Historical aerial photographs, from 1937 to the present, show Skagit Delta tidal marshes prograding into Skagit Bay for most of the record, but the progradation rates have been steadily declining and the marshes have begun to erode in recent decades despite the large suspended sediment load provided by the Skagit River. In an area of the delta isolated from direct riverine sediment supply by anthropogenic blockage of historical distributaries, 0.5-m tall marsh cliffs along with concave marsh profiles indicate wave erosion is contributing to marsh retreat. This is further supported by a “natural experiment” provided by rocky outcrops that shelter high marsh in their lee, while being bounded by 0.5-m lower eroded marsh to windward and on either side. Coastal wetlands with high sediment supply are thought to be resilient to sea level rise, but the case of the Skagit Delta shows this is not necessarily true. A combination of sea level rise and wave-generated erosion may overwhelm sediment supply. Additionally, anthropogenic obstruction of historical distributaries and levee construction along the remaining distributaries likely increase the jet momentum of river discharge, forcing much suspended sediment to bypass the tidal marshes and be exported from Skagit Bay. Adaptive response to the threat of climate change related sea level rise and increased wave frequency or intensity should consider the efficacy of restoring historical distributaries and managed retreat of constrictive river levees to maximize sediment delivery to delta marshes.
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U.S. Geological Survey
405 N Goodwin
Urbana, IL 61801
http://il.water.usgs.gov/
The Neosho River and its primary tributary, the Cottonwood River, are the main sources of inflow to John Redmond Reservoir in east-central Kansas. Storage loss in the reservoir resulting from sedimentation has been estimated to be 765 acre-feet per year for 1964–2014. The 1964–2014 sedimentation rate was almost 90 percent larger than the projected design sedimentation rate of 404 acre-feet per year, and resulted in a loss of about 40 percent of the original (1964) conservation (multi-purpose) pool storage capacity. To help maintain storage in the reservoir, the Kansas Water Office has implemented more than two dozen stream bank erosion control projects to reduce the annual sediment load entering the reservoir and initiated a dredging project to restore nearly 2,000 acre-feet of storage near the dam to provide additional water supply to downstream water users. Storm events during May through July 2015 caused large inflows of water and sediment into the reservoir. Initially, flood waters were held back in the reservoir in order to decrease downstream flooding in Oklahoma. Later, retained reservoir flood waters were released at high rates (up to 25,400 acre-feet per day, the maximum allowed for the reservoir) for extended periods.
\nThe U.S. Geological Survey, in cooperation with the Kansas Water Office, computed the suspended-sediment inflows and retention in John Redmond Reservoir during May through July 2015. Computations relied upon previously published turbidity-suspended sediment relations at water-quality monitoring sites located upstream and downstream from the reservoir. During the 3-month period, approximately 872,000 tons of sediment entered the reservoir, and 57,000 tons were released through the reservoir outlet. The average monthly trapping efficiency during this period was 93 percent, and monthly averages ranged from 83 to 97 percent. During the study period, an estimated 980 acre-feet of storage was lost, over 2.4 times the design annual sedimentation rate of the reservoir. Storm inflows during the 3-month analysis period reduced reservoir storage in the conservation pool approximately 1.6 percent. This indicates that large inflows, coupled with minimal releases, can have substantial effects on reservoir storage and lifespan.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165040","collaboration":"Prepared in cooperation with the Kansas Water Office","usgsCitation":"Foster, G.M., 2016, Effects of May through July 2015 storm events on suspended sediment loads, sediment trapping efficiency, and storage capacity of John Redmond Reservoir, east-central Kansas: U.S. Geological Survey Scientific Investigations Report 2016–5040, 10 p., https://dx.doi.org/10.3133/sir20165040.","productDescription":"Report: iv, 10 p.; Appendix 1","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-069900","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":320856,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5040/sir20165040.pdf","text":"Report","size":"1.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5040"},{"id":320857,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5040/sir20165040_appendix 1.xlsx","text":"Appendix 1","size":"20.3 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2016–5040 Appendix 1"},{"id":320855,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5040/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cottonwood River watershed, John Redmond Reservoir, Upper Neosho watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98,\n 38\n ],\n [\n -98,\n 39\n ],\n [\n -95,\n 39\n ],\n [\n -95,\n 38\n ],\n [\n -98,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
The Moscow-Pullman basin, located on the eastern margin of the Columbia River flood basalt province, consists of a subsurface mosaic of interlayered Miocene sediments and lava flows of the Imnaha, Grande Ronde, Wanapum, and Saddle Mountains Basalts of the Columbia River Basalt Group. This sequence is ~1800 ft (550 m) thick in the east around Moscow, Idaho, and exceeds 2300 ft (700 m) in the west at Pullman, Washington. Most flows entered from the west into a topographic low, partially surrounded by steep mountainous terrain. These flows caused a rapid rise in base level and deposition of immature sediments. This field guide focuses on the upper Grande Ronde Basalt, Wanapum Basalt, and sediments of the Latah Formation.
Late Grande Ronde flows terminated midway into the basin to begin the formation of a topographic high that now separates a thick sediment wedge of the Vantage Member to the east of the high from a thin layer to the west. Disrupted by lava flows, streams were pushed from a west-flowing direction to a north-northwest orientation and drained the basin through a gap between steptoes toward Palouse, Washington. Emplacement of the Roza flow of the Wanapum Basalt against the western side of the topographic high was instrumental in this process, plugging west-flowing drainages and increasing deposition of Vantage sediments east of the high. The overlying basalt of Lolo covered both the Roza flow and Vantage sediments, blocking all drainages, and was in turn covered by sediments interlayered with local Saddle Mountains Basalt flows. Reestablishment of west-flowing drainages has been slow.
The uppermost Grande Ronde, the Vantage, and the Wanapum contain what is known as the upper aquifer. The water supply is controlled, in part, by thickness, composition, and distribution of the Vantage sediments. A buried channel of the Vantage likely connects the upper aquifer to Palouse, Washington, outside the basin. This field guide locates outcrops; relates them to stratigraphic well data; outlines paleogeographic basin evolution from late Grande Ronde to the present time; and notes structures, basin margin differences, and features that influence upper aquifer water supply.
