Thoroughly investigating the characteristics of new generation hyperspectral and high spatial resolution spaceborne sensors will advance the study of agricultural crops. Therefore, we compared the performances of hyperspectral Deutsches Zentrum fur Luftund Raumfahrt- (DLR) Earth Sensing Imaging Spectrometer (DESIS) and high spatial resolution PlanetScope in classifying eight crop types in California's Central Valley during the 2020 growing season. The DESIS sensor onboard the International Space Station collects data at 235 hyperspectral narrowbands (HNB) each with 2.55 nm bandwidth from 400–1000 nm and 30 m spatial resolution. In contrast, PlanetScope Dove-R data have four multispectral broadbands (MBB) with 3–4 m spatial resolution. We obtained best classification accuracies using 14 DESIS HNB from the August 2020 image, with an overall accuracy of 85% and producer's and user's accuracies of 72–100% and 75–100%, respectively, for the eight crops. The best classification accuracies using PlanetScope data were obtained using an image mosaic pair from June and August 2020; this resulted in an overall accuracy of 79% and producer's and user's accuracies of 56–100% and 61–100%, respectively. Combining the best 14 DESIS HNB from August 2020 with the 4 PlanetScope MBB from August 2020 yielded an overall accuracy of 82% and producer's and user's accuracies of 65–100% and 60–94%, respectively. On one-to-one single date comparisons of DESIS versus PlanetScope data, the hyperspectral data always outperformed high spatial resolution data in crop type classification. Nevertheless, high spatial resolution data will remain invaluable in assessing within-field variability and crop biophysical/biochemical modeling in precision agriculture.
","language":"English","publisher":"IEEE","doi":"10.1109/JSTARS.2022.3204223","usgsCitation":"Aneece, I.P., Foley, D., Thenkabail, P., Oliphant, A., and Teluguntla, P.G., 2022, New generation hyperspectral data From DESIS compared to high spatial resolution PlanetScope data for crop type classification: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 15, p. 7846-7858, https://doi.org/10.1109/JSTARS.2022.3204223.","productDescription":"13 p.","startPage":"7846","endPage":"7858","ipdsId":"IP-140341","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":446530,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1109/jstars.2022.3204223","text":"Publisher Index Page"},{"id":435698,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XM63RK","text":"USGS data release","linkHelpText":"PlanetScope and DESIS spectral library of agricultural crops in California's Central Valley for the 2020 growing season"},{"id":407512,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Aneece, Itiya P. 0000-0002-1201-5459","orcid":"https://orcid.org/0000-0002-1201-5459","contributorId":208265,"corporation":false,"usgs":true,"family":"Aneece","given":"Itiya","middleInitial":"P.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":853176,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foley, Daniel 0000-0002-2051-6325","orcid":"https://orcid.org/0000-0002-2051-6325","contributorId":208266,"corporation":false,"usgs":true,"family":"Foley","given":"Daniel","email":"","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":853177,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thenkabail, Prasad 0000-0002-2182-8822","orcid":"https://orcid.org/0000-0002-2182-8822","contributorId":220239,"corporation":false,"usgs":true,"family":"Thenkabail","given":"Prasad","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":853178,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Oliphant, Adam 0000-0001-8622-7932 aoliphant@usgs.gov","orcid":"https://orcid.org/0000-0001-8622-7932","contributorId":192325,"corporation":false,"usgs":true,"family":"Oliphant","given":"Adam","email":"aoliphant@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":853179,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teluguntla, Pardhasaradhi G. 0000-0001-8060-9841","orcid":"https://orcid.org/0000-0001-8060-9841","contributorId":297051,"corporation":false,"usgs":true,"family":"Teluguntla","given":"Pardhasaradhi","email":"","middleInitial":"G.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":853180,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236388,"text":"70236388 - 2022 - Characterization of vegetated and ponded wetlands with implications towards coastal wetland marsh collapse","interactions":[],"lastModifiedDate":"2023-06-08T14:54:19.254192","indexId":"70236388","displayToPublicDate":"2022-09-05T10:14:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1198,"text":"Catena","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of vegetated and ponded wetlands with implications towards coastal wetland marsh collapse","docAbstract":"Coastal wetlands provide numerous ecosystem services; yet these ecosystems are increasingly vulnerable to climate change stressors, especially excessive flooding from sea-level rise and storm events. This study highlights the important contribution of vegetation belowground biomass to marsh stability and identifies loss of vegetation as a critical driver of marsh collapse. We investigated the shear strength of salt marshes and unvegetated interior ponds using a modified cone penetrometer along a chronosequence of wetland marsh collapse (0 to 21 + years following pond formation) to characterize changes in the structural integrity of the marsh soil. Following conversion from vegetated marsh to open water pond, the surficial soils experienced a dramatic loss in shear strength resulting from the loss of vegetation and compaction of soil pore space. The Cone Penetrometer Testing (CPT) data indicate that higher shear strength in the surficial layers of the vegetated marsh sites were never recovered, up to 21 + years following marsh collapse. Coupled with significant elevation loss from marsh collapse, additional sea-level rise, deep subsidence, and reduced sedimentation may contribute to conditions that can exceed critical flooding thresholds, making recovery from marsh collapse difficult or impossible. Therefore, characterizing mechanisms and thresholds of marsh collapse are critical for identifying those coastal marshes that are vulnerable to collapse before conversion from vegetated marsh to open water occurs.","language":"English","publisher":"Elsevier","doi":"10.1016/j.catena.2022.106547","usgsCitation":"Cadigan, J.A., Jafari, N., Stagg, C., Laurenzano, C., Harris, B.D., Meselhe, A.E., Dugas, J., and Couvillion, B., 2022, Characterization of vegetated and ponded wetlands with implications towards coastal wetland marsh collapse: Catena, v. 218, 106547, 9 p.; Data Release, https://doi.org/10.1016/j.catena.2022.106547.","productDescription":"106547, 9 p.; Data Release","ipdsId":"IP-123995","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":446532,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.catena.2022.106547","text":"Publisher Index Page"},{"id":406222,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":417830,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EW3N0D"}],"country":"United States","state":"Louisiana","city":"Port Sulphur","otherGeospatial":"Gulf of Mexico, Mississippi River Deltaic Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.98626708984375,\n 29.3067588581613\n ],\n [\n -89.48501586914062,\n 29.3067588581613\n ],\n [\n -89.48501586914062,\n 29.54956657394792\n ],\n [\n -89.98626708984375,\n 29.54956657394792\n ],\n [\n -89.98626708984375,\n 29.3067588581613\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"218","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cadigan, Jack A. 0000-0002-1200-8275","orcid":"https://orcid.org/0000-0002-1200-8275","contributorId":296178,"corporation":false,"usgs":false,"family":"Cadigan","given":"Jack","email":"","middleInitial":"A.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":850851,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jafari, Navid H.","contributorId":214730,"corporation":false,"usgs":false,"family":"Jafari","given":"Navid H.