Magma intrusion rate is a key parameter in eruption triggering but is poorly quantified in existing geodetic studies. Here we examine two episodes of rapid inflation in this context. Two noneruptive microseismic swarms were recorded at Semisopochnoi Volcano, Alaska in 2014–2015. We use differential SAR techniques and TerraSAR‐X images to document surface deformation from 2011 to 2015, which comprises island‐wide radial inflation totaling ~25 cm (+/−1 cm) line of sight displacement in 2014–2015. Multiple source geometries are tested in an inversion of the deformation data, and InSAR data are best fit by a spheroid trending to the northeast and plunging to the southeast, with a major axis of ~4 km and minor axes of ~1 km, directly under the central caldera of Semisopochnoi. In 2014, a modeled influx of 0.043 km3 of magma caused line of sight displacement of ~17 cm. This magma was stored at a depth of ~8 km, until 2015 when 0.029 km3 was added. Along with the definition of inflation source parameters, the recorded seismic events are relocated using differential travel times. These relocated events outline a linear aseismic area within a larger zone of shallow (<10 km) seismicity. This aseismic region aligns with the centroid of the deformation model. Based on these geodetic and seismic models, the plumbing system at Semisopochnoi is interpreted as a spheroidal magma storage zone at a depth of ˜8 km below a linear feature of partial melt. The observed deformation and seismicity appear to result from rapid injection into this main storage region.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GC008720","usgsCitation":"Degrandpre, K., Pesicek, J.D., Lu, Z., DeShon, H.R., and Roman, D., 2019, High rates of inflation during a noneruptive episode of seismic unrest at Semisopochnoi Volcano, Alaska in 2014–2015: Journal of Geophysical Research, v. 20, no. 12, p. 6163-6186, https://doi.org/10.1029/2019GC008720.","productDescription":"24 p.","startPage":"6163","endPage":"6186","ipdsId":"IP-098799","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":380068,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Semisopochnoi Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 179.47265625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 51.839171715043946\n ],\n [\n 179.77203369140625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 52.04742324502936\n ],\n [\n 179.47265625,\n 51.839171715043946\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"20","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Degrandpre, Kimberly","contributorId":244311,"corporation":false,"usgs":false,"family":"Degrandpre","given":"Kimberly","email":"","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pesicek, Jeremy D. 0000-0001-7964-5845","orcid":"https://orcid.org/0000-0001-7964-5845","contributorId":202042,"corporation":false,"usgs":true,"family":"Pesicek","given":"Jeremy","email":"","middleInitial":"D.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":803747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lu, Zhong","contributorId":199794,"corporation":false,"usgs":false,"family":"Lu","given":"Zhong","affiliations":[],"preferred":false,"id":803748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"DeShon, Heather R.","contributorId":244313,"corporation":false,"usgs":false,"family":"DeShon","given":"Heather","email":"","middleInitial":"R.","affiliations":[{"id":20301,"text":"SMU","active":true,"usgs":false}],"preferred":false,"id":803749,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":803777,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215201,"text":"70215201 - 2019 - Geometric targets for UAS Lidar","interactions":[],"lastModifiedDate":"2020-10-13T22:43:27.669449","indexId":"70215201","displayToPublicDate":"2019-12-14T11:14:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Geometric targets for UAS Lidar","docAbstract":"Lidar from small unoccupied aerial systems (UAS) is a viable method for collecting geospatial data associated with a wide variety of applications. Point clouds from UAS lidar require a means for accuracy assessment, calibration, and adjustment. In order to carry out these procedures, specific locations within the point cloud must be precisely found. To do this, artificial targets may be used for rural settings, or anywhere there is a lack of identifiable and measurable features in the scene. This paper presents the design of lidar targets for precise location based on geometric structure. The targets and associated mensuration algorithm were tested in two scenarios to investigate their performance under different point densities, and different levels of algorithmic rigor. The results show that the targets can be accurately located within point clouds from typical scanning parameters to <2 cm
The development of mercury (Hg) stable isotope measurements has enhanced the study of Hg sources and transformations in the environment. As a result of the mixing of inorganic Hg (iHg) and methylmercury (MeHg) species within organisms of the aquatic food web, understanding species-specific Hg stable isotopic compositions is of significant importance. The lack of MeHg isotope measurements is due to the analytical difficulty in the separation of the MeHg from the total Hg pool, with only a few methods having been tested over the past decade with varying degrees of success, and only a handful of environmentally relevant measurements. Here, we present a novel anion-exchange resin separation method using AG 1-X4 that further isolates MeHg from the sample matrix, following a distillation pretreatment, in order to obtain ambient MeHg stable isotopic compositions. This method avoids the use of organic reagents, does not require complex instrumentation, and is applicable across matrices. Separation tests across sediment, water, and biotic matrices showed acceptable recoveries (98 ± 5%, n = 54) and reproducible δ202Hg isotope results (2 SDs ≤ 0.15‰) down to 5 ng of MeHg. The measured MeHg pools in natural matrices, such as plankton and sediments, showed large deviations from the non-speciated total Hg measurement, indicating that there is an important isotopic shift during methylation that is not recorded by typical measurements, but is vital in order to assess sources of Hg during bioaccumulation.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Worldwide, human activities have modified hydrology and nutrient loading regimes in coastal wetlands. Understanding the interplay between these drivers and subsequent response of wetland plant communities is essential to informing wetland management and restoration efforts. Recent restoration strategies in Louisiana proposes to use sediment diversions from the Mississippi River to build land in adjacent wetlands and reduce the rate of land to open water conversion. In conjunction with sediment delivery, diversions can increase nutrient loads and water levels in the receiving basins. We conducted a greenhouse mesocosm experiment in which we exposed three common tidal freshwater and brackish marsh plants (Panicum hemitomon, Sagittaria lancifolia, and Spartina patens) to two nitrate loading rates [high (35 g N m2 year−1) and low (0.25 g N m2 year−1)], and two flooding treatments (with and without diversion pulsing). Experimental units were set at two different elevations within the treatment tanks to simulate both a healthy and degraded marsh. Plant growth metrics and soil physicochemical properties were measured monthly. Final total biomass was determined at the study’s conclusion. Growth responses differed between species but were not significantly influenced by the treatments. Soil redox potential decreased significantly following the increase in flooding associated with the diversion pulse, but recovered to pre-diversion levels after a 3-month recovery period. Our study suggests short flooding pulses with a recovery period may be key for maintaining healthy marshes, however there remains a need for longer-term empirical studies to understand marsh response to pressures associated with river sediment diversions over time.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-019-09699-8","usgsCitation":"McCoy, M.M., Sloey, T.M., Howard, R.J., and Hester, M.W., 2019, Response of tidal marsh vegetation to pulsed increases in flooding and nitrogen: Wetlands Ecology and Management, v. 28, p. 119-135, https://doi.org/10.1007/s11273-019-09699-8.","productDescription":"17 p.","startPage":"119","endPage":"135","ipdsId":"IP-106945","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":370302,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana","otherGeospatial":"Jean Lafitte National Historical Park and Preserve, Lake Salvador Wildlife Management Area ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 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M","contributorId":149516,"corporation":false,"usgs":false,"family":"Sloey","given":"Taylor","email":"","middleInitial":"M","affiliations":[{"id":17763,"text":"University of Louisiana, Lafayette","active":true,"usgs":false}],"preferred":false,"id":777557,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Howard, Rebecca J. 0000-0001-7264-4364 howardr@usgs.gov","orcid":"https://orcid.org/0000-0001-7264-4364","contributorId":2429,"corporation":false,"usgs":true,"family":"Howard","given":"Rebecca","email":"howardr@usgs.gov","middleInitial":"J.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":455,"text":"National Wetlands Research Center","active":true,"usgs":true}],"preferred":true,"id":777555,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hester, Mark W.","contributorId":195572,"corporation":false,"usgs":false,"family":"Hester","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":34316,"text":"University of Louisiana at Lafayette, Lafayette, LA, USA","active":true,"usgs":false}],"preferred":false,"id":777558,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70250490,"text":"70250490 - 2019 - Yukon-Kuskokwim Delta Berry Outlook: Final Report","interactions":[],"lastModifiedDate":"2023-12-13T12:49:20.331267","indexId":"70250490","displayToPublicDate":"2019-12-13T06:49:00","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Yukon-Kuskokwim Delta Berry Outlook: Final Report","docAbstract":"No abstract available.