","largerWorkTitle":"Field Guide","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.0041(05)","usgsCitation":"Bush, J.H., Garwood, D.L., and Dunlap, P., 2016, Geology and geologic history of the Moscow-Pullman basin, Idaho and Washington, from late Grande Ronde to late Saddle Mountains time: GSA Field Guides, v. 41, p. 151-174, https://doi.org/10.1130/2016.0041(05).","productDescription":"24 p. 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Pamela pdunlap@usgs.gov","contributorId":5329,"corporation":false,"usgs":true,"family":"Dunlap","given":"Pamela","email":"pdunlap@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":700835,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70171537,"text":"fs20163035 - 2016 - May through July 2015 storm event effects on suspended-sediment loads, sediment trapping efficiency, and storage capacity of John Redmond Reservoir","interactions":[],"lastModifiedDate":"2016-07-26T08:58:35","indexId":"fs20163035","displayToPublicDate":"2016-06-20T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-3035","title":"May through July 2015 storm event effects on suspended-sediment loads, sediment trapping efficiency, and storage capacity of John Redmond Reservoir","docAbstract":"The Neosho River and its primary tributary, the Cottonwood River, are the main sources of inflow to John Redmond Reservoir in east-central Kansas. Storm events during May through July 2015 caused large inflows of water and sediment into the reservoir. The U.S. Geological Survey, in cooperation with the Kansas Water Office, and funded in part through the Kansas State Water Plan Fund, computed the suspended-sediment inflows to, and trapping efficiency of, John Redmond Reservoir during May through July 2015. This fact sheet summarizes the quantification of suspended-sediment loads to and from the reservoir during May through July 2015 storm events and describes reservoir sediment trapping efficiency and effects on water-storage capacity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163035","collaboration":"Prepared in cooperation with the Kansas Water Office, and funded in part through the State Water Plan Fund","usgsCitation":"Foster, G.M., and King, L.R., 2016, May through July 2015 storm event effects on suspended-sediment loads, sediment trapping efficiency, and storage capacity of John Redmond Reservoir: U.S. Geological Survey Fact Sheet 2016–3035, 4 p., https://dx.doi.org/10.3133/fs20163035.","productDescription":"4 p","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-074296","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":323828,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3035/fs20163035.pdf","text":"Report","size":"2.07 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3035"},{"id":323827,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3035/coverthb.jpg"}],"country":"United States","state":"Kansas","otherGeospatial":"Cottonwood River watershed, John Redmond Reservoir, Upper Neosho watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98,\n 38\n ],\n [\n -98,\n 39\n ],\n [\n -95,\n 39\n ],\n [\n -95,\n 38\n ],\n [\n -98,\n 38\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, Kansas 66049
The Gold King mine water release that occurred on 5 August 2015 near the historical mining community of Silverton, Colorado, highlights the environmental legacy that abandoned mines have on the environment. During reclamation efforts, a breach of collapsed workings at the Gold King mine sent 3 million gallons of acidic and metal-rich mine water into the upper Animas River, a tributary to the Colorado River basin. The Gold King mine is located in the scenic, western San Juan Mountains, a region renowned for its volcano-tectonic and gold-silver-base metal mineralization history. Prior to mining, acidic drainage from hydrothermally altered areas was a major source of metals and acidity to streams, and it continues to be so. In addition to abandoned hard rock metal mines, uranium mine waste poses a long-term storage and immobilization challenge in this area. Uranium resources are mined in the Colorado Plateau, which borders the San Juan Mountains on the west. Uranium processing and repository sites along the Animas River near Durango, Colorado, are a prime example of how the legacy of mining must be managed for the health and well-being of future generations. The San Juan Mountains are part of a geoenvironmental nexus where geology, mining, agriculture, recreation, and community issues converge. This trip will explore the geology, mining, and mine cleanup history in which a community-driven, watershed-based stakeholder process is an integral part. Research tools and historical data useful for understanding complex watersheds impacted by natural sources of metals and acidity overprinted by mining will also be discussed.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/2016.0044(16)","usgsCitation":"Yager, D.B., Fey, D.L., Chapin, T., and Johnson, R.H., 2016, New perspectives on a 140-year legacy of mining and abandoned mine cleanup in the San Juan Mountains, Colorado: Field Guides, v. 44, p. 377-419, https://doi.org/10.1130/2016.0044(16).","productDescription":"43 p.","startPage":"377","endPage":"419","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-075687","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":330900,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.511962890625,\n 37.00255267215955\n ],\n [\n -108.511962890625,\n 38.51378825951165\n ],\n [\n -105.699462890625,\n 38.51378825951165\n ],\n [\n -105.699462890625,\n 37.00255267215955\n ],\n [\n -108.511962890625,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582443f4e4b09065cdf30523","contributors":{"authors":[{"text":"Yager, Douglas B. 0000-0001-5074-4022 dyager@usgs.gov","orcid":"https://orcid.org/0000-0001-5074-4022","contributorId":798,"corporation":false,"usgs":true,"family":"Yager","given":"Douglas","email":"dyager@usgs.gov","middleInitial":"B.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":639496,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fey, David L. dfey@usgs.gov","contributorId":713,"corporation":false,"usgs":true,"family":"Fey","given":"David","email":"dfey@usgs.gov","middleInitial":"L.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":639497,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapin, Thomas 0000-0001-6587-0734 tchapin@usgs.gov","orcid":"https://orcid.org/0000-0001-6587-0734","contributorId":758,"corporation":false,"usgs":true,"family":"Chapin","given":"Thomas","email":"tchapin@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":639498,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Raymond H. rhjohnso@usgs.gov","contributorId":707,"corporation":false,"usgs":true,"family":"Johnson","given":"Raymond","email":"rhjohnso@usgs.gov","middleInitial":"H.","affiliations":[],"preferred":true,"id":639499,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70173954,"text":"70173954 - 2016 - State-dependent resource harvesting with lagged information about system states","interactions":[],"lastModifiedDate":"2016-06-20T09:47:59","indexId":"70173954","displayToPublicDate":"2016-06-17T15:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"State-dependent resource harvesting with lagged information about system states","docAbstract":"Markov decision processes (MDPs), which involve a temporal sequence of actions conditioned on the state of the managed system, are increasingly being applied in natural resource management. This study focuses on the modification of a traditional MDP to account for those cases in which an action must be chosen after a significant time lag in observing system state, but just prior to a new observation. In order to calculate an optimal decision policy under these conditions, possible actions must be conditioned on the previous observed system state and action taken. We show how to solve these problems when the state transition structure is known and when it is uncertain. Our focus is on the latter case, and we show how actions must be conditioned not only on the previous system state and action, but on the probabilities associated with alternative models of system dynamics. To demonstrate this framework, we calculated and simulated optimal, adaptive policies for MDPs with lagged states for the problem of deciding annual harvest regulations for mallards (Anas platyrhynchos) in the United States. In this particular example, changes in harvest policy induced by the use of lagged information about system state were sufficient to maintain expected management performance (e.g. population size, harvest) even in the face of an uncertain system state at the time of a decision.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0157373","usgsCitation":"Johnson, F.A., Fackler, P.L., Boomer, G., Zimmerman, G.S., Williams, B.K., Nichols, J.D., and Dorazio, R., 2016, State-dependent resource harvesting with lagged information about system states: PLoS ONE, v. 11, no. 6, p. 1-21, https://doi.org/10.1371/journal.pone.0157373.","productDescription":"21 p.","startPage":"1","endPage":"21","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-071587","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":470874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0157373","text":"Publisher Index Page"},{"id":323944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"6","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-17","publicationStatus":"PW","scienceBaseUri":"576913e8e4b07657d19ff273","contributors":{"authors":[{"text":"Johnson, Fred A. 0000-0002-5854-3695 fjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-5854-3695","contributorId":2773,"corporation":false,"usgs":true,"family":"Johnson","given":"Fred","email":"fjohnson@usgs.gov","middleInitial":"A.","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":639749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fackler, Paul L.","contributorId":17487,"corporation":false,"usgs":true,"family":"Fackler","given":"Paul","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":639750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boomer, G Scott","contributorId":172150,"corporation":false,"usgs":false,"family":"Boomer","given":"G Scott","affiliations":[{"id":26994,"text":"Div. of Migratory Bird Management, U.S. Fish and Wildlife Service, MD","active":true,"usgs":false}],"preferred":false,"id":639751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":639752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Byron K. 0000-0001-7644-1396","orcid":"https://orcid.org/0000-0001-7644-1396","contributorId":86616,"corporation":false,"usgs":true,"family":"Williams","given":"Byron","email":"","middleInitial":"K.","affiliations":[{"id":554,"text":"Science and Decisions Center","active":true,"usgs":true}],"preferred":false,"id":639753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":639754,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dorazio, Robert 0000-0003-2663-0468 bob_dorazio@usgs.gov","orcid":"https://orcid.org/0000-0003-2663-0468","contributorId":172151,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert","email":"bob_dorazio@usgs.gov","affiliations":[{"id":5051,"text":"FLWSC-Orlando","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":639755,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70173791,"text":"sir20165075 - 2016 - United States copper metal and scrap use and trade patterns, 1995‒2014","interactions":[],"lastModifiedDate":"2016-07-12T12:34:09","indexId":"sir20165075","displayToPublicDate":"2016-06-17T13:30:00","publicationYear":"2016","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":"2016-5075","title":"United States copper metal and scrap use and trade patterns, 1995‒2014","docAbstract":"In 1995, China accounted for 10 percent of world copper consumption. By 2014, China accounted for about 49 percent of world copper consumption. This change has affected global copper and copper scrap prices, the sources of copper supply, and U.S. trade of copper-containing materials.
