Mount Rushmore National Memorial (hereafter referred to as “the memorial”), in western South Dakota, is maintained by the National Park Service (NPS) and includes 1,278 acres of land in the east-central part of the Black Hills. An ongoing challenge for NPS managers at the memorial is providing water from sustainable and reliable sources for operations, staff, and the increasing number of visitors. In 2020, the U.S. Geological Survey (USGS) and NPS completed a hydrological study of the Burnpit well (well 5), a 580-foot-deep open hole groundwater well completed in metamorphic (crystalline) rock at the memorial. The purpose of this study was to estimate the geological and hydraulic properties of the aquifer supplying the well and to determine the water quality of the groundwater from the well. The study provides NPS staff and managers background information for assessing future uses for the well. Methods for data collection and analysis for the study included borehole and video camera analysis in 2020, aquifer testing by the NPS in 2009 and the USGS in 2020, and water-quality sampling in 2020.
Borehole camera video generally matched the lithology recorded in the well log. Fractures recorded in the well log and observed with the borehole camera, including more than 20 less prominent fractures and rough sidewall areas, indicated a fractured aquifer. The fractures are the primary conduits for groundwater flow through the rock and into the well.
Transmissivity was estimated for the upper and lower water-level drawdown zones at the Burnpit well with data from the NPS and USGS using the Theis and Cooper-Jacob methods. Transmissivity for the NPS test using the Theis method was 9.0 and 11 feet squared per day (ft2/d) for the upper and lower drawdown zones, respectively. Using the Cooper-Jacob method, the transmissivity was 22 and 14 ft2/d for the upper and lower drawdown zones of the aquifer, respectively. Transmissivity estimates from data from the USGS test were similar. The Theis method, applied to the upper and lower drawdown zones of the aquifer, produced transmissivity estimates of 7.7 and 10 ft2/d, and the Cooper-Jacob method produced estimates of 9.7 and 12 ft2/d, respectively.
Storativity (specific yield) estimated using the Theis method for the NPS aquifer-test data was 0.85 and 0.92 for the upper and lower drawdown zones of the aquifer, respectively. The Cooper-Jacob method applied to the NPS aquifer-test data produced storativity estimates of 0.11 and 0.50 for the upper and lower drawdown zones, respectively. The Theis method applied to the USGS aquifer-test data estimated storativity values of 0.77 and 1.0 for the upper and lower drawdown zones, respectively. The Cooper-Jacob method estimated storativity of 0.50 and 0.60 for the upper and lower drawdown zones of the USGS aquifer test, respectively. The estimated storativity values from the NPS and USGS aquifer tests for the upper and lower drawdown zones were higher than expected for limestones and schists.
The hypothetical equilibrium drawdown for the Burnpit well was estimated after the NPS test in 2009 at no more, and possibly less, than 35 gallons per minute. The NPS noted that the sustainable yield likely was overestimated because the water level did not stabilize during the NPS aquifer test. The specific capacity for the NPS aquifer test in 2009 was 0.16 gallon per minute per foot ([gal/min]/ft) of drawdown at 3 hours, and the specific capacity for the USGS aquifer test in 2020 was 0.13 (gal/min)/ft of drawdown at 3 hours. The rate of water-level recovery after pumping ceased was 0.017 and 0.013 (gal/min)/ft for the NPS and USGS aquifer tests, respectively. The water-level recovery rate was nearly an order of magnitude less than the specific capacity estimated during pumping, indicating that water levels in the Burnpit well may not recover quickly enough during pumping to provide for a continuous source of water.
Water-quality samples were collected at the Burnpit well on June 24 and July 23, 2020, and analyzed for field-measured properties, major ions, metals, nutrients, and perchlorate. Iron, zinc, and lithium concentrations for unfiltered samples in the well were at least three times greater than the mean filtered sample concentrations reported for crystalline aquifers in the Black Hills. Manganese concentrations were less than the mean concentration for crystalline aquifers but exceeded the U.S. Environmental Protection Agency (EPA) secondary drinking-water standards. The iron concentration from the June 24 sample was about 11 times greater than the EPA secondary drinking-water standards and mean concentrations from crystalline aquifers in the Black Hills. Arsenic concentrations in Burnpit well samples collected in 2020 were greater than the EPA primary drinking-water standard and the mean concentration for crystalline aquifers in the Black Hills. Arsenic occurs naturally in the rock of crystalline aquifers, and concentrations from samples in the Black Hills commonly exceed the EPA primary drinking-water standard of 10 micrograms per liter. High concentrations of arsenic, iron, and manganese metals in the Burnpit well make groundwater from the well in its natural state unusable as a drinking-water source, and water treatment would be necessary to reduce the trace element concentrations to less than the EPA primary and secondary drinking-water standards. However, if the memorial has immediate nonpotable water requirements, such as for construction and fire suppression, groundwater from the Burnpit well could provide water without causing additional stress to current (2021) drinking-water sources.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215059","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Eldridge, W.G., Hoogestraat, G.K., and Rice, S.E., 2021, Borehole analysis, single-well aquifer testing, and water quality for the Burnpit well, Mount Rushmore National Memorial, South Dakota: U.S. Geological Survey Scientific Investigations Report 2021–5059, 29 p., https://doi.org/10.3133/sir20215059.","productDescription":"Report: vii, 29 p.; Data Release; Dataset","numberOfPages":"40","onlineOnly":"Y","ipdsId":"IP-126498","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":386673,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98OZQN9","text":"USGS data release","description":"USGS data release","linkHelpText":"Borehole video and aquifer test data for the Burnpit well, Mount Rushmore National Memorial, South Dakota, 2020"},{"id":386672,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5059/sir20215059.pdf","text":"Report","size":"2.83 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5059"},{"id":386674,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS dataset","linkHelpText":"— USGS water data for the Nation"},{"id":386671,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5059/coverthb.jpg"}],"country":"United States","state":"South Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.0625,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 43.40903821777055\n ],\n [\n -103.2440185546875,\n 44.52392653654213\n ],\n [\n -104.0625,\n 44.52392653654213\n ],\n [\n -104.0625,\n 43.40903821777055\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue
Bismarck, ND 58503
1608 Mountain View Road
Rapid City, SD 57702
Amphibian larvae are commonly used as indicators of aquatic ecosystem health because they are susceptible to contaminants. However, there is limited information on how species characteristics and trophic position influence contaminant loads in larval amphibians. Importantly, there remains a need to understand whether grazers (frogs and toads [anurans]) and predators (salamanders) provide comparable information on contaminant accumulation or if they are each indicative of unique environmental processes and risks. To better understand the role of trophic position in contaminant accumulation, we analyzed composite tissues for 10 metals from larvae of multiple co-occurring anuran and salamander species from 20 wetlands across the United States. We examined how metal concentrations varied with body size (anurans and salamanders) and developmental stage (anurans) and how the digestive tract (gut) influenced observed metal concentrations. Across all wetlands, metal concentrations were greater in anurans than salamanders for all metals tested except mercury (Hg), selenium (Se), and zinc (Zn). Concentrations of individual metals in anurans decreased with increasing weight and developmental stage. In salamanders, metal concentrations were less correlated with weight, indicating diet played a role in contaminant accumulation. Based on batches of similarly sized whole-body larvae compared to larvae with their digestive tracts removed, our results indicated that tissue type strongly affected perceived concentrations, especially for anurans (gut represented an estimated 46–97% of all metals except Se and Zn). This suggests the reliability of results based on whole-body sampling could be biased by metal, larval size, and development. Overall, our data shows that metal concentrations differs between anurans and salamanders, which suggests that metal accumulation is unique to feeding behavior and potentially trophic position. To truly characterize exposure risk in wetlands, species of different life histories, sizes and developmental stages should be included in biomonitoring efforts.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envpol.2021.117638","usgsCitation":"Smalling, K., Oja, E.B., Cleveland, D.M., Davenport, J.D., Eagles-Smith, C., Campbell Grant, E.H., Kleeman, P.M., Halstead, B., Stemp, K.M., Tornabene, B., Bunnell, Z.J., and Hossack, B., 2021, Metal accumulation varies with life history, size, and development of larval amphibians: Environmental Pollution, v. 287, 117638, 10 p., https://doi.org/10.1016/j.envpol.2021.117638.","productDescription":"117638, 10 p.","ipdsId":"IP-127103","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":489089,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envpol.2021.117638","text":"Publisher Index Page"},{"id":436291,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9Q3LM78","text":"USGS data release","linkHelpText":"Metal concentrations in sediment and amphibian tissues from wetlands sampled across the United States"},{"id":387228,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"287","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Oja, Emily Bea 0000-0002-8621-9665","orcid":"https://orcid.org/0000-0002-8621-9665","contributorId":261164,"corporation":false,"usgs":true,"family":"Oja","given":"Emily","email":"","middleInitial":"Bea","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819407,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":819408,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davenport, Jon D 0000-0002-9911-2779","orcid":"https://orcid.org/0000-0002-9911-2779","contributorId":261166,"corporation":false,"usgs":false,"family":"Davenport","given":"Jon","email":"","middleInitial":"D","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":819409,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Eagles-Smith, Collin A. 0000-0003-1329-5285","orcid":"https://orcid.org/0000-0003-1329-5285","contributorId":221745,"corporation":false,"usgs":true,"family":"Eagles-Smith","given":"Collin A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819410,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Campbell Grant, Evan H. 0000-0003-4401-6496 ehgrant@usgs.gov","orcid":"https://orcid.org/0000-0003-4401-6496","contributorId":150443,"corporation":false,"usgs":true,"family":"Campbell Grant","given":"Evan","email":"ehgrant@usgs.gov","middleInitial":"H.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":819411,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kleeman, Patrick M. 0000-0001-6567-3239 pkleeman@usgs.gov","orcid":"https://orcid.org/0000-0001-6567-3239","contributorId":3948,"corporation":false,"usgs":true,"family":"Kleeman","given":"Patrick","email":"pkleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":819412,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":819413,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Stemp, Kenzi M 0000-0001-7566-8513","orcid":"https://orcid.org/0000-0001-7566-8513","contributorId":261169,"corporation":false,"usgs":false,"family":"Stemp","given":"Kenzi","email":"","middleInitial":"M","affiliations":[{"id":36626,"text":"Appalachian State University","active":true,"usgs":false}],"preferred":false,"id":819414,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tornabene, Brian J.","contributorId":200041,"corporation":false,"usgs":false,"family":"Tornabene","given":"Brian J.","affiliations":[],"preferred":false,"id":819415,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bunnell, Zachary J 0000-0001-6142-8703","orcid":"https://orcid.org/0000-0001-6142-8703","contributorId":261172,"corporation":false,"usgs":true,"family":"Bunnell","given":"Zachary","email":"","middleInitial":"J","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819416,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hossack, Blake R. 0000-0001-7456-9564","orcid":"https://orcid.org/0000-0001-7456-9564","contributorId":229347,"corporation":false,"usgs":true,"family":"Hossack","given":"Blake R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":819417,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221582,"text":"70221582 - 2021 - Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin","interactions":[],"lastModifiedDate":"2021-06-24T14:50:56.03881","indexId":"70221582","displayToPublicDate":"2021-06-23T09:46:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1542,"text":"Environmental Health Perspectives","active":true,"publicationSubtype":{"id":10}},"title":"Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin","docAbstract":"Private wells are an important source of drinking water in Kewaunee County, Wisconsin. Due to the region’s fractured dolomite aquifer, these wells are vulnerable to contamination by human and zoonotic gastrointestinal pathogens originating from land-applied cattle manure and private septic systems.
