Naval Air Station Whiting Field is located near Milton, Florida and is one of the Navy's two primary pilot training bases. Commissioned in 1943, historic operations at Whiting Field generated industrial wastes that contaminated soil and the water-table aquifer. The Environmental Protection Agency placed Whiting Field on the Superfund program’s National Priorities List of contaminated sites in 1994. The U.S. Geological Survey was tasked with studying the contaminant migration and remediation processes at this site. A numerical model is under development to better define groundwater flow patterns, discharge to surface water, and the potential fate of contaminants. An initial model discretized the water-table aquifer into 5 layers, with the top layer between land surface and elevation -50 feet National Geodetic Vertical Datum of 1929 (NGVD29). However, with land surface ranging from 3.3 to 206.6 feet NGVD29, the top layer thickness is over 250 feet at highest land elevations. To more accurately simulate contaminant transport, refining the resolution in this top model layer is necessary.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Fourth international symposium on bioremediation and sustainable environmental technologies","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Fourth International Symposium on Bioremediation and Sustainable Environmental Technologies","conferenceDate":"May 22-25, 2017","conferenceLocation":"Miami, FL","language":"English","usgsCitation":"Swain, E.D., Campbell, B.G., and Landmeyer, J., 2017, Implications of refining vertical resolution of hydraulic conductivity in the numerical modeling of groundwater flow to surface water, NAS Whiting Field, Florida, in Fourth international symposium on bioremediation and sustainable environmental technologies, Miami, FL, May 22-25, 2017, 1 p.","productDescription":"1 p.","ipdsId":"IP-079891","costCenters":[{"id":269,"text":"FLWSC-Ft. Lauderdale","active":true,"usgs":true}],"links":[{"id":375024,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Naval Air Station Whiting Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.06132888793944,\n 30.679553982390203\n ],\n [\n -86.99077606201172,\n 30.679553982390203\n ],\n [\n -86.99077606201172,\n 30.750392622606626\n ],\n [\n -87.06132888793944,\n 30.750392622606626\n ],\n [\n -87.06132888793944,\n 30.679553982390203\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swain, Eric D. 0000-0001-7168-708X edswain@usgs.gov","orcid":"https://orcid.org/0000-0001-7168-708X","contributorId":1538,"corporation":false,"usgs":true,"family":"Swain","given":"Eric","email":"edswain@usgs.gov","middleInitial":"D.","affiliations":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"preferred":true,"id":657436,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Campbell, Bruce G. 0000-0003-4800-6674 bcampbel@usgs.gov","orcid":"https://orcid.org/0000-0003-4800-6674","contributorId":995,"corporation":false,"usgs":true,"family":"Campbell","given":"Bruce","email":"bcampbel@usgs.gov","middleInitial":"G.","affiliations":[{"id":559,"text":"South Carolina Water Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Landmeyer, James 0000-0002-5640-3816 jlandmey@usgs.gov","orcid":"https://orcid.org/0000-0002-5640-3816","contributorId":3257,"corporation":false,"usgs":true,"family":"Landmeyer","given":"James","email":"jlandmey@usgs.gov","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":789729,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70220302,"text":"70220302 - 2017 - Preliminary-assessment and upgrade of a groundwater flow model of the Seacoast Bedrock Aquifer, New Hampshire","interactions":[],"lastModifiedDate":"2021-06-02T15:23:50.809272","indexId":"70220302","displayToPublicDate":"2017-12-31T10:19:40","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary-assessment and upgrade of a groundwater flow model of the Seacoast Bedrock Aquifer, New Hampshire","docAbstract":"In 2003 and 2004, the U.S. Geological Survey investigated the availability of groundwater resources in a 160-square mile area of coastal New Hampshire (Figure 1) using a regional groundwater flow model (Mack, 2009). At that time, population growth and increasing water demand prompted concern for the sustainability of the region’s groundwater resources in a fractured-crystalline bedrock-aquifer with little storage. The groundwater flow model developed for the previous study incorporated detailed water-use information for 2003-4 and simulated the effects of projected increases in water use. However, poor stream representation may reduce the effectiveness of the original model head simulations. Improvements to the model, made by incorporating the USGS’s MODLFOW-2005 Newton formulation (MODFLOW-NWT, Niswonger and others, 2011) and by more accurately representing stream characteristics, are presented in an example simulating approximate changes in water use. Groundwater heads in an area of relatively larger population change, near the center of the Seacoast’s fractured bedrock aquifer, were simulated with the upgraded model using published 2004, and approximated 2015, water use rates. This area is situated at a local topographic high point and near the junction of three towns, where drainages flow westward, toward Great Bay, and eastward, toward the Atlantic Ocean (Figure 1).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the MODFLOW and more 2017 conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"MODFLOW and More 2017","conferenceDate":"May 21-24, 2017","language":"English","usgsCitation":"Mack, T., 2017, Preliminary-assessment and upgrade of a groundwater flow model of the Seacoast Bedrock Aquifer, New Hampshire, in Proceedings of the MODFLOW and more 2017 conference, May 21-24, 2017, p. 40-44.","productDescription":"5 p.","startPage":"40","endPage":"44","ipdsId":"IP-087643","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":386128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Hampshire","otherGeospatial":"Seacoast Bedrock Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.71418762207031,\n 43.04079076668198\n ],\n [\n -70.71075439453125,\n 43.071395809535375\n ],\n [\n -70.78628540039062,\n 43.08493742707592\n ],\n [\n -70.8570098876953,\n 43.1405770781429\n ],\n [\n -70.9881591796875,\n 43.033764503405315\n ],\n [\n -70.98953247070311,\n 42.82663145362289\n ],\n [\n -70.81443786621094,\n 42.82209892875648\n ],\n [\n -70.74783325195311,\n 42.976520698105524\n ],\n [\n -70.71418762207031,\n 43.03777960950732\n ],\n [\n -70.71418762207031,\n 43.04079076668198\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mack, Thomas J. 0000-0002-0496-3918","orcid":"https://orcid.org/0000-0002-0496-3918","contributorId":218727,"corporation":false,"usgs":true,"family":"Mack","given":"Thomas J.","affiliations":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":815071,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70212544,"text":"70212544 - 2017 - Preliminary assessment of porphyry copper deposits in the Sierra Maestra, Cuba","interactions":[],"lastModifiedDate":"2020-08-24T12:43:12.673457","indexId":"70212544","displayToPublicDate":"2017-12-31T09:34:01","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Preliminary assessment of porphyry copper deposits in the Sierra Maestra, Cuba","docAbstract":"The U.S. Geological Survey’s “three-step” form of mineral-resource assessment was used to obtain a preliminary estimate of copper resources in undiscovered porphyry deposits of the Paleogene Sierra Maestra Arc. Results of this preliminary assessment suggest that a mean of 3.2 undiscovered deposits are likely present. This estimate is comparable to results from an independently-derived porphyry deposit density model, which points to 3.9 undiscovered deposits. Monte Carlo simulation results further show that the mean estimate of undiscovered copper resources in this porphyry copper tract is in the order of 12 million metric tons.\nNotwithstanding having been a relatively short-lived (20-25 Ma) magmatic event, the Sierra Maestra Arc was a particularly favorable environment for the formation of porphyry copper deposits.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"GEOCIENCIAS 2017 Proceedings volume \"Memorias, Trabajos y Resumenes\"","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"Convención de Ciencias de la Tierra (GEOCIENCIAS 2017)","conferenceDate":"April 3-7, 2017","conferenceLocation":"La Habana, Cuba","language":"English","publisher":"VII Convención de Ciencias de la Tierra (GEOCIENCIAS2017)","usgsCitation":"Zurcher, L., Gray, F., Hayes, T., Orris, G.J., Gettings, M.E., Cocker, M.D., and Gass, L., 2017, Preliminary assessment of porphyry copper deposits in the Sierra Maestra, Cuba, in GEOCIENCIAS 2017 Proceedings volume \"Memorias, Trabajos y Resumenes\", La Habana, Cuba, April 3-7, 2017, 5 p.","productDescription":"5 p.","ipdsId":"IP-084482","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science 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Lukas 0000-0001-5575-1192 lzurcher@usgs.gov","orcid":"https://orcid.org/0000-0001-5575-1192","contributorId":172674,"corporation":false,"usgs":true,"family":"Zurcher","given":"Lukas","email":"lzurcher@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796775,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gray, Floyd 0000-0002-0223-8966","orcid":"https://orcid.org/0000-0002-0223-8966","contributorId":201529,"corporation":false,"usgs":true,"family":"Gray","given":"Floyd","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796776,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayes, Timothy 0000-0002-1224-4219","orcid":"https://orcid.org/0000-0002-1224-4219","contributorId":206109,"corporation":false,"usgs":true,"family":"Hayes","given":"Timothy","email":"","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orris, Greta J. 0000-0002-2340-9955 greta@usgs.gov","orcid":"https://orcid.org/0000-0002-2340-9955","contributorId":3472,"corporation":false,"usgs":true,"family":"Orris","given":"Greta","email":"greta@usgs.gov","middleInitial":"J.