Fish consumption advisories are used to inform citizens in the United States about noncommercial game fish with hazardous levels of methylmercury (MeHg). The US Environmental Protection Agency (USEPA) suggests issuing a fish consumption advisory when concentrations of MeHg in fish exceed a human health screening value of 300 ng/g. However, states have authority to develop their own systems for issuing fish consumption advisories for MeHg. Five states in the south central United States (Arkansas, Louisiana, Mississippi, Oklahoma, and Texas) issue advisories for the general human population when concentrations of MeHg exceed 700 ng/g to 1000 ng/g. The objective of the present study was to estimate the increase in fish consumption advisories that would occur if these states followed USEPA recommendations. The authors used the National Descriptive Model of Mercury in Fish to estimate the mercury concentrations in 5 size categories of largemouth bass–equivalent fish at 766 lentic and lotic sites within the 5 states. The authors found that states in this region have not issued site‐specific fish consumption advisories for most of the water bodies that would have such advisories if USEPA recommendations were followed. One outcome of the present study may be to stimulate discussion between scientists and policy makers at the federal and state levels about appropriate screening values to protect the public from the health hazards of consuming MeHg‐contaminated game fish.
","language":"English","publisher":"Wiley","doi":"10.1002/etc.3185","usgsCitation":"Adams, K., Drenner, R.W., Chumchal, M.M., and Donato, D.I., 2015, Disparity between state fish consumption advisory systems for methylmercury and US Environmental Protection Agency recommendations: A case study of the South Central United States: Environmental Toxicology and Chemistry, v. 35, no. 1, p. 247-251, https://doi.org/10.1002/etc.3185.","productDescription":"5 p.","startPage":"247","endPage":"251","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-064147","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":318156,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Louisiana, Mississippi, Oklahoma, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.330078125,\n 30.391830328088137\n ],\n [\n -88.505859375,\n 32.0639555946604\n ],\n [\n -88.04443359375,\n 35.02999636902566\n ],\n [\n -90.17578124999999,\n 35.10193405724606\n ],\n [\n -89.6484375,\n 36.01356058518153\n ],\n [\n -90.3515625,\n 36.03133177633187\n ],\n [\n -90.02197265625,\n 36.36822190085111\n ],\n [\n -90.263671875,\n 36.527294814546245\n ],\n [\n -94.59228515625,\n 36.527294814546245\n ],\n [\n -94.6142578125,\n 37.020098201368114\n ],\n [\n -103.0078125,\n 37.03763967977139\n ],\n [\n -103.02978515625,\n 36.527294814546245\n ],\n [\n -103.1396484375,\n 31.970803930433096\n ],\n [\n -106.69921875,\n 31.98944183792288\n ],\n [\n -106.5673828125,\n 31.89621446335144\n ],\n [\n -104.87548828125,\n 30.486550842588485\n ],\n [\n -104.58984375,\n 29.668962525992505\n ],\n [\n -103.1396484375,\n 28.92163128242129\n ],\n [\n -102.67822265625,\n 29.80251790576445\n ],\n [\n -102.23876953125,\n 29.80251790576445\n ],\n [\n -101.337890625,\n 29.7453016622136\n ],\n [\n -100.5029296875,\n 28.844673680771795\n ],\n [\n -99.7119140625,\n 27.586197857692664\n ],\n [\n -99.49218749999999,\n 27.078691552927534\n ],\n [\n -98.96484375,\n 26.352497858154024\n ],\n [\n -98.02001953125,\n 26.05678288577881\n ],\n [\n -97.36083984375,\n 25.780107118422244\n ],\n [\n -97.05322265625,\n 25.958044673317843\n ],\n [\n -97.294921875,\n 27.01998400798257\n ],\n [\n -96.78955078125,\n 27.955591004642553\n ],\n [\n -94.85595703125,\n 29.19053283229458\n ],\n [\n -93.8232421875,\n 29.554345125748267\n ],\n [\n -93.2080078125,\n 29.649868677972304\n ],\n [\n -92.28515625,\n 29.439597566602902\n ],\n [\n -92.0654296875,\n 29.439597566602902\n ],\n [\n -91.7578125,\n 29.286398892934763\n ],\n [\n -91.0986328125,\n 28.97931203672246\n ],\n [\n -89.71435546875,\n 28.998531814051795\n ],\n [\n -88.92333984375,\n 28.9023972285585\n ],\n [\n -88.330078125,\n 30.391830328088137\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-01-01","publicationStatus":"PW","scienceBaseUri":"56c6f93fe4b0946c6524072d","contributors":{"authors":[{"text":"Adams, Kimberly","contributorId":167057,"corporation":false,"usgs":false,"family":"Adams","given":"Kimberly","email":"","affiliations":[{"id":24605,"text":"Biology Department, Texas Christian University, Fort Worth, Texas, USA","active":true,"usgs":false}],"preferred":false,"id":620875,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drenner, Ray W.","contributorId":46407,"corporation":false,"usgs":true,"family":"Drenner","given":"Ray","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":620876,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chumchal, Matthew M.","contributorId":84659,"corporation":false,"usgs":true,"family":"Chumchal","given":"Matthew","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":620877,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Donato, David I. 0000-0002-5412-0249 didonato@usgs.gov","orcid":"https://orcid.org/0000-0002-5412-0249","contributorId":2234,"corporation":false,"usgs":true,"family":"Donato","given":"David","email":"didonato@usgs.gov","middleInitial":"I.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":620874,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159804,"text":"70159804 - 2015 - Changes in seasonality and timing of peak streamflow in snow and semi-arid climates of the north-central United States, 1910–2012","interactions":[],"lastModifiedDate":"2017-10-12T20:00:47","indexId":"70159804","displayToPublicDate":"2015-11-24T16:45:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Changes in seasonality and timing of peak streamflow in snow and semi-arid climates of the north-central United States, 1910–2012","docAbstract":"Changes in the seasonality and timing of annual peak streamflow in the north-central USA are likely because of changes in precipitation and temperature regimes. A source of long-term information about flood events across the study area is the U.S. Geological Survey peak streamflow database. However, one challenge of answering climate-related questions with this dataset is that even in snowmelt-dominated areas, it is a mixed population of snowmelt/spring rain generated peaks and summer/fall rain generated peaks. Therefore, a process was developed to divide the annual peaks into two populations, or seasons, snowmelt/spring, and summer/fall. The two series were then tested for the hypotheses that because of changes in precipitation regimes, the odds of summer/fall peaks have increased and, because of temperature changes, snowmelt/spring peaks happen earlier. Over climatologically and geographically similar regions in the north-central USA, logistic regression was used to model the odds of getting a summer/fall peak. When controlling for antecedent wet and dry conditions and geographical differences, the odds of summer/fall peaks occurring have increased across the study area. With respect to timing within the seasons, trend analysis showed that in northern portions of the study region, snowmelt/spring peaks are occurring earlier. The timing of snowmelt/spring peaks in three regions in the northern part of the study area is earlier by 8.7– 14.3 days. These changes have implications for water interests, such as potential changes in lead-time for flood forecasting or changes in the operation of flood-control dams.
