The southwestern U.S. is a global hotspot of climate change. Models project that temperatures will continue to rise through the end of the 21st century, accompanied by significant changes to the hydrological cycle. Within the Sonoran Desert, a limited number of studies have documented climate change impacts on the phenology of native plant species. Much of this phenological work to understand climate change impacts to phenology builds on research conducted nearly three decades ago to define flowering triggers and developmental requirements for native keystone Sonoran Desert woody species. Here we expand on the drivers and explore recent phenological trends for six species using a unique 36-year observational data set. We use statistical models to determine which aspects of climate influence the probability of flowering, and how flowering time may respond to climate change. We move beyond traditional models of phenology by incorporating different metrics of moisture availability in addition to temperature, weather, and climate at several time scales, including daily, weekly, seasonal, and antecedent conditions. Our results provide evidence of a trend towards earlier flowering (on the order of 1–4 days per decade) for five of the six species analyzed, and no trend for one species. The species we evaluated had contrasting phenological responses to different aspects of climate, suggesting individualistic changes in phenology and the potential of divergent plant community flowering patterns under future climate change. Understanding recent changes in flowering phenology and their climatic triggers is important to anticipating whether plant species can attract pollinators, reproduce, and persist within the community under continued climate change.
Land disposal of sewage wastewater through septic systems and cesspools is a major cause of elevated concentrations of nitrogen in the shallow coastal aquifers of southern New England. The discharge of nitrogen from these sources at the coast is affecting the environmental health of coastal saltwater bodies. In response, local, State, and Federal agencies are considering expensive actions to mitigate these effects, including installing municipal sewer systems. To increase the understanding of the effects of municipal sewering on groundwater quality discharging to coastal surface waters, a network of multilevel monitoring wells was established in a densely developed coastal neighborhood on the Maravista peninsula, Falmouth, Massachusetts, which was undergoing conversion from onsite septic disposal to municipal sewering.
The geohydrology of the study area on the peninsula is generally characterized as consisting of fine to coarse, well-sorted sands containing 2.9 to 9.3 meters of fresh groundwater and a flow system characterized by a groundwater divide slightly west of the center of the peninsula. The magnitude of hydraulic gradients at the water table is gently sloping, ranging from 0.000032 to 0.00059, and affected by daily and bimonthly tidal fluctuations from adjacent coastal ponds. On the western side of the divide, upgradient from Little Pond, average linear groundwater velocities and traveltimes along shallow flow paths, estimated from observed hydraulic gradients and estimated aquifer hydraulic conductivity and effective porosity, range from 0.076 to 0.094 meters per day and 7.8 to 9.7 years, respectively.
The groundwater monitoring network consists of 14 profile sites on the peninsula that each include a multilevel sampler for water-quality data collection and a shallow monitoring well for groundwater-level measurements. The study area encompasses about 230 residences that transitioned from onsite septic disposal to municipal sewering between spring 2017 and summer 2019. An additional multilevel sampler that was in a residential coastal setting but not undergoing sewering also was sampled periodically as a reference site.
Elevated nitrogen, as compared to typical uncontaminated, fresh groundwater in the Cape Cod aquifer, predominately as nitrate, was measured in 15 water-quality profiles at nitrate concentrations as great as 26.2 milligrams per liter as nitrogen (n=749; mean and median values were 5.1 and 4.1 milligrams per liter as nitrogen, respectively). At all 14 profile sites and the reference profile site on a nearby peninsula, wastewater effects were denoted by increased nitrate, boron, and specific conductance, and by decreased pH and dissolved oxygen. The highest concentrations of nitrate typically occurred in the deepest one-half of the freshwater zone and in intervals of suboxic and oxic groundwater.
Thickness-weighted mean and maximum nitrate concentrations, and total nitrate mass from four sampling rounds, provided a metric to evaluate expected changes at the 14 profile sites on the peninsula. Nitrate concentrations varied moderately by site between sampling rounds through both the presewering (June 2016 and April 2017) and transitional periods (April 2018 and June 2019). Nitrate concentrations greater than the U.S. Environmental Protection Agency maximum contaminant level for nitrate in drinking water (10 milligrams per liter as nitrogen), were detected at 9 of the 14 profile sites and at the reference site. The average of the mean thickness-weighted nitrate concentrations for the four full sampling rounds was greater than 5.0 milligrams per liter as nitrogen at 8 sites (7 profile sites and the reference site) and greater than 8 milligrams per liter as nitrogen at 3 profile sites. The total nitrate mass per square meter of land area at each profile site ranged from 1,830 to 36,800 milligrams per square meter. Nitrate mass flux, across a 500-meter-long section upgradient from Little Pond and covering about 15 percent of the total pond shoreline length, ranged from 124.3 to 192.6 kilograms per year for the four full sampling rounds under three groundwater-flow conditions.
The expected improvements in groundwater quality in the freshwater zone should be characterized by decreases in concentrations of dissolved total and inorganic nitrogen and common ions such as boron, chloride, and fluoride. A statistical analysis using the Regional Kendall test for sampling points grouped in specific depth ranges confirmed that water-quality changes were statistically significant in at least one depth group during the 3-year sampling period (nitrate: −0.76 milligram per liter per year; specific conductance: −12.1 microsiemens per centimeter at 25 degrees Celsius per year; dissolved oxygen: 0.82 milligram per liter per year); however, the rate at which the water-quality improvements will result in decreases in nitrate mass loads to the coastal ponds primarily depends on groundwater traveltimes and the rate of flushing of wastewater constituents from the aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215130","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency’s Southeast New England Program","usgsCitation":"McCobb, T.D., Barbaro, J.R., LeBlanc, D.R., and Belaval, M., 2021, Evaluating the effects of replacing septic systems with municipal sewers on groundwater quality in a densely developed coastal neighborhood, Falmouth, Massachusetts, 2016–19: U.S. Geological Survey Scientific Investigations Report 2021–5130, 39 p., https://doi.org/10.3133/sir20215130.","productDescription":"Report viii, 39 p.; Data Release; Dataset","numberOfPages":"39","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126300","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":393105,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5130/images/"},{"id":393103,"rank":4,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"- USGS water data for the Nation"},{"id":393102,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GEMMN6","text":"USGS data release","linkHelpText":"Baseline groundwater-quality data from a densely developed coastal neighborhood, Falmouth, Massachusetts (2016–2020) (ver. 3.0, April 2021)"},{"id":393101,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5130/sir20215130.pdf","text":"Report","size":"8.63 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5130"},{"id":393100,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5130/coverthb.jpg"},{"id":393104,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5130/sir20215130.XML"}],"country":"United States","state":"Massachusetts","city":"Falmouth","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.65788269042969,\n 41.52245918082221\n ],\n [\n -70.39627075195312,\n 41.52245918082221\n ],\n [\n -70.39627075195312,\n 41.725205507257016\n ],\n [\n -70.65788269042969,\n 41.725205507257016\n ],\n [\n -70.65788269042969,\n 41.52245918082221\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
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10 Bearfoot Road
Northborough, MA 01532
No abstract available.
