Natural resources have always been a strength for Indiana. Once largely covered by forest, the State now includes a mix of forest, farmland, wetlands, and small lakes. In fact, farms and forested areas make up more than 80 percent of the land. The Ohio River forms the southern border, and the south shore of Lake Michigan contrasts with urban development in the northwestern corner. Indiana Dunes National and State Parks reside just east of the Chicago metropolitan area.
Agriculture adds more than $30 billion to the economy in Indiana, which placed eighth in the country for agricultural exports at $4.6 billion in 2017. Indiana ranks in the top five States nationally for the production of corn and soybeans. The “Hoosier State” also grows sizable crops of popcorn, spearmint, peppermint, pumpkins, tomatoes, and watermelon. Additionally, hogs, cattle, dairy, and poultry contribute to the agricultural economy. Other industries important to Indiana include manufacturing, medicine, energy, and mining. Mineral sources vary from coal, building stone, and gypsum to sand, gravel, and shale.
Landsat can help monitor the condition of natural resources and the effects of extreme weather events. Here are several ways Landsat has benefited Indiana.
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\"}}]}","contact":"Program Coordinator, National Land Imaging Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
We present the eruption sequence for the east flank of Santa Ana volcano, which we divide into the sections above and below the Tierra Blanca Joven (TBJ) formation. The sequence below the TBJ suggests a series of mafic magmatic eruptions that began before 7,800 cal BP and continued until after 5,800 cal BP. These eruptions emplaced tephra-fall and pyroclastic-density-current deposits. The sequence above the TBJ are deposits from recent eruptions during the last 1.5 ky. Most of these eruptions were phreatomagmatic with associated pyroclastic surges. The two sequences demonstrate a shift in eruptive style possibly related to an increase in water volume in the hydrothermal system.
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R14-1-R14-5.","productDescription":"5 p.","startPage":"R14-1","endPage":"R14-5","ipdsId":"IP-141070","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":425876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425875,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://opamss.org.sv/ova_doc/publicaciones-de-congreso-xiv-congreso-geologico-de-america-central/","linkFileType":{"id":5,"text":"html"}}],"country":"El Salvador","otherGeospatial":"Santa Ana volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.66204930512441,\n 13.861989677410719\n ],\n [\n -89.66204930512441,\n 13.795862836104703\n ],\n [\n -89.55685050851551,\n 13.795862836104703\n ],\n [\n -89.55685050851551,\n 13.861989677410719\n ],\n [\n -89.66204930512441,\n 13.861989677410719\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lemus, Dennis","contributorId":293267,"corporation":false,"usgs":false,"family":"Lemus","given":"Dennis","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846905,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846906,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garcia, Angela V.","contributorId":293247,"corporation":false,"usgs":false,"family":"Garcia","given":"Angela","email":"","middleInitial":"V.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846907,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escobar, Demetrio","contributorId":293248,"corporation":false,"usgs":false,"family":"Escobar","given":"Demetrio","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846908,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hernandez, Alexander","contributorId":293268,"corporation":false,"usgs":false,"family":"Hernandez","given":"Alexander","affiliations":[{"id":63269,"text":"Universidad de El Salvador, Facultad Multidisciplinaria de Occidente","active":true,"usgs":false}],"preferred":false,"id":846909,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Alvarenga, Estefany","contributorId":293269,"corporation":false,"usgs":false,"family":"Alvarenga","given":"Estefany","email":"","affiliations":[{"id":63269,"text":"Universidad de El Salvador, Facultad Multidisciplinaria de Occidente","active":true,"usgs":false}],"preferred":false,"id":846910,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70233224,"text":"70233224 - 2022 - Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana","interactions":[],"lastModifiedDate":"2024-02-22T15:33:26.223523","indexId":"70233224","displayToPublicDate":"2022-07-18T09:19:44","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana","docAbstract":"The Acajutla debris-avalanche deposit is dated to about 40,000 cal BP. The dating is based on two 14C dates on pieces of wood from the debris-avalanche deposit recovered from a core at the Santa Águeda School Center. The debris-avalanche deposit overlies a 1.2-m-thick paleosol and four ash layers. One of these ash layers is geochemically correlated to the Los Chocoyos ash from Atitlán Caldera, while the others are possibly from eruptions of Coatepeque and Ilopango Calderas. The new data, collected in well reports from the years 1966 to 2019, include deposit thicknesses identified in 25 wells for water monitoring, bathymetric data from nautical charts, GEBCO’s grids, updated topographic data from LIDAR, and a reassessment of the deposit’s lateral limits. This new data will allow us to better constrain the volume of the Acajutla debris-avalanche deposit.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"Spanish","publisher":"COAMSS/OPAMSS","usgsCitation":"Garcia, A.V., Harpel, C., Hernandez, W., Escobar, D., Mixco, L.E., Lewis, C., and Scott Cummings, L., 2022, Nuevos datos: Avalancha de escombros de Acajutla, volcán Santa Ana, in Memoria, XIV congreso geologica de America Central & VII congreso geologico nacional, p. R54-1-R54-4.","productDescription":"4 p.","startPage":"R54-1","endPage":"R54-4","ipdsId":"IP-141071","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":425867,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://opamss.org.sv/ova_doc/publicaciones-de-congreso-xiv-congreso-geologico-de-america-central/"},{"id":425868,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"El Salvador","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89.60202528025114,\n 13.862680525535808\n ],\n [\n -89.68026892718665,\n 13.862680525535808\n ],\n [\n -89.68026892718665,\n 13.783268152881789\n ],\n [\n -89.60202528025114,\n 13.783268152881789\n ],\n [\n -89.60202528025114,\n 13.862680525535808\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garcia, Angela V.","contributorId":293247,"corporation":false,"usgs":false,"family":"Garcia","given":"Angela","email":"","middleInitial":"V.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846839,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harpel, Christopher 0000-0001-8587-7845","orcid":"https://orcid.org/0000-0001-8587-7845","contributorId":204746,"corporation":false,"usgs":true,"family":"Harpel","given":"Christopher","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846840,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hernandez, Walter","contributorId":218214,"corporation":false,"usgs":false,"family":"Hernandez","given":"Walter","email":"","affiliations":[{"id":39782,"text":"Ministerio de Medio Ambiente y Recursos Naturales, San Salvador, El Salvador","active":true,"usgs":false}],"preferred":false,"id":846841,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Escobar, Demetrio","contributorId":293248,"corporation":false,"usgs":false,"family":"Escobar","given":"Demetrio","email":"","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846842,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mixco, Luis E.","contributorId":293249,"corporation":false,"usgs":false,"family":"Mixco","given":"Luis","email":"","middleInitial":"E.","affiliations":[{"id":63262,"text":"Ministerio de Ambiente y Recursos Naturales de El Salvador","active":true,"usgs":false}],"preferred":false,"id":846843,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lewis, Charles 0000-0001-5848-937X","orcid":"https://orcid.org/0000-0001-5848-937X","contributorId":293250,"corporation":false,"usgs":false,"family":"Lewis","given":"Charles","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":846844,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Scott Cummings, Linda","contributorId":293251,"corporation":false,"usgs":false,"family":"Scott Cummings","given":"Linda","email":"","affiliations":[{"id":63265,"text":"PaleoResearch Institute","active":true,"usgs":false}],"preferred":false,"id":846845,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236309,"text":"70236309 - 2022 - Tephrochronology of the Miocene Monterey and Modelo Formations, California","interactions":[],"lastModifiedDate":"2022-09-01T12:17:20.986718","indexId":"70236309","displayToPublicDate":"2022-07-17T07:15:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1727,"text":"GSA Special Papers","active":true,"publicationSubtype":{"id":10}},"title":"Tephrochronology of the Miocene Monterey and Modelo