","language":"English","publisher":"John Wiley & Sons","doi":"10.1002/hyp.10693","usgsCitation":"Ryberg, K.R., Akyuz, F.A., Wiche, G.J., and Lin, W., 2015, Changes in seasonality and timing of peak streamflow in snow and semi-arid climates of the north-central United States, 1910–2012: Hydrological Processes, v. 30, no. 8, p. 1208-1218, https://doi.org/10.1002/hyp.10693.","productDescription":"11 p.","startPage":"1208","endPage":"1218","onlineOnly":"N","additionalOnlineFiles":"N","temporalStart":"1910-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-059283","costCenters":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":311699,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, Iowa, Kansas, Minnesota, Missouri, Montana, Nebraska, North Dakota, South Dakota, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.7353515625,\n 48.574789910928864\n ],\n [\n -91.23046875,\n 43.45291889355465\n ],\n [\n -90.263671875,\n 42.00032514831621\n ],\n [\n -91.4501953125,\n 40.38002840251183\n ],\n [\n -90.8349609375,\n 39.13006024213511\n ],\n [\n -102.1728515625,\n 38.61687046392973\n ],\n [\n -105.77636718749999,\n 38.92522904714054\n ],\n [\n -109.4677734375,\n 45.01141864227728\n ],\n [\n -113.09326171875,\n 48.99463598353408\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -93.7353515625,\n 48.574789910928864\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","issue":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-11-11","publicationStatus":"PW","scienceBaseUri":"56558a32e4b071e7ea53dedf","chorus":{"doi":"10.1002/hyp.10693","url":"http://dx.doi.org/10.1002/hyp.10693","publisher":"Wiley-Blackwell","authors":"Ryberg Karen R., Akyüz F. Adnan, Wiche Gregg J., Lin Wei","journalName":"Hydrological Processes","publicationDate":"11/11/2015","auditedOn":"4/11/2016"},"contributors":{"authors":[{"text":"Ryberg, Karen R. 0000-0002-9834-2046 kryberg@usgs.gov","orcid":"https://orcid.org/0000-0002-9834-2046","contributorId":1172,"corporation":false,"usgs":true,"family":"Ryberg","given":"Karen","email":"kryberg@usgs.gov","middleInitial":"R.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580536,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Akyuz, F. Adnan","contributorId":140760,"corporation":false,"usgs":false,"family":"Akyuz","given":"F.","email":"","middleInitial":"Adnan","affiliations":[{"id":13555,"text":"North Dakota Climate Office","active":true,"usgs":false}],"preferred":false,"id":580538,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wiche, Gregg J. gjwiche@usgs.gov","contributorId":1675,"corporation":false,"usgs":true,"family":"Wiche","given":"Gregg","email":"gjwiche@usgs.gov","middleInitial":"J.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":580539,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lin, Wei","contributorId":93805,"corporation":false,"usgs":true,"family":"Lin","given":"Wei","email":"","affiliations":[],"preferred":false,"id":580537,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70158680,"text":"sir20155131 - 2015 - Aquifer geometry, lithology, and water levels in the Anza–Terwilliger area—2013, Riverside and San Diego Counties, California","interactions":[],"lastModifiedDate":"2015-11-25T08:14:40","indexId":"sir20155131","displayToPublicDate":"2015-11-24T16:00:00","publicationYear":"2015","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":"2015-5131","title":"Aquifer geometry, lithology, and water levels in the Anza–Terwilliger area—2013, Riverside and San Diego Counties, California","docAbstract":"The population of the Anza–Terwilliger area relies solely on groundwater pumped from the alluvial deposits and surrounding bedrock formations for water supply. The size, characteristics, and current conditions of the aquifer system in the Anza–Terwilliger area are poorly understood, however. In response to these concerns, the U.S. Geological Survey, in cooperation with the High Country Conservancy and Rancho California Water District, undertook a study to (1) improve mapping of groundwater basin geometry and lithology and (2) to resume groundwater-level monitoring last done during 2004–07 in the Anza–Terwilliger area.
\nInversion of gravity data, including new data collected for this study, was done to estimate the thickness of the alluvial deposits that form the Cahuilla and Terwilliger groundwater basins and to understand the geometry of the underlying basement complex. After processing of the gravity data, the thickness of the alluvial aquifer materials was modeled by using all available lithology, density, and geophysical data.
\nThe thickest alluvial deposits (greater than 500 feet) are in the northern part of the study area along the south side of the San Jacinto fault zone, in the southern part of the Cahuilla groundwater basin, and in the western part of the Terwilliger groundwater basin. Through most of the area of alluvial materials, the thickness of the alluvium estimated from gravity data is less than 400 feet.
\nAnalysis of more than 900 drillers’ logs indicated that in areas having relatively thick alluvium, particularly along the San Jacinto fault zone and in the Terwilliger Valley, the alluvium is predominantly composed of sands and gravels. Fine-textured sediments appeared to be discontinuous rather than forming laterally extensive, low-permeability layers. More than 500 drillers’ logs indicated only bedrock is present, indicating that the fractured bedrock is an important source of groundwater, primarily for domestic use, in the study area. The depths of the holes drilled into the bedrock indicated that fractures potentially supplying water to wells persist in the upper few hundred feet and that the permeable zone of the fractured bedrock extends to depths greater than weathered zones in the upper part of the basement complex.
\nWater-level data were collected from 59 wells during fall 2013. These data indicated that hydraulic head did not vary substantially with well depth and that the measured water levels in bedrock and alluvium were similar. Large offsets in groundwater altitude across the San Jacinto fault zone indicated that the fault zone is a barrier to groundwater flow in the northeastern part of the Anza Valley.
\nOn the basis of data from 33 wells, water levels mostly declined between the fall of 2006 and the fall of 2013; the median decline was 5.1 feet during this period, for a median rate of decline of about 0.7 feet/year. Based on data from 40 wells, water-level changes between fall 2004 and fall 2013 were variable in magnitude and trend, but had a median decline of 2.4 feet and a median rate of decline of about 0.3 feet/ year. These differences in apparent rates of groundwater-level change highlight the value of ongoing water-level measurements to distinguish decadal, or longer term, trends in groundwater storage often associated with climatic variability and trends. Fifty-four long-term hydrographs indicated the sensitivity of groundwater levels to climatic conditions; they also showed a general decline in water levels across the study area since 1986 and, in some cases, dating back to the 1950s.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155131","collaboration":"Prepared in cooperation with the High Country Conservancy and Rancho California Water District","usgsCitation":"Landon, M.K., Morita, A.Y., Nawikas, J.M., Christensen, A.H., Faunt, C.C., and Langenheim, V.E., 2015, Aquifer geometry, lithology, and water levels in the Anza–Terwilliger Area—2013, Riverside and San Diego Counties, California: U.S. Geological Survey Scientific Investigations Report 2015–5131, 30 p.\nhttps://dx.doi.org/10.3133/sir20155131.","productDescription":"Report: iv, 30 p.; Appendixes: 1-4","numberOfPages":"40","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2013-01-01","temporalEnd":"2013-12-31","ipdsId":"IP-057158","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":311697,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5131/sir20155131.pdf","text":"Report","size":"2.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5131"},{"id":311696,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5131/coverthb.jpg"},{"id":311698,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5131/sir20155131_appendixes.xlsx","text":"Appendixes 1–4","size":"422 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5131 Appendixes 1-4"}],"country":"United States","state":"California","county":"Riverside County, San Diego County","otherGeospatial":"Anza–Terwilliger Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.82174682617188,\n 33.426856918285004\n ],\n [\n -116.82174682617188,\n 33.59803478218408\n ],\n [\n -116.51481628417967,\n 33.59803478218408\n ],\n [\n -116.51481628417967,\n 33.426856918285004\n ],\n [\n -116.82174682617188,\n 33.426856918285004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819
http://ca.water.usgs.gov
Wildlife managers need reliable methods to estimate large carnivore densities and population trends; yet large carnivores are elusive, difficult to detect, and occur at low densities making traditional approaches intractable. Recent advances in spatial capture-recapture (SCR) models have provided new approaches for monitoring trends in wildlife abundance and these methods are particularly applicable to large carnivores. We applied SCR models in a Bayesian framework to estimate mountain lion densities in the Bitterroot Mountains of west central Montana. We incorporate an existing resource selection function (RSF) as a density covariate to account for heterogeneity in habitat use across the study area and include data collected from harvested lions. We identify individuals through DNA samples collected by (1) biopsy darting mountain lions detected in systematic surveys of the study area, (2) opportunistically collecting hair and scat samples, and (3) sampling all harvested mountain lions. We included 80 DNA samples collected from 62 individuals in the analysis. Including information on predicted habitat use as a covariate on the distribution of activity centers reduced the median estimated density by 44%, the standard deviation by 7%, and the width of 95% credible intervals by 10% as compared to standard SCR models. Within the two management units of interest, we estimated a median mountain lion density of 4.5 mountain lions/100 km2 (95% CI = 2.9, 7.7) and 5.2 mountain lions/100 km2 (95% CI = 3.4, 9.1). Including harvested individuals (dead recovery) did not create a significant bias in the detection process by introducing individuals that could not be detected after removal. However, the dead recovery component of the model did have a substantial effect on results by increasing sample size. The ability to account for heterogeneity in habitat use provides a useful extension to SCR models, and will enhance the ability of wildlife managers to reliably and economically estimate density of wildlife populations, particularly large carnivores.
It is thought that grass carp (Ctenopharyngodon idella) eggs must remain suspended in the water column in order to hatch successfully. Using sand, the effects of varying sediment levels on grass carp eggs were tested at different developmental states and temperatures. Survival was high (15–35%, depending on temperature and trial) in the unburied treatment where eggs rested on a sand bed but were not covered by sediment. Survival was lower in the partial burial (5–10%) and very low (0–4%) in the full burial treatment. In all treatments, delayed hatching (organisms remaining in membranes past the stage of hatching competence) was noted. Deformities such as missing heads and pericardial edema occurred at high rates in the partial and full burials. Eggs that come in contact with the benthos and are resuspended in the water column should be considered in embryonic drift models.
