Since the 1970s, temporal variations of hydrothermal discharge and thermal output from the numerous hydrothermal features in the Yellowstone Plateau Volcanic Field (YPVF) have been studied by measuring the chloride flux in the major rivers. In this study, the sources, fate, and flux of solutes in the Fall River and its major tributaries, in southwest Yellowstone National Park, were determined. The considerable precipitation in southwest YPVF and high groundwater flow through Quaternary rhyolites results in river solute fluxes that originate from shallow non-thermal groundwater and deep-thermal water. Specific conductance serves as a surrogate measure for thirteen riverine solute concentrations. Combining continuous 15-minute specific conductance and discharge data, the annual chloride, arsenic, fluoride, and silica fluxes from the Fall River were determined to be 11%, 5%, 25%, and 19% of the total flux exiting YPVF. Approximately 11% of the Fall River chloride flux is from non-thermal waters, which is larger than the previous estimate of 4 to 6%. Furthermore, a large proportion of fluoride and silica in the Fall River are derived from water-rock interaction in the shallow non-thermal groundwater system and the non-thermal weathering rate (30 ± 2 t/yr·km2) is higher than other rivers draining the Yellowstone caldera. Consequently, 73 ± 3% of the annual total dissolved solid flux in the Fall River is from thermal sources. Synoptic sampling of river water and discharge measurements was performed during low-flow conditions that allowed for the determination of solute sources and their downstream fate. It was determined that chloride, sodium, arsenic, rubidium, lithium, and boron are primarily (>89%) associated with thermal waters and the Bechler River is the primary source of most hydrothermal solutes in the Fall River, but the major source of arsenic is Boundary Creek. Using the chloride inventory method, the thermal water discharge from several thermal areas was also determined.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.107021","usgsCitation":"McCleskey, R., Hurwitz, S., White, E.B., Roth, D.A., Susong, D., Hungerford, J., and Olson, L.A., 2020, Sources, fate, and flux of riverine solutes in the Southwest Yellowstone Plateau Volcanic Field, USA: Journal of Volcanology and Geothermal Research, v. 403, 107021, 15 p., https://doi.org/10.1016/j.jvolgeores.2020.107021.","productDescription":"107021, 15 p.","ipdsId":"IP-118755","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":382915,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.03881835937499,\n 43.476840397778936\n ],\n [\n -108.929443359375,\n 43.476840397778936\n ],\n [\n -108.929443359375,\n 45.01141864227728\n ],\n [\n -111.03881835937499,\n 45.01141864227728\n ],\n [\n -111.03881835937499,\n 43.476840397778936\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"403","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McCleskey, R. Blaine 0000-0002-2521-8052","orcid":"https://orcid.org/0000-0002-2521-8052","contributorId":205663,"corporation":false,"usgs":true,"family":"McCleskey","given":"R. 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Few comparisons guide users in dataset selection, examine a large spatial extent, and include multiple metrics. This study compares metrics from 10 leading remote sensing datasets against a network of PhenoCam near-surface cameras throughout the Western United States from 2002-2014. Correlation (R2) and mean bias varied substantially by dataset, metric, and land cover. The closest association with PhenoCam measured phenology metrics represented a date (SOS, PIRGd, POS, and EOS) rather than a duration (length of spring, length of growing season), with R2 of individual datasets ranging from 0.03 (SOS) – 0.55 (PIRGd), and absolute value of mean bias ranging from 0.38 (PIRGd) – 37.92 days (EOS). Datasets had higher agreement with PhenoCam metrics in shrublands, grasslands, and deciduous/broadleaf forests than in evergreen forests. Productivity metrics agreed worse than phenology metrics, though some datasets showed high correlations in deciduous/broadleaf forests. Using the two datasets that agreed best with PhenoCam metrics and covered 1982-2016, we conducted a trend analysis to study changes to growing seasons. Trends in phenology exhibited substantial spatial heterogeneity in the direction of trend for both datasets. Variability of metrics increased over time in some areas, particularly in the Southwest. Approximately 60% of pixels had consistent trend direction (both earlier and later) for SOS, POS, and EOS. In all ecoregions except Mediterranean California EOS trended toward a later date. This study provides a comprehensive comparison of remote sensing datasets across many important phenology and productivity metrics and discusses considerations for users to make informed decisions about their data choices.
","language":"English","publisher":"MDPI","doi":"10.3390/rs12162538","usgsCitation":"Graves, T., Berman, E.E., Mikle, N., Merkle, J., Johnston, A.N., and Chong, G.W., 2020, Comparative performance and trend of remotely sensed phenology and productivity metrics across the Western United States: Remote Sensing, v. 12, no. 16, 2538, 27 p., https://doi.org/10.3390/rs12162538.","productDescription":"2538, 27 p.","ipdsId":"IP-117118","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":455728,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12162538","text":"Publisher Index Page"},{"id":436832,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YL7B2F","text":"USGS data release","linkHelpText":"Historical trend analysis of phenology dates across the Western US from 1982 to 2016"},{"id":377480,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, 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\"}}]}","volume":"12","issue":"16","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Graves, Tabitha A. 0000-0001-5145-2400","orcid":"https://orcid.org/0000-0001-5145-2400","contributorId":202084,"corporation":false,"usgs":true,"family":"Graves","given":"Tabitha A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":796143,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berman, Ethan Edward 0000-0001-6112-6211","orcid":"https://orcid.org/0000-0001-6112-6211","contributorId":238131,"corporation":false,"usgs":true,"family":"Berman","given":"Ethan","email":"","middleInitial":"Edward","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":796144,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mikle, Nathaniel 0000-0002-6529-8210 nmikle@usgs.gov","orcid":"https://orcid.org/0000-0002-6529-8210","contributorId":177026,"corporation":false,"usgs":true,"family":"Mikle","given":"Nathaniel","email":"nmikle@usgs.gov","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":796145,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Merkle, Jerod 0000-0003-0100-1833","orcid":"https://orcid.org/0000-0003-0100-1833","contributorId":224370,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":796146,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnston, Aaron N. 0000-0003-4659-0504","orcid":"https://orcid.org/0000-0003-4659-0504","contributorId":201768,"corporation":false,"usgs":true,"family":"Johnston","given":"Aaron","email":"","middleInitial":"N.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":796147,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chong, Geneva W. 0000-0003-3883-5153 geneva_chong@usgs.gov","orcid":"https://orcid.org/0000-0003-3883-5153","contributorId":419,"corporation":false,"usgs":true,"family":"Chong","given":"Geneva","email":"geneva_chong@usgs.gov","middleInitial":"W.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":796148,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215775,"text":"70215775 - 2020 - Bidirectional connectivity via fish ladders in a large Neotropical river: Response to a comment","interactions":[],"lastModifiedDate":"2020-10-29T22:19:15.40488","indexId":"70215775","displayToPublicDate":"2020-08-06T17:11:42","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3301,"text":"River Research and Applications","active":true,"publicationSubtype":{"id":10}},"title":"Bidirectional connectivity via fish ladders in a large Neotropical river: Response to a comment","docAbstract":"In a recent article, we described fitting electronic tags to the fish Prochilodus lineatus to document how a fishway connected aquatic habitats downstream and upstream of a major dam. Moreover, given that tagged fish remained upstream or downstream for periods extending months and years before returning to the fishway, and that observed patterns of passage were consistent with seasonal migratory cycles, and building on existing literature, we speculated that the fishway allows fish access to spawning habitats upstream and feeding habitats downstream. Our interpretation of the movement data resulted in several comments from Pelicice, Pompeu, and Agostinho (2020) and they outline various reasons by which, in their opinion, some of our conclusions may be mistaken. Their critique is threefold. First, they argue that the percentage of fish attracted into the fishway is too low to consider the fishway an effective link between the reservoir and the river downstream. We contend that without estimates of population size it is impossible to judge if 28% passage is “limited”; conceivably, the absolute number of fish passed may still be enough to maintain a viable population. Second, they assert that because receivers were located only in the fishway it is unknown if fish that used the fishway remained near the dam, or if they continued their migration. We counter with a brief literature review that documents P. lineatus migrating through reservoirs and spawning in tributaries. Third, they advocate for a broader conservation perspective and for additional research. We agree and, in the article, had already expressed this view that fishways are only a temporary fix and that we support their use only as an element of a broader environmental management package. We also agree with the need for more research but argue that procrastinating on conservation action may not be wise because we do not know if the research will be done, how long it will take, or what the cost may be of waiting.