We determined the magnitude of the health burden associated with contamination of private wells in Kewaunee County by feces-borne gastrointestinal pathogens.
This study used data from a year-long countywide pathogen occurrence study as inputs into a quantitative microbial risk assessment (QMRA) to predict the total cases of acute gastrointestinal illness (AGI) caused by private well contamination in the county. Microbial source tracking was used to associate predicted cases of illness with bovine, human, or unknown fecal sources.
Results suggest that private well contamination could be responsible for as many as 301 AGI cases per year in Kewaunee County, and that 230 and 12 cases per year were associated with a bovine and human fecal source, respectively. Furthermore, Cryptosporidium parvum was predicted to cause 190 cases per year, the most out of all 8 pathogens included in the QMRA.
This study has important implications for land use and water resource management in Kewaunee County and informs the public health impacts of consuming drinking water produced in other similarly vulnerable hydrogeological settings.
","language":"English","doi":"10.1289/EHP7815","usgsCitation":"Burch, T., Stokdyk, J.P., Spencer, S.K., Kieke, B.A., Firnstahl, A.D., Muldoon, M.A., and Borchardt, M.A., 2021, Quantitative microbial risk assessment for contaminated private wells in the fractured dolomite aquifer of Kewaunee County, Wisconsin: Environmental Health Perspectives, v. 129, no. 6, 067003, 9 p., https://doi.org/10.1289/EHP7815.","productDescription":"067003, 9 p.","ipdsId":"IP-120047","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":451765,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1289/ehp7815","text":"Publisher Index Page"},{"id":386700,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","county":"Kewaunee County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-87.3761,44.6754],[-87.3774,44.674],[-87.381,44.6636],[-87.3858,44.6545],[-87.3911,44.6473],[-87.3944,44.6442],[-87.3966,44.6378],[-87.4045,44.6302],[-87.4085,44.6257],[-87.4137,44.6235],[-87.4223,44.6145],[-87.4263,44.61],[-87.4341,44.6056],[-87.442,44.6011],[-87.4428,44.5934],[-87.4468,44.5893],[-87.4502,44.5816],[-87.4544,44.5721],[-87.4604,44.5622],[-87.4664,44.555],[-87.4738,44.5455],[-87.476,44.5369],[-87.4761,44.5305],[-87.4796,44.5223],[-87.4851,44.5106],[-87.488,44.4974],[-87.4959,44.4706],[-87.5046,44.4575],[-87.5041,44.4534],[-87.5062,44.4457],[-87.5064,44.4375],[-87.5074,44.4279],[-87.5121,44.4188],[-87.5163,44.408],[-87.5191,44.3998],[-87.5212,44.3907],[-87.5209,44.3816],[-87.5218,44.3734],[-87.5232,44.3688],[-87.5279,44.3602],[-87.5351,44.3521],[-87.5386,44.3422],[-87.5368,44.338],[-87.5408,44.3331],[-87.5454,44.3277],[-87.6445,44.3273],[-87.7665,44.3271],[-87.7655,44.4146],[-87.7646,44.5017],[-87.7643,44.5888],[-87.7628,44.6477],[-87.7582,44.6522],[-87.7555,44.6558],[-87.7547,44.6608],[-87.7507,44.6667],[-87.7435,44.673],[-87.7389,44.6775],[-87.6413,44.6757],[-87.5193,44.6753],[-87.4384,44.6754],[-87.3973,44.6753],[-87.3761,44.6754]]]},\"properties\":{\"name\":\"Kewaunee\",\"state\":\"WI\"}}]}","volume":"129","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Burch, Tucker R.","contributorId":195801,"corporation":false,"usgs":false,"family":"Burch","given":"Tucker R.","affiliations":[],"preferred":false,"id":818182,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stokdyk, Joel P. 0000-0003-2887-6277 jstokdyk@usgs.gov","orcid":"https://orcid.org/0000-0003-2887-6277","contributorId":193848,"corporation":false,"usgs":true,"family":"Stokdyk","given":"Joel","email":"jstokdyk@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818183,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spencer, Susan K.","contributorId":210972,"corporation":false,"usgs":false,"family":"Spencer","given":"Susan","email":"","middleInitial":"K.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":818184,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kieke, Burney A","contributorId":195802,"corporation":false,"usgs":false,"family":"Kieke","given":"Burney","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":818185,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Firnstahl, Aaron D. 0000-0003-2686-7596 afirnstahl@usgs.gov","orcid":"https://orcid.org/0000-0003-2686-7596","contributorId":168296,"corporation":false,"usgs":true,"family":"Firnstahl","given":"Aaron","email":"afirnstahl@usgs.gov","middleInitial":"D.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818186,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Muldoon, Maureen A.","contributorId":198974,"corporation":false,"usgs":false,"family":"Muldoon","given":"Maureen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":818187,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Borchardt, Mark A. 0000-0002-6471-2627","orcid":"https://orcid.org/0000-0002-6471-2627","contributorId":210973,"corporation":false,"usgs":false,"family":"Borchardt","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":38162,"text":"United States Department of Agriculture Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":818188,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70221438,"text":"tm6G1 - 2021 - Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming","interactions":[],"lastModifiedDate":"2021-06-24T13:59:13.479713","indexId":"tm6G1","displayToPublicDate":"2021-06-23T08:54:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"6-G1","displayTitle":"Probabilistic Methodology for the Assessment of Original and Recoverable Coal Resources, Illustrated with an Application to a Coal Bed in the Fort Union Formation, Wyoming","title":"Probabilistic methodology for the assessment of original and recoverable coal resources, illustrated with an application to a coal bed in the Fort Union Formation, Wyoming","docAbstract":"The U.S. Geological Survey (USGS) has been using its Circular 891 for evaluating uncertainty in coal resource assessments for more than 35 years. Calculated cell tonnages are assigned to four qualitative reliability classes depending exclusively on distance to the nearest drill hole. The main appeal of this methodology, simplicity, is also its main drawback. Reliability may depend so marginally on distance to the nearest drill hole that, over time, it has become evident that Circular 891 is inadequate for modeling reliability and is limited by other shortcomings. The present publication describes the use of geostatistics as an approach allowing a more satisfactory performance than that which is achieved following Circular 891. Geostatistics takes advantage of partly random and partly organized fluctuations in attributes such as coal thickness, coal density, and elevation of the top of a coal bed, borrowing concepts and tools that have been standard features in statistics and risk analysis for decades. Considering that readers interested in this study may not have the background to go directly into the details of the methodology, we start by explaining geostatistical concepts and modeling techniques. The remainder of the publication is devoted to formulating the assessment methodology, applying it to data from the Fillmore Ranch coal bed in the Fort Union Formation in Wyoming, and explaining the computer software applied for performing calculations and displays. The assessment methodology has been designed to report three different forms of resources: coal in place, coal mineable by surface mining methods, and coal mineable by underground mining methods. These three types of resources are reported graphically by displaying both the magnitude and the reliability of total coal resources and resources at the cell scale. In the case of the Fillmore Ranch coal bed example, there is a 90-percent probability that the resources in place are 9.687 ± 0.383 billion short tons (bst), while the coal available for underground mining is 2.279 ± 0.160 bst, and that available for surface mining is only 0.240 ± 0.025 bst because of the steep dip to the west away from the outcrop. These magnitudes are derived from numerical probability distributions not following any specific form.
