Changes in municipal and industrial point-source discharges over time have been an important factor affecting nutrient trends in many of the Nation’s streams and rivers. This report documents how three U.S. Environmental Protection Agency (EPA) national datasets—the Permit Compliance System, the Integrated Compliance Information System, and the Clean Watersheds Needs Survey—were evaluated for use in the U.S. Geological Survey National Water-Quality Assessment project to assess the causes of nutrient trends. This report also describes how a database of total nitrogen load and total phosphorous load was generated for select wastewater treatment facilities in the United States based on information reported in the EPA Clean Watersheds Needs Survey. Nutrient loads were calculated for the years 1978, 1980, 1982, 1984, 1986, 1988, 1990, 1992, 1996, 2000, 2004, 2008, and 2012 based on average nitrogen and phosphorous concentrations for reported treatment levels and on annual reported flow values.
The EPA Permit Compliance System (PCS) and Integrated Compliance Information System (ICIS), which monitor point-source facility discharges, together are the Nation’s most spatially comprehensive dataset for nutrients released to surface waters. However, datasets for many individual facilities are incomplete, the PCS/ICIS historical data date back only to 1989, and historical data are available for only a limited number of facilities. Additionally, inconsistencies in facility reporting make it difficult to track or identify changes in nutrient discharges over time. Previous efforts made by the U.S. Geological Survey to “fill in” gaps in the PCS/ICIS data were based on statistical methods—missing data were filled in through the use of a statistical model based on the Standard Industrial Classification code, size, and flow class of the facility and on seasonal nutrient discharges of similar facilities. This approach was used to estimate point-source loads for a single point in time; it was not evaluated for use in generating a consistent data series over time.
Another national EPA dataset that is available is the Clean Watersheds Needs Survey (CWNS), conducted every 4 years beginning 1973. The CWNS is an assessment of the capital needs of wastewater facilities to meet the water-quality goals set in the Clean Water Act. Data collected about these facilities include location and contact information for the facilities; population served; flow and treatment level of the facility; estimated capital needs to upgrade, repair, or improve facilities for water quality; and nonpoint-source best management practices.
Total nitrogen and total phosphorous load calculations for each of the CWNS years were based on treatment level information and average annual outflow (in million gallons per day) from each of the facilities that had reported it. Treatment levels categories (such as Primary, Secondary, or Advanced) were substituted with average total nitrogen and total phosphorous concentrations for each treatment level based on those reported in literature. The CWNS dataset, like the PCS/ICIS dataset, has years where facilities did not report either a treatment level or an annual average outflow, or both. To fill in the data gaps, simple linear assumptions were made based on each facility’s responses to the survey in years bracketing the data gap or immediately before or after the data gap if open ended. Treatment level and flow data unique to each facility were used to complete the CWNS dataset for that facility.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175115","usgsCitation":"Ivahnenko, Tamara, 2017, Evaluation and use of U.S. Environmental Protection Agency Clean Watersheds Needs Survey data to quantify nutrient loads to surface water, 1978–2012: U.S. Geological Survey Scientific Investigations Report 2017–5115, 11 p., https://doi.org/10.3133/sir20175115.","productDescription":"Report: iv, 11 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","ipdsId":"IP-082278","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":349388,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5115/coverthb.jpg"},{"id":349584,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7MG7MNN","text":"USGS Data Release","description":"USGS Data Release","linkHelpText":"National USEPA Clean Watershed Needs Survey WWTP nutrient load data 1978 to 2012"},{"id":349389,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5115/sir20175115.pdf","text":"Report","size":"864 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5115"}],"contact":"Program Coordinator, National Water Quality Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The Rio Grande aquifer system constitutes one of the important areas being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173047","usgsCitation":"Musgrove, MaryLynn, 2017, Groundwater quality in the Rio Grande aquifer system, southwestern United States: U.S. Geological Survey Fact Sheet 2017–3047, 4 p., https://doi.org/10.3133/fs20173047.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-084174","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":346610,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3047/fs20173047.pdf","text":"Report","size":"3.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3047"},{"id":346609,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3047/coverthb.jpg"}],"country":"United States","otherGeospatial":"Rio Grande Aquifer System, Southwestern United States Groundwater provides nearly 50 ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109,\n 29\n ],\n [\n -103,\n 29\n ],\n [\n -103,\n 38\n ],\n [\n -109,\n 38\n ],\n [\n -109,\n 29\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
We present a prototype operational loss model based on UCERF3-ETAS, which is the third Uniform California Earthquake Rupture Forecast with an Epidemic Type Aftershock Sequence (ETAS) component. As such, UCERF3-ETAS represents the first earthquake forecast to relax fault segmentation assumptions and to include multi-fault ruptures, elastic-rebound, and spatiotemporal clustering, all of which seem important for generating realistic and useful aftershock statistics. UCERF3-ETAS is nevertheless an approximation of the system, however, so usefulness will vary and potential value needs to be ascertained in the context of each application. We examine this question with respect to statewide loss estimates, exemplifying how risk can be elevated by orders of magnitude due to triggered events following various scenario earthquakes. Two important considerations are the probability gains, relative to loss likelihoods in the absence of main shocks, and the rapid decay of gains with time. Significant uncertainties and model limitations remain, so we hope this paper will inspire similar analyses with respect to other risk metrics to help ascertain whether operationalization of UCERF3-ETAS would be worth the considerable resources required.
","language":"English","publisher":"EERI","doi":"10.1193/011817EQS017M","usgsCitation":"Field, E., Porter, K., and Milner, K., 2017, A prototype operational earthquake loss model for California based on UCERF3-ETAS – A first look at valuation: Earthquake Spectra, v. 33, no. 4, p. 1279-1299, https://doi.org/10.1193/011817EQS017M.","productDescription":"21 p.","startPage":"1279","endPage":"1299","ipdsId":"IP-087973","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":349888,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"33","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-11-01","publicationStatus":"PW","scienceBaseUri":"5a60faeae4b06e28e9c22988","contributors":{"authors":[{"text":"Field, Edward H. 0000-0001-8172-7882 field@usgs.gov","orcid":"https://orcid.org/0000-0001-8172-7882","contributorId":1165,"corporation":false,"usgs":true,"family":"Field","given":"Edward H.","email":"field@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":724680,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Porter, Keith","contributorId":191074,"corporation":false,"usgs":false,"family":"Porter","given":"Keith","affiliations":[],"preferred":false,"id":724681,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Milner, Kevn","contributorId":201228,"corporation":false,"usgs":false,"family":"Milner","given":"Kevn","email":"","affiliations":[],"preferred":false,"id":724682,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70187894,"text":"fs20173040 - 2017 - Groundwater quality in the Piedmont and Blue Ridge crystalline-rock aquifers, eastern United States","interactions":[],"lastModifiedDate":"2020-09-18T19:56:51.396583","indexId":"fs20173040","displayToPublicDate":"2017-12-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3040","title":"Groundwater quality in the Piedmont and Blue Ridge crystalline-rock aquifers, eastern United States","docAbstract":"Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The Piedmont and Blue Ridge crystalline-rock aquifers constitute one of the important areas being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173040","usgsCitation":"Lindsey, Bruce, 2017, Groundwater quality in the Piedmont and Blue Ridge crystalline-rock aquifers, eastern United States (ver. 1.1, September 2020): U.S. Geological Survey Fact Sheet 2017–3040, 4 p., https://doi.org/10.3133/fs20173040.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-083351","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":378456,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2017/3040/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":346605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3040/coverthb.jpg"},{"id":346606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3040/fs20173040_ver1.1.pdf","text":"Report","size":"4.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3040"}],"country":"United States","otherGeospatial":"Piedmont and Blue Ridge Crystalline-Rock Aquifers","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87,\n 32\n ],\n [\n -74,\n 32\n ],\n [\n -74,\n 41\n ],\n [\n -87,\n 41\n ],\n [\n -87,\n 32\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 7, 2017; Version 1.1: September 16, 2020","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
From January to April 2016, the U.S. Geological Survey (USGS), the Mojave Water Agency, and other local water districts made approximately 1,200 water-level measurements in about 645 wells located within 15 separate groundwater basins, collectively referred to as the Mojave River and Morongo groundwater basins. These data document recent conditions and, when compared with older data, changes in groundwater levels. A water-level contour map was drawn using data measured in 2016 that shows the elevation of the water table and general direction of groundwater movement for most of the groundwater basins. Historical water-level data stored in the USGS National Water Information System (https://waterdata.usgs.gov/nwis/) database were used in conjunction with data collected for this study to construct 37 hydrographs to show long-term (1930–2016) and short-term (1990–2016) water-level changes in the study area.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3391","collaboration":"Prepared in cooperation with the Mojave Water Agency","usgsCitation":"Dick, M.C., and Kjos, A.R., 2017, Regional water table (2016) in the Mojave River and Morongo groundwater basins, southwestern Mojave Desert, California: U.S. Geological Survey Scientific Investigations Map 3391, scale 1:170,000, https://doi.org/10.3133/sim3391.","productDescription":"Map: 42.62 x 37.53 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-083520","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":349478,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3391/coverthb_.jpg"},{"id":349479,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3391/sim3391.pdf","text":"Report","size":"34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3391"},{"id":349633,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7GB2291","text":"Data Release","description":"SIM 3391","linkHelpText":"Regional Water Table (2016) in the Mojave River and Morongo Groundwater Basins, Southwestern Mojave Desert, California Data Release"}],"country":"United States","state":"California","otherGeospatial":"Mojave Desert, Mojave River and Morongo groundwater basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116,\n 34.0833\n ],\n [\n -117.8333,\n 34.0833\n ],\n [\n -117.8333,\n 35.25\n ],\n [\n -116,\n 35.25\n ],\n [\n -116,\n 34.0833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
The Navajo (N) aquifer is an extensive aquifer and the primary source of groundwater in the 5,400-square-mile Black Mesa area in northeastern Arizona. Availability of water is an important issue in northeastern Arizona because of continued water requirements for industrial and municipal use by a growing population and because of low precipitation in the arid climate of the Black Mesa area. Precipitation in the area typically is between 6 and 16 inches per year.