","language":"English","publisher":"University of Massachusetts","usgsCitation":"Evans, A., Lopez, B., MacLean, M., Morelli, T.L., Narayanan, R., and Ocana, M., 2021, Adaptation actions for resistance, resilience, and transformation, 5 p.","productDescription":"5 p.","ipdsId":"IP-135166","costCenters":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":410720,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":410719,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.massecan.org/resources-massecan/rrt"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Evans, A.","contributorId":300017,"corporation":false,"usgs":false,"family":"Evans","given":"A.","affiliations":[],"preferred":false,"id":859274,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lopez, Bianca","contributorId":300018,"corporation":false,"usgs":false,"family":"Lopez","given":"Bianca","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":859275,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"MacLean, Meghan G.","contributorId":300019,"corporation":false,"usgs":false,"family":"MacLean","given":"Meghan G.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":859276,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":859277,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Narayanan, R.","contributorId":300020,"corporation":false,"usgs":false,"family":"Narayanan","given":"R.","email":"","affiliations":[],"preferred":false,"id":859278,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ocana, M.","contributorId":300130,"corporation":false,"usgs":false,"family":"Ocana","given":"M.","email":"","affiliations":[],"preferred":false,"id":859462,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70232949,"text":"70232949 - 2021 - EDNA monitoring in the upper Mississippi River","interactions":[],"lastModifiedDate":"2022-07-14T14:31:03.922793","indexId":"70232949","displayToPublicDate":"2022-01-01T09:08:04","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"EDNA monitoring in the upper Mississippi River","docAbstract":"This report describes the joint efforts of USGS UMESC and the U.S. Fish and Wildlife Service to monitor bigheaded carps in the Upper Mississippi River. 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2021 - Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico","interactions":[],"lastModifiedDate":"2022-09-12T17:03:23.740912","indexId":"70227684","displayToPublicDate":"2021-12-31T11:51:41","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"4","title":"Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico","docAbstract":"Reliable information about the frequency of annual peak streamflow is needed for floodplain management, objective assessment of flood risk, and cost-effective design of dams, levees, other flood-control structures, and roads, bridges, and culverts. Generalized skew coefficients are among the data needed for log-Pearson type III peak-streamflow frequency analyses of annual peak streamflows. A technique is presented to estimate generalized skew coefficients used for log-Pearson type III peak-streamflow frequency analyses of annual peak streamflow from natural watersheds (minimal regulation and minimal impervious cover). The estimation of generalized skew coefficients was based on annual and historical peak streamflow data from an initial set of 444 selected USGS streamgaging stations (streamgages) with at least 30 years of recorded annual peak streamflows from natural watersheds in Texas, Oklahoma, and the part of New Mexico east of the Great Continental Divide. The primary focus was to obtain information that could be used to update previously published generalized skew coefficients in Texas.\n\nOf the 444 candidate streamgages, 341 were used in the final construction of statistical models. Two generalized additive models (GAMs) were used to predict generalized skew based on a 2-dimensional smooth on projected Albers equal area coordinates of either (1) the locations of the centroids of the gaged watersheds or (2) the streamgage locations. To create maps of generalized skew coefficients, predictions were made on a 1-kilometer grid and contour lines were superimposed. The centroid-location map, with a mean-squared error (MSE) of 0.216, is preferred. Generalized skew coefficients from the centroid-location map, along with the MSE, are useful for computing weighted-skew values when conducting frequency analyses of annual peak streamflow following the guidelines set forth in Bulletin 17C. Based on the results of the study, text revision of the TxDOT Hydraulic Design Manual could be made.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Generalized skew update and regional study of distribution shape for Texas flood frequency analyses","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"Texas Tech University Center for Multidisciplinary Research in Transportation","doi":"10.18738/T8/SVLCOQ","collaboration":"Texas Department of Transportation","usgsCitation":"Asquith, W.H., Yesildirek, M.V., Landers, R.N., Cleveland, T.G., Fang, Z.N., and Zhang, J., 2021, Technique to estimate generalized skew coefficients of annual peak streamflow for natural watershed conditions in Texas, Oklahoma, and eastern New Mexico, chap. 4 of Generalized skew update and regional study of distribution shape for Texas flood frequency analyses, p. 31-58, https://doi.org/10.18738/T8/SVLCOQ.","productDescription":"28 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To facilitate emergency responses and mitigate losses, the U.S. Geological Survey provides a rapid hazard estimation system for earthquake-triggered landslides and liquefaction using geospatial susceptibility proxies and ShakeMap ground motion estimates. However, the resolution and accuracy of these models are often limited by coarse-granularity and large uncertainties of available geospatial features provided at a regional scale. Recently, with the advancement of remote sensing technologies, synthetic aperture radar (SAR) images are captured and analyzed to obtain a rapid estimate of earthquake-induced correlation changes between pre- and post-event images. These correlation changes indicate ground failures and building damage t, showing the potential to provide supplementary information for rapid hazard and loss estimation. However, the exact causes of changes in satellite images are not directly ascertained by the DPM alone. For example, changes could be due to building damage, landslides, liquefaction, noise or any combination thereof. More importantly, the occurrence and intensity of landslides, liquefaction, and building damages are spatially correlated, which makes it yet more challenging to distinguish the sources of any such changes.
In this study, we develop a generalized causal graph-based Bayesian Network that models the physical interdependencies between geospatial features, seismic ground failures and building damage, as well as DPMs. Geospatial features provide physical insights for estimating ground failure occurrence while DPMs contain event-specific surface change observations. This physics-informed causal graph incorporate these variables with complex physical relationships in one holistic Bayesian updating scheme to effectively fuse information from both geospatial models and remote sensing data. This framework is scalable and flexible enough to deal with highly complex multi-hazard combinations. We then develop a stochastic variational inference algorithm to jointly update the intractable posterior probabilities of unobserved landslides, liquefaction, and building damage at different locations efficiently. In addition, a local graphical model pruning algorithm is presented to reduce the computational cost of large-scale seismic ground failure estimation. We apply this framework to September 2018 Hokkaido Iburi-Tobu, Japan (M6.6) earthquake and January 2020 Southwest Puerto Rico (M6.4) earthquake to evaluate the performance of our algorithm
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 17th World Conference on Earthquake Engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"The 17th World Conference on Earthquake Engineering","conferenceDate":"September 27-October 2, 2021","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan","usgsCitation":"Xu, S., Dimasaka, J., Wald, D.J., and Noh, H., 2021, Bayesian updating of seismic ground failure estimates via causal graphical models and satellite imagery, in Proceedings of the 17th World Conference on Earthquake Engineering, Sendai, Japan, September 27-October 2, 2021, 12 p.","productDescription":"12 p.","ipdsId":"IP-127995","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":426079,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":421116,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Xu, S.","contributorId":330153,"corporation":false,"usgs":false,"family":"Xu","given":"S.","affiliations":[{"id":78827,"text":"State University of New York at Stony Brook","active":true,"usgs":false}],"preferred":false,"id":884126,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dimasaka, J.","contributorId":330154,"corporation":false,"usgs":false,"family":"Dimasaka","given":"J.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884127,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":884128,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noh, H.","contributorId":330155,"corporation":false,"usgs":false,"family":"Noh","given":"H.","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":884129,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225677,"text":"70225677 - 2021 - Multi-period response spectra","interactions":[],"lastModifiedDate":"2022-04-18T16:30:04.442385","indexId":"70225677","displayToPublicDate":"2021-12-31T11:29:28","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"title":"Multi-period response spectra","docAbstract":"Multi-period response spectra (MPRS) are incorporated in the development of seismic design ground motions in the 2020 edition of the NEHRP Recommended Seismic Provisions for New Buildings and Other Structures (2020 NEHRP Provisions) and are approved for adoption in the American Society of Civil Engineers (ASCE) Standard, Minimum Design Loads and Associated Criteria for Buildings and Other Structures (ASCE/SEI 7-22). MPRS are incorporated in these design regulations because it was discovered that the standard spectral shape based on two periods and one reference site class was substantially understating spectral response in moderately long period structures located on soft soil sites where ground motion hazard is dominated by large magnitude events. These are the motions that are relevant to tall buildings in the Los Angeles region and of interest to the Los Angeles Tall Buildings Seismic Design Council (LATBSDC). The MPRS incorporation updated Chapters 11, 20, 21, and 22 of the 2020 NEHRP Provisions (a.k.a. FEMA P-2082); changes are described in detail in the commentary of FEMA P-2082. The MPRS also influenced the development of the 2018 U.S. Geological Survey (USGS) National Seismic Hazard Model (NSHM) for the conterminous U.S. because valid ground motion models for all periods and site classes of interest were required. FEMA P-2082 is complemented by the FEMA P-2078 technical report that provides a procedure for approximating MPRS outside of the conterminous U.S. This paper presents a condensed version of the relevant sections of FEMA P-2082 and FEMA P-2078 that would interest the LATBSDC.