","language":"English","publisher":"Wiley","doi":"10.1002/rra.3687","usgsCitation":"Celestino, L., Sanz-Ronda, F., Miranda, L.E., Makrakis, M., Pinheiro Dias, J., and Makrakis, S., 2020, Bidirectional connectivity via fish ladders in a large Neotropical river: Response to a comment: River Research and Applications, v. 36, no. 7, p. 1377-1381, https://doi.org/10.1002/rra.3687.","productDescription":"5 p.","startPage":"1377","endPage":"1381","ipdsId":"IP-117690","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":486800,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/11449/200842","text":"External Repository"},{"id":379944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Brazil","otherGeospatial":"Parana River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -53.4539794921875,\n -22.91286328803374\n ],\n [\n -52.4267578125,\n -22.91286328803374\n ],\n [\n -52.4267578125,\n -22.212834764522576\n ],\n [\n -53.4539794921875,\n -22.212834764522576\n ],\n [\n -53.4539794921875,\n -22.91286328803374\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"36","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Celestino, L.F.","contributorId":244135,"corporation":false,"usgs":false,"family":"Celestino","given":"L.F.","affiliations":[{"id":48852,"text":"Companhia Energética de São Paulo","active":true,"usgs":false}],"preferred":false,"id":803383,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanz-Ronda, F.J.","contributorId":207046,"corporation":false,"usgs":false,"family":"Sanz-Ronda","given":"F.J.","email":"","affiliations":[{"id":37437,"text":"Universidad de Valladolid","active":true,"usgs":false}],"preferred":false,"id":803384,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":803385,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Makrakis, M. C.","contributorId":244136,"corporation":false,"usgs":false,"family":"Makrakis","given":"M. C.","affiliations":[{"id":48853,"text":"Universidade Estadual do Oeste do Paraná","active":true,"usgs":false}],"preferred":false,"id":803386,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pinheiro Dias, J. H.","contributorId":244137,"corporation":false,"usgs":false,"family":"Pinheiro Dias","given":"J. H.","affiliations":[{"id":48854,"text":"Universidade Estadual Paulista","active":true,"usgs":false}],"preferred":false,"id":803387,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Makrakis, S.","contributorId":244138,"corporation":false,"usgs":false,"family":"Makrakis","given":"S.","affiliations":[{"id":48853,"text":"Universidade Estadual do Oeste do Paraná","active":true,"usgs":false}],"preferred":false,"id":803388,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70215007,"text":"70215007 - 2020 - Evaluation of acute and chronic toxicity of nickel and zinc to 2 sensitive freshwater benthic invertebrates using refined testing methods","interactions":[],"lastModifiedDate":"2020-10-29T15:13:00.246324","indexId":"70215007","displayToPublicDate":"2020-08-06T11:44:51","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of acute and chronic toxicity of nickel and zinc to 2 sensitive freshwater benthic invertebrates using refined testing methods","docAbstract":"The US Environmental Protection Agency (USEPA) is reviewing the protectiveness of the national ambient water quality criteria (WQC) for nickel (Ni) and zinc (Zn) and compiling toxicity databases to update the WQC. An amphipod (Hyalella azteca) and a unionid mussel (Lampsilis siliquoidea) have shown high sensitivity to Ni and Zn in previous studies. However, there remained uncertainties regarding the influence of test duration (48 vs 96 h) and the presence and absence of food in acute exposures with the amphipod, and there were also concerns about poor control of amphipod growth and reproduction and mussel growth in chronic exposures. We conducted acute 48‐ and 96‐h water‐only toxicity tests to evaluate the influence of feeding and test durations on the toxicity of dissolved Ni and Zn to the amphipod; we also used recently refined test methods to conduct chronic Ni and Zn toxicity tests to evaluate the sensitivity of the amphipod (6‐wk exposure) and the mussel (4‐ and 12‐wk exposures). The 96‐h 50% effect concentrations (EC50s) of 916 µg Ni/L and 99 µg Zn/L from acute amphipod tests without feeding decreased from the 48‐h EC50s by 62 and 33%, respectively, whereas the 96‐h EC50s of 2732 µg Ni/L and 194 µg Zn/L from the tests with feeding decreased from the 48‐h EC50s by 10 and 26%, indicating that the presence or absence of food had apparent implications for the 96‐h EC50. Our chronic 6‐wk EC20s for the amphipod (4.5 µg Ni/L and 35 µg Zn/L) were 50 to 67% lower than the 6‐wk EC20s from previous amphipod tests, and our chronic 4‐wk EC20s for the mussel (41 µg Ni/L and 66 µg Zn/L) were similar to or up to 42% lower than the 4‐wk EC20s from previous mussel tests. The lower EC20s from the present study likely reflect more accurate estimates of inherent sensitivity to Ni and Zn due to the refined test conditions. Finally, increasing the chronic test duration from 4 to 12 wk substantially increased the toxicity of Zn to the mussel, whereas the 4‐ and 12‐wk Ni effect needs to be re‐evaluated to understand the large degree of variation in organism responses observed in the present study.