Formations, California","docAbstract":"Tuff beds have been known in the Miocene Monterey and Modelo Formations since the initial descriptions; however, age control and correlation is predominantly biostratigraphy. Here we combine tephrochronology and biostratigraphy in order to provide numerical age control for eight sedimentary sequences of the Monterey and Modelo Formations from Monterey, California to Orange County, California. We correlate 38 tuffs and tephra beds in the Monterey and Modelo Formations to 26 different dated tuffs found mainly in non-marine sequences in Nevada, Idaho and New Mexico. We also include geochemical data for an additional 19 tuffs in the Monterey and Modelo Formations for which there are no known correlative tuffs and geochemical data for 11 additional tuffs in other units that will add to the Miocene tephrostratigraphy. The identified tuffs range in age from 16 to 7 Ma with 31 tuffs erupted from volcanic centers of the Snake River Plain of northern Nevada to eastern Idaho. Twelve other tuffs erupted from the Southern Nevada Volcanic Field, one from the Sonoma Volcanic Field, north of San Francisco, and the eruptive source of 12 other tuffs is uncertain. These tuffs provide useful correlations of marine sequences deposited at varying depths along offshore Miocene California and possible insight into the distribution of air-fall tephra from so-called super eruptions","language":"English","publisher":"Geological Society of America","doi":"10.1130/2022.2556(08)","usgsCitation":"Knott, J.R., Sarna-Wojcicki, A., Barron, J.A., Wan, E., Heizler, N., and Martinez, P., 2022, Tephrochronology of the Miocene Monterey and Modelo Formations, California: GSA Special Papers, https://doi.org/10.1130/2022.2556(08).","ipdsId":"IP-122368","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447098,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/2022.2556(08)","text":"Publisher Index Page"},{"id":406061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"Online First","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knott, Jeffrey R. 0000-0002-4600-5961","orcid":"https://orcid.org/0000-0002-4600-5961","contributorId":218427,"corporation":false,"usgs":false,"family":"Knott","given":"Jeffrey","email":"","middleInitial":"R.","affiliations":[{"id":39844,"text":"CSU Fullerton, Department of Geological Sciences","active":true,"usgs":false}],"preferred":false,"id":850545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sarna-Wojcicki, Andrei M. 0000-0002-0244-9149","orcid":"https://orcid.org/0000-0002-0244-9149","contributorId":296073,"corporation":false,"usgs":true,"family":"Sarna-Wojcicki","given":"Andrei M.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":850546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barron, John A. 0000-0002-9309-1145 jbarron@usgs.gov","orcid":"https://orcid.org/0000-0002-9309-1145","contributorId":2222,"corporation":false,"usgs":true,"family":"Barron","given":"John","email":"jbarron@usgs.gov","middleInitial":"A.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":850547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wan, Elmira 0000-0002-9255-112X ewan@usgs.gov","orcid":"https://orcid.org/0000-0002-9255-112X","contributorId":296074,"corporation":false,"usgs":true,"family":"Wan","given":"Elmira","email":"ewan@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":850548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heizler, Nancy","contributorId":296075,"corporation":false,"usgs":false,"family":"Heizler","given":"Nancy","email":"","affiliations":[{"id":16150,"text":"New Mexico Bureau of Geology and Mineral Resources","active":true,"usgs":false}],"preferred":false,"id":850549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Martinez, Priscilla","contributorId":296076,"corporation":false,"usgs":false,"family":"Martinez","given":"Priscilla","email":"","affiliations":[{"id":63349,"text":"California State University Fullerton","active":true,"usgs":false}],"preferred":false,"id":850550,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263335,"text":"70263335 - 2022 - The effects of earthquake experience on intentions to respond to Earthquake Early Warnings","interactions":[],"lastModifiedDate":"2025-02-06T17:05:30.263398","indexId":"70263335","displayToPublicDate":"2022-07-17T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":20070,"text":"Frontiers in Communications","active":true,"publicationSubtype":{"id":10}},"title":"The effects of earthquake experience on intentions to respond to Earthquake Early Warnings","docAbstract":"Warning systems are essential for providing people with information so they can take protective action in response to perils. Systems need to be human-centered, which requires an understanding of the context within which humans operate. Therefore, our research sought to understand the human context for Earthquake Early Warning (EEW) in Aotearoa New Zealand, a location where no comprehensive EEW system existed in 2019 when we did this study. We undertook a survey of people's previous experiences of earthquakes, their perceptions of the usefulness of a hypothetical EEW system, and their intended responses to a potential warning (for example, Drop, Cover, Hold (DCH), staying still, performing safety actions). Results showed little difference in perceived usefulness of an EEW system between those with and without earthquake experience, except for a weak relationship between perceived usefulness and if a respondent's family or friends had previously experienced injury, damage or loss from an earthquake. Previous earthquake experience was, however, associated with various intended responses to a warning. The more direct, or personally relevant a person's experiences were, the more likely they were to intend to take a useful action on receipt of an EEW. Again, the type of experience which showed the largest difference was having had a family member or friend experience injury, damage or loss. Experience of participation in training, exercises or drills did not seem to prompt the correct intended actions for earthquake warnings; however, given the hypothetical nature of the study, it is possible people did not associate their participation in drills, for example, with a potential action that could be taken on receipt of an EEW. Our analysis of regional differences highlighted that intentions to mentally prepare on receipt of a warning were significantly higher for Canterbury region participants, most likely related to strong shaking and subsequent impacts experienced during the 2010–11 Canterbury Earthquake Sequence. Our research reinforces that previous experience can influence earthquake-related perceptions and behaviors, but in different ways depending on the context. Public communication and interventions for EEW could take into consideration different levels and types of experiences of the audience for greater success in response.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fcomm.2022.857004","usgsCitation":"Becker, J., Vinnell, L., McBride, S., Nakayachi, K., Doyle, E., Potter, S., and Bostrom, A., 2022, The effects of earthquake experience on intentions to respond to Earthquake Early Warnings: Frontiers in Communications, v. 7, 857004, 14 p., https://doi.org/10.3389/fcomm.2022.857004.","productDescription":"857004, 14 p.","ipdsId":"IP-133466","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":487627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fcomm.2022.857004","text":"Publisher Index Page"},{"id":481762,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[173.02037,-40.91905],[173.24723,-41.332],[173.95841,-40.9267],[174.24759,-41.34916],[174.24852,-41.77001],[173.87645,-42.23318],[173.22274,-42.97004],[172.71125,-43.37229],[173.08011,-43.85334],[172.30858,-43.86569],[171.45293,-44.24252],[171.18514,-44.8971],[170.6167,-45.90893],[169.83142,-46.35577],[169.33233,-46.64124],[168.41135,-46.61994],[167.76374,-46.2902],[166.67689,-46.21992],[166.50914,-45.8527],[167.04642,-45.11094],[168.30376,-44.12397],[168.94941,-43.93582],[169.66781,-43.55533],[170.52492,-43.03169],[171.12509,-42.51275],[171.56971,-41.76742],[171.94871,-41.51442],[172.09723,-40.9561],[172.79858,-40.49396],[173.02037,-40.91905]]],[[[174.61201,-36.1564],[175.33662,-37.2091],[175.3576,-36.52619],[175.80889,-36.79894],[175.95849,-37.55538],[176.7632,-37.88125],[177.43881,-37.96125],[178.01035,-37.57982],[178.51709,-37.69537],[178.27473,-38.58281],[177.97046,-39.16634],[177.20699,-39.14578],[176.93998,-39.44974],[177.03295,-39.87994],[176.88582,-40.06598],[176.50802,-40.60481],[176.01244,-41.28962],[175.23957,-41.68831],[175.0679,-41.42589],[174.65097,-41.28182],[175.22763,-40.45924],[174.90016,-39.90893],[173.82405,-39.50885],[173.85226,-39.1466],[174.5748,-38.79768],[174.74347,-38.02781],[174.69702,-37.38113],[174.29203,-36.71109],[174.319,-36.53482],[173.841,-36.12198],[173.05417,-35.23713],[172.63601,-34.52911],[173.00704,-34.45066],[173.5513,-35.00618],[174.32939,-35.2655],[174.61201,-36.1564]]]]},\"properties\":{\"name\":\"New Zealand\"}}]}","volume":"7","noUsgsAuthors":false,"publicationDate":"2022-07-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Becker, Julia S.","contributorId":217541,"corporation":false,"usgs":false,"family":"Becker","given":"Julia S.