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gettings, Mark E. 0000-0002-2910-2321 mgetting@usgs.gov","orcid":"https://orcid.org/0000-0002-2910-2321","contributorId":602,"corporation":false,"usgs":true,"family":"Gettings","given":"Mark","email":"mgetting@usgs.gov","middleInitial":"E.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cocker, Mark D. 0000-0001-9435-5862 mcocker@usgs.gov","orcid":"https://orcid.org/0000-0001-9435-5862","contributorId":4297,"corporation":false,"usgs":true,"family":"Cocker","given":"Mark","email":"mcocker@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gass, Leila 0000-0002-3436-262X lgass@usgs.gov","orcid":"https://orcid.org/0000-0002-3436-262X","contributorId":3770,"corporation":false,"usgs":true,"family":"Gass","given":"Leila","email":"lgass@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796781,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70195693,"text":"70195693 - 2017 - Population genetic structure and gene flow of Adélie penguins (Pygoscelis adeliae) breeding throughout the western Antarctic Peninsula","interactions":[],"lastModifiedDate":"2018-05-20T12:44:45","indexId":"70195693","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":814,"text":"Antarctic Science","onlineIssn":"1365-2079","printIssn":"0954-1020","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Population genetic structure and gene flow of Adélie penguins (Pygoscelis adeliae) breeding throughout the western Antarctic Peninsula","title":"Population genetic structure and gene flow of Adélie penguins (Pygoscelis adeliae) breeding throughout the western Antarctic Peninsula","docAbstract":"Adélie penguins (Pygoscelis adeliae) are responding to ocean–climate variability throughout the marine ecosystem of the western Antarctic Peninsula (WAP) where some breeding colonies have declined by 80%. Nuclear and mitochondrial DNA (mtDNA) markers were used to understand historical population genetic structure and gene flow given relatively recent and continuing reductions in sea ice habitats and changes in numbers of breeding adults at colonies throughout the WAP. Genetic diversity, spatial genetic structure, genetic signatures of fluctuations in population demography and gene flow were assessed in four regional Adélie penguin colonies. The analyses indicated little genetic structure overall based on bi-parentally inherited microsatellite markers (FST =-0.006–0.004). No significant variance was observed in overall haplotype frequency (mtDNA ΦST =0.017; P=0.112). Some comparisons with Charcot Island were significant, suggestive of female-biased philopatry. Estimates of gene flow based on a two-population coalescent model were asymmetrical from the species’ regional core to its northern range. Breeding Adélie penguins of the WAP are a panmictic population and hold adequate genetic diversity and dispersal capacity to be resilient to environmental change.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0954102017000293","usgsCitation":"Gorman, K.B., Talbot, S.L., Sonsthagen, S.A., Sage, G.K., Gravley, M.C., Fraser, W.R., and Williams, T.D., 2017, Population genetic structure and gene flow of Adélie penguins (Pygoscelis adeliae) breeding throughout the western Antarctic Peninsula: Antarctic Science, v. 29, no. 6, p. 499-510, https://doi.org/10.1017/S0954102017000293.","productDescription":"12 p.","startPage":"499","endPage":"510","ipdsId":"IP-072678","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":352118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"6","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-28","publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2e4","contributors":{"authors":[{"text":"Gorman, Kristen B.","contributorId":42437,"corporation":false,"usgs":true,"family":"Gorman","given":"Kristen","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":729788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Talbot, Sandra L. 0000-0002-3312-7214 stalbot@usgs.gov","orcid":"https://orcid.org/0000-0002-3312-7214","contributorId":140512,"corporation":false,"usgs":true,"family":"Talbot","given":"Sandra","email":"stalbot@usgs.gov","middleInitial":"L.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":729716,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":729717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sage, George K. 0000-0003-1431-2286 ksage@usgs.gov","orcid":"https://orcid.org/0000-0003-1431-2286","contributorId":87833,"corporation":false,"usgs":true,"family":"Sage","given":"George","email":"ksage@usgs.gov","middleInitial":"K.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":false,"id":729719,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gravley, Megan C. 0000-0002-4947-0236 mgravley@usgs.gov","orcid":"https://orcid.org/0000-0002-4947-0236","contributorId":202812,"corporation":false,"usgs":true,"family":"Gravley","given":"Megan","email":"mgravley@usgs.gov","middleInitial":"C.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":729718,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Williams, Tony D.","contributorId":202813,"corporation":false,"usgs":false,"family":"Williams","given":"Tony","email":"","middleInitial":"D.","affiliations":[{"id":29801,"text":"Department of Biological Sciences, Simon Fraser University, Burnaby, BC","active":true,"usgs":false}],"preferred":false,"id":729721,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fraser, William R.","contributorId":197704,"corporation":false,"usgs":false,"family":"Fraser","given":"William","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":729720,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196914,"text":"70196914 - 2017 - Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia","interactions":[],"lastModifiedDate":"2018-05-10T14:36:58","indexId":"70196914","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3444,"text":"Southeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia","docAbstract":"The relationships between environmental variables and the growth rates of fishes are important and rapidly expanding topics in fisheries ecology. We used an informationtheoretic approach to evaluate the influence of lake surface area and total phosphorus on the age-specific growth rates of Lepomis macrochirus (Bluegill) in 6 small impoundments in central Georgia. We used model averaging to create composite models and determine the relative importance of the variables within each model. Results indicated that surface area was the most important factor in the models predicting growth of Bluegills aged 1–4 years; total phosphorus was also an important predictor for the same age-classes. These results suggest that managers can use water quality and lake morphometry variables to create predictive models specific to their waterbody or region to help develop lake-specific management plans that select for and optimize local-level habitat factors for enhancing Bluegill growth.
","language":"English","publisher":"Eagle Hill Institute","doi":"10.1656/058.016.0406","usgsCitation":"Jennings, C.A., and Sundmark, A.P., 2017, Influence of lake surface area and total phosphorus on annual bluegill growth in small impoundments of central Georgia: Southeastern Naturalist, v. 16, no. 4, p. 546-566, https://doi.org/10.1656/058.016.0406.","productDescription":"21 p.","startPage":"546","endPage":"566","ipdsId":"IP-077977","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Charlie Elliot Wildlife Center","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.30908203125,\n 32.7503226078097\n ],\n [\n -83.03466796874999,\n 32.7503226078097\n ],\n [\n -83.03466796874999,\n 33.779147331286474\n ],\n [\n -84.30908203125,\n 33.779147331286474\n ],\n [\n -84.30908203125,\n 32.7503226078097\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"4","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2dc","contributors":{"authors":[{"text":"Jennings, Cecil A. 0000-0002-6159-6026 jennings@usgs.gov","orcid":"https://orcid.org/0000-0002-6159-6026","contributorId":874,"corporation":false,"usgs":true,"family":"Jennings","given":"Cecil","email":"jennings@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":734984,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sundmark, Aaron P.","contributorId":204804,"corporation":false,"usgs":false,"family":"Sundmark","given":"Aaron","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":735042,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70197037,"text":"70197037 - 2017 - Declining occurrence and low colonization probability in freshwater mussel assemblages: A dynamic occurrence modeling approach","interactions":[],"lastModifiedDate":"2020-12-16T16:55:45.695979","indexId":"70197037","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5254,"text":"Freshwater Mollusk Biology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Declining occurrence and low colonization probability in freshwater mussel assemblages: A dynamic occurrence modeling approach","docAbstract":"Mussel monitoring data are abundant, but methods for analyzing long-term trends in these data are often uninformative or have low power to detect changes. We used a dynamic occurrence model, which accounted for imperfect species detection in surveys, to assess changes in species occurrence in a longterm data set (1986–2011) for the Tar River basin of North Carolina, USA. Occurrence of all species decreased steadily over the time period studied. Occurrence in 1986 ranged from 0.19 for Utterbackia imbecillis to 0.60 for Fusconaia masoni. Occurrence in 2010–2011 ranged from 0.10 for Lampsilis radiata to 0.40 for F. masoni. The maximum difference between occurrence in 1986 and 2011 was a decline of 0.30 for Alasmidonta undulata. Mean persistence for all species was high (0.97, 95% CI ¼ 0.95–0.99); however, mean colonization probability was very low (,0.01, 95% CI ¼ ,0.01–0.01). These results indicate that mussels persisted at sites already occupied but that they have not colonized sites where they had not occurred previously. Our findings highlight the importance of modeling approaches that incorporate imperfect detection in estimating species occurrence and revealing temporal trends to inform conservation planning.
