Total nitrogen loads at 14 water-quality monitoring stations were calculated by using discrete measurements of total nitrogen and continuous streamflow data for the period 2005–13 (water years 2006–13). Total nitrogen loads were calculated by using the LOADEST computer program.
Overall, for water years 2006–13, streamflow in Connecticut was generally above normal. Total nitrogen yields ranged from 1,160 to 23,330 pounds per square mile per year. Total nitrogen loads from the French River at North Grosvenordale and the Still River at Brookfield Center, Connecticut, declined noticeably during the study period. An analysis of the bias in estimated loads indicated unbiased results at all but one station, indicating generally good fit for the LOADEST models.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161007","collaboration":"Prepared in cooperation with the Connecticut Department of Energy and Environmental Protection","usgsCitation":"Mullaney, J.R., 2016, Nitrogen loads from selected rivers in the Long Island Sound Basin, 2005–13, Connecticut and Massachusetts: U.S. Geological Survey Open-File Report 2016–1007, 14 p., https://dx.doi.org/10.3133/ofr20161007.","productDescription":"Report: iv, 14 p.; Table 2","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070502","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":319223,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1007/coverthb.jpg"},{"id":319224,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1007/ofr20161007.pdf","size":"6.36 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1007"},{"id":319225,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2016/1007/ofr20161007_table2.xlsx","text":"Table 2. Total nitrogen load and yield and selected regression model and flux-bias diagnostics for each water-quality monitoring station, Long Island Sound Basin, water years 2006–13.","size":"22.7 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2016-1007"}],"country":"United States","state":"Connecticut, Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.4820556640625,\n 42.17561739661684\n ],\n [\n -71.806640625,\n 42.12674735753131\n ],\n [\n -71.8011474609375,\n 41.31907562295136\n ],\n [\n -72.3834228515625,\n 41.25716209782705\n ],\n [\n -72.93823242187499,\n 41.22824901518532\n ],\n [\n -73.65234375,\n 40.992337919312284\n ],\n [\n -73.740234375,\n 41.10005163093046\n ],\n [\n -73.4930419921875,\n 41.21998578493921\n ],\n [\n -73.56994628906249,\n 41.29431726315258\n ],\n [\n -73.4820556640625,\n 42.17561739661684\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
101 Pitkin Street
East Hartford, CT 06108
Or Visit our Web site at:
http://newengland.water.usgs.gov
With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models. The Standardized Precipitation Evapotranspiration Index (SPEI) is a drought index that has potential advantages in modeling ecological response variables, including a flexible computation of the index over different timescales. However, little development has been made in terms of the choice of timescale for SPEI. We developed a Bayesian modeling approach for estimating the timescale for SPEI and demonstrated its use in modeling wetland hydrologic dynamics in two different eras (i.e., historical [pre-1970] and contemporary [post-2003]). Our goal was to determine whether differences in climate between the two eras could explain changes in the amount of water in wetlands. Our results showed that wetland water surface areas tended to be larger in wetter conditions, but also changed less in response to climate fluctuations in the contemporary era. We also found that the average timescale parameter was greater in the historical period, compared with the contemporary period. We were not able to determine whether this shift in timescale was due to a change in the timing of wet–dry periods or whether it was due to changes in the way wetlands responded to climate. Our results suggest that perhaps some interaction between climate and hydrologic response may be at work, and further analysis is needed to determine which has a stronger influence. Despite this, we suggest that our modeling approach enabled us to estimate the relevant timescale for SPEI and make inferences from those estimates. Likewise, our approach provides a mechanism for using prior information with future data to assess whether these patterns may continue over time. We suggest that ecologists consider using temporally scalable climate indices in conjunction with Bayesian analysis for assessing the role of climate in ecological systems.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.2092","usgsCitation":"Post van der Burg, M., Anteau, M.J., McCauley, L.A., and Wiltermuth, M.T., 2016, A Bayesian approach for temporally scaling climate for modeling ecological systems: Ecology and Evolution, v. 6, no. 9, p. 2978-2987, https://doi.org/10.1002/ece3.2092.","productDescription":"10 p.","startPage":"2978","endPage":"2987","numberOfPages":"10","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-063141","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":471122,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.2092","text":"Publisher Index Page"},{"id":319621,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"6","issue":"9","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-28","publicationStatus":"PW","scienceBaseUri":"56fceaabe4b0a6037df29c96","contributors":{"authors":[{"text":"Post van der Burg, Max 0000-0002-3943-4194 maxpostvanderburg@usgs.gov","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":4947,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","email":"maxpostvanderburg@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anteau, Michael J. 0000-0002-5173-5870 manteau@usgs.gov","orcid":"https://orcid.org/0000-0002-5173-5870","contributorId":3427,"corporation":false,"usgs":true,"family":"Anteau","given":"Michael","email":"manteau@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":625618,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McCauley, Lisa A. lmccauley@usgs.gov","contributorId":5048,"corporation":false,"usgs":true,"family":"McCauley","given":"Lisa","email":"lmccauley@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":625617,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wiltermuth, Mark T. 0000-0002-8871-2816 mwiltermuth@usgs.gov","orcid":"https://orcid.org/0000-0002-8871-2816","contributorId":708,"corporation":false,"usgs":true,"family":"Wiltermuth","given":"Mark","email":"mwiltermuth@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":625619,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168400,"text":"sir20165019 - 2016 - Social-value maps for Arapaho, Roosevelt, Medicine Bow, Routt, and White River National Forests, Colorado and Wyoming","interactions":[],"lastModifiedDate":"2016-03-28T08:40:41","indexId":"sir20165019","displayToPublicDate":"2016-03-25T17:00:00","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":"2016-5019","title":"Social-value maps for Arapaho, Roosevelt, Medicine Bow, Routt, and White River National Forests, Colorado and Wyoming","docAbstract":"The continued pressures of population growth on the life-sustaining, economic, and cultural ecosystem services provided by our national forests, particularly those located near rapidly growing urban areas, present ongoing challenges to forest managers. Achieving an effective assessment of these ecosystem services includes a proper accounting of the ecological, economic, and social values attributable to them. However, assessments of ecosystem goods and services notably lack information describing the spatial distribution and relative intensity of social values—the perceived, nonmarket values derived particularly from cultural ecosystem services. A geographic information system (GIS) tool developed to fill this need, Social Values for Ecosystem Services (SolVES; http://solves.cr.usgs.gov), now provides the capability to generate social-value maps at a range of spatial scales. This report presents some of the methods behind SolVES, procedures needed to apply the tool, the first formal map products resulting from its application at a regional scale, and a discussion of the management implications associated with this type of information.
In this study, we use SolVES to identify the location and relative intensity of social values as derived from survey responses gathered from residents living in counties adjacent to Arapaho, Roosevelt, Medicine Bow, Routt, and White River National Forests. The results, presented as a series of social-value maps, represent the first publicly available spatial data on social-value intensity for the southern Rocky Mountain region. Our analysis identified high-value areas for social values including aesthetic, biodiversity, and life sustaining within wilderness areas. Other values, like recreation, show high-value areas both within wilderness and throughout the general forest areas, which can be attributed to people using the forests for a diverse set of recreational activities. The economic social-value type was lower within wilderness areas, which was an expected outcome because of the restrictions inside wilderness areas that preclude resource extraction, development, and motorized or mechanized recreation.
Providing spatially explicit social-value information collected from residents in counties adjacent to these national forests can assist in facilitating the U.S. Department of Agriculture Forest Service’s Multiple-Use Sustained-Yield Act of 1960 (16 U.S.C. 528) note and the 2012 Forest Service Planning Rule (36 CFR Part 219), which encourage public participation in planning and management. By making these maps available to the public, we hope to encourage exploration of potential uses of these data for resource management and planning.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165019","usgsCitation":"Ancona, Z.H., Semmens, D.J., and Sherrouse, B.C., 2016, Social-value maps for Arapaho, Roosevelt, Medicine Bow, Routt, and White River National Forests, Colorado and Wyoming: U.S. Geological Survey Scientific Investigations Report 2016–5019, 31 p., https://dx.doi.org/10.3133/sir20165019.","productDescription":"Report: vi, 30 p.; Social Value Rasters; Metadata; Read Me; Spatial Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066270","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":318822,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/sir/2016/5019/","text":"Social Value Rasters","size":"881 kB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2016-5019 Social Value 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Box 25046, Mail Stop 980
Denver, CO 80225
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within the limit of California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data, acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
The Offshore of Santa Cruz map area is located in central California, on the Pacific Coast about 98 km south of San Francisco. The city of Santa Cruz (population, about 63,000), the largest incorporated city in the map area and the county seat of Santa Cruz County, lies on uplifted marine terraces between the shoreline and the northwest-trending Santa Cruz Mountains, part of California’s Coast Ranges. All of California’s State Waters in the map area is part of the Monterey Bay National Marine Sanctuary.
The map area is cut by an offshore section of the San Gregorio Fault Zone, and it lies about 20 kilometers southwest of the San Andreas Fault Zone. Regional folding and uplift along the coast has been attributed to a westward bend in the San Andreas Fault Zone and to right-lateral movement along the San Gregorio Fault Zone. Most of the coastal zone is characterized by low, rocky cliffs and sparse, small pocket beaches backed by low, terraced hills. Point Santa Cruz, which forms the north edge of Monterey Bay, provides protection for the beaches in the easternmost part of the map area by sheltering them from the predominantly northwesterly waves.
The shelf in the map area is underlain by variable amounts (0 to 25 m) of upper Quaternary shelf, estuarine, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene. The inner shelf is characterized by bedrock outcrops that have local thin sediment cover, the result of regional uplift, high wave energy, and limited sediment supply. The midshelf occupies part of an extensive, shore-parallel mud belt. The thickest sediment deposits, inferred to consist mainly of lowstand nearshore deposits, are found in the southeastern and northwestern parts of the map area.
