Geologic maps are valuable tools in planetary science. Though planetary geologic maps are similar to terrestrial (Earthbased) geologic maps, the nature of planetary exploration introduces unique challenges for geologic mappers. Terrestrial geologic mappers prepare products from field-based observation, often comparing or refining those with aerial and (or) orbital images. Planetary geologic mapping relies almost exclusively on remote observations, which are made by orbiting spacecraft. Therefore, with a few exceptions for locations with rovers, landers, or crewed surface missions, planetary geologic mappers are not able to observe their map area in detail or at smaller scales. As a result, they must interpret and describe their map features differently than those used in terrestrial geologic maps. For example, terrestrial geologic mappers commonly divide units by lithology (rock type) or grain size. However, planetary geologic mappers often do not have detailed enough information to know what type of rock or grain size is present, and instead must divide the planet’s surface into geologic units using differences in tone, color, and surface texture (at multiple scales). Cross-cutting relationships, where apparent, can provide excellent—and often crucial—observations for identifying and subdividing geologic units using orbital datasets. The process of creating planetary maps has evolved over time, from original hand-drawn maps created during the early 1800s through the late 1900s, to the fully digital products created today. Modern-day planetary geologic mapping uses Geographic Information Systems (GIS) software and tools to visualize data, delineate units and landforms, and accurately convey spatial relationships to a map user using cartographic features represented by points, lines, and polygons. This tutorial was written to familiarize both new and experienced planetary geologic mappers with ArcGIS Pro, a commonly used GIS software package developed by Esri. This tutorial introduces new planetary geologic mappers to fundamental concepts and best practices in planetary geologic mapping. For mappers with experience using ArcMap (a previous version of ArcGIS), this tutorial will help to familiarize users with new changes in layout and functionality, so current projects may be migrated into the ArcGIS Pro environment. No prior knowledge is required, although a general familiarity with geology, GIS, and planetary science is recommended. This tutorial includes links to helpful glossaries of common GIS terms and other GIS and planetary science resources in appendix 1. For additional information on planetary geologic mapping, see the U.S Geological Survey (USGS) Astrogeology NASA Planetary Geologic Mapping Program website and the Planetary Mapping Guidelines. Numerous ArcGIS tutorials are also available from Esri’s Tutorials page, through the Esri Academy catalog, and Esri’s ArcGIS Pro Resources page.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm11B14","usgsCitation":"Black, S.R., 2023, User’s Guide to planetary image analysis and geologic mapping in ArcGIS Pro: U.S. Geological Survey Techniques and Methods 11–B14, 180 p., https://doi.org/10.3133/tm11B14.","productDescription":"vi, 180 p.","onlineOnly":"Y","ipdsId":"IP-133084","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":413590,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/11/b14/tm11b14.pdf","text":"Report","size":"88.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 11-B14"},{"id":413589,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/11/b14/coverthb.jpg"},{"id":413591,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm11B13","text":"TM 11-B13 —","description":"TM 11-B13","linkHelpText":"Planetary geologic mapping protocol—2022"}],"contact":"Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Stationarity of species’ ecological tolerances is a first-order assumption of paleoenvironmental reconstruction based upon analog methods. To test this and other assumptions used in quantitative analysis of foraminiferal faunas for paleoceanographic reconstruction, we analyzed paired alkenone unsaturation ratio (
Improved simulations of streamflow and base flow for selected sites within and adjacent to the Mississippi River Alluvial Plain area are important for modeling groundwater flow because surface-water flows have a substantial effect on groundwater levels. One method for simulating streamflow and base flow, random forest (RF) models, was developed from the data at gaged sites and, in turn, was used to make monthly mean streamflow and base-flow predictions at 162 ungaged sites in the study area. Daily streamflow observations and computed base flow from 247 streamgages were used as the basis for the development of these RF models. RF models were constructed from basin and climatic characteristics and related to observed monthly mean streamflow values; models were used to compute monthly base-flow estimates from selected streamgages in and adjacent to the Mississippi River Alluvial Plain extent, which includes streamflows from parts of Alabama, Arkansas, Colorado, Florida, Illinois, Indiana, Kansas, Kentucky, Louisiana, Mississippi, Missouri, New Mexico, Tennessee, and Texas. The explanatory variables for the models were selected to represent physical characteristics and climatic time series for the contributing drainage basins to the streamgages and ungaged locations of interest. The Nash-Sutcliffe efficiency between observed and simulated monthly mean streamflow was greater than 0.80 for 155 of the 247 streamgages, with a median Nash-Sutcliffe efficiency value of 0.83. The streamflow and base-flow simulations can be used to improve inflow values and to verify the Mississippi River Alluvial Plain groundwater flow model. The statistical model, input data, and response data (simulated monthly mean streamflows) are available as a U.S. Geological Survey software release and a U.S. Geological Survey data release.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225079","programNote":"Water Availability and Use Science Program","usgsCitation":"Dietsch, B.J., Asquith, W.H., Breaker, B.K., Westenbroek, S.M., and Kress, W.H., 2023, Simulation of monthly mean and monthly base flow of streamflow using random forests for the Mississippi River Alluvial Plain, 1901 to 2018: U.S. Geological Survey Scientific Investigations Report 2022–5079, 17 p., https://doi.org/10.3133/sir20225079.","productDescription":"Report: v, 17 p.; Tables: 4; Data Release; Dataset; Software Release","numberOfPages":"28","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105480","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science 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U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
Because tension infiltrometers apply water through a disk of finite size, the infiltrated water moves laterally as well as downward. Only the vertical component of this flow is indicative of the hydraulic conductivity K, so the algorithm for computing K must include a way of isolating that component from the total flow. Some commonly used formulas correct for the multidimensional effects by subtracting an estimate of the laterally spreading flow. For disks smaller than about 200 mm in diameter, however, lateral spreading constitutes so much of the total flow that these subtractive formulas lose considerable accuracy, and sometimes overcorrect so severely as to produce a negative number for K. Other methods rely on empiricisms that are not completely consistent with unsaturated-flow theory and that require prior knowledge of certain soil properties. We developed a new formula that uses a multiplicative factor instead of a subtracted term to achieve the needed correction. For testing we conducted numerical experiments with synthetic data produced by solving the Richardson-Richards equation using the code VS2DRTI, for diverse media and a range of disk sizes, including the widely used 45-mm diameter. We compared K values calculated from our formula to the actual K used to generate the simulated data, as well as to results from other published formulas. This comparison shows that our method provides an algorithm based in unsaturated-flow theory that produces more reliable values for small disks without requiring prior knowledge of soil properties.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022WR032475","usgsCitation":"Nimmo, J.R., and Voss, P.R., 2023, Improved calculation of hydraulic conductivity for small-disk tension infiltrometers: Water Resources Research, v. 59, no. 3, e2022WR032475, 16 p., https://doi.org/10.1029/2022WR032475.","productDescription":"e2022WR032475, 16 p.","ipdsId":"IP-144414","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":499247,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022wr032475","text":"Publisher Index Page"},{"id":418222,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"59","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Nimmo, John R. 0000-0001-8191-1727 jrnimmo@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-1727","contributorId":757,"corporation":false,"usgs":true,"family":"Nimmo","given":"John","email":"jrnimmo@usgs.gov","middleInitial":"R.