The U.S. Geological Survey water-monitoring program in the Black Mesa area began in 1971 and provides information about the long-term effects of groundwater withdrawals from the N aquifer for industrial and municipal uses. This report presents results of data collected as part of the monitoring program in the Black Mesa area from January 2013 to December 2015. The monitoring program includes measurements of (1) groundwater withdrawals (pumping), (2) groundwater levels, (3) spring discharge, (4) surface-water discharge, and (5) groundwater chemistry.
In 2013, total groundwater withdrawals were 3,980 acre-feet (ft), in 2014 total withdrawals were 4,170 acre-ft, and in 2015 total withdrawals were 3,970 acre-ft. From 2013 to 2015 total withdrawals varied by less than 5 percent.
From 2014 to 2015, annually measured water levels in the Black Mesa area declined in 9 of 15 wells that were available for comparison in the unconfined areas of the N aquifer, and the median change was -0.1 feet. Water levels declined in 3 of 16 wells measured in the confined area of the aquifer. The median change for the confined area of the aquifer was 0.6 feet. From the prestress period (prior to 1965) to 2015, the median water-level change for 34 wells in both the confined and unconfined areas was -13.2 feet; the median water-level changes were -1.7 feet for 16 wells measured in the unconfined areas and -42.3 feet for 18 wells measured in the confined area.
Spring flow was measured at four springs in 2014. Flow fluctuated during the period of record for Burro Spring and Unnamed Spring near Dennehotso, but a decreasing trend was statistically significant (p<0.05) at Moenkopi School Spring and Pasture Canyon Spring. Discharge at Burro Spring has remained relatively constant since it was first measured in the 1980s and discharge at Unnamed Spring near Dennehotso has fluctuated for the period of record. Trend analysis for discharge at Moenkopi and Pasture Canyon Springs yielded a slope significantly different (p<0.05) from zero.
Continuous records of surface-water discharge in the Black Mesa area were collected from streamflow-gaging stations at the following sites: Moenkopi Wash at Moenkopi 09401260 (1976 to 2015), Dinnebito Wash near Sand Springs 09401110 (1993 to 2015), Polacca Wash near Second Mesa 09400568 (1994 to 2015), and Pasture Canyon Springs 09401265 (2004 to 2015). Median winter flows (November through February) of each water year were used as an index of the amount of groundwater discharge at the above-named sites. For the period of record of each streamflow-gaging station, the median winter flows have generally remained constant, which suggests no change in groundwater discharge.
In 2014, water samples collected from four springs in the Black Mesa area were analyzed for selected chemical constituents, and the results were compared with previous analyses. Dissolved solids, chloride, and sulfate concentrations increased at Moenkopi School Spring during the more than 25 years of record at that site. Concentrations of dissolved solids, chloride, and sulfate at Pasture Canyon Spring have not varied significantly (p>0.05) since the early 1980s, and there is no increasing or decreasing trend in those data. Concentrations of dissolved solids, chloride, and sulfate at Burro Spring and Unnamed Spring near Dennehotso have varied for the period of record, but there is no increasing or decreasing statistical trend in the data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171127","collaboration":"Prepared in cooperation with the Navajo Nation and the Arizona Department of Water Resources","usgsCitation":"Macy, J.P., and Mason, J.P., 2017, Groundwater, surface-water, and water-chemistry data, Black Mesa area, northeastern Arizona—2013–2015: U.S. Geological Survey Open-File Report 2017–1127, 49 p., https://doi.org/10.3133/ofr20171127.","productDescription":"v., 49 p.","numberOfPages":"58","onlineOnly":"Y","ipdsId":"IP-083213","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":349866,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1127/coverthb.jpg"},{"id":349867,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1127/ofr20171127.pdf","text":"Report","size":"2.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1127"}],"country":"United States","state":"Arizona","otherGeospatial":"Black Mesa area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.5,\n 35.5\n ],\n [\n -109.5,\n 35.5\n ],\n [\n -109.5,\n 37\n ],\n [\n -111.5,\n 37\n ],\n [\n -111.5,\n 35.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
In almost all past inversions of large-earthquake ground motions for rupture behavior, the goal of the inversion is to find the “best fitting” rupture model that predicts ground motions which optimize some function of the difference between predicted and observed ground motions. This type of inversion was pioneered in the linear-inverse sense by Olson and Apsel (1982), who minimized the square of the difference between observed and simulated motions (“least squares”) while simultaneously minimizing the rupture-model norm (by setting the null-space component of the rupture model to zero), and has been extended in many ways, one of which is the use of nonlinear inversion schemes such as simulated annealing algorithms that optimize some other misfit function. For example, the simulated annealing algorithm of Piatanesi and others (2007) finds the rupture model that minimizes a “cost” function which combines a least-squares and a waveform-correlation measure of misfit.
All such inversions that look for a unique “best” model have at least three problems. (1) They have removed the null-space component of the rupture model—that is, an infinite family of rupture models that all fit the data equally well have been narrowed down to a single model. Some property of interest in the rupture model might have been discarded in this winnowing process. (2) Smoothing constraints are commonly used to yield a unique “best” model, in which case spatially rough rupture models will have been discarded, even if they provide a good fit to the data. (3) No estimate of confidence in the resulting rupture models can be given because the effects of unknown errors in the Green’s functions (“theory errors”) have not been assessed. In inversion for rupture behavior, these theory errors are generally larger than the data errors caused by ground noise and instrumental limitations, and so overfitting of the data is probably ubiquitous for such inversions.
Recently, attention has turned to the inclusion of theory errors in the inversion process. Yagi and Fukahata (2011) made an important contribution by presenting a method to estimate the uncertainties in predicted large-earthquake ground motions due to uncertainties in the Green’s functions. Here we derive their result and compare it with the results of other recent studies that look at theory errors in a Bayesian inversion context particularly those by Bodin and others (2012), Duputel and others (2012), Dettmer and others (2014), and Minson and others (2014).
