The U.S. Geological Survey (USGS) monitors water quality and suspended-sediment transport in the San Francisco Bay (bay) as part of a multi-agency effort to address management, water supply, and ecological concerns. The San Francisco Bay area is home to millions of people, and the bay teems both with resident and with migratory wildlife, plants, and fish. Freshwater mixes with salt water in the bay, which is subject both to riverine influences (floods, droughts, managed reservoir releases and freshwater diversions) and to marine influences (tides, waves, effects of salt water). To understand this environment, the USGS, along with its partners (see “Acknowledgements”), has been monitoring the bay’s waters continuously since 1988. Several water-quality variables are of particular importance to State and Federal resource managers and are monitored at key locations throughout the bay (fig. 1). Salinity, which indicates the relative mixing of fresh and ocean waters in the bay, is derived from specific conductance measurements. Water temperature, along with salinity, affects the density of water, which controls gravity-driven circulation patterns and stratification in the water column. Turbidity, a measure of light scattered from suspended particles in the water, is used to estimate suspended-sediment concentration (SSC). Suspended sediment affects the bay in multiple ways: attenuation of sunlight in the water column, affecting phytoplankton growth; deposition on tidal marsh and intertidal mudflats, which can help sustain these habitats as sea level rises; deposition in ports and shipping channels, which can necessitate dredging; and often, adsorption of contaminants, affecting their distribution and concentrations in the environment. Dissolved oxygen concentration, essential to a healthy ecosystem and a fundamental indicator of water quality, is affected by water temperature, salinity, ecosystem metabolism, tidal currents, and wind. Tidal currents in the bay reverse four times a day, and wind direction and intensity typically vary on a daily cycle. Consequently, salinity, water temperature, SSC, and dissolved-oxygen concentration vary spatially and temporally throughout the bay. Therefore, continuous measurements are needed to observe these changes. The purpose of this fact sheet is to provide information about these variables, as well as internet links to access these continuous water-quality data collected by the USGS.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20183013","usgsCitation":"Buchanan, P.A., Downing-Kunz, M.A., Schoellhamer, D.H., and Livsey, D.N., 2018, Continuous water-quality and suspended-sediment transport monitoring in the San Francisco Bay, California, water years 2014–15 (ver. 1.1, May 2018): U.S. Geological Survey Fact Sheet 2018–3013, 5 p., https://doi.org/10.3133/fs20183013.","productDescription":"5 p.","ipdsId":"IP-062545","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":354204,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2018/3013/fs20183013_versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"},"description":"Fact Sheet 2018-3013 Version History"},{"id":352337,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2018/3013/coverthb.jpg"},{"id":352338,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2018/3013/fs20183013_v1.1.pdf","text":"Report","size":"650 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Fact Sheet 2018-3013"}],"country":"United States","state":"California","otherGeospatial":"San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5689697265625,\n 37.399619108675594\n ],\n [\n -121.76834106445311,\n 37.399619108675594\n ],\n [\n -121.76834106445311,\n 38.19825933797085\n ],\n [\n -122.5689697265625,\n 38.19825933797085\n ],\n [\n -122.5689697265625,\n 37.399619108675594\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: March 8, 2018; Version 1.1: May 15, 2018","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175160","usgsCitation":"Terziotti, Silvia, Capel, P.D., Tesoriero, A.J., Hopple, J.A., and Kronholm, S.C., 2017, Estimates of nitrate loads and yields from groundwater to streams in the Chesapeake Bay watershed based on land use and geology: U.S. Geological Survey Scientific Investigations Report 2017–5160, 20 p., https://doi.org/10.3133/sir20175160.","productDescription":"Report: iv, 20 p.; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-086227","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":352267,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F79G5KP4","text":"USGS data release","description":"USGS data release","linkHelpText":"Datasets and metadata for estimates of nitrate loads and yields from groundwater to streams in the 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U.S. Geological Survey
3916 Sunset Ridge Road
Raleigh, NC 27607
This report is a user guide for the low-flow analysis methods provided with version 1.0 of the Surface Water Toolbox (SWToolbox) computer program. The software combines functionality from two software programs—U.S. Geological Survey (USGS) SWSTAT and U.S. Environmental Protection Agency (EPA) DFLOW. Both of these programs have been used primarily for computation of critical low-flow statistics. The main analysis methods are the computation of hydrologic frequency statistics such as the 7-day minimum flow that occurs on average only once every 10 years (7Q10), computation of design flows including biologically based flows, and computation of flow-duration curves and duration hydrographs. Other annual, monthly, and seasonal statistics can also be computed. The interface facilitates retrieval of streamflow discharge data from the USGS National Water Information System and outputs text reports for a record of the analysis. Tools for graphing data and screening tests are available to assist the analyst in conducting the analysis.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Statistical analysis in Book 4: Hydrologic analysis and interpretation","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm4A11","collaboration":"Prepared in cooperation with the U.S. Environment Protection Agency","usgsCitation":"Kiang, J.E., Flynn, K.M., Zhai, Tong, Hummel, Paul, and Granato, Gregory, 2018, SWToolbox: A surface-water tool-box for statistical analysis of streamflow time series: U.S. Geological Survey Techniques and Methods, book 4, chap. A–11, 33 p., https://doi.org/10.3133/tm4A11.","productDescription":"Report: vii, 34 p.; Software Download and Release Notes","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086817","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":352238,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/04/a11/coverthb.jpg"},{"id":352239,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/04/a11/tm4a11.pdf","text":"Report","size":"5.08 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 4-A11"},{"id":352240,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://water.usgs.gov/osw/swtoolbox/","text":"Software Download and Release Notes","linkHelpText":"- SWToolbox Software Information"}],"publicComments":"This report is Chapter 11 of Section A: Statistical analysis in Book 4: Hydrologic analysis and interpretation.","contact":"Chief, Analysis and Prediction Branch
Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 415
Reston, VA 20192
Freshwater mussels are among the most-imperiled organisms worldwide, although they provide a variety of important functions in the streams and rivers they inhabit. Among Atlantic-slope rivers, the Delaware River is known for its freshwater mussel diversity and biomass; however, limited data are available on the freshwater mussel fauna in the lower, non-tidal portion of the river. This section of the Delaware River has experienced decades of water-quality degradation from both industrial and municipal sources, primarily as a function of one of its major tributaries, the Lehigh River. We completed semi-quantitative snorkel surveys in 53.5 of the 121 km of the river to document mussel community composition and the continued impacts from pollution (particularly inputs from the Lehigh River) on mussel fauna. We detected changes in mussel catch per unit effort (CPUE) below the confluence of the Lehigh River, with significant declines in the dominant species Elliptio complanata (Eastern Elliptio) as we moved downstream from its confluence—CPUE dropped from 179 to 21 mussels/h. Patterns in mussel distribution around the Lehigh confluence matched chemical signatures of Lehigh water input. Specifically, Eastern Elliptio CPUE declined more quickly moving downstream on the Pennsylvania bank, where Lehigh River water input was more concentrated compared to the New Jersey bank. A definitive causal link remains to be established between the Lehigh River and the dramatic shifts in mussel community composition, warranting continued investigation as it relates to mussel conservation and restoration in the basin.