","language":"English","publisher":"Freshwater Mollusk Conservation Society","doi":"10.31931/fmbc.v20i1.2017.13-19","usgsCitation":"Pandolfo, T.J., Kwak, T.J., Cope, W., Heise, R.J., Nichols, R.B., and Pacifici, K., 2017, Declining occurrence and low colonization probability in freshwater mussel assemblages: A dynamic occurrence modeling approach: Freshwater Mollusk Biology and Conservation, v. 20, no. 1, p. 13-19, https://doi.org/10.31931/fmbc.v20i1.2017.13-19.","productDescription":"7 p.","startPage":"13","endPage":"19","ipdsId":"IP-070553","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":469227,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.31931/fmbc.v20i1.2017.13-19","text":"Publisher Index Page"},{"id":354162,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina","otherGeospatial":"Tar River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.365234375,\n 35.37113502280101\n ],\n [\n -77.6513671875,\n 35.37113502280101\n ],\n [\n -77.6513671875,\n 36.527294814546245\n ],\n [\n -79.365234375,\n 36.527294814546245\n ],\n [\n -79.365234375,\n 35.37113502280101\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"1","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2d6","contributors":{"authors":[{"text":"Pandolfo, Tamara J.","contributorId":146388,"corporation":false,"usgs":false,"family":"Pandolfo","given":"Tamara","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":735347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":735325,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cope, W. Gregory","contributorId":70353,"corporation":false,"usgs":true,"family":"Cope","given":"W. Gregory","affiliations":[],"preferred":false,"id":735348,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Heise, Ryan J.","contributorId":145789,"corporation":false,"usgs":false,"family":"Heise","given":"Ryan","email":"","middleInitial":"J.","affiliations":[{"id":16149,"text":"North Carolina Wildlife Resources Commission, 1003 Consolidated Rd., Elizabeth City, NC 27909","active":true,"usgs":false}],"preferred":false,"id":735349,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nichols, Robert B.","contributorId":182112,"corporation":false,"usgs":false,"family":"Nichols","given":"Robert","email":"","middleInitial":"B.","affiliations":[{"id":35598,"text":"North Carolina Wildlife Resources Commission ","active":true,"usgs":false}],"preferred":false,"id":735350,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pacifici, Krishna","contributorId":26564,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":735351,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70197040,"text":"70197040 - 2017 - Occupancy and abundance of Eleutherodactylus wightmanae and E. brittoni along elevational gradients in west-central Puerto Rico","interactions":[],"lastModifiedDate":"2020-12-16T16:49:09.712772","indexId":"70197040","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5533,"text":"Caribbean Naturalist","onlineIssn":"2326-7119","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Occupancy and abundance of Eleutherodactylus wightmanae and E. brittoni along elevational gradients in west-central Puerto Rico","title":"Occupancy and abundance of Eleutherodactylus wightmanae and E. brittoni along elevational gradients in west-central Puerto Rico","docAbstract":"Populations of Eleutherodactylus species in Puerto Rico have declined in recent decades due to habitat loss and long-term climatic changes. The conservation of these habitat specialists requires an understanding of factors influencing their abundance and distribution, which at present is scant. We estimated occupancy probability and the probability of encountering ≥2 individuals of E. wightmanae (Melodius Coqui or Wightman's Robber Frog) and E. brittoni (Grass Coqui), species with contrasting habitat affinities, using multi-season, multi-state occupancy models. These parameters also served as an index of abundance (non-presence, 1, and ≥2 individuals). We modeled parameters as a function of seasonal temperature and humidity, long-term average monthly precipitation, and habitat covariates measured at survey sites along 2 elevation gradients in the southern slopes of west-central Puerto Rico. We collected survey data using passive acoustic recorders during 3 seasonal periods between February and July 2015. Occupancy patterns of both species was unimodal, containing higher probabilities (e.g., ≥0.5) at elevations between 400 m and 700 m, where long-term monthly precipitation varied between 120 mm and 160 mm. Chances of encountering ≥2 individuals increased with ground cover for E. brittoni, and decreased with increasing canopy cover for E. wightmanae. Seasonal temperature and relative humidity did not influence occupancy or the probability of encountering ≥2 individuals, likely because covariates varied within known tolerance levels for Eleutherodactylus. Our findings help reduce local extinction probability through management of habitat conditions that increase the likelihood of encountering ≥2 individuals. We also detailed an analytical framework suitable to test hypotheses aimed at predicting potential impacts from land use and climatic changes, and species responses to conservation actions.
","language":"English","publisher":"Eagle Hill Institute","usgsCitation":"Monroe, K.D., Collazo, J., Pacifici, K., Reich, B.J., Puente-Rolon, A.R., and Terando, A.J., 2017, Occupancy and abundance of Eleutherodactylus wightmanae and E. brittoni along elevational gradients in west-central Puerto Rico: Caribbean Naturalist, v. 40, p. 1-18.","productDescription":"18 p.","startPage":"1","endPage":"18","onlineOnly":"Y","ipdsId":"IP-077348","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":354159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":381419,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.eaglehill.us/CANAonline/CANA-access-pages/CANA-regular/CANA-040-Collazo.shtml"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.445068359375,\n 17.78007412664325\n ],\n [\n -65.19287109375,\n 17.78007412664325\n ],\n [\n -65.19287109375,\n 18.729501999072138\n ],\n [\n -67.445068359375,\n 18.729501999072138\n ],\n [\n -67.445068359375,\n 17.78007412664325\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee788e4b0da30c1bfc2d2","contributors":{"authors":[{"text":"Monroe, Kelen D.","contributorId":200135,"corporation":false,"usgs":false,"family":"Monroe","given":"Kelen","email":"","middleInitial":"D.","affiliations":[{"id":33914,"text":"North Carolina State University, Raleigh","active":true,"usgs":false}],"preferred":false,"id":735339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collazo, Jaime A. 0000-0002-1816-7744 jaime_collazo@usgs.gov","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":173448,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime A.","email":"jaime_collazo@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":735337,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pacifici, Krishna","contributorId":26564,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":735340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reich, Brian J.","contributorId":150871,"corporation":false,"usgs":false,"family":"Reich","given":"Brian","email":"","middleInitial":"J.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":735341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Puente-Rolon, Alberto R.","contributorId":42498,"corporation":false,"usgs":true,"family":"Puente-Rolon","given":"Alberto","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":735342,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Terando, Adam J. 0000-0002-9280-043X aterando@usgs.gov","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":173447,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","email":"aterando@usgs.gov","middleInitial":"J.","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":735343,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196355,"text":"70196355 - 2017 - Long-term monitoring data provide evidence of declining species richness in a river valued for biodiversity conservation","interactions":[],"lastModifiedDate":"2018-04-03T14:24:41","indexId":"70196355","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Long-term monitoring data provide evidence of declining species richness in a river valued for biodiversity conservation","docAbstract":"Free-flowing river segments provide refuges for many imperiled aquatic biota that have been extirpated elsewhere in their native ranges. These biodiversity refuges are also foci of conservation concerns because species persisting within isolated habitat fragments may be particularly vulnerable to local environmental change. We have analyzed long-term (14- and 20-y) survey data to assess evidence of fish species declines in two southeastern U.S. rivers where managers and stakeholders have identified potentially detrimental impacts of current and future land uses. The Conasauga River (Georgia and Tennessee) and the Etowah River (Georgia) form free-flowing headwaters of the extensively dammed Coosa River system. These rivers are valued in part because they harbor multiple species of conservation concern, including three federally endangered and two federally threatened fishes. We used data sets comprising annual surveys for fish species at multiple, fixed sites located at river shoals to analyze occupancy dynamics and temporal changes in species richness. Our analyses incorporated repeated site-specific surveys in some years to estimate and account for incomplete species detection, and test for species-specific (rarity, mainstem-restriction) and year-specific (elevated frequencies of low- or high-flow days) covariates on occupancy dynamics. In the Conasauga River, analysis of 26 species at 13 sites showed evidence of temporal declines in colonization rates for nearly all taxa, accompanied by declining species richness. Four taxa (including one federally endangered species) had reduced occupancy across the Conasauga study sites, with three of these taxa apparently absent for at least the last 5 y of the study. In contrast, a similar fauna of 28 taxa at 10 sites in the Etowah River showed no trends in species persistence, colonization, or occupancy. None of the tested covariates showed strong effects on persistence or colonization rates in either river. Previous studies and observations identified contaminants, nutrient loading, or changes in benthic habitat as possible causes for fish species declines in the Conasauga River. Our analysis provides baseline information that could be used to assess effectiveness of future management actions in the Conasauga or Etowah rivers, and illustrates the use of dynamic occupancy models to evaluate evidence of faunal decline from time-series data.
","language":"English","publisher":"Scientific Journals","doi":"10.3996/122016-JFWM-090","usgsCitation":"Freeman, M., Hagler, M.M., Bumpers, P.M., Wheeler, K., Wenger, S., and Freeman, B.J., 2017, Long-term monitoring data provide evidence of declining species richness in a river valued for biodiversity conservation: Journal of Fish and Wildlife Management, v. 8, no. 2, p. 418-434, https://doi.org/10.3996/122016-JFWM-090.","productDescription":"17p.","startPage":"418","endPage":"434","ipdsId":"IP-082143","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":353118,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Conasauga River, Etowah River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.49560546875,\n 33.99802726234877\n ],\n [\n -83.9959716796875,\n 33.99802726234877\n ],\n [\n -83.9959716796875,\n 35.007502842952896\n ],\n [\n -85.49560546875,\n 35.007502842952896\n ],\n [\n -85.49560546875,\n 33.99802726234877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-01","publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2e2","contributors":{"authors":[{"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":732551,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hagler, Megan M.","contributorId":203870,"corporation":false,"usgs":false,"family":"Hagler","given":"Megan","email":"","middleInitial":"M.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":732552,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bumpers, Phillip M.","contributorId":203871,"corporation":false,"usgs":false,"family":"Bumpers","given":"Phillip","email":"","middleInitial":"M.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":732553,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wheeler, Kit","contributorId":203872,"corporation":false,"usgs":false,"family":"Wheeler","given":"Kit","email":"","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":732554,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wenger, Seth J.","contributorId":177838,"corporation":false,"usgs":false,"family":"Wenger","given":"Seth J.","affiliations":[],"preferred":false,"id":732555,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Freeman, Byron J.","contributorId":49782,"corporation":false,"usgs":false,"family":"Freeman","given":"Byron","email":"","middleInitial":"J.","affiliations":[{"id":12697,"text":"University of Georgia","active":true,"usgs":false}],"preferred":false,"id":732556,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196906,"text":"70196906 - 2017 - Spatial ecology and movement of reintroduced Canada lynx","interactions":[],"lastModifiedDate":"2018-05-11T14:19:18","indexId":"70196906","displayToPublicDate":"2017-12-31T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Spatial ecology and movement of reintroduced Canada lynx","docAbstract":"Understanding movement behavior and identifying areas of landscape connectivity is critical for the conservation of many species. However, collecting fine‐scale movement data can be prohibitively time consuming and costly, especially for rare or endangered species, whereas existing data sets may provide the best available information on animal movement. Contemporary movement models may not be an option for modeling existing data due to low temporal resolution and large or unusual error structures, but inference can still be obtained using a functional movement modeling approach. We use a functional movement model to perform a population‐level analysis of telemetry data collected during the reintroduction of Canada lynx to Colorado. Little is known about southern lynx populations compared to those in Canada and Alaska, and inference is often limited to a few individuals due to their low densities. Our analysis of a population of Canada lynx fills significant gaps in the knowledge of Canada lynx behavior at the southern edge of its historical range. We analyzed functions of individual‐level movement paths, such as speed, residence time, and tortuosity, and identified a region of connectivity that extended north from the San Juan Mountains, along the continental divide, and terminated in Wyoming at the northern edge of the Southern Rocky Mountains. Individuals were able to traverse large distances across non‐boreal habitat, including exploratory movements to the Greater Yellowstone area and beyond. We found evidence for an effect of seasonality and breeding status on many of the movement quantities and documented a potential reintroduction effect. Our findings provide the first analysis of Canada lynx movement in Colorado and substantially augment the information available for conservation and management decisions. The functional movement framework can be extended to other species and demonstrates that information on movement behavior can be obtained using existing data sets.