Coastal sediment transport in the map area is characterized by northwest-to-southeast littoral transport of sediment that is derived mainly from ephemeral streams in the Santa Cruz Mountains and also from local coastal-bluff erosion. During the last approximately 300 years, as much as 18 million cubic yards (14 million cubic meters) of sand-sized sediment has been eroded from the area between Año Nuevo Island and Point Año Nuevo and transported south; this mass of eroded sand is now enriching beaches in the map area. Sediment transport is within the Santa Cruz littoral cell, which terminates in the submarine Monterey Canyon.
Benthic species observed in the Offshore of Santa Cruz map area are natives of the cold-temperate biogeographic zone that is called either the “Oregonian province” or the “northern California ecoregion.” This biogeographic province is maintained by the long-term stability of the southward-flowing California Current, the eastern limb of the North Pacific subtropical gyre that flows from southern British Columbia to Baja California. At its midpoint off central California, the California Current transports subarctic surface (0–500 m deep) waters southward, about 150 to 1,300 km from shore. Seasonal northwesterly winds that are, in part, responsible for the California Current, generate coastal upwelling. The south end of the Oregonian province is at Point Conception (about 300 km south of the map area), although its associated phylogeographic group of marine fauna may extend beyond to the area offshore of Los Angeles in southern California. The ocean off of central California has experienced a warming over the last 50 years that is driving an ecosystem shift away from the productive subarctic regime towards a depopulated subtropical environment.
Biological productivity resulting from coastal upwelling supports populations of Sooty Shearwater, Western Gull, Common Murre, Cassin’s Auklet, and many other less populous bird species. In addition, an observable recovery of Humpback and Blue Whales has occurred in the area; both species are dependent on coastal upwelling to provide nutrients. The large extent of exposed inner shelf bedrock supports large forests of “bull kelp,” which is well adapted for high-wave-energy environments. The kelp beds are the northernmost known habitat for the population of southern sea otters. Common fish species found in the kelp beds and rocky reefs include lingcod and various species of rockfish and greenling.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20161024","usgsCitation":"Cochrane, G.R., Dartnell, P., Johnson, S.Y., Erdey, M.D., Golden, N.E., Greene, H.G., Dieter, B.E., Hartwell, S.R., Ritchie, A.C., Finlayson, D.P., Endris, C.A., Watt, J.T., Davenport, C.W., Sliter, R.W., Maier, K.L., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series — Offshore of Santa Cruz, California: U.S. Geological Survey Open-File Report 2016–1024, pamphlet 40 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20161024.","productDescription":"Pamphlet: iv, 40 p.; 10 Sheets: 60.0 x 36.0 inches or smaller; Data Catalog; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-058743","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438627,"rank":21,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TM785G","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Santa Cruz, California"},{"id":318983,"rank":19,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20161025","text":"Open-File Report 2016–1025","linkHelpText":"California State Waters Map Series—Offshore of Aptos, California, by Guy R. 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Each GIS data file is listed with a brief description, a small image, and links to the metadata files and the downloadable data files."},{"id":318971,"rank":9,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet9.pdf","text":"Sheet 9","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 9 PDF","linkHelpText":"Local (Offshore of Santa Cruz Map Area) and Regional (Offshore from Pigeon Point to Southern Monterey Bay) Shallow-Subsurface Geology and Structure, California By Samuel Y. Johnson, Stephen R. Hartwell, Janet T. Watt, Ray W. Sliter, and Katherine L. Maier"},{"id":318969,"rank":7,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet7.pdf","text":"Sheet 7","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 7 PDF","linkHelpText":"Potential Marine Benthic Habitats, Offshore of Santa Cruz Map Area, California By Bryan E. Dieter, Charles A. Endris, H. Gary Greene, and Mercedes D. Erdey"},{"id":318968,"rank":6,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet6.pdf","text":"Sheet 6","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 6 PDF","linkHelpText":"Ground-Truth Studies, Offshore of Santa Cruz Map Area, California By Nadine E. Golden, Guy R. Cochrane, and Lisa M. Krigsman"},{"id":318967,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet5.pdf","text":"Sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 5 PDF","linkHelpText":"Seafloor Character, Offshore of Santa Cruz Map Area, California By Mercedes D. Erdey and Guy R. Cochrane"},{"id":318966,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet4.pdf","text":"Sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 4 PDF","linkHelpText":"Data Integration and Visualization, Offshore of Santa Cruz Map Area, California By Peter Dartnell"},{"id":318965,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 3 PDF","linkHelpText":"Acoustic Backscatter, Offshore of Santa Cruz Map Area, California By Peter Dartnell, Andrew C. Ritchie, and David P. Finlayson"},{"id":318975,"rank":11,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Pamphlet"},{"id":318977,"rank":13,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_metadata.html"},{"id":318970,"rank":8,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet8.pdf","text":"Sheet 8","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 8 PDF","linkHelpText":"Seismic-Reflection Profiles, Offshore of Santa Cruz Map Area, California by Samuel Y. Johnson, Stephen R. Hartwell, and Ray W. Sliter"},{"id":399098,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_104092.htm"},{"id":318976,"rank":12,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1024/coverthb.jpg"},{"id":318964,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 2 PDF","linkHelpText":"Shaded-Relief Bathymetry, Offshore of Santa Cruz Map Area, California By Peter Dartnell, Andrew C. Ritchie, and David P. Finlayson"},{"id":318963,"rank":1,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1024/ofr20161024_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1024 Sheet 1 PDF","linkHelpText":"Colored Shaded-Relief Bathymetry, Offshore of Santa Cruz Map Area, California By Peter Dartnell, Andrew C. Ritchie, and David P. 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Cochrane and others."}],"country":"United States","state":"California","city":"Santa Cruz","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.1997,\n 36.8425\n ],\n [\n -122.0333,\n 36.8425\n ],\n [\n -122.0333,\n 37.0033\n ],\n [\n -122.1997,\n 37.0033\n ],\n [\n -122.1997,\n 36.8425\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar bathymetric data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow subsurface geology.
\nThe Offshore of Aptos map area is located on the Pacific Coast, on the north side of Monterey Bay, about 105 km southeast of San Francisco. The largest incorporated city in the map area, Capitola, and numerous unincorporated towns including Aptos, lie on uplifted marine terraces between the shoreline, and the northwest-trending Santa Cruz Mountains, part of California’s Coast Ranges. The map area includes the northernmost part of Santa Cruz Harbor, and Moss Landing Harbor is located about 10 km south of the map area. The offshore part of the map area is entirely within California’s State Waters and is also part of the Monterey Bay National Marine Sanctuary. In the southern part of the map area, the Soquel Canyon State Marine Conservation Area extends eastward from the limit of California’s State Waters across Soquel Canyon.
\nThe Offshore of Aptos map area is on the western margin of North American Plate—the only continental margin in the world delineated largely by transform faults. The San Andreas Fault Zone cuts through the Santa Cruz Mountains just 3.5 km northeast of the map area. The San Gregorio Fault Zone, another major plate-boundary structure, cuts through Monterey Canyon about 13 km southwest of the map area. Ongoing deformation associated with and between these major fault zones has uplifted the Santa Cruz Mountains and formed well-developed sets of marine terraces that characterize almost the entire coastal zone of the map area.
\nThe offshore part of the map area consists of relatively flat and shallow continental shelf which is underlain by variable amounts (0 to 30 m) of upper Quaternary shelf, estuarine, and fluvial sediments deposited as sea level fluctuated in the late Pleistocene. Along the southern edge of the map area, the shelf is incised by the head of Soquel Canyon, a northeast-trending tributary to the west-trending Monterey Canyon. During the last sea-level lowstand (the Last Glacial Maximum [LGM], about 21,000 years ago) Soquel Creek flowed through a paleochannel across the emergent shelf into the head of Soquel Canyon. This canyon was disconnected from its onshore watershed during the post-LGM sea-level rise of about 125 m; the abandoned paleochannel was subsequently filled with marine sediment. Coastal sediment in the Offshore of Aptos map area is supplied by coastal watersheds and bluff erosion. Sediment transport in this part of the Santa Cruz littoral cell is primarily from the northwest to the southeast and terminates in the submarine Monterey Canyon. Longshore drift is impeded by jetties at Santa Cruz Harbor, resulting in high beach erosion rates east of the harbor. Sediment dredged from the harbor mouth (estimated 300,000 yds3/yr) is currently being used to nourish beaches directly to the east. Farther downcoast, the rapidly eroding beach at Capitola was stabilized by construction of an about 75-m-long groin.
\nThis part of central California is exposed to large North Pacific swells from the northwest throughout the year. North Pacific swell heights range from 2 to 10 meters, with larger swells occurring from October to May. During El Niño-Southern Oscillation (ENSO) events, winter storms track farther south than they do in normal (non-ENSO) years, thereby impacting the map area more frequently and with waves of larger heights. Bedrock exposed in coastal cliffs is relatively erosion-resistant, and significant erosional events primarily are restricted to storm-wave activity that also erodes the overlying unconsolidated marine-terrace sediments.
\nThe Offshore of Aptos map area lies within the cold-temperate biogeographic zone that is called either the “Oregonian” province or the “northern California ecoregion.” This biogeographic province is maintained by the long-term stability of the southward-flowing California Current, the eastern limb of the North Pacific subtropical gyre that flows from southern British Columbia to Baja California. At its midpoint off central California, the California Current transports subarctic surface (0–500 m deep) waters southward, about 150 to 1,300 km from shore. Seasonal northwesterly winds that are, in part, responsible for the California Current, generate coastal upwelling. The south end of the Oregonian province is at Point Conception (about 310 km southeast of the map area), although its associated phylogeographic group of marine fauna may extend beyond to the area offshore of Los Angeles in southern California. The ocean off of central California has experienced a warming over the last 50 years that is driving an ecosystem shift away from the productive subarctic regime towards a depopulated subtropical environment.
