Sample descriptions and analytical data for more than 200 K/Ar and 40Ar/39Ar analyses from rocks of the Alaska-Aleutian Range batholith of south-central Alaska are reported here. Samples were collected over a period of 20 years by Bruce R. Reed and Marvin A. Lanphere (both U.S. Geological Survey) as part of their studies of the batholith.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181033","usgsCitation":"Koeneman, L.L., and Wilson, F.H., comps., 2018, Legacy K/Ar and 40Ar/39Ar geochronologic data from the Alaska-\nAleutian Range batholith of south-central Alaska: U.S. Geological Survey Open-File Report 2018–1033, 8 p.,\n1 plate, https://doi.org/10.3133/ofr20181033.","productDescription":"Plate: 16.96 x 27.64 inches; Pamphlet: iii, 8 p.; 2 Tables; Metadata","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086126","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"links":[{"id":352666,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1033/coverthb.jpg"},{"id":352667,"rank":2,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/of/2018/1033/ofr20181033.pdf","text":"Report","size":"50.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1033"},{"id":352670,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2018/1033/ofr20181033_table02.csv","text":"Table 2","size":"7 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2018-1033 Table 2"},{"id":352669,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2018/1033/ofr20181033_table01.csv","text":"Table 1","size":"83 KB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2018-1033 Table 1"},{"id":352671,"rank":6,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2018/1033/ofr20181033_metadata.zipx","text":"Metadata","size":"19 KB zipx","description":"OFR 2018-1033 Metadata"},{"id":352668,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1033/ofr20181033_pamphlet.pdf","text":"Pamphlet","size":"404 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1033 Pamphlet"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.25,\n 59.75\n ],\n [\n -151.875,\n 59.75\n ],\n [\n -151.875,\n 62\n ],\n [\n -155.25,\n 62\n ],\n [\n -155.25,\n 59.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Alaska Science Center staff
U.S. Geological Survey
4210 University Dr.
Anchorage, AK 99508
Alaska Mineral Resources
Alaska Science Center
Noninvasive geophysical estimation of soil moisture has potential to improve understanding of flow in the unsaturated zone for problems involving agricultural management, aquifer recharge, and optimization of landfill design and operations. In principle, several geophysical techniques (e.g., electrical resistivity, electromagnetic induction, and nuclear magnetic resonance) offer insight into soil moisture, but data‐analysis tools are needed to “translate” geophysical results into estimates of soil moisture, consistent with (1) the uncertainty of this translation and (2) direct measurements of moisture. Although geostatistical frameworks exist for this purpose, straightforward and user‐friendly tools are required to fully capitalize on the potential of geophysical information for soil‐moisture estimation. Here, we present MoisturEC, a simple R program with a graphical user interface to convert measurements or images of electrical conductivity (EC) to soil moisture. Input includes EC values, point moisture estimates, and definition of either Archie parameters (based on experimental or literature values) or empirical data of moisture vs. EC. The program produces two‐ and three‐dimensional images of moisture based on available EC and direct measurements of moisture, interpolating between measurement locations using a Tikhonov regularization approach.
","language":"English","publisher":"Wiley","doi":"10.1111/gwat.12650","usgsCitation":"Terry, N., Day-Lewis, F.D., Werkema, D.D., and Lane, J.W., 2018, MoisturEC: a new R program for moisture content estimation from electrical conductivity data: Groundwater, v. 56, no. 5, p. 823-831, https://doi.org/10.1111/gwat.12650.","productDescription":"9 p.","startPage":"823","endPage":"831","ipdsId":"IP-093542","costCenters":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"links":[{"id":468844,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8920296","text":"External Repository"},{"id":437956,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7862FC7","text":"USGS data release","linkHelpText":"MoisturEC"},{"id":353241,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-31","publicationStatus":"PW","scienceBaseUri":"5afee6e6e4b0da30c1bfbf08","contributors":{"authors":[{"text":"Terry, Neil 0000-0002-3965-340X nterry@usgs.gov","orcid":"https://orcid.org/0000-0002-3965-340X","contributorId":192554,"corporation":false,"usgs":true,"family":"Terry","given":"Neil","email":"nterry@usgs.gov","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":732933,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Day-Lewis, Frederick D. 0000-0003-3526-886X daylewis@usgs.gov","orcid":"https://orcid.org/0000-0003-3526-886X","contributorId":1672,"corporation":false,"usgs":true,"family":"Day-Lewis","given":"Frederick","email":"daylewis@usgs.gov","middleInitial":"D.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":732934,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Werkema, Dale D.","contributorId":190401,"corporation":false,"usgs":false,"family":"Werkema","given":"Dale","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":732935,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, John W. Jr. 0000-0002-3558-243X jwlane@usgs.gov","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":189168,"corporation":false,"usgs":true,"family":"Lane","given":"John","suffix":"Jr.","email":"jwlane@usgs.gov","middleInitial":"W.","affiliations":[{"id":493,"text":"Office of Ground Water","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":false,"id":732936,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196100,"text":"ofr20181042 - 2018 - Digital representation of exposures of Precambrian bedrock in parts of Dickinson and Iron Counties, Michigan, and Florence and Marinette Counties, Wisconsin","interactions":[],"lastModifiedDate":"2018-04-04T14:46:32","indexId":"ofr20181042","displayToPublicDate":"2018-04-04T09:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1042","title":"Digital representation of exposures of Precambrian bedrock in parts of Dickinson and Iron Counties, Michigan, and Florence and Marinette Counties, Wisconsin","docAbstract":"The U.S. Geological Survey (USGS) conducted a program of bedrock geologic mapping in much of the central and western Upper Peninsula of Michigan from the 1940s until the late 1990s. Geologic studies in this region are hampered by a scarcity of bedrock exposures because of a nearly continuous blanket of unconsolidated sediments resulting from glaciation of the region during the Pleistocene ice ages. The USGS mapping, done largely at a scale of 1:24,000, routinely recorded the location and extent of exposed bedrock to provide both an indication of where direct observations were made and a guide for future investigations to expedite location of observable rock exposures. The locations of outcrops were generally shown as colored or patterned overlays on printed geologic maps. Although those maps have been scanned and are available as Portable Document Format (PDF) files, no further digital portrayal of the outcrops had been done. We have conducted a prototype study of digitizing and improving locational accuracy of the outcrop locations in parts of Dickinson County, Michigan, to form a data layer that can be used with other data layers in geographic information system applications.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181042","usgsCitation":"Cannon, W.F., Schulte, Ruth, and Bickerstaff, Damon, 2018, Digital representation of exposures of Precambrian bedrock in parts of Dickinson and Iron Counties, Michigan, and Florence and Marinette Counties, Wisconsin: \nU.S. Geological Survey Open-File Report 2018–1042, 3 p., https://doi.org/10.3133/ofr20181042.","productDescription":"Report: 3 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-091916","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"links":[{"id":352778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1042/coverthb.jpg"},{"id":352779,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1042/ofr20181042.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1042"},{"id":352780,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7SJ1JH0","text":"USGS data release","description":"USGS data release"}],"country":"United States","state":"Michigan, Wisconsin","county":"Dickinson County, Florence County, Iron County, Marinette County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.1333,\n 45.75\n ],\n [\n -87.7,\n 45.75\n ],\n [\n -87.7,\n 46.1\n ],\n [\n -88.1333,\n 46.1\n ],\n [\n -88.1333,\n 45.75\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Mineral and Environmental Resources
12201 Sunrise Valley Drive
Mail Stop 954
Reston, VA 20192
Models that formulate mathematical linkages between fish use and habitat characteristics are applied for many purposes. For riverine fish, these linkages are often cast as resource selection functions with variables including depth and velocity of water and distance to nearest cover. Ecologists are now recognizing the role that detection plays in observing organisms, and failure to account for imperfect detection can lead to spurious inference. Herein, we present a flexible N-mixture model to associate habitat characteristics with the abundance of riverine salmonids that simultaneously estimates detection probability. Our formulation has the added benefits of accounting for demographics variation and can generate probabilistic statements regarding intensity of habitat use. In addition to the conceptual benefits, model application to data from the Trinity River, California, yields interesting results. Detection was estimated to vary among surveyors, but there was little spatial or temporal variation. Additionally, a weaker effect of water depth on resource selection is estimated than that reported by previous studies not accounting for detection probability. N-mixture models show great promise for applications to riverine resource selection.