","language":"English","publisher":"Wiley","doi":"10.1111/ecog.03030","usgsCitation":"Buderman, F.E., Hooten, M., Ivan, J., and Shenk, T., 2017, Spatial ecology and movement of reintroduced Canada lynx: Ecography, v. 41, no. 1, p. 126-139, https://doi.org/10.1111/ecog.03030.","productDescription":"14 p.","startPage":"126","endPage":"139","ipdsId":"IP-072342","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":354099,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.02783203125,\n 44.1151978766043\n ],\n [\n -109.94293212890625,\n 44.1151978766043\n ],\n [\n -109.94293212890625,\n 44.88895839978044\n ],\n [\n -111.02783203125,\n 44.88895839978044\n ],\n [\n -111.02783203125,\n 44.1151978766043\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-22","publicationStatus":"PW","scienceBaseUri":"5afee789e4b0da30c1bfc2de","contributors":{"authors":[{"text":"Buderman, Frances E.","contributorId":171634,"corporation":false,"usgs":false,"family":"Buderman","given":"Frances","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":734972,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":734971,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ivan, Jacob S.","contributorId":200243,"corporation":false,"usgs":false,"family":"Ivan","given":"Jacob S.","affiliations":[],"preferred":false,"id":734973,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shenk, Tanya","contributorId":204778,"corporation":false,"usgs":false,"family":"Shenk","given":"Tanya","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":734974,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70194814,"text":"sir20175141 - 2017 - Groundwater-flow budget for the lower Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia and parts of Florida and Alabama, 2008–12","interactions":[],"lastModifiedDate":"2018-01-02T13:28:49","indexId":"sir20175141","displayToPublicDate":"2017-12-29T15:45:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5141","title":"Groundwater-flow budget for the lower Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia and parts of Florida and Alabama, 2008–12","docAbstract":"As part of the National Water Census program in the Apalachicola-Chattahoochee-Flint (ACF) River Basin, the U.S. Geological Survey evaluated the groundwater budget of the lower ACF, with particular emphasis on recharge, characterizing the spatial and temporal relation between surface water and groundwater, and groundwater pumping. To evaluate the hydrologic budget of the lower ACF River Basin, a groundwater-flow model, constructed using MODFLOW-2005, was developed for the Upper Floridan aquifer and overlying semiconfining unit for 2008–12. Model input included temporally and spatially variable specified recharge, estimated using a Precipitation-Runoff Modeling System (PRMS) model for the ACF River Basin, and pumping, partly estimated on the basis of measured agricultural pumping rates in Georgia. The model was calibrated to measured groundwater levels and base flows, which were estimated using hydrograph separation.
The simulated groundwater-flow budget resulted in a small net cumulative loss of groundwater in storage during the study period. The model simulated a net loss in groundwater storage for all the subbasins as conditions became substantially drier from the beginning to the end of the study period. The model is limited by its conceptualization, the data used to represent and calibrate the model, and the mathematical representation of the system; therefore, any interpretations should be considered in light of these limitations. In spite of these limitations, the model provides insight regarding water availability in the lower ACF River Basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175141","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"Jones, L.E., Painter, Jaime, LaFontaine, Jacob, Sepulveda, Nicasio, and Sifuentes, D.F., 2017, Groundwater-flow budget for the lower Apalachicola-Chattahoochee-Flint River Basin in southwestern Georgia and parts of \nFlorida and Alabama, 2008–12: U.S. Geological Survey Scientific Investigations Report 2017–5141, 76 p., \nhttps://doi.org/10.3133/sir20175141.","productDescription":"Report: viii, 76 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":350246,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5141/coverthb.jpg"},{"id":350249,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/sir20175133","text":"Scientific Investigations Report 2017-5133","linkHelpText":"- Simulations of Hydrologic Response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States"},{"id":350247,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5141/sir20175141.pdf","text":"Report","size":"10.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5141"},{"id":350248,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DV1HCG","text":"USGS data release","description":"USGS data release"}],"country":"United States","state":"Alabama, Florida, Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.5,\n 30.5\n ],\n [\n -83.75,\n 30.5\n ],\n [\n -83.75,\n 32.25\n ],\n [\n -85.5,\n 32.25\n ],\n [\n -85.5,\n 30.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
A suite of hydrologic models has been developed for the Apalachicola-Chattahoochee-Flint River Basin (ACFB) as part of the National Water Census, a U.S. Geological Survey research program that focuses on developing new water accounting tools and assessing water availability and use at the regional and national scales. Seven hydrologic models were developed using the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, process-based system that simulates the effects of precipitation, temperature, land cover, and water use on basin hydrology. A coarse-resolution PRMS model was developed for the entire ACFB, and six fine-resolution PRMS models were developed for six subbasins of the ACFB. The coarse-resolution model was loosely coupled with a groundwater model to better assess the effects of water use on streamflow in the lower ACFB, a complex geologic setting with karst features. The PRMS coarse-resolution model was used to provide inputs of recharge to the groundwater model, which in turn provide simulations of groundwater flow that were aggregated with PRMS-based simulations of surface runoff and shallow-subsurface flow. Simulations without the effects of water use were developed for each model for at least the calendar years 1982–2012 with longer periods for the Potato Creek subbasin (1942–2012) and the Spring Creek subbasin (1952–2012). Water-use-affected flows were simulated for 2008–12. Water budget simulations showed heterogeneous distributions of precipitation, actual evapotranspiration, recharge, runoff, and storage change across the ACFB. Streamflow volume differences between no-water-use and water-use simulations were largest along the main stem of the Apalachicola and Chattahoochee River Basins, with streamflow percentage differences largest in the upper Chattahoochee and Flint River Basins and Spring Creek in the lower Flint River Basin. Water-use information at a shorter time step and a fully coupled simulation in the lower ACFB may further improve water availability estimates and hydrologic simulations in the basin.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175133","collaboration":"U.S. Geological Survey National Water Census and Water Availability and Use Science Program","usgsCitation":"LaFontaine, J.H., Jones, L.E., and Painter, J.A., 2017, Simulations of hydrologic response in the Apalachicola-Chattahoochee-Flint River Basin, Southeastern United States: U.S. Geological Survey Scientific Investigations Report 2017–5133, 112 p., https://doi.org/10.3133/sir20175133.","productDescription":"Report: x, 112 p.; Data Release","numberOfPages":"126","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075742","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":350203,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5133/sir20175133.pdf","text":"Report","size":"32.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5133"},{"id":350251,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20175141","text":"Scientific Investigations Report 2017-5141","linkHelpText":"- Groundwater-Flow Budget for the Lower Apalachicola-Chattahoochee-Flint River Basin in Southwestern Georgia and Parts of Florida and Alabama, 2008–12"},{"id":350202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5133/coverthb.jpg"},{"id":350244,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7FJ2F1R","text":"USGS data release","description":"USGS data release","linkHelpText":"Model Input and Output for Hydrologic Simulations of the Apalachicola-Chattahoochee-Flint River Basin using the Precipitation Runoff Modeling System"}],"country":"United States","otherGeospatial":"Apalachicola-Chattahoochee-Flint River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.660400390625,\n 29.726222319395504\n ],\n [\n -83.397216796875,\n 29.726222319395504\n ],\n [\n -83.397216796875,\n 34.88593094075317\n ],\n [\n -85.660400390625,\n 34.88593094075317\n ],\n [\n -85.660400390625,\n 29.726222319395504\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, South Atlantic Water Science Center
U.S. Geological Survey
720 Gracern Road
Stephenson Center, Suite 129
Columbia, SC 29210
The Devonian-age Marcellus Shale and the Ordovician-age Utica Shale, which have the potential for natural gas development, underlie Pike County and neighboring counties in northeastern Pennsylvania. In 2015, the U.S. Geological Survey, in cooperation with the Pike County Conservation District, conducted a study that expanded on a previous more limited 2012 study to assess baseline shallow groundwater quality in bedrock aquifers in Pike County prior to possible extensive shale-gas development. Seventy-nine water wells ranging in depths from 80 to 610 feet were sampled during June through September 2015 to provide data on the presence of methane and other aspects of existing groundwater quality in the various bedrock geologic units throughout the county, including concentrations of inorganic constituents commonly present at low values in shallow, fresh groundwater but elevated in brines associated with fluids extracted from geologic formations during shale-gas development. All groundwater samples collected in 2015 were analyzed for bacteria, dissolved and total major ions, nutrients, selected dissolved and total inorganic trace constituents (including metals and other elements), radon-222, gross alpha- and gross beta-particle activity, dissolved gases (methane, ethane, and propane), and, if sufficient methane was present, the isotopic composition of methane. Additionally, samples from 20 wells distributed throughout the county were analyzed for selected man-made volatile organic compounds, and samples from 13 wells where waters had detectable gross alpha activity were analyzed for radium-226 on the basis of relatively elevated gross alpha-particle activity.