","language":"English","publisher":"U.S. Geological Survey","collaboration":"Western Alaska Landscape Conservation Cooperative","usgsCitation":"Herman-Mercer, N.M., and Loehman, R.A., 2019, Yukon-Kuskokwim Delta Berry Outlook: Final Report, v, 46 p.","productDescription":"v, 46 p.","ipdsId":"IP-098985","costCenters":[{"id":37316,"text":"WMA - Integrated Information Dissemination Division","active":true,"usgs":true}],"links":[{"id":423509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":423504,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.sciencebase.gov/catalog/item/5ca655d1e4b0c3b0064c2703"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -167.41101641468245,\n 64.14447313670226\n ],\n [\n -167.41101641468245,\n 59.43905568894709\n ],\n [\n -157.8962443697305,\n 59.43905568894709\n ],\n [\n -157.8962443697305,\n 64.14447313670226\n ],\n [\n -167.41101641468245,\n 64.14447313670226\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Herman-Mercer, Nicole M. 0000-0001-5933-4978 nhmercer@usgs.gov","orcid":"https://orcid.org/0000-0001-5933-4978","contributorId":3927,"corporation":false,"usgs":true,"family":"Herman-Mercer","given":"Nicole","email":"nhmercer@usgs.gov","middleInitial":"M.","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":890134,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loehman, Rachel A. 0000-0001-7680-1865 rloehman@usgs.gov","orcid":"https://orcid.org/0000-0001-7680-1865","contributorId":187605,"corporation":false,"usgs":true,"family":"Loehman","given":"Rachel","email":"rloehman@usgs.gov","middleInitial":"A.","affiliations":[{"id":118,"text":"Alaska Science Center Geography","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":false,"id":890135,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70207510,"text":"70207510 - 2019 - Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model","interactions":[],"lastModifiedDate":"2019-12-22T14:03:15","indexId":"70207510","displayToPublicDate":"2019-12-12T14:00:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1818,"text":"Geoscientific Model Development","active":true,"publicationSubtype":{"id":10}},"title":"Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model","docAbstract":"Ecosystem dynamic models are useful for understanding ecosystem characteristics over time and space because of their efficiency over direct field measurements and applicability to broad spatial extents. Their application, however, is challenging due to internal model uncertainties and complexities arising from distinct qualities of the ecosystems being analyzed. The sagebrush-steppe in western North America, for example, has substantial spatial and temporal heterogeneity as well as variability due to anthropogenic disturbance, invasive species, climate change, and altered fire regimes, which collectively make modelling dynamic ecosystem processes difficult. Ecosystem Demography (EDv2.2) is a robust ecosystem dynamic model, initially developed for tropical forests, that simulates energy, water, and carbon fluxes at fine scales. Although EDv2.2 has since been tested on different ecosystems via development of different Plant Function Types (PFT), it still lacks a shrub PFT. In this study, we developed and parameterized a shrub PFT representative of sagebrush (Artemisia spp.) ecosystems in order to initialize and test it within EDv2.2, and to promote future broad-scale analysis of restoration activities, climate change, and fire regimes in the sagebrush-steppe. Specifically, we parameterized the sagebrush PFT within EDv2.2 to estimate gross primary production (GPP), using data from two sagebrush study sites in the northern Great Basin. To accomplish this, we employed a three-tier approach: 1) To initially parameterize the sagebrush PFT, we fitted allometric relationships for sagebrush using field-collected data, information from existing sagebrush literature, and parameters from other land models. 2) To determine influential parameters in GPP prediction, we used a sensitivity analysis to identify the five most sensitive parameters. 3) To improve model performance and validate results, we optimized these five parameters using an exhaustive search method to estimate GPP, and compared results with observations from two Eddy Covariance (EC) sites in the study area. Our modeled results were encouraging, with reasonable fidelity to observed values, although some negative biases (i.e., seasonal underestimates of GPP) were apparent.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/gmd-12-4585-2019","usgsCitation":"Pandit, K., Dasthi, H., Glenn, N., Flores, A., Maguire, K.C., Shinneman, D.J., Flerchinger, G., and Fellow, A., 2019, Developing and optimizing shrub parameters representing sagebrush (Artemisia spp.) ecosystems in the Northern Great Basin using the Ecosystem Demography (EDv2.2) model: Geoscientific Model Development, v. 12, p. 4585-4601, https://doi.org/10.5194/gmd-12-4585-2019.","productDescription":"17 p.","startPage":"4585","endPage":"4601","ipdsId":"IP-102648","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":458969,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-12-4585-2019","text":"Publisher Index Page"},{"id":370607,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.56347656249999,\n 42.032974332441405\n ],\n [\n -118.16894531249999,\n 35.35321610123823\n ],\n [\n -112.2802734375,\n 34.59704151614417\n ],\n [\n -109.248046875,\n 38.37611542403604\n ],\n [\n -110.0830078125,\n 43.13306116240612\n ],\n [\n -112.8955078125,\n 44.02442151965934\n ],\n [\n -115.6201171875,\n 43.58039085560784\n ],\n [\n -119.35546875000001,\n 44.15068115978094\n ],\n [\n -121.025390625,\n 44.08758502824516\n ],\n [\n -122.56347656249999,\n 42.032974332441405\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-11-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pandit, Karun","contributorId":221464,"corporation":false,"usgs":false,"family":"Pandit","given":"Karun","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778308,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dasthi, Hamid","contributorId":221465,"corporation":false,"usgs":false,"family":"Dasthi","given":"Hamid","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778309,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Glenn, Nancy","contributorId":181558,"corporation":false,"usgs":false,"family":"Glenn","given":"Nancy","affiliations":[],"preferred":false,"id":778310,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Flores, Alejandro","contributorId":221466,"corporation":false,"usgs":false,"family":"Flores","given":"Alejandro","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":778311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Maguire, Kaitlin C. 0000-0001-8193-2384","orcid":"https://orcid.org/0000-0001-8193-2384","contributorId":203419,"corporation":false,"usgs":true,"family":"Maguire","given":"Kaitlin","email":"","middleInitial":"C.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778312,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":778307,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Flerchinger, Gerald","contributorId":221467,"corporation":false,"usgs":false,"family":"Flerchinger","given":"Gerald","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":778313,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fellow, Aaron","contributorId":221468,"corporation":false,"usgs":false,"family":"Fellow","given":"Aaron","email":"","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":778314,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70210612,"text":"70210612 - 2019 - Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system","interactions":[],"lastModifiedDate":"2020-06-12T17:22:56.373722","indexId":"70210612","displayToPublicDate":"2019-12-12T12:19:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5966,"text":"Biomolecules","active":true,"publicationSubtype":{"id":10}},"title":"Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system","docAbstract":"The variable lymphocyte receptors (VLRs) consist of leucine rich repeats (LRRs) and comprise the humoral antibodies produced by lampreys and hagfishes. The diversity of the molecules is generated by stepwise genomic rearrangements of LRR cassettes dispersed throughout the VLRB locus. Previously, target-specific monovalent VLRB antibodies were isolated from sea lamprey larvae after immunization with model antigens. Further, the cloned VLR cDNAs from activated lamprey leukocytes were transfected into human cell lines or yeast to select best binders. Here, we expand on the overall utility of the VLRB technology by introducing it into a filamentous phage display system. We first tested the efficacy of isolating phage into which known VLRB molecules were cloned after a series of dilutions. These experiments showed that targeted VLRB clones could easily be recovered even after extensive dilutions (1 to 109). We further utilized the system to isolate target-specific “lampribodies” from phage display libraries from immunized animals and observed an amplification of binders with relative high affinities by competitive binding. The lampribodies can be individually purified and ostensibly utilized for applications for which conventional monoclonal antibodies are employed.