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":926487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vinnell, Lauren","contributorId":292282,"corporation":false,"usgs":false,"family":"Vinnell","given":"Lauren","email":"","affiliations":[{"id":13571,"text":"Massey University","active":true,"usgs":false}],"preferred":false,"id":926488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":926489,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Nakayachi, K.","contributorId":350629,"corporation":false,"usgs":false,"family":"Nakayachi","given":"K.","affiliations":[{"id":48933,"text":"Doshisha University","active":true,"usgs":false}],"preferred":false,"id":926490,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doyle, Emma","contributorId":118709,"corporation":false,"usgs":true,"family":"Doyle","given":"Emma","affiliations":[],"preferred":false,"id":926585,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Potter, Sally H.","contributorId":217521,"corporation":false,"usgs":false,"family":"Potter","given":"Sally H.","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":926586,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bostrom, Ann 0000-0002-6399-3404","orcid":"https://orcid.org/0000-0002-6399-3404","contributorId":239575,"corporation":false,"usgs":false,"family":"Bostrom","given":"Ann","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":926587,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70233571,"text":"70233571 - 2022 - Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","interactions":[],"lastModifiedDate":"2022-07-26T12:04:54.409941","indexId":"70233571","displayToPublicDate":"2022-07-16T06:59:21","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2342,"text":"Journal of Hydrology","active":true,"publicationSubtype":{"id":10}},"title":"Assessing spatial transferability of a random forest metamodel for predicting drainage fraction","docAbstract":"Fully distributed hydrological models are widely used in groundwater management, but model speed and data requirements impede their use for decision support purposes. Metamodels provide a simpler and faster model which emulates the underlying complex model using machine learning techniques. However, metamodel predictions beyond the ranges, in space and/or time, of training data are highly uncertain, and thus it is important to assess the predictive model performance to ranges outside the training data, i.e., model transferability. We present a novel methodology for evaluating model transferability to areas not contained in the training data set, based on various metrics that quantify the differences in covariate distributions between training and testing data. The transferability method can be employed as a screening tool to assess the suitability of a metamodel for spatial prediction beyond its training domain. We evaluated this transferability approach on a Random Forest metamodel of a 1000 km2 fully distributed coupled groundwater model for predicting drainage fraction, the partitioning of infiltrating water between drains and groundwater. We conducted spatial cross-validation on 9 holdout sub-basins to assess metamodel transferability beyond sampling locations and compared this estimate with a random split-sample validation test. Using mappable covariates only, the metamodel showed high performance (R2 = 0.79) tested on a 20% randomly sampled holdout. Conversely, metamodel performance significantly decreased for the 9 spatial holdouts (R2 ranging from 0.13 to 0.61). We document that the proposed transferability metric correlates with metamodel predictive performance, and demonstrate its use to assess model transferability to datasets outside the training data spatial domain.
Secondary forests regenerating from human disturbance are increasingly becoming a predominant forest type in many regions, and they play a significant role in forest community dynamics. Understanding the factors that underlie the variation in species responses during secondary succession is important for understanding community assembly and biodiversity monitoring and management. Because species vary in ecology and behaviour, responses to ecosystem change should vary among species. Here, we show that habitat type (riparian, upland), phylogeny, and species traits mediate anuran and lizard probability of occurrence and species richness in pasture and secondary forest.
Sarapiquí and Osa Peninsula, Costa Rica.
We used phylogenetic occupancy models to estimate assemblage-level and species-specific responses to forest succession in 30 chronosequence sites that include pasture, secondary forest regenerating from pasture, and mature forest sites.
For the majority of species, we found increasing probability of occurrence in upland habitats as forest regenerated from pasture to secondary forest and similar probability of occurrence in riparian habitats across pasture, secondary forest, and mature forest sites. Species' responses to forest stage were phylogenetically correlated, and the trend was especially strong for anuran response to pasture sites. Anurans with lentic larval habitat had a positive occupancy response to pasture upland habitat, and anurans with lotic larval habitat had a variable response to different forest stages compared to mature forest.
Our study, which focuses on sites that are minimally isolated from mature forest reference sites, indicated that anuran and lizard occupancy rapidly recovered to a level similar to mature forest in a relatively short time span (approximately 20 years). Riparian habitats are key ecosystem features in our system and provide refugia for organisms in early successional stages. Maintenance of vegetation along streams shows that we can mitigate forest conversion by maintaining riparian buffers.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13601","usgsCitation":"Thompson, M.E., Halstead, B., and Donnelly, M., 2022, Riparian buffers provide refugia during secondary forest succession: Diversity and Distributions, v. 28, no. 9, p. 2008-2019, https://doi.org/10.1111/ddi.13601.","productDescription":"12 p.","startPage":"2008","endPage":"2019","ipdsId":"IP-132963","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447102,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13601","text":"Publisher Index Page"},{"id":404477,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Costa Rica","otherGeospatial":"Osa Peninsula, Sarapiquí","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.9,\n 10.3\n ],\n [\n -83.9,\n 10.5\n ],\n [\n -84.1,\n 10.5\n ],\n [\n -84.1,\n 10.3\n ],\n [\n -83.9,\n 10.3\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.4,\n 8.32\n ],\n [\n -83.3,\n 8.32\n ],\n [\n -83.3,\n 8.52\n ],\n [\n -83.4,\n 8.52\n ],\n [\n -83.4,\n 8.32\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","issue":"9","noUsgsAuthors":false,"publicationDate":"2022-07-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Michelle E.","contributorId":210341,"corporation":false,"usgs":false,"family":"Thompson","given":"Michelle","email":"","middleInitial":"E.","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":847546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":847547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Donnelly, Maureen A.","contributorId":293649,"corporation":false,"usgs":false,"family":"Donnelly","given":"Maureen A.","affiliations":[{"id":63353,"text":"Department of Biological Sciences, Florida International University, Miami, Florida, USA","active":true,"usgs":false}],"preferred":false,"id":847548,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70233184,"text":"70233184 - 2022 - Evapotranspiration covers at uranium mill tailings sites","interactions":[],"lastModifiedDate":"2023-03-24T16:50:15.597022","indexId":"70233184","displayToPublicDate":"2022-07-15T09:09:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3674,"text":"Vadose Zone Journal","active":true,"publicationSubtype":{"id":10}},"title":"Evapotranspiration covers at uranium mill tailings sites","docAbstract":"Waste isolation is a key strategy for mitigating risk from municipal solid waste (MSW) and hazardous waste streams. Conventional covers at MSW facilities are designed for a 30-yr post-closure period where compacted soils and geosynthetics are used to minimize percolation into buried waste. Recently, evapotranspiration (ET) covers have shown beneficial use for MSW management. Evapotranspiration covers encourage infiltration, storage, and transpiration of precipitation to minimize percolation. Such covers may also have beneficial use for long-term waste issues, such as at Uranium Mill Tailings Radiation Control Act (UMTRCA) sites. These sites were covered by a clay radon barrier to create tortuous flow paths that allow radioactive decay and attenuation of short-lived, radon-222 gas. For long-term waste isolation, an ET-radon cover may provide greater resilience by exploiting natural processes instead of resisting them. This update presents a review of the current state-of-the-science regarding ET covers and considerations for long-term applications.