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":875702,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voss, Paige R.","contributorId":310446,"corporation":false,"usgs":false,"family":"Voss","given":"Paige","email":"","middleInitial":"R.","affiliations":[{"id":67192,"text":"USGS, now at University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":875703,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70262846,"text":"70262846 - 2023 - Distribution of northern long-eared bat summer-habitat derived from historical data collected on the Monongahela National Forest, West Virginia, USA","interactions":[],"lastModifiedDate":"2025-01-24T17:08:47.242141","indexId":"70262846","displayToPublicDate":"2023-03-01T11:05:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Distribution of northern long-eared bat summer-habitat derived from historical data collected on the Monongahela National Forest, West Virginia, USA","docAbstract":"Species distribution models enable resource managers to avoid and mitigate impacts to, or enhance habitat of, target species at the landscape level. Persistent declines of northern long-eared bats (Myotis septentrionalis) due to white-nose syndrome have made acquisition of contemporary data difficult. Therefore, use of legacy data may be necessary for creation of species distribution models. We used historical roost and capture records, both individually and in combination, to assess the distribution and availability of northern long-eared bat habitat across the 670,000-ha Monongahela Na- tional Forest (MNF), West Virginia, USA. We created random forest presence/pseudo-absence models to examine influences of various biotic and abi- otic predictors on both roosting and foraging presence locations of northern long-eared bats. Predicted northern long-eared bat habitat was abundant (43.1% of the MNF) and widely dispersed. Generally, all models suggested that northern long-eared bat habitat was characterized by interior forests containing linear edge features. We observed only 3.4% spatial overlap of habitat based on complete model agreement, but 38.5% of all habitat areas resulted from agreement between capture-only and combination models. Our models provide important assessments of habitat availability necessary for addressing state and federal conservation requirements on the MNF and adjacent eastern West Virginia mountains.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"De La Cruz, J., Ford, W., Jones, S.B., Johnson, J., and Silvis, A., 2023, Distribution of northern long-eared bat summer-habitat derived from historical data collected on the Monongahela National Forest, West Virginia, USA: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 10, p. 114-124.","productDescription":"11 p.","startPage":"114","endPage":"124","ipdsId":"IP-144150","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481130,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2023/distribution-northern-long-eared-bat-summer-habitat-monongahela-national-forest-west"},{"id":481153,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"West Virginia","otherGeospatial":"Monongahela National Forest","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79.92561978482631,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.52966246222408\n ],\n [\n -80.6422160008137,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.104444484140316\n ],\n [\n -79.92561978482631,\n 38.52966246222408\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"De La Cruz, J.L.","contributorId":349847,"corporation":false,"usgs":false,"family":"De La Cruz","given":"J.L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":924990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":924991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jones, S. Beaux","contributorId":346278,"corporation":false,"usgs":false,"family":"Jones","given":"S.","email":"","middleInitial":"Beaux","affiliations":[{"id":82811,"text":"The Water Institute, Baton Rouge, Louisiana, USA","active":true,"usgs":false}],"preferred":false,"id":924992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, J.R.","contributorId":349849,"corporation":false,"usgs":false,"family":"Johnson","given":"J.R.","affiliations":[{"id":32872,"text":"John Hopkins University, Applied Physics Laboratory","active":true,"usgs":false}],"preferred":false,"id":924993,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silvis, A.","contributorId":349851,"corporation":false,"usgs":false,"family":"Silvis","given":"A.","affiliations":[{"id":40299,"text":"West Virginia Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924994,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70265046,"text":"70265046 - 2023 - Using public litigation records to identify priority science needs for managing public lands","interactions":[],"lastModifiedDate":"2025-03-31T15:28:46.250573","indexId":"70265046","displayToPublicDate":"2023-03-01T10:24:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1468,"text":"Ecology and Society","active":true,"publicationSubtype":{"id":10}},"title":"Using public litigation records to identify priority science needs for managing public lands","docAbstract":"Relevant science is essential for effective natural resource decision making, including on public lands managed by the United States Department of the Interior (DOI) Bureau of Land Management (BLM), that cover 1/10th of the United States. Most of the BLM’s management decisions require analyses under the National Environmental Policy Act, and the use of science in these decisions is often challenged. Using coproduction, we assembled an interagency team of scientists and resource managers to develop a method for using public litigation to identify priority science needs for the BLM. We searched publicly available case documents finalized from 2015–2019 in Wyoming, Colorado, Utah, and New Mexico within federal courts and the DOI Office of Hearings and Appeals, and identified 108 case documents that involved challenges to the BLM’s use of science. We retained 48 case documents that contained at least one challenge about the BLM’s use of science for a specific resource. We categorized all challenges in each case document according to the proposed action, affected resource, type of science challenged (data about resources, science relevant to potential impacts, methods for analyzing potential impacts, and mitigation actions), and specific nature of the challenge (e.g., challenging direct effects analysis). We identified priority science needs based on the frequency of challenges, the number of states where similar challenges occurred, whether the BLM lost the challenge, and whether the case was remanded. Top needs related to oil and gas development actions and included science about effects on air quality and climate, water, and socioeconomics; data for air quality and climate; and methods for analyzing potential impacts to cultural resources and air quality and climate. The BLM can use this information to prioritize actions (e.g., funding new research or science syntheses) to strengthen its science foundation for decision-making.
","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ES-13708-280111","usgsCitation":"Foster, A.C., Carter, S.K., Haby, T.S., Espy, L., and Barton, M., 2023, Using public litigation records to identify priority science needs for managing public lands: Ecology and Society, v. 28, no. 1, 11, 27 p., https://doi.org/10.5751/ES-13708-280111.","productDescription":"11, 27 p.","ipdsId":"IP-130700","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":488927,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5751/es-13708-280111","text":"Publisher Index Page"},{"id":484021,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado, New Mexico, Utah, 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Starting from a base year of 2001, NLCD releases a land cover database every 2–3-years. The recent release of NLCD2019 extends the database to 18 years. We implemented a stratified random sample to collect land cover reference data for the 2016 and 2019 components of the NLCD2019 database at Level II and Level I of the classification hierarchy. For both dates, Level II land cover overall accuracies (OA) were 77.5% ± 1% (± value is the standard error) when agreement was defined as a match between the map label and primary reference label only, and increased to 87.1% ± 0.7% when agreement was defined as a match between the map label and either the primary or alternate reference label. At Level I of the classification hierarchy, land cover OA was 83.1% ± 0.9% for both 2016 and 2019 when agreement was defined as a match between the map label and primary reference label only, and increased to 90.3% ± 0.7% when agreement also included the alternate reference label. The Level II and Level I OA for the 2016 land cover in the NLCD2019 database were 5% higher compared to the 2016 land cover component of the NLCD2016 database when agreement was defined as a match between the map label and primary reference label only. No improvement was realized by the NLCD2019 database when agreement also included the alternate reference label. User’s accuracies (UA) for forest loss and grass gain were>70% when agreement included either the primary or alternate label, and UA was generally<50% for all other change themes. Producer’s accuracies (PA) were>70% for grass loss and gain and water gain and generally<50% for the other change themes. We conducted a post-analysis review for map-reference agreement to identify patterns of disagreement, and these findings are discussed in the context of potential adjustments to mapping and reference data collection procedures that may lead to improved map accuracy going forward.