","language":"English","publisher":"MDPI","doi":"10.3390/biom9120868","usgsCitation":"Hassan, K.M., Hansen, J.D., Herrin, B.R., and Amemiya, C.T., 2019, Generation of lamprey monoclonal antibodies (Lampribodies) using the phage display system: Biomolecules, v. 9, no. 12, 868, 18 p., https://doi.org/10.3390/biom9120868.","productDescription":"868, 18 p.","ipdsId":"IP-107248","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":458972,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/biom9120868","text":"Publisher Index Page"},{"id":375562,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"12","noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Hassan, Khan M A","contributorId":225255,"corporation":false,"usgs":false,"family":"Hassan","given":"Khan","email":"","middleInitial":"M A","affiliations":[{"id":41083,"text":"University of California-Merced, Molecular Cell Biology, Merced CA 95343","active":true,"usgs":false}],"preferred":false,"id":790844,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, John D. 0000-0002-3006-2734","orcid":"https://orcid.org/0000-0002-3006-2734","contributorId":220725,"corporation":false,"usgs":true,"family":"Hansen","given":"John","middleInitial":"D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":790845,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrin, Brantley R","contributorId":225256,"corporation":false,"usgs":false,"family":"Herrin","given":"Brantley","email":"","middleInitial":"R","affiliations":[{"id":41084,"text":"Emory University, Department of Pathology and Laboratory Medicine, Atlanta GA 30322 USA","active":true,"usgs":false}],"preferred":false,"id":790846,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amemiya, Chris T","contributorId":225257,"corporation":false,"usgs":false,"family":"Amemiya","given":"Chris","email":"","middleInitial":"T","affiliations":[{"id":41083,"text":"University of California-Merced, Molecular Cell Biology, Merced CA 95343","active":true,"usgs":false}],"preferred":false,"id":790847,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211501,"text":"70211501 - 2019 - Improved genetic identification of acipenseriform embryos with application to the endangered pallid sturgeon Scaphirhynchus albus","interactions":[],"lastModifiedDate":"2020-07-29T14:47:44.047032","indexId":"70211501","displayToPublicDate":"2019-12-12T09:46:04","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2285,"text":"Journal of Fish Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Improved genetic identification of acipenseriform embryos with application to the endangered pallid sturgeon Scaphirhynchus albus","title":"Improved genetic identification of acipenseriform embryos with application to the endangered pallid sturgeon Scaphirhynchus albus","docAbstract":"We produced pallid sturgeon Scaphirhynchus albus embryos at five pre‐hatch developmental stages and isolated and quantified genomic DNA from four of the stages using four commercial DNA isolation kits. Genomic DNA prepared using the kit that produced the largest yields and concentrations were used for microsatellite DNA analyses of 10–20 embryos at each of the five developmental stages. We attempted to genotype the hatchery‐produced embryos at 19 microsatellite loci and confirmed reliable genotyping by comparing the microsatellite genotypes to those of known parents. Embryos at stages 5 and 8 did not produce reliable genotyping while those at stages 14, 24 and 33 did. We used the same DNA isolation method on 262 wild‐caught acipenseriform embryos collected from the lower Yellowstone River. A total of 200 of the wild embryos were successfully identified to stages 8 to 34 and the rest could not be staged. Using a combination of single nucleotide polymorphism and microsatellite markers, 249 of the wild‐caught embryos were genetically identified as paddlefish Polyodon spathula , five were identified as shovelnose sturgeon Scaphirhynchus platorynchus and eight failed to amplify. None were identified as pallid sturgeon. This study demonstrates that early‐stage wild‐spawned acipenseriform embryos can be genetically identified less than 24 h post‐spawn. This methodology will be useful for recovery efforts for endangered pallid sturgeon and can be applied to other acipenseriform species.","language":"English","publisher":"Wiley","doi":"10.1111/jfb.14230","usgsCitation":"Kashiwagi, T., Delonay, A.J., Braaten, P., Chojnacki, K., Gocker, R.M., and Heist, E.J., 2019, Improved genetic identification of acipenseriform embryos with application to the endangered pallid sturgeon Scaphirhynchus albus: Journal of Fish Biology, v. 96, no. 2, p. 486-495, https://doi.org/10.1111/jfb.14230.","productDescription":"10 p.","startPage":"486","endPage":"495","ipdsId":"IP-111396","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":376841,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-01-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Kashiwagi, Tom","contributorId":236836,"corporation":false,"usgs":false,"family":"Kashiwagi","given":"Tom","email":"","affiliations":[{"id":47549,"text":"Center for Fisheries Aquaculture and Aquatic Sciences, Southern Illinois University Carbondale, Carbondale, IL","active":true,"usgs":false}],"preferred":false,"id":794373,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeLonay, Aaron J. 0000-0002-3752-2799 adelonay@usgs.gov","orcid":"https://orcid.org/0000-0002-3752-2799","contributorId":2725,"corporation":false,"usgs":true,"family":"DeLonay","given":"Aaron","email":"adelonay@usgs.gov","middleInitial":"J.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":794374,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Braaten, Patrick 0000-0003-3362-420X pbraaten@usgs.gov","orcid":"https://orcid.org/0000-0003-3362-420X","contributorId":152682,"corporation":false,"usgs":true,"family":"Braaten","given":"Patrick","email":"pbraaten@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":794375,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chojnacki, Kimberly 0000-0001-6091-3977 kchojnacki@usgs.gov","orcid":"https://orcid.org/0000-0001-6091-3977","contributorId":221080,"corporation":false,"usgs":true,"family":"Chojnacki","given":"Kimberly","email":"kchojnacki@usgs.gov","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":794376,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gocker, Rachel M.","contributorId":236837,"corporation":false,"usgs":false,"family":"Gocker","given":"Rachel","email":"","middleInitial":"M.","affiliations":[{"id":47549,"text":"Center for Fisheries Aquaculture and Aquatic Sciences, Southern Illinois University Carbondale, Carbondale, IL","active":true,"usgs":false}],"preferred":false,"id":794377,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heist, Edward J.","contributorId":221082,"corporation":false,"usgs":false,"family":"Heist","given":"Edward","email":"","middleInitial":"J.","affiliations":[{"id":40317,"text":"Southern Illinois