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2017-0027","usgsCitation":"Som, N.A., Perry, R.W., Jones, E.C., De Juilio, K., Petros, P., Pinnix, W.D., and Rupert, D.L., 2018, N-mix for fish: estimating riverine salmonid habitat selection via N-mixture models: Canadian Journal of Fisheries and Aquatic Sciences, v. 75, no. 7, p. 1048-1058, https://doi.org/10.1139/cjfas-2017-0027.","productDescription":"11 p.","startPage":"1048","endPage":"1058","ipdsId":"IP-086618","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":501089,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/1807/82344","text":"External Repository"},{"id":353134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"75","issue":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6e8e4b0da30c1bfbf2f","contributors":{"authors":[{"text":"Som, Nicholas A.","contributorId":203773,"corporation":false,"usgs":false,"family":"Som","given":"Nicholas","email":"","middleInitial":"A.","affiliations":[{"id":36713,"text":"Statistician, USFWS - Arcata Fisheries Program, Humboldt State University","active":true,"usgs":false}],"preferred":false,"id":732647,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":732646,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, Edward C. 0000-0001-7255-1475 ejones@usgs.gov","orcid":"https://orcid.org/0000-0001-7255-1475","contributorId":203917,"corporation":false,"usgs":true,"family":"Jones","given":"Edward","email":"ejones@usgs.gov","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":732648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"De Juilio, Kyle","contributorId":203918,"corporation":false,"usgs":false,"family":"De Juilio","given":"Kyle","affiliations":[{"id":36756,"text":"Yurok Tribal Fisheries Program, Weaverville, CA 96093","active":true,"usgs":false}],"preferred":false,"id":732649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Petros, Paul","contributorId":203920,"corporation":false,"usgs":false,"family":"Petros","given":"Paul","email":"","affiliations":[{"id":36758,"text":"Humboldt State University, Department of Fisheries Biology, Arcata, CA 95521","active":true,"usgs":false}],"preferred":false,"id":732651,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pinnix, William D.","contributorId":203925,"corporation":false,"usgs":false,"family":"Pinnix","given":"William","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":732666,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Rupert, Derek L.","contributorId":203919,"corporation":false,"usgs":false,"family":"Rupert","given":"Derek","email":"","middleInitial":"L.","affiliations":[{"id":36757,"text":"U.S. Fish and Wildlife Service, Arcata FWO, Arcata, CA 95521","active":true,"usgs":false}],"preferred":false,"id":732650,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196367,"text":"70196367 - 2018 - Relating river discharge and water temperature to the recruitment of age‐0 White Sturgeon (Acipenser transmontanus Richardson, 1836) in the Columbia River using over‐dispersed catch data","interactions":[],"lastModifiedDate":"2018-04-04T11:04:31","indexId":"70196367","displayToPublicDate":"2018-04-04T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2166,"text":"Journal of Applied Ichthyology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Relating river discharge and water temperature to the recruitment of age‐0 White Sturgeon (Acipenser transmontanus Richardson, 1836) in the Columbia River using over‐dispersed catch data","title":"Relating river discharge and water temperature to the recruitment of age‐0 White Sturgeon (Acipenser transmontanus Richardson, 1836) in the Columbia River using over‐dispersed catch data","docAbstract":"The goals were to (i) determine if river discharge and water temperature during various early life history stages were predictors of age‐0 White Sturgeon, Acipenser transmontanus, recruitment, and (ii) provide an example of how over‐dispersed catch data, including data with many zero observations, can be used to better understand the effects of regulated rivers on the productivity of depressed sturgeon populations. An information theoretic approach was used to develop and select negative binomial and zero‐inflated negative binomial models that model the relation of age‐0 White Sturgeon survey data from three contiguous Columbia River reservoirs to river discharge and water temperature during spawning, egg incubation, larval, and post‐larval phases. Age‐0 White Sturgeon were collected with small mesh gill nets in The Dalles and John Day reservoirs from 1997 to 2014 and a bottom trawl in Bonneville Reservoir from 1989 to 2006. Results suggest that seasonal river discharge was positively correlated with age‐0 recruitment; notably that discharge, 16 June–31 July was positively correlated to age‐0 recruitment in all three reservoirs. The best approximating models for two of the three reservoirs also suggest that seasonal water temperature may be a determinant of age‐0 recruitment. Our research demonstrates how over‐dispersed catch data can be used to better understand the effects of environmental conditions on sturgeon populations caused by the construction and operation of dams.
","language":"English","publisher":"Wiley","doi":"10.1111/jai.13570","usgsCitation":"Counihan, T.D., and Chapman, C.G., 2018, Relating river discharge and water temperature to the recruitment of age‐0 White Sturgeon (Acipenser transmontanus Richardson, 1836) in the Columbia River using over‐dispersed catch data: Journal of Applied Ichthyology, v. 34, no. 2, p. 279-289, https://doi.org/10.1111/jai.13570.","productDescription":"11 p.","startPage":"279","endPage":"289","ipdsId":"IP-074421","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":460965,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jai.13570","text":"Publisher Index Page"},{"id":353138,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Columbia River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.12353515624999,\n 45.213003555993964\n ],\n [\n -117.12524414062501,\n 45.213003555993964\n ],\n [\n -117.12524414062501,\n 46.800059446787316\n ],\n [\n -124.12353515624999,\n 46.800059446787316\n ],\n [\n -124.12353515624999,\n 45.213003555993964\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"34","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-23","publicationStatus":"PW","scienceBaseUri":"5afee6e8e4b0da30c1bfbf33","contributors":{"authors":[{"text":"Counihan, Timothy D. 0000-0003-4967-6514 tcounihan@usgs.gov","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":4211,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy","email":"tcounihan@usgs.gov","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":732626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Chapman, Colin G.","contributorId":197963,"corporation":false,"usgs":false,"family":"Chapman","given":"Colin","email":"","middleInitial":"G.","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":732627,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196313,"text":"ofr20171157 - 2018 - Barrier-island and estuarine-wetland physical-change assessment after Hurricane Sandy","interactions":[],"lastModifiedDate":"2025-05-13T16:22:30.827212","indexId":"ofr20171157","displayToPublicDate":"2018-04-03T10:15:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1157","title":"Barrier-island and estuarine-wetland physical-change assessment after Hurricane Sandy","docAbstract":"The Nation’s eastern coast is fringed by beaches, dunes, barrier islands, wetlands, and bluffs. These natural coastal barriers provide critical benefits and services, and can mitigate the impact of storms, erosion, and sea-level rise on our coastal communities. Waves and storm surge resulting from Hurricane Sandy, which made landfall along the New Jersey coast on October 29, 2012, impacted the U.S. coastline from North Carolina to Massachusetts, including Assateague Island, Maryland and Virginia, and the Delmarva coastal system. The storm impacts included changes in topography, coastal morphology, geology, hydrology, environmental quality, and ecosystems.
In the immediate aftermath of the storm, light detection and ranging (lidar) surveys from North Carolina to New York documented storm impacts to coastal barriers, providing a baseline to assess vulnerability of the reconfigured coast. The focus of much of the existing coastal change assessment is along the ocean-facing coastline; however, much of the coastline affected by Hurricane Sandy includes the estuarine-facing coastlines of barrier-island systems. Specifically, the wetland and back-barrier shorelines experienced substantial change as a result of wave action and storm surge that occurred during Hurricane Sandy (see also USGS photograph, http://coastal.er.usgs.gov/hurricanes/sandy/photo-comparisons/virginia.php). Assessing physical shoreline and wetland change (land loss as well as land gains) can help to determine the resiliency of wetland systems that protect adjacent habitat, shorelines, and communities.
To address storm impacts to wetlands, a vulnerability assessment should describe both long-term (for example, several decades) and short-term (for example, Sandy’s landfall) extent and character of the interior wetlands and the back-barrier-shoreline changes. The objective of this report is to describe several new wetland vulnerability assessments based on the detailed physical changes estimated from observations. The scope includes understanding changes caused by both short- and long-term processes using both remotely sensed and in situ observations to characterize changes to the wetland in terms of accretion/expansion and erosion/contraction. Accretion may be due to net vertical and (or) horizontal deposition, including estuarine-shoreline change due to overwash. Wetland erosion may be due to elevated waves and water levels in the estuary itself. We included additional information based on wave runup and storm-surge elevations based on models and elevation data. We then developed a predictive assessment for wetland vulnerability that describes the likelihood of changes of the estuarine shoreline and the landward extent of sand overwash driven by processes occurring on the ocean-facing shoreline. This assessment is intended to be linked to the beach and dune vulnerability assessments that have been developed previously.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171157","usgsCitation":"Plant, N.G., Smith, K.E.L., Passeri, D.L., Smith, C.G., and Bernier, J.C., 2018, Barrier-island and estuarine-wetland physical-change assessment after Hurricane Sandy: U.S. Geological Survey Open-File Report 2017–1157, 36 p., https://doi.org/10.3133/ofr20171157.","productDescription":"viii, 36 p.","numberOfPages":"45","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073468","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":353051,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1157/coverthb.jpg"},{"id":353052,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1157/ofr20171157.pdf","text":"Report","size":"7.19 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1157"}],"contact":"Director, St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Coal combustion byproducts (CCBs), which are composed of fly ash, bottom ash, and flue gas desulfurization material, produced at the coal-fired San Juan Generating Station (SJGS), located in San Juan County, New Mexico, have been buried in former surface-mine pits at the San Juan Mine, also referred to as the San Juan Coal Mine, since operations began in the early 1970s. This report, prepared by the U.S. Geological Survey in cooperation with the Mining and Minerals Division of the New Mexico Energy, Minerals and Natural Resources Department, describes results of a hydrogeologic assessment, including numerical groundwater modeling, to identify the timing of groundwater recovery and potential pathways for groundwater transport of metals that may be leached from stored CCBs and reach hydrologic receptors after operations cease. Data collected for the hydrologic assessment indicate that groundwater in at least one centrally located reclaimed surface-mining pit has already begun to recover.
