Bag 3015, Hamilton 3240, New Zealand.","active":true,"usgs":false}],"preferred":false,"id":758761,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Collier, Kevin J.","contributorId":213943,"corporation":false,"usgs":false,"family":"Collier","given":"Kevin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":758762,"contributorType":{"id":2,"text":"Editors"},"rank":2},{"text":"Quinn, John M.","contributorId":47469,"corporation":false,"usgs":true,"family":"Quinn","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":758763,"contributorType":{"id":2,"text":"Editors"},"rank":3},{"text":"Howard-Williams, Clive","contributorId":213944,"corporation":false,"usgs":false,"family":"Howard-Williams","given":"Clive","email":"","affiliations":[],"preferred":false,"id":758764,"contributorType":{"id":2,"text":"Editors"},"rank":4}],"authors":[{"text":"Parshotam, Aroon","contributorId":213925,"corporation":false,"usgs":false,"family":"Parshotam","given":"Aroon","email":"","affiliations":[{"id":38930,"text":"Waikato University, New Zealand","active":true,"usgs":false}],"preferred":false,"id":758703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758702,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202471,"text":"70202471 - 2019 - Patterns of big sagebrush plant community composition and stand structure in the western United States","interactions":[],"lastModifiedDate":"2019-06-18T10:33:26","indexId":"70202471","displayToPublicDate":"2019-03-04T15:41:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Patterns of big sagebrush plant community composition and stand structure in the western United States","docAbstract":"Big sagebrush (Artemisia tridentata Nutt.) plant communities are found in western North America and comprise a mix of shrubs, forbs, and grasses. Climate, topography, and soil water availability are important factors that shape big sagebrush stand structure and plant community composition; however, most studies have focused on understanding these relationships at sites in a small portion of the big sagebrush region. Our goal was to characterize detailed stand structure and plant composition patterns and identify environmental variables related to those patterns by sampling 15 sites distributed across the western United States. In each site, we characterized stand structure at the individual shrub level and at the site level. We quantified size distributions and assessed relationships among canopy volume, age, and height. We also characterized functional type cover and species composition and related those to climatic, topographic, and edaphic variables. Mean big sagebrush age ranged from 21 (± 8) to 57 (± 22) yr at individual sites, mean height ranged from 0.23 (± 0.12) to 0.67 (± 0.23) m, and mean canopy volume ranged from 0.03 (± 0.04) to 0.62 (± 0.51) m3. Bare ground and litter contributed the most cover (mean = 64%), followed by big sagebrush (mean = 39% of vascular plant cover). There was a negative relationship between big sagebrush cover and grass and forb cover. Species composition was related to both climate and elevation, likely because these variables influence water availability. Although our study was limited to 15 field sites, our detailed descriptions of widely distributed sites provide insight into the magnitude of variability in big sagebrush plant community structure.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rama.2018.11.013","usgsCitation":"Pennington, V.E., Bradford, J.B., Palmquist, K.A., Renne, R.R., and Lauenroth, W.K., 2019, Patterns of big sagebrush plant community composition and stand structure in the western United States: Rangeland Ecology and Management, v. 72, no. 3, p. 505-514, https://doi.org/10.1016/j.rama.2018.11.013.","productDescription":"10 p.","startPage":"505","endPage":"514","ipdsId":"IP-093250","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":361717,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"72","issue":"3","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pennington, Victoria E.","contributorId":138850,"corporation":false,"usgs":false,"family":"Pennington","given":"Victoria","email":"","middleInitial":"E.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":758721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradford, John B. 0000-0001-9257-6303 jbradford@usgs.gov","orcid":"https://orcid.org/0000-0001-9257-6303","contributorId":611,"corporation":false,"usgs":true,"family":"Bradford","given":"John","email":"jbradford@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":758720,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmquist, Kyle A.","contributorId":169517,"corporation":false,"usgs":false,"family":"Palmquist","given":"Kyle","email":"","middleInitial":"A.