Notably, in all these studies, the estimates of theory error were obtained from theoretical considerations alone; none of the investigators actually measured Green’s function errors. Large earthquakes typically have aftershocks, which, if their rupture surfaces are physically small enough, can be considered point evaluations of the real Green’s functions of the Earth. Here we simulate smallaftershock ground motions with (erroneous) theoretical Green’s functions. Taking differences between aftershock ground motions and simulated motions to be the “theory error,” we derive a statistical model of the sources of discrepancies between the theoretical and real Green’s functions. We use this model with an extended frequency-domain version of the time-domain theory of Yagi and Fukahata (2011) to determine the expected variance 2 τ caused by Green’s function error in ground motions from a larger (nonpoint) earthquake that we seek to model.
We also differ from the above-mentioned Bayesian inversions in our handling of the nonuniqueness problem of seismic inversion. We follow the philosophy of Segall and Du (1993), who, instead of looking for a best-fitting model, looked for slip models that answered specific questions about the earthquakes they studied. In their Bayesian inversions, they inductively derived a posterior probability-density function (PDF) for every model parameter. We instead seek to find two extremal rupture models whose ground motions fit the data within the error bounds given by 2 τ , as quantified by using a chi-squared test described below. So, we can ask questions such as, “What are the rupture models with the highest and lowest average rupture speed consistent with the theory errors?” Having found those models, we can then say with confidence that the true rupture speed is somewhere between those values. Although the Bayesian approach gives a complete solution to the inverse problem, it is computationally demanding: Minson and others (2014) needed 1010 forward kinematic simulations to derive their posterior probability distribution. In our approach, only about107 simulations are needed. Moreover, in practical application, only a small set of rupture models may be needed to answer the relevant questions—for example, determining the maximum likelihood solution (achievable through standard inversion techniques) and the two rupture models bounding some property of interest.
The specific property that we wish to investigate is the correlation between various rupturemodel parameters, such as peak slip velocity and rupture velocity, in models of real earthquakes. In some simulations of ground motions for hypothetical large earthquakes, such as those by Aagaard and others (2010) and the Southern California Earthquake Center Broadband Simulation Platform (Graves and Pitarka, 2015), rupture speed is assumed to correlate locally with peak slip, although there is evidence that rupture speed should correlate better with peak slip speed, owing to its dependence on local stress drop. We may be able to determine ways to modify Piatanesi and others’s (2007) inversion’s “cost” function to find rupture models with either high or low degrees of correlation between pairs of rupture parameters. We propose a cost function designed to find these two extremal models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171151","usgsCitation":"Spudich, P., Cirella, A., Scognamiglio, L., and Tinti, E., 2017, Analysis of the variability in ground-motion synthesis and inversion: U.S. Geological Survey Open-File Report 2017–1151, 39 p., https://doi.org/10.3133/ofr20171151.","productDescription":"iv, 39 p.","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-087954","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":349837,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1151/ofr20171151_.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1151"},{"id":349836,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1151/coverthb.jpg"}],"contact":"Director,
Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025
Interest in translational ecology (TE) – a research approach that yields useful scientific outcomes through ongoing collaboration between scientists and stakeholders – is growing among both of these groups. Translational ecology brings together participants from different cultures and with different professional incentives. We address ways to cultivate a culture of TE, such as investing time in understanding one another's decision context and incentives, and outline common entry points to translational research, such as working through boundary organizations, building place-based research programs, and being open to opportunities as they arise. We also highlight common institutional constraints on scientists and practitioners, and ways in which collaborative research can overcome these limitations, emphasizing considerations for navigating TE within current institutional frameworks, but also pointing out ways in which institutions are evolving to facilitate translational research approaches.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.1734","usgsCitation":"Hallett, L.M., Morelli, T.L., Gerber, L.R., Moritz, M.A., Schwartz, M.W., Stephenson, N.L., Tank, J.L., Williamson, M.A., and Woodhouse, C.A., 2017, Navigating translational ecology: Creating opportunities for scientist participation: Frontiers in Ecology and the Environment, v. 15, no. 10, p. 578-586, https://doi.org/10.1002/fee.1734.","productDescription":"9 p.","startPage":"578","endPage":"586","ipdsId":"IP-074729","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":469241,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.1734","text":"Publisher Index Page"},{"id":349876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"10","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faeae4b06e28e9c22985","contributors":{"authors":[{"text":"Hallett, Lauren M.","contributorId":175310,"corporation":false,"usgs":false,"family":"Hallett","given":"Lauren","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":724698,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":724699,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gerber, Leah R.","contributorId":147236,"corporation":false,"usgs":false,"family":"Gerber","given":"Leah","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":724700,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Moritz, Max A.","contributorId":182434,"corporation":false,"usgs":false,"family":"Moritz","given":"Max","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724701,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schwartz, Mark W.","contributorId":145938,"corporation":false,"usgs":false,"family":"Schwartz","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":724702,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":724697,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tank, Jennifer L.","contributorId":201231,"corporation":false,"usgs":false,"family":"Tank","given":"Jennifer","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":724703,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Williamson, Matthew A.","contributorId":201232,"corporation":false,"usgs":false,"family":"Williamson","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724704,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Woodhouse, Connie A.","contributorId":187601,"corporation":false,"usgs":false,"family":"Woodhouse","given":"Connie","email":"","middleInitial":"A.","affiliations":[{"id":32413,"text":"University of Arizona, Tucson, AZ, USA, 85721","active":true,"usgs":false}],"preferred":false,"id":724705,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70189113,"text":"fs20173055 - 2017 - Groundwater quality in the glacial aquifer system, United States","interactions":[],"lastModifiedDate":"2017-12-07T11:04:24","indexId":"fs20173055","displayToPublicDate":"2017-12-07T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-3055","title":"Groundwater quality in the glacial aquifer system, United States","docAbstract":"Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The glacial aquifer system constitutes one of the important areas being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173055","usgsCitation":"Stackelberg, Paul, 2017, Groundwater quality in the glacial aquifer system, United States: U.S. Geological Survey Fact Sheet 2017–3055, 4 p., https://doi.org/10.3133/fs20173055.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-082639","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":346613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3055/coverthb.jpg"},{"id":346614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3055/fs20173055.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3055"}],"country":"United States","otherGeospatial":"Glacial Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.541015625,\n 37.579412513438385\n ],\n [\n -67.060546875,\n 37.579412513438385\n ],\n [\n -67.060546875,\n 49.210420445650286\n ],\n [\n -124.541015625,\n 49.210420445650286\n ],\n [\n -124.541015625,\n 37.579412513438385\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
Groundwater provides nearly 50 percent of the Nation’s drinking water. To help protect this vital resource, the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) Project assesses groundwater quality in aquifers that are important sources of drinking water (Burow and Belitz, 2014). The Cambrian-Ordovician aquifer system constitutes one of the important areas being evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173056","usgsCitation":"Stackelberg, Paul, 2017, Groundwater quality in the Cambrian-Ordovician aquifer system, midwestern United States: U.S. Geological Survey Fact Sheet 2017–3056, 4 p., https://doi.org/10.3133/fs20173056.","productDescription":"4 p.","numberOfPages":"4","ipdsId":"IP-082365","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":346612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3056/fs20173056.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3056"},{"id":346611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3056/coverthb.jpg"}],"country":"United States","state":"Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Wisconsin","otherGeospatial":"Cambrian-Ordovician Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.52587890625,\n 38.54816542304656\n ],\n [\n -83.671875,\n 38.54816542304656\n ],\n [\n -83.671875,\n 46.66451741754235\n ],\n [\n -96.52587890625,\n 46.66451741754235\n ],\n [\n -96.52587890625,\n 38.54816542304656\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"National Water-Quality Assessment (NAWQA) Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, Virginia 20192
Comparing sea otter recovery in California (CA) and British Columbia (BC) reveals key ecosystem properties that shape top-down effects in seagrass communities. We review potential ecosystem drivers of sea otter foraging in CA and BC seagrass beds, including the role of coastline complexity and environmental stress on sea otter effects. In BC, we find greater species richness across seagrass trophic assemblages. Furthermore, Cancer spp. crabs, an important link in the seagrass trophic cascade observed in CA, are less common. Additionally, the more recent reintroduction of sea otters, more complex coastline, and reduced environmental stress in BC seagrass habitats supports the hypotheses that sea otter foraging pressure is currently reduced there. In order to manage the ecosystem features that lead to regional differences in top predator effects in seagrass communities, we review our findings, their spatial and temporal constraints, and present a social-ecological framework for future research.