","language":"English","publisher":"Eagle Hill Publications","doi":"10.1656/045.025.0106","usgsCitation":"Blakeslee, C.J., Silldorff, E.L., and Galbraith, H.S., 2018, Changes in freshwater mussel communities linked to legacy pollution in the Lower Delaware River: Northeastern Naturalist, v. 25, no. 1, p. 101-116, https://doi.org/10.1656/045.025.0106.","productDescription":"16 p.","startPage":"101","endPage":"116","ipdsId":"IP-073172","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":352300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lower Delaware River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.25909423828125,\n 40.18097176388719\n ],\n [\n -74.7344970703125,\n 40.18097176388719\n ],\n [\n -74.7344970703125,\n 40.99855696412671\n ],\n [\n -75.25909423828125,\n 40.99855696412671\n ],\n [\n -75.25909423828125,\n 40.18097176388719\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"25","issue":"1","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70ce4b0da30c1bfc06f","contributors":{"authors":[{"text":"Blakeslee, Carrie J. 0000-0002-0801-5325 cblakeslee@usgs.gov","orcid":"https://orcid.org/0000-0002-0801-5325","contributorId":5462,"corporation":false,"usgs":true,"family":"Blakeslee","given":"Carrie","email":"cblakeslee@usgs.gov","middleInitial":"J.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":730391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Silldorff, Erik L.","contributorId":203041,"corporation":false,"usgs":false,"family":"Silldorff","given":"Erik","email":"","middleInitial":"L.","affiliations":[{"id":36569,"text":"Delaware River Basin Commission","active":true,"usgs":false}],"preferred":false,"id":730392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Galbraith, Heather S. 0000-0003-3704-3517 hgalbraith@usgs.gov","orcid":"https://orcid.org/0000-0003-3704-3517","contributorId":4519,"corporation":false,"usgs":true,"family":"Galbraith","given":"Heather","email":"hgalbraith@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":730393,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70195888,"text":"70195888 - 2018 - Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks","interactions":[],"lastModifiedDate":"2018-03-07T14:59:58","indexId":"70195888","displayToPublicDate":"2018-03-07T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks","title":"Human Bacteroides and total coliforms as indicators of recent combined sewer overflows and rain events in urban creeks","docAbstract":"Combined sewer overflows (CSOs) are a known source of human fecal pollution and human pathogens in urban water bodies, which may present a significant public health threat. To monitor human fecal contamination in water, bacterial fecal indicator organisms (FIOs) are traditionally used. However, because FIOs are not specific to human sources and do not correlate with human pathogens, alternative fecal indicators detected using qPCR are becoming of interest to policymakers. For this reason, this study measured correlations between the number and duration of CSOs and mm of rainfall, concentrations of traditional FIOs and alternative indicators, and the presence of human pathogens in two urban creeks. Samples were collected May–July 2016 and analyzed for concentrations of FIOs (total coliforms and E. coli) using membrane filtration as well as for three alternative fecal indicators (human Bacteroides HF183 marker, human polyomavirus (HPoV), pepper mild mottle virus (PMMoV)) and nine human pathogens using qPCR. Four of the nine pathogens analyzed were detected at these sites including adenovirus, Enterohemorrhagic E. coli, norovirus, and Salmonella. Among all indicators studied, human Bacteroides and total coliforms were significantly correlated with recent CSO and rainfall events, while E. coli, PMMoV, and HPoV did not show consistent significant correlations. Further, human Bacteroides were a more specific indicator, while total coliforms were a more sensitive indicator of CSO and rainfall events. Results may have implications for the use and interpretation of these indicators in future policy or monitoring programs.
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however, water-quality standards for suspended sediment in Minnesota are based on grab field sampling and total suspended solids (TSS) laboratory analysis methods that have underrepresented concentrations of suspended sediment in rivers compared to U.S. Geological Survey equal-width-increment or equal-discharge-increment (EWDI) field sampling and suspended sediment concentration (SSC) laboratory analysis methods. Because of this underrepresentation, the U.S. Geological Survey, in collaboration with the Minnesota Pollution Control Agency, collected concurrent grab and EWDI samples at eight sites to compare results obtained using different combinations of field sampling and laboratory analysis methods.
Study results determined that grab field sampling and TSS laboratory analysis results were biased substantially low compared to EWDI sampling and SSC laboratory analysis results, respectively. Differences in both field sampling and laboratory analysis methods caused grab and TSS methods to be biased substantially low. The difference in laboratory analysis methods was slightly greater than field sampling methods.
Sand-sized particles had a strong effect on the comparability of the field sampling and laboratory analysis methods. These results indicated that grab field sampling and TSS laboratory analysis methods fail to capture most of the sand being transported by the stream. The results indicate there is less of a difference among samples collected with grab field sampling and analyzed for TSS and concentration of fines in SSC. Even though differences are present, the presence of strong correlations between SSC and TSS concentrations provides the opportunity to develop site specific relations to address transport processes not captured by grab field sampling and TSS laboratory analysis methods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185023","collaboration":"Prepared in collaboration with the Minnesota Pollution Control Agency, Clean Water Fund","usgsCitation":"Groten, J.T., and Johnson, G.D., 2018, Comparability of river suspended-sediment sampling and laboratory analysis methods: U.S. Geological Survey Scientific Investigations Report 2018–5023, 23 p., https://doi.org/10.3133/sir20185023.","productDescription":"Report: vi, 21 p.; Appendix","numberOfPages":"34","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088277","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":352247,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5023/sir20185023_appendix.zip","text":"Appendix","size":"3.43 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2018–5023 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\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Nitrate contamination of groundwater in agricultural areas poses a major challenge to the sustainability of water resources. Aquifer vulnerability models are useful tools that can help resource managers identify areas of concern, but quantifying nitrogen (N) inputs in such models is challenging, especially at large spatial scales. We sought to improve regional nitrate (NO3−) input functions by characterizing unsaturated zone NO3− transport to groundwater through use of surrogate, machine-learning metamodels of a process-based N flux model. The metamodels used boosted regression trees (BRTs) to relate mappable landscape variables to parameters and outputs of a previous “vertical flux method” (VFM) applied at sampled wells in the Fox, Wolf, and Peshtigo (FWP) river basins in northeastern Wisconsin. In this context, the metamodels upscaled the VFM results throughout the region, and the VFM parameters and outputs are the metamodel response variables. The study area encompassed the domain of a detailed numerical model that provided additional predictor variables, including groundwater recharge, to the metamodels. We used a statistical learning framework to test a range of model complexities to identify suitable hyperparameters of the six BRT metamodels corresponding to each response variable of interest: NO3− source concentration factor (which determines the local NO3− input concentration); unsaturated zone travel time; NO3− concentration at the water table in 1980, 2000, and 2020 (three separate metamodels); and NO3− “extinction depth”, the eventual steady state depth of the NO3−front. The final metamodels were trained to 129 wells within the active numerical flow model area, and considered 58 mappable predictor variables compiled in a geographic information system (GIS). These metamodels had training and cross-validation testing R2 values of 0.52 – 0.86 and 0.22 – 0.38, respectively, and predictions were compiled as maps of the above response variables. Testing performance was reasonable, considering that we limited the metamodel predictor variables to mappable factors as opposed to using all available VFM input variables. Relationships between metamodel predictor variables and mapped outputs were generally consistent with expectations, e.g. with greater source concentrations and NO3− at the groundwater table in areas of intensive crop use and well drained soils. Shorter unsaturated zone travel times in poorly drained areas likely indicated preferential flow through clay soils, and a tendency for fine grained deposits to collocate with areas of shallower water table. Numerical estimates of groundwater recharge were important in the metamodels and may have been a proxy for N input and redox conditions in the northern FWP, which had shallow predicted NO3− extinction depth. The metamodel results provide proof-of-concept for regional characterization of unsaturated zone NO3− transport processes in a statistical framework based on readily mappable GIS input variables.