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A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental raster layers representing soil forming factors to samples to predict 15 soil properties (pH, texture fractions, rock, electrical conductivity, gypsum, CaCO3, sodium adsorption ratio, available water capacity, bulk density, erodibility, organic matter) at 7 depths, depth to restrictive layer, and surface rock size and cover. Cross-validations resulted in coefficient of determinations averaging 0.52, with a range of 0.20 to 0.76; and mean absolute errors ranged from 3% to 98% of training data averages with a mean of 41%. Uncertainty estimates were also developed by creating relative prediction intervals (RPIs) for the entire study area, which allow end users to evaluate uncertainty relative to original data distributions. Average error increased with higher RPI values (higher uncertainty), and areas with the highest RPI are consistently under-sampled, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was also observed in areas with shale parent materials and physiographic settings uncommon relative to the broader study area.","language":"English","publisher":"Wiley","doi":"10.1002/saj2.20080","usgsCitation":"Nauman, T.W., and Duniway, M.C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 170-1194, https://doi.org/10.1002/saj2.20080.","productDescription":"25 p.","startPage":"170","endPage":"1194","onlineOnly":"Y","ipdsId":"IP-108106","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436834,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SK0DO2","text":"USGS data release","linkHelpText":"Predictive soil property maps with prediction uncertainty at 30-meter resolution for the Colorado River Basin above Lake Mead"},{"id":377090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado, Wyoming, Utah, Nevada, Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.61132812499999,\n 35.67514743608467\n ],\n [\n -107.490234375,\n 39.50404070558415\n ],\n [\n -108.720703125,\n 42.68243539838623\n ],\n [\n -110.302734375,\n 42.5530802889558\n ],\n [\n -112.1484375,\n 41.21172151054787\n ],\n [\n -113.818359375,\n 38.06539235133249\n ],\n [\n -115.6201171875,\n 37.3002752813443\n ],\n [\n -116.3671875,\n 36.527294814546245\n ],\n [\n -112.587890625,\n 34.56085936708384\n ],\n [\n -106.61132812499999,\n 35.67514743608467\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794893,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211646,"text":"70211646 - 2020 - Evaluation of genetic structuring within GIS‐derived Brook Trout management units","interactions":[],"lastModifiedDate":"2021-01-25T15:51:59.457998","indexId":"70211646","displayToPublicDate":"2020-08-06T10:05:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of genetic structuring within GIS‐derived Brook Trout management units","docAbstract":"Delineation of management units across broad spatial scales can help to visualize population structuring and identify conservation opportunities. Geographical information system (GIS) approaches can be useful for developing broad‐scale management units, especially when paired with field data that can validate the GIS‐based delineations. Genetic data can be useful for evaluating whether management units accurately represent population structuring. The Eastern Brook Trout Joint Venture, a regionwide collaborative group, delineated patch‐based management units for Brook Trout Salvelinus fontinalis by using GIS approaches to inform conservation strategies across the eastern United States. The objectives of this research were to (1) evaluate how well the patches predicted Brook Trout genetic structuring in Connecticut, USA; (2) modify the patches as needed to represent contemporary genetic structuring; and (3) identify catchment‐ and patch‐scale riverscape characteristics that predict genetic diversity. Patches with dams and high levels of upstream impervious surfaces (>3%) had increased intrapatch genetic structuring, which we incorporated into our revised patch delineation algorithm. Patch area and catchment area were the best predictors of genetic diversity, suggesting the importance of maintaining connectivity and incorporating patch‐scale processes into conservation actions. The modified patch layer could be used as the basis for Brook Trout management units to help predict population structuring in the absence of watershed‐scale genetic data, allowing opportunities for Brook Trout conservation to be identified.
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Center","active":true,"usgs":true}],"preferred":true,"id":794914,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Welsh, Amy B.","contributorId":192239,"corporation":false,"usgs":false,"family":"Welsh","given":"Amy","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":794915,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Whiteley, Andrew R.","contributorId":150155,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":794916,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vokoun, Jason C.","contributorId":236998,"corporation":false,"usgs":false,"family":"Vokoun","given":"Jason C.","affiliations":[{"id":47587,"text":"University of CT","active":true,"usgs":false}],"preferred":false,"id":794917,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211706,"text":"70211706 - 2020 - Methods for rapid quality assessment for national-scale land surface change monitoring","interactions":[],"lastModifiedDate":"2020-08-07T13:34:27.240855","indexId":"70211706","displayToPublicDate":"2020-08-06T08:30:46","publicationYear":"2020","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":"Methods for rapid quality assessment for national-scale land surface change monitoring","docAbstract":"Providing rapid access to land surface change data and information is a goal of the U.S. Geological Survey. Through the Land Change Monitoring, Assessment, and Projection (LCMAP) initiative, we have initiated a monitoring capability that involves generating a suite of ten annual land cover and land surface change datasets across the United States at a 30-m spatial resolution. During the LCMAP automated production on a tile-by-tile basis, erroneous data can occasionally be generated due to hardware or software failure. While crucial to assure the quality of the data, rapid evaluation of results at the pixel level during production is a substantial challenge because of the massive data volumes. Traditionally, product quality relies on the validation after production, which is inefficient to reproduce the whole product when an error occurs. This paper presents a method for automatically evaluating LCMAP results during the production phase based on fourteen indices to quickly find and flag erroneous tiles in the LCMAP products. The methods involved two types of comparisons: comparing LCMAP values across the temporal record to measure internal consistency and calculating agreement with multiple intervals of the National Land Cover Database (NLCD) data to measure the consistency with existing products. We developed indices on a tile-by-tile basis in order to quickly find and flag potential erroneous tiles by comparing with surrounding tiles using local outlier factor analysis. The analysis integrates all indices into a local outlier score (LOS) to detect erroneous tiles distinct from neighbor tiles. Our analysis showed that the methods were sensitive to partially erroneous tiles in the simulated data with a LOS higher than 2. The rapid quality assessment methods also successfully identified erroneous tiles during the LCMAP production, in which land surface change results were not properly saved to the products. The LOS map and indices for rapid quality assessment also point to directions for further investigations. A map of all LOS values by tile for the published LCMAP shows all LOS values are below 2. We also investigated tiles with high LOS to ensure the distinction with neighboring tiles was reasonable. An index in this study shows the overall agreement between LCMAP and NLCD on a tile basis is above 71.5% and has an average at 89.1% across the 422 tiles in the conterminous U.S. The workflow is suitable for other studies with a large volume of image products.","language":"English","publisher":"MDPI","doi":"10.3390/rs12162524","usgsCitation":"Zhou, Q., Barber, C., and Xian, G.Z., 2020, Methods for rapid quality assessment for national-scale land surface change monitoring: Remote Sensing, v. 12, no. 16, 2524, 18 p., https://doi.org/10.3390/rs12162524.","productDescription":"2524, 18 p.","ipdsId":"IP-120030","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":455740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs12162524","text":"Publisher Index 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0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":223103,"corporation":false,"usgs":true,"family":"Zhou","given":"Qiang","email":"","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":795198,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barber, Christopher P. 0000-0003-0570-1140","orcid":"https://orcid.org/0000-0003-0570-1140","contributorId":223102,"corporation":false,"usgs":true,"family":"Barber","given":"Christopher","middleInitial":"P.