The stocking of fish in riverine systems to re-establish stocks for conservation and management appears limited to a few species and often occurs in reaches impacted by impoundments. Stocking of sport fish species such as centrarchids and ictalurids is often restricted to lentic environments, although stocking in lotic environments is feasible with variable success. R. L. Harris Dam on the Tallapoosa River, Alabama is the newest and uppermost dam facility on the river (operating since 1983); flows from the dam have been managed adaptively for multiple stakeholder objectives since 2005. One of the stakeholders’ primary objectives is to provide quality sport fisheries in the Tallapoosa River in the managed area below the dam. Historically, ictalurids and cyprinids dominated the river above Lake Martin. However, investigations after Harris Dam closed have detected a shift in community structure to domination by centrarchids. Flow management (termed the Green Plan) has been occurring since March 2005; however, sport fish populations as measured by recruitment of age-1 sport fishes below the dam has not responded adequately to flow management. The objectives of this research were to: (1) determine if stocking Channel Catfish Ictalurus punctatus and Redbreast Sunfish Lepomis auritus influences year-class strength; (2) estimate vital rates (i.e. growth, mortality, and recruitment) for Channel Catfish populations for use in an age-based population model; and (3) identify age-specific survivorship and fecundity rates contributing to Channel Catfish population stability. No marked Redbreast Sunfish were recaptured due to poor marking efficacy and therefore no further analysis was conducted with this species. Stocked Channel Catfish, similarly, were not recaptured, leaving reasons for non-recapture unknown. Matrix models exploring vital rates illustrated survival to age-1 for Channel Catfish to be less than 0.03% and that survival through ages 2 – 4 had equal contribution to overall population growth, indicating recruitment limitation may impact population size and stability. Results from this study indicate stock enhancement of sport fish populations below Harris Dam may not be an effective management technique at this time.
","language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Lloyd, M.C., Lai, Q., Sammons, S., and Irwin, E.R., 2017, Experimental stocking of sport fish in the regulated Tallapoosa River to determine critical periods for recruitment: Cooperator Science Series 128-2017, 24 p.","productDescription":"24 p.","ipdsId":"IP-086160","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":353855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":353845,"type":{"id":15,"text":"Index Page"},"url":"https://digitalmedia.fws.gov/cdm/ref/collection/document/id/2218"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.7208251953125,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 32.93953889877841\n ],\n [\n -85.48324584960936,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 33.6283419913718\n ],\n [\n -85.7208251953125,\n 32.93953889877841\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee79ce4b0da30c1bfc2f0","contributors":{"authors":[{"text":"Lloyd, M. Clint","contributorId":204520,"corporation":false,"usgs":false,"family":"Lloyd","given":"M.","email":"","middleInitial":"Clint","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":734239,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lai, Quan","contributorId":204521,"corporation":false,"usgs":false,"family":"Lai","given":"Quan","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":734240,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sammons, Steve","contributorId":204522,"corporation":false,"usgs":false,"family":"Sammons","given":"Steve","email":"","affiliations":[{"id":36951,"text":"Auburnj University","active":true,"usgs":false}],"preferred":false,"id":734241,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734238,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70195106,"text":"70195106 - 2017 - Considerations in comparing the U.S. Geological Survey one‐year induced‐seismicity hazard models with “Did You Feel It?” and instrumental data","interactions":[],"lastModifiedDate":"2018-02-08T12:43:25","indexId":"70195106","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Considerations in comparing the U.S. Geological Survey one‐year induced‐seismicity hazard models with “Did You Feel It?” and instrumental data","docAbstract":"The recent steep increase in seismicity rates in Oklahoma, southern Kansas, and other parts of the central United States led the U.S. Geological Survey (USGS) to develop, for the first time, a probabilistic seismic hazard forecast for one year (2016) that incorporates induced seismicity. In this study, we explore a process to ground‐truth the hazard model by comparing it with two databases of observations: modified Mercalli intensity (MMI) data from the “Did You Feel It?” (DYFI) system and peak ground acceleration (PGA) values from instrumental data. Because the 2016 hazard model was heavily based on earthquake catalogs from 2014 to 2015, this initial comparison utilized observations from these years. Annualized exceedance rates were calculated with the DYFI and instrumental data for direct comparison with the model. These comparisons required assessment of the options for converting hazard model results and instrumental data from PGA to MMI for comparison with the DYFI data. In addition, to account for known differences that affect the comparisons, the instrumental PGA and DYFI data were declustered, and the hazard model was adjusted for local site conditions. With these adjustments, examples at sites with the most data show reasonable agreement in the exceedance rates. However, the comparisons were complicated by the spatial and temporal completeness of the instrumental and DYFI observations. Furthermore, most of the DYFI responses are in the MMI II–IV range, whereas the hazard model is oriented toward forecasts at higher ground‐motion intensities, usually above about MMI IV. Nevertheless, the study demonstrates some of the issues that arise in making these comparisons, thereby informing future efforts to ground‐truth and improve hazard modeling for induced‐seismicity applications.
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fishes","interactions":[],"lastModifiedDate":"2018-03-28T10:55:15","indexId":"70194563","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Assessing diet compositions of Lake Ontario predators using fatty acid profiles of prey fishes","docAbstract":"Fatty acid profiles are used in food web studies to assess trophic interactions between predator and prey. The present study provides the first comprehensive fatty acid dataset for important prey and predator species in Lake Ontario. Three major prey fish (alewife, rainbow smelt, and round goby) were collected at three sites along the southern shore of Lake Ontario during the spring and fall of 2013, and predator species were collected in similar locations during the summer of 2013. Fatty acid compositions were compared among all prey species, all predator species, and information from both predator and prey was used to infer foraging differences among predators. Seasonal differences in fatty acids were found within each prey species studied. Differences among prey species were greater than any spatio-temporal differences detected within species. Fatty acids of predators revealed species-specific differences that matched known foraging habits. Chinook and Coho salmon, which are known to select alewife as their dominant prey item, had relatively little variation in fatty acid profiles. Conversely, brown trout, lake trout, yellow perch and esocids had highly variable fatty acid profiles and likely highly variable diet compositions. In general, our data suggested three dominant foraging patterns: 1) diet composed of nearly exclusively alewife for Chinook and Coho Salmon; 2) a mixed diet of alewife and round goby for brown and lake trout, and both rock and smallmouth bass; 3) a diet that is likely comprised of forage fishes other than those included in our study for northern pike and chain pickerel.