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings of the 2021 Los Angeles tall buildings confrerence","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"2021 Los Angeles Tall Buildings Conference","conferenceDate":"Nov 12, 2021","conferenceLocation":"Los Angeles, CA","language":"English","publisher":"Los Angeles Tall Buildings Structural Design Council","usgsCitation":"Rezaeian, S., Luco, N., and Kircher, C.A., 2021, Multi-period response spectra, in Proceedings of the 2021 Los Angeles tall buildings confrerence, Los Angeles, CA, Nov 12, 2021, p. 110-129.","productDescription":"20 p.","startPage":"110","endPage":"129","ipdsId":"IP-134522","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":398946,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":398945,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.latallbuildings.org/past-conference-proceedings"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rezaeian, Sanaz 0000-0001-7589-7893 srezaeian@usgs.gov","orcid":"https://orcid.org/0000-0001-7589-7893","contributorId":4395,"corporation":false,"usgs":true,"family":"Rezaeian","given":"Sanaz","email":"srezaeian@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":826188,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":826189,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kircher, C. 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Beginning in 2015, the benthic preyfish survey expanded from US-only to incorporate lake-wide sampling sites which increased the survey’s spatial coverage, and resumed sampling in eastern embayments (Black River, Chaumont, Guffin, and Henderson Bays) that were historically sampled during a September bottom trawl survey to index yellow perch from 1978 to 2007. In 2021, the collaborative benthic preyfish survey completed 195 bottom trawl tows across main lake and embayments at depths from 5 to 226 m. New embayment sites at Bay of Quinte, Sodus, and Little Sodus Bay were added to the survey in 2021 to compare fish communities across nearshore sites. In total, the 2021 survey sampled 109,178 fish from 35 species. Round goby (Neogobius melanostomus) was the most numerically abundant species comprising 44% of the total catch, followed by deepwater sculpin (Myoxocephalus thompsonii), and alewife (Alosa pseudoharengus) at 17% and 11%, respectively. Deepwater sculpin accounted for most (406 kg) of the fish biomass sampled during the 2021 survey (total=1,995 kg), followed by round goby (257 kg), and common carp (252 kg). Slimy sculpin (Cottus cognatus) biomass was higher in 2021 than in 2020, when spatial coverage was reduced. Deepwater sculpin biomass remained high in 2021 and similar to observations since 2019. White perch biomass (Morone americana) in Black River Bay has increased compared to observations from historical surveys. Yellow perch (Perca flavescens) accounted for most of the benthic preyfish biomass across the embayments surveyed in 2021 except for the Bay of Quinte and Black River Bay, where white perch accounted for a greater proportion of the fish community biomass.
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Highlights of findings from the CLSM WG include: the interpreted presence of Botryococcus; incomplete blocking of laser light from highly reflective materials; surface roughening and surface flattening induced by differential BIB milling dependent on location and scale of measurement; substitution of uranium for iron in sulfides; red-shift of reflectance and auto-fluorescence from below the sample surface; positive alteration from laser-induced photo-oxidation of the sample surface including fluorescence blue-shift; blueshift associated to higher fluorescence intensity regions in amorphous organic matter; need for fluorescence spectroscopy standardization as applied via CLSM; and the suitability of CLSM to predict solid bitumen reflectance via calibration to an extant data set. Due to the inability of WG members to continue participating in a WG format, the CLSM WG is hereby finalized. 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This has led to increases in generalists’ population numbers across landscapes that were previously less suitable for their current demographic rates. Of particular concern are growing populations of common ravens (Corvus corax; ravens), because predation by ravens is linked to population declines of sensitive species. Ecosystem managers seek management options for mitigating the adverse effects of raven predation where unsustainable predator–prey conflicts exist. We present 3 case studies examining how manipulating reproductive success of ravens influences demographic rates of 2 sensitive prey species. Two case studies examine impacts of removing raven nests or oiling raven eggs on nest survival of greater sage-grouse (Centrocercus urophasianus; sage-grouse) within Wyoming and the Great Basin of California and Nevada, USA, respectively. The third case study uses Mojave desert tortoise (Gopherus agassizii; tortoise) decoys to examine effects of oiling raven eggs on depredation rates of juvenile tortoises in the Mojave Desert in California. Initial trial years from all 3 case studies were consistent in finding improved vital rates associated with the application of strategies for reducing reproductive success of ravens. Specifically, removal of raven nests resulted in increased nest survival of sage-grouse within treatment areas where predation by ravens was the primary cause of nest failure. In addition, nest survival of sage-grouse and survival of juvenile tortoise decoys was higher following a treatment of oiling the eggs of ravens in their nests at 2 sites within the Great Basin and 4 tortoise conservation areas in the Mojave Desert in California. Along with specialized technologies that can make techniques such as egg-oiling more feasible, these findings support these management practices as important tools for managing ravens, especially in areas where breeding ravens have negative impacts on sensitive prey species.
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\"}}]}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pasley, Nathaniel Kyle 0000-0001-7441-495X","orcid":"https://orcid.org/0000-0001-7441-495X","contributorId":272301,"corporation":false,"usgs":true,"family":"Pasley","given":"Nathaniel","email":"","middleInitial":"Kyle","affiliations":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831940,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70240350,"text":"70240350 - 2021 - Synthesis of nest predation impacts of common ravens on sensitive avian species","interactions":[],"lastModifiedDate":"2023-02-06T16:08:53.197614","indexId":"70240350","displayToPublicDate":"2021-12-31T10:06:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Synthesis of nest predation impacts of common ravens on sensitive avian species","docAbstract":"Decades of mounting scientific evidence have revealed that common raven (Corvus corax; raven) population numbers have been increasing across nearly all regions of their geographic range in North America. Concomitantly, numerous native wildlife species have experienced elevated predation rates from ravens as populations have increased and expanded their range. Managers are concerned that increased raven predation of many threatened and endangered avian species in the U.S. and Canada during nesting periods may be hampering species recovery. We explored the literature to aggregate existing knowledge and evaluate the impacts of raven predation on nests and young of sensitive avian species. We used this information to develop a simple relative index for each species, the “Raven Impact Index” (RII). The RII incorporated the species demographic rates, abundance of ravens in relation to each sensitive species’ breeding range, and the degree of overlap between raven and sensitive prey distributions. We also developed a second relative descriptor describing our confidence in each RII, termed a “Impact Credibility Index (ICI).” The species ICI was based on the number of published studies and the type of evidence presented (e.g., circumstantial vs. direct). We found evidence of nest predation on 8 sensitive avian species and suspected nest predation on 1 additional species. All species shared aspects of nesting biology that suggested they would likely be susceptible to raven nest predation. The RII varied among prey species, with greater sage-grouse (Centrocercus urophasianus) having the highest relative impact values, followed by snowy plover (Charadrius nivosus nivosus), marbled murrelet (Brachyramphus marmoratus), and Gunnison sage-grouse (Centrocercus minimus). Our species RII is intended to inform management decisions regarding actions that mitigate the negative effects of raven predation of sensitive avian species. Although elevated nest predation may be of high conservation concern, it is important to recognize that all of the sensitive native prey species we established an RII for also face multiple conservation threats.