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":758722,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Renne, Rachel R.","contributorId":213935,"corporation":false,"usgs":false,"family":"Renne","given":"Rachel","email":"","middleInitial":"R.","affiliations":[{"id":38934,"text":"School of Forestry and Environmental Studies, Yale University, New Haven, CT 06511, USA","active":true,"usgs":false}],"preferred":false,"id":758723,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lauenroth, William K.","contributorId":80982,"corporation":false,"usgs":false,"family":"Lauenroth","given":"William","email":"","middleInitial":"K.","affiliations":[{"id":7098,"text":"University of Wyoming, Department of Botany, 1000 E. University Avenue, Laramie, WY 82071, USA","active":true,"usgs":false}],"preferred":false,"id":758724,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70202465,"text":"70202465 - 2019 - Physical, biogeochemical, and meteorological factors responsible for interannual changes in cyanobacterial community composition and biovolume over two decades in a eutrophic lake","interactions":[],"lastModifiedDate":"2019-03-04T15:28:51","indexId":"70202465","displayToPublicDate":"2019-03-04T15:28:45","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Physical, biogeochemical, and meteorological factors responsible for interannual changes in cyanobacterial community composition and biovolume over two decades in a eutrophic lake","docAbstract":"This study used a 20-year dataset (1995–2014) to identify factors affecting cyanobacterial community composition (CCC) and abundance in a eutrophic lake. We hypothesized that differences in thermal structure, nutrients, and meteorology drive interannual variability in CCC and abundance. Cluster analysis differentiated dominant cyanobacteria into rare, low abundance, or sporadically occurring taxa. The bloom-forming genera were Microcystis and Aphanizomenon, accounting for ~ 70% of total cyanobacterial biovolume (BV) on average, whereas unusually high abundance of Planktothrix, Synechococcus, and Oscillatoria were clear outliers in three of the years. Variability in CCC was significantly correlated (P < 0.05, R > 0.3) with ice duration, Kjeldahl nitrogen (TKN), and spring nitrite + nitrate (NO2+3); ice duration and TKN were associated with the occurrence of primarily non-bloom-forming genera. Pairwise correlations tested linear, exponential, and polynomial correlates of absolute and relative total Cyanophyta, Microcystis, or Aphanizomenon BV. TKN, total nitrogen (TN) and phosphorus (TP), TN:TP ratio, Schmidt stability, and rainfall correlated with total Cyanophyta, Microcystis, and Aphanizomenon BV, whereas ice cover, NO2+3, and TKN correlated with relative Microcystis and Aphanizomenon BV. Despite increasing TN:TP ratio over two decades, cyanobacterial abundance had not changed significantly. These data suggest differing responses of cyanobacterial genera to important environmental factors over two decades.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-018-3810-x","usgsCitation":"Weirich, C.A., Robertson, D.M., and Miller, T.R., 2019, Physical, biogeochemical, and meteorological factors responsible for interannual changes in cyanobacterial community composition and biovolume over two decades in a eutrophic lake: Hydrobiologia, v. 828, no. 1, p. 165-182, https://doi.org/10.1007/s10750-018-3810-x.","productDescription":"18 p.","startPage":"165","endPage":"182","ipdsId":"IP-095911","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":361713,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"828","issue":"1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Weirich, Chelsea A. 0000-0002-2481-4987","orcid":"https://orcid.org/0000-0002-2481-4987","contributorId":213923,"corporation":false,"usgs":false,"family":"Weirich","given":"Chelsea","email":"","middleInitial":"A.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":758700,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Robertson, Dale M. 0000-0001-6799-0596","orcid":"https://orcid.org/0000-0001-6799-0596","contributorId":204668,"corporation":false,"usgs":true,"family":"Robertson","given":"Dale","email":"","middleInitial":"M.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":758699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Todd R. 0000-0002-2113-1662","orcid":"https://orcid.org/0000-0002-2113-1662","contributorId":213924,"corporation":false,"usgs":false,"family":"Miller","given":"Todd","email":"","middleInitial":"R.","affiliations":[{"id":7200,"text":"University of Wisconsin-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":758701,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202460,"text":"70202460 - 2019 - Influenza A prevalence and subtype diversity in migrating teal sampled along the United States Gulf Coast","interactions":[],"lastModifiedDate":"2019-06-18T10:31:03","indexId":"70202460","displayToPublicDate":"2019-03-04T15:18:53","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":948,"text":"Avian