Results of the 2015 study show that groundwater quality generally met most drinking-water standards for constituents and properties included in analyses, but groundwater samples from some wells had one or more constituents or properties, including arsenic, iron, manganese, pH, bacteria, sodium, chloride, sulfate, total dissolved solids, and radon-222, that did not meet (commonly termed failed or exceeded) primary or secondary maximum contaminant levels (MCLs) or Health Advisories (HA) for drinking water. Except for iron, dissolved and total concentrations of major ions and most trace constituents generally were similar. Only 1 of 79 well-water samples had any constituent that exceeded a MCL, with an arsenic concentration of about 30 micrograms per liter (µg/L) that was higher than the MCL of 10 µg/L. However, total arsenic concentrations were higher than the HA of 2 µg/L in samples from another 12 of 79 wells (about 15 percent). Secondary maximum contaminant levels (SMCLs) were exceeded most frequently by pH and concentrations of iron and manganese. The pH was outside of the SMCL range of 6.5–8.5 in samples from 24 of 79 wells (30 percent), ranging from 5.5 to 9.2; more samples had pH values less than 6.5 than had pH values greater than 8.5. Total iron concentrations typically were much greater than dissolved iron concentrations, indicating substantial presence of iron in particulate phase, and exceeded the SMCL of 300 µg/L more often [35 of 79 samples (44 percent)] than dissolved iron concentrations [samples from 8 of 79 wells (10 percent)]. Total manganese concentrations exceeded the SMCL of 50 µg/L in samples from 31 of 79 wells (39 percent) and the HA of 300 µg/L in samples from 13 of 79 wells (about 16 percent). A few (1–2) samples had concentrations of sodium, chloride, sulfate, or TDS higher than the SMCLs of 60, 250, 250, and 500 mg/L, respectively. However, dissolved sodium concentrations were higher than the HA of 20 mg/L in samples from 15 of 79 wells (nearly 20 percent). Total coliform bacteria were detected in samples from 25 of 79 wells (32 percent) but Escherichia coli were not detected in any sample. Radon-222 activities ranged from 11 to 5,100 picocuries per liter (pCi/L), with a median of 1,440 pCi/L, and exceeded the proposed and the alternate proposed drinking-water standards of 300 and 4,000 pCi/L, respectively, in samples from 60 of 79 wells (75 percent) and in samples from 2 of 79 wells (3 percent), respectively.
Groundwater samples from all wells were analyzed for dissolved methane by one contract laboratory that determined water from 19 of the 79 wells (24 percent) had concentrations of methane greater than the reporting level of 0.010 milligrams per liter (mg/L) with a maximum methane concentration of 2.5 mg/L. Methane concentrations in 18 replicate samples submitted to a second laboratory for dissolved gas and isotopic analysis generally were higher by as much as a factor of 2.7 from those determined by the first laboratory, indicating potential bias related to combined sampling and analytical methods, and therefore, caution needs to be used when comparing methane results determined by different methods. The isotopic composition of methane in 9 of 10 samples with sufficient dissolved methane (about 0.3 mg/L) for isotopic analysis is consistent with values reported for methane of microbial origin produced through carbon dioxide reduction; an isotopic shift in 1 or 2 samples may indicate subsequent methane oxidation. The low concentrations of ethane relative to methane in these samples further indicate that the methane may be of microbial origin. Groundwater samples with relatively elevated methane concentrations (near or greater than 0.3 mg/L) also had chemical compositions that differed in some respects from groundwater with relatively low methane concentrations (less than 0.3 mg/L) by having higher pH (greater than 8) and higher concentrations of sodium, lithium, boron, fluoride, arsenic, and bromide and chloride/bromide ratios indicative of mixing with a small amount of brine of probable natural occurrence.
The spatial distribution of groundwater compositions differs by topographic setting and lithology and generally shows that (1) relatively dilute, slightly acidic, oxygenated, calcium-carbonate type waters tend to occur in the uplands underlain by the undivided Poplar Gap and Packerton members of the Catskill Formation in southwestern Pike County; (2) waters of near neutral pH with the highest amounts of hardness (calcium and magnesium) generally occur in areas of intermediate altitudes underlain by other members of the Catskill Formation; and (3) waters with pH values greater than 8, low oxygen concentrations, and the highest arsenic, sodium, lithium, bromide, and methane concentrations can be present in deep wells in uplands but most frequently occur in stream valleys, especially at low altitudes (less than about 1,200 feet above North American Vertical Datum of 1988) where groundwater may be discharging regionally, such as to the Delaware River in northern and eastern Pike County. Thus, the baseline assessment of groundwater quality in Pike County prior to gas-well development shows that shallow (less than about 1,000 feet deep) groundwater generally meets primary drinking-water standards for inorganic constituents but varies spatially, with methane and some constituents present in high concentrations in brine (and connate waters from gas and oil reservoirs) present at low to moderate concentrations in some parts of Pike County.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175110","collaboration":"Prepared in cooperation with the Pike County Conservation District","usgsCitation":"Senior, L.A., and Cravotta, C.A., III, 2017: Baseline assessment of groundwater quality in Pike County, Pennsylvania, 2015: U.S. Geological Survey Scientific Investigations Report 2017–5110, 181 p., https://doi.org/10.3133/sir20175110.","productDescription":"Report: xii, 181 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-088156","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":350196,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5980c3c0e4b0a38ca278a8c9","text":"USGS Data Release","linkHelpText":"Field properties and results of laboratory analysis of groundwater samples collected from 79 wells in Pike County, Pennsylvania, 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Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070-2424
http://pa.water.usgs.gov
Geochemical Modeling for Mine Site Characterization and Remediation is the fourth of six volumes in the Management Technologies for Metal Mining Infl uenced Water series about technologies for management of metal mine and metallurgical process drainage.
This handbook describes the important components of hydrogeochemical modeling for mine environments, primarily those mines where sulfi de minerals are present—metal mines and coal mines.
It provides general guidelines on the strengths and limitations of geochemical modeling and an overview of its application to the hydrogeochemistry of both unmined mineralized sites and those contaminated from mineral extraction and mineral processing.
The handbook includes an overview of the models behind the codes, explains vital geochemical computations, describes several modeling processes, provides a compilation of codes, and gives examples of their application, including both successes and failures.
Hydrologic modeling is also included because mining contaminants most often migrate by surface water and groundwater transport, and contaminant concentrations are a function of water residence time as well as pathways.
This is an indispensable resource for mine planners and engineers, environmental managers, land managers, consultants, researchers, government regulators, nongovernmental organizations, students, stakeholders, and anyone with an interest in mining influenced water.
Streamflow distribution maps for the Cannon River and St. Louis River drainage basins were developed by the U.S. Geological Survey, in cooperation with the Legislative-Citizen Commission on Minnesota Resources, to illustrate relative and cumulative streamflow distributions. The Cannon River was selected to provide baseline data to assess the effects of potential surficial sand mining, and the St. Louis River was selected to determine the effects of ongoing Mesabi Iron Range mining. Each drainage basin (Cannon, St. Louis) was subdivided into nested drainage basins: the Cannon River was subdivided into 152 nested drainage basins, and the St. Louis River was subdivided into 353 nested drainage basins. For each smaller drainage basin, the estimated volumes of groundwater discharge (as base flow) and surface runoff flowing into all surface-water features were displayed under the following conditions: (1) extreme low-flow conditions, comparable to an exceedance-probability quantile of 0.95; (2) low-flow conditions, comparable to an exceedance-probability quantile of 0.90; (3) a median condition, comparable to an exceedance-probability quantile of 0.50; and (4) a high-flow condition, comparable to an exceedance-probability quantile of 0.02.