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/962c-56f0","usgsCitation":"Coates, P.S., Webb, W.C., Dettenmaier, S.J., Harju, S.M., and Delehanty, D.J., 2021, Synthesis of nest predation impacts of common ravens on sensitive avian species: Human–Wildlife Interactions, v. 15, no. 3, p. 350-371, https://doi.org/10.26077/962c-56f0.","productDescription":"22 p.","startPage":"350","endPage":"371","ipdsId":"IP-130901","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412743,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863523,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Webb, William C.","contributorId":174938,"corporation":false,"usgs":false,"family":"Webb","given":"William","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":863524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harju, Seth M. 0000-0003-0444-7881","orcid":"https://orcid.org/0000-0003-0444-7881","contributorId":238889,"corporation":false,"usgs":false,"family":"Harju","given":"Seth","email":"","middleInitial":"M.","affiliations":[{"id":47817,"text":"Heron Ecological","active":true,"usgs":false}],"preferred":false,"id":863526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":863527,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240352,"text":"70240352 - 2021 - A desert tortoise-common raven viable conflict threshold","interactions":[],"lastModifiedDate":"2023-02-06T16:05:37.397759","indexId":"70240352","displayToPublicDate":"2021-12-31T10:03:42","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"A desert tortoise-common raven viable conflict threshold","docAbstract":"Since 1966, common raven (Corvus corax; raven) abundance has increased throughout much of this species’ Holarctic distribution, fueled by an ever-expanding supply of anthropogenic resource subsidies (e.g., water, food, shelter, and nesting substrate) to ecoregion specific raven population carrying capacities. Consequently, ravens are implicated in declines of both avian and reptilian species of conservation concern, including the California (USA) endangered and federally threatened Mojave desert tortoise (Gopherus agassizii; desert tortoise). While ravens are a natural predator of desert tortoises, the inter-generational stability of desert tortoise populations is expected to be compromised as annual juvenile survival is suppressed below 0.77 through a combination of raven depredation and other sources of mortality. To estimate the extent to which raven depredation suppresses desert tortoise recruitment within the Mojave Desert of California, we collected data from 274 variable-radius point counts, 78 desert tortoise decoy stations, and 8 control stations during the spring of 2020. Additionally, we complied a geodatabase of previously active raven nests, observed between 2013 and 2020. Raven density estimates from 4 monitoring areas ranged between 0.63 (eastern most) and 2.44 (western most) raven km-2 (95% CI: 0.35–1.14 and 1.33–4.48, respectively). We used a Bayesian shared frailty model to estimate the effects of raven density and distance to the nearest previously active raven nest on the annual “survival” of juvenile desert tortoise decoys (75-mm Midline Carapace Length), which we then converted into survival estimates for 0- to 10-year-old desert tortoises by adjusting exposure to reflect natural activity patterns. At the 1.72-km median distance from the nearest previously active raven nest, the estimated annual survival of desert tortoises decreased as raven density increased, ranging among conservation areas from 0.774 (eastern most) to 0.733 (western most). Accordingly, our model predicts that desert tortoise populations exposed to raven densities in excess of 0.89 raven km-2, at a distance
","language":"English","publisher":"Berryman Institute","doi":"10.26077/eeca-1eec","usgsCitation":"Holcomb, K.L., Coates, P.S., Prochazka, B.G., Shields, T., and Boarman, W., 2021, A desert tortoise-common raven viable conflict threshold: Human–Wildlife Interactions, v. 15, no. 3, p. 405-421, https://doi.org/10.26077/eeca-1eec.","productDescription":"17 p.","startPage":"405","endPage":"421","ipdsId":"IP-130973","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412742,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Mojave Basin & Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.97303916756042,\n 35.71726205140463\n ],\n [\n -117.97303916756042,\n 34.34636579137755\n ],\n [\n -114.99857556861961,\n 34.34636579137755\n ],\n [\n -114.99857556861961,\n 35.71726205140463\n ],\n [\n -117.97303916756042,\n 35.71726205140463\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Holcomb, Kerry L.","contributorId":296962,"corporation":false,"usgs":false,"family":"Holcomb","given":"Kerry","email":"","middleInitial":"L.","affiliations":[{"id":64256,"text":"U.S. Fish and Wildlife Service, Carlsbad Fish and Wildlife Office, 777 East Tahquitz Canyon Way, Suite 208, Palm Springs, California, 92262, USA","active":true,"usgs":false}],"preferred":false,"id":863528,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863529,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prochazka, Brian G. 0000-0001-7270-5550 bprochazka@usgs.gov","orcid":"https://orcid.org/0000-0001-7270-5550","contributorId":174839,"corporation":false,"usgs":true,"family":"Prochazka","given":"Brian","email":"bprochazka@usgs.gov","middleInitial":"G.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863530,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shields, Timothy","contributorId":296963,"corporation":false,"usgs":false,"family":"Shields","given":"Timothy","affiliations":[{"id":64257,"text":"Hardshell Labs, Inc., P.O. Box 362, Haines, Alaska, 99827, USA","active":true,"usgs":false}],"preferred":false,"id":863531,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boarman, William I.","contributorId":302114,"corporation":false,"usgs":false,"family":"Boarman","given":"William I.","affiliations":[{"id":65416,"text":"Hardshell Labs","active":true,"usgs":false}],"preferred":false,"id":863532,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240335,"text":"70240335 - 2021 - A rapid assessment function to estimate common raven population densities: Implications for targeted management","interactions":[],"lastModifiedDate":"2023-02-06T15:59:35.728824","indexId":"70240335","displayToPublicDate":"2021-12-31T09:58:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"A rapid assessment function to estimate common raven population densities: Implications for targeted management","docAbstract":"Common raven (Corvus corax; raven) populations have increased over the past 5 decades within the western United States. Raven population increases have been largely attributed to growing resource subsidies from expansion of human enterprise. Concomitantly, managers are becoming increasingly concerned about elevated adverse effects on multiple sensitive prey species, damage to livestock and agriculture, and human safety. Managers could benefit from a rapid but reliable method to estimate raven densities across spatiotemporal scales to monitor raven populations more efficiently and inform targeted and adaptive management frameworks. However, obtaining estimates of raven density is data- and resource-intensive, which renders monitoring within an adaptive framework unrealistic. To address this need, we developed a rapid survey protocol for resource managers to estimate site-level density based on the average number of ravens per survey. Specifically, we first estimated raven densities at numerous field sites with robust distance sampling procedures and then used regression to investigate the relationship between those density estimates and the number of ravens per survey, which revealed a strong correlation (R2 = 0.86). For management application, we provide access to R function software through a web-based interface to estimate density using number of ravens per survey, which we refer to as a Rapid Assessment Function (RAF). Then, using a simulation analysis of data from sites with abundant surveys and the RAF, we estimated raven density based on different numbers of surveys to help inform how many surveys are needed to achieve reliable estimates within this rapid assessment. While more robust procedures of distance sampling are the preferred methods for estimating raven densities from count surveys, the RAF tool presented herein provides a reliable approximation for informing management decisions when managers are faced with resource and small sample size constraints.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/1svg-ej32","usgsCitation":"Brussee, B.E., Coates, P.S., O’Neil, S.T., Dettenmaier, S.J., Jackson, P.J., Howe, K., and Delehanty, D.J., 2021, A rapid assessment function to estimate common raven population densities: Implications for targeted management: Human–Wildlife Interactions, v. 15, no. 3, p. 433-446, https://doi.org/10.26077/1svg-ej32.","productDescription":"14 p.","startPage":"433","endPage":"446","ipdsId":"IP-130930","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":412741,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -114.64724871350592,\n 35.13961157171347\n ],\n [\n -114.11719290812712,\n 36.263508018383206\n ],\n [\n -113.31573256140439,\n 43.69803466511934\n ],\n [\n -120.81998484033755,\n 43.75896375090363\n ],\n [\n -121.47400838048571,\n 39.959291447247125\n ],\n [\n -118.79678730789294,\n 37.043166083806\n ],\n [\n -114.64699890679083,\n 35.109972173503834\n ],\n [\n -114.64724871350592,\n 35.13961157171347\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"3","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863449,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863450,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863451,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":863452,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":863453,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Howe, Kristy B.","contributorId":192078,"corporation":false,"usgs":false,"family":"Howe","given":"Kristy B.","affiliations":[],"preferred":false,"id":863454,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":863455,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70224527,"text":"70224527 - 2021 - Exploring probabilistic seismic risk assessment to monitor the Sendai Framework for Disaster Risk Reduction","interactions":[],"lastModifiedDate":"2024-02-28T16:23:59.120117","indexId":"70224527","displayToPublicDate":"2021-12-31T09:57:42","publicationYear":"2021","noYear":false,"publicationType":{"id":26,"text":"Extramural-Authored Publication Paper"},"publicationSubtype":{"id":31,"text":"Extramural-Authored Publication"},"title":"Exploring probabilistic seismic risk assessment to monitor the Sendai Framework for Disaster Risk Reduction","docAbstract":"The Sendai Framework for Disaster Risk Reduction (SFDRR) calls upon the systematic collection of damage and loss data between 2015 and 2030 to monitor a number of disaster indicators. These indicators include the number of deaths, number of injured people, number of people affected by disasters, and direct economic losses. These results can then be compared with previous periods in order to track progress in disaster risk reduction. However, there is an important limitation with such an approach when measuring disaster