","language":"English","publisher":"American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America","doi":"10.1002/vzj2.20222","usgsCitation":"Caldwell, T., Tabatabai, S., Huntington, J., Davies, G.E., and Fuhrmann, M., 2022, Evapotranspiration covers at uranium mill tailings sites: Vadose Zone Journal, v. 21, no. 5, e20222, 11 p., https://doi.org/10.1002/vzj2.20222.","productDescription":"e20222, 11 p.","ipdsId":"IP-120446","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":447104,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/vzj2.20222","text":"Publisher Index Page"},{"id":403892,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Conterminous United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n 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Sarah","contributorId":293202,"corporation":false,"usgs":false,"family":"Tabatabai","given":"Sarah","email":"","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":846712,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huntington, Jena 0000-0002-9291-1404","orcid":"https://orcid.org/0000-0002-9291-1404","contributorId":204033,"corporation":false,"usgs":true,"family":"Huntington","given":"Jena","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846713,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davies, Gwendolyn Elizabeth 0000-0003-1538-8610","orcid":"https://orcid.org/0000-0003-1538-8610","contributorId":293203,"corporation":false,"usgs":true,"family":"Davies","given":"Gwendolyn","email":"","middleInitial":"Elizabeth","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846714,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuhrmann, Mark","contributorId":293204,"corporation":false,"usgs":false,"family":"Fuhrmann","given":"Mark","email":"","affiliations":[{"id":12536,"text":"U.S. Nuclear Regulatory Commission","active":true,"usgs":false}],"preferred":false,"id":846715,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232990,"text":"70232990 - 2022 - The formation mechanisms for mid-latitude ice scarps on Mars","interactions":[],"lastModifiedDate":"2022-07-15T13:30:06.924916","indexId":"70232990","displayToPublicDate":"2022-07-15T08:25:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1963,"text":"Icarus","active":true,"publicationSubtype":{"id":10}},"title":"The formation mechanisms for mid-latitude ice scarps on Mars","docAbstract":"Mid-latitude exposed ice scarps have recently been identified on Mars (Dundas et al., 2018; 2021). The presence of such surface ice exposures at relatively low latitudes was itself a mystery, and the formation dynamics of such scarps have also not been explained. In this work we model the ice ablation rates of several identified mid-latitude scarps. We find that, given certain characteristics of their geographic setting, the orientation and growth of the scarps can be explained by energy balance models.","language":"English","publisher":"Elsevier","doi":"10.1016/j.icarus.2022.115174","usgsCitation":"Williams, K.E., Dundas, C., and Kahre, M.A., 2022, The formation mechanisms for mid-latitude ice scarps on Mars: Icarus, v. 386, 115174, 12 p., https://doi.org/10.1016/j.icarus.2022.115174.","productDescription":"115174, 12 p.","ipdsId":"IP-138177","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":447106,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.icarus.2022.115174","text":"Publisher Index Page"},{"id":403785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars","volume":"386","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Williams, Kaj E. 0000-0003-1755-1872 kewilliams@usgs.gov","orcid":"https://orcid.org/0000-0003-1755-1872","contributorId":196988,"corporation":false,"usgs":true,"family":"Williams","given":"Kaj","email":"kewilliams@usgs.gov","middleInitial":"E.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":846629,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dundas, Colin M. 0000-0003-2343-7224","orcid":"https://orcid.org/0000-0003-2343-7224","contributorId":237028,"corporation":false,"usgs":true,"family":"Dundas","given":"Colin M.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":846630,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kahre, Melinda A.","contributorId":61942,"corporation":false,"usgs":true,"family":"Kahre","given":"Melinda","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":846631,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70233546,"text":"70233546 - 2022 - Scanning the horizon for invasive plant threats using a data-driven approach","interactions":[],"lastModifiedDate":"2022-07-25T12:25:01.412207","indexId":"70233546","displayToPublicDate":"2022-07-15T07:21:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5071,"text":"NeoBiota","active":true,"publicationSubtype":{"id":10}},"title":"Scanning the horizon for invasive plant threats using a data-driven approach","docAbstract":"Early detection and eradication of invasive plants are more cost-effective than managing well-established invasive plant populations and their impacts. However, there is high uncertainty around which taxa are likely to become invasive in a given area. Horizon scanning that combines a data-driven approach with rapid risk assessment and consensus building among experts can help identify invasion threats. We performed a horizon scan of potential invasive plant threats to Florida, USA—a state with a high influx of introduced species, conditions that are generally favorable for plant establishment, and a history of negative impacts from invasive plants. We began with an initial list of 2128 non-native plant taxa that are known invaders or crop pests. We built on previous invasive species horizon scans by developing data-based criteria to prioritize 100 taxa for rapid risk assessment. The semi-automated prioritization process included selecting taxa “on the horizon” (i.e., not yet in the target location and not on a noxious weed list) with climate matching, naturalization history, “weediness” record, and global commonness. We derived overall invasion risk scores with rapid risk assessment by evaluating the likelihood of each of the taxa arriving, establishing, and having an impact in Florida. Then, following a consensus-building discussion, we identified six plant taxa as high risk, with overall risk scores ranging from 75 to 100 out of a possible 125. The six taxa are globally distributed, easily transported to new areas, found in regions with climates similar to Florida’s, and can impact native plant communities, human health, or agriculture. Finally, we evaluated our initial and final lists for potential biases. Assessors tended to assign higher risk scores to taxa that had more available information. In addition, we identified biases towards four plant families and certain geographical regions of origin. Our horizon scan approach identified taxa conforming to metrics of high invasion risk and used a methodology refined for plants that can be applied to other locations.