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2016.12.008","usgsCitation":"Happell, A., Pattridge, R., Rinchard, J., and Walsh, M., 2017, Assessing diet compositions of Lake Ontario predators using fatty acid profiles of prey fishes: Journal of Great Lakes Research, v. 43, no. 5, p. 838-845, https://doi.org/10.1016/j.jglr.2016.12.008.","productDescription":"8 p.","startPage":"838","endPage":"845","ipdsId":"IP-079964","costCenters":[{"id":324,"text":"Great Lakes Science 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isotopes","docAbstract":"The forage fish communities of the Laurentian Great Lakes continue to experience changes that have altered ecosystem structure, yet little is known about how they partition resources. Seasonal, spatial and body size variation in δ13C and δ15N was used to assess isotopic niche overlap and resource and habitat partitioning among the five common offshore Lake Ontario forage fish species (n = 2037) [Alewife (Alosa pseudoharengus), Rainbow Smelt (Osmerus mordax), Round Goby (Neogobius melanostomus), and Deepwater (Myoxocephalus thompsonii) and Slimy (Cottus cognatus) Sculpin]. Round Goby had the largest isotopic niche (6.1‰2, standard ellipse area (SEAC)), followed by Alewife (3.4‰2) while Rainbow Smelt, Slimy Sculpin and Deepwater Sculpin had the smallest and similar niche size (1.7-1.8‰2), with only the Sculpin species showing significant isotopic niche overlap (>63%). Stable isotopes in Alewife, Round Goby and Rainbow Smelt varied with location, season and size, but did not in the Sculpin spp. Lake Ontario forage fish species have partitioned food and habitat resources, and non-native Alewife and Round Goby have the largest isotopic niche, suggestive of a boarder ecological niche, and may contribute to their current high abundance.","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2016-0150","usgsCitation":"Mumby, J., Johson, T., Stewart, T., Halfyard, E., Walsh, M., Weidel, B., Lantry, J., and Fisk, A., 2017, Feeding ecology and niche overlap of Lake Ontario offshore forage fish assessed with stable isotopes: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 5, p. 759-771, https://doi.org/10.1139/cjfas-2016-0150.","productDescription":"13 p.","startPage":"759","endPage":"771","ipdsId":"IP-082414","costCenters":[{"id":324,"text":"Great Lakes Science 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program","interactions":[],"lastModifiedDate":"2017-12-06T09:35:07","indexId":"70194564","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Cooperative science to inform Lake Ontario management: Research from the 2013 Lake Ontario CSMI program","docAbstract":"Since the mid-1970s, successful Lake Ontario management actions including nutrient load and pollution reductions, habitat restoration, and fish stocking have improved Lake Ontario. However, several new obstacles to maintenance and restoration have emerged. This special issue presents management-relevant research from multiple agency surveys in 2011 and 2012 and the 2013 Cooperative Science and Monitoring Initiative (CSMI), that span diverse lake habitats, species, and trophic levels. This research focused on themes of nutrient loading and fate; vertical dynamics of primary and secondary production; fish abundance and behavior; and food web structure. Together these papers identify the status of many of the key drivers of the Lake Ontario ecosystem and contribute to addressing lake-scale questions and management information needs in Lake Ontario and the other Great Lakes and connecting water bodies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2017.07.008","usgsCitation":"Watkins, J.M., Weidel, B., Fisk, A.T., and Rudstam, L.G., 2017, Cooperative science to inform Lake Ontario management: Research from the 2013 Lake Ontario CSMI program: Journal of Great Lakes Research, v. 43, no. 5, p. 779-781, https://doi.org/10.1016/j.jglr.2017.07.008.","productDescription":"3 p.","startPage":"779","endPage":"781","ipdsId":"IP-089210","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":469244,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2017.07.008","text":"Publisher Index 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Canada","active":true,"usgs":false}],"preferred":false,"id":724494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rudstam, Lars G.","contributorId":56609,"corporation":false,"usgs":false,"family":"Rudstam","given":"Lars","email":"","middleInitial":"G.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":724495,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194562,"text":"70194562 - 2017 - Reply to the discussion of Pinter et al. on ‘Fluvial system response to late Pleistocene-Holocene sea-level change on Santa Rosa Island, Channel Islands National Park, California’ by Schumann et al. (2016)","interactions":[],"lastModifiedDate":"2017-12-06T09:43:31","indexId":"70194562","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Reply to the discussion of Pinter et al. on ‘Fluvial system response to late Pleistocene-Holocene sea-level change on Santa Rosa Island, Channel Islands National Park, California’ by Schumann et al. (2016)","docAbstract":"We appreciate the thoughtful discussion offered by Pinter et al. (2017) because it gives us an opportunity to elucidate some of the main points of our study, address some apparent misinterpretations, and recapitulate one of our conclusions. Pinter et al.’s discussion emphasizes and reinforces some of the important concepts we presented but also raises questions regarding specific aspects of our study, including that: (1) base level is the dominant control on fluvial system change on Santa Rosa Island (SRI); (2) post-Last Glacial Maximum (LGM) fluvial aggradation on SRI occurred at uniform rates; and (3) the transition from aggradation to incision on SRI occurred during the last 500 to ≤150 years.","largerWorkTitle":"Geomorphology","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.04.032","usgsCitation":"Schumann, R.R., and Pigati, J.S., 2017, Reply to the discussion of Pinter et al. on ‘Fluvial system response to late Pleistocene-Holocene sea-level change on Santa Rosa Island, Channel Islands National Park, California’ by Schumann et al. (2016): Geomorphology, v. 301, p. 144-146, https://doi.org/10.1016/j.geomorph.2017.04.032.","productDescription":"3 p.","startPage":"144","endPage":"146","ipdsId":"IP-086290","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":349739,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Channel Islands National Park, Santa Rosa Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.27076721191405,\n 34.00599664251842\n ],\n [\n -120.25772094726561,\n 33.989487811032085\n ],\n [\n -120.23986816406249,\n 33.978100540958756\n ],\n [\n -120.21995544433594,\n 33.96500329452545\n ],\n [\n -120.20896911621092,\n 33.94905609818093\n ],\n [\n -120.19866943359375,\n 33.92569945434823\n ],\n [\n -120.18768310546875,\n 33.90860516848035\n ],\n [\n -120.17532348632812,\n 33.904616008362325\n ],\n [\n -120.16090393066405,\n 33.89834695102012\n ],\n [\n -120.14579772949217,\n 33.889797493644444\n ],\n [\n -120.13137817382811,\n 33.88580745357739\n ],\n [\n -120.11146545410156,\n 33.884097379274905\n ],\n [\n -120.09429931640625,\n 33.889797493644444\n ],\n [\n -120.07575988769531,\n 33.897207072889486\n ],\n [\n -120.06202697753906,\n 33.90176649398624\n ],\n [\n -120.05104064941405,\n 33.90518589980854\n ],\n [\n -120.03524780273436,\n 33.91430364480298\n ],\n [\n -120.02632141113283,\n 33.920571528675076\n ],\n [\n -120.00778198242186,\n 33.92740869431181\n ],\n [\n -119.9871826171875,\n 33.92797843334449\n ],\n [\n -119.970703125,\n 33.93082707134273\n ],\n [\n -119.9604034423828,\n 33.93367561406283\n ],\n [\n -119.95628356933594,\n 33.9416510267666\n ],\n [\n -119.95903015136717,\n 33.954182308688196\n ],\n [\n -119.96452331542969,\n 33.96158628979907\n ],\n [\n -119.96177673339842,\n 33.97525348507592\n ],\n [\n -119.96177673339842,\n 33.985502440044165\n ],\n [\n -119.96658325195312,\n 33.99290369455045\n ],\n [\n -119.97962951660158,\n 33.994611584814606\n ],\n [\n -119.99061584472655,\n 33.99347299511967\n ],\n [\n -120.00778198242186,\n 33.99518087394024\n ],\n [\n -120.02288818359374,\n 33.9997350496171\n ],\n [\n -120.03044128417969,\n 34.008273470938335\n ],\n [\n -120.02906799316406,\n 34.02705497597962\n ],\n [\n -120.02357482910155,\n 34.035590649919314\n ],\n [\n -120.02975463867188,\n 34.04640127074641\n ],\n [\n -120.0421142578125,\n 34.05265942137599\n ],\n [\n -120.06614685058594,\n 34.049814864716275\n ],\n [\n -120.0970458984375,\n 34.04014265821754\n ],\n [\n -120.12931823730467,\n 34.04128062212254\n ],\n [\n -120.15129089355469,\n 34.03672867489511\n ],\n [\n -120.17326354980469,\n 34.02477865123825\n ],\n [\n -120.19729614257814,\n 34.01794931066773\n ],\n [\n -120.23300170898438,\n 34.02477865123825\n ],\n [\n -120.25634765624999,\n 34.01851844336969\n ],\n [\n -120.27076721191405,\n 34.00599664251842\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"301","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faede4b06e28e9c229b1","contributors":{"authors":[{"text":"Schumann, R. Randall 0000-0001-8158-6960 rschumann@usgs.gov","orcid":"https://orcid.org/0000-0001-8158-6960","contributorId":1569,"corporation":false,"usgs":true,"family":"Schumann","given":"R.","email":"rschumann@usgs.gov","middleInitial":"Randall","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":724486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigati, Jeffrey S. 0000-0001-5843-6219 jpigati@usgs.gov","orcid":"https://orcid.org/0000-0001-5843-6219","contributorId":201167,"corporation":false,"usgs":true,"family":"Pigati","given":"Jeffrey","email":"jpigati@usgs.gov","middleInitial":"S.