risk due to earthquakes. Even in countries with significant seismic risk, it is plausible to witness a 15 year period without any destructive earthquakes (e.g., Nicaragua, Haiti, Myanmar). This situation can lead to the perception that efficient measures are being undertaken to reduce the impact of earthquakes, when in reality the trend could be the opposite. An alternative approach to monitor the SFDRR indicators is through probabilistic risk models. These models allow the estimation of the indicators of the SFDRR probabilistically (e.g., average annual economic losses, average annual fatalities), which do not depend on the occurrence of destructive events during the period of interest. Although seismic activity can be assumed as stationary over several decades, in order to evaluate the evolution of the SFDRR over these time frames, the consistent updating of the risk model has to be considered in order to reflect the evolution and change of the built environment and its vulnerability, such as the introduction of new design regulations or the implementation of retrofitting campaigns. A comparison of the various risk indicators throughout time allows assessing whether the potential losses caused by earthquakes are decreasing or increasing, as well as where risk reduction measures should be prioritized. This study discusses how the global seismic risk model released in December 2018 by the Global Earthquake Model (GEM) Foundation and its partners can be explored to monitor the SFDRR, and more importantly, how it can be modified to assess which measures should be endorsed to respect the 2030 targets.
","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Silva, V., Calderon, A., Costa, C., Dabbeek, J., Martins, L., Rao, A., Yepes-Estrada, C., Acevedo, A., Crowley, H., Journeay, M., and Pittore, M., 2021, Exploring probabilistic seismic risk assessment to monitor the Sendai Framework for Disaster Risk Reduction, 11 p.","productDescription":"11 p.","ipdsId":"IP-116586","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425821,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/"},{"id":426074,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":true,"publicationStatus":"PW","contributors":{"authors":[{"text":"Silva, V.","contributorId":211393,"corporation":false,"usgs":false,"family":"Silva","given":"V.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823879,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Calderon, A.","contributorId":211395,"corporation":false,"usgs":false,"family":"Calderon","given":"A.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823880,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Costa, C.","contributorId":265967,"corporation":false,"usgs":false,"family":"Costa","given":"C.","affiliations":[{"id":54846,"text":"Global Earthquake Model Foundation","active":true,"usgs":false}],"preferred":false,"id":823881,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dabbeek, J.","contributorId":211396,"corporation":false,"usgs":false,"family":"Dabbeek","given":"J.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823882,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martins, L.","contributorId":211398,"corporation":false,"usgs":false,"family":"Martins","given":"L.","email":"","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823883,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rao, A.","contributorId":211399,"corporation":false,"usgs":false,"family":"Rao","given":"A.","affiliations":[{"id":38243,"text":"GEM Foundation Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823884,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Yepes-Estrada, Catalina","contributorId":222353,"corporation":false,"usgs":false,"family":"Yepes-Estrada","given":"Catalina","email":"","affiliations":[{"id":40531,"text":"Global Earthquake Model Foundation, Pavia, Italy","active":true,"usgs":false}],"preferred":false,"id":823885,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Acevedo, A.","contributorId":211403,"corporation":false,"usgs":false,"family":"Acevedo","given":"A.","email":"","affiliations":[{"id":38244,"text":"Department of Civil Engineering, Universidad EAFIT, Medellin, Colombia","active":true,"usgs":false}],"preferred":false,"id":823886,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Crowley, H.","contributorId":211404,"corporation":false,"usgs":false,"family":"Crowley","given":"H.","email":"","affiliations":[{"id":38245,"text":"EUCENTRE Pavia Italy","active":true,"usgs":false}],"preferred":false,"id":823887,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Journeay, M.","contributorId":211405,"corporation":false,"usgs":false,"family":"Journeay","given":"M.","affiliations":[{"id":38246,"text":"Geological Survey of Canada, Vancouver Canada","active":true,"usgs":false}],"preferred":false,"id":823889,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Pittore, M.","contributorId":211406,"corporation":false,"usgs":false,"family":"Pittore","given":"M.","affiliations":[{"id":38247,"text":"GFZ Potsdam Germany","active":true,"usgs":false}],"preferred":false,"id":823890,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70224525,"text":"70224525 - 2021 - Near real-time updating of pager loss estimates","interactions":[],"lastModifiedDate":"2024-02-21T15:52:50.673441","indexId":"70224525","displayToPublicDate":"2021-12-31T09:51:40","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Near real-time updating of pager loss estimates","docAbstract":"Initial alerts by PAGER (Prompt Assessment of Global Earthquakes for Response) within minutes following an earthquake include several uncertainties, mainly due to potential inaccuracies in location, depth, fault delineation, and shaking estimates. We enhance an updating framework by incorporating early reports of fatalities within the first 24 hours, or so, of an earthquake to update PAGER’s overall fatality estimates and its resulting alert level. Though initial loss reports by officials or the media are uncertain and often undercount the eventual reported impacts, their temporal evolution provides predictive constraints for the PAGER model. The proposed framework helps capture these in a systematic way to minimize potential large fluctuations in PAGER alerts as ShakeMap (the USGS product which estimates how an area is affected by an earthquake) gets updated in the early hours after an earthquake. The new framework also accounts for uncertainties associated with early fatality reports as well as PAGER model-related uncertainties in order to improve the overall impact forecast. This updating framework improves the loss estimate and alert level to the correct level within the first 24 hours even when the initial estimation from PAGER is assumed to be off by two levels of alert, which is plausible due to potential over- or under-estimation of the PAGER model. While test results are very encouraging, our future work aims at implementation of operational PAGER model updating, which entails additional challenges in acquiring useful data, estimating their credibility, and developing rigorously tested operational code and protocols","conferenceTitle":"17th World Conference on Earthquake Engineering, 17WCEE","conferenceDate":"September 13-18, 2020","conferenceLocation":"Sendai, Japan","language":"English","publisher":"Japan Association for Earthquake Engineering","usgsCitation":"Engler, D., Jaiswal, K.S., Noh, H.Y., and Wald, D.J., 2021, Near real-time updating of pager loss estimates, 17th World Conference on Earthquake Engineering, 17WCEE, Sendai, Japan, September 13-18, 2020, 10 p.","productDescription":"10 p.","ipdsId":"IP-116417","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":425820,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425817,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wcee.nicee.org/wcee/seventeenth_conf_sendai_japan/","linkFileType":{"id":5,"text":"html"}}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Engler, Davis 0000-0002-7133-3545","orcid":"https://orcid.org/0000-0002-7133-3545","contributorId":265963,"corporation":false,"usgs":false,"family":"Engler","given":"Davis","affiliations":[{"id":27102,"text":"USGS student contractor","active":true,"usgs":false}],"preferred":false,"id":823867,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaiswal, Kishor S. 0000-0002-5803-8007 kjaiswal@usgs.gov","orcid":"https://orcid.org/0000-0002-5803-8007","contributorId":149796,"corporation":false,"usgs":true,"family":"Jaiswal","given":"Kishor","email":"kjaiswal@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823868,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Noh, Hae Young","contributorId":265961,"corporation":false,"usgs":false,"family":"Noh","given":"Hae","email":"","middleInitial":"Young","affiliations":[{"id":54844,"text":"Carnegie Mellon University (now at Stanford University)","active":true,"usgs":false}],"preferred":false,"id":823869,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":823870,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211491,"text":"70211491 - 2021 - The use of boundary-spanning organizations to bridge the knowledge-action gap in North America","interactions":[],"lastModifiedDate":"2022-04-18T14:54:19.239963","indexId":"70211491","displayToPublicDate":"2021-12-31T09:46:42","publicationYear":"2021","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"The use of boundary-spanning organizations to bridge the knowledge-action gap in North America","docAbstract":"The goals of boundary-spanning organizations include communicating among researchers, stakeholders, and resource managers to improve decision-making. These efforts span public agencies, environmental non-governmental organizations, and private stakeholders and occur throughout Canada, the USA, and Mexico. We describe how the core philosophy of boundary-spanning organizations may help address conservation challenges in these countries. We profile a subset of the more than 100 boundary-spanning organizations, identifying some of their core accomplishments and the challenges they face. Scientific information generally is acknowledged as useful to resource management by recipients of the information. It is more difficult to infer whether the information transmitted by boundary-spanning organizations contributed to conservation decisions or whether the outcomes of those decisions differed from the potential outcomes in the absence of such information. Several examples of sustained enthusiasm for boundary-spanning efforts indicate that the organizations help to bridge the knowledge to action gap in North America.
