Lake Michigan supports popular fisheries for Chinook Salmon Oncorhynchus tshawytscha that have been sustained by stocking since the late 1960s. Natural recruitment of Chinook Salmon in Lake Michigan has increased in the past few decades and currently contributes more than 50% of Chinook Salmon recruits. We hypothesized that selective forces differ for naturalized populations born in the wild and hatchery populations, resulting in divergent life history characteristics with implications for Chinook Salmon population production and the Lake Michigan fishery. First, we conducted a historical analysis to determine if life history characteristics changed through time as the Chinook Salmon population became increasingly naturalized. Next, we conducted a 2-year field study of naturalized and hatchery stocked Chinook Salmon spawning populations to quantify differences in fecundity, egg size, timing of spawning, and size at maturity. In general, our results did not indicate significant life history divergence between naturalized and hatchery-stocked Chinook Salmon populations in Lake Michigan. Although historical changes in adult sex ratio were correlated with the proportion of naturalized individuals, changes in weight at maturity were better explained by density-dependent factors. The field study revealed no divergence in fecundity, timing of spawning, or size at maturity, and only small differences in egg size (hatchery > naturalized). For the near future, our results suggest that the limited life history differences observed between Chinook Salmon of naturalized and hatchery origin will not lead to large differences in characteristics important to the dynamics of the population or fishery.
","language":"English","publisher":"Taylor & Francis","publisherLocation":"Abingdon, UK","doi":"10.1080/02755947.2016.1204392","usgsCitation":"Kerns, J., Rogers, M.W., Bunnell, D., Claramunt, R., and Collingsworth, P.D., 2016, Comparing life history characteristics of Lake Michigan’s naturalized and stocked Chinook Salmon: North American Journal of Fisheries Management, v. 36, no. 5, p. 1106-1118, https://doi.org/10.1080/02755947.2016.1204392.","productDescription":"13 p.","startPage":"1106","endPage":"1118","numberOfPages":"13","ipdsId":"IP-073139","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":331215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.803466796875,\n 45.336701909968134\n ],\n [\n -85.36376953125,\n 44.824708282300236\n ],\n [\n -85.71533203125,\n 44.41808794374846\n ],\n [\n -86.143798828125,\n 44.10336537791152\n ],\n [\n -86.12182617187499,\n 43.32517767999296\n ],\n [\n -85.924072265625,\n 42.68243539838623\n ],\n [\n -85.98999023437499,\n 42.35042512243457\n ],\n [\n -86.253662109375,\n 42.09822241118974\n ],\n [\n -86.6162109375,\n 41.63186741069748\n ],\n [\n -87.022705078125,\n 41.46742831254425\n ],\n [\n -87.593994140625,\n 41.45919537950706\n ],\n [\n -87.879638671875,\n 41.72213058512578\n ],\n [\n -87.989501953125,\n 42.15525946577863\n ],\n [\n -88.143310546875,\n 42.601619944327965\n ],\n [\n -88.11035156249999,\n 43.100982876188546\n ],\n [\n -87.967529296875,\n 43.67581809328341\n ],\n [\n -87.8466796875,\n 44.19795903948531\n ],\n [\n -87.6708984375,\n 44.53567453241317\n ],\n [\n -88.0224609375,\n 44.43377984606822\n ],\n [\n -88.1982421875,\n 44.56699093657141\n ],\n [\n -88.05541992187499,\n 45.058001435398275\n ],\n [\n -87.802734375,\n 45.336701909968134\n ],\n [\n -87.275390625,\n 45.66780526567164\n ],\n [\n -87.0556640625,\n 46.057985244793024\n ],\n [\n -86.7919921875,\n 46.23305294479828\n ],\n [\n -86.099853515625,\n 46.255846818480315\n ],\n [\n -85.25390625,\n 46.31658418182218\n ],\n [\n -84.74853515625,\n 46.14178273759234\n ],\n [\n -84.495849609375,\n 45.75985868785574\n ],\n [\n -84.803466796875,\n 45.336701909968134\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2016-08-31","publicationStatus":"PW","scienceBaseUri":"5836b8dce4b0d9329c801c51","contributors":{"authors":[{"text":"Kerns, Janice A","contributorId":150933,"corporation":false,"usgs":false,"family":"Kerns","given":"Janice A","affiliations":[{"id":18145,"text":"Wisconsin Cooperative Fishery Research Unit - 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2016 - Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system","interactions":[],"lastModifiedDate":"2016-11-29T10:22:40","indexId":"sir20165137","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5137","title":"Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system","docAbstract":"The hydrogeology and hydrologic characteristics of the Ozark Plateaus aquifer system were characterized as part of ongoing U.S. Geological Survey efforts to assess groundwater availability across the Nation. The need for such a study in the Ozark Plateaus physiographic province (Ozark Plateaus) is highlighted by increasing demand on groundwater resources by the 5.3 million people of the Ozark Plateaus, water-level declines in some areas, and potential impacts of climate change on groundwater availability. The subject study integrates knowledge gained through local investigation within a regional perspective to develop a regional conceptual model of groundwater flow in the Ozark Plateaus aquifer system (Ozark system), a key phase of groundwater availability assessment. The Ozark system extends across much of southern Missouri and northwestern and north-central Arkansas and smaller areas of southeastern Kansas and northeastern Oklahoma. The region is one of the major karst landscapes in the United States, and karst aquifers are predominant in the Ozark system. Groundwater flow is ultimately controlled by aquifer and confining unit lithologies and stratigraphic relations, geologic structure, karst development, and the character of surficial lithologies and regolith mantle. The regolith mantle is a defining element of Ozark Plateaus karst, affecting recharge, karst development, and vulnerability to surface-derived contaminants. Karst development is more advanced—as evidenced by larger springs, hydraulic characteristics, and higher well yields—in the Salem Plateau and in the northern part of the Springfield Plateau (generally north of the Arkansas-Missouri border) as compared with the southern part of the Springfield Plateau in Arkansas, largely due to thinner, less extensive regolith and purer carbonate lithology.
Precipitation is the ultimate source of all water to the Ozark system, and the hydrologic budget for the Ozark system includes inputs from recharge, losing-stream sections, and groundwater inflows and losses of water to gaining-stream sections, groundwater withdrawals, and surface-water and groundwater outflows to neighboring systems. Groundwater recharge, estimated by a soil-water-balance model, represents about 24 percent, or 11 inches, of 43.9 inches annual precipitation. Recharge is spatially variable, being greater in the northern Springfield Plateau and Salem Plateau than in the southern Springfield Plateau (generally south of the Arkansas border) because of differences in regolith mantle extent and thickness and carbonate lithology and hydraulic properties. Increased precipitation and decreased agricultural land use during the period 1951 through 2011 increased recharge by approximately 5 percent. Although all Ozark streams have losing, neutral, and gaining sections, they are dominantly gaining and are a net sink for groundwater with nearly 90 percent of groundwater recharge returned to springs and streams. Groundwater pumping is a small but important loss of water in the Ozark system hydrologic budget; water-level declines and local cones of depression have been observed around pumping centers and strong concerns exist over potential effects on stream and spring flow.
Data indicate that societal needs for freshwater resources in the Ozark Plateaus will continue to increase and will do so in the context of changing climate and hydrology. Groundwater will continue to be an important part of supporting these societal needs and also local ecosystems. The unique character and hydrogeologic variability across the Ozark system will control how the system responds to future stress. Groundwater of the Ozark system in the northern study area is more dynamic, has greater storage and larger flux, and has greater potential for further development than in the part of the study area south of the Arkansas-Missouri border. Further south in Arkansas, a line exists, roughly defined as 5 miles south of the Springfield Plateau-Boston Mountains boundary, beyond which further extensive municipal or commercial development appears unlikely under current economic and resource-need conditions. A small part of the Ozark system groundwater budget is currently drafted for use, leaving an apparently large component available for further development and use—particularly in the northern Springfield Plateau and Salem Plateau; however, the effects of increased pumping on groundwater’s role in maintaining ecosystems and ecosystem services are not quantitatively well understood, and the close relation between groundwater and surface water highlights the importance of further quantitative assessment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165137","collaboration":"Prepared in cooperation with the Groundwater Resources Program","usgsCitation":"Hays, P.D., Knierim, K.J., Breaker, Brian, Westerman, D.A., and Clark, B.R., 2016, Hydrogeology and hydrologic conditions of the Ozark Plateaus aquifer system: U.S. Geological Survey Scientific Investigations Report 2016–5137, 61 p., https://dx.doi.org/10.3133/sir20165137. \n\n","productDescription":"Report: vii, 61 p.; Appendixes: 1-2","numberOfPages":"73","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071467","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331147,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5137/coverthb.jpg"},{"id":331148,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5137/sir20165137.pdf","description":"SIR 2016–5137"},{"id":331149,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5137/downloads","text":"Appendix 1 & 2","description":"SIR 2016–5137 Appendix 1 & 2"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark Plateaus Aquifer System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1263427734375,\n 38.81831117374662\n ],\n [\n -90.3076171875,\n 38.89103282648846\n ],\n [\n -90.4559326171875,\n 38.8225909761771\n ],\n [\n -90.692138671875,\n 38.70694605159386\n ],\n [\n -90.8734130859375,\n 38.556757147352215\n ],\n [\n -91.0546875,\n 38.59970036588819\n ],\n [\n -91.2689208984375,\n 38.66835610151506\n ],\n [\n -91.3897705078125,\n 38.71551876930462\n ],\n [\n -91.571044921875,\n 38.68122173079789\n ],\n [\n -91.7083740234375,\n 38.70265930723801\n ],\n [\n -91.9171142578125,\n 38.65119833229951\n ],\n [\n -92.07092285156249,\n 38.57823196583313\n ],\n [\n -92.197265625,\n 38.586820096127674\n ],\n [\n -92.3345947265625,\n 38.6897975322717\n ],\n [\n -92.4884033203125,\n 38.90813299596705\n ],\n [\n -92.57080078125,\n 38.96368010198575\n ],\n [\n -92.74108886718749,\n 38.98503278695909\n ],\n [\n -92.87841796875,\n 38.997841307500714\n ],\n [\n -92.92236328125,\n 39.091699613104595\n ],\n [\n -92.9058837890625,\n 39.17691709496078\n ],\n [\n -92.8619384765625,\n 39.2407625100131\n ],\n [\n -92.9608154296875,\n 39.317300373271024\n ],\n [\n -93.0926513671875,\n 39.38101803294523\n ],\n [\n -93.2025146484375,\n 