\nThis report considers changes to the copper and copper scrap industries of the United States. For the study period, 1995 through 2014, U.S. refined copper production from all sources (primary and secondary materials) decreased from 2.28 million metric tons (Mt) of copper to 1.05 Mt (a 54 percent decrease). During the same period, U.S. copper scrap net exports increased from 0.203 Mt to 0.737 Mt (a 263 percent increase and a compound annual growth rate of about 7.0 percent per year). Copper and copper scrap prices (in constant 2014 dollars) rose such that 2014 prices were about 48 percent greater than 1995 prices. From 1995 through 2014, Chinese imports of copper scrap from the United States grew from 0.061 Mt to 0.569 Mt (an increase of about 830 percent and a compound annual growth rate of about 12.5 percent per year). In 2011, Chinese imports of U.S. copper scrap peaked at 0.745 Mt of contained copper. In 1995, Chinese imports of U.S. copper scrap accounted for 17 percent of U.S. copper scrap exports. By 2014, Chinese imports accounted for 69 percent of U.S. copper scrap exports (by weight), and Chinese imports of U.S. copper scrap were valued at $1.45 billion.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165075","usgsCitation":"Goonan, T.G., 2016, United States copper metal and scrap use and trade patterns, 1995–2014: U.S. Geological Survey Scientific Investigations Report 2016–5075, 10 p., https://dx.doi.org/10.3133/sir20165075.","productDescription":"10 p.","startPage":"1","endPage":"10","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-070019","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":323728,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5075/sir20165075.pdf","text":"Report","size":"522 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5075"},{"id":323727,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5075/coverthb.jpg"}],"country":"United 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States\"}}]}","contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Or visit the USGS Minerals Information Web site at
http://minerals.usgs.gov/minerals/
Models and data used to describe species–area relationships confound sampling with ecological process as they fail to acknowledge that estimates of species richness arise due to sampling. This compromises our ability to make ecological inferences from and about species–area relationships. We develop and illustrate hierarchical community models of abundance and frequency to estimate species richness. The models we propose separate sampling from ecological processes by explicitly accounting for the fact that sampled patches are seldom completely covered by sampling plots and that individuals present in the sampling plots are imperfectly detected. We propose a multispecies abundance model in which community assembly is treated as the summation of an ensemble of species-level Poisson processes and estimate patch-level species richness as a derived parameter. We use sampling process models appropriate for specific survey methods. We propose a multispecies frequency model that treats the number of plots in which a species occurs as a binomial process. We illustrate these models using data collected in surveys of early-successional bird species and plants in young forest plantation patches. Results indicate that only mature forest plant species deviated from the constant density hypothesis, but the null model suggested that the deviations were too small to alter the form of species–area relationships. Nevertheless, results from simulations clearly show that the aggregate pattern of individual species density–area relationships and occurrence probability–area relationships can alter the form of species–area relationships. The plant community model estimated that only half of the species present in the regional species pool were encountered during the survey. The modeling framework we propose explicitly accounts for sampling processes so that ecological processes can be examined free of sampling artefacts. Our modeling approach is extensible and could be applied to a variety of study designs and allows the inclusion of additional environmental covariates.
","language":"English","publisher":"Blackwell Pub. Ltd.","doi":"10.1002/ece3.2244","usgsCitation":"Yamaura, Y., Connor, E.F., Royle, A., Itoh, K., Sato, K., Taki, H., and Mishima, Y., 2016, Estimating species – area relationships by modeling abundance and frequency subject to incomplete sampling: Ecology and Evolution, v. 6, no. 14, p. 4836-4848, https://doi.org/10.1002/ece3.2244.","productDescription":"13 p.","startPage":"4836","endPage":"4848","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076034","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":470875,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2244","text":"Publisher Index Page"},{"id":325580,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"14","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2016-06-17","publicationStatus":"PW","scienceBaseUri":"57934445e4b0eb1ce79e8bf0","contributors":{"authors":[{"text":"Yamaura, Yuichi","contributorId":173082,"corporation":false,"usgs":false,"family":"Yamaura","given":"Yuichi","affiliations":[{"id":16855,"text":"Hokkaido University","active":true,"usgs":false}],"preferred":false,"id":643387,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Edward F.","contributorId":173083,"corporation":false,"usgs":false,"family":"Connor","given":"Edward","email":"","middleInitial":"F.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":643388,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":643389,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Itoh, Katsuo","contributorId":173084,"corporation":false,"usgs":false,"family":"Itoh","given":"Katsuo","email":"","affiliations":[{"id":27146,"text":"Itoh Research of Applied Plant Studies","active":true,"usgs":false}],"preferred":false,"id":643390,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sato, Kiyoshi","contributorId":173085,"corporation":false,"usgs":false,"family":"Sato","given":"Kiyoshi","email":"","affiliations":[{"id":27146,"text":"Itoh Research of Applied Plant Studies","active":true,"usgs":false}],"preferred":false,"id":643391,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Taki, Hisatomo","contributorId":173086,"corporation":false,"usgs":false,"family":"Taki","given":"Hisatomo","email":"","affiliations":[{"id":27146,"text":"Itoh Research of Applied Plant Studies","active":true,"usgs":false}],"preferred":false,"id":643392,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mishima, Yoshio","contributorId":173121,"corporation":false,"usgs":false,"family":"Mishima","given":"Yoshio","email":"","affiliations":[],"preferred":false,"id":643393,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70260147,"text":"70260147 - 2016 - Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years","interactions":[],"lastModifiedDate":"2024-10-29T12:23:50.270565","indexId":"70260147","displayToPublicDate":"2016-06-17T07:22:06","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Late Pleistocene and Holocene tephrostratigraphy of interior Alaska and Yukon: Key beds and chronologies over the past 30,000 years","docAbstract":"From May 2011 to August 2014, the U.S. Geological Survey (USGS) collected gravity data at more than 2,300 stations and physical property measurements on more than 640 rock samples from outcrops in the eastern Mojave Desert, California and Nevada. Gravity, magnetic, and physical-property data are used to study and locate regional crustal structures as an aid to understanding the geologic framework related to mineral resources of the eastern Mojave Desert.