Numerous porphyry copper-molybdenum-gold and epithermal deposits define a belt that extends from Eastern Alaska to western Yukon, Canada. An orientation study conducted near the Taurus porphyry deposit was designed to test methods that require minimal sample collection, preparation, and analytical time to determine the viability of indicator mineral studies as a reconnaissance exploration method. Bulk stream sediments and altered and mineralized rocks were sieved to the 0.105−0.25 millimeter fraction (+140, −60 mesh) and passed over a shaking table to create a moderate to heavy mineral separate that was mounted in epoxy and subsequently analyzed using automated scanning electron microscope (SEM) techniques. Seven polished thin sections of core were also analyzed. Among the advantages of automated SEM techniques compared to visual mineral identification are that thousands of grains can be rapidly identified in each sample (about 1 hour per sample) and small quantities of indicator minerals that may be missed during traditional visual analyses can be detected. Automated SEM analyses of stream sediment and rock samples show that specific minerals (chalcopyrite, bornite, and jarosite) are indicators of potential mineralized areas. Svanbergite, an aluminum sulfate phosphate mineral, was identified in mineralized rocks and in nearly all stream sediment samples (up to 9 kilometers) downstream from the Taurus and other porphyry occurrences but not epithermal occurrences. It was not identified in areas with no known mineralization and thus it is possibly one of the best indicator minerals for porphyry copper (+/- molybdenum, gold) occurrences.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20221046","usgsCitation":"Kelley, K.D., Pfaff, K., and Graham, G.E., 2022, Results of automated scanning electron microscope (SEM) analyses of rock and stream sediment samples from the Taurus porphyry copper deposit area, Tanacross quadrangle, eastern Alaska: U.S. Geological Survey Open-File Report 2022–1046, 12 p., https://doi.org/10.3133/ofr20221046.","productDescription":"Report: vi, 12 p.; Table; Data Release","onlineOnly":"Y","ipdsId":"IP-132987","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":403682,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2022/1046/table1_1.csv","text":"Table 1.1","size":"12.0 kB","linkFileType":{"id":7,"text":"csv"},"description":"OFR 2022-1046 Table 1.1"},{"id":403617,"rank":3,"type":{"id":30,"text":"Data 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Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
P.O. Box 25046, Mail Stop 973
Denver, CO 80225
Minerals occurring in marine environments span the globe and encompass a broad range of mineral categories, forming within varied geologic and oceanographic settings. They occur in coastal regions, either from the continuation or mechanical reworking of terrestrial mineralization, as well as in the deep ocean, from diagenetic, hydrogenetic, and hydrothermal processes. The oceans cover most of the Earth’s surface and as a result, any inventory of global resources is incomplete without the inclusion of marine minerals. This study by the U.S. Geological Survey reviews current knowledge regarding deep-ocean and coastal marine minerals within the marine areas surrounding Alaska, including the Alaska Outer Continental Shelf (OCS). For the purposes of this study, we have divided these areas in to eight regions: (1) Gulf of Alaska seamounts, (2) Chukchi Borderland, (3) Canada Basin, (4) Aleutian Arc, (5) Seward Peninsula, (6) Goodnews Bay, (7) Bristol Bay and Alaska Peninsula, and (8) southern and southeastern Alaska. The Alaska OCS encompasses several areas broadly conducive to marine mineral formation, including extensional basins resulting from an active subduction zone where massive sulfide deposits may form, deep abyssal plains with conditions that may lead to manganese nodule formation, seamounts that can provide substrate for the growth of ferromanganese crusts, and erosional settings and submerged continental crust where placer deposits are found. For deep-ocean hydrothermal minerals and manganese nodules, the Alaska OCS contains prospective regions, including the Canada Basin and the Aleutian Arc; however, no such minerals have yet been identified. We explore the probability that these minerals occur based on reviews of existing geologic and oceanographic data within the relevant sections. In regions far from shore data are limited. Deep-ocean ferromanganese crusts are known to occur in two regions: (1) the Gulf of Alaska seamounts and (2) the Chukchi Borderland in the Arctic Ocean. Limited sampling has occurred in both regions, and along the Chukchi Borderland the sampling was outside of the OCS and the U.S. Exclusive Economic Zone. Data relevant to coastal minerals is more extensive, and in some places fairly systematic sampling was conducted. Several nearshore placer deposits have been exploited for decades; however, the potential for nearshore extension of terrestrial ore deposits is less well considered. This contribution considers the state of knowledge regarding marine mineral occurrences within the Alaska regions and identifies the data gaps in order to help inform future marine mineral related research efforts around Alaska.
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Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission St.
Santa Cruz, CA 95060
Eastern black rails (Laterallus jamaicensis jamaicensis) are among the rarest and least-studied birds in North America and were recently listed as threatened under the U.S. Endangered Species Act. Spatial models that predict habitat quality across the subspecies range are therefore needed to inform conservation, recovery, and monitoring efforts for this rare bird. We used data from 47,585 call-broadcast surveys collected at 7906 sites over a 3-decade period (1990s, 2000s, 2010s; 23 total years) to build species distribution models for eastern black rails. We used hierarchical Bayesian occupancy models and predictive model selection to develop multi-scale models that optimally predict habitat suitability for eastern black rails within tidal wetlands while also accounting for imperfect detection of these cryptic birds during field surveys. We also used raster regression techniques to translate model predictions into 30-m resolution maps of habitat suitability for eastern black rails within tidal wetlands along the eastern seaboard of the United States. The model predicted suitability of breeding habitat as a function of wetland attributes (e.g., cover of high marsh and terrestrial border), hydrologic modification, and disturbance from human development measured over multiple spatial scales. We also found differences in habitat relationships for eastern black rails when compared to models that included both North American subspecies of black rail. Important results included negative effects of shrub-scrub wetlands, and strong positive effects of high marsh, terrestrial border, and impoundments on breeding season occupancy. Our study provides an example of integrating detection-non-detection data and modern statistical methods to build predictive distribution models for an extremely rare species, while also providing rigorous predictions of breeding habitat quality for the eastern black rail within tidal wetlands. These models will facilitate optimal monitoring, habitat conservation, and recovery planning efforts for eastern black rails and provide a foundation for future research and conservation of this imperiled bird.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02222","usgsCitation":"Stevens, B., Conway, C.J., Luke, K., Weldon, A., Hand, C., Schwarzer, A., Smith, F., Watson, C., and Watts, B.D., 2022, Large-scale distribution models for optimal prediction of Eastern black rail habitat within tidal ecosystems: Global Ecology and Conservation, v. 38, e02222, 12 p., https://doi.org/10.1016/j.gecco.2022.e02222.","productDescription":"e02222, 12 p.","ipdsId":"IP-136723","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":467176,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02222","text":"Publisher Index Page"},{"id":430022,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"38","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":275853,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan S.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":903426,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903427,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luke, Kirsten","contributorId":338653,"corporation":false,"usgs":false,"family":"Luke","given":"Kirsten","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903428,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Weldon, Aimee","contributorId":338654,"corporation":false,"usgs":false,"family":"Weldon","given":"Aimee","email":"","affiliations":[{"id":81183,"text":"Atlantic Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903429,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hand, Christy","contributorId":338655,"corporation":false,"usgs":false,"family":"Hand","given":"Christy","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903430,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schwarzer, Amy","contributorId":338656,"corporation":false,"usgs":false,"family":"Schwarzer","given":"Amy","email":"","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":903431,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Smith, Fletcher","contributorId":338657,"corporation":false,"usgs":false,"family":"Smith","given":"Fletcher","email":"","affiliations":[{"id":36378,"text":"Georgia Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":903432,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Watson, Craig","contributorId":338659,"corporation":false,"usgs":false,"family":"Watson","given":"Craig","email":"","affiliations":[{"id":81184,"text":"Atlanti Coast Joint Venture","active":true,"usgs":false}],"preferred":false,"id":903433,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Watts, Bryan D.","contributorId":338660,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D.","affiliations":[{"id":37406,"text":"College of William & Mary","active":true,"usgs":false}],"preferred":false,"id":903434,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70274308,"text":"70274308 - 2022 - Fluid transport and storage in the Cascadia forearc influenced by overriding plate