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Mark W.","contributorId":236772,"corporation":false,"usgs":false,"family":"Schwartz","given":"Mark","email":"","middleInitial":"W.","affiliations":[{"id":47545,"text":"Department of Environmental Science and Policy, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":794307,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fleishman, Erica","contributorId":236773,"corporation":false,"usgs":false,"family":"Fleishman","given":"Erica","affiliations":[{"id":47546,"text":"Department of Environmental Science and Policy, University of California, Davis, and Department of Fish, Wildlife and Conservation Biology, Colorado State University, Fort Collins, CO","active":true,"usgs":false}],"preferred":false,"id":794308,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williamson, Matthew A.","contributorId":236774,"corporation":false,"usgs":false,"family":"Williamson","given":"Matthew A.","affiliations":[{"id":47545,"text":"Department of Environmental Science and Policy, University of California, Davis","active":true,"usgs":false}],"preferred":false,"id":794309,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, John N.","contributorId":236775,"corporation":false,"usgs":false,"family":"Williams","given":"John","email":"","middleInitial":"N.","affiliations":[{"id":47547,"text":"Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional-Unidad Oaxaca, Instituto Politécnico Nacional, Mexico","active":true,"usgs":false}],"preferred":false,"id":794311,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Morelli, Toni Lyn 0000-0001-5865-5294 tmorelli@usgs.gov","orcid":"https://orcid.org/0000-0001-5865-5294","contributorId":197458,"corporation":false,"usgs":true,"family":"Morelli","given":"Toni","email":"tmorelli@usgs.gov","middleInitial":"Lyn","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":794310,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231183,"text":"70231183 - 2021 - Stop 3 – The Petersburg “Granite” redefined: Recognition and implications of Silurian to Devonian rocks in central-eastern Virginia","interactions":[],"lastModifiedDate":"2022-05-03T14:37:56.400105","indexId":"70231183","displayToPublicDate":"2021-12-31T09:22:07","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Stop 3 – The Petersburg “Granite” redefined: Recognition and implications of Silurian to Devonian rocks in central-eastern Virginia","docAbstract":"Introduction Although the Petersburg Granite had long been in practical use as a building stone since the 1830s (Watson, 1906; 1907; 1910; Darton, 1911; Steidtmann, 1945), it was first formally defined as a geologic unit by Anna Jonas on the 1928 geologic map of Virginia. Anna Jonas defined this unit as a Precambrian coarse-grained porphyritic biotite granite that was intruded by finer grained granite and cut by pegmatite (Nelson, 1928). This belt of mostly granitic rocks extends from near Ashland, Virginia north of Richmond, to near Stony Creek, south of Petersburg, Virginia (e.g., Virginia Division of Mineral Resources, 1993) and is bounded by the Hylas fault zone to the northwest, the Mesozoic Richmond basin to the west, and the newly recognized Nottoway River fault zone to the southwest (e.g., Carter and others, 2020; 2021). The eastern boundary of this belt is covered by Coastal Plain sediments, but geophysical and deep borehole data suggest an orogen-scale suture separates it from the Neoproterozoic Chesapeake block to the east (Figure 1; Carter and others, 2021).
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"From the Eastern Piedmont to the Coastal Plain: a cross section through the Richmond Area Fall Zone: Guidebook for 2021 Virginia Geologic Field Conference","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"William and Mary","usgsCitation":"Carter, M.W., McAleer, R.J., Occhi, M., Holm-Denoma, C., Vazquez, J.A., and Owens, B.E., 2021, Stop 3 – The Petersburg “Granite” redefined: Recognition and implications of Silurian to Devonian rocks in central-eastern Virginia, in From the Eastern Piedmont to the Coastal Plain: a cross section through the Richmond Area Fall Zone: Guidebook for 2021 Virginia Geologic Field Conference, p. 18-25.","productDescription":"8 p.","startPage":"18","endPage":"25","ipdsId":"IP-137667","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":400054,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399998,"type":{"id":15,"text":"Index Page"},"url":"https://vgfc.blogs.wm.edu/past-conferences/"}],"country":"United States","state":"Virginia","otherGeospatial":"Petersburg granite","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 36.75\n ],\n [\n -77.25,\n 36.75\n ],\n [\n -77.25,\n 38\n ],\n [\n -78,\n 38\n ],\n [\n -78,\n 36.75\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Carter, Mark W. 0000-0003-0460-7638 mcarter@usgs.gov","orcid":"https://orcid.org/0000-0003-0460-7638","contributorId":4808,"corporation":false,"usgs":true,"family":"Carter","given":"Mark","email":"mcarter@usgs.gov","middleInitial":"W.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"preferred":true,"id":841878,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":841879,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Occhi, Marcie","contributorId":191116,"corporation":false,"usgs":false,"family":"Occhi","given":"Marcie","affiliations":[],"preferred":false,"id":841880,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":841881,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":841882,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Owens, Brent E.","contributorId":178190,"corporation":false,"usgs":false,"family":"Owens","given":"Brent","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":841883,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240354,"text":"70240354 - 2021 - SMaRT: A science-based tiered framework for common ravens","interactions":[],"lastModifiedDate":"2023-02-06T15:23:17.560502","indexId":"70240354","displayToPublicDate":"2021-12-31T09:18:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":13291,"text":"Human–Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"SMaRT: A science-based tiered framework for common ravens","docAbstract":"Large-scale increases and expansion of common raven (Corvus corax; raven) populations are occurring across much of North America, leading to increased negative consequences for livestock and agriculture, human health and safety, and sensitive species conservation. We describe a science-based adaptive management framework that incorporates recent quantitative analyses and mapping products for addressing areas with elevated raven numbers and minimizing potential adverse impacts to sensitive species, agricultural damage, and human safety. The framework comprises 5 steps: (1) desktop analysis; (2) field assessments; (3) comparison of raven density estimates to an ecological threshold (in terms of either density or density plus distance to nearest active or previous nest); (4) prescribing management options using a 3-tiered process (i.e., habitat improvements, subsidy reductions, and direct actions using StallPOPd.V4 software); and (5) post-management monitoring. The framework is integrated within the Science-based Management of Ravens Tool (SMaRT), a web-based application outfitted with a user-friendly interface that guides managers through each step to develop a fully customized adaptive plan for raven management. In the SMaRT interface, users can: (1) interact with pre-loaded maps of raven occurrence and density and define their own areas of interest within the Great Basin to delineate proposed survey or treatment sites; (2) enter site-level density estimates from distance sampling methods or perform estimation of raven densities using the rapid assessment protocol that we provide; (3) compare site-level density estimates to an identified ecological threshold; and (4) produce a list of potential management options for their consideration. The SMaRT supports decision-making by operationalizing scientific products for raven management and facilitates realization of diverse management goals including sensitive species conservation, protection of livestock and agriculture, safeguarding human health, and addressing raven overabundance and expansion. We illustrate the use of the framework through SMaRT using an example of greater sage-grouse (Centrocercus urophasianus) conservation efforts within the Great Basin, USA.
