{"pageNumber":"154","pageRowStart":"3825","pageSize":"25","recordCount":46651,"records":[{"id":70248097,"text":"70248097 - 2022 - 2021 National park visitor spending effects: Economic contributions to local communities, states, and the nation","interactions":[],"lastModifiedDate":"2023-09-05T15:55:29.98934","indexId":"70248097","displayToPublicDate":"2022-06-01T10:53:59","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":53,"text":"Natural Resource Report","active":false,"publicationSubtype":{"id":1}},"seriesNumber":"NPS/NRSS/EQD/NRR-2022/2395","title":"2021 National park visitor spending effects: Economic contributions to local communities, states, and the nation","docAbstract":"The National Park Service (NPS) manages the Nation’s most iconic destinations that attract millions of visitors from across the Nation and around the world. Trip-related spending by NPS visitors generates and supports economic activity within park gateway communities. This report summarizes the annual economic contribution analysis that measures how NPS visitor spending cycles through local economies, generating business sales and supporting jobs and income.\nIn 2021, the National Park System received over 297 million recreation visits (up 25% from 2020). Visitors to national parks spent an estimated $20.5 billion in local gateway regions (up 41% from 2020). The estimated contribution of this spending to the national economy was 322,600 jobs, $14.6 billion in labor income, $24.3 billion in value added, and $42.5 billion in economic output. The lodging sector saw the highest direct effects, with $7 billion in economic output directly contributed to this sector nationally. The restaurants sector saw the next greatest effects, with $4.2 billion in economic output directly contributed to this sector nationally.\nResults from the Visitor Spending Effects report series are available online via an interactive tool. Users can view year-by-year trend data and explore current year visitor spending, jobs, labor income, value added, and economic output effects by sector for national, state, and local economies. The interactive tool is available at https://www.nps.gov/subjects/socialscience/vse.htm.","language":"English","publisher":"National Park Service","usgsCitation":"Cullinane Thomas, C., Flyr, M., and Koontz, L., 2022, 2021 National park visitor spending effects: Economic contributions to local communities, states, and the nation: Natural Resource Report NPS/NRSS/EQD/NRR-2022/2395, v, 63 p.","productDescription":"v, 63 p.","ipdsId":"IP-139225","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":420493,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://www.nps.gov/nature/customcf/NPS_Data_Visualization/docs/NPS_2021_Visitor_Spending_Effects.pdf"},{"id":420494,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cullinane Thomas, Catherine M. 0000-0001-8168-1271","orcid":"https://orcid.org/0000-0001-8168-1271","contributorId":328910,"corporation":false,"usgs":true,"family":"Cullinane Thomas","given":"Catherine M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":881839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Flyr, Matthew 0000-0002-4723-3763","orcid":"https://orcid.org/0000-0002-4723-3763","contributorId":291828,"corporation":false,"usgs":false,"family":"Flyr","given":"Matthew","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":881840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koontz, Lynne koontzl@usgs.gov","contributorId":2174,"corporation":false,"usgs":false,"family":"Koontz","given":"Lynne","email":"koontzl@usgs.gov","affiliations":[{"id":7016,"text":"Environmental Quality Division, National Park Service, Fort Collins, Colorado","active":true,"usgs":false}],"preferred":false,"id":881841,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70237855,"text":"70237855 - 2022 - The population genetics of the causative agent of snake fungal disease indicate recent introductions to the USA","interactions":[],"lastModifiedDate":"2022-10-27T15:51:09.02612","indexId":"70237855","displayToPublicDate":"2022-06-01T10:44:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2979,"text":"PLoS Biology","active":true,"publicationSubtype":{"id":10}},"title":"The population genetics of the causative agent of snake fungal disease indicate recent introductions to the USA","docAbstract":"

Snake fungal disease (SFD; ophidiomycosis), caused by the pathogen Ophidiomyces ophiodiicola (Oo), has been documented in wild snakes in North America and Eurasia, and is considered an emerging disease in the eastern United States of America. However, a lack of historical disease data has made it challenging to determine whether Oo is a recent arrival to the USA or whether SFD emergence is due to other factors. Here, we examined the genomes of 82 Oo strains to determine the pathogen’s history in the eastern USA. Oo strains from the USA formed a clade (Clade II) distinct from European strains (Clade I), and molecular dating indicated that these clades diverged too recently (approximately 2,000 years ago) for transcontinental dispersal of Oo to have occurred via natural snake movements across Beringia. A lack of nonrecombinant intermediates between clonal lineages in Clade II indicates that Oo has actually been introduced multiple times to North America from an unsampled source population, and molecular dating indicates that several of these introductions occurred within the last few hundred years. Molecular dating also indicated that the most common Clade II clonal lineages have expanded recently in the USA, with time of most recent common ancestor mean estimates ranging from 1985 to 2007 CE. The presence of Clade II in captive snakes worldwide demonstrates a potential mechanism of introduction and highlights that additional incursions are likely unless action is taken to reduce the risk of pathogen translocation and spillover into wild snake populations.

","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pbio.3001676","usgsCitation":"Ladner, J.T., Palmer, J.M., Ettinger, C.L., Stajich, J.E., Farrell, T.M., Glorioso, B.M., Lawson, B., Price, S.J., Stengle, A.G., Grear, D.A., and Lorch, J.M., 2022, The population genetics of the causative agent of snake fungal disease indicate recent introductions to the USA: PLoS Biology, v. 20, no. 6, e3001676, 24 p., https://doi.org/10.1371/journal.pbio.3001676.","productDescription":"e3001676, 24 p.","ipdsId":"IP-137982","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447577,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pbio.3001676","text":"Publisher Index Page"},{"id":408806,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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dgrear@usgs.gov","orcid":"https://orcid.org/0000-0002-5478-1549","contributorId":189819,"corporation":false,"usgs":true,"family":"Grear","given":"Daniel","email":"dgrear@usgs.gov","middleInitial":"A.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":855940,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":298587,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":855941,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70232214,"text":"70232214 - 2022 - Late Paleozoic flexural extension and overprinting shortening in the southern Ozark dome, Arkansas, USA: Evolving fault kinematics in the foreland of the Ouachita orogen","interactions":[],"lastModifiedDate":"2022-06-14T13:55:55.492663","indexId":"70232214","displayToPublicDate":"2022-06-01T08:52:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3524,"text":"Tectonics","active":true,"publicationSubtype":{"id":10}},"title":"Late Paleozoic flexural extension and overprinting shortening in the southern Ozark dome, Arkansas, USA: Evolving fault kinematics in the foreland of the Ouachita orogen","docAbstract":"