\nSeafloor habitats in the Offshore of Aptos map area lie within the “Shelf” Megahabitat class and include patchy rocky habitats, gravel-rich scour depressions, minor bedrock habitat, and predominantly sandy inner shelf. Sandy shelf habitats grade offshore to mud-dominated habitats on the midshelf in deeper water. Biological productivity resulting from coastal upwelling supports populations of Sooty Shearwater, Western Gull, Common Murre, Cassin’s Auklet, and many other less populous bird species. In addition, an observable recovery of Humpback and Blue Whales has occurred in the area; both species are dependent on coastal upwelling to provide nutrients. California sea lions and Pacific harbor seals are abundant in the map area. Common bottlenose dolphins are often observed very close to shore and in the surf zone. The large extent of exposed inner shelf bedrock supports large forests of “bull kelp,” which is well adapted for high-wave-energy environments. The kelp beds are the northernmost known habitat for the population of southern sea otters. Common fish species found in the kelp beds and rocky reefs include blue rockfish, black rockfish, olive rockfish, kelp rockfish, gopher rockfish, black-and-yellow rockfish, painted greenling, kelp greenling, and lingcod.
\n","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20161025","usgsCitation":"Cochrane, G.R., Johnson, S.Y., Dartnell, P., Greene, H.G., Erdey, M.D., Dieter, B.E., Golden, N.E., Hartwell, S.R., Ritchie, A.C., Kvitek, R.G., Maier, K.L., Endris, C.A., Davenport, C.W., Watt, J.T., Sliter, R.W., Finlayson, D.P., and Krigsman, L.M. (G.R. Cochrane and S.A. Cochran, eds.), 2016, California State Waters Map Series — Offshore of Aptos, California: U.S. Geological Survey Open-File Report 2016–1025, pamphlet 43 p., 10 sheets, scale 1:24,000, https://dx.doi.org/10.3133/ofr20161025.","productDescription":"Pamphlet: iv, 43 p.; 10 Sheets: 55.0 x 36.0 inches or smaller; Data Catalog; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-059274","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438628,"rank":21,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K35RQB","text":"USGS data release","linkHelpText":"California State Waters Map Series Data Catalog--Offshore of Aptos, California"},{"id":318989,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/ofr20151191","text":"Open-File Report 2015–1191,","linkHelpText":"California State Waters Map Series—Offshore of Scott Creek, California, by Guy R. Cochrane and others."},{"id":399015,"rank":20,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_104093.htm"},{"id":318984,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2016/1025/coverthb.jpg"},{"id":318985,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_pamphlet.pdf","text":"Pamphlet","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Pamphlet PDF"},{"id":318986,"rank":3,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_metadata.html"},{"id":318987,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://dx.doi.org/10.5066/F7K35RQB","text":"Data Catalog","linkFileType":{"id":5,"text":"html"},"linkHelpText":"The GIS data layers for this map are accessible from “Data Catalog—Offshore of Aptos, California,” which is part of California State Waters Map Series Data Catalog. 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Cochrane and others."},{"id":318991,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/sim3306","text":"Scientific Investigations Map 3306,","linkHelpText":"California State Waters Map Series—Offshore of San Gregorio, California, by Guy R. Cochrane and others."},{"id":318992,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://dx.doi.org/10.3133/ofr20141214","text":"Open-File Report 2014–1214,","linkHelpText":"California State Waters Map Series—Offshore of Half Moon Bay, California, by Guy R. Cochrane and others."},{"id":318993,"rank":10,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet1.pdf","text":"Sheet 1","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 1 PDF","linkHelpText":"Colored Shaded-Relief Bathymetry, Offshore of Aptos Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":318994,"rank":11,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet2.pdf","text":"Sheet 2","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 2 PDF","linkHelpText":"Shaded-Relief Bathymetry, Offshore of Aptos Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":318995,"rank":12,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet3.pdf","text":"Sheet 3","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 3 PDF","linkHelpText":"Acoustic Backscatter, Offshore of Aptos Map Area, California By Peter Dartnell, Andrew C. Ritchie, David P. Finlayson, and Rikk G. Kvitek"},{"id":318996,"rank":13,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet4.pdf","text":"Sheet 4","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 4 PDF","linkHelpText":"Data Integration and Visualization, Offshore of Aptos Map Area, California By Peter Dartnell"},{"id":318997,"rank":14,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet5.pdf","text":"Sheet 5","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 5 PDF","linkHelpText":"Seafloor Character, Offshore of Aptos Map Area, California By Mercedes D. Erdey and Guy R. Cochrane"},{"id":318998,"rank":15,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet6.pdf","text":"Sheet 6","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 6 PDF","linkHelpText":"Ground-Truth Studies, Offshore of Aptos Map Area, California By Nadine E. Golden, Guy R. Cochrane, and Lisa M. Krigsman"},{"id":318999,"rank":16,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet7.pdf","text":"Sheet 7","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 7 PDF","linkHelpText":"Potential Marine Benthic Habitats, Offshore of Aptos Map Area, California By Bryan E. Dieter, Charles A. Endris, H. Gary Greene, and Mercedes D. Erdey"},{"id":319001,"rank":18,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2016/1025/ofr20161025_sheet9.pdf","text":"Sheet 9","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2016-1025 Sheet 9 PDF","linkHelpText":"Local (Offshore of Aptos Map Area) and Regional (Offshore from Pigeon Point to Southern Monterey Bay) Shallow-Subsurface Geology and Structure, California By Samuel Y. Johnson, Stephen R. Hartwell, Janet T. Watt, Ray W. Sliter, and Katherine L. 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Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
http://walrus.wr.usgs.gov/
The U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) was recently established to provide airborne lidar data coverage on a national scale. As part of a broader research effort of the USGS to develop an effective remote sensing-based methodology for the creation of an operational biomass Essential Climate Variable (Biomass ECV) data product, we evaluated the performance of airborne lidar data at various pulse densities against Landsat 8 satellite imagery in estimating above ground biomass for forests and woodlands in a study area in east-central Arizona, U.S. High point density airborne lidar data, were randomly sampled to produce five lidar datasets with reduced densities ranging from 0.5 to 8 point(s)/m2, corresponding to the point density range of 3DEP to provide national lidar coverage over time. Lidar-derived aboveground biomass estimate errors showed an overall decreasing trend as lidar point density increased from 0.5 to 8 points/m2. Landsat 8-based aboveground biomass estimates produced errors larger than the lowest lidar point density of 0.5 point/m2, and therefore Landsat 8 observations alone were ineffective relative to airborne lidar for generating a Biomass ECV product, at least for the forest and woodland vegetation types of the Southwestern U.S. While a national Biomass ECV product with optimal accuracy could potentially be achieved with 3DEP data at 8 points/m2, our results indicate that even lower density lidar data could be sufficient to provide a national Biomass ECV product with accuracies significantly higher than that from Landsat observations alone.
","language":"English","publisher":"Cloud Publications","usgsCitation":"Wu, Z., Dye, D.G., Stoker, J.M., Vogel, J.M., Velasco, M.G., and Middleton, B.R., 2016, Evaluating lidar point densities for effective estimation of aboveground biomass: International Journal of Advanced Remote Sensing and GIS, v. 5, no. 1, p. 1483-1499.","productDescription":"17 p.","startPage":"1483","endPage":"1499","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-068758","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":323961,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":319210,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://technical.cloud-journals.com/index.php/IJARSG/article/view/Tech-559"}],"volume":"5","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"576913b9e4b07657d19ff045","contributors":{"authors":[{"text":"Wu, Zhuoting 0000-0001-7393-1832 zwu@usgs.gov","orcid":"https://orcid.org/0000-0001-7393-1832","contributorId":4953,"corporation":false,"usgs":true,"family":"Wu","given":"Zhuoting","email":"zwu@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":498,"text":"Office of Land Remote Sensing (Geography)","active":true,"usgs":true}],"preferred":true,"id":623214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dye, Dennis G. 0000-0002-7100-272X ddye@usgs.gov","orcid":"https://orcid.org/0000-0002-7100-272X","contributorId":4233,"corporation":false,"usgs":true,"family":"Dye","given":"Dennis","email":"ddye@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":623216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":623215,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vogel, John M. 0000-0002-8226-1188 jvogel@usgs.gov","orcid":"https://orcid.org/0000-0002-8226-1188","contributorId":3167,"corporation":false,"usgs":true,"family":"Vogel","given":"John","email":"jvogel@usgs.gov","middleInitial":"M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":623218,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Velasco, Miguel G. 0000-0003-2559-7934 mvelasco@usgs.gov","orcid":"https://orcid.org/0000-0003-2559-7934","contributorId":2103,"corporation":false,"usgs":true,"family":"Velasco","given":"Miguel","email":"mvelasco@usgs.gov","middleInitial":"G.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":623219,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Middleton, Barry R. 0000-0001-8924-4121 bmiddleton@usgs.gov","orcid":"https://orcid.org/0000-0001-8924-4121","contributorId":3947,"corporation":false,"usgs":true,"family":"Middleton","given":"Barry","email":"bmiddleton@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":623217,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70169151,"text":"70169151 - 2016 - Nest survival is influenced by parental behaviour and heterospecifics in a mixed-species colony","interactions":[],"lastModifiedDate":"2018-03-06T15:58:07","indexId":"70169151","displayToPublicDate":"2016-03-23T11:45:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1961,"text":"Ibis","active":true,"publicationSubtype":{"id":10}},"title":"Nest survival is influenced by parental behaviour and heterospecifics in a mixed-species colony","docAbstract":"Studies of avian nest success often focus on examining influences of variation in environmental and seasonal factors. However, in-depth evaluations can also incorporate variation in individual incubation behaviour to further advance our understanding of avian reproductive ecology. We examined these relationships in colonially nesting Black-crowned Night-Herons Nycticorax nycticorax using intensive video-monitoring methods to quantify incubation behaviours. We modelled nest survival as a function of both extrinsic factors and incubation behaviours over a 3-year period (2010–12) on Alcatraz Island, USA. Model-averaged parameter estimates indicated that nest survival increased as a function of greater incubation constancy (% of time spent incubating eggs within a 24-h period), and average daily precipitation throughout the nesting stage. Common Ravens Corvus corax are the only known nest predator of Night-Herons on Alcatraz Island, as on many other coastal Pacific islands. We also investigated the effects of heterospecific nesting of California Gulls Larus californicus and Western Gulls Larus occidentalis in a mixed-species colony with Night-Herons, based on nesting proximity data collected over a 2-year period (2011–12). This second analysis indicated that, in addition to incubation behaviours, nesting heterospecifics are an important factor for explaining variation in Night-Heron nest survival. However, contrary to our original expectation, we found that Night-Herons experienced increased nest survival with increasing distance from gull colony boundaries. These results may apply to other areas with multiple colonial nesting species and similar predator communities and climatic patterns.