University, Fisheries and Illinois Aquaculture Center","active":true,"usgs":false}],"preferred":false,"id":794378,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208062,"text":"70208062 - 2019 - High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data","interactions":[],"lastModifiedDate":"2020-01-29T16:34:42","indexId":"70208062","displayToPublicDate":"2019-12-12T07:32:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3250,"text":"Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data","docAbstract":"The areas characterized by dynamic and rapid morphological changes need accurate topography information with frequent updates, especially if these are populated and involve infrastructures. This is particularly true in active volcanic areas such as Mount (Mt.) Etna, located in the northeastern portion of Sicily, Italy. The Mt. Etna volcano is periodically characterized by explosive and effusive eruptions and represents a potential hazard for several thousands of local people and hundreds of tourists present on the volcano itself. In this work, a high-resolution, high vertical accuracy digital surface model (DSM) of Mt. Etna was derived from Pleiades satellite data using the National Aeronautics and Space Administration (NASA) Ames Stereo Pipeline (ASP) tool set. We believe that this is the first time that the ASP using Pleiades imagery has been applied to Mt. Etna with sub-meter vertical root mean square error (RMSE) results. The model covers an area of about 400 km2 with a spatial resolution of 2 m and centers on the summit portion of the volcano. The model was validated by using a set of reference ground control points (GCP) obtaining a vertical RMSE of 0.78 m. The described procedure provides an avenue to obtain DSMs at high spatial resolution and elevation accuracy in a relatively short amount of processing time, making the procedure itself suitable to reproduce topographies often indispensable during the emergency management case of volcanic eruptions.
","language":"English","publisher":"MDPI","doi":"10.3390/rs11242983","usgsCitation":"Palaseanu-Lovejoy, M., Bisson, M., Spinetti, C., Buongiorno, M.F., Alexandrov, O., and Cecere, T., 2019, High-resolution and accurate topography reconstruction of Mount Etna from Pleiades satellite data: Remote Sensing, v. 11, no. 24, 2983, 17 p., https://doi.org/10.3390/rs11242983.","productDescription":"2983, 17 p.","ipdsId":"IP-112349","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":458977,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs11242983","text":"Publisher Index Page"},{"id":437261,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IGLDYE","text":"USGS data release","linkHelpText":"Digital Surface Model of Mt. Etna, Italy, derived from 2015 Pleiades Satellite Imagery"},{"id":371637,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Italy","otherGeospatial":"Mount Etna","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 14.84733581542969,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.62238973852369\n ],\n [\n 15.128173828125,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.84015683604136\n ],\n [\n 14.84733581542969,\n 37.62238973852369\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"24","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-12","publicationStatus":"PW","contributors":{"authors":[{"text":"Palaseanu-Lovejoy, Monica 0000-0002-3786-5118 mpal@usgs.gov","orcid":"https://orcid.org/0000-0002-3786-5118","contributorId":3639,"corporation":false,"usgs":true,"family":"Palaseanu-Lovejoy","given":"Monica","email":"mpal@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":5061,"text":"National Cooperative Geologic Mapping and Landslide Hazards","active":true,"usgs":true},{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":780322,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bisson, Marina 0000-0002-7104-9210","orcid":"https://orcid.org/0000-0002-7104-9210","contributorId":221724,"corporation":false,"usgs":false,"family":"Bisson","given":"Marina","email":"","affiliations":[{"id":40408,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Pisa, via Della Faggiola, Pisa, 56126, Italy","active":true,"usgs":false}],"preferred":false,"id":780323,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Spinetti, Claudia 0000-0002-1861-5666","orcid":"https://orcid.org/0000-0002-1861-5666","contributorId":221725,"corporation":false,"usgs":false,"family":"Spinetti","given":"Claudia","email":"","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780324,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Buongiorno, Maria Fabrizia 0000-0002-6095-6974","orcid":"https://orcid.org/0000-0002-6095-6974","contributorId":221726,"corporation":false,"usgs":false,"family":"Buongiorno","given":"Maria","email":"","middleInitial":"Fabrizia","affiliations":[{"id":40409,"text":"Istituto Nazionale di Geofisica e Vulcanologia, Sezione ONT, via di Vigna Murata, Roma, 00143, Italy","active":true,"usgs":false}],"preferred":false,"id":780325,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Alexandrov, Oleg","contributorId":167662,"corporation":false,"usgs":false,"family":"Alexandrov","given":"Oleg","email":"","affiliations":[{"id":24796,"text":"NASA Ames Research Center","active":true,"usgs":false}],"preferred":false,"id":780326,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cecere, Thomas 0000-0001-5254-8404 tcecere@usgs.gov","orcid":"https://orcid.org/0000-0001-5254-8404","contributorId":221727,"corporation":false,"usgs":true,"family":"Cecere","given":"Thomas","email":"tcecere@usgs.gov","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":780327,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70208616,"text":"70208616 - 2019 - Earthquakes, ShakeMap","interactions":[],"lastModifiedDate":"2020-02-21T06:58:05","indexId":"70208616","displayToPublicDate":"2019-12-12T06:57:38","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Earthquakes, ShakeMap","docAbstract":"ShakeMap® is an open-source software program employed to automatically produce a suite of maps and products that portray the geographical extent and severity of potentially damaging shaking following an earthquake. ShakeMap’s primary purpose is to provide post-earthquake situational awareness for emergency management and response as well as damage and loss estimation. The availability of ShakeMaps immediately after a significant earthquake is critical for the identification of areas likely to be most damaged. Principal users include first responders, utility companies, response and aid agencies, scientists and engineers, and the media. Maps are made publicly available via the Internet within several minutes of an earthquake’s occurrence. ShakeMap is widely deployed in seismically active, well-instrumented portions of the USA and internationally in numerous countries including Italy, Iceland, Greece, Costa Rica, and Switzerland, among others, and the US...