Diseases","active":true,"publicationSubtype":{"id":10}},"title":"Influenza A prevalence and subtype diversity in migrating teal sampled along the United States Gulf Coast","docAbstract":"Wild birds in the order Anseriformes are important reservoirs for influenza A viruses (IAV); however, IAV prevalence and subtype diversity may vary by season, even at the same location. To better understand the ecology of IAV during waterfowl migration through the Gulf Coast of the United States (Louisiana and Texas), surveillance of blue-winged (Spatula discors) and American green-winged (Anas carolinensis) teal was conducted annually during the spring (live-capture; 2012-2017) and fall (hunter-harvested; 2007-2017) at times inferred to coincide with northward and southward movements, respectively, for these waterfowl species. During spring migration, 266 low pathogenicity (LP) IAV positive samples were recovered from 7,547 paired cloacal/oropharyngeal (COP) samples (prevalence: 3.5%; annual range: 1.3%-8.4%). During fall migration, 650 LP IAV positive samples were recovered from 9,493 COP samples (prevalence: 6.8%; annual range: 0.4%-23.5%). Overall, 34 and 20 different IAV subtypes were recovered during fall and spring sampling, respectively. Consistent with previous results for fall migrating ducks, H3 and H4 HA subtypes were most common; however, H4 subtype viruses predominated every year. This is in contrast to the predominance of LP H7 and H10 HA subtype viruses in both species during spring. The N6 and N8 NA subtypes, which were usually associated with H4, were most common during fall; the N6 subtype was not recovered in the spring. These consistent seasonal trends in IAV subtype detection in both species are currently not understood and highlight the need for further research regarding potential drivers of spatiotemporal patterns of infection such as population immunity.
","language":"English","publisher":"American Association of Avian Pathologists","doi":"10.1637/11850-041918-Reg.1","usgsCitation":"Carter, D., Link, P.T., Walther, P., Ramey, A.M., Stallknecht, D.E., and Poulson, R., 2019, Influenza A prevalence and subtype diversity in migrating teal sampled along the United States Gulf Coast: Avian Diseases, v. 63, no. SP1, p. 165-171, https://doi.org/10.1637/11850-041918-Reg.1.","productDescription":"7 p.","startPage":"165","endPage":"171","ipdsId":"IP-097180","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":467849,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11312343","text":"External Repository"},{"id":361708,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"SP1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Deborah","contributorId":213914,"corporation":false,"usgs":false,"family":"Carter","given":"Deborah","affiliations":[{"id":38928,"text":"University of Georgia Southeastern Cooperative Wildlife Disease Study","active":true,"usgs":false}],"preferred":false,"id":758675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Link, Paul T.","contributorId":53611,"corporation":false,"usgs":false,"family":"Link","given":"Paul","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":758676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walther, Patrick","contributorId":213915,"corporation":false,"usgs":false,"family":"Walther","given":"Patrick","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":758677,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":758674,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stallknecht, David E.","contributorId":14323,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David","email":"","middleInitial":"E.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":758678,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":758679,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202870,"text":"70202870 - 2019 - Carbon accumulation and vertical accretion in a restored vs. historic salt marsh in southern Puget Sound, Washington, United States","interactions":[],"lastModifiedDate":"2019-09-16T11:51:50","indexId":"70202870","displayToPublicDate":"2019-03-04T14:52:44","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3271,"text":"Restoration Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Carbon accumulation and vertical accretion in a restored vs. historic salt marsh in southern Puget Sound, Washington, United States","docAbstract":"Few comparisons exist between vertical accretion (VA) and carbon accumulation rates (CARs), in restored vs. historic (i.e., reference) marshes. Here we compare these processes in a formerly diked, sparsely vegetated, restored salt marsh (Six Gill Slough, SG), whose surface is subsided relative to the tidal frame, to an adjacent, relatively pristine, historic salt marsh (Animal Slough, AS). Six sediment cores were collected at