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":795199,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xian, George Z. 0000-0001-5674-2204 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organic soil horizons in Interior Alaska","docAbstract":"Boreal ecosystems comprise one tenth of the world’s land surface and contain over 20 % of the global soil carbon (C) stocks. Boreal soils are unique in that its mineral soil is covered by what can be quite thick layers of organic soil. These organic soil layers, or horizons, can differ in their state of decomposition, source vegetation, and disturbance history. These differences result in varying soil properties (bulk density, C concentration, and nitrogen (N) concentration) among soil horizons. Here we summarize these soil properties, as represented by over 3000 samples from Interior Alaska, and examine how soil drainage and stand age affect these attributes. The summary values presented here can be used to gap-fill large datasets when important soil properties were not measured, provide data to initialize process-based models, and validate model results. These data are available at https://doi.org/10.5066/P960N1F9 (Manies, 2019).","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-12-1745-2020","usgsCitation":"Manies, K.L., Waldrop, M., and Harden, J.W., 2020, Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska: Earth System Science Data (ESSD), v. 12, p. 1745-1757, https://doi.org/10.5194/essd-12-1745-2020.","productDescription":"13 p.","startPage":"1745","endPage":"1757","ipdsId":"IP-109891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":455749,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-12-1745-2020","text":"Publisher Index Page"},{"id":377481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.3125,\n 63.54855223203644\n ],\n [\n -142.734375,\n 63.54855223203644\n ],\n [\n -142.734375,\n 68.13885164925573\n ],\n [\n -160.3125,\n 68.13885164925573\n ],\n [\n -160.3125,\n 63.54855223203644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":796141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harden, Jennifer W. 0000-0002-6570-8259 jharden@usgs.gov","orcid":"https://orcid.org/0000-0002-6570-8259","contributorId":1971,"corporation":false,"usgs":true,"family":"Harden","given":"Jennifer","email":"jharden@usgs.gov","middleInitial":"W.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":796142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211605,"text":"sir20205077 - 2020 - Steps taken for calculating estimated ultimate recoveries of wells in the Eagle Ford Group and associated Cenomanian–Turonian strata, U.S. Gulf Coast, Texas, 2018","interactions":[],"lastModifiedDate":"2020-08-06T19:01:37.143617","indexId":"sir20205077","displayToPublicDate":"2020-08-06T05:52:21","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5077","displayTitle":"Steps Taken for Calculating Estimated Ultimate Recoveries of Wells in the Eagle Ford Group and Associated Cenomanian–Turonian Strata, U.S. Gulf Coast, Texas, 2018","title":"Steps taken for calculating estimated ultimate recoveries of wells in the Eagle Ford Group and associated Cenomanian–Turonian strata, U.S. Gulf Coast, Texas, 2018","docAbstract":"In 2018, the U.S. Geological Survey published an assessment of technically recoverable continuous oil and gas resources of the Eagle Ford Group and associated Cenomanian–Turonian strata in the U.S. Gulf Coast of Texas. Estimated ultimate recoveries (EURs) were calculated with production data from IHS MarkitTM using DeclinePlus software in the Harmony interface. These EURs were a major component of the aforementioned quantitative resource assessment fact sheet. The calculated mean EURs for each oil assessment unit (AU) ranged from 113,000 barrels of oil in the Cenomanian–Turonian Mudstone Continuous Oil AU to 223,000 barrels of oil in the Submarine Plateau-Karnes Trough Continuous Oil AU. The calculated mean EURs for each gas AU ranged from 2.261 billion cubic feet of gas in the Submarine Plateau-Karnes Trough Continuous Gas AU to 3.116 billion cubic feet of gas in the Eagle Ford Marl Continuous Gas AU.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205077","usgsCitation":"Leathers-Miller, H.M., 2020, Steps taken for calculating estimated ultimate recoveries of wells in the Eagle Ford Group and associated Cenomanian–Turonian strata, U.S. Gulf Coast, Texas, 2018: U.S. Geological Survey Scientific Investigations Report 2020–5077, 5 p., https://doi.org/10.3133/sir20205077.","productDescription":"5 p.","numberOfPages":"5","onlineOnly":"Y","ipdsId":"IP-102505","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":377009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5077/sir20205077.pdf","text":"Report","size":"5.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5077"},{"id":377008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5077/coverthb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2626953125,\n 32.80574473290688\n ],\n [\n -98.85498046875,\n 30.35391637229704\n ],\n [\n -100.72265625,\n 29.22889003019423\n ],\n [\n -100.2392578125,\n 28.246327971048842\n ],\n [\n -99.51416015625,\n 27.527758206861886\n ],\n [\n -99.31640625,\n 27.078691552927534\n ],\n [\n -96.85546875,\n 28.70986084394286\n ],\n [\n -93.8232421875,\n 30.543338954230222\n ],\n [\n -92.35107421874999,\n 31.147006308556566\n ],\n [\n -94.2626953125,\n 32.80574473290688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, Mail Stop 939
Denver, CO 80225
Agassiz’s desert tortoise (Gopherus agassizii), a threatened species of the southwestern United States, has severely declined to the point where 76% of populations in critical habitat (Tortoise Conservation Areas) are below viability. The potential for rapid recovery of wild populations is low because females require 12–20 years to reach reproductive maturity and produce few eggs annually. We report on a 34‐year mark‐recapture study of tortoises initiated in 1979 at the Desert Tortoise Research Natural Area in the western Mojave Desert, California, USA, and provide substantive data on challenges faced by the species. In 1980, the United States Congress designated the Research Natural Area and protected the land from recreational vehicles, livestock grazing, and mining with a wildlife‐permeable fence. The 7.77‐km2 study area, centered on interpretive facilities, included land both within the Natural Area and outside the fence. We expected greater benefits to accrue to the tortoises and habitat inside compared to outside. Our objectives were to conduct a demographic study, analyze and model changes in the tortoise population and habitat, and compare the effectiveness of fencing to protect populations and habitat inside the fence versus outside, where populations and habitat were unprotected. We conducted surveys in spring in each of 7 survey years from 1979, when the fence was under construction, through 2012. We compared populations inside to those outside the fence by survey year for changes in distribution, structure by size and relative age, sex ratios, death rates of adults, and causes of death for all sizes of tortoises. We used a Bayesian implementation of a Jolly Seber model for mark‐recapture data. We modeled detection, density, growth and transition of tortoises to larger size‐age classes, movements from inside the protective fence to outside and vice versa, and survival. After the second and subsequent survey years, we added surveys to monitor vegetation and habitat changes, conduct health assessments, and collect data on counts of predators and predator sign. At the beginning of the study, counts and densities for all sizes of tortoises were high, but densities were approximately 24% higher inside the fence than outside. By 2002, the low point in densities, densities had declined 90% inside the fence and 95% outside. Between 2002 and 2012, the population inside the fence showed signs of improving with a 54% increase in density. Outside the fence, densities remained low. At the end of the study, when we considered the initial differences in location, densities inside the fence were roughly 2.5 times higher than outside. The pattern of densities was similar for male and female adults. When evaluating survival by blocks of years, survivorship was higher in 1979–1989 than in 1989–2002 (the low point) and highest from 2002 to 2012. Recruitment and survival of adult females into the population was important for growing the population, but survival of all sizes, including juveniles, was also critical.