Geology, Energy & Minerals Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 954
Reston, VA 20192
The handbooks and synchronized MP4 recordings provide hands-on instruction for creating and analyzing vegetation live fractional cover (LFC) maps. The methods and protocols used in the instruction materials follow those developed and recorded in Rangoonwala and Ramsey (2019). The LFC mapping and geographic information system (GIS) analyses highlight the consortium of rangeland conservancies covering the semiarid central region of Kenya (approximately 44,000 square kilometers).
The instruction materials are separated into two parts: processing and map-product creation based on remote-sensing images and GIS analyses of the created maps for rangeland management. The image processing is conducted using the advanced and professional software package SeNtinels Application Platform (SNAP) that is supported and maintained by the European Space Agency. SNAP is a free image analyses software package available for download. It is largely icon driven but offers simple to advanced program inserts and batch processing. The GIS analyses are conducted using the software package Quantum Geographic Information System (QGIS), another free and downloadable software. QGIS is compatible with numerous software, including the Esri suite of ArcGIS software and database structure. The image data includes both high-spatial-resolution Sentinel-2 optical data and Sentinel-1 synthetic aperture radar (SAR). Both datasets are freely available via a public portal that is maintained by the European Space Agency.
The image-processing instruction handbook covers all aspects of acquiring and processing satellite-image data and importing vector-data sources into SNAP. The GIS analysis handbook covers final creation of map products from the maps created in SNAP and creation of GIS procedures in QGIS that are needed to manage the rangeland resources for wildlife and pastoral grazing. Although focused on the Kenyan conservancies and their semiarid environment, the processing methods and procedures are applicable for similar environments and management, and to a large part, even for integrated mapping and GIS functionality of any managed landscape resource.
The instruction handbooks are synchronized to MP4 training videos created with U.S. Geological Survey-licensed Camtasia 9 software.
The workbook and MP4 video combinations are suitable for a single user or a workshop setting.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211001","collaboration":"Prepared in cooperation with the U.S. Agency for International Development","usgsCitation":"Rangoonwala, A., and Ramsey, E., III, 2021, Grassland live fractional cover map creation and Geographic Information System (GIS) analysis for rangeland management supporting Kenya Northern Rangelands Trust Conservancies: U.S. Geological Survey Report 2021–1001, 59 p., https://doi.org/10.3133/ofr20211001.","productDescription":"Report: v, 59 p.; 2 Companion Files","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-119513","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":386330,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1001/coverthb.jpg"},{"id":386331,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001.pdf","text":"Report","size":"5.35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1001"},{"id":386397,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_SNAP_video/ofr20211001_SNAP_video_player.html","text":"Section I—SNAP Video Player","linkHelpText":"— SeNtinels Application Platform (SNAP)"},{"id":386334,"rank":4,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_video/ofr20211001_QGIS_video_player.html","text":"Section II—QGIS Video Player","description":"OFR 2021–1001 Video Player","linkHelpText":"— Quantum Geographic Information System (QGIS)"},{"id":386578,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_SNAP_video/","text":"Section I—SNAP package"},{"id":391246,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_shapefile","text":"Section II—QGIS Shapefiles"},{"id":386579,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2021/1001/ofr20211001_QGIS_video/","text":"Section II—QGIS package"}],"country":"Kenya","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[40.993,-0.85829],[41.58513,-1.68325],[40.88477,-2.08255],[40.63785,-2.49979],[40.26304,-2.57309],[40.12119,-3.27768],[39.80006,-3.68116],[39.60489,-4.34653],[39.20222,-4.67677],[37.7669,-3.67712],[37.69869,-3.09699],[34.07262,-1.05982],[33.90371,-0.95],[33.89357,0.10981],[34.18,0.515],[34.6721,1.17694],[35.03599,1.90584],[34.59607,3.05374],[34.47913,3.5556],[34.005,4.24988],[34.6202,4.84712],[35.29801,5.506],[35.81745,5.33823],[35.81745,4.77697],[36.15908,4.44786],[36.85509,4.44786],[38.12091,3.59861],[38.43697,3.58851],[38.67114,3.61607],[38.89251,3.50074],[39.55938,3.42206],[39.85494,3.83879],[40.76848,4.25702],[41.1718,3.91909],[41.85508,3.91891],[40.98105,2.78452],[40.993,-0.85829]]]},\"properties\":{\"name\":\"Kenya\"}}]}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, Louisiana 70506
The Albuquerque Basin, located in central New Mexico, is about 100 miles long and 25–40 miles wide. The basin is hydrologically defined as the extent of consolidated and unconsolidated deposits of Tertiary and Quaternary age that encompasses the structural Rio Grande Rift between San Acacia to the south and Cochiti Lake to the north. A 20-percent population increase in the basin from 1990 to 2000 and a 22-percent population increase from 2000 to 2010 resulted in an increased demand for water in areas within the basin. Drinking-water supplies throughout the basin were obtained solely from groundwater resources until December 2008, when the Albuquerque Bernalillo County Water Utility Authority (ABCWUA) began treatment and distribution of surface water from the Rio Grande through the San Juan-Chama Drinking Water Project.
An initial network of wells was established by the U.S. Geological Survey (USGS) in cooperation with the City of Albuquerque from April 1982 through September 1983 to monitor changes in groundwater levels throughout the Albuquerque Basin. In 1983, this network consisted of 6 wells with analog-to-digital recorders and 27 wells where water levels were measured monthly. As of 2020, the network consisted of 120 wells and piezometers. A piezometer is a specialized well open to a specific depth in the aquifer, often of small diameter and nested with other piezometers screened at different depths. The USGS, in cooperation with the ABCWUA, the New Mexico Office of the State Engineer, and Bernalillo County, measures water levels from the wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at the sites through water year 2020 (October 1, 2019, through September 30, 2020). Water levels that were collected from discontinued wells in previous water years were published in previous USGS reports.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1139","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Jurney, E.R., and Bell, M.T., 2021, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2020: U.S. Geological Survey Data Series 1139, 40 p., https://doi.org/10.3133/ds1139.","productDescription":"iv, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-128111","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":386657,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1139/coverthb.jpg"},{"id":386658,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1139/ds1139.pdf","text":"Report","size":"6.24 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":386659,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/ds/1139/images"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.3583984375,\n 34.261756524459805\n ],\n [\n -106.14990234375,\n 34.261756524459805\n ],\n [\n -106.14990234375,\n 35.65729624809628\n ],\n [\n -107.3583984375,\n 35.65729624809628\n ],\n [\n -107.3583984375,\n 34.261756524459805\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Understanding of morphodynamic processes associated with large-scale floods has recently improved following significant advances of modern technologies. Nevertheless, a clear link between flood discharge and in-channel sedimentation processes remains to be resolved. The hydrological and geomorphological data available for the meandering Powder River (Montana, USA) since 1977 makes it a perfect laboratory to investigate connections between flood discharge and point-bar sedimentation processes. This study focuses on a point-bar that accreted laterally ca 70 m during a 50-year recurrence flood, which lasted about 14 days in May 1978. In September 2018, a trench ca 2 m deep and 70 m long was excavated through the axial point-bar deposits, and the 1978 flood deposits were delineated based on georeferenced pre-flood and post-flood cross-section surveys. Sedimentological data show that point-bar deposits accumulated at the early and late flood stages, when the flow was confined to the channel, and have similarities with classical facies models in terms of palaeocurrent patterns and vertical grain-size trend. However, during high-stage flood conditions, when the flow overtopped the bar, cross-cutting of the bar and armouring were typical processes. Integration of sedimentological and palaeo-hydrological data highlight that the relation between channel cross-sectional area and flood discharge play a key role in preserving bar deposits. The integrated approach adopted here provides a basis for advancing palaeoflood hydrology beyond the stage of estimating peak discharges to the next stage of estimating palaeoflood hydrographs.