A recent geological record of inundation by tsunamis or storm surges is evidenced by deposits found within the first few meters of the modern surface at five wetlands on the northern California coast. The study sites include three locations in the Crescent City area (Marhoffer Creek marsh, Elk Creek wetland, and Sand Mine marsh), O’rekw marsh in the lower Redwood Creek alluvial valley, and Pillar Point marsh at the northern end of Half Moon Bay.
Contact Information
Pacific Coastal & Marine Science Center
U.S. Geological Survey
Pacific Science Center
2885 Mission St.
Santa Cruz, CA 95060
White-tailed deer (Odocoileus virginianus) are among the most impactful herbivores in the eastern United States. Legacy forest effects, those accrued from intense herbivory over time, manifest as low seedling regeneration, high cover of plant species that are infrequently browsed by deer, presence or expansion of nonnative or invasive plant species, few herbaceous species, and diminished capacity for recovery. Interfering vegetation (that is, species that increase in cover and density due to avoidance by deer, such as American beech sprouts, Pennsylvania sedge, and hay-scented fern) increase competition for light and hinder recruitment of trees into the forest canopy.
The lower Hudson Valley in New York has been heavily browsed by white-tailed deer since the early 20th century. The region has some of the lowest tree regeneration rates in New York State as a result of deer browsing and subsequent increases in interfering vegetation. The U.S. Geological Survey and the State University of New York College of Environmental Science and Forestry studied sites where deer hunting is permitted (case sites) and nearby sites where hunting is currently prohibited (control sites) to assess and identify forest structure and composition differences.
Instead of using deer exclosures, which are time-consuming and expensive to install and maintain, we used a case-control study because such studies are well-suited to effects with long latency and rare outcomes. Case-control studies seek to describe the relation between an outcome of interest (in this study, forest understory recovery from chronic herbivory) and forest condition. We inferred recovery by comparing these characteristics on adjacent sites in the lower Hudson Valley with similar forest communities and land uses but different deer population management histories. Case plots were on lands where deer management has taken place annually for several decades. Control plots were on lands where deer populations have not been consistently managed to lowered abundance. We accounted for differences in forest recovery not attributable to deer by first matching case and control plots along several important environmental gradients (slope, aspect, elevation, moisture, canopy openness). By controlling for these gradients, we looked for associations between measured forest conditions and deer herbivory reduction through population management.
We surveyed more than 200 plots in upland forest types across case and control sites where we assessed forest condition by estimating density (number per unit area) and composition and cover (percent) of important vegetation constituents in ground, shrub, subcanopy, and canopy layers of the forest. We recorded 37 tree species, 22 shrub species, 57 herbaceous species, and 19 species of grasses and sedges in our plot surveys, including a number of nonnative and invasive plants. We also estimated the ages of a number of common canopy trees by counting rings from cores extracted from individual stems.
Effects of more than 100 years of chronic deer browsing manifested in low herbaceous ground cover and little to no tree recruitment (saplings) on lands without deer management. In contrast, sustained deer management resulted in forests with conditions that indicated substantial recovery from chronic herbivory in the ground, shrub, and subcanopy layers. Sites with ongoing deer management exhibited greater ground cover of tree seedlings and herbs and less ground cover of interfering vegetation and nonnative species. The well-developed sub-canopy layer of small trees, saplings, and tall shrubs on sites with deer management indicates a high potential for sapling recruitment to the canopy of the future forest.
Of the 25 subcanopy trees sampled on control sites, most were more than 100 years old, indicating little to no regeneration in areas sampled for more than 100 years. The forest canopy, a relic of land uses of bygone days, requires a source of young trees to replace itself as older trees die. Without an abundant layer of young trees in the subcanopy, a forest cannot be sustained over time. Reduction in deer herbivory promotes forest recovery and could benefit Harriman and Bear Mountain State Parks (the control sites for the study), but removal of interfering vegetation may be necessary to mitigate legacy effects where they currently hinder ground layer recovery. To successfully promote a more desirable forest condition that includes elimination of nonnative plant species, promotion of tree recruitment into the forest canopy, and development of diverse and abundant herbaceous cover in ground layer vegetation, future management decisions could include information on herbivory reduction and management of interfering vegetation where necessary.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191116","collaboration":"Prepared in cooperation with the New York State Parks, Recreation, and Historic Preservation","usgsCitation":"Kilheffer, C.R., Underwood, H.B., Leopold, D.J., and Guerrieri, R., 2019, Evaluating legacy effects of hyperabundant white-tailed deer (Odocoileus virginianus) in forested stands of Harriman and Bear Mountain State Parks, New York: U.S. Geological Survey Open-File Report 2019–1116, 36 p., https://doi.org/10.3133/ofr20191116.","productDescription":"Report: viii, 35 p.; Dataset","numberOfPages":"48","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111113","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":369987,"rank":2,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.7910/DVN/3ZFKFS","linkFileType":{"id":5,"text":"html"},"linkHelpText":"- Data for evaluation of effects of white-tailed deer at Harriman and Bear Mountain State Parks, New York"},{"id":370098,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1116/ofr20191116.pdf","text":"Report","size":"16.