39.40224434029275\n ],\n [\n -93.350830078125,\n 39.30454987014581\n ],\n [\n -93.482666015625,\n 39.287545585410435\n ],\n [\n -93.70788574218749,\n 39.223742741391305\n ],\n [\n -93.98803710937499,\n 39.15136267949029\n ],\n [\n -95.2349853515625,\n 38.25112269630296\n ],\n [\n -95.2789306640625,\n 36.45221769643571\n ],\n [\n -95.0152587890625,\n 35.30840140169162\n ],\n [\n -94.19128417968749,\n 35.35321610123823\n ],\n [\n -93.834228515625,\n 35.47409160773029\n ],\n [\n -93.482666015625,\n 35.40696093270201\n ],\n [\n -93.16955566406249,\n 35.25459097465022\n ],\n [\n -93.065185546875,\n 35.15584570226544\n ],\n [\n -92.6641845703125,\n 35.08845057036537\n ],\n [\n -92.55432128906249,\n 34.95349314197422\n ],\n [\n -92.427978515625,\n 34.836349990763864\n ],\n [\n -92.1148681640625,\n 34.8183131456094\n ],\n [\n -90.3515625,\n 35.97800618085566\n ],\n [\n -89.35729980468749,\n 37.01132594307015\n ],\n [\n -89.4561767578125,\n 37.25656608611523\n ],\n [\n -89.4287109375,\n 37.37015718405753\n ],\n [\n -89.46716308593749,\n 37.45741810262938\n ],\n [\n -89.5166015625,\n 37.58811876638322\n ],\n [\n -89.56054687499999,\n 37.71859032558816\n ],\n [\n -89.70886230468749,\n 37.82280243352756\n ],\n [\n -89.8187255859375,\n 37.900865092570065\n ],\n [\n -89.9560546875,\n 37.97451499202459\n ],\n [\n -90.164794921875,\n 38.07836562996712\n ],\n [\n -90.2801513671875,\n 38.14751758025121\n ],\n [\n -90.3680419921875,\n 38.268375880204744\n ],\n [\n -90.32409667968749,\n 38.40194908237822\n ],\n [\n -90.252685546875,\n 38.53097889440024\n ],\n [\n -90.2032470703125,\n 38.62974534092597\n ],\n [\n -90.19775390625,\n 38.70694605159386\n ],\n [\n -90.1263427734375,\n 38.81831117374662\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
401 Hardin Road
Little Rock, AR 72211
Information concerning the availability, use, and quality of water in West Baton Rouge Parish, Louisiana, is critical for proper water-resource management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. Information on the availability, past and current use, use trends, and water quality from groundwater and surface-water sources in the parish is presented. Previously published reports and data stored in the U.S. Geological Survey’s National Water Information System (http://waterdata.usgs.gov/nwis) are the primary sources of the information presented here.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20163068","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., and Prakken, L.B., 2016, Water resources of West Baton Rouge Parish, Louisiana: U.S. Geological Survey Fact Sheet 2016–3068, 6 p., https://dx.doi.org/10.3133/fs20163068.","productDescription":"6 p.","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-073099","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":330988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2016/3068/coverthb.jpg"},{"id":330989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2016/3068/fs20163068.pdf","text":"Fact Sheet","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2016–3068"}],"country":"United States","state":"Louisiana","county":"West Baton Rouge Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-91.3051,30.6529],[-91.2993,30.6516],[-91.2977,30.6493],[-91.2972,30.6401],[-91.2999,30.631],[-91.3057,30.6182],[-91.3132,30.6018],[-91.3164,30.5903],[-91.3159,30.5816],[-91.3117,30.5752],[-91.3085,30.5739],[-91.2947,30.5711],[-91.2666,30.571],[-91.2502,30.5632],[-91.2459,30.5559],[-91.2438,30.55],[-91.2444,30.5454],[-91.246,30.5395],[-91.2588,30.5294],[-91.2811,30.5189],[-91.2848,30.5158],[-91.2843,30.5098],[-91.28,30.5052],[-91.2684,30.5047],[-91.2503,30.5097],[-91.2094,30.5229],[-91.201,30.5178],[-91.1973,30.5073],[-91.196,30.4396],[-91.1997,30.42],[-91.2152,30.3939],[-91.2338,30.3757],[-91.2412,30.362],[-91.2418,30.3579],[-91.236,30.3446],[-91.2307,30.3414],[-91.2228,30.3409],[-91.2042,30.3454],[-91.1873,30.3468],[-91.1619,30.3421],[-91.1508,30.3375],[-91.145,30.3315],[-91.1419,30.3237],[-91.3144,30.3246],[-91.3202,30.3443],[-91.3371,30.3526],[-91.3714,30.3874],[-91.3947,30.3956],[-91.3947,30.4094],[-91.4127,30.4322],[-91.4143,30.4318],[-91.4524,30.4743],[-91.4535,30.4753],[-91.4604,30.4707],[-91.4853,30.4972],[-91.4815,30.4972],[-91.4821,30.5114],[-91.4147,30.5118],[-91.4152,30.5191],[-91.4147,30.5255],[-91.4147,30.5406],[-91.4078,30.5406],[-91.4056,30.5557],[-91.4009,30.5621],[-91.3993,30.569],[-91.3977,30.569],[-91.3945,30.569],[-91.3648,30.5689],[-91.3653,30.579],[-91.3653,30.5845],[-91.3653,30.5877],[-91.3631,30.59],[-91.3498,30.6041],[-91.3355,30.616],[-91.3201,30.6329],[-91.3174,30.637],[-91.312,30.6484],[-91.3338,30.6539],[-91.3312,30.6585],[-91.3051,30.6529]]]},\"properties\":{\"name\":\"West Baton Rouge\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120,
Baton Rouge, LA 70816
There are many types of changes occurring over the Earth's landscapes that can be detected and monitored using Landsat data. Here we focus on monitoring “within-state,” gradual changes in vegetation in contrast with traditional monitoring of “abrupt” land-cover conversions. Gradual changes result from a variety of processes, such as vegetation growth and succession, damage from insects and disease, responses to shifts in climate, and other factors. Despite the prevalence of gradual changes across the landscape, they are largely ignored by the remote sensing community. Gradual changes are best characterized and monitored using time-series analysis, and with the successful launch of Landsat 8 we now have appreciable data continuity that extends the Landsat legacy across the previous 43 years. In this study, we conducted three related analyses: (1) comparison of spectral values acquired by Landsats 7 and 8, separated by eight days, to ensure compatibility for time-series evaluation; (2) tracking of multitemporal signatures for different change processes across Landsat 5, 7, and 8 sensors using anniversary-date imagery; and (3) tracking the same type of processes using all available acquisitions. In this investigation, we found that data representing natural vegetation from Landsats 5, 7, and 8 were comparable and did not indicate a need for major modification prior to use for long-term monitoring. Analyses using anniversary-date imagery can be very effective for assessing long term patterns and trends occurring across the landscape, and are especially good for providing insights regarding trends related to long-term and continuous trends of growth or decline. We found that use of all available data provided a much more comprehensive level of understanding of the trends occurring, providing information about rate, duration, and intra- and inter-annual variability that could not be readily gleaned from the anniversary date analyses. We observed that using all available clear Landsat 5–8 observations with the new Continuous Change Detection and Classification (CCDC) algorithm was very effective for illuminating vegetation trends. There are a number of potential challenges for assessing gradual changes, including atmospheric impacts, algorithm development and visualization of the changes. One of the biggest challenges for studying gradual change will be the lack of appropriate data for validating results and products.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2016.02.060","usgsCitation":"Vogelmann, J., Gallant, A.L., Shi, H., and Zhu, Z., 2016, Perspectives on monitoring gradual change across the continuity of Landsat sensors using time-series data: Remote Sensing of Environment, v. 185, p. 258-270, https://doi.org/10.1016/j.rse.2016.02.060.","productDescription":"13 p.","startPage":"258","endPage":"270","ipdsId":"IP-066052","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":470406,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2016.02.060","text":"Publisher Index Page"},{"id":341856,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"185","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"592e84b8e4b092b266f10d2c","contributors":{"authors":[{"text":"Vogelmann, James 0000-0002-0804-5823 vogel@usgs.gov","orcid":"https://orcid.org/0000-0002-0804-5823","contributorId":192352,"corporation":false,"usgs":true,"family":"Vogelmann","given":"James","email":"vogel@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":5055,"text":"Land Change Science","active":true,"usgs":true}],"preferred":true,"id":696377,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gallant, Alisa L. 0000-0002-3029-6637 gallant@usgs.gov","orcid":"https://orcid.org/0000-0002-3029-6637","contributorId":2940,"corporation":false,"usgs":true,"family":"Gallant","given":"Alisa","email":"gallant@usgs.gov","middleInitial":"L.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696378,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shi, Hua 0000-0001-7013-1565 hshi@usgs.gov","orcid":"https://orcid.org/0000-0001-7013-1565","contributorId":646,"corporation":false,"usgs":true,"family":"Shi","given":"Hua","email":"hshi@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696379,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zhu, Zhe 0000-0001-8283-6407 zhezhu@usgs.gov","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":168792,"corporation":false,"usgs":true,"family":"Zhu","given":"Zhe","email":"zhezhu@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":696380,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170208,"text":"70170208 - 2016 - Acoustic Doppler velocimeter backscatter for quantification of suspended sediment concentration in South San Francisco Bay, USA","interactions":[],"lastModifiedDate":"2019-12-14T06:29:29","indexId":"70170208","displayToPublicDate":"2016-11-23T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Acoustic Doppler velocimeter backscatter for quantification of suspended sediment concentration in South San Francisco Bay, USA","docAbstract":"A data set was acquired on a shallow mudflat in south San Francisco Bay that featured simultaneous, co-located optical and acoustic sensors for subsequent estimation of suspended sediment concentrations (SSC). The optical turbidity sensor output was converted to SSC via an empirical relation derived at a nearby site using bottle sample estimates of SSC. The acoustic data was obtained using an acoustic Doppler velocimeter. Backscatter and noise were combined to develop another empirical relation between the optical estimates of SSC and the relative backscatter from the acoustic velocimeter. The optical and acoustic approaches both reproduced similar general trends in the data and have merit. Some seasonal variation in the dataset was evident, with the two methods differing by greater or lesser amounts depending on which portion of the record was examined. It is hypothesized that this is the result of flocculation, affecting the two signals by different degrees, and that the significance or mechanism of the flocculation has some seasonal variability. In the earlier portion of the record (March), there is a clear difference that appears in the acoustic approach between ebb and flood periods, and this is not evident later in the record (May). The acoustic method has promise but it appears that characteristics of flocs that form and break apart may need to be accounted for to improve the power of the method. This may also be true of the optical method: both methods involve assuming that the sediment characteristics (size, size distribution, and shape) are constant.