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V.","contributorId":302942,"corporation":false,"usgs":false,"family":"Stehman","given":"Stephen V.","affiliations":[{"id":65585,"text":"College of Environmental Science and Forestry, State University of New York, Syracuse","active":true,"usgs":false}],"preferred":false,"id":865983,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sorenson, Daniel G. 0000-0003-0365-9444 dsorenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0365-9444","contributorId":2898,"corporation":false,"usgs":true,"family":"Sorenson","given":"Daniel","email":"dsorenson@usgs.gov","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":865984,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gass, Leila 0000-0002-3436-262X 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Basin","interactions":[],"lastModifiedDate":"2023-06-01T14:08:40.055086","indexId":"70244090","displayToPublicDate":"2023-03-01T08:44:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin","docAbstract":"Quantifying the effects of streamflow alteration on assemblage response is central to understanding the role humans play in shaping aquatic environments. These changes represent a level of complexity that impedes developing quantitative links between flow and ecological response because stream hydrology is strongly intertwined with natural and anthropogenic factors. Better management outcomes require disentangling these linkages. Benthic macroinvertebrate data were combined with GIS-derived natural and anthropogenic basin characteristics to identify factors associated with changes in flow processes and assemblage characteristics. Models linking streamflow metrics and macroinvertebrate response at basin and subregion scales were developed using boosted regression tree (BRT) analysis. Basin-scale BRT analyses revealed that links between macroinvertebrate response and flow metrics were often obscured, whereas more homogeneous subregions were better able to discern relations with flow. Urban land cover was the primary factor accounting for changes in flow characteristics. Elevation, land cover, and high flow frequency were the principal variables driving changes in assemblage structure within subregions. Assemblage metrics and traits were equally useful for building response models and were affected similarly by streamflow alteration. Results indicate that response models should be developed based on upland and coastal subregions. However, when defining subregions, care should be taken to maintain data sufficiency. Developing practical flow-protection standards that support a balance between human water requirements and ecological integrity requires models that reduce uncertainty and identify management-relevant drivers. However, effective management often differs by location and models developed at the subregion level may be more applicable to management and stakeholder interests.","language":"English","publisher":"Wiley","doi":"10.1002/eco.2508","usgsCitation":"Kennen, J., and Cuffney, T.F., 2023, Unravelling the influence of landscape alteration from flow alteration on benthic macroinvertebrate assemblage response in the Delaware River Basin: Ecohydrology, v. 16, no. 2, e2508, 41 p., https://doi.org/10.1002/eco.2508.","productDescription":"e2508, 41 p.","ipdsId":"IP-128360","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":498861,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eco.2508","text":"Publisher Index Page"},{"id":417646,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, New Jersey, New York, Pennsylvania","otherGeospatial":"Delaware River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.07997541667648,\n 38.70906787639316\n ],\n [\n -74.80531721355018,\n 39.00778043156808\n ],\n [\n -74.33839826823872,\n 40.4450386444411\n ],\n [\n -73.72865705730113,\n 40.994591290300974\n ],\n [\n -73.7835886979261,\n 42.478350475454334\n ],\n [\n -75.40956526042564,\n 42.295772510663625\n ],\n [\n -75.42055158855078,\n 41.8349594674406\n ],\n [\n -76.34340315105082,\n 40.43667721449637\n ],\n [\n -75.78859358073888,\n 39.713504216020766\n ],\n [\n -75.76662092448927,\n 39.578152174338356\n ],\n [\n -75.66225080730156,\n 39.41283383409595\n ],\n [\n -75.50294904948888,\n 39.22584914314203\n ],\n [\n -75.47548322917642,\n 39.042631522344635\n ],\n [\n -75.33266096355176,\n 38.846103881559685\n ],\n [\n -75.07997541667648,\n 38.70906787639316\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Kennen, Jonathan G. 0000-0002-5426-4445 jgkennen@usgs.gov","orcid":"https://orcid.org/0000-0002-5426-4445","contributorId":574,"corporation":false,"usgs":true,"family":"Kennen","given":"Jonathan G.","email":"jgkennen@usgs.gov","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cuffney, Thomas F. 0000-0003-1164-5560 tcuffney@usgs.gov","orcid":"https://orcid.org/0000-0003-1164-5560","contributorId":517,"corporation":false,"usgs":true,"family":"Cuffney","given":"Thomas","email":"tcuffney@usgs.gov","middleInitial":"F.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":874462,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70241112,"text":"70241112 - 2023 - Large increases in methane emissions expected from North America’s largest wetland complex","interactions":[],"lastModifiedDate":"2023-03-10T15:10:35.729452","indexId":"70241112","displayToPublicDate":"2023-03-01T08:33:52","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Large increases in methane emissions expected from North America’s largest wetland complex","docAbstract":"Natural methane (CH4) emissions from aquatic ecosystems may rise because of human-induced climate warming, although the magnitude of increase is highly uncertain. Using an exceptionally large CH4 flux dataset (~19,000 chamber measurements) and remotely sensed information, we modeled plot- and landscape-scale wetland CH4 emissions from the Prairie Pothole Region (PPR), North America’s largest wetland complex. Plot-scale CH4 emissions were driven by hydrology, temperature, vegetation, and wetland size. Historically, landscape-scale PPR wetland CH4 emissions were largely dependent on total wetland extent. However, regardless of future wetland extent, PPR CH4 emissions are predicted to increase by two- or threefold by 2100 under moderate or severe warming scenarios, respectively. Our findings suggest that international efforts to decrease atmospheric CH4 concentrations should jointly account for anthropogenic and natural emissions to maintain climate mitigation targets to the end of the century.