both AS and SG ~six years after restoration. Cores were analyzed for bulk density, % loss of ignition, % organic carbon, and 210Pb. We found that sharp changes in bulk density in surface layers of SG cores were highly reliable markers for the onset of restoration. The mean VA since restoration at SG (0.79 (sd=0.29) cm yr-1) was ~twice that of AS (0.41 (sd=0.16) cm yr-1). In comparison, the VA at AS over 50 years was 0.30 (sd=0.09) cm yr-1. VA consisted almost entirely of inorganic sediment at SG whereas at AS it was ~55%. Mean CARs at SG were somewhat greater than at AS, but the difference was not significant due to high variability (SG: 81 - 210 g C m-2 yr-1; AS: 115 - 168 g C m-2 yr-1). The mean CAR at AS over the past 50 years was 118 (sd=23) g C m-2 yr-1. This study demonstrates that a sparsely vegetated, restored salt marsh can quickly begin to accumulate carbon and that historic and restored marshes can have similar CARs despite highly divergent formation processes.","language":"English","publisher":"Society for Ecological Research","doi":"10.1111/rec.12941","usgsCitation":"Drexler, J.Z., Woo, I., Fuller, C.C., and Nakai, G., 2019, Carbon accumulation and vertical accretion in a restored vs. historic salt marsh in southern Puget Sound, Washington, United States: Restoration Ecology, v. 27, no. 5, p. 1117-1127, https://doi.org/10.1111/rec.12941.","productDescription":"11 p.","startPage":"1117","endPage":"1127","ipdsId":"IP-104450","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":362664,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington ","otherGeospatial":"Puget Sound","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.71393775939941,\n 47.06579564376744\n ],\n [\n -122.67788887023924,\n 47.06579564376744\n ],\n [\n -122.67788887023924,\n 47.09694798930915\n ],\n [\n -122.71393775939941,\n 47.09694798930915\n ],\n [\n -122.71393775939941,\n 47.06579564376744\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"5","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Drexler, Judith Z. 0000-0002-0127-3866 jdrexler@usgs.gov","orcid":"https://orcid.org/0000-0002-0127-3866","contributorId":167492,"corporation":false,"usgs":true,"family":"Drexler","given":"Judith","email":"jdrexler@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":760349,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Woo, Isa 0000-0002-8447-9236 iwoo@usgs.gov","orcid":"https://orcid.org/0000-0002-8447-9236","contributorId":2524,"corporation":false,"usgs":true,"family":"Woo","given":"Isa","email":"iwoo@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":760351,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":760350,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nakai, Glynnis","contributorId":172123,"corporation":false,"usgs":false,"family":"Nakai","given":"Glynnis","email":"","affiliations":[{"id":26986,"text":"US Fish and Wildlife Service, Nisqually Nat'l Wildlife Refuge, Olympia, WA","active":true,"usgs":false}],"preferred":false,"id":760352,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215596,"text":"70215596 - 2019 - Unprocessed atmospheric nitrate in waters of the Northern Forest Region in the USA and Canada","interactions":[],"lastModifiedDate":"2020-10-25T18:19:51.970621","indexId":"70215596","displayToPublicDate":"2019-03-04T13:16:41","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Unprocessed atmospheric nitrate in waters of the Northern Forest Region in the USA and Canada","docAbstract":"Little is known about the regional extent and variability of nitrate from atmospheric deposition that is transported to streams without biological processing in forests. We measured water chemistry and isotopic tracers (δ18O and δ15N) of nitrate sources across the Northern Forest Region of the U.S. and Canada and reanalyzed data from other studies to determine when, where, and how unprocessed atmospheric nitrate was transported in catchments. These inputs were more widespread and numerous than commonly recognized, but with high spatial and temporal variability. Only 6 of 32 streams had high fractions (>20%) of unprocessed atmospheric nitrate during baseflow. Seventeen had high fractions during stormflow or snowmelt, which corresponded to large fractions in near-surface soil waters or groundwaters, but not deep groundwater. The remaining 10 streams occasionally had some (<20%) unprocessed atmospheric nitrate during stormflow or baseflow. Large, sporadic events may continue to be cryptic due to atmospheric deposition variation among storms and a near complete lack of monitoring for these events. A general lack of observance may bias perceptions of occurrence; sustained monitoring of chronic nitrogen pollution effects on forests with nitrate source apportionments may offer insights needed to advance the science as well as assess regulatory and management schemes.