lithology","interactions":[],"lastModifiedDate":"2026-03-26T16:14:27.133091","indexId":"70274308","displayToPublicDate":"2022-07-14T10:55:18","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Fluid transport and storage in the Cascadia forearc influenced by overriding plate lithology","docAbstract":"Subduction of hydrated oceanic lithosphere can carry water deep into the Earth, with consequences for a range of tectonic and magmatic processes. Most of the fluid is released in the forearc where it plays a critical role in controlling the mechanical properties and seismic behaviour of the subduction megathrust. Here we present results from three-dimensional inversions of data from nearly 400 long-period magnetotelluric sites, including 64 offshore, to provide insights into the distribution of fluids in the forearc of the Cascadia subduction zone. We constrain the geometry of the electrically resistive Siletz terrane, a thickened section of oceanic crust accreted to North America in the Eocene, and the conductive accretionary complex underthrust along the margin. We find that fluids accumulate over timescales exceeding 1 My above the plate in metasedimentary units, while the mafic rocks of Siletzia remain dry. Fluid concentrations tend to peak at slab depths of 17.5 and 30 km, suggesting control by metamorphic processes, but also concentrate around the edges of Siletzia, suggesting that this mafic block is impermeable, with dehydration fluids escaping up-dip along the megathrust. Our results demonstrate that the lithology of the overriding crust can play a critical role in controlling fluid transport in a subduction zone.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-022-00981-8","usgsCitation":"Egbert, G.D., Yang, B., Bedrosian, P.A., Key, K., Livelybrooks, D., Schultz, A., Kelbert, A., and Parris, B., 2022, Fluid transport and storage in the Cascadia forearc influenced by overriding plate lithology: Nature Geoscience, v. 15, p. 677-682, https://doi.org/10.1038/s41561-022-00981-8.","productDescription":"6 p.","startPage":"677","endPage":"682","ipdsId":"IP-130443","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501581,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"British Columbia, California, Oregon, Washington","otherGeospatial":"Cascadia forearc","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.49903582325707,\n 50.200003229484764\n ],\n [\n -132,\n 50.200003229484764\n ],\n [\n -132,\n 40\n ],\n [\n -121.49903582325707,\n 40\n ],\n [\n -121.49903582325707,\n 50.200003229484764\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","noUsgsAuthors":false,"publicationDate":"2022-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Egbert, Gary D.","contributorId":187462,"corporation":false,"usgs":false,"family":"Egbert","given":"Gary","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":957814,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yang, Bo","contributorId":149369,"corporation":false,"usgs":false,"family":"Yang","given":"Bo","email":"","affiliations":[{"id":13653,"text":"University South Florida","active":true,"usgs":false}],"preferred":false,"id":957815,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"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":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":957816,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Key, Kerry","contributorId":367853,"corporation":false,"usgs":false,"family":"Key","given":"Kerry","affiliations":[{"id":87628,"text":"Lamont-Doherty Earth Observatory, Columbia Univ.","active":true,"usgs":false}],"preferred":false,"id":957817,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Livelybrooks, Dean","contributorId":367854,"corporation":false,"usgs":false,"family":"Livelybrooks","given":"Dean","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":957818,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schultz, Adam","contributorId":197380,"corporation":false,"usgs":false,"family":"Schultz","given":"Adam","affiliations":[],"preferred":false,"id":957819,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kelbert, Anna 0000-0003-4395-398X akelbert@usgs.gov","orcid":"https://orcid.org/0000-0003-4395-398X","contributorId":184053,"corporation":false,"usgs":true,"family":"Kelbert","given":"Anna","email":"akelbert@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":957820,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Parris, Blake","contributorId":367855,"corporation":false,"usgs":false,"family":"Parris","given":"Blake","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":957821,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232943,"text":"ofr20191023E - 2022 - Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium","interactions":[],"lastModifiedDate":"2026-03-25T16:59:26.525114","indexId":"ofr20191023E","displayToPublicDate":"2022-07-14T10:34:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1023","chapter":"E","displayTitle":"Alaska Focus Area Definition for Data Acquisition for Potential Domestic Sources of Critical Minerals in Alaska for Antimony, Barite, Beryllium, Chromium, Fluorspar, Hafnium, Magnesium, Manganese, Uranium, Vanadium, and Zirconium","title":"Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium","docAbstract":"Phase 3 of the Earth Mapping Resources Initiative (Earth MRI) focuses on geologic belts that are favorable for hosting mineral systems that could contain the critical minerals antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium. Prior phases of the Earth MRI program in Alaska focused only on rare earth elements, aluminum, cobalt, graphite, lithium, niobium, platinum-group metals, tantalum, tin, titanium, and tungsten. An additional 11 critical minerals planed for future phases of Earth MRI (As, Bi, Cs, Ga, Ge, In, Re, Rb, Sc, Sr, Te) are considered prospective in these focus areas. Together, Alaska focus areas address 22 of the 35 minerals or mineral material groups presently deemed critical. This report describes the methodology and techniques utilized to define focus areas for future data acquisition in Alaska; the conterminous United States are covered in a separate report.
Focus areas are identified using a mineral systems framework, which accounts for all the possible tectonic and geologic settings where co-genetic mineral deposits may form. These deposits contain many commodities, including byproduct and critical minerals. Large system-scale processes may be evaluated using such a framework to determine the influence they play on critical mineral endowment within the deposits. Analyzing larger mineral systems provides an integrated and broad context to determine how and where critical minerals are sourced, transported, and deposited in geologic systems.
Statewide geological, geochemical, geophysical, and mineral occurrence datasets informed the delineation of focus areas in Alaska. For some mineral systems, previously published data-driven prospectivity analyses for critical mineral-bearing deposit types provided the basis for focus areas. We report a total of 22 new focus areas that are prospective for phase 3 critical minerals. These new focus areas represent four different mineral systems that are known or suspected to occur in Alaska. An additional 55 focus areas that were previously identified for phase 1 and phase 2 commodities were also identified as being prospective for phase 3 critical minerals. Collectively, 102 focus areas in Alaska have known or suspected potential for hosting phase 1, phase 2, and (or) phase 3 critical minerals. These focus areas represent 17 different mineral systems also containing critical minerals that are planned for consideration in future Earth MRI phases. Thus, the focus areas delineated herein, and in previous reports for Alaska, are comprehensive for all critical minerals as presently defined and may be used to guide the collection of new geologic, geochemical, and geophysical data in the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023E","collaboration":"Prepared in cooperation with the Alaska Division of Geology & Geophysics","usgsCitation":"Kreiner, D.C., Jones, J.V., III, and Case, G. N., 2022, Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium, chap. 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Niobium, Platinum Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":501525,"rank":11,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113261.htm","linkFileType":{"id":5,"text":"html"}},{"id":403736,"rank":8,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023B","text":"Open-File Report 2019-1023-B","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Resources of 11 Critical Minerals in the Conterminous United States, Hawaii, and Puerto Rico—Aluminum, Cobalt, Graphite, Lithium, Niobium, Platinum-Group Elements, Rare Earth Elements, Tantalum, Tin, Titanium, and Tungsten"},{"id":403735,"rank":7,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/ofr20191023A","text":"Open-File Report 2019-1023-A","linkHelpText":"- Focus Areas for Data Acquisition for Potential Domestic Sources of Critical Minerals—Rare Earth 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\"}}]}","contact":"Director, Alaska Science Center
U.S. Geological Survey
4210 University Drive
Anchorage, Alaska 99508
The Earth Mapping Resources Initiative (Earth MRI) is conducted in phases to identify areas for acquiring new geologic framework data to identify potential domestic resources of the 35 mineral materials designated as critical minerals for the United States. This report describes the data sources and summary results for 13 critical minerals evaluated in the conterminous United States and Puerto Rico during phase 3 of the study (antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium). Phases 1 and 2 of the Earth MRI addressed aluminum, cobalt, graphite, lithium, niobium, platinum-group elements (PGEs), rare earth elements (REEs), tantalum, tin, titanium, and tungsten. Critical minerals in Alaska are covered in a separate report. No focus areas for phase 3 critical minerals are delineated for Hawaii.
The geologic, geochemical, topographic, and geophysical mapping provided by the Earth MRI documents geologic features that reflect the extent of individual mineral systems and provides information about critical mineral deposits that may not have been previously considered. The mineral-systems approach links critical mineral commodities to deposit types that represent the manifestations of large mineral systems.
