{"pageNumber":"3","pageRowStart":"50","pageSize":"25","recordCount":676,"records":[{"id":70259657,"text":"70259657 - 2023 - Ecological associations of non-native ungulates on the Hawaiian Island of Lāna‘i","interactions":[],"lastModifiedDate":"2024-10-19T13:24:47.508554","indexId":"70259657","displayToPublicDate":"2023-10-19T08:23:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Ecological associations of non-native ungulates on the Hawaiian Island of Lāna‘i","docAbstract":"

Sustained-yield hunting of introduced ungulates in the Hawaiian Islands often conflicts with the conservation of native species, but there is little reliable data to guide effective management. European mouflon sheep (Ovis musimon; mouflon) and axis deer (Axis axis; deer) were introduced on the island of Lāna‘i to provide additional hunting opportunities. Managers will require better information regarding the ecological associations of introduced ungulate species, relative to the habitats occupied, to resolve longstanding conflicts between native species conservation and sustained-yield hunting on islands. To address this information need, we modeled sheep and deer ecological associations, habitat-use, and suitability using data obtained from an intensive aerial survey completed in 2013 and temporally matching environmental data. In habitat suitability models evaluated by Receiver Operating Characteristic (ROC) metrics, predictor importance in a generalized linear model (GLM) of deer decreased in the following order: afternoon cloud cover, topographic slope, mean annual precipitation (MAP), elevation, normalized difference vegetation index (NDVI), and bare soil index. In a random GLM model of mouflon, predictor importance decreased in the following order: afternoon cloud cover, deer habitat suitability, NDVI, bare soil index, topographic slope, elevation, and MAP. Mouflon were restricted to lower elevation arid slopes, whereas deer were more broadly distributed throughout upland environments of the island. The presence of deer was also an important predictor for mouflon distribution, although mouflon was not an important predictor of deer, suggesting asymmetrical competition. Removal of the more abundant deer population may lead to an increase in abundance and distribution of mouflon without containment. This work represents the first habitat suitability analysis for all nonnative ungulates on any entire Hawaiian island. Our results are applicable to other islands where conflicts may arise with introduced ungulates, sustained-yield hunting, and native species conservation.


","language":"English","publisher":"Berryman Insititute","doi":"10.26077/be03-b519","usgsCitation":"Hess, S.C., Brinck, K., Leopold, C.R., Muise, J., and Sprague, J., 2023, Ecological associations of non-native ungulates on the Hawaiian Island of Lāna‘i: Human-Wildlife Interactions, v. 17, no. 2, 14 p., https://doi.org/10.26077/be03-b519.","productDescription":"14 p.","ipdsId":"IP-134608","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":463043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Lāna‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.25852174158157,\n 21.072831141997597\n ],\n [\n -157.25852174158157,\n 20.56423494187817\n ],\n [\n -156.5879617183619,\n 20.56423494187817\n ],\n [\n -156.5879617183619,\n 21.072831141997597\n ],\n [\n -157.25852174158157,\n 21.072831141997597\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hess, Steven C.","contributorId":176679,"corporation":false,"usgs":false,"family":"Hess","given":"Steven","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":916161,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":3847,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","email":"kbrinck@usgs.gov","affiliations":[],"preferred":false,"id":916162,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Leopold, Christina R","contributorId":345275,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","email":"","middleInitial":"R","affiliations":[{"id":82536,"text":"0000-0003-0499-3196","active":true,"usgs":false}],"preferred":false,"id":916163,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Muise, Jacob","contributorId":240997,"corporation":false,"usgs":false,"family":"Muise","given":"Jacob","email":"","affiliations":[{"id":48185,"text":"KIA Hawaii","active":true,"usgs":false}],"preferred":false,"id":916164,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sprague, Jonathan","contributorId":240998,"corporation":false,"usgs":false,"family":"Sprague","given":"Jonathan","email":"","affiliations":[{"id":48186,"text":"Pulama Lana‘i","active":true,"usgs":false}],"preferred":false,"id":916165,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256523,"text":"70256523 - 2023 - An agent-based model to quantify energetics, movement and habitat selection of mid-continent mallards in the Mississippi Alluvial Valley","interactions":[],"lastModifiedDate":"2024-08-07T12:17:58.180684","indexId":"70256523","displayToPublicDate":"2023-09-06T07:15:27","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"An agent-based model to quantify energetics, movement and habitat selection of mid-continent mallards in the Mississippi Alluvial Valley","docAbstract":"

The dynamics of wintering waterfowl populations at the landscape scale are the result of complex interactions of environmental, behavioral and energetic drivers. Agent-based models provide a method to directly link these factors in a spatially explicit framework and allow the emergence of patterns from the aggregation of individual agent actions. We adapted the Spatially-explicit Waterbird Agent-based Model Program (SWAMP), originally developed for waterfowl in central California, to simulate a basin-scale population of mallards (A. platyrhynchos) wintering in the Mississippi Alluvial Valley over a four-month period (November–February). Simulated agents move within the landscape, foraging on areas made available based on a probabilistic inundation status, and converting food resources to endogenous energy. The model uses a high-resolution map of eastern Arkansas waterfowl habitats and incorporates a hierarchical habitat selection system that enables mallards to relocate at increasing scales in response to changing food availability. We validated the performance of modeled mallard body condition and behavioral metrics under a range of environmental conditions against expected outcomes derived from empirical data and found that the simulation produced realistic representations of changes in flight distances, energy expenditure, lipid storage, and foraging habitat use in response to depleting food resources over time. We discuss the model's applicability as a tool to quantify waterfowl response to a range of environmental conditions and to evaluate scenarios of landscape composition and configuration in the context of waterfowl population management.


","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2023.110488","usgsCitation":"Weller, F.G., Webb, E.B., Fogenburg, S., Beatty, W., Kesler, D., Blenk, R.H., Ringelman, K.M., Miller, M.L., and Eadie, J., 2023, An agent-based model to quantify energetics, movement and habitat selection of mid-continent mallards in the Mississippi Alluvial Valley: Ecological Modelling, v. 485, 110488, https://doi.org/10.1016/j.ecolmodel.2023.110488.","productDescription":"110488","ipdsId":"IP-151506","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":442176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.lsu.edu/agrnr_pubs/679","text":"Publisher Index Page"},{"id":432333,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"485","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weller, Florian G.","contributorId":341013,"corporation":false,"usgs":false,"family":"Weller","given":"Florian","email":"","middleInitial":"G.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":907803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, Elisabeth B. 0000-0003-3851-6056 ewebb@usgs.gov","orcid":"https://orcid.org/0000-0003-3851-6056","contributorId":3981,"corporation":false,"usgs":true,"family":"Webb","given":"Elisabeth","email":"ewebb@usgs.gov","middleInitial":"B.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fogenburg, Sean","contributorId":341014,"corporation":false,"usgs":false,"family":"Fogenburg","given":"Sean","email":"","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":907805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beatty, William S. 0000-0003-0013-3113","orcid":"https://orcid.org/0000-0003-0013-3113","contributorId":224795,"corporation":false,"usgs":true,"family":"Beatty","given":"William S.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":907806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kesler, Dylan","contributorId":341015,"corporation":false,"usgs":false,"family":"Kesler","given":"Dylan","affiliations":[{"id":37290,"text":"The Institute for Bird Populations","active":true,"usgs":false}],"preferred":false,"id":907807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blenk, Robert H.","contributorId":341016,"corporation":false,"usgs":false,"family":"Blenk","given":"Robert","email":"","middleInitial":"H.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":907808,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ringelman, Kevin M.","contributorId":341017,"corporation":false,"usgs":false,"family":"Ringelman","given":"Kevin","email":"","middleInitial":"M.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":907809,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Miller, Matt L.","contributorId":341018,"corporation":false,"usgs":false,"family":"Miller","given":"Matt","email":"","middleInitial":"L.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":907810,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Eadie, John M.","contributorId":341020,"corporation":false,"usgs":false,"family":"Eadie","given":"John M.","affiliations":[{"id":7214,"text":"University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":907811,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70256607,"text":"70256607 - 2023 - Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub","interactions":[],"lastModifiedDate":"2024-08-26T15:28:07.371737","indexId":"70256607","displayToPublicDate":"2023-08-11T10:19:49","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub","docAbstract":"

Objective

A central challenge in applied ecology is understanding how organisms are spatially and temporally distributed and how management might be tailored to maintain or restore species distributions. The niche concept is central to understanding species distributions, but the diversity of niche definitions requires that multiple dimensions be considered. For example, the Grinnellian niche concept focuses on environmental conditions that allow species to persist, the Eltonian niche concept stresses the influence of biotic interactions, and the fundamental niche concept considers both abiotic and biotic environmental features to define spaces that organisms could occupy.

Methods

We combined abiotic (A), biotic (B), and movement (M) information (collectively, BAM model) to map the multidimensional niche of Prairie Chub Macrhybopsis australis, a regionally endemic freshwater fish currently under review for listing under the Endangered Species Act. We estimated A using remotely sensed environmental riverscape variables, B using the spatial distribution of a hybridization zone between Prairie Chub and Shoal Chub M. hyostoma, and M using data from a mark–recapture study.

Result

The BAM model estimated the spatial extent of multiple niches, including the Grinnellian (A; extent = 944 km of river), Eltonian (B; 2974 km), and fundamental niche (overlap of A + B; 645 km) niches. When A, B, and M components were combined, the estimated extent of the Prairie Chub niche was 645 km.

Conclusion

Our work shows that the realized, multidimensional niche of Prairie Chub includes medium to large rivers with high habitat connectivity in the upper–middle Red River basin upstream of the distribution of Shoal Chub. The current Prairie Chub distribution could be maintained by preventing further habitat fragmentation and maintaining the environmental gradient separating Prairie Chub from Shoal Chub. Expansion of the species distribution may be possible through restoration of longitudinal fluvial connectivity.

","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10860","usgsCitation":"Steffensmeier, Z.D., Brewer, S.K., Wedgeworth, M., Starks, T.A., Rodger, A.W., Nguyen, E., and Perkin, J., 2023, Conservation at the nexus of niches: Multidimensional niche modeling to improve management of Prairie Chub: North American Journal of Fisheries Management, v. 43, no. 5, p. 1205-1224, https://doi.org/10.1002/nafm.10860.","productDescription":"20 p.","startPage":"1205","endPage":"1224","ipdsId":"IP-142909","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433158,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-08-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Steffensmeier, Zachary D.","contributorId":341344,"corporation":false,"usgs":false,"family":"Steffensmeier","given":"Zachary","email":"","middleInitial":"D.","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brewer, Shannon K. 0000-0002-1537-3921 skbrewer@usgs.gov","orcid":"https://orcid.org/0000-0002-1537-3921","contributorId":2252,"corporation":false,"usgs":true,"family":"Brewer","given":"Shannon","email":"skbrewer@usgs.gov","middleInitial":"K.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":908271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wedgeworth, Maeghen","contributorId":341345,"corporation":false,"usgs":false,"family":"Wedgeworth","given":"Maeghen","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Starks, Trevor A.","contributorId":145640,"corporation":false,"usgs":false,"family":"Starks","given":"Trevor","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":908273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rodger, Anthony W.","contributorId":302586,"corporation":false,"usgs":false,"family":"Rodger","given":"Anthony","email":"","middleInitial":"W.","affiliations":[{"id":27443,"text":"Oklahoma Department of Wildlife Conservation","active":true,"usgs":false}],"preferred":false,"id":908274,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Nguyen, Erin","contributorId":341346,"corporation":false,"usgs":false,"family":"Nguyen","given":"Erin","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":908275,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Perkin, Joshuah S.","contributorId":238286,"corporation":false,"usgs":false,"family":"Perkin","given":"Joshuah S.","affiliations":[{"id":47708,"text":"Department of Wildlife and Fisheries Sciences, Texas A&M University, College Station, TX","active":true,"usgs":false}],"preferred":false,"id":908276,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70249411,"text":"70249411 - 2023 - The blue carbon reservoirs from Maine to Long Island, NY","interactions":[],"lastModifiedDate":"2023-10-10T15:33:18.522773","indexId":"70249411","displayToPublicDate":"2023-08-08T10:23:52","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"The blue carbon reservoirs from Maine to Long Island, NY","docAbstract":"

In response to the New England Governor and Eastern Canadian Premier 2017 Climate Change Action Plan recommendation to “manage blue carbon resources to preserve and enhance their existing carbon reservoirs,” the U.S. Environmental Protection Agency (EPA) convened a New England Blue Carbon Inventory Workgroup, comprised of a variety of federal, state, academic, and non-profit organizations to develop an inventory of blue carbon stocks from Maine to Long Island, New York. The Workgroup focused its inventory efforts on salt marshes and eelgrass meadows, leveraging existing habitat maps for geographic data. Existing data for soil organic carbon stocks were then used to calculate blue carbon stock estimates. For visual display purposes, sediment carbon heat maps were developed to highlight areas of greatest carbon accumulation. The habitat distribution and sediment carbon heat maps can be accessed on the Northeast Ocean Data Portal (www.northeastoceandata.org/eelgrass) which is a public source of expert-reviewed, interactive maps and data on the ocean ecosystem, economy, and culture of the northeastern United States and can be used to facilitate decision making by government agencies, tribal nations, businesses, non-governmental organizations (NGOs), academic institutions, and individuals. Based on available data and Workgroup calculations, the target geographic area has an estimated 218,222 acres of eelgrass meadows, salt marsh and saline Phragmites, which are estimated to provide a reservoir of 7,523,568 megagrams of blue carbon, or the equivalent to the annual carbon emissions from over 5,944,024 passenger vehicles. Due to data limitations, the carbon stock estimate represents a mere fraction of the actual quantity of accumulated carbon in these habitats. The findings from the Workgroup’s efforts and the resulting map products can help inform land and coastal management policies, fisheries management, and climate change mitigation practices. Further refinements and expansion of data are needed, including more detailed habitat maps, deeper soil core data for soil organic carbon content, and inclusion of more marine flora into calculations.