","language":"English","publisher":"Wiley","doi":"10.1111/ibi.12351","usgsCitation":"Brussee, B.E., Coates, P.S., Hothem, R.L., Howe, K., Casazza, M.L., and Eadie, J.M., 2016, Nest survival is influenced by parental behaviour and heterospecifics in a mixed-species colony: Ibis, v. 158, no. 2, p. 315-326, https://doi.org/10.1111/ibi.12351.","productDescription":"12 p.","startPage":"315","endPage":"326","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073084","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":319208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"158","issue":"2","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-10","publicationStatus":"PW","scienceBaseUri":"56f3b01ce4b0f59b85dfca20","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hothem, Roger L. roger_hothem@usgs.gov","contributorId":1721,"corporation":false,"usgs":true,"family":"Hothem","given":"Roger","email":"roger_hothem@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howe, Kristy khowe@usgs.gov","contributorId":167379,"corporation":false,"usgs":true,"family":"Howe","given":"Kristy","email":"khowe@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eadie, John M.","contributorId":65219,"corporation":false,"usgs":false,"family":"Eadie","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7082,"text":"University of California - Davis","active":true,"usgs":false}],"preferred":false,"id":623237,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70168900,"text":"fs20163012 - 2016 - Surface water-quality activities of the U.S. Geological Survey in New England","interactions":[],"lastModifiedDate":"2017-06-30T10:18:40","indexId":"fs20163012","displayToPublicDate":"2016-03-23T11:30:00","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":"2016-3012","title":"Surface water-quality activities of the U.S. Geological Survey in New England","docAbstract":"The U.S. Geological Survey (USGS) collaborates with a variety of Federal, State, local, and tribal partners on scientific projects to provide reliable and impartial water-quality data and interpretation to resource managers, planners, stakeholders, and the general public. The themes related to surface water quality include the following:
\n• Water quality monitoring networks
• Effects of best management practices and low impact development on water quality
• Load estimation techniques and total maximum daily load assistance
• Mercury studies
• Toxics and emerging contaminants
• Eutrophication and nuisance algal blooms
Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Or visit our Web site at
http://newengland.water.usgs.gov/
The U.S. Geological Survey (USGS) has actively pursued research in the effects of climate change on the hydrology of New England. Ongoing focus areas of climate change science activities of the USGS in New England include the following:
\n• Hydrologic climate-response data (initiating or expanding long-term hydrologic climate-response data collection networks to detect and monitor climate-related changes in hydrology)
• Relations between historical climatic and hydrologic variation (investigating historic relations between long-term climatic variation and hydrologic variation and connecting these relations to future conditions as forecasted by the Interagency Panel on Climate Change (Nakićenović and others, 2000)
• Hydrologic change on natural and human (incorporating climate change scenarios into assessments of how flows in streams or water levels in aquifers may change)
• Relations between climatic and hydrologic variation (characterizing impacts of climate change on floods and droughts, the long-term availability of water supplies for societal and natural uses in response to climate changes)
• Developing tools to forecast ecosystem change and water resources management (developing regionally consistent tools for forecasting ecosystem change and resource management)
This fact sheet presents recent climate change investigations of the USGS in New England using selected recent publications. These publications highlight the broad spectrum of expertise and commitment to understanding the relations of climate change and water resources in New England.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163011","usgsCitation":"U.S. Geological Survey, 2016, Climate change science activities of the U.S. Geological Survey in New England: U.S. Geological Survey Fact Sheet 2016–3011, 4 p., https://dx.doi.org/10.3133/fs20163011.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-070470","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":319052,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20163012","text":"Fact Sheet 2016-3012","size":"1 MB","description":"FS2016-3011"},{"id":319051,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/publication/fs20163010","text":"Fact Sheet 2016-3010","size":"871 KB","description":"FS2016-3011"},{"id":319050,"rank":4,"type":{"id":7,"text":"Companion 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
Or visit our Web site at
http://newengland.water.usgs.gov/
The U.S. Geological Survey (USGS) has a long history of working with the Federal Highway Administration (FHWA) and State transportation agencies to provide data and information to address various issues related to water resources and the Nation’s transportation infrastructure. These issues include the following:
\n• Streamgaging data networks
• Flow frequencies and flow statistics
• Water-quality investigations
• Bridge scour and sediment transport
• Hydrologic and hydraulic flood modeling
In New England, the USGS is conducting investigations to improve flood flow estimation techniques, to define channel characteristics at bankfull discharge, and to document storm tide as a result of major coastal storms. Current locally focused investigations include examination of flow frequency in rural, urban, and small watersheds; documentation of extreme inland floods along with flood-frequency updates; examination of the effects of roadway blasting on groundwater quality; and determinations of the effects of road salting on the quality of runoff and receiving waters.
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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
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Undiscovered potash resources in the Central Asia Salt Basin (CASB) of Turkmenistan, Uzbekistan, Tajikistan, and Afghanistan were assessed as part of a global mineral resource assessment led by the U.S. Geological Survey. The term “potash” refers to potassium-bearing, water-soluble salts derived from evaporite basins, where seawater dried up and precipitated various salt compounds; the word for the element “potassium” is derived from potash. Potash is produced worldwide at amounts exceeding 30 million metric tons per year, mostly for use in fertilizers. The term “potash” is used by industry to refer to potassium chloride, as well as potassium in sulfate, nitrate, and oxide forms. For the purposes of this assessment, the term “potash” refers to potassium ores and minerals and potash ore grades. Resource and production values are usually expressed by industry in terms of K2O (potassium oxide) or muriate of potash (KCl, potassium chloride).
\nThe CASB hosts significant discovered potash resources and originated in an inland sea during Late Jurassic time. Seawater flowed into the CASB, mostly from its extreme northwestern margin near the modern Caspian Sea, during several evaporation episodes that deposited at least five different packages of evaporites, with virtually all potash in the second and fourth packages. In this study, the CASB was subdivided into three tracts (permissive areas) for evaluation: the Amu Darya tract in the west, the Gissar tract in the center, and the Afghan-Tajik tract in the east. The Gissar and Amu Darya tracts were quantitatively assessed, whereas the Afghan‑Tajik tract was only qualitatively assessed because of the commonly extreme depth (as deep as 7 km) of the Jurassic salt, extensive deformation, and a lack of known potash deposits.
\nTwo approaches were used to estimate amounts of undiscovered potash in the CASB. Stratabound evaporite deposits in the Amu Darya tract were evaluated using an Adaptive Geometric Estimation (AGE) approach, which estimates in-place potash volumes and tonnages. The Gissar tract was evaluated by using the AGE approach for stratabound deposits and the three-part form of assessment of Singer and Menzie (2005) for discrete halokinetic deposits. In the three-part form of assessment, numbers of undiscovered deposits were estimated and combined with grade and tonnage models to probabilistically forecast the amount of undiscovered potash. The Amu Darya tract is estimated to contain 38 billion metric tons of undiscovered potash as K2O by using the AGE approach for stratabound deposits. The hybrid stratabound-halokinetic Gissar tract is estimated to contain between 1 and 16 billion metric tons of undiscovered potash as K2O.