Each of the 13 critical mineral commodities for phase 3 of the Earth MRI is discussed in terms of its importance to the Nation’s economy, modes of occurrence, mineral systems, and deposit types, and is accompanied by maps and tables listing examples of focus areas in the conterminous United States and Puerto Rico. Examples of important mineral systems for this group of 13 critical minerals include basin brine path systems for barite and fluorspar, Carlin-type systems and Coeur d’Alene systems for antimony, chemical weathering and volcanogenic seafloor systems for manganese, Climax-type systems for beryllium, mafic magmatic systems for chromium, marine evaporite systems for potash and magnesium, meteoric recharge systems for uranium, petroleum systems for helium, and placer systems for zirconium and hafnium.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191023D","collaboration":"Prepared in cooperation with the Association of American State Geologists","usgsCitation":"Hammarstrom, J.M., Dicken, C.L., Woodruff, L.G., Andersen, A.K., Brennan, S., Day, W.C., Drenth, B.J., Foley, N.K., Hall, S., Hofstra, A.H., McCafferty, A.E., Shah, A.K., and Ponce, D.A., 2022, Focus areas for data acquisition for potential domestic resources of 13 critical minerals in the conterminous United States and Puerto Rico—Antimony, barite, beryllium, chromium, fluorspar, hafnium, helium, magnesium, manganese, potash, uranium, vanadium, and zirconium, chap. 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Mineral Resources Program
U.S. Geological Survey
913 National Center
Reston, VA 20192
Drought and nutrient pollution can affect the dynamics of stream ecosystems in diverse ways. While the individual effects of both stressors are broadly examined in the literature, we still know relatively little about if and how these stressors interact. Here, we performed a mesocosm experiment that explores the compounded effects of seasonal drought via water withdrawals and nutrient pollution (1.0 mg/L of N and 0.1 mg/L of P) on a subset of Ozark stream community fauna and ecosystem processes. We observed biological responses to individual stressors as well as both synergistic and antagonistic stressor interactions. We found that drying negatively affected periphyton assemblages, macroinvertebrate colonization, and leaf litter decomposition in shallow habitats. However, in deep habitats, drought-based increases in fish density caused trophic cascades that released algal communities from grazing pressures; while nutrient enrichment caused bottom-up cascades that influenced periphyton variables and crayfish growth rates. Finally, the combined effects of drought and nutrient enrichment interacted antagonistically to increase survival in longear sunfish; and stressors acted synergistically on grazers causing a trophic cascade that increased periphyton variables. Because stressors can directly and indirectly impact biota—and that the same stressor pairing can act differentially on various portions of the community simultaneously—our broad understanding of individual stressors might not adequately inform our knowledge of multi-stressor systems.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0269222","usgsCitation":"Fournier, R., and Magoulick, D.D., 2022, Drought and nutrient pollution produce multiple interactive effects in stream ecosystems: PLoS ONE, v. 17, no. 7, e0269222, 16 p., https://doi.org/10.1371/journal.pone.0269222.","productDescription":"e0269222, 16 p.","ipdsId":"IP-111266","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447113,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0269222","text":"Publisher Index Page"},{"id":433446,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-07-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Fournier, R.J.","contributorId":341731,"corporation":false,"usgs":false,"family":"Fournier","given":"R.J.","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":908837,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":908838,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232969,"text":"70232969 - 2022 - Warming temperatures affect meadow-wide nectar resources, with implications for plant-pollinator communities","interactions":[],"lastModifiedDate":"2022-07-14T13:57:25.353308","indexId":"70232969","displayToPublicDate":"2022-07-14T08:39:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Warming temperatures affect meadow-wide nectar resources, with implications for plant-pollinator communities","docAbstract":"Nectar production may be a point of sensitivity that can help link primary and secondary trophic responses to climate shifts, and is therefore important to our understanding of ecosystem responses. We evaluated the nectar response of two widespread native forbs, Balsamorhiza sagittata and Eriogonum umbellatum, to experimental warming in a high-elevation sagebrush meadow in the Teton Range, WY, USA, over two years, 2015 and 2016. Warming treatments reduced the occurrence of nighttime freezing and nectar volume but increased sugar concentration in nectar in both species in both years. Warming effects were also evident in a consistent increase in the number of flowers produced by B. sagittata. Our research suggests that warming associated with climate change has the potential to induce shifts in the nectar-feeding community by changing nectar characteristics such as volume and sugar concentration to which nectar feeders are adapted.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.4162","usgsCitation":"McCombs, A.L., Debinski, D., Reinhardt, K., Germino, M., and Caragea, P., 2022, Warming temperatures affect meadow-wide nectar resources, with implications for plant-pollinator communities: Ecosphere, v. 13, no. 7, e4162, 9 p., https://doi.org/10.1002/ecs2.4162.","productDescription":"e4162, 9 p.","ipdsId":"IP-132652","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447116,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecs2.4162","text":"External Repository"},{"id":403740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Grand Teton National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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C.","contributorId":36903,"corporation":false,"usgs":false,"family":"Peters","given":"Debra P. C.","affiliations":[{"id":25579,"text":"USDA-ARS Jornada Experimental Range, Las Cruces, NM 88003","active":true,"usgs":false}],"preferred":false,"id":846596,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"McCombs, Audrey L.","contributorId":293175,"corporation":false,"usgs":false,"family":"McCombs","given":"Audrey","email":"","middleInitial":"L.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":846584,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Debinski, Diane","contributorId":293176,"corporation":false,"usgs":false,"family":"Debinski","given":"Diane","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":846585,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reinhardt, Keith","contributorId":178543,"corporation":false,"usgs":false,"family":"Reinhardt","given":"Keith","email":"","affiliations":[],"preferred":false,"id":846586,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":846587,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Caragea, Petrutza","contributorId":293177,"corporation":false,"usgs":false,"family":"Caragea","given":"Petrutza","email":"","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":846588,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232960,"text":"70232960 - 2022 - Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions","interactions":[],"lastModifiedDate":"2022-07-14T13:39:00.417801","indexId":"70232960","displayToPublicDate":"2022-07-14T08:29:28","publicationYear":"2022","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"seriesTitle":{"id":11125,"text":"Special Papers of the Geological Society of London","active":true,"publicationSubtype":{"id":24}},"title":"Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions","docAbstract":"Subaerial volcaniclastic deposits are produced principally by volcanic debris avalanches, pyroclastic density currents, lahars, and tephra falls. Those deposits have widely ranging geomorphic and sedimentologic characteristics; they can mantle, modify, or create new topography, and their emplacement and subsequent reworking can have an outsized impact on the geomorphic and sedimentologic responses of watersheds surrounding, and channels draining, volcanoes. Volcaniclastic deposits provide a wealth of information about eruptive histories, volcanic processes, and landscape responses to eruptions. The volcanic processes that produce these deposits, and consequently the character and sedimentary structures of the deposits themselves, are influenced by initiation mechanism. Deposit preservation is affected by deposit magnitude, texture, and composition, depositional environment, and climate regime. Innovative analyses of deposits from several modern eruptions and advancements in physical and numerical modelling have vastly improved our understanding of volcanic processes, interpretations of eruptive histories, and recognition of the hazards posed by volcanic eruptions. This contribution highlights and summarizes major advances that have occurred in the past few\ndecades in understanding of volcaniclastic deposits and linkages with volcanic processes.