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1116"},{"id":369982,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1116/coverthb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"Harriman, Bear Mountain State Parks","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-73.9525,41.59],[-73.9526,41.5841],[-73.9578,41.5751],[-73.9685,41.562],[-73.9866,41.5472],[-73.9948,41.5374],[-73.9975,41.526],[-73.9985,41.4788],[-74.0002,41.4543],[-73.9997,41.4498],[-73.9955,41.4475],[-73.9869,41.4451],[-73.9828,41.4401],[-73.9703,41.4222],[-73.9643,41.4135],[-73.9573,41.4016],[-73.9495,41.3947],[-73.9507,41.3916],[-73.9546,41.3834],[-73.9586,41.3699],[-73.9621,41.3472],[-73.9653,41.3432],[-73.9696,41.3391],[-73.9765,41.3338],[-73.9821,41.3279],[-73.9834,41.3248],[-73.9829,41.3212],[-73.971,41.306],[-73.9601,41.2982],[-73.9474,41.2921],[-73.9444,41.2907],[-73.9439,41.288],[-73.9476,41.2853],[-73.9638,41.271],[-73.9694,41.2652],[-73.9726,41.2616],[-73.9732,41.2584],[-73.9739,41.2552],[-73.9722,41.2511],[-73.9668,41.247],[-73.959,41.2378],[-73.9334,41.2057],[-73.9222,41.1888],[-73.9104,41.1705],[-73.8922,41.1417],[-73.8905,41.1321],[-73.892,41.0968],[-73.892,41.0677],[-73.8899,41.0523],[-73.8936,40.9965],[-73.9015,40.9976],[-73.9057,40.9994],[-73.9841,41.0339],[-74.0005,41.0409],[-74.0246,41.0521],[-74.1533,41.1098],[-74.2129,41.1344],[-74.2253,41.1395],[-74.2338,41.1431],[-74.3179,41.1791],[-74.3259,41.1823],[-74.3343,41.1864],[-74.3677,41.2033],[-74.3831,41.2111],[-74.4101,41.2248],[-74.5363,41.284],[-74.605,41.3152],[-74.6492,41.3359],[-74.6955,41.3576],[-74.6913,41.3598],[-74.6901,41.3621],[-74.69,41.3639],[-74.6912,41.3662],[-74.6934,41.3683],[-74.6938,41.3688],[-74.6962,41.3713],[-74.6985,41.373],[-74.7011,41.3753],[-74.7064,41.3803],[-74.7105,41.3842],[-74.7126,41.3866],[-74.7137,41.389],[-74.7154,41.3917],[-74.7175,41.3929],[-74.7205,41.3947],[-74.7247,41.3958],[-74.7278,41.3963],[-74.732,41.3973],[-74.7349,41.3987],[-74.7376,41.4003],[-74.7392,41.4025],[-74.7409,41.4066],[-74.7421,41.4094],[-74.7419,41.4103],[-74.7415,41.4123],[-74.7412,41.4145],[-74.7405,41.4166],[-74.7391,41.4197],[-74.7384,41.4229],[-74.7376,41.4261],[-74.7389,41.4286],[-74.7408,41.4298],[-74.7438,41.4305],[-74.7461,41.4303],[-74.7487,41.4287],[-74.7506,41.4274],[-74.7523,41.4328],[-74.7535,41.4373],[-74.7559,41.4401],[-74.7589,41.4451],[-74.7601,41.4501],[-74.7588,41.4573],[-74.7557,41.4614],[-74.7514,41.4659],[-74.7513,41.4686],[-74.7537,41.4741],[-74.7579,41.4814],[-74.7597,41.4868],[-74.7591,41.4896],[-74.756,41.4923],[-74.7541,41.4945],[-74.5928,41.4989],[-74.4781,41.5031],[-74.4743,41.5085],[-74.4688,41.5139],[-74.4668,41.522],[-74.4599,41.5302],[-74.4488,41.5364],[-74.4469,41.5423],[-74.4338,41.5545],[-74.4276,41.5589],[-74.4201,41.5666],[-74.4084,41.5724],[-74.3985,41.5778],[-74.3941,41.5809],[-74.3867,41.5854],[-74.3749,41.5889],[-74.3675,41.5916],[-74.3583,41.5938],[-74.3521,41.5982],[-74.3404,41.5954],[-74.3187,41.6084],[-74.3156,41.6115],[-74.2989,41.6182],[-74.281,41.6257],[-74.2754,41.6284],[-74.2667,41.6324],[-74.2606,41.6337],[-74.2502,41.6291],[-74.25,41.6059],[-74.2458,41.6036],[-74.1907,41.5913],[-74.187,41.5908],[-74.1858,41.5944],[-74.1282,41.5833],[-74.1325,41.6152],[-74.1246,41.6133],[-74.0983,41.6089],[-74.0886,41.5988],[-74.0677,41.604],[-74.0575,41.5926],[-74.0521,41.5816],[-73.9999,41.5855],[-73.9525,41.59]]]},\"properties\":{\"name\":\"Orange\",\"state\":\"NY\"}}]}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
12100 Beech Forest Road
Laurel, MD 20708-4039
The location of a point on the landscape within a stream network (hydrologic position) can be an important predictive measure in hydrology. Hydrologic position is defined here by two metrics: lateral position and distance from stream to divide, both measured horizontally. Lateral position (dimensionless) is the relative position of a point between the stream and its watershed divide. Distance from stream to divide (units of length) is an indicator of position within a watershed: generally small near a confluence and generally large in headwater areas. Watersheds and watershed divides are defined here by Thiessen polygons rather than topographic divides. Lateral position and distance from stream to divide are also defined in the context of hydrologic order. Hydrologic order “n” is defined as the network of streams, and associated divides, of order n and higher. And given that a point can have different positions in different hydrologic orders the term multiorder hydrologic position (MOHP) is used to describe the ensemble of hydrologic positions. MOHP was mapped across the conterminous United States for nine hydrologic orders at a spatial resolution of 30 m (about 8.7 billion pixels). There are 18 metrics for each pixel. Four case studies are presented that use MOHP metrics as explanatory factors in random forest machine learning models. The case studies show that lower order MOHP metrics can serve as indicators of hydrologic process while higher‐order metrics serve as indicators of location. MOHP is shown to have utility as a predictor variable across a large range of scales (50,000 to 8,000,000 km2).
Glass composition-based correlations of volcanic ash (tephra) traditionally rely on extensive manual plotting. Many previous statistical methods for testing correlations are limited by using geochemical means, masking diagnostic variability. We suggest that machine learning classifiers can expedite correlation, quickly narrowing the list of likely candidates using well-trained models. Eruptives from Alaska's Aleutian Arc-Alaska Peninsula and Wrangell volcanic field were used as a test environment for 11 supervised classification algorithms, trained on nearly 2000 electron probe microanalysis measurements of glass major oxides, representing 10 volcanic sources. Artificial neural networks and random forests were consistently among the top-performing learners (accuracy and kappa > 0.96). Their combination as an average ensemble effectively improves their performance. Using this combined model on tephras from Eklutna Lake, south-central Alaska, showed that predictions match traditional methods and can speed correlation. Although classifiers are useful tools, they should aid expert analysis, not replace it. The Eklutna Lake tephras are mostly from Redoubt Volcano. Besides tephras from known Holocene-active sources, Holocene tephra geochemically consistent with Pleistocene Emmons Lake Volcanic Center (Dawson tephra), but from a yet unknown source, is evident. These tephras are mostly anchored by a highly resolved varved chronology and represent new important regional stratigraphic markers.