","largerWorkType":{"id":24,"text":"Conference Paper"},"largerWorkTitle":"Proceedings of the 35th International Conference on Coastal Engineering","largerWorkSubtype":{"id":19,"text":"Conference Paper"},"conferenceTitle":"35th International Conference on Coastal Engineering","conferenceDate":"November 17-20, 2016","conferenceLocation":"Antalya, Turkey","language":"English","usgsCitation":"Öztürk, M., and Work, P.A., 2016, Acoustic Doppler velocimeter backscatter for quantification of suspended sediment concentration in South San Francisco Bay, USA, in Proceedings of the 35th International Conference on Coastal Engineering, Antalya, Turkey, November 17-20, 2016, 13 p.","productDescription":"13 p.","ipdsId":"IP-068263","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":340083,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -123.00292968749999,\n 37.31775185163688\n ],\n [\n -121.84936523437499,\n 37.31775185163688\n ],\n [\n -121.84936523437499,\n 38.156156969924915\n ],\n [\n -123.00292968749999,\n 38.156156969924915\n ],\n [\n -123.00292968749999,\n 37.31775185163688\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58fb1a4ce4b0c3010a8087b9","contributors":{"authors":[{"text":"Öztürk, Mehmet mozturk@usgs.gov","contributorId":168560,"corporation":false,"usgs":true,"family":"Öztürk","given":"Mehmet","email":"mozturk@usgs.gov","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":692415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":626466,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70176092,"text":"ds1015 - 2016 - Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008 ","interactions":[],"lastModifiedDate":"2016-11-23T11:43:49","indexId":"ds1015","displayToPublicDate":"2016-11-22T13:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1015","title":"Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008 ","docAbstract":"The U.S. Geological Survey, in cooperation with the Tennessee Department of Environment and Conservation (TDEC), measured continuous discharge at 4 water-quality monitoring sites and developed stage-discharge ratings for 10 additional water-quality monitoring sites in the Elk River Basin during 2006 through 2008. The discharge data were collected to support stream load assessments by TDEC. Annual nitrogen and phosphorus loads were estimated for the four sites where continuous daily discharge records were collected. Reported loads for the period 2006 through 2008 are not representative of long-term mean annual conditions at the sites in this study, however, because of severe drought conditions in the Elk River Basin during this period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1015","collaboration":"Prepared in cooperation with the Tennessee Department of Environment and Conservation","usgsCitation":"Hoos, A.B., Williams, S.D., and Wolfe, W.J., 2016, Stage-discharge relations and annual nitrogen and phosphorus load estimates for stream sites in the Elk River Basin, 2006–2008: U.S. Geological Survey Data Series 1015, 9 p., https://dx.doi.org/10.3133/ds1015.","productDescription":"Report: v, 9 p.; Table 3","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-041936","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":331068,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1015/coverthb.jpg"},{"id":331069,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1015/ds1015.pdf","text":"Report","size":"753 KB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Series 1015"},{"id":331070,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/ds/1015/ds1015_table3.xlsx","text":"Table 3 ","size":"101 KB","linkFileType":{"id":3,"text":"xlsx"},"description":"Table 3","linkHelpText":"- Stage-discharge ratings for 10 partial-record stage-discharge sites in the Elk River Basin"}],"country":"United States","state":"Alabama, Tennessee","otherGeospatial":"Elk River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -85.93505859374999,\n 35.94243575255426\n ],\n [\n -85.5615234375,\n 35.902399875143615\n ],\n [\n -85.53955078125,\n 35.86679541749027\n ],\n [\n -85.5230712890625,\n 35.82672127366604\n ],\n [\n -85.5230712890625,\n 35.746512259918504\n ],\n [\n -85.5340576171875,\n 35.64836915737426\n ],\n [\n -85.5670166015625,\n 35.53222622770337\n ],\n [\n -85.58349609375,\n 35.429344044107154\n ],\n [\n -85.6439208984375,\n 35.32633026307483\n ],\n [\n -85.74829101562499,\n 35.178298352001214\n ],\n [\n -85.9130859375,\n 35.03449433167976\n ],\n [\n -86.0888671875,\n 34.912962495216966\n ],\n [\n -86.253662109375,\n 34.8183131456094\n ],\n [\n -86.5008544921875,\n 34.72355492704221\n ],\n [\n -86.90185546874999,\n 34.615126683462194\n ],\n [\n -87.044677734375,\n 34.610605760914666\n ],\n [\n -87.264404296875,\n 34.56538299699511\n ],\n [\n -87.3907470703125,\n 34.615126683462194\n ],\n [\n -87.451171875,\n 34.72355492704221\n ],\n [\n -87.4896240234375,\n 34.854382885097905\n ],\n [\n -87.462158203125,\n 35.0254981588326\n ],\n [\n -87.418212890625,\n 35.19625600786368\n ],\n [\n -87.4072265625,\n 35.483038134069574\n ],\n [\n -87.2589111328125,\n 35.545635932499415\n ],\n [\n -87.1160888671875,\n 35.66622234103479\n ],\n [\n -86.934814453125,\n 35.75988604933661\n ],\n [\n -86.80847167968749,\n 35.80444911191491\n ],\n [\n -86.5283203125,\n 35.86679541749027\n ],\n [\n -85.93505859374999,\n 35.94243575255426\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Chief, Lower Mississippi-Gulf Water Science Center-Tennessee
640 Grassmere Park
Suite 100
Nashville, TN 37211
http://tn.water.usgs.gov
Results of a flood-hazard analysis conducted by the U.S. Geological Survey, in cooperation with the Argonne National Laboratory, for four headwater streams within the Argonne National Laboratory property indicate that the 1-percent and 0.2-percent annual exceedance probability floods would cause multiple roads to be overtopped. Results indicate that most of the effects on the infrastructure would be from flooding of Freund Brook. Flooding on the Northeast and Southeast Drainage Ways would be limited to overtopping of one road crossing for each of those streams. The Northwest Drainage Way would be the least affected with flooding expected to occur in open grass or forested areas.
The Argonne Site Sustainability Plan outlined the development of hydrologic and hydraulic models and the creation of flood-plain maps of the existing site conditions as a first step in addressing resiliency to possible climate change impacts as required by Executive Order 13653 “Preparing the United States for the Impacts of Climate Change.” The Hydrological Simulation Program-FORTRAN is the hydrologic model used in the study, and the Hydrologic Engineering Center‒River Analysis System (HEC–RAS) is the hydraulic model. The model results were verified by comparing simulated water-surface elevations to observed water-surface elevations measured at a network of five crest-stage gages on the four study streams. The comparison between crest-stage gage and simulated elevations resulted in an average absolute difference of 0.06 feet and a maximum difference of 0.19 feet.
In addition to the flood-hazard model development and mapping, a qualitative stream assessment was conducted to evaluate stream channel and substrate conditions in the study reaches. This information can be used to evaluate erosion potential.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165132","collaboration":"Prepared in cooperation with the Argonne National Laboratory","usgsCitation":"Soong, D.T., Murphy, E.A., Straub, T.D., and Zeeb, H.L., 2016, Flood-hazard analysis of four headwater streams draining the Argonne National Laboratory property, DuPage County, Illinois: U.S. Geological Survey Scientific Investigations Report 2016-5132, 57 p., https://dx.doi.org/10.3133/sir20165132.","productDescription":"vii, 57 p.","numberOfPages":"69","onlineOnly":"Y","ipdsId":"IP-075928","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":331075,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5132/sir20165132.pdf","text":"Report","size":"67.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5132"},{"id":331074,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5132/coverthb.jpg"}],"country":"United States","state":"Illinois","county":"DuPage County","otherGeospatial":"Sawmill Creek Watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.02177429199219,\n 41.6872711837914\n ],\n [\n -88.02177429199219,\n 41.77873679916478\n ],\n [\n -87.9287338256836,\n 41.77873679916478\n ],\n [\n -87.9287338256836,\n 41.6872711837914\n ],\n [\n -88.02177429199219,\n 41.6872711837914\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Illinois-Iowa Water Science Center
U.S. Geological Survey
405 North Goodwin Avenue
Urbana, Illinois 61801
http://il.water.usgs.gov
High-resolution airborne magnetic and gravity gradiometry data provide the geophysical framework for evaluating the exploration potential of hidden iron oxide deposits in Mesoproterozoic basement rocks of southeast Missouri. The data are used to calculate mineral prospectivity for iron oxide-apatite (IOA) ± rare earth element (REE) and iron oxide-copper-gold (IOCG) deposits. Results delineate the geophysical footprints of all known iron oxide deposits and reveal several previously unrecognized prospective areas. The airborne data are also inverted to three-dimensional density and magnetic susceptibility models over four concealed deposits at Pea Ridge (IOA ± REE), Boss (IOCG), Kratz Spring (IOA), and Bourbon (IOCG). The Pea Ridge susceptibility model shows a magnetic source that is vertically extensive and traceable to a depth of greater than 2 km. A smaller density source, located within the shallow Precambrian basement, is partly coincident with the magnetic source at Pea Ridge. In contrast, the Boss models show a large (625-m-wide), vertically extensive, and coincident dense and magnetic stock with shallower adjacent lobes that extend more than 2,600 m across the shallow Precambrian paleosurface. The Kratz Spring deposit appears to be a smaller volume of iron oxides and is characterized by lower density and less magnetic rock compared to the other iron deposits. A prospective area identified south of the Kratz Spring deposit shows the largest volume of coincident dense and nonmagnetic rock in the subsurface, and is interpreted as prospective for a hematite-dominant lithology that extends from the top of the Precambrian to depths exceeding 2 km. The Bourbon deposit displays a large bowl-shaped volume of coincident high density and high-magnetic susceptibility rock, and a geometry that suggests the iron mineralization is vertically restricted to the upper parts of the Precambrian basement. In order to underpin the evaluation of the prospectivity and three-dimensional models, an extensive statistical summary of density and apparent magnetic susceptibility measurements is presented that includes data on several hundred samples taken from the deposits, altered wall rocks, and unaltered country rocks.
","language":"English","publisher":"Society of Economic Geologists","doi":"10.2113/econgeo.111.8.1859","usgsCitation":"McCafferty, A.E., Phillips, J., and Driscoll, R.L., 2016, Magnetic and gravity gradiometry framework for Mesoproterozoic iron oxide-apatite and iron oxide-copper-gold deposits, southeast Missouri, USA: Economic Geology, v. 111, no. 8, https://doi.org/10.2113/econgeo.111.8.1859.","productDescription":"24 p.","startPage":"1882","ipdsId":"IP-069306","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":438504,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78P5XM4","text":"USGS data release","linkHelpText":"Helicopter magnetic and gravity gradiometry survey over the Pea Ridge iron mine and surrounding area, southeast Missouri, 2014"},{"id":331203,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Missouri","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.65869140625,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 38.788345355085625\n ],\n [\n -89.05517578125,\n 35.88905007936091\n ],\n [\n -92.65869140625,\n 35.88905007936091\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"111","issue":"8","edition":"1859","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"58356728e4b0070c0abfb6d2","contributors":{"authors":[{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":654216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Driscoll, Rhonda L. 0000-0001-7725-8956 rdriscoll@usgs.gov","orcid":"https://orcid.org/0000-0001-7725-8956","contributorId":745,"corporation":false,"usgs":true,"family":"Driscoll","given":"Rhonda","email":"rdriscoll@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":654217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178537,"text":"70178537 - 2016 - Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability","interactions":[],"lastModifiedDate":"2021-04-26T15:42:46.518202","indexId":"70178537","displayToPublicDate":"2016-11-22T00:00:00","publicationYear":"2016","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":"Landscape and flow metrics affecting the distribution of a federally-threatened fish: Improving management, model fit, and model transferability","docAbstract":"Truncated distributions of pelagophilic fishes have been observed across the Great Plains of North America, with water use and landscape fragmentation implicated as contributing factors. Developing conservation strategies for these species is hindered by the existence of multiple competing flow regime hypotheses related to species persistence. Our primary study objective was to compare the predicted distributions of one pelagophil, the Arkansas River Shiner Notropis girardi, constructed using different flow regime metrics. Further, we investigated different approaches for improving temporal transferability of the species distribution model (SDM). We compared four hypotheses: mean annual flow (a baseline), the 75th percentile of daily flow, the number of zero-flow days, and the number of days above 55th percentile flows, to examine the relative importance of flows during the spawning period. Building on an earlier SDM, we added covariates that quantified wells in each catchment, point source discharges, and non-native species presence to a structured variable framework. We assessed the effects on model transferability and fit by reducing multicollinearity using Spearman’s rank correlations, variance inflation factors, and principal component analysis, as well as altering the regularization coefficient (β) within MaxEnt. The 75th percentile of daily flow was the most important flow metric related to structuring the species distribution. The number of wells and point source discharges were also highly ranked. At the default level of β, model transferability was improved using all methods to reduce collinearity; however, at higher levels of β, the correlation method performed best. Using β = 5 provided the best model transferability, while retaining the majority of variables that contributed 95% to the model. This study provides a workflow for improving model transferability and also presents water-management options that may be considered to improve the conservation status of pelagophils.