Faults and folds on the southern flank of the Ozark dome in northern Arkansas, USA, record flexural extension in a foreland area followed by shortening in response to the late Paleozoic Ouachita orogeny. Map-scale structures and an analysis of fault-slip data collected systematically during geologic mapping demonstrate that most deformation in the area accommodated north-south extension as the southern margin of Laurentia was flexed beneath the thrust load of the Ouachita belt, probably during Middle Pennsylvanian. Extension was concentrated in northeast- and west-northwest-trending structural zones having sets of discontinuous, often en echelon normal and strike-slip faults and associated monoclinal folds. Reactivation of basement weaknesses that underlie these zones is indicated by their close match to oblique-rift models in which both the proportions of normal and strike-slip faulting and the internal extension directions vary with orientation of the zones. Subsequent propagation of north-south Ouachita shortening into the foreland formed small-offset strike-slip and sparse reverse faults that overprinted older extensional structures. Strike-slip faults were concentrated in reactivated northeast-trending structural zones. In two areas, reverse faults and local anticlines were developed in the footwalls of older normal faults, both near intersections of northeast- and west-northwest-trending structural zones. These are interpreted as areas of incipient inversion due to compressional stress concentrations at fault-block corners. Spatial overlap of areas of north-south shortening and fluid flux marked by silicification or lead-zinc mineralization indicates that regional fluid flow of brines was coeval with and may have enhanced inversion during Late Pennsylvanian to early Permian.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021TC006706","usgsCitation":"Hudson, M., and Turner, K.J., 2022, Late Paleozoic flexural extension and overprinting shortening in the southern Ozark dome, Arkansas, USA: Evolving fault kinematics in the foreland of the Ouachita orogen: Tectonics, v. 41, e2021TC006706, 27 p., https://doi.org/10.1029/2021TC006706.","productDescription":"e2021TC006706, 27 p.","ipdsId":"IP-125523","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":447580,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021tc006706","text":"Publisher Index Page"},{"id":435830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92TLPU2","text":"USGS data release","linkHelpText":"Fault data collected between 1996 and 2019 from the Buffalo River watershed area, northern Arkansas"},{"id":402148,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Kansas, Missouri, Oklahoma","otherGeospatial":"Ozark dome","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.63623046875,\n 35.51434313431818\n ],\n [\n -92.83447265624999,\n 35.60371874069731\n ],\n [\n -92.30712890625,\n 35.71083783530009\n ],\n [\n -88.22021484375,\n 36.24427318493909\n ],\n [\n -88.3740234375,\n 37.055177106660814\n ],\n [\n -89.14306640625,\n 37.125286284966805\n ],\n [\n -89.5166015625,\n 37.71859032558816\n ],\n [\n -90.28564453124999,\n 38.09998264736481\n ],\n [\n -91.34033203125,\n 38.238180119798635\n ],\n [\n -92.0654296875,\n 38.34165619279595\n ],\n [\n -93.4716796875,\n 38.22091976683121\n ],\n [\n -94.52636718749999,\n 37.47485808497102\n ],\n [\n -94.9658203125,\n 37.23032838760387\n ],\n [\n -95.1416015625,\n 36.58024660149866\n ],\n [\n -95.361328125,\n 35.90684930677121\n ],\n [\n -94.89990234375,\n 35.496456056584165\n ],\n [\n -94.63623046875,\n 35.51434313431818\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationDate":"2022-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Hudson, Mark R. 0000-0003-0338-6079 mhudson@usgs.gov","orcid":"https://orcid.org/0000-0003-0338-6079","contributorId":1236,"corporation":false,"usgs":true,"family":"Hudson","given":"Mark R.","email":"mhudson@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":844677,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turner, Kenzie J. 0000-0002-4940-3981 kturner@usgs.gov","orcid":"https://orcid.org/0000-0002-4940-3981","contributorId":496,"corporation":false,"usgs":true,"family":"Turner","given":"Kenzie","email":"kturner@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":844678,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237405,"text":"70237405 - 2022 - Overturning stereotypes: The fuzzy boundary between recreational and subsistence inland fisheries","interactions":[],"lastModifiedDate":"2022-10-12T13:52:58.684473","indexId":"70237405","displayToPublicDate":"2022-06-01T08:41:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1652,"text":"Fish and Fisheries","active":true,"publicationSubtype":{"id":10}},"title":"Overturning stereotypes: The fuzzy boundary between recreational and subsistence inland fisheries","docAbstract":"Inland recreational fisheries provide numerous socio- economic benefits to fishers, families and communities. Recreationally harvested fish are also frequently consumed and may provide affordable and sustainable but undervalued contributions to human nutrition. Quantifying the degree to which recreationally harvested fish contribute to food security and subsistence is impeded by lack of data on harvest and consumption and by the difficulty in differentiating among recreational and subsistence fisheries. Recreational harvest records tend to be limited to wealthy, food- secure countries and well- monitored fisheries with clear regulations or permitting systems. These records often neglect components of recreational harvest among food- insecure fishers who are potentially more likely to have consumption as a motivation. Here, we highlight the ‘fuzzy boundary’ that can exist between inland recreational and subsistence fisheries and argue that unreported consumption is likely to be a hidden contributor to food security in some populations. We draw on local case studies from around the world to highlight specific instances where recreationally harvested fish species contribute food and subsistence benefits to participating communities. We use these examples to highlight the diversity of ways that inland recreational fisheries contribute to human nutrition, knowledge gaps in understanding recreational fishing for food, and consequences of not accounting for them as food fisheries in policy and management. The aim of this paper is to draw the attention of resource managers and policy makers, create greater social awareness of the importance of recreational fisheries and bring to light this hidden contribution of inland fisheries to nutrition and subsistence.","language":"English","publisher":"Wiley","doi":"10.1111/faf.12688","usgsCitation":"Nyboer, E.A., Embke, H.S., Robertson, A., Arlinghaus, R., Bower, S., Baigun, C., Beard, T., Cooke, S.J., Cowx, I.G., Koehn, J.D., Lyach, R., Milardi, M., Potts, W.M., and Lynch, A., 2022, Overturning stereotypes: The fuzzy boundary between recreational and subsistence inland fisheries: Fish and Fisheries, v. 23, no. 6, p. 1282-1298, https://doi.org/10.1111/faf.12688.","productDescription":"17 p.","startPage":"1282","endPage":"1298","ipdsId":"IP-135314","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":500571,"rank":0,"type":{"id":41,"text":"Open Access External Repository 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]\n }\n }\n ]\n}","volume":"23","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Nyboer, Elizabeth A.","contributorId":250650,"corporation":false,"usgs":false,"family":"Nyboer","given":"Elizabeth","email":"","middleInitial":"A.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":854401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Embke, Holly Susan 0000-0002-9897-7068","orcid":"https://orcid.org/0000-0002-9897-7068","contributorId":270754,"corporation":false,"usgs":true,"family":"Embke","given":"Holly","email":"","middleInitial":"Susan","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":854402,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Ashley","contributorId":270755,"corporation":false,"usgs":false,"family":"Robertson","given":"Ashley","affiliations":[{"id":56210,"text":"George Mason U.","active":true,"usgs":false}],"preferred":false,"id":854403,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arlinghaus, Robert","contributorId":32425,"corporation":false,"usgs":false,"family":"Arlinghaus","given":"Robert","email":"","affiliations":[{"id":17980,"text":"Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Berlin, Germany","active":true,"usgs":false}],"preferred":false,"id":854404,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bower, Shannon D.","contributorId":166936,"corporation":false,"usgs":false,"family":"Bower","given":"Shannon D.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":854405,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baigun, Claudio","contributorId":292267,"corporation":false,"usgs":false,"family":"Baigun","given":"Claudio","email":"","affiliations":[{"id":62854,"text":"Institute of Research and Environmental Engineering","active":true,"usgs":false}],"preferred":false,"id":854406,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Beard, T. Douglas Jr. 0000-0003-2632-2350","orcid":"https://orcid.org/0000-0003-2632-2350","contributorId":245522,"corporation":false,"usgs":true,"family":"Beard","given":"T. Douglas","suffix":"Jr.","affiliations":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":854407,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cooke, Steve J.","contributorId":220492,"corporation":false,"usgs":false,"family":"Cooke","given":"Steve","email":"","middleInitial":"J.","affiliations":[{"id":17786,"text":"Carleton University","active":true,"usgs":false}],"preferred":false,"id":854408,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cowx, Ian. G.","contributorId":220479,"corporation":false,"usgs":false,"family":"Cowx","given":"Ian.","email":"","middleInitial":"G.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":854409,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Koehn, John D.","contributorId":220481,"corporation":false,"usgs":false,"family":"Koehn","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":27292,"text":"Arthur Rylah Institute for Environmental Research","active":true,"usgs":false}],"preferred":false,"id":854410,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Lyach, Roman","contributorId":295283,"corporation":false,"usgs":false,"family":"Lyach","given":"Roman","email":"","affiliations":[{"id":63822,"text":"The Institute for Evaluations and Social Analyses","active":true,"usgs":false}],"preferred":false,"id":854411,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Milardi, Marco","contributorId":201384,"corporation":false,"usgs":false,"family":"Milardi","given":"Marco","email":"","affiliations":[],"preferred":false,"id":854412,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Potts, Warren M.","contributorId":295287,"corporation":false,"usgs":false,"family":"Potts","given":"Warren","email":"","middleInitial":"M.","affiliations":[{"id":63825,"text":"Rhodes University","active":true,"usgs":false}],"preferred":false,"id":854413,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Lynch, Abigail J. 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":207361,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","middleInitial":"J.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":854414,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70250193,"text":"70250193 - 2022 - Evolving magma temperature and volatile contents over the 2008–2018 summit eruption of Kīlauea Volcano","interactions":[],"lastModifiedDate":"2023-11-28T13:28:07.883022","indexId":"70250193","displayToPublicDate":"2022-06-01T07:24:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Evolving magma temperature and volatile contents over the 2008–2018 summit eruption of Kīlauea Volcano","docAbstract":"
Magma rheology and volatile contents exert primary and highly nonlinear controls on volcanic activity. Subtle changes in these magma properties can modulate eruption style and hazards, making in situ inference of their temporal evolution vital for volcano monitoring. Here, we study thousands of impulsive magma oscillations within the shallow conduit and lava lake of Kīlauea Volcano, Hawai‘i, USA, over the 2008–2018 summit eruptive sequence, encoded by “very-long-period” seismic events and ground deformation. Inversion of these data with a petrologically informed model of magma dynamics reveals significant variation in temperature and highly disequilibrium volatile contents over days to years, within a transport network that evolved over the eruption. Our results suggest a framework for inferring subsurface magma dynamics associated with prolonged eruptions in near real time that synthesizes petrologic and geophysical volcano monitoring approaches.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.1126/sciadv.abm4310","usgsCitation":"Crozier, J.A., and Karlstrom, L., 2022, Evolving magma temperature and volatile contents over the 2008–2018 summit eruption of Kīlauea Volcano: Science Advances, v. 8, no. 22, eabm4310, 9 p., https://doi.org/10.1126/sciadv.abm4310.","productDescription":"eabm4310, 9 p.","ipdsId":"IP-134239","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447589,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1126/sciadv.abm4310","text":"Publisher Index Page"},{"id":423013,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kīlauea Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.4622437871858,\n 19.5593064442494\n ],\n [\n -155.4622437871858,\n 19.256216654399836\n ],\n [\n -155.02279066218574,\n 19.256216654399836\n ],\n [\n -155.02279066218574,\n 19.5593064442494\n ],\n [\n -155.4622437871858,\n 19.5593064442494\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"8","issue":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Crozier, Joshua Allen 0000-0001-8996-3441","orcid":"https://orcid.org/0000-0001-8996-3441","contributorId":331790,"corporation":false,"usgs":true,"family":"Crozier","given":"Joshua","email":"","middleInitial":"Allen","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":888784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karlstrom, Leif 0000-0002-2197-2349","orcid":"https://orcid.org/0000-0002-2197-2349","contributorId":261729,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Leif","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":888785,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70243331,"text":"70243331 - 2022 - Luminescence sediment tracing reveals the complex dynamics of colluvial wedge formation","interactions":[],"lastModifiedDate":"2023-05-09T11:59:46.047485","indexId":"70243331","displayToPublicDate":"2022-06-01T06:51:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3338,"text":"Science","active":true,"publicationSubtype":{"id":10}},"title":"Luminescence sediment tracing reveals the complex dynamics of colluvial wedge formation","docAbstract":"
Paleoearthquake studies that inform seismic hazard rely on assumptions of sediment transport that remain largely untested. Here, we test a widespread conceptual model and a new numerical model on the formation of colluvial wedges, a key deposit used to constrain the timing of paleoearthquakes. We perform this test by applying luminescence, a sunlight-sensitive sediment tracer, at a field site displaying classic colluvial wedge morphostratigraphy. The model and data comparison reveals complex sediment transport processes beyond the predictions of either conceptual or numerical models, including periods of simultaneous debris and wash facies forming processes, erosion, and reworking. These processes could lead to preservation bias, such as incomplete or overinterpretable paleoearthquake records, given the right environmental conditions. Attention to the site-specific mechanics of fault zone depositional systems, such as via sediment tracing, may buffer against the possible effects of preservation bias on paleoseismic study.
","language":"English","publisher":"Science","doi":"10.1126/sciadv.abo0747","usgsCitation":"Gray, H., DuRoss, C., Nicovich, S., and Gold, R.D., 2022, Luminescence sediment tracing reveals the complex dynamics of colluvial wedge formation: Science, v. 8, no. 22, eabo0747, 11 p., https://doi.org/10.1126/sciadv.abo0747.","productDescription":"eabo0747, 11 p.","ipdsId":"IP-132616","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":447592,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abo0747","text":"External Repository"},{"id":416851,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"22","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gray, Harrison J. 0000-0002-4555-7473","orcid":"https://orcid.org/0000-0002-4555-7473","contributorId":207019,"corporation":false,"usgs":true,"family":"Gray","given":"Harrison J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":872065,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DuRoss, Christopher 0000-0002-6963-7451 cduross@usgs.gov","orcid":"https://orcid.org/0000-0002-6963-7451","contributorId":152321,"corporation":false,"usgs":true,"family":"DuRoss","given":"Christopher","email":"cduross@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":872066,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nicovich, Sylvia","contributorId":210054,"corporation":false,"usgs":false,"family":"Nicovich","given":"Sylvia","affiliations":[{"id":38060,"text":"Department of Earth Sciences, Montana State University, Bozeman, MT","active":true,"usgs":false}],"preferred":false,"id":872067,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gold, Ryan D. 0000-0002-4464-6394 rgold@usgs.gov","orcid":"https://orcid.org/0000-0002-4464-6394","contributorId":3883,"corporation":false,"usgs":true,"family":"Gold","given":"Ryan","email":"rgold@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":872068,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70249673,"text":"70249673 - 2022 - Inversion of induced polarization-affected towed-transient electromagnetic data in a lateritic regolith geology: A case study from western Tanzania","interactions":[],"lastModifiedDate":"2023-10-24T11:47:23.229236","indexId":"70249673","displayToPublicDate":"2022-06-01T06:37:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1808,"text":"Geophysics","active":true,"publicationSubtype":{"id":10}},"title":"Inversion of induced polarization-affected towed-transient electromagnetic data in a lateritic regolith geology: A case study from western Tanzania","docAbstract":"