\nChapter 1 of this report provides an overview of the history of the CASB and summarizes evaporite potash deposition, halokinesis, and dissolution processes that have affected the current distribution of potash-bearing salt in the CASB. Chapter 2 describes the Gissar tract, an uplifted region that contains a mix of stratabound and halokinetic potash deposits and all of the discovered and exploited potash deposits of the CASB. Chapter 3 describes the Amu Darya tract, where evaporite deposits remain flat-lying and undeformed since their original deposition. Chapter 4 describes the highly deformed and compressed Afghan-Tajik tract and what is known of the deeply-buried Jurassic salt. Chapter 5 describes the spatial databases included with this report, which contain a collection of CASB potash information. Appendixes A and B summarize descriptive models for stratabound and halokinetic potash-bearing salt deposits, respectively. Appendix C summarizes the AGE method used to evaluate the Gissar and Amu Darya tracts. Appendixes D and E contain grade and thickness data for the Gissar and Amu Darya tracts. Appendix F provides the SYSTAT script used to estimate undiscovered K2 O in a CASB tract. Appendix G provides a potash glossary, and appendix H provides biographies of assessment participants.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Global mineral resource assessment (Scientific Investigations Report 2010-5090)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105090AA","usgsCitation":"Wynn, Jeff, Orris, G.J., Dunlap, Pamela, Cocker, M.D., and Bliss, J.D., 2016, Geology and undiscovered resource assessment of the potash-bearing Central Asia Salt Basin, Turkmenistan, Uzbekistan, Tajikistan, and Afghanistan: U.S. Geological Survey Scientific Investigations Report SIR 2010–5090–AA, 106 p., and spatial data, https://dx.doi.org/10.3133/sir20105090AA.","productDescription":"Report: xi, 106 p.; GIS Data","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-053583","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":318376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5090/aa/sir20105090aa.pdf","text":"Report","size":"7.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2010-5090-AA report PDF"},{"id":318377,"rank":3,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sir/2010/5090/aa/sir20105090aa_gis.zip","text":"GIS Data","size":"4.1 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2010-5090-AA GIS Data"},{"id":318375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2010/5090/aa/coverthb.jpg"}],"country":"Afghanistan, Tajikistan, Turkmenistan, Uzbekistan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 62.38037109374999,\n 40.094882122321174\n ],\n [\n 60.3369140625,\n 40.01078714046552\n ],\n [\n 61.14990234374999,\n 36.89719446989036\n ],\n [\n 70.07080078125,\n 35.94243575255426\n ],\n [\n 70.24658203125,\n 37.94419750075404\n ],\n [\n 70.9716796875,\n 38.94232097947902\n ],\n [\n 62.38037109374999,\n 40.094882122321174\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Contact Information, Mineral Resources Program
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12201 Sunrise Valley Drive
913 National Center
Reston, VA 20192
http://minerals.usgs.gov/
Alaska has one of the most rapidly changing climates on earth and is experiencing an accelerated rate of human disturbance, including resource extraction and transportation infrastructure development. Combined, these factors increase the state’s vulnerability to biological invasion, which can have acute negative impacts on ecological integrity and subsistence practices. Of growing concern is the spread of Alaska’s first documented freshwater aquatic invasive plant Elodea spp. (elodea). In this study, we modeled the suitable habitat of elodea using global and state-specific species occurrence records and environmental variables, in concert with an ensemble of model algorithms. Furthermore, we sought to incorporate local subsistence concerns by using Native Alaskan knowledge and available statewide subsistence harvest data to assess the potential threat posed by elodea to Chinook salmon (Oncorhynchus tshawytscha) and whitefish (Coregonus nelsonii) subsistence. State models were applied to future climate (2040–2059) using five general circulation models best suited for Alaska. Model evaluations indicated that our results had moderate to strong predictability, with area under the receiver-operating characteristic curve values above 0.80 and classification accuracies ranging from 66 to 89 %. State models provided a more robust assessment of elodea habitat suitability. These ensembles revealed different levels of management concern statewide, based on the interaction of fish subsistence patterns, known spawning and rearing sites, and elodea habitat suitability, thus highlighting regions with additional need for targeted monitoring. Our results suggest that this approach can hold great utility for invasion risk assessments and better facilitate the inclusion of local stakeholder concerns in conservation planning and management.
","language":"English","publisher":"Springer","doi":"10.1007/s00267-016-0692-4","usgsCitation":"Luizza, M., Evangelista, P., Jarnevich, C.S., West, A., and Stewart, H., 2016, Integrating subsistence practice and species distribution modeling: assessing invasive elodea’s potential impact on Native Alaskan subsistence of Chinook salmon and whitefish: Environmental Management, v. 58, no. 1, p. 144-163, https://doi.org/10.1007/s00267-016-0692-4.","productDescription":"19 p.","startPage":"144","endPage":"163","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-067317","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":325696,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Agriculture","active":true,"usgs":false}],"preferred":false,"id":643643,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70169142,"text":"ds983 - 2016 - Water temperature profiles for reaches of the Raging River during summer baseflow, King County, western Washington, July 2015","interactions":[],"lastModifiedDate":"2016-03-23T08:58:18","indexId":"ds983","displayToPublicDate":"2016-03-22T12:00:00","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":"983","title":"Water temperature profiles for reaches of the Raging River during summer baseflow, King County, western Washington, July 2015","docAbstract":"Re-introducing wood into rivers where it was historically removed is one approach to improving habitat conditions in rivers of the Pacific Northwest. The Raging River drainage basin, which flows into the Snoqualmie River at Fall City, western Washington, was largely logged during the 20th century and wood was removed from its channel. To improve habitat conditions for several species of anadromous salmonids that spawn and rear in the Raging River, King County Department of Transportation placed untethered log jams in a 250-meter reach where wood was historically removed. The U.S. Geological Survey measured longitudinal profiles of near-streambed temperature during summer baseflow along 1,026 meters of channel upstream, downstream, and within the area of wood placements. These measurements were part of an effort by King County to monitor the geomorphic and biological responses to these wood placements. Near-streambed temperatures averaged over about 1-meter intervals were measured with a fiber‑optic distributed temperature sensor every 30 minutes for 7 days between July 7 and 13, 2015. Vertical temperature profiles were measured coincident with the longitudinal temperature profile at four locations at 0 centimeters (cm) (at the streambed), and 35 and 70 cm beneath the streambed to document thermal dynamics of the hyporheic zone and surface water in the study reach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds983","collaboration":"Prepared in cooperation with King County Department of Natural Resource and Parks","usgsCitation":"Gendaszek, A.S., and Opatz, C.C., 2016, Water temperature profiles for reaches of the Raging River during summer baseflow, King County, western Washington, July 2015: U.S. Geological Survey Data Series 983, 8 p.,\nhttps://dx.doi.org/10.3133/ds983.","productDescription":"Report: iii, 8 p.; Tables 1-3","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070544","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":319142,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0983/coverthb.jpg"},{"id":319143,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0983/ds983.pdf","text":"Report","size":"4.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 983"},{"id":319144,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0983/ds983_table01.xlsx","text":"Table 1","size":"1.2 MB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 983 Table 1"},{"id":319145,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0983/ds983_table02.xlsx","text":"Table 2","size":"56 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 983 Table 2"},{"id":319146,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0983/ds983_table03.xlsx","text":"Table 3","size":"70 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 983 Table 3"}],"country":"United States","state":"Washington","county":"King County","otherGeospatial":"Raging River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8639,\n 47.45\n ],\n [\n -121.8639,\n 47.4569\n ],\n [\n -121.8583,\n 47.4569\n ],\n [\n -121.8583,\n 47.45\n ],\n [\n -121.8639,\n 47.45\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
http://wa.water.usgs.gov
Freshwater fish move vertically and horizontally through the aquatic landscape for a variety of reasons, such as to find and exploit patchy resources or to locate essential habitats (e.g., for spawning). Inherent challenges exist with the assessment of fish populations because they are moving targets. We submit that quantifying and describing the spatial ecology of fish and their habitat is an important component of freshwater fishery assessment and management. With a growing number of tools available for studying the spatial ecology of fishes (e.g., telemetry, population genetics, hydroacoustics, otolith microchemistry, stable isotope analysis), new knowledge can now be generated and incorporated into biological assessment and fishery management. For example, knowing when, where, and how to deploy assessment gears is essential to inform, refine, or calibrate assessment protocols. Such information is also useful for quantifying or avoiding bycatch of imperiled species. Knowledge of habitat connectivity and usage can identify critically important migration corridors and habitats and can be used to improve our understanding of variables that influence spatial structuring of fish populations. Similarly, demographic processes are partly driven by the behavior of fish and mediated by environmental drivers. Information on these processes is critical to the development and application of realistic population dynamics models. Collectively, biological assessment, when informed by knowledge of spatial ecology, can provide managers with the ability to understand how and when fish and their habitats may be exposed to different threats. Naturally, this knowledge helps to better evaluate or develop strategies to protect the long-term viability of fishery production. Failure to understand the spatial ecology of fishes and to incorporate spatiotemporal data can bias population assessments and forecasts and potentially lead to ineffective or counterproductive management actions.