Mountain watersheds recently burned by wildfire often experience greater amounts of runoff and increased rates of sediment transport relative to similar unburned areas. Given the sedimentation and debris flow threats caused by increases in erosion, more work is needed to better understand the physical mechanisms responsible for the observed increase in sediment transport in burned environments and the time scale over which a heightened geomorphic response can be expected. In this study, we quantified the relative importance of different hillslope erosion mechanisms during two postwildfire rainstorms at a drainage basin in Southern California by combining terrestrial laser scanner-derived maps of topographic change, field measurements, and numerical modeling of overland flow and sediment transport. Numerous debris flows were initiated by runoff at our study area during a long-duration storm of relatively modest intensity. Despite the presence of a well-developed rill network, numerical model results suggest that the majority of eroded hillslope sediment during this long-duration rainstorm was transported by raindrop-induced sediment transport processes, highlighting the importance of raindrop-driven processes in supplying channels with potential debris flow material. We also used the numerical model to explore relationships between postwildfire storm characteristics, vegetation cover, soil infiltration capacity, and the total volume of eroded sediment from a synthetic hillslope for different end-member erosion regimes. This study adds to our understanding of sediment transport in steep, postwildfire landscapes and shows how data from field monitoring can be combined with numerical modeling of sediment transport to isolate the processes leading to increased erosion in burned areas.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2016JF003867","usgsCitation":"McGuire, L., Kean, J.W., Staley, D.M., Rengers, F.K., and Wasklewicz, T.A., 2016, Constraining the relative importance of raindrop- and flow-driven sediment transport mechanisms in postwildfire environments and implications for recovery time scales: Journal of Geophysical Research, v. 121, no. 11, p. 2211-2237, https://doi.org/10.1002/2016JF003867.","productDescription":"27 p.","startPage":"2211","endPage":"2237","ipdsId":"IP-077491","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":470407,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016jf003867","text":"Publisher Index Page"},{"id":342596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Arroyo Seco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.383333,\n 34.441667\n ],\n [\n -117.875,\n 34.441667\n ],\n [\n -117.875,\n 34.2\n ],\n [\n -118.383333,\n 34.2\n ],\n [\n -118.383333,\n 34.441667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"11","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-22","publicationStatus":"PW","scienceBaseUri":"5944ee16e4b062508e333607","contributors":{"authors":[{"text":"McGuire, Luke lmcguire@usgs.gov","contributorId":167018,"corporation":false,"usgs":true,"family":"McGuire","given":"Luke","email":"lmcguire@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":698465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698394,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698395,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rengers, Francis K. 0000-0002-1825-0943 frengers@usgs.gov","orcid":"https://orcid.org/0000-0002-1825-0943","contributorId":150422,"corporation":false,"usgs":true,"family":"Rengers","given":"Francis","email":"frengers@usgs.gov","middleInitial":"K.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":698396,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wasklewicz, Thad A.","contributorId":39275,"corporation":false,"usgs":true,"family":"Wasklewicz","given":"Thad","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":698397,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178466,"text":"70178466 - 2016 - Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays","interactions":[],"lastModifiedDate":"2018-08-07T12:05:31","indexId":"70178466","displayToPublicDate":"2016-11-21T15:10:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1290,"text":"Comparative Biochemistry and Physiology, Part D: Genomics and Proteomics","active":true,"publicationSubtype":{"id":10}},"title":"Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays","docAbstract":"Environmental studies increasingly identify the presence of both contaminants of emerging concern (CECs) and legacy contaminants in aquatic environments; however, the biological effects of these compounds on resident fishes remain largely unknown. High throughput methodologies were employed to establish partial transcriptomes for three wild-caught, non-model fish species; smallmouth bass (Micropterus dolomieu), white sucker (Catostomus commersonii) and brown bullhead (Ameiurus nebulosus). Sequences from these transcriptome databases were utilized in the development of a custom nCounter CodeSet that allowed for direct multiplexed measurement of 50 transcript abundance endpoints in liver tissue. Sequence information was also utilized in the development of quantitative real-time PCR (qPCR) primers. Cross-species hybridization allowed the smallmouth bass nCounter CodeSet to be used for quantitative transcript abundance analysis of an additional non-model species, largemouth bass (Micropterus salmoides). We validated the nCounter analysis data system with qPCR for a subset of genes and confirmed concordant results. Changes in transcript abundance biomarkers between sexes and seasons were evaluated to provide baseline data on transcript modulation for each species of interest.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cbd.2016.07.001","usgsCitation":"Hahn, C.M., Iwanowicz, L., Cornman, R.S., Mazik, P.M., and Blazer, V., 2016, Transcriptome discovery in non-model wild fish species for the development of quantitative transcript abundance assays: Comparative Biochemistry and Physiology, Part D: Genomics and Proteomics, v. 20, p. 27-40, https://doi.org/10.1016/j.cbd.2016.07.001.","productDescription":"14 p.","startPage":"27","endPage":"40","ipdsId":"IP-071925","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":331166,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"20","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"583415aae4b0070c0abed80e","contributors":{"authors":[{"text":"Hahn, Cassidy M. cmhahn@usgs.gov","contributorId":5321,"corporation":false,"usgs":true,"family":"Hahn","given":"Cassidy","email":"cmhahn@usgs.gov","middleInitial":"M.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":654096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iwanowicz, Luke R. liwanowicz@usgs.gov","contributorId":386,"corporation":false,"usgs":true,"family":"Iwanowicz","given":"Luke R.","email":"liwanowicz@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":654097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":654098,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mazik, Patricia M. 0000-0002-8046-5929 pmazik@usgs.gov","orcid":"https://orcid.org/0000-0002-8046-5929","contributorId":2318,"corporation":false,"usgs":true,"family":"Mazik","given":"Patricia","email":"pmazik@usgs.gov","middleInitial":"M.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":654099,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":149414,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":false,"id":654095,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178475,"text":"70178475 - 2016 - Inference of population structure and demographic history in Taxodium distichum, a coniferous tree in North America, based on amplicon sequence analysis","interactions":[],"lastModifiedDate":"2016-11-30T12:35:55","indexId":"70178475","displayToPublicDate":"2016-11-21T14:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":724,"text":"American Journal of Botany","active":true,"publicationSubtype":{"id":10}},"title":"Inference of population structure and demographic history in Taxodium distichum, a coniferous tree in North America, based on amplicon sequence analysis","docAbstract":"PREMISE OF THE STUDY: Studies of natural genetic variation can elucidate the genetic basis of phenotypic variation and the past population structure of species. Our study species, Taxodium distichum, is a unique conifer that inhabits the flood plains and swamps of North America. Morphological and ecological differences in two varieties, T. distichum var. distichum (bald cypress) and T. distichum var. imbricarium (pond cypress), are well known, but little is known about the level of genetic differentiation between the varieties and the demographic history of local populations.
METHODS: We analyzed nucleotide polymorphisms at 47 nuclear loci from 96 individuals collected from the Mississippi River Alluvial Valley (MRAV), and Gulf Coastal populations in Texas, Louisiana, and Florida using high-throughput DNA sequencing. Standard population genetic statistics were calculated, and demographic parameters were estimated using a composite-likelihood approach.
KEY RESULTS: Taxodium distichum in North America can be divided into at least three genetic groups, bald cypress in the MRAV and Texas, bald cypress in Florida, and pond cypress in Florida. The levels of genetic differentiation among the groups were low but significant. Several loci showed the signatures of positive selection, which might be responsible for local adaptation or varietal differentiation.
CONCLUSIONS: Bald cypress was genetically differentiated into two geographical groups, and the boundary was located between the MRAV and Florida. This differentiation could be explained by population expansion from east to west. Despite the overlap of the two varieties’ ranges, they were genetically differentiated in Florida. The estimated demographic parameters suggested that pond cypress split from bald cypress during the late Miocene.
Three in-stream experiments were conducted to determine whether sea lamprey, Petromyzon marinus L., tissue extract (alarm cue) and 2-phenylethylamine hydrochloride (PEA HCl, a putative predator cue) influenced the distribution of migrating adult sea lamprey. Experiments evaluated sea lamprey movement when an odour was applied to (1) a tributary of a larger stream; and (2) half of a stream channel. Fewer sea lamprey entered the tributary and side of the river scented with sea lamprey tissue extract compared to the control treatment. Sea lamprey did not avoid the tributary and side of the river scented with PEA HCl. A final laboratory experiment found no difference in the avoidance response of sea lamprey to PEA HCl mixed with river water vs PEA HCl mixed with water from Lake Huron. As such, the lack of sea lamprey response to PEA HCl in the stream was unlikely to have been caused by the presence of the river water. Rather, the difference between laboratory and field results may be attributed to the complexity of the physical environment.
","language":"English","publisher":"Wiley","doi":"10.1111/fme.12198","usgsCitation":"Di Rocco, R., Johnson, N., Brege, L., Imre, I., and Brown, G., 2016, Sea lamprey avoid areas scented with conspecific tissue extract in Michigan streams: Fisheries Management and Ecology, v. 23, no. 6, p. 548-560, https://doi.org/10.1111/fme.12198.","productDescription":"13 p.","startPage":"548","endPage":"560","ipdsId":"IP-077130","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":331157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","otherGeospatial":"Ocqueoc River, Silver Creek","volume":"23","issue":"6","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-09","publicationStatus":"PW","scienceBaseUri":"583415abe4b0070c0abed812","contributors":{"authors":[{"text":"Di Rocco, Richard","contributorId":126735,"corporation":false,"usgs":false,"family":"Di Rocco","given":"Richard","affiliations":[{"id":6585,"text":"Algoma University","active":true,"usgs":false}],"preferred":false,"id":654130,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654129,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brege, Linnea 0000-0002-7495-3619 lbrege@usgs.gov","orcid":"https://orcid.org/0000-0002-7495-3619","contributorId":176976,"corporation":false,"usgs":true,"family":"Brege","given":"Linnea","email":"lbrege@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Imre, I.","contributorId":25398,"corporation":false,"usgs":true,"family":"Imre","given":"I.","affiliations":[],"preferred":false,"id":654132,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brown, G.E.","contributorId":58131,"corporation":false,"usgs":true,"family":"Brown","given":"G.E.","email":"","affiliations":[],"preferred":false,"id":654133,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178481,"text":"70178481 - 2016 - Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake","interactions":[],"lastModifiedDate":"2016-11-21T11:35:07","indexId":"70178481","displayToPublicDate":"2016-11-21T12:30:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1019,"text":"Biological Journal of the Linnean Society","active":true,"publicationSubtype":{"id":10}},"title":"Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake","docAbstract":"All examples of lake charr (Salvelinus namaycush) diversity occur within the largest, deepest lakes of North America (i.e. > 2000 km2). We report here Rush Lake (1.3 km2) as the first example of a small lake with two lake charr morphs (lean and huronicus). Morphology, diet, life history, and genetics were examined to demonstrate the existence of morphs and determine the potential influence of evolutionary processes that led to their formation or maintenance. Results showed that the huronicus morph, caught in deep-water, had a deeper body, smaller head and jaws, higher eye position, greater buoyancy, and deeper peduncle than the shallow-water lean morph. Huronicus grew slower to a smaller adult size, and had an older mean age than the lean morph. Genetic comparisons showed low genetic divergence between morphs, indicating incomplete reproductive isolation. Phenotypic plasticity and differences in habitat use between deep and shallow waters associated with variation in foraging opportunities seems to have been sufficient to maintain the two morphs, demonstrating their important roles in resource polymorphism. Rush Lake expands previous explanations for lake charr intraspecific diversity, from large to small lakes and from reproductive isolation to the presence of gene flow associated with strong ecological drivers.