","language":"English","publisher":"Environmental Protection Agency","usgsCitation":"Colarusso, P., Libohova, Z., Shumchenia, E., Eagle, M.J., Christian, M., Vincent, R., and Johnson, B., 2023, The blue carbon reservoirs from Maine to Long Island, NY, 31 p.","productDescription":"31 p.","ipdsId":"IP-146585","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":421823,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421698,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.northeastoceandata.org/files/metadata/Themes/Habitat/EPABlueCarbonReport.pdf","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Connecticut, Maine, Massachusetts, New Hampshire, New York, Rhode Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.61511760727777,\n 45.22754478031186\n ],\n [\n -69.46772958832273,\n 44.45391256542621\n ],\n [\n -70.41808310369574,\n 44.0249641018155\n ],\n [\n -71.18235185260586,\n 43.43128866248264\n ],\n [\n -71.38130070335747,\n 41.926291445574776\n ],\n [\n -72.88727252479508,\n 41.59585766120409\n ],\n [\n -74.23639888538038,\n 41.298612606793824\n ],\n [\n -74.24300744809933,\n 40.29712569967327\n ],\n [\n -72.41592450378879,\n 40.62059607271837\n ],\n [\n -69.64618289257739,\n 41.250157632666884\n ],\n [\n -69.77860402969628,\n 42.24177812265074\n ],\n [\n -70.24692368777268,\n 42.525861412000836\n ],\n [\n -70.24992804020442,\n 43.441149255128096\n ],\n [\n -67.02233592375215,\n 44.65254677637256\n ],\n [\n -67.61511760727777,\n 45.22754478031186\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Colarusso, Philip D.","contributorId":218700,"corporation":false,"usgs":false,"family":"Colarusso","given":"Philip D.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":885521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Libohova, Zamir","contributorId":330648,"corporation":false,"usgs":false,"family":"Libohova","given":"Zamir","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shumchenia, Emily","contributorId":330649,"corporation":false,"usgs":false,"family":"Shumchenia","given":"Emily","email":"","affiliations":[{"id":78947,"text":"Northeast Regional Ocean Council","active":true,"usgs":false}],"preferred":false,"id":885523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eagle, Meagan J. 0000-0001-5072-2755 meagle@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":242890,"corporation":false,"usgs":true,"family":"Eagle","given":"Meagan","email":"meagle@usgs.gov","middleInitial":"J.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":885524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Christian, Megan","contributorId":330651,"corporation":false,"usgs":false,"family":"Christian","given":"Megan","email":"","affiliations":[{"id":63834,"text":"United States Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":885525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vincent, Robert","contributorId":330652,"corporation":false,"usgs":false,"family":"Vincent","given":"Robert","email":"","affiliations":[{"id":12444,"text":"Massachusetts Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":885526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Johnson, Beverly","contributorId":330653,"corporation":false,"usgs":false,"family":"Johnson","given":"Beverly","email":"","affiliations":[{"id":33413,"text":"Bates College","active":true,"usgs":false}],"preferred":false,"id":885527,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70247520,"text":"70247520 - 2023 - The transmission patterns of the endosymbiont Wolbachia within the Hawaiian Drosophilidae adaptive radiation","interactions":[],"lastModifiedDate":"2023-08-11T13:26:41.062961","indexId":"70247520","displayToPublicDate":"2023-07-27T07:04:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5837,"text":"Genes and Genomics","onlineIssn":"2092-9293","printIssn":"1976-9571","active":true,"publicationSubtype":{"id":10}},"displayTitle":"The transmission patterns of the endosymbiont Wolbachia within the Hawaiian Drosophilidae adaptive radiation","title":"The transmission patterns of the endosymbiont Wolbachia within the Hawaiian Drosophilidae adaptive radiation","docAbstract":"
The evolution of endosymbionts and their hosts can lead to highly dynamic interactions with varying fitness effects for both the endosymbiont and host species. Wolbachia, a ubiquitous endosymbiont of arthropods and nematodes, can have both beneficial and detrimental effects on host fitness. We documented the occurrence and patterns of transmission of Wolbachia within the Hawaiian Drosophilidae and examined the potential contributions of Wolbachia to the rapid diversification of their hosts. Screens for Wolbachia infections across a minimum of 140 species of Hawaiian Drosophila and Scaptomyza revealed species-level infections of 20.0%, and across all 399 samples, a general infection rate of 10.3%. Among the 44 Wolbachia strains we identified using a modified Wolbachia multi-locus strain typing scheme, 30 (68.18%) belonged to supergroup B, five (11.36%) belonged to supergroup A, and nine (20.45%) had alleles with conflicting supergroup assignments. Co-phylogenetic reconciliation analysis indicated that Wolbachia strain diversity within their endemic Hawaiian Drosophilidae hosts can be explained by vertical (e.g., co-speciation) and horizontal (e.g., host switch) modes of transmission. Results from stochastic character trait mapping suggest that horizontal transmission is associated with the preferred oviposition substrate of the host, but not the host’s plant family or island of occurrence. For Hawaiian Drosophilid species of conservation concern, with 13 species listed as endangered and 1 listed as threatened, knowledge of Wolbachia strain types, infection status, and potential for superinfection could assist with conservation breeding programs designed to bolster population sizes, especially when wild populations are supplemented with laboratory-reared, translocated individuals. Future research aimed at improving the understanding of the mechanisms of Wolbachia transmission in nature, their impact on the host, and their role in host species formation may shed light on the influence of Wolbachia as an evolutionary driver, especially in Hawaiian ecosystems.
","language":"English","publisher":"MDPI","doi":"10.3390/genes14081545","usgsCitation":"Corpuz, R.L., Bellinger, M.R., Veillet, A., Magnacca, K.N., and Price, D.K., 2023, The transmission patterns of the endosymbiont Wolbachia within the Hawaiian Drosophilidae adaptive radiation: Genes and Genomics, v. 14, no. 8, 1545, 23 p., https://doi.org/10.3390/genes14081545.","productDescription":"1545, 23 p.","ipdsId":"IP-153520","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":442628,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/genes14081545","text":"Publisher Index Page"},{"id":419698,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Corpuz, Renee L.","contributorId":318229,"corporation":false,"usgs":false,"family":"Corpuz","given":"Renee","email":"","middleInitial":"L.","affiliations":[{"id":37485,"text":"University of Hawai‘i - Hilo","active":true,"usgs":false}],"preferred":false,"id":879977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bellinger, Mona Renee 0000-0001-5274-9572","orcid":"https://orcid.org/0000-0001-5274-9572","contributorId":301018,"corporation":false,"usgs":true,"family":"Bellinger","given":"Mona","email":"","middleInitial":"Renee","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":879978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Veillet, Anne","contributorId":196674,"corporation":false,"usgs":false,"family":"Veillet","given":"Anne","email":"","affiliations":[],"preferred":false,"id":879979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Magnacca, Karl N.","contributorId":173504,"corporation":false,"usgs":false,"family":"Magnacca","given":"Karl","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":879980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Price, Donald K. 0000-0003-2501-8373","orcid":"https://orcid.org/0000-0003-2501-8373","contributorId":302769,"corporation":false,"usgs":false,"family":"Price","given":"Donald","email":"","middleInitial":"K.","affiliations":[{"id":65554,"text":"School of Life Sciences, University of Nevada, Las Vegas, NV","active":true,"usgs":false}],"preferred":false,"id":879981,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70247100,"text":"sir20235079 - 2023 - Techniques for estimating the magnitude and frequency of peak flows on small streams in Minnesota, excluding the Rainy River Basin, based on data through water year 2019","interactions":[],"lastModifiedDate":"2026-03-13T13:20:17.495243","indexId":"sir20235079","displayToPublicDate":"2023-07-25T10:09:21","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":"2023-5079","displayTitle":"Techniques for Estimating the Magnitude and Frequency of Peak Flows on Small Streams in Minnesota, Excluding the Rainy River Basin, Based on Data Through Water Year 2019","title":"Techniques for estimating the magnitude and frequency of peak flows on small streams in Minnesota, excluding the Rainy River Basin, based on data through water year 2019","docAbstract":"

Annual peak-flow data collected at U.S. Geological Survey streamgages in Minnesota and adjacent areas of neighboring states of Iowa and South Dakota were analyzed to develop and update regional regression equations that can be used to estimate the magnitude and frequency of peak streamflow for ungaged streams in Minnesota, excluding the Lake of the Woods-Rainy River Basin upstream from Kenora, Ontario, Canada. Hydraulic engineers use peak-flow frequency estimates to inform designs of bridges, culverts, and dams, and water managers use the estimates for regulation and planning activities. Peak-flow estimates are provided for the 66.7-, 50-, 20-, 10-, 4-, 2-, 1-, and 0.2-percent annual exceedance probabilities (AEPs), which are equivalent to annual flood-frequency recurrence intervals of 1.5-, 2-, 5-, 10-, 25-, 50-, 100-, and 500-years, respectively. The estimates were computed by applying the expected moments algorithm to fit a Pearson Type III distribution to the logarithms of annual peak flows for 298 streamgages based on annual peak-flow data collected through water year 2019. The study area is represented by six hydrologic regions delineated on the basis of a pattern of residuals of statewide regressions, using basin characteristics such as drainage area, main-channel slope, lake area, storage area, and mean annual runoff as explanatory variables. The concept and principles of hydrologic landscape units was used to validate the regions. Residual analysis of the regional regression equations was used to subsequently develop equations relating the peak flow estimates for selected AEPs using 17 characteristics tested as explanatory variables in the regression analysis.

The equations developed in this study can be used to produce AEPs within the six regions and to update equations developed in earlier, similar studies in Minnesota. Furthermore, updating the equations in StreamStats, a web-based geographic information system tool developed by the U.S. Geological Survey, will allow hydraulic engineers and water managers to obtain AEPs and basin characteristics for user-selected locations on streams through an interactive map.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235079","collaboration":"Prepared in cooperation with the Minnesota Department of Transportation","usgsCitation":"Sanocki, C.A., and Levin, S.B., 2023, Techniques for estimating the magnitude and frequency of peak flows on small streams in Minnesota, excluding the Rainy River Basin, based on data through water year 2019: U.S. Geological Survey Scientific Investigations Report 2023–5079, 15 p., https://doi.org/10.3133/sir20235079.","productDescription":"Report: v, 15 p.; 2 Data Releases; Dataset","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-127955","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":419307,"rank":7,"type":{"id":39,"text":"HTML 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\"}}]}","contact":"

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U.S. Geological Survey
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","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-07-25","noUsgsAuthors":false,"publicationDate":"2023-07-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Sanocki, Christopher A. 0000-0001-6714-5421 sanocki@usgs.gov","orcid":"https://orcid.org/0000-0001-6714-5421","contributorId":3142,"corporation":false,"usgs":true,"family":"Sanocki","given":"Christopher","email":"sanocki@usgs.gov","middleInitial":"A.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878899,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Levin, Sara B. 0000-0002-2448-3129","orcid":"https://orcid.org/0000-0002-2448-3129","contributorId":209947,"corporation":false,"usgs":true,"family":"Levin","given":"Sara B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":878900,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70245422,"text":"70245422 - 2023 - Lightning rings and gravity waves: Insights into the giant eruption plumefrom Tonga’s Hunga Volcano on 15 January 2022","interactions":[],"lastModifiedDate":"2023-06-23T11:36:48.443262","indexId":"70245422","displayToPublicDate":"2023-06-20T06:33:14","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Lightning rings and gravity waves: Insights into the giant eruption plumefrom Tonga’s Hunga Volcano on 15 January 2022","docAbstract":"

On 15 January 2022, Hunga Volcano in Tonga produced the most violent eruption in the modern satellite era, sending a water-rich plume at least 58 km high. Using a combination of satellite- and ground-based sensors, we investigate the astonishing rate of volcanic lightning (>2,600 flashes min−1) and what it reveals about the dynamics of the submarine eruption. In map view, lightning locations form radially expanding rings. We show that the initial lightning ring is co-located with an internal gravity wave traveling >80 m s−1 in the stratospheric umbrella cloud. Buoyant oscillations of the plume's overshooting top generated the gravity waves, which enhanced turbulent particle interactions and triggered high-current electrical discharges at unusually high altitudes. Our analysis attributes the intense lightning activity to an exceptional mass eruption rate (>5 × 109 kg s−1), rapidly expanding umbrella cloud, and entrainment of abundant seawater vaporized from magma-water interaction at the submarine vent.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022GL102341","usgsCitation":"Van Eaton, A.R., Lapierre, J., Behnke, S.A., Vagasky, C., Schultz, C.J., Pavolonis, M.J., Bedka, K., and Khlopenkov, K., 2023, Lightning rings and gravity waves: Insights into the giant eruption plumefrom Tonga’s Hunga Volcano on 15 January 2022: Geophysical Research Letters, v. 50, no. 12, e2022GL102341, 10 p., https://doi.org/10.1029/2022GL102341.","productDescription":"e2022GL102341, 10 p.","ipdsId":"IP-151641","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":443013,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022gl102341","text":"Publisher Index Page"},{"id":418389,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tonga","otherGeospatial":"Hunga Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.2503367072947,\n -19.19107806045814\n ],\n [\n -176.2503367072947,\n -21.898036687649522\n ],\n [\n -173.20032150771877,\n -21.898036687649522\n ],\n [\n -173.20032150771877,\n -19.19107806045814\n ],\n [\n -176.2503367072947,\n -19.19107806045814\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"12","noUsgsAuthors":false,"publicationDate":"2023-06-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":876092,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lapierre, Jeff","contributorId":311229,"corporation":false,"usgs":false,"family":"Lapierre","given":"Jeff","email":"","affiliations":[{"id":67363,"text":"Advanced Environmental Monitoring (AEM), Germantown, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":876093,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Behnke, Sonja A.","contributorId":311230,"corporation":false,"usgs":false,"family":"Behnke","given":"Sonja","email":"","middleInitial":"A.","affiliations":[{"id":67364,"text":"Los Alamos National Laboratory, Los Alamos, New Mexico, USA","active":true,"usgs":false}],"preferred":false,"id":876094,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vagasky, Chris","contributorId":311231,"corporation":false,"usgs":false,"family":"Vagasky","given":"Chris","email":"","affiliations":[{"id":67366,"text":"Vaisala Inc., Louisville, Colorado, USA","active":true,"usgs":false}],"preferred":false,"id":876095,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schultz, Christopher J.","contributorId":311232,"corporation":false,"usgs":false,"family":"Schultz","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":67367,"text":"NASA Marshall Space Flight Center, Huntsville, Alabama, USA","active":true,"usgs":false}],"preferred":false,"id":876096,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":876097,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bedka, Kristopher","contributorId":311233,"corporation":false,"usgs":false,"family":"Bedka","given":"Kristopher","email":"","affiliations":[{"id":67368,"text":"NASA Langley Research Center, Hampton, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":876098,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khlopenkov, Konstantin","contributorId":311234,"corporation":false,"usgs":false,"family":"Khlopenkov","given":"Konstantin","email":"","affiliations":[{"id":67369,"text":"Science Systems and Applications, Inc, Hampton, Virginia, USA","active":true,"usgs":false}],"preferred":false,"id":876099,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243924,"text":"70243924 - 2023 - A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome","interactions":[],"lastModifiedDate":"2023-05-25T15:10:07.037036","indexId":"70243924","displayToPublicDate":"2023-05-25T09:44:21","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome","docAbstract":"