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2018.02.029","usgsCitation":"Nolan, B.T., Green, C.T., Juckem, P.F., Liao, L., and Reddy, J.E., 2018, Metamodeling and mapping of nitrate flux in the unsaturated zone and groundwater, Wisconsin, USA: Journal of Hydrology, v. 559, p. 428-441, https://doi.org/10.1016/j.jhydrol.2018.02.029.","productDescription":"14 p.","startPage":"428","endPage":"441","ipdsId":"IP-088948","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":468931,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jhydrol.2018.02.029","text":"Publisher Index Page"},{"id":437988,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ZC81VR","text":"USGS data release","linkHelpText":"Data Release for Metamodeling and Mapping of Nitrate Flux in the Unsaturated Zone and Groundwater, Wisconsin, USA"},{"id":352259,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90,\n 43.5\n ],\n [\n -87.38525390624999,\n 43.5\n ],\n [\n -87.38525390624999,\n 46\n ],\n [\n -90,\n 46\n ],\n [\n -90,\n 43.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"559","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee70ce4b0da30c1bfc074","contributors":{"authors":[{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":730332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Green, Christopher T. 0000-0002-6480-8194 ctgreen@usgs.gov","orcid":"https://orcid.org/0000-0002-6480-8194","contributorId":1343,"corporation":false,"usgs":true,"family":"Green","given":"Christopher","email":"ctgreen@usgs.gov","middleInitial":"T.","affiliations":[{"id":438,"text":"National Research Program - 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2018 - Groundwater-level analysis of selected wells in the Hoosic River Valley near Hoosick Falls, New York, for aquifer framework and properties","interactions":[],"lastModifiedDate":"2018-03-05T14:27:58","indexId":"ofr20181015","displayToPublicDate":"2018-03-05T11:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1015","title":"Groundwater-level analysis of selected wells in the Hoosic River Valley near Hoosick Falls, New York, for aquifer framework and properties","docAbstract":"The U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, analyzed groundwater levels, drilling record logs, and field water-quality data from selected wells, and the surficial geology in the Hoosic River valley south of the village of Hoosick Falls, New York, to provide information about the framework and properties of a confined aquifer. The aquifer, which consists of ice-contact sand and gravel overlain by lacustrine clay and silt, was evaluated by the New York State Department of Environmental Conservation as part of their investigation of alternate water supplies for the village whose wellfield has been affected by perfluorooctanoic acid. Wells inventoried in the study area were classified as confined, water table, or transitional between the two aquifer conditions. Groundwater levels in three confined-aquifer wells and a transitional-aquifer well responded to pumping of a test production well finished in the confined aquifer. Groundwater levels in a water-table well showed no detectable water-level change in response to test-well pumping. Analysis of drawdown and recovery data from the three confined-aquifer wells and a transitional-aquifer well through the application of the Theis type-curve method provided estimates of aquifer properties. Representation of a constant-head boundary in the analysis where an unnamed pond and fluvial-terrace deposits abut the valley wall resulted in satisfactory matches of the Theis type curves with the observed water-level responses. Aquifer transmissivity estimates ranged from 1,160 to 1,370 feet squared per day. Aquifer storativity estimates ranged from 5.2×10–5 to 1.1×10–3 and were consistent with the inferred degree of confinement and distance from the represented recharge boundary.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181015","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Williams, J.H., and Heisig, P.M., 2018, Groundwater-level analysis of selected wells in the Hoosic River Valley near Hoosick Falls, New York, for aquifer framework and properties: U.S. Geological Survey Open-File Report 2018–1015, 14 p., https://doi.org/10.3133/ofr20181015. ","productDescription":"v, 14 p.","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-092144","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":352081,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1015/ofr20181015.pdf","text":"Report","size":"741 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OF 2018-1015"},{"id":352080,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1015/coverthb.jpg"}],"country":"United States","state":"New York","city":"Hoosick Falls","otherGeospatial":"Hoosic River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.37021827697754,\n 42.870680346240626\n ],\n [\n -73.3417224884033,\n 42.870680346240626\n ],\n [\n -73.3417224884033,\n 42.88961171193983\n ],\n [\n -73.37021827697754,\n 42.88961171193983\n ],\n [\n -73.37021827697754,\n 42.870680346240626\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Human actions, including nutrient pollution, are causing the widespread degradation of coastal habitats, and efforts to restore these valuable ecosystems have been largely unsuccessful or of limited scope. We provide an example of successful restoration linking effective management of nutrients to the successful recovery of submersed aquatic vegetation along thousands of kilometers of coastline in Chesapeake Bay, United States. We also show that biodiversity conservation can be an effective path toward recovery of coastal systems. Our study validates 30 years of environmental policy and provides a road map for future ecological restoration.
","language":"English","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1715798115","usgsCitation":"Lefcheck, J., Orth, R.J., Dennison, W., Wilcox, D.J., Murphy, R.R., Keisman, J.L., Gurbisz, C., Hannam, M.P., Landry, J.B., Moore, K.A., Patrick, C.J., Testa, J., Weller, D.E., and Batiuk, R., 2018, Long-term nutrient reductions lead to the unprecedented recovery of a temperate coastal region: PNAS, v. 115, no. 14, p. 3658-3662, https://doi.org/10.1073/pnas.1715798115.","productDescription":"5 p.","startPage":"3658","endPage":"3662","ipdsId":"IP-093374","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":468933,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1715798115","text":"Publisher Index Page"},{"id":395541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Cassie","contributorId":199774,"corporation":false,"usgs":false,"family":"Gurbisz","given":"Cassie","email":"","affiliations":[],"preferred":false,"id":833356,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hannam, Michael P.","contributorId":199775,"corporation":false,"usgs":false,"family":"Hannam","given":"Michael","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":833357,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Landry, J. Brooke","contributorId":199776,"corporation":false,"usgs":false,"family":"Landry","given":"J.","email":"","middleInitial":"Brooke","affiliations":[],"preferred":false,"id":833358,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Moore, Kenneth A.","contributorId":140569,"corporation":false,"usgs":false,"family":"Moore","given":"Kenneth","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":833359,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Patrick, Christopher J.","contributorId":199778,"corporation":false,"usgs":false,"family":"Patrick","given":"Christopher","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":833360,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Testa, Jeremy","contributorId":199779,"corporation":false,"usgs":false,"family":"Testa","given":"Jeremy","affiliations":[],"preferred":false,"id":833361,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Weller, Donald E.","contributorId":206834,"corporation":false,"usgs":false,"family":"Weller","given":"Donald","email":"","middleInitial":"E.","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":833362,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Batiuk, Richard A.","contributorId":244451,"corporation":false,"usgs":false,"family":"Batiuk","given":"Richard A.","affiliations":[{"id":48913,"text":"CoastWise Partners","active":true,"usgs":false}],"preferred":false,"id":833363,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70193917,"text":"sir20175139 - 2018 - Collection methods and quality assessment for Escherichia coli, water quality, and microbial source tracking data within Tumacácori National Historical Park and the upper Santa Cruz River, Arizona, 2015-16","interactions":[],"lastModifiedDate":"2018-07-23T08:55:22","indexId":"sir20175139","displayToPublicDate":"2018-03-05T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5139","title":"Collection methods and quality assessment for Escherichia coli, water quality, and microbial source tracking data within Tumacácori National Historical Park and the upper Santa Cruz River, Arizona, 2015-16","docAbstract":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Performance assessment of ocean color satellite data has generally relied on statistical metrics chosen for their common usage and the rationale for selecting certain metrics is infrequently explained. Commonly reported statistics based on mean squared errors, such as the coefficient of determination (r2), root mean square error, and regression slopes, are most appropriate for Gaussian distributions without outliers and, therefore, are often not ideal for ocean color algorithm performance assessment, which is often limited by sample availability. In contrast, metrics based on simple deviations, such as bias and mean absolute error, as well as pair-wise comparisons, often provide more robust and straightforward quantities for evaluating ocean color algorithms with non-Gaussian distributions and outliers. This study uses a SeaWiFS chlorophyll-a validation data set to demonstrate a framework for satellite data product assessment and recommends a multi-metric and user-dependent approach that can be applied within science, modeling, and resource management communities.