","language":"English","publisher":"Wiley","doi":"10.1111/jai.12918","usgsCitation":"George, A.E., Chapman, D., Deters, J.E., Erwin, S.O., and Hayer, C., 2015, Effects of sediment burial on grass carp, Ctenopharyngodon idella (Valenciennes,1844), eggs: Journal of Applied Ichthyology, v. 31, no. 6, p. 1120-1126, https://doi.org/10.1111/jai.12918.","productDescription":"7 p.","startPage":"1120","endPage":"1126","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-060743","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":471628,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.12918","text":"Publisher Index Page"},{"id":311679,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2015-10-12","publicationStatus":"PW","scienceBaseUri":"56558a32e4b071e7ea53dee1","contributors":{"authors":[{"text":"George, Amy E. 0000-0003-1150-8646 ageorge@usgs.gov","orcid":"https://orcid.org/0000-0003-1150-8646","contributorId":3950,"corporation":false,"usgs":true,"family":"George","given":"Amy","email":"ageorge@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":580503,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Duane 0000-0002-1086-8853 dchapman@usgs.gov","orcid":"https://orcid.org/0000-0002-1086-8853","contributorId":1291,"corporation":false,"usgs":true,"family":"Chapman","given":"Duane","email":"dchapman@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":580504,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deters, Joseph E. jdeters@usgs.gov","contributorId":3240,"corporation":false,"usgs":true,"family":"Deters","given":"Joseph","email":"jdeters@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":580505,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Erwin, Susannah O. 0000-0002-2799-0118 serwin@usgs.gov","orcid":"https://orcid.org/0000-0002-2799-0118","contributorId":5183,"corporation":false,"usgs":true,"family":"Erwin","given":"Susannah","email":"serwin@usgs.gov","middleInitial":"O.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":580506,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hayer, Cari-Ann chayer@usgs.gov","contributorId":150040,"corporation":false,"usgs":true,"family":"Hayer","given":"Cari-Ann","email":"chayer@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":false,"id":580507,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70158939,"text":"sir20155150 - 2015 - Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California","interactions":[],"lastModifiedDate":"2016-01-07T10:17:48","indexId":"sir20155150","displayToPublicDate":"2015-11-24T10:30:00","publicationYear":"2015","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":"2015-5150","title":"Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California","docAbstract":"The Borrego Valley is a small valley (110 square miles) in the northeastern part of San Diego County, California. Although the valley is about 60 miles northeast of city of San Diego, it is separated from the Pacific Ocean coast by the mountains to the west and is mostly within the boundaries of Anza-Borrego Desert State Park. From the time the basin was first settled, groundwater has been the only source of water to the valley. Groundwater is used for agricultural, recreational, and municipal purposes. Over time, groundwater withdrawal through pumping has exceeded the amount of water that has been replenished, causing groundwater-level declines of more than 100 feet in some parts of the basin. Continued pumping has resulted in an increase in pumping lifts, reduced well efficiency, dry wells, changes in water quality, and loss of natural groundwater discharge. As a result, the U.S. Geological Survey began a cooperative study of the Borrego Valley with the Borrego Water District (BWD) in 2009. The purpose of the study was to develop a greater understanding of the hydrogeology of the Borrego Valley Groundwater Basin (BVGB) and to provide tools to help evaluate the potential hydrologic effects of future development. The objectives of the study were to (1) improve the understanding of groundwater conditions and land subsidence, (2) incorporate this improved understanding into a model that would assist in the management of the groundwater resources in the Borrego Valley, and (3) use this model to test several management scenarios. This model provides the capability for the BWD and regional stakeholders to quantify the relative benefits of various options for increasing groundwater storage. The study focuses on the period 1945–2010, with scenarios 50 years into the future.
\nThis report documents and presents (1) an analysis of the conceptual model, (2) a description of the hydrologic features, (3) a compilation and analysis of water-quality data, (4) the measurement and analysis of land subsidence by using geophysical and remote sensing techniques, (5) the development and calibration of a two-dimensional borehole-groundwater-flow model to estimate aquifer hydraulic conductivities, (6) the development and calibration of a three-dimensional (3-D) integrated hydrologic flow model, (7) a water-availability analysis with respect to current climate variability and land use, and (8) potential future management scenarios. The integrated hydrologic model, referred to here as the “Borrego Valley Hydrologic Model” (BVHM), is a tool that can provide results with the accuracy needed for making water-management decisions, although potential future refinements and enhancements could further improve the level of spatial and temporal resolution and model accuracy. Because the model incorporates time-varying inflows and outflows, this tool can be used to evaluate the effects of temporal changes in recharge and pumping and to compare the relative effects of different water-management scenarios on the aquifer system. Overall, the development of the hydrogeologic and hydrologic models, data networks, and hydrologic analysis provides a basis for assessing surface and groundwater availability and potential water-resource management guidelines.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155150","collaboration":"Prepared in cooperation with the Borrego Water District","usgsCitation":"Faunt, C.C., Stamos, C.L., Flint, L.E., Wright, M.T., Burgess, M.K., Sneed, Michelle, Brandt, Justin, Martin, Peter, and Coes, A.L., 2015, Hydrogeology, hydrologic effects of development, and simulation of groundwater flow in the Borrego Valley, San Diego County, California: U.S. Geological Survey Scientific Investigations Report 2015–5150, 135 p., https://dx.doi.org/10.3133/sir20155150.","productDescription":"xiv, 135 p.","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-024573","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":311633,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5150/sir20155150.pdf","text":"Report","size":"21.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5150"},{"id":311632,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5150/coverthb.jpg"},{"id":311671,"rank":3,"type":{"id":18,"text":"Project Site"},"url":"https://dx.doi.org/10.5066/F7S180J9","text":"Borrego Valley Groundwater Conditions"},{"id":314004,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5150/sir20155150_input.zip","text":"Model Input","size":"420 KB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5150 Model Input files"},{"id":314005,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2015/5150/sir20155150_output.zip","text":"Model Output","size":"45 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2015-5150 Model Output files"}],"country":"United States","state":"California","otherGeospatial":"Borrego Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.54708862304686,\n 32.89342578969234\n ],\n [\n -116.54708862304686,\n 33.34659043589842\n ],\n [\n -115.73272705078124,\n 33.34659043589842\n ],\n [\n -115.73272705078124,\n 32.89342578969234\n ],\n [\n -116.54708862304686,\n 32.89342578969234\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, California Water Science Center
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Anthropogenic warming contributed to the 2014 East African drought by increasing East African and west Pacific temperatures, and increasing the gradient between standardized western and central Pacific SST causing reduced rainfall, evapotranspiration, and soil moisture.
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Therefore, it is important to determine whether that wolf–prey system fits a long-standing model relating wolf density to prey biomass. Using data from 2005 to 2012 after elk population fluctuations dampened 10 years subsequent to wolf reintroduction, we found that NR prey biomass predicted wolf density. This finding and the trajectory of the regression extend the validity of the model to prey densities 19% higher than previous data and suggest that the model would apply to wolf–prey systems of even higher prey biomass.