","language":"English","publisher":"Kluwer Academic Publishers","doi":"10.1007/s10661-016-5228-0","usgsCitation":"Cooke, S., Martins, E.G., Struthers, D.P., Gutowsky, L.F., Powers, M.H., Doka, S.E., Dettmers, J.M., Crook, D.A., Lucas, M.C., Holbrook, C., and Krueger, C., 2016, A moving target—incorporating knowledge of the spatial ecology of fish into the assessment and management of freshwater fish populations: Environmental Monitoring and Assessment, v. 188, no. 239, 18 p., https://doi.org/10.1007/s10661-016-5228-0.","productDescription":"18 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-073964","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":471130,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://durham-repository.worktribe.com/file/1387959/1/Accepted%20Journal%20Article","text":"External Repository"},{"id":326047,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"188","issue":"239","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-22","publicationStatus":"PW","scienceBaseUri":"57a315b9e4b006cb45558a20","contributors":{"authors":[{"text":"Cooke, Steven J.","contributorId":56132,"corporation":false,"usgs":false,"family":"Cooke","given":"Steven J.","affiliations":[{"id":36574,"text":"Carleton University, Ottawa, Ontario","active":true,"usgs":false}],"preferred":false,"id":644582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martins, Eduardo G","contributorId":173417,"corporation":false,"usgs":false,"family":"Martins","given":"Eduardo","email":"","middleInitial":"G","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":644583,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Struthers, Daniel P","contributorId":173418,"corporation":false,"usgs":false,"family":"Struthers","given":"Daniel","email":"","middleInitial":"P","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":644584,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gutowsky, Lee F G","contributorId":149696,"corporation":false,"usgs":false,"family":"Gutowsky","given":"Lee","email":"","middleInitial":"F G","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":644585,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Powers, Michael H. 0000-0002-4480-7856 mhpowers@usgs.gov","orcid":"https://orcid.org/0000-0002-4480-7856","contributorId":851,"corporation":false,"usgs":true,"family":"Powers","given":"Michael","email":"mhpowers@usgs.gov","middleInitial":"H.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":644586,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Doka, Susan E.","contributorId":173419,"corporation":false,"usgs":false,"family":"Doka","given":"Susan","email":"","middleInitial":"E.","affiliations":[{"id":13677,"text":"Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":644587,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dettmers, John M.","contributorId":27395,"corporation":false,"usgs":true,"family":"Dettmers","given":"John","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":644588,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Crook, David A","contributorId":173420,"corporation":false,"usgs":false,"family":"Crook","given":"David","email":"","middleInitial":"A","affiliations":[{"id":12877,"text":"Charles Darwin University","active":true,"usgs":false}],"preferred":false,"id":644589,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lucas, Martyn C.","contributorId":18725,"corporation":false,"usgs":true,"family":"Lucas","given":"Martyn","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":644590,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":644581,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Krueger, Charles C.","contributorId":73131,"corporation":false,"usgs":true,"family":"Krueger","given":"Charles C.","affiliations":[],"preferred":false,"id":644591,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70169299,"text":"70169299 - 2016 - Permissible Home Range Estimation (PHRE) in restricted habitats: A new algorithm and an evaluation for sea otters","interactions":[],"lastModifiedDate":"2016-03-24T08:44:05","indexId":"70169299","displayToPublicDate":"2016-03-22T09:45:00","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":"Permissible Home Range Estimation (PHRE) in restricted habitats: A new algorithm and an evaluation for sea otters","docAbstract":"Parametric and nonparametric kernel methods dominate studies of animal home ranges and space use. Most existing methods are unable to incorporate information about the underlying physical environment, leading to poor performance in excluding areas that are not used. Using radio-telemetry data from sea otters, we developed and evaluated a new algorithm for estimating home ranges (hereafter Permissible Home Range Estimation, or “PHRE”) that reflects habitat suitability. We began by transforming sighting locations into relevant landscape features (for sea otters, coastal position and distance from shore). Then, we generated a bivariate kernel probability density function in landscape space and back-transformed this to geographic space in order to define a permissible home range. Compared to two commonly used home range estimation methods, kernel densities and local convex hulls, PHRE better excluded unused areas and required a smaller sample size. Our PHRE method is applicable to species whose ranges are restricted by complex physical boundaries or environmental gradients and will improve understanding of habitat-use requirements and, ultimately, aid in conservation efforts.
","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"PLoS ONE","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0150547","collaboration":"USFWS","usgsCitation":"Tarjan, L.M., and Tinker, M.T., 2016, Permissible Home Range Estimation (PHRE) in restricted habitats: A new algorithm and an evaluation for sea otters: PLoS ONE, v. 11, no. 3, https://doi.org/10.1371/journal.pone.0150547.","productDescription":"20 p.","startPage":"e0150547","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-072967","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":471131,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0150547","text":"Publisher Index Page"},{"id":438630,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76H4FH8","text":"USGS data release","linkHelpText":"Geospatial data collected from tagged sea otters in central California, 1998-2012"},{"id":319336,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-22","publicationStatus":"PW","scienceBaseUri":"56f50fcce4b0f59b85e1eb77","contributors":{"authors":[{"text":"Tarjan, Lily M","contributorId":167782,"corporation":false,"usgs":false,"family":"Tarjan","given":"Lily","email":"","middleInitial":"M","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":623488,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tinker, M. Tim 0000-0002-3314-839X ttinker@usgs.gov","orcid":"https://orcid.org/0000-0002-3314-839X","contributorId":2796,"corporation":false,"usgs":true,"family":"Tinker","given":"M.","email":"ttinker@usgs.gov","middleInitial":"Tim","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":623487,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70168728,"text":"ds981 - 2016 - Irrigation water use in Kansas, 2013","interactions":[],"lastModifiedDate":"2016-03-22T10:13:22","indexId":"ds981","displayToPublicDate":"2016-03-22T00:00:00","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":"981","title":"Irrigation water use in Kansas, 2013","docAbstract":"This report, prepared by the U.S. Geological Survey in cooperation with the Kansas Department of Agriculture, Division of Water Resources, presents derivative statistics of 2013 irrigation water use in Kansas. The published regional and county-level statistics from the previous 4 years (2009–12) are shown with the 2013 statistics and are used to calculate a 5-year average. An overall Kansas average and regional averages also are calculated and presented. Total reported irrigation water use in 2013 was 3.3 million acre-feet of water applied to 3.0 million irrigated acres.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds981","collaboration":"Prepared in cooperation with the Kansas Department of Agriculture, Division of Water Resources","usgsCitation":"Lanning-Rush, J.L., 2016, Irrigation water use in Kansas, 2013: U.S. Geological Survey Data Series 981, 12 p., https://dx.doi.org/10.3133/ds981.","productDescription":"Report: iv, 12 p.; Tables 6-12; Appendix: 2 p.","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-070156","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":318889,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/0981/ds981.pdf","text":"Report","size":"617 kB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 981"},{"id":318890,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/0981/downloads/ds981_tables6to12.xlsx","text":"Tables 6–12","size":"80 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"DS 981 Tables "},{"id":318888,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/0981/coverthb_new.jpg"},{"id":318893,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/ds/0981/downloads/ds981_appendix.pdf","text":"Water-Use Report","size":"1.34 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 981 Appendix"}],"country":"United States","state":"Kansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.29541015625,\n 39.99395569397331\n ],\n [\n -102.06298828125,\n 40.01078714046552\n ],\n [\n -102.052001953125,\n 36.98500309285596\n ],\n [\n -94.603271484375,\n 36.99377838872517\n ],\n [\n -94.6142578125,\n 39.13006024213511\n ],\n [\n -94.76806640624999,\n 39.172658670429946\n ],\n [\n -94.82299804687499,\n 39.2407625100131\n ],\n [\n -94.910888671875,\n 39.29179704377487\n ],\n [\n -94.8779296875,\n 39.38526381099777\n ],\n [\n -94.976806640625,\n 39.436192999314095\n ],\n [\n -95.086669921875,\n 39.53793974517628\n ],\n [\n -95.042724609375,\n 39.57182223734374\n ],\n [\n -95.042724609375,\n 39.63953756436671\n ],\n [\n -94.9658203125,\n 39.73253798438173\n ],\n [\n -94.910888671875,\n 39.73253798438173\n ],\n [\n -94.888916015625,\n 39.78321267821705\n ],\n [\n -94.910888671875,\n 39.842286020743394\n ],\n [\n -94.95483398437499,\n 39.8928799002948\n ],\n [\n -95.03173828125,\n 39.90973623453719\n ],\n [\n -95.06469726562499,\n 39.87601941962116\n ],\n [\n -95.174560546875,\n 39.926588421909436\n ],\n [\n -95.29541015625,\n 39.99395569397331\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
4821 Quail Crest Place
Lawrence, KS 66049
Digital flood-inundation maps for a 12.5-mile reach of the Big Papillion Creek from 0.6 mile upstream from the State Street Bridge to the 72nd Street Bridge in Omaha, Nebraska, were created by the U.S. Geological Survey (USGS) in cooperation with the Papio-Missouri River Natural Resources District. The flood-inundation maps, which can be accessed through the USGS Flood Inundation Mapping Science Web site at http://water.usgs.gov/osw/flood_inundation/, depict estimates of the areal extent and depth of flooding corresponding to selected water levels (stages) at the USGS streamgage on the Big Papillion Creek at Fort Street at Omaha, Nebraska (station 06610732). Near-real-time stages at this streamgage may be obtained on the Internet from the USGS National Water Information System at http://waterdata.usgs.gov/ or the National Weather Service Advanced Hydrologic Prediction Service at http:/water.weather.gov/ahps/, which also forecasts flood hydrographs at this site.
\nFlood profiles were computed for the 12.5-mile reach by means of a one-dimensional step-backwater model. The model was calibrated by using the current (2015) stage-discharge relation at streamgages for the Big Papillion Creek at Fort Street at Omaha, Nebraska, and the Big Papillion Creek at Q Street at Omaha, Nebraska. The hydraulic model was then used to compute 15 water-surface profiles for flood stages at 1-foot (ft) intervals referenced to the streamgage datum for the Big Papillion Creek at Fort Street and ranging from 18 ft (or near bankfull) to 32 ft, which exceeds the “major flood stage” as defined by the National Weather Service. The simulated water-surface profiles were then combined with a Geographic Information System digital elevation model (derived from light detection and ranging data having a 1.18-ft vertical accuracy and 3.28-ft horizontal resolution) to delineate the area flooded at each flood stage (water level).