Control methods that target specific traits of an invasive species can produce results contrary to the aims of management. If targeted phenotypes exhibit heritability, then it follows that the invasive species could evolve greater resistance to the applied control measures over time. Additional complications emerge if those traits targeted by control are also inversely related to reproductive success. Given this, prudent considerations for invasive species management are to quantify the heritability of traits selected through control measures and gauge their relationship with reproductive success. Herein we provide a case study utilizing long-term field data and a multi-generational pedigree of an experimentally-closed population of brown treesnakes (N = 426; Boiga irregularis) on Guam. We employed an “animal model” to estimate the narrow-sense heritability (h2) for annual body condition, a trait related to both susceptibility to a primary tool used for brown treesnake control (i.e., live-lure traps) and annual reproductive success. Annual body condition displayed significant heritability [h2 = 0.149 (95% highest posterior density interval: 0.059–0.220)]. Considering a negative effect of body condition on susceptibility to trap capture but positive effect on reproductive success, significant heritability of body condition suggests the potential for live-lure traps to lose efficacy over time while also eliciting an undesirable effect on brown treesnake fecundity. Our results highlight the potential for negative repercussions that can stem from management actions, while also serving to underscore the evolutionary implications that are often overlooked but subsumed within invasive species control.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-021-02588-3","usgsCitation":"Levine, B., Douglas, M.R., Yackel Adams, A.A., Lardner, B., Reed, R., Savidge, J.A., and Douglas, M.E., 2021, Trait heritability and its implications for the management of an invasive vertebrate: Biological Invasions, v. 23, p. 3447-3456, https://doi.org/10.1007/s10530-021-02588-3.","productDescription":"10 p.","startPage":"3447","endPage":"3456","ipdsId":"IP-124543","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":386980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2021-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Levine, Brenna A","contributorId":243207,"corporation":false,"usgs":false,"family":"Levine","given":"Brenna A","affiliations":[{"id":38022,"text":"University of Tulsa","active":true,"usgs":false}],"preferred":false,"id":818727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, Marlis R","contributorId":243208,"corporation":false,"usgs":false,"family":"Douglas","given":"Marlis","email":"","middleInitial":"R","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":818728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lardner, Bjorn","contributorId":225066,"corporation":false,"usgs":false,"family":"Lardner","given":"Bjorn","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":818731,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Savidge, Julie A.","contributorId":175196,"corporation":false,"usgs":false,"family":"Savidge","given":"Julie","email":"","middleInitial":"A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":818732,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Douglas, Michael E","contributorId":243209,"corporation":false,"usgs":false,"family":"Douglas","given":"Michael","email":"","middleInitial":"E","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":818733,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70220649,"text":"sim3475 - 2021 - Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado","interactions":[],"lastModifiedDate":"2021-06-24T13:13:16.405477","indexId":"sim3475","displayToPublicDate":"2021-06-22T14:35:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3475","displayTitle":"Surficial Geology of the Northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado","title":"Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado","docAbstract":"The San Luis Valley and associated underlying basin of south-central Colorado and north-central New Mexico is the largest structural and hydrologic basin of the Rio Grande Rift and fluvial system. The surrounding San Juan and Sangre de Cristo Mountains reveal evidence of widespread volcanism and transtensional tectonism beginning in the Oligocene and continuing to the present, as seen in fault displacement of Pleistocene to Holocene deposits along the eastern basin-bounding Sangre de Cristo fault system and fault zones along the western margin of the basin. The San Luis basin can generally be subdivided into northern and southern basins at the structural and physiographic high terrain of the San Luis Hills in the center of the basin, proximal to the Colorado-New Mexico stateline. The northern San Luis Valley can be subdivided into two subbasins at approximately the latitude of the Great Sand Dunes and San Luis Lakes, where the endorheic northern subbasin surface and subsurface flow currently accumulate in a series of playa lakes. To the south of this playa region, the Rio Grande has captured basin hydrology into a through-going fluvial system cutting through the San Luis Hills, carving the Rio Grande gorge, and ultimately flowing into the Gulf of Mexico. This surficial geologic map of the northern San Luis Valley, paired with the Alamosa, CO 1:100,000-scale geologic map (U.S. Geological Survey Scientific Investigations Map 3342) provides new and compiled geologic mapping that characterizes basin deposits and locates the traces of active faults, with the goal to provide geospatial data for future investigations related to western North American neotectonics, Pleistocene paleoclimate, and related geomorphic processes. In addition, present natural and anthropogenic water bodies have been located and updated for hydrologic modeling and water-usage investigations.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3475","usgsCitation":"Ruleman, C.A., and Brandt, T.R., 2021, Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3475, 2 sheets, scale 1:75,000, https://doi.org/10.3133/sim3475.","productDescription":"4 Sheets: 52.81 x 75.84 inches or smaller; ReadMe; Data Release","onlineOnly":"Y","ipdsId":"IP-092739","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":386092,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3436","text":"Scientific Investigations Map 3346—","linkHelpText":"Geologic map of the Poncha Pass area, Chaffee, Fremont, and Saguache Counties, Colorado"},{"id":385874,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3342","text":"Scientific Investigations Map 3342—","linkHelpText":"Geologic map of the Alamosa 30’ × 60’ quadrangle, south-central Colorado"},{"id":385873,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PUTQYK","text":"USGS data release","linkHelpText":"Data release for Surficial Geology of the Northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado"},{"id":385872,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3475/ReadMe.txt","size":"7.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3475 Read Me"},{"id":385875,"rank":5,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3475/sim3475_sheet2.pdf","text":"Sheet 2","size":"527 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Box 25046, MS-980
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Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Groundwater is an important source of drinking water in California. Water-borne diseases caused by microbial contamination are a growing concern. The MI test, a membrane filtration method for the chromogenic/fluorogenic detection of total coliforms and Escherichia coli, was used for samples collected January to April 2014 from 42 domestic wells in the southeastern San Joaquin Valley. The wells were sampled as part of the Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP), a cooperative study between the U.S. Geological Survey and the California State Water Resources Control Board. Polymerase chain reaction analysis and sequencing of deoxyribonucleic acid (DNA) were used for 34 target and nontarget colonies that grew on the MI media from samples collected from 13 of the domestic wells to identify what genera of bacteria could exist in groundwater used by domestic wells. Gene sequences obtained using the Sanger method were entered into the basic local alignment search tool (BLAST) database, and 17 genera of bacteria were identified. Of these, 13 genera contain species that are human pathogens or opportunistic human pathogens. All the genera that include human pathogens are naturally present in soil, plants, or water; one of the pathogens also can be found in fecal matter. Six of the human pathogens were from non-target colony growth on the MI media. Target and non-target microbial growth on MI media are indicators of the possible presence of pathogenic bacteria even if the bacteria naturally are from soil rather than from a fecal source.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215030","collaboration":"Prepared in cooperation with California State Water Resources Control BoardDirector,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Streams in the Catskill Mountains region of New York provide many important ecological and economic services, including recreational angling and serving as a drinking water supply to New York City. Many streams in this region were adversely affected by acid deposition during the late 20th century, impairing water quality and aquatic ecosystems. More recently, the level of acid deposition has declined while changes in climate have become more pronounced. As a result, biological and chemical data are needed to determine the current condition of stream ecosystems in the Catskill Mountains region. The U.S. Geological Survey, in cooperation with the Rondout Neversink Stream Program, surveyed fish communities and water chemistry annually between 2017 and 2019 at 23 sites in the upper Neversink River and upper Rondout Creek watersheds to compile a contemporary baseline dataset and assess potential biological recovery from reduced acidification.