The eastern Mojave Desert is host to a world-class rare earth element carbonatite deposit located at Mountain Pass, California. Carbonatites are typically defined as magmatic rocks with high modal abundances of primary carbonate minerals >50 weight percent and elevated abundances of rare earth elements (REEs) (Nelson and others, 1988; Woolley and Kempe, 1989). The “Sulphide Queen” carbonatite ore deposit is a composite, tabular body made up of sills and dikes of REE-bearing sovites and beforsites that occurs just south of the Clark Mountain Range along a north-northwest trending fault-bounded block that extends along the northeast edge of the Mescal Range and northwestern extent of Ivanpah Mountains. This early to middle Proterozoic block is composed of a 1.7 Ga metamorphic complex of gneiss and schist that underwent widespread metamorphism and associated plutonism during the Ivanpah orogeny (Miller and others, 2007). Subsequently, these rocks were intruded by a series of granitoids, which included the 1.4 Ga (DeWitt and others, 1987) ultrapotassic alkaline suite of intrusions that are spatially and temporally associated with hundreds of dikes, outcrops, and a carbonatite ore body. The relative age sequence of this intrusive suite of alkaline rocks from oldest to youngest includes shonkinite, mesosyenite, syenite, quartz syenite, potassic granite, carbonatite, and late shonkinite dikes (Olson and others, 1954; Wooden and Miller, 1990; Haxel, 2005; Miller and others, 2007).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161070","usgsCitation":"Denton, K.M., and Ponce, D.A., 2018, Gravity and magnetic studies of the eastern Mojave Desert, California and Nevada (ver 1.1, August 2018): U.S. Geological Survey Open-File Report 2016-1070, 20 p., https://doi.org/10.3133/ofr20161070.","productDescription":"Report: iv, 20 p.; 3 Tables; Metadata; version history","onlineOnly":"Y","additionalOnlineFiles":"Y","temporalStart":"2011-05-01","ipdsId":"IP-064300","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":323913,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_table01_gravity_data_v1.1.xlsx","text":"Table 1 version 1.1","size":"472 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1070 Table 1 ver. 1.1"},{"id":323914,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_table02_rock_property_data.xlsx","text":"Table 2","size":"128 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1070 Table 2"},{"id":323915,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_table03_rock_modifier_data.xlsx","text":"Table 3","size":"20 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1070 Table 3"},{"id":323910,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1070/coverthb_.jpg"},{"id":323911,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_v1.1.pdf","text":"Report","size":"6.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1070"},{"id":323912,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1070/ofr20161070_metadata.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"OFR 2016-1070 Metadata"},{"id":356639,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/of/2016/1070/versionHist.txt"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Mojave Desert","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.5,\n 35.0\n ],\n [\n -115.5,\n 35.5\n ],\n [\n -115.0,\n 35.5\n ],\n [\n -115.0,\n 35.0\n ],\n [\n -115.5,\n 35.0\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"ver. 1.1: August 2018; ver. 1: June 2016","contact":"Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Tucson
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Resource managers rely on abundance or density metrics derived from beach seine surveys to make vital decisions that affect fish population dynamics and assemblage structure. However, abundance and density metrics may be biased by imperfect capture and lack of geographic closure during sampling. Currently, there is considerable uncertainty about the capture efficiency of juvenile Chinook salmon (Oncorhynchus tshawytscha) by beach seines. Heterogeneity in capture can occur through unrealistic assumptions of closure and from variation in the probability of capture caused by environmental conditions. We evaluated the assumptions of closure and the influence of environmental conditions on capture efficiency and abundance estimates of Chinook salmon from beach seining within the Sacramento–San Joaquin Delta and the San Francisco Bay. Beach seine capture efficiency was measured using a stratified random sampling design combined with open and closed replicate depletion sampling. A total of 56 samples were collected during the spring of 2014. To assess variability in capture probability and the absolute abundance of juvenile Chinook salmon, beach seine capture efficiency data were fitted to the paired depletion design using modified N-mixture models. These models allowed us to explicitly test the closure assumption and estimate environmental effects on the probability of capture. We determined that our updated method allowing for lack of closure between depletion samples drastically outperformed traditional data analysis that assumes closure among replicate samples. The best-fit model (lowest-valued Akaike Information Criterion model) included the probability of fish being available for capture (relaxed closure assumption), capture probability modeled as a function of water velocity and percent coverage of fine sediment, and abundance modeled as a function of sample area, temperature, and water velocity. Given that beach seining is a ubiquitous sampling technique for many species, our improved sampling design and analysis could provide significant improvements in density and abundance estimation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161099","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Perry, R.W., Kirsch, J.E., and Hendrix, A.N., 2016, Estimating juvenile Chinook salmon (Oncorhynchus tshawytscha) abundance from beach seine data collected in the Sacramento–San Joaquin Delta and San Francisco Bay, California: U.S. Geological Survey Open-File Report 2016–1099, 21 p., https://dx.doi.org/10.3133/ofr20161099.","productDescription":"iv, 21 p.","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074132","costCenters":[{"id":552,"text":"San Francisco Bay-Delta","active":false,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":323938,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1099/coverthb.jpg"},{"id":323939,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1099/ofr20161099.pdf","text":"Report","size":"2.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1099"}],"country":"United States","state":"California","otherGeospatial":"Sacramento-San Joaquin Delta, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5579833984375,\n 37.31338308990806\n ],\n [\n -122.5579833984375,\n 39.21523130910493\n ],\n [\n -121.1077880859375,\n 39.21523130910493\n ],\n [\n -121.1077880859375,\n 37.31338308990806\n ],\n [\n -122.5579833984375,\n 37.31338308990806\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
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Seattle, Washington 98115
http://wfrc.usgs.gov/
Hawai‘i has experienced numerous destructive tsunamis and the potential for future inundation has been described over the years using various historical events and scenarios. To support tsunami preparedness and risk-reduction planning in Hawai‘i, this study documents the variations among 91 coastal communities and 4 counties in the amounts, types, and percentages of developed land, residents, employees, community-support businesses, dependent-care facilities, public venues, and critical facilities in a composite extreme tsunami-inundation zone associated with two great Aleutian moment magnitude (Mw) 9.3 and 9.6 earthquake scenarios. These earthquake scenarios are considered to provide the maximum tsunami scenario for the Hawaiian Islands. According to 2010 U.S. Census Bureau data, the Hawai‘i extreme tsunami-inundation zone contains approximately 248,749 residents and 91,528 households (18 and 20 percent, respectively, of State totals). The residential population in tsunami-prone areas is racially diverse, with most residents identifying themselves as White (47 percent of the total exposed population), Asian (48 percent), or Native Hawaiian and Other Pacific Islander (29 percent), either alone or in combination with one or more other races (note that race categories do not sum to 100 percent because individuals were able to report multiple races in the 2010 U.S. Census). A total of 50,016 households are renter-occupied, making up 55 percent of total households in the extreme inundation zone. The extreme tsunami-inundation zone contains 18,693 businesses (37 percent of State totals) and 245,827 employees (42 percent of the State labor force). The employee population in the extreme tsunami-inundation zone is largely in the accommodation and food services and retail-trade sectors. Although occupancy values are not known for each facility, the extreme tsunami-inundation zone also contains numerous community-support businesses (for example, religious organizations and markets), dependent-care facilities (for example, child-day-care facilities and schools), public venues (for example, colleges and entertainment venues), and critical facilities (for example, fire stations and electric companies).