The U.S. Geological Survey numerical modeling package MODFLOW–NWT was used with MODPATH particle-tracking software to identify advective flow paths from CCB storage areas toward potential hydrologic receptors. Results indicate that groundwater at CCB storage areas will recover to the former steady state, or in some locations, groundwater may recover to a new steady state in 6,600 to 10,600 years at variable rates depending on the proximity to a residual cone-of-groundwater depression caused by mine dewatering and regional oil and gas pumping as well as on actual, rather than estimated, groundwater recharge and evapotranspirational losses. Advective particle-track modeling indicates that the number of particles and rates of advective transport will vary depending on hydraulic properties of the mine spoil, particularly hydraulic conductivity and porosity. Modeling results from the most conservative scenario indicate that particles can migrate from CCB repositories to either the Shumway Arroyo alluvium after 1,320 years and from there to the San Juan River alluvium after 1,520 years or from southernmost CCB repositories directly to the San Juan River alluvium after 2,400 years after the cessation of mining.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175155","collaboration":"Prepared in cooperation with the Mining and Minerals Division of the State of New Mexico Energy, Minerals and Natural Resources Department","usgsCitation":"Stewart, A.M., 2018, Hydrologic assessment and numerical simulation of groundwater flow, San Juan Mine, San Juan County, New Mexico, 2010–13: U.S. Geological Survey Scientific Investigations Report 2017–5155, 94 p., https://doi.org/10.3133/sir20175155.","productDescription":"Report: xi, 94 p.; Data Releases","numberOfPages":"110","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-080017","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":352877,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7Q81BJK","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Chemical analyses for arsenic, calcium, chloride, sodium, sulfate, sulfide and dissolved solids, August 2011 through December 2013, from groundwater sampled at or in the vicinity of the San Juan Coal Mine, New Mexico"},{"id":353249,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75719JV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW–NWT and MODPATH5 models used to identify potential flow paths from San Juan Mine to hydrologic receptors, San Juan County, New Mexico"},{"id":352876,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5155/sir20175155.pdf","text":"Report","size":"6.00 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5155"},{"id":352875,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5155/coverthb.jpg"}],"country":"United States","state":"New Mexico","county":"San Juan County","otherGeospatial":"San Juan Mine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.5,\n 36.7167\n ],\n [\n -108.1,\n 36.72099868793134\n ],\n [\n -108.1,\n 37\n ],\n [\n -108.5,\n 37\n ],\n [\n -108.5,\n 36.7167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque, NM 87113
One of the primary goals of biological assessment of streams is to identify which of a suite of chemical stressors is limiting their ecological potential. Elevated metal concentrations in streams are often associated with low pH, yet the effects of these two potentially limiting factors of freshwater biodiversity are rarely considered to interact beyond the effects of pH on metal speciation. Using a dataset from two continents, a biogeochemical model of the toxicity of metal mixtures (Al, Cd, Cu, Pb, Zn) and quantile regression, we addressed the relative importance of both pH and metals as limiting factors for macroinvertebrate communities. Current environmental quality standards for metals proved to be protective of stream macroinvertebrate communities and were used as a starting point to assess metal mixture toxicity. A model of metal mixture toxicity accounting for metal interactions was a better predictor of macroinvertebrate responses than a model considering individual metal toxicity. We showed that the direct limiting effect of pH on richness was of the same magnitude as that of chronic metal toxicity, independent of its influence on the availability and toxicity of metals. By accounting for the direct effect of pH on macroinvertebrate communities, we were able to determine that acidic streams supported less diverse communities than neutral streams even when metals were below no-effect thresholds. Through a multivariate quantile model, we untangled the limiting effect of both pH and metals and predicted the maximum diversity that could be expected at other sites as a function of these variables. This model can be used to identify which of the two stressors is more limiting to the ecological potential of running waters.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2017.12.097","usgsCitation":"Fornaroli, R., Ippolito, A., Tolkkinen, M.J., Mykra, H., Muotka, T., Balistrieri, L.S., and Schmidt, T., 2018, Disentangling the effects of low pH and metal mixture toxicity on macroinvertebrate diversity: Environmental Pollution, v. 235, p. 889-898, https://doi.org/10.1016/j.envpol.2017.12.097.","productDescription":"10 p.","startPage":"889","endPage":"898","ipdsId":"IP-079637","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":437964,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7R20ZJW","text":"USGS data release","linkHelpText":"Disentangling the effects of low pH and metal mixture toxicity on macroinvertebrate diversity: datasets"},{"id":353113,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"235","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6e9e4b0da30c1bfbf3d","contributors":{"authors":[{"text":"Fornaroli, Riccardo","contributorId":201354,"corporation":false,"usgs":false,"family":"Fornaroli","given":"Riccardo","email":"","affiliations":[],"preferred":false,"id":732575,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ippolito, Alessio","contributorId":201355,"corporation":false,"usgs":false,"family":"Ippolito","given":"Alessio","email":"","affiliations":[],"preferred":false,"id":732576,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tolkkinen, Mari J.","contributorId":201357,"corporation":false,"usgs":false,"family":"Tolkkinen","given":"Mari","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":732578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mykra, Heikki","contributorId":201358,"corporation":false,"usgs":false,"family":"Mykra","given":"Heikki","email":"","affiliations":[],"preferred":false,"id":732579,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Muotka, Timo","contributorId":201359,"corporation":false,"usgs":false,"family":"Muotka","given":"Timo","email":"","affiliations":[],"preferred":false,"id":732580,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Balistrieri, Laurie S. 0000-0002-6359-3849 balistri@usgs.gov","orcid":"https://orcid.org/0000-0002-6359-3849","contributorId":1406,"corporation":false,"usgs":true,"family":"Balistrieri","given":"Laurie","email":"balistri@usgs.gov","middleInitial":"S.","affiliations":[{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":732574,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"preferred":true,"id":732577,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196335,"text":"70196335 - 2018 - An epidemiological model of virus transmission in salmonid fishes of the Columbia River Basin","interactions":[],"lastModifiedDate":"2018-04-03T11:10:18","indexId":"70196335","displayToPublicDate":"2018-04-03T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"An epidemiological model of virus transmission in salmonid fishes of the Columbia River Basin","docAbstract":"We have developed a dynamic epidemiological model informed by records of viral presence and genotypes to evaluate potential transmission routes maintaining a viral pathogen in economically and culturally important anadromous fish populations. In the Columbia River Basin, infectious hematopoietic necrosis virus (IHNV) causes severe disease, predominantly in juvenile steelhead trout (Oncorhynchus mykiss) and less frequently in Chinook salmon (O. tshawytscha). Mortality events following IHNV infection can be devastating for individual hatchery programs. Despite reports of high local mortality and extensive surveillance efforts, there are questions about how viral transmission is maintained. Modeling this system offers important insights into disease transmission in natural aquatic systems, as well as about the data requirements for generating accurate estimates about transmission routes and infection probabilities. We simulated six scenarios in which testing rates and the relative importance of different transmission routes varied. The simulations demonstrated that the model accurately identified routes of transmission and inferred infection probabilities accurately when there was testing of all cohort-sites. When testing records were incomplete, the model accurately inferred which transmission routes exposed particular cohort-sites but generated biased infection probabilities given exposure. After validating the model and generating guidelines for result interpretation, we applied the model to data from 14 annual cohorts (2000–2013) at 24 focal sites in a sub-region of the Columbia River Basin, the lower Columbia River (LCR), to quantify the relative importance of potential transmission routes in this focal sub-region. We demonstrate that exposure to IHNV via the return migration of adult fish is an important route for maintaining IHNV in the LCR sub-region, and the probability of infection following this exposure was relatively high at 0.16. Although only 1% of cohort-sites experienced self-exposure by infected juvenile fish, this transmission route had the greatest probability of infection (0.22). Increased testing and/or determining whether transmission can occur from cohort-sites without testing records (e.g., determining there was no testing record because there were no fish at the cohort-site) are expected to improve inference about infection probabilities. Increased use of secure water supplies and continued use of biosecurity protocols may reduce IHNV transmission from adult fish and juvenile fish within the site, respectively, to juvenile salmonids at hatcheries. Models and conclusions from this study are potentially relevant to understanding the relative importance of transmission routes for other important aquatic pathogens in salmonids, including the agents of bacterial kidney disease and coldwater disease, and the basic approach may be useful for other pathogens and hosts in other geographic regions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2018.03.002","usgsCitation":"Ferguson, P.F., Breyta, R., Brito, I.L., Kurath, G., and LaDeau, S.L., 2018, An epidemiological model of virus transmission in salmonid fishes of the Columbia River Basin: Ecological Modelling, v. 377, p. 1-15, https://doi.org/10.1016/j.ecolmodel.2018.03.002.","productDescription":"15 p.","startPage":"1","endPage":"15","ipdsId":"IP-091422","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":468859,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2018.03.002","text":"Publisher Index Page"},{"id":353082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"377","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6eae4b0da30c1bfbf4f","contributors":{"authors":[{"text":"Ferguson, Paige