","language":"English","publisher":"Berryman Institute","doi":"10.26077/9f56-ea2c","usgsCitation":"Dettenmaier, S.J., Coates, P.S., Roth, C.L., Webster, S.C., O’Neil, S.T., Holcomb, K.L., Tull, J.C., and Jackson, P.J., 2021, SMaRT: A science-based tiered framework for common ravens: Human–Wildlife Interactions, v. 15, no. 3, p. 575-597, https://doi.org/10.26077/9f56-ea2c.","productDescription":"23 p.","startPage":"575","endPage":"597","ipdsId":"IP-130933","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436077,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B5ANSM","text":"USGS data release","linkHelpText":"Science-based Management of Ravens Tool (SMaRT)"},{"id":412737,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin 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USGS research, which is supported by the USGS and BOEM, includes population monitoring, and quantifying breeding success and predator disturbances. This information is preliminary or provisional and is subject to revision. Learn more: https://www.usgs.gov/centers/alaska-science-center/science/cook-inlet-seabird-and-forage-fish-study
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This paper, the second of two parts, addresses processing the collected data to inform inputs for probabilistic bank erosion estimates in BSTEM. Measuring the intrinsic soil properties for BSTEM is discussed in part one. Soil critical shear stress and soil erodibility coefficients were calibrated by Unified Soil Classification soil type to observed erosion on the American River. Adjustments were made in the probability density functions for these parameters to reflect field-measured variability and carry forward the reduction in error achieved during calibration. The resulting calibrated values were tested at additional sites, validating the resulting critical shear stress and soil erodibility coefficient values and probability density functions for more robust probabilistic bank erosion estimates using BSTEM.","conferenceTitle":"10th International Conference on Scour and Erosion (ICSE-10)","conferenceDate":"October 18-20, 2021","language":"English","publisher":"ASCE","usgsCitation":"Rivas, T.M., AuBuchon, J., Shidlovskaya, A., Langendoen, E., Work, P.A., Livsey, D.N., Timchenko, A., Jemes, K., and Briaud, J., 2021, Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 2: Probabilistic numerical simulation of bank erosion, 10th International Conference on Scour and Erosion (ICSE-10), October 18-20, 2021, 10 p.","productDescription":"10 p.","ipdsId":"IP-116981","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425819,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/publications/publication/risk-informed-levee-erosion-countermeasure-site-selection-and-design-in-the-sacramento-area-part-2-probabilistic-numerical-simulation-of-bank-erosion","linkFileType":{"id":5,"text":"html"}},{"id":425814,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","city":"Sacramento","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62841736139654,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.61545545028389\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivas, Todd M.","contributorId":256771,"corporation":false,"usgs":false,"family":"Rivas","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":51857,"text":"Sacramento District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shidlovskaya, Anna","contributorId":256773,"corporation":false,"usgs":false,"family":"Shidlovskaya","given":"Anna","email":"","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":813242,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813243,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813244,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813245,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Timchenko, Anna","contributorId":334257,"corporation":false,"usgs":false,"family":"Timchenko","given":"Anna","email":"","affiliations":[],"preferred":false,"id":895117,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Jemes, Kellie","contributorId":334259,"corporation":false,"usgs":false,"family":"Jemes","given":"Kellie","email":"","affiliations":[],"preferred":false,"id":895118,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Briaud, Jean-Louis","contributorId":334258,"corporation":false,"usgs":false,"family":"Briaud","given":"Jean-Louis","email":"","affiliations":[],"preferred":false,"id":895119,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70219211,"text":"70219211 - 2021 - Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing","interactions":[],"lastModifiedDate":"2024-02-21T14:39:05.70841","indexId":"70219211","displayToPublicDate":"2021-12-31T08:31:46","publicationYear":"2021","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing","docAbstract":"USACE partnered with the United States Department of Agriculture, Agricultural Research Service, United States Geological Survey, and Texas A&M University to evaluate the erodibility of the river banks and levees to inform probabilistic numerical simulations using the Bank Stability and Toe Erosion Model (BSTEM). This paper discusses the measurement of the intrinsic erosion and geotechnical properties of the soil for improved BSTEM results and is the first of two parts. Sampling and testing were conducted at select sites to obtain the soil stratigraphy and collect samples for estimation of engineering properties. Soil erodibility was measured using the Erosion Function Apparatus test, the Borehole Erosion Test, the Pocket Erodometer Test, and the mini-Jet Erosion Test. Critical evaluation of previously existing datasets and the collection of these new datasets helped to provide better definition of the range in erosion parameters for improved probabilistic erosion estimates using BSTEM for risk-informed levee erosion countermeasure site selection and design.","conferenceTitle":"10th International Conference on Scour and Erosion (ICSE-10)","conferenceDate":"October 18-20, 2021","language":"English","publisher":"ASCE","collaboration":"US Army Corps of Engineers","usgsCitation":"Rivas, T.M., AuBuchon, J., Shidlovskaya, A., Langendoen, E., Work, P.A., Livsey, D.N., Timchenko, A., and Briaud, J., 2021, Risk-informed levee erosion countermeasure site selection and design in the Sacramento area part 1: Soil sampling, testing, and data processing, 10th International Conference on Scour and Erosion (ICSE-10), October 18-20, 2021, 11 p.","productDescription":"11 p.","ipdsId":"IP-117504","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":425813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":425812,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.issmge.org/publications/publication/risk-informed-levee-erosion-countermeasure-site-selection-and-design-in-the-sacramento-area-part-2-probabilistic-numerical-simulation-of-bank-erosion","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","city":"Sacramento","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62841736139654,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.459433276808824\n ],\n [\n -121.38485568051728,\n 38.61545545028389\n ],\n [\n -121.62841736139654,\n 38.61545545028389\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rivas, Todd M.","contributorId":256771,"corporation":false,"usgs":false,"family":"Rivas","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":51857,"text":"Sacramento District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813234,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"AuBuchon, Jonathan","contributorId":256772,"corporation":false,"usgs":false,"family":"AuBuchon","given":"Jonathan","email":"","affiliations":[{"id":51859,"text":"Albuquerque District, United States Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":813235,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shidlovskaya, Anna","contributorId":256773,"corporation":false,"usgs":false,"family":"Shidlovskaya","given":"Anna","email":"","affiliations":[{"id":51860,"text":"Department of Civil Engineering, Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":813236,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":813237,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Work, Paul A. 0000-0002-2815-8040 pwork@usgs.gov","orcid":"https://orcid.org/0000-0002-2815-8040","contributorId":168561,"corporation":false,"usgs":true,"family":"Work","given":"Paul","email":"pwork@usgs.gov","middleInitial":"A.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813238,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Livsey, Daniel N. 0000-0002-2028-6128 dlivsey@usgs.gov","orcid":"https://orcid.org/0000-0002-2028-6128","contributorId":181870,"corporation":false,"usgs":true,"family":"Livsey","given":"Daniel","email":"dlivsey@usgs.gov","middleInitial":"N.