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wmon.1052","usgsCitation":"Berry, K.H., Yee, J.L., Shields, T.A., and Stockton, L., 2020, The catastrophic decline of tortoises at a fenced natural area: Wildlife Monographs, v. 205, no. 1, p. 1-53, https://doi.org/10.1002/wmon.1052.","productDescription":"53 p.","startPage":"1","endPage":"53","ipdsId":"IP-114548","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455757,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wmon.1052","text":"Publisher Index Page"},{"id":436835,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY0HVH","text":"USGS data release","linkHelpText":"Demography and Habitat of Desert Tortoises at the Desert Tortoise Research Natural Area, Western Mojave Desert, California (1978 - 2014)"},{"id":436836,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY0HVH","text":"USGS data release","linkHelpText":"Demography and Habitat of Desert Tortoises at the Desert Tortoise Research Natural Area, Western Mojave Desert, California (1978 - 2014)"},{"id":385754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Desert Tortoise Research Natural Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.1634521484375,\n 34.97150033361733\n ],\n [\n -117.3504638671875,\n 34.97150033361733\n ],\n [\n -117.3504638671875,\n 35.48527461007853\n ],\n [\n -118.1634521484375,\n 35.48527461007853\n ],\n [\n -118.1634521484375,\n 34.97150033361733\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"205","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shields, Timothy A.","contributorId":190759,"corporation":false,"usgs":false,"family":"Shields","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":815992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockton, Laura","contributorId":258217,"corporation":false,"usgs":false,"family":"Stockton","given":"Laura","email":"","affiliations":[{"id":52242,"text":"Bakersfield, CA","active":true,"usgs":false}],"preferred":false,"id":815993,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211604,"text":"ds1129 - 2020 - Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019","interactions":[],"lastModifiedDate":"2020-08-04T21:34:42.312972","indexId":"ds1129","displayToPublicDate":"2020-08-04T14:33:20","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1129","displayTitle":"Water-Level Data for the Albuquerque Basin and Adjacent Areas, Central New Mexico, Period of Record Through September 30, 2019","title":"Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019","docAbstract":"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 2019, 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 120 wells and piezometers in the network; this report, prepared in cooperation with the ABCWUA, presents water-level data collected by USGS personnel at those 120 sites through water year 2019 (October 1, 2018, through September 30, 2019). Water levels that were collected from those 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/ds1129","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Beman, J.E., 2020, Water-level data for the Albuquerque Basin and adjacent areas, central New Mexico, period of record through September 30, 2019: U.S. Geological Survey Data Series 1129, 40 p., https://doi.org/10.3133/ds1129.","productDescription":"iii, 40 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-120239","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":377000,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1129/coverthb.jpg"},{"id":377001,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1129/ds1129.pdf","text":"Report","size":"5.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1129"}],"country":"United States","state":"New Mexico","city":"Albuquerque","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.611083984375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 33.797408767572485\n ],\n [\n -105.941162109375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 36.06686213257888\n ],\n [\n -107.611083984375,\n 33.797408767572485\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
Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201053","usgsCitation":"Rigler, E.J., and Claycomb, A.E., 2020, Adjusted geomagnetic data—Theoretical basis and validation: U.S. Geological Survey Open-File Report 2020–1053, 19 p., https://doi.org/10.3133/ofr20201053.","productDescription":"iv, 19 p.","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":376988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1053/coverthb.jpg"},{"id":376989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1053/ofr20201053.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1053"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562
Information concerning the availability, use, and quality of water in Evangeline Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 282.66 million gallons per day (Mgal/d) of water were withdrawn in Evangeline Parish, including about 122.05 Mgal/d from groundwater sources and 160.61 Mgal/d from surface-water sources. Withdrawals for agricultural use, composed of aquaculture, general irrigation, livestock, and rice irrigation, accounted for 45 percent (126.86 Mgal/d) of the total water withdrawn. Withdrawals for power-generation use accounted for about 52 percent (146.33 Mgal/d) of the total water withdrawn. Other categories of use included public supply, industry, and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 1980.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203020","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"Murphy, C.J., and White, V.E., 2020, Water resources of Evangeline Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3020, 6 p., https://doi.org/10.3133/fs20203020.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-103346","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376884,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 2014–2015"},{"id":376882,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2020/3020/coverthb.jpg"},{"id":376883,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2020/3020/fs20203020.pdf","text":"Report","size":"1.04 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2020–3020"}],"country":"United States","state":"Louisiana","county":"Evangeline Parish","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-92.2809,30.9653],[-92.2811,30.9365],[-92.2381,30.8924],[-92.2377,30.8486],[-92.2132,30.8487],[-92.2127,30.7948],[-92.2079,30.7889],[-92.2073,30.7848],[-92.1977,30.7798],[-92.1918,30.7785],[-92.187,30.7758],[-92.1816,30.7694],[-92.1774,30.7685],[-92.1694,30.7677],[-92.1729,30.6758],[-92.175,30.6762],[-92.1797,30.6661],[-92.1861,30.667],[-92.1887,30.6647],[-92.1945,30.6596],[-92.2009,30.6564],[-92.2008,30.6477],[-92.205,30.639],[-92.2034,30.6372],[-92.2055,30.6353],[-92.2044,30.6331],[-92.206,30.6299],[-92.2038,30.6257],[-92.2064,30.6216],[-92.2107,30.6198],[-92.2117,30.6129],[-92.2113,30.569],[-92.2622,30.5682],[-92.263,30.5385],[-92.2795,30.5388],[-92.4148,30.5405],[-92.4227,30.5386],[-92.4285,30.5363],[-92.4397,30.5362],[-92.4508,30.532],[-92.4592,30.5246],[-92.4622,30.5163],[-92.4659,30.5108],[-92.4637,30.5008],[-92.4657,30.4967],[-92.471,30.4939],[-92.4805,30.4924],[-92.4874,30.4878],[-92.4942,30.4818],[-92.6304,30.4827],[-92.6305,30.4859],[-92.6284,30.4896],[-92.6237,30.4929],[-92.6232,30.4974],[-92.618,30.5021],[-92.6165,30.5067],[-92.6176,30.5135],[-92.6246,30.5185],[-92.6241,30.5208],[-92.6162,30.5259],[-92.6051,30.531],[-92.6,30.5434],[-92.5958,30.5457],[-92.5948,30.5517],[-92.5932,30.5554],[-92.588,30.5559],[-92.5859,30.5618],[-92.5844,30.5683],[-92.5871,30.5719],[-92.5903,30.5732],[-92.593,30.5796],[-92.5979,30.5832],[-92.5986,30.8726],[-92.5989,30.8945],[-92.5658,30.8948],[-92.5605,30.899],[-92.5537,30.9031],[-92.5484,30.9032],[-92.5451,30.9009],[-92.5366,30.8978],[-92.5275,30.8997],[-92.5253,30.8943],[-92.5142,30.8953],[-92.4961,30.9037],[-92.484,30.9138],[-92.4798,30.9226],[-92.4861,30.9536],[-92.484,30.9559],[-92.4728,30.9587],[-92.4664,30.9574],[-92.4568,30.9589],[-92.451,30.9626],[-92.4404,30.9686],[-92.4271,30.9733],[-92.4154,30.9788],[-92.4123,30.9853],[-92.405,30.994],[-92.3948,30.9968],[-92.3869,31.0033],[-92.3784,31.0029],[-92.3746,30.9974],[-92.3676,30.9916],[-92.3606,30.9925],[-92.3601,30.9898],[-92.3611,30.988],[-92.3579,30.9848],[-92.3419,30.9817],[-92.3408,30.9794],[-92.3418,30.9758],[-92.345,30.9739],[-92.3439,30.9703],[-92.3316,30.9736],[-92.3252,30.9704],[-92.331,30.9635],[-92.3171,30.9636],[-92.315,30.9655],[-92.2809,30.9653]]]},\"properties\":{\"name\":\"Evangeline\",\"state\":\"LA\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Information concerning the availability, use, and quality of water in Avoyelles Parish, Louisiana, is critical for proper water-supply management. The purpose of this fact sheet is to present information that can be used by water managers, parish residents, and others for stewardship of this vital resource. In 2014, about 70 million gallons per day (Mgal/d) of water were withdrawn in Avoyelles Parish, including about 59.27 Mgal/d from groundwater sources and 10.95 Mgal/d from surface-water sources. Withdrawals for agricultural use—composed of aquaculture, general irrigation, livestock, and rice irrigation—accounted for 93 percent (65.59 Mgal/d) of the total water withdrawn. Other categories of use included public supply and rural domestic. Water-use data collected at 5-year intervals from 1960 to 2010 and again in 2014 indicated that water withdrawals peaked in 2014.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20203011","collaboration":"Prepared in cooperation with the Louisiana Department of Transportation and Development","usgsCitation":"White, V.E., 2020, Water resources of Avoyelles Parish, Louisiana: U.S. Geological Survey Fact Sheet 2020–3011, 6 p., https://doi.org/10.3133/fs20203011.","productDescription":"Report: 6 p.; Data Release","numberOfPages":"6","onlineOnly":"N","ipdsId":"IP-102165","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":376881,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F78051VM","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Water withdrawals by source and category in Louisiana Parishes, 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Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