F. B.","contributorId":203803,"corporation":false,"usgs":false,"family":"Ferguson","given":"Paige","email":"","middleInitial":"F. B.","affiliations":[{"id":36722,"text":"Department of Biological Sciences, University of Alabama, Box 870344, Tuscaloosa, AL 35487","active":true,"usgs":false}],"preferred":false,"id":732373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Breyta, Rachel","contributorId":150355,"corporation":false,"usgs":false,"family":"Breyta","given":"Rachel","affiliations":[],"preferred":false,"id":732374,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brito, Ilana L.","contributorId":177102,"corporation":false,"usgs":false,"family":"Brito","given":"Ilana","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":732375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kurath, Gael 0000-0003-3294-560X gkurath@usgs.gov","orcid":"https://orcid.org/0000-0003-3294-560X","contributorId":2629,"corporation":false,"usgs":true,"family":"Kurath","given":"Gael","email":"gkurath@usgs.gov","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":732372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"LaDeau, Shannon L.","contributorId":172640,"corporation":false,"usgs":false,"family":"LaDeau","given":"Shannon","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":732376,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70194955,"text":"sir20185007 - 2018 - Data analysis considerations for pesticides determined by National Water Quality Laboratory schedule 2437","interactions":[],"lastModifiedDate":"2021-05-28T14:27:51.335925","indexId":"sir20185007","displayToPublicDate":"2018-04-02T14:00:00","publicationYear":"2018","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":"2018-5007","title":"Data analysis considerations for pesticides determined by National Water Quality Laboratory schedule 2437","docAbstract":"In 2013, the U.S. Geological Survey National Water Quality Laboratory (NWQL) made a new method available for the analysis of pesticides in filtered water samples: laboratory schedule 2437. Schedule 2437 is an improvement on previous analytical methods because it determines the concentrations of 225 fungicides, herbicides, insecticides, and associated degradates in one method at similar or lower concentrations than previously available methods. Additionally, the pesticides included in schedule 2437 were strategically identified in a prioritization analysis that assessed likelihood of occurrence, prevalence of use, and potential toxicity. When the NWQL reports pesticide concentrations for analytes in schedule 2437, the laboratory also provides supplemental information useful to data users for assessing method performance and understanding data quality. That supplemental information is discussed in this report, along with an initial analysis of analytical recovery of pesticides in water-quality samples analyzed by schedule 2437 during 2013–2015. A total of 523 field matrix spike samples and their paired environmental samples and 277 laboratory reagent spike samples were analyzed for this report (1,323 samples total). These samples were collected in the field as part of the U.S. Geological Survey National Water-Quality Assessment groundwater and surface-water studies and as part of the NWQL quality-control program. This report reviews how pesticide samples are processed by the NWQL, addresses how to obtain all the data necessary to interpret pesticide concentrations, explains the circumstances that result in a reporting level change or the occurrence of a raised reporting level, and describes the calculation and assessment of recovery. This report also discusses reasons why a data user might choose to exclude data in an interpretive analysis and outlines the approach used to identify the potential for decreased data quality in the assessment of method recovery. The information provided in this report is essential to understanding pesticide data determined by schedule 2437 and should be reviewed before interpretation of these data.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185007","collaboration":"National Water Quality Program","usgsCitation":"Shoda, M.E., Nowell, L.H., Stone, W.W., Sandstrom, M.W., and Bexfield, L.M., 2018, Data analysis considerations for pesticides determined by National Water Quality Laboratory schedule 2437: U.S. Geological Survey Scientific Investigations Report 2018-5007, 458 p., https://doi.org/10.3133/sir20185007.","productDescription":"Report: vi, 458 p.; 2 Data Releases; Table","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-088656","costCenters":[{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":452,"text":"National Water Quality Laboratory","active":true,"usgs":true}],"links":[{"id":352965,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5007/sir20185007.pdf","text":"Report","size":"7.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5007"},{"id":352964,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5007/coverthb.jpg"},{"id":352966,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2018/5007/sir20185007_table4-v4.xlsx","text":"Table 4","size":"75.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Summary statistics for the recovery of schedule 2437 pesticides in lab reagent spikes, and groundwater and surface-water spike samples"},{"id":352967,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F75H7DS8","text":"USGS data release","description":"USGS data release","linkHelpText":"National Water-Quality Assessment Project replicate surface water and groundwater pesticide data analyzed by the USGS National Water Quality Laboratory schedule 2437, water years 2013–15"},{"id":352968,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QZ28G4","text":"USGS data release","description":"USGS data release","linkHelpText":"Recovery data for surface water, groundwater and lab reagent samples analyzed by the USGS National Water Quality Laboratory schedule 2437, water years 2013–15"}],"contact":"Program Coordinator, National Water Quality Program
U.S. Geological Survey
413 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
We analyzed a five year, high frequency time series generated by an in situ fluorescent dissolved organic matter (fDOM) sensor installed near the Connecticut River’s mouth, investigating high temporal resolution DOM dynamics in a larger watershed and longer time series than previously addressed. We identified a gradient between large, saturating summer fDOM responses to discharge and linear, subdued responses during colder months. Seasonal response patterns were not consistent with multiple linear regression. Alternatively, we binned measurements across the yearly cycle using environmental indices, such as temperature, and applied moving regression, a novel approach which produced superior fits to calendar day binning. Spatially averaged watershed soil temperature at 10 cm was the best overall index of discharge-fDOM response. DOM fractionation showed fDOM was primarily a surrogate for hydrophobic organic acid (HPOA) concentrations. HPOAs were highly correlated with discharge, but hydrophilics (HPIs) were not. Discharge dependent DOM concentrations driven by the HPOA fraction may be controlled by soil temperature and water table position relative to organic and mineral soil horizons. HPI concentrations were correlated with average watershed soil temperature at 10 cm but were rather stationary throughout the year, further indicating a consistent groundwater source for this nonfluorescent DOM. We present a resolved subseasonal empirical model of DOM concentrations and fluxes, showing that riverine DOM flux and quality depend heavily on seasonal terrestrial carbon dynamics and hydrologic flow paths. High frequency monitoring reveals readily discernible patterns demonstrating that upland biogeochemical signals are maintained even at this large watershed scale.
Lake Mead National Recreational Area (LMNRA) serves as critical habitat for several federally listed species and supplies water for municipal, domestic, and agricultural use in the Southwestern U.S. Contaminant sources and concentrations vary among the sub-basins within LMNRA. To investigate whether exposure to environmental contaminants is associated with alterations in male common carp (Cyprinus carpio) gamete quality and endocrine- and reproductive parameters, data were collected among sub-basins over 7 years (1999–2006). Endpoints included sperm quality parameters of motility, viability, mitochondrial membrane potential, count, morphology, and DNA fragmentation; plasma components were vitellogenin (VTG), 17ß-estradiol, 11-keto-testosterone, triiodothyronine, and thyroxine. Fish condition factor, gonadosomatic index, and gonadal histology parameters were also measured. Diminished biomarker effects were noted in 2006, and sub-basin differences were indicated by the irregular occurrences of contaminants and by several associations between chemicals (e.g., polychlorinated biphenyls, hexachlorobenzene, galaxolide, and methyl triclosan) and biomarkers (e.g., plasma thyroxine, sperm motility and DNA fragmentation). By 2006, sex steroid hormone and VTG levels decreased with subsequent reduced endocrine disrupting effects. The sperm quality bioassays developed and applied with carp complemented endocrine and reproductive data, and can be adapted for use with other species.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envres.2018.01.041","usgsCitation":"Jenkins, J.A., Rosen, M.R., Dale, R.O., Echols, K.R., Torres, L., Wieser, C.M., Kersten, C.A., and Goodbred, S., 2018, Sperm quality biomarkers complement reproductive and endocrine parameters in investigating environmental contaminants in common carp (Cyprinus carpio) from the Lake Mead National Recreation Area: Environmental Research, v. 163, p. 149-164, https://doi.org/10.1016/j.envres.2018.01.041.","productDescription":"16 p.","startPage":"149","endPage":"164","ipdsId":"IP-087819","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":468863,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envres.2018.01.041","text":"Publisher Index Page"},{"id":437967,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7NS0SS8","text":"USGS data 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Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1040","title":"Trends and habitat associations of waterbirds using the South Bay Salt Pond Restoration Project, San Francisco Bay, California","docAbstract":"The aim of the South Bay Salt Pond Restoration Project (hereinafter “Project”) is to restore 50–90 percent of former salt evaporation ponds to tidal marsh in San Francisco Bay (SFB). However, hundreds of thousands of waterbirds use these ponds over winter and during fall and spring migration. To ensure that existing waterbird populations are supported while tidal marsh is restored in the Project area, managers plan to enhance the habitat suitability of ponds by adding islands and berms to change pond topography, manipulating water salinity and depth, and selecting appropriate ponds to maintain for birds. To help inform these actions, we used 13 years of monthly (October–April) bird abundance data from Project ponds to (1) assess trends in waterbird abundance since the inception of the Project, and (2) evaluate which pond habitat characteristics were associated with highest abundances of different avian guilds and species. For comparison, we also evaluated waterbird abundance trends in active salt production ponds using 10 years of monthly survey data.