Vapor‐dominated hydrothermal systems are characterized by localized and elevated heat and gas flux. In these systems, steam and gas ascend from a boiling water reservoir, steam condenses beneath a low‐permeability cap layer, and liquid water descends, driven by gravity (“heat pipe” model). We combine magnetic, electromagnetic, and geoelectrical methods and CO2 flux and subsurface temperature measurements in the Solfatara Plateau Thermal Area in the Yellowstone Caldera to address several fundamental questions: (1) What are the structural and/or lithological controls on heat and mass transport in vapor‐dominated areas? (2) What is the geometry and size of convecting multiphase thermal plumes? (3) Are thermal plumes associated with subsurface rock alteration and demagnetization? Magnetic and electromagnetic data inversions suggest an asymmetric 50‐ to 100‐m thick basin of glacial deposits with the thickest part adjacent to the margin of a rhyolite flow. The 3‐D electrical conductivity model in the glacial basin reveals a narrow vertical conductor interpreted as a focused multiphase plume, which coincides at the ground surface with the heat and CO2 flux maxima. The magnetic data suggest that destruction of magnetic minerals due to rock alteration associated with the hydrothermal plume occurs mainly near the ground surface. We propose a model where the buoyant multiphase plume forms in response to decompression, boiling, and phase separation of pressurized thermal groundwater that discharges from the brecciated base of a rhyolite flow into the basin of glacial deposits. Results from multiphase groundwater flow and heat transport numerical simulations corroborate the first‐order characteristics of this model.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB016202","usgsCitation":"Bouligand, C., Hurwitz, S., Vandemeulebrouck, J., Byrdina, S., Kass, M.A., and Lewicki, J.L., 2019, Heat and mass transport in a vapor-dominated hydrothermal area in Yellowstone National Park, USA: Inferences from magnetic, electrical, electromagnetic, subsurface temperature and diffuse CO2 flux measurements: Journal of Geophysical Research B: Solid Earth, v. 124, no. 1, p. 291-309, https://doi.org/10.1029/2018JB016202.","productDescription":"19 p.","startPage":"291","endPage":"309","ipdsId":"IP-098703","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":488797,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/e8f358ca-154d-4c4f-a21a-cd87db837f8c","text":"External Repository"},{"id":361674,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.75,\n 44.5833\n ],\n [\n -110.5,\n 44.5833\n ],\n [\n -110.5,\n 44.75\n ],\n [\n -110.75,\n 44.75\n ],\n [\n -110.75,\n 44.5833\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"124","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bouligand, Claire","contributorId":71662,"corporation":false,"usgs":true,"family":"Bouligand","given":"Claire","affiliations":[],"preferred":false,"id":758657,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hurwitz, Shaul 0000-0001-5142-6886 shaulh@usgs.gov","orcid":"https://orcid.org/0000-0001-5142-6886","contributorId":2169,"corporation":false,"usgs":true,"family":"Hurwitz","given":"Shaul","email":"shaulh@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":758658,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vandemeulebrouck, Jean","contributorId":101973,"corporation":false,"usgs":true,"family":"Vandemeulebrouck","given":"Jean","email":"","affiliations":[],"preferred":false,"id":758659,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Byrdina, Svetlana","contributorId":213911,"corporation":false,"usgs":false,"family":"Byrdina","given":"Svetlana","email":"","affiliations":[],"preferred":false,"id":758660,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kass, Mason