","language":"English","publisher":"Wiley","doi":"10.1111/bij.12913","usgsCitation":"Chavarie, L., Muir, A., Zimmerman, M.S., Baillie, S.M., Hansen, M.J., Nate, N.A., Yule, D.L., Middel, T., Bentzen, P., and Krueger, C., 2016, Challenge to the model of lake charr evolution: Shallow- and deep-water morphs exist within a small postglacial lake: Biological Journal of the Linnean Society, https://doi.org/10.1111/bij.12913.","ipdsId":"IP-078858","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":462033,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/bij.12913","text":"Publisher Index Page"},{"id":331153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-16","publicationStatus":"PW","scienceBaseUri":"583415ace4b0070c0abed814","contributors":{"authors":[{"text":"Chavarie, Louise","contributorId":156227,"corporation":false,"usgs":false,"family":"Chavarie","given":"Louise","email":"","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":654136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muir, Andrew M.","contributorId":103933,"corporation":false,"usgs":false,"family":"Muir","given":"Andrew M.","affiliations":[],"preferred":false,"id":654137,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zimmerman, Mara S.","contributorId":152687,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Mara","email":"","middleInitial":"S.","affiliations":[{"id":13269,"text":"Washington Department of Fish & Wildlife","active":true,"usgs":false}],"preferred":false,"id":654138,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baillie, Shauna M.","contributorId":176176,"corporation":false,"usgs":false,"family":"Baillie","given":"Shauna","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":654139,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":654140,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nate, Nancy A.","contributorId":26626,"corporation":false,"usgs":true,"family":"Nate","given":"Nancy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":654141,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yule, Daniel L. dyule@usgs.gov","contributorId":139525,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","email":"dyule@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":654142,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Middel, Trevor","contributorId":176991,"corporation":false,"usgs":false,"family":"Middel","given":"Trevor","affiliations":[],"preferred":false,"id":654143,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bentzen, Paul","contributorId":176178,"corporation":false,"usgs":false,"family":"Bentzen","given":"Paul","email":"","affiliations":[],"preferred":false,"id":654144,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Krueger, Charles C.","contributorId":73131,"corporation":false,"usgs":true,"family":"Krueger","given":"Charles C.","affiliations":[],"preferred":false,"id":654145,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70178474,"text":"70178474 - 2016 - Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis","interactions":[],"lastModifiedDate":"2017-01-17T19:03:21","indexId":"70178474","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis","docAbstract":"This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000–2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m−2·year−1) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.
","language":"English","publisher":"MDPI","doi":"10.3390/rs8110944","usgsCitation":"Wylie, B.K., Howard, D., Dahal, D., Gilmanov, T., Ji, L., Zhang, L., and Smith, K., 2016, Grassland and cropland net ecosystem production of the U.S. Great Plains: Regression tree model development and comparative analysis: Remote Sensing, v. 8, no. 11, p. 1-28, https://doi.org/10.3390/rs8110944.","productDescription":"Article 944; 28 p.","startPage":"1","endPage":"28","ipdsId":"IP-057161","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":462035,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs8110944","text":"Publisher Index Page"},{"id":331161,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Plains","volume":"8","issue":"11","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-11","publicationStatus":"PW","scienceBaseUri":"583415ace4b0070c0abed816","contributors":{"authors":[{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654160,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Howard, Daniel 0000-0002-7563-7538","orcid":"https://orcid.org/0000-0002-7563-7538","contributorId":56946,"corporation":false,"usgs":true,"family":"Howard","given":"Daniel","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":654161,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dahal, Devendra 0000-0001-9594-1249 ddahal@usgs.gov","orcid":"https://orcid.org/0000-0001-9594-1249","contributorId":5622,"corporation":false,"usgs":true,"family":"Dahal","given":"Devendra","email":"ddahal@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654162,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gilmanov, Tagir","contributorId":6351,"corporation":false,"usgs":true,"family":"Gilmanov","given":"Tagir","affiliations":[],"preferred":false,"id":654163,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ji, Lei 0000-0002-6133-1036 lji@usgs.gov","orcid":"https://orcid.org/0000-0002-6133-1036","contributorId":139587,"corporation":false,"usgs":true,"family":"Ji","given":"Lei","email":"lji@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":654164,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Li","contributorId":98139,"corporation":false,"usgs":true,"family":"Zhang","given":"Li","affiliations":[],"preferred":false,"id":654165,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Kelcy 0000-0001-6811-1485 kelcy.smith.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6811-1485","contributorId":176844,"corporation":false,"usgs":true,"family":"Smith","given":"Kelcy","email":"kelcy.smith.ctr@usgs.gov","affiliations":[],"preferred":false,"id":654166,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70178470,"text":"70178470 - 2016 - Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","interactions":[],"lastModifiedDate":"2018-09-13T14:45:17","indexId":"70178470","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model","docAbstract":"Reducing uncertainty in data inputs at relevant spatial scales can improve tidal marsh forecasting models, and their usefulness in coastal climate change adaptation decisions. The Marsh Equilibrium Model (MEM), a one-dimensional mechanistic elevation model, incorporates feedbacks of organic and inorganic inputs to project elevations under sea-level rise scenarios. We tested the feasibility of deriving two key MEM inputs—average annual suspended sediment concentration (SSC) and aboveground peak biomass—from remote sensing data in order to apply MEM across a broader geographic region. We analyzed the precision and representativeness (spatial distribution) of these remote sensing inputs to improve understanding of our study region, a brackish tidal marsh in San Francisco Bay, and to test the applicable spatial extent for coastal modeling. We compared biomass and SSC models derived from Landsat 8, DigitalGlobe WorldView-2, and hyperspectral airborne imagery. Landsat 8-derived inputs were evaluated in a MEM sensitivity analysis. Biomass models were comparable although peak biomass from Landsat 8 best matched field-measured values. The Portable Remote Imaging Spectrometer SSC model was most accurate, although a Landsat 8 time series provided annual average SSC estimates. Landsat 8-measured peak biomass values were randomly distributed, and annual average SSC (30 mg/L) was well represented in the main channels (IQR: 29–32 mg/L), illustrating the suitability of these inputs across the model domain. Trend response surface analysis identified significant diversion between field and remote sensing-based model runs at 60 yr due to model sensitivity at the marsh edge (80–140 cm NAVD88), although at 100 yr, elevation forecasts differed less than 10 cm across 97% of the marsh surface (150–200 cm NAVD88). Results demonstrate the utility of Landsat 8 for landscape-scale tidal marsh elevation projections due to its comparable performance with the other sensors, temporal frequency, and cost. Integration of remote sensing data with MEM should advance regional projections of marsh vegetation change by better parameterizing MEM inputs spatially. Improving information for coastal modeling will support planning for ecosystem services, including habitat, carbon storage, and flood protection.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.1582","usgsCitation":"Byrd, K.B., Windham-Myers, L., Leeuw, T., Downing, B.D., Morris, J.T., and Ferner, M.C., 2016, Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model: Ecosphere, v. 7, no. 11, e01582; 27 p., https://doi.org/10.1002/ecs2.1582.","productDescription":"e01582; 27 p.","ipdsId":"IP-073438","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":470411,"rank":4,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.1582","text":"Publisher Index Page"},{"id":438505,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F76M34Z1","text":"USGS data release","linkHelpText":"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model"},{"id":331164,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":335610,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F76M34Z1","text":"Data release for journal article titled, \"Forecasting tidal marsh elevation and habitat change through fusion of Earth observations and a process model\""}],"country":"United States","state":"California","otherGeospatial":"Rush Ranch Open Space Preserve, Suisun Slough, Suisun Marsh","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.05501556396483,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.212288054388175\n ],\n [\n -121.99802398681642,\n 38.17802085110361\n ],\n [\n -122.05501556396483,\n 38.17802085110361\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-14","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed81a","chorus":{"doi":"10.1002/ecs2.1582","url":"http://dx.doi.org/10.1002/ecs2.1582","publisher":"Wiley-Blackwell","authors":"Byrd Kristin B., Windham-Myers Lisamarie, Leeuw Thomas, Downing Bryan, Morris James T., Ferner Matthew C.","journalName":"Ecosphere","publicationDate":"11/2016","auditedOn":"11/29/2016"},"contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":654113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":654114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leeuw, Thomas","contributorId":176970,"corporation":false,"usgs":false,"family":"Leeuw","given":"Thomas","email":"","affiliations":[],"preferred":false,"id":654115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Downing, Bryan D. 0000-0002-2007-5304 bdowning@usgs.gov","orcid":"https://orcid.org/0000-0002-2007-5304","contributorId":1449,"corporation":false,"usgs":true,"family":"Downing","given":"Bryan","email":"bdowning@usgs.gov","middleInitial":"D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":654116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morris, James T.","contributorId":29118,"corporation":false,"usgs":true,"family":"Morris","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":654117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ferner, Matthew C.","contributorId":176972,"corporation":false,"usgs":false,"family":"Ferner","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":654118,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70178472,"text":"70178472 - 2016 - Intermittent surface water connectivity: Fill and spill vs. fill and merge dynamics","interactions":[],"lastModifiedDate":"2017-01-03T16:04:08","indexId":"70178472","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3750,"text":"Wetlands","onlineIssn":"1943-6246","printIssn":"0277-5212","active":true,"publicationSubtype":{"id":10}},"title":"Intermittent surface water connectivity: Fill and spill vs. fill and merge dynamics","docAbstract":"Intermittent surface connectivity can influence aquatic systems, since chemical and biotic movements are often associated with water flow. Although often referred to as fill and spill, wetlands also fill and merge. We examined the effects of these connection types on water levels, ion concentrations, and biotic communities of eight prairie pothole wetlands between 1979 and 2015. Fill and spill caused pulsed surface water connections that were limited to periods following spring snow melt. In contrast, two wetlands connected through fill and merge experienced a nearly continuous, 20-year surface water connection and had completely coincident water levels. Fill and spill led to minimal convergence in dissolved ions and macroinvertebrate composition, while these constituents converged under fill and merge. The primary factor determining differences in response was duration of the surface water connection between wetland pairs. Our findings suggest that investigations into the effects of intermittent surface water connections should not consider these connections generically, but need to address the specific types of connections. In particular, fill and spill promotes external water exports while fill and merge favors internal storage. The behaviors of such intermittent connections will likely be accentuated under a future with more frequent and severe climate extremes.
","language":"English","publisher":"Springer","doi":"10.1007/s13157-016-0830-z","usgsCitation":"Leibowitz, S.G., Mushet, D.M., and Newton, W.E., 2016, Intermittent surface water connectivity: Fill and spill vs. fill and merge dynamics: Wetlands, v. 36, no. s2, p. 323-342, https://doi.org/10.1007/s13157-016-0830-z.","productDescription":"20 p.","startPage":"323","endPage":"342","ipdsId":"IP-074809","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":331163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"s2","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2016-10-12","publicationStatus":"PW","scienceBaseUri":"583415ade4b0070c0abed818","contributors":{"authors":[{"text":"Leibowitz, Scott G.","contributorId":156432,"corporation":false,"usgs":false,"family":"Leibowitz","given":"Scott","email":"","middleInitial":"G.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":654122,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654121,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Newton, Wesley E. 0000-0002-1377-043X wnewton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-043X","contributorId":3661,"corporation":false,"usgs":true,"family":"Newton","given":"Wesley","email":"wnewton@usgs.gov","middleInitial":"E.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":654123,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178439,"text":"70178439 - 2016 - Special issue “The phreatic eruption of Mt. Ontake volcano in 2014”","interactions":[],"lastModifiedDate":"2016-11-21T14:38:59","indexId":"70178439","displayToPublicDate":"2016-11-21T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1430,"text":"Earth, Planets and Space","active":true,"publicationSubtype":{"id":10}},"title":"Special issue “The phreatic eruption of Mt. Ontake volcano in 2014”","docAbstract":"Mt. Ontake volcano erupted at 11:52 on September 27, 2014, claiming the lives of at least 58 hikers. This eruption was the worst volcanic disaster in Japan since the 1926 phreatic eruption of Mt. Tokachidake claimed 144 lives (Table 1). The timing of the eruption contributed greatly to the heavy death toll: near midday, when many hikers were near the summit, and during a weekend of clear weather conditions following several rainy weekends. The importance of this timing is reflected by the fact that a somewhat larger eruption of Mt. Ontake in 1979 resulted in injuries but no deaths. In 2014, immediate precursors were detected with seismometers and tiltmeters about 10 min before the eruption, but the eruption started before a warning was issued.