We assessed bird guild and species abundance trends through time, and created separate trend curves for Project and salt production ponds using data from every pond that was counted in a year. We divided abundance data into three seasons—fall (October–November), winter (December–February), and spring (March–April). We used the resulting curves to assess which periods had the highest bird abundance and to identify increasing or decreasing trends for each guild and species.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181040","usgsCitation":"De La Cruz, S.E.W., Smith, L.M., Moskal, S.M., Strong, C., Krause, J., Wang, Y., and Takekawa, J.Y., 2018, Trends and habitat associations of waterbirds using the South Bay Salt Pond Restoration Project, San Francisco Bay, California: U.S. Geological Survey Open-File Report 2018–1040, 136 p., https://doi.org/10.3133/ofr20181040.","productDescription":"viii, 136 p.","numberOfPages":"148","onlineOnly":"Y","ipdsId":"IP-080192","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":353069,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1040/coverthb.jpg"},{"id":353070,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1040/ofr20181040.pdf","text":"Report","size":"6.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1040"}],"country":"United States","state":"California","otherGeospatial":"South San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.20642089843749,\n 37.406164630829345\n ],\n [\n -121.90704345703124,\n 37.406164630829345\n ],\n [\n -121.90704345703124,\n 37.645771969647\n ],\n [\n -122.20642089843749,\n 37.645771969647\n ],\n [\n -122.20642089843749,\n 37.406164630829345\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) is a common soil contaminant at current and former military facilities, including many training and testing ranges. Because RDX is readily transported through soils to the subsurface, this nitramine explosive now also impacts groundwater and drinking water at numerous locations across the country. A significant issue with RDX contamination on ranges and at other military installations is that it often occurs over expansive areas, where in situ or ex situ treatment technologies are difficult to implement. One potential alternative for military ranges and other facilities is monitored natural attenuation (MNA), in which contaminant degradation by natural processes, including biodegradation, are evaluated. However, one limitation of this approach for RDX is the inability to accurately evaluate whether the nitramine is biodegrading under field conditions, as rates may be relatively slow. One potential technique to overcome this limitation is the use of compound-specific stable isotope analysis (CSIA), where biological contaminant destruction can be documented as changes in the ratio of stable isotopes of specific elements in a molecule; for RDX, ratios of 15N/14N and 13C/12C are relevant. The objective of this project is to validate a CSIA method to confirm and constrain rates of aerobic and anaerobic biodegradation of RDX at field sites. This technique can be utilized by DoD to provide critical data to support MNA as a remedy for treating this energetic in groundwater, and confirm the effectiveness of in situ enhanced bioremediation remedies. The stable isotopic composition of NO3- and NO2- was also measured when these anions co-occurred with RDX to evaluate whether these potential degradation products from RDX could be used to further demonstrate MNA in the field.
","language":"English","publisher":"U.S. Department of Defense, Environmental Science and Technology Certification Program","usgsCitation":"Hatzinger, P.B., Fuller, M.E., Sturchio, N.C., and Bohlke, J., 2018, Validation of stable isotope ratio analysis to document the biodegradation and natural attenuation of RDX, ESTCP Project ER-201208, 144 p.","productDescription":"144 p.","ipdsId":"IP-097005","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":359533,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":357693,"type":{"id":15,"text":"Index Page"},"url":"https://www.serdp-estcp.org/Program-Areas/Environmental-Restoration/Contaminated-Groundwater/Persistent-Contamination/ER-201208"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5befe5bde4b045bfcadf7f46","contributors":{"authors":[{"text":"Hatzinger, Paul B.","contributorId":149376,"corporation":false,"usgs":false,"family":"Hatzinger","given":"Paul","email":"","middleInitial":"B.","affiliations":[{"id":17721,"text":"Shaw Environmental, Princeton, NJ","active":true,"usgs":false}],"preferred":false,"id":746143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Mark E.","contributorId":192618,"corporation":false,"usgs":false,"family":"Fuller","given":"Mark","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":746144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sturchio, Neil C.","contributorId":149375,"corporation":false,"usgs":false,"family":"Sturchio","given":"Neil","email":"","middleInitial":"C.","affiliations":[{"id":15289,"text":"University of Illinois, Ven Te Chow Hydrosystems Laboratory","active":true,"usgs":false}],"preferred":false,"id":746145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":746142,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227850,"text":"70227850 - 2018 - Assessing the risk of dreissenid mussel invasion in Texas based on lake physical characteristics and potential for downstream dispersal","interactions":[],"lastModifiedDate":"2024-03-22T16:13:08.183249","indexId":"70227850","displayToPublicDate":"2018-04-01T11:10:35","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Assessing the risk of dreissenid mussel invasion in Texas based on lake physical characteristics and potential for downstream dispersal","docAbstract":"ebra mussels (Dreissena polymorpha) and quagga mussels (Dreissena bugensis) were likely introduced from Ponto-Caspian Eurasia to the Laurentian Great Lakes inadvertently via ballast water release in the 1980s and have since spread across the US, including Texas. Their spread into the state, including reservoirs in both Brazos River and Colorado River basins, has resulted in a need to delimit suitable dreissenid habitat and dispersal potential in Texas. The objective of our research was to assess invasion risk in Texas by 1) predicting distribution of suitable habitat of zebra and quagga mussels using Maxent models; 2) refining lake-specific predictions for present zebra mussels via collection of physicochemical data; and 3) assessing the potential for downstream spread of zebra mussels by applying environmental DNA (eDNA) methods in the Leon and Lampasas Rivers downstream from the invaded Lakes Belton and Stillhouse Hollow, respectively.
Maxent models did not predict the occurrence of suitable habitat for quagga mussels within Texas. However, our models accurately identified global zebra mussel habitat (AUC = 0.919), and Bioclim layers representing temperature and precipitation data both strongly influenced predictions. Predicted “hotspots” of suitable zebra mussel habitat in Texas occurred along the Red and Sabine Rivers of north and east Texas, as well as patches of suitable habitat in central Texas between the Colorado and Brazos Rivers and extending inland along the Gulf Coast. Most of the Texas panhandle, west Texas extending toward El Paso, and the Rio Grande valley were predicted to provide poor habitat suitability.
Collection of physicochemical data (dissolved oxygen, pH, specific conductance, and temperature on-site as well as laboratory analysis for Ca, N, and P) from zebra mussel invaded lakes and a subset of identified high-risk lakes of North and Central Texas, did not aid predictions. Visual inspection of biplots of the first three components of a principle component analysis, which together accounted for ~80% of data variability, did not reveal separation between invaded and uninvaded lakes, and logistic regression analysis also failed to identify predictive relationships between measured variables and invasion status.
Using eDNA analysis, we detected the presence of zebra mussel eDNA at 11 of 12 sites and up to at least 90.7 river km downstream from a pair of infested reservoirs. Rate of positive detection among water samples at each site ranged from 1/5 to 5/5, and within positive water samples, rate of detection among technical replicates ranged from 1/8 to 8/8, suggesting considerable heterogeneity in the zebra mussel eDNA signal in both rivers. Furthermore, no clear spatial pattern in detection rate occurred.
Thus, a monitoring strategy that combines traditional sampling (e.g. settlement substrate samplers and microscopy) at sites immediately below a dam, and transitioning to more sensitive eDNA analysis at distances further from the dam may represent the most successful strategy for detection of dreissenid mussel downstream dispersal. Overall, we have demonstrated that while quagga mussels do not appear to represent an invasive threat in Texas, suitable habitat for continuing zebra mussel invasion exists within Texas, and stream and river connections may contribute to their spread. The threat of continued expansion of this poster-child for negative invasive species impacts warrants further prevention efforts, management, and research.
","language":"English","publisher":"Texas Tech University","usgsCitation":"Barnes, M., and Patino, R., 2018, Assessing the risk of dreissenid mussel invasion in Texas based on lake physical characteristics and potential for downstream dispersal, 28 p.","productDescription":"28 p.","ipdsId":"IP-093396","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":426898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":426897,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://tpwd.texas.gov/landwater/water/aquatic-invasives/research2.phtml","linkFileType":{"id":5,"text":"html"}}],"country":"United States","otherGeospatial":"continental United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n 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Atlanta","active":true,"usgs":true}],"preferred":true,"id":832423,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196965,"text":"70196965 - 2018 - Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska","interactions":[],"lastModifiedDate":"2018-05-15T16:50:33","indexId":"70196965","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3907,"text":"Scientific Data","active":true,"publicationSubtype":{"id":10}},"title":"Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska","docAbstract":"Arctic tundra landscapes are composed of a complex mosaic of patterned ground features, varying in soil moisture, vegetation composition, and surface hydrology over small spatial scales (10–100 m). The importance of microtopography and associated geomorphic landforms in influencing ecosystem structure and function is well founded, however, spatial data products describing local to regional scale distribution of patterned ground or polygonal tundra geomorphology are largely unavailable. Thus, our understanding of local impacts on regional scale processes (e.g., carbon dynamics) may be limited. We produced two key spatiotemporal datasets spanning the Arctic Coastal Plain of northern Alaska (~60,000 km2) to evaluate climate-geomorphological controls on arctic tundra productivity change, using (1) a novel 30 m classification of polygonal tundra geomorphology and (2) decadal-trends in surface greenness using the Landsat archive (1999–2014). These datasets can be easily integrated and adapted in an array of local to regional applications such as (1) upscaling plot-level measurements (e.g., carbon/energy fluxes), (2) mapping of soils, vegetation, or permafrost, and/or (3) initializing ecosystem biogeochemistry, hydrology, and/or habitat modeling.
