Near-surface soil conditions can significantly alter the amplitude and frequency content of incoming ground motions – often with profound consequences for the built environment – and are thus important inputs to any ground-motion prediction. Previous soil-velocity models (SVM) have predicted shear-wave velocity profiles based on the time-averaged shear-wave velocity in the upper 30 m (VS30). This article presents a generic soil-velocity model that accounts both for near-surface conditions (VS30) and deeper geologic structure, as represented to the depth at which the profile reaches a velocity of 1.0 km/s (Z1.0). To demonstrate the advantages of our new SVM, we apply it to the Cascadia Region of North America, where numerous geologic basins and glaciated landscapes give rise to a wide range of VS30 and Z1.0 combinations. This soil velocity model yields good estimates of site response across all site conditions, and significantly improves upon a model calibrated using only VS30 data. In conjunction with existing models that describe the deep velocity structure of the region (e.g., (Stephenson et al., 2017) [27]; the proposed model is particularly suited for use in regional-scale predictions of site response, liquefaction, landslides, infrastructure damage, and loss. The proposed methodology is broadly applicable to the development of SVMs elsewhere, and with improved understanding of near-surface and deep velocity structures, can facilitate more accurate ground-motion predictions globally.
International efforts to restore degraded ecosystems will continue to expand over the coming decades, yet the factors contributing to the effectiveness of long‐term restoration across large areas remain largely unexplored. At large scales, outcomes are more complex and synergistic than the additive impacts of individual restoration projects. Here, we propose a cumulative‐effects conceptual framework to inform restoration design and implementation and to comprehensively measure ecological outcomes. To evaluate and illustrate this approach, we reviewed long‐term restoration in several large coastal and riverine areas across the US: the greater Florida Everglades; Gulf of Mexico coast; lower Columbia River and estuary; Puget Sound; San Francisco Bay and Sacramento–San Joaquin Delta; Missouri River; and northeastern coastal states. Evidence supported eight modes of cumulative effects of interacting restoration projects, which improved outcomes for species and ecosystems at landscape and regional scales. We conclude that cumulative effects, usually measured for ecosystem degradation, are also measurable for ecosystem restoration. The consideration of evidence‐based cumulative effects will help managers of large‐scale restoration capitalize on positive feedback and reduce countervailing effects.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/fee.2274","usgsCitation":"Diefenderfer, H.L., Steyer, G., Harwell, M.C., LoSchiavo, A.J., Neckles, H.A., Burdick, D.M., Johnson, G.E., Buenau, K.E., Trujillo, E., Callaway, J.C., Thom, R.M., Ganju, N., and Twilley, R.R., 2021, Applying cumulative effects to strategically advance large‐scale ecosystem restoration: Frontiers in Ecology and the Environment, v. 19, no. 2, p. 108-117, https://doi.org/10.1002/fee.2274.","productDescription":"10 p.","startPage":"108","endPage":"117","ipdsId":"IP-107430","costCenters":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"links":[{"id":454325,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/fee.2274","text":"Publisher Index Page"},{"id":385579,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Gulf of Mexico, San Francisco Bay/Sacramento Delta, Puget Sound, Gulf of Maine, Virginia Coastal Bays, Lower Columbia River and Estuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.66796875,\n 25.562265014427492\n ],\n [\n -82.44140625,\n 25.562265014427492\n ],\n [\n -82.44140625,\n 30.372875188118016\n ],\n [\n -99.66796875,\n 30.372875188118016\n ],\n [\n -99.66796875,\n 25.562265014427492\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.1484375,\n 45.398449976304086\n ],\n [\n -70.400390625,\n 44.33956524809713\n ],\n [\n -74.35546875,\n 40.78054143186033\n ],\n [\n -77.16796875,\n 38.95940879245423\n ],\n [\n -76.728515625,\n 36.80928470205937\n ],\n [\n -74.1796875,\n 36.59788913307022\n ],\n [\n 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0000-0001-6153-4565","orcid":"https://orcid.org/0000-0001-6153-4565","contributorId":257980,"corporation":false,"usgs":false,"family":"Diefenderfer","given":"Heida","email":"","middleInitial":"L.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":815454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Steyer, Gregory 0000-0001-7231-0110","orcid":"https://orcid.org/0000-0001-7231-0110","contributorId":218813,"corporation":false,"usgs":true,"family":"Steyer","given":"Gregory","affiliations":[{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":815455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harwell, Matthew C. 0000-0001-6765-7857","orcid":"https://orcid.org/0000-0001-6765-7857","contributorId":248373,"corporation":false,"usgs":false,"family":"Harwell","given":"Matthew","email":"","middleInitial":"C.","affiliations":[{"id":49874,"text":"US EPA, Gulf Ecosystem Measurement & Modeling Division, Ctr for Envtl Measurement and Modeling","active":true,"usgs":false}],"preferred":false,"id":815456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LoSchiavo, Andrew J","contributorId":257981,"corporation":false,"usgs":false,"family":"LoSchiavo","given":"Andrew","email":"","middleInitial":"J","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":815457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Neckles, Hilary A. 0000-0002-5662-2314 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0000-0002-7364-286X","orcid":"https://orcid.org/0000-0002-7364-286X","contributorId":205456,"corporation":false,"usgs":false,"family":"Callaway","given":"John","email":"","middleInitial":"C.","affiliations":[{"id":37110,"text":"Dept. of Environmental Science, University of San Francisco, 2130 Fulton St., San Francisco, CA 94117","active":true,"usgs":false}],"preferred":false,"id":815463,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Thom, Ronald M. 0000-0002-8639-6709","orcid":"https://orcid.org/0000-0002-8639-6709","contributorId":257985,"corporation":false,"usgs":false,"family":"Thom","given":"Ronald","email":"","middleInitial":"M.","affiliations":[{"id":52195,"text":"Pacific Northwest National Lab","active":true,"usgs":false}],"preferred":false,"id":815464,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":815465,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Twilley, Robert R.","contributorId":34585,"corporation":false,"usgs":false,"family":"Twilley","given":"Robert","email":"","middleInitial":"R.","affiliations":[{"id":5115,"text":"Louisiana State University","active":true,"usgs":false}],"preferred":false,"id":815466,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70216772,"text":"70216772 - 2021 - Evaluation of seismic hazard models with fragile geologic features","interactions":[],"lastModifiedDate":"2021-01-19T16:04:47.842953","indexId":"70216772","displayToPublicDate":"2020-10-28T09:20:35","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of seismic hazard models with fragile geologic features","docAbstract":"We provide an overview of a 2019 workshop on the use of fragile geologic features (FGFs) to evaluate seismic hazard models. FGFs have been scarcely utilized in the evaluation of seismic hazard models, despite nearly 30 yr having passed since the first recognition of their potential value. Recently, several studies have begun to focus on the implementation of FGFs in seismic hazard modeling. The workshop was held to capture a “snapshot” of the state‐of‐the‐art in FGF work and to define key research areas that would increase confidence in FGF‐based evaluation of seismic hazard models. It was held at the annual meeting of the Southern California Earthquake Center on 8 September 2019, and the conveners were Mark Stirling (University of Otago, New Zealand) and Michael Oskin (University of California, Davis). The workshop attracted 44 participants from a wide range of disciplines. The main topics of discussion were FGF fragility age estimation (age at which an FGF achieved its current fragile geometry), fragility estimation, FGF‐based evaluation of seismic hazard models, and ethical considerations relating to documentation and preservation of FGFs. There are now many scientists working on, or motivated to work on, FGFs, and more types of FGFs are being worked on than just the precariously balanced rock (PBR) variety. One of the ideas presented at the workshop is that fragility ages for FGFs should be treated stochastically rather than assuming that all share a common age. In a similar vein, new studies propose more comprehensive methods of fragility assessment beyond peak ground acceleration and peak ground velocity‐based approaches. Two recent studies that apply PBRs to evaluate probabilistic seismic hazard models use significantly different methods of evaluation. Key research needs identified from the workshop will guide future, focused efforts that will ultimately facilitate the uptake of FGFs in seismic hazard analysis.