","language":"English","publisher":"Springer","doi":"10.1186/s40623-016-0548-4","usgsCitation":"Yamaoka, K., Geshi, N., Hashimoto, T., Ingebritsen, S.E., and Oikawa, T., 2016, Special issue “The phreatic eruption of Mt. Ontake volcano in 2014”: Earth, Planets and Space, v. 68, Article 175; 8 p., https://doi.org/10.1186/s40623-016-0548-4.","productDescription":"Article 175; 8 p.","ipdsId":"IP-080065","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":470410,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40623-016-0548-4","text":"Publisher Index Page"},{"id":331171,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"68","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-10","publicationStatus":"PW","scienceBaseUri":"583415b2e4b0070c0abed820","contributors":{"authors":[{"text":"Yamaoka, Koshun","contributorId":176955,"corporation":false,"usgs":false,"family":"Yamaoka","given":"Koshun","email":"","affiliations":[],"preferred":false,"id":654082,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Geshi, Nobuo","contributorId":176957,"corporation":false,"usgs":false,"family":"Geshi","given":"Nobuo","email":"","affiliations":[],"preferred":false,"id":654084,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hashimoto, Tasheki","contributorId":176956,"corporation":false,"usgs":false,"family":"Hashimoto","given":"Tasheki","email":"","affiliations":[],"preferred":false,"id":654083,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ingebritsen, Steven E. 0000-0001-6917-9369 seingebr@usgs.gov","orcid":"https://orcid.org/0000-0001-6917-9369","contributorId":818,"corporation":false,"usgs":true,"family":"Ingebritsen","given":"Steven","email":"seingebr@usgs.gov","middleInitial":"E.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":654081,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oikawa, Teruki","contributorId":176958,"corporation":false,"usgs":false,"family":"Oikawa","given":"Teruki","email":"","affiliations":[],"preferred":false,"id":654085,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70178048,"text":"sir20165085 - 2016 - Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012","interactions":[],"lastModifiedDate":"2016-12-19T13:51:19","indexId":"sir20165085","displayToPublicDate":"2016-11-17T09:16:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5085","title":"Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012","docAbstract":"In 2012, a late season tropical depression developed into a tropical storm and later a hurricane. The hurricane, named “Hurricane Sandy,” gained strength to a Category 3 storm on October 25, 2012, and underwent several transitions on its approach to the mid-Atlantic region of the eastern coast of the United States. By October 28, 2012, Hurricane Sandy had strengthened into the largest hurricane ever recorded in the North Atlantic and was tracking parallel to the east coast of United States, heading toward New Jersey. On October 29, 2012, the storm turned west-northwest and made landfall near Atlantic City, N.J. The high winds and wind-driven storm surge caused massive damage along the entire coastline of New Jersey. Millions of people were left without power or communication networks. Many homes were completely destroyed. Sand dunes were eroded, and the barrier island at Mantoloking was breached, connecting the ocean with Barnegat Bay.
Several days before the storm made landfall in New Jersey, the U.S. Geological Survey (USGS) made a decision to deploy a temporary network of storm-tide sensors and barometric pressure sensors from Virginia to Maine to supplement the existing USGS and National Oceanic and Atmospheric Administration (NOAA) networks of permanent tide monitoring stations. After the storm made landfall, the USGS conducted a sensor data recovery and high-water-mark collection campaign in cooperation with the Federal Emergency Management Agency (FEMA).
Peak storm-tide elevations documented at USGS tide gages, tidal crest-stage gages, temporary storm sensor locations, and high-water-mark sites indicate the area from southern Monmouth County, N.J., north through Raritan Bay, N.J., had the highest peak storm-tide elevations during this storm. The USGS tide gages at Raritan River at South Amboy and Raritan Bay at Keansburg, part of the New Jersey Tide Telemetry System, each recorded peak storm-tide elevations of greater than 13 feet (ft)—more than 5 ft higher than the previously recorded period-of-record maximum. A comparison of peak storm-tide elevations to preliminary FEMA Coastal Flood Insurance Study flood elevations indicated that these areas experienced the highest recurrence intervals along the coast of New Jersey. Analysis showed peak storm-tide elevations exceeded the 100-year FEMA flood elevations in many parts of Middlesex, Union, Essex, Hudson, and Bergen Counties, and peak storm-tide elevations at many locations in Monmouth County exceeded the 500-year recurrence interval.
A level 1 HAZUS (HAZards United States) analysis was done for the counties in New Jersey affected by flooding to estimate total building stock losses. The aggregated total building stock losses estimated by HAZUS for New Jersey, on the basis of the final inundation verified by USGS high-water marks, was almost $19 billion. A comparison of Hurricane Sandy with historic coastal storms showed that peak storm-tide elevations associated with Hurricane Sandy exceeded most of the previously documented elevations associated with the storms of December 1992, March 1962, September 1960, and September 1944 at many coastal communities in New Jersey. This scientific investigation report was prepared in cooperation with FEMA to document flood processes and flood damages resulting from this storm and to assist in future flood mitigation actions in New Jersey.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165085","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency","usgsCitation":"Suro, T.P., Deetz, Anna, and Hearn, Paul, 2016, Documentation and hydrologic analysis of Hurricane Sandy in New Jersey, October 29–30, 2012: U.S. Geological Survey Scientific Investigations Report 2016–5085, 73 p., https://dx.doi.org/10.3133/sir20165085.","productDescription":"Report: ix, 73 p.; 5 Tables","numberOfPages":"87","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-055579","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":330616,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table4.xls","text":"Table 4","size":"45 KB xls","description":"SIR 2016-5085","linkHelpText":"- Description of U.S. Geological Survey sensors temporarily deployed for Hurricane Sandy with peak storm tide elevations, annual exceedance probabilities, and estimated recurrence intervals in New Jersey, October 29–30, 2012 \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330617,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table5.xls","text":"Table 5","size":"144 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 169 high-water-mark sites along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012, and the corresponding Federal Emergency Management Agency flood elevations for the 10-, 50-, 100-, and 500-year recurrence intervals \t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330618,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table6.xls","text":"Table 6","size":"61 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations for selected historic coastal floods and peak storm-tide elevations during Hurricane Sandy, October 29–30, 2012, at selected U.S. Geological Survey permanent monitoring tide gages in New Jersey"},{"id":330619,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table7.xls","text":"Table 7","size":"74 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak storm-tide elevations at 82 high-water-mark sites flagged and surveyed after the December 1992 storm in New Jersey, peak storm-tide elevations from the closest high-water-mark sites flagged and surveyed after Hurricane Sandy, October 29–30, 2012, and peak storm-tide elevations from the nearest U.S. Geological Survey tide gage along the coast of New Jersey during Hurricane Sandy, October 29–30, 2012\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t"},{"id":330614,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085.pdf","text":"Report","size":"85.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5085"},{"id":330615,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2016/5085/sir20165085_table3.xls","text":"Table 3","size":"77.5 KB xls","description":"SIR 2016-5085","linkHelpText":"- Peak-of-record tide elevations and peak storm-tide elevations at U.S. Geological Survey permanent monitoring tide gages in New Jersey, October 29–30, 2012\t\t\t\t\t\t"},{"id":330613,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5085/coverthb.jpg"}],"country":"United States","state":"New 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville NJ, 08648
http://nj.usgs.gov/
The cumulative effects of increasing human use of the ocean and coastal zone have contributed to a rapid decline in ocean and coastal resources. As a result, scientists are investigating how multiple, overlapping stressors accumulate in the environment and impact ecosystems. These investigations are the foundation for the development of new tools that account for and predict cumulative effects in order to more adequately prevent or mitigate negative effects. Despite scientific advances, legal requirements, and management guidance, those who conduct assessments—including resource managers, agency staff, and consultants—continue to struggle to thoroughly evaluate cumulative effects, particularly as part of the environmental assessment process. Even though 45 years have passed since the United States National Environmental Policy Act was enacted, which set a precedent for environmental assessment around the world, defining impacts, baseline, scale, and significance are still major challenges associated with assessing cumulative effects. In addition, we know little about how practitioners tackle these challenges or how assessment aligns with current scientific recommendations. To shed more light on these challenges and gaps, we undertook a comparative study on how cumulative effects assessment (CEA) is conducted by practitioners operating under some of the most well-developed environmental laws around the globe: California, USA; British Columbia, Canada; Queensland, Australia; and New Zealand. We found that practitioners used a broad and varied definition of impact for CEA, which led to differences in how baseline, scale, and significance were determined. We also found that practice and science are not closely aligned and, as such, we highlight opportunities for managers, policy makers, practitioners, and scientists to improve environmental assessment.
","language":"English","publisher":"Elsevier","publisherLocation":"New York, NY","doi":"10.1016/j.eiar.2016.06.008","usgsCitation":"Foley, M.M., Mease, L., Martone, R.G., Prahler, E.E., Morrison, T.H., Clarke Murray, C., and Wojcik, D., 2016, The challenges and opportunities in cumulative effects assessment: Environmental Impact Assessment Review, v. 62, p. 122-134, https://doi.org/10.1016/j.eiar.2016.06.008.","productDescription":"13 p.","startPage":"122","endPage":"134","ipdsId":"IP-074481","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":331107,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"62","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582ecfe9e4b04d580bd43524","contributors":{"authors":[{"text":"Foley, Melissa M. 0000-0002-5832-6404 mfoley@usgs.gov","orcid":"https://orcid.org/0000-0002-5832-6404","contributorId":4861,"corporation":false,"usgs":true,"family":"Foley","given":"Melissa","email":"mfoley@usgs.gov","middleInitial":"M.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":654030,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mease, Lindley A","contributorId":176938,"corporation":false,"usgs":false,"family":"Mease","given":"Lindley A","affiliations":[],"preferred":false,"id":654031,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Martone, Rebecca G","contributorId":176939,"corporation":false,"usgs":false,"family":"Martone","given":"Rebecca","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":654032,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Prahler, Erin E","contributorId":176940,"corporation":false,"usgs":false,"family":"Prahler","given":"Erin","email":"","middleInitial":"E","affiliations":[],"preferred":false,"id":654033,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morrison, Tiffany H","contributorId":176941,"corporation":false,"usgs":false,"family":"Morrison","given":"Tiffany","email":"","middleInitial":"H","affiliations":[],"preferred":false,"id":654034,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Clarke Murray, Cathryn","contributorId":176942,"corporation":false,"usgs":false,"family":"Clarke Murray","given":"Cathryn","email":"","affiliations":[],"preferred":false,"id":654035,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wojcik, Deborah","contributorId":176943,"corporation":false,"usgs":false,"family":"Wojcik","given":"Deborah","email":"","affiliations":[],"preferred":false,"id":654036,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70175252,"text":"sir20165093 - 2016 - Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013","interactions":[],"lastModifiedDate":"2016-11-17T16:24:46","indexId":"sir20165093","displayToPublicDate":"2016-11-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5093","title":"Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013","docAbstract":"Despite widespread and ongoing implementation of conservation practices throughout the Chesapeake Bay watershed, water quality continues to be degraded by excess sediment and nutrient inputs. While the Chesapeake Bay Program has developed and maintains a large-scale and long-term monitoring network to detect improvements in water quality throughout the watershed, fewer resources have been allocated for monitoring smaller watersheds, even though water-quality improvements that may result from the implementation of conservation practices are likely to be first detected at smaller watershed scales.
In 2010, the U.S. Geological Survey partnered with the U.S. Environmental Protection Agency and the U.S. Department of Agriculture to initiate water-quality monitoring in four selected small watersheds that were targeted for increased implementation of conservation practices. Smith Creek watershed is an agricultural watershed in the Shenandoah Valley of Virginia that is dominated by cattle and poultry production, and the Upper Chester River watershed is an agricultural watershed on the Eastern Shore of Maryland that is dominated by row-cropping activities. The Conewago Creek watershed is an agricultural watershed in southeastern Pennsylvania that is characterized by mixed agricultural activities. The fourth watershed, Difficult Run, is a suburban watershed in northern Virginia that is dominated by medium density residential development. The objective of this study was to investigate spatial and temporal variations in water chemistry and suspended sediment in these four relatively small watersheds that represent a range of land-use patterns and underlying geology to (1) characterize current water-quality conditions in these watersheds, and (2) identify the dominant sources, sinks, and transport processes in each watershed.