","language":"English","publisher":"Nature","doi":"10.1038/sdata.2018.58","usgsCitation":"Lara, M.J., Nitze, I., Grosse, G., and McGuire, A.D., 2018, Tundra landform and vegetation productivity trend maps for the Arctic Coastal Plain of northern Alaska: Scientific Data, v. 5, p. 1-10, https://doi.org/10.1038/sdata.2018.58.","productDescription":"Article number: 180058; 10 p.","startPage":"1","endPage":"10","ipdsId":"IP-088497","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468870,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/sdata.2018.58","text":"Publisher Index Page"},{"id":354201,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Arctic Coastal Plain","volume":"5","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5afee6ece4b0da30c1bfbf73","contributors":{"authors":[{"text":"Lara, Mark J.","contributorId":194640,"corporation":false,"usgs":false,"family":"Lara","given":"Mark","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":735152,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nitze, Ingmar","contributorId":191057,"corporation":false,"usgs":false,"family":"Nitze","given":"Ingmar","affiliations":[],"preferred":false,"id":735153,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grosse, Guido","contributorId":101475,"corporation":false,"usgs":true,"family":"Grosse","given":"Guido","affiliations":[{"id":34291,"text":"University of Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":735154,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McGuire, A. David 0000-0003-4646-0750 ffadm@usgs.gov","orcid":"https://orcid.org/0000-0003-4646-0750","contributorId":166708,"corporation":false,"usgs":true,"family":"McGuire","given":"A.","email":"ffadm@usgs.gov","middleInitial":"David","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":735151,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196860,"text":"70196860 - 2018 - Efficacy of using data from angler-caught Burbot to estimate population rate functions","interactions":[],"lastModifiedDate":"2018-05-07T11:07:24","indexId":"70196860","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Efficacy of using data from angler-caught Burbot to estimate population rate functions","docAbstract":"The effective management of a fish population depends on the collection of accurate demographic data from that population. Since demographic data are often expensive and difficult to obtain, developing cost‐effective and efficient collection methods is a high priority. This research evaluates the efficacy of using angler‐supplied data to monitor a nonnative population of Burbot Lota lota. Age and growth estimates were compared between Burbot collected by anglers and those collected in trammel nets from two Wyoming reservoirs. Collection methods produced different length‐frequency distributions, but no difference was observed in age‐frequency distributions. Mean back‐calculated lengths at age revealed that netted Burbot grew faster than angled Burbot in Fontenelle Reservoir. In contrast, angled Burbot grew slightly faster than netted Burbot in Flaming Gorge Reservoir. Von Bertalanffy growth models differed between collection methods, but differences in parameter estimates were minor. Estimates of total annual mortality (A) of Burbot in Fontenelle Reservoir were comparable between angled (A = 35.4%) and netted fish (33.9%); similar results were observed in Flaming Gorge Reservoir for angled (29.3%) and netted fish (30.5%). Beverton–Holt yield‐per‐recruit models were fit using data from both collection methods. Estimated yield differed by less than 15% between data sources and reservoir. Spawning potential ratios indicated that an exploitation rate of 20% would be required to induce recruitment overfishing in either reservoir, regardless of data source. Results of this study suggest that angler‐supplied data are useful for monitoring Burbot population dynamics in Wyoming and may be an option to efficiently monitor other fish populations in North America.
","language":"English","publisher":"Wiley","doi":"10.1002/nafm.10031","usgsCitation":"Brauer, T.A., Rhea, D.T., Walrath, J.D., and Quist, M.C., 2018, Efficacy of using data from angler-caught Burbot to estimate population rate functions: North American Journal of Fisheries Management, v. 38, no. 2, p. 346-354, https://doi.org/10.1002/nafm.10031.","productDescription":"9 p.","startPage":"346","endPage":"354","ipdsId":"IP-088701","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":353973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Green River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.2423095703125,\n 41.000629848685385\n ],\n [\n -109.324951171875,\n 41.000629848685385\n ],\n [\n -109.324951171875,\n 42.20817645934742\n ],\n [\n -110.2423095703125,\n 42.20817645934742\n ],\n [\n -110.2423095703125,\n 41.000629848685385\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"38","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-04-10","publicationStatus":"PW","scienceBaseUri":"5afee6ece4b0da30c1bfbf77","contributors":{"authors":[{"text":"Brauer, Tucker A.","contributorId":204716,"corporation":false,"usgs":false,"family":"Brauer","given":"Tucker","email":"","middleInitial":"A.","affiliations":[{"id":36977,"text":"Department of Fish and Wildlife Sciences, University of Idaho","active":true,"usgs":false}],"preferred":false,"id":734788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rhea, Darren T.","contributorId":204717,"corporation":false,"usgs":false,"family":"Rhea","given":"Darren","email":"","middleInitial":"T.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":734789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walrath, John D.","contributorId":204718,"corporation":false,"usgs":false,"family":"Walrath","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":36596,"text":"Wyoming Game and Fish Department","active":true,"usgs":false}],"preferred":false,"id":734790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Quist, Michael C. 0000-0001-8268-1839 mquist@usgs.gov","orcid":"https://orcid.org/0000-0001-8268-1839","contributorId":171392,"corporation":false,"usgs":true,"family":"Quist","given":"Michael","email":"mquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":false,"id":734787,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70196876,"text":"70196876 - 2018 - Increased scientific rigor will improve reliability of research and effectiveness of management","interactions":[],"lastModifiedDate":"2018-05-08T13:21:48","indexId":"70196876","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Increased scientific rigor will improve reliability of research and effectiveness of management","docAbstract":"Rigorous science that produces reliable knowledge is critical to wildlife management because it increases accurate understanding of the natural world and informs management decisions effectively. Application of a rigorous scientific method based on hypothesis testing minimizes unreliable knowledge produced by research. To evaluate the prevalence of scientific rigor in wildlife research, we examined 24 issues of the Journal of Wildlife Management from August 2013 through July 2016. We found 43.9% of studies did not state or imply a priori hypotheses, which are necessary to produce reliable knowledge. We posit that this is due, at least in part, to a lack of common understanding of what rigorous science entails, how it produces more reliable knowledge than other forms of interpreting observations, and how research should be designed to maximize inferential strength and usefulness of application. Current primary literature does not provide succinct explanations of the logic behind a rigorous scientific method or readily applicable guidance for employing it, particularly in wildlife biology; we therefore synthesized an overview of the history, philosophy, and logic that define scientific rigor for biological studies. A rigorous scientific method includes 1) generating a research question from theory and prior observations, 2) developing hypotheses (i.e., plausible biological answers to the question), 3) formulating predictions (i.e., facts that must be true if the hypothesis is true), 4) designing and implementing research to collect data potentially consistent with predictions, 5) evaluating whether predictions are consistent with collected data, and 6) drawing inferences based on the evaluation. Explicitly testing a priori hypotheses reduces overall uncertainty by reducing the number of plausible biological explanations to only those that are logically well supported. Such research also draws inferences that are robust to idiosyncratic observations and unavoidable human biases. Offering only post hoc interpretations of statistical patterns (i.e., a posteriorihypotheses) adds to uncertainty because it increases the number of plausible biological explanations without determining which have the greatest support. Further, post hocinterpretations are strongly subject to human biases. Testing hypotheses maximizes the credibility of research findings, makes the strongest contributions to theory and management, and improves reproducibility of research. Management decisions based on rigorous research are most likely to result in effective conservation of wildlife resources.