Escalated wildfire activity within the western U.S. has widespread societal impacts and long-term consequences for the imperiled sagebrush (Artemisia spp.) biome. Shifts from historical fire regimes and the interplay between frequent disturbance and invasive annual grasses may initiate permanent state transitions as wildfire frequency outpaces sagebrush communities’ innate capacity to recover. Therefore, wildfire management is at the core of conservation plans for sagebrush ecosystems, especially critical habitat for species of conservation concern such as the greater sage-grouse (Centrocercus urophasianus; hereafter sage-grouse). Fuel breaks help facilitate wildfire suppression by modifying behavior through fuels modification and allowing safe access points for containment by firefighters. The Bureau of Land Management has proposed to roughly double the existing fuel break network in the western U.S., centered on the Great Basin. To our knowledge, no broad-scale examination of fuel break effectiveness or the environmental conditions under which fuel breaks are expected to be most effective has been conducted. We performed a retrospective assessment of probability of fuel break contributing to wildfire containment on recorded wildfire and fuel break interactions from 1985 to 2018 within the western U.S. We characterized environmental, fuels, and weather conditions within 500 m of wildfire contact, and within 5 km of the approaching wildfire. We used a binomial mixed model within a Bayesian framework to identify relationships between these variables and fuel break success. Fuel breaks were least successful in areas classified as having low resilience to disturbance and low resistance to invasion, in areas composed of primarily woody fuels, and when operating in high temperature and low precipitation conditions. Fuel breaks were most effective in areas where fine fuels dominated and in areas that were readily accessible. Maintenance history and fuel break type also contributed to the probability of containment. Overall results indicate a complex and sometimes paradoxical relationship between landscape characteristics that promote wildfire spread and those that impact fuel break effectiveness. Finally, we developed predictive maps of fuel break effectiveness by fuel break type to further elucidate these complex relationships and to inform urgently needed fuel break placement and maintenance priorities across the sagebrush biome.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.117903","usgsCitation":"Weise, C.L., Brussee, B.E., Crist, M.R., Shinneman, D.J., Coates, P.S., Aldridge, C.L., Heinrichs, J.A., and Ricca, M.A., 2023, A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome: Journal of Environmental Management, v. 341, 117903, 15 p., https://doi.org/10.1016/j.jenvman.2023.117903.","productDescription":"117903, 15 p.","ipdsId":"IP-145845","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":435315,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OPI1N3","text":"USGS data release","linkHelpText":"Predictive Maps of Fuel Break Effectiveness by Treatment Type and Underlying Resilience to Disturbance and Resistance to Invasion Across the Western U.S."},{"id":417439,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.72004583519214,\n 49.050273759992564\n ],\n [\n -121.72004583519214,\n 36.57497861377678\n ],\n [\n -103.96634293196246,\n 36.57497861377678\n ],\n [\n -103.96634293196246,\n 49.050273759992564\n ],\n [\n -121.72004583519214,\n 49.050273759992564\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"341","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weise, Cali L.","contributorId":305785,"corporation":false,"usgs":false,"family":"Weise","given":"Cali","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":873833,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873834,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873745,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":873746,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crist, Michele R. 0000-0002-3506-3402","orcid":"https://orcid.org/0000-0002-3506-3402","contributorId":202575,"corporation":false,"usgs":true,"family":"Crist","given":"Michele","email":"","middleInitial":"R.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":873835,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":873836,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034 jheinrichs@usgs.gov","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":193742,"corporation":false,"usgs":true,"family":"Heinrichs","given":"Julie","email":"jheinrichs@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":873837,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":873747,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70243199,"text":"cir1508 - 2023 - Yellowstone Volcano Observatory 2022 annual report","interactions":[],"lastModifiedDate":"2026-02-04T19:54:12.136173","indexId":"cir1508","displayToPublicDate":"2023-05-04T08:56:25","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1508","displayTitle":"Yellowstone Volcano Observatory 2022 Annual Report","title":"Yellowstone Volcano Observatory 2022 annual report","docAbstract":"

The Yellowstone Volcano Observatory (YVO) monitors volcanic and hydrothermal activity associated with the Yellowstone magmatic system, carries out research into magmatic processes occurring beneath Yellowstone Caldera, and issues timely warnings and guidance related to potential future geologic hazards. This report summarizes the activities and findings of YVO during the year 2022, focusing on the Yellowstone volcanic system. Highlights of YVO research and related activities during 2022 include deployments of seismometers in Norris Geyser Basin and Upper Geyser Basin to investigate interactions between hydrothermal features and influences from external influences, geological studies of post-glacial hydrothermal activity, refining the ages of Yellowstone volcanic units and updating existing maps of geologic deposits, new mapping of ash-flow deposits on the Sour Creek dome, installation of a new continuous gas monitoring station near Mud Volcano, sampling of gas emissions and thermal waters around Yellowstone National Park to monitor water chemistry over space and time, research into the age and history of Steamboat Geyser in Norris Geyser Basin, and assessment of thermal output based on satellite imagery and chloride flux in rivers.

The most noteworthy event of the year was not geophysical, but meteorological. Combined runoff from rain and snowmelt caused substantial flooding in Yellowstone National Park, which caused damage to park roads and infrastructure. Steamboat Geyser, in Norris Geyser Basin, continued the pattern of frequent eruptions that began in 2018 with 11 water eruptions in 2022, the lowest number of annual eruptions in the current eruptive sequence. Total seismicity—2,429 located earthquakes—was slightly less than the 2,773 earthquakes located in 2021 and at the upper end of the historical average range of about 1,500–2,500 earthquakes per year. Overall subsidence of the caldera floor, ongoing since late 2015 or early 2016, continued at rates of a few centimeters (1–2 inches) per year. Satellite deformation measurements indicated the possibility of slight uplift amounting to about 1 centimeter (less than 1 inch) along the north caldera rim in 2021, but satellite data spanning 2022 show no uplift in that area. Throughout 2022, the aviation color code for Yellowstone Caldera remained at “green” and the volcano alert level remained at “normal.”

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1508","usgsCitation":"Yellowstone Volcano Observatory, 2023, Yellowstone Volcano Observatory 2022 annual report: U.S. Geological Survey Circular 1508, 49 p., https://doi.org/10.3133/cir1508.","productDescription":"v, 49 p.","numberOfPages":"49","onlineOnly":"N","ipdsId":"IP-149199","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":416682,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1508/covrthb.jpg"},{"id":416683,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1508/cir1508.pdf","text":"Report","size":"28 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":499548,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114711.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.22714973143812,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 43.34546446716831\n ],\n [\n -108.61352791332872,\n 45.10797687707381\n ],\n [\n -111.22714973143812,\n 45.10797687707381\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Yellowstone Volcano Observatory
U.S. Geological Survey
1300 SE Cardinal Court, Suite 100
Vancouver, WA 98683

Email: yvowebteam@usgs.gov

","tableOfContents":"","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"publishedDate":"2023-05-04","noUsgsAuthors":false,"publicationDate":"2023-05-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Yellowstone Volcano Observatory","contributorId":127797,"corporation":true,"usgs":false,"organization":"Yellowstone Volcano Observatory","id":871446,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70242949,"text":"70242949 - 2023 - Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models","interactions":[],"lastModifiedDate":"2023-06-09T15:19:19.221059","indexId":"70242949","displayToPublicDate":"2023-04-20T06:46:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models","docAbstract":"
  1. Sea otters are apex predators that can exert considerable influence over the nearshore communities they occupy. Since facing near extinction in the early 1900s, sea otters are making a remarkable recovery in Southeast Alaska, particularly in Glacier Bay, the largest protected tidewater glacier fjord in the world. The expansion of sea otters across Glacier Bay offers both a challenge to monitoring and stewardship and an unprecedented opportunity to study the top-down effect of a novel apex predator across a diverse and productive ecosystem.
  2. Our goal was to integrate monitoring data across trophic levels, space, and time to quantify and map the predator–prey interaction between sea otters and butter clams Saxidomus gigantea, one of the dominant large bivalves in Glacier Bay and a favoured prey of sea otters.
  3. We developed a spatially-referenced mechanistic differential equation model of butter clam dynamics that combined both environmental drivers of local population growth and estimates of otter abundance from aerial survey data. We embedded this model in a Bayesian statistical framework and fit it to clam survey data from 43 intertidal and subtidal sites across Glacier Bay.
  4. Prior to substantial sea otter expansion, we found that butter clam density was structured by an environmental gradient driven by distance from glacier (represented by latitude) and a quadratic effect of current speed. Estimates of sea otter attack rate revealed spatial heterogeneity in sea otter impacts and a negative relationship with local shoreline complexity.
  5. Sea otter exploitation of productive butter clam habitat substantially reduced the abundance and altered the distribution of butter clams across Glacier Bay, with potential cascading consequences for nearshore community structure and function. Spatial variation in estimated sea otter predation processes further suggests that community context and local environmental conditions mediate the top-down influence of sea otters on a given prey. Overall, our framework provides high-resolution insights about the interaction among components of this food web and could be applied to a variety of other systems involving invasive species, epidemiology or migration.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2656.13929","usgsCitation":"Leach, C.B., Weitzman, B.P., Bodkin, J., Esler, D., Esslinger, G.G., Kloecker, K.A., Monson, D., Womble, J., and Hooten, M.B., 2023, Revealing the extent of sea otter impacts on bivalve prey through multi-trophic monitoring and mechanistic models: Journal of Animal Ecology, v. 92, no. 5, p. 1230-1243, https://doi.org/10.1111/1365-2656.13929.","productDescription":"14 p.","startPage":"1230","endPage":"1243","ipdsId":"IP-134411","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":443800,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2656.13929","text":"Publisher Index Page"},{"id":435367,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9LODH0Z","text":"USGS data release","linkHelpText":"Intertidal and Subtidal Sea Otter Prey Sampling in Mixed Sediment Habitat in Glacier Bay National Park and Preserve, Alaska, 1998 to 2011"},{"id":416170,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"92","issue":"5","noUsgsAuthors":false,"publicationDate":"2023-04-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Leach, Clinton B.","contributorId":270703,"corporation":false,"usgs":false,"family":"Leach","given":"Clinton","email":"","middleInitial":"B.","affiliations":[{"id":13606,"text":"CSU","active":true,"usgs":false}],"preferred":false,"id":870322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Weitzman, Benjamin P.","contributorId":304409,"corporation":false,"usgs":false,"family":"Weitzman","given":"Benjamin","email":"","middleInitial":"P.","affiliations":[{"id":66056,"text":"USFWS-MMM","active":true,"usgs":false}],"preferred":false,"id":870323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bodkin, James L. 0000-0003-1641-4438","orcid":"https://orcid.org/0000-0003-1641-4438","contributorId":264733,"corporation":false,"usgs":false,"family":"Bodkin","given":"James L.","affiliations":[{"id":40616,"text":"former USGS PI","active":true,"usgs":false}],"preferred":false,"id":870324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Esler, Daniel 0000-0001-5501-4555 desler@usgs.gov","orcid":"https://orcid.org/0000-0001-5501-4555","contributorId":5465,"corporation":false,"usgs":true,"family":"Esler","given":"Daniel","email":"desler@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":12437,"text":"Simon Fraser University, Centre for Wildlife Ecology","active":true,"usgs":false}],"preferred":true,"id":870325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Esslinger, George G. 0000-0002-3459-0083 gesslinger@usgs.gov","orcid":"https://orcid.org/0000-0002-3459-0083","contributorId":131009,"corporation":false,"usgs":true,"family":"Esslinger","given":"George","email":"gesslinger@usgs.gov","middleInitial":"G.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":870326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kloecker, Kimberly A. 0000-0002-2461-968X kkloecker@usgs.gov","orcid":"https://orcid.org/0000-0002-2461-968X","contributorId":3442,"corporation":false,"usgs":true,"family":"Kloecker","given":"Kimberly","email":"kkloecker@usgs.gov","middleInitial":"A.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":870327,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Monson, Daniel 0000-0002-4593-5673 dmonson@usgs.gov","orcid":"https://orcid.org/0000-0002-4593-5673","contributorId":196670,"corporation":false,"usgs":true,"family":"Monson","given":"Daniel","email":"dmonson@usgs.gov","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":870328,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Womble, Jamie N.","contributorId":267709,"corporation":false,"usgs":false,"family":"Womble","given":"Jamie N.","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":870329,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hooten, Mevin B. 0000-0002-1614-723X","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":292295,"corporation":false,"usgs":false,"family":"Hooten","given":"Mevin","email":"","middleInitial":"B.","affiliations":[{"id":12430,"text":"University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":870330,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243875,"text":"70243875 - 2023 - Time-lapse seafloor surveys reveal how turbidity currents and internal tides in Monterey Canyon interact with the seabed at centimeter-scale","interactions":[],"lastModifiedDate":"2023-05-24T17:02:25.974335","indexId":"70243875","displayToPublicDate":"2023-04-06T11:56:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Time-lapse seafloor surveys reveal how turbidity currents and internal tides in Monterey Canyon interact with the seabed at centimeter-scale","docAbstract":"