","language":"English","publisher":"Optica Publishing Group","doi":"10.1364/OE.26.007404","usgsCitation":"Seegers, B.N., Stumpf, R., Schaeffer, B., Loftin, K.A., and Werdell, P., 2018, Performance metrics for the assessment of satellite data products: An ocean color case study: Optics Express, v. 26, no. 6, p. 7404-7422, https://doi.org/10.1364/OE.26.007404.","productDescription":"19 p.","startPage":"7404","endPage":"7422","ipdsId":"IP-093210","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":488729,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1364/oe.26.007404","text":"Publisher Index Page"},{"id":483985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"26","issue":"6","noUsgsAuthors":false,"publicationDate":"2018-03-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Seegers, Bridget N.","contributorId":334288,"corporation":false,"usgs":false,"family":"Seegers","given":"Bridget","email":"","middleInitial":"N.","affiliations":[{"id":80107,"text":"Morgan State University, National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":932328,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stumpf, Richard P.","contributorId":193799,"corporation":false,"usgs":false,"family":"Stumpf","given":"Richard P.","affiliations":[],"preferred":false,"id":932329,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schaeffer, Blake A.","contributorId":334287,"corporation":false,"usgs":false,"family":"Schaeffer","given":"Blake A.","affiliations":[{"id":35215,"text":"Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":932330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Loftin, Keith A. 0000-0001-5291-876X","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":221964,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":932331,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Werdell, P. Jeremy","contributorId":269868,"corporation":false,"usgs":false,"family":"Werdell","given":"P. Jeremy","affiliations":[{"id":37453,"text":"National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":932332,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70195813,"text":"70195813 - 2018 - Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation","interactions":[],"lastModifiedDate":"2018-03-02T16:06:47","indexId":"70195813","displayToPublicDate":"2018-03-02T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation","title":"Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation","docAbstract":"Through their dam-building activities and subsequent water storage, beaver have the potential to restore riparian ecosystems and offset some of the predicted effects of climate change by modulating streamflow. Thus, it is not surprising that reintroducing beaver to watersheds from which they have been extirpated is an often-used restoration and climate-adaptation strategy. Identifying sites for reintroduction, however, requires detailed information about habitat factors—information that is not often available at broad spatial scales. Here we explore the potential for beaver relocation throughout the Snohomish River Basin in Washington, USA with a model that identifies some of the basic building blocks of beaver habitat suitability and does so by relying solely on remotely sensed data. More specifically, we developed a generalized intrinsic potential model that draws on remotely sensed measures of stream gradient, stream width, and valley width to identify where beaver could become established if suitable vegetation were to be present. Thus, the model serves as a preliminary screening tool that can be applied over relatively large extents. We applied the model to 5,019 stream km and assessed the ability of the model to correctly predict beaver habitat by surveying for beavers in 352 stream reaches. To further assess the potential for relocation, we assessed land ownership, use, and land cover in the landscape surrounding stream reaches with varying levels of intrinsic potential. Model results showed that 33% of streams had moderate or high intrinsic potential for beaver habitat. We found that no site that was classified as having low intrinsic potential had any sign of beavers and that beaver were absent from nearly three quarters of potentially suitable sites, indicating that there are factors preventing the local population from occupying these areas. Of the riparian areas around streams with high intrinsic potential for beaver, 38% are on public lands and 17% are on large tracts of privately-owned timber land. Thus, although there are a large number of areas that could be suitable for relocation and restoration using beavers, current land use patterns may substantially limit feasibility in these areas.
","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0192538","usgsCitation":"Dittbrenner, B.J., Pollack, M.M., Schilling, J.W., Olden, J., Lawler, J.J., and Torgersen, C.E., 2018, Modeling intrinsic potential for beaver (Castor canadensis) habitat to inform restoration and climate change adaptation: PLoS ONE, v. 13, no. 2, e0192538; 15 p., https://doi.org/10.1371/journal.pone.0192538.","productDescription":"e0192538; 15 p.","ipdsId":"IP-093284","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":468936,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0192538","text":"Publisher Index Page"},{"id":352193,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Snohomish River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.37945556640624,\n 47.372314620566925\n ],\n [\n -120.9979248046875,\n 47.372314620566925\n ],\n [\n -120.9979248046875,\n 48.08908799881762\n ],\n [\n -122.37945556640624,\n 48.08908799881762\n ],\n [\n -122.37945556640624,\n 47.372314620566925\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-28","publicationStatus":"PW","scienceBaseUri":"5afee70ee4b0da30c1bfc088","contributors":{"authors":[{"text":"Dittbrenner, Benjamin J.","contributorId":202890,"corporation":false,"usgs":false,"family":"Dittbrenner","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":730022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pollack, Michael M.","contributorId":202891,"corporation":false,"usgs":false,"family":"Pollack","given":"Michael","email":"","middleInitial":"M.","affiliations":[{"id":36546,"text":"National Oceanic and Atmospheric Administration – Northwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":730023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schilling, Jason W.","contributorId":202892,"corporation":false,"usgs":false,"family":"Schilling","given":"Jason","email":"","middleInitial":"W.","affiliations":[{"id":36547,"text":"Tulalip Tribes Natural Resources","active":true,"usgs":false}],"preferred":false,"id":730024,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Olden, Julian D.","contributorId":202893,"corporation":false,"usgs":false,"family":"Olden","given":"Julian D.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":730025,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lawler, Joshua J.","contributorId":73327,"corporation":false,"usgs":false,"family":"Lawler","given":"Joshua","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":730026,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":730021,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70202334,"text":"70202334 - 2018 - Volcanic ash deposition, eelgrass beds, and inshore habitat loss from the 1920s to the 1990s at Chignik, Alaska","interactions":[],"lastModifiedDate":"2019-02-22T16:48:55","indexId":"70202334","displayToPublicDate":"2018-03-01T16:48:50","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1587,"text":"Estuarine, Coastal and Shelf Science","active":true,"publicationSubtype":{"id":10}},"title":"Volcanic ash deposition, eelgrass beds, and inshore habitat loss from the 1920s to the 1990s at Chignik, Alaska","docAbstract":"We quantified the shallowing of the seafloor in five of six bays examined in the Chignik region of the Alaska Peninsula, confirming National Ocean Service observations that 1990s hydrographic surveys were shallower than previous surveys from the 1920s. Castle Bay, Chignik Lagoon, Hook Bay, Kujulik Bay and Mud Bay lost volume as calculated from Mean Lower Low Water (Chart Datum) to the deepest depths and four of these sites lost volume from Mean High Water to the deepest depths. Calculations relative to each datum were made because tidal datum records exhibited an increase in tidal range in this region from the 1920s to the 1990s. Our analysis showed that Mud Bay is quickly disappearing while Chignik Lagoon is being reduced to narrow channels. Anchorage Bay was the only site that increased in depth over time, perhaps due to erosion. Volcanoes dominate the landscape of the Chignik area. They have blanketed the region in deep ash deposits before the time frame of this study, and