","language":"English","publisher":"Canadian Journal of Zoology","doi":"10.1139/cjz-2015-0002","usgsCitation":"Mech, L.D., and Barber-Meyer, S., 2015, Yellowstone wolf (Canis lupus) denisty predicted by elk (Cervus elaphus) biomass: Canadian Journal of Zoology, v. 93, no. 6, p. 499-502, https://doi.org/10.1139/cjz-2015-0002.","productDescription":"4 p.","startPage":"499","endPage":"502","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-062098","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":488395,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/68840","text":"External Repository"},{"id":311956,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Montana, Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.9619140625,\n 45.205263456162385\n ],\n [\n -110.85205078124999,\n 45.24008561090264\n ],\n [\n -110.7861328125,\n 45.251688256117646\n ],\n [\n -110.67626953125,\n 45.251688256117646\n ],\n [\n -110.5938720703125,\n 45.24395342262324\n ],\n [\n -110.4290771484375,\n 45.182036837015886\n ],\n [\n -110.2972412109375,\n 45.1433047394883\n ],\n [\n -109.4732666015625,\n 45.154927133618465\n ],\n [\n -109.3414306640625,\n 45.18978009667531\n ],\n [\n -109.18212890625,\n 45.20913363773731\n ],\n [\n -109.149169921875,\n 45.166547157856016\n ],\n [\n -109.149169921875,\n 45.06964120886863\n ],\n [\n -109.2315673828125,\n 44.84029065139799\n ],\n [\n -109.2205810546875,\n 44.695992981720714\n ],\n [\n -109.1656494140625,\n 44.59046718130883\n ],\n [\n -109.06677246093749,\n 44.53175879707938\n ],\n [\n -109.072265625,\n 44.469071224701096\n ],\n [\n -109.2205810546875,\n 44.422011314236634\n ],\n [\n -109.34692382812499,\n 44.378839759088585\n ],\n [\n -109.3963623046875,\n 44.34349388385857\n ],\n [\n -109.4073486328125,\n 44.296332880058706\n ],\n [\n -109.3359375,\n 44.3002644115815\n ],\n [\n -109.21508789062499,\n 44.34742225636393\n ],\n [\n -109.127197265625,\n 44.327777761284445\n ],\n [\n -109.09423828125,\n 44.284536706018905\n ],\n [\n -109.1436767578125,\n 44.19402066387343\n ],\n [\n -109.2425537109375,\n 44.142797828180605\n ],\n [\n -109.21508789062499,\n 44.08363928284644\n ],\n [\n -109.1546630859375,\n 44.06390660801779\n ],\n [\n -109.039306640625,\n 44.036269809534616\n ],\n [\n -109.017333984375,\n 43.957236472025635\n ],\n [\n -108.9898681640625,\n 43.810747313446996\n ],\n [\n -109.01184082031249,\n 43.6599240747891\n ],\n [\n -109.017333984375,\n 43.61619382369188\n ],\n [\n -111.3134765625,\n 43.620170616189924\n ],\n [\n -111.29150390625,\n 44.836395454104796\n ],\n [\n -111.3519287109375,\n 44.85586880735725\n ],\n [\n -111.4892578125,\n 44.883120442385646\n ],\n [\n -111.5167236328125,\n 45.00365115687189\n ],\n [\n -111.51123046875,\n 45.178164812206376\n ],\n [\n -111.566162109375,\n 45.259422036351694\n ],\n [\n -111.5277099609375,\n 45.36758436884978\n ],\n [\n -111.434326171875,\n 45.390735154248894\n ],\n [\n -111.2640380859375,\n 45.413876460821086\n ],\n [\n -111.13220214843749,\n 45.4524242413431\n ],\n [\n -111.060791015625,\n 45.43700828867389\n ],\n [\n -111.005859375,\n 45.390735154248894\n ],\n [\n -111.00036621093749,\n 45.31352900692261\n ],\n [\n -110.950927734375,\n 45.259422036351694\n ],\n [\n -110.9619140625,\n 45.205263456162385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"93","issue":"6","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5662c75ce4b06a3ea36c67d1","contributors":{"authors":[{"text":"Mech, L. David 0000-0003-3944-7769 david_mech@usgs.gov","orcid":"https://orcid.org/0000-0003-3944-7769","contributorId":2518,"corporation":false,"usgs":true,"family":"Mech","given":"L.","email":"david_mech@usgs.gov","middleInitial":"David","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":581136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber-Meyer, Shannon 0000-0002-3048-2616 sbarber-meyer@usgs.gov","orcid":"https://orcid.org/0000-0002-3048-2616","contributorId":150236,"corporation":false,"usgs":true,"family":"Barber-Meyer","given":"Shannon","email":"sbarber-meyer@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":581137,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159787,"text":"70159787 - 2015 - Persistent U(IV) and U(VI) following in-situ recovery (ISR) mining of a sandstone uranium deposit, Wyoming, USA","interactions":[],"lastModifiedDate":"2018-09-04T16:23:32","indexId":"70159787","displayToPublicDate":"2015-11-23T11:45:00","publicationYear":"2015","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":"Persistent U(IV) and U(VI) following in-situ recovery (ISR) mining of a sandstone uranium deposit, Wyoming, USA","docAbstract":"Drill-core samples from a sandstone-hosted uranium (U) deposit in Wyoming were characterized to determine the abundance and distribution of uranium following in-situ recovery (ISR) mining with oxygen- and carbon dioxide-enriched water. Concentrations of uranium, collected from ten depth intervals, ranged from 5 to 1920 ppm. A composite sample contained 750 ppm uranium with an average oxidation state of 54% U(VI) and 46% U(IV). Scanning electron microscopy (SEM) indicated rare high uranium (∼1000 ppm U) in spatial association with P/Ca and Si/O attributed to relict uranium minerals, possibly coffinite, uraninite, and autunite, trapped within low permeability layers bypassed during ISR mining. Fission track analysis revealed lower but still elevated concentrations of U in the clay/silica matrix and organic matter (several 10 s ppm) and yet higher concentrations associated with Fe-rich/S-poor sites, likely iron oxides, on altered chlorite or euhedral pyrite surfaces (but not on framboidal pyrite). Organic C (<1.62%), total S (<0.31%), and P (<0.03%) were in low abundance relative to the overall bulk composition. Microbial community analysis showed a diverse group of bacteria present with a wide range of putative metabolisms, and provides evidence for a variety of redox microenvironments co-existing in core samples. Although the uranium minerals persisting in low permeability areas in association with organic carbon were less affected by oxidizing solutions during mining, the likely sequestration of uranium within labile iron oxides following mining and sensitivity to changes in redox conditions requires careful attention during groundwater restoration.
\n\n
A groundwater-flow model was developed for the Bad River Watershed and surrounding area by using the U.S. Geological Survey (USGS) finite-difference code MODFLOW-NWT. The model simulates steady-state groundwater-flow and base flow in streams by using the streamflow routing (SFR) package. The objectives of this study were to: (1) develop an improved understanding of the groundwater-flow system in the Bad River Watershed at the regional scale, including the sources of water to the Bad River Band of Lake Superior Chippewa Reservation (Reservation) and groundwater/surface-water interactions; (2) provide a quantitative platform for evaluating future impacts to the watershed, which can be used as a starting point for more detailed investigations at the local scale; and (3) identify areas where more data are needed. This report describes the construction and calibration of the groundwater-flow model that was subsequently used for analyzing potential locations for the collection of additional field data, including new observations of water-table elevation for refining the conceptualization and corresponding numerical model of the hydrogeologic system.
\nThe study area can be conceptually divided into three primary hydrogeologic environments. The first encompasses the southern uplands with relatively low topographic relief, where groundwater-flow is unconfined and occurs primarily in sandy till and glacial outwash overlying Archean-aged crystalline bedrock. The second includes a transitional area of higher topographic relief and shallow depth to bedrock, in the vicinity of ridges formed by steeply dipping, early-Proterozoic aged metasedimentary units of the Marquette Range Supergroup (including the Ironwood Formation), and late-Proterozoic igneous units associated with the Midcontinent Rift System (MRS). Groundwater-flow in this area likely occurs primarily through connected networks of bedrock fractures that are not well characterized, and also in isolated pockets of Quaternary deposits. The third and last hydrogeologic environment includes lowlands along Lake Superior where a deep sandstone aquifer is confined by thick deposits of clay-rich till.
\nModel input was compiled by using both published and unpublished data. Constant flux boundary conditions for the model perimeter were developed from a regional analytic element model described in appendix 1 of this report. Pumping from 26 high-capacity wells within the model area was included. The SFR stream network was developed from the National Hydrography Dataset (NHDPlus Version 2) and hydrography from the Wisconsin Department of Natural Resources (WDNR). Hydraulic conductivity values were determined for each model cell by interpolation from a network of pilot points, within zones representing major hydrogeologic units.
\nRecharge to the groundwater system was estimated on a cell-by-cell basis by using the Soil Water Balance code (SWB), with gridded daily temperature and precipitation data for the period 1980–2011, and GIS coverages of soil and land-surface conditions. Estimated recharge varies considerably, following spatial patterns in the precipitation and soil hydrologic group inputs. The lowest recharge values occur in the Superior lowlands, whereas the highest values occur in the upland areas, especially those underlain by sandy soils, and in the vicinity of bedrock hills.
\nThe model was calibrated to groundwater-levels and base flows obtained from the USGS National Water Information System (NWIS) database, and groundwater-levels obtained from the WDNR and Band River Band well-construction databases. Calibration was performed via nonlinear regression by using the parameter-estimation software suite PEST. Groundwater levels and base-flow observations in the calibration dataset were well simulated by the calibrated model, with reasonable values of hydraulic conductivity. The pilot-point parameters that were most constrained by observations during model calibration coincided with the locations containing the most wells (head observations)—especially the population centers of Ashland, Mellen, and other communities along the major highway corridors.
\nResults from the calibrated model illustrate differences in the nature of groundwater-flow within the watershed. In the southern part of the watershed, where bedrock is shallow, groundwater flow paths are relatively short, extending from local recharge areas to adjacent first and second-order streams. In contrast, laterally continuous deposits of clay-rich till covering the Superior Lowlands isolate most smaller streams from the sandstone aquifer, allowing for longer flow paths toward larger streams such as the Bad, Marengo, and White Rivers. Approximately three-quarters of all first-order stream cells were dry in the Superior Lowlands, compared to only half of first-order stream cells in the southern bedrock uplands.
\nThe model was used to delineate the groundwatershed for the Bad and Kakagon Rivers. “Groundwatershed” is defined as the area contributing groundwater discharge to one of these streams and their tributaries. The groundwatershed was found to align closely with the surface-watershed, with the most notable exception occurring along the southwestern half of Birch Hill, where surface water drains southwest towards the Potato River, and groundwater flows north and east towards Lake Superior. Similarly, the contributing area of groundwater-flow to the Reservation was delineated. Results indicate the off-Reservation groundwater contributing area to be limited in comparison to the extent of the watershed, extending southward into the highlands underlain by MRS igneous rock units, but not further into the area underlain by the Marquette Range Supergroup.