Here we show how ultra-high resolution seabed mapping using new technology can help to understand processes that sculpt submarine canyons. Time-lapse seafloor surveys were conducted in the axis of Monterey Canyon, ∼50 km from the canyon head (∼1,840 m water depth) over an 18-month period. These surveys comprised 5-cm resolution multibeam bathymetry, 1-cm resolution lidar bathymetry, and 2-mm resolution stereophotographic imagery. Bathymetry data reveal centimeter-scale textures that would be undetectable by more traditional survey methods. Upward-looking Acoustic Doppler Current Profilers at the site recorded the flow character of internal tides and the passage of three turbidity currents, while sediment cores collected from the site record flow deposits. Combined with flow and core data, the bathymetry shows how turbidity currents and internal tides modify the seabed. The turbidity currents drape sediment across the site, infilling bedform troughs and smoothing erosional features carved by the internal tides (e.g., rippled scours). Turbidity currents with speeds of 0.9–3.3 m/s failed to cause notable bedform movement, which is surprising given that flows with similar speeds produced rapid bedform migration elsewhere, including the upper Monterey Canyon. The lack of migration may be related to the character of the underlying substrate or indicate that turbidity currents at the site lack dense, near-bed layers. The scale of scours produced by the internal tides (≤0.7 m/s) approaches the scale of features recorded in the ancient rock record. Thus, these results illustrate how the scale gap between seabed mapping technology and the rock record may eventually be bridged.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006705","usgsCitation":"Wolfson-Schwehr, M., Paull, C.K., Caress, D.W., Gwiazda, R., Nieminski, N.M., Talling, P.J., Carvajal, C., Simmons, S.M., and Troni, G., 2023, Time-lapse seafloor surveys reveal how turbidity currents and internal tides in Monterey Canyon interact with the seabed at centimeter-scale: Journal of Geophysical Research: Earth Surface, v. 128, no. 4, e2022JF006705, 22 p., https://doi.org/10.1029/2022JF006705.","productDescription":"e2022JF006705, 22 p.","ipdsId":"IP-136210","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006705","text":"Publisher Index Page"},{"id":417402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Monterey Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.095833,\n 36.704167\n ],\n [\n -122.095833,\n 36.7\n ],\n [\n -122.0875,\n 36.7\n ],\n [\n -122.0875,\n 36.704167\n ],\n [\n -122.095833,\n 36.704167\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfson-Schwehr, Monica","contributorId":175112,"corporation":false,"usgs":false,"family":"Wolfson-Schwehr","given":"Monica","email":"","affiliations":[],"preferred":false,"id":873579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":873580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caress, David W.","contributorId":147392,"corporation":false,"usgs":false,"family":"Caress","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":873581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gwiazda, Roberto","contributorId":147193,"corporation":false,"usgs":false,"family":"Gwiazda","given":"Roberto","email":"","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":873582,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nieminski, Nora Maria 0000-0002-4465-8731","orcid":"https://orcid.org/0000-0002-4465-8731","contributorId":279764,"corporation":false,"usgs":true,"family":"Nieminski","given":"Nora","email":"","middleInitial":"Maria","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873583,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talling, Peter J.","contributorId":195515,"corporation":false,"usgs":false,"family":"Talling","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":873584,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carvajal, Cristian","contributorId":204133,"corporation":false,"usgs":false,"family":"Carvajal","given":"Cristian","email":"","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":873585,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simmons, Stephen M.","contributorId":305699,"corporation":false,"usgs":false,"family":"Simmons","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":873586,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Troni, Giancarlo","contributorId":305700,"corporation":false,"usgs":false,"family":"Troni","given":"Giancarlo","email":"","affiliations":[{"id":66274,"text":"Pontifica Universidad Catolica de Chile","active":true,"usgs":false}],"preferred":false,"id":873587,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70240656,"text":"dr1167 - 2023 - Analysis of the United States documented unplugged orphaned oil and gas well dataset","interactions":[],"lastModifiedDate":"2026-02-04T20:02:22.307176","indexId":"dr1167","displayToPublicDate":"2023-02-17T05:55:00","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1167","displayTitle":"Analysis of the United States Documented Unplugged Orphaned Oil and Gas Well Dataset","title":"Analysis of the United States documented unplugged orphaned oil and gas well dataset","docAbstract":"

In coordination with the U.S. Department of the Interior (DOI) and in response to the Bipartisan Infrastructure Law (BIL), the U.S. Geological Survey (USGS) produced a documented unplugged orphaned oil and gas well dataset (called the DOW dataset hereafter) that contains the location and status of these wells nationwide as of 2022. The DOW dataset includes 117,672 wells across 27 states. The data were compiled from publicly available, and often online, state agency sources. The USGS reformatted, produced coordinate locations when needed, and conducted quality control evaluations on the source material. The DOW dataset has numerous potential uses related to the topic of orphaned well locations. For example, the DOW dataset has contributed to the template behind the DOI's future orphaned well program database. Until the DOI database is complete, the USGS's DOW dataset may serve as an interim product, helping the DOI fulfill obligations in the BIL legislation.

The DOW dataset can also be utilized in future USGS estimates of greenhouse gas emissions from Federal lands. The DOW dataset is available as a USGS data release with metadata describing the sources and processing steps used in its production. An online map is also available for exploration and interaction with the DOW data. In addition to describing the DOW dataset and its generation, this report includes analyses of orphaned well completion year and well depth, produced by combining the DOW dataset with the proprietary IHS Markit database.

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Director, Geology, Energy & Minerals Science Center
U.S. Geological Survey
956 National Center
Reston, VA 20192

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"publishedDate":"2023-02-17","revisedDate":"2023-04-06","noUsgsAuthors":false,"publicationDate":"2023-02-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Merrill, Matthew D. 0000-0003-3766-847X mmerrill@usgs.gov","orcid":"https://orcid.org/0000-0003-3766-847X","contributorId":174817,"corporation":false,"usgs":true,"family":"Merrill","given":"Matthew","email":"mmerrill@usgs.gov","middleInitial":"D.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864151,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grove, Claire A. 0000-0002-4024-454X","orcid":"https://orcid.org/0000-0002-4024-454X","contributorId":295483,"corporation":false,"usgs":true,"family":"Grove","given":"Claire","email":"","middleInitial":"A.","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":864152,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gianoutsos, Nicholas J. 0000-0002-6510-6549 ngianoutsos@usgs.gov","orcid":"https://orcid.org/0000-0002-6510-6549","contributorId":3607,"corporation":false,"usgs":true,"family":"Gianoutsos","given":"Nicholas","email":"ngianoutsos@usgs.gov","middleInitial":"J.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":864153,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Freeman, Philip A. 0000-0002-0863-7431 pfreeman@usgs.gov","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":193093,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","email":"pfreeman@usgs.gov","affiliations":[{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":864154,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70240258,"text":"70240258 - 2023 - Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau","interactions":[],"lastModifiedDate":"2023-03-01T17:23:31.199955","indexId":"70240258","displayToPublicDate":"2023-01-30T07:12:05","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau","docAbstract":"

Livestock removal is increasingly used as a management option to mitigate the negative impacts of grazing-related disturbances on rangelands. Removal generally increases plant cover, but it is unclear when, where, and by how much plant and soil cover changes can be expected. On the Colorado Plateau, complex geology, topography, soils, and climate all interact to mediate the relationship between land cover, climate, and disturbance. In this study we used new developments in land cover mapping and analysis to assess landscape plant and bare soil cover up to 30 years after livestock removal from two grazing allotments in Capitol Reef National Park, Utah, USA. Results indicate that livestock removal increases plant cover 0.17-0.32% per year and reduces bare soil cover 0.34-0.41% per year, although these rates may be suppressed by warming temperatures. Soils, assessed through Soil Geomorphic Units, played a strong but complex role in mediating land cover changes through time. These results suggest that livestock removal is an effective strategy for increasing plant cover and reducing bare soil on the Colorado Plateau, but including soil information in decision making will enhance efficiency by improving manager's ability to prioritize management actions effectively across space and through time.

","language":"English","publisher":"IOP Science","doi":"10.1088/1748-9326/acb728","usgsCitation":"McNellis, B.E., Knight, A.C., Nauman, T.W., Chambers, S., Brungard, C., Fick, S., Livensperger, C., Borthwick, C., and Duniway, M.C., 2023, Livestock removal increases plant cover across a heterogeneous dryland landscape on the Colorado Plateau: Environmental Research Letters, v. 18, 034034, 18 p., https://doi.org/10.1088/1748-9326/acb728.","productDescription":"034034, 18 p.","ipdsId":"IP-145563","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":444677,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.1088/1748-9326/acb728","text":"Publisher Index Page"},{"id":435478,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EK2PHY","text":"USGS data release","linkHelpText":"Plant cover, climate, grazing disturbance, and soil class data from 1991-2020 compiled from remotely sensed data on two retired grazing allotments in Capitol Reef National Park, Utah, USA"},{"id":412611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"18","noUsgsAuthors":false,"publicationDate":"2023-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"McNellis, Brandon E 0000-0001-9604-8727","orcid":"https://orcid.org/0000-0001-9604-8727","contributorId":271065,"corporation":false,"usgs":true,"family":"McNellis","given":"Brandon","email":"","middleInitial":"E","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863113,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knight, Anna C. 0000-0002-9455-2855","orcid":"https://orcid.org/0000-0002-9455-2855","contributorId":255113,"corporation":false,"usgs":true,"family":"Knight","given":"Anna","email":"","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":863114,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":863115,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chambers, Samuel Norton 0000-0002-4734-2855","orcid":"https://orcid.org/0000-0002-4734-2855","contributorId":297994,"corporation":false,"usgs":true,"family":"Chambers","given":"Samuel Norton","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":863116,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brungard, C.W.","contributorId":301936,"corporation":false,"usgs":false,"family":"Brungard","given":"C.W.","email":"","affiliations":[{"id":65369,"text":"Department of Plant and Environmental Sciences. New Mexico State University. Las Cruces, NM, USA","active":true,"usgs":false}],"preferred":false,"id":863117,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fick, S.E.","contributorId":301937,"corporation":false,"usgs":false,"family":"Fick","given":"S.E.","email":"","affiliations":[{"id":65370,"text":"formerly: US Geological Survey, Southwest Biological Science Center, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":863118,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Livensperger, C.G.","contributorId":301938,"corporation":false,"usgs":false,"family":"Livensperger","given":"C.G.","affiliations":[{"id":65371,"text":"National Park Service, Capitol Reef National Park, Fruita, UT; National Park Service, Northern Colorado Plateau Network, Moab, UT","active":true,"usgs":false}],"preferred":false,"id":863119,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Borthwick, C.G.","contributorId":301939,"corporation":false,"usgs":false,"family":"Borthwick","given":"C.G.","email":"","affiliations":[{"id":65372,"text":"National Park Service, Capitol Reef National Park, Fruita, UT","active":true,"usgs":false}],"preferred":false,"id":863120,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"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":863121,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70239930,"text":"sir20225089 - 2023 - Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming","interactions":[],"lastModifiedDate":"2026-02-23T19:20:42.551781","indexId":"sir20225089","displayToPublicDate":"2023-01-26T12:30:05","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":"2022-5089","displayTitle":"Interaction of a Legacy Groundwater Contaminant Plume with the Little Wind River from 2015 Through 2017, Riverton Processing Site, Wyoming","title":"Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming","docAbstract":"

The Riverton Processing site was a uranium mill 4 kilometers southwest of Riverton, Wyoming, that prepared uranium ore for nuclear reactors and weapons from 1958 to 1963. The U.S. Department of Energy completed surface remediation of the uranium tailings in 1989; however, groundwater below and downgradient from the tailings site and nearby Little Wind River was not remediated. Beginning in 2010, a series of floods along the Little Wind River began to mobilize contaminants in the unsaturated zone, resulting in substantial increases of uranium and other contaminants of concern in monitoring wells completed inside the contaminant plume. In 2011, the U.S. Department of Energy started a series of university and Government agency retrospective and field investigations to understand the processes controlling contaminant increases in the groundwater plume. The goals of the field investigations described in this report were to (1) identify and quantify the contaminant flux and potential associated biological effects from groundwater associated with the legacy plume as it enters a perennial stream reach, and (2) assess chemical exposure and potential effects to biological receptors from the interaction of the contaminant plume and the river.

Field investigations along the Little Wind River were completed by the U.S. Geological Survey during 2015–17 in cooperation with the U.S. Department of Energy Office of Legacy Management to characterize: (1) seepage areas and seepage rates; (2) pore-water and bed sediment chemistry and hyporheic exchange and reactive loss; and (3) exposure pathways and biological receptors. All data collected during the study are contained in two U.S. Geological Survey data releases, available at https://doi.org/10.5066/F7BR8QX4 and https://doi.org/10.5066/P9J9VJBR. A variety of tools and methods were used during the field characterizations. Streambed temperature mapping, electrical resistivity tomography, electromagnetic induction, fiber-optic distributed temperature sensing, tube seepage meters, vertical thermal sensor arrays, and an environmental tracer (radon) were used to identify areas of groundwater seepage and associated seepage rates along specific sections of the study reach of the river. Drive points, minipiezometers, diffusive equilibrium in thin-film/diffusive gradients in thin-film probes, bed-sediment samples, and equal discharge increment sampling methods were used to characterize pore-water chemistry, estimate hyporheic exchange and reactive loss of selected chemical constituents, and quantify contaminant loadings entering the study reach. Sampling and analysis of surface sediments, filamentous algae, periphytic algae, and macroinvertebrates were used to characterize biological exposure pathways, metal uptake, and receptors.