some have had smaller ash-producing eruptions during the time frame of this study. Remobilization of land-deposited ash and redeposition in marine areas - in some locations facilitated by extensive eelgrass (Zostera marina) beds (covering 54% of Chignik Lagoon and 68% of Mud Bay in 2010) - is the most likely cause of shallowing in the marine environment. Loss of shallow water marine habitat may alter future abundance and distribution of several fish, invertebrate and avian species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecss.2017.12.001","usgsCitation":"Zimmermann, M., Ruggerone, G.T., Freymueller, J., Kinsman, N., Ward, D.H., and Hogrefe, K.R., 2018, Volcanic ash deposition, eelgrass beds, and inshore habitat loss from the 1920s to the 1990s at Chignik, Alaska: Estuarine, Coastal and Shelf Science, v. 202, p. 69-86, https://doi.org/10.1016/j.ecss.2017.12.001.","productDescription":"18 p.","startPage":"69","endPage":"86","ipdsId":"IP-088301","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":468937,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecss.2017.12.001","text":"Publisher Index Page"},{"id":437991,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7VQ31X8","text":"USGS data release","linkHelpText":"Map of eelgrass distribution and field survey points in Chignik Lagoon and Mud Bay, Alaska in 2010"},{"id":361480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Chignik","volume":"202","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmermann, Mark","contributorId":49479,"corporation":false,"usgs":true,"family":"Zimmermann","given":"Mark","affiliations":[],"preferred":false,"id":757875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ruggerone, Gregory T.","contributorId":48068,"corporation":false,"usgs":true,"family":"Ruggerone","given":"Gregory","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":757876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Freymueller, Jeffrey T.","contributorId":96841,"corporation":false,"usgs":false,"family":"Freymueller","given":"Jeffrey T.","affiliations":[{"id":26875,"text":"Michigan State University, East Lansing, MI","active":true,"usgs":false}],"preferred":false,"id":757877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kinsman, Nicole","contributorId":95737,"corporation":false,"usgs":true,"family":"Kinsman","given":"Nicole","affiliations":[],"preferred":false,"id":757878,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ward, David H. 0000-0002-5242-2526 dward@usgs.gov","orcid":"https://orcid.org/0000-0002-5242-2526","contributorId":3247,"corporation":false,"usgs":true,"family":"Ward","given":"David","email":"dward@usgs.gov","middleInitial":"H.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":757874,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hogrefe, Kyle R. khogrefe@usgs.gov","contributorId":4264,"corporation":false,"usgs":true,"family":"Hogrefe","given":"Kyle","email":"khogrefe@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":757879,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70201617,"text":"70201617 - 2018 - Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America","interactions":[],"lastModifiedDate":"2018-12-18T15:53:14","indexId":"70201617","displayToPublicDate":"2018-03-01T15:53:23","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America","docAbstract":"Carbon cycling in the coastal zone affects global carbon budgets and is critical for understanding the urgent issues of hypoxia, acidification, and tidal wetland loss. However, there are no regional carbon budgets spanning the three main ecosystems in coastal waters: tidal wetlands, estuaries, and shelf waters. Here we construct such a budget for eastern North America using historical data, empirical models, remote sensing algorithms, and process‐based models. Considering the net fluxes of total carbon at the domain boundaries, 59 ± 12% (± 2 standard errors) of the carbon entering is from rivers and 41 ± 12% is from the atmosphere, while 80 ± 9% of the carbon leaving is exported to the open ocean and 20 ± 9% is buried. Net lateral carbon transfers between the three main ecosystem types are comparable to fluxes at the domain boundaries. Each ecosystem type contributes substantially to exchange with the atmosphere, with CO2uptake split evenly between tidal wetlands and shelf waters, and estuarine CO2outgassing offsetting half of the uptake. Similarly, burial is about equal in tidal wetlands and shelf waters, while estuaries play a smaller but still substantial role. The importance of tidal wetlands and estuaries in the overall budget is remarkable given that they, respectively, make up only 2.4 and 8.9% of the study domain area. This study shows that coastal carbon budgets should explicitly include tidal wetlands, estuaries, shelf waters, and the linkages between them; ignoring any of them may produce a biased picture of coastal carbon cycling.
","language":"English","publisher":"AGU","doi":"10.1002/2017GB005790","usgsCitation":"Najjar, R., Herrmann, M., Alexander, R.B., Boyer, E., Burdige, D., Butman, D., Cai, W., Canuel, E., Chen, R., Friedrichs, M.A., Feagin, R., Griffith, P.C., Hinson, A., Holmquist, J., Hu, X., Kemp, W., Kroeger, K.D., Mannino, A., McCallister, S., McGillis, W., Mulholland, M., Pilskaln, C.H., Salisbury, J., Signorini, S., St. Laurent, P., Tian, H., Tzortziou, M., Vlahos, P., Wan, Z., and Zimmerman, R.C., 2018, Carbon budget of tidal wetlands, estuaries, and shelf waters of eastern North America: Global Biogeochemical Cycles, v. 32, no. 3, p. 389-416, https://doi.org/10.1002/2017GB005790.","productDescription":"28 p.","startPage":"389","endPage":"416","ipdsId":"IP-092980","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":468939,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/2017gb005790","text":"External Repository"},{"id":360518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"32","issue":"3","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-11","publicationStatus":"PW","scienceBaseUri":"5c1a1534e4b0708288c23542","contributors":{"authors":[{"text":"Najjar, R.G.","contributorId":211647,"corporation":false,"usgs":false,"family":"Najjar","given":"R.G.","affiliations":[{"id":38295,"text":"Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania, USA.","active":true,"usgs":false}],"preferred":false,"id":754585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Herrmann, M.","contributorId":211648,"corporation":false,"usgs":false,"family":"Herrmann","given":"M.","email":"","affiliations":[{"id":38295,"text":"Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, Pennsylvania, USA.","active":true,"usgs":false}],"preferred":false,"id":754586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alexander, Richard B. 0000-0001-9166-0626 ralex@usgs.gov","orcid":"https://orcid.org/0000-0001-9166-0626","contributorId":541,"corporation":false,"usgs":true,"family":"Alexander","given":"Richard","email":"ralex@usgs.gov","middleInitial":"B.","affiliations":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true}],"preferred":true,"id":754587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyer, E.W.","contributorId":56358,"corporation":false,"usgs":false,"family":"Boyer","given":"E.W.","email":"","affiliations":[{"id":6738,"text":"The Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":754588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burdige, D. 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Stream habitat and fish population characteristics, including biomass, abundance, growth, size, and movement, were assessed over a 9-year period (4 years pre- and 5 years postlogging). The logged catchment was located on private industrial forestland and had been previously logged in 1966. The reference catchment was covered by an unharvested, fire-regenerated forest approximately 150–160 years old, which was unroaded and managed as a Research Natural Area by the USDA Forest Service. A single clearcut harvest unit of the upper 40% of the treatment catchment was implemented following current forest practice regulations, including the retention of riparian buffer of standing trees adjacent to fish bearing channels. No statistically significant negative effects on coastal cutthroat trout or coho salmon occurred following logging, and in fact, both late-summer density and total biomass of age-1+ coastal cutthroat trout increased in the logged catchment following logging. Increases in age-1+ coastal cutthroat were greatest closest to the harvest area and declined downstream as distance from the logged area increased. In contrast to the previous timber harvest in the catchment when few logging regulations existed, current forest practice regulations and logging techniques appear to have reduced acute negative effects on coastal cutthroat trout.