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Volcanic processes in the sedimentary record: When volcanoes meet the environment","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Geological Society of London","doi":"10.1144/SP520-2021-142","usgsCitation":"Major, J.J., 2022, Subaerial volcaniclastic deposits — Influences of initiation mechanisms and transport behaviour on characteristics and distributions, chap. of Volcanic processes in the sedimentary record: When volcanoes meet the environment: Special Papers of the Geological Society of London, v. 520, 72 p., https://doi.org/10.1144/SP520-2021-142.","productDescription":"72 p.","ipdsId":"IP-138407","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":447118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/sp520-2021-142","text":"Publisher Index Page"},{"id":403724,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"520","noUsgsAuthors":false,"publicationDate":"2022-07-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Major, Jon J. 0000-0003-2449-4466 jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":846570,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70232972,"text":"70232972 - 2022 - Gill-net selectivity for fifteen fish species of the upper San Francisco Estuary","interactions":[],"lastModifiedDate":"2022-07-14T13:27:46.712019","indexId":"70232972","displayToPublicDate":"2022-07-14T08:19:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Gill-net selectivity for fifteen fish species of the upper San Francisco Estuary","docAbstract":"Gill-net size selectivity for 15 fish species occurring in the upper San Francisco Estuary was estimated from a data set compiled from multiple studies which together contained 7,096 individual fish observations from 882 gill net sets. The gill nets considered in this study closely resembled the American Fisheries Society’s recommended standardized experimental gill nets for sampling inland waters. Relationships between gill-net mesh sizes and the sizes for each fish species retained in them were estimated indirectly using generalized linear modeling and maximum likelihood. Selectivity curves are provided for each species to inform researchers about population characteristics of fishes sampled with similar gill nets.","language":"English","publisher":"University of California","doi":"10.15447/sfews.2022v20iss2art4","usgsCitation":"Wulff, M.L., Feyrer, F.V., and Young, M.J., 2022, Gill-net selectivity for fifteen fish species of the upper San Francisco Estuary: San Francisco Estuary and Watershed Science, v. 20, no. 2, 4, 10 p., https://doi.org/10.15447/sfews.2022v20iss2art4.","productDescription":"4, 10 p.","ipdsId":"IP-101973","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":447121,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2022v20iss2art4","text":"Publisher Index Page"},{"id":403721,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Napa River, Petaluma River, San Francisco Estuary, San Pablo Bay, Suisun Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14187622070311,\n 38.052416771864834\n ],\n [\n -122.17758178710939,\n 38.07620357665235\n ],\n [\n -122.21603393554688,\n 38.08052761936274\n ],\n [\n -122.25036621093749,\n 38.11619121500379\n ],\n [\n -122.25723266601562,\n 38.156156969924915\n ],\n [\n -122.26684570312499,\n 38.19286295796692\n ],\n [\n -122.26409912109375,\n 38.285624966683756\n ],\n [\n -122.2723388671875,\n 38.34704152882893\n ],\n [\n -122.61291503906249,\n 38.24788726821097\n ],\n [\n -122.6348876953125,\n 38.23278669950994\n ],\n [\n -122.57858276367186,\n 38.17235306715556\n ],\n [\n -122.53463745117186,\n 38.13779704369437\n ],\n [\n -122.49618530273438,\n 38.09674050228651\n ],\n [\n -122.49893188476561,\n 38.06647354576796\n ],\n [\n -122.29980468749999,\n 38.002655740556705\n ],\n [\n -122.24624633789062,\n 38.0513353697269\n ],\n [\n -122.19955444335938,\n 38.04160203158016\n ],\n [\n -122.18032836914062,\n 38.025376925938865\n ],\n [\n -122.15011596679688,\n 38.01239425385966\n ],\n [\n -122.11303710937499,\n 38.025376925938865\n ],\n [\n -122.07733154296875,\n 38.039438891821746\n ],\n [\n -122.00729370117188,\n 38.048091067457236\n ],\n [\n -121.81365966796874,\n 38.01239425385966\n ],\n [\n -121.84936523437499,\n 38.08701320402273\n ],\n [\n -121.91253662109376,\n 38.059986139487975\n ],\n [\n -121.90017700195312,\n 38.07512252602562\n ],\n [\n -121.92214965820311,\n 38.098901948321256\n ],\n [\n -121.97021484374999,\n 38.08701320402273\n ],\n [\n -122.00180053710936,\n 38.095659755295614\n ],\n [\n -121.97708129882812,\n 38.11727165830543\n ],\n [\n -121.9976806640625,\n 38.15075747130226\n ],\n [\n -122.07595825195312,\n 38.13779704369437\n ],\n [\n -122.0745849609375,\n 38.11078875872392\n ],\n [\n -122.14187622070311,\n 38.052416771864834\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-06-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Wulff, Marissa L. 0000-0003-0121-9066","orcid":"https://orcid.org/0000-0003-0121-9066","contributorId":229534,"corporation":false,"usgs":true,"family":"Wulff","given":"Marissa","email":"","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846589,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Feyrer, Frederick V. 0000-0003-1253-2349 ffeyrer@usgs.gov","orcid":"https://orcid.org/0000-0003-1253-2349","contributorId":178379,"corporation":false,"usgs":true,"family":"Feyrer","given":"Frederick","email":"ffeyrer@usgs.gov","middleInitial":"V.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846590,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Young, Matthew J. 0000-0001-9306-6866 mjyoung@usgs.gov","orcid":"https://orcid.org/0000-0001-9306-6866","contributorId":206255,"corporation":false,"usgs":true,"family":"Young","given":"Matthew","email":"mjyoung@usgs.gov","middleInitial":"J.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":846591,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232967,"text":"70232967 - 2022 - Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin","interactions":[],"lastModifiedDate":"2022-07-14T13:18:11.726912","indexId":"70232967","displayToPublicDate":"2022-07-14T08:08:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":11122,"text":"BMC Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Genetic diversity and connectivity of chemosynthetic cold seep mussels from the U.S. Atlantic margin","docAbstract":"Background
Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae.
Results
A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi. No evidence was found for genetic diversification across depth in G. childressi, likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments.
Conclusions
These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
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2022 - Impact of climate change on mollusks and other invertebrate resources at the Dominican University of California archaeological site (CA-MRN-254), Marin County, California","interactions":[],"lastModifiedDate":"2022-07-26T11:39:17.783231","indexId":"70233568","displayToPublicDate":"2022-07-14T06:33:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3217,"text":"Quaternary International","active":true,"publicationSubtype":{"id":10}},"title":"Impact of climate change on mollusks and other invertebrate resources at the Dominican University of California archaeological site (CA-MRN-254), Marin County, California","docAbstract":"We have identified and provided ecological interpretations of 30 taxa recovered at two shellmounds at the Dominican University of California archaeology site in Marin County, California (CA-MRN-254). A Q-mode cluster analysis was used to group the samples according to their faunal similarity. The clusters ranged from a diverse grouping of 100 samples with 27 taxa (Cluster A) to those with a more restricted assemblage (4–9 taxa in Clusters B to E). The Q-mode clusters were then used to interpret the variability in food resources utilized through the 1800 years of site occupation. During the Intermediate Middle Period (A.D.100-300), the inhabitants appeared to be selective in the marine taxa they used, evident by the presence of Cluster B and E assemblages. A diverse (Cluster A) assemblage was then utilized at the site at one or both of the shellmounds through the remainder of the occupancy period, including the Middle/Late Period Transition (A.D. 700–900) and Late Period Phase 1C (A.D. 900–1300), coincident with the extensive drought conditions of the Medieval Climatic Anomaly (MCA) in the San Francisco Bay area. These findings suggest the marine invertebrate resources utilized by the site occupants were not significantly affected by the persistent aridity associated with the MCA.
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2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.
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