\nStable isotope samples were collected from 54 wells within the watershed, to investigate sources of groundwater. Oxygen-18 (δ 18O) values lower than -13.0 per mil were documented in the sampling, and likely indicate the presence of recharge water from the last glacial period (>9,500 years old) beneath the northern portion of the Reservation, in the vicinity of Odanah, Wisconsin.
\nFinally, a new data-worth analysis of potential new monitoring-well locations was performed by using the model. The relative worth of new measurements was evaluated based on their ability to increase confidence in model predictions of groundwater levels and base flows at 35 locations, under the condition of a proposed open-pit iron mine. Results of the new data-worth analysis, and other inputs and outputs from the Bad River model, are available through an online dynamic web mapping service at (http://wim.usgs.gov/badriver/).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155162","collaboration":"Prepared in cooperation with the Bad River Band of Lake Superior Chippewa; U.S. Bureau of Indian Affairs","usgsCitation":"Leaf, A.T., Fienen, M.N., Hunt, R.J., and Buchwald, C.A., 2015, Groundwater/Surface-Water Interactions in the Bad\n River Watershed, Wisconsin: U.S. Geological Survey Scientific Investigations Report 2015–5162, 110 p., https://dx.doi.org/10.3133/sir20155162.","productDescription":"viii, 110 p.","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-061535","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":311584,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5162/coverthb.jpg"},{"id":311585,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5162/sir20155162.pdf","text":"Report","size":"24.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015=5162"},{"id":332726,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F7Z0368H","text":"MODFLOW-NWT model used to evaluate groundwater/surface-water interactions in the Bad River Watershed, Wisconsin"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Bad River Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.06842041015625,\n 46.36777895261494\n ],\n [\n -91.06842041015625,\n 46.76996843356982\n ],\n [\n -90.43121337890625,\n 46.76996843356982\n ],\n [\n -90.43121337890625,\n 46.36777895261494\n ],\n [\n -91.06842041015625,\n 46.36777895261494\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wisconsin Water Science Center
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8505 Research W
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http://wi.water.usgs.gov/
In this study, water and whole rock samples from hydraulically fractured wells in the Marcellus Shale (Middle Devonian), and water from conventional wells producing from Upper Devonian sandstones were analyzed for lithium concentrations and isotope ratios (δ7Li). The distribution of lithium concentrations in different mineral groups was determined using sequential extraction. Structurally bound Li, predominantly in clays, accounted for 75-91 wt. % of total Li, whereas exchangeable sites and carbonate cement contain negligible Li (< 3%). Up to 20% of the Li is present in the oxidizable fraction (organic matter and sulfides). The δ7Li values for whole rock shale in Greene Co., Pennsylvania, and Tioga Co., New York, ranged from -2.3 to + 4.3‰, similar to values reported for other shales in the literature. The δ7Li values in shale rocks with stratigraphic depth record progressive weathering of the source region; the most weathered and clay-rich strata with isotopically light Li are found closest to the top of the stratigraphic section. Diagenetic illite-smectite transition could also have partially affected the bulk Li content and isotope ratios of the Marcellus Shale.
\nIn Greene Co., southwest Pennsylvania, the Upper Devonian sandstone formation waters have δ7Li values of + 14.6 ± 1.2 (2SD, n = 25), and are distinct from Marcellus Shale formation waters which have δ7Li of + 10.0 ± 0.8 (2SD, n = 12). These two formation waters also maintain distinctive 87Sr/86Sr ratios suggesting hydrologic separation between these units. Applying temperature-dependent illitilization model to Marcellus Shale, we found that Li concentration in clay minerals increased with Li concentration in pore fluid during diagenetic illite-smectite transition. Samples from north central PA show a much smaller range in both δ7Li and 87Sr/86Sr than in southwest Pennsylvania. Spatial variations in Li and δ7Li values show that Marcellus formation waters are not homogeneous across the Appalachian Basin. Marcellus formation waters in the northeastern Pennsylvania portion of the basin show a much smaller range in both δ7Li and 87Sr/86Sr, suggesting long term, cross-formational fluid migration in this region. Assessing the impact of potential mixing of fresh water with deep formation water requires establishment of a geochemical and isotopic baseline in the shallow, fresh water aquifers, and site specific characterization of formation water, followed by long-term monitoring, particularly in regions of future shale gas development.
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W.","contributorId":150019,"corporation":false,"usgs":false,"family":"Hammack","given":"Richard","email":"","middleInitial":"W.","affiliations":[{"id":17887,"text":"National Energy Technology Laboratory, Department of Energy","active":true,"usgs":false}],"preferred":false,"id":580430,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70157563,"text":"sir20155138 - 2015 - Geohydrology and water quality of the stratified-drift aquifers in Upper Buttermilk Creek and Danby Creek Valleys, Town of Danby, Tompkins County, New York","interactions":[],"lastModifiedDate":"2015-11-24T09:07:20","indexId":"sir20155138","displayToPublicDate":"2015-11-20T11:00:00","publicationYear":"2015","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":"2015-5138","title":"Geohydrology and water quality of the stratified-drift aquifers in Upper Buttermilk Creek and Danby Creek Valleys, Town of Danby, Tompkins County, New York","docAbstract":"In 2006, the U.S. Geological Survey, in cooperation with the Town of Danby and the Tompkins County Planning Department, began a study of the stratified-drift aquifers in the upper Buttermilk Creek and Danby Creek valleys in the Town of Danby, Tompkins County, New York. In the northern part of the north-draining upper Buttermilk Creek valley, there is only one sand and gravel aquifer, a confined basal unit that overlies bedrock. In the southern part of upper Buttermilk Creek valley, there are as many as four sand and gravel aquifers, two are unconfined and two are confined. In the south-draining Danby Creek valley, there is an unconfined aquifer consisting of outwash and kame sand and gravel (deposited by glacial meltwaters during the late Pleistocene Epoch) and alluvial silt, sand, and gravel (deposited by streams during the Holocene Epoch). In addition, throughout the study area, there are several small local unconfined aquifers where large tributaries deposited alluvial fans in the valley.
\nThe principal sources of recharge to the unconfined aquifers in the study area include direct infiltration of precipitation (rain and snowmelt) at land surface, unchanneled surface runoff from adjacent hillsides that seeps into the aquifer along the edges of the valley, groundwater inflow from adjacent till and bedrock that enters the aquifer along the sides of the valley, and seepage loss from upland-tributary streams where they flow over their alluvial fans in the valley. The percentages of all sources of recharge to the contiguous unconfined aquifer in Danby Creek valley include 16 percent from precipitation that falls directly over the aquifer, 55 percent from unchanneled surface runoff and groundwater inflow from hillsides, and 29 percent from losing tributary streams that cross the aquifer. The total annual recharge to the contiguous unconfined aquifer is 2.56 cubic feet per second (604 million gallons per year).
\nThe principal sources of recharge to the confined aquifers include precipitation that falls directly on the surficial confining unit, which then slowly flows vertically downward through the fine-grained sediments and enters the confined aquifer, and groundwater inflow from till and bedrock that borders the aquifer along adjacent hillsides and at the bottom of the valley. In addition, there is substantial amounts of recharge to the confined aquifers where the confining units are locally absent (forming windows) and where parts of the confining units consist of sediments of low to moderate permeability (forming a semiconfining layer).
\nIn the northern part of the study area (upper Buttermilk Creek valley), groundwater in the stratified-drift aquifers discharges to (1) domestic and commercial wells; (2) Buttermilk Creek in the area near the northern town border, and (3) and a small unnamed stream in a ravine in Buttermilk State Park just north of the town border. In the southern part of the study area (Danby Creek valley), groundwater discharges (1) to domestic, commercial, and farm wells; (2) to Danby Creek; (3) to a large wetland in the central parts of Danby Creek valley; and (4) as losses because of plant uptake and evaporation. About 300 people depend on groundwater from the upper Buttermilk Creek and Danby Creek stratified-drift aquifer system.
\nAn unconfined surficial aquifer about 8,000 feet (ft) long and as much as 800 ft wide, with a saturated thickness of about 20 ft, occupies the lower (southeastern most) 8,000 ft of Danby Creek valley within the Town of Danby. However, because the aquifer is thin, the volume of water stored in the aquifer is small and the potential for induced recharge from Danby Creek during summer periods of low flow is also small, an array of wells would probably be needed to provide sustainable continuous amount of water to large water users such as municipalities and industries. Additional data and a groundwater flow model would be required to estimate sustainable withdrawal from the confined aquifers in upper Buttermilk Creek valley. Well data from water-well drillers through 2012 indicate that the confined aquifers in upper Buttermilk Creek valley are thin (typically about 10 feet thick) and the reported well-yield data suggest these aquifers may not be capable of supplying sufficient water to meet the needs of municipalities and industries. However, additional geohydrologic data leading to calibration of a groundwater flow model would be needed to properly evaluate the long-term (multiple years) potential yield of the confined aquifer system in upper Buttermilk Creek valley and of the unconfined aquifer in Danby Creek valley.