Areas of focused groundwater discharge identified by the fiber-optic distributed temperature sensing surveys corresponded closely with areas of elevated electrical conductivity identified by the electromagnetic induction survey results in the top 5 meters of sediment. During three monitoring periods in 2016, the mean vertical seepage rate measured with tube seepage meters was 0.45 meter per day, ranging from −0.02 to 1.55 meters per day. Five of the 11 locations where vertical thermal profile data were collected along the study reach during August 2017 indicated mean upwelling values ranging from 0.11 to 0.23 meter per day. Radon data collected from the Little Wind River during June, July, and August 2016 indicated a consistent inflow of groundwater to the central part of the study reach, in the area congruous with the center of the previously mapped groundwater plume discharge zone. During August 2017, the greatest attenuation of uranium from reactive loss in pore-water samples was observed at three locations along the study reach, at depths between 6 and 15 centimeters, and similar trends in molybdenum attenuation were also observed. Bed-sediment concentration profiles collected during 2017 also indicated attenuation of uranium and molybdenum from groundwater during hyporheic mixing of surface water with the legacy plume during groundwater upwelling into the river. Streamflow measurements combined with equal discharge increment water sampling along the study reach indicated an increase in dissolved uranium concentrations in the downstream direction during 2016 and 2017. Net uranium load entering the Little Wind River study reach was about 290 and 435 grams per day during 2016 and 2017, respectively. Biological samples indicated that low levels of uranium and molybdenum exposure were confined to the benthos in the Little Wind River within and immediately downstream from the perimeter of the groundwater plume. Concentrations of molybdenum and uranium in filamentous algae were consistently low at all sites in the study reach with no indication of increased exposure of dissolved bioavailable molybdenum or uranium at sites next to or downstream from the groundwater plume.

Comparison of the August 2017 results from electromagnetic induction, tube seepage meters, vertical thermal profiling, and pore-water chemistry surveys were in general agreement in identifying areas with upwelling groundwater conditions along the study reach. However, the electroconductivity values measured with electromagnetic induction in the top 100 centimeters of sediment did not agree with sodium concentrations measured in pore-water samples collected at similar streambed depths. Differences and similarities between multiple methods can result in additional insights into hydrologic and biogeochemical processes that may be occurring along a reach of a river system interacting with shallow groundwater inputs. It may be advantageous to apply a variety of geophysical, geochemical, hydrologic, and biological tools at other Uranium Mill Tailings Remedial Action (https://www.energy.gov/sites/prod/files/2014/10/f19/UMTRCA.pdf) sites during the investigation of legacy contaminant plume interactions with surface-water systems.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, Va.","doi":"10.3133/sir20225089","collaboration":"Prepared in cooperation with the U.S. Department of Energy","usgsCitation":"Naftz, D.L., Fuller, C.C., Runkel, R.L., Solder, J., Gardner, W.P., Terry, N., Briggs, M.A., Short, T.M., Cain, D.J., Dam, W.L., Byrne, P.A., and Campbell, J.R., 2023, Interaction of a legacy groundwater contaminant plume with the Little Wind River from 2015 through 2017, Riverton Processing site, Wyoming: U.S. Geological Survey Scientific Investigations Report 2022–5089, 66 p., https://doi.org/10.3133/sir20225089.","productDescription":"Report: xi, 66 p.; 3 Datasets; 2 Data Releases","numberOfPages":"82","onlineOnly":"Y","ipdsId":"IP-123760","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":412328,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7BR8QX4","text":"USGS data release","linkHelpText":"Hydrologic, biogeochemical, and radon data collected within and adjacent to the Little Wind River near Riverton, Wyoming (ver. 1.1, January 2019)"},{"id":412329,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J9VJBR","text":"USGS data release","linkHelpText":"Geophysical data collected within and adjacent to the Little Wind River near Riverton, Wyoming"},{"id":412324,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5089/coverthb.jpg"},{"id":412325,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5089/sir20225089.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5089"},{"id":412330,"rank":7,"type":{"id":28,"text":"Dataset"},"url":"https://gems.lm.doe.gov/","text":"U.S. Department of Energy Office of Legacy Management Geospatial Environmental Mapping System database","linkHelpText":"—Riverton, WY, Processing site"},{"id":412331,"rank":8,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":412332,"rank":9,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":500452,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114285.htm","linkFileType":{"id":5,"text":"html"}},{"id":412327,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5089/images"},{"id":412326,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5089/sir20225089.XML"}],"country":"United States","state":"Wyoming","city":"Riverton","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -109,\n 43.5\n ],\n [\n -109,\n 42.5\n ],\n [\n -107.5,\n 42.5\n ],\n [\n -107.5,\n 43.5\n ],\n [\n -109,\n 43.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2023-01-26","noUsgsAuthors":false,"publicationDate":"2023-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Naftz, David L. 0000-0003-1130-6892 dlnaftz@usgs.gov","orcid":"https://orcid.org/0000-0003-1130-6892","contributorId":1041,"corporation":false,"usgs":true,"family":"Naftz","given":"David","email":"dlnaftz@usgs.gov","middleInitial":"L.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true},{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862398,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":862401,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Runkel, Robert L. 0000-0003-3220-481X runkel@usgs.gov","orcid":"https://orcid.org/0000-0003-3220-481X","contributorId":685,"corporation":false,"usgs":true,"family":"Runkel","given":"Robert","email":"runkel@usgs.gov","middleInitial":"L.","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Solder, John 0000-0002-0660-3326","orcid":"https://orcid.org/0000-0002-0660-3326","contributorId":222003,"corporation":false,"usgs":true,"family":"Solder","given":"John","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":862407,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gardner, W. 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The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

","language":"English","publisher":"Nature","doi":"10.1038/s41597-023-01929-2","usgsCitation":"Buscombe, D.D., Wernette, P., Fitzpatrick, S., Favela, J., Goldstein, E.B., and Enwright, N., 2023, A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments: Scientific Data, v. 10, 46, 18 p., https://doi.org/10.1038/s41597-023-01929-2.","productDescription":"46, 18 p.","ipdsId":"IP-136940","costCenters":[{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444749,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41597-023-01929-2","text":"Publisher Index Page"},{"id":413278,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2023-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Buscombe, Daniel D. 0000-0001-6217-5584","orcid":"https://orcid.org/0000-0001-6217-5584","contributorId":198817,"corporation":false,"usgs":false,"family":"Buscombe","given":"Daniel","middleInitial":"D.","affiliations":[],"preferred":false,"id":864788,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wernette, Phillipe Alan 0000-0002-8902-5575","orcid":"https://orcid.org/0000-0002-8902-5575","contributorId":259274,"corporation":false,"usgs":true,"family":"Wernette","given":"Phillipe Alan","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":864789,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzpatrick, Sharon 0000-0001-6513-9132","orcid":"https://orcid.org/0000-0001-6513-9132","contributorId":288329,"corporation":false,"usgs":false,"family":"Fitzpatrick","given":"Sharon","email":"","affiliations":[{"id":39151,"text":"California State University Sacramento","active":true,"usgs":false}],"preferred":false,"id":864790,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Favela, Jaycee 0000-0001-9175-8324","orcid":"https://orcid.org/0000-0001-9175-8324","contributorId":288328,"corporation":false,"usgs":false,"family":"Favela","given":"Jaycee","email":"","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":864791,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Goldstein, Evan B. 0000-0001-9358-1016","orcid":"https://orcid.org/0000-0001-9358-1016","contributorId":184210,"corporation":false,"usgs":false,"family":"Goldstein","given":"Evan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":864792,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Enwright, Nicholas 0000-0002-7887-3261","orcid":"https://orcid.org/0000-0002-7887-3261","contributorId":216198,"corporation":false,"usgs":true,"family":"Enwright","given":"Nicholas","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":864793,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70238945,"text":"70238945 - 2023 - Effects of structure and volcanic stratigraphy on groundwater and surface water flow: Hat Creek basin, California, USA","interactions":[],"lastModifiedDate":"2023-03-31T15:02:55.092427","indexId":"70238945","displayToPublicDate":"2022-12-16T06:57:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1923,"text":"Hydrogeology Journal","active":true,"publicationSubtype":{"id":10}},"title":"Effects of structure and volcanic stratigraphy on groundwater and surface water flow: Hat Creek basin, California, USA","docAbstract":"

Hydrogeologic systems in the southern Cascade Range in California (USA) develop in volcanic rocks where morphology, stratigraphy, extensional structures, and attendant basin geometry play a central role in groundwater flow paths, groundwater/surface-water interactions, and spring discharge locations. High-volume springs (greater than 3 m3/s) flow from basin-filling (<800 ka) volcanic rocks in the Hat Creek and Fall River tributaries and contribute approximately half of the average annual flow of the Pit River, the largest tributary to Shasta Lake. A hydrogeologic conceptual framework is constructed for the Hat Creek basin combining new geologic mapping, water-well lithologic logs, a database of active faults, LiDAR mapping of faults and volcanic landforms, streamflow measurements and airborne thermal infrared remote sensing of stream temperature. These data are used to integrate the geologic structure and the volcanic and volcaniclastic stratigraphy to create a three-dimensional interpretation of the hydrogeology in the basin. Two large streamflow gains from focused groundwater discharge near Big Spring and north of Sugarloaf Peak result from geologic barriers that restrict lateral groundwater flow and force water into Hat Creek. The inferred groundwater-flow barriers divide the aquifer system into at least three leaky compartments. The two downstream compartments lose streamflow in the upstream reaches (immediately downstream of the groundwater-flow barriers) and gain in downstream reaches with the greatest inflows immediately upstream of the barriers.

","language":"English","publisher":"Springer","doi":"10.1007/s10040-022-02545-x","usgsCitation":"Marcelli, M.F., Burns, E., Muffler, L.P., Meigs, A.J., Curtis, J.A., and Torgersen, C.E., 2023, Effects of structure and volcanic stratigraphy on groundwater and surface water flow: Hat Creek basin, California, USA: Hydrogeology Journal, v. 31, p. 219-240, https://doi.org/10.1007/s10040-022-02545-x.","productDescription":"22 p.","startPage":"219","endPage":"240","ipdsId":"IP-129472","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"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":445103,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10040-022-02545-x","text":"Publisher Index Page"},{"id":435541,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P921523S","text":"USGS data release","linkHelpText":"Data release of hydrogeologic data of the Hat Creek basin, Shasta County, California"},{"id":435540,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DMJYT7","text":"USGS data release","linkHelpText":"Airborne thermal infrared imagery and longitudinal stream temperature profiles, Hat Creek, California, August 2018"},{"id":410694,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Hat Creek Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.12182405478094,\n 41.076918975510125\n ],\n [\n -122.12182405478094,\n 40.46841961145353\n ],\n [\n -122.70599738660988,\n 40.46841961145353\n ],\n [\n -122.70599738660988,\n 41.076918975510125\n ],\n [\n -122.12182405478094,\n 41.076918975510125\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationDate":"2022-12-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Marcelli, Marina Francesca 0000-0002-7195-0367","orcid":"https://orcid.org/0000-0002-7195-0367","contributorId":300036,"corporation":false,"usgs":true,"family":"Marcelli","given":"Marina","email":"","middleInitial":"Francesca","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859302,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":859303,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muffler, L.J. Patrick 0000-0001-6638-7218 pmuffler@usgs.gov","orcid":"https://orcid.org/0000-0001-6638-7218","contributorId":3322,"corporation":false,"usgs":true,"family":"Muffler","given":"L.J.","email":"pmuffler@usgs.gov","middleInitial":"Patrick","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":859304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meigs, Andrew J","contributorId":300037,"corporation":false,"usgs":false,"family":"Meigs","given":"Andrew","email":"","middleInitial":"J","affiliations":[{"id":65004,"text":"College of Earth, Ocean and Atmospheric Sciences Oregon State University","active":true,"usgs":false}],"preferred":false,"id":859305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Curtis, Jennifer A. 0000-0001-7766-994X jacurtis@usgs.gov","orcid":"https://orcid.org/0000-0001-7766-994X","contributorId":300038,"corporation":false,"usgs":true,"family":"Curtis","given":"Jennifer","email":"jacurtis@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859306,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Torgersen, Christian E. 0000-0001-8325-2737 ctorgersen@usgs.gov","orcid":"https://orcid.org/0000-0001-8325-2737","contributorId":146935,"corporation":false,"usgs":true,"family":"Torgersen","given":"Christian","email":"ctorgersen@usgs.gov","middleInitial":"E.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":859307,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70239296,"text":"70239296 - 2023 - An interactive viewer to improve operational aftershock forecasts","interactions":[],"lastModifiedDate":"2023-01-06T14:43:02.358938","indexId":"70239296","displayToPublicDate":"2022-11-14T08:40:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"An interactive viewer to improve operational aftershock forecasts","docAbstract":"