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":850852,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stagg, Camille 0000-0002-1125-7253","orcid":"https://orcid.org/0000-0002-1125-7253","contributorId":220330,"corporation":false,"usgs":true,"family":"Stagg","given":"Camille","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":850853,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laurenzano, Claudia 0000-0003-1406-8658","orcid":"https://orcid.org/0000-0003-1406-8658","contributorId":215853,"corporation":false,"usgs":false,"family":"Laurenzano","given":"Claudia","affiliations":[{"id":25340,"text":"Cherokee Nation Technologies","active":true,"usgs":false}],"preferred":false,"id":850854,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harris, Brian D. 0000-0001-5771-1880","orcid":"https://orcid.org/0000-0001-5771-1880","contributorId":296180,"corporation":false,"usgs":false,"family":"Harris","given":"Brian","email":"","middleInitial":"D.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":850855,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Meselhe, Amina E.","contributorId":296186,"corporation":false,"usgs":false,"family":"Meselhe","given":"Amina","email":"","middleInitial":"E.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":850856,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Dugas, Jason 0000-0001-6094-7560","orcid":"https://orcid.org/0000-0001-6094-7560","contributorId":205300,"corporation":false,"usgs":true,"family":"Dugas","given":"Jason","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":850857,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Couvillion, Brady 0000-0001-5323-1687","orcid":"https://orcid.org/0000-0001-5323-1687","contributorId":222810,"corporation":false,"usgs":true,"family":"Couvillion","given":"Brady","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":850858,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70236396,"text":"70236396 - 2022 - Stratigraphy and eruption history of maars in the Clear Lake Volcanic Field, California","interactions":[],"lastModifiedDate":"2022-09-05T14:00:23.511563","indexId":"70236396","displayToPublicDate":"2022-09-05T08:24:50","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5232,"text":"Frontiers in Earth Science","onlineIssn":"2296-6463","active":true,"publicationSubtype":{"id":10}},"title":"Stratigraphy and eruption history of maars in the Clear Lake Volcanic Field, California","docAbstract":"The Clear Lake Volcanic Field (CLVF) is the northernmost and youngest field in a chain of volcanic provinces in the California Coast Range mountains. Effusive and explosive volcanic activity in the field has spanned at least 2.1 million years, with the youngest eruptions comprising a series of maar craters at the edges of, and within, Clear Lake itself. This work documents the first direct ages for many of these maar deposits, and builds the stratigraphic basis for interpreting eruptive processes and dynamics of the young eruptions which produced them. Detailed stratigraphy has distinguished maar eruption products from pyroclastic deposits (monolithologic falls and flows, previously mapped together with maars as a single unit), and established a set of 6 eruption facies from maar deposit lithology, grain size parameters, and depositional structures. Radiocarbon dates from carbon films found on clasts at 3 outcrops have constrained several of these maar eruptions to ~8500-13,500 years BP, coinciding with eruptive periods previously estimated based on lake core tephrachronology. Part of this period also coincides with indigenous occupation (< 12,000 years BP), which suggests that oral histories of Pomo and other local tribes may contain descriptions of volcanic phenomena experienced by local residents of the CLVF. Collaboration between volcanologists and indigenous historians may add a valuable human dimension to the youngest eruptions of the Clear Lake Volcanic Field; combined, geologic and ethnographic avenues of research will help build a richer eruption history for future volcanic hazard assessment.","language":"English","publisher":"Frontiers Media","doi":"10.3389/feart.2022.911129","usgsCitation":"Ball, J.L., 2022, Stratigraphy and eruption history of maars in the Clear Lake Volcanic Field, California: Frontiers in Earth Science, v. 10, 911129, 18 p., https://doi.org/10.3389/feart.2022.911129.","productDescription":"911129, 18 p.","ipdsId":"IP-140117","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":446534,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/feart.2022.911129","text":"Publisher Index Page"},{"id":435700,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9OH9DSD","text":"USGS data release","linkHelpText":"Clear Lake Volcanic Field maar deposit grain size distributions, Inman grain size statistics, and radiocarbon ages"},{"id":406217,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Clear Lake Volcanic Field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.01940917968751,\n 38.606139634147866\n ],\n [\n -122.29980468749999,\n 38.606139634147866\n ],\n [\n -122.29980468749999,\n 39.25990481501755\n ],\n [\n -123.01940917968751,\n 39.25990481501755\n ],\n [\n -123.01940917968751,\n 38.606139634147866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-09-02","publicationStatus":"PW","contributors":{"editors":[{"text":"Hollomon Graettinger, Alison","contributorId":296219,"corporation":false,"usgs":false,"family":"Hollomon Graettinger","given":"Alison","email":"","affiliations":[{"id":27119,"text":"University of Missouri-Kansas City","active":true,"usgs":false}],"preferred":false,"id":850889,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Ball, Jessica L. 0000-0002-7837-8180 jlball@usgs.gov","orcid":"https://orcid.org/0000-0002-7837-8180","contributorId":205012,"corporation":false,"usgs":true,"family":"Ball","given":"Jessica","email":"jlball@usgs.gov","middleInitial":"L.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":850870,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70236363,"text":"70236363 - 2022 - Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes","interactions":[],"lastModifiedDate":"2022-09-05T13:24:05.252457","indexId":"70236363","displayToPublicDate":"2022-09-05T08:18:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3910,"text":"Frontiers in Ecology and Evolution","onlineIssn":"2296-701X","active":true,"publicationSubtype":{"id":10}},"title":"Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes","docAbstract":"The expansion of human infrastructure has contributed to novel risks and disturbance regimes in most ecosystems, leading to considerable uncertainty about how species will respond to altered landscapes. A recent assessment revealed that whooping cranes (Grus americana), an endangered migratory waterbird species, avoid wind-energy infrastructure during migration. However, uncertainties regarding collective impacts of other types of human infrastructure, such as power lines on migration, variable drought conditions, and continued construction of wind energy infrastructure may compromise ongoing recovery efforts for whooping cranes. Droughts are increasing in frequency and severity throughout the whooping crane migration corridor, and the impacts of drought on stopover habitat use are largely unknown. Moreover, decision-based analyses are increasingly advocated to guide recovery planning for endangered species, yet applications remain rare. Using GPS locations from 57 whooping cranes from 2010 through 2016 in the United States Great Plains, we assessed habitat selection and avoidance of potential disturbances during migration relative to drought conditions, and we used these results in an optimization analysis to select potential sites for new wind energy developments that minimize relative habitat loss for whooping cranes and maximize wind energy potential. Drought occurrence and severity varied spatially and temporally across the migration corridor during our study period. Whooping cranes rarely used areas <5 km from human settlements and wind energy infrastructure under both drought and non-drought conditions, and <2 km from power lines during non-drought conditions, with the lowest likelihood of use near wind energy infrastructure. Whooping cranes differed in their selection of wetland and cropland land cover types depending on drought or non-drought conditions. We identified scenarios for wind energy expansion across the migration corridor and in select states, which are robust to uncertain drought conditions, where future loss of highly selected stopover habitats could be minimized under a common strategy. Our approach was to estimate functional habitat loss while integrating current disturbances, potential future disturbances, and uncertainty in drought conditions. Therefore, dynamic models describing potential costs associated with risk-averse behaviors resulting from future developments can inform proactive conservation before population impacts occur.