The resulting data indicated that brook trout (Salvelinus fontinalis) were present at every study site, although slimy sculpin (Cottus cognatus) was the most abundant species at most sites. Stream pH ranged from 4.8 to 7.0 across all sites and generally increased from upstream to downstream. Similarly, the number of species present and the ratio of brown trout (Salmo trutta) to brook trout increased at sites in each subwatershed from upstream to downstream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1137","collaboration":"Prepared in cooperation with the Rondout Neversink Stream Program","usgsCitation":"Winterhalter, D.R., George, S.D., and Baldigo, B.P., 2021, Survey of fish assemblages in the upper Neversink River and upper Rondout Creek, 2017–19: U.S. Geological Survey Data Series 1137, 55 p., https://doi.org/10.3133/ds1137.","productDescription":"Report: viii, 55 p.; Data Release","numberOfPages":"55","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-1118329","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":386501,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1137/ds1137_2pg_spread.pdf","text":"Report (2-page spread)","size":"4.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1137 (2-page spread)","linkHelpText":"- To be printed on tabloid paper"},{"id":386500,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1137/ds1137.pdf","text":"Report","size":"3.85 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1137"},{"id":386499,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1137/coverthb2.jpg"},{"id":386502,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4V25","text":"USGS data release","linkHelpText":"Adirondack and Catskill stream-fish survey dataset (ver. 3.0, November 2020)"}],"country":"United States","state":"New York","otherGeospatial":"Upper Neversink River, Upper Rondout Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.157470703125,\n 41.66060124302088\n ],\n [\n -73.7841796875,\n 41.66060124302088\n ],\n [\n -73.7841796875,\n 42.40317854182803\n ],\n [\n -75.157470703125,\n 42.40317854182803\n ],\n [\n -75.157470703125,\n 41.66060124302088\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Drift-feeding fish are typically considered size-selective predators. Yet, few studies have explicitly tested which aspect of prey “size” best explains size selection by drift-foraging fish. Here, we develop a Bayesian discrete choice model to evaluate how attributes of both prey and predator simultaneously influence size-selective foraging. We apply the model to a large dataset of paired invertebrate drift (n = 784) and rainbow trout (Oncorhynchus mykiss) diets (n = 1028). We characterized prey “size” using six metrics (length, width, area, hemispherical area, volume, mass) and used pseudo-R2 to determine which metric best explained observed prey selection across seven taxa. We found that rainbow trout are positively size-selective, they are selecting prey based upon differences in prey width, and size-selectivity increases with fish length. Rainbow trout demonstrated strong selection for the adult and pupae stages of aquatic insects relative to their larval stages. Our study provides strong empirical evidence for size-selective foraging in rainbow trout and demonstrates prey selection is based primarily upon width, not length or area as has been widely reported.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2020-0113","usgsCitation":"Dodrill, M., Yackulic, C., Kennedy, T., Yard, M.D., and Josh Korman, 2021, As the prey thickens: Rainbow trout select prey based upon width not length: Canadian Journal of Fisheries and Aquatic Sciences, v. 78, no. 7, p. 809-819, https://doi.org/10.1139/cjfas-2020-0113.","productDescription":"11 p.","startPage":"809","endPage":"819","ipdsId":"IP-118180","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":436296,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P923AX7C","text":"USGS data release","linkHelpText":"Rainbow trout diet and invertebrate drift data from 2012-2015 for the Colorado River, Grand Canyon, Arizona"},{"id":387195,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.44256591796875,\n 36.923547681089296\n ],\n [\n -111.533203125,\n 36.925743371044966\n ],\n [\n -111.64306640625,\n 36.8708321556463\n ],\n [\n -111.719970703125,\n 36.74328605437939\n ],\n [\n -111.88201904296875,\n 36.551568887374\n ],\n [\n -111.90948486328125,\n 36.39696752441776\n ],\n [\n -111.86004638671875,\n 36.219902972702606\n ],\n [\n -111.84906005859375,\n 36.1245647481333\n ],\n [\n -111.94244384765625,\n 36.0624217151089\n ],\n [\n -112.0330810546875,\n 36.09127994838079\n ],\n [\n -112.14019775390625,\n 36.10237644873644\n ],\n [\n -112.1484375,\n 36.046878280461684\n ],\n [\n -111.917724609375,\n 36.006895355244666\n ],\n [\n -111.7913818359375,\n 36.09349937380574\n ],\n [\n -111.77215576171874,\n 36.255348043040904\n ],\n [\n -111.81610107421875,\n 36.366010258936925\n ],\n [\n -111.78314208984375,\n 36.54053616262899\n ],\n [\n -111.61560058593749,\n 36.767492156196745\n ],\n [\n -111.44256591796875,\n 36.923547681089296\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"78","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dodrill, Michael J. 0000-0002-7038-7170","orcid":"https://orcid.org/0000-0002-7038-7170","contributorId":206439,"corporation":false,"usgs":true,"family":"Dodrill","given":"Michael","middleInitial":"J.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":819339,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":819340,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kennedy, Theodore 0000-0003-3477-3629","orcid":"https://orcid.org/0000-0003-3477-3629","contributorId":221741,"corporation":false,"usgs":true,"family":"Kennedy","given":"Theodore","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":819341,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yard, Michael D. 0000-0002-6580-6027 myard@usgs.gov","orcid":"https://orcid.org/0000-0002-6580-6027","contributorId":169281,"corporation":false,"usgs":true,"family":"Yard","given":"Michael","email":"myard@usgs.gov","middleInitial":"D.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":819342,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Josh Korman","contributorId":261146,"corporation":false,"usgs":false,"family":"Josh Korman","affiliations":[{"id":52750,"text":"Ecometric Research, Inc., 3560 West 22nd Avenue, Vancouver, British Columbia V6S 1J3, Canada","active":true,"usgs":false}],"preferred":false,"id":819343,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221489,"text":"cir1479 - 2021 - The North American Breeding Bird Survey in Mexico, 2008 to 2018—A status report","interactions":[],"lastModifiedDate":"2021-06-21T17:42:27.497155","indexId":"cir1479","displayToPublicDate":"2021-06-21T08:55:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1479","displayTitle":"The North American Breeding Bird Survey in Mexico, 2008 to 2018—A Status Report","title":"The North American Breeding Bird Survey in Mexico, 2008 to 2018—A status report","docAbstract":"Collection of avian population data has repeatedly been identified as a high priority for bird conservation in Mexico. To meet this need, in 2008 the North American Breeding Bird Survey (BBS), a volunteer-based survey, was expanded to include northern Mexico. The BBS in Mexico (Mexican BBS) is managed by the North American Bird Conservation Initiative (NABCI), Mexico’s National Coordination Office inside the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO).
During 2008–18, 252 surveys were conducted along 68 routes in Mexico, with geographic coverage varying from year to year. Of these 68 routes, 36 were surveyed three or more times. Thirty-one observers conducted the surveys, and 21 of these observers conducted two or more surveys. Just two observers conducted more than one-third of the 252 surveys, and both observers were paid to conduct the surveys. The low availability of local observers who are qualified, willing, and able to volunteer their services to conduct BBS surveys may prove to be the biggest obstacle to the success of the Mexican BBS program, especially in the context of Mexico’s ongoing safety and security concerns.
Apart from the amount of data collected, many surveys did not adhere to pre-established quality-control requirements, and this would result in the exclusion of a large percentage of the data from potential trend analyses. Only 31 percent of the surveys met all the quality-control criteria. Additional observer training may help resolve this issue. Of greater concern is the selection of region-specific sampling date windows during which the surveys are conducted. Observers consistently conducted surveys outside the preliminarily prescribed sampling date window, reflecting the need to re-evaluate the regional appropriateness of this date window.
Regardless of the quality of the data, the quantity of data available from 2008 to 2018 is insufficient for trend analysis using methods typically employed by U.S. Geological Survey BBS analysts. Reaching minimum sample size thresholds for statistical analysis will require a substantial increase in effort. During 2008–18, no strata (defined as the intersection of State and Bird Conservation Region boundaries) reached the suggested minimum of 14 sampled routes, and most routes were not run consistently.
This report provides information needed for an evaluation of the merits of continuing to invest in the Mexican BBS program in its current form. Such an evaluation should consider the likelihood of achieving the primary project goal of producing reliable long-term population trend estimates, a projected timeline for meeting this goal, and include an assessment of the potential value of any additional data products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1479","usgsCitation":"U.S. Geological Survey and Mexican National Commission for the Knowledge and Use of Biodiversity, 2021, The North American Breeding Bird Survey in Mexico, 2008 to 2018—A Status Report: U.S. Geological Survey Circular 1479, 33 p., https://doi.org/10.3133/cir1479.","productDescription":"Report: v, 33 p.; Data Release","numberOfPages":"33","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120948","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":436297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9L4KBDC","text":"USGS data release","linkHelpText":"The North American Breeding Bird Survey in Mexico, 2008-2018 - unprocessed data"},{"id":386569,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1479/cir1479.pdf","text":"Report","size":"14.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1479"},{"id":386568,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1479/coverthb.jpg"},{"id":386570,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://www.doi.org/10.5066/P9L4KBDC","text":"USGS data release","linkHelpText":"The North American Breeding Bird Survey in Mexico, 2008–2018—unprocessed data"}],"country":"Mexico","state":"Baja California, Baja California Sur, Chihuahua, Coahuila, Nuevo Leon, Sonora, Tamaulipas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.09423828125,\n 26.303264239389534\n ],\n [\n -108.336181640625,\n 27.049341619870376\n ],\n [\n -108.17138671875,\n 26.83387451505858\n ],\n [\n -107.808837890625,\n 26.185018250078308\n ],\n [\n -106.61132812499999,\n 25.54244147012483\n ],\n [\n -106.051025390625,\n 26.814266197561462\n ],\n [\n -104.501953125,\n 26.322960198925365\n ],\n [\n -104.21630859375,\n 26.775039386999605\n ],\n [\n -103.370361328125,\n 26.56887654795065\n ],\n [\n -103.436279296875,\n 25.3241665257384\n ],\n [\n -102.908935546875,\n 24.716895455859337\n ],\n [\n -102.469482421875,\n 25.06569718553588\n ],\n [\n -101.898193359375,\n 24.986058021167594\n ],\n [\n -100.74462890625,\n 24.5271348225978\n ],\n [\n -100.404052734375,\n 23.200960808078566\n ],\n [\n -100.1953125,\n 23.271627053918277\n ],\n [\n -100.08544921874999,\n 22.806567100271522\n ],\n [\n -99.42626953125,\n 22.664709810176827\n ],\n [\n -98.909912109375,\n 22.31958944283391\n ],\n [\n -98.63525390624999,\n 22.370396344320053\n ],\n [\n -98.184814453125,\n 22.451648819126202\n ],\n [\n -97.7947998046875,\n 22.17214491738176\n ],\n [\n -97.7288818359375,\n 24.307053283225915\n ],\n [\n -97.38830566406249,\n 25.18505888358067\n ],\n [\n -97.1246337890625,\n 25.962983554822678\n ],\n [\n -97.37182617187499,\n 25.86416657624641\n ],\n [\n -97.7398681640625,\n 26.03704188651584\n ],\n [\n -98.2342529296875,\n 26.046912801683984\n ],\n [\n -99.085693359375,\n 26.441065564038418\n ],\n [\n -99.50866699218749,\n 27.36201054924028\n ],\n [\n -99.51416015625,\n 27.581329075043357\n ],\n [\n -100.579833984375,\n 28.7965462417692\n ],\n [\n -101.4532470703125,\n 29.76437737516313\n ],\n [\n -102.32666015625,\n 29.854937397596693\n ],\n [\n -102.623291015625,\n 29.72145191669099\n ],\n [\n -103.216552734375,\n 28.98411731593083\n ],\n [\n -104.249267578125,\n 29.501768632523262\n ],\n [\n -104.5513916015625,\n 29.625996273660785\n ],\n [\n -104.6832275390625,\n 30.178373310707887\n ],\n [\n -105.0018310546875,\n 30.680439786468128\n ],\n [\n -106.46301269531249,\n 31.765537409484374\n ],\n [\n -106.5673828125,\n 31.770207631866715\n ],\n [\n -108.1988525390625,\n 31.774877618507386\n ],\n [\n -108.204345703125,\n 31.320794146937374\n ],\n [\n -111.0443115234375,\n 31.325486676506983\n ],\n [\n -114.466552734375,\n 32.37996146435729\n ],\n [\n -114.81811523437501,\n 32.509761735919426\n ],\n [\n -114.71923828124999,\n 32.72721987021932\n ],\n [\n -117.13623046874999,\n 32.54218257955074\n ],\n [\n -117.10876464843749,\n 32.29177633471201\n ],\n [\n -116.73522949218751,\n 31.81689688674699\n ],\n [\n -116.75170898437501,\n 31.63467554954133\n ],\n [\n -116.53198242187499,\n 31.245681880715527\n ],\n [\n -115.64208984374999,\n 29.616445727622548\n ],\n [\n -115.6640625,\n 27.737022779516813\n ],\n [\n -113.25256347656249,\n 26.115985925333536\n ],\n [\n -111.68701171875,\n 23.83560098662095\n ],\n [\n -109.8687744140625,\n 22.71032284205226\n ],\n [\n -109.2041015625,\n 23.427968862308678\n ],\n [\n -110.54443359375,\n 25.27450351782018\n ],\n [\n -109.434814453125,\n 26.367263860129366\n ],\n [\n -109.09423828125,\n 26.303264239389534\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD
Western North American sagebrush shrublands and steppe face accelerating risks from fire-driven feedback loops that transition these ecosystems into self-reinforcing states dominated by invasive annual grasses. In response, sagebrush conservation decision-making is increasingly done through the lens of resilience to fire and annual grass invasion resistance. Operationalizing resilience and resistance concepts requires place-based understanding of resilience and resistance variation among landscapes over time. Place-based insights allow for landscape prioritization in targeted areas of significance such as protected-area sagebrush ecosystems that exhibit inherently low resilience and are therefore at high risk of loss. We used a multi-scale approach to evaluate sagebrush resiliency and strategic planning across 1) the US National Park system, 2) a regional suite of five parks, and 3) for two specific park case studies. First, we summarized broad patterns of relative resilience to fire and resistance to annual grass invasion across all parks with sagebrush ecosystems. We found that national parks represented ~11% of US protected-area sagebrush ecosystems and reflected a similar low-resilience bias that occurs across the biome, broadly. Climate change is likely to shift both low- and high-resilience park sagebrush ecosystems towards moderate resiliency, creating new opportunities and constraints for park conservation. Approximately seventy park units include at least some sagebrush shrublands or steppe, but we identified 40 parks with substantial amounts (>20% of park area) that can be included in an agency-wide conservation strategy. Second, we examined detailed patterns of resilience and resistance, fire history and fire risk, cheatgrass (Bromus tectorum) invasion, and sagebrush shrub (Artemisia spp.) persistence in five national park units in Columbia Basin and Snake River Plain sagebrush steppe, contextualized by the broader summary. In these five parks, fire frequency and size increased in recent decades. Cheatgrass invasion and sagebrush persistence correlated strongly with resilience, burn frequency (0–3 fires since ~1940), and burn probability, but with important variation, in part mediated by local-scale topography. Third, we used these insights to assemble strategic sagebrush ecosystem fire protection mapping scenarios in two additional parks – Lava Beds National Monument and Great Basin National Park. Readily available and periodically updated geospatial data including soil surveys, fire histories, vegetation inventories, and long-term monitoring support resiliency-based adaptive management through tactical planning of pre-fire protection, post-fire restoration, and triage. Our assessment establishes the precarious importance of the US national park system to sagebrush ecosystem conservation and an operational strategy for place-based and science-supported conservation.
Imperfect detection is an important problem when counting wildlife, but new technologies such as unmanned aerial systems (UAS) can help overcome this obstacle. We used data collected by a UAS and a Bayesian closed capture-mark-recapture model to estimate abundance and distribution while accounting for imperfect detection of aggregated Florida manatees (Trichechus manatus latirostris) at thermal refuges to assess use of current and new warmwater sources in winter. Our UAS hovered for 10 min and recorded 4 K video over sites in Collier County, FL. Open-source software was used to create recapture histories for 10- and 6-min time periods. Mean estimates of probability of detection for 1-min intervals at each canal varied by survey and ranged between 0.05 and 0.92. Overall, detection probability for sites varied between 0.62 and 1.00 across surveys and length of video (6 and 10 min). Abundance varied by survey and location, and estimates indicated that distribution changed over time, with use of the novel source of warmwater increasing over time. The highest cumulative estimate occurred in the coldest winter, 2018 (N = 158, CI 141–190). Methods here reduced survey costs, increased safety and obtained rigorous abundance estimates at aggregation sites previously too difficult to monitor.
A typical problem for magnetic surveys with small Unmanned Aerial Systems (sUAS) is the heading error caused by undesired magnetic signals that originate from the aircraft. This can be addressed by suspending the magnetometers on sufficiently long tethers. However, tethered payloads require skilled pilots and are difficult to fly safely. Alternatively, the magnetometer can be fixed on the aircraft. In this case, aircraft magnetic signals are removed from the recordings with a process referred to as magnetic compensation, which requires parameters estimated from calibration flights flown in an area with magnetically low-gradients prior to the survey. We present open-source software fully written in Python to process data and compute compensations for two fundamentally different magnetometer systems (scalar and vector). We used the software to compare the precision of two commercially available systems by flying dense grid patterns over a 135 × 150 m area using different suspension configurations. The accuracy of the magnetic recordings is assessed using both standard deviations of the calibration pattern and tie-line cross-over differences from the survey. After compensation, the vector magnetometer provides the lowest heading error. However, the magnetic field intensity recovered with this system is relative and needs to be adjusted with absolute data if absolute intensity values are needed. Overall, the highest accuracy of all suspension configurations tested was obtained by fixing the magnetometer 0.5 m below the sUAS onto a self-built carbon-fiber frame, which also offered greater stability and allowed fully autonomous flights in a wide range of conditions.
Density-dependent (DD) and density-independent (DI) effects play an important role in shaping fish growth rates, an attribute that correlates with many life history traits in fishes. Consequently, understanding the extent to which DD and DI effects influence growth rates is valuable for fisheries assessments because it can inform managers about how populations may respond as environmental conditions continue to change (e.g., threats from climate change). We used a Rio Grande Cutthroat Trout Oncorhynchus clarkii virginalis (RGCT) capture–mark–recapture data set collected over 2 years along a temperature and density gradient in northern New Mexico streams to test the extent to which DD and DI effects interact to influence specific growth rates. We found that temperature (DI) and density (DD) interacted with RGCT life stage (i.e., immature or mature) to affect growth rates. We only detected evidence of a negative DD effect on RGCT growth for the immature fraction of a population when exposed to the warmest stream temperatures. Our results suggest that competition most strongly affected the immature portion of RGCT populations, and this effect was only detectable when temperatures were warmest and energetic stress was likely at its highest. The quadratic relationship between temperature and growth rates also demonstrated that stream temperatures were below as well as above optimal growth temperatures for RGCT. Growth rates in our RGCT populations were influenced by complex interactions of DD and DI effects, and our results suggest that the negative consequences of warming trends associated with climate change on RGCT populations may be exacerbated by DD effects.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10319","usgsCitation":"Huntsman, B., Lynch, A., and Caldwell, C.A., 2021, Interacting effects of density-dependent and density-independent factors on growth rates in southwestern Cutthroat Trout populations: Transactions of the American Fisheries Society, v. 150, no. 5, p. 651-664, https://doi.org/10.1002/tafs.10319.","productDescription":"14 p.","startPage":"651","endPage":"664","ipdsId":"IP-125845","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"links":[{"id":396915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.3861083984375,\n 35.55904339525896\n ],\n [\n -104.2437744140625,\n 35.55904339525896\n ],\n [\n -104.2437744140625,\n 36.98500309285596\n ],\n [\n -106.3861083984375,\n 36.98500309285596\n ],\n [\n -106.3861083984375,\n 35.55904339525896\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"150","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-08-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Huntsman, Brock M.","contributorId":288215,"corporation":false,"usgs":false,"family":"Huntsman","given":"Brock M.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":837566,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":216203,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":837567,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caldwell, Colleen A. 0000-0002-4730-4867 ccaldwel@usgs.gov","orcid":"https://orcid.org/0000-0002-4730-4867","contributorId":3050,"corporation":false,"usgs":true,"family":"Caldwell","given":"Colleen","email":"ccaldwel@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":837568,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221546,"text":"70221546 - 2021 - Comparison of historical water temperature measurements with landsat analysis ready data provisional surface temperature estimates for the Yukon River in Alaska","interactions":[],"lastModifiedDate":"2021-06-23T12:24:25.466012","indexId":"70221546","displayToPublicDate":"2021-06-19T07:08:29","publicationYear":"2021","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":"Comparison of historical water temperature measurements with landsat analysis ready data provisional surface temperature estimates for the Yukon River in Alaska","docAbstract":"Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns.