Streamflow distribution maps were developed using flow-duration curve exceedance-probability quantiles in conjunction with Soil-Water-Balance model outputs; both the flow-duration curve and Soil-Water-Balance models were built upon previously published U.S. Geological Survey reports. The selected streamflow distribution maps provide a proactive water management tool for State cooperators by illustrating flow rates during a range of hydraulic conditions. Furthermore, after the nested drainage basins are highlighted in terms of surface-water flows, the streamflows can be evaluated in the context of meeting specific ecological flows under different flow regimes and potentially assist with decisions regarding groundwater and surface-water appropriations. Presented streamflow distribution maps are foundational work intended to support the development of additional streamflow distribution maps that include statistical constraints on the selected flow conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3390","collaboration":"Prepared in cooperation with the Legislative-Citizen Commission on Minnesota Resources","usgsCitation":"Smith, E.A., Sanocki, C.A., Lorenz, D.L., and Jacobsen, K.E., 2017, Streamflow distribution maps for the Cannon River drainage basin, southeast Minnesota, and the St. Louis River drainage basin, northeast Minnesota: U.S. Geological Survey Scientific Investigations Map 3390, pamphlet 16 p., 2 sheets, https://doi.org/10.3133/sim3390.","productDescription":"Pamphlet: vii, 16 p.; 2 Sheets: 22.0 inches x 11.0 inches; Data Releases","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-060395","costCenters":[{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true}],"links":[{"id":350215,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3390/sim3390.pdf","text":"Pamphlet","size":"976 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3390 Pamphlet"},{"id":350216,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3390/sim3390_sheet1.pdf","text":"Sheet 1","size":"480 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3390 Sheet 1"},{"id":350217,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3390/sim3390_sheet2.pdf","text":"Sheet 2","size":"590 kB","linkFileType":{"id":1,"text":"pdf"}},{"id":350214,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3390/coverthb.jpg"},{"id":350218,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F72V2DMN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the Cannon River drainage basin, southeast Minnesota, 1995-2010"},{"id":350219,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Z60MJ0","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Soil-Water-Balance model data sets for the St. Louis River drainage basin, northeast Minnesota, 1995-2010"}],"country":"United States","state":"Minnesota","otherGeospatial":"Cannon River Drainage Basin, St. Louis River Drainage Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.25,\n 46.63435070293566\n ],\n [\n -91.5,\n 46.63435070293566\n ],\n [\n -91.5,\n 47.89056441663247\n ],\n [\n -93.25,\n 47.89056441663247\n ],\n [\n -93.25,\n 46.63435070293566\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.8,\n 43.75\n ],\n [\n -92.75,\n 43.75\n ],\n [\n -92.75,\n 44.6\n ],\n [\n -93.8,\n 44.6\n ],\n [\n -93.8,\n 43.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Streambed scour potential was evaluated at 52 river- and stream-spanning bridges in Alaska that lack a quantitative scour analysis or have unknown foundation details. All sites were evaluated for stream stability and long-term scour potential. Contraction scour and abutment scour were calculated for 52 bridges, and pier scour was calculated for 11 bridges that had piers. Vertical contraction (pressure flow) scour was calculated for sites 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 also was calculated for large recorded floods at 13 sites.
Channel instability at 11 sites was related to human activities (in-channel mining, dredging, and channel relocation). Eight of the dredged sites are located on active unstable alluvial fans and were graded to protect infrastructure. The trend toward aggradation during major floods at these sites reduces confidence in scour estimates.
Vertical contraction and pressure flow occurred during the 0.2-percent or smaller annual exceedance probability floods at eight sites. Contraction scour exceeded 5 feet (ft) at four sites, and total scour at piers (pier scour plus contraction scour) exceeded 5 ft at four sites. Debris accumulation increased calculated pier scour at six sites by an average of 2.4 ft. Total scour at abutments exceeded 5 ft at 10 sites. Scour estimates seemed excessive at two piers where equations did not account for channel armoring, and at four abutments 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/sir20175149","collaboration":"Prepared in cooperation with the Alaska Department of Transportation and Public Facilities","usgsCitation":"Beebee, R.A., Dworsky, K.L., and Knopp, S.J., 2017, Streambed scour evaluations and conditions at selected bridge sites in Alaska, 2013–15: U.S. Geological Survey Scientific Investigations Report 2017-5149, 67 p., https://doi.org/10.3133/sir20175149.","productDescription":"Report: vi, 67 p.; Appendix","numberOfPages":"78","onlineOnly":"Y","ipdsId":"IP-083845","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":350212,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5149/sir20175149.pdf","text":"Report","size":"4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5049"},{"id":350211,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5149/coverthb.jpg"},{"id":350213,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5149/sir20175149_appendix1.xlsx","text":"Appendix 1","size":"500 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5049"}],"country":"United 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\":{\"name\":\"Alaska\",\"nation\":\"USA \"}}]}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508-4560
https://alaska.usgs.gov/
Populations of migratory birds present unique conservation challenges given the often vast distances separating critical resources throughout the annual cycle. Migration areas close to the breeding grounds represent a link between two key stages of the annual cycle, and understanding migration ecology as birds exit the breeding grounds may be particularly informative for successful conservation. We studied migration phenology and stopover ecology of an endangered subspecies of the Red Knot Calidris canutus rufa at a migration area relatively close to its breeding range. Using mark-recapture/resight data and a Jolly-Seber model for open populations, we described the arrival and departure schedules, stopover duration, and passage population size at the Mingan Archipelago, Quebec, Canada. Red Knots arrived at the study area in two distinct waves of birds separated by approximately 22 days. Nearly 30% of the passage population arrived in the first wave of arrivals during 15–18 July, and approximately 22% arrived in a second wave during 8–11 August. The sex-ratio in the stopover population at the time of the first wave was slightly skewed toward females, whereas the second wave was heavily skewed toward males. Because males remain on the breeding grounds to care for young, this may reflect successful
breeding in the year of our study. The estimated stopover duration (population mean) was 11 days (95% credible interval: 10.3–11.7 days), but stopover persistence was variable throughout the season. We estimated a passage population size of 9,450 birds (8,355–10,710), a minimum estimate for reasons related to the duration of our sampling. Mingan Archipelago is thus an important migration area for this endangered subspecies and could be a priority in conservation planning. Our results also emphasize the advantages of mark-recapture/resight approaches for estimating migration phenology and stopover persistence.
Climate change refugia, areas buffered from climate change relative to their surroundings, are of increasing interest as natural resource managers seek to prioritize climate adaptation actions. However, evidence that refugia buffer the effects of anthropogenic climate change is largely missing.
Focusing on the climate-sensitive Belding’s ground squirrel (Urocitellus beldingi), we predicted that highly connected Sierra Nevada meadows that had warmed less or shown less precipitation change over the last century would have greater population persistence, as measured by short-term occupancy, fewer extirpations over the twentieth century, and long-term persistence measured through genetic diversity.
Across California, U. beldingi were more likely to persist over the last century in meadows with high connectivity that were defined as refugial based on a suite of temperature and precipitation factors. In Yosemite National Park, highly connected refugial meadows were more likely to be occupied by U. beldingi. More broadly, populations inhabiting Sierra Nevada meadows with colder mean winter temperatures had higher values of allelic richness at microsatellite loci, consistent with higher population persistence in temperature-buffered sites. Furthermore, both allelic richness and gene flow were higher in meadows that had higher landscape connectivity, indicating the importance of metapopulation processes. Conversely, anthropogenic refugia, sites where populations appeared to persist due to food or water supplementation, had lower connectivity, genetic diversity, and gene flow, and thus might act as ecological traps. This study provides evidence that validates the climate change refugia concept in a contemporary context and illustrates how to integrate field observations and genetic analyses to test the effectiveness of climate change refugia and connectivity.
Climate change refugia will be important for conserving populations as well as genetic diversity and evolutionary potential. Our study shows that in-depth modeling paired with rigorous fieldwork can identify functioning climate change refugia for conservation.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s40665-017-0036-5","usgsCitation":"Morelli, T.L., Maher, S.P., Lim, M.C., Kastely, C., Eastman, L.M., Flint, L.E., Flint, A.L., Beissinger, S.R., and Moritz, C., 2017, Climate change refugia and habitat connectivity promote species persistence: Climate Change Responses, v. 4, 8, 12 p., https://doi.org/10.1186/s40665-017-0036-5.","productDescription":"8, 12 p.","ipdsId":"IP-091107","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":469229,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40665-017-0036-5","text":"Publisher Index Page"},{"id":376819,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.94873046875,\n 37.51844023887861\n ],\n [\n -119.2236328125,\n 37.51844023887861\n ],\n [\n -119.2236328125,\n 38.199338565983844\n ],\n [\n -119.94873046875,\n 38.199338565983844\n ],\n [\n -119.94873046875,\n 37.51844023887861\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","noUsgsAuthors":false,"publicationDate":"2017-12-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":794322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maher, Sean P.","contributorId":7998,"corporation":false,"usgs":true,"family":"Maher","given":"Sean","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":794323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lim, Marisa C. W.","contributorId":236825,"corporation":false,"usgs":false,"family":"Lim","given":"Marisa","email":"","middleInitial":"C. W.","affiliations":[],"preferred":false,"id":794324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kastely, Christina","contributorId":236826,"corporation":false,"usgs":false,"family":"Kastely","given":"Christina","email":"","affiliations":[],"preferred":false,"id":794325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eastman, Lindsey M.","contributorId":236827,"corporation":false,"usgs":false,"family":"Eastman","given":"Lindsey","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794362,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Flint, Lorraine E. 0000-0002-7868-441X lflint@usgs.gov","orcid":"https://orcid.org/0000-0002-7868-441X","contributorId":1184,"corporation":false,"usgs":true,"family":"Flint","given":"Lorraine","email":"lflint@usgs.gov","middleInitial":"E.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794363,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flint, Alan L. 0000-0002-5118-751X aflint@usgs.gov","orcid":"https://orcid.org/0000-0002-5118-751X","contributorId":1492,"corporation":false,"usgs":true,"family":"Flint","given":"Alan","email":"aflint@usgs.gov","middleInitial":"L.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":794364,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Beissinger, Steven R.","contributorId":100534,"corporation":false,"usgs":true,"family":"Beissinger","given":"Steven","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":794365,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Moritz, Craig","contributorId":149462,"corporation":false,"usgs":false,"family":"Moritz","given":"Craig","email":"","affiliations":[{"id":17742,"text":"Research School of Biology, The Australian Nat'l U, Acton, Australia","active":true,"usgs":false}],"preferred":false,"id":794366,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70194811,"text":"sir20175129 - 2017 - The 2014 eruptions of Pavlof Volcano, Alaska","interactions":[],"lastModifiedDate":"2018-01-22T10:51:38","indexId":"sir20175129","displayToPublicDate":"2017-12-22T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-5129","title":"The 2014 eruptions of Pavlof Volcano, Alaska","docAbstract":"Pavlof Volcano is one of the most frequently active volcanoes in the Aleutian Island arc, having erupted more than 40 times since observations were first recorded in the early 1800s . The volcano is located on the Alaska Peninsula (lat 55.4173° N, long 161.8937° W), near Izembek National Wildlife Refuge. The towns and villages closest to the volcano are Cold Bay, Nelson Lagoon, Sand Point, and King Cove, which are all within 90 kilometers (km) of the volcano (fig. 1). Pavlof is a symmetrically shaped stratocone that is 2,518 meters (m) high, and has about 2,300 m of relief. The volcano supports a cover of glacial ice and perennial snow roughly 2 to 4 cubic kilometers (km3) in volume, which is mantled by variable amounts of tephra fall, rockfall debris, and pyroclastic-flow deposits produced during historical eruptions. Typical Pavlof eruptions are characterized by moderate amounts of ash emission, lava fountaining, spatter-fed lava flows, explosions, and the accumulation of unstable mounds of spatter on the upper flanks of the volcano. The accumulation and subsequent collapse of spatter piles on the upper flanks of the volcano creates hot granular avalanches, which erode and melt snow and ice, and thereby generate watery debris-flow and hyperconcentrated-flow lahars.