A. 0000-0001-6119-2593 mkass@usgs.gov","orcid":"https://orcid.org/0000-0001-6119-2593","contributorId":613,"corporation":false,"usgs":true,"family":"Kass","given":"Mason","email":"mkass@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":758661,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewicki, Jennifer L. 0000-0003-1994-9104 jlewicki@usgs.gov","orcid":"https://orcid.org/0000-0003-1994-9104","contributorId":5071,"corporation":false,"usgs":true,"family":"Lewicki","given":"Jennifer","email":"jlewicki@usgs.gov","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":758662,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201030,"text":"sir20185161 - 2019 - Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate","interactions":[],"lastModifiedDate":"2019-03-06T09:26:09","indexId":"sir20185161","displayToPublicDate":"2019-03-04T10:11:16","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-5161","displayTitle":"Assessment of Columbia and Willamette River Flood Stage on the Columbia Corridor Levee System at Portland, Oregon, in a Future Climate","title":"Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate","docAbstract":"To support Levee Ready Columbia’s (LRC’s) effort to re-certify levees along the Columbia and Willamette Rivers and remain accredited, two 2-dimensional hydraulic models, Adaptive Hydraulics and Delft3D-Flexible Mesh, were used to simulate the effects of plausible extreme high water during the 2030 to 2059 period. The Columbia River was simulated from Bonneville Dam, situated at river mile (RM) 145, to the mouth of Columbia River, and the Willamette River was simulated from Willamette Falls, RM 26.2, to the Columbia River confluence. Inputs to the models included light detection and ranging (lidar) and bathymetric mapping data to determine bed level, and boundary conditions in the form of daily inflow hydrographs and water levels in the ocean offshore of the mouth of the Columbia River.
Future conditions were based on climate science data developed by the U.S. Army Corps of Engineers and others. These conditions included future streamflow and coastal ocean water levels. The hypothetical, extreme but plausible, upstream boundary was based on scaling up the hydrographs from the 1996 flood. Scaling factors were determined by comparing the peak flow rankings determined from flood frequency analyses of historical unregulated periods and 2040s simulated unregulated winter streamflow. The comparison resulted in scaling up the Columbia River hydrograph by 40-percent and scaling up the Willamette River and Lower Columbia River tributaries hydrographs by 20-percent. The downstream ocean boundary was based on a combination of sea-level change, high tide, and storm surge.
The models were calibrated for two historical periods: (1) from January 15 to February 28, 1996, and (2) from April 12 to July 12, 1997. The two models compared well to the measured water-surface elevation over the historical periods and had good performance statistics, with root-mean square error ranging from 0.085 to 0.32 meters, Nash-Sutcliffe values greater than 0.96, and bias ranging from -0.03 to 0.28 meters. The simulated peak stage in the Columbia River at Vancouver, Washington, for 1996 was 9.60 and 9.98 meters (31.5 and 32.7 feet) compared to the measured peak of 9.89 meters (32.5 feet). Future peak stage then was simulated with boundary conditions representing extreme but plausible future conditions at the inflow sites and the ocean boundary.