The global climate has been changing over the last century due to greenhouse gas emissions and will continue to change over this century, accelerating without effective global efforts to reduce emissions. Ticks and tick-borne diseases (TTBDs) are inherently climate-sensitive due to the sensitivity of tick lifecycles to climate. Key direct climate and weather sensitivities include survival of individual ticks, and the duration of development and host-seeking activity of ticks. These sensitivities mean that in some regions a warming climate may increase tick survival, shorten life-cycles and lengthen the duration of tick activity seasons. Indirect effects of climate change on host communities may, with changes in tick abundance, facilitate enhanced transmission of tick-borne pathogens. High temperatures, and extreme weather events (heat, cold, and flooding) are anticipated with climate change, and these may reduce tick survival and pathogen transmission in some locations. Studies of the possible effects of climate change on TTBDs to date generally project poleward range expansion of geographical ranges (with possible contraction of ranges away from the increasingly hot tropics), upslope elevational range spread in mountainous regions, and increased abundance of ticks in many current endemic regions. However, relatively few studies, using long-term (multi-decade) observations, provide evidence of recent range changes of tick populations that could be attributed to recent climate change. Further integrated ‘One Health’ observational and modeling studies are needed to detect changes in TTBD occurrence, attribute them to climate change, and to develop predictive models of public- and animal-health needs to plan for TTBD emergence.
","language":"English","publisher":"Entomological Society of America","doi":"10.1093/jme/tjaa220","usgsCitation":"Ogden, N.H., Beard, C.B., Ginsberg, H., and Tsao, J.I., 2021, Possible effects of climate change on ixodid ticks and the pathogens they transmit: Predictions and observations: Journal of Medical Entomology, v. 58, no. 4, p. 1536-1545, https://doi.org/10.1093/jme/tjaa220.","productDescription":"10 p.","startPage":"1536","endPage":"1545","ipdsId":"IP-121581","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":454330,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjaa220","text":"Publisher Index Page"},{"id":379905,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ogden, Nicholas H.","contributorId":147667,"corporation":false,"usgs":false,"family":"Ogden","given":"Nicholas","email":"","middleInitial":"H.","affiliations":[{"id":16890,"text":"Public Health Agency of Canada","active":true,"usgs":false}],"preferred":false,"id":803337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beard, Charles B.","contributorId":148018,"corporation":false,"usgs":false,"family":"Beard","given":"Charles","email":"","middleInitial":"B.","affiliations":[{"id":16974,"text":"US Centers for Disease Control and Prevention (CDC)","active":true,"usgs":false}],"preferred":false,"id":803338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ginsberg, Howard S. 0000-0002-4933-2466 hginsberg@usgs.gov","orcid":"https://orcid.org/0000-0002-4933-2466","contributorId":147665,"corporation":false,"usgs":true,"family":"Ginsberg","given":"Howard S.","email":"hginsberg@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":803339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tsao, Jean I.","contributorId":140905,"corporation":false,"usgs":false,"family":"Tsao","given":"Jean","email":"","middleInitial":"I.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":803340,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216439,"text":"70216439 - 2021 - Estimating the contribution of tributary sand inputs to controlled flood deposits for sandbar restoration using elemental tracers, Colorado River, Grand Canyon National Park, Arizona","interactions":[],"lastModifiedDate":"2021-05-14T11:50:15.092644","indexId":"70216439","displayToPublicDate":"2020-10-28T07:37:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1786,"text":"Geological Society of America Bulletin","active":true,"publicationSubtype":{"id":10}},"title":"Estimating the contribution of tributary sand inputs to controlled flood deposits for sandbar restoration using elemental tracers, Colorado River, Grand Canyon National Park, Arizona","docAbstract":"Completion of Glen Canyon Dam in 1963 resulted in complete elimination of sediment delivery from the upstream Colorado River basin to Grand Canyon and nearly complete control of spring snowmelt floods responsible for creating channel and bar morphology. Management of the river ecosystem in Grand Canyon National Park now relies on dam-release floods to redistribute tributary-derived sediment accumulated on the channel bed to higher-elevation sandbars. Here, we used multivariate mixing analysis of sediment elemental compositions to evaluate the extent to which flood deposits derive from tributary-supplied sand compared to reworked, relict predam sediment. The concentrations of seven major and trace elements (Fe, Ca, K, Ti, Rb, Sr, and Zr) were measured in very fine−, fine-, and medium-grained sand from flood deposits using X-ray fluorescence and interpreted using a Bayesian mixing model to characterize the proportion of sand originating from the Paria River, the only major tributary within the study reach. Flood deposits from the 2013 and 2014 controlled floods contained 69% ± 16% and 84% ± 20% Paria River−derived material, respectively, with substantial variation among sites. Based on a sand mass balance, we calculated that under decreasing storage conditions since 1963, ∼77%−83% of the annual Paria River sand flux needs to be retained within the mass of active sand stored in Marble Canyon each year to reach the observed concentration of Paria River sand at sample locations. This finding suggests that the use of controlled floods may continue to be effective for sandbar maintenance, provided sand inputs from the Paria River do not decline.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/B35642.1","usgsCitation":"Chapman, K.A., Best, R.J., Smith, M.E., Mueller, E.R., Grams, P.E., and Parnell, R., 2021, Estimating the contribution of tributary sand inputs to controlled flood deposits for sandbar restoration using elemental tracers, Colorado River, Grand Canyon National Park, Arizona: Geological Society of America Bulletin, v. 133, no. 5-6, p. 1141-1156, https://doi.org/10.1130/B35642.1.","productDescription":"16 p.","startPage":"1141","endPage":"1156","ipdsId":"IP-116064","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436648,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C0IN56","text":"USGS data release","linkHelpText":"Tributary sand input data, Colorado River, Grand Canyon National Park, Arizona"},{"id":380590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.027099609375,\n 35.71083783530009\n ],\n [\n -111.258544921875,\n 35.71083783530009\n ],\n [\n -111.258544921875,\n 36.958671131530316\n ],\n [\n -114.027099609375,\n 36.958671131530316\n ],\n [\n -114.027099609375,\n 35.71083783530009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"133","issue":"5-6","noUsgsAuthors":false,"publicationDate":"2020-10-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Chapman, Katherine A. kchapman@usgs.gov","contributorId":5368,"corporation":false,"usgs":true,"family":"Chapman","given":"Katherine","email":"kchapman@usgs.gov","middleInitial":"A.","affiliations":[],"preferred":true,"id":805134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Best, Rebecca J.","contributorId":198804,"corporation":false,"usgs":false,"family":"Best","given":"Rebecca","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":805135,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, M. Elliot","contributorId":255572,"corporation":false,"usgs":false,"family":"Smith","given":"M.","email":"","middleInitial":"Elliot","affiliations":[],"preferred":false,"id":805136,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mueller, Erich R. 0000-0001-8202-154X emueller@usgs.gov","orcid":"https://orcid.org/0000-0001-8202-154X","contributorId":4930,"corporation":false,"usgs":true,"family":"Mueller","given":"Erich","email":"emueller@usgs.gov","middleInitial":"R.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812614,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Grams, Paul E. 0000-0002-0873-0708 pgrams@usgs.gov","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":1830,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","email":"pgrams@usgs.gov","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":812615,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Parnell, Roderic A.","contributorId":41922,"corporation":false,"usgs":true,"family":"Parnell","given":"Roderic A.","affiliations":[],"preferred":false,"id":812616,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216003,"text":"70216003 - 2021 - Carrying capacity of spatially distributed metapopulations","interactions":[],"lastModifiedDate":"2021-01-19T16:35:18.526261","indexId":"70216003","displayToPublicDate":"2020-10-28T07:32:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3653,"text":"Trends in Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Carrying capacity of spatially distributed metapopulations","docAbstract":"Carrying capacity is a key concept in ecology. A body of theory, based on the logistic equation, has extended predictions of carrying capacity to spatially distributed, dispersing populations. However, this theory has only recently been tested empirically. The experimental results disagree with some theoretical predictions of when they are extended to a population dispersing randomly in a two-patch system. However, they are consistent with a mechanistic model of consumption on an exploitable resource (consumer–resource model). We argue that carrying capacity, defined as the total equilibrium population, is not a fundamental property of ecological systems, at least in the context of spatial heterogeneity. Instead, it is an emergent property that depends on the population’s intrinsic growth and dispersal rates.