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2018.01.030","usgsCitation":"Bateman, D.S., Gresswell, R.E., Warren, D., Hockman-Wert, D., Leer, D.W., Light, J.T., and Stednick, J.D., 2018, Fish response to contemporary timber harvest practices in a second-growth forest from the central Coast Range of Oregon: Forest Ecology and Management, v. 411, p. 142-157, https://doi.org/10.1016/j.foreco.2018.01.030.","productDescription":"16 p.","startPage":"142","endPage":"157","ipdsId":"IP-084981","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":468940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.foreco.2018.01.030","text":"Publisher Index Page"},{"id":356934,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","volume":"411","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5b98a2eae4b0702d0e84300c","contributors":{"authors":[{"text":"Bateman, Douglas S. 0000-0002-5609-2085 doug_bateman@usgs.gov","orcid":"https://orcid.org/0000-0002-5609-2085","contributorId":207396,"corporation":false,"usgs":false,"family":"Bateman","given":"Douglas","email":"doug_bateman@usgs.gov","middleInitial":"S.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":743727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gresswell, Robert E. 0000-0003-0063-855X bgresswell@usgs.gov","orcid":"https://orcid.org/0000-0003-0063-855X","contributorId":152031,"corporation":false,"usgs":true,"family":"Gresswell","given":"Robert","email":"bgresswell@usgs.gov","middleInitial":"E.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":743726,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Warren, Dana","contributorId":192215,"corporation":false,"usgs":false,"family":"Warren","given":"Dana","affiliations":[],"preferred":false,"id":743728,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hockman-Wert, David 0000-0003-2436-6237 dhockman-wert@usgs.gov","orcid":"https://orcid.org/0000-0003-2436-6237","contributorId":3891,"corporation":false,"usgs":true,"family":"Hockman-Wert","given":"David","email":"dhockman-wert@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":743729,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leer, David W.","contributorId":207397,"corporation":false,"usgs":false,"family":"Leer","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":743730,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Light, Jeffrey T.","contributorId":207398,"corporation":false,"usgs":false,"family":"Light","given":"Jeffrey","email":"","middleInitial":"T.","affiliations":[{"id":37530,"text":"Weyerhaeuser","active":true,"usgs":false}],"preferred":false,"id":743731,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Stednick, John D.","contributorId":207399,"corporation":false,"usgs":false,"family":"Stednick","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":37531,"text":"Colorado Statte University","active":true,"usgs":false}],"preferred":false,"id":743732,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202014,"text":"70202014 - 2018 - Sensitivity of Na+/K+-ATPase isoforms to acid and aluminum explains differential effects on Atlantic salmon osmoregulation in fresh water and seawater","interactions":[],"lastModifiedDate":"2019-02-05T14:32:53","indexId":"70202014","displayToPublicDate":"2018-03-01T14:32:44","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Sensitivity of Na+/K+-ATPase isoforms to acid and aluminum explains differential effects on Atlantic salmon osmoregulation in fresh water and seawater","docAbstract":"Atlantic salmon (Salmo salar) smolts are sensitive to acid rain and associated increases in dissolved inorganic aluminum (Al) resulting in decreased seawater tolerance at this critical life stage. Salmon have two major isoforms of the catalytic alpha subunit of Na+/K+-ATPase (NKA), with NKAα1a being the major freshwater (FW) isoform and NKAα1b the major seawater (SW) isoform. Here we evaluate physiological markers of SW preparedness and NKAα1a and NKAα1b isoforms after short-term exposure to acidified water and acidified water with added Al (acid–Al). Atlantic salmon smolts were exposed to low ion FW (Control), low ion acidic water (pH 5.2; acid), and low ion acidic water (pH 5.2) with moderate levels of added inorganic Al (35 μg·L−1; acid–Al) for 4 days. Acid exposure resulted in loss of salinity tolerance (higher plasma chloride (Cl) after SW exposure) and significantly decreased the levels of gill NKAα1b but not NKAα1a protein abundance. Acid–Al exposure resulted in loss of plasma Cl in FW and higher plasma Cl in SW and decreased NKAα1a and NKAα1b abundance. The loss of salinity tolerance in smolts can be explained by the differential sensitivity of NKA isoforms to acid.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2017-0198","usgsCitation":"Regish, A.M., Kelly, J.T., O'Dea, M., and McCormick, S.D., 2018, Sensitivity of Na+/K+-ATPase isoforms to acid and aluminum explains differential effects on Atlantic salmon osmoregulation in fresh water and seawater: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 8, p. 1319-1328, https://doi.org/10.1139/cjfas-2017-0198.","productDescription":"20 p.","startPage":"1319","endPage":"1328","ipdsId":"IP-085421","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":361033,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"75","issue":"8","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Regish, Amy M. 0000-0003-4747-4265 aregish@usgs.gov","orcid":"https://orcid.org/0000-0003-4747-4265","contributorId":5415,"corporation":false,"usgs":true,"family":"Regish","given":"Amy","email":"aregish@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":756692,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kelly, John T.","contributorId":212827,"corporation":false,"usgs":false,"family":"Kelly","given":"John","email":"","middleInitial":"T.","affiliations":[{"id":38688,"text":"Department of Biology & Environmental Science, University of New Haven","active":true,"usgs":false}],"preferred":false,"id":756693,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O'Dea, Michael F.","contributorId":212828,"corporation":false,"usgs":false,"family":"O'Dea","given":"Michael F.","affiliations":[],"preferred":false,"id":756694,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCormick, Stephen D. 0000-0003-0621-6200 smccormick@usgs.gov","orcid":"https://orcid.org/0000-0003-0621-6200","contributorId":139214,"corporation":false,"usgs":true,"family":"McCormick","given":"Stephen","email":"smccormick@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":756695,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70199155,"text":"70199155 - 2018 - Convergence of marine megafauna movement patterns in coastal and open oceans","interactions":[],"lastModifiedDate":"2018-09-07T14:11:59","indexId":"70199155","displayToPublicDate":"2018-03-01T14:11:45","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Convergence of marine megafauna movement patterns in coastal and open oceans","docAbstract":"The extent of increasing anthropogenic impacts on large marine vertebrates partly depends on the animals’ movement patterns. Effective conservation requires identification of the key drivers of movement including intrinsic properties and extrinsic constraints associated with the dynamic nature of the environments the animals inhabit. However, the relative importance of intrinsic versus extrinsic factors remains elusive. We analyze a global dataset of ∼2.8 million locations from >2,600 tracked individuals across 50 marine vertebrates evolutionarily separated by millions of years and using different locomotion modes (fly, swim, walk/paddle). Strikingly, movement patterns show a remarkable convergence, being strongly conserved across species and independent of body length and mass, despite these traits ranging over 10 orders of magnitude among the species studied. This represents a fundamental difference between marine and terrestrial vertebrates not previously identified, likely linked to the reduced costs of locomotion in water. Movement patterns were primarily explained by the interaction between species-specific traits and the habitat(s) they move through, resulting in complex movement patterns when moving close to coasts compared with more predictable patterns when moving in open oceans. This distinct difference may be associated with greater complexity within coastal microhabitats, highlighting a critical role of preferred habitat in shaping marine vertebrate global movements. Efforts to develop understanding of the characteristics of vertebrate movement should consider the habitat(s) through which they move to identify how movement patterns will alter with forecasted severe ocean changes, such as reduced Arctic sea ice cover, sea level rise, and declining oxygen content.