For several decades, induced polarization (IP) effects on transient electromagnetic (TEM) responses have been observed. These effects can manifest as late-time negative transients or as rapidly decaying curves and are usually associated with highly polarizable bodies. If neglected, IP effects can lead to erroneous resistivity models. Recent work allows IP effects to be incorporated into the inversion of TEM data on a more routine basis. In a recent field survey in western Tanzania, strongly IP-affected TEM signals are observed using a towed-transient electromagnetic (tTEM) system. The survey have been carried out to locate drinking water resources in a weathered regolith setting. In these settings, an inversion of tTEM data using a resistivity-only forward model (i.e., IP neglected) cannot fit the data and severely limits the value of the TEM data for hydrogeologic interpretation. To account for IP effects, we have applied a modified version of the Cole-Cole model called the maximum phase angle (MPA) model to invert IP-affected tTEM data. The MPA model incorporates four inversion model parameters: resistivity (⁠ρ\">ρ), MPA (⁠ϕmax\">ϕmax⁠), relaxation time (⁠τ\">τ), and frequency exponent (⁠c\">c). The MPA model fits the data well and improves the reliability of the resistivity model. In much of the surveyed region, the inverted models using MPA display a three-layer system consisting of an upper resistive laterite layer of varying thickness and an intermediate polarizable conductive unit overlying more resistive weathered basement rocks. The conductive polarizable layer is interpreted as a chemically weathered saprolite separating the surficial and deeper aquifers. Overall, tTEM inversion results provide a local understanding of groundwater systems, especially in such regions with very limited subsurface knowledge.

","language":"English","publisher":"Society of Exploration Geophysics","doi":"10.1190/geo2021-0396.1","usgsCitation":"Maurya, P.K., Grombacher, D., Lind, J., Lane, J.W., and Auken, E., 2022, Inversion of induced polarization-affected towed-transient electromagnetic data in a lateritic regolith geology: A case study from western Tanzania: Geophysics, v. 87, no. 4, p. B247-B254, https://doi.org/10.1190/geo2021-0396.1.","productDescription":"8 p.","startPage":"B247","endPage":"B254","ipdsId":"IP-129592","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":422061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Tanzania","city":"Kaguruka, Kitagata","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.26631758554646,\n -4.307213040551957\n ],\n [\n 30.26631758554646,\n -4.45300782969197\n ],\n [\n 30.49585195666816,\n -4.45300782969197\n ],\n [\n 30.49585195666816,\n -4.307213040551957\n ],\n [\n 30.26631758554646,\n -4.307213040551957\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 30.2257012708871,\n -4.777233849561554\n ],\n [\n 30.2257012708871,\n -4.817521702247873\n ],\n [\n 30.294869797065786,\n -4.817521702247873\n ],\n [\n 30.294869797065786,\n -4.777233849561554\n ],\n [\n 30.2257012708871,\n -4.777233849561554\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"87","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Maurya, Pradip Kumar","contributorId":214855,"corporation":false,"usgs":false,"family":"Maurya","given":"Pradip","email":"","middleInitial":"Kumar","affiliations":[{"id":13419,"text":"Aarhus University, Denmark","active":true,"usgs":false}],"preferred":false,"id":886697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Grombacher, Denys","contributorId":331071,"corporation":false,"usgs":false,"family":"Grombacher","given":"Denys","email":"","affiliations":[],"preferred":false,"id":886698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lane, John W. 0000-0002-3558-243X","orcid":"https://orcid.org/0000-0002-3558-243X","contributorId":219742,"corporation":false,"usgs":true,"family":"Lane","given":"John","email":"","middleInitial":"W.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":886675,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lind, Johan","contributorId":331072,"corporation":false,"usgs":false,"family":"Lind","given":"Johan","email":"","affiliations":[],"preferred":false,"id":886699,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Auken, Esben","contributorId":193991,"corporation":false,"usgs":false,"family":"Auken","given":"Esben","email":"","affiliations":[],"preferred":false,"id":886700,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70262182,"text":"70262182 - 2022 - Review of field methods for monitoring Asian bears","interactions":[],"lastModifiedDate":"2025-01-15T15:48:50.052344","indexId":"70262182","displayToPublicDate":"2022-06-01T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Review of field methods for monitoring Asian bears","docAbstract":"

Efficient and effective monitoring methods are required to assess population status and gauge efficacy of conservation actions for threatened species. Here we review the spectrum of field methods useful for monitoring distribution, occupancy, abundance, and population trend for the five species of Asian terrestrial bears. Methods reviewed include expert opinion, local knowledge, bear sign, visual observations, camera traps, DNA-based methods (hair and scat derived), and radio telemetry. We examine the application of each method in terms of realizing specific monitoring objectives, their assumptions, challenges, and advantages. Our goal is to assist researchers in matching appropriate field methods with sought-after project objectives and to highlight shortfalls and trade-offs. Methods vary greatly in terms of cost, logistics, required number and expertise of staff, and the reliability of the data they provide. Many Asian bear population assessments have relied on expert opinion, local interviews, and sign surveys to provide estimates of distribution, abundance, and trend, in part because these are inexpensive and relatively easy to employ. However, increasing use of camera traps and DNA-based methods now allow for better monitoring via occupancy or rigorous capture–recapture population estimation, with the caveat that these methods may be restricted by inadequate budgets or logistical constraints. For distribution monitoring, camera traps and DNA yield the most definitive records of presence, but in low density bear populations, sign and local knowledge may be more effective. For occupancy, camera traps and DNA are advantageous in providing definitive detections in known time periods. For abundance/density or population trend monitoring in relatively small areas (<10,000 km2), bears must be individually identified and used in a mark-recapture design. This requires DNA from collections of hair or scat, or a camera-based survey in which natural chest marks are clearly visible and individually distinguishable. DNA-methods or camera traps within individual identification is best for population trend when sufficient funding is available. Alternatively, careful use of local knowledge or expert opinion may be viable options, but come with greater uncertainty. For the foreseeable future, we believe that expert opinion will likely continue to play a large part in monitoring Asian bears, but these opinions should be informed by more rigorous data from the other methods we discuss.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02080","usgsCitation":"Proctor, M., Garshelis, D., Thatte, P., Steinmetz, R., Crudge, B., McLellan, B., McShea, W., Ngoprasert, D., Nawaz, M., Wong, S.T., Sharma, S., Fuller, A.K., Dharaiya, N., Pigeon, K., Fredriksson, G., Wang, D., Li, S., and Hwang, M., 2022, Review of field methods for monitoring Asian bears: Global Ecology and Conservation, v. 35, e02080, 25 p., https://doi.org/10.1016/j.gecco.2022.e02080.","productDescription":"e02080, 25 p.","ipdsId":"IP-135698","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467182,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02080","text":"Publisher Index Page"},{"id":466419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Proctor, Michael F.","contributorId":348345,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael F.","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923379,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garshelis, David L.","contributorId":348346,"corporation":false,"usgs":false,"family":"Garshelis","given":"David L.","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923380,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thatte, Prachi","contributorId":348347,"corporation":false,"usgs":false,"family":"Thatte","given":"Prachi","affiliations":[{"id":83342,"text":"Tata Institute of Fundamental Research","active":true,"usgs":false}],"preferred":false,"id":923381,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steinmetz, Robert","contributorId":348348,"corporation":false,"usgs":false,"family":"Steinmetz","given":"Robert","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923382,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crudge, Brian","contributorId":348349,"corporation":false,"usgs":false,"family":"Crudge","given":"Brian","affiliations":[{"id":83344,"text":"University of South-Eastern Norway","active":true,"usgs":false}],"preferred":false,"id":923383,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McLellan, Bruce N.","contributorId":348350,"corporation":false,"usgs":false,"family":"McLellan","given":"Bruce N.","affiliations":[{"id":83345,"text":"Lands and Natural Resource Operations and Rural Development","active":true,"usgs":false}],"preferred":false,"id":923384,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McShea, William J.","contributorId":348351,"corporation":false,"usgs":false,"family":"McShea","given":"William J.","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":923385,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ngoprasert, Dusit","contributorId":348352,"corporation":false,"usgs":false,"family":"Ngoprasert","given":"Dusit","affiliations":[{"id":83346,"text":"King Mongkut’s University of Technology Thonburi","active":true,"usgs":false}],"preferred":false,"id":923386,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nawaz, M. 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The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project has partnered with the Bureau of Land Management to provide annual maps of rangeland vegetation condition across the Western United States from 1985 to present. Annual mapping can assist land managers and scientists with monitoring changes to vegetation composition, evaluating past management practices, targeting future improvements, determining locations of critical wildlife habitat, and assessing landscape health and fragmentation. Impacts of climate variability and long-term change are often gradual and frequently do not present as a land cover change (for example, shrubland to grassland); however, RCMAP fractional vegetation cover data capture these gradual changes.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223036","usgsCitation":"Rigge, M.B., 2022, Rangeland Condition Monitoring Assessment and Projection (RCMAP): U.S. Geological Survey Fact Sheet 2022–3036, 2 p., https://doi.org/10.3133/fs20223036.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-139947","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401389,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223036/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401374,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3036/coverthb.jpg"},{"id":401375,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3036/fs20223036.pdf","text":"Report","size":"8.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3036"},{"id":401377,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3036/images"},{"id":401376,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3036/fs20223036.XML"}],"contact":"

Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-31","noUsgsAuthors":false,"publicationDate":"2022-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Rigge, Matthew B. 0000-0003-4471-8009 mrigge@usgs.gov","orcid":"https://orcid.org/0000-0003-4471-8009","contributorId":751,"corporation":false,"usgs":true,"family":"Rigge","given":"Matthew","email":"mrigge@usgs.gov","middleInitial":"B.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":843952,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70231718,"text":"fs20223034 - 2022 - LANDFIRE data and applications","interactions":[],"lastModifiedDate":"2022-09-27T12:09:57.750701","indexId":"fs20223034","displayToPublicDate":"2022-05-31T08:24:35","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-3034","displayTitle":"LANDFIRE Data and Applications","title":"LANDFIRE data and applications","docAbstract":"

LANDFIRE is a Federal program that provides a suite of spatial datasets indicating areas of disturbance, vegetation and fuels distributions and structure, and historical conditions. The level of detail presented in LANDFIRE’s classifications of disturbance, vegetation, and fuels is unparalleled and can be used in a variety of applications, including (1) modeling wildfire risk and fire behavior, (2) modeling habitat and species ranges, (3) understanding how disturbances affect the landscape, and (4) researching departure from precolonial conditions. Additionally, the all-lands paradigm of LANDFIRE mapping creates spatial data that do not stop at jurisdictional boundaries. The primary research and management applications of LANDFIRE data are detailed in this fact sheet, providing users with a well-rounded understanding of the potential of LANDFIRE’s spatial data layers.

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LANDFIRE help desk
Earth Resources Observation and Science Center
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","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-05-31","noUsgsAuthors":false,"publicationDate":"2022-05-31","publicationStatus":"PW","contributors":{"authors":[{"text":"La Puma, Inga P. 0000-0002-6865-820X","orcid":"https://orcid.org/0000-0002-6865-820X","contributorId":206011,"corporation":false,"usgs":false,"family":"La Puma","given":"Inga","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":843525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hatten, Timothy D. 0000-0003-3413-4325","orcid":"https://orcid.org/0000-0003-3413-4325","contributorId":291959,"corporation":false,"usgs":false,"family":"Hatten","given":"Timothy D.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":false,"id":843526,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70237968,"text":"70237968 - 2022 - River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin","interactions":[],"lastModifiedDate":"2022-11-02T11:49:35.521099","indexId":"70237968","displayToPublicDate":"2022-05-31T06:47:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12802,"text":"ESSOAr","active":true,"publicationSubtype":{"id":10}},"title":"River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin","docAbstract":"

Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.

","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511255.1","usgsCitation":"Choi, J., Quion, K.M., Reed, A., and Harvey, J., 2022, River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin: ESSOAr, 22 p., https://doi.org/10.1002/essoar.10511255.1.","productDescription":"22 p.","ipdsId":"IP-139945","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435833,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TEBOUR","text":"USGS data release","linkHelpText":"RiverMET: Workflow and scripts for river metabolism estimation including Illinois River Basin application, 2005 - 2020"},{"id":409056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Illinois River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.901423683579,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 42.70071815175049\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quion, Katherine Michelle Bernabe 0000-0003-2388-7508","orcid":"https://orcid.org/0000-0003-2388-7508","contributorId":298787,"corporation":false,"usgs":true,"family":"Quion","given":"Katherine","email":"","middleInitial":"Michelle Bernabe","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Ariel 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":298788,"corporation":false,"usgs":false,"family":"Reed","given":"Ariel","affiliations":[],"preferred":false,"id":856405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856406,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70231834,"text":"70231834 - 2022 - Spatially explicit management of genetic diversity using ancestry probability surfaces","interactions":[],"lastModifiedDate":"2022-12-15T14:48:20.853298","indexId":"70231834","displayToPublicDate":"2022-05-30T15:16:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2717,"text":"Methods in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Spatially explicit management of genetic diversity using ancestry probability surfaces","docAbstract":"

1. Ecological restoration and conservation efforts are increasing worldwide and the management of intraspecific genetic variation in plants and animals, an important component of biodiversity, is increasingly valued. As a result, tailorable, spatially explicit approaches to map genetic variation are needed to support decision-making and management frameworks related to the recovery of threatened and endangered species and the maintenance of genetic resources in species utilized by humans, such as for restoration or agricultural purposes.

2. Here, we describe and demonstrate a workflow to spatially interpolate patterns of genetic differentiation using novel functions in the R package POPMAPS (Population Management using Ancestry Probability Surfaces). Our approach uses empirical genetic data to estimate ancestry coefficients across a user-defined landscape correlated with patterns of differentiation in the focal species. The resulting surface, which we term the ancestry probability surface, includes two components: hard population boundaries and estimations of uncertainty that represent confidence in population assignments (i.e., ancestry probabilities).

3. An ancestry probability surface developed for Hilaria jamesii, an important graminoid utilized in restoration across the western United States, demonstrates the functionality of POPMAPS. Genetic distances among empirical sites correlated better with least-cost distances across suitable habitat than with geographic distances, informing the surface over which the interpolation was conducted (i.e., a model indicating habitat suitability). A jackknifing procedure identified parameter values resulting in robust population assignments across the species’ range, which were utilized in downstream analyses to estimate ancestry coefficients from empirical data. Ancestry coefficients were translated into ancestry probabilities, which tended to be low for cells that were intermediate in distance between empirical sampling locations representing different populations or when influenced by empirical sampling locations with mixed genetic ancestry.

4. POPMAPS allows users to tailor parameter values and analytical approaches and thereby incorporate species-specific biological characteristics and desired levels of uncertainty into maps illustrating patterns of genetic differentiation. Ancestry probability surfaces may be used to guide management or investigate further ecological or evolutionary hypotheses. We discuss how maps produced by POPMAPS can inform multiple management challenges including species recovery planning and the utilization of commonly used species in restoration.

","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.13902","usgsCitation":"Massatti, R., and Winkler, D.E., 2022, Spatially explicit management of genetic diversity using ancestry probability surfaces: Methods in Ecology and Evolution, v. 13, no. 12, p. 2668-2681, https://doi.org/10.1111/2041-210X.13902.","productDescription":"14 p.","startPage":"2668","endPage":"2681","ipdsId":"IP-133238","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447618,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13902","text":"Publisher Index Page"},{"id":435837,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VLOA5","text":"USGS data release","linkHelpText":"POPMAPS: An R package to estimate ancestry probability surfaces"},{"id":401362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843924,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274304,"text":"70274304 - 2022 - Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","interactions":[],"lastModifiedDate":"2026-03-26T16:59:44.858337","indexId":"70274304","displayToPublicDate":"2022-05-30T11:52:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","title":"Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","docAbstract":"

Erebus volcano, Antarctica, with its persistent phonolite lava lake, is a classic example of an evolved, CO2-rich rift volcano. Seismic studies provide limited images of the magmatic system. Here we show using magnetotelluric data that a steep, melt-related conduit of low electrical resistivity originating in the upper mantle undergoes pronounced lateral re-orientation in the deep crust before reaching shallower magmatic storage and the summit lava lake. The lateral turn represents a structural fault-valve controlling episodic flow of magma and CO2 vapour, which replenish and heat the high level phonolite differentiation zone. This magmatic valve lies within an inferred, east-west structural trend forming part of an accommodation zone across the southern termination of the Terror Rift, providing a dilatant magma pathway. Unlike H2O-rich subduction arc volcanoes, CO2-dominated Erebus geophysically shows continuous magmatic structure to shallow crustal depths of < 1 km, as the melt does not experience decompression-related volatile supersaturation and viscous stalling.