The Black-capped Petrel or Diablotin Pterodroma hasitata has a fragmented and declining population estimated at c.1,000 breeding pairs. On land, the species nests underground in steep ravines with dense understorey vegetation. The only confirmed breeding sites are located in the mountain ranges of Hispaniola in the Caribbean, where habitat loss and degradation are continuing threats. Other nesting populations may still remain undiscovered but, to locate them, laborious in situ nest searches must be conducted over expansive geographical areas. To focus nest-search efforts more efficiently, we analysed the environmental characteristics of Black-capped Petrel nesting habitat and modeled suitable habitat on Hispaniola using openly available environmental datasets. We used a univariate generalized linear model to compare the habitat characteristics of active Black-capped Petrel nests sites with those of potentially available sites (i.e. random pseudo-absences). Elevation, distance to coast, and the influence of tree cover and density emerged as important environmental variables. We then applied multivariate generalized linear models to these environmental variables that showed a significant relationship with petrel nesting activity. We used the top performing model of habitat suitability model to create maps of predicted suitability for Hispaniola. In addition to areas of known petrel activity, the model identified possible nesting areas for Black-capped Petrels in habitats not previously considered suitable. Based on model results, we estimated the total area of predicted suitable nesting habitat for Black-capped Petrels on Hispaniola and found that forest loss due to hurricanes, forest fires, and encroachment from agriculture had severely decreased availability of predicted suitable habitat between 2000 and 2018.
","language":"English","publisher":"Cambridge University Press","doi":"10.1017/S0959270920000490","usgsCitation":"Satge, Y.G., Rupp, E., Brown, A.J., and Jodice, P.G., 2021, Habitat modelling locates nesting areas of the endangered Black-capped Petrel Pterodroma hasitata on Hispaniola and identifies habitat loss: Bird Conservation International, v. 31, no. 4, p. 573-590, https://doi.org/10.1017/S0959270920000490.","productDescription":"18 p.","startPage":"573","endPage":"590","ipdsId":"IP-115786","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454339,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1017/s0959270920000490","text":"Publisher Index Page"},{"id":436651,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FWJPBD","text":"USGS data release","linkHelpText":"Nesting habitat suitability for the Black-capped Petrel Pterodroma hasitata on Hispaniola, Supplementary Material"},{"id":395771,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Dominican Republic, Haiti","otherGeospatial":"Hispaniola","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -68.09326171875,\n 18.823116948090494\n ],\n [\n -69.2138671875,\n 19.621892180319374\n ],\n [\n -71.136474609375,\n 20.159098270646936\n ],\n [\n -72.916259765625,\n 20.24158281954221\n ],\n [\n -73.71826171874999,\n 19.766703551716976\n ],\n [\n -74.92675781249999,\n 18.396230138028827\n ],\n [\n -73.201904296875,\n 17.602139123350838\n ],\n [\n -71.47705078125,\n 17.45547257997284\n ],\n [\n -68.79638671875,\n 17.90556881196468\n ],\n [\n -68.21411132812499,\n 18.3336694457713\n ],\n [\n -68.09326171875,\n 18.823116948090494\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-10-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Satge, Y. G.","contributorId":275774,"corporation":false,"usgs":false,"family":"Satge","given":"Y.","email":"","middleInitial":"G.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":834273,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rupp, E.","contributorId":265431,"corporation":false,"usgs":false,"family":"Rupp","given":"E.","email":"","affiliations":[],"preferred":false,"id":834274,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brown, A. J.","contributorId":197185,"corporation":false,"usgs":false,"family":"Brown","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":834275,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jodice, Patrick G.R. 0000-0001-8716-120X","orcid":"https://orcid.org/0000-0001-8716-120X","contributorId":219852,"corporation":false,"usgs":true,"family":"Jodice","given":"Patrick","middleInitial":"G.R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":834276,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215758,"text":"70215758 - 2021 - Surface elevation change evaluation in mangrove forests using a low‐cost, rapid‐scan terrestrial laser scanner","interactions":[],"lastModifiedDate":"2021-01-19T16:39:46.16129","indexId":"70215758","displayToPublicDate":"2020-10-26T08:13:11","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7183,"text":"Limnology and Oceanography Methods","active":true,"publicationSubtype":{"id":10}},"title":"Surface elevation change evaluation in mangrove forests using a low‐cost, rapid‐scan terrestrial laser scanner","docAbstract":"Mangrove forests have adapted to sea level rise (SLR) increases by maintaining their forest floor elevation via belowground root growth and surface sediment deposits. Researchers use surface elevation tables (SETs) to monitor surface elevation change (SEC) in mangrove forests, after which this information is used to assess SLR resiliency or to dictate active forest management for vulnerable systems. This method requires significant investments in terms of time and human resources and is limited in the number of points it can measure per plot. We use a low‐cost, portable terrestrial laser scanning (TLS) system to assess SEC for three mangrove forests on Pohnpei Island (Federated States of Micronesia). Cloth simulation filtering was used for ground detection, after which results were refined by filtering points using angular orientation. Digital elevation models then were generated via kriging interpolation for data collected in 2017 and 2019, after which the heights of corresponding points were compared across years. Extreme elevation changes, due to disturbances such as footprints or fallen logs, were removed using interquartile range analysis. The TLS‐obtained average SEC ranged between −6.92 and +6.01 mm, which exhibited an average consistency of 72% when compared to simultaneously collected SET data (root mean square error = 1.36 mm). We contend that this approach represents an improvement over the manual method, where very few points typically are used, that is, ≅ 36 points vs. ≅ 30,000 points in the case of TLS, and could contribute to improved monitoring and management of these rapidly changing forest environments.
The western purple martin (Progne subis arboricola), an avian insectivore, is a species of conservation concern throughout the Pacific Northwest. Compared to the well-studied eastern subspecies (Progne subis subis), little is known of the life history and biology of the western subspecies. Availability of breeding habitat is believed to be a major limiting factor for western purple martins in forested habitat, but fundamental information on their current distribution and selection of nesting habitat is deficient. To fill this gap, we compared habitat characteristics at three spatial scales (snag-level, stand-level [48.6 ha], landscape-level [314 ha]) surrounding nest snags occupied by purple martins in western Oregon to unoccupied sites. We found habitat for nesting purple martins was defined by the presence of moderately decayed snags with nest cavities, located well away from closed-canopy forest in sufficiently large (>15 ha) open areas. Our modeling efforts suggested suitable habitat was rare within the study region because: 1) snags were scarce on private industrial forest lands and 2) large disturbed patches were uncommon on federal lands. We conclude that a disturbance regime characterized by infrequent but major stand-replacing events, such as fire or timber harvest, is likely the key to maintaining breeding habitat for purple martins in upland forests in western Oregon.
System-of-systems approaches for integrated assessments have become prevalent in recent years. Such approaches integrate a variety of models from different disciplines and modeling paradigms to represent a socio-environmental (or social-ecological) system aiming to holistically inform policy and decision-making processes. Central to the system-of-systems approaches is the representation of systems in a multi-tier framework with nested scales. Current modeling paradigms, however, have disciplinary-specific lineage, leading to inconsistencies in the conceptualization and integration of socio-environmental systems. In this paper, a multidisciplinary team of researchers, from engineering, natural and social sciences, have come together to detail socio-technical practices and challenges that arise in the consideration of scale throughout the socio-environmental modeling process. We identify key paths forward, focused on explicit consideration of scale and uncertainty, strengthening interdisciplinary communication, and improvement of the documentation process. We call for a grand vision (and commensurate funding) for holistic system-of-systems research that engages researchers, stakeholders, and policy makers in a multi-tiered process for the co-creation of knowledge and solutions to major socio-environmental problems.