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.ade1112","usgsCitation":"Bansal, S., Post van der Burg, M., Fern, R., Jones, J., Lo, R., McKenna, O.P., Tangen, B., Zhang, Z., and Gleason, R.A., 2023, Large increases in methane emissions expected from North America’s largest wetland complex: Science Advances, v. 9, no. 9, eade1112, 14 p., https://doi.org/10.1126/sciadv.ade1112.","productDescription":"eade1112, 14 p.","ipdsId":"IP-137112","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":444325,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.ade1112","text":"Publisher Index Page"},{"id":435429,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PKI29C","text":"USGS data release","linkHelpText":"Methane flux model for wetlands of the Prairie Pothole Region of North America: Model input data and programming code"},{"id":413952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.103,\n 47.104\n ],\n [\n -99.103,\n 47.096\n ],\n [\n -99.091,\n 47.096\n ],\n [\n -99.091,\n 47.104\n ],\n [\n -99.103,\n 47.104\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866116,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Post van der Burg, Max 0000-0002-3943-4194","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":219400,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866117,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fern, Rachel","contributorId":302984,"corporation":false,"usgs":false,"family":"Fern","given":"Rachel","affiliations":[{"id":27442,"text":"Texas parks and Wildlife Department","active":true,"usgs":false}],"preferred":false,"id":866118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jones, John W. 0000-0001-6117-3691 jwjones@usgs.gov","orcid":"https://orcid.org/0000-0001-6117-3691","contributorId":2220,"corporation":false,"usgs":true,"family":"Jones","given":"John","email":"jwjones@usgs.gov","middleInitial":"W.","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":866119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lo, Rachel 0000-0003-1014-7076","orcid":"https://orcid.org/0000-0003-1014-7076","contributorId":303000,"corporation":false,"usgs":true,"family":"Lo","given":"Rachel","email":"","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":866151,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKenna, Owen P. 0000-0002-5937-9436 omckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-5937-9436","contributorId":198598,"corporation":false,"usgs":true,"family":"McKenna","given":"Owen","email":"omckenna@usgs.gov","middleInitial":"P.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":866121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhang, Zhen 0000-0003-0899-1139","orcid":"https://orcid.org/0000-0003-0899-1139","contributorId":149173,"corporation":false,"usgs":false,"family":"Zhang","given":"Zhen","email":"","affiliations":[],"preferred":false,"id":866123,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gleason, Robert A. 0000-0001-5308-8657 rgleason@usgs.gov","orcid":"https://orcid.org/0000-0001-5308-8657","contributorId":2402,"corporation":false,"usgs":true,"family":"Gleason","given":"Robert","email":"rgleason@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":866124,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243279,"text":"70243279 - 2023 - Wildfire-induced shifts in groundwater discharge to streams identified with paired air and stream water temperature analyses","interactions":[],"lastModifiedDate":"2023-05-05T12:02:31.291377","indexId":"70243279","displayToPublicDate":"2023-03-01T06:59:25","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Wildfire-induced shifts in groundwater discharge to streams identified with paired air and stream water temperature analyses","docAbstract":"Within the western United States, increasingly severe and frequent wildfires may alter the magnitude, timing, and quality of water exported from burned areas by streams. Post-fire hydrologic studies often focus on peak stream flow responses to shifts in runoff generation or on annual streamflow yield response to changes in evapotranspiration following fire. However, the magnitude and duration of wildfire effects on groundwater recharge, changes in subsurface routing, and consequences for stream low flows sourced predominately by baseflow are poorly understood. Here, we demonstrate an approach using the amplitude and phase of paired annual air and stream water temperature signals to broadly identify changes in watershed subsurface flow contributions after fire. Watersheds were classified using pre-fire temperature data, as having air-coupled (i.e., reduced apparent groundwater signature), deep groundwater, or shallow groundwater stream temperature signals. Changes in pre- and post-fire paired air and stream water temperature metrics were compared for locations (n = 17) spanning a large range of physiographic and climatic conditions across the western United States. Pre- and post-fire comparisons were computed by quantile using bootstrapped confidence intervals (ci = 95), as well as in aggregate using Kruskal-Wallis and post-hoc Dunn tests. Statistical comparisons of pre- and post-fire temperature metrics suggest that overall, watersheds classified as having minimal groundwater influence are the most likely to experience fire-induced subsurface hydrologic change. More specifically, watersheds classified as having air-coupled or shallow groundwater signals experienced increases in the magnitude of groundwater discharge, with more stable annual thermal regimes post-fire that are less-coupled to ambient air temperature. These findings form the basis of a conceptual framework for watershed resistance to subsurface hydrologic change following fire that can be broadly applied as a first approximation for water management, impacts on aquatic habitat, and post-wildfire response planning.
The Quaternary Clear Lake Volcanic Field (CLVF) in the Northern California Coast Range is the youngest of a string of northward-younging volcanic centers in the state. The CLVF is located within the broad San Andreas Transform Fault System and has been active intermittently for ∼2 million years. Heat beneath the CLVF supports The Geysers, one of the largest producing geothermal fields in the world.
Previous geophysical studies proposed the existence of a magma reservoir beneath Mount Hannah, which is northeast of The Geysers, near the geographic center of the CLVF. The lateral extent, depth, and presence of melt within this reservoir are poorly constrained, as is the relationship between this body and the broader magmatic plumbing of the CLVF. To gain a clearer and more comprehensive picture of the CLVF magma source region, a gravity dataset was compiled and the first 3-D gravity inversions of the CLVF were completed.
Field and synthetic model inversions from the current study both indicate that the gravity low roughly centered on Mount Hannah is not accurately explained by a 5–7 km thick lens of Mesozoic Great Valley Sequence (
The prolonged eruption history of the CLVF, coupled with the compositional variation of erupted rocks over time and space, is consistent with the existence of several, potentially ephemeral, melt-bearing bodies as opposed to one large melt body. Given the density and location of the recovered anomaly, rhyolite-MELTS thermodynamic modeling suggests the existence of 10–30% rhyodacitic melt within the proposed silicic magma reservoir at about 700 °C and 8 km depth (210 MPa). Independent petrologic, geochemical, and seismic evidence indicates that this silicic partial melt zone is underlain by basaltic melt in the lower to middle crust (13 to 21 km depth), which is fed by a mantle source.