","language":"English","publisher":"Nature","doi":"10.1038/s41467-022-30627-7","usgsCitation":"Hill, G.J., Wannamaker, P.E., Maris, V., Stodt, J.A., Kordy, M., Unsworth, M.J., Bedrosian, P.A., Wallin, E.L., Uhlmann, D.F., Ogawa, Y., and Kyle, P.R., 2022, Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica: Nature Communications, v. 13, 2989, 10 p., https://doi.org/10.1038/s41467-022-30627-7.","productDescription":"2989, 10 p.","ipdsId":"IP-138531","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-30627-7","text":"Publisher Index Page"},{"id":501590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Mount Erebus","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 165,\n -78\n ],\n [\n 170,\n -78\n ],\n [\n 170,\n -77\n ],\n [\n 165,\n -77\n ],\n [\n 165,\n -78\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Hill, Graham J","contributorId":367839,"corporation":false,"usgs":false,"family":"Hill","given":"Graham","middleInitial":"J","affiliations":[{"id":79730,"text":"Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":957801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wannamaker, Phil E","contributorId":367840,"corporation":false,"usgs":false,"family":"Wannamaker","given":"Phil","middleInitial":"E","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":957802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maris, Virginie","contributorId":194006,"corporation":false,"usgs":false,"family":"Maris","given":"Virginie","affiliations":[],"preferred":false,"id":957803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stodt, J. A.","contributorId":367843,"corporation":false,"usgs":false,"family":"Stodt","given":"J.","middleInitial":"A.","affiliations":[{"id":87627,"text":"Numerical Resources LLC","active":true,"usgs":false}],"preferred":false,"id":957804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kordy, Michael","contributorId":367844,"corporation":false,"usgs":false,"family":"Kordy","given":"Michael","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":957805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Unsworth, Martyn J.","contributorId":367845,"corporation":false,"usgs":false,"family":"Unsworth","given":"Martyn","middleInitial":"J.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":957806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":957807,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wallin, Erin L.","contributorId":367846,"corporation":false,"usgs":false,"family":"Wallin","given":"Erin","middleInitial":"L.","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":957808,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Uhlmann, Danny F.","contributorId":367847,"corporation":false,"usgs":false,"family":"Uhlmann","given":"Danny","middleInitial":"F.","affiliations":[{"id":35541,"text":"University of Lausanne","active":true,"usgs":false}],"preferred":false,"id":957809,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ogawa, Yasuo","contributorId":302663,"corporation":false,"usgs":false,"family":"Ogawa","given":"Yasuo","email":"","affiliations":[{"id":38251,"text":"Tokyo Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":957810,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kyle, Philip R.","contributorId":174414,"corporation":false,"usgs":false,"family":"Kyle","given":"Philip","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":957811,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70256653,"text":"70256653 - 2022 - Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","interactions":[],"lastModifiedDate":"2024-08-29T15:02:58.967417","indexId":"70256653","displayToPublicDate":"2022-05-30T09:59:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","docAbstract":"

Over the last decade, spatial capture–recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies — individual animals observed at specific locations and times — can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional “building blocks” of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3771","usgsCitation":"Gardner, B., McClintock, B., Converse, S.J., and Hostetter, N.J., 2022, Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference: Ecology, v. 103, e3771, 13 p., https://doi.org/10.1002/ecy.3771.","productDescription":"e3771, 13 p.","ipdsId":"IP-130421","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"text":"Publisher Index Page"},{"id":433312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, B.","contributorId":341497,"corporation":false,"usgs":false,"family":"Gardner","given":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClintock, B.T.","contributorId":341498,"corporation":false,"usgs":false,"family":"McClintock","given":"B.T.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":908508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908510,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236349,"text":"70236349 - 2022 - P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","interactions":[],"lastModifiedDate":"2022-09-02T14:09:03.829255","indexId":"70236349","displayToPublicDate":"2022-05-30T09:01:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","docAbstract":"

This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S-wave velocity (VS) and P-wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model’s success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P-wave velocity profile and the S-wave velocity close to the resolution depth.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac201","usgsCitation":"Bas, E.E., Seylabi, E., Yong, A., Tehrani, H., and Asimaki, D., 2022, P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California: Geophysical Journal International, v. 231, no. 1, p. 536-551, https://doi.org/10.1093/gji/ggac201.","productDescription":"16 p.","startPage":"536","endPage":"551","ipdsId":"IP-132480","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447625,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20220804-250008000","text":"External Repository"},{"id":406134,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58618164062499,\n 38.75408327579141\n ],\n [\n -122.684326171875,\n 37.900865092570065\n ],\n [\n -122.3876953125,\n 36.98500309285596\n ],\n [\n -121.46484375,\n 35.6126508187567\n ],\n [\n -120.4541015625,\n 34.32529192442733\n ],\n [\n -120.14648437499999,\n 33.60546961227188\n ],\n [\n -119.05883789062501,\n 32.8334428466495\n ],\n [\n -117.18017578125,\n 32.565333160841035\n ],\n [\n -114.47753906249999,\n 32.7503226078097\n ],\n [\n -114.42260742187499,\n 32.99023555965106\n ],\n [\n -114.6533203125,\n 33.073130945006625\n ],\n [\n -114.70825195312501,\n 33.38558626887102\n ],\n [\n -114.47753906249999,\n 33.63291573870479\n ],\n [\n -114.49951171875,\n 33.925129700072\n ],\n [\n -114.071044921875,\n 34.32529192442733\n ],\n [\n -114.444580078125,\n 34.67839374011646\n ],\n [\n -114.664306640625,\n 35.03899204678081\n ],\n [\n -116.46606445312499,\n 36.465471886798134\n ],\n [\n -119.58618164062499,\n 38.75408327579141\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bas, Elif Ecem","contributorId":296121,"corporation":false,"usgs":false,"family":"Bas","given":"Elif","email":"","middleInitial":"Ecem","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seylabi, Elnaz","contributorId":296122,"corporation":false,"usgs":false,"family":"Seylabi","given":"Elnaz","email":"","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yong, Alan K. 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":296123,"corporation":false,"usgs":true,"family":"Yong","given":"Alan K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tehrani, Hesam","contributorId":296124,"corporation":false,"usgs":false,"family":"Tehrani","given":"Hesam","email":"","affiliations":[],"preferred":false,"id":850707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asimaki, Domniki","contributorId":146598,"corporation":false,"usgs":false,"family":"Asimaki","given":"Domniki","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":850705,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"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}]}} ,{"id":70232385,"text":"70232385 - 2022 - Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA","interactions":[],"lastModifiedDate":"2022-07-01T12:09:43.70979","indexId":"70232385","displayToPublicDate":"2022-05-28T18:02:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA","docAbstract":"

Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al2O3, Fe2O3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104054","usgsCitation":"Chatterjee, S., Mastalerz, M., Drobniak, A., and Karacan, C.O., 2022, Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA: International Journal of Coal Geology, v. 259, 104054, 14 p., https://doi.org/10.1016/j.coal.2022.104054.","productDescription":"104054, 14 p.","ipdsId":"IP-138032","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":402804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.56103515625,\n 40.49709237269567\n ],\n [\n -87.5390625,\n 39.35129035526705\n ],\n [\n -87.56103515625,\n 38.839707613545144\n ],\n [\n -87.86865234374999,\n 38.06539235133249\n ],\n [\n -88.11035156249999,\n 37.90953361677018\n ],\n [\n -88.154296875,\n 37.77071473849609\n ],\n [\n -87.451171875,\n 37.92686760148135\n ],\n [\n -87.099609375,\n 37.87485339352928\n ],\n [\n -86.81396484375,\n 38.048091067457236\n ],\n [\n -86.572265625,\n 37.89219554724437\n ],\n [\n -86.396484375,\n 38.11727165830543\n ],\n [\n -86.63818359375,\n 38.95940879245423\n ],\n [\n -86.8359375,\n 40.111688665595956\n ],\n [\n -87.03369140625,\n 40.463666324587685\n ],\n [\n -87.56103515625,\n 40.49709237269567\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chatterjee, Snahamoy","contributorId":292652,"corporation":false,"usgs":false,"family":"Chatterjee","given":"Snahamoy","email":"","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":845399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastalerz, Maria","contributorId":292654,"corporation":false,"usgs":false,"family":"Mastalerz","given":"Maria","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drobniak, Agnieszka","contributorId":292655,"corporation":false,"usgs":false,"family":"Drobniak","given":"Agnieszka","email":"","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":845402,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256676,"text":"70256676 - 2022 - Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","interactions":[],"lastModifiedDate":"2024-08-30T15:04:17.421329","indexId":"70256676","displayToPublicDate":"2022-05-28T09:39:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","docAbstract":"

Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3772","usgsCitation":"Hostetter, N.J., Regehr, E., Wilson, R., Royle, A., and Converse, S.J., 2022, Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model: Ecology, v. 103, no. 10, e3772, 13 p., https://doi.org/10.1002/ecy.3772.","productDescription":"e3772, 13 p.","ipdsId":"IP-130471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447644,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3772","text":"Publisher Index Page"},{"id":433369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"eastern Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.16804284685193,\n 70.64271222820804\n ],\n [\n -176.4405798143377,\n 63.26571385602864\n ],\n [\n -160.5704824801017,\n 63.43804844317145\n ],\n [\n -160.5759226643521,\n 70.4273607365736\n ],\n [\n -176.16804284685193,\n 70.64271222820804\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regehr, E.V.","contributorId":341555,"corporation":false,"usgs":false,"family":"Regehr","given":"E.V.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, R.R.","contributorId":341556,"corporation":false,"usgs":false,"family":"Wilson","given":"R.R.","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231783,"text":"sir20225029 - 2022 - Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019","interactions":[],"lastModifiedDate":"2026-04-09T17:09:27.463858","indexId":"sir20225029","displayToPublicDate":"2022-05-27T10:43:00","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":"2022-5029","displayTitle":"Hydrogeology and Groundwater Quality in the San Agustin Basin, New Mexico, 1975–2019","title":"Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019","docAbstract":"

This report describes the findings of a U.S. Geological Survey study, completed in cooperation with the Bureau of Land Management, focused on better understanding the present-day (1975–2019) hydrogeology and groundwater quality of the San Agustin Basin in west-central New Mexico to support sustainable groundwater resource management. The basin hosts a relatively undeveloped basin-fill and alluvium aquifer system and is topographically divided into east and west subbasins by the McClure Hills. Groundwater chemistry and groundwater elevation data were compiled, collected, and interpreted in the context of groundwater flow and quality. The analyses presented in this report consider groundwater chemistry data collected within the last decade (2010–19) and groundwater elevation data collected from 1975 through 2019 to provide insight into present-day conditions. Groundwater elevations show that groundwater typically moves from the highlands to the lowlands, with a prominent east to west regional trend. Groundwater elevations were lowest in the southwestern portion of the west subbasin, where estimated flow directions suggest underflow through the local highlands into the northern East Fork Gila River watershed, which is further supported by historical groundwater elevation data from the northern East Fork Gila River watershed. Gradual groundwater elevation gradients (about 2 feet per mile) near the east and west subbasin divide suggest that groundwater slowly flows from the east subbasin to the west subbasin.