","language":"English","publisher":"National Academy of Sciences of the United States of America","doi":"10.1073/pnas.1716137115","usgsCitation":"Sequeira, A., Rodriguez, J., Eguíluz, V., Harcourt, R., Hindell, M., Sims, D., Duarte, C., Costa, D., Fernandez-Gracia, J., Ferreira, L., Hays, G., Heupel, M., Meekan, M., Aven, A., Bailleul, F., Baylis, A.M., Berumen, M.L., Braun, C.D., Burns, J., Caley, M., Campbell, R., Carmichael, R., Clua, E., Einoder, L.D., Friedlaender, A., Goebel, M.E., Goldsworthy, S., Guinet, C., Gunn, J., Hamer, D., Hammerschlag, N., Hammill, M., Hückstädt, L., Humphries, N., Lea, M., Lowther, A., Mackay, A., McHuron, E., Mckenzie, J., McLeay, L., McMahon, C., Mengersen, K., Muelbert, M.M., Pagano, A.M., Page, B., Queiroz, N., Robinson, P.W., Shaffer, S.A., Shivji, M., Skomal, G., Thorrold, S., Villegas-Amtmann, S., Weise, M., Wells, R., Wetherbee, B., Wiebkin, A., Wienecke, B., and Thums, M., 2018, Convergence of marine megafauna movement patterns in coastal and open oceans: Proceedings of the National Academy of Sciences, v. 115, no. 12, p. 3072-3077, https://doi.org/10.1073/pnas.1716137115.","productDescription":"6 p.","startPage":"3072","endPage":"3077","ipdsId":"IP-089008","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":468941,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.1716137115","text":"Publisher Index Page"},{"id":437992,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7RV0MK4","text":"USGS data release","linkHelpText":"Locations Collected 1985-2015 from Female Polar Bears (Ursus maritimus) with Dependent Young Instrumented in the Southern Beaufort Sea with Satellite-linked Transmitters by the USGS"},{"id":357121,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"115","issue":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5b98a2eae4b0702d0e84300e","contributors":{"authors":[{"text":"Sequeira, A. 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Nitrogen (N) and phosphorus (P) commonly stimulate phytoplankton production in lakes, but recent observations from lakes from an agricultural region suggest that nitrate may have a subsidy‐stress effect on chlorophyll a (Chl a). It is unclear, however, how generalizable this effect might be. Here, we analyzed a large water quality dataset of 2385 lakes spanning 60 regions across 17 states in the Northeastern and Midwestern U.S. to determine if N subsidy‐stress effects on phytoplankton are common and to identify regional landscape characteristics promoting N stress effects in lakes. We used a Bayesian hierarchical modeling framework to test our hypothesis that Chl a–total N (TN) threshold relationships would be common across the central agricultural region of the U.S. (“the Corn Belt”), where lake N and P concentrations are high. Data aggregated across all regions indicated that high TN concentrations had a negative effect on Chl a in lakes with concurrent high total P. This large‐scale pattern was driven by relationships within only a subset of regions, however. Eight regions were identified as having Chl a–TN threshold relationships, but only two of these regions located within the Corn Belt clearly demonstrated this subsidy‐stress relationship. N stress effects were not consistent across other intense agricultural regions, as we hypothesized. These findings suggest that interactions among regional land use and land cover, climate, and hydrogeology may be important in determining the synergistic conditions leading to N subsidy‐stress effects on lake phytoplankton.
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\"}}]}","volume":"63","issue":"S1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-14","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc09e","contributors":{"authors":[{"text":"Filstrup, Christopher T.","contributorId":169032,"corporation":false,"usgs":false,"family":"Filstrup","given":"Christopher","email":"","middleInitial":"T.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":734713,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":734651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oliver, Samantha K.","contributorId":169273,"corporation":false,"usgs":false,"family":"Oliver","given":"Samantha K.","affiliations":[],"preferred":false,"id":734714,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stow, Craig A.","contributorId":204103,"corporation":false,"usgs":false,"family":"Stow","given":"Craig","email":"","middleInitial":"A.","affiliations":[{"id":36843,"text":"NOAA, Great Lakes Environmental Research Lab","active":true,"usgs":false}],"preferred":false,"id":734715,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Webster, Katherine E.","contributorId":147903,"corporation":false,"usgs":false,"family":"Webster","given":"Katherine","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734716,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stanley, Emily H.","contributorId":55725,"corporation":false,"usgs":false,"family":"Stanley","given":"Emily","email":"","middleInitial":"H.","affiliations":[{"id":12951,"text":"Center for Limnology, University of Wisconsin Madison","active":true,"usgs":false}],"preferred":false,"id":734717,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Downing, John A.","contributorId":70348,"corporation":false,"usgs":true,"family":"Downing","given":"John A.","affiliations":[],"preferred":false,"id":734718,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196711,"text":"70196711 - 2018 - Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","interactions":[],"lastModifiedDate":"2019-06-27T07:53:35","indexId":"70196711","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2620,"text":"Limnology and Oceanography","active":true,"publicationSubtype":{"id":10}},"title":"Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie","docAbstract":"Large lakes provide a variety of ecological services to surrounding cities and communities. Many of these services are supported by ecological processes that are threatened by the increasing prevalence of cyanobacterial blooms which occur as aquatic ecosystems experience cultural eutrophication. Over the past 10 yr, Lake Erie experienced cyanobacterial blooms of increasing severity and frequency, which have resulted in impaired drinking water for the surrounding communities. Cyanobacterial blooms may impact ecological processes that support other services, but many of these impacts have not been documented. Secondary production (production of primary consumers) is an important process that supports economically important higher trophic levels. Cyanobacterial blooms may influence secondary production because cyanobacteria are a poor‐quality food resource and cyanotoxins may be harmful to consumers. Over 3 yr at 34 sites across the western basin of Lake Erie, we measured three indices of secondary production that focus on the dominant bivalve taxa: (1) growth of a native unionid mussel, (2) the size of young‐of‐year dreissenid mussels, and (3) the mass of colonizing animals on a Hester‐Dendy sampler. Associations between these indices and cyanobacterial data were estimated to assess whether cyanobacteria are associated with variation in secondary production in the western basin of Lake Erie. The results suggest cyanobacterial abundance alone is only weakly associated with secondary production, but that cyanotoxins have a larger effect on secondary production. Given recurring late‐summer cyanobacterial blooms, this impact on secondary production has the potential to undermine Lake Erie's ability to sustain important ecosystem services.