The general study design involved two components. The first included intensive routine water-quality monitoring at an existing streamgage within each study area (including continuous water-quality monitoring as well as discrete water-quality sampling) to develop a detailed understanding of the temporal and hydrologic variability in stream chemistry and sediment transport in each watershed. The second component involved extensive water-quality monitoring at various sites throughout each watershed to develop a detailed understanding of spatial patterns. Both components were used to improve understanding of sources and transport processes affecting stream chemistry, including nutrients and suspended sediments, and their implications for detecting long-term trends related to best management practices. This report summarizes the results of monitoring that was performed from April 2010 through September 2013.
Summaries for each of the four small watersheds are presented below. Each watershed has a more descriptive and detailed section in the report, but these summaries may be particularly useful for some watershed managers and stakeholders desiring slightly less technical detail.
Smith Creek is a 105.39-mi2 watershed within the Shenandoah Valley that drains to the North Fork Shenandoah River. The long-term Smith Creek base-flow index is 72.3 percent, indicating that on average, approximately 72 percent of Smith Creek flow was base flow, which suggests that Smith Creek streamflow is dominated by groundwater discharge rather than stormwater runoff. A series of cluster and principal components analyses demonstrated that the majority of the variability in Smith Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed for turbidity, suspended sediments, total nitrogen, ammonium, orthophosphate, iron, total phosphorus, and the ratio of calcium to magnesium. Statistically significant inverse correlations with flow were observed for specific conductance, magnesium, δ15N of nitrate, pH, bicarbonate, calcium, and δ18O of nitrate. Of particular note, flow and nitrate were not statistically significantly correlated, likely because of the relatively complex concentration-discharge relationship observed in continuous and discrete datasets. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, turbidity, orthophosphate, total phosphorus, suspended-sediment concentration, and silica were higher during the warm season, but pH, dissolved oxygen, and sulfate were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loads in Smith Creek using the continuous water-quality monitors. The mean Smith Creek in-stream sediment load was approximately 6,900 tons per year, with nearly 90 percent of the sediment load over the 3-year study period contributed during the eight largest storm events during that period. The Smith Creek total phosphorus load was approximately 21,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods, although a substantial phosphorus load still occurs during base-flow conditions. The Smith Creek total nitrogen load was approximately 400,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions (as was the case for sediment and total phosphorus) and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Smith Creek watershed revealed how the complex geology and hydrology interacted to result in variable water chemistry. During relatively dry and low base-flow periods, much of the discharge in Smith Creek was contributed by a single dominant spring—Lacey Spring. During wetter base-flow periods, the flows in Smith Creek were largely generated by a mixture of headwater springs and forested mountain tributaries with very different geochemical composition. The headwater springs generally issued from limestone bedrock and were characterized as having relatively high nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The undeveloped, high-gradient, forested mountain sites were generally characterized by low ionic strength waters with low nutrient concentrations. Nitrate isotope data from the limestone springs generally were consistent with manure-derived nitrogen sources (such as cattle and poultry), although the possibility of other mixed sources cannot be excluded. Nitrate isotope data from the undeveloped, high-gradient forested mountain sites were more consistent with nitrogen from undisturbed soils, atmospheric deposition, or nitrogen fixation. Regardless of the nitrogen source, oxygen isotope data indicate that the nitrate was largely a result of nitrification. Land-use data indicate that manure sources of nitrogen dominated watershed nitrogen inputs. Phosphorus sources were less well studied. The presence of a single point-source discharge near the town of New Market contributed the majority of the phosphorus to Smith Creek under base-flow conditions, but nonpoint sources of phosphorus dominated the loading to Smith Creek during stormflow periods.
Implementation of conservation practices increased in the Smith Creek watershed during the study period, and even though a broad range of practice types was implemented, the most common practices included stream fencing (for cattle exclusion), the development of nutrient management plans, conservation crop rotation, and the planting of cover crops. While the implementation of these conservation practices is encouraging, results indicate small increases in nitrate concentrations at the streamgage over the last 29 years, concurrent with small decreases in nitrate fluxes. It will likely be years before the cumulative effect of these practices can be detected in the Smith Creek water quality, and the magnitude of the effect of these conservation practices detected in Smith Creek will depend largely on whether nutrient loading (of manure and commercial fertilizer) is reduced over time.
The Upper Chester River watershed includes the 36-square-mile (mi2) watershed area around several nontidal tributaries that drain into the tidal Chester River. The streamgage is on Chesterville Branch, the largest nontidal tributary (approximately 6.12 mi2) and is the site for continuous water-quality monitoring for this project. The base-flow index at Chesterville Branch is about 72 percent and indicates that, as in most of the Coastal Plain, groundwater is the greatest contributor to streamflow. As such, more than 90 percent of the nitrogen in the stream is in the form of nitrate from groundwater. Continuous and discrete data collected at Chesterville Branch show the effects of streamflow and season on water quality. Significantly positive correlations with flow were observed for ammonium, dissolved and total phosphorus, sediment, and turbidity as runoff carried these constituents from the land surface into Chesterville Branch. Other constituents that increased significantly with flow include potassium, sulfate, iron, and manganese, which are likely contributed from near-stream areas and ponds with high organic-matter content. Total nitrogen, pH, and specific conductance, along with chemical constituents associated with groundwater inputs including nitrate, calcium, ratio of calcium to magnesium, silica, bicarbonate, and sodium, were negatively correlated with flow because concentrations of these constituents were diluted by runoff.
Seasonal differences in water chemistry, which are most likely related to increased biologic effects on the uptake and release of chemicals in the stream and near-stream areas, also were observed. Water temperature, orthophosphate, δ15N of nitrate, bicarbonate, sodium, and the ratio of sodium to chloride were higher during the warm season, and dissolved oxygen, total nitrogen, nitrate, magnesium, sulfate, and manganese were higher during the cool season.
Surrogate-regression models developed by using continuous water-quality data showed that the annual sediment load for the 2013 water year was about 2,600 tons, with more than 90 percent of this sediment contributed during two storms. The total phosphorus load in 2013 was about 13,000 pounds with more than 90 percent contributed during the same two storms as sediment. The load of total nitrogen, 140,000 pounds, accumulated steadily throughout the 2013 water year as nitrate in groundwater continuously discharged into the stream. The same two large storms that contributed 90 percent of the suspended-sediment and total phosphorus load only contributed about 20 percent of the annual total nitrogen load.
Extensive water-quality monitoring of stream base flow throughout the Upper Chester River watershed identified how differences in land use and hydrogeology affected water chemistry. In parts of the watershed with well-drained soil and thick sandy aquifer sediments, concentrations of nitrate and other chemicals associated with fertilizer and lime application increased in streams as agricultural land use increased. More than 90 percent of the nitrogen in streams from these areas was in the form of nitrate, and concentrations ranged from about 5 milligrams per liter (mg/L) to 8 mg/L as nitrogen in the two largest tributaries. Stream nitrate concentrations were about 1 mg/L as nitrogen where soils were more poorly drained, the surficial aquifer sediments were thinner, and forests and wetlands were more widespread than agriculture. Nitrate isotope data were consistent with inorganic fertilizers ± atmospheric deposition and N2 fixation as sources of nitrogen, and with nitrification as the dominant nitrate-forming process. Nitrate reduction was indicated by elevated δ15N and δ18O values in some samples from streams draining watersheds with poorly drained soils. An analysis of land-use data and SPARROW modeling input data attributed almost 90 percent of the nitrogen sources in the Upper Chester River watershed to inorganic fertilizer and fixation of atmospheric nitrogen by legumes, which is in agreement with the isotopic characteristics of nitrate in this watershed. Local sources of manure are limited in this area. Total phosphorus concentrations during base flow ranged from below detection to about 0.2 mg/L. Stream phosphorus concentrations during base flow were generally lower than those measured during storms because most phosphorus transport likely occurs as phosphorus attached to sediment particles during runoff. Because manure is not widely used in this area, the major source of phosphorus is likely fertilizer.
The implementation of conservation practices in the Upper Chester River watershed increased substantially during the study period, with a total implementation of 1,194 U.S. Department of Agriculture-compliant practices. The most frequently used practices were oriented towards nutrient and sediment control, including cover crops, nutrient management planning, conservation crop rotation, conservation tillage, and irrigation management. The current Chesapeake Bay model for this area predicts that implementation of best management practices should result in a 13-percent decrease in overall delivery of nitrogen to the Upper Chester River. Because most nitrogen travels through the groundwater system for years to decades before being discharged to streams, the time period of monitoring was not sufficient to see the effects of these practices on water quality. The magnitude of the effect that may eventually be detected will depend on the degree to which nitrate leaching into the groundwater system is reduced over time. Loadings of phosphorus and sediment are primarily transported during large runoff events and are difficult to control and analyze for trends because of their timing and episodic nature.
Conewago Creek has two primary monitoring locations—one near the middle of the 47-mi2 watershed and the other near the outlet just upstream of the Susquehanna River. The base-flow index was 47.3 percent for 2012–2013, indicating that on average, approximately 53 percent of the streamflow in Conewago Creek exited the watershed as surface flow, which suggests that the stormwater runoff was somewhat greater than groundwater discharge (base flow). A series of cluster and principal components analyses demonstrated that the majority of the variability in the Conewago Creek water quality could be attributed to hydrologic and seasonal variability. Statistically significant positive correlations with flow were observed at both monitoring sites for ammonium, total phosphorus, orthophosphate, iron, and manganese; additionally, at the upstream monitoring station, total nitrogen demonstrated a statistically significant positive correlation with flow. Statistically significant inverse correlations with flow were observed at both sites for water temperature, specific conductance (at the downstream site only), sulfate, chloride, calcium, and magnesium. Statistically significant seasonal patterns were observed for several water-quality constituents. Water temperature, phosphorus (upstream site only), and orthophosphate were higher during the warm season, and nitrate and total nitrogen (upstream site only) were higher during the cool season.
Surrogate regression models were developed to compute sediment and nutrient load in Conewago Creek by using the continuous water-quality monitors and water-quality samples. Conewago Creek sediment load was approximately 9,900 tons in 2012 and approximately 18,900 tons in 2013, with nearly 80 percent of the sediment load in 2013 contributed by the three largest storm events. Annual total nitrogen loads could not be estimated due to poor model performance. The addition of continued monitoring or a continuously recording nitrate sensor could improve estimates of total nitrogen loads. During 2012 and 2013, phosphorus loads in Conewago Creek were approximately 50,000 pounds in each year.
Combining data from one high-flow synoptic sampling with the data from routine sampling revealed how the geology and hydrology interact to result in variable water chemistry throughout the Conewago Creek watershed. The areas above the upstream gage in the headwaters are generally underlain by forested non-carbonate bedrock and are characterized by relatively low nitrate, specific conductance, calcium, and magnesium, as well as relatively low concentrations of phosphorus, ammonium, iron, and manganese. The more developed, agricultural areas below the upstream site were generally characterized by higher ionic strength waters with higher nutrient and metal concentrations. An analysis of land-use data and SPAtially Referenced Regressions On Watershed (SPARROW) modeling data indicates that manure sources of nitrogen dominate the input of nitrogen to the watershed.
Implementation of conservation practices increased in the Conewago Creek watershed during the study period, and while a broad range of practice types were implemented, the most common practices included residue and tillage management, cover crops, nutrient management, terracing, and stream fencing (for animal exclusion or bank restoration). While the implementation of these conservation practices is encouraging, the cumulative effects of these practices probably will not be detected in Conewago Creek water quality for several years. The magnitude of the effects of these conservation practices on water quality in Conewago Creek will depend largely on the extent to which nutrient loading (septic, manure, and commercial fertilizer) and sediment-producing activities are reduced over time.