Recovering species are often limited to much smaller areas than they historically occupied. Conservation planning for the recovering species is often based on this limited range, which may simply be an artifact of where the surviving population persisted. Southern sea otters (Enhydra lutris nereis) were hunted nearly to extinction but recovered from a small remnant population on a remote stretch of the California outer coast, where most of their recovery has occurred. However, studies of recently-recolonized estuaries have revealed that estuaries can provide southern sea otters with high quality habitats featuring shallow waters, high production and ample food, limited predators, and protected haul-out opportunities. Moreover, sea otters can have strong effects on estuarine ecosystems, fostering seagrass resilience through their consumption of invertebrate prey. Using a combination of literature reviews, population modeling, and prey surveys we explored the former estuarine habitats outside the current southern sea otter range to determine if these estuarine habitats can support healthy sea otter populations. We found the majority of studies and conservation efforts have focused on populations in exposed, rocky coastal habitats. Yet historical evidence indicates that sea otters were also formerly ubiquitous in estuaries. Our habitat-specific population growth model for California’s largest estuary—San Francisco Bay—determined that it alone can support about 6,600 sea otters, more than double the 2018 California population. Prey surveys in estuaries currently with (Elkhorn Slough and Morro Bay) and without (San Francisco Bay and Drakes Estero) sea otters indicated that the availability of prey, especially crabs, is sufficient to support healthy sea otter populations. Combining historical evidence with our results, we show that conservation practitioners could consider former estuarine habitats as targets for sea otter and ecosystem restoration. This study reveals the importance of understanding how recovering species interact with all the ecosystems they historically occupied, both for improved conservation of the recovering species and for successful restoration of ecosystem functions and processes.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.8100","usgsCitation":"Hughes, B.B., Wasson, K., Tinker, M., Williams, S.L., Carswell, L., Boyer, K.E., Beck, M.W., Eby, R., Scoles, R., Staedler, M.M., Espinosa, S., Hessing-Lewis, M., Foster, E.U., Beheshti, K., Grimes, T.M., Becker, B.H., Needles, L., Tomoleoni, J.A., Rudebusch, J., Hines, E.M., and Silliman, B.R., 2019, Species recovery and recolonization of past habitats: Lessons for science and conservation from sea otters in estuaries: PeerJ, v. 7, e8100, 30 p., https://doi.org/10.7717/peerj.8100.","productDescription":"e8100, 30 p.","ipdsId":"IP-098446","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458985,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.8100","text":"Publisher Index Page"},{"id":371544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Elkhorn Slough, Morro Bay, San Francisco Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.62390136718749,\n 37.38325280195101\n ],\n [\n -121.8878173828125,\n 37.38325280195101\n ],\n [\n -121.8878173828125,\n 38.229550455326134\n ],\n [\n -122.62390136718749,\n 38.229550455326134\n ],\n [\n -122.62390136718749,\n 37.38325280195101\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.8170928955078,\n 36.79993834872292\n ],\n [\n -121.73057556152344,\n 36.79993834872292\n ],\n [\n -121.73057556152344,\n 36.87110680999585\n ],\n [\n -121.8170928955078,\n 36.87110680999585\n ],\n [\n -121.8170928955078,\n 36.79993834872292\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.97869873046875,\n 35.25907654252574\n ],\n [\n -120.76171875,\n 35.25907654252574\n ],\n [\n -120.76171875,\n 35.458432791026304\n ],\n [\n -120.97869873046875,\n 35.458432791026304\n ],\n [\n -120.97869873046875,\n 35.25907654252574\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Hughes, Brent B.","contributorId":201240,"corporation":false,"usgs":false,"family":"Hughes","given":"Brent","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":780221,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wasson, Kerstin","contributorId":221786,"corporation":false,"usgs":false,"family":"Wasson","given":"Kerstin","email":"","affiliations":[{"id":6949,"text":"University of California, Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":780222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tinker, M. 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San Luis Obispo","active":true,"usgs":false}],"preferred":false,"id":780237,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Tomoleoni, Joseph A. 0000-0001-6980-251X jtomoleoni@usgs.gov","orcid":"https://orcid.org/0000-0001-6980-251X","contributorId":167551,"corporation":false,"usgs":true,"family":"Tomoleoni","given":"Joseph","email":"jtomoleoni@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780220,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Rudebusch, Jane","contributorId":221796,"corporation":false,"usgs":false,"family":"Rudebusch","given":"Jane","affiliations":[{"id":6690,"text":"San Francisco State University","active":true,"usgs":false}],"preferred":false,"id":780238,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Hines, Ellen Marie","contributorId":147831,"corporation":false,"usgs":false,"family":"Hines","given":"Ellen","email":"","middleInitial":"Marie","affiliations":[],"preferred":false,"id":780239,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Silliman, Brian R","contributorId":221797,"corporation":false,"usgs":false,"family":"Silliman","given":"Brian","email":"","middleInitial":"R","affiliations":[{"id":12643,"text":"Duke University","active":true,"usgs":false}],"preferred":false,"id":780240,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70207164,"text":"70207164 - 2019 - Is the timing, pace and success of the monarch migration associated with sun angle?","interactions":[],"lastModifiedDate":"2019-12-10T17:09:34","indexId":"70207164","displayToPublicDate":"2019-12-10T17:06:18","publicationYear":"2019","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":"Is the timing, pace and success of the monarch migration associated with sun angle?","docAbstract":"A basic question concerning the monarch butterfly’s fall migration is which monarchs succeed in reaching overwintering sites in Mexico, which fail—and why. We document the timing and pace of the fall migration, ask whether the sun’s position in the sky is associated with the pace of the migration, and whether timing affects success in completing the migration. Using data from the Monarch Watch tagging program, we explore whether the fall monarch migration is associated with the daily maximum vertical angle of the sun above the horizon (Sun Angle at Solar Noon, SASN) or whether other processes are more likely to explain the pace of the migration. From 1998 to 2015, more than 1.38 million monarchs were tagged and 13,824 (1%) were recovered in Mexico. The pace of migration was relatively slow early in the migration but increased in late September and declined again later in October as the migrating monarchs approached lower latitudes. This slow-fast-slow pacing in the fall migration is consistent with monarchs reaching latitudes with the same SASN, day after day, as they move south to their overwintering sites. The observed pacing pattern and overall movement rates are also consistent with monarchs migrating at a pace determined by interactions among SASN, temperature, and daylength. The results suggest monarchs successfully reaching the Monarch Butterfly Biosphere Reserve (MBBR) migrate within a “migration window” with an SASN of about 57° at the leading edge of the migration and 46° at the trailing edge. Migrants reaching locations along the migration route with SASN outside this migration window may be considered early or late migrants. We noted several years with low overwintering abundance of monarchs, 2004 and 2011–2014, with high percentages of late migrants. This observation suggests a possible effect of migration timing on population size. The migration window defined by SASN might serve as a framework against which to establish the influence of environmental factors on the size, geographic distribution, and timing of past and future fall migrations.","language":"English","publisher":"Frontiers","doi":"10.3389/fevo.2019.00442","usgsCitation":"Taylor, O.R., Lovett, J., Gibo, D.L., Weiser, E.L., Thogmartin, W.E., Semmens, D.J., Diffendorfer, J., Pleasants, J.M., Pecoraro, S., and Grundel, R., 2019, Is the timing, pace and success of the monarch migration associated with sun angle?: Frontiers in Ecology and Evolution, v. 7, 442, https://doi.org/10.3389/fevo.2019.00442.","productDescription":"442","ipdsId":"IP-107707","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":458988,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fevo.2019.00442","text":"Publisher Index 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Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5119","displayTitle":"Trends in Streamflow and Concentrations and Flux of Nutrients and Total Suspended Solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana","title":"Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana","docAbstract":"The U.S. Geological Survey (USGS), in cooperation with The Nature Conservancy, completed a study to estimate and assess trends in streamflow and annual mean concentrations and flux of nutrients (nitrate plus nitrite, total Kjeldahl nitrogen, and total phosphorus) and total suspended solids at three USGS streamgages (hereafter referred to as “study gages”) on the Upper White River at Muncie (USGS station 03347000), near Nora (USGS station 03351000), and near Centerton (USGS station 03354000), Indiana. Water-quality data used in the analyses were collected by several agencies between calendar years 1991 and 2017, and streamflow (discharge) data were collected by the USGS. For most of the water-quality constituents, there were suitable data to facilitate an analysis of the 26-year period extending from calendar years 1991 to 2017 (water years 1992 to 2017); however, shorter analytical periods were necessary for total Kjeldahl nitrogen for the study gages at Muncie and near Centerton and for total suspended solids for the study gage near Centerton.