Eruptions in the past ∼8.5–13.5 thousand years; high regional heat flow; 3He enrichment of hydrothermal fluids; and our modeling, which suggests the presence of a mid-crustal, silicic partial melt zone, point to a still-active CLVF. The relatively low estimates of partial melt (10–30%) predicted by thermodynamic modeling indicates that an injection of new magma into the imaged partial melt zone is needed to generate sufficient melt to incite future eruptions. Despite the low percent melt estimates within the proposed silicic partial melt zone the potential for future volcanic eruption remains. Due to the proximity of the CLVF to cities surrounding Clear Lake and the densely populated San Francisco Bay Area, continued research and monitoring of the volcanic field are warranted. The geophysical and petrologic modeling presented here improves our understanding of the CLVF magma plumbing system and allows us to better characterize its associated volcanic hazards.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2023.107758","usgsCitation":"Mitchell, M.A., Peacock, J., and Burgess, S.D., 2023, Imaging the magmatic plumbing of the Clear Lake Volcanic Field using 3-D gravity inversions: Journal of Volcanology and Geothermal Research, v. 435, 107758, 41 p., https://doi.org/10.1016/j.jvolgeores.2023.107758.","productDescription":"107758, 41 p.","ipdsId":"IP-137378","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":444342,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jvolgeores.2023.107758","text":"Publisher Index Page"},{"id":415702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.25068446644411,\n 39.211549683768425\n ],\n [\n -123.25068446644411,\n 38.316770047400155\n ],\n [\n -122.29803029534756,\n 38.316770047400155\n ],\n [\n -122.29803029534756,\n 39.211549683768425\n ],\n [\n -123.25068446644411,\n 39.211549683768425\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"435","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":299110,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"Albert","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":869389,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peacock, Jared R. 0000-0002-0439-0224","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":210082,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":869390,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Burgess, Seth D. 0000-0002-4238-3797 sburgess@usgs.gov","orcid":"https://orcid.org/0000-0002-4238-3797","contributorId":200371,"corporation":false,"usgs":true,"family":"Burgess","given":"Seth","email":"sburgess@usgs.gov","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":869391,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70256611,"text":"70256611 - 2023 - A review of lethal thermal tolerance among freshwater mussels (Bivalvia: Unionida) within the North American faunal region","interactions":[],"lastModifiedDate":"2024-08-07T11:10:46.111024","indexId":"70256611","displayToPublicDate":"2023-03-01T06:08:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5056,"text":"Environmental Reviews","active":true,"publicationSubtype":{"id":10}},"title":"A review of lethal thermal tolerance among freshwater mussels (Bivalvia: Unionida) within the North American faunal region","docAbstract":"Floods are one of the most frequent and expensive natural disasters that occur across the United States. Rapid, high-water events that occur in local areas—flash floods—are especially difficult for emergency managers to predict and provide advance warning to the public, and insufficient data can hamper postflood recovery efforts. Central Texas is hilly, and it is known as a “flash flood alley” because of its high-intensity rains, shallow soils, and steep terrain, all of which combined can result in loss of life and property damage. For example, the flash flood event during July 2002 claimed 12 lives in central Texas, including 1 in the town of Utopia, which is on the east bank of the Sabinal River in a flash-flood-prone area along the Balcones Escarpment. During the flood event, the peak discharge recorded on July 5, 2002, at U.S. Geological Survey (USGS) streamgage 08198000 Sabinal River near Sabinal, Tex. (hereinafter referred to as the “Sabinal gage”), was 108,000 cubic feet per second (corresponding to a stream stage [also called gage height] of 33.74 feet). To put the 2002 flood into context, during a typical year the median daily discharge in the Sabinal River at the Sabinal gage is only about 23 cubic feet per second. In 2021, the USGS, in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board, developed a flood warning toolset for the Sabinal River near Utopia. This study builds on earlier USGS flood work on the Medina River in Bandera County. The newly developed toolset consists of a newly installed USGS streamgage to collect continuous stream stage data (streamgage 08197970 Sabinal River at Utopia, Tex.; hereinafter referred to as the “Utopia gage”) 13 miles upstream from the Sabinal gage, a hydraulic model developed for the Sabinal River near Utopia, and an online library of digital flood-inundation maps referenced to the stream stage at the Utopia gage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233001","issn":"2327-6932 (online)","collaboration":"Prepared in cooperation with the Bandera County River Authority and Groundwater District and the Texas Water Development Board","usgsCitation":"Choi, N., 2023, Flood warning toolset for the Sabinal River near Utopia, Texas: U.S. Geological Survey Fact Sheet 2023–3001 (ver. 2.0, September 2023), 4 p., https://doi.org/10.3133/fs20233001.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-136337","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":421082,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/fs/2023/3001/versionHist.txt","linkFileType":{"id":2,"text":"txt"}},{"id":499562,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114429.htm","linkFileType":{"id":5,"text":"html"}},{"id":421081,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/sir20235001","text":"Scientific Investigations Report 2023–5001","description":"SIR 2023-5001","linkHelpText":"- Flood-Inundation Maps Created Using a Synthetic Rating Curve for a 10-Mile Reach of the Sabinal River and a 7-Mile Reach of the West Sabinal River Near Utopia, Texas, 2021"},{"id":421080,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3001/fs20233001.pdf","text":"Report","size":"1.79 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023-3001"},{"id":414773,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3001/coverthb.jpg"}],"country":"United States","state":"Texas","city":"Utopia","otherGeospatial":"Sabinal River, West Sabinal River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -99.6333,\n 29.75\n ],\n [\n -99.6333,\n 29.6\n ],\n [\n -99.5,\n 29.6\n ],\n [\n -99.5,\n 29.75\n ],\n [\n -99.6333,\n 29.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: February 2023; Version 2.0: September 2023","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Overview
Creation of Flood Warning Toolset
References Cited
Coastal resources are increasingly affected by erosion, extreme weather events, sea level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying effects on coastal landscapes because of the compounding of geologic, oceanographic, ecologic, and socioeconomic factors that exist at a given location. An assessment framework is introduced in this report that synthesizes existing datasets that cover the variability of the landscape, and hazards that may act on the landscape, to evaluate the likelihood of coastal change along the U.S. coastline on a decadal scale. The pilot study that aided in the development of the framework was run in the northeastern United States (from Maine to Virginia) and consists of datasets derived from a variety of Federal, State, and local sources.
First, a decision-tree-based dataset was built that describes the resistance or integrity of the coastal landscape (called the fabric dataset for the purposes of this report) and includes land cover, elevation, slope, long-term (more than 50 years) shoreline change, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (for example, flooding, wave power, and probability of storm overwash) and persistent or perpetual hazards (for example, relative sea level rise rate, short-term [about 30-year] shoreline erosion rate, and storm recurrence interval). The fabric dataset was then merged with the coastal hazards databases, and a model training dataset made up of hundreds of polygons was generated from these combined data to support machine learning.
The pilot study resulted in location-specific, 10-meter-resolution data classified into five raster datasets that include intrinsic characteristics of the coast used to determine the resistance of the landscape to change, the persistent and event hazards that act on the coast, the machine learning output (coastal change likelihood) based on the cumulative effects of the fabric and hazards datasets, and an estimate of the hazard type (event or persistent) that is the most likely to influence coastal change. Final outcomes are intended to be used as a first-order planning tool to determine which areas of the coast are more likely to change in response to future potential coastal hazards and to examine elements and drivers that make change in a location more likely.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1169","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Pendleton, E.A., Lentz, E.E., Sterne, T.K., and Henderson, R.E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: U.S. Geological Survey Data Report 1169, 56 p., https://doi.org/10.3133/dr1169.","productDescription":"Report: viii, 56 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141482","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413447,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96A2Q5X","text":"USGS data release","linkHelpText":"Coastal change likelihood in the U.S. northeast region—Maine to Virginia"},{"id":413449,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1169/images/"},{"id":499552,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114426.htm","linkFileType":{"id":5,"text":"html"}},{"id":413448,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.XML"},{"id":413446,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1169/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1169"},{"id":413445,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1169"},{"id":413444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1169/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.69060371478722,\n 36.642989353191695\n ],\n [\n -75.63687331767586,\n 36.61725767144067\n ],\n [\n -73.59796884586575,\n 40.01648840470193\n ],\n [\n -70.80274185094413,\n 41.01068598755887\n ],\n [\n -69.57806852142825,\n 41.099580604299234\n ],\n [\n -69.90622192003855,\n 42.11383028198776\n ],\n [\n -70.57455282622757,\n 43.02711001288796\n ],\n [\n -67.01522918769722,\n 44.713652472389384\n ],\n [\n -67.51934985246183,\n 45.183064414796405\n ],\n [\n -71.15470019361379,\n 43.73883250370727\n ],\n [\n -71.3079883194934,\n 41.86654548530123\n ],\n [\n -73.98393265041173,\n 41.230559521628294\n ],\n [\n -76.3263141198747,\n 39.710722533017474\n ],\n [\n -77.25368632549163,\n 38.78457338969852\n ],\n [\n -76.72175177570705,\n 36.72418776523644\n ],\n [\n -75.97037108334082,\n 36.81887134243617\n ],\n [\n -75.94893824149692,\n 36.817996199366135\n ],\n [\n -76.69060371478722,\n 36.642989353191695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
The sensitivity of wildland plants to temperature can be directly measured using experimental manipulations of temperature in situ. We show that soil surface temperature and plant density (per square meter) have a significant impact on the germination, growth, and phenology of Bromus tectorum L., cheatgrass, a short-statured invasive winter-annual grass, and assess a new experimental temperature manipulation method: the application of black and white gravel to warm and cool the soil surface.