The Difficult Run watershed is a 57.82-mi2 watershed that drains to the Potomac River. The long-term Difficult Run base-flow index (from 1936 to 2010) was 57.9, indicating that approximately 58 percent of streamflow exited the watershed as base flow and 42 percent as stormflow; however, with continued development and urbanization of the watershed, the base-flow index has decreased to 50 percent during the last 20 years. This base-flow index was less than those of the other watersheds evaluated in this study, likely because the Difficult Run watershed largely is underlain by crystalline piedmont metamorphic rocks and has a greater proportion of impervious urban land cover. A series of cluster and principal components analyses indicated that most of the variability in Difficult Run water quality could be attributed to hydrologic variability and seasonality. Statistically significant positive correlations with flow were observed for turbidity, dissolved oxygen, suspended sediments, ammonium, orthophosphate, iron, and total phosphorus. Statistically significant inverse correlations with flow were observed for water temperature, pH, specific conductance, bicarbonate, calcium, magnesium, nitrate, δ15N of nitrate, and silica. Statistically significant seasonal patterns were observed for numerous water-quality constituents: water temperature, ammonium, orthophosphate, and δ15N of nitrate were higher during the warm season, and dissolved oxygen, nitrate, and manganese were higher during the cool season. Surrogate regression models were developed to compute sediment and nutrient loading rates. The Difficult Run sediment load was approximately 8,000 tons per year, with greater than 95 percent of the sediment load in the 2013 water year contributed by the seven largest storm events. The total phosphorus load in Difficult Run was approximately 14,000 pounds of phosphorus per year, with the majority of the load contributed during stormflow periods. The total nitrogen load in Difficult Run is estimated to have been approximately 140,000 pounds per year, with total nitrogen accumulation less dominated by stormflow contributions than that of phosphorus and strongly affected by base-flow export of nitrogen from the basin.
Extensive water-quality monitoring throughout the Difficult Run watershed revealed relatively uniform generation of flow per unit of watershed area, as well as spatial variation in water quality that is strongly related to land-use activities. Elevated nitrate concentrations were observed in a subset of monitoring sites that are inversely correlated with population density and positively correlated to the septic system density within each subwatershed. The majority of the elevated nitrate concentrations for these sites are hypothesized to be caused by nitrate leaching from septic systems, more so than homeowner fertilizer usage among these subwatersheds that have lower population densities than other parts of the watershed. Nitrate isotope data, temporal patterns in the water-quality data, mass-balance computations, and a separate land-use analysis all generally indicate that leachate from septic systems was the likely source of the elevated nitrate. Another group of water-quality sites have relatively low nitrogen concentrations, are located in areas that are served by city sewer lines, and have experienced stream restoration activities. A final group of sites drained the areas with the highest imperviousness and had strongly elevated specific conductance, chloride, and sodium, which were likely caused by a combination of road salting and other anthropogenic sources draining these urbanized areas in the watershed. A fourth group of sites represents a mixture of water sources and had water quality similar to that at the Difficult Run streamgage. Analysis of the nitrate isotope data generally indicates a broad range of composition indicative of mixed natural and anthropogenic nitrogen sources. Implementation of conservation practices increased in the Difficult Run watershed during the study period, and while a broad range of practice types was implemented, the most common practices included stream restoration. While the implementation of these conservation practices is encouraging, the cumulative effect of these practices probably will not be detected in Difficult Run water quality for several years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165093","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency Chesapeake Bay Program","usgsCitation":"Hyer, K.E., Denver, J.M., Langland, M.J., Webber, J.S., Böhlke, J.K., Hively, W.D., and Clune, J.W., 2016, Spatial and temporal variation of stream chemistry associated with contrasting geology and land-use patterns in the Chesapeake Bay watershed—Summary of results from Smith Creek, Virginia; Upper Chester River, Maryland; Conewago Creek, Pennsylvania; and Difficult Run, Virginia, 2010–2013: U.S. Geological Survey Scientific Investigations Report 2016–5093, 211 p., https://dx.doi.org/10.3133/sir20165093.","productDescription":"Report: xix, 211 p.","startPage":"1","endPage":"211","numberOfPages":"236","onlineOnly":"N","ipdsId":"IP-067371","costCenters":[{"id":614,"text":"Virginia Water Science 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U.S. Geological Survey
1730 East Parham Road
Richmond, VA 23228
Context: The Columbian sharp-tailed grouse (Tympanuchus phasianellus columbianus) is a subspecies of conservation concern in the western United States, currently occupying ≤10% of its historic range. Land and management agencies are employing translocation techniques to restore Columbian sharp-tailed grouse (CSTG) populations. However, establishing self-sustaining populations by translocating grouse often is unsuccessful, owing, in part, to low survivorship of translocated grouse following release.
Aims: We measured and modelled patterns of CSTG mortality for 150 days following translocation into historic range, to better understand patterns and causes of success or failure in conservation efforts to re-establish grouse populations.
Methods: We conducted two independent multi-year translocations and evaluated individual and temporal factors associated with CSTG survival up to 150 days following their release. Both translocations were reintroduction attempts in Nevada, USA, to establish viable populations of CSTG into their historic range.
Key results: We observed a clear temporal threshold in survival probability, with CSTG mortality substantially higher during the first 50 days following release than during the subsequent 100 days. Additionally, translocated yearling grouse exhibited higher overall survival (0.669 ± 0.062) than did adults (0.420 ± 0.052) across the 150-day period and higher survival than adults both before and after the 50-day temporal threshold.
Conclusions: Translocated CSTG are especially vulnerable to mortality for 50 days following release, whereas translocated yearling grouse are more resistant to mortality than are adult grouse. On the basis of the likelihood of survival, yearling CSTG are better candidates for population restoration through translocation than are adult grouse.
Implications: Management actions that ameliorate mortality factors for 50 days following translocation and translocations that employ yearling grouse will increase the likelihood of population establishment.
","language":"English","publisher":"Commonwealth Scientific and Industrial Research Organisation","publisherLocation":"East Melbourne, Austrailia","doi":"10.1071/WR15158","usgsCitation":"Mathews, S.R., Coates, P.S., and Delehanty, D., 2016, Survival of translocated sharp-tailed grouse: Temporal threshold and age effects: Wildlife Research, v. 43, no. 3, p. 220-227, https://doi.org/10.1071/WR15158.","productDescription":"8 p.","startPage":"220","endPage":"227","ipdsId":"IP-075276","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":470413,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1071/wr15158","text":"Publisher Index Page"},{"id":331095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.21240234375001,\n 36.01356058518153\n ],\n [\n -120.21240234375001,\n 47.78363463526376\n ],\n [\n -108.80859375,\n 47.78363463526376\n ],\n [\n -108.80859375,\n 36.01356058518153\n ],\n [\n -120.21240234375001,\n 36.01356058518153\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"3","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"582ecfede4b04d580bd4352a","contributors":{"authors":[{"text":"Mathews, Steven R. 0000-0002-3165-9460 smathews@usgs.gov","orcid":"https://orcid.org/0000-0002-3165-9460","contributorId":176922,"corporation":false,"usgs":true,"family":"Mathews","given":"Steven","email":"smathews@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":653983,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":653982,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Delehanty, David J.","contributorId":86683,"corporation":false,"usgs":true,"family":"Delehanty","given":"David J.","affiliations":[],"preferred":false,"id":653984,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178409,"text":"70178409 - 2016 - Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms","interactions":[],"lastModifiedDate":"2016-12-16T13:08:30","indexId":"70178409","displayToPublicDate":"2016-11-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms","docAbstract":"We evaluate the relative impact of petroleum spill and storm surge on near-shore wetland loss by quantifying the lateral movement of coastal shores in upper Barataria Bay, Louisiana (USA), between June 2009 and October 2012, a study period that extends from the year prior to the Deepwater Horizon spill to 2.5 years following the spill. We document a distinctly different pattern of shoreline loss in the 2 years following the spill, both from that observed in the year prior to the spill, during which there was no major cyclonic storm, and from change related to Hurricane Isaac, which made landfall in August 2012. Shoreline erosion following oiling was far more spatially extensive and included loss in areas protected from wave-induced erosion. We conclude that petroleum exposure can substantially increase shoreline recession particularly in areas protected from storm-induced degradation and disproportionally alters small oil-exposed barrier islands relative to natural erosion.
","language":"English","publisher":"American Geophysical Union","publisherLocation":"Washington, D.C.","doi":"10.1002/2016GL070624","collaboration":"Jet Propulsion Laboratory, California Institute of Technology","usgsCitation":"Rangoonwala, A., Jones, C.E., and Ramsey, E.W., 2016, Wetland shoreline recession in the Mississippi River Delta from petroleum oiling and cyclonic storms: Geophysical Research Letters, v. 43, no. 22, p. 11,652-11,660, https://doi.org/10.1002/2016GL070624.","productDescription":"9 p.","startPage":"11,652","endPage":"11,660","ipdsId":"IP-074987","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":462037,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2016gl070624","text":"Publisher Index Page"},{"id":331102,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.966667,\n 29.516667\n ],\n [\n -89.966667,\n 29.408333\n ],\n [\n -89.816667,\n 29.408333\n ],\n [\n -89.816667,\n 29.516667\n ],\n [\n -89.966667,\n 29.516667\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"43","issue":"22","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2016-11-17","publicationStatus":"PW","scienceBaseUri":"582ecfebe4b04d580bd43526","contributors":{"authors":[{"text":"Rangoonwala, Amina 0000-0002-0556-0598 rangoonwalaa@usgs.gov","orcid":"https://orcid.org/0000-0002-0556-0598","contributorId":3455,"corporation":false,"usgs":true,"family":"Rangoonwala","given":"Amina","email":"rangoonwalaa@usgs.gov","affiliations":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":654022,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Cathleen E.","contributorId":11890,"corporation":false,"usgs":true,"family":"Jones","given":"Cathleen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":654023,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramsey, Elijah W. III 0000-0002-4518-5796 ramseye@usgs.gov","orcid":"https://orcid.org/0000-0002-4518-5796","contributorId":2883,"corporation":false,"usgs":true,"family":"Ramsey","given":"Elijah","suffix":"III","email":"ramseye@usgs.gov","middleInitial":"W.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":false,"id":654024,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70178405,"text":"70178405 - 2016 - Comparison of the mineral composition of the sediment found in two Mars dunefields: Ogygis Undae and Gale crater – three distinct endmembers identified","interactions":[],"lastModifiedDate":"2016-12-16T13:06:00","indexId":"70178405","displayToPublicDate":"2016-11-17T00:00:00","publicationYear":"2016","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1427,"text":"Earth and Planetary Science Letters","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of the mineral composition of the sediment found in two Mars dunefields: Ogygis Undae and Gale crater – three distinct endmembers identified","docAbstract":"The composition of two dune fields, Ogygis Undae and the NE–SW trending dune field in Gale crater (the “Bagnold Dune Field” and “Western Dune Field”), were analyzed using thermal emission spectra from the Mars Global Surveyor (MGS) Thermal Emission Spectrometer (TES) and the Mars Odyssey Thermal Emission Imaging System (THEMIS). The Gale crater dune field was used as a baseline as other orbital compositional analyses have been conducted, and in situ sampling results will soon be available.
Results from unmixing thermal emission spectra showed a spatial variation between feldspar mineral abundances and pyroxene mineral abundances in Ogygis Undae. Other datasets, including nighttime thermal inertia values, also showed variation throughout the dune field. One explanation proposed for this variation is a bimodal distribution of two sand populations. This distribution is seen in some terrestrial dune fields.
The two dune fields varied in both mineral types present and in uniformity of composition. These differences point to different source lithologies and different distances travelled from source material. Examining these differences further will allow for a greater understanding of aeolian processes on Mars.
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