Temporal trends in streamflows at the study gages for the period extending from water years 1978 to 2017 were assessed using Exploration and Graphics for RivEr Trends (EGRET) and Mann-Kendall and Pettitt tests. With just one exception, the annual maximum and mean daily streamflows and the annual minimum 7-day mean streamflows at the study gages demonstrated upward trends (increasing streamflows) in the EGRET analyses. The exception was the annual 7-day minimum streamflow at the study gage near Nora, which indicated no trend. Mann-Kendall tests also indicated that the average trend for the annual maximum daily, annual mean daily, and annual 7-day minimum streamflow statistics between water years 1978 and 2017 was upward at each of the study gages; however, only the trends in the annual mean daily streamflows at the study gage at Muncie and the annual maximum daily streamflows at the study gages near Nora and near Centerton were statistically significant at a 0.05 probability level. The Pettitt tests indicated that a statistically significant step trend (abrupt change) in annual mean daily streamflows occurred at each of the study gages around water year 2001.
The seasonal distributions of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentrations at the study gages were evaluated to identify patterns and other distinguishing characteristics by examining boxplots of concentrations as a function of month of the year. Seasonal distributions of nitrate plus nitrite concentrations and total suspended solids concentrations differed from each other but were generally similar among the three study gages for a given constituent. Median concentrations of nitrate plus nitrite were highest during the January–June months, whereas median concentrations of total suspended solids were highest during June and July. Seasonal distributions of total phosphorus concentrations were similar at the study gages near Nora and near Centerton, but the seasonal distribution was noticeably different at the study gage at Muncie, which had monthly median concentrations that were substantially lower than at the two downstream study gages (near Nora and near Centerton). The seasonal distribution of total Kjeldahl nitrogen concentrations differed in pattern among the three study gages; however, in general, some of the higher monthly median total Kjeldahl nitrogen concentrations at each study gage were associated with the late spring and summer periods.
The Weighted Regressions on Time, Discharge, and Season (WRTDS) method implemented in EGRET was used to estimate water-year annual mean daily concentrations and flux of nutrients and total suspended solids, as well as estimates of concentrations and flux that were “normalized” to remove the effect of year-to-year variation in streamflow. The approximate coefficients of determination for the WRTDS regression models ranged from a high of 0.82 for total phosphorus for the study gage near Centerton to a low of 0.19 for nitrate plus nitrite for the study gage near Nora.
Loads and yields of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen were estimated for analytical periods consisting of the longest periods of concurrent record at the three study gages. Loads of each of the constituents increased sequentially from the most upstream study gage to the most downstream study gage; however, the same was not true for yields. The highest yields of total suspended solids, total phosphorus, and total Kjeldahl nitrogen occurred at the most upstream study gage (at Muncie); however, the highest yield of nitrate plus nitrite occurred at the most downstream study gage (near Centerton).
WRTDS bootstrap tests were used to assess the magnitude, direction, and likelihood of changes in annual flow-normalized mean daily concentrations and flux of total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen at the study gages between water years 1997 and 2017. Changes in flow-normalized concentrations and flux of the constituents between water years 1997 and 2017 were mostly downward (decreasing). The exceptions were likely to highly likely upward (increasing) changes in (1) flow-normalized annual mean daily concentration and annual flux for total suspended solids and total phosphorus at the study gage at Muncie, (2) flow-normalized annual mean daily total phosphorus concentration at the study gage near Centerton, (3) flow-normalized annual flux of total phosphorus at the study gage near Centerton, and (4) flow-normalized annual mean daily nitrate plus nitrite concentration at the study gage near Centerton. Although an upward change in flow-normalized nitrate plus nitrite concentrations was likely at the study gage near Centerton, flow-normalized annual flux of nitrate plus nitrite at that study gage was determined to have a highly likely downward change.
EGRET and Exploration and Graphics for RivEr Trends Confidence Intervals (EGRETci) analyses can be used to improve our understanding of how concentrations and flux change as functions of time and streamflow, as well as provide information on how the relations between streamflow and constituent concentrations have changed within the calendar year between any 2 years included in the analyses. Examples of those uses, illustrating changes between calendar years 1992 and 2017, were given for total suspended solids concentrations at the study gage near Nora and for nitrate plus nitrite concentrations at the study gage near Centerton.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195119","collaboration":"Prepared in cooperation with The Nature Conservancy","usgsCitation":"Koltun, G.F., 2019, Trends in streamflow and concentrations and flux of nutrients and total suspended solids in the Upper White River at Muncie, near Nora, and near Centerton, Indiana: U.S. Geological Survey Scientific Investigations Report 2019–5119, 34 p., https://doi.org/10.3133/sir20195119.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"46","onlineOnly":"Y","ipdsId":"IP-109722","costCenters":[{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true}],"links":[{"id":399602,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109513.htm"},{"id":370134,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VN5RKV","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Total suspended solids, total phosphorus, nitrate plus nitrite, and total Kjeldahl nitrogen concentration data for the White River at Muncie, near Nora, and near Centerton, Indiana, 1991–2017"},{"id":370133,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5119/sir20195119.pdf","text":"Report","size":"3.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019–5119"},{"id":370132,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5119/coverthb.jpg"}],"country":"United States","state":"Indiana","county":"Morgan County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -86.8311,\n 39.2633\n ],\n [\n -84.9667,\n 39.2633\n ],\n [\n -84.9667,\n 40.3608\n ],\n [\n -86.8311,\n 40.3608\n ],\n [\n -86.8311,\n 39.2633\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Ohio-Kentucky-Indiana Water Science Center
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Volcanoes are the main pathway to the surface for volatiles that are stored within the Earth. Carbon dioxide (CO2) is of particular interest because of its potential for climate forcing. Understanding the balance of CO2 that is transferred from the Earth’s surface to the Earth’s interior, hinges on accurate quantification of the long-term emissions of volcanic CO2 to the atmosphere. Here we present an updated evaluation of the world’s volcanic CO2 emissions that takes advantage of recent improvements in satellite-based monitoring of sulfur dioxide, the establishment of ground-based networks for semi-continuous CO2-SO2 gas sensing and a new approach to estimate key volcanic gas parameters based on magma compositions. Our results reveal a global volcanic CO2 flux of 51.3 ± 5.7 Tg CO2/y (11.7 × 1011 mol CO2/y) for non-eruptive degassing and 1.8 ± 0.9 Tg/y for eruptive degassing during the period from 2005 to 2015. While lower than recent estimates, this global volcanic flux implies that a significant proportion of the surface-derived CO2 subducted into the Earth’s mantle is either stored below the arc crust, is efficiently consumed by microbial activity before entering the deeper parts of the subduction system, or becomes recycled into the deep mantle to potentially form diamonds.
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