","language":"English","publisher":"Frontiers Media","doi":"10.3389/fevo.2022.931260","usgsCitation":"Ellis, K.S., Pearse, A.T., Brandt, D.A., Bidwell, M., Harrell, W.C., Butler, M.J., and Post van der Burg, M., 2022, Balancing future renewable energy infrastructure siting and associated habitat loss for migrating whooping cranes: Frontiers in Ecology and Evolution, v. 10, 931260, 17 p., https://doi.org/10.3389/fevo.2022.931260.","productDescription":"931260, 17 p.","ipdsId":"IP-138784","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":446538,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2022.931260","text":"Publisher Index Page"},{"id":435701,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P902I4WO","text":"USGS data release","linkHelpText":"Whooping crane migration habitat selection disturbance data and maps"},{"id":406216,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Kansas, Montana, Nebraska, North Dakota, South Dakota, Oklahoma, Texas","otherGeospatial":"Great Plains","volume":"10","noUsgsAuthors":false,"publicationDate":"2022-08-12","publicationStatus":"PW","contributors":{"editors":[{"text":"Hamilton, Diana","contributorId":296218,"corporation":false,"usgs":false,"family":"Hamilton","given":"Diana","email":"","affiliations":[{"id":12803,"text":"Mount Allison University","active":true,"usgs":false}],"preferred":false,"id":850888,"contributorType":{"id":2,"text":"Editors"},"rank":1}],"authors":[{"text":"Ellis, Kristen S. 0000-0003-2759-3670","orcid":"https://orcid.org/0000-0003-2759-3670","contributorId":251877,"corporation":false,"usgs":true,"family":"Ellis","given":"Kristen","email":"","middleInitial":"S.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850802,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pearse, Aaron T. 0000-0002-6137-1556 apearse@usgs.gov","orcid":"https://orcid.org/0000-0002-6137-1556","contributorId":1772,"corporation":false,"usgs":true,"family":"Pearse","given":"Aaron","email":"apearse@usgs.gov","middleInitial":"T.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850803,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brandt, David A. 0000-0001-9786-307X dbrandt@usgs.gov","orcid":"https://orcid.org/0000-0001-9786-307X","contributorId":149929,"corporation":false,"usgs":true,"family":"Brandt","given":"David","email":"dbrandt@usgs.gov","middleInitial":"A.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850804,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bidwell, Mark T.","contributorId":139204,"corporation":false,"usgs":false,"family":"Bidwell","given":"Mark T.","affiliations":[{"id":12696,"text":"Environmental Canada","active":true,"usgs":false}],"preferred":false,"id":850805,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Harrell, Wade C.","contributorId":147143,"corporation":false,"usgs":false,"family":"Harrell","given":"Wade","email":"","middleInitial":"C.","affiliations":[{"id":16793,"text":"USFWS, Ecological Services, Austwell, TX","active":true,"usgs":false}],"preferred":false,"id":850806,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Butler, Matthew J.","contributorId":296149,"corporation":false,"usgs":false,"family":"Butler","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":850807,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Post van der Burg, Max 0000-0002-3943-4194","orcid":"https://orcid.org/0000-0002-3943-4194","contributorId":216013,"corporation":false,"usgs":true,"family":"Post van der Burg","given":"Max","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":850808,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70236497,"text":"70236497 - 2022 - Predictive models of selective cattle use of large, burned landscapes in semiarid sagebrush-steppe","interactions":[],"lastModifiedDate":"2022-09-09T12:14:02.910302","indexId":"70236497","displayToPublicDate":"2022-09-05T07:10:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3228,"text":"Rangeland Ecology and Management","onlineIssn":"1551-5028","printIssn":"1550-7424","active":true,"publicationSubtype":{"id":10}},"title":"Predictive models of selective cattle use of large, burned landscapes in semiarid sagebrush-steppe","docAbstract":"The fire-exotic annual grass cycle is a severe threat to shrub-steppe rangelands, and a greater understanding of how livestock grazing relates to the problem is needed to guide effective management interventions. Grazing effects vary throughout shrub-steppe rangelands because livestock are selective in their use within pastures. Thus, knowing where cattle are located and concentrate their use in a postfire landscape is important for enhancing plant community resiliency to disturbance and resistance to exotic annual grass invasion. We asked how the distribution and intensity of cattle use varied across 113 000 ha of recently burned, environmentally varied shrub-steppe. Generalized linear mixed effects models were used to determine the relationship of cattle dung (presence/absence and counts), which was recorded during the third to fifth postfire year (after grazing deferment) on 1166 (531-m2) plots, to water sources, burn severity, grass cover, and topographic predictors. Our distribution and intensity of use models revealed similar relationships between cattle use and landscape predictors. Cattle use was greater in areas that were flatter and closer to water and that had moderate burn severity and less heat load and ruggedness. Slope had the strongest effect on cattle use of the predictors. The probability of cattle being present decreased by 10% for every 5° increase in slope until slope exceeded 15°, and then the effect of slope weakened. Despite moderate slopes
Relative Sea Levels (RSLs) derived primarily from marine bivalves near Petermann Glacier, NW Greenland, constrain past regional ice-mass changes through glacial isostatic adjustment (GIA) modeling. Oxygen isotopes measured on bivalves corrected for shell-depth habitat and document changing meltwater input. Rapid RSL fall of up to 62 m/kyr indicates ice loss at or prior to ∼9 ka. Transition to an RSL stillstand starting at ∼6 ka reflects renewed ice-mass loading followed by further mass loss over the past few millennia. GIA simulations of rapid early RSL fall suggest a low regional upper-mantle viscosity. Early loss of grounded ice tracks atmospheric warming and pre-dates the eventual collapse of Petermann Glacier's floating ice tongue near ∼7 ka, suggesting grounding zone stabilization during early phases of deglaciation. We hypothesize mid-Holocene regrowth of regional ice caps in response to cooling and increased precipitation, following loss of the floating shelf ice. Remnants of these ice caps remain present but are now melting.
As North America collided with Africa to form Pangea during the Alleghanian orogeny, crystalline and sedimentary rocks in the southeastern United States were thrust forelandward along the Appalachian décollement. We examined Ps receiver functions to better constrain the kinematics of this prominent subsurface structure. From Southeastern Suture of the Appalachian Margin Experiment (SESAME) and other EarthScope stations on the Blue Ridge–Piedmont crystalline megathrust, we find large arrivals from a 5–10-km-deep converter. We argue that a strong contrast in dipping anisotropic foliation occurs at the subhorizontal Appalachian décollement, and propose that such a geometry may be typical for décollement structures. Conversion polarity flips can be explained by an east-dipping foliation, but this orientation is at odds with the overlying northeast-trending surface tectonic grain. We suggest that prior to late Alleghanian northwest-directed head-on collision, the Appalachian décollement accommodated early Alleghanian west-vergence, independent of the overlying Blue Ridge–Piedmont structural inheritance. The geophysical expression of dipping anisotropic foliation provides a powerful tool for investigating subsurface kinematics, especially where they are obscured by overlying fabric, to disentangle the tectonic complexities that embody oblique collisional orogens.
Coyotes (Canis latrans) are expanding their range and due to conflicts with the public and concerns of Coyotes affecting natural resources such as game or sensitive species, there is interest and often a demand to monitor Coyote populations. A challenge to monitoring is that traditional invasive methods involving live-capture of individual animals are costly and can be controversial. Natural resource management agencies can benefit from contemporary noninvasive genetic sampling approaches aimed at determining key aspects of Coyote ecology (e.g., population density and food habits). However, the efficacy of such approaches under different environmental conditions is poorly understood. Our objectives were to 1) examine accumulation and nuclear DNA degradation rates of Coyote scats in metropolitan and rural sites in Florida to help optimize methods to estimate population density; and 2) explore new genetic methods for determining diet of Coyotes based on vertebrate, plant, and invertebrate species DNA identified in scat. Recently developed DNA metabarcoding approaches make it possible to simultaneously identify DNA from multiple prey species in predator scat samples, but an exploration of this tool for assessing Coyote diet has not been pursued. We observed that scat accumulation rates (0.02 scats/km/day) did not vary between sites and fecal DNA amplification success decreased and genotyping errors increased over time with exposure to sun and precipitation. DNA sampling allowed us to generate a Coyote density estimate for the urban environment of eight Coyotes per 100 km2, but lack of recaptures in the rural area precluded density estimation. DNA metabarcoding showed promise for assessing diet contributions of vertebrate species to Coyote diet. Feral Swine (Sus scrofa) were detected as prey at higher frequencies than previously reported. We identify several considerations that can be used to optimize future noninvasive sampling efforts for Coyotes in the southeastern United States. We also discuss strengths and drawbacks of utilizing DNA metabarcoding for assessing diet of generalist carnivores such as Coyotes.