Quantitative analyses of groundwater chemistry data show that groundwater in both subbasins has similar chemical characteristics. A systematic east to west groundwater evolution in water chemistry was not observed despite evidenced subbasin connectivity. The absence of this pattern suggests that groundwater mixing is regionally prevalent, sediment reactivity is low and variable, and (or) recharge conditions are comparable in both subbasins. Groundwater chemistry was generally independent of aquifer type, suggesting that the aquifers are hydrologically well connected. Corrected carbon-14 groundwater age estimates in the basin ranged from 232 to 13,916 years before present with a median of 5,409 years. A wide range of groundwater ages is therefore present in the basin, with waters commonly being thousands of years old, thereby supporting generally slow regional groundwater movement. A component of relatively young groundwater, for which estimated ages could not be accurately computed, is also present in the basin, and it may commonly mix with older waters. The spatial distribution of categorical and quantitative groundwater ages indicates that most recharge likely occurs in the highlands through mountain-block recharge and as focused recharge within arroyos, although evidence of modern (1953 and after) groundwater was minimal at sampled sites.

Median annual gradients (groundwater elevation change over time) indicate that most groundwater elevations in the lowlands changed little (−0.2 to 0.2 foot per year) from 1975 through 2019. Groundwater elevations in the highlands varied more annually, which is likely due to recharge from precipitation events. These more variable groundwater elevations in the highlands compared with the lowlands, along with groundwater ages, provide further evidence that most groundwater recharge takes place in the highlands, with minimal recharge in the lowlands. Median groundwater elevation change for all sites was −0.05 foot per year. Temporal consistency of lowland groundwater elevations suggests that regional groundwater dynamics have been more or less stable through time under current climate and development conditions, although median annual gradients indicate that groundwater elevations may have slightly declined on average between 1975 and 2019.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225029","collaboration":"Prepared in cooperation with Bureau of Land Management and in collaboration with New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Pepin, J.D., Travis, R.E., Blake, J.M., Rinehart, A., and Koning, D., 2022, Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019: U.S. Geological Survey Scientific Investigations Report 2022–5029, 61 p., 4 app., https://doi.org/10.3133/sir20225029.","productDescription":"Report: x, 61 p.; 6 Tables; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120066","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":502386,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113080.htm","linkFileType":{"id":5,"text":"html"}},{"id":401145,"rank":11,"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":401143,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.csv","text":"Table 3.1","size":"29.5 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 3.1"},{"id":401142,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.xlsx","text":"Table 3.1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 3.1"},{"id":401141,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.csv","text":"Table 2.1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 2.1"},{"id":401140,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.xlsx","text":"Table 2.1","size":"27.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 2.1"},{"id":401138,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.xlsx","text":"Table 1.1","size":"116 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 1.1"},{"id":401137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5029/images"},{"id":401134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5029/coverthb.jpg"},{"id":401135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.pdf","text":"Report","size":"8.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5029"},{"id":401139,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.csv","text":"Table 1.1","size":"146 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 1.1"},{"id":401136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.XML"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Agustin Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 34.5\n ],\n [\n -107.333,\n 34.5\n ],\n [\n -107.333,\n 33.333\n ],\n [\n -108.666,\n 33.333\n ],\n [\n -108.666,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"publishedDate":"2022-05-27","noUsgsAuthors":false,"publicationDate":"2022-05-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Pepin, Jeff D. 0000-0002-7410-9979","orcid":"https://orcid.org/0000-0002-7410-9979","contributorId":222161,"corporation":false,"usgs":true,"family":"Pepin","given":"Jeff","email":"","middleInitial":"D.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Travis, Rebecca E. 0000-0001-8601-7791 rtravis@usgs.gov","orcid":"https://orcid.org/0000-0001-8601-7791","contributorId":5562,"corporation":false,"usgs":true,"family":"Travis","given":"Rebecca E.","email":"rtravis@usgs.gov","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Blake, Johanna M. 0000-0003-4667-0096 jmtblake@usgs.gov","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":169698,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","email":"jmtblake@usgs.gov","middleInitial":"M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rinehart, Alex","contributorId":194395,"corporation":false,"usgs":false,"family":"Rinehart","given":"Alex","affiliations":[],"preferred":false,"id":843821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Koning, Daniel","contributorId":58355,"corporation":false,"usgs":true,"family":"Koning","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":843822,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231769,"text":"70231769 - 2022 - Fundamental science and engineering questions in planetary cave exploration","interactions":[],"lastModifiedDate":"2022-11-16T16:50:05.401052","indexId":"70231769","displayToPublicDate":"2022-05-27T08:48:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9967,"text":"JGR Planets","active":true,"publicationSubtype":{"id":10}},"title":"Fundamental science and engineering questions in planetary cave exploration","docAbstract":"

Nearly half a century ago, two papers postulated the likelihood of lunar lava tube caves using mathematical models. Today, armed with an array of orbiting and fly-by satellites and survey instrumentation, we have now acquired cave data across our solar system—including the identification of potential cave entrances on the Moon, Mars, and at least six other planetary bodies. These discoveries gave rise to the study of planetary caves. To help advance this field, we leveraged the expertise of an interdisciplinary group to identify a strategy to explore caves beyond Earth. Focusing primarily on astrobiology, the cave environment, geology, robotics, instrumentation, and human exploration, our goal was to produce a framework to guide this subdiscipline through at least the next decade. To do this, we first assembled a list of 198 science and engineering questions. Then, through a series of social surveys, 114 scientists and engineers winnowed down the list to the top 53 highest priority questions. This exercise resulted in identifying emerging and crucial research areas that require robust development to ultimately support a robotic mission to a planetary cave—principally the Moon and/or Mars. With the necessary financial investment and institutional support, the research and technological development required to achieve these necessary advancements over the next decade are attainable. Subsequently, we will be positioned to robotically examine lunar caves and search for evidence of life within martian caves; in turn, this will set the stage for human exploration and potential habitation of both the lunar and martian subsurface.

","language":"English","publisher":"Wiley","doi":"10.1029/2022JE007194","usgsCitation":"Wynne, J.J., Titus, T.N., Agha-Mohammadi, A., Azua-Bustos, A., Boston, P.J., de Leon, P., Demirel-Floyd, C., de Waele, J., Jones, H., Malaska, M.J., Miller, A.Z., Sapers, H.M., Sauro, F., Sonderegger, D.L., Uckert, K., Wong, U.Y., Alexander, E.C., Chiao, L., Cushing, G.E., DeDecker, J., Fairen, A.G., Frumkin, A., Harris, G.L., Kearney, M.L., Kerber, L.A., Leveille, R.J., Manyapu, K., Massironi, M., Mylroie, J.E., Onac, B.P., Parazynski, S.E., Phillips-Lander, C.M., Prettyman, T.H., Schulze-Makuch, D., Wagner, R.V., Whittaker, W.L., and Williams, K.E., 2022, Fundamental science and engineering questions in planetary cave exploration: JGR Planets, v. 127, no. 11, e2022JE007194, 32 p., https://doi.org/10.1029/2022JE007194.","productDescription":"e2022JE007194, 32 p.","ipdsId":"IP-131152","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":447653,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2022je007194","text":"External Repository"},{"id":401298,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"127","issue":"11","noUsgsAuthors":false,"publicationDate":"2022-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Wynne, J. 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Preparation and mitigation efforts for widespread landslide hazards can be aided by a large-scale, well-labeled landslide inventory with high location accuracy. Recent smallscale studies for pixel-wise labeling of potential landslide areas in remotely-sensed images using deep learning (DL) showed potential but were based on data from very small, homogeneous regions with unproven model transferability. In this paper we consider a more realistic and practical setting for large-scale heterogeneous landslide data collection and DL-based labeling. In this setting, remotely sensed images are collected sequentially in temporal batches, where each batch focuses on images from a particular ecoregion, but different batches can focus on different ecoregions with distinct landscape characteristics. For such a scenario, we study the following questions: (1) How well do DL models trained in homogeneous regions perform when they are transferred to different ecoregions, (2) Does increasing the spatial coverage in the data improve model performance in a given ecoregion (even when the extra data do not come from the ecoregion), and (3) Can a landslide pixel labeling model be incrementally updated with new data, but without access to the old data and without losing performance on the old data (so that researchers can share models obtained from proprietary datasets)' We address these questions by extending the Learning without Forgetting framework, which is used for incremental training of image classification models, to the setting of incremental training of semantic segmentation models (e.g., identifying all landslide pixels in an image). We call the resulting extension Task-Specific Model Updates (TSMU). TSMU semantic segmentation framework consists of an encoder shared by all ecoregions to capture the similarities between them, and ecoregion-specific decoders to capture the nuances of each ecoregion. This framework is continually updated using a threestage training procedure for each new addition of an ecoregion without having to revisit data from old ecoregions and without losing performance on them.