\nCommunity exposure to tsunamis in Hawai‘i varies considerably—some communities may experience great losses that reflect only a small part of their community and others may experience relatively small losses that devastate them. Among the 91 communities and 4 counties, Urban Honolulu has the highest number of people and businesses in the extreme tsunami-inundation zone, and Hanalei has the highest percentages of its people and businesses in this zone. Urban Honolulu has the highest combination of the number and percentage of people, businesses, and facilities in the hazard zone. This report will further the dialogue on societal risk to tsunami hazards in Hawai‘i and help identify future preparedness, mitigation, response, and recovery planning needs within coastal communities and economic sectors of the State of Hawaii.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165053","collaboration":"Prepared in cooperation with the Hawai‘i Emergency Management Agency","usgsCitation":"Jones, J.L., Jamieson, M.R., and Wood, N.J., 2016, Community exposure to tsunami hazards in Hawai‘i: U.S. Geological Survey Scientific Investigation Report 2016–5053, 32 p., https://dx.doi.org/10.3133/sir20165053.","productDescription":"v, 32 p.","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-069839","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":323921,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5053/coverthb.jpg"},{"id":323922,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5053/sir20165053.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 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\"}}]}","contact":"Contact Information, Western Geographic Science Center
U.S. Geological Survey
345 Middlefield Road, MS 531
Menlo Park, CA 94025
http://geography.wr.usgs.gov/
Between 1999 and 2014, 126 archival time–depth recorders (TDRs) were used to examine the foraging behavior of southern sea otters (Enhydra lutris nereis) off the coast of California, in both resource-abundant (recently occupied, low sea otter density) and resource-limited (long-occupied, high sea otter density) locations. Following predictions of foraging theory, sea otters generally behaved as energy rate maximizers. Males and females without pups employed similar foraging strategies to optimize rates of energy intake in resource-limited habitats, with some exceptions. Both groups increased overall foraging effort and made deeper, longer and more energetically costly dives as resources became limited, but males were more likely than females without pups to utilize extreme dive profiles. In contrast, females caring for young pups (≤10 weeks) prioritized parental care over energy optimization. The relative importance of parental care versus energy optimization for adult females with pups appeared to reflect developmental changes as dependent young matured. Indeed, contrary to females during the initial stages of lactation, females with large pups approaching weaning once again prioritized optimizing energy intake. The increasing prioritization of energy optimization over the course of lactation was possible due to the physiological development of pups and likely driven by the energetic deficit incurred by females early in lactation. Our results suggest that regardless of resource availability, females at the end of lactation approach a species-specific ceiling for percent time foraging and that reproductive females in the central portion of the current southern sea otter range are disproportionately affected by resource limitation.
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Through overland runoff and tile drainage, zoonotic pathogens can contaminate surface water and streambed sediment and could affect both wildlife and human health. This study examined the environmental occurrence of gene markers for livestock-related bacterial, protozoan, and viral pathogens and antibiotic resistance in surface waters within the South Fork Iowa River basin before and after periods of swine manure application on agricultural land. Increased concentrations of indicator bacteria after manure application exceeding Iowa's state bacteria water quality standards suggest that swine manure contributes to diminished water quality and may pose a risk to human health. Additionally, the occurrence of HEV and numerous bacterial pathogen genes for Escherichia coli, Enterococcus spp., Salmonella sp., and Staphylococcus aureus in both manure samples and in corresponding surface water following periods of manure application suggests a potential role for swine in the spreading of zoonotic pathogens to the surrounding environment. During this study, several zoonotic pathogens were detected including Shiga-toxin producing E. coli, Campylobacter jejuni, pathogenic enterococci, and S. aureus; all of which can pose mild to serious health risks to swine, humans, and other wildlife. This research provides the foundational understanding required for future assessment of the risk to environmental health from livestock-related zoonotic pathogen exposures in this region. This information could also be important for maintaining swine herd biosecurity and protecting the health of wildlife near swine facilities.
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Fecal coliforms (FC) are commonly employed as fecal indicator bacteria, but do not distinguish between human and non-human sources of fecal contamination. Human Bacteroides and humanLachnospiraceae, two genetic markers for human-associated indicator bacteria, were used to identify sewage signals in two urban rivers and the estuary that drains to Lake Michigan. Grab samples were collected from the rivers throughout 2012 and 2013 and hourly samples were collected in the estuary across the hydrograph during summer 2013. Human Bacteroides and human Lachnospiraceae were highly correlated with each other in river samples (Pearson’s r = 0.86), with average concentrations at most sites elevated during wet weather. These human indicators were found during baseflow, indicating that sewage contamination is chronic in these waterways. FC are used for determining total maximum daily loads (TMDLs) in management plans; however, FC concentrations alone failed to prioritize river reaches with potential health risks. While 84% of samples with >1000 CFU/100 ml FC had sewage contamination, 52% of samples with moderate (200–1000 CFU/100 ml) and 46% of samples with low (<200 CFU/100 ml) FC levels also had evidence of human sewage. Load calculations in the in the Milwaukee estuary revealed storm-driven sewage contamination varied greatly among events and was highest during an event with a short duration of intense rain. This work demonstrates urban areas have unrecognized sewage inputs that may not be adequately prioritized for remediation by the TMDL process. Further analysis using these approaches could determine relationships between land use, storm characteristics, and other factors that drive sewage contamination in urban waterways.