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":724487,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70194566,"text":"70194566 - 2017 - Contaminant gradients in trees: Directional tree coring reveals boundaries of soil and soil-gas contamination with potential applications in vapor intrusion assessment","interactions":[],"lastModifiedDate":"2017-12-20T14:52:07","indexId":"70194566","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","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":"Contaminant gradients in trees: Directional tree coring reveals boundaries of soil and soil-gas contamination with potential applications in vapor intrusion assessment","docAbstract":"Contaminated sites pose ecological and human-health risks through exposure to contaminated soil and groundwater. Whereas we can readily locate, monitor, and track contaminants in groundwater, it is harder to perform these tasks in the vadose zone. In this study, tree-core samples were collected at a Superfund site to determine if the sample-collection location around a particular tree could reveal the subsurface location, or direction, of soil and soil-gas contaminant plumes. Contaminant-centroid vectors were calculated from tree-core data to reveal contaminant distributions in directional tree samples at a higher resolution, and vectors were correlated with soil-gas characterization collected using conventional methods. Results clearly demonstrated that directional tree coring around tree trunks can indicate gradients in soil and soil-gas contaminant plumes, and the strength of the correlations were directly proportionate to the magnitude of tree-core concentration gradients (spearman’s coefficient of -0.61 and -0.55 in soil and tree-core gradients, respectively). Linear regression indicates agreement between the concentration-centroid vectors is significantly affected by in-planta and soil concentration gradients and when concentration centroids in soil are closer to trees. Given the existing link between soil-gas and vapor intrusion, this study also indicates that directional tree coring might be applicable in vapor intrusion assessment.","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.7b03466","usgsCitation":"Wilson, J.L., Samaranayake, V., Limmer, M.A., Schumacher, J., and Burken, J.G., 2017, Contaminant gradients in trees: Directional tree coring reveals boundaries of soil and soil-gas contamination with potential applications in vapor intrusion assessment: Environmental Science & Technology, v. 51, no. 24, p. 14055-14064, https://doi.org/10.1021/acs.est.7b03466.","productDescription":"10 p.","startPage":"14055","endPage":"14064","ipdsId":"IP-086172","costCenters":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":349735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","city":"Vienna","otherGeospatial":"Vienna Wells","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.52685546875,\n 37.779398571318765\n ],\n [\n -91.29638671875,\n 37.779398571318765\n ],\n [\n -91.29638671875,\n 38.61687046392973\n ],\n [\n -92.52685546875,\n 38.61687046392973\n ],\n [\n -92.52685546875,\n 37.779398571318765\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"51","issue":"24","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-08","publicationStatus":"PW","scienceBaseUri":"5a60faece4b06e28e9c229a6","contributors":{"authors":[{"text":"Wilson, Jordan L. 0000-0003-0490-9062 jlwilson@usgs.gov","orcid":"https://orcid.org/0000-0003-0490-9062","contributorId":5416,"corporation":false,"usgs":true,"family":"Wilson","given":"Jordan","email":"jlwilson@usgs.gov","middleInitial":"L.","affiliations":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":724504,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Samaranayake, V.A. 0000-0002-1892-8363","orcid":"https://orcid.org/0000-0002-1892-8363","contributorId":201176,"corporation":false,"usgs":false,"family":"Samaranayake","given":"V.A.","email":"","affiliations":[],"preferred":false,"id":724507,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Limmer, Matthew A.","contributorId":200927,"corporation":false,"usgs":false,"family":"Limmer","given":"Matthew","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":724505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schumacher, John G. jschu@usgs.gov","contributorId":2055,"corporation":false,"usgs":true,"family":"Schumacher","given":"John G.","email":"jschu@usgs.gov","affiliations":[{"id":396,"text":"Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":725289,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Burken, Joel G.","contributorId":21218,"corporation":false,"usgs":true,"family":"Burken","given":"Joel","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":724506,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194559,"text":"70194559 - 2017 - Stratigraphic intervals for oil and tar sands deposits in the Uinta Basin, Utah","interactions":[],"lastModifiedDate":"2017-12-06T10:09:10","indexId":"70194559","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2789,"text":"Mountain Geologist","active":true,"publicationSubtype":{"id":10}},"title":"Stratigraphic intervals for oil and tar sands deposits in the Uinta Basin, Utah","docAbstract":"No abstract available.
","language":"English","publisher":"The Mountain Geologist","usgsCitation":"Johnson, R.C., Birdwell, J.E., and Lillis, P.G., 2017, Stratigraphic intervals for oil and tar sands deposits in the Uinta Basin, Utah: Mountain Geologist, v. 54, no. 4, p. 227-265.","productDescription":"39 p.","startPage":"227","endPage":"265","ipdsId":"IP-090720","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":349742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Uinta Basin","volume":"54","issue":"4","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faeee4b06e28e9c229b7","contributors":{"authors":[{"text":"Johnson, Ronald C. 0000-0002-6197-5165 rcjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-6197-5165","contributorId":1550,"corporation":false,"usgs":true,"family":"Johnson","given":"Ronald","email":"rcjohnson@usgs.gov","middleInitial":"C.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":724472,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Birdwell, Justin E. 0000-0001-8263-1452 jbirdwell@usgs.gov","orcid":"https://orcid.org/0000-0001-8263-1452","contributorId":3302,"corporation":false,"usgs":true,"family":"Birdwell","given":"Justin","email":"jbirdwell@usgs.gov","middleInitial":"E.","affiliations":[{"id":569,"text":"Southwest Climate Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":724473,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lillis, Paul G. 0000-0002-7508-1699 plillis@usgs.gov","orcid":"https://orcid.org/0000-0002-7508-1699","contributorId":1817,"corporation":false,"usgs":true,"family":"Lillis","given":"Paul","email":"plillis@usgs.gov","middleInitial":"G.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":724474,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70194561,"text":"70194561 - 2017 - Chemical elements in the environment: multi-element geochemical datasets from continental to national scale surveys on four continents","interactions":[],"lastModifiedDate":"2025-05-14T19:00:58.320661","indexId":"70194561","displayToPublicDate":"2017-12-06T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":835,"text":"Applied Geochemistry","active":true,"publicationSubtype":{"id":10}},"title":"Chemical elements in the environment: multi-element geochemical datasets from continental to national scale surveys on four continents","docAbstract":"During the last 10-20 years, Geological Surveys around the world have undertaken a major effort towards delivering fully harmonized and tightly quality-controlled low-density multi-element soil geochemical maps and datasets of vast regions including up to whole continents. Concentrations of between 45 and 60 elements commonly have been determined in a variety of different regolith types (e.g., sediment, soil). The multi-element datasets are published as complete geochemical atlases and made available to the general public. Several other geochemical datasets covering smaller areas but generally at a higher spatial density are also available. These datasets may, however, not be found by superficial internet-based searches because the elements are not mentioned individually either in the title or in the keyword lists of the original references. This publication attempts to increase the visibility and discoverability of these fundamental background datasets covering large areas up to whole continents.","language":"English","publisher":"Elsevier","doi":"10.1016/j.apgeochem.2017.11.010","usgsCitation":"Caritat, P.D., Reimann, C., Smith, D.B., and Wang, X., 2017, Chemical elements in the environment: multi-element geochemical datasets from continental to national scale surveys on four continents: Applied Geochemistry, v. 89, p. 150-159, https://doi.org/10.1016/j.apgeochem.2017.11.010.","productDescription":"10 p.","startPage":"150","endPage":"159","ipdsId":"IP-092659","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":349740,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":469243,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.apgeochem.2017.11.010","text":"Publisher Index Page"}],"volume":"89","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faede4b06e28e9c229b3","contributors":{"authors":[{"text":"Caritat, Patrice de","contributorId":201164,"corporation":false,"usgs":false,"family":"Caritat","given":"Patrice","email":"","middleInitial":"de","affiliations":[],"preferred":false,"id":724483,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reimann, Clemens","contributorId":201165,"corporation":false,"usgs":false,"family":"Reimann","given":"Clemens","email":"","affiliations":[],"preferred":false,"id":724484,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David B. 0000-0001-8396-9105 dsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-8396-9105","contributorId":138565,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"dsmith@usgs.gov","middleInitial":"B.