","language":"English","publisher":"Wiley","doi":"10.1002/jwmg.21413","usgsCitation":"Sells, S.N., Bassing, S.B., Barker, K.J., Forshee, S.C., Keever, A., Goerz, J.W., and Mitchell, M.S., 2018, Increased scientific rigor will improve reliability of research and effectiveness of management: Journal of Wildlife Management, v. 82, no. 3, p. 485-494, https://doi.org/10.1002/jwmg.21413.","productDescription":"10 p.","startPage":"485","endPage":"494","ipdsId":"IP-091041","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468873,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.21413","text":"Publisher Index Page"},{"id":354011,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"82","issue":"3","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2018-01-14","publicationStatus":"PW","scienceBaseUri":"5afee6ece4b0da30c1bfbf75","contributors":{"authors":[{"text":"Sells, Sarah N.","contributorId":171706,"corporation":false,"usgs":false,"family":"Sells","given":"Sarah","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":734896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bassing, Sarah B.","contributorId":198688,"corporation":false,"usgs":false,"family":"Bassing","given":"Sarah","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":734897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barker, Kristin J.","contributorId":204755,"corporation":false,"usgs":false,"family":"Barker","given":"Kristin","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":734898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Forshee, Shannon C.","contributorId":204756,"corporation":false,"usgs":false,"family":"Forshee","given":"Shannon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":734899,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Keever, Allison","contributorId":187743,"corporation":false,"usgs":false,"family":"Keever","given":"Allison","email":"","affiliations":[],"preferred":false,"id":734900,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Goerz, James W.","contributorId":204757,"corporation":false,"usgs":false,"family":"Goerz","given":"James","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":734901,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mitchell, Michael S. 0000-0002-0773-6905 mmitchel@usgs.gov","orcid":"https://orcid.org/0000-0002-0773-6905","contributorId":3716,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"mmitchel@usgs.gov","middleInitial":"S.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":734889,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70196473,"text":"70196473 - 2018 - Springs as hydrologic refugia in a changing climate? A remote sensing approach","interactions":[],"lastModifiedDate":"2018-04-10T16:51:18","indexId":"70196473","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Springs as hydrologic refugia in a changing climate? A remote sensing approach","docAbstract":"Spring‐fed wetlands are ecologically important habitats in arid and semi‐arid regions. Springs have been suggested as possible hydrologic refugia from droughts and climate change; however, springs that depend on recent precipitation or snowmelt for recharge may be vulnerable to warming and drought intensification. Springs that are expected to maintain their ecohydrologic function in a warmer, drier climate may be priorities for conservation and restoration. Identifying such springs is difficult because many springs lack hydrologic records of adequate temporal extent and resolution to assess their resilience to water cycle changes. This study demonstrates proof‐of‐concept for the assessment of certain spring types (i.e., helocrene, hypocrene, and hillslope springs) in terms of hydrologic and ecological resilience to climatic water stress using freely available remote‐sensing and climate data. We used the Normalized Difference Vegetation Index (NDVI) from 1985 through 2011 to delineate surface‐moisture zones (SMZs) associated with 39 clusters of 172 springs in a montane sage‐steppe landscape in southeastern Oregon, USA. We developed and synthesized seven NDVI‐based indicators of SMZ resilience to interannual changes in water availability: (1) mean and (2) standard deviation of July NDVI; (3) mean difference in July NDVI and (4) difference in coefficient of variation for July NDVI between each SMZ and its surrounding watershed; (5) response of SMZ July NDVI to 90‐day antecedent precipitation; (6) response of SMZ July NDVI to the previous winter's snowpack; and (7) range of NDVI values from an exceptionally wet year followed by three dry years. Because all resilience indicators were highly inter‐correlated, we derived an overall metric of SMZ resilience using principal components analysis that accounted for 66% of total variance. This overall resilience score was positively correlated with SMZ elevation, slope, mean annual precipitation, and with the number of associated springs. Resilience was greater for SMZs on topographically shaded, north‐facing slopes. Several high‐resilience SMZs were located immediately below persistent snowbanks, suggesting a possible source of steady recharge throughout the growing season. The approach presented here—if combined with field assessments of spring hydrogeology, discharge, and groundwater age—could help identify spring‐fed wetlands that are most likely to serve as hydrologic refugia from climate change.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.2155","usgsCitation":"Cartwright, J.M., and Johnson, H.M., 2018, Springs as hydrologic refugia in a changing climate? A remote sensing approach: Ecosphere, v. 9, no. 3, p. 1-22, https://doi.org/10.1002/ecs2.2155.","productDescription":"e02155; 22 p.","startPage":"1","endPage":"22","ipdsId":"IP-088217","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":468874,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2155","text":"Publisher Index Page"},{"id":353310,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Steens Mountain Cooperative Manage-ment and Protection Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 42.3333\n ],\n [\n -118.1667,\n 42.3333\n ],\n [\n -118.1667,\n 43.1667\n ],\n [\n -119,\n 43.1667\n ],\n [\n -119,\n 42.3333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","issue":"3","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-03-24","publicationStatus":"PW","scienceBaseUri":"5afee6ede4b0da30c1bfbf93","contributors":{"authors":[{"text":"Cartwright, Jennifer M. 0000-0003-0851-8456 jmcart@usgs.gov","orcid":"https://orcid.org/0000-0003-0851-8456","contributorId":5386,"corporation":false,"usgs":true,"family":"Cartwright","given":"Jennifer","email":"jmcart@usgs.gov","middleInitial":"M.","affiliations":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":581,"text":"Tennessee Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733120,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Henry M. 0000-0002-7571-4994 hjohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7571-4994","contributorId":869,"corporation":false,"usgs":true,"family":"Johnson","given":"Henry","email":"hjohnson@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":733121,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70196563,"text":"70196563 - 2018 - Genome-wide SNP data and morphology support the distinction of two new species of Kovarikia Soleglad, Fet & Graham, 2014 endemic to California (Scorpiones, Vaejovidae)","interactions":[],"lastModifiedDate":"2018-04-17T10:09:27","indexId":"70196563","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3808,"text":"ZooKeys","active":true,"publicationSubtype":{"id":10}},"title":"Genome-wide SNP data and morphology support the distinction of two new species of Kovarikia Soleglad, Fet & Graham, 2014 endemic to California (Scorpiones, Vaejovidae)","docAbstract":"Morphologically conserved taxa such as scorpions represent a challenge to delimit. We recently discovered populations of scorpions in the genus Kovarikia Soleglad, Fet & Graham, 2014 on two isolated mountain ranges in southern California. We generated genome-wide single nucleotide polymorphism data and used Bayes factors species delimitation to compare alternative species delimitation scenarios which variously placed scorpions from the two localities with geographically adjacent species or into separate lineages. We also estimated a time-calibrated phylogeny of Kovarikia and examined and compared the morphology of preserved specimens from across its distribution. Genetic results strongly support the distinction of two new lineages, which we describe and name here. Morphology among the species of Kovarikia was relatively conserved, despite deep genetic divergences, consistent with recent studies of stenotopic scorpions with limited vagility. Phylogeographic structure discovered in several previously described species also suggests additional cryptic species are probably present in the genus.
","language":"English","publisher":"Pensoft","doi":"10.3897/zookeys.739.20628","usgsCitation":"Bryson, R.W., Wood, D.A., Graham, M.R., Soleglad, M.E., and McCormack, J., 2018, Genome-wide SNP data and morphology support the distinction of two new species of Kovarikia Soleglad, Fet & Graham, 2014 endemic to California (Scorpiones, Vaejovidae): ZooKeys, v. 739, p. 79-106, https://doi.org/10.3897/zookeys.739.20628.","productDescription":"28 p.","startPage":"79","endPage":"106","ipdsId":"IP-097285","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468872,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/zookeys.739.20628","text":"Publisher Index Page"},{"id":353478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 32.5\n ],\n [\n -116.5,\n 32.5\n ],\n [\n -116.5,\n 34.261756524459805\n ],\n [\n -119,\n 34.261756524459805\n ],\n [\n -119,\n 32.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"739","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-02-22","publicationStatus":"PW","scienceBaseUri":"5afee6ede4b0da30c1bfbf8b","contributors":{"authors":[{"text":"Bryson, Robert W. Jr.","contributorId":204308,"corporation":false,"usgs":false,"family":"Bryson","given":"Robert","suffix":"Jr.","email":"","middleInitial":"W.","affiliations":[{"id":36911,"text":"University of Washington, Seattle; Occidental College","active":true,"usgs":false}],"preferred":false,"id":733607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wood, Dustin A. 0000-0002-7668-9911 dawood@usgs.gov","orcid":"https://orcid.org/0000-0002-7668-9911","contributorId":4179,"corporation":false,"usgs":true,"family":"Wood","given":"Dustin","email":"dawood@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":733606,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Matthew R.","contributorId":196613,"corporation":false,"usgs":false,"family":"Graham","given":"Matthew","email":"","middleInitial":"R.","affiliations":[{"id":34649,"text":"Eastern Connectictut State University","active":true,"usgs":false}],"preferred":false,"id":733608,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Soleglad, Michael E.","contributorId":204309,"corporation":false,"usgs":false,"family":"Soleglad","given":"Michael","email":"","middleInitial":"E.","affiliations":[{"id":36912,"text":"Winchester, CA","active":true,"usgs":false}],"preferred":false,"id":733609,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McCormack, John E.","contributorId":204310,"corporation":false,"usgs":false,"family":"McCormack","given":"John E.","affiliations":[{"id":36913,"text":"Occidental College","active":true,"usgs":false}],"preferred":false,"id":733610,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70197995,"text":"70197995 - 2018 - Improving near‐real‐time coseismic landslide models: Lessons learned from the 2016 Kaikōura, New Zealand, earthquake","interactions":[],"lastModifiedDate":"2018-07-05T10:23:59","indexId":"70197995","displayToPublicDate":"2018-04-01T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Improving near‐real‐time coseismic landslide models: Lessons learned from the 2016 Kaikōura, New Zealand, earthquake","docAbstract":"The U.S. Geological Survey (USGS) is developing near‐real‐time global earthquake‐triggered‐landslide products to augment the USGS Prompt Assessment of Global Earthquakes for Response (PAGER) system. The 14 November 2016
Interactions between wildlife hosts act as transmission routes for directly transmitted pathogens and vary in ways that affect transmission efficiency. Identifying drivers of contact variation can allow both contact inference and estimation of transmission dynamics despite limited data. In desert tortoises, mating strategy, burrow use and seasonal change influence numerous behaviours and likely shape contact patterns. In this study, we ask to what extent tortoise contact behaviour varies between sexes and seasons, and whether space or burrow-use data can be used to infer contact characteristics consistent with those recorded by proximity loggers. We identified sex and season-biased contact behaviour in both wild and captive populations indicative of female-female avoidance and seasonal male mate-seeking behaviour. Space and burrow-use patterns were informative, but did not always predict the extent of sex or seasonal biases on contact. We discuss the implications these findings have for transmission patterns and disease mitigation in tortoise populations.