3535 S. Sherwood Forest Blvd., Suite 120
Baton Rouge, LA 70816
Passive acoustic monitoring using autonomous recording units (ARUs) is a fast-growing area of wildlife research especially for rare, cryptic species that vocalize. Northern Spotted Owl (Strix occidentalis caurina) populations have been monitored since the mid-1980s using mark–recapture methods. To evaluate an alternative survey method, we used ARUs to detect calls of Northern Spotted Owls and Barred Owls (S. varia), a congener that has expanded its range into the Pacific Northwest and threatens Northern Spotted Owl persistence. We set ARUs at 30 500-ha hexagons (150 ARU stations) with recent Northern Spotted Owl activity and high Barred Owl density within Northern Spotted Owl demographic study areas in Oregon and Washington, and set ARUs to record continuously each night from March to July, 2017. We reviewed spectrograms (visual representations of sound) and tagged target vocalizations to extract calls from ~160,000 hr of recordings. Even in a study area with low occupancy rates on historical territories (Washington’s Olympic Peninsula), the probability of detecting a Northern Spotted Owl when it was present in a hexagon exceeded 0.95 after 3 weeks of recording. Environmental noise, mainly from rain, wind, and streams, decreased detection probabilities for both species over all study areas. Using demographic information about known Northern Spotted Owls, we found that weekly detection probabilities of Northern Spotted Owls were higher when ARUs were closer to known nests and activity centers and when owls were paired, suggesting passive acoustic data alone could help locate Northern Spotted Owl pairs on the landscape. These results demonstrate that ARUs can effectively detect Northern Spotted Owls when they are present, even in a landscape with high Barred Owl density, thereby facilitating the use of passive, occupancy-based study designs to monitor Northern Spotted Owl populations.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa017","usgsCitation":"Duchac, L.S., Lesmeister, D., Dugger, K.M., Ruff, Z.J., and Davis, R.J., 2020, Passive acoustic monitoring effectively detects Northern Spotted Owls and Barred Owls over a range of forest conditions: Condor, v. 122, no. 3, duaa017, 22 p., https://doi.org/10.1093/condor/duaa017.","productDescription":"duaa017, 22 p.","ipdsId":"IP-113895","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":455771,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa017","text":"Publisher Index Page"},{"id":396168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon, Washington","otherGeospatial":"Klamath Mountains, Olympic Peninsula, Oregon Coast Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.51904296875,\n 47.24194882163242\n ],\n [\n -122.838134765625,\n 47.24194882163242\n ],\n [\n -122.838134765625,\n 48.23199134320962\n ],\n [\n -124.51904296875,\n 48.23199134320962\n ],\n [\n -124.51904296875,\n 47.24194882163242\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.12353515624999,\n 43.731414013769\n ],\n [\n -123.321533203125,\n 43.731414013769\n ],\n [\n -123.321533203125,\n 44.6061127451739\n ],\n [\n -124.12353515624999,\n 44.6061127451739\n ],\n [\n -124.12353515624999,\n 43.731414013769\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.64013671874999,\n 42.415346114253616\n ],\n [\n -122.64038085937499,\n 42.415346114253616\n ],\n [\n -122.64038085937499,\n 43.004647127794435\n ],\n [\n -123.64013671874999,\n 43.004647127794435\n ],\n [\n -123.64013671874999,\n 42.415346114253616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"122","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-04-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Duchac, Leila S.","contributorId":279674,"corporation":false,"usgs":false,"family":"Duchac","given":"Leila","email":"","middleInitial":"S.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835345,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lesmeister, Damon B.","contributorId":279675,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon B.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835346,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dugger, Katie M. 0000-0002-4148-246X","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":36037,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"","middleInitial":"M.","affiliations":[{"id":517,"text":"Oregon Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":835344,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ruff, Zachary J.","contributorId":279676,"corporation":false,"usgs":false,"family":"Ruff","given":"Zachary","email":"","middleInitial":"J.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Davis, Raymond J.","contributorId":279677,"corporation":false,"usgs":false,"family":"Davis","given":"Raymond","email":"","middleInitial":"J.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":835348,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70211424,"text":"ofr20201073 - 2020 - Ecological forecasting—21st century science for 21st century management","interactions":[],"lastModifiedDate":"2024-03-04T18:30:12.945694","indexId":"ofr20201073","displayToPublicDate":"2020-08-04T07:20:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1073","displayTitle":"Ecological Forecasting—21st Century Science for 21st Century Management","title":"Ecological forecasting—21st century science for 21st century management","docAbstract":"Natural resource managers are coping with rapid changes in both environmental conditions and ecosystems. Enabled by recent advances in data collection and assimilation, short-term ecological forecasting may be a powerful tool to help resource managers anticipate impending near-term changes in ecosystem conditions or dynamics. Managers may use the information in forecasts to minimize the adverse effects of ecological stressors and optimize the effectiveness of management actions. To explore the potential for ecological forecasting to enhance natural resource management, the U.S. Geological Survey (USGS) convened a workshop titled \"Building Capacity for Applied Short-Term Ecological Forecasting\" on May 29—31, 2019, with participants from several Federal agencies, including the Bureau of Land Management, the U.S. Fish and Wildlife Service, the National Park Service, and the National Oceanic and Atmospheric Administration as well as all mission areas within the USGS.
Participants broadly agreed that short-term ecological forecasting—on the order of days to years into the future—has tremendous potential to improve the quality and timeliness of information available to guide resource management decisions. Participants considered how ecological forecasting could directly affect their agency missions and specified numerous critical tools for addressing natural resource management concerns in the 21st century that could be enhanced by ecological forecasting. Given this breadth of possible applications for forecast products, participants developed a repeatable framework for evaluating potential value of a forecast product for enhancing resource management. Applying that process to a large list of forecast ideas that were developed in a brainstorming session, participants identified a small set of promising forecast products that illustrate the value of ecological forecasting for informing resource management. Workshop outcomes also include insights about important likely obstacles and next steps. In particular, reliable production and delivery of operational ecological forecasts will require a sustained commitment by research agencies, in partnership with resource management agencies, to maintain and improve forecasting tools and capabilities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201073","usgsCitation":"Bradford, J.B., Weltzin, J.F., McCormick, M., Baron, J., Bowen, Z., Bristol, S., Carlisle, D., Crimmins, T., Cross, P., DeVivo, J., Dietze, M., Freeman, M., Goldberg, J., Hooten, M., Hsu, L., Jenni, K., Keisman, J., Kennen, J., Lee, K., Lesmes, D., Loftin, K., Miller, B.W., Murdoch, P., Newman, J., Prentice, K.L., Rangwala, I., Read, J., Sieracki, J., Sofaer, H., Thur, S., Toevs, G., Werner, F., White, C.L., White, T., and Wiltermuth, M., 2020, Ecological forecasting—21st century science for 21st century management: U.S. Geological Survey Open-File Report 2020–1073, 54 p., https://doi.org/10.3133/ofr20201073.","productDescription":"vii, 54 p.","numberOfPages":"54","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-114740","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":392,"text":"Minnesota Water Science Center","active":true,"usgs":true},{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":376787,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1073/ofr20201073.pdf","text":"Report","size":"598 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1073"},{"id":376786,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1073/coverthb.jpg"}],"contact":"Director, Southwest Biological Science Center