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":724482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Xueqiu","contributorId":201166,"corporation":false,"usgs":false,"family":"Wang","given":"Xueqiu","email":"","affiliations":[],"preferred":false,"id":724485,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70191367,"text":"sir20175122 - 2017 - Detection of microcystin and other cyanotoxins in lakes at Isle Royale National Park, Pictured Rocks National Lakeshore, and Sleeping Bear Dunes National Lakeshore, northern Michigan, 2012–13","interactions":[],"lastModifiedDate":"2018-09-12T17:05:27","indexId":"sir20175122","displayToPublicDate":"2017-12-05T16:20:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5122","title":"Detection of microcystin and other cyanotoxins in lakes at Isle Royale National Park, Pictured Rocks National Lakeshore, and Sleeping Bear Dunes National Lakeshore, northern Michigan, 2012–13","docAbstract":"Although cyanotoxins released during algal blooms have become an increasing concern in surface waters across the United States, the presence of cyanotoxins in northern Michigan lakes had not been evaluated in detail. The U.S. Geological Survey and National Park Service (NPS) led a 2-year study (2012 and 2013) to determine the presence of microcystin and other algal toxins in several inland lakes at Isle Royale National Park (hereafter referred to as ISRO, Pictured Rocks National Lakeshore (hereafter referred to as PIRO), and Sleeping Bear Dunes National Lakeshore (hereafter referred to as SLBE). Samples also were collected at four sites in Lake Michigan within the SLBE. The two analytical techniques used in the study were enzyme-linked immunosorbent assays (ELISA) for microcystin, cylindrospermopsin, and saxitoxin; and liquid chromatography/tandem mass spectrometry (LC/MS/MS) for a larger suite of algal toxins. Neither cylindrospermopsin nor saxitoxin were detected in the 211 samples. Microcystin was detected in 31 percent of samples (65 of 211 samples) analyzed by the ELISA method, but no sample results exceeded the World Health Organization recreational health advisory standard for microcystin (10 micrograms per liter [µg/L]). However, about 10 percent of the samples (21 of 211 samples) that were collected from PIRO and SLBE and were analyzed by ELISA for microcystin had concentrations greater than the U.S. Environmental Protection Agency (EPA) drinking water 10-day health advisory of 0.3 µg/L for children preschool age and younger (less than 6-years old). One sample collected in 2012 from SLBE exceeded the EPA drinking water 10-day health advisory of 1.6 µg/L for school-age children through adults (6-years old and older). In 2012, the highest concentration of 2.7 µg/L was detected in Florence Lake within SLBE. Many visitors enjoy recreation in or on the water and camp in the backcountry at these national parks where the most common source of drinking water is filtered surface water.
Approximately 18 percent of the samples (39 of 211 samples) were analyzed by LC/MS/MS to confirm the ELISA results and to evaluate the samples for a larger suite of algal toxins. In general, the microcystin results between the ELISA and LC/MS/MS methods were similar; although, the ELISA results tended to be slightly higher than the summation of LC/MS/MS microcystin congeners. The slightly higher ELISA results might be because the ELISA microcystin method is reactive with the ADDA functional group common to all microcystins, and because not all microcystin congeners are included in the LC/MS/MS method. The LC/MS/MS method indicated that the congener microcystin-LR was the most frequently detected, followed by microcystin-WR and microcystin-YR.
Sixteen of the lakes included in this study also were monitored by the NPS for nutrients. Total phosphorus (TP) concentrations were, on average, highest at the ISRO lakes, whereas total nitrogen (TN) concentrations were highest at SLBE. The average annual TN:TP ratios for the 16 lakes within the national park and national lakeshores ranged from ratios of 20 to 89. Overall, results indicated a slight increase in percentage of microcystin detections with an increase in the TN:TP ratio (R-squared 0.269 and 0.340, respectively [2012 and 2013 combined dataset] derived from linear regression).
This study also indicated that even in the absence of visible algal blooms, microcystin may be present. Most microcystin concentrations did not exceed the EPA’s 10-day health advisory drinking-water benchmark. In general, these results provide a useful baseline with which to evaluate potential future changes in algal toxin concentrations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175122","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Fuller, L.M., Brennan, A.K., Fogarty, L.R., Loftin, K.A., Johnson, H.E., VanderMeulen, D.D., and Lafrancois, B.M., 2017, Detection of microcystin and other cyanotoxins in lakes at Isle Royale National Park, Pictured Rocks National Lakeshore, and Sleeping Bear Dunes National Lakeshore, northern Michigan, 2012–13: U.S. Geological Survey Scientific Investigations Report 2017–5122, 44 p., https://doi.org/10.3133/sir20175122.","productDescription":"vi, 44 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-071309","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":349614,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5122/coverthb.jpg"},{"id":349615,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5122/sir20175122.pdf","text":"Report","size":"9.76 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5122"}],"country":"United States","state":"Michigan","otherGeospatial":"Isle Royale National Park, Pictured Rocks National Lakeshore, Sleeping Bear Dunes National Shoreline","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.32571411132812,\n 47.8085431415187\n ],\n [\n -88.3795166015625,\n 47.8085431415187\n ],\n [\n -88.3795166015625,\n 48.20728655738642\n ],\n [\n -89.32571411132812,\n 48.20728655738642\n ],\n [\n -89.32571411132812,\n 47.8085431415187\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.63131713867188,\n 46.417979059090115\n ],\n [\n -86.00234985351562,\n 46.417979059090115\n ],\n [\n -86.00234985351562,\n 46.693725378358955\n ],\n [\n -86.63131713867188,\n 46.693725378358955\n ],\n [\n -86.63131713867188,\n 46.417979059090115\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.17469787597656,\n 44.8281172855491\n ],\n [\n -85.79704284667969,\n 44.8281172855491\n ],\n [\n -85.79704284667969,\n 45.16509478442965\n ],\n [\n -86.17469787597656,\n 45.16509478442965\n ],\n [\n -86.17469787597656,\n 44.8281172855491\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
Connectivity among individual marine protected areas (MPAs) is one of the most important considerations in the design of integrated MPA networks. To provide such information for managers in Hawaii, USA, a numerical circulation model was developed to determine the role of ocean currents in transporting coral larvae from natal reefs throughout the high volcanic islands of the Maui Nui island complex in the southeastern Hawaiian Archipelago. Spatially- and temporally-varying wind, wave, and circulation model outputs were used to drive a km-scale, 3-dimensional, physics-based circulation model for Maui Nui. The model was calibrated and validated using satellite-tracked ocean surface current drifters deployed during coral-spawning conditions, then used to simulate the movement of the larvae of the dominant reef-building coral, Porites compressa, from 17 reefs during eight spawning events in 2010–2013. These simulations make it possible to investigate not only the general dispersal patterns from individual coral reefs, but also how anomalous conditions during individual spawning events can result in large deviations from those general patterns. These data also help identify those reefs that are dominated by self-seeding and those where self-seeding is limited to determine their relative susceptibility to stressors and potential roadblocks to recovery. Overall, the numerical model results indicate that many of the coral reefs in Maui Nui seed reefs on adjacent islands, demonstrating the interconnected nature of the coral reefs in Maui Nui and providing a key component of the scientific underpinning essential for the design of a mutually supportive network of MPAs to enhance conservation of coral reefs.