We monitored height, seed production, and phenological responses of cheatgrass, seeded into colored gravel at low and high densities at two sites in the western USA: Boise, ID and Cheyenne, WY. Soil surface temperature and volumetric water content were measured to assess treatment effects on soil surface microclimate.
Black gravel increased mean temperatures of the surface soil by 1.6 and 2.6 °C compared to white gravel in Cheyenne and Boise, respectively, causing 21–24 more days with soil temperatures > 0 °C, earlier cheatgrass germination, and up to 2.8-fold increases in cheatgrass height. Higher seeding density of cheatgrass led to 1.4-fold taller plants on black gravel plots at both sites, but not white gravel at the Boise site, indicating a possible thermal benefit or reduction of water demand due to plant clustering in warmer treatments.
Manipulating soil-surface albedo altered the soil microclimate and thus growth and phenology of cheatgrass, whose life history and growth form confer a strong dependency on soil-surface conditions.
","language":"English","publisher":"Springer","doi":"10.1007/s11104-023-05929-4","usgsCitation":"Maxwell, T.M., Germino, M., Romero, S., Porensky, L., Blumenthal, D.M., Brown, C., and Adler, P.B., 2023, Experimental manipulation of soil-surface albedo alters phenology and growth of Bromus tectorum (cheatgrass): Plant and Soil, v. 487, p. 325-339, https://doi.org/10.1007/s11104-023-05929-4.","productDescription":"15 p.","startPage":"325","endPage":"339","ipdsId":"IP-141275","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":413664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","city":"Boise, Cheyenne","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.35917072312003,\n 43.73889472078221\n ],\n [\n -116.35917072312003,\n 43.464686153948634\n ],\n [\n -116.05823287588329,\n 43.464686153948634\n ],\n [\n -116.05823287588329,\n 43.73889472078221\n ],\n [\n -116.35917072312003,\n 43.73889472078221\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.67448200616478,\n 41.2486838873686\n ],\n [\n -104.94665093511199,\n 41.2486838873686\n ],\n [\n -104.94665093511199,\n 41.04211139242014\n ],\n [\n -104.67448200616478,\n 41.04211139242014\n ],\n [\n -104.67448200616478,\n 41.2486838873686\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"487","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Maxwell, Toby M. 0000-0001-5171-0705","orcid":"https://orcid.org/0000-0001-5171-0705","contributorId":302845,"corporation":false,"usgs":false,"family":"Maxwell","given":"Toby","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":865613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":865614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romero, Seth","contributorId":302846,"corporation":false,"usgs":false,"family":"Romero","given":"Seth","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":865615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":865616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blumenthal, Dana M.","contributorId":203896,"corporation":false,"usgs":false,"family":"Blumenthal","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":36745,"text":"USDA-ARS Rangeland Resources Research Unit","active":true,"usgs":false}],"preferred":false,"id":865617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Cynthia S.","contributorId":302847,"corporation":false,"usgs":false,"family":"Brown","given":"Cynthia S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":865618,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Adler, Peter B.","contributorId":64789,"corporation":false,"usgs":false,"family":"Adler","given":"Peter","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":865619,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70240728,"text":"sir20225125 - 2023 - Modeling flow and water quality in reservoir and river reaches of the Mahoning River Basin, Ohio","interactions":[],"lastModifiedDate":"2026-02-23T20:55:47.151064","indexId":"sir20225125","displayToPublicDate":"2023-02-27T16:09:05","publicationYear":"2023","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":"2022-5125","displayTitle":"Modeling Flow and Water Quality in Reservoir and River Reaches of the Mahoning River Basin, Ohio","title":"Modeling flow and water quality in reservoir and river reaches of the Mahoning River Basin, Ohio","docAbstract":"The U.S. Army Corps of Engineers (USACE) is considering changes to the management of water surface elevation in four lakes in the Mahoning River Basin. These changes would affect the timing and amounts of water released to the Mahoning River and could affect the water quality of those releases. To provide information on possible water-quality effects from these operational changes, flow and water-quality models were constructed for Berlin Lake, Lake Milton, Michael J Kirwan Reservoir, Mosquito Creek Lake, Mosquito Creek, and the Mahoning River from the dams downstream to Lowellville, Ohio.
The models were calibrated for two calendar years each, with model years selected depending on the availability of water-quality data. Models were developed with CE-QUAL-W2 version 4.2 (Wells, S.A., 2020, CE-QUAL-W2—A two-dimensional, laterally averaged, hydrodynamic and water quality model [version 4.2]: Portland State University, variously paged), a two-dimensional, laterally averaged hydrodynamic and water-quality model. Modeled constituents included flow, velocity, ice cover, water temperature, total dissolved solids (TDS), sulfate, chloride, inorganic suspended sediment, nitrate, ammonia, total Kjeldahl nitrogen, orthophosphate, total phosphorus, dissolved and particulate organic matter, algae, and dissolved oxygen. Iron was included for the lake models, but not the river.