","language":"English","publisher":"Brill","doi":"10.1163/1568539X-00003477","usgsCitation":"Aiello, C.M., Esque, T., Nussear, K.E., Emblidge, P., and Hudson, P.J., 2018, Associating sex-biased and seasonal behaviour with contact patterns and transmission risk in Gopherus agassizii: Behaviour, v. 155, no. 7-9, p. 585-619, https://doi.org/10.1163/1568539X-00003477.","productDescription":"35 p.","startPage":"585","endPage":"619","ipdsId":"IP-094040","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":353642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"155","issue":"7-9","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5afee6ede4b0da30c1bfbf89","contributors":{"authors":[{"text":"Aiello, Christina M. 0000-0002-2399-5464 caiello@usgs.gov","orcid":"https://orcid.org/0000-0002-2399-5464","contributorId":5617,"corporation":false,"usgs":true,"family":"Aiello","given":"Christina","email":"caiello@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":733823,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Esque, Todd 0000-0002-4166-6234 tesque@usgs.gov","orcid":"https://orcid.org/0000-0002-4166-6234","contributorId":195896,"corporation":false,"usgs":true,"family":"Esque","given":"Todd","email":"tesque@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":733822,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nussear, K. E.","contributorId":204375,"corporation":false,"usgs":false,"family":"Nussear","given":"K.","email":"","middleInitial":"E.","affiliations":[{"id":36924,"text":"Univerisity of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":733824,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Emblidge, P. G.","contributorId":204376,"corporation":false,"usgs":false,"family":"Emblidge","given":"P. G.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":733825,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hudson, Peter J.","contributorId":204377,"corporation":false,"usgs":false,"family":"Hudson","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":733826,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70229333,"text":"70229333 - 2018 - A multistate open robust design: population dynamics, reproductive effort, and phenology of sea turtles from tagging data","interactions":[],"lastModifiedDate":"2022-03-03T23:44:02.137553","indexId":"70229333","displayToPublicDate":"2018-03-31T17:32:47","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1459,"text":"Ecological Monographs","active":true,"publicationSubtype":{"id":10}},"title":"A multistate open robust design: population dynamics, reproductive effort, and phenology of sea turtles from tagging data","docAbstract":"Understanding population dynamics, and how it is influenced by exogenous and endogenous factors, is important to the study and conservation of species. Moreover, for migratory species, the phenology and duration of use of a given location can also influence population structure and dynamics. For many species, breeding abundance, survival, and reproductive performance, as well as phenology of nesting, are often the most accessible, and therefore practical, elements of their life history to study. For a population of hawksbill sea turtles (Eretmochelys imbricata), we modeled population change for nesters and total adult females, survival, and breeding probability, from 25 years of intensive tagging data. We modeled breeding probability as a function of the number of years since last breeding, and tested for differences between neophyte and experienced nesters. For each year, we also estimated the number of clutches deposited per female, and phenology of use, for neophytes and experienced nesters. In order to implement the analysis we developed a novel generalized multistate open robust design mark-recapture modeling framework, with parameters for survival and transition probabilities, and for each primary period, state structure and arrival, persistence, and detection probabilities. Derived parameters included abundance of observable and unobservable components of the population, residence time, expected arrival and departure periods, and per-period intensity of study area use. Abundance of nesters increased over most of the time series. Survival probability was 0.935 (se = 0.01). All hawksbills skipped at least one year of nesting. Breeding probability increased by skipping a second year, but then decreased thereafter. Subsequent breeding probability was lower for neophyte nesters than for experienced nesters, but the effect was weaker than the effect of years since breeding. Clutch frequency varied by year, with no discernable pattern of differences between neophytes and experienced nesters. Mean arrival and departure dates also varied, with a slight shift of nesting activity to earlier in the season. The multistate open robust design model developed here provides a flexible framework for modeling the dynamics of structured migratory populations, and the phenology and duration of their seasonal use of study areas.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecm.1329","usgsCitation":"Kendall, W.L., Stapleton, S., White, G., Richardson, J.I., Pearson, K., and Mason, P., 2018, A multistate open robust design: population dynamics, reproductive effort, and phenology of sea turtles from tagging data: Ecological Monographs, v. 89, no. 1, e01329, 17 p., https://doi.org/10.1002/ecm.1329.","productDescription":"e01329, 17 p.","ipdsId":"IP-092105","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":396735,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Antigua","otherGeospatial":"Caribbean, Jumby Bay Leeward Islands, Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -61.7677116394043,\n 17.148171901233166\n ],\n [\n -61.74179077148437,\n 17.148171901233166\n ],\n [\n -61.74179077148437,\n 17.163918137304176\n ],\n [\n -61.7677116394043,\n 17.163918137304176\n ],\n [\n -61.7677116394043,\n 17.148171901233166\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"89","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-08-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837068,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stapleton, Seth","contributorId":287796,"corporation":false,"usgs":false,"family":"Stapleton","given":"Seth","affiliations":[{"id":54555,"text":"umn","active":true,"usgs":false}],"preferred":false,"id":837067,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Gary C.","contributorId":287795,"corporation":false,"usgs":false,"family":"White","given":"Gary C.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":837066,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richardson, James I.","contributorId":287794,"corporation":false,"usgs":false,"family":"Richardson","given":"James","email":"","middleInitial":"I.","affiliations":[{"id":54555,"text":"umn","active":true,"usgs":false}],"preferred":false,"id":837065,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pearson, Kristen N.","contributorId":287793,"corporation":false,"usgs":false,"family":"Pearson","given":"Kristen N.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":837064,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Mason, Peri","contributorId":287792,"corporation":false,"usgs":false,"family":"Mason","given":"Peri","email":"","affiliations":[{"id":32856,"text":"Queens College","active":true,"usgs":false}],"preferred":false,"id":837063,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70196047,"text":"ofr20181038 - 2018 - Factors affecting long-term trends in surface-water quality in the Gwynns Falls watershed, Baltimore City and County, Maryland, 1998–2016","interactions":[],"lastModifiedDate":"2018-03-30T16:32:53","indexId":"ofr20181038","displayToPublicDate":"2018-03-30T16:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2018-1038","title":"Factors affecting long-term trends in surface-water quality in the Gwynns Falls watershed, Baltimore City and County, Maryland, 1998–2016","docAbstract":"Factors affecting water-quality trends in urban streams are not well understood, despite current regulatory requirements and considerable ongoing investments in gray and green infrastructure. To address this gap, long-term water-quality trends and factors affecting these trends were examined in the Gwynns Falls, Maryland, watershed during 1998–2016 in cooperation with Blue Water Baltimore. Data on water-quality constituents and potential factors of influence were obtained from multiple sources and compiled for analysis, with a focus on data collected as part of the National Science Foundation funded Long-Term Ecological Research project, the Baltimore Ecosystem Study.
Variability in climate (specifically, precipitation) and land cover can overwhelm actions taken to improve water quality and can present challenges for meeting regulatory goals. Analysis of land cover during 2001–11 in the Gwynns Falls watershed indicated minimal change during the study time frame; therefore, land-cover change is likely not a factor affecting trends in water quality. However, a modest increase in annual precipitation and a significant increase in winter precipitation were apparent in the region. A higher proportion of runoff producing storms was observed in the winter and a lower proportion in the summer, indicating that climate change may affect water quality in the watershed. The increase in precipitation was not reflected in annual or seasonal trends of streamflow in the watershed. Nonetheless, these precipitation changes may exacerbate the inflow and infiltration of water to gray infrastructure and reduce the effectiveness of green infrastructure. For streamflow and most water-quality constituents examined, no discernable trends were noted over the timeframe examined. Despite the increases in precipitation, no trends were observed for annual or seasonal discharge at the various sites within the study area. In some locations, nitrate, phosphate, and total nitrogen show downward trends, and total phosphorus and chloride show upward trends.
Sanitary sewer overflows (gray infrastructure) and best management practices (green infrastructure) were identified as factors affecting water-quality change. The duration of sanitary sewer overflows was positively correlated with annual loads of nutrients and bacteria, and the drainage area of best management practices was negatively correlated with annual loads of phosphate and sulfate. Results of the study indicate that continued investments in gray and green infrastructure are necessary for urban water-quality improvement. Although this outcome is not unexpected, long-term datasets such as the one used in this study, allow the effects of gray and green infrastructures to be quantified.
Results of this study have implications for the Gwynns Falls watershed and its residents and Baltimore City and County managers. Moreover, outcomes are relevant to other watersheds in the metropolitan region that do not have the same long-term dataset. Further, this study has established a framework for ongoing statistical analysis of primary factors affecting urban water-quality trends as regulatory programs mature.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181038","collaboration":"Prepared in cooperation with Blue Water Baltimore","usgsCitation":"Majcher, E.H., Woytowitz, E.L., Reisinger, A.J., and Groffman, P.M., 2018, Factors affecting long-term trends in surface-water quality in the Gwynns Falls watershed, Baltimore City and County, Maryland, 1998–2016: U.S. Geological Survey Open-File Report 2018–1038, 27 p., https://doi.org/10.3133/ofr20181038.","productDescription":"Report: viii, 27 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-094705","costCenters":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"links":[{"id":352999,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1038/coverthb2.jpg"},{"id":353000,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1038/ofr20181038.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1038"},{"id":353001,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76T0KTJ","text":"USGS data release","description":"USGS data release","linkHelpText":"Nutrient, bacteria, ammonia, total Kjeldahl nitrogen, & total suspended solids annual loads; green & gray infrastructure; land cover change; & climate data in the Gwynns Falls subwatersheds, Baltimore, Maryland, 1998-2016 "}],"country":"United States","state":"Maryland","county":"Baltimore County","city":"Baltimore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.8833,\n 39.5\n ],\n [\n -76.5,\n 39.5\n ],\n [\n -76.5,\n 39.1667\n ],\n [\n -76.8833,\n 39.1667\n ],\n [\n -76.8833,\n 39.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, MD-DE-DC Water Science Center
U.S. Geological Survey
5522 Research Park Drive
Baltimore, MD 21228