","language":"English","publisher":"Elsevier","publisherLocation":"Amsterdam","doi":"10.1016/j.watres.2016.05.056","usgsCitation":"Olds, H., Dila, D., Bootsma, M., Corsi, S., and McLellan, S., 2016, Quantification of human-associated fecal indicators reveal sewage from urban watersheds as a source of pollution to Lake Michigan: Water Research, v. 100, no. 1, p. 556-567, https://doi.org/10.1016/j.watres.2016.05.056.","productDescription":"12 p.","startPage":"556","endPage":"567","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073855","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"links":[{"id":323742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Kinnickinnic River, Menomonee River, Milwaukee River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.97147750854492,\n 42.99297119949901\n ],\n [\n -87.97147750854492,\n 43.05496631251752\n ],\n [\n -87.85989761352539,\n 43.05496631251752\n ],\n [\n -87.85989761352539,\n 42.99297119949901\n ],\n [\n -87.97147750854492,\n 42.99297119949901\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"100","issue":"1","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5763bf9ce4b07657d19b5bd4","contributors":{"authors":[{"text":"Olds, Hayley T. 0000-0002-6701-6459 htemplar@usgs.gov","orcid":"https://orcid.org/0000-0002-6701-6459","contributorId":5002,"corporation":false,"usgs":true,"family":"Olds","given":"Hayley T.","email":"htemplar@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":false,"id":639198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dila, Deborah K.","contributorId":172000,"corporation":false,"usgs":false,"family":"Dila","given":"Deborah K.","affiliations":[{"id":26971,"text":"School of Freshwater Sciences, UW-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":639199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bootsma, Melinda J.","contributorId":172001,"corporation":false,"usgs":false,"family":"Bootsma","given":"Melinda J.","affiliations":[{"id":26971,"text":"School of Freshwater Sciences, UW-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":639200,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Corsi, Steven R. 0000-0003-0583-5536 srcorsi@usgs.gov","orcid":"https://orcid.org/0000-0003-0583-5536","contributorId":172002,"corporation":false,"usgs":true,"family":"Corsi","given":"Steven R.","email":"srcorsi@usgs.gov","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":639201,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLellan, Sandra L.","contributorId":172003,"corporation":false,"usgs":false,"family":"McLellan","given":"Sandra L.","affiliations":[{"id":26971,"text":"School of Freshwater Sciences, UW-Milwaukee","active":true,"usgs":false}],"preferred":false,"id":639202,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70173921,"text":"70173921 - 2016 - Taxonomy of the order Mononegavirales: update 2016","interactions":[],"lastModifiedDate":"2016-07-15T15:00:30","indexId":"70173921","displayToPublicDate":"2016-06-16T13:15:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":892,"text":"Archives of Virology","active":true,"publicationSubtype":{"id":10}},"title":"Taxonomy of the order Mononegavirales: update 2016","docAbstract":"In 2016, the order Mononegavirales was emended through the addition of two new families (Mymonaviridae and Sunviridae), the elevation of the paramyxoviral subfamily Pneumovirinae to family status (Pneumoviridae), the addition of five free-floating genera (Anphevirus, Arlivirus, Chengtivirus, Crustavirus, and Wastrivirus), and several other changes at the genus and species levels. This article presents the updated taxonomy of the order Mononegavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV).
","language":"English","publisher":"Springer Vienna","doi":"10.1007/s00705-016-2880-1","usgsCitation":"Afonso, C., Kurath, G., and Authors, 8.A., 2016, Taxonomy of the order Mononegavirales: update 2016: Archives of Virology, v. 161, no. 8, p. 2351-2360, https://doi.org/10.1007/s00705-016-2880-1.","productDescription":"10 p.","startPage":"2351","endPage":"2360","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075674","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":470878,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s00705-016-2880-1","text":"External Repository"},{"id":323740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":323739,"type":{"id":15,"text":"Index Page"},"url":"https://link.springer.com/article/10.1007/s00705-016-2880-1"}],"volume":"161","issue":"8","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-05-23","publicationStatus":"PW","scienceBaseUri":"5763bf9ce4b07657d19b5bd7","contributors":{"authors":[{"text":"Afonso, C.L.","contributorId":42066,"corporation":false,"usgs":true,"family":"Afonso","given":"C.L.","affiliations":[],"preferred":false,"id":639467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kurath, Gael 0000-0003-3294-560X gkurath@usgs.gov","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":2629,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","email":"gkurath@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":639343,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Authors, 82 Additional","contributorId":171949,"corporation":false,"usgs":false,"family":"Authors","given":"82","email":"","middleInitial":"Additional","affiliations":[{"id":26970,"text":"Various","active":true,"usgs":false}],"preferred":false,"id":639344,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70171109,"text":"sir20165067 - 2016 - Geologic and geophysical models for Osage County, Oklahoma, with implications for groundwater resources","interactions":[],"lastModifiedDate":"2025-05-14T18:51:50.000213","indexId":"sir20165067","displayToPublicDate":"2016-06-16T10:00:00","publicationYear":"2016","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":"2016-5067","title":"Geologic and geophysical models for Osage County, Oklahoma, with implications for groundwater resources","docAbstract":"This report summarizes a three-dimensional (3-D) geologic model that was constructed to provide a framework to investigate groundwater resources of the Osage Nation in northeastern Oklahoma. This report also presents an analysis of an airborne electromagnetic (AEM) survey that assessed the spatial variation of electrical resistivity to depths as great as 300 meters in the subsurface. The report and model provide support for a countywide assessment of groundwater resources, emphasizing the Upper Pennsylvanian rock units in the shallow subsurface of central and eastern Osage County having electrical resistivity properties that may indicate aquifers.
\nSurface outcrops and subsurface stratigraphic picks on wire-line geophysical logs of Upper Pennsylvanian–Lower Permian sedimentary rock were used to construct a 3-D model of the geologic subsurface as an aid for evaluating groundwater resources in Osage County. Quaternary alluvium and terraces along major streams and the Arkansas River are included in the geologic framework model. Data from the AEM survey were subjected to quality-control procedures, truncated at depth of investigation (DOI), and then used to build a 3-D electrical resistivity model making use of secondary and tertiary interpolation profiles between primary data profiles. The AEM data highlight westward-inclined resistivity gradients that parallel the shallow dip of bedrock strata; bodies have resistivity >30 ohm-meters, and extend as much as 10 kilometers (km) down the dip of host geologic units. Volume analysis and internal imaging of an integrated 3-D geology and electrical resistivity model give a proxy for likely aquifer units with large relative volumes of high resistivity: Quaternary alluvium, Elgin Sandstone Lentil in the upper part of the Vamoosa Group, Tallant Formation, and parts of a combined Wann-Iola-Chanute Formation. Less voluminous, high-resistivity bodies correspond to intervals in the lower part of the Vamoosa Group in the east-central part of the county and probable limestone intervals in the upper part of the Vanoss Group in the northwest part of the county. Northwestern and eastern troughs of potable water previously defined for central Osage County generally correspond to down-dip projections of high-resistivity bodies associated with the Elgin Sandstone Lentil of the Vamoosa Group and Tallant Formation, respectively.