\nDuring 2007–10, groundwater samples were collected from 13 wells including 7 wells that are completed in the confined sand and gravel aquifers, 1 well that is completed in the unconfined aquifer, and 5 wells that are completed in the bedrock aquifers. Calcium dominates the cation composition and bicarbonate dominates the anion composition in most groundwater. Water quality in the study area generally meets state and Federal drinking-water standards but concentrations of some constituents exceeded the standards. The standards that were exceeded include sodium (3 samples), dissolved solids (1 sample), iron (3 samples), manganese (8 samples), and arsenic (1 sample).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155138","collaboration":"Prepared in cooperation with the Town of Danby and the Tompkins County Planning Department","usgsCitation":"Miller, T.S., 2015, Geohydrology and water quality of the stratified-drift aquifers in upper Buttermilk Creek and Danby Creek valleys, Town of Danby, Tompkins County, New York: U.S. Geological Survey Scientific Investigations Report 2015–5138, 66 p., https://dx.doi.org/10.3133/sir20155138.","productDescription":"viii, 66 p.","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-055138","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":311559,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5138/coverthb.jpg"},{"id":311560,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5138/sir20155138.pdf","text":"Report","size":"6.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5138"}],"country":"United States","state":"New York","county":"Tompkins County","city":"Danby","otherGeospatial":"Danby Creek, Upper Buttermilk Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.51290893554688,\n 42.357782825014176\n ],\n [\n -76.51290893554688,\n 42.428524987525385\n ],\n [\n -76.43051147460938,\n 42.428524987525385\n ],\n [\n -76.43051147460938,\n 42.357782825014176\n ],\n [\n -76.51290893554688,\n 42.357782825014176\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
30 Brown Road
Ithaca, NY 14850
http://ny.water.usgs.gov
Information requests:
(518) 285-5602
Next-Generation Radar (NEXRAD) has become an integral component in the estimation of precipitation (Kitzmiller and others, 2013). The high spatial and temporal resolution of NEXRAD has revolutionized the ability to estimate precipitation across vast regions, which is especially beneficial in areas without a dense rain-gage network. With the improved precipitation estimates, hydrologic models can produce reliable streamflow forecasts for areas across the United States. NEXRAD data from the National Weather Service (NWS) has been an invaluable tool used by the U.S. Geological Survey (USGS) for numerous projects and studies; NEXRAD data processing techniques similar to those discussed in this Fact Sheet have been developed within the USGS, including the NWS Quantitative Precipitation Estimates archive developed by Blodgett (2013).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153076","collaboration":"Prepared in cooperation with the DuPage County Stormwater Management Department","usgsCitation":"Ortel, T.W., and Spies, R.R., 2015, NEXRAD Quantitative precipitation estimates, data acquisition, and processing for the DuPage County, Illinois, streamflow-simulation modeling system: U.S. Geological Survey Fact Sheet 2015–3076, 2 p., https://dx.doi.org/10.3133/fs20153076.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-057259","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3076/coverthb.jpg"},{"id":311539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3076/fs20153076.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3076"}],"country":"United States","state":"Illinois","county":"DuPage County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.49212646484374,\n 41.281934557995356\n ],\n [\n -88.49212646484374,\n 42.04929263868686\n ],\n [\n -87.6104736328125,\n 42.04929263868686\n ],\n [\n -87.6104736328125,\n 41.281934557995356\n ],\n [\n -88.49212646484374,\n 41.281934557995356\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, IL 61801–2347
Phone: (217) 328–USGS (8747)
http://il.water.usgs.gov/
The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153066","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice implementation in the Great Lakes restoration initiative priority watersheds—Eagle Creek, tributary to Maumee River, Ohio: U.S. U.S. Geological Survey Fact Sheet 2015–3066, 6 p., https://dx.doi.org/10.3133/fs20153066.","productDescription":"6 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070205","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311089,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3066/coverthb.jpg"},{"id":311090,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3066/fs20153066.pdf","text":"Report","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3066"},{"id":311091,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153065","text":"Fact Sheet 2015-3065","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3066"},{"id":311092,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153067","text":"Fact Sheet 2015-3067","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3066"}],"country":"United States","state":"Ohio","otherGeospatial":"Eagle Creek, Maumee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.72749328613281,\n 40.730608477796636\n ],\n [\n -83.72749328613281,\n 41.000629848685385\n ],\n [\n -83.57574462890625,\n 41.000629848685385\n ],\n [\n -83.57574462890625,\n 40.730608477796636\n ],\n [\n -83.72749328613281,\n 40.730608477796636\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, IL 61801
http://il.water.usgs.gov
The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153065","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin: U.S. Geological Survey Fact Sheet 2015–3065, 6 p., https://dx.doi.org/10.3133/fs20153065.","productDescription":"6 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070173","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":311075,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153066","text":"Fact Sheet 2015-3066","size":"1.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"},{"id":311076,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153067","text":"Fact Sheet 2015-3067","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"},{"id":311073,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3065/coverthb.jpg"},{"id":311074,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3065/fs20153065.pdf","text":"Report","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3065"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Upper East River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.6376953125,\n 44.21764696919354\n ],\n [\n -88.6376953125,\n 44.66865287227321\n ],\n [\n -87.703857421875,\n 44.66865287227321\n ],\n [\n -87.703857421875,\n 44.21764696919354\n ],\n [\n -88.6376953125,\n 44.21764696919354\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, IL 61801
http://il.water.usgs.gov
The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20153067","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture-Natural Resources Conservation Service","usgsCitation":"Merriman, K.R., 2015, Development of an assessment tool for agricultural best management practice Implementation in the Great Lakes Restoration Initiative priority watersheds—Alger Creek, tributary to Saginaw River, Michigan: U.S. Geological Survey Fact Sheet 2015–3067, 6 p., https://dx.doi.org/10.3133/fs20153067.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070206","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":438665,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7ST7P39","text":"USGS data release","linkHelpText":"Daily loads of nutrients, sediment, and chloride at Great Lakes Restoration Initiative USGS edge-of-field and tile stations"},{"id":311095,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153065","text":"Fact Sheet 2015-3065","size":"1.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3067"},{"id":311096,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20153066","text":"Fact Sheet 2015-3066","size":"1.28 MB","description":"FS 2015-3067"},{"id":311093,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2015/3067/coverthb.jpg"},{"id":311094,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2015/3067/fs20153067.pdf","size":"1.16 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2015-3067"}],"country":"United States","state":"Michigan","otherGeospatial":"Alger Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.166015625,\n 42.52879629320373\n ],\n [\n -85.166015625,\n 43.937461690316646\n ],\n [\n -82.77099609375,\n 43.937461690316646\n ],\n [\n -82.77099609375,\n 42.52879629320373\n ],\n [\n -85.166015625,\n 42.52879629320373\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave>
Urbana, IL 6180
http://il.water.usgs.gov
","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"publishedDate":"2015-11-19","noUsgsAuthors":false,"publicationDate":"2015-11-19","publicationStatus":"PW","scienceBaseUri":"564ef2b7e4b064dd1d095556","contributors":{"authors":[{"text":"Merriman, Katherine R. 0000-0002-1303-2410 kmerriman@usgs.gov","orcid":"https://orcid.org/0000-0002-1303-2410","contributorId":4973,"corporation":false,"usgs":true,"family":"Merriman","given":"Katherine","email":"kmerriman@usgs.gov","middleInitial":"R.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":false,"id":579479,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70159002,"text":"ofr20151185 - 2015 - A one-dimensional model of solid-earth electrical resistivity beneath Florida","interactions":[],"lastModifiedDate":"2015-11-19T14:52:35","indexId":"ofr20151185","displayToPublicDate":"2015-11-19T13:00:00","publicationYear":"2015","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2015-1185","title":"A one-dimensional model of solid-earth electrical resistivity beneath Florida","docAbstract":"
An estimated one-dimensional layered model of electrical resistivity beneath Florida was developed from published geological and geophysical information. The resistivity of each layer is represented by plausible upper and lower bounds as well as a geometric mean resistivity. Corresponding impedance transfer functions, Schmucker-Weidelt transfer functions, apparent resistivity, and phase responses are calculated for inducing geomagnetic frequencies ranging from 10−5 to 100 hertz. The resulting one-dimensional model and response functions can be used to make general estimates of time-varying electric fields associated with geomagnetic storms such as might represent induction hazards for electric-power grid operation. The plausible upper- and lower-bound resistivity structures show the uncertainty, giving a wide range of plausible time-varying electric fields.