U.S. Geological Survey
2255 N. Gemini Drive
Flagstaff, AZ 86001
The accurate mapping of streams and their streamflow conditions in terms of presence or absence of surface water is important to both understanding physical, chemical, and biological processes in streams and to managing land, water, and ecological resources. This document describes a field form, FLOwPER (FLOw PERmanence), available within a mobile application (app), for standardized data collection of the presence or absence of surface flow in streams. The FLOwPER Database is a publicly available geodataset that can be used for research and management applications. This document provides instructions on how to (1) access and download the FLOwPER field form within the mobile app service, (2) use and complete a FLOwPER field form, and (3) view and download data from the FLOwPER Database.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201075","collaboration":"Prepared in cooperation with the United States Forest Service and the Bureau of Land Management","usgsCitation":"Jaeger, K.L., Burnett, J., Heaston, E.D., Wondzell, S.M., Chelgren, N., Dunham, J.B., Johnson, S., and Brown, M., 2020, FLOwPER user guide—For collection of FLOw PERmanence field observations: U.S. Geological Survey Open-File Report 2020–1075, 40 p., https://doi.org/10.3133/ofr20201075.","productDescription":"Report: vi, 40 p.; Appendix","onlineOnly":"Y","ipdsId":"IP-118616","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":436839,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13WFKYW","text":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence field observations, Jan 2021 - Dec 2021"},{"id":407336,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://www.sciencebase.gov/catalog/item/5edea67582ce7e579c6e5845","text":"USGS data release","description":"USGS data release","linkHelpText":"FLOwPER Database: StreamFLOw PERmanence Field Observations"},{"id":376985,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1075/coverthb.jpg"},{"id":377862,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075_appendix01.pdf","text":"Appendix 1","size":"507 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075 Appendix 1"},{"id":376986,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1075/ofr20201075.pdf","text":"Report","size":"5.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1075"}],"contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The Midcontinent Rift System (MRS) of North America is one of the world’s largest continental rifts and has an age of 1.1 Ga (giga-annum). The MRS hosts a diverse suite of magmatic and hydrothermal mineral deposits in the Lake Superior region where rift rocks are exposed at or near the surface. As part of the construction of a database summarizing information on mineral deposits in the MRS, data from regional mineral deposits were downloaded from the U.S. Geological Survey (USGS) Mineral Resources Data System (MRDS), the USGS Mineral Deposit Database (USMIN), and the Ontario Ministry of Energy, Northern Development and Mines Mineral Deposit Inventory (MDI). Deposits related to MRS rocks or mineralizing events were identified and compiled into a database to develop a space/time classification for MRS-related mineral deposits. Information from MRDS, USMIN, and MDI records and from the extensive literature describing MRS mineral deposits was used to classify each entry by deposit type, host rock age and type, and estimated mineralization age. Most deposits were readily classified because of unique mineralogy, location, or well-constrained host rock. These deposits were then put into a tectonic evolutionary framework for the MRS, which showed that many deposits formed within discrete spatial and temporal stages of rift evolution.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201069","usgsCitation":"Woodruff, L.G., Schulz, K.J., Dicken, C.L., and Nicholson, S.W., 2020, Mineral resource database for deposits related to the Mesoproterozoic Midcontinent Rift System, United States and Canada: U.S. Geological Survey Open-File Report 2020–1069, 20 p., https://doi.org/10.3133/ofr20201069.","productDescription":"Report: vi, 20 p.; 2 Tables","numberOfPages":"20","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113694","costCenters":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"links":[{"id":436840,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HTATKY","text":"USGS data release","linkHelpText":"Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376912,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.csv","text":"Table 1","size":"171 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376911,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069_table1.xlsx","text":"Table 1","size":"124 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Database of mineral deposits related to the Mesoproterozoic Midcontinent Rift System (MRS) in the northern United States and northern Ontario, Canada"},{"id":376909,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1069/coverthb.jpg"},{"id":376910,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1069/ofr20201069.pdf","text":"Report","size":"13.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1069"}],"country":"United States, Canada","otherGeospatial":"Mesoproterozoic Midcontinent Rift System","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -82.2216796875,\n 40.245991504199026\n ],\n [\n -81.8701171875,\n 50.792047064406866\n ],\n [\n -96.6357421875,\n 51.23440735163459\n ],\n [\n -96.1083984375,\n 43.45291889355465\n ],\n [\n -97.5146484375,\n 43.739352079154706\n ],\n [\n -97.55859375,\n 41.541477666790286\n ],\n [\n -99.931640625,\n 41.376808565702355\n ],\n [\n -100.1513671875,\n 37.16031654673677\n ],\n [\n -94.7021484375,\n 37.09023980307208\n ],\n [\n -94.833984375,\n 39.53793974517628\n ],\n [\n -82.2216796875,\n 40.245991504199026\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Coordinator, Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
Impacts of land use, specifically soil disturbance, are linked to reductions of soil organic carbon (SOC) stocks. Correspondingly, ecosystem restoration is promoted to sequester SOC to mitigate anthropogenic greenhouse gas emissions, which are exacerbating global climate change. Restored wetlands have relatively high potential to sequester carbon compared to other ecosystems, but SOC accumulation rates are variable, which leads to high uncertainty in sequestration rates. To assess soil properties and carbon sequestration rates of freshwater mineral soil wetlands, we analyzed an extensive database of SOC concentrations from the Prairie Pothole Region (549 wetlands over 160,000 km2), which is considered one of the largest wetland ecosystems in North America. We demonstrate that SOC of wetland catchments varies among inner, transition, toe slope, and upland landscape positions (LSPs), as well as among land uses and soil depth segments. Soil organic carbon concentrations were greatest in the inner portion of the catchment (66 Mg ha−1) and progressively decrease towards the upland LSP (43 Mg ha−1). We also conducted a regional extrapolation based on LSP- and land-use-specific SOC stocks, and estimated that wetland and upland areas of PPR wetland catchments contain 141 and 178 Tg of SOC in the upper 15 cm of the soil profile, respectively. Regressing SOC by restoration age (years restored) showed that sequestration rates, which differ by LSP and depth, ranged from 0.35 to 1.10 Mg ha−1 year−1. Using these SOC sequestration rates, along with data from natural and cropland reference sites, we estimated that it takes 20 to 64 years for SOC levels of restored wetlands to return to natural reference conditions, depending on LSP and depth segment. Accounting for LSP reduces uncertainty and should refine future assessments of the greenhouse gas mitigation potential from wetland restoration.