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Center Director, USGS Geosciences and Environmental Change Science Center
Box 25046, Mail Stop 980
Denver, CO 80225
Urban stormwater runoff is a critical source of degradation to stream ecosystems globally. Despite broad appreciation by stream ecologists of negative effects of stormwater runoff, stormwater management objectives still typically center on flood and pollution mitigation without an explicit focus on altered hydrology. Resulting management approaches are unlikely to protect the ecological structure and function of streams adequately. We present critical elements of stormwater management necessary for protecting stream ecosystems through 5 principles intended to be broadly applicable to all urban landscapes that drain to a receiving stream: 1) the ecosystems to be protected and a target ecological state should be explicitly identified; 2) the postdevelopment balance of evapotranspiration, stream flow, and infiltration should mimic the predevelopment balance, which typically requires keeping significant runoff volume from reaching the stream; 3) stormwater control measures (SCMs) should deliver flow regimes that mimic the predevelopment regime in quality and quantity; 4) SCMs should have capacity to store rain events for all storms that would not have produced widespread surface runoff in a predevelopment state, thereby avoiding increased frequency of disturbance to biota; and 5) SCMs should be applied to all impervious surfaces in the catchment of the target stream. These principles present a range of technical and social challenges. Existing infrastructural, institutional, or governance contexts often prevent application of the principles to the degree necessary to achieve effective protection or restoration, but significant potential exists for multiple co-benefits from SCM technologies (e.g., water supply and climate-change adaptation) that may remove barriers to implementation. Our set of ideal principles for stream protection is intended as a guide for innovators who seek to develop new approaches to stormwater management rather than accept seemingly insurmountable historical constraints, which guarantee future, ongoing degradation.
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As such, they provide very useful information to private industry regarding potential and non-potential prospect areas, provide the U.S. Geological Survey with useful information on mineral occurrences that are used in national assessments for particular mineral deposits, and provide other U.S. Federal agencies (Bureau of Indian Affairs, Bureau of Land Management, and Environmental Protection Agency) information relevant to land management, permitting, and leasing.
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U.S. Geological Survey
904 W. Riverside Ave., Room 202
Spokane, WA 99201
FAX 509/368-3199
http://geomaps.wr.usgs.gov/gmeg/
Flooding occurred in the central and southeastern United States during December 2015 and January 2016. The flooding was the result of more than 20 inches of rain falling in a 19 day period from December 12 to December 31, 2015. U.S. Geological Survey streamgages recorded 23 peaks of record during the subsequent flooding, with a total of 172 streamgages recording peaks that ranked in the top 5 all time for the period of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161092","usgsCitation":"Holmes, R.R., Jr., Watson, K.M., and Harris, T.E., 2016, Preliminary peak stage and streamflow data at selected U.S. Geological Survey streamgages for flooding in the central and southeastern United States during December 2015 and January 2016: U.S. Geological Survey Open-File Report 2016–1092, 27 p., https://dx.doi.org/10.3133/ofr20161092.","productDescription":"iv, 28 p.","startPage":"1","endPage":"28","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-074393","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":323762,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1092/ofr20161092.pdf","text":"Report","size":"38.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 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States\"}}]}","contact":"Chief, Office of Surface Water
U.S. Geological Survey
415 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
Or visit the Office of Surface Water Web site at:
http://water.usgs.gov/osw/
Ulaanbaatar, the capital city of Mongolia (fig. 1), is dependent on groundwater for its municipal and industrial water supply. The population of Mongolia is about 3 million people, with about one-half the population residing in or near Ulaanbaatar (World Population Review, 2016). Groundwater is drawn from a network of shallow wells in an alluvial aquifer along the Tuul River. Evidence indicates that current water use may not be sustainable from existing water sources, especially when factoring the projected water demand from a rapidly growing urban population (Ministry of Environment and Green Development, 2013). In response, the Government of Mongolia Ministry of Environment, Green Development, and Tourism (MEGDT) and the Freshwater Institute, Mongolia, requested technical assistance on groundwater modeling through the U.S. Army Corps of Engineers (USACE) to the U.S. Geological Survey (USGS). Scientists from the USGS and USACE provided two workshops in 2015 to Mongolian hydrology experts on basic principles of groundwater modeling using the USGS groundwater modeling program MODFLOW-2005 (Harbaugh, 2005). The purpose of the workshops was to bring together representatives from the Government of Mongolia, local universities, technical experts, and other key stakeholders to build in-country capacity in hydrogeology and groundwater modeling.
A preliminary steady-state groundwater-flow model was developed as part of the workshops to demonstrate groundwater modeling techniques to simulate groundwater conditions in alluvial deposits along the Tuul River in the vicinity of Ulaanbaatar. ModelMuse (Winston, 2009) was used as the graphical user interface for MODFLOW for training purposes during the workshops. Basic and advanced groundwater modeling concepts included in the workshops were groundwater principles; estimating hydraulic properties; developing model grids, data sets, and MODFLOW input files; and viewing and evaluating MODFLOW output files. A key to success was developing in-country technical capacity and partnerships with the Mongolian University of Science and Technology; Freshwater Institute, Mongolia, a non-profit organization; United Nations Educational, Scientific and Cultural Organization (UNESCO); the Government of Mongolia; and the USACE.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161096","collaboration":"Prepared in cooperation with U.S. Army Corps of Engineers; U.S. Pacific Command; United Nations Educational, Scientific and Cultural Organization (UNESCO) and International Center for Integrated Water Resources Management under the auspices of UNESCO; Government of Mongolia Ministry of Environment, Green Development, and Tourism; and Freshwater Institute, Mongolia","usgsCitation":"Valder, J.F., Carter, J.M., Anderson, M.T., Davis, K.W., Haynes M.A., and Dechinlhundev, Dorjsuren, 2016, Building groundwater modeling capacity in Mongolia: U.S. Geological Survey Open-File Report 2016–1096, 1 sheet, https://dx.doi.org/10.3133/ofr20161096.","productDescription":"Sheet: 60.00 x 36.00 inches","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-075136","costCenters":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":323764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1096/ofr20161096.pdf","text":"Report","size":"8.91 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016–1096"},{"id":323763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1096/coverthb.jpg"}],"country":"Mongolia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[87.75126,49.2972],[88.80557,49.47052],[90.71367,50.33181],[92.23471,50.80217],[93.10422,50.49529],[94.14757,50.48054],[94.81595,50.01343],[95.81403,49.97747],[97.25973,49.72606],[98.23176,50.4224],[97.82574,51.011],[98.86149,52.04737],[99.98173,51.63401],[100.88948,51.51686],[102.06522,51.25992],[102.25591,50.51056],[103.67655,50.08997],[104.62155,50.27533],[105.88659,50.40602],[106.8888,50.2743],[107.86818,49.79371],[108.47517,49.28255],[109.40245,49.29296],[110.66201,49.13013],[111.58123,49.37797],[112.89774,49.54357],[114.36246,50.2483],[114.96211,50.14025],[115.4857,49.80518],[116.6788,49.88853],[116.1918,49.1346],[115.48528,48.13538],[115.74284,47.72654],[116.30895,47.85341],[117.29551,47.69771],[118.06414,48.06673],[118.86657,47.74706],[119.77282,47.04806],[119.66327,46.69268],[118.87433,46.80541],[117.4217,46.67273],[116.71787,46.3882],[115.9851,45.72724],[114.46033,45.33982],[113.46391,44.80889],[112.43606,45.01165],[111.87331,45.10208],[111.34838,44.45744],[111.66774,44.07318],[111.82959,43.74312],[111.12968,43.40683],[110.4121,42.87123],[109.2436,42.51945],[107.74477,42.48152],[106.12932,42.13433],[104.96499,41.59741],[104.52228,41.90835],[103.31228,41.90747],[101.83304,42.51487],[100.84587,42.6638],[99.51582,42.52469],[97.45176,42.74889],[96.3494,42.72564],[95.76245,43.31945],[95.30688,44.24133],[94.68893,44.35233],[93.48073,44.97547],[92.13389,45.11508],[90.94554,45.28607],[90.58577,45.71972],[90.97081,46.88815],[90.28083,47.69355],[88.8543,48.06908],[88.01383,48.59946],[87.75126,49.2972]]]},\"properties\":{\"name\":\"Mongolia\"}}]}","contact":"Director, South Dakota Water Science Center
U.S. Geological Survey
1608 Mountain View Road
Rapid City, South Dakota 57702
Or visit the South Dakota Water Science Center Web site at:
http://sd.water.usgs.gov/
Invasive species introductions in Arctic and Subarctic ecosystems are growing as climate change manifests and human activity increases in high latitudes. The aquatic plants of the genus Elodea are potential invaders to Arctic and Subarctic ecosystems circumpolar and at least one species is already established in Alaska, USA. To illustrate the problems of preventing, eradicating, containing, and mitigating aquatic, invasive plants in Arctic and Subarctic ecosystems, we review the invasion dynamics of Elodea and provide recommendations for research and management efforts in Alaska. Foremost, we conclude the remoteness of Arctic and Subarctic systems such as Alaska is no longer a protective attribute against invasions, as transportation pathways now reach throughout these regions. Rather, high costs of operating in remote Arctic and Subarctic systems hinders detection of infestations and limits eradication or mitigation, emphasizing management priorities of prevention and containment of aquatic plant invaders in Alaska and other Arctic and Subarctic systems.