The Failure Forecast Method (FFM) was introduced as an empirical model for forecasting catastrophic material failures related to natural hazards, such as landslides and volcanic eruptions, with mixed success. During the 2018 eruption of Kilauea volcano, Hawaii, the draining of the summit magma reservoir into the Lower East Rift Zone resulted in the formation of a new caldera at the summit. I tested the applicability of the FFM to caldera collapse by analyzing the cyclical earthquake swarms and ground deformation that occurred between 62 sudden major caldera collapse events. The progression of both the cumulative moment release of the cyclical earthquakes and the GNSS displacement show a major change in mid-June. In late May through early June, the progression of the parameters is consistent with strain localization or creep progression related to the development or activation of the ring fault system. From late June until the end of the eruption, parameter progression is roughly steady with initial accelerating increases in cumulative moment and displacement that shift to approximately linear progression. Analysis of repeating earthquake families in the cyclical swarms showed that the behavior of the repeaters was consistent with that of the cyclical swarms as a whole and suggested that each family undergoes its own progression of activation to termination. While the FFM analysis identified the system change in mid-June, it did not demonstrate an ability to forecast collapse events or the end of the eruption.
The scarcity of strong ground motion records presents a challenge for making reliable performance assessments of tall buildings whose seismic design is controlled by large-magnitude and close-distance earthquakes. This challenge can be addressed using broadband ground-motion simulation methods to generate records with site-specific characteristics of large-magnitude events. In this paper, simulated site-specific earthquake seismograms, developed through a related project that was organized through the Southern California Earthquake Center (SCEC) Ground Motion Simulation Validation (GMSV) Technical Activity Group, are used for nonlinear response history analyses of two archetype tall buildings for sites in San Francisco, Los Angeles, and San Bernardino. The SCEC GMSV team created the seismograms using the Broadband Platform (BBP) simulations for five site-specific earthquake scenarios. The two buildings are evaluated using nonlinear dynamic analyses under comparable record suites selected from the simulated BBP catalog and recorded motions from the NGA-West database. The collapse risks and structural response demands (maximum story drift ratio, peak floor acceleration, and maximum story shear) under the BBP and NGA suites are compared. In general, this study finds that use of the BBP simulations resolves concerns about estimation biases in structural response analysis which are caused by ground motion scaling, unrealistic spectral shapes, and overconservative spectral variations. While there are remaining concerns that strong coherence in some kinematic fault rupture models may lead to an overestimation of velocity pulse effects in the BBP simulations, the simulations are shown to generally yield realistic pulse-like features of near-fault ground motion records.
","language":"English","publisher":"Wiley","doi":"10.1002/eqe.3364","usgsCitation":"Zhong, K., Lin, T., Deierlein, G., Graves, R., Silva, F., and Luco, N., 2021, Tall building performance-based seismic design using SCEC broadband platform site-specific ground motion simulations: Earthquake Engineering and Structural Dynamics, v. 50, no. 1, p. 81-98, https://doi.org/10.1002/eqe.3364.","productDescription":"18 p.","startPage":"81","endPage":"98","ipdsId":"IP-119067","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":454358,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/2346/88059","text":"External Repository"},{"id":387857,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Los Angeles, San Bernardino, San Francisco","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.39790916442873,\n 37.78178983833927\n ],\n [\n -122.3886823654175,\n 37.78178983833927\n ],\n [\n -122.3886823654175,\n 37.78921753609959\n ],\n [\n -122.39790916442873,\n 37.78921753609959\n ],\n [\n -122.39790916442873,\n 37.78178983833927\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.26359510421753,\n 34.05214385256302\n ],\n [\n -118.25709342956542,\n 34.05214385256302\n ],\n [\n -118.25709342956542,\n 34.0581704783008\n ],\n [\n -118.26359510421753,\n 34.0581704783008\n ],\n [\n -118.26359510421753,\n 34.05214385256302\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.29613304138182,\n 34.09673784258847\n ],\n [\n -117.27553367614746,\n 34.09673784258847\n ],\n [\n -117.27553367614746,\n 34.11812893261633\n ],\n [\n -117.29613304138182,\n 34.11812893261633\n ],\n [\n -117.29613304138182,\n 34.09673784258847\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-10-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhong, Kuanshi","contributorId":264127,"corporation":false,"usgs":false,"family":"Zhong","given":"Kuanshi","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":820928,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lin, Ting","contributorId":264128,"corporation":false,"usgs":false,"family":"Lin","given":"Ting","affiliations":[{"id":36331,"text":"Texas Tech University","active":true,"usgs":false}],"preferred":false,"id":820929,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Deierlein, Greg","contributorId":264129,"corporation":false,"usgs":false,"family":"Deierlein","given":"Greg","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":820930,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Graves, Robert 0000-0001-9758-453X rwgraves@usgs.gov","orcid":"https://orcid.org/0000-0001-9758-453X","contributorId":140738,"corporation":false,"usgs":true,"family":"Graves","given":"Robert","email":"rwgraves@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":820931,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Silva, Fabio","contributorId":264130,"corporation":false,"usgs":false,"family":"Silva","given":"Fabio","email":"","affiliations":[{"id":54387,"text":"SCEC","active":true,"usgs":false}],"preferred":false,"id":820932,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Luco, Nico 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":145730,"corporation":false,"usgs":true,"family":"Luco","given":"Nico","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":820933,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70229080,"text":"70229080 - 2021 - Balancing transferability and complexity of species distribution models for rare species conservation","interactions":[],"lastModifiedDate":"2022-02-28T15:12:14.941976","indexId":"70229080","displayToPublicDate":"2020-10-20T09:07:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Balancing transferability and complexity of species distribution models for rare species conservation","docAbstract":"Species distribution models (SDMs) are valuable for rare species conservation and are commonly used to extrapolate predictions of habitat suitability geographically to regions where species occurrence is unknown (i.e., transferability). Spatially structured cross-validation can be used to infer transferability, yet, few studies have evaluated how delineation of cross-validation folds affects model complexity and predictions. We developed SDMs using multiple cross-validation approaches to understand the implications for predicting habitat suitability for northern Idaho ground squirrels, a rare, federally threatened species that has been extensively surveyed in regions where known populations occur, resulting in >8000 presence locations.
Idaho, USA.
We delineated cross-validation folds by mimicking the manner in which predictions would be geographically extrapolated or by using existing dispersal barriers. We varied the distance between, number, and directionality of folds. We conducted a grid search on statistical regularization parameters to optimize model complexity, covering a range of values exceeding that typically implemented. For each cross-validation approach, we selected optimal regularization and model complexity based on out-of-sample predictive ability.
Delineation of cross-validation folds substantially affected resulting model complexity and extrapolated predictions. All cross-validation approaches resulted in models with apparently high out-of-sample predictive ability, yet optimal model complexity varied substantially among the approaches. Regularization demonstrated a noisy relationship between model complexity and prediction, where local optima in predictive performance were common at small values.
Subtle modelling decisions can have large consequences for predictions of habitat suitability and transferability of SDMs. When transferability is the goal, cross-validation approaches should be considered carefully and mimic the manner in which spatial extrapolation will occur, else overly complex models with inflated assessments of predictive accuracy may result. Further, spatially structured cross-validation may not guard against over-parameterization, and assessing a broader range of regularization parameters may be necessary to optimize model complexity for transferability.