\nThe availability of these flood-inundation maps, along with Internet information regarding current stage from the USGS streamgage and forecasted high-flow stages from the National Weather Service, will provide emergency management personnel and residents with information that is critical for flood response activities such as evacuations and road closures, as well as for postflood recovery efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20155152","collaboration":"Prepared in cooperation with the Papio-Missouri River Natural Resources District","usgsCitation":"Strauch, K.R., Dietsch, B.J., and Anderson, K.J., 2016, Flood-inundation maps for a 12.5-mile reach of Big Papillion Creek at Omaha, Nebraska: U.S. Geological Survey Scientific Investigations Report 2015–5152, 11 p., https://dx.doi.org/10.3133/sir20155152.","productDescription":"Report: v, 11 p.; Datasets; Metadata","numberOfPages":"11","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-066029","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":314625,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_river_areas_GRIDS.zip","text":"River area GRIDS","size":"52.9 MB","description":"SIR 2015–5152 River area GRIDS"},{"id":314626,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_river_areas_shapefiles.zip","text":"River area shapefiles","size":"2.2 MB","description":"SIR 2015–5152 River area shapefiles"},{"id":314628,"rank":7,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_metadata_BigPapio_at_Fort_FIM_GRID.txt","text":"GRID metadata","size":"20.0 kb","description":"SIR 2015–5152 GRID metadata"},{"id":314629,"rank":8,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_metadata_BigPapio_at_Fort_FIM_shapefile.txt","text":"Shapefile metadata","size":"20.0 kb","description":"SIR 2015–5152 Shapefile metadata"},{"id":314466,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152.pdf","text":"Report","size":"2.11 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2015–5152"},{"id":314622,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_levee_areas_GRIDS.zip","text":"Levee area GRIDS","size":"1.6 MB","description":"SIR 2015–5152 Levee area GRIDS"},{"id":314623,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://pubs.usgs.gov/sir/2015/5152/sir20155152_dataset_levee_areas_shapefiles.zip","text":"Levee area shapefiles","size":"144 kb","description":"SIR 2015–5152 Levee area shapefiles"},{"id":314465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2015/5152/coverthb.jpg"}],"country":"United States","state":"Nebraska","city":"Omaha","otherGeospatial":"Big Papillion Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.16676330566406,\n 41.23702755320388\n ],\n [\n -96.16676330566406,\n 41.35052580597025\n ],\n [\n -96.01020812988281,\n 41.35052580597025\n ],\n [\n -96.01020812988281,\n 41.23702755320388\n ],\n [\n -96.16676330566406,\n 41.23702755320388\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
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Avian botulism type E is responsible for extensive waterbird mortality on the Great Lakes, yet the actual site of toxin exposure remains unclear. Beached carcasses are often used to describe the spatial aspects of botulism mortality outbreaks, but lack specificity of offshore toxin source locations. We detail methodology for developing a neural network model used for predicting waterbird carcass motions in response to wind, wave, and current forcing, in lieu of a complex analytical relationship. This empirically trained model uses current velocity, wind velocity, significant wave height, and wave peak period in Lake Michigan simulated by the Great Lakes Coastal Forecasting System. A detailed procedure is further developed to use the model for back-tracing waterbird carcasses found on beaches in various parts of Lake Michigan, which was validated using drift data for radiomarked common loon (Gavia immer) carcasses deployed at a variety of locations in northern Lake Michigan during September and October of 2013. The back-tracing model was further used on 22 non-radiomarked common loon carcasses found along the shoreline of northern Lake Michigan in October and November of 2012. The model-estimated origins of those cases pointed to some common source locations offshore that coincide with concentrations of common loons observed during aerial surveys. The neural network source tracking model provides a promising approach for identifying locations of botulinum neurotoxin type E intoxication and, in turn, contributes to developing an understanding of the dynamics of toxin production and possible trophic transfer pathways.
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An alternative is to emulate the process model with a statistical “metamodel.” Built on a dataset of collocated numerical model input and output, a groundwater flow model was emulated using a Bayesian Network, an Artificial neural network, and a Gradient Boosted Regression Tree. The response of interest was surface water depletion expressed as the source of water-to-wells. The results have application for managing allocation of groundwater. Each technique was tuned using cross validation and further evaluated using a held-out dataset. A numerical MODFLOW-USG model of the Lake Michigan Basin, USA, was used for the evaluation. The performance and interpretability of each technique was compared pointing to advantages of each technique. The metamodel can extend to unmodeled areas.
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The objectives of the study were to quantify inorganic and organic accretion rates in back-barrier and mainland marsh and estuarine environments. Various field and laboratory methods were used to achieve these objectives, including subsurface imaging using Ground Penetrating Radar (GPR), sediment sampling, lithologic and microfossil analyses, and geochronology techniques to produce barrier island stratigraphic cross sections to help interpret the recent (last 2000 years) geologic evolution of the island.
This data series report is an archive of GPR and associated Global Positioning System (GPS) data collected in April 2013 from Dauphin Island and adjacent barrier-island environments. In addition to GPR data, marsh core and vibracore data were also collected collected but are not reported (or included) in the current report. Data products, including elevation-corrected subsurface profile images of the processed GPR data, unprocessed digital GPR trace data, post-processed GPS data, Geographic Information System (GIS) files and accompanying Federal Geographic Data Committee (FGDC) metadata, can be downloaded from the Data Downloads page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds982","usgsCitation":"Forde, A.S., Smith, C.G., and Reynolds, B.J., 2016, Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013: U.S. Geological Survey Data Series 982, https://dx.doi.org/10.3133/ds982.","productDescription":"HTML Document","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-068738","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":438631,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7H70DSZ","text":"USGS data release","linkHelpText":"Sedimentary Data Collected in April 2013 From Dauphin Island and Salt Marshes of Coastal Alabama"},{"id":318974,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/usgs_thumb.jpg"},{"id":318878,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/0982/index.html"}],"country":"United States","state":"Alabama","otherGeospatial":"Dauphin Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.32832628252302,\n 30.222222538874064\n ],\n [\n -88.0647962871504,\n 30.23742743361879\n ],\n [\n -88.07755483450525,\n 30.261370373972056\n ],\n [\n -88.12286967511226,\n 30.286827097172534\n ],\n [\n -88.13694807219358,\n 30.27124988630632\n ],\n [\n -88.34636422878367,\n 30.235907049955017\n ],\n [\n -88.32832628252302,\n 30.222222538874064\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, St. Petersburg Coastal and Marine Science Center
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Global positioning system (GPS) wildlife collars have revolutionized wildlife research. Studies of predation by free-ranging carnivores have particularly benefited from the application of location clustering algorithms to determine when and where predation events occur. These studies have changed our understanding of large carnivore behavior, but the gains have concentrated on obligate carnivores. Facultative carnivores, such as grizzly/brown bears (Ursus arctos), exhibit a variety of behaviors that can lead to the formation of GPS clusters. We combined clustering techniques with field site investigations of grizzly bear GPS locations (n = 732 site investigations; 2004–2011) to produce 174 GPS clusters where documented behavior was partitioned into five classes (large-biomass carcass, small-biomass carcass, old carcass, non-carcass activity, and resting). We used multinomial logistic regression to predict the probability of clusters belonging to each class. Two cross-validation methods—leaving out individual clusters, or leaving out individual bears—showed that correct prediction of bear visitation to large-biomass carcasses was 78–88%, whereas the false-positive rate was 18–24%. As a case study, we applied our predictive model to a GPS data set of 266 bear-years in the Greater Yellowstone Ecosystem (2002–2011) and examined trends in carcass visitation during fall hyperphagia (September–October). We identified 1997 spatial GPS clusters, of which 347 were predicted to be large-biomass carcasses. We used the clustered data to develop a carcass visitation index, which varied annually, but more than doubled during the study period. Our study demonstrates the effectiveness and utility of identifying GPS clusters associated with carcass visitation by a facultative carnivore.
","language":"English","publisher":"Springer","doi":"10.1007/s00442-016-3594-5","usgsCitation":"Ebinger, M.R., Haroldson, M.A., van Manen, F.T., Costello, C., Bjornlie, D., Thompson, D.J., Gunther, K.A., Fortin, J., Teisberg, J.E., Pils, S.R., White, P.J., Cain, S.L., and Cross, P.C., 2016, Detecting grizzly bear use of ungulate carcasses using global positioning system telemetry and activity data: Oecologia, v. 181, no. 3, p. 695-708, https://doi.org/10.1007/s00442-016-3594-5.","productDescription":"14 p.","startPage":"695","endPage":"708","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-065281","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":318930,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Greater Yellowstone Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.895751953125,\n 42.49640294093708\n ],\n [\n -111.895751953125,\n 45.767522962149904\n ],\n [\n -108.5888671875,\n 45.767522962149904\n ],\n [\n -108.5888671875,\n 42.49640294093708\n ],\n [\n -111.895751953125,\n 42.49640294093708\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"181","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-03-14","publicationStatus":"PW","scienceBaseUri":"56ebc71ae4b0f59b85d9940b","chorus":{"doi":"10.1007/s00442-016-3594-5","url":"http://dx.doi.org/10.1007/s00442-016-3594-5","publisher":"Springer Nature","authors":"Ebinger Michael R., Haroldson Mark A., van Manen Frank T., Costello Cecily M., Bjornlie Daniel D., Thompson Daniel J., Gunther Kerry A., Fortin Jennifer K., Teisberg Justin E., Pils Shannon R., White P. 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Box 644236, Pullman, WA 99164","active":true,"usgs":false}],"preferred":false,"id":622847,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pils, Shannon R","contributorId":167609,"corporation":false,"usgs":false,"family":"Pils","given":"Shannon","email":"","middleInitial":"R","affiliations":[{"id":24778,"text":"US Forest Service, Shoshone National Forest, Wapiti Ranger District, 203A Yellowstone Avenue, Cody, WY 82414,USA","active":true,"usgs":false}],"preferred":false,"id":622848,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"White, P J","contributorId":167610,"corporation":false,"usgs":false,"family":"White","given":"P","email":"","middleInitial":"J","affiliations":[{"id":24779,"text":"National Park Service, Yellowstone Center for Resources, P.O. Box 168, Yellowstone National Park, WY 82190, USA","active":true,"usgs":false}],"preferred":false,"id":622849,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Cain, Steven L.","contributorId":145511,"corporation":false,"usgs":false,"family":"Cain","given":"Steven","email":"","middleInitial":"L.","affiliations":[{"id":16139,"text":"National Park Service, Grand Teton National Park, Moose, Wyoming 83012, USA","active":true,"usgs":false}],"preferred":false,"id":622850,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Cross, Paul C. 0000-0001-8045-5213 pcross@usgs.gov","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":2709,"corporation":false,"usgs":true,"family":"Cross","given":"Paul","email":"pcross@usgs.gov","middleInitial":"C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":622851,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70169116,"text":"70169116 - 2016 - Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation","interactions":[],"lastModifiedDate":"2017-11-22T17:32:53","indexId":"70169116","displayToPublicDate":"2016-03-17T09:30:00","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}},"displayTitle":"Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation","title":"Exploring climate niches of ponderosa pine (Pinus ponderosa Douglas ex Lawson) haplotypes in the western United States: Implications for evolutionary history and conservation","docAbstract":"Ponderosa pine (Pinus ponderosa Douglas ex Lawson) occupies montane environments throughout western North America, where it is both an ecologically and economically important tree species. A recent study using mitochondrial DNA analysis demonstrated substantial genetic variation among ponderosa pine populations in the western U.S., identifying 10 haplotypes with unique evolutionary lineages that generally correspond spatially with distributions of the Pacific (P. p. var. ponderosa) and Rocky Mountain (P. p. var. scopulorum) varieties. To elucidate the role of climate in shaping the phylogeographic history of ponderosa pine, we used nonparametric multiplicative regression to develop predictive climate niche models for two varieties and 10 haplotypes and to hindcast potential distribution of the varieties during the last glacial maximum (LGM), ~22,000 yr BP. Our climate niche models performed well for the varieties, but haplotype models were constrained in some cases by small datasets and unmeasured microclimate influences. The models suggest strong relationships between genetic lineages and climate. Particularly evident was the role of seasonal precipitation balance in most models, with winter- and summer-dominated precipitation regimes strongly associated with P. p. vars. ponderosa and scopulorum, respectively. Indeed, where present-day climate niches overlap between the varieties, introgression of two haplotypes also occurs along a steep clinal divide in western Montana. Reconstructed climate niches for the LGM suggest potentially suitable climate existed for the Pacific variety in the California Floristic province, the Great Basin, and Arizona highlands, while suitable climate for the Rocky Mountain variety may have existed across the southwestern interior highlands. These findings underscore potentially unique phylogeographic origins of modern ponderosa pine evolutionary lineages, including potential adaptations to Pleistocene climates associated with discrete temporary glacial refugia. Our predictive climate niche models may inform strategies for further genetic research (e.g., sampling design) and conservation that promotes haplotype compatibility with projected changes in future climate.