The two calibrated models compared well in their simulations of extreme but plausible future conditions. For the 0-meter sea-level change scenario, the simulated peak stage in the Columbia River at Vancouver was 11.15 and 11.39 meters (36.6 and 37.4 feet); and for the 1-meter sea-level change scenario, the simulated peak stage in the Columbia River was 11.25 and 11.54 meters (36.9 and 37.9 feet). The total increase in stage as compared to the 1996 measured peak stage ranged from 1.26 to 1.65 meters (4.13 to 5.40 feet).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185161","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers and Levee Ready Columbia","usgsCitation":"Wherry, S.A., Wood, T.M., Moritz, H.R., and Duffy, K.B., 2019, Assessment of Columbia and Willamette River flood stage on the Columbia Corridor Levee System at Portland, Oregon, in a future climate: U.S. Geological Survey Scientific Investigations Report 2018-5161, 44 p., https://doi.org/10.3133/sir20185161.","productDescription":"vii, 44 p.","numberOfPages":"56","onlineOnly":"Y","ipdsId":"IP-096367","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":361538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5161/coverthb.jpg"},{"id":361539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5161/sir20185161.pdf","text":"Report","size":"5.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5161"}],"country":"United States","state":"Oregon","city":"Portland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.112548828125,\n 44.953136827528816\n ],\n [\n -119.9981689453125,\n 44.953136827528816\n ],\n [\n -119.9981689453125,\n 46.5172957536981\n ],\n [\n -124.112548828125,\n 46.5172957536981\n ],\n [\n -124.112548828125,\n 44.953136827528816\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Sucralose is an increasingly popular artificial sweetener and has been found in the environment in groundwater, surface water, and wastewater treatment plant effluent. Its chemical properties make it strongly recalcitrant in the environment and it has been used as a conservative tracer of human wastewater in recent years. Most current methods of sucralose analysis use high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) instrumentation. In this study, we describe an analytical method using silylation derivatization and gas chromatography mass spectrometry (GC-MS) for the quantification of sucralose in groundwater samples. This method employs a deuterium-labeled internal standard to account for reaction and sample processing imprecision. The deuterated internal standard as well as experiments using negative ion chemical ionization gas chromatography-mass spectrometry strongly indicate that sucralose is derivatized at all five hydroxyl positions with trimethyl silyl groups. A previously developed GC-MS method with derivatization of sucralose in environmental samples did not employ an internal standard for quantification. As such, this method represents a more robust methodological approach for sucralose quantification in environmental samples. The method detection limit based on a set of 15 method blanks was calculated to be 21.8 ng L−1, which is competitive with most methods in the published literature and sufficient to detect sucralose in water with ∼0.1% wastewater contribution. The method was applied to 37 groundwater samples from drinking water wells in California's Central Valley, in which five samples (13.5%) were found to contain sucralose at concentrations greater than the 21.8 ng L−1 detection limit.
Coastal areas in the U.S. and worldwide have experienced massive population and land use changes contributing to significant degradation of coastal ecosystems. Excess nutrient pollution causes coastal ecosystem degradation, and both regulatory and management efforts have targeted reducing nutrient and sediment loading to coastal rivers. Decadal trends in flow-normalized nutrient and sediment loads were determined for 95 monitoring locations on 88 U.S. coastal rivers, including tributaries of the Great Lakes, between 2002 and 2012 for nitrogen (N), phosphorus (P), and sediment. N and P loading from urban watersheds generally decreased between 2002 and 2012. In contrast, N and P trends in agricultural watersheds were variable indicating uneven progress in decreasing nutrient loading. Coherent decreases in N loading from agricultural watersheds occurred in the Lake Erie basin, but limited benefit is expected from these changes because P is the primary driver of degradation in the lake. Nutrient loading from undeveloped watersheds was low, but increased between 2002 and 2012, possibly indicating degradation of coastal watersheds with a lower intensity of anthropogenic influence. Regional differences in trends were evident, with stable nutrient loads from the Mississippi River to the Gulf of Mexico, but commonly decreasing N loads and increasing P loads in Chesapeake Bay. Compared to global rivers, coastal rivers of the conterminous U.S have somewhat lower TN yields and slightly higher TP yields, but similarities exist among land use, nutrient sources, and changes in nutrient loads. Despite widespread decreases in N loading in coastal watersheds, recent N:P ratios remained elevated compared to historic values in many areas. Additional progress in reducing N and P loading to U.S. coastal waters, particularly outside of urban areas, would benefit coastal ecosystems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2018.10.437","usgsCitation":"Oelsner, G.P., and Stets, E.G., 2019, Recent trends in nutrient and sediment loading to coastal areas of the conterminous U.S.: Insights and global context: Science of the Total Environment, v. 654, p. 1225-1240, https://doi.org/10.1016/j.scitotenv.2018.10.437.","productDescription":"16 p. 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