The U.S. Geological Survey (USGS) issues forecasts for aftershocks about 20 minutes after most earthquakes above M 5 in the United States and its territories, and updates these forecasts 75 times during the first year. Most of the forecasts are issued automatically, but some forecasts require manual intervention to maintain accuracy. It is important to identify the sequences whose forecasts will benefit from a modified approach so the USGS can provide accurate information to the public. The oaftools R package (Paris and Michael, 2022) includes functions that analyze and plot earthquake sequences and their forecasts to identify which sequences require such intervention. The package includes the Operational Aftershock Forecast (OAF) Viewer, which incorporates the functions into an interactive web environment that can be used to explore aftershock sequences. The OAF Viewer starts with a global map and table of mainshocks. After a mainshock has been selected, the map and a new table show its aftershocks and the OAF Viewer generates five analytical plots: (1) magnitude–time, which is used to look for patterns in the data; (2) cumulative number, to see how the productivity of the sequence compares to a Reasenberg and Jones (1989) aftershock model over time; (3) magnitude–frequency, to compare the ratio of large to small magnitudes and extrapolate to higher magnitudes with sparse data and lower magnitudes with incomplete data; (4) forecast success, to compare the forecasts with observations for a sequence; and (5) parameter–time, which examines the temporal evolution of the forecast model parameters. The user can interact with the functions provided by the oaftools package through the OAF Viewer or by incorporating the functions into their own analysis methods. The OAF Viewer will help seismologists understand complexities in the data, communicate with the public and emergency managers, and improve the OAF system by maintaining operational awareness.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0220220108","usgsCitation":"Paris, G.M., and Michael, A.J., 2023, An interactive viewer to improve operational aftershock forecasts: Seismological Research Letters, v. 94, no. 1, p. 473-484, https://doi.org/10.1785/0220220108.","productDescription":"12 p.","startPage":"473","endPage":"484","ipdsId":"IP-138814","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":435560,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9PZTYEN","text":"USGS data release","linkHelpText":"OAF Tools - R package"},{"id":411488,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Paris, Gabrielle Madison 0000-0001-5008-1441","orcid":"https://orcid.org/0000-0001-5008-1441","contributorId":300636,"corporation":false,"usgs":true,"family":"Paris","given":"Gabrielle","email":"","middleInitial":"Madison","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861018,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Michael, Andrew J. 0000-0002-2403-5019 michael@usgs.gov","orcid":"https://orcid.org/0000-0002-2403-5019","contributorId":1280,"corporation":false,"usgs":true,"family":"Michael","given":"Andrew","email":"michael@usgs.gov","middleInitial":"J.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":861019,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70238971,"text":"sir20225108 - 2022 - Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21","interactions":[],"lastModifiedDate":"2026-04-27T19:08:13.835336","indexId":"sir20225108","displayToPublicDate":"2022-12-19T12:06:14","publicationYear":"2022","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":"2022-5108","displayTitle":"Hydrogeologic Characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from Water-Level and Water-Chemistry Data, 2015–21","title":"Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21","docAbstract":"

Jewel Cave National Monument is in the western Black Hills of South Dakota and contains an extensive cave network, including various subterranean water bodies (cave lakes) that are believed to represent the regionally important Madison aquifer. Recent investigations have sought to improve understanding of hydrogeologic characteristics of cave lakes in Jewel Cave. The U.S. Geological Survey, in cooperation with the National Park Service, collected water-level and water-chemistry data within and near Jewel Cave to better understand groundwater interactions in Jewel Cave and to evaluate recharge characteristics of cave lakes. Continuous water-level data were collected at two cave lakes (Hourglass and New Years Lakes) from 2018 to 2021, and discrete measurements were collected by National Park Service staff from 2015 to 2021. Water samples were collected from one stream, one rain collector, three springs, and two cave lakes. The approach for this study included comparing water-level data collected from two cave lakes to historical climate data and using multivariate statistical analyses to evaluate water samples collected during this study and from previous investigations. This study builds on interpretations from previous investigations that collected similar datasets and performed similar analyses.

Hydrographs of Hourglass and News Years Lakes from 2015 to 2021 demonstrated the variability of groundwater levels in Jewel Cave in response to dry and wet climate conditions. Hourglass Lake displayed small (up to 4.8 feet), gradual water-level changes, whereas New Years Lake displayed relatively large (up to at least 27.5 feet) and rapid water-level changes. Hourglass and New Years Lakes are about 0.4 mile apart at the land surface, and the water-level elevation between the lakes varied from 61 to 93.5 feet from 2016 to 2021. The proximity and relatively small elevation difference of Hourglass and New Years Lakes indicated different recharge sources and (or) mechanisms were responsible for hydrograph dissimilarities. Water-level changes at Hourglass Lake were similar to water-level changes at a well completed in the Madison aquifer about 9 miles south of Jewel Cave National Monument, which indicated Hourglass Lake may be recharged similar to the regional Madison aquifer along outcrops north of Jewel Cave. New Years Lake displayed almost no similarities to the well completed in the Madison aquifer—indicating a more direct connection to local recharge rather than solely from outcrops recharging the regional Madison aquifer.

Results from multivariate statistical analyses of water-chemistry data were used to evaluate recharge observations from water-level data. The water chemistry of Hourglass Lake indicated its water was chemically more similar to precipitation than other groundwater sites sampled. A conceptual karst recharge model indicated that the dominant recharge source to Hourglass Lake was diffuse allogenic recharge from vertical movement of infiltrated precipitation through vertical or near-vertical fractures that extend through the Minnelusa Formation and unsaturated zone of the Madison Limestone. The water chemistry of New Years Lake was chemically similar to Hell Canyon Creek about 0.2 mile from New Years Lake at the land surface. Streamflow loss zones (concentrated allogenic recharge) along Hell Canyon Creek have not been mapped, but their presence in the Jewel Cave area has been speculated by previous investigations. A fault observed in the cave ceiling above New Years Lake by National Park Service staff could provide a natural conduit for direct recharge from Hell Canyon Creek to New Years Lake if the fault is extensive. Additional water-chemistry and water-level data, as well as streamflow data upstream and downstream of the potential streamflow loss zone along Hell Canyon Creek, are needed to prove the presence of this loss zone and discern further correlations between streamflow and water levels in New Years Lake. Observations from previous investigations and this study indicated recharge to Jewel Cave is complex and occurs on various timescales that are affected temporally by precipitation patterns and spatially by hydrologic connection with the overlying Minnelusa aquifer of the Minnelusa Formation.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225108","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Medler, C.J., 2022, Hydrogeologic characteristics of Hourglass and New Years Cave Lakes at Jewel Cave National Monument, South Dakota, from water-level and water-chemistry data, 2015–21: U.S. Geological Survey Scientific Investigations Report 2022–5108, 47 p., https://doi.org/10.3133/sir20225108.","productDescription":"Report: viii, 47 p.; Dataset","numberOfPages":"60","onlineOnly":"Y","ipdsId":"IP-137086","costCenters":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"links":[{"id":410721,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225108/full","text":"Report"},{"id":410718,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":410717,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5108/images"},{"id":410715,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.pdf","text":"Report","size":"8.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5108"},{"id":410714,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5108/coverthb.jpg"},{"id":410716,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5108/sir20225108.XML"},{"id":503566,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113998.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"South Dakota","otherGeospatial":"Jewel Cave National Monument","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.2,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.738808274610875\n ],\n [\n -104.0,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.35675372367402\n ],\n [\n -103.2,\n 43.738808274610875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"

Director, Dakota Water Science Center
U.S. Geological Survey
821 East Interstate Avenue, Bismarck, ND 58503
1608 Mountain View Road, Rapid City, SD 57702

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-12-19","noUsgsAuthors":false,"publicationDate":"2022-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Medler, Colton J. 0000-0001-6119-5065","orcid":"https://orcid.org/0000-0001-6119-5065","contributorId":201463,"corporation":false,"usgs":true,"family":"Medler","given":"Colton","email":"","middleInitial":"J.","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":859461,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70238777,"text":"70238777 - 2022 - Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain","interactions":[],"lastModifiedDate":"2022-12-12T13:54:40.518768","indexId":"70238777","displayToPublicDate":"2022-12-08T07:41:22","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3554,"text":"The Cryosphere","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain","docAbstract":"

Dynamic natural processes govern snow distribution in mountainous environments throughout the world. Interactions between these different processes create spatially variable patterns of snow depth across a landscape. Variations in accumulation and redistribution occur at a variety of spatial scales, which are well established for moderate mountain terrain. However, spatial patterns of snow depth variability in steep, complex mountain terrain have not been fully explored due to insufficient spatial resolutions of snow depth measurement. Recent advances in uncrewed aerial systems (UASs) and structure from motion (SfM) photogrammetry provide an opportunity to map spatially continuous snow depths at high resolutions in these environments. Using UASs and SfM photogrammetry, we produced 11 snow depth maps at a steep couloir site in the Bridger Range of Montana, USA, during the 2019–2020 winter. We quantified the spatial scales of snow depth variability in this complex mountain terrain at a variety of resolutions over 2 orders of magnitude (0.02 to 20 m) and time steps (4 to 58 d) using variogram analysis in a high-performance computing environment. We found that spatial resolutions greater than 0.5 m do not capture the complete patterns of snow depth spatial variability within complex mountain terrain and that snow depths are autocorrelated within horizontal distances of 15 m at our study site. The results of this research have the potential to reduce uncertainty currently associated with snowpack and snow water resource analysis by documenting and quantifying snow depth variability and snowpack evolution on relatively inaccessible slopes in complex terrain at high spatial and temporal resolutions.

","language":"English","publisher":"Copernicus Journals","doi":"10.5194/tc-16-4907-2022","usgsCitation":"Miller, Z., Peitzsch, E.H., Sproles, E.A., Birkeland, K.W., and Palomaki, R.T., 2022, Assessing the seasonal evolution of snow depth spatial variability and scaling in complex mountain terrain: The Cryosphere, v. 16, no. 12, p. 4907-4930, https://doi.org/10.5194/tc-16-4907-2022.","productDescription":"24 p.","startPage":"4907","endPage":"4930","ipdsId":"IP-139965","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":445693,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/tc-16-4907-2022","text":"Publisher Index Page"},{"id":435598,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCIA1R","text":"USGS data release","linkHelpText":"2020 winter timeseries of UAS derived digital surface models (DSMs) from the Hourglass study site, Bridger Mountains, Montana, USA"},{"id":410274,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana","otherGeospatial":"Bridger Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -110.94,\n 45.84\n ],\n [\n -110.94,\n 45.830\n ],\n [\n -110.93,\n 45.83\n ],\n [\n -110.93,\n 45.84\n ],\n [\n -110.94,\n 45.84\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"16","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Miller, Zachary 0000-0002-6876-6710","orcid":"https://orcid.org/0000-0002-6876-6710","contributorId":214464,"corporation":false,"usgs":true,"family":"Miller","given":"Zachary","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858561,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Peitzsch, Erich H. 0000-0001-7624-0455","orcid":"https://orcid.org/0000-0001-7624-0455","contributorId":202576,"corporation":false,"usgs":true,"family":"Peitzsch","given":"Erich","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":858562,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sproles, Eric A. 0000-0003-1245-1653","orcid":"https://orcid.org/0000-0003-1245-1653","contributorId":299760,"corporation":false,"usgs":false,"family":"Sproles","given":"Eric","email":"","middleInitial":"A.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858563,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Birkeland, Karl W.","contributorId":209943,"corporation":false,"usgs":false,"family":"Birkeland","given":"Karl","email":"","middleInitial":"W.","affiliations":[{"id":38033,"text":"U.S.D.A. Forest Service National Avalanche Center, Bozeman, Montana, USA","active":true,"usgs":false}],"preferred":false,"id":858564,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Palomaki, Ross T. 0000-0002-3304-9914","orcid":"https://orcid.org/0000-0002-3304-9914","contributorId":299761,"corporation":false,"usgs":false,"family":"Palomaki","given":"Ross","email":"","middleInitial":"T.","affiliations":[{"id":64943,"text":"Montana State University Earth Sciences Department","active":true,"usgs":false}],"preferred":false,"id":858565,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70237595,"text":"cir1497 - 2022 - U.S. Geological Survey—Department of the Interior Region 11, Alaska—2021–22 biennial science report","interactions":[],"lastModifiedDate":"2022-10-27T16:47:26.966721","indexId":"cir1497","displayToPublicDate":"2022-10-13T12:52:14","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":307,"text":"Circular","code":"CIR","onlineIssn":"2330-5703","printIssn":"1067-084X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1497","displayTitle":"U.S. Geological Survey—Department of the Interior Region 11, Alaska—2021–22 Biennial Science Report","title":"U.S. Geological Survey—Department of the Interior Region 11, Alaska—2021–22 biennial science report","docAbstract":"

U.S. Geological Survey (USGS) Mission: The USGS national mission is to monitor, analyze, and predict the current and evolving dynamics of complex human and natural Earth-system interactions, and to deliver actionable information at scales and timeframes relevant to decision-makers. Consistent with the national mission, the USGS in Alaska provides timely and objective scientific information to help address issues and inform management decisions across five inter-connected themes:

The USGS in Alaska consists of approximately 350 scientists and support staff working in three Alaska-based science centers, a Cooperative Research Unit, and USGS centers outside Alaska, with a combined annual science budget of about $60 million. In the last 5 years, USGS research in Alaska has produced many scientific benefits resulting from more than 1,050 publications. Publications relevant to Alaska can be conveniently searched by keyword through the USGS Publications Warehouse at https://pubs.er.usgs.gov/search?q=Alaska.

