The U.S. Geological Survey National Water Dashboard supplies critical information to decision makers, emergency managers, and the public during extreme hydrologic events (such as droughts and floods) and during normal hydrologic conditions. It informs decision making that can help protect lives and property before and during extreme hydrologic events. The National Water Dashboard draws upon the extensive site-specific hydrologic data housed in the U.S. Geological Survey National Water Information System database (https://doi.org/10.5066/F7P55KJN) and also links to the U.S. Geological Survey WaterAlert system, which provides users with instant and customized updates about water conditions. Overall, the National Water Dashboard is part of the U.S. Geological Survey's effort to respond to 21st century science needs.
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Hydroclimate on ‘Uvea (Wallis et Futuna) is controlled by rainfall associated with the South Pacific Convergence Zone (SPCZ), the southern hemisphere's largest precipitation feature. To extend the short observational precipitation record, the hydrogen isotopic composition of the algal lipid biomarker dinosterol (δ2Hdinosterol) was measured in sediment cores from two volcanic crater lakes on ‘Uvea. The modern lakes differ morphologically and chemically but both contain freshwater within the photic zone, support phytoplankton communities inclusive of dinosterol-producing dinoflagellates, and experience identical climate conditions. δ2Hdinosterol values track lake water isotope ratios, ultimately controlled in the tropics by precipitation amount and evaporative enrichment. However, in 88-m-deep Lac Lalolalo a steadily decreasing trend in sedimentary δ2Hdinosterol values from −227‰ around year 988 CE to modern values as low as −303‰, suggests this lake's evolution from an active volcanic setting to the present system strongly influenced δ2Hdinosterol values. Although current hydrology and water isotope systematics may now reflect precipitation and evaporation in this lake, the interaction between these processes and large changes in basin morphology, geochemistry, and hydrology obstruct the recovery of a climate signal from Lac Lalolalo's sedimentary δ2Hdinosterol records. This work emphasizes the importance of site replication and the use of complementary climate reconstruction tools, especially when using molecular proxies that may be sensitive to more than one environmental parameter. Contrary to its neighbor, duplicate δ2Hdinosterol records from 23-m-deep Lac Lanutavake varied between −277‰ and −297‰ and indicate slightly drier conditions during the time-period known as the Medieval Climate Anomaly (MCA, 950–1250 CE). The δ2Hdinosterol signal in Lac Lanutavake was muted compared to published records from ‘Upolu (Samoa) and Efate (Vanuatu) indicating that ‘Uvea's location is not as sensitive to precipitation variability at sites farther from the SPCZ central axis. Lithogenic runoff proxies combined with δ2Hdinosterol support the interpretation of a relatively dry MCA on ‘Uvea, ‘Upolu, and Efate, potentially due to less intense precipitation, a contracted, or a more zonally oriented SPCZ.
The endangered mountain yellow-legged frog (Rana muscosa) has been reduced to <10 isolated populations in the wild. Due to frequent catastrophic events (floods, droughts, wildfires), the recent dynamics of these populations have been erratic, making the future of the species highly uncertain. In 2018, a recovery plan was developed to improve the species status by reducing the impacts of various threats (predation, disease, habitat destruction), as well as reinforcing wild populations through the reintroduction of captive-bred frogs. The short-term goal stated in this plan was to reach a minimum of 20 populations of 50 adults each (hereafter, the 20/50 target), before the species can be considered for downlisting from the U.S. Endangered Species Act. However, there is no guarantee that this 20/50 target will be sufficient to ensure the species persistence in the long run. Using 19 years of mark-recapture data, we estimated populations' demographic trends and assessed the viability of R. muscosa from a starting state of 20 populations of 50 adults each (i.e., the downlisting criteria). Our results reveal that, from this 20/50 state, the species has high chances of persistence only at a short time horizon (50 years). Moreover, >80% of populations would be extinct 50 years later. Therefore, the species will not be able to persist without implementation of the reintroduction program. We found that it is more important to increase the number of suitable sites occupied by R. muscosa than to simply reinforce or augment existing populations. Expanding the current distribution by establishing new populations at suitable sites, even after the “20 populations” mark has been reached, would increase the likelihood of the species' persistence in the longer term.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12666","usgsCitation":"Chambert, T., Backlin, A.R., Gallegos, E., Baskerville-Bridges, B., and Fisher, R., 2022, Defining relevant conservation targets for the endangered Southern California distinct population segment of the mountain yellow-legged frog (Rana muscosa): Conservation Science and Practice, v. 4, no. 5, e12666, 10 p., https://doi.org/10.1111/csp2.12666.","productDescription":"e12666, 10 p.","ipdsId":"IP-136151","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":448619,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12666","text":"Publisher Index Page"},{"id":397864,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Palomar Mountain, San Bernardino Mountains, San Gabriel Mountains, San Jacinto Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.795166015625,\n 33.95247360616282\n ],\n [\n -