Ground deformation during caldera collapse at Kīlauea Volcano in 2018 was recorded in unprecedented detail on a network of real-time GNSS (Global Navigation Satellite System) and tilt instruments. Observations informed hazard assessments during the eruption and now yield insight into collapse dynamics and the magma system. The caldera grew in size over 78 days in a series of repeating, quasi-periodic
Double-crested cormorants (Nannopterum auritum) have been implicated as causes of fish population declines in many locations across their breeding range. Two challenges facing managers are identifying fisheries population metrics indicative of cormorant impacts and determining when this evidence becomes actionable. Building upon existing studies, we conducted a meta-analysis of eight data-rich systems across the Laurentian Great Lakes region of the United States for common fish population responses to changes in cormorant abundance. Specifically, we examined trends in mean total female length at age-3 (TL3), female mean length and age at 50 % maturity, and mean age evenness as indicated by Shannon’s Equitability Index. Annual observations for these metrics were independently regressed linearly against cormorant density by system for walleye (Sander vitreus), yellow perch (Perca flavescens), smallmouth bass (Micropterus dolomieu), and northern pike (Esox lucius) populations. TL3 was the most sensitive with 9 of the 14 datasets statistically significant (r2 range 0.29 to 0.86). Maturity metrics were moderately sensitive to trends in cormorant predation with mean total length at 50 % maturity significant in 4 out of 11 datasets (r2 range 0.27–0.41) and mean age at 50 % maturity significant in 3 out of 11 datasets (r2 range 0.12 – 0.51). Least sensitive was age evenness with the Shannon Index significant in 3 out of 12 datasets (r2 typically < 0.25). Of metrics tested, TL3 was the most reliable indicator of changes in cormorant effects despite varying system changes and management responses among locations.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.08.022","usgsCitation":"Schultz, D.W., Dorr, B.S., Fielder, D.G., Jackson, J.R., and DeBruyne, R.L., 2022, Indicators of fish population responses to avian predation with focus on double-crested cormorants: Journal of Great Lakes Research, v. 48, no. 6, p. 1659-1668, https://doi.org/10.1016/j.jglr.2022.08.022.","productDescription":"10 p.","startPage":"1659","endPage":"1668","ipdsId":"IP-140185","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":467164,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.08.022","text":"Publisher Index Page"},{"id":406437,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Minnesota, New York","otherGeospatial":"Bays de Noc, East Lake Ontario, East Vermillion Lake, Lake Oneida, Leech Lake, Les Cheneaux Islands, Green Bay, Saginaw Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.68841552734374,\n 46.922131240914\n ],\n [\n -94.06768798828122,\n 46.922131240914\n ],\n [\n -94.06768798828122,\n 47.37045451569322\n ],\n [\n -94.68841552734374,\n 47.37045451569322\n ],\n [\n -94.68841552734374,\n 46.922131240914\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -92.74658203125,\n 47.75779097897638\n ],\n [\n -92.120361328125,\n 47.75779097897638\n ],\n [\n -92.120361328125,\n 48.085418575511966\n ],\n [\n -92.74658203125,\n 48.085418575511966\n ],\n [\n -92.74658203125,\n 47.75779097897638\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n 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Center","active":true,"usgs":false}],"preferred":false,"id":851265,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fielder, David G.","contributorId":127535,"corporation":false,"usgs":false,"family":"Fielder","given":"David","email":"","middleInitial":"G.","affiliations":[{"id":7024,"text":"Michigan Department of Natural Resources, Fisheries Research Station","active":true,"usgs":false}],"preferred":false,"id":851266,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, James R.","contributorId":55709,"corporation":false,"usgs":false,"family":"Jackson","given":"James","email":"","middleInitial":"R.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":851267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":851268,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70236899,"text":"70236899 - 2022 - Measured efficacy, bioaccumulation, and leaching of a transfluthrin-based insecticidal paint: A case study with a nuisance, nonbiting aquatic insect","interactions":[],"lastModifiedDate":"2022-12-01T16:10:07.961907","indexId":"70236899","displayToPublicDate":"2022-09-03T06:40:36","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3035,"text":"Pest Management Science","active":true,"publicationSubtype":{"id":10}},"title":"Measured efficacy, bioaccumulation, and leaching of a transfluthrin-based insecticidal paint: A case study with a nuisance, nonbiting aquatic insect","docAbstract":"Pest management professionals will require a diverse, adaptive abatement toolbox to combat advanced challenges from disease vector and nuisance insect populations. Designed for post-application longevity, insecticidal paints offer extended residual effects on targeted insect pest populations; a measured understanding of active ingredient bioavailability over time is valuable to fully assess treatment efficacy and potential environmental risks. This study was initiated because a nuisance net-spinning caddisfly, Smicridea fasciatella, is lowering the quality of life for riverfront residents at the type locality.
We tested the efficacy and potential mobility of a transfluthrin-based paint (a.i. 0.50%), comparing the impacts of UV exposure and substrate texture over time. Direct UV exposure decreased efficacy (β ± S.E. = 0.008 ± 0.001, P < 0.001) and a coarse texture maintained greater efficacy (β ± S.E. = −3.7 ± 1.3, P = 0.004) over time. Notably, the coarse texture + indirect UV treatment maintained 100% mortality after 240 days. UV exposure and substrate texture did not have a significant impact on leachate concentrations over time, and successive immersion tests indicated a two-phase emission pattern. Bioaccumulation increased with time on the cuticle of dead adult S. fasciatella; after 24 h of direct exposure the concentration of transfluthrin was 25.3 ± 0.9 ng/caddisfly with a maximum concentration of 345 ng/caddisfly after 7 days.
Our predictions were validated with measured, time-dependent impacts on efficacy, leachability, and bioaccumulation. Because of the mobility of active ingredient in the environment, insecticidal paints merit low-impact protocols to improve public health outcomes and environmental safety. © 2022 Society of Chemical Industry.
","language":"English","publisher":"Wiley","doi":"10.1002/ps.7163","usgsCitation":"Cavallaro, M.C., Sanders, C., and Hladik, M.L., 2022, Measured efficacy, bioaccumulation, and leaching of a transfluthrin-based insecticidal paint: A case study with a nuisance, nonbiting aquatic insect: Pest Management Science, v. 78, no. 12, p. 5413-5422, https://doi.org/10.1002/ps.7163.","productDescription":"10 p.","startPage":"5413","endPage":"5422","ipdsId":"IP-142496","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":407124,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"78","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-09-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Cavallaro, Michael C.","contributorId":296789,"corporation":false,"usgs":false,"family":"Cavallaro","given":"Michael","email":"","middleInitial":"C.","affiliations":[{"id":64177,"text":"Bullhead City Pest Abatement District","active":true,"usgs":false}],"preferred":false,"id":852487,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sanders, Corey 0000-0001-7743-6396","orcid":"https://orcid.org/0000-0001-7743-6396","contributorId":204711,"corporation":false,"usgs":true,"family":"Sanders","given":"Corey","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852488,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hladik, Michelle L. 0000-0002-0891-2712","orcid":"https://orcid.org/0000-0002-0891-2712","contributorId":203857,"corporation":false,"usgs":true,"family":"Hladik","given":"Michelle","middleInitial":"L.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":852489,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70236294,"text":"fs20223048 - 2022 - Assessment of undiscovered conventional oil and gas resources of the Montana Thrust Belt Province, 2021","interactions":[],"lastModifiedDate":"2026-03-25T16:19:06.54123","indexId":"fs20223048","displayToPublicDate":"2022-09-02T11:50:00","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":"2022-3048","displayTitle":"Assessment of Undiscovered Conventional Oil and Gas Resources of the Montana Thrust Belt Province, 2021","title":"Assessment of undiscovered conventional oil and gas resources of the Montana Thrust Belt Province, 2021","docAbstract":"Using a geology-based assessment methodology, the U.S. Geological Survey estimated means of 783 million barrels of conventional oil and 17,606 billion (17.6 trillion) cubic feet of conventional gas in the Montana Thrust Belt Province.