A whole-basin model, with the four lake models and river model, was used to run model scenarios to examine the effects of altered lake water surface elevations on flow and water quality in the lakes, the lake outflows, and the Mahoning River. The initial whole-basin model, with calendar year 2013 hydrology and measured or typical water quality, was designated as scenario 0. Mahoning River flows for calendar year 2013 were close to a 20-year median flow. Four additional scenarios were constructed based on reservoir operations model (RES-SIM) model water surface elevations for the four lakes as provided by USACE. Scenario 1 was the RES-SIM base case, scenario 2 kept Berlin Lake water surface elevations higher in summer, scenario 3 allowed 25 percent of summer flood storage to extend the guide curve, and scenario 4 allowed more flexibility in lake management by removing any downstream Mahoning River minimum flow requirements. The Mahoning River model was not changed in any scenarios but received altered flows from the lakes. Significant findings from this study include the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Graphite Creek is an unusual flake graphite deposit located on the Seward Peninsula, Alaska, USA. We present field observations, uranium-lead (U–Pb) monazite and titanite geochronology, carbon (C) and sulfur (S) stable isotope geochemistry, and graphite Raman spectroscopy data from this deposit that support a new model of flake graphite ore genesis in high-grade metamorphic environments. The Graphite Creek deposit is within the second sillimanite metamorphic zone of the Kigluaik Mountains gneiss dome. Flake graphite, hosted in sillimanite-gneiss and quartz-biotite paragneiss, occurs as disseminations and in sets of very high grade (up to 50 wt.% graphite), semi-massive to massive graphite lenses 0.2 to 1 m wide containing quartz, sillimanite, inclusions of garnet porphyroblasts, K-feldspar, and tourmaline. Restitic garnet, sillimanite, graphite, and biotite accumulations indicate a high degree of anatexis and melt loss. Strong yttrium depletion in monazite, high europium ratios (Eu/Eu*), and excursions of high strontium and thorium concentrations are consistent with biotite dehydration melting. Monazite and titanite U–Pb ages record peak metamorphism from ~ 97 to 92 million years ago (Ma) and a retrograde event at ~ 85 Ma. Raman spectroscopy confirms the presence of carbonaceous material and highly ordered, crystalline graphite. Graphite δ13CVPDB values of − 30 to − 12‰ and pyrrhotite δ34SVCDT values of − 14 to 10‰ are consistent with derivation from organic carbon and sulfur in sedimentary rocks, respectively. These data collectively suggest that formation of massive graphite lenses occurred approximately synchronously with high-temperature metamorphism and anatexis of a highly carbonaceous pelitic protolith. Melt extraction and fluid release associated with anatexis were likely crucial for concentrating graphite. High-temperature, graphitic migmatite sequences within high-strain shear zones may be favorable for the occurrence of high-grade flake graphite deposits.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-023-01161-3","usgsCitation":"Case, G.N., Karl, S.M., Regan, S., Johnson, C.A., Ellison, E.T., Caine, J., Holm-Denoma, C., Pianowski, L., and Benowitz, J.A., 2023, Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA: Mineralium Deposita, v. 58, p. 939-962, https://doi.org/10.1007/s00126-023-01161-3.","productDescription":"24 p.","startPage":"939","endPage":"962","ipdsId":"IP-135671","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":444354,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00126-023-01161-3","text":"Publisher Index Page"},{"id":435431,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J50EKX","text":"USGS data release","linkHelpText":"Data for Uranium-Lead Geochronology, Carbon and Sulfur Stable Isotopes, and Raman Spectroscopy from Graphite Creek, Alaska"},{"id":413658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Graphite Creek graphite deposit, Seward Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.1974510679629,\n 65.1\n ],\n [\n -166.1974510679629,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 65.1\n ],\n [\n -166.1974510679629,\n 65.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","noUsgsAuthors":false,"publicationDate":"2023-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":865622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":865623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regan, Sean P.","contributorId":219815,"corporation":false,"usgs":false,"family":"Regan","given":"Sean P.","affiliations":[{"id":13599,"text":"University of Alaska - Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellison, Eric T 0000-0002-6761-1397","orcid":"https://orcid.org/0000-0002-6761-1397","contributorId":302853,"corporation":false,"usgs":false,"family":"Ellison","given":"Eric","email":"","middleInitial":"T","affiliations":[{"id":52978,"text":"Department of Geological Sciences, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":865626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":865627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pianowski, Laura 0000-0002-5346-8251","orcid":"https://orcid.org/0000-0002-5346-8251","contributorId":218817,"corporation":false,"usgs":true,"family":"Pianowski","given":"Laura","email":"","affiliations":[],"preferred":true,"id":865629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benowitz, Jeff A. 0000-0003-2294-9172","orcid":"https://orcid.org/0000-0003-2294-9172","contributorId":229570,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":41671,"text":"Geophysical Institute and Geochronology Laboratory, University of Alaska–Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70247443,"text":"70247443 - 2023 - Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey","interactions":[],"lastModifiedDate":"2023-08-08T12:23:17.972689","indexId":"70247443","displayToPublicDate":"2023-02-27T07:19:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey","docAbstract":"A solid formulation of the pesticide TFM (4-nitro-3-(trifluoromethyl)-phenol) was developed in the 1980s for application in small tributaries during treatments to control invasive sea lamprey (Petromyzon marinus Linnaeus, 1758). Several initial inert ingredients were discontinued and substituted, culminating with an interim formulation that unacceptably softens and rapidly decays in warm conditions. A new TFM bar formulation was developed to resolve poor thermal stability and it was registered with the U.S. Environmental Protection Agency and Health Canada Pesticide Management Regulatory Agency in 2020. Laboratory studies compared the thermostability and dissolution (i.e., TFM release) of the interim and new formulation of TFM bars that were held at 20 C or 45 C for 24 hours prior to evaluation. Field tests compared the dissolution of the interim and new formulation of TFM bars when applied in three small tributaries in Michigan. Laboratory tests show that the new formulation bars remain usable when held at 45 C for 24 hours; whereas, the interim formulation bars partially liquify and are not usable. Field tests indicate the new formulation bars have superior characteristics including a near consistent release of TFM for 1013 hours when applied in waters with a velocity of < 0.06 m/sec. A near consistent release of TFM was observed for a maximum of about 6 hours in one field application of the interim formulation bars. Water temperature and water velocity influenced both formulations; however, the greatest effects were observed with interim formulation bars where higher initial TFM concentrations were followed by precipitous TFM concentration decreases in tributaries with the highest water temperature or velocity. Field treatment applications will provide data for refining application parameters such as the number of bars required per unit discharge at various water temperatures and the acceptable water velocity range for applications.","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2023.14.2.11","usgsCitation":"Luoma, J.A., Schueller, J., Schloesser, N., Johnson, T., and Kirkeeng, C., 2023, Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey: Management of Biological Invasions, v. 14, no. 2, p. 347-362, https://doi.org/10.3391/mbi.2023.14.2.11.","productDescription":"16 p.","startPage":"347","endPage":"362","ipdsId":"IP-139416","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":444356,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.2.11","text":"Publisher Index Page"},{"id":435432,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P910SHBL","text":"USGS data release","linkHelpText":"Data Release for Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey"},{"id":419593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.06,\n 44.18\n ],\n [\n -84.06,\n 44.13\n ],\n [\n -84.00,\n 44.13\n ],\n [\n -84.00,\n 44.18\n ],\n [\n -84.06,\n 44.18\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Todd 0000-0003-2152-8528","orcid":"https://orcid.org/0000-0003-2152-8528","contributorId":261519,"corporation":false,"usgs":true,"family":"Johnson","given":"Todd","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirkeeng, Courtney A. 0000-0002-7141-1216","orcid":"https://orcid.org/0000-0002-7141-1216","contributorId":237026,"corporation":false,"usgs":true,"family":"Kirkeeng","given":"Courtney","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879660,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262037,"text":"70262037 - 2023 - A big data–model integration approach for predicting epizootics and population recovery in a keystone