","language":"English","publisher":"MDPI","doi":"10.3390/rs13122394","usgsCitation":"Baughman, C., and Conaway, J., 2021, Comparison of historical water temperature measurements with landsat analysis ready data provisional surface temperature estimates for the Yukon River in Alaska: Remote Sensing, v. 13, no. 12, 2394, 45 p., https://doi.org/10.3390/rs13122394.","productDescription":"2394, 45 p.","ipdsId":"IP-127623","costCenters":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true}],"links":[{"id":451818,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs13122394","text":"Publisher Index Page"},{"id":436300,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MCNPGK","text":"USGS data release","linkHelpText":"Historical Landsat-Derived Water Surface Temperature for Three Large Alaska Rivers 1984-2022"},{"id":386643,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Yukon River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -164.53125,\n 61.48075950007598\n ],\n [\n -158.81835937499997,\n 61.48075950007598\n ],\n [\n -158.81835937499997,\n 63.35212928507874\n ],\n [\n -164.53125,\n 63.35212928507874\n ],\n [\n -164.53125,\n 61.48075950007598\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2021-06-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Baughman, Carson 0000-0002-9423-9324 cbaughman@usgs.gov","orcid":"https://orcid.org/0000-0002-9423-9324","contributorId":169657,"corporation":false,"usgs":true,"family":"Baughman","given":"Carson","email":"cbaughman@usgs.gov","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true}],"preferred":true,"id":818015,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conaway, Jeff 0000-0002-3036-592X","orcid":"https://orcid.org/0000-0002-3036-592X","contributorId":214226,"corporation":false,"usgs":true,"family":"Conaway","given":"Jeff","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":818016,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221855,"text":"70221855 - 2021 - Sediment transport, turbidity, and dissolved oxygen responses to annual streambed drawdowns for downstream fish passage in a flood control reservoir","interactions":[],"lastModifiedDate":"2021-07-12T17:40:19.227133","indexId":"70221855","displayToPublicDate":"2021-06-18T12:39:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Sediment transport, turbidity, and dissolved oxygen responses to annual streambed drawdowns for downstream fish passage in a flood control reservoir","docAbstract":"Sediment transport, turbidity, and dissolved oxygen were evaluated during six consecutive water years (2013–2018) of drawdowns of a flood control reservoir in the upper Willamette Valley, Oregon, USA. The drawdowns were conducted to allow volitional passage of endangered juvenile chinook salmon through the dam's regulating outlets by lowering the reservoir elevation to a point where the historical streambed was exposed and transported water and sediment through the reservoir dam. Sediment loads during the drawdown were highest in the first year of monitoring, with a computed value of 40,200 metric tons over a 5-day drawdown, followed by 5 years of lower sediment loads and lower sediment transport rates, suggesting that much of the stored sediment within the reservoir thalweg was transported out of the reservoir in the early years of the consecutive drawdowns. Suspended sediment concentrations (SSC) computed using turbidity and streamflow data resulted in maximum SSC at the onset of the drawdowns, with the highest computed values occurring during the water year 2017 drawdown at 17,500 mg/L (turbidity = 2,990 FNU), and average drawdown SSC values ranging from 654 to 3,950 mg/L for the six years of monitoring. Computed SSC were on the lower range of concentrations that could be harmful to out-migrating juvenile salmon published in other studies. High amounts of particulate organic matter and sand-sized material in drawdown SSC samples affected relations between turbidity and SSC, requiring the use of multiple surrogate regression models over short time frames. Dissolved oxygen minimum values were recorded in two of the monitoring years, with a minimum value of 0.71 and 3.4 mg/L recorded at the onset of the drawdowns in water years 2016 and 2018, respectively. Dissolved oxygen values below 4 mg/L lasted for 1 h, suggesting a rapidly expressed chemical oxygen demand. The response of suspended sediment loads and SSC highlight the site-specific nature of reservoir drawdowns, and the need for evaluation of expected sediment responses for drawdowns being considered at other locations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2021.113068","usgsCitation":"Schenk, L.N., and Bragg, H.M., 2021, Sediment transport, turbidity, and dissolved oxygen responses to annual streambed drawdowns for downstream fish passage in a flood control reservoir: Journal of Environmental Management, v. 295, 113068, 11 p., https://doi.org/10.1016/j.jenvman.2021.113068.","productDescription":"113068, 11 p.","ipdsId":"IP-119744","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":387132,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Fall Creek Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.69325256347656,\n 43.923862711777446\n ],\n [\n -122.73548126220703,\n 43.9429004110983\n ],\n [\n -122.69565582275389,\n 43.95130472827632\n ],\n [\n -122.65342712402344,\n 43.97305156068593\n ],\n [\n -122.66578674316406,\n 43.97972228837853\n ],\n [\n -122.71076202392577,\n 43.96069638244953\n ],\n [\n -122.75333404541016,\n 43.959460723283826\n ],\n [\n -122.76226043701173,\n 43.958472177448414\n ],\n [\n -122.76191711425781,\n 43.93820336335502\n ],\n [\n -122.7509307861328,\n 43.93721446391471\n ],\n [\n -122.73616790771484,\n 43.93251696697599\n ],\n [\n -122.70767211914064,\n 43.92336814487696\n ],\n [\n -122.69256591796876,\n 43.92287357386489\n ],\n [\n -122.69325256347656,\n 43.923862711777446\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"295","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schenk, Liam N. 0000-0002-2491-0813 lschenk@usgs.gov","orcid":"https://orcid.org/0000-0002-2491-0813","contributorId":4273,"corporation":false,"usgs":true,"family":"Schenk","given":"Liam","email":"lschenk@usgs.gov","middleInitial":"N.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819009,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bragg, Heather M. 0000-0002-0013-4573 hmbragg@usgs.gov","orcid":"https://orcid.org/0000-0002-0013-4573","contributorId":239645,"corporation":false,"usgs":true,"family":"Bragg","given":"Heather","email":"hmbragg@usgs.gov","middleInitial":"M.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819010,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70221701,"text":"70221701 - 2021 - New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino-acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi)","interactions":[],"lastModifiedDate":"2021-11-01T15:36:01.701983","indexId":"70221701","displayToPublicDate":"2021-06-18T10:07:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1751,"text":"Geobiology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi)","title":"New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino-acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi)","docAbstract":"In order to reconstruct the ecosystem structure of chemosynthetic environments in the fossil record, geochemical proxies must be developed. Here, we present a suite of novel compound-specific isotope parameters for tracing chemosynthetic production with a focus on understanding nitrogen dynamics in deep-sea cold seep environments. We examined the chemosymbiotic bivalve Bathymodiolus childressi from three geographically distinct seep sites on the NE Atlantic Margin and compared isotope data to non-chemosynthetic littoral mussels to test whether water depth, seep activity, and/or mussel bed size are linked to differences in chemosynthetic production. The bulk isotope analysis of carbon (δ13C) and nitrogen (δ15N), and δ15N values of individual amino acids (δ15NAA) in both gill and muscle tissues, as well as δ15NAA-derived parameters including trophic level (TL), baseline δ15N value (δ15NPhe), and a microbial resynthesis index (ΣV), were used to investigate specific geochemical signatures of chemosynthesis. Our results show that δ15NAA values provide a number of new proxies for relative reliance on chemosynthesis, including TL, ∑V, and both δ15N values and molar percentages (Gly/Glu mol% index) of specific AA. Together, these parameters suggested that relative chemoautotrophy is linked to both degree of venting from seeps and mussel bed size. Finally, we tested a Bayesian mixing model using diagnostic AA δ15N values, showing that percent contribution of chemoautotrophic versus heterotrophic production to seep mussel nutrition can be directly estimated from δ15NAA values. Our results demonstrate that δ15NAA analysis can provide a new set of geochemical tools to better understand mixotrophic ecosystem function and energetics, and suggest extension to the study of ancient chemosynthetic environments in the fossil record.