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Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508

","tableOfContents":"","publishedDate":"2022-10-13","noUsgsAuthors":false,"publicationDate":"2022-10-13","publicationStatus":"PW","contributors":{"editors":[{"text":"Powers, Elizabeth M. 0000-0002-4688-1195","orcid":"https://orcid.org/0000-0002-4688-1195","contributorId":255448,"corporation":false,"usgs":false,"family":"Powers","given":"Elizabeth","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":854584,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Williams, Dee M. 0000-0003-0400-479X dmwilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-0400-479X","contributorId":224715,"corporation":false,"usgs":true,"family":"Williams","given":"Dee M.","email":"dmwilliams@usgs.gov","affiliations":[{"id":113,"text":"Alaska Regional Director's Office","active":true,"usgs":true}],"preferred":false,"id":854585,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70236829,"text":"ofr20221081 - 2022 - A sagebrush conservation design to proactively restore America’s sagebrush biome","interactions":[],"lastModifiedDate":"2022-09-22T16:12:53.285869","indexId":"ofr20221081","displayToPublicDate":"2022-09-22T10:55:00","publicationYear":"2022","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":"2022-1081","displayTitle":"A Sagebrush Conservation Design to Proactively Restore America’s Sagebrush Biome","title":"A sagebrush conservation design to proactively restore America’s sagebrush biome","docAbstract":"

A working group of experts with diverse professional backgrounds and disciplinary expertise was assembled to conceptualize a spatially explicit conservation design to support and inform the Sagebrush Conservation Strategy Part 2. The goal was to leverage recent advancements in remotely sensed landcover products to develop spatially and temporally explicit maps of sagebrush rangeland condition and landscape threats. In addition, the group sought to provide a common basis for understanding the state of sagebrush rangelands through time.

First, the study team developed a spatially explicit model to assess geographic patterns in sagebrush ecological integrity and used this model to identify core sagebrush areas (CSAs), growth opportunity areas (GOAs), and other rangeland areas (ORAs) across the biome. Among the identified rangelands, 33.4 million acres were classified as CSAs; 84.3 million acres as GOAs; and 127.2 million acres as ORAs as of 2020. Second, the team sought to demonstrate the ecological relevance of the identified CSAs and GOAs by comparing these data with independent datasets for sagebrush obligate species of conservation concern. Geographical patterns in sagebrush ecological integrity were strongly associated with the occurrence of high-priority species and also displayed clear links to population performance for greater sage-grouse. Third, the team parsed out the type, location, and acres of primary threats within the different categories (CSAs, GOAs, and ORAs) to help focus active management by identifying places where multiagency and organization efforts can protect CSAs and GOAs that have higher levels of integrity with lower cumulative threats. The assessment of the condition of the sagebrush biome (that is, the location, amount, and conservation status) indicated that complex ecosystem function problems are driving ~73 percent of the demonstrated threats within the CSAs and GOAs (rather than point-source problems, such as human development). Fourth, the team developed trend estimates for the identified CSAs and GOAs and three selected primary threats (invasive annual grasses, conifer encroachment, and human modification) to the sagebrush biome from 2001 to 2020. Results showed that an average of 1.3 million acres per year have transitioned to ORAs at an annual rate of −1.34 percent. Fifth, the team developed an approach to integrate climate change effects into the threat-based landscape conservation design and conducted an initial assessment on the magnitude of near-term climate effects in the context of observed historical trends. The team’s analysis suggests that climate change alone is unlikely to be the dominant threat to sagebrush ecological integrity in the next few decades, although interactions of climate with wildfire and invasive annual grasses may be an important threat, especially in the longer term.

A spatial overlap analysis was performed and highlighted 45.8 million acres of shared priorities among existing conservation frameworks to help anchor and guide collaborative landscape-scale conservation of areas that still have no to low threats. This information is critical to provide context for decisions about the volume and nature of conservation actions and funding requirements.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221081","collaboration":"Prepared in cooperation with the Western Association of Fish and Wildlife Agencies and the U.S. Fish and Wildlife Service","usgsCitation":"Doherty, K., Theobald, D.M., Bradford, J.B., Wiechman, L.A., Bedrosian, G., Boyd, C.S., Cahill, M., Coates, P.S., Creutzburg, M.K., Crist, M.R., Finn, S.P., Kumar, A.V., Littlefield, C.E., Maestas, J.D., Prentice, K.L., Prochazka, B.G., Remington, T.E., Sparklin, W.D., Tull, J.C., Wurtzebach, Z., and Zeller, K.A., 2022, A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081.","productDescription":"Report: viii, 38 p.; Data Release; 3 Figures: 7.99 × 6.10 inches or smaller","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-138940","costCenters":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"links":[{"id":407103,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig10.pdf","text":"Figure 10, full size","size":"5.46 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Conifer 2020"},{"id":407104,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig11.pdf","text":"Figure 11, full size","size":"9.45 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Human Modification 2020"},{"id":407025,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081_fig09.pdf","text":"Figure 9, full size","size":"5.57 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Invasive Annual Grass 2020"},{"id":407023,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1081/ofr20221081.pdf","text":"Report","size":"32.4 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":407022,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1081/coverthb.jpg"},{"id":407024,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94Y5CDV","text":"USGS data release","linkHelpText":"Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.6845703125,\n 33\n ],\n [\n -101.25,\n 33\n ],\n [\n -101.25,\n 49\n ],\n [\n -121.6845703125,\n 49\n ],\n [\n -121.6845703125,\n 33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Sagebrush Ecosystem Specialist
Land Management Research Program
Ecosystems Mission Area
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526

Contact Pubs Warehouse

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effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota"},"lastModifiedDate":"2026-04-01T15:49:03.755037","indexId":"sir20175070C","displayToPublicDate":"2022-09-13T06:05:15","publicationYear":"2022","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":"2017-5070","chapter":"C","displayTitle":"Potential Effects of Energy Development on Environmental Resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water Resources","title":"Potential effects of energy development on environmental resources of the Williston Basin in Montana, North Dakota, and South Dakota—Water resources","docAbstract":"

The Williston Basin has been a leading oil and gas producing area for more than 50 years. While oil production initially peaked within the Williston Basin in the mid-1980s, production rapidly increased in the mid-2000s, largely because of improved horizontal (directional) drilling and hydraulic fracturing methods. In 2012, energy development associated with the Bakken Formation was identified as a priority requiring collaboration toward improved timeliness of issuing permits for new wells combined with reasonable measures to maintain environmental quality. Shortly thereafter, the Bakken Federal Executive Group was created to address common challenges associated with energy development. The Bakken Federal Executive Group partner agencies identified a gap in current understanding of the cumulative environmental challenges attributed to energy development throughout the area, resulting in an effort to aggregate scientific data and identify additional research and information needs related to natural resources within areas of energy development in the Williston Basin. As part of this effort, water resources in the area (including groundwater; streams and rivers; and lakes, reservoirs, and wetlands) were characterized and described in terms of physical occurrence, flow characteristics, recharge, water quality, and water use. Similarly, waters produced during energy-development activities also were characterized even though these waters are not considered usable resources within the area. Groundwater resources were characterized by the major hydrogeologic units, or aquifers, identifying the units that supply most groundwater used for domestic, stock, agricultural, and industrial purposes. The groundwater characterization included other deeper hydrogeologic units in the Williston Basin that may be a useable source of water with treatment, have utility as a reservoir for reinjection of produced waters, or be a source of minerals and energy resources. A generalized groundwater budget and flow system identifying the sources of recharge (stream infiltration, precipitation, and movement [leakage] from other aquifers) and the general groundwater flow direction is included for each of the major hydrogeologic units. Rivers and streams within the Williston Basin with 10 or more years of continuous streamflow data were identified. For a subset of these sites, streamflow characteristics, including the monthly and annual mean flow, were generated to identify seasonal and interannual changes in streamflow and thus provide information on the drivers and reliability of streamflow at the seasonal or multiyear scale. Daily streamflow and annual extreme flows (peak and low flow) also were estimated for the subset of sites. The daily streamflow and annual extreme flow values provide information on short-term or extreme events that are relevant to infrastructure design and evaluating spills, leaks, or accidental discharges of water or petroleum products. Surface-water features (lakes, ponds, and wetlands) were classified using the Cowardin system and identified on the National Wetlands Inventory maps generated by the U.S. Fish and Wildlife Service. The spatial distribution of the surface-water features was analyzed by State, county, and specifically in comparison to the Prairie Pothole Region. The proximity of the surface-water features to energy development infrastructure (specifically oil or gas well pads) was evaluated. It was determined that, although oil or gas wells are often near a surface-water feature, most surface-water features do not have wells nearby, with the exception of wells in the Prairie Pothole Region. Water-quality data were aggregated from two data sources: (1) the Water-Quality Portal, sponsored by the U.S. Geological Survey (USGS), U.S. Environmental Protection Agency (EPA), and National Water Quality Monitoring Council; and (2) a data compilation completed as part of the USGS National Water-Quality Assessment project. The Water-Quality Portal integrates publicly available water-quality data from databases maintained by the USGS, EPA, and U.S. Department of Agriculture, including water-quality data from Tribal, State, and local databases. Water-quality data for 15 commonly measured water-quality constituents were aggregated for groundwater, rivers and streams, and lakes and reservoirs. For each aggregated dataset (groundwater, rivers and streams, and lakes and reservoirs), analyses of the water-quality data included summary statistics, maps of spatial distribution of constituent values, boxplots of constituent values by timeframe or hydrogeologic unit, spatial comparisons of site locations and constituent values to petroleum well density, and comparisons of the constituent values measured to EPA drinking-water standards/guidelines. Produced water includes all fluids brought to the surface along with the targeted hydrocarbons as part of the oil and gas exploration and extraction processes. These fluids may include formation water (waters that co-exist with rock/oil/gas), hydraulic fracturing fluids, and other combinations of water and chemicals used during oil and gas well drilling, development, treatments, recompletions, and workovers. Produced water datasets were aggregated from two sources: the USGS National Produced Waters Geochemical database (ver. 2.1) and a series of projects focused specifically on sampling produced water in the Williston Basin from 2010 to 2014. The National Produced Waters Geochemical database was useful for a general understanding of produced-water chemistry. Produced waters are characterized by extreme salinity and contain elevated concentrations of other constituents (including arsenic, barium, cadmium, lead, zinc, radium-226/radium-228, and ammonium) that could negatively affect water and aquatic resources if released. Produced waters also have a generally unique chemical (isotopic) signature that may be useful in tracking water from different geologic units; for example, the oxygen/deuterium and strontium ratio values measured in brine waters from the Bakken Formation are distinct from brines collected from other geologic units in the Williston Basin.

Water-use information related to energy production in the area also was aggregated and summarized. The summary of water use is not limited to oil and gas production but includes water used to produce all types of energy resources in the Williston Basin, including coal/lignite, thermoelectric power, oil and gas, hydropower, biomass and biofuels, wind, geothermal, and solar. Each State has its own methods for regulating and reporting water usage within its jurisdiction. These methods can introduce problems when examining water use from sources, such as the Missouri River or Fox Hills aquifer, that are shared across political boundaries. Without the one-to-one match for usage types and amounts used from a water source, it is difficult to develop a comprehensive water budget for the water source being evaluated. A large amount of freshwater is required to prepare a well for oil and gas well production; in some cases, 3 to 7 million gallons of water are needed per well. The EPA estimates that hydraulic fracturing in the Williston Basin uses between 70 to 140 billion gallons per year. Water also is used for myriad other purposes related to ancillary oil and gas extraction. In addition to water used for immediate energy development, the expanded human workforce migrating into the area and other support staff who have moved into the area during the development also use water.

Research and information needs were identified that could be relevant in the evaluation of the effects of energy development on water resources. Information needs related to the evaluation of groundwater resources include the following: improved potentiometric-surface maps for glacial units; availability of a uniform stream network digital geographic coverage that spans the international boundary with Canada; enhanced surface-water use information with regards to the gain and loss of streamflow to shallow groundwater, which would increase understanding groundwater and surface-water interactions; and expanded geophysical assessments. Gaps in the availability of streamflow data include the lack of information on ice-jam flooding despite potential for effects to infrastructure (pipelines, roads, and facilities) and an understanding of the cumulative effects of largely undocumented stock and diversion dams. Although this study resulted in the aggregation of a large quantity of water-quality data, the availability of consistently collected, systematically processed and reported data over large parts of the Williston Basin is sparse. Few samples have been analyzed for constituents that may indicate the effect of energy development on water resources. Constituents that could be considered include boron, chloride, bromide, iodine, fluoride, manganese, lithium, radium, strontium isotopes, volatile organic compounds, and isotopes of inorganic ions (such as hydrogen and carbon). Collaboration between Tribal, Federal, State, and local entities to identify a common study design, common monitoring constituents, and consistent sampling locations would generate datasets with broad utility and would likely result in overall cost savings for monitoring over time. Similarly, there is a need for standardized sample collection, processing, laboratory analytical methods, and the collection of ancillary data for produced waters sampling. Additional characterization of the range of chemical, microbial, and isotopic compositions and quantities of “end-member” produced waters, and the collection of time-series datasets to document the changes in produced waters during and after well development also were needs identified during this study. Water-use estimates would be improved through the implementation of comprehensive studies of water use from groundwater and surface-water sources using consistent methodologies across the Williston Basin. The submission of chemical and water data related to hydraulic fracturing collected by the oil and gas industry would add to the quantity of available data. Consistent implementation of regulations and monitoring controls across political boundaries (State, county, and international) would further improve the consistency of data available for the estimates of water use.

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Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601

Contact Pubs Warehouse

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","affiliations":[{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849559,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Bender, David A. 0000-0002-1269-0948 dabender@usgs.gov","orcid":"https://orcid.org/0000-0002-1269-0948","contributorId":985,"corporation":false,"usgs":true,"family":"Bender","given":"David","email":"dabender@usgs.gov","middleInitial":"A.","affiliations":[{"id":562,"text":"South Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849560,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Frankforter, Jill D. 0000-0003-0371-2313 jdfrankf@usgs.gov","orcid":"https://orcid.org/0000-0003-0371-2313","contributorId":1739,"corporation":false,"usgs":true,"family":"Frankforter","given":"Jill","email":"jdfrankf@usgs.gov","middleInitial":"D.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849561,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Galloway, Joel M. 0000-0002-9836-9724 jgallowa@usgs.gov","orcid":"https://orcid.org/0000-0002-9836-9724","contributorId":1562,"corporation":false,"usgs":true,"family":"Galloway","given":"Joel","email":"jgallowa@usgs.gov","middleInitial":"M.","affiliations":[{"id":478,"text":"North Dakota Water Science Center","active":true,"usgs":true},{"id":34685,"text":"Dakota Water Science Center","active":true,"usgs":true}],"preferred":true,"id":849562,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70237014,"text":"70237014 - 2022 - Great Lakes spatial priorities study","interactions":[],"lastModifiedDate":"2022-09-28T16:23:24.798526","indexId":"70237014","displayToPublicDate":"2022-09-01T11:17:12","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":5134,"text":"NOAA Technical Memorandum","active":true,"publicationSubtype":{"id":1}},"seriesNumber":"NOS CS 51","title":"Great Lakes spatial priorities study","docAbstract":"

Spatial data about the bathymetry, habitat characteristics, underlying geology, and other features of the ocean and inland seas are essential for decision-making. Marine research and management organizations use these data to help ensure safe navigation, promote sustainable fisheries, extract energy, and protect marine habitats in the coastal and ocean waters of the U.S. Exclusive Economic Zone (EEZ) and Laurentian Great Lakes. Many of these organizations may have overlapping or shared mapping interests without knowing it.