","language":"English","publisher":"Springer","doi":"10.1007/s10750-016-2767-x","usgsCitation":"Carey, M.P., Sethi, S.A., Larsen, S.J., and Rich, C.F., 2016, A primer on potential impacts, management priorities, and future directions for Elodea spp. in high latitude systems: learning from the Alaskan experience: Hydrobiologia, v. 777, no. 1, p. 1-19, https://doi.org/10.1007/s10750-016-2767-x.","productDescription":"19 p.","startPage":"1","endPage":"19","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063244","costCenters":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"links":[{"id":323718,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"777","issue":"1","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2016-04-26","publicationStatus":"PW","scienceBaseUri":"57626e1ce4b07657d199cd5c","contributors":{"authors":[{"text":"Carey, Michael P. 0000-0002-3327-8995 mcarey@usgs.gov","orcid":"https://orcid.org/0000-0002-3327-8995","contributorId":5397,"corporation":false,"usgs":true,"family":"Carey","given":"Michael","email":"mcarey@usgs.gov","middleInitial":"P.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":638837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sethi, Suresh A","contributorId":171843,"corporation":false,"usgs":false,"family":"Sethi","given":"Suresh","email":"","middleInitial":"A","affiliations":[{"id":26952,"text":"U.S. Fish and Wildlife Service, Anchorage, AK; Fisheries, Aquatic Science and Technology Lab, Alaska Pacific University, Anchorage, AK and U.S. Geological Survey New York Cooperative Fish and Wildlife Research Unit, Cornell University, Ithaca, NY","active":true,"usgs":false}],"preferred":false,"id":638838,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larsen, Sabrina J","contributorId":171844,"corporation":false,"usgs":false,"family":"Larsen","given":"Sabrina","email":"","middleInitial":"J","affiliations":[{"id":26953,"text":"Fisheries, Aquatic Science and Technology Lab, Alaska Pacific University, Anchorage, AK and U.S. Forest Service, Santa Fe National Forest, Santa Fe, NM","active":true,"usgs":false}],"preferred":false,"id":638839,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rich, Cecil F","contributorId":171845,"corporation":false,"usgs":false,"family":"Rich","given":"Cecil","email":"","middleInitial":"F","affiliations":[{"id":26954,"text":"Fisheries and Ecological Services Division, U.S. Fish and Wildlife Service, Anchorage, AK and U.S. Forest Service, Santa Fe National Forest, Santa Fe, NM","active":true,"usgs":false}],"preferred":false,"id":638840,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70171034,"text":"ds1000 - 2016 - Benthos and plankton community data for selected rivers and harbors along the western Lake Michigan shoreline, 2014","interactions":[],"lastModifiedDate":"2016-06-23T16:22:02","indexId":"ds1000","displayToPublicDate":"2016-06-15T16:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1000","title":"Benthos and plankton community data for selected rivers and harbors along the western Lake Michigan shoreline, 2014","docAbstract":"Benthos (benthic invertebrates) and plankton (zooplankton and phytoplankton) communities were sampled in 2014 at 10 Wisconsin rivers and harbors, including 4 sites in Great Lakes Areas of Concern and 6 less degraded comparison sites with similar physical and chemical characteristics, including climate, latitude, geology, and land use. Previous U.S. Geological Survey sampling was completed in 2012, but because of ongoing sediment remediation at three of the Areas of Concern (AOCs) and unusually hot and dry conditions in many areas during 2012, additional sampling was added in 2014. Comparable sampling methods were used in 2012 and 2014. Benthos were collected by using Hester-Dendy artificial substrate samplers and composite Ponar grab samples of bottom sediment; zooplankton were collected by using tows from depth to the surface with a 63-micrometer mesh plankton net; phytoplankton were collected by using whole water samples composited from set depth intervals. This report describes the study areas and field sampling methods for 2014, and it presents data on taxonomic identification and abundance of benthos and plankton that can serve as a basis for evaluation of related Beneficial Use Impairments (BUIs) at the AOCs. Physical and chemical data were sampled concurrently (specific conductance, temperature, pH, dissolved oxygen, chlorophyll a, total and volatile suspended solids in water samples; particle size and volatile-on-ignition of sediment in benthic grab samples). The results of field quality assurance-quality control are also presented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1000","collaboration":"Prepared in cooperation with the Wisconsin Department of Natural Resources and the U.S. Environmental Protection Agency-Great Lakes National Program Office","usgsCitation":"Scudder Eikenberry, B.C., Burns, D.J., Templar, H.A., Bell, A.H., and Mapel, K.T., 2016, Benthos and plankton community data for selected rivers and harbors along the western Lake Michigan shoreline, 2014: U.S. Geological Survey Data Series 1000, 29 p. plus 8 appendixes, https://dx.doi.org/10.3133/ds1000.","productDescription":"Report: viii, 29 p.; 8 Appendixes; Metadata","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-072354","costCenters":[{"id":677,"text":"Wisconsin Water Science 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U.S. Geological Survey
8505 Research Way
Middleton, WI 53562-3586
http://wi.water.usgs.gov/