","language":"English","publisher":"Wiley","doi":"10.1111/ddi.13174","usgsCitation":"Helmstetter, N.A., Conway, C.J., Stevens, B.S., and Goldberg, A., 2021, Balancing transferability and complexity of species distribution models for rare species conservation: Diversity and Distributions, v. 27, no. 1, p. 95-108, https://doi.org/10.1111/ddi.13174.","productDescription":"14 p.","startPage":"95","endPage":"108","ipdsId":"IP-121750","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":454360,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ddi.13174","text":"Publisher Index Page"},{"id":396549,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","county":"Adams County, Valley 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Nolan A.","contributorId":287004,"corporation":false,"usgs":false,"family":"Helmstetter","given":"Nolan","email":"","middleInitial":"A.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":836429,"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":836428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stevens, Bryan S.","contributorId":171809,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":836430,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goldberg, Amanda R.","contributorId":280029,"corporation":false,"usgs":false,"family":"Goldberg","given":"Amanda R.","affiliations":[{"id":39599,"text":"ui","active":true,"usgs":false}],"preferred":false,"id":836431,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219196,"text":"70219196 - 2021 - Signatures of hydrologic function across the critical zone observatory network","interactions":[],"lastModifiedDate":"2021-03-30T12:05:44.485187","indexId":"70219196","displayToPublicDate":"2020-10-18T06:50:52","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Signatures of hydrologic function across the critical zone observatory network","docAbstract":"Despite a multitude of small catchment studies, we lack a deep understanding of how variations in critical zone architecture lead to variations in hydrologic states and fluxes. This study characterizes hydrologic dynamics of 15 catchments of the U.S. Critical Zone Observatory (CZO) network where we hypothesized that our understanding of subsurface structure would illuminate patterns of hydrologic partitioning. The CZOs collect data sets that characterize the physical, chemical, and biological architecture of the subsurface, while also monitoring hydrologic fluxes such as streamflow, precipitation, and evapotranspiration. For the first time, we collate time series of hydrologic variables across the CZO network and begin the process of examining hydrologic signatures across sites. We find that catchments with low baseflow indices and high runoff sensitivity to storage receive most of their precipitation as rain and contain clay‐rich regolith profiles, prominent argillic horizons, and/or anthropogenic modifications. In contrast, sites with high baseflow indices and low runoff sensitivity to storage receive the majority of precipitation as snow and have more permeable regolith profiles. The seasonal variability of water balance components is a key control on the dynamic range of hydraulically connected water in the critical zone. These findings lead us to posit that water balance partitioning and streamflow hydraulics are linked through the coevolution of critical zone architecture but that much work remains to parse these controls out quantitatively.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR026635","usgsCitation":"Wlostowski, A.N., Molotch, N.P., Anderson, S.P., Brantley, S.L., Chorover, J., Dralle, D., Kumar, P., Li, L., Lohse, K.A., Mallard, J., McIntosh, J.C., Murphy, S.F., Parrish, E., Safeeq, M., Seyfried, M., Shi, Y., and Harman, C., 2021, Signatures of hydrologic function across the critical zone observatory network: Water Resources Research, v. 57, no. 3, e2019WR026635, 28 p., https://doi.org/10.1029/2019WR026635.","productDescription":"e2019WR026635, 28 p.","ipdsId":"IP-117846","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":454369,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019wr026635","text":"Publisher Index 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P. 0000-0002-6796-6649","orcid":"https://orcid.org/0000-0002-6796-6649","contributorId":172732,"corporation":false,"usgs":false,"family":"Anderson","given":"Suzanne","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":813174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brantley, Susan L. 0000-0003-4320-2342","orcid":"https://orcid.org/0000-0003-4320-2342","contributorId":184201,"corporation":false,"usgs":false,"family":"Brantley","given":"Susan","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":813175,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chorover, Jon 0000-0001-9497-0195","orcid":"https://orcid.org/0000-0001-9497-0195","contributorId":139472,"corporation":false,"usgs":false,"family":"Chorover","given":"Jon","email":"","affiliations":[],"preferred":false,"id":813176,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dralle, David 0000-0002-1944-2103","orcid":"https://orcid.org/0000-0002-1944-2103","contributorId":256752,"corporation":false,"usgs":false,"family":"Dralle","given":"David","email":"","affiliations":[{"id":13243,"text":"University of California Berkeley","active":true,"usgs":false}],"preferred":false,"id":813177,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kumar, Praveen 0000-0002-4787-0308","orcid":"https://orcid.org/0000-0002-4787-0308","contributorId":256753,"corporation":false,"usgs":false,"family":"Kumar","given":"Praveen","email":"","affiliations":[{"id":36403,"text":"University of Illinois","active":true,"usgs":false}],"preferred":false,"id":813178,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Li, Li 0000-0002-1641-3710","orcid":"https://orcid.org/0000-0002-1641-3710","contributorId":197290,"corporation":false,"usgs":false,"family":"Li","given":"Li","affiliations":[],"preferred":false,"id":813179,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lohse, Kathleen A. 0000-0003-1779-6773","orcid":"https://orcid.org/0000-0003-1779-6773","contributorId":196995,"corporation":false,"usgs":false,"family":"Lohse","given":"Kathleen","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":813180,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mallard, John 0000-0002-0494-9024","orcid":"https://orcid.org/0000-0002-0494-9024","contributorId":256757,"corporation":false,"usgs":false,"family":"Mallard","given":"John","email":"","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":813181,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"McIntosh, Jennifer C. 0000-0001-5055-4202","orcid":"https://orcid.org/0000-0001-5055-4202","contributorId":150557,"corporation":false,"usgs":false,"family":"McIntosh","given":"Jennifer","email":"","middleInitial":"C.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":813182,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Murphy, Sheila F. 0000-0002-5481-3635 sfmurphy@usgs.gov","orcid":"https://orcid.org/0000-0002-5481-3635","contributorId":1854,"corporation":false,"usgs":true,"family":"Murphy","given":"Sheila","email":"sfmurphy@usgs.gov","middleInitial":"F.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":813183,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Parrish, Eric","contributorId":256760,"corporation":false,"usgs":false,"family":"Parrish","given":"Eric","email":"","affiliations":[{"id":36621,"text":"University of Colorado","active":true,"usgs":false}],"preferred":false,"id":813184,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Safeeq, Mohammad 0000-0003-0529-3925","orcid":"https://orcid.org/0000-0003-0529-3925","contributorId":77814,"corporation":false,"usgs":false,"family":"Safeeq","given":"Mohammad","email":"","affiliations":[{"id":6641,"text":"University of California at Merced","active":true,"usgs":false}],"preferred":false,"id":813185,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Seyfried, Mark 0000-0001-8081-0713","orcid":"https://orcid.org/0000-0001-8081-0713","contributorId":256763,"corporation":false,"usgs":false,"family":"Seyfried","given":"Mark","email":"","affiliations":[{"id":51849,"text":"United States Department of Agriculture - Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813186,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Shi, Yuning 0000-0003-0118-5847","orcid":"https://orcid.org/0000-0003-0118-5847","contributorId":256765,"corporation":false,"usgs":false,"family":"Shi","given":"Yuning","email":"","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":813187,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Harman, Ciaran 0000-0002-3185-002X","orcid":"https://orcid.org/0000-0002-3185-002X","contributorId":242780,"corporation":false,"usgs":false,"family":"Harman","given":"Ciaran","email":"","affiliations":[{"id":48526,"text":"Department of Environmental Health and Engineering, Johns Hopkins University","active":true,"usgs":false}],"preferred":false,"id":813188,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70254939,"text":"70254939 - 2021 - Hierarchical computing for hierarchical models in ecology","interactions":[],"lastModifiedDate":"2024-06-12T00:14:56.560958","indexId":"70254939","displayToPublicDate":"2020-10-17T19:13:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"title":"Hierarchical computing for hierarchical models in ecology","docAbstract":"Increasing aridity is a challenge for forest managers and reducing stand density to minimize competition is a recognized strategy to mitigate drought impacts on growth. In many dry forests, the most widespread and common forest management programs currently being implemented focus on restoration of historical stand structures, primarily to minimize fire risk and enhance watershed function. The implications of these restoration projects for drought vulnerability are not well understood. Here, we examined how planned restoration treatments in the Four Forests Restoration Initiative, the largest forest restoration project in the United States, would alter landscape‐scale patterns of forest growth and drought vulnerability throughout the 21st century. Using drought‐growth relationships developed within the landscape, we considered a suite of climate and treatment scenarios and estimated average forest growth and the proportion of years with extremely low growth as a measure of vulnerability to long‐term decline. Climatic shifts projected for this landscape include higher temperatures and shifting seasonal precipitation that promotes lower soil moisture availability in the early growing season and greater hot‐dry stress, conditions negatively associated with tree growth. However, drought severity and the magnitude of future growth declines was moderated by the thinning treatments. Compared to historical conditions, proportional growth in mid‐century declines by ~40% if thinning ceases or continues at the status quo pace. By comparison, proportional growth declines by only 20% if the Four Forest Restoration Initiative treatments are fully implemented, and < 10% if stands are thinned even more intensively than currently planned. Furthermore, restoration treatments resulted in dramatically fewer years with extremely low growth in the future, a recognized precursor to forest decline and eventual tree mortality. Benefits from density reduction for mitigating drought‐induced growth declines are more apparent in mid‐century and under RCP4.5 than under RCP8.5 at the end of the century. Future climate is inherently uncertain, and our results only reflect the climate projections from the representative suite of models examined. Nevertheless, these results indicate that forest restoration projects designed for other objectives also have substantial benefits for minimizing future drought vulnerability in dry forests and provide additional incentive to accelerate the pace of restoration.