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\"}}]}","contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225-0046
http://geohazards.cr.usgs.gov/
Contours maps (such as topographic maps) compress the information of a function over a two-dimensional area into a discrete set of closed lines that connect points of equal value (isolines), striking a fine balance between expressiveness and cognitive simplicity. They allow humans to perform many common sense reasoning tasks about the underlying function (e.g. elevation).
\nThis paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Spatial information theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-319-23374-1_18","usgsCitation":"Usery, E.L., and Hahmann, T., 2015, What is in a contour map? A region-based logical formalization of contour semantics, chap. of Spatial information theory: 12th International Conference, COSIT 2015 Santa Fe, NM, USA, October 12–16, 2015, proceedings, v. 9368, p. 375-399, https://doi.org/10.1007/978-3-319-23374-1_18.","productDescription":"25 p.","startPage":"375","endPage":"399","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065389","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":311569,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9368","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2015-12-15","publicationStatus":"PW","scienceBaseUri":"564ef2bbe4b064dd1d095566","contributors":{"authors":[{"text":"Usery, E. Lynn 0000-0002-2766-2173 usery@usgs.gov","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":231,"corporation":false,"usgs":true,"family":"Usery","given":"E.","email":"usery@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":580288,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hahmann, Torsten","contributorId":149994,"corporation":false,"usgs":false,"family":"Hahmann","given":"Torsten","email":"","affiliations":[{"id":17881,"text":"Assistant Professor, University of Maine","active":true,"usgs":false}],"preferred":false,"id":580289,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159728,"text":"70159728 - 2015 - Biofilm formation of Francisella noatunensis subsp. orientalis","interactions":[],"lastModifiedDate":"2016-12-19T11:59:44","indexId":"70159728","displayToPublicDate":"2015-11-19T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3685,"text":"Veterinary Microbiology","active":true,"publicationSubtype":{"id":10}},"title":"Biofilm formation of Francisella noatunensis subsp. orientalis","docAbstract":"Francisella noatunensis subsp. orientalis (Fno) is an emergent fish pathogen in both marine and fresh water environments. The bacterium is suspected to persist in the environment even without the presence of a suitable fish host. In the present study, the influence of different abiotic factors such as salinity and temperature were used to study the biofilm formation of different isolates of Fno including intracellular growth loci C (iglC)and pathogenicity determinant protein A (pdpA) knockout strains. Finally, we compared the susceptibility of planktonic and biofilm to three disinfectants used in the aquaculture and ornamental fish industry, namely Virkon®, bleach and hydrogen peroxide. The data indicates that Fno is capable of producing biofilms within 24 h where both salinity as well as temperature plays a role in the growth and biofilm formation of Fno. Mutations in theiglC or pdpA, both known virulence factors, do not appear to affect the capacity of Fno to produce biofilms, and the minimum inhibitory concentration, and minimum biocidal concentration for the three disinfectants were lower than the minimum biofilm eradication concentration values. This information needs to be taken into account if trying to eradicate the pathogen from aquaculture facilities or aquariums.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.vetmic.2015.10.007","usgsCitation":"Soto, E., Halliday-Wimmonds, I., Francis, S., Kearney, M.T., and Hansen, J.D., 2015, Biofilm formation of Francisella noatunensis subsp. orientalis: Veterinary Microbiology, v. 181, no. 3-4, p. 313-317, https://doi.org/10.1016/j.vetmic.2015.10.007.","productDescription":"5 p.","startPage":"313","endPage":"317","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-061353","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":311568,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"181","issue":"3-4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564ef2b5e4b064dd1d095552","contributors":{"authors":[{"text":"Soto, Esteban","contributorId":64142,"corporation":false,"usgs":true,"family":"Soto","given":"Esteban","email":"","affiliations":[],"preferred":false,"id":580224,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halliday-Wimmonds, Iona","contributorId":149970,"corporation":false,"usgs":false,"family":"Halliday-Wimmonds","given":"Iona","email":"","affiliations":[{"id":17866,"text":"Department of Biomedical Sciences, Ross University School of Veterinary Medicine, PO Box 334, Basseterre, St. Kitts, West Indies","active":true,"usgs":false}],"preferred":false,"id":580225,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Francis, Stewart","contributorId":177541,"corporation":false,"usgs":false,"family":"Francis","given":"Stewart","email":"","affiliations":[],"preferred":false,"id":656130,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kearney, Michael T.","contributorId":149971,"corporation":false,"usgs":false,"family":"Kearney","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":17867,"text":"Department of Pathobiological Sciences, School of Veterinary Medicine, Louisiana State University, Baton Rouge, Louisiana, USA 70803","active":true,"usgs":false}],"preferred":false,"id":580226,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, John D. 0000-0002-3006-2734 jhansen@usgs.gov","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":3440,"corporation":false,"usgs":true,"family":"Hansen","given":"John","email":"jhansen@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":580223,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70154881,"text":"70154881 - 2015 - Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24","interactions":[],"lastModifiedDate":"2016-08-17T11:33:56","indexId":"70154881","displayToPublicDate":"2015-11-19T12:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24","docAbstract":"The horseshoe crab fishery on the US Atlantic coast represents a compelling fishery management story for many reasons, including ecological complexity, health and human safety ramifications, and socio-economic conflicts. Knowledge of stock status and assessment and monitoring capabilities for the species have increased greatly in the last 15 years and permitted managers to make more informed harvest recommendations. Incorporating the bioenergetics needs of migratory shorebirds, which feed on horseshoe crab eggs, into the management framework for horseshoe crabs was identified as a goal, particularly in the Delaware Bay region where the birds and horseshoe crabs exhibit an important ecological interaction. In response, significant effort was invested in studying the population dynamics, migration ecology, and the ecologic relationship of a key migratory shorebird, the Red Knot, to horseshoe crabs. A suite of models was developed that linked Red Knot populations to horseshoe crab populations through a mass gain function where female spawning crab abundance determined what proportion of the migrating Red Knot population reached a critical body mass threshold. These models were incorporated in an adaptive management framework wherein optimal harvest decisions for horseshoe crab are recommended based on several resource-based and value-based variables and thresholds. The current adaptive framework represents a true multispecies management effort where additional data over time are employed to improve the predictive models and reduce parametric uncertainty. The possibility of increasing phenologic asynchrony between the two taxa in response to climate change presents a potential challenge to their ecologic interaction in Delaware Bay.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Changing Global Perspectives on Horseshoe Crab Biology, Conservation and Management","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","publisherLocation":"Cham","doi":"10.1007/978-3-319-19542-1_24","usgsCitation":"Millard, M.J., Sweka, J.A., McGowan, C., and Smith, D., 2015, Assessment and Mmanagement of North American horseshoe crab populations, with emphasis on a multispecies framework for Delaware Bay, U.S.A. populations: Chapter 24, chap. of Changing Global Perspectives on Horseshoe Crab Biology, Conservation and Management, p. 407-431, https://doi.org/10.1007/978-3-319-19542-1_24.","startPage":"407","endPage":"431","numberOfPages":"25","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-059817","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":326655,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57b58abee4b03bcb0104bb5e","contributors":{"authors":[{"text":"Millard, Michael J.","contributorId":23411,"corporation":false,"usgs":false,"family":"Millard","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":645754,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sweka, John A.","contributorId":80945,"corporation":false,"usgs":true,"family":"Sweka","given":"John","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":645755,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McGowan, Conor P. cmcgowan@usgs.gov","contributorId":145496,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor P.","email":"cmcgowan@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":564308,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, David R.","contributorId":173756,"corporation":false,"usgs":false,"family":"Smith","given":"David R.","affiliations":[],"preferred":false,"id":645756,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70159331,"text":"sir20155154 - 2015 - Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2012","interactions":[],"lastModifiedDate":"2022-03-15T17:20:23.416494","indexId":"sir20155154","displayToPublicDate":"2015-11-19T12:00:00","publicationYear":"2015","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":"2015-5154","title":"Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2012","docAbstract":"Streambed scour potential was evaluated at 18 river- and stream-spanning bridges in Alaska that have unknown foundation details or a lack of existing scour analysis. All sites were evaluated for stream stability and long-term scour potential. Contraction scour and abutment scour were calculated for 17 bridges, and pier scour was calculated for 7 bridges that had piers. Vertical contraction (pressure flow) scour was calculated for sites with overtopping floods (where the modeled water surface was higher than the superstructure of the bridge). In most cases, hydraulic models of the 1- and 0.2-percent annual exceedance probability floods (also known as the 100- and 500-year floods, respectively) were used to derive hydraulic variables for the scour calculations. Alternate flood values were used in scour calculations for sites where smaller floods overtopped a bridge or where standard flood-frequency estimation techniques did not apply. Scour was also calculated for large recorded floods at several sites. Equations for scour in cohesive soils were used for sites where streambed sediment was silt-sized or smaller.
\nChannel instability at four sites was related to human activities (in-channel mining, dredging, and channel relocation). Three of the dredged sites are located on active unstable alluvial fans and were graded to inhibit aggradation. The trend toward aggradation during major floods at these sites greatly reduces confidence in scour estimates.