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":813239,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Timchenko, Anna","contributorId":334257,"corporation":false,"usgs":false,"family":"Timchenko","given":"Anna","email":"","affiliations":[],"preferred":false,"id":895115,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Briaud, Jean-Louis","contributorId":334258,"corporation":false,"usgs":false,"family":"Briaud","given":"Jean-Louis","email":"","affiliations":[],"preferred":false,"id":895116,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70241610,"text":"70241610 - 2021 - Estimating trends of common raven populations in North America, 1966—2018","interactions":[],"lastModifiedDate":"2024-09-11T16:27:03.40755","indexId":"70241610","displayToPublicDate":"2021-12-31T08:28:07","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1914,"text":"Human-Wildlife Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Estimating trends of common raven populations in North America, 1966—2018","docAbstract":"Over the last half century, common raven (Corvus corax; raven) populations have increased in abundance across much of North America. Ravens are generalist predators known to depredate the eggs and young of several sensitive species. Quantifying raven population increases at multiple spatial scales across North America will help wildlife resource managers identify areas where population increases present the greatest risk to species conservation. We used a hierarchical Bayesian modeling approach to analyze trends of standardized raven counts from 1966 to 2018 using Breeding Bird Survey data within each Level I and II ecoregion of the United States and Canada. We also compared raven abundance within and outside the distributions of 9 sensitive or endangered species. Although we found substantial evidence that raven populations have increased across North America, populations varied in growth rates and relative abundances among regions. We found 73% of Level I (11/15) and II (25/34) ecoregions demonstrated positive annual population growth rates ranging from 0.2–9.4%. We found higher raven abundance inside versus outside the distributions of 7 of the 9 sensitive species included in our analysis. Gunnison sage-grouse (Centrocercus minimus) had the highest discrepancy, with 293% more ravens within compared to outside of their range, followed by greater sandhill crane (Antigone canadensis tabida; 280%), and greater sage-grouse (C. urophasianus; 204%). Only 2 species, least tern (Sternula antillarum) and piping plover (Charadrius melodus), indicated lower raven abundance within relative to outside their distributions. Our findings will help wildlife resource managers identify regional trends in abundance of ravens and anticipate which sensitive species are at greatest risk from elevated raven populations. Future research directed at identifying the underlying regional drivers of these trends could help elucidate the most appropriate and responsive management actions and, thereby, guide the development of raven population management plans to mitigate impacts to sensitive species.
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Seth M. 0000-0003-0444-7881","orcid":"https://orcid.org/0000-0003-0444-7881","contributorId":238889,"corporation":false,"usgs":false,"family":"Harju","given":"Seth","email":"","middleInitial":"M.","affiliations":[{"id":47817,"text":"Heron Ecological","active":true,"usgs":false}],"preferred":false,"id":867486,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867487,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dettenmaier, Seth J. 0000-0001-6325-8808","orcid":"https://orcid.org/0000-0001-6325-8808","contributorId":302087,"corporation":false,"usgs":true,"family":"Dettenmaier","given":"Seth","email":"","middleInitial":"J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867488,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dinkins, Jonathan B.","contributorId":177565,"corporation":false,"usgs":false,"family":"Dinkins","given":"Jonathan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":867489,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":867490,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chenaille, Michael P. 0000-0003-3387-7899 mchenaille@usgs.gov","orcid":"https://orcid.org/0000-0003-3387-7899","contributorId":194661,"corporation":false,"usgs":true,"family":"Chenaille","given":"Michael","email":"mchenaille@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":867491,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70259394,"text":"70259394 - 2021 - Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019","interactions":[],"lastModifiedDate":"2024-10-07T14:00:24.63834","indexId":"70259394","displayToPublicDate":"2021-12-31T08:26:56","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18741,"text":"Incasciences, Revista del Instituto Geológico, Minero y Metalúrgico","active":true,"publicationSubtype":{"id":10}},"title":"Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019","docAbstract":"Using a volcanic monitoring data set from Ubinas volcano, we applied a process-based model of pre-eruptive seismic patterns to the 2019 eruptive episode with the goal of identifying possible seismic precursors in order to help forecast future eruptions. This conceptual model, based on geologic processes, is divided into four seismicity stages: Stage 1. Characterized by the occurrence of deep seismicity associated with deep intrusion(s); Stage 2. Occurrence of distal volcano – tectonic seismicity in response to magma intrusion(s) into the upper crustal reservoir; Stage 3. Dominated by seismicity associated with vent – clearing; and Stage 4. Corresponding to the occurrence of repetitive seismicity related with final magma ascent. \nIn the 2019 eruptive episode, we identified the last three stages: seismicity associated with the intrusion of new magma (Stage 2), seismicity associated with an opened and vent – clearing inside of the volcanic system (Stage 3) and repetitive seismicity that suggested the magma ascent towards shallower depths (Stage 4), however, no surficial lava was observed. Because Ubinas is an active system with frequent eruptions, Stage 2 was very brief; however, we were still able to identify the transition from phreatomagmatic to magmatic activity.\nThe model allows us to provide a process-based interpretation to the volcanic monitoring observations from Ubinas volcano. Additionally, this model will aid in future assessment of unrest and contribute to eruption forecasting.","language":"English","publisher":"Instituto Geológico, Minero y Metalúrgico","usgsCitation":"Ortega, M.A., McCausland, W., White, R., Anccasi, R.M., and Ccallata, B., 2021, Aplicación de un modelo basado en procesos de patrones de sismicidad pre – eruptiva al volcán Ubinas, episodio eruptivo 2019: Incasciences, Revista del Instituto Geológico, Minero y Metalúrgico, v. 1, no. 1, p. 53-61.","productDescription":"9 p.","startPage":"53","endPage":"61","ipdsId":"IP-122050","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":462661,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Peru","otherGeospatial":"Ubinas Volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.92669701072916,\n -16.328296011041587\n ],\n [\n -70.92669701072916,\n -16.37059339221304\n ],\n [\n -70.87807952769546,\n -16.37059339221304\n ],\n [\n -70.87807952769546,\n -16.328296011041587\n ],\n [\n -70.92669701072916,\n -16.328296011041587\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"1","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ortega, Mayra A.","contributorId":344962,"corporation":false,"usgs":false,"family":"Ortega","given":"Mayra","email":"","middleInitial":"A.","affiliations":[{"id":82443,"text":"INGEMMET (Peru)","active":true,"usgs":false}],"preferred":false,"id":915137,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCausland, Wendy 0000-0002-8683-1440","orcid":"https://orcid.org/0000-0002-8683-1440","contributorId":344963,"corporation":false,"usgs":true,"family":"McCausland","given":"Wendy","email":"","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":915138,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"White, Randall A. 0000-0003-4074-8577","orcid":"https://orcid.org/0000-0003-4074-8577","contributorId":344964,"corporation":false,"usgs":false,"family":"White","given":"Randall A.","affiliations":[{"id":82444,"text":"none, retired USGS","active":true,"usgs":false}],"preferred":false,"id":915139,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anccasi, Rosa M.","contributorId":344965,"corporation":false,"usgs":false,"family":"Anccasi","given":"Rosa","email":"","middleInitial":"M.","affiliations":[{"id":82443,"text":"INGEMMET (Peru)","active":true,"usgs":false}],"preferred":false,"id":915140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ccallata, Beto","contributorId":190928,"corporation":false,"usgs":false,"family":"Ccallata","given":"Beto","email":"","affiliations":[],"preferred":false,"id":915141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}