","language":"English","publisher":"Wiley","doi":"10.1111/gbi.12458","usgsCitation":"Vokhshoori, N., McCarthy, M., Close, H., Demopoulos, A., and Prouty, N.G., 2021, New geochemical tools for investigating resource and energy functions at deep-sea cold seeps using amino-acid δ15N in chemosymbiotic mussels (Bathymodiolus childressi): Geobiology, v. 19, no. 6, p. 601-617, https://doi.org/10.1111/gbi.12458.","productDescription":"17 p.","startPage":"601","endPage":"617","ipdsId":"IP-121092","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":386867,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"19","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Vokhshoori, Natasha","contributorId":260681,"corporation":false,"usgs":false,"family":"Vokhshoori","given":"Natasha","email":"","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":818469,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCarthy, Matt","contributorId":260682,"corporation":false,"usgs":false,"family":"McCarthy","given":"Matt","email":"","affiliations":[{"id":6948,"text":"UC Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":818470,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Close, Hilary","contributorId":199931,"corporation":false,"usgs":false,"family":"Close","given":"Hilary","affiliations":[],"preferred":false,"id":818471,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Demopoulos, Amanda 0000-0003-2096-4694","orcid":"https://orcid.org/0000-0003-2096-4694","contributorId":222192,"corporation":false,"usgs":true,"family":"Demopoulos","given":"Amanda","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":818472,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":818473,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70221695,"text":"70221695 - 2021 - Nutrient limitation of algae and macrophytes in streams: Integrating laboratory bioassays, field experiments, and field data","interactions":[],"lastModifiedDate":"2021-06-29T14:31:23.162807","indexId":"70221695","displayToPublicDate":"2021-06-18T09:13:25","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient limitation of algae and macrophytes in streams: Integrating laboratory bioassays, field experiments, and field data","docAbstract":"Successful eutrophication control strategies need to address the limiting nutrient. We conducted a battery of laboratory and in situ nutrient-limitation tests with waters collected from 9 streams in an agricultural region of the upper Snake River basin, Idaho, USA. Laboratory tests used the green alga Raphidocelis subcapitata, the macrophyte Lemna minor (duckweed) with native epiphytes, and in situ nutrient-limitation tests of periphyton were conducted with nutrient-diffusing substrates (NDS). In the duckweed/epiphyte test, P saturation occurred when concentrations reached about 100 μg/L. Chlorophyll a in epiphytic periphyton was stimulated at low P additions and by about 100 μg/L P, epiphytic periphyton chlorophyll a appeared to be P saturated. Both duckweed and epiphyte response patterns with total N were weaker but suggested a growth stimulation threshold for duckweed when total N concentrations exceeded about 300 μg/L and approached saturation at the highest N concentration tested, 1300 μg/L. Nutrient uptake by epiphytes and macrophytes removed up to 70 and 90% of the N and P, respectively. The green algae and the NDS nutrient-limitation test results were mostly congruent; N and P co-limitation was the most frequent result for both test series. Across all tests, when N:P molar ratios >30 (mass ratios >14), algae or macrophyte growth was P limited; N limitation was observed at N:P molar ratios up to 23 (mass ratios up to 10). A comparison of ambient periphyton chlorophyll a concentrations with chlorophyll a accrued on control artificial substrates in N-limited streams, suggests that total N concentrations associated with a periphyton chlorophyll a benchmark for desirable or undesirable conditions for recreation would be about 600 to 1000 μg/L total N, respectively. For P-limited streams, the corresponding benchmark concentrations were about 50 to 90 μg/L total P, respectively. Our approach of integrating controlled experiments and matched biomonitoring field surveys was cost effective and more informative than either approach alone.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0252904","usgsCitation":"Mebane, C.A., Ray, A.M., and Marcarelli, A.M., 2021, Nutrient limitation of algae and macrophytes in streams: Integrating laboratory bioassays, field experiments, and field data: PLoS ONE, v. 16, no. 6, e0252904, 27 p., https://doi.org/10.1371/journal.pone.0252904.","productDescription":"e0252904, 27 p.","ipdsId":"IP-127847","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":451823,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0252904","text":"Publisher Index Page"},{"id":386850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Cottonwood Creek, Stalker Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -113.98040771484375,\n 42.176126260952934\n ],\n [\n -113.76480102539062,\n 42.176126260952934\n ],\n [\n -113.76480102539062,\n 42.33063116562984\n ],\n [\n -113.98040771484375,\n 42.33063116562984\n ],\n [\n -113.98040771484375,\n 42.176126260952934\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.16468620300293,\n 43.311127198613335\n ],\n [\n -114.15696144104004,\n 43.320744323395154\n ],\n [\n -114.16399955749512,\n 43.32218051659263\n ],\n [\n -114.17404174804688,\n 43.31118965238512\n ],\n [\n -114.18365478515625,\n 43.316560436671395\n ],\n [\n -114.19017791748047,\n 43.32823713177707\n ],\n [\n -114.20339584350586,\n 43.34365692013493\n ],\n [\n -114.21223640441895,\n 43.33966188522517\n ],\n [\n -114.20125007629395,\n 43.328986361785745\n ],\n [\n -114.18837547302246,\n 43.313625299426235\n ],\n [\n -114.18022155761719,\n 43.307005107782196\n ],\n [\n -114.17326927185059,\n 43.306755275110774\n ],\n [\n -114.16468620300293,\n 43.311127198613335\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2021-06-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Mebane, Christopher A. 0000-0002-9089-0267 cmebane@usgs.gov","orcid":"https://orcid.org/0000-0002-9089-0267","contributorId":110,"corporation":false,"usgs":true,"family":"Mebane","given":"Christopher","email":"cmebane@usgs.gov","middleInitial":"A.","affiliations":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"preferred":true,"id":818448,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ray, Andrew M.","contributorId":167601,"corporation":false,"usgs":false,"family":"Ray","given":"Andrew","email":"","middleInitial":"M.","affiliations":[{"id":5106,"text":"National Park Service, Yellowstone National Park, Mammoth, Wyoming 82190","active":true,"usgs":false}],"preferred":false,"id":818449,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Marcarelli, Amy M 0000-0002-4175-9211","orcid":"https://orcid.org/0000-0002-4175-9211","contributorId":257363,"corporation":false,"usgs":false,"family":"Marcarelli","given":"Amy","email":"","middleInitial":"M","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":818450,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222520,"text":"70222520 - 2021 - When hazard avoidance is not an option: Lessons learned from monitoring the postdisaster Oso landslide, USA","interactions":[],"lastModifiedDate":"2021-09-14T16:42:15.893843","indexId":"70222520","displayToPublicDate":"2021-06-18T07:36:05","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2604,"text":"Landslides","active":true,"publicationSubtype":{"id":10}},"title":"When hazard avoidance is not an option: Lessons learned from monitoring the postdisaster Oso landslide, USA","docAbstract":"On 22 March 2014, a massive, catastrophic landslide occurred near Oso, Washington, USA, sweeping more than 1 km across the adjacent valley flats and killing 43 people. For the following 5 weeks, hundreds of workers engaged in an exhaustive search, rescue, and recovery effort directly in the landslide runout path. These workers could not avoid the risks posed by additional large-scale slope collapses. In an effort to ensure worker safety, multiple agencies cooperated to swiftly deploy a monitoring and alerting system consisting of sensors, automated data processing and web-based display, along with defined communication protocols and clear calls to action for emergency management and search personnel. Guided by the principle that an accelerating landslide poses a greater threat than a steadily moving or stationary mass, the system was designed to detect ground motion and vibration using complementary monitoring techniques. Near real-time information was provided by continuous GPS, seismometers/geophones, and extensometers. This information was augmented by repeat-assessment techniques such as terrestrial and aerial laser scanning and time-lapse photography. Fortunately, no major additional landsliding occurred. However, we did detect small headscarp failures as well as slow movement of the remaining landslide mass with the monitoring system. This was an exceptional response situation and the lessons learned are applicable to other landslide disaster crises. They underscore the need for cogent landslide expertise and ready-to-deploy monitoring equipment, the value of using redundant monitoring techniques with distinct goals, the benefit of clearly defined communication protocols, and the importance of continued research into forecasting landslide behavior to allow timely warning.
Tungsten (W) is rarely found in natural waters, yet it can be introduced into the food chain and cause potentially toxic effects. Uptake of W by plants and vegetables, or trace presence of W in drinking water are possible vectors for ingestion of W by humans. The latter is recognized as a possible cause of lymphatic leukemia. Increased uses of W might result in a degradation of water resources, with attendant adverse effects on biota and human health. Therefore, this study was aimed at investigating regional occurrence and speciation of W in aquatic systems in Sardinia, Italy, factors affecting W mobility and possible relations with other oxyanion-forming trace elements such as Sb, As and Mo. Although our results are specifically from Sardinia, the implications are broader and should prompt future studies in other areas with known high W concentrations.
A total of 350 sample sites are reported here, including surface waters, groundwaters, mine drainages, thermal waters and local seawater. The waters were analyzed for major and trace components, including W, Sb, As and Mo. The waters showed a variety of major chemical compositions and W concentrations. High concentrations of W were found in some mine waters and drainages from slag heaps, with W, Sb and As up to 140, 5000 and 800 μg L−1, respectively. The highest concentrations of W occurred under slightly alkaline pH and oxygenated conditions, and were likely due to the dissolution of scheelite [CaWO4] hosted in materials with which the water came into contact. High W concentrations also were observed in thermal waters, under alkaline pH and reducing conditions, and sometimes coincided with relatively high concentrations either of As or Mo.
Previous studies of W geochemistry have focused on WO42− as the major dissolved form of W. For this study, we have augmented the thermodynamic database in PHREEQC to include possible formation of many other W-bearing complexes gleaned from the literature. The results of the speciation calculations with the newly added complexation reactions shows that the neutral species CaWO4° and MgWO4° are particularly dominant in most W-bearing waters and lead to undersaturation with respect to scheelite and other W-bearing minerals.
Assessing W contamination in water systems and establishing W limits in drinking water may prevent potential adverse effects of W on human and ecosystem health.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gexplo.2021.106846","usgsCitation":"Cidu, R., Biddau, R., Frau, F., Wanty, R., and Naitza, S., 2021, Regional occurrence of aqueous tungsten and relations with antimony, arsenic and molybdenum concentrations (Sardinia, Italy): Journal of Geochemical Exploration, v. 229, 106846, 16 p., https://doi.org/10.1016/j.gexplo.2021.106846.","productDescription":"106846, 16 p.","ipdsId":"IP-127822","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":399886,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Sardinia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 7.943115234375001,\n 38.865374851611634\n ],\n [\n 9.920654296875,\n 38.865374851611634\n ],\n [\n 9.920654296875,\n 41.31082388091818\n ],\n [\n 7.943115234375001,\n 41.31082388091818\n ],\n [\n 7.943115234375001,\n 38.865374851611634\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"229","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cidu, Rosa","contributorId":290729,"corporation":false,"usgs":false,"family":"Cidu","given":"Rosa","affiliations":[{"id":16820,"text":"University of Cagliari","active":true,"usgs":false}],"preferred":false,"id":841689,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biddau, Riccardo","contributorId":290730,"corporation":false,"usgs":false,"family":"Biddau","given":"Riccardo","affiliations":[{"id":16820,"text":"University of Cagliari","active":true,"usgs":false}],"preferred":false,"id":841690,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frau, Franco","contributorId":290731,"corporation":false,"usgs":false,"family":"Frau","given":"Franco","affiliations":[{"id":16820,"text":"University of Cagliari","active":true,"usgs":false}],"preferred":false,"id":841691,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wanty, Richard B. 0000-0002-2063-6423","orcid":"https://orcid.org/0000-0002-2063-6423","contributorId":209899,"corporation":false,"usgs":true,"family":"Wanty","given":"Richard","middleInitial":"B.","affiliations":[],"preferred":true,"id":841692,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Naitza, Stefano","contributorId":290732,"corporation":false,"usgs":false,"family":"Naitza","given":"Stefano","email":"","affiliations":[{"id":16820,"text":"University of Cagliari","active":true,"usgs":false}],"preferred":false,"id":841693,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}