Seismic instruments were first installed on Pavlof Volcano in the early 1970s, and since then eruptive episodes have been better characterized and specific processes have been documented with greater certainty. The application of remote sensing techniques, including the use of infrasound data, has also aided the study of more recent eruptions. Although Pavlof Volcano is located in a remote part of Alaska, it is visible from Cold Bay, Sand Point, and Nelson Lagoon, making distal observations of eruptive activity possible, weather permitting. A busy air-travel corridor that is utilized by a numerous transcontinental and regional air carriers passes near Pavlof Volcano. The frequency of air travel across the region results in a relatively large number of airborne observations of eruptive activity. During the 2014 Pavlof eruptions, the Alaska Volcano Observatory received observations and photographs from pilots and local observers, which aided evaluation of the eruptive activity and the areas affected by eruptive products.
This report outlines the chronology of events associated with the 2014 eruptive activity at Pavlof Volcano, provides documentation of the style and character of the eruptive episodes, and reports briefly on the eruptive products and impacts. The principal observations are described and portrayed on maps and photographs, and the 2014 eruptive activity is compared to historical eruptions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175129","usgsCitation":"Waythomas, C.F., Haney, M.M., Wallace, K.L., Cameron, C.E., and Schneider, D.J., 2017, The 2014 eruptions of Pavlof Volcano, Alaska: U.S. Geological Survey Scientific Investigations Report 2017-5129, 27 p., https://doi.org/10.3133/sir20175129. ","productDescription":"vi, 27 p.","numberOfPages":"36","onlineOnly":"Y","ipdsId":"IP-075355","costCenters":[{"id":121,"text":"Alaska Volcano Observatory","active":false,"usgs":true}],"links":[{"id":350485,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix1.xlsx","text":"Appendix 1","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350486,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2a.xlsx","text":"Appendix 2A","size":"40 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350487,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129_appendix2b.xlsx","text":"Appendix 2B","size":"150 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017-5129"},{"id":350186,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5129/sir20175129.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5129"},{"id":350185,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5129/coverthb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Pavlof Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -162.2,\n 55.25\n ],\n [\n -161.7,\n 55.25\n ],\n [\n -161.7,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.547280698640805\n ],\n [\n -162.2,\n 55.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Volcano Observatory
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Anchorage, AK 99508
The U.S. Geological Survey (USGS), in support of the Massachusetts Estuaries Project (MEP), delineated groundwater-contributing areas to various hydrologic receptors including ponds, streams, and coastal water bodies throughout southeastern Massachusetts, including portions of the Plymouth-Carver aquifer system and all of Cape Cod. These contributing areas were delineated over a 6-year period from 2003 through 2008 by using previously published regional USGS groundwater-flow models for the Plymouth-Carver region (Masterson and others, 2009), the Sagamore (western) and Monomoy (eastern) flow lenses of Cape Cod (Walter and Whealan, 2005), and lower Cape Cod (Masterson, 2004). The original USGS groundwater-contributing areas were subsequently revised in some locations by the MEP to remove modeling artifacts or to make the contributing areas more consistent with site-specific hydrologic conditions without further USGS review. This report describes the process used to create the USGS groundwater-contributing areas and provides these model results in their original format in a single, publicly accessible publication.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1074","collaboration":"Prepared in cooperation with the Massachusetts Department of Environmental Protection","usgsCitation":"Carlson, C.S., Masterson, J.P., Walter, D.A., and Barbaro, J.R., 2017, Development of simulated groundwater-contributing areas to selected streams, ponds, coastal water bodies, and production wells in the Plymouth-Carver region and Cape Cod, Massachusetts: U.S. Geological Survey Data Series 1074, 17 p., https://doi.org/10.3133/ds1074.","productDescription":"Report: iv, 17 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-087594","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":350104,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7V69H2Z","text":"USGS data release","description":"USGS data release","linkHelpText":"Simulated Groundwater-Contributing Areas to Selected Streams, Ponds, Coastal Water Bodies, and Production Wells, Plymouth-Carver Region and Cape Cod, Massachusetts"},{"id":350102,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1074/coverthb.jpg"},{"id":350103,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1074/ds1074.pdf","text":"Report","size":"5.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1074"}],"country":"United States","state":"Massachusetts","otherGeospatial":"Cape Cod, Plymouth-Carver Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -71.015625,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 41.50857729743935\n ],\n [\n -69.88128662109375,\n 42.167475010395336\n ],\n [\n -71.015625,\n 42.167475010395336\n ],\n [\n -71.015625,\n 41.50857729743935\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
In support of earthquake hazard studies and ground motion simulations in the Pacific Northwest, three-dimensional P- and S-wave velocity (VP and VS, respectively) models incorporating the Cascadia subduction zone were previously developed for the region encompassed from about 40.2°N. to 50°N. latitude, and from about 122°W. to 129°W. longitude. This report describes updates to the Cascadia velocity property volumes of model version 1.3 (V1.3), herein called model version 1.6 (V1.6). As in model V1.3, the updated V1.6 model volume includes depths from 0 kilometers (mean sea level) to 60 kilometers, and it is intended to be a reference for researchers who have used, or are planning to use, this model in their Earth science investigations. To this end, it is intended that the VP and VS property volumes of model V1.6 will be considered a template for a community velocity model of the Cascadia region as additional results become available. With the recent and ongoing development of the National Crustal Model, we envision any future versions of this model will be directly integrated with that effort.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171152","collaboration":"Earthquake Hazards Ground Motion Investigations","usgsCitation":"Stephenson, W.J., Reitman, N.G., and Angster, S.J., 2017, P- and S-wave velocity models incorporating the Cascadia subduction zone for 3D earthquake ground motion simulations, version 1.6—Update for Open-File Report 2007–1348 (ver. 1.1, Sept. 10, 2019): U.S. Geological Survey Open-File Report 2017–1152, 17 p., https://doi.org/10.3133/ofr20171152. [Supersedes USGS Open-File Report 2007–1348.]","productDescription":"Report: vi, 17 p.; Read Me; Data Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088666","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":350108,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NS0SWM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data for P- and S-wave Seismic Velocity Models Incorporating the Cascadia Subduction Zone for 3D Earthquake Ground Motion simulations-Update for Open-File Report 2007-1348"},{"id":350107,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2017/1152/Readme.txt","text":"Read Me","size":"3.82 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2017–1152 Read Me"},{"id":350105,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1152/coverthb2.jpg"},{"id":350106,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1152/ofr20171152.pdf","text":"Report","size":"9.72 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017–1152"},{"id":367615,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2017/1152/versionHist.txt","text":"Version History","size":"4.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2017–1152 Version History"}],"country":"Canada, United States","state":"Oregon, Vancouver, Washington","otherGeospatial":"Cascadia Subduction Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -128.583984375,\n 40.36328834091583\n ],\n [\n -121.61865234375,\n 40.36328834091583\n ],\n [\n -121.61865234375,\n 49.92293545449574\n ],\n [\n -128.583984375,\n 49.92293545449574\n ],\n [\n -128.583984375,\n 40.36328834091583\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: December 20, 2017; Version 1.1: September 11, 2019","contact":"Center Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046 Mail Stop 966
Denver, CO 80225
Earthquake-triggered ground failure, such as landsliding and liquefaction, can contribute significantly to losses, but our current ability to accurately include them in earthquake-hazard analyses is limited. The development of robust and widely applicable models requires access to numerous inventories of ground failures triggered by earthquakes that span a broad range of terrains, shaking characteristics, and climates. We present an openly accessible, centralized earthquake-triggered groundfailure inventory repository in the form of a ScienceBase Community to provide open access to these data with the goal of accelerating research progress. The ScienceBase Community hosts digital inventories created by both U.S. Geological Survey (USGS) and non-USGS authors. We present the original digital inventory files (when available) as well as an integrated database with uniform attributes. We also summarize the mapping methodology and level of completeness as reported by the original author(s) for each inventory. This document describes the steps taken to collect, process, and compile the inventories and the process for adding additional ground-failure inventories to the ScienceBase Community in the future.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1064","usgsCitation":"Schmitt, R.G., Tanyas, Hakan, Nowicki Jessee, M.A., Zhu, Jing, Biegel, K.M., Allstadt, K.E., Jibson, R.W., Thompson, E.M., van Westen, C.J., Sato, H.P., Wald, D.J., Godt, J.W., Gorum, Tolga, Xu, Chong, Rathje, E.M., Knudsen, K.L., 2017, An open repository of earthquake-triggered ground-failure inventories: U.S. Geological Survey Data Series 1064, 17 p., https://doi.org/10.3133/ds1064.","productDescription":"Report: iii, 17 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-088662","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":438123,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7DN43Z6","text":"USGS data release","linkHelpText":"landslides-metadata"},{"id":350095,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1064/coverthb.jpg"},{"id":350096,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1064/ds1064.pdf","text":"Report","size":"2.81 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1064"},{"id":350097,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70DB4","text":"USGS data release","description":"USGS data release","linkHelpText":"An Open Repository of Earthquake-Triggered Ground-Failure Inventories"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS–966