species","interactions":[],"lastModifiedDate":"2025-01-10T14:56:44.15352","indexId":"70262037","displayToPublicDate":"2023-02-27T00:00:00","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"A big data–model integration approach for predicting epizootics and population recovery in a keystone species","docAbstract":"Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2827","usgsCitation":"Barrile, G., Augustine, D.J., Porensky, L., Duchardt, C., Shoemaker, K., Hartway, C., Derner, J.D., Hunter, E.A., and Davidson, A.D., 2023, A big data–model integration approach for predicting epizootics and population recovery in a keystone species: Ecological Applications, v. 33, no. 4, e2827, 23 p., https://doi.org/10.1002/eap.2827.","productDescription":"e2827, 23 p.","ipdsId":"IP-142779","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2827","text":"Publisher Index Page"},{"id":465980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.72151393816772,\n 49.06842429079816\n ],\n [\n -106.74407678276975,\n 44.935250564946244\n ],\n [\n -105.02441274593369,\n 40.78587530801761\n ],\n [\n -105.18708495048385,\n 35.23907808129579\n ],\n [\n -110.74352872625728,\n 31.51616164533567\n ],\n [\n -101.12343743446304,\n 28.915776651344572\n ],\n [\n -100.73547916940133,\n 38.76206177537206\n ],\n [\n -100.82267130820097,\n 44.32291601997803\n ],\n [\n -101.89899715272732,\n 47.61230649548153\n ],\n [\n -111.72151393816772,\n 49.06842429079816\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":922769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, David J.","contributorId":189957,"corporation":false,"usgs":false,"family":"Augustine","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":922770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":922771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duchardt, Courtney J.","contributorId":347959,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney J.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":922772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":922773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartway, Cynthia R.","contributorId":347961,"corporation":false,"usgs":false,"family":"Hartway","given":"Cynthia R.","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":922774,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Derner, Justin D.","contributorId":195928,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":922775,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922776,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davidson, Ana D. 0000-0003-4701-5923","orcid":"https://orcid.org/0000-0003-4701-5923","contributorId":304176,"corporation":false,"usgs":false,"family":"Davidson","given":"Ana","email":"","middleInitial":"D.","affiliations":[{"id":65991,"text":"CNHP","active":true,"usgs":false}],"preferred":false,"id":922777,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70255281,"text":"70255281 - 2023 - Can angler-assisted broodstock collection programs improve harvest rates of hatchery-produced steelhead?","interactions":[],"lastModifiedDate":"2024-06-14T12:18:19.195278","indexId":"70255281","displayToPublicDate":"2023-02-25T07:15:37","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1528,"text":"Environmental Biology of Fishes","active":true,"publicationSubtype":{"id":10}},"title":"Can angler-assisted broodstock collection programs improve harvest rates of hatchery-produced steelhead?","docAbstract":"Fish that exhibit high foraging activity or bold behavior can be particularly vulnerable to angling. If these traits are heritable, selection through harvest can drive phenotypic change, eventually rendering a target population less vulnerable to angling and consequently impacting the quality of the fishery. In this study, we used parental-based tags to investigate whether vulnerability to angling might be heritable in steelhead trout (Oncorhynchus mykiss) spawned at a hatchery in western Oregon, USA. We found modest evidence to support the hypothesis that vulnerability to angling is a heritable trait in steelhead. However, our data unexpectedly revealed that steelhead collected with in-river traps produced nearly twice as many adult offspring as steelhead collected by anglers. This difference in adult-to-adult production is explained in part through lower egg-to-fry survival of steelhead produced with angler-caught broodstock, possibly related to collection stress and greater time in captivity experienced by angler-caught broodstock. Our findings suggest that managers could improve broodstock fitness and program efficiencies by preferentially spawning fish collected with traps, and limiting use of broodstock collected by anglers. Additional research is needed to identify mechanisms contributing to higher juvenile mortality of steelhead produced with angler-caught broodstock.
To assess the status and trends of conditions of surface waters throughout Mississippi, the U.S. Geological Survey, in cooperation with the Mississippi Department of Environmental Quality (MDEQ), summarized concentrations and estimated loads, yields, trends, and spatial and temporal patterns of total nitrogen (TN) and total phosphorus (TP) at 20 stream sites in MDEQ’s ambient water-quality monitoring network and 2 stream sites in the U.S. Geological Survey’s National Water-Quality Assessment Project’s monitoring network.
Comparison of streamflow at the time of water-quality sample collection to flow-duration curves for each site showed that samples were relatively evenly spread over a wide range of flows, indicating that load estimations were representative of a wide range of flows. Relation of streamflow to concentrations of TN and TP varied among sites and land use. Sites with high agriculture land use in the drainage basin tended to have a positive correlation between streamflow and concentration, suggesting influence of event-driven nonpoint-source runoff. Sites near urban (developed) areas tended to have a negative correlation between streamflow and concentration, suggesting chronic point-source influences during low-flow conditions. Sites with high forest land use and lower agriculture and urban (developed) land use showed little to no association between streamflow and concentration.
Seasonal distributions of concentrations of TN and TP also corresponded closely with variations in land use. Sites near urban (developed) land had the highest concentrations in late summer and fall, sites with a high percentage of agricultural land had the highest concentrations in the spring, and sites that were primarily forested or with little developed land did not exhibit substantial changes in concentration across seasons.
Eight sites had statistical likelihoods for upward trends of TN loads, and seven sites had statistical likelihoods for downward trends. Trends in TN loads at six sites were considered “about as likely as not,” meaning that a site has an equal chance of having an upward or downward trend. Trend results of mean annual flow-normalized loads of TP for the period of analysis (2008–18) showed that 16 sites had upward trends, 3 sites had downward trends, and 2 sites were considered “about as likely as not.”
Results from our study were compared to results from existing regional models to assess accuracy of predictions at a local scale. Comparisons of yields predicted from 2012 regional-scale SPAtially Referenced Regressions on Watershed attributes (SPARROW) to results from this study showed the 2012 SPARROW-predicted estimates varied in consistency with results from this study. The 2012 SPARROW-prediction model underestimated TN yields, more often and by a slightly larger degree, more than it overestimated TN yields. The 2012 SPARROW-predicted model tended to underestimate yields at study sites with higher yields. All four sites in the predominantly agricultural area of northwest Mississippi, locally known as the Mississippi Delta, were underestimated by 2012 SPARROW. For TP, yield comparisons at sites with lower yields were consistent, yields at sites with midrange yields tended to be overestimated by SPARROW, and yields at sites with high yields tended to be underestimated by SPARROW. TP yields at four sites in the Mississippi Delta were underestimated by the 2012 SPARROW-predicted model.
Results of select sites from our study were also compared to other published load estimates from an earlier time period to evaluate possible trends. Comparison of TN yields at four sites and TP yields at three sites from the study-derived estimates to estimates made from data spanning 1993–2004 showed decreasing TN yields at all four sites and decreasing TP yields at two of three sites, with increasing yields of TP at the Yazoo River lower site. Also, a third comparison of the TN and TP yields of the Yazoo River lower site of this study to estimates made from data spanning 1996–97 showed decreasing TN yields but similar TP yields. This suggests that TN yields may have decreased over the last 20–30 years, but TP yields remain constant or are increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235003","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Hicks, M.B., Crain, A.S., and Segrest, N.G., 2023, Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18: U.S. Geological Survey Scientific Investigations Report 2023–5003, 77 p., https://doi.org/10.3133/sir20235003.","productDescription":"Report: x, 77 p.; Data Release; Dataset","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-130707","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VV2U70","text":"USGS data release","linkHelpText":"Datasets of streamflow, nutrient concentrations, loads 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211