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223048","usgsCitation":"Schenk, C.J., Mercier, T.J., Woodall, C.A., Le, P.A., Cicero, A.D., Drake, R.M., II., Ellis, G.S., Finn, T.M., Gardner, M.H., Gelman, S.E., Hearon, J.S., Johnson, B.G., Lagesse, J.H., Leathers-Miller, H.M., Marra, K.R., Timm, K.K., and Young, S.S., 2022, Assessment of undiscovered conventional oil and gas resources of the Montana Thrust Belt Province, 2021: U.S. Geological Survey Fact Sheet 2022−3048, 2 p., https://doi.org/10.3133/fs20223048.","productDescription":"2 p.","onlineOnly":"Y","ipdsId":"IP-132654","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":406005,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J6IUZ2","text":"USGS data release","linkHelpText":"USGS National and Global Oil and Gas Assessment Project-Montana Thrust Belt Province: Assessment Unit Boundaries, Assessment Input Data, and Fact Sheet Data Tables"},{"id":406004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3048/fs20223048.pdf","text":"Report","size":"9.42 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3048"},{"id":406003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3048/coverthb.jpg"},{"id":501505,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113430.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Montana","otherGeospatial":"Cordilleran orogenic belt, Montana Thrust Belt Province","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.411376953125,\n 48.97300592158682\n ],\n [\n -114.927978515625,\n 48.64016871811908\n ],\n [\n -115.059814453125,\n 48.40003249610685\n ],\n [\n -115.24658203125,\n 48.158757304569235\n ],\n [\n -115.213623046875,\n 47.85740289465826\n ],\n [\n -115.02685546875,\n 47.52461999690651\n ],\n [\n -114.71923828124999,\n 47.24194882163242\n ],\n [\n -114.36767578124999,\n 47.025206001585396\n ],\n [\n -114.136962890625,\n 46.77749276376827\n ],\n [\n -114.224853515625,\n 46.483264729155586\n ],\n [\n -114.2578125,\n 46.23305294479828\n ],\n [\n -114.169921875,\n 45.97406038956237\n ],\n [\n -113.917236328125,\n 45.744526980468436\n ],\n [\n -113.6865234375,\n 45.521743896993634\n ],\n [\n -113.44482421875,\n 45.166547157856016\n ],\n [\n -113.104248046875,\n 44.84029065139799\n ],\n [\n -112.774658203125,\n 44.574817404670306\n ],\n [\n -112.54394531249999,\n 44.457309801319305\n ],\n [\n -111.73095703125,\n 44.55916341529182\n ],\n [\n -111.456298828125,\n 44.59829048984011\n ],\n [\n -110.93994140625,\n 45.07352060670971\n ],\n [\n -110.401611328125,\n 45.56021795715051\n ],\n [\n -110.203857421875,\n 46.14178273759234\n ],\n [\n -110.401611328125,\n 46.50595444552049\n ],\n [\n -111.11572265625,\n 46.77749276376827\n ],\n [\n -111.357421875,\n 47.07760411715964\n ],\n [\n -112.30224609374999,\n 47.286681888764214\n ],\n [\n -112.576904296875,\n 47.517200697839414\n ],\n [\n -112.54394531249999,\n 48.011975126709956\n ],\n [\n -112.928466796875,\n 48.32703913063476\n ],\n [\n -113.170166015625,\n 48.46563710044979\n ],\n [\n -113.466796875,\n 49.001843917978526\n ],\n [\n -115.411376953125,\n 48.97300592158682\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
U.S. Geological Survey
Box 25046, MS-939
Denver, CO 80225-0046
The Deepwater Horizon (DWH) disaster released 3.19 million barrels of crude oil into the Gulf of Mexico (GOM) in 2010, overlapping the habitat of pelagic fish populations. Using mahi-mahi (Coryphaena hippurus)─a highly migratory marine teleost present in the GOM during the spill─as a model species, laboratory experiments demonstrate injuries to physiology and behavior following oil exposure. However, more than a decade postspill, impacts on wild populations remain unknown. To address this gap, we exposed wild mahi-mahi to crude oil or control conditions onboard a research vessel, collected fin clip samples, and tagged them with electronic tags prior to release into the GOM. We demonstrate profound effects on survival and reproduction in the wild. In addition to significant changes in gene expression profiles and predation mortality, we documented altered acceleration and habitat use in the first 8 days oil-exposed individuals were at liberty as well as a cessation of apparent spawning activity for at least 37 days. These data reveal that even a brief and low-dose exposure to crude oil impairs fitness in wild mahi-mahi. These findings offer new perspectives on the lasting impacts of the DWH blowout and provide insight about the impacts of future deep-sea oil spills.
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change studies in Hawai'i","docAbstract":"Gridded bioclimatic variables representing yearly, seasonal, and monthly means and extremes in temperature and precipitation have been widely used for ecological modeling purposes and in broader climate change impact and biogeographical studies. As a result of their utility, numerous sets of bioclimatic variables have been developed on a global scale (e.g., WorldClim) but rarely represent the finer regional scale pattern of climate in Hawai'i. Recognizing the value of having such regionally downscaled products, we integrated more detailed projections from recent climate models developed for Hawai'i with current climatological datasets to generate updated regionally defined bioclimatic variables. We derived updated bioclimatic variables from new projections of baseline and future monthly minimum, mean, and maximum temperature (Tmin, Tmean, Tmax) and mean precipitation (Pmean) data at 250 m resolution. We used the most up-to-date dynamically downscaled projections based on the Weather Research and Forecasting (WRF) model from the International Pacific Research Center (IPRC) and the National Center for Atmospheric Research (NCAR). We summarized the monthly data from these two climate projections into a suite of 19 standard bioclimatic variables that provide detailed information about annual and seasonal mean climatic conditions for the Hawaiian Islands. These bioclimatic variables are available for three climate scenarios: baseline climate (1990-2009) and future climate (2080-2099) under representative concentration pathway (RCP) 4.5 (IPRC projections only) and RCP 8.5 (both IPRC and NCAR projections) climate scenarios. The resulting dataset provides a more robust set of climate products that can be used for modeling purposes, impact studies, and management planning.
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\"}}]}","volume":"45","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":862133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kaiser, Lauren R.","contributorId":200422,"corporation":false,"usgs":false,"family":"Kaiser","given":"Lauren","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":862134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xue, Lulin","contributorId":301129,"corporation":false,"usgs":false,"family":"Xue","given":"Lulin","email":"","affiliations":[{"id":6648,"text":"National Center for Atmospheric Research","active":true,"usgs":false}],"preferred":false,"id":862135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wang, Yaping","contributorId":191943,"corporation":false,"usgs":false,"family":"Wang","given":"Yaping","email":"","affiliations":[],"preferred":false,"id":862136,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236194,"text":"sir20225080 - 2022 - Water-level and recoverable water in storage changes, High Plains aquifer, predevelopment to 2017 and 2015–17","interactions":[],"lastModifiedDate":"2022-09-02T14:00:26.433161","indexId":"sir20225080","displayToPublicDate":"2022-09-02T07:50:04","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":"2022-5080","displayTitle":"Water-Level and Recoverable Water in Storage Changes, High Plains Aquifer, Predevelopment to 2017 and 2015–17","title":"Water-level and recoverable water in storage changes, High Plains aquifer, predevelopment to 2017 and 2015–17","docAbstract":"The High Plains aquifer underlies 111.8 million acres (about 175,000 square miles) in parts of eight States—Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. Water-level declines began in parts of the High Plains aquifer soon after the beginning of substantial groundwater irrigation (about 1950). This report presents water-level changes and change in recoverable water in storage in the High Plains aquifer from predevelopment (about 1950) to 2017 and from 2015 to 2017.