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M.","contributorId":167660,"corporation":false,"usgs":false,"family":"Potter","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":24794,"text":"Department of Forestry and Environmental Resources, North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":623047,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hipkins, Valerie D.","contributorId":167661,"corporation":false,"usgs":false,"family":"Hipkins","given":"Valerie","email":"","middleInitial":"D.","affiliations":[{"id":24795,"text":"National Forest Genetics Laboratory, USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":623048,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70168418,"text":"sir20165021 - 2016 - Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","interactions":[],"lastModifiedDate":"2016-03-18T08:13:50","indexId":"sir20165021","displayToPublicDate":"2016-03-17T00:00:00","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":"2016-5021","title":"Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10","docAbstract":"The Albuquerque area of New Mexico has two principal sources of water: (1) groundwater from the Santa Fe Group aquifer system, and (2) surface water from the Rio Grande. From 1960 to 2002, pumping from the Santa Fe Group aquifer system caused groundwater levels to decline more than 120 feet while water-level declines along the Rio Grande in Albuquerque were generally less than 40 feet. These differences in water-level declines in the Albuquerque area have resulted in a great deal of interest in quantifying the river-aquifer interaction associated with the Rio Grande.
In 2003, the U.S. Geological Survey, in cooperation with the Bureau of Reclamation, acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program, and the U.S. Army Corps of Engineers, began a study to characterize the hydrogeology of the Rio Grande inner valley alluvial aquifer in the Albuquerque area of New Mexico. The study provides hydrologic data in order to enhance the understanding of rates of water leakage from the Rio Grande to the alluvial aquifer, groundwater flow through the aquifer, and discharge of water from the aquifer to riverside drains. The study area extends about 20 miles along the Rio Grande in the Albuquerque area. Piezometers and surface-water gages were installed in paired transects at eight locations. Nested piezometers, completed at various depths in the alluvial aquifer, and surface-water gages, installed in the Rio Grande and riverside drains, were instrumented with pressure transducers. Water-level and water-temperature data were collected from 2009 to 2010.
Water levels from the piezometers indicated that groundwater movement was usually away from the river towards the riverside drains. Annual mean horizontal groundwater gradients in the inner valley alluvial aquifer ranged from 0.0024 (I-25 East) to 0.0144 (Pajarito East). The median hydraulic conductivity values of the inner valley alluvial aquifer, determined from slug tests, ranged from 30 feet per day (ft/d) (Montaño) to 120 ft/d (Central) for paired transects, with a median hydraulic conductivity for all transects of 50 ft/d. Daily mean groundwater fluxes from the river through the inner valley alluvial aquifer computed using Darcy’s Law and the slug test results ranged from about 0.01 ft/d (Montaño West) to between 1.0 and 2.0 ft/d (Central East). Median annual groundwater fluxes from the river through the inner valley alluvial aquifer determined using the Suzuki-Stallman method was greatest at Alameda East (0.50 ft/d) and lowest at Alameda West (0.25 ft/d). The results from both methods agreed reasonably well.
Seepage investigations conducted by measuring discharge in the east and west riverside drains provided information for computing changes in flow within the drains and for evaluating results from Darcy’s Law and Suzuki-Stallman method flux calculations. Discharge measured in the east riverside drain between the Barelas Bridge and the I-25 bridge indicated that the flow in the east riverside drain increased by an average of 56.5 cubic feet per day per linear foot (ft3/d/ft) of drain. Discharge measured in the west riverside drain between the Central bridge and the I-25 bridge indicated that flow increased between west drain miles 0 and 4, an average of 53.8 ft3/d/ft of drain, and that flow increased between west drain miles 7 and 10, an average of 44.9 ft3/d/ft of drain. In comparison to the seepage measurement results, the groundwater fluxes from the river through the inner valley alluvial aquifer calculated from Darcy’s Law (qslug) and by the Suzuki-Stallman method (qheat) would account for 20–36 percent or 53–95 percent, respectively, of the total flow in the east riverside drain and 22–31 percent or 19–26 percent, respectively, of the total flow in the west drain. These results indicate that the drains likely also receive water from outside the inner valley.
The spatial variability of horizontal hydraulic gradients and groundwater fluxes can be primarily attributed to variability in the distances between the river and riverside drains throughout the study area and geologic heterogeneities in the alluvial aquifer. Temporal variability in the water levels, which control the horizontal hydraulic gradients and fluxes between the Rio Grande and the riverside drains, can be primarily attributed to seasonal fluctuations in river stage and irrigation practices.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165021","collaboration":"Prepared in cooperation with Bureau of Reclamation acting as fiscal agent for the Middle Rio Grande Endangered Species Collaborative Program","usgsCitation":"Rankin, D.R., Oelsner, G.P., McCoy, K.J., Moret, G.J.M., Worthington, J.A., and Bandy-Baldwin, K.M., 2016, Groundwater hydrology and estimation of horizontal groundwater flux from the Rio Grande at selected locations in Albuquerque, New Mexico, 2009–10: U.S. Geological Survey Scientific Investigations Report 2016–5021, 89 p., https://dx.doi.org/10.3133/sir20165021.","productDescription":"viii, 89 p.","numberOfPages":"101","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-033038","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":318926,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5021/sir20165021.pdf","text":"Report","size":"5.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016–5021"},{"id":318925,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5021/coverthb.jpg"}],"country":"United States","state":"New Mexico","city":"Albuquerque","otherGeospatial":"Rio Grande","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.7266845703125,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 35.290468565908775\n ],\n [\n -106.56051635742188,\n 34.95011635301367\n ],\n [\n -106.7266845703125,\n 34.95011635301367\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
5338 Montogmery Blvd., NE Suite 400
Albuquerque, NM 87109–1311
—The Southern Mule Deer is a mobile but non-migratory large mammal found throughout southern California and is a covered species in the San Diego Multi-Species Conservation Plan. We assessed deer movement and population connectivity across California State Route 67 and two smaller roads in eastern San Diego County using non-invasive genetic sampling. We collected deer scat pellets between April and November 2015, and genotyped pellets at 15 microsatellites and a sex determination marker. We successfully genotyped 71 unique individuals from throughout the study area and detected nine recapture events. Recaptures were generally found close to original capture locations (within 1.5 km). We did not detect recaptures across roads; however, pedigree analysis detected 21 first order relative pairs, of which approximately 20% were found across State Route 67. Exact tests comparing allele frequencies between groups of individuals in pre-defined geographic clusters detected significant genetic differentiation across State Route 67. In contrast, the assignment-based algorithm of STRUCTURE supported a single genetic cluster across the study area. Our data suggest that State Route 67 may reduce, but does not preclude, movement and gene flow of Southern Mule Deer.
","language":"English","publisher":"The Journal of the Western Section of The Wildlife Society","publisherLocation":"Albany, CA","usgsCitation":"Mitelberg, A., and Vandergast, A.G., 2016, Non-invasive genetic sampling of Southern Mule Deer (Odocoileus hemionus fuliginatus) reveals limited movement across California State Route 67 in San Diego County: Western Wildlife, v. 3, p. 8-18.","productDescription":"11 p.","startPage":"8","endPage":"18","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-076907","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":325878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":325873,"type":{"id":15,"text":"Index Page"},"url":"https://wwjournal.org/index.php/current-volume"}],"country":"United States","state":"California","city":"San Diego","volume":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a072b9e4b060ce18fb2dda","contributors":{"authors":[{"text":"Mitelberg, Anna amitelberg@usgs.gov","contributorId":173293,"corporation":false,"usgs":true,"family":"Mitelberg","given":"Anna","email":"amitelberg@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644117,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vandergast, Amy G. 0000-0002-7835-6571 avandergast@usgs.gov","orcid":"https://orcid.org/0000-0002-7835-6571","contributorId":3963,"corporation":false,"usgs":true,"family":"Vandergast","given":"Amy","email":"avandergast@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":644116,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}