Releases of oil and gas (OG) wastewaters can have complex effects on stream-water quality and downstream organisms, due to sediment-water interactions and groundwater/surface water exchange. Previously, elevated concentrations of sodium (Na), chloride (Cl), barium (Ba), strontium (Sr), and lithium (Li), and trace hydrocarbons were determined to be key markers of OG wastewater releases when combined with Sr and radium (Ra) isotopic compositions. Here, we assessed the persistence of an OG wastewater spill in a creek in North Dakota using a combination of geochemical measurements and modeling, hydrologic analysis, and geophysical investigations. OG wastewater comprised 0.1 to 0.3% of the stream-water compositions at downstream sites in February and June 2015 but could not be quantified in 2016 and 2017. However, OG-wastewater markers persisted in sediments and pore water for 2.5 years after the spill and up to 7.2-km downstream from the spill site. Concentrations of OG wastewater constituents were highly variable depending on the hydrologic conditions. Electromagnetic measurements indicated substantially higher electrical conductivity under the bank adjacent to a seep 7.2 km downstream from the spill site. Geomorphic investigations revealed mobilization of sediment is an important contaminant transport process. Labile Ba, Ra, Sr, and ammonium (NH4) concentrations extracted from sediments indicated sediments are a long-term reservoir of these constituents, both in the creek and on the floodplain. Using the drivers of ecological effects identified at this intensively studied site we identified 41 watersheds across the North Dakota landscape that may be subject to similar episodic inputs from OG wastewater spills. Effects of contaminants released to the environment during OG waste management activities remain poorly understood; however, analyses of Ra and Sr isotopic compositions, as well as trace inorganic and organic compound concentrations at these sites in pore-water provide insights into potentials for animal and human exposures well outside source-remediation zones.
The eastern Adirondack Highlands of northern New York host dozens of iron oxide-apatite (IOA) deposits containing magnetite and rare earth element (REE)-bearing apatite. We use new aeromagnetic, aeroradiometric, ground gravity, and sample petrophysical and geochemical data to image and understand these deposits and their geologic framework. Aeromagnetic total field data reflect highly magnetic leucogranite host rock and major structures that likely served as fluid conduits for the hydrothermal system. Bandpass filtering of the aeromagnetic data reveals individual deposits that were verified in the field or from historical records. A three-dimensional inversion for magnetic susceptibility images these deposits at depth, allowing inference of plunge directions and relative size. Radiometric data highlight variations in the surface geology and several large tailings piles that contain REE-bearing apatite. Within the host rock, eTh (equivalent Th), K and the eTh/K ratio are variable with high eTh/K near several of the IOA deposits. Areas with elevated K or low eTh/K representing potassic alteration appear to be rare; instead elevated eTh/K ratios likely reflect widespread sodic alteration overprinting potassic alteration. Bouguer gravity anomalies show limited correspondence to the surface geology, radiometric data, or magnetic data, but do exhibit ~10-km wide highs in areas where deposits are observed. Two-dimensional forward models of the gravity and magnetic data show that deeper dense material beneath the leucogranite is quantitatively feasible. If these dense rocks represent intrusions that were emplaced or still cooling at the time of mineralization, they may have served as a heat source that helped to drive the hydrothermal system. Combining datasets, we find that deposits occur towards the distal ends of major structures within the host leucogranite and mostly above gravity highs. The geophysical modeling thus suggests that IOA deposits formed in structural, thermal, and chemical traps near the distal ends of the hydrothermal system.
","language":"English","publisher":"Society for Exploration Geophysics","doi":"10.1190/geo2019-0783.1","usgsCitation":"Shah, A.K., Taylor, R.D., Walsh, G.J., and Phillips, J., 2021, Integrated geophysical imaging of rare-earth-element-bearing iron oxide-apatite deposits in the eastern Adirondack Highlands, New York: Geophysics, v. 86, no. 1, p. B37-B54, https://doi.org/10.1190/geo2019-0783.1.","productDescription":"18 p.","startPage":"B37","endPage":"B54","ipdsId":"IP-117777","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454380,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1190/geo2019-0783.1","text":"Publisher Index Page"},{"id":380582,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Adirondack Highlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2451171875,\n 43.42100882994726\n ],\n [\n -73.6083984375,\n 43.42100882994726\n ],\n [\n -73.6083984375,\n 44.809121700077355\n ],\n [\n -76.2451171875,\n 44.809121700077355\n ],\n [\n -76.2451171875,\n 43.42100882994726\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"86","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805129,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Taylor, Ryan D. 0000-0002-8845-5290","orcid":"https://orcid.org/0000-0002-8845-5290","contributorId":245004,"corporation":false,"usgs":true,"family":"Taylor","given":"Ryan","email":"","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805130,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walsh, Gregory J. 0000-0003-4264-8836 gwalsh@usgs.gov","orcid":"https://orcid.org/0000-0003-4264-8836","contributorId":873,"corporation":false,"usgs":true,"family":"Walsh","given":"Gregory","email":"gwalsh@usgs.gov","middleInitial":"J.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":805131,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Phillips, Jeffrey 0000-0002-6459-2821 jeff@usgs.gov","orcid":"https://orcid.org/0000-0002-6459-2821","contributorId":127453,"corporation":false,"usgs":true,"family":"Phillips","given":"Jeffrey","email":"jeff@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":805132,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232558,"text":"70232558 - 2021 - Nutrient limitation of phytoplankton in Chesapeake Bay: Development of an empirical approach for water-quality management","interactions":[],"lastModifiedDate":"2022-07-07T12:01:41.226961","indexId":"70232558","displayToPublicDate":"2020-10-13T06:57:47","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Nutrient limitation of phytoplankton in Chesapeake Bay: Development of an empirical approach for water-quality management","docAbstract":"Understanding the temporal and spatial roles of nutrient limitation on phytoplankton growth is necessary for developing successful management strategies. Chesapeake Bay has well-documented seasonal and spatial variations in nutrient limitation, but it remains unknown whether these patterns of nutrient limitation have changed in response to nutrient management efforts. We analyzed historical data from nutrient bioassay experiments (1992–2002) and data from long-term, fixed-site water-quality monitoring program (1990–2017) to develop empirical approaches for predicting nutrient limitation in the surface waters of the mainstem Bay. Results from classification and regression trees (CART) matched the seasonal and spatial patterns of bioassay-based nutrient limitation in the 1992–2002 period much better than two simpler, non-statistical approaches. An ensemble approach of three selected CART models satisfactorily reproduced the bioassay-based results (classification rate = 99%). This empirical approach can be used to characterize nutrient limitation from long-term water-quality monitoring data on much broader geographic and temporal scales than would be feasible using bioassays, providing a new tool for informing water-quality management. Results from our application of the approach to 21 tidal monitoring stations for the period of 2007–2017 showed modest changes in nutrient limitation patterns, with expanded areas of nitrogen-limitation and contracted areas of nutrient saturation (i.e., not limited by nitrogen or phosphorus). These changes imply that long-term reductions in nitrogen load have led to expanded areas with nutrient-limited phytoplankton growth in the Bay, reflecting long-term water-quality improvements in the context of nutrient enrichment. However, nutrient limitation patterns remain unchanged in the majority of the mainstem, suggesting that nutrient loads should be further reduced to achieve a less nutrient-saturated ecosystem.