","language":"English","publisher":"Association for the Sciences of Limnology and Oceanography (ASLO)","doi":"10.1002/lno.10733","usgsCitation":"Larson, J.H., Evans, M.A., Kennedy, R.J., Bailey, S., Loftin, K.A., Laughrey, Z.R., Femmer, R., Schaeffer, J., Richardson, W.B., Wynne, T., Nelson, J., and Duris, J.W., 2018, Associations between cyanobacteria and indices of secondary production in the western basin of Lake Erie: Limnology and Oceanography, v. 63, no. S1, p. S232-S243, https://doi.org/10.1002/lno.10733.","productDescription":"12 p.","startPage":"S232","endPage":"S243","ipdsId":"IP-085475","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":468950,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/lno.10733","text":"Publisher Index Page"},{"id":437994,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QSRU0E","text":"USGS data release","linkHelpText":"Temperature and invertebrate community composition at nearshore Great Lakes sites, 2013-2016"},{"id":353756,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Erie","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.51669311523438,\n 41.51063406062076\n ],\n [\n -82.77923583984375,\n 41.51063406062076\n ],\n [\n -82.77923583984375,\n 42.04011410708205\n ],\n [\n -83.51669311523438,\n 42.04011410708205\n ],\n [\n -83.51669311523438,\n 41.51063406062076\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"63","issue":"S1","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-23","publicationStatus":"PW","scienceBaseUri":"5afee70fe4b0da30c1bfc0a4","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734118,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Evans, Mary Anne 0000-0002-1627-7210 maevans@usgs.gov","orcid":"https://orcid.org/0000-0002-1627-7210","contributorId":4883,"corporation":false,"usgs":true,"family":"Evans","given":"Mary","email":"maevans@usgs.gov","middleInitial":"Anne","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":734119,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Robert J. 0000-0003-2135-5022 rkennedy@usgs.gov","orcid":"https://orcid.org/0000-0003-2135-5022","contributorId":3652,"corporation":false,"usgs":true,"family":"Kennedy","given":"Robert","email":"rkennedy@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734120,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bailey, Sean 0000-0003-0361-7914 sbailey@usgs.gov","orcid":"https://orcid.org/0000-0003-0361-7914","contributorId":198515,"corporation":false,"usgs":true,"family":"Bailey","given":"Sean","email":"sbailey@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734121,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Loftin, Keith A. 0000-0001-5291-876X kloftin@usgs.gov","orcid":"https://orcid.org/0000-0001-5291-876X","contributorId":868,"corporation":false,"usgs":true,"family":"Loftin","given":"Keith","email":"kloftin@usgs.gov","middleInitial":"A.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":734122,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Laughrey, Zachary R. 0000-0002-7630-2078 zlaughrey@usgs.gov","orcid":"https://orcid.org/0000-0002-7630-2078","contributorId":198516,"corporation":false,"usgs":true,"family":"Laughrey","given":"Zachary","email":"zlaughrey@usgs.gov","middleInitial":"R.","affiliations":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"preferred":true,"id":734123,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Femmer, Robin 0000-0003-4896-918X rfemmer@usgs.gov","orcid":"https://orcid.org/0000-0003-4896-918X","contributorId":198517,"corporation":false,"usgs":true,"family":"Femmer","given":"Robin","email":"rfemmer@usgs.gov","affiliations":[],"preferred":true,"id":734124,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schaeffer, Jeff 0000-0003-3430-0872 jschaeffer@usgs.gov","orcid":"https://orcid.org/0000-0003-3430-0872","contributorId":2041,"corporation":false,"usgs":true,"family":"Schaeffer","given":"Jeff","email":"jschaeffer@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":734125,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Richardson, William B. 0000-0002-7471-4394 wrichardson@usgs.gov","orcid":"https://orcid.org/0000-0002-7471-4394","contributorId":3277,"corporation":false,"usgs":true,"family":"Richardson","given":"William","email":"wrichardson@usgs.gov","middleInitial":"B.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":734126,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wynne, Timothy","contributorId":147819,"corporation":false,"usgs":false,"family":"Wynne","given":"Timothy","affiliations":[{"id":16942,"text":"National Oceanic and Atmospheric Administration, Silver Spring, Maryland","active":true,"usgs":false}],"preferred":false,"id":734127,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Nelson, J. C. 0000-0002-7105-0107 jcnelson@usgs.gov","orcid":"https://orcid.org/0000-0002-7105-0107","contributorId":459,"corporation":false,"usgs":true,"family":"Nelson","given":"J. C.","email":"jcnelson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":false,"id":734128,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Duris, Joseph W. 0000-0002-8669-8109 jwduris@usgs.gov","orcid":"https://orcid.org/0000-0002-8669-8109","contributorId":172426,"corporation":false,"usgs":true,"family":"Duris","given":"Joseph","email":"jwduris@usgs.gov","middleInitial":"W.","affiliations":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"preferred":false,"id":734129,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70195819,"text":"70195819 - 2018 - Species distribution modeling in regions of high need and limited data: waterfowl of China","interactions":[],"lastModifiedDate":"2018-03-05T10:59:14","indexId":"70195819","displayToPublicDate":"2018-03-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5641,"text":"Avian Research","active":true,"publicationSubtype":{"id":10}},"title":"Species distribution modeling in regions of high need and limited data: waterfowl of China","docAbstract":"Background
A number of conservation and societal issues require understanding how species are distributed on the landscape, yet ecologists are often faced with a lack of data to develop models at the resolution and extent desired, resulting in inefficient use of conservation resources. Such a situation presented itself in our attempt to develop waterfowl distribution models as part of a multi-disciplinary team targeting the control of the highly pathogenic H5N1 avian influenza virus in China.
Methods
Faced with limited data, we built species distribution models using a habitat suitability approach for China’s breeding and non-breeding (hereafter, wintering) waterfowl. An extensive review of the literature was used to determine model parameters for habitat modeling. Habitat relationships were implemented in GIS using land cover covariates. Wintering models were validated using waterfowl census data, while breeding models, though developed for many species, were only validated for the one species with sufficient telemetry data available.
Results
We developed suitability models for 42 waterfowl species (30 breeding and 39 wintering) at 1 km resolution for the extent of China, along with cumulative and genus level species richness maps. Breeding season models showed highest waterfowl suitability in wetlands of the high-elevation west-central plateau and northeastern China. Wintering waterfowl suitability was highest in the lowland regions of southeastern China. Validation measures indicated strong performance in predicting species presence. Comparing our model outputs to China’s protected areas indicated that breeding habitat was generally better covered than wintering habitat, and identified locations for which additional research and protection should be prioritized.
Conclusions
These suitability models are the first available for many of China’s waterfowl species, and have direct utility to conservation and habitat planning and prioritizing management of critically important areas, providing an example of how this approach may aid others faced with the challenge of addressing conservation issues with little data to inform decision making.
Species detection error (i.e., imperfect and variable detection probability) is an essential consideration when investigators map distributions and interpret habitat associations. When fish detection error that is due to highly variable instream environments needs to be addressed, sand‐bed streams of the Great Plains represent a unique challenge. We quantified seining detection probability for diminutive Great Plains fishes across a range of sampling conditions in two sand‐bed rivers in Oklahoma. Imperfect detection resulted in underestimates of species occurrence using naïve estimates, particularly for less common fishes. Seining detection probability also varied among fishes and across sampling conditions. We observed a quadratic relationship between water depth and detection probability, in which the exact nature of the relationship was species‐specific and dependent on water clarity. Similarly, the direction of the relationship between water clarity and detection probability was species‐specific and dependent on differences in water depth. The relationship between water temperature and detection probability was also species dependent, where both the magnitude and direction of the relationship varied among fishes. We showed how ignoring detection error confounded an underlying relationship between species occurrence and water depth. Despite imperfect and heterogeneous detection, our results support that determining species absence can be accomplished with two to six spatially replicated seine hauls per 200‐m reach under average sampling conditions; however, required effort would be higher under certain conditions. Detection probability was low for the Arkansas River Shiner Notropis girardi, which is federally listed as threatened, and more than 10 seine hauls per 200‐m reach would be required to assess presence across sampling conditions. Our model allows scientists to estimate sampling effort to confidently assess species occurrence, which maximizes the use of available resources. Increased implementation of approaches that consider detection error promote ecological advancements and conservation and management decisions that are better informed.
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","language":"English","publisher":"ACS","doi":"10.1021/acs.est.7b06388","usgsCitation":"Hladik, M., Main, A., and Goulson, D., 2018, Environmental risks and challenges associated with neonicotinoid insecticides: Environmental Science & Technology, v. 52, no. 6, p. 3329-3335, https://doi.org/10.1021/acs.est.7b06388.","productDescription":"7 p.","startPage":"3329","endPage":"3335","ipdsId":"IP-092573","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":468954,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://figshare.com/articles/journal_contribution/Environmental_risks_and_challenges_associated_with_neonicotinoid_insecticides/23455121","text":"External Repository"},{"id":352161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-26","publicationStatus":"PW","scienceBaseUri":"5afee712e4b0da30c1bfc0d2","contributors":{"authors":[{"text":"Hladik, Michelle L. 0000-0002-0891-2712 mhladik@usgs.gov","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":189904,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle L.","email":"mhladik@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":729914,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Main, Anson 0000-0001-9539-760X","orcid":"https://orcid.org/0000-0001-9539-760X","contributorId":202852,"corporation":false,"usgs":false,"family":"Main","given":"Anson","email":"","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":729915,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Goulson, Dave 0000-0003-4421-2876","orcid":"https://orcid.org/0000-0003-4421-2876","contributorId":202853,"corporation":false,"usgs":false,"family":"Goulson","given":"Dave","email":"","affiliations":[{"id":36533,"text":"Univesity of Sussex, Brighton, U.K.","active":true,"usgs":false}],"preferred":false,"id":729916,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}