","language":"English","publisher":"Institute of Electrical and Electronics Engineers","doi":"10.1109/JSTARS.2022.3177025","usgsCitation":"Nagendra, S., Kifer, D., Mirus, B., Pei, T., Lawson, K., Manjunatha, S.B., Li, W., Nguyen, H., Qiu, T., Tran, S., and Shen, C., 2022, Constructing a large-scale landslide database across heterogeneous environments using task-specific model updates: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 15, p. 4349-4370, https://doi.org/10.1109/JSTARS.2022.3177025.","productDescription":"23 p.","startPage":"4349","endPage":"4370","ipdsId":"IP-137285","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2022.3177025","text":"Publisher Index Page"},{"id":401292,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nagendra, Savinay","contributorId":292084,"corporation":false,"usgs":false,"family":"Nagendra","given":"Savinay","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kifer, Daniel","contributorId":292085,"corporation":false,"usgs":false,"family":"Kifer","given":"Daniel","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":843803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pei, Te","contributorId":292087,"corporation":false,"usgs":false,"family":"Pei","given":"Te","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lawson, Kathryn","contributorId":292089,"corporation":false,"usgs":false,"family":"Lawson","given":"Kathryn","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Manjunatha, Srikanth Banagere","contributorId":292090,"corporation":false,"usgs":false,"family":"Manjunatha","given":"Srikanth","email":"","middleInitial":"Banagere","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Weixin","contributorId":292093,"corporation":false,"usgs":false,"family":"Li","given":"Weixin","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843807,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nguyen, Hien","contributorId":292096,"corporation":false,"usgs":false,"family":"Nguyen","given":"Hien","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843808,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Qiu, Tong","contributorId":292099,"corporation":false,"usgs":false,"family":"Qiu","given":"Tong","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843809,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Tran, Sarah","contributorId":292102,"corporation":false,"usgs":false,"family":"Tran","given":"Sarah","email":"","affiliations":[{"id":37314,"text":"Google Inc.","active":true,"usgs":false}],"preferred":false,"id":843810,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Shen, Chaopeng","contributorId":152465,"corporation":false,"usgs":false,"family":"Shen","given":"Chaopeng","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":843811,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70231799,"text":"sir20225021 - 2022 - Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","interactions":[],"lastModifiedDate":"2026-04-09T16:53:55.498502","indexId":"sir20225021","displayToPublicDate":"2022-05-26T12:05:53","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":"2022-5021","displayTitle":"Status and Understanding of Groundwater Quality in the Sacramento Metropolitan Domestic-Supply Aquifer Study Unit, 2017: California GAMA Priority Basin Project","title":"Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","docAbstract":"

Groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit (SacMetro-DSA) was studied from August to November 2017 as part of the second phase of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is in parts of Amador, Placer, Sacramento, and Sutter Counties, and the extent of the study unit was defined by the location of three California Department of Water Resources groundwater subbasins: the North American, the South American, and the Cosumnes. The SacMetro-DSA focused on groundwater resources used for domestic drinking-water supply, which generally correspond to shallower parts of aquifer systems than those of groundwater resources used for public drinking water supply in the same area. The assessments characterized the quality of untreated groundwater, not the quality of drinking water.

This study included two components: (1) a status assessment, which characterized the status of the quality of the groundwater resources used for domestic supply and (2) an understanding assessment, which evaluated the natural and human factors potentially affecting water quality in those resources. The first component of this study—the status assessment—was based on water-quality data collected from 49 sites sampled by the U.S. Geological Survey for the GAMA Priority Basin Project in 2017. The samples were analyzed for volatile organic compounds, pesticides, and naturally present inorganic constituents, such as major ions and trace elements. To provide context, concentrations of constituents measured in groundwater were compared to U.S. Environmental Protection Agency and California State Water Resources Control Board Division of Drinking Water regulatory and non-regulatory benchmarks for drinking-water quality. The status assessment used a grid-based method to estimate the proportion of the groundwater resources that had concentrations of water-quality constituents approaching or above benchmark concentrations. This method provides statistically unbiased results at the study-area scale and permits comparisons to other GAMA Priority Basin Project study areas. The second component of this study—the understanding assessment—identified the natural and human factors that potentially affect groundwater quality by evaluating land-use characteristics, groundwater age, and geochemical and hydrologic conditions of the domestic-supply aquifer and related these data to constituents identified in the status assessment for further evaluation.

In the SacMetro-DSA study unit, arsenic was the only inorganic constituent detected above health-based benchmarks and was detected in 10 percent of the domestic-supply aquifer system. Inorganic constituents were detected above the non-health-based California State Water Resources Control Board—Division of Drinking Water secondary maximum contaminant levels (SMCL-CA) in 16 percent of the system. The inorganic constituents detected above the SMCL-CA were chloride, iron, manganese, and total dissolved solids (TDS). Organic constituents (volatile organic compounds and pesticides) with health-based benchmarks were not detected above health-based benchmarks; however, chloroform was detected at concentrations higher than 10 percent of the health-based benchmark (80 micrograms per liter) in 2 percent of the domestic-supply aquifer system. Of the 310 organic constituents analyzed, 16 constituents were detected; however, only bentazon and chloroform had detection frequencies greater than 10 percent.

Inorganic constituents with health-based benchmarks that were evaluated in the understanding assessment included arsenic and hexavalent chromium. Arsenic and hexavalent chromium are natural constituents of aquifer sediments in the study unit and did not appear to be influenced by anthropogenic processes; rather, the presence of arsenic and hexavalent chromium appeared to be related to geochemical conditions controlled by oxidation–reduction reactions in the aquifer system. Naturally occurring inorganic constituents with SMCL-CAs evaluated in the understanding assessment were the trace elements iron and manganese, the major ion chloride, and TDS. Like arsenic and hexavalent chromium, the presence of iron and manganese was most strongly related to geochemical conditions in the aquifer system, specifically reducing conditions, which were most common near the western edge of the study unit close to the Sacramento River. Concentrations of chloride and TDS are indicators of salinity and were correlated with variables related to well location and included redox, agricultural land use, and elevation. Chloride and TDS were positively correlated to reducing conditions, and agricultural land use was negatively correlated to elevation and well depth. Observed correlations among variables were likely driven by the characteristics of the western part of the study unit, such as its higher proportion of agricultural land use and its relatively low elevation. A large portion of the western edge of the study unit is located in the center of the Sacramento Valley, defined by the location of the Sacramento River. The special-interest constituent perchlorate, also included in the understanding assessment, has natural and anthropogenic sources. Perchlorate was detected frequently and at moderate relative concentrations. In some areas of the study unit, concentrations of perchlorate were higher than what might be expected in nature; therefore, anthropogenic introduction of perchlorate or anthropogenically induced migration of native perchlorate could be occurring.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225021","collaboration":"Prepared in cooperation with the California State Water Resources Control Board","programNote":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program","usgsCitation":"Bennett, G.L., V, 2022, Status and understanding of groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey Scientific Investigations Report 2022–5021, 52 p., https://doi.org/10.3133/sir20225021.","productDescription":"Report: xi, 52 p.; Data Release","numberOfPages":"52","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-125530","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":401167,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H4P0XF","text":"Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project","description":"Bennett, G.L., V, 2022, Potential explanatory variables for groundwater quality in the Sacramento Metropolitan Domestic-Supply Aquifer study unit, 2017—California GAMA Priority Basin Project: U.S. Geological Survey data release, available at https://doi.org/10.5066/P9H4P0XF."},{"id":401166,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5021/images"},{"id":401165,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.xml"},{"id":401164,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5021/sir20225021.pdf","text":"Report","size":"20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Report 2022–5021"},{"id":401163,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5021/covrthb.jpg"},{"id":502376,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113075.htm","linkFileType":{"id":5,"text":"html"}},{"id":401191,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225021/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"Scientific Investigations Report 2022–5021"}],"country":"United States","state":"California","otherGeospatial":"Sacramento Metropolitan Domestic-Supply Aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.51953124999999,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 37.87485339352928\n ],\n [\n -120.5419921875,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 39.232253141714885\n ],\n [\n -122.51953124999999,\n 37.87485339352928\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

GAMA Project Chief
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, CA 95819

","tableOfContents":"","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"publishedDate":"2022-05-26","noUsgsAuthors":false,"publicationDate":"2022-05-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Bennett, George L. V 0000-0002-6239-1604 georbenn@usgs.gov","orcid":"https://orcid.org/0000-0002-6239-1604","contributorId":1373,"corporation":false,"usgs":true,"family":"Bennett","given":"George","suffix":"V","email":"georbenn@usgs.gov","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843862,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70233186,"text":"70233186 - 2022 - Reducing uncertainty in climate change responses of inland fishes: A decision-path approach","interactions":[],"lastModifiedDate":"2022-07-18T14:28:41.751238","indexId":"70233186","displayToPublicDate":"2022-05-25T09:25:46","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5803,"text":"Conservation Science and Practice","active":true,"publicationSubtype":{"id":10}},"title":"Reducing uncertainty in climate change responses of inland fishes: A decision-path approach","docAbstract":"

Climate change will continue to be an important consideration for conservation practitioners. However, uncertainty in identifying appropriate management strategies, particularly for understudied species and regions, constrains the implementation of science-based solutions and adaptation strategies. Here, we share a decision-path approach to reduce uncertainty in climate change responses of inland fishes to inform conservation and adaptation planning. With the Fish and Climate Change database (FiCli), a comprehensive, online, public database of peer-reviewed literature on documented and projected climate impacts to inland fishes, users can identify relevant studies and associated management recommendations via geographic regions, response types (i.e., fish assemblage dynamics, demographic, distributional, evolutionary, phenological), fish taxa, and traits (e.g., thermal guilds, feeding type, parental care, habitat type) and use a suite of summary tools to make more informed decisions. For both data-rich and data-poor scenarios, we demonstrate that this approach can reduce uncertainty in understanding climate change responses. Using thermal sensitivity as an example, we also establish the utility of FiCli database to address other user-defined, management-relevant questions via supplementary analyses. This decision-path approach can be applied to rapid assessments, management decisions, and policy development and may serve as a model for other conservation decision-making processes.