Groundwater discharge zones in streams are important habitats for aquatic organisms. The use of discharge zones for thermal refuge and spawning by fish and other biota renders them susceptible to potential focused discharge of groundwater contamination. Currently, there is a paucity of information about discharge zones as a potential exposure pathway of chemicals to stream ecosystems. Using thermal mapping technologies to locate groundwater discharges, shallow groundwater and surface water from three rivers in the Chesapeake Bay Watershed, USA were analyzed for phytoestrogens, pesticides and their degradates, steroid hormones, sterols and bisphenol A. A Bayesian censored regression model was used to compare groundwater and surface water chemical concentrations. The most frequently detected chemicals in both ground and surface water were the phytoestrogens genistein (79%) and formononetin (55%), the herbicides metolachlor (50%) and atrazine (74%), and the sterol cholesterol (88%). There was evidence suggesting groundwater discharge zones could be a unique exposure pathway of chemicals to surface water systems, in our case, metolachlor sulfonic acid (posterior mean concentration = 150 ng/L in groundwater and 4.6 ng/L in surface water). Our study also demonstrated heterogeneity of chemical concentration in groundwater discharge zones within a stream for the phytoestrogen formononetin, the herbicides metolachlor and atrazine, and cholesterol. Results support the hypothesis that discharge zones are an important source of exposure of phytoestrogens and herbicides to aquatic organisms. To manage critical resources within the Chesapeake Bay Watershed, more work is needed to characterize exposure in discharge zones more broadly across time and space.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.142873","usgsCitation":"Thompson, T.J., Briggs, M., Phillips, P.J., Blazer, V., Smalling, K., Kolpin, D., and Wagner, T., 2021, Groundwater discharges as a source of phytoestrogens and other agriculturally derived contaminants to streams: Science of the Total Environment, v. 755, 142873, 11 p., https://doi.org/10.1016/j.scitotenv.2020.142873.","productDescription":"142873, 11 p.","ipdsId":"IP-122288","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true},{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"links":[{"id":454389,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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J.","contributorId":261148,"corporation":false,"usgs":false,"family":"Thompson","given":"Tyler","email":"","middleInitial":"J.","affiliations":[{"id":36985,"text":"Penn State University","active":true,"usgs":false}],"preferred":false,"id":819369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Briggs, Martin A. 0000-0003-3206-4132","orcid":"https://orcid.org/0000-0003-3206-4132","contributorId":257637,"corporation":false,"usgs":true,"family":"Briggs","given":"Martin A.","affiliations":[{"id":486,"text":"OGW Branch of Geophysics","active":true,"usgs":true}],"preferred":true,"id":819370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Phillips, Patrick J. 0000-0001-5915-2015 pjphilli@usgs.gov","orcid":"https://orcid.org/0000-0001-5915-2015","contributorId":172757,"corporation":false,"usgs":true,"family":"Phillips","given":"Patrick","email":"pjphilli@usgs.gov","middleInitial":"J.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blazer, Vicki S. 0000-0001-6647-9614 vblazer@usgs.gov","orcid":"https://orcid.org/0000-0001-6647-9614","contributorId":150384,"corporation":false,"usgs":true,"family":"Blazer","given":"Vicki S.","email":"vblazer@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":819372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smalling, Kelly L. 0000-0002-1214-4920","orcid":"https://orcid.org/0000-0002-1214-4920","contributorId":214623,"corporation":false,"usgs":true,"family":"Smalling","given":"Kelly L.","affiliations":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819373,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kolpin, Dana W. 0000-0002-3529-6505","orcid":"https://orcid.org/0000-0002-3529-6505","contributorId":204154,"corporation":false,"usgs":true,"family":"Kolpin","given":"Dana W.","affiliations":[{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true},{"id":589,"text":"Toxic Substances Hydrology Program","active":true,"usgs":true},{"id":351,"text":"Iowa Water Science Center","active":true,"usgs":true}],"preferred":true,"id":819374,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":819368,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70223706,"text":"70223706 - 2021 - More than one way to kill a spruce forest: The role of fire and climate in the late-glacial termination of spruce woodlands across the southern Great Lakes","interactions":[],"lastModifiedDate":"2021-09-02T12:53:25.277365","indexId":"70223706","displayToPublicDate":"2020-10-08T07:44:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2242,"text":"Journal of Ecology","active":true,"publicationSubtype":{"id":10}},"title":"More than one way to kill a spruce forest: The role of fire and climate in the late-glacial termination of spruce woodlands across the southern Great Lakes","docAbstract":"The landslide complex at Gold Basin, Washington, has been drawing considerable attention after a catastrophic runout of the nearby landslide at Oso, Washington, in 2014. To evaluate potential threats of the Gold Basin landslide to the campground down the slope, remote sensing and numerical modeling were integrated to monitor recent landslide activity and simulate hypothetical runout scenarios. Bare-earth LiDAR DEM (digital elevation model) differencing, InSAR (Interferometric Synthetic Aperture Radar), and offset tracking of SAR images reveal that localized collapses at the headscarps have been the primary type of landslide activity at Gold Basin from 2005 to 2019, and currently no signs indicative of movement of a large centralized block or a deep-seated main body were detected. The maximum horizontal deformation rate is 5 m/year occurring primarily from headscarp recession of the middle lobe, and the annual landsliding volume of the whole landslide complex averages 1.03 × 105 m3. From three-dimensional limit equilibrium analysis of generalized terrace structures, the maximum landslide volume is estimated as 2.0 × 106 m3. Simulations of hypothetical runout scenarios were carried out using the depth-averaged two-phase model D-claw with above-obtained landslide geometry constraints. The simulation results demonstrate that debris flows with volume less than 105 m3 only pose limited threats to the campground, while volumes over 106 m3 could cause severe damages. Consequently, the estimated maximum landslide volume of 2.0 × 106 m3 suggests a potential risk to the campground nearby. Adaption of our methodology could prove useful for evaluating other similar landslides globally for hazards prevention and mitigation.
","language":"English","publisher":"Springer","doi":"10.1007/s10346-020-01533-0","usgsCitation":"Xu, Y., George, D.L., Kim, J., Lu, Z., Riley, M., Griffin, T., and de la Fuente, J., 2021, Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: An application to the Gold Basin landslide complex, northern Washington: Landslides, v. 18, p. 1131-1141, https://doi.org/10.1007/s10346-020-01533-0.","productDescription":"11 p.","startPage":"1131","endPage":"1141","ipdsId":"IP-118305","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":464027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Gold Basin landslide complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.70,\n 48.2855\n ],\n [\n -121.70,\n 48.0755\n ],\n [\n -121.855,\n 48.0755\n ],\n [\n -121.855,\n 48.2855\n ],\n [\n -121.70,\n 48.2855\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","noUsgsAuthors":false,"publicationDate":"2020-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, Yuankun","contributorId":261747,"corporation":false,"usgs":false,"family":"Xu","given":"Yuankun","email":"","affiliations":[{"id":52987,"text":"Roy M. Huffington Department of Earth Sciences, Southern Methodist University, Dallas, TX 75205, USA","active":true,"usgs":false}],"preferred":false,"id":918501,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"George, David L. 0000-0002-5726-0255 dgeorge@usgs.gov","orcid":"https://orcid.org/0000-0002-5726-0255","contributorId":3120,"corporation":false,"usgs":true,"family":"George","given":"David","email":"dgeorge@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":918502,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kim, Jin-Woo","contributorId":69486,"corporation":false,"usgs":true,"family":"Kim","given":"Jin-Woo","affiliations":[],"preferred":false,"id":918503,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lu, Zhong","contributorId":344911,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":918504,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riley, Mark","contributorId":346244,"corporation":false,"usgs":false,"family":"Riley","given":"Mark","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":918505,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Griffin, Todd","contributorId":346245,"corporation":false,"usgs":false,"family":"Griffin","given":"Todd","email":"","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":918506,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"de la Fuente, Juan","contributorId":346246,"corporation":false,"usgs":false,"family":"de la Fuente","given":"Juan","affiliations":[{"id":37389,"text":"U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":918507,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70217785,"text":"70217785 - 2021 - Dendritic reidite from the Chesapeake Bay impact horizon, Ocean Drilling Program Site 1073 (offshore northeastern USA): A fingerprint of distal ejecta?","interactions":[],"lastModifiedDate":"2021-02-02T12:38:43.102242","indexId":"70217785","displayToPublicDate":"2020-10-07T06:33:22","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"Dendritic reidite from the Chesapeake Bay impact horizon, Ocean Drilling Program Site 1073 (offshore northeastern USA): A fingerprint of distal ejecta?","docAbstract":"High-pressure minerals provide records of processes not normally preserved in Earth’s crust. Reidite, a quenchable polymorph of zircon, forms at pressures >20 GPa during shock compression. However, there is no broad consensus among empirical, experimental, and theoretical studies on the nature of the polymorphic transformation. Here we decipher a multistage history of reidite growth recorded in a zircon grain in distal impact ejecta (offshore northeastern United States) from the ca. 35 Ma Chesapeake Bay impact event which, remarkably, experienced near-complete conversion (89%) to reidite. The grain displays two distinctive reidite habits: (1) intersecting sets of planar lamellae that are dark in cathodoluminescence (CL); and (2) dendritic epitaxial overgrowths on the lamellae that are luminescent in CL. While the former is similar to that described in literature, the latter has not been previously reported. A two-stage growth model is proposed for reidite formation at >40 GPa in Chesapeake Bay impact ejecta: formation of lamellar reidite by shearing during shock compression, followed by dendrite growth, also at high pressure, via recrystallization. The dendritic reidite is interpreted to nucleate on lamellae and replace damaged zircon adjacent to lamellae, which may be amorphous ZrSiO4 or possibly an intermediate phase, all before quenching. These results provide new insights on the microstructural evolution of the high-pressure polymorphic transformation over the microseconds-long interval of reidite stability during meteorite impact. Given the formation conditions, dendritic reidite may be a unique indicator of distal ejecta.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G47860.1","usgsCitation":"Cavosie, A.J., Biren, M.C., Hodges, K.V., Wartho, J., Horton,, J., and Koeberl, C., 2021, Dendritic reidite from the Chesapeake Bay impact horizon, Ocean Drilling Program Site 1073 (offshore northeastern USA): A fingerprint of distal ejecta?: Geology, v. 49, no. 2, p. 201-205, https://doi.org/10.1130/G47860.1.","productDescription":"5 p.","startPage":"201","endPage":"205","ipdsId":"IP-118547","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":486996,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"text":"External Repository"},{"id":382866,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia","otherGeospatial":"Chesapeake Bay impact","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n 37.055177106660814\n ],\n [\n -75.1025390625,\n 37.055177106660814\n ],\n [\n -75.1025390625,\n 38.591113776147445\n ],\n [\n -76.2890625,\n 38.591113776147445\n ],\n [\n -76.2890625,\n 37.055177106660814\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"49","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-10-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Cavosie, Aaron J.","contributorId":248705,"corporation":false,"usgs":false,"family":"Cavosie","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":49985,"text":"Curtin University, Perth, WA, 6102, Australia","active":true,"usgs":false}],"preferred":false,"id":809646,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Biren, Marc C","contributorId":248706,"corporation":false,"usgs":false,"family":"Biren","given":"Marc","email":"","middleInitial":"C","affiliations":[{"id":36436,"text":"Arizona State University, Tempe, AZ","active":true,"usgs":false}],"preferred":false,"id":809647,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hodges, Kip V. 0000-0003-2805-8899","orcid":"https://orcid.org/0000-0003-2805-8899","contributorId":229558,"corporation":false,"usgs":false,"family":"Hodges","given":"Kip","email":"","middleInitial":"V.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":809648,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wartho, Jo-Anne","contributorId":248707,"corporation":false,"usgs":false,"family":"Wartho","given":"Jo-Anne","email":"","affiliations":[{"id":49986,"text":"GEOMAR Helmholltz Centre for Ocean Research, Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":809649,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Horton,, J. Wright Jr. 0000-0001-6756-6365","orcid":"https://orcid.org/0000-0001-6756-6365","contributorId":219824,"corporation":false,"usgs":true,"family":"Horton,","given":"J. Wright","suffix":"Jr.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":809650,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Koeberl, Christian","contributorId":219447,"corporation":false,"usgs":false,"family":"Koeberl","given":"Christian","email":"","affiliations":[],"preferred":false,"id":809651,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217997,"text":"70217997 - 2021 - Evaluating the dynamics of groundwater, lakebed transport, nutrient inflow and algal blooms in Upper Klamath Lake, Oregon, USA","interactions":[],"lastModifiedDate":"2021-02-11T19:59:24.92397","indexId":"70217997","displayToPublicDate":"2020-10-06T13:54:50","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the dynamics of groundwater, lakebed transport, nutrient inflow and algal blooms in Upper Klamath Lake, Oregon, USA","docAbstract":"Transport of nutrients to lakes can occur via surface-water inflow, atmospheric deposition, groundwater (GW) inflow and benthic processes. Identifying and quantifying within-lake nutrient sources and recycling processes is challenging. Prior studies in hypereutrophic Upper Klamath Lake, Oregon, USA, indicated that ~60% of the early summer phosphorus (P) load to the lake was internal and hypothesized to be lakebed sediment release. Dynamic nutrient transport processes were examined to better characterize the nutrient sources. One-dimensional heat transport models calibrated to observed lakebed temperatures and a cross-sectional GW flow model provided estimates of GW-inflow rates that were greatest in spring and decreased through summer. One-dimensional solute transport models calibrated to observed lakebed pore-water dissolved silica (Si) and dissolved phosphate-phosphorus (DP) concentrations indicated that nutrients were transported from the lakebed by advection, diffusion, and enhanced mixing by benthic organisms and waves, and that DP removal occurred near the lakebed interface. Estimated water, Si, DP and total-phosphorus (TP) budgets indicated that GW contributed 21% of lake water inflow and at least 26, 20 and 16% of total Si, DP and TP inflow, respectively, when conservatively assuming background GW nutrient concentrations. However, lakebed GW (LGW) is enriched in nutrients during flow through lakebed sediment and the estimated GW contribution increased to 29 (33), 49 (67) and 43% (61%) of total Si, DP and TP inflow, respectively, if 20% (50%) of GW inflow to the lake was assumed to have LGW concentrations. Net nutrient inflow to the lake was greatest in spring and coincident with the annual diatom bloom. Inflowing dissolved nutrients appear to be assimilated by diatoms during the spring and become available for the summer Aphanizomenon flos-aquae bloom when the diatoms senesce. Thus, nutrient-enriched GW inflow and nutrient recycling by successive algal blooms must be considered when evaluating internal nutrient loading to lakes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2020.142768","usgsCitation":"Essaid, H.I., Kuwabara, J.S., Corson-Dosch, N., Carter, J.L., and Topping, B.R., 2021, Evaluating the dynamics of groundwater, lakebed transport, nutrient inflow and algal blooms in Upper Klamath Lake, Oregon, USA: Science of the Total Environment, v. 765, 142768, 16 p., https://doi.org/10.1016/j.scitotenv.2020.142768.","productDescription":"142768, 16 p.","ipdsId":"IP-115458","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":436656,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98C5H5N","text":"USGS data release","linkHelpText":"MODFLOW, MT3D-USGS and VS2DH simulations used to estimate groundwater and nutrient inflow to Upper Klamath Lake, Oregon"},{"id":383225,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Klamath Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.10273742675781,\n 42.21987327563142\n ],\n [\n -121.79374694824219,\n 42.21987327563142\n ],\n [\n -121.79374694824219,\n 42.6026307853624\n ],\n [\n -122.10273742675781,\n 42.6026307853624\n ],\n [\n -122.10273742675781,\n 42.21987327563142\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"765","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Essaid, Hedeff I. 0000-0003-0154-8628 hiessaid@usgs.gov","orcid":"https://orcid.org/0000-0003-0154-8628","contributorId":2284,"corporation":false,"usgs":true,"family":"Essaid","given":"Hedeff","email":"hiessaid@usgs.gov","middleInitial":"I.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":810172,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kuwabara, James S. 0000-0003-2502-1601 kuwabara@usgs.gov","orcid":"https://orcid.org/0000-0003-2502-1601","contributorId":3374,"corporation":false,"usgs":true,"family":"Kuwabara","given":"James","email":"kuwabara@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":810173,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Corson-Dosch, Nicholas 0000-0002-6776-6241","orcid":"https://orcid.org/0000-0002-6776-6241","contributorId":202630,"corporation":false,"usgs":true,"family":"Corson-Dosch","given":"Nicholas","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":810174,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carter, James L. 0000-0002-0104-9776","orcid":"https://orcid.org/0000-0002-0104-9776","contributorId":215951,"corporation":false,"usgs":true,"family":"Carter","given":"James","email":"","middleInitial":"L.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":810175,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Topping, Brent R. 0000-0002-7887-4221 btopping@usgs.gov","orcid":"https://orcid.org/0000-0002-7887-4221","contributorId":1484,"corporation":false,"usgs":true,"family":"Topping","given":"Brent","email":"btopping@usgs.gov","middleInitial":"R.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":810176,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}