Denver, CO 80225–0046
Recent developments of community abundance models (CAMs) enable us to analyze communities subject to imperfect detection. However, existing CAMs assume spatial closure, that is, that individuals are always present in the sampling plots, which is often violated in field surveys. Violation of this assumption, such as in the presence of spatial temporary emigration, can lead to the underestimates of detection probability and overestimates of population densities and diversity metrics. Here, we propose a model that simultaneously accommodates both temporary emigration and imperfect detection by integrating CAMs and a form of hierarchical distance sampling for open populations. Expected values of species richness are obtained via the summation of occupancy (or incidence) probabilities, based on species‐level densities, across all species of the community. Simulations were used to examine the effects of spatial temporary emigration on the estimation of biological communities. We also applied the proposed model to empirical data and constructed area‐based rarefaction curves accounting for temporary emigration. Simulation experiments showed that temporary emigration can decrease the local species richness (α diversity) based on densities and increase the species turnover (β diversity). Raw species counts can overestimate or underestimate α diversity in the presence of temporary emigration, but the specific biases depend on the values of detection and emigration probabilities. Our newly proposed model yielded unbiased estimates of α, β, and γ diversity in the presence of temporary emigration. The application to empirical data suggested that accounting for temporary emigration lowered area‐based rarefaction curves because availability probabilities of individual species were estimated to be <1. Temporary emigration prevails in field surveys and has broad significance for understanding the ecology and function of biological communities and separation of imperfect detection and temporary emigration resolves long‐standing issues in the use of count data. We therefore suggest that the consideration of temporary emigration would contribute to understanding the nature and role of biological communities.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2028","usgsCitation":"Yamaura, Y., and Royle, A., 2017, Community distance sampling models allowing for imperfect detection and temporary emigration: Ecosphere, v. 8, no. 12, p. 1-15, https://doi.org/10.1002/ecs2.2028.","productDescription":"e02028, 15 p.","startPage":"1","endPage":"15","ipdsId":"IP-089901","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469230,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2028","text":"Publisher Index Page"},{"id":363415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"12","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":173122,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","email":"","affiliations":[{"id":16855,"text":"Hokkaido University","active":true,"usgs":false}],"preferred":false,"id":761807,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":761806,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70192576,"text":"ofr20171137 - 2017 - Visualization of groundwater withdrawals","interactions":[],"lastModifiedDate":"2018-02-21T11:46:21","indexId":"ofr20171137","displayToPublicDate":"2017-12-19T13:30:00","publicationYear":"2017","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":"2017-1137","title":"Visualization of groundwater withdrawals","docAbstract":"Generating an informative display of groundwater withdrawals can sometimes be difficult because the symbols for closely spaced wells can overlap. An alternative method for displaying groundwater withdrawals is to generate a “footprint” of the withdrawals. WellFootprint version 1.0 implements the Footprint algorithm with two optional variations that can speed up the footprint calculation. ModelMuse has been modified in order to generate the input for WellFootprint and to read and graphically display the output from WellFootprint.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171137","usgsCitation":"Winston, R.B., and Goode, D.J., 2017, Visualization of groundwater withdrawals: U.S. Geological Survey Open-File Report 2017–1137, 8 p., https://doi.org/10.3133/ofr20171137.","productDescription":"Report: vi, 8 p.; Application Site","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-089907","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":438124,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4TQ8","text":"USGS data release","linkHelpText":"WellFootprint Software Release"},{"id":350110,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1137/ofr20171137.pdf","text":"Report","size":"1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1137"},{"id":350113,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/F70C4TQ8","linkHelpText":"- WellFootprint source code and examples: U.S. Geological Survey software release"},{"id":350109,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1137/coverthb.jpg"}],"contact":"Director, Integrated Modeling and Prediction Division
U.S. Geological Survey
MS 415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Manganese is an essential element for modern industrial societies. Its principal use is in steelmaking, where it serves as a purifying agent in iron-ore refining and as an alloy that converts iron into steel. Although the amount of manganese consumed to make a ton of steel is small, ranging from 6 to 9 kilograms, it is an irreplaceable component in the production of this fundamental material. The United States has been totally reliant on imports of manganese for many decades and will continue to be so for at least the near future. There are no domestic reserves, and although some large low-grade resources are known, they are far inferior to manganese ores readily available on the international market. World reserves of manganese are about 630 million metric tons, and annual global consumption is about 16 million metric tons. Current reserves are adequate to meet global demand for several decades. Global resources in traditional land-based deposits, including both reserves and rocks sufficiently enriched in manganese to be ores in the future, are much larger, at about 17 billion metric tons. Manganese resources in seabed deposits of ferromanganese nodules and crusts are larger than those on land and have not been fully quantified. No production from seabed deposits has yet been done, but current research and development activities are substantial and may bring parts of these seabed resources into production in the future. The advent of economically successful seabed mining could substantially alter the current scenario of manganese supply by providing a large new source of manganese in addition to traditional land-based deposits.
From a purely geologic perspective, there is no global shortage of proven ores and potential new ores that could be developed from the vast tonnage of identified resources. Reserves and resources are very unevenly distributed, however. The Kalahari manganese district in South Africa contains 70 percent of the world’s identified resources and about 25 percent of its reserves. South Africa, Brazil, and Ukraine together accounted for nearly 65 percent of reserves in 2013. The combination of total import reliance for manganese, the mineral commodity’s essential uses in our industrialized society, and the potential for supply disruptions because of the limited sources of the ore makes manganese among the most critical minerals for the United States.
Manganese is the 12th most abundant element in Earth’s crust. Its concentration varies among common types of rocks, mostly in the range of from 0.1 to 0.2 percent. The highest quality manganese ores contain from 40 to 45 percent manganese. The formation of these ores requires specialized geologic conditions that concentrate manganese at several hundred times its average crustal abundance. The dominant processes in forming the world’s principal deposits take place in the oceans. As a result, most important manganese deposits occur in ancient marine sedimentary rocks that are now exposed on continents as a result of subsequent tectonic uplift and erosion. In many cases, other processes have further enriched these manganiferous sedimentary rocks to form some of today’s highest grade ores. Modern seabed resources of ferromanganese nodules cover vast areas of the present ocean floor and are still forming by complex interactions of marine microorganisms, manganese dissolved in seawater, and chemical processes on the seabed.
Manganese is ubiquitous in soil, water, and air. It occurs most often in solid form but can become soluble under acidic conditions. Manganese mining, like any activity that disturbs large areas of Earth’s surface, has the potential to produce increases in manganese concentrations that could be harmful to humans or the environment if not properly controlled. Although manganese is an essential nutrient for humans and most other organisms, overexposure can lead to neurotoxicity in humans. Workers at manganese mining and processing facilities have the greatest potential to inhale manganese-rich dust. Without proper protective equipment, these workers may develop a permanent neurological disorder known as manganism. Each manganese mine is unique and presents its own suite of potential hazards and preventative measures. Likewise, various nations have their own sets of standards to ensure safe mining, isolation of mine waste, treatment of mine waters, and mine closure and restoration. Interest in mining trace metals contained in ferromanganese nodules and crusts on the seabed has increased rapidly in the past decade. Prime areas for future research include overcoming the technological challenges presented by mining as deep as 6,500 meters below sea level and understanding and mitigating the potential impacts of seabed mining on marine ecosystems.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1802L","isbn":"978-1-4113-3991-0","usgsCitation":"Cannon, W.F., Kimball, B.E., and Corathers, L.A., 2017, Manganese, chap. L of Schulz, K.J., DeYoung, J.H., Jr., Seal, R.R., II, and Bradley, D.C., eds., Critical mineral resources of the United States—Economic and environmental geology and prospects for future supply: U.S. Geological Survey Professional Paper 1802, p. L1–L28, https://doi.org/10.3133/pp1802L.","productDescription":"viii, 28 p.","numberOfPages":"40","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-046161","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":334193,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1802/l/pp1802l.pdf","text":"Report","size":"7.29 MB","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1802 K"},{"id":334192,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1802/l/coverthb1.jpg"}],"contact":"Mineral Resources Program Coordinator
U.S. Geological Survey
913 National Center
Reston, VA 20192
Email: minerals@usgs.gov
https://minerals.usgs.gov