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fmars.2017.00381","usgsCitation":"Storlazzi, C.D., van Ormondt, M., Chen, Y., and Elias, E.P., 2017, Modeling fine-scale coral larval dispersal and interisland connectivity to help designate mutually-supporting coral reef marine protected areas: Insights from Maui Nui, Hawaii: Frontiers in Marine Science, v. 4, 381, 14 p., https://doi.org/10.3389/fmars.2017.00381.","productDescription":"381, 14 p.","ipdsId":"IP-074125","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":469246,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fmars.2017.00381","text":"Publisher Index Page"},{"id":438128,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NK3C59","text":"USGS data release","linkHelpText":"Physics-based numerical circulation model outputs of ocean surface circulation during the 2010-2013 summer coral-spawning seasons in Maui Nui, Hawaii, USA"},{"id":349887,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kahoolawe, Lanai, Maui, Molokai","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -157.40386962890625,\n 20.406420474920292\n ],\n [\n -155.85479736328125,\n 20.406420474920292\n ],\n [\n -155.85479736328125,\n 21.299610604945606\n ],\n [\n -157.40386962890625,\n 21.299610604945606\n ],\n [\n -157.40386962890625,\n 20.406420474920292\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-05","publicationStatus":"PW","scienceBaseUri":"5a60faeee4b06e28e9c229bc","contributors":{"authors":[{"text":"Storlazzi, Curt D. 0000-0001-8057-4490 cstorlazzi@usgs.gov","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":140584,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","email":"cstorlazzi@usgs.gov","middleInitial":"D.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":724730,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"van Ormondt, Maarten","contributorId":200365,"corporation":false,"usgs":false,"family":"van Ormondt","given":"Maarten","email":"","affiliations":[],"preferred":false,"id":724731,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chen, Yi-Leng","contributorId":173747,"corporation":false,"usgs":false,"family":"Chen","given":"Yi-Leng","email":"","affiliations":[{"id":27289,"text":"Department of Meteorology, University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":724732,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Elias, Edwin P. L.","contributorId":194055,"corporation":false,"usgs":false,"family":"Elias","given":"Edwin","email":"","middleInitial":"P. L.","affiliations":[],"preferred":false,"id":724733,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194538,"text":"70194538 - 2017 - Evidence for the interior evolution of Ceres from geologic analysis of fractures","interactions":[],"lastModifiedDate":"2017-12-05T13:03:39","indexId":"70194538","displayToPublicDate":"2017-12-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Evidence for the interior evolution of Ceres from geologic analysis of fractures","docAbstract":"Ceres is the largest asteroid belt object, and the Dawn spacecraft observed Ceres since 2015. Dawn observed two morphologically distinct linear features on Ceres's surface: secondary crater chains and pit chains. Pit chains provide unique insights into Ceres's interior evolution. We interpret pit chains called the Samhain Catenae as the surface expression of subsurface fractures. Using the pit chains' spacings, we estimate that the localized thickness of Ceres's fractured, outer layer is approximately ≥58 km, at least ~14 km greater than the global average. We hypothesize that extensional stresses, induced by a region of upwelling material arising from convection/diapirism, formed the Samhain Catenae. We derive characteristics for this upwelling material, which can be used as constraints in future interior modeling studies. For example, its predicted location coincides with Hanami Planum, a high-elevation region with a negative residual gravity anomaly, which may be surficial evidence for this proposed region of upwelling material.","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017GL075086","usgsCitation":"Scully, J.E., Buczkowski, D., Schmedemann, N., Raymond, C.A., Castillo-Rogez, J., King, S., Bland, M.T., Ermakov, A., O’Brien, D., Marchi, S., Longobardo, A., Russell, C., Fu, R., and Neveu, M., 2017, Evidence for the interior evolution of Ceres from geologic analysis of fractures: Geophysical Research Letters, v. 44, p. 9564-9572, https://doi.org/10.1002/2017GL075086.","productDescription":"9 p.","startPage":"9564","endPage":"9572","ipdsId":"IP-090919","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":469245,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2017gl075086","text":"Publisher Index Page"},{"id":349690,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"44","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-10-14","publicationStatus":"PW","scienceBaseUri":"5a60faf5e4b06e28e9c22a04","contributors":{"authors":[{"text":"Scully, Jennifer E. 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While this mold is known to be worldwide, these instances represent the first cases in Nuiqsut and only the second instance on a single fish on the North Slope, occurring in 1980. We describe the collaborative work on monitoring this emerging disease. Because fish constitute a critical component of the diet in Nuiqsut and fishing is an integral part of Inupiaq nutritional and cultural subsistence activities overall, individual subsistence fishers, local governmental entities, and Alaska Native organizations representing Nuiqsut requested an examination of affected fish and information on possible drivers of this emerging disease. The collaborative work described here ranges from recording fishermen observations, acquiring fish and mold specimens, histopathology, and molecular identification of the mold. This work, not currently grant-funded, begins with Native observation that incorporates western scientific methods and involves local, state, and federal departments as well as for-profit and non-profit organizations. Additionally, we report the more recent (2016) observation of this disease in a second species of whitefish, Pikuktuuq, humpback whitefish (Coregonus pidschain).
","language":"English","publisher":"Elsevier","doi":"10.1016/j.polar.2017.07.002","usgsCitation":"Sformo, T.L., Adams, B., Seigle, J.C., Ferguson, J.A., Purcell, M.K., Stimmelmayr, R., Welch, J.H., Ellis, L.M., Leppi, J.C., and George, J., 2017, Observations and first reports of saprolegniosis in Aanaakłiq, broad whitefish (Coregonus nasus), from the Colville River near Nuiqsut, Alaska: Polar Science, v. 14, p. 78-82, https://doi.org/10.1016/j.polar.2017.07.002.","productDescription":"5 p.","startPage":"78","endPage":"82","ipdsId":"IP-084054","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":488741,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.polar.2017.07.002","text":"Publisher Index Page"},{"id":349689,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"Nuiqsut","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.39111328124997,\n 69.6914318644638\n ],\n [\n -148.86474609374997,\n 69.6914318644638\n ],\n [\n -148.86474609374997,\n 70.8698912672041\n ],\n [\n -153.39111328124997,\n 70.8698912672041\n ],\n [\n -153.39111328124997,\n 69.6914318644638\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"14","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a60faf5e4b06e28e9c22a00","contributors":{"authors":[{"text":"Sformo, Todd L.","contributorId":201120,"corporation":false,"usgs":false,"family":"Sformo","given":"Todd","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":724384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Adams, Billy","contributorId":201121,"corporation":false,"usgs":false,"family":"Adams","given":"Billy","email":"","affiliations":[],"preferred":false,"id":724385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seigle, John C.","contributorId":201122,"corporation":false,"usgs":false,"family":"Seigle","given":"John","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":724386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ferguson, Jayde A.","contributorId":201123,"corporation":false,"usgs":false,"family":"Ferguson","given":"Jayde","email":"","middleInitial":"A.","affiliations":[{"id":7058,"text":"Alaska Department of Fish and Game","active":true,"usgs":false}],"preferred":false,"id":724387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Purcell, Maureen K. 0000-0003-0154-8433 mpurcell@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8433","contributorId":168475,"corporation":false,"usgs":true,"family":"Purcell","given":"Maureen","email":"mpurcell@usgs.gov","middleInitial":"K.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":724383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Stimmelmayr, Raphaela","contributorId":201124,"corporation":false,"usgs":false,"family":"Stimmelmayr","given":"Raphaela","email":"","affiliations":[],"preferred":false,"id":724388,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Welch, Joseph H.","contributorId":201125,"corporation":false,"usgs":false,"family":"Welch","given":"Joseph","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":724389,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ellis, Leah M.","contributorId":201126,"corporation":false,"usgs":false,"family":"Ellis","given":"Leah","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":724390,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leppi, Jason C.","contributorId":201127,"corporation":false,"usgs":false,"family":"Leppi","given":"Jason","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":724391,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"George, John C.","contributorId":201128,"corporation":false,"usgs":false,"family":"George","given":"John C.","affiliations":[],"preferred":false,"id":724392,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}