","language":"English","publisher":"Wiley","doi":"10.1111/csp2.12724","usgsCitation":"Lynch, A., Myers, B., Wong, J.P., Chu, C., Tingley, R.W., Falke, J.A., Kwak, T.J., Paukert, C.P., and Krabbenhoft, T.J., 2022, Reducing uncertainty in climate change responses of inland fishes: A decision-path approach: Conservation Science and Practice, v. 4, no. 7, e12724, 15 p., https://doi.org/10.1111/csp2.12724.","productDescription":"e12724, 15 p.","ipdsId":"IP-123065","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":447669,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12724","text":"Publisher Index Page"},{"id":435839,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9F6HA3M","text":"USGS data release","linkHelpText":"FiCli: Fish and Climate Change Database (2021 Update)"},{"id":403898,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":846721,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Myers, Bonnie 0000-0002-3170-2633","orcid":"https://orcid.org/0000-0002-3170-2633","contributorId":219702,"corporation":false,"usgs":true,"family":"Myers","given":"Bonnie","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":846722,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wong, Jesse P.","contributorId":264850,"corporation":false,"usgs":false,"family":"Wong","given":"Jesse","email":"","middleInitial":"P.","affiliations":[{"id":12909,"text":"George Mason University","active":true,"usgs":false}],"preferred":false,"id":846723,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chu, Cindy","contributorId":176496,"corporation":false,"usgs":false,"family":"Chu","given":"Cindy","email":"","affiliations":[],"preferred":false,"id":846724,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tingley, Ralph W. III 0000-0002-1689-2133","orcid":"https://orcid.org/0000-0002-1689-2133","contributorId":189812,"corporation":false,"usgs":true,"family":"Tingley","given":"Ralph","suffix":"III","email":"","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":846725,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Falke, Jeffrey A. 0000-0002-6670-8250 jfalke@usgs.gov","orcid":"https://orcid.org/0000-0002-6670-8250","contributorId":5195,"corporation":false,"usgs":true,"family":"Falke","given":"Jeffrey","email":"jfalke@usgs.gov","middleInitial":"A.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":846726,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kwak, Thomas J. 0000-0002-0616-137X tkwak@usgs.gov","orcid":"https://orcid.org/0000-0002-0616-137X","contributorId":834,"corporation":false,"usgs":true,"family":"Kwak","given":"Thomas","email":"tkwak@usgs.gov","middleInitial":"J.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":846727,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Paukert, Craig P. 0000-0002-9369-8545","orcid":"https://orcid.org/0000-0002-9369-8545","contributorId":245524,"corporation":false,"usgs":true,"family":"Paukert","given":"Craig","middleInitial":"P.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":846728,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Krabbenhoft, Trevor J.","contributorId":176498,"corporation":false,"usgs":false,"family":"Krabbenhoft","given":"Trevor","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":846729,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262306,"text":"70262306 - 2022 - Within-marsh and landscape features structure ribbed mussel distribution in Georgia, USA, marshes","interactions":[],"lastModifiedDate":"2025-01-22T18:38:40.548941","indexId":"70262306","displayToPublicDate":"2022-05-25T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"title":"Within-marsh and landscape features structure ribbed mussel distribution in Georgia, USA, marshes","docAbstract":"

Ribbed mussels, Geukensia demissa, are marsh fauna that are used in coastal management and restoration due to the ecosystem services they provide. Ribbed mussel restoration efforts may be improved with a greater understanding of the environmental drivers of ribbed mussel distribution at multiple spatial scales to predict areas where restoration could be successful. This study sought to estimate the effects of within-marsh (4 m) and landscape (500 m) factors on ribbed mussel distribution. Ribbed mussel densities were surveyed at 11 sites along the coast of Georgia, USA, and overlaid with spatial data for within-marsh factors (elevation, distance to marsh features, slope) as well as landscape factors (percent cover by subtidal creek, forest, and development within a 500-m radius). The distribution model was then validated using three previously unsurveyed marshes and explained 55% of the variance in ribbed mussel abundance. Ribbed mussel abundances and occupancy were most sensitive to changes in within-marsh factors (elevation and distance to subtidal creeks, bodies of water inundated during the full tidal cycle) but were also sensitive to landscape features (percent landcover of forests and development). The highest ribbed mussel densities were found in mid-elevation areas (~ 0.7 m NAVD88), far from subtidal creeks, and in marshes surrounded with forest and development. These results contrast with distributions in the northeastern USA, where ribbed mussels are distributed along subtidal creek banks. This work suggests that restoration may be most effective when focused on appropriate elevations and at locations away from the marsh-creek ecotone.

","language":"English","publisher":"Springer Nature","doi":"10.1007/s12237-022-01090-w","usgsCitation":"Annis, W., Hunter, E.A., and Carroll, J., 2022, Within-marsh and landscape features structure ribbed mussel distribution in Georgia, USA, marshes: Estuaries and Coasts, v. 45, p. 2660-2674, https://doi.org/10.1007/s12237-022-01090-w.","productDescription":"15 p.","startPage":"2660","endPage":"2674","ipdsId":"IP-132397","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481086,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1007/s12237-022-01090-w","text":"External Repository"},{"id":480947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -82.27652817835495,\n 32.30818582903797\n ],\n [\n -82.27652817835495,\n 31.035143348707706\n ],\n [\n -80.80318017426605,\n 31.035143348707706\n ],\n [\n -80.80318017426605,\n 32.30818582903797\n ],\n [\n -82.27652817835495,\n 32.30818582903797\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"45","noUsgsAuthors":false,"publicationDate":"2022-05-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Annis, William K.","contributorId":348800,"corporation":false,"usgs":false,"family":"Annis","given":"William K.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923780,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923781,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carroll, John M.","contributorId":348801,"corporation":false,"usgs":false,"family":"Carroll","given":"John M.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923782,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242085,"text":"70242085 - 2022 - S/P amplitude ratios derived from single-component seismograms and their potential use in constraining focal mechanisms for micro-earthquake sequences","interactions":[],"lastModifiedDate":"2023-04-06T12:07:36.628847","indexId":"70242085","displayToPublicDate":"2022-05-24T07:05:37","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10542,"text":"The Seismic Record","active":true,"publicationSubtype":{"id":10}},"title":"S/P amplitude ratios derived from single-component seismograms and their potential use in constraining focal mechanisms for micro-earthquake sequences","docAbstract":"

Focal mechanisms, which reflect the sense of slip in earthquakes, provide important constraints for understanding crustal tectonics and earthquake source physics, including the interactions among earthquakes during mainshock–aftershock sequences or seismic swarms. Focal mechanisms of small (magnitude ≲3.5) earthquakes are usually determined by first‐motion P‐wave polarities, sometimes supplemented by the ratio of S‐wave to P‐wave amplitudes (S/P). However, focal mechanisms of such events can be difficult to reliably constrain, particularly with sparse recording networks or very small magnitude events. Here, we describe a method for deriving S/P amplitude ratios from P/P and S/S amplitude ratios measured on single seismic components between pairs of nearby events, as is often performed during correlation‐based earthquake detection and relocation. These measurements can be transformed into relative S/P amplitude ratios, or they can be combined with a smaller number of traditional S/P amplitude ratios to provide a single‐channel estimation of full S/P ratios, even for low signal‐to‐noise‐ratio events not routinely cataloged and not amenable to traditional S/P ratio processing. This approach has the potential to greatly expand the applicability of S/P amplitude ratios, providing additional constraints for focal mechanisms of small earthquakes, particularly for spatially concentrated seismicity sequences.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320220002","usgsCitation":"Shelly, D.R., Skoumal, R.J., and Hardebeck, J.L., 2022, S/P amplitude ratios derived from single-component seismograms and their potential use in constraining focal mechanisms for micro-earthquake sequences: The Seismic Record, v. 2, no. 2, p. 118-126, https://doi.org/10.1785/0320220002.","productDescription":"9 p.","startPage":"118","endPage":"126","ipdsId":"IP-137250","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":447675,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320220002","text":"Publisher Index Page"},{"id":415330,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.56538035717716,\n 39.564841490302626\n ],\n [\n -123.56538035717716,\n 39.046509232532856\n ],\n [\n -122.97786452831625,\n 39.046509232532856\n ],\n [\n -122.97786452831625,\n 39.564841490302626\n ],\n [\n -123.56538035717716,\n 39.564841490302626\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":868784,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Skoumal, Robert John 0000-0002-6960-481X rskoumal@usgs.gov","orcid":"https://orcid.org/0000-0002-6960-481X","contributorId":299165,"corporation":false,"usgs":true,"family":"Skoumal","given":"Robert","email":"rskoumal@usgs.gov","middleInitial":"John","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":868785,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hardebeck, Jeanne L. 0000-0002-6737-7780","orcid":"https://orcid.org/0000-0002-6737-7780","contributorId":254964,"corporation":false,"usgs":true,"family":"Hardebeck","given":"Jeanne","email":"","middleInitial":"L.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":868786,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ]}