\nVertical contraction and pressure flow occurred during 1 percent or smaller annual exceedance probability floods at five sites, including three aggradation sites. Contraction scour exceeded 5 feet at two sites, and total scour at piers (pier scour plus contraction scour) exceeded 5 feet at two sites. Debris accumulation increased calculated pier scour at six sites by an average of 1.2 feet. Total scour at abutments including contraction scour exceeded 5 feet at seven sites. Scour estimates seemed excessive at aggradation sites where upstream sediment supply controls scour and deposition processes, at cohesive soil sites where conservative assumptions were made for soil strength and flood duration, and for abutment scour at sites where failure of the embankment and attendant channel widening would reduce scour.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155154","collaboration":"Prepared in cooperation with the Alaska Department of Transporation and Public Facilities","usgsCitation":"Beebee, R.A., and Schauer, P.V., 2015, Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2012: U.S. Geological Survey Scientific Investigations Report 2015–5154, 45 p., https://dx.doi.org/10.3133/sir20155154.","productDescription":"Report: vi, 45 p.; Appendix","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2012-01-01","temporalEnd":"2012-12-31","ipdsId":"IP-064803","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":311582,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2015/5154/sir20155154_appendixa.xlsx","text":"Appendix A","size":"105 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2015-5154 Appendix A"},{"id":311546,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5154/sir20155154.pdf","text":"Report","size":"3.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015-5154 PDF"},{"id":311545,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5154/coverthb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -131.748046875,\n 54.265224078605655\n ],\n [\n -129.7265625,\n 55.37911044801047\n ],\n [\n -131.044921875,\n 56.70450561416937\n ],\n [\n -135.439453125,\n 59.80063426102869\n ],\n [\n -137.63671875,\n 59.265880628258095\n ],\n [\n -139.658203125,\n 60.326947742998414\n ],\n [\n -140.888671875,\n 60.413852350464914\n ],\n [\n -141.064453125,\n 67.57571741708057\n ],\n [\n -145.546875,\n 67.60922060496382\n ],\n [\n -149.677734375,\n 66.65297740055279\n ],\n [\n -154.16015625,\n 64.20637724320852\n ],\n [\n -154.16015625,\n 61.98026726504402\n ],\n [\n -152.666015625,\n 59.80063426102869\n ],\n [\n -151.962890625,\n 59.085738569819505\n ],\n [\n -148.359375,\n 59.80063426102869\n ],\n [\n -146.162109375,\n 60.45721779774397\n ],\n [\n -142.91015625,\n 59.84481485969105\n ],\n [\n -140.44921875,\n 59.712097173322924\n ],\n [\n -136.845703125,\n 58.03137242177637\n ],\n [\n -133.41796874999997,\n 54.316523240258284\n ],\n [\n -131.748046875,\n 54.265224078605655\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.86328125,\n 57.37393841871411\n ],\n [\n -152.578125,\n 58.53959476664049\n ],\n [\n -151.78710937499997,\n 58.07787626787517\n ],\n [\n -152.490234375,\n 57.18390185831188\n ],\n [\n -154.423828125,\n 56.559482483762245\n ],\n [\n -154.86328125,\n 57.37393841871411\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Alaska Science Center
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4210 University Drive
Anchorage, Alaska 99508-4560
http://alaska.usgs.gov
Piscine reovirus (PRV) is a double stranded non-enveloped RNA virus detected in farmed and wild salmonids. This study examined the phylogenetic relationships among different PRV sequence types present in samples from salmonids in Western Canada and the US, including Alaska (US), British Columbia (Canada) and Washington State (US). Tissues testing positive for PRV were partially sequenced for segment S1, producing 71 sequences that grouped into 10 unique sequence types. Sequence analysis revealed no identifiable geographical or temporal variation among the sequence types. Identical sequence types were found in fish sampled in 2001, 2005 and 2014. In addition, PRV positive samples from fish derived from Alaska, British Columbia and Washington State share identical sequence types. Comparative analysis of the phylogenetic tree indicated that Canada/US Pacific Northwest sequences formed a subgroup with some Norwegian sequence types (group II), distinct from other Norwegian and Chilean sequences (groups I, III and IV). Representative PRV positive samples from farmed and wild fish in British Columbia and Washington State were subjected to genome sequencing using next generation sequencing methods. Individual analysis of each of the 10 partial segments indicated that the Canadian and US PRV sequence types clustered separately from available whole genome sequences of some Norwegian and Chilean sequences for all segments except the segment S4. In summary, PRV was genetically homogenous over a large geographic distance (Alaska to Washington State), and the sequence types were relatively stable over a 13 year period.
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2015 - Evidence of population resistance to extreme low flows in a fluvial-dependent fish species","interactions":[],"lastModifiedDate":"2015-11-19T09:34:11","indexId":"70159742","displayToPublicDate":"2015-11-19T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Evidence of population resistance to extreme low flows in a fluvial-dependent fish species","docAbstract":"Extreme low streamflows are natural disturbances to aquatic populations. Species in naturally intermittent streams display adaptations that enhance persistence during extreme events; however, the fate of populations in perennial streams during unprecedented low-flow periods is not well-understood. Biota requiring swift-flowing habitats may be especially vulnerable to flow reductions. We estimated the abundance and local survival of a native fluvial-dependent fish species (Etheostoma inscriptum) across 5 years encompassing historic low flows in a sixth-order southeastern USA perennial river. Based on capturemark-recapture data, the study shoal may have acted as a refuge during severe drought, with increased young-of-the-year (YOY) recruitment and occasionally high adult immigration. Contrary to expectations, summer and autumn survival rates (30 days) were not strongly depressed during low-flow periods, despite 25%-80% reductions in monthly discharge. Instead, YOY survival increased with lower minimum discharge and in response to small rain events that increased low-flow variability. Age-1+ fish showed the opposite pattern, with survival decreasing in response to increasing low-flow variability. Results from this population dynamics study of a small fish in a perennial river suggest that fluvial-dependent species can be resistant to extreme flow reductions through enhanced YOY recruitment and high survival
","language":"English","publisher":"NRC Research Press","doi":"10.1139/cjfas-2015-0173","usgsCitation":"Katz, R.A., and Freeman, M., 2015, Evidence of population resistance to extreme low flows in a fluvial-dependent fish species: Canadian Journal of Fisheries and Aquatic Sciences, v. 11, no. 29, p. 1776-1787, https://doi.org/10.1139/cjfas-2015-0173.","productDescription":"12 p.","startPage":"1776","endPage":"1787","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065219","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":311557,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"11","issue":"29","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"564ef2b9e4b064dd1d09555c","contributors":{"authors":[{"text":"Katz, Rachel A.","contributorId":149995,"corporation":false,"usgs":false,"family":"Katz","given":"Rachel","email":"","middleInitial":"A.","affiliations":[{"id":17882,"text":"Odum School of Ecology, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":580305,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Freeman, Mary 0000-0001-7615-6923 mcfreeman@usgs.gov","orcid":"https://orcid.org/0000-0001-7615-6923","contributorId":3528,"corporation":false,"usgs":true,"family":"Freeman","given":"Mary","email":"mcfreeman@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":580304,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70159743,"text":"70159743 - 2015 - Estimating occupancy dynamics for large-scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States","interactions":[],"lastModifiedDate":"2015-11-19T09:30:04","indexId":"70159743","displayToPublicDate":"2015-11-19T10:30:00","publicationYear":"2015","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Estimating occupancy dynamics for large-scale monitoring networks: amphibian breeding occupancy across protected areas in the northeast United States","docAbstract":"Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected within which intensive sub-sampling occurs. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among sub-samples. Implementing such an analysis within a classic likelihood framework is computationally prohibitive with species occurrence data when accounting for detection probabilities. Bayesian methods offer an alternative framework to make this analysis feasible. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. Using data from a regional monitoring program of wood frogs (Lithobates sylvaticus) and spotted salamanders (Ambystoma maculatum) in vernal pools, we analyzed data using static and dynamic occupancy frameworks. We analyzed observations from 2004-2013collected within 14 protected areas located throughout the northeast United States . We use the data set to estimate trends in occupancy at both the regional and individual protected area level. We show that occupancy at the regional level was relatively stable for both species. Much more variation occurred within individual study areas, with some populations declining and some increasing for both species. We found some evidence for a latitudinal gradient in trends among protected areas. However, support for this pattern is overestimated when the hierarchical nature of the data collection is not controlled for in the analysis. For both species, occupancy appeared to be declining in the most southern areas, while occupancy was stable or increasing in more northern areas. These results shed light on the range-level population status of these pond-breeding amphibians and our approach provides a framework that can be used to examine drivers of change including among-year and among-site variation in occurrence dynamics, while properly accounting for nested structure of data collection.
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