Water-level changes from predevelopment to 2017, by well, ranged from a rise of 84 feet to a decline of 262 feet; the range for 99 percent of the wells was from a rise of 39 feet to a decline of 200 feet. Water-level changes from 2015 to 2017, by well, ranged from a rise of 41 feet to a decline of 21 feet; the range for 99 percent of the wells was from a rise of 14 feet to a decline of 10 feet. The area-weighted, average water-level changes in the aquifer were an overall decline of 16.8 feet from predevelopment to 2017 and a rise of 0.1 foot from 2015 to 2017. Total recoverable water in storage in the aquifer in 2017 was about 2.91 billion acre-feet, which was a decline of about 291.8 million acre-feet since predevelopment and a rise of 0.1 million acre-feet from 2015 to 2017.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225080","programNote":"Groundwater and Streamflow Information Program","usgsCitation":"McGuire, V.L., and Strauch, K.R., 2022, Water-level and recoverable water in storage changes, High Plains aquifer, predevelopment to 2017 and 2015–17: U.S. Geological Survey Scientific Investigations Report 2022–5080, 15 p., https://doi.org/10.3133/sir20225080.","productDescription":"Report: vi, 15 p.; Data Release; Dataset","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-106333","costCenters":[{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true}],"links":[{"id":405912,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5080/sir20225080.pdf","text":"Report","size":"5.31 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5080"},{"id":405911,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5080/coverthb.jpg"},{"id":405915,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":405914,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5080/images"},{"id":405913,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5080/sir20225080.XML"},{"id":405916,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YN7PY3","text":"USGS data release","linkHelpText":"Data from maps of water-level changes in the High Plains aquifer in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming, predevelopment (about 1950) to 2017 and 2015–17"}],"country":"United States","state":"Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, Wyoming","otherGeospatial":"High Plains aquifer","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -103.4912109375,\n 33.32134852669881\n ],\n [\n -102.216796875,\n 32.24997445586331\n ],\n [\n -100.45898437499999,\n 34.52466147177172\n ],\n [\n -99.6240234375,\n 36.06686213257888\n ],\n [\n -99.6240234375,\n 38.09998264736481\n ],\n [\n -97.20703125,\n 40.74725696280421\n ],\n [\n -97.119140625,\n 42.293564192170095\n ],\n [\n -98.9208984375,\n 42.87596410238256\n ],\n [\n -101.7333984375,\n 43.77109381775651\n ],\n [\n -103.22753906249999,\n 42.90816007196054\n ],\n [\n -105.0732421875,\n 42.52069952914966\n ],\n [\n -105.908203125,\n 42.293564192170095\n ],\n [\n -105.8203125,\n 41.07935114946899\n ],\n [\n -104.150390625,\n 40.97989806962013\n ],\n [\n -104.7216796875,\n 39.50404070558415\n ],\n [\n -104.67773437499999,\n 38.30718056188316\n ],\n [\n -103.1396484375,\n 38.20365531807149\n ],\n [\n -103.974609375,\n 37.23032838760387\n ],\n [\n -103.88671875,\n 35.496456056584165\n ],\n [\n -104.1064453125,\n 34.95799531086792\n ],\n [\n -103.4912109375,\n 33.32134852669881\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nebraska Water Science Center
U.S. Geological Survey
5231 South 19th Street
Lincoln, NE 68512
In 2020, the Appropriations Committee for the U.S. House of Representatives directed the CDC to develop a national One Health framework to combat zoonotic diseases, including sylvatic plague, which is caused by the flea-borne bacterium Yersinia pestis. This review builds upon that multisectoral objective. We aim to increase awareness of Y. pestis and to highlight examples of plague mitigation for One Health purposes (i.e., to achieve optimal health outcomes for people, animals, plants, and their shared environment). We draw primarily upon examples from the USA, but also discuss research from Madagascar and Uganda where relevant, as Y. pestis has emerged as a zoonotic threat in those foci.
Historically, the bulk of plague research has been directed at the disease in humans. This is not surprising, given that Y. pestis is a scourge of human history. Nevertheless, the ecology of Y. pestis is inextricably linked to other mammals and fleas under natural conditions. Accumulating evidence demonstrates Y. pestis is an unrelenting threat to multiple ecosystems, where the bacterium is capable of significantly reducing native species abundance and diversity while altering competitive and trophic relationships, food web connections, and nutrient cycles. In doing so, Y. pestis transforms ecosystems, causing “shifting baselines syndrome” in humans, where there is a gradual shift in the accepted norms for the condition of the natural environment. Eradication of Y. pestis in nature is difficult to impossible, but effective mitigation is achievable; we discuss flea vector control and One Health implications in this context.
There is an acute need to rapidly expand research on Y. pestis, across multiple host and flea species and varied ecosystems of the Western US and abroad, for human and environmental health purposes. The fate of many wildlife species hangs in the balance, and the implications for humans are profound in some regions. Collaborative multisectoral research is needed to define the scope of the problem in each epidemiological context and to identify, refine, and implement appropriate and effective mitigation practices.
","language":"English","publisher":"Springer","doi":"10.1007/s40475-022-00265-6","usgsCitation":"Eads, D.A., Biggins, D.E., Wimsatt, J., Eisen, R., Hinnebusch, B.J., Matchett, M.R., Goldberg, A., Livieri, T., Hacker, G., Novak, M., Buttke, D., Grassel, S.M., Hughes-Clarke, J., and Atiku, L., 2022, Exploring and mitigating plague for One Health purposes: Current Tropical Medicine Reports, v. 9, p. 169-184, https://doi.org/10.1007/s40475-022-00265-6.","productDescription":"16 p.","startPage":"169","endPage":"184","ipdsId":"IP-136407","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":446558,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11358858","text":"External Repository"},{"id":406299,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2022-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Eads, David A. 0000-0002-4247-017X deads@usgs.gov","orcid":"https://orcid.org/0000-0002-4247-017X","contributorId":173639,"corporation":false,"usgs":true,"family":"Eads","given":"David","email":"deads@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":851035,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biggins, Dean E. 0000-0003-2078-671X bigginsd@usgs.gov","orcid":"https://orcid.org/0000-0003-2078-671X","contributorId":2522,"corporation":false,"usgs":true,"family":"Biggins","given":"Dean","email":"bigginsd@usgs.gov","middleInitial":"E.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":851036,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wimsatt, Jeffrey","contributorId":173421,"corporation":false,"usgs":false,"family":"Wimsatt","given":"Jeffrey","email":"","affiliations":[],"preferred":false,"id":851037,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Eisen, Rebecca J.","contributorId":148027,"corporation":false,"usgs":false,"family":"Eisen","given":"Rebecca J.","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":false,"id":851038,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hinnebusch, B. Joseph","contributorId":295326,"corporation":false,"usgs":false,"family":"Hinnebusch","given":"B.","email":"","middleInitial":"Joseph","affiliations":[{"id":13450,"text":"NIH","active":true,"usgs":false}],"preferred":false,"id":851039,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Matchett, Marc R.","contributorId":193409,"corporation":false,"usgs":false,"family":"Matchett","given":"Marc","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":851040,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Goldberg, Amanda R.","contributorId":288043,"corporation":false,"usgs":false,"family":"Goldberg","given":"Amanda R.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":851041,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Livieri, Travis","contributorId":279912,"corporation":false,"usgs":false,"family":"Livieri","given":"Travis","affiliations":[{"id":6753,"text":"Prairie Wildlife Research","active":true,"usgs":false}],"preferred":false,"id":851042,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hacker, Gregory","contributorId":296262,"corporation":false,"usgs":false,"family":"Hacker","given":"Gregory","email":"","affiliations":[{"id":33266,"text":"California Department of Public Health","active":true,"usgs":false}],"preferred":false,"id":851043,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Novak, Mark","contributorId":45229,"corporation":false,"usgs":false,"family":"Novak","given":"Mark","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":851044,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Buttke, Danielle","contributorId":225082,"corporation":false,"usgs":false,"family":"Buttke","given":"Danielle","affiliations":[],"preferred":false,"id":851045,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Grassel, Shaun M.","contributorId":150648,"corporation":false,"usgs":false,"family":"Grassel","given":"Shaun","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":851046,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Hughes-Clarke, John","contributorId":41698,"corporation":false,"usgs":false,"family":"Hughes-Clarke","given":"John","email":"","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":851047,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Atiku, Linda","contributorId":296263,"corporation":false,"usgs":false,"family":"Atiku","given":"Linda","email":"","affiliations":[{"id":64008,"text":"Uganda Virus Research Institute","active":true,"usgs":false}],"preferred":false,"id":851048,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70245397,"text":"70245397 - 2022 - Modelling the transport and deposition of ash following a magnitude 7 eruption: The distal Mazama tephra","interactions":[],"lastModifiedDate":"2023-06-22T12:05:22.567726","indexId":"70245397","displayToPublicDate":"2022-09-02T06:59:56","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Modelling the transport and deposition of ash following a magnitude 7 eruption: The distal Mazama tephra","docAbstract":"Volcanic ash transport and dispersion models (VATDMs) are necessary for forecasting tephra dispersal during volcanic eruptions and are a useful tool for estimating the eruption source parameters (ESPs) of prehistoric eruptions. Here we use Ash3D, an Eulerian VATDM, to simulate the tephra deposition from the ~ 7.7 ka climactic eruption of Mount Mazama. We investigate how best to apply a VATDM using the ESPs characteristic of a large magnitude eruption (M ≥ 7). We simplify the approach to focus on the distal deposit as if it were formed by a single phase of Plinian activity. Our results demonstrate that it is possible to use modern wind profiles to simulate the tephra dispersal from a prehistoric eruption; however, this introduces an inherent uncertainty to the subsequent simulations where we explore different ESPs. We show, using the well-documented distal Mazama tephra, that lateral umbrella cloud spreading, rather than advection–diffusion alone, must be included in the VATDM to reproduce the width of the isopachs. In addition, the Ash3D particle size distribution must be modified to simulate the transport and deposition of distal fine-grained (< 125 µm) Mazama ash. With these modifications, the Ash3D simulations reproduce the thickness and grain size of the Mazama tephra deposit. Based on our simulations, however, we conclude that the exact relationship between mass eruption rate and the scale of umbrella cloud spreading remains unresolved. Furthermore, for ground-based grain size distributions to be input directly into Ash3D, further research is required into the atmospheric and particle processes that control the settling behaviour of fine volcanic ash.