We use gravity, magnetic, seismic reflection, well, and outcrop data to determine the three-dimensional shape and structural features of south-central Alaska’s Susitna basin. This basin is located within the Aleutian-Alaskan convergent margin region and is expected to show effects of regional subduction zone processes. Aeromagnetic data, when filtered to highlight anomalies associated with sources within the upper few kilometers, show numerous linear northeast-trending highs and some linear north-trending highs. Comparisons to seismic reflection and well data show that these highs correspond to areas where late Paleocene to early Eocene volcanic layers have been locally uplifted due to folding and/or faulting. The combined magnetic and seismic reflection data suggest that the linear highs represent northeast-trending folds and north-striking faults. Several lines of evidence suggest that the northeast-trending folds formed during the middle Eocene to early Miocene and may have continued to be active in the Pliocene. The north-striking faults, which in some areas appear to cut the northeast-trending folds, show evidence of Neogene and probable modern movement. Gravity data facilitate estimates of the shape and depth of the basin. This was accomplished by separating the observed gravity anomaly into two components—one representing low-density sedimentary fill within the basin and one representing density heterogeneities within the underlying crystalline basement. We then used the basin anomaly, seismic reflection data, and well data to estimate the depth of the basin. Together, the magnetic, gravity, and reflection seismic analyses reveal an asymmetric basin comprising sedimentary rock over 4 km thick with steep, fault-bounded sides to the southwest, west, and north and a mostly gentle rise toward the east. Relations to the broader tectonic regime are suggested by fold axis orientations within the Susitna basin and neighboring Cook Inlet basin, which are roughly parallel to the easternmost part of the Alaska-Aleutian trench and associated Wadati-Benioff zone as it trends from northeast to north-northeast to northeast. An alignment between forearc basin folds and the subduction zone trench has been observed at other convergent margins, attributed to strain partitioning generated by regional rheologic variations that are associated with the subducting plate and arc magmatism. The asymmetric shape of the basin, especially its gentle rise to the east, may reflect uplift associated with flat-slab subduction of the Yakutat microplate, consistent with previous work that suggested Yakutat influence on the nearby Talkeetna Mountains and western Alaska Range. Yakutat subduction may also have contributed to Neogene and later reverse slip along north-striking faults within the Susitna basin.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02165.1","usgsCitation":"Shah, A.K., Phillips, J., Lewis, K.A., Stanley, R.G., Haeussler, P., and Potter, C.J., 2020, Three-dimensional shape and structure of the Susitna basin, south-central Alaska, from geophysical data: Geosphere, v. 16, no. 4, p. 969-990, https://doi.org/10.1130/GES02165.1.","productDescription":"22 p.","startPage":"969","endPage":"990","ipdsId":"IP-103718","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":455808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02165.1","text":"Publisher Index Page"},{"id":380589,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","city":"South Central Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -154.775390625,\n 57.844750992891\n ],\n [\n -145.634765625,\n 57.844750992891\n ],\n [\n -145.634765625,\n 62.71446210149774\n ],\n [\n -154.775390625,\n 62.71446210149774\n ],\n [\n -154.775390625,\n 57.844750992891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":805103,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805104,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lewis, Kristen A. 0000-0003-4991-3399 klewis@usgs.gov","orcid":"https://orcid.org/0000-0003-4991-3399","contributorId":4120,"corporation":false,"usgs":true,"family":"Lewis","given":"Kristen","email":"klewis@usgs.gov","middleInitial":"A.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":805105,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanley, Richard G. 0000-0001-6192-8783 rstanley@usgs.gov","orcid":"https://orcid.org/0000-0001-6192-8783","contributorId":1832,"corporation":false,"usgs":true,"family":"Stanley","given":"Richard","email":"rstanley@usgs.gov","middleInitial":"G.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":805106,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Haeussler, Peter J. 0000-0002-1503-6247","orcid":"https://orcid.org/0000-0002-1503-6247","contributorId":219956,"corporation":false,"usgs":true,"family":"Haeussler","given":"Peter J.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":805107,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Potter, Christopher J. 0000-0002-2300-6670 cpotter@usgs.gov","orcid":"https://orcid.org/0000-0002-2300-6670","contributorId":1026,"corporation":false,"usgs":true,"family":"Potter","given":"Christopher","email":"cpotter@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":805108,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70211518,"text":"sir20205069 - 2020 - Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019","interactions":[],"lastModifiedDate":"2020-08-05T18:38:22.157905","indexId":"sir20205069","displayToPublicDate":"2020-07-31T18:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5069","displayTitle":"Incipient Bed-Movement and Flood-Frequency Analysis using Hydrophones to Estimate Flushing Flows on the Upper Colorado River, Colorado, 2019","title":"Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019","docAbstract":"In 2019, the U.S. Geological Survey, in cooperation with the Upper Colorado River Wild and Scenic Stakeholder Group, studied the magnitude and recurrence interval of streamflow (discharge) needed to initiate bed movement of gravel-sized and finer sediment in a segment of the Colorado River in Colorado to better understand sediment movement and its relation to flow regimes of the river. The study area extended from the confluence of the Blue and Colorado Rivers near Kremmling, Colorado, downstream to the confluence of the Eagle and Colorado Rivers near Dotsero, Colo. Bed movement occurred more frequently and at lower streamflows from State Bridge to Catamount Bridge compared to the study area upstream from State Bridge. As a result, the flushing flow was characterized in the study area using two definitions: the “upstream flushing flow” for locations above State Bridge and the “downstream flushing flow” for locations below State Bridge.
Acoustic data from stationary hydrophones continuously deployed in the spring and summer of 2019 and longitudinal hydrophone acoustic profiles manually collected in summer 2019 were used to identify the streamflow needed for incipient gravel-bed movement and establish flushing flows defined for this study. The upstream flushing flow was defined as 3,000 cubic feet per second (ft3/s) at streamgage 09058000 Colorado River near Kremmling, Colo. (the Kremmling streamgage) based on the underwater acoustic data from the downstream location at the Radium stationary site (2,950 ft3/s at the Kremmling streamgage which was rounded to 3,000 ft3/s). The downstream flushing flow was defined as 2,400 ft3/s at the Kremmling streamgage or 3,100 ft3/s at streamgage 09060799 Colorado River at Catamount Bridge, Colo. (the Catamount Bridge streamgage) based on the more conservative streamflow associated with the flushing flow defined using underwater acoustic data from the downstream location at the above Catamount Bridge stationary site (2,310 ft3/s at the Kremmling streamgage which was rounded to 2,400 ft3/s and 3,040 ft3/s at the Catamount Bridge streamgage which was rounded to 3,100 ft3/s).
The annual series of peak-streamflow data at the Kremmling streamgage were used to estimate annual exceedance probability (AEP) streamflows to compare to the flushing flow. Results from the Denver Water Platte and Colorado Simulation Model were used to generate daily peak-streamflows for a future conditions scenario provided for this report. The upstream flushing flow of approximately 3,000 ft3/s at the Kremmling streamgage has an AEP near 0.50 (2-year return period) depending on the period of historical record and an AEP near 0.43 (2.33-year return period) for the future period. The downstream flushing flow of approximately 2,400 ft3/s at the Kremmling streamgage has an AEP near 0.67 (1.5-year return period) depending on the period of historical record and an AEP near 0.67 (1.5-year return period) for the future period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205069","collaboration":"Prepared in cooperation with the Upper Colorado River Wild and Scenic Stakeholder Group and the Colorado River Water Conservation District","usgsCitation":"Kohn, M.S., Marineau, M.D., Hempel, L.A., and McDonald, R.R., 2020, Incipient bed-movement and flood-frequency analysis using hydrophones to estimate flushing flows on the upper Colorado River, Colorado, 2019: U.S. Geological Survey Scientific Investigations Report 2020–5069, 39 p., https://doi.org/10.3133/sir20205069.","productDescription":"Report: viii, 39 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-114386","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":376916,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J5L78O","text":"USGS data release","linkHelpText":"Acoustic, Spatial, and Sediment Size Data 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Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS 415
Denver, CO 80225
The documentation of hybrids between distantly related taxa can illustrate an initial step to explain how genes might move between species that do not exhibit complete reproductive isolation. In birds, some of the most phylogenetically distant hybrid combinations occur between genera. Traditionally, morphological and plumage characters have been used to assign the identity of the parental species of a putative hybrid, although recently, nuclear introns also have been used. Here, we demonstrate how high-throughput short-read DNA sequence data can be used to identify the parentage of a putative intergeneric hybrid, in this case between a blue-winged warbler (Vermivora cyanoptera) and a cerulean warbler (Setophaga cerulea). This hybrid had mitochondrial DNA of a cerulean warbler, indicating the maternal parent. For hundreds of single nucleotide polymorphisms within six regions of the nuclear genome that differentiate blue-winged warblers and golden-winged warblers (Vermivora chrysoptera), the hybrid had roughly equal ancestry assignment to blue-winged and cerulean warblers, suggesting a blue-winged warbler as the paternal parent species and demonstrating that this was a first generation (F1) hybrid between these species. Unlike other recently characterized intergeneric warbler hybrids, this individual hybrid learned to song match its maternal parent species, suggesting that it might have been the result of an extra-pair mating and raised in a cerulean warbler nest.
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