In a multi-jurisdictional planning environment, it can be challenging and cumbersome to determine where other entities have shared or overlapping mapping interests, especially across a transnational region such as the Great Lakes. State and provincial governments, federal governments, academia, tribes and First Nations, and other stakeholders from both the U.S. and Canada all have mapping interests across Great Lakes waters. Identifying and communicating target geographies for new data collection that are shared among multiple organizations can both help to avoid redundancy of new mapping efforts, and create opportunities for greater efficiency through collaboration.

To address this issue, a spatial priorities study was conducted using a geospatial tool developed by the National Ocean Services National Centers for Coastal and Ocean Science (NCCOS). The tool provided an easy-to-use online interface in which programs can identify their priorities in a simple and straightforward way. This study asked representatives of Great Lakes management and science organizations to identify the areas for which they needed maps of lakebed features on a near-term, mid-term, and long-term timeframe, and why. Then, the responses were analyzed and overlaid to determine areas of shared mapping need and opportunity and to determine the types of map products needed.

The analysis revealed high interest among multiple organizations in discrete geographies including the Minnesota and Wisconsin shoreline from Duluth to the eastern extent of the Bayfield Peninsula, Green Bay in Lake Michigan, and the southern coastlines of Lake Erie and Lake Ontario, the St. Marys River, and the northern Lake Superior coastal waters near Grand Portage, MN. Lower priority mapping interest were distributed widely across all lakes, but tended to be concentrated in nearshore areas (<30 m depth).

The analysis also indicated that the top mapping justifications were Habitat/biota/natural area, Benthic exploration, Commercial and recreational fishing, and Scientific research. The top desired map product types were Elevation, Substrate/sub-bottom geologic characterization, and Habitat map/characterization, although participants on some lakes noted other less prevalent product types.

Following from previously conducted NOAA and non-NOAA Federal spatial prioritization exercises, the results of this regional focus can help mapping organizations better understand how their priorities align with the needs of regional organizations, allow for more efficient coordination and funding, and enable partners to leverage assets and resources to fill their most pressing data and information gaps across Great Lakes waters. The U.S. Mapping Coordination Site hosts the results of this study and other spatial prioritization studies. Through this website, one can interact with the study results along with recent and planned mapping efforts.

NOAA intends to update their spatial priorities on a three- to five-year basis. Future studies should strive to expand participation of federal agencies, state and local governments, federally-recognized tribes, academia, and private industry (among other stakeholders) to seek out ocean mapping partnerships in conjunction with the National Ocean Mapping, Exploration and Characterization (NOMEC) goals map once, use many times.”

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Lavas","interactions":[],"lastModifiedDate":"2024-10-28T11:50:33.184333","indexId":"70259940","displayToPublicDate":"2022-08-03T06:46:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"The Shallow Magmatic Plumbing System of the Deccan Traps, Evidence from Plagioclase Megacrysts and Their Host Lavas","docAbstract":"

We investigate the shallow plumbing system of the Deccan Traps Large Igneous Province using rock and mineral data from Giant Plagioclase Basalt (GPB) lava flows from around the entire province, but with a focus on the Saurashtra Peninsula, the Malwa Plateau, and the base and top of the Western Ghats (WG) lava pile. GPB lavas in the WG typically occur at the transition between chemically distinct basalt formations. Most GPB samples are evolved basalts, with high Fe and Ti contents, and show major and trace elements and Sr-Nd-Pb isotopic compositions generally similar to those of previously studied Deccan basalts. Major element modeling suggests that high-Fe, evolved melts typical of GPB basalts may derive from less evolved Deccan basalts by low-pressure fractional crystallization in a generally dry magmatic plumbing system. The basalts are strongly porphyritic, with 6–25% of mm- to cm-sized plagioclase megacrysts, frequently occurring as crystal clots, plus relatively rare olivine and clinopyroxene. The plagioclase crystals are mostly labradoritic, but some show bytownitic cores (general range of anorthite mol%: 78–55). A common feature is a strong Fe enrichment at the plagioclase rims, indicating interaction with an Fe-rich melt similar to that represented by the matrix compositions (FeOt up to 16–17 wt%). Plagioclase minor and trace elements and Sr isotopic compositions analyzed by laser ablation inductively coupled plasma mass spectrometry show evidence of a hybrid and magma mixing origin. In particular, several plagioclase crystals show variable 87Sr/86Sri, which only partially overlaps with the 87Sr/86Sri of the surrounding matrix. Diffusion modeling suggests residence times of decades to centuries for most plagioclase megacrysts. Notably, some plagioclase crystal clots show textural evidence of deformation as recorded by electron back-scatter diffraction analyses and chemical maps, which suggest that the plagioclase megacrysts were deformed in a crystal-rich environment in the presence of melt. We interpret the plagioclase megacrysts as remnants of a crystal mush originally formed in the shallow plumbing system of the Deccan basalts. In this environment, plagioclase acquired a zoned composition due to the arrival of chemically distinct basaltic magmas. Prior to eruption, a rapidly rising but dense Fe-rich magma was capable of disrupting the shallow level crystal mush, remobilizing part of it and carrying a cargo of buoyant plagioclase megacrysts. Our findings suggest that basaltic magmas from the Deccan Traps, and possibly from LIPs in general, are produced within complex transcrustal magmatic plumbing systems with widespread crystal mushes developed in the shallow crust.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/petrology/egac075","usgsCitation":"Marzoli, A., Renne, P.R., Andreasen, R., Spiess, R., Chiaradia, M., Ruth, D.C., Tholt, A., Pande, K., and Costa, F.J., 2022, The Shallow Magmatic Plumbing System of the Deccan Traps, Evidence from Plagioclase Megacrysts and Their Host Lavas: Journal of Petrology, v. 63, no. 9, egac075, https://doi.org/10.1093/petrology/egac075.","productDescription":"egac075","ipdsId":"IP-137938","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":489006,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/28790e6d-08a1-4f02-b893-195df971663e","text":"External Repository"},{"id":463238,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"63","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-08-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Marzoli, A.","contributorId":167328,"corporation":false,"usgs":false,"family":"Marzoli","given":"A.","email":"","affiliations":[{"id":24687,"text":"Universitá Degli Studi di Padova, Padova, Italy","active":true,"usgs":false}],"preferred":false,"id":916902,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Renne, Paul R. 0000-0003-1769-5235","orcid":"https://orcid.org/0000-0003-1769-5235","contributorId":229577,"corporation":false,"usgs":false,"family":"Renne","given":"Paul","email":"","middleInitial":"R.","affiliations":[{"id":37390,"text":"Department of Earth and Planetary Science, University of California, Berkeley","active":true,"usgs":false}],"preferred":false,"id":916903,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreasen, R","contributorId":345557,"corporation":false,"usgs":false,"family":"Andreasen","given":"R","email":"","affiliations":[{"id":37318,"text":"Aarhus University","active":true,"usgs":false}],"preferred":false,"id":916904,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spiess, R","contributorId":345558,"corporation":false,"usgs":false,"family":"Spiess","given":"R","email":"","affiliations":[{"id":82629,"text":"Universita di Padova","active":true,"usgs":false}],"preferred":false,"id":916905,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chiaradia, M","contributorId":345561,"corporation":false,"usgs":false,"family":"Chiaradia","given":"M","email":"","affiliations":[{"id":82630,"text":"Universite de Geneve","active":true,"usgs":false}],"preferred":false,"id":916906,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruth, Dawn Catherine Sweeney 0000-0001-9369-9364","orcid":"https://orcid.org/0000-0001-9369-9364","contributorId":334908,"corporation":false,"usgs":true,"family":"Ruth","given":"Dawn","email":"","middleInitial":"Catherine Sweeney","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":916907,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tholt, A.J.","contributorId":345562,"corporation":false,"usgs":false,"family":"Tholt","given":"A.J.","email":"","affiliations":[{"id":38176,"text":"Berkeley Geochronology Center","active":true,"usgs":false}],"preferred":false,"id":916908,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pande, K","contributorId":345563,"corporation":false,"usgs":false,"family":"Pande","given":"K","email":"","affiliations":[{"id":7210,"text":"Indian Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":916909,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Costa, Fabio J. V.","contributorId":289278,"corporation":false,"usgs":false,"family":"Costa","given":"Fabio","email":"","middleInitial":"J. V.","affiliations":[{"id":62093,"text":"Policia Federal","active":true,"usgs":false}],"preferred":false,"id":916910,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70231868,"text":"70231868 - 2022 - Geologic controls on groundwater salinity reversal in North Coles Levee Oil Field, southern San Joaquin Valley, California, USA","interactions":[],"lastModifiedDate":"2022-06-01T12:19:18.602617","indexId":"70231868","displayToPublicDate":"2022-05-29T07:16:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1534,"text":"Environmental Earth Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Geologic controls on groundwater salinity reversal in North Coles Levee Oil Field, southern San Joaquin Valley, California, USA","docAbstract":"

This paper documents a reversal in the groundwater salinity depth gradient in the North Coles Levee Oil Field in the San Joaquin Valley, California. Salinity, measured in mg/L, was mapped with water quality data from groundwater and oil and gas wells and salinity estimated from oil and gas well borehole geophysical logs using Archie's equation. The resulting three-dimensional salinity volume shows groundwater salinity increasing with depth through the Tulare and San Joaquin Formations to about 50,000 mg/L at 1100 m depth, then decreasing to 10,000–31,000 mg/L in the Etchegoin Formation at 1400 m depth. The high salinity zone occurs near the base of the San Joaquin Formation in sand lenses in shales that have been interpreted as representing a mudflat environment. The groundwater and produced water geochemistry show formation waters lie on the seawater dilution line, indicating the salinity structure is largely the result of dilution or evaporation of seawater and not due to water–rock interactions. Instead, changing depositional environments linked to decreasing sea level may be responsible for variably saline water at or near the time of deposition, leading to a salinity reversal preserved in connate waters. The steepness of the salinity reversal varies laterally, possibly due to post-depositional freshwater recharge allowed by thick sands, alternatively, by a change in connate water composition due to a lateral facies change present at the time of deposition. These results illustrate geologic and paleogeographic processes that drive the vertical salinity structure of groundwater in shallow alluvial basins.

","language":"English","publisher":"Springer","doi":"10.1007/s12665-022-10362-4","usgsCitation":"Flowers, M.D., Shimabukuro, D.H., Stephens, M.J., Warden, J.G., Gillespie, J., and Chang, W., 2022, Geologic controls on groundwater salinity reversal in North Coles Levee Oil Field, southern San Joaquin Valley, California, USA: Environmental Earth Sciences, v. 81, 317, 16 p., https://doi.org/10.1007/s12665-022-10362-4.","productDescription":"317, 16 p.","ipdsId":"IP-127486","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":447640,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s12665-022-10362-4","text":"Publisher Index Page"},{"id":435838,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GHJDL4","text":"USGS data release","linkHelpText":"Geophysical and geological data for select petroleum wells in North Coles Levee Oil Field, Kern County, California"},{"id":401525,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"North Coles Levee Oil Field, southern San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.794921875,\n 34.74161249883172\n ],\n [\n -117.92724609375,\n 34.74161249883172\n ],\n [\n -117.92724609375,\n 35.746512259918504\n ],\n [\n -119.794921875,\n 35.746512259918504\n ],\n [\n -119.794921875,\n 34.74161249883172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"81","noUsgsAuthors":false,"publicationDate":"2022-05-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Flowers, Michael D 0000-0002-7765-7057","orcid":"https://orcid.org/0000-0002-7765-7057","contributorId":291849,"corporation":false,"usgs":false,"family":"Flowers","given":"Michael","email":"","middleInitial":"D","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":844007,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shimabukuro, David H. 0000-0002-6106-5284","orcid":"https://orcid.org/0000-0002-6106-5284","contributorId":208209,"corporation":false,"usgs":false,"family":"Shimabukuro","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37762,"text":"California State University, Sacramento","active":true,"usgs":false}],"preferred":false,"id":844008,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stephens, Michael J. 0000-0001-8995-9928","orcid":"https://orcid.org/0000-0001-8995-9928","contributorId":205895,"corporation":false,"usgs":true,"family":"Stephens","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844009,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Warden, John G. 0000-0003-1384-458X","orcid":"https://orcid.org/0000-0003-1384-458X","contributorId":215846,"corporation":false,"usgs":true,"family":"Warden","given":"John","email":"","middleInitial":"G.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844010,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gillespie, Janice M. 0000-0003-1667-3472","orcid":"https://orcid.org/0000-0003-1667-3472","contributorId":203915,"corporation":false,"usgs":true,"family":"Gillespie","given":"Janice M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":844011,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chang, Will 0000-0002-0796-0763","orcid":"https://orcid.org/0000-0002-0796-0763","contributorId":208210,"corporation":false,"usgs":false,"family":"Chang","given":"Will","email":"","affiliations":[{"id":37763,"text":"Hypergradient LLC","active":true,"usgs":false}],"preferred":false,"id":844012,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}