Chronic wasting disease (CWD) continues to expand in distribution and prevalence across North America. Upon detection, either for the first time in a novel area or in a region with an existing outbreak, wildlife management agencies are tasked with responding to mitigate the disease. This response often entails creation or modification of a management zone with modified rules and regulations that support an agency's disease management plan. To guide the process of creating an appropriately sized CWD management zone, assuming that wild deer movements are a major risk factor for disease spread, we used data from global positioning system (GPS)-collared white-tailed deer (Odocoileus virginianus) in southeastern Minnesota and southcentral Pennsylvania, USA, between 2018 and 2021 to estimate long-distance movements associated with dispersal and migratory behaviors. These contrasting study areas with active CWD outbreaks permitted an evaluation of deer movement dynamics in different parts of their range. We quantified the proportion, distribution, timing, and orientation of dispersing and migratory deer. We observed 21% of female and 58% of male yearlings disperse from their apparent natal home range in Minnesota, while in Pennsylvania 4% of female and 68% of male yearlings dispersed. We also documented 20% of females and 6% of males migrated between seasonal home ranges in Minnesota, while in Pennsylvania no females and 5% of males migrated. The average distance deer dispersed or migrated in Minnesota was 20 km and 11 km, respectively, while in Pennsylvania male deer dispersed only about 4 km. Both sexes in Minnesota tended to disperse in a consistent, westerly direction; however, there was no directional preference observed for migratory deer or for dispersing deer in Pennsylvania. We found differences between natal and adult home range size for both sexes in Minnesota but not for males in Pennsylvania. Our results identify the considerable variability in dispersal and migration dynamics of white-tailed deer in disparate landscapes, which is important to agencies managing CWD. We summarize the distribution of these movements and suggest agencies use this information to help make decisions about optimal management zone size. We suggest the development of formalized assessments of the tradeoffs associated with optimizing decisions about creation of CWD management zones across white-tailed deer populations that exhibit variation in dispersal behavior and suggest careful evaluation to avoid using an arbitrary size or shape to create disease management zones.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22306","usgsCitation":"Jennelle, C., Walter, W., Crawford, J., Rosenberry, C., and Wallingford, B., 2022, Movement of white‐tailed deer in contrasting landscapes influences management of chronic wasting disease: Journal of Wildlife Management, v. 86, no. 8, e22306, 21 p., https://doi.org/10.1002/jwmg.22306.","productDescription":"e22306, 21 p.","ipdsId":"IP-136125","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, 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\"}}]}","volume":"86","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Jennelle, Christopher S.","contributorId":349404,"corporation":false,"usgs":false,"family":"Jennelle","given":"Christopher S.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walter, W. David 0000-0003-3068-1073","orcid":"https://orcid.org/0000-0003-3068-1073","contributorId":219540,"corporation":false,"usgs":true,"family":"Walter","given":"W. David","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Crawford, Joanne","contributorId":349406,"corporation":false,"usgs":false,"family":"Crawford","given":"Joanne","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":924294,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rosenberry, Christopher S.","contributorId":349408,"corporation":false,"usgs":false,"family":"Rosenberry","given":"Christopher S.","affiliations":[{"id":83357,"text":"Bureau of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":924295,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wallingford, Bret D.","contributorId":349410,"corporation":false,"usgs":false,"family":"Wallingford","given":"Bret D.","affiliations":[{"id":83357,"text":"Bureau of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":924296,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245165,"text":"70245165 - 2022 - Microgravity change during the 2008-2018 Kı̄lauea summit eruption: Nearly a decade of subsurface mass accumulation","interactions":[],"lastModifiedDate":"2023-06-19T18:30:40.041096","indexId":"70245165","displayToPublicDate":"2022-09-01T13:15:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Microgravity change during the 2008-2018 Kı̄lauea summit eruption: Nearly a decade of subsurface mass accumulation","docAbstract":"Results from nine microgravity campaigns from Kı̄lauea, Hawaiʻi, spanning most of the volcano's 2008–2018 summit eruption, indicate persistent mass accumulation at shallow levels. A weighted least squares approach is used to recover microgravity results from a network of benchmarks around Kı̄lauea's summit, eliminate instrumental drift, and restore suspected data tares. A total mass of 1.9 × 1011 kg was determined from these microgravity campaigns to have accumulated below Kı̄lauea Caldera during 2009–2015 at an estimated depth of 1.3 km below sea level. Only a fraction of this mass is reflected in surface deformation, and this is consistent with previously reported discrepancies between subsurface mass accumulation and observed surface deformation. The discrepancy, amongst other independent evidence from gas emissions, seismicity, and continuous gravimetry, indicate densification of magma in the reservoirs below the volcano summit. This densification may have been driven by degassing through the summit vent. It is hypothesized that during the final years of the summit eruption, magma densification resulted in a buildup of pressure in the reservoirs that may have contributed to the lower East Rift Zone outbreak of 2018. The observed mass accumulation beneath Kı̄lauea could not have been detected through other techniques and illustrates the importance of microgravity measurements in volcano monitoring.
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","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Barnhart, P.D., Quiogue, Z.C., Frasch, E., Vice, D., Hopkins, C.B., Yackel Adams, A.A., Reed, R., and Nafus, M., 2022, Boiga irregularis (brown treesnake): Herpetological Review, v. 53, no. 3, p. 444-445.","productDescription":"2 p.","startPage":"444","endPage":"445","ipdsId":"IP-126596","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":420632,"type":{"id":24,"text":"Thumbnail"},"url":"http://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Cocos Island, Guam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 144.65870053775183,\n 13.24430283369017\n ],\n [\n 144.64368253164076,\n 13.24430283369017\n ],\n [\n 144.64368253164076,\n 13.232788830081347\n ],\n [\n 144.65870053775183,\n 13.232788830081347\n ],\n [\n 144.65870053775183,\n 13.24430283369017\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"53","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barnhart, Patrick D 0000-0002-3966-9444","orcid":"https://orcid.org/0000-0002-3966-9444","contributorId":224635,"corporation":false,"usgs":true,"family":"Barnhart","given":"Patrick","email":"","middleInitial":"D","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":880820,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quiogue, Zachary C.","contributorId":270995,"corporation":false,"usgs":false,"family":"Quiogue","given":"Zachary","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":880821,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Frasch, Elisabeth","contributorId":328628,"corporation":false,"usgs":false,"family":"Frasch","given":"Elisabeth","email":"","affiliations":[{"id":54632,"text":"Research Corporation of the University of Guam","active":true,"usgs":false}],"preferred":false,"id":880822,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Vice, Diane","contributorId":328629,"corporation":false,"usgs":false,"family":"Vice","given":"Diane","affiliations":[{"id":78429,"text":"Guam Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":880823,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hopkins, Charlene Beverly 0000-0002-2537-8275","orcid":"https://orcid.org/0000-0002-2537-8275","contributorId":328630,"corporation":false,"usgs":true,"family":"Hopkins","given":"Charlene","email":"","middleInitial":"Beverly","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":880824,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yackel Adams, Amy A. 0000-0002-7044-8447 yackela@usgs.gov","orcid":"https://orcid.org/0000-0002-7044-8447","contributorId":3116,"corporation":false,"usgs":true,"family":"Yackel Adams","given":"Amy","email":"yackela@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":880825,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Reed, Robert 0000-0001-8349-6168","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":267796,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":880826,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Nafus, Melia Gail 0000-0002-7325-3055","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":245717,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia Gail","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":880827,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238578,"text":"70238578 - 2022 - Advancing geophysical techniques to image a stratigraphic hydrothermal resource","interactions":[],"lastModifiedDate":"2022-11-30T17:25:35.358802","indexId":"70238578","displayToPublicDate":"2022-09-01T11:18:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1827,"text":"Geothermal Resources Council Transactions","active":true,"publicationSubtype":{"id":10}},"title":"Advancing geophysical techniques to image a stratigraphic hydrothermal resource","docAbstract":"Sedimentary-hosted geothermal energy systems are permeable structural, structural-stratigraphic, and/or stratigraphic horizons with sufficient temperature for direct use and/or electricity generation. Sedimentary-hosted (i.e., stratigraphic) geothermal reservoirs may be present in multiple locations across the central and eastern Great Basin of the USA, thereby constituting a potentially large base of untapped, economically accessible energy resources. Sandia National Laboratories has partnered with a multi-disciplinary group of collaborators to evaluate a stratigraphic system in Steptoe Valley, Nevada using both established and novel geophysical imaging techniques. The goal of this study is to inform an optimized strategy for subsequent exploration and development of this and analogous resources. Building from prior Nevada Play Fairway Analysis (PFA), this team is primarily 1) collecting additional geophysical data, 2) employing novel joint geophysical inversion/modeling techniques to update existing 3D geologic models, and 3) integrating the geophysical results to produce a working, geologically constrained thermo-hydrological reservoir model. Prior PFA work highlights Steptoe Valley as a favorable resource basin that likely has both sedimentary and hydrothermal characteristics. However, there remains significant uncertainty on the nature and architecture of the resource(s) at depth, which increases the risk in exploratory drilling. Newly acquired gravity, magnetic, magnetotelluric, and controlled-source electromagnetic data, in conjunction with new and preceding geoscientific measurements and observations, are being integrated and evaluated in this study for efficacy in understanding stratigraphic geothermal resources and mitigating exploration risk. Furthermore, the influence of hydrothermal activity on sedimentary-hosted reservoirs in favorable structural settings (i.e., whether fault-controlled systems may locally enhance temperature and permeability in some deep stratigraphic reservoirs) will also be evaluated. This paper provides details and current updates on the course of this study in-progress.","language":"English","publisher":"Geothermal Rising","usgsCitation":"Schwering, P., Winn, C., Jaysaval, P., Knox, H., Siler, D.L., Hardwick, C., Ayling, B., Faulds, J., Mlawsky, E., McConville, E., Norbeck, J., Hinz, N., Matson, G., and Queen, J., 2022, Advancing geophysical techniques to image a stratigraphic hydrothermal resource: Geothermal Resources Council Transactions, v. 46, p. 976-991.","productDescription":"16 p.","startPage":"976","endPage":"991","ipdsId":"IP-141659","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":409862,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":409843,"type":{"id":15,"text":"Index Page"},"url":"https://www.geothermal-library.org/index.php?mode=pubs&action=view&record=1034650","linkFileType":{"id":5,"text":"html"}}],"volume":"46","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schwering, Paul","contributorId":299507,"corporation":false,"usgs":false,"family":"Schwering","given":"Paul","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":857964,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winn, Carmen","contributorId":299508,"corporation":false,"usgs":false,"family":"Winn","given":"Carmen","email":"","affiliations":[{"id":34829,"text":"Sandia National Laboratories","active":true,"usgs":false}],"preferred":false,"id":857965,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jaysaval, Piyoosh","contributorId":299509,"corporation":false,"usgs":false,"family":"Jaysaval","given":"Piyoosh","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":857966,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Knox, Hunter","contributorId":299510,"corporation":false,"usgs":false,"family":"Knox","given":"Hunter","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":857967,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Siler, Drew L. 0000-0001-7540-8244","orcid":"https://orcid.org/0000-0001-7540-8244","contributorId":203341,"corporation":false,"usgs":true,"family":"Siler","given":"Drew","email":"","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":857968,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hardwick, Christian","contributorId":299511,"corporation":false,"usgs":false,"family":"Hardwick","given":"Christian","affiliations":[{"id":17626,"text":"Utah Geological Survey","active":true,"usgs":false}],"preferred":false,"id":857969,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayling, Bridget","contributorId":299512,"corporation":false,"usgs":false,"family":"Ayling","given":"Bridget","affiliations":[{"id":64865,"text":"Great Basin Center for Geothermal Energy; 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Marine research and management organizations use these data to help ensure safe navigation, promote sustainable fisheries, extract energy, and protect marine habitats in the coastal and ocean waters of the U.S. Exclusive Economic Zone (EEZ) and Laurentian Great Lakes. Many of these organizations may have overlapping or shared mapping interests without knowing it.
In a multi-jurisdictional planning environment, it can be challenging and cumbersome to determine where other entities have shared or overlapping mapping interests, especially across a transnational region such as the Great Lakes. State and provincial governments, federal governments, academia, tribes and First Nations, and other stakeholders from both the U.S. and Canada all have mapping interests across Great Lakes waters. Identifying and communicating target geographies for new data collection that are shared among multiple organizations can both help to avoid redundancy of new mapping efforts, and create opportunities for greater efficiency through collaboration.
To address this issue, a spatial priorities study was conducted using a geospatial tool developed by the National Ocean Services National Centers for Coastal and Ocean Science (NCCOS). The tool provided an easy-to-use online interface in which programs can identify their priorities in a simple and straightforward way. This study asked representatives of Great Lakes management and science organizations to identify the areas for which they needed maps of lakebed features on a near-term, mid-term, and long-term timeframe, and why. Then, the responses were analyzed and overlaid to determine areas of shared mapping need and opportunity and to determine the types of map products needed.
The analysis revealed high interest among multiple organizations in discrete geographies including the Minnesota and Wisconsin shoreline from Duluth to the eastern extent of the Bayfield Peninsula, Green Bay in Lake Michigan, and the southern coastlines of Lake Erie and Lake Ontario, the St. Marys River, and the northern Lake Superior coastal waters near Grand Portage, MN. Lower priority mapping interest were distributed widely across all lakes, but tended to be concentrated in nearshore areas (<30 m depth).
The analysis also indicated that the top mapping justifications were Habitat/biota/natural area, Benthic exploration, Commercial and recreational fishing, and Scientific research. The top desired map product types were Elevation, Substrate/sub-bottom geologic characterization, and Habitat map/characterization, although participants on some lakes noted other less prevalent product types.
Following from previously conducted NOAA and non-NOAA Federal spatial prioritization exercises, the results of this regional focus can help mapping organizations better understand how their priorities align with the needs of regional organizations, allow for more efficient coordination and funding, and enable partners to leverage assets and resources to fill their most pressing data and information gaps across Great Lakes waters. The U.S. Mapping Coordination Site hosts the results of this study and other spatial prioritization studies. Through this website, one can interact with the study results along with recent and planned mapping efforts.
NOAA intends to update their spatial priorities on a three- to five-year basis. Future studies should strive to expand participation of federal agencies, state and local governments, federally-recognized tribes, academia, and private industry (among other stakeholders) to seek out ocean mapping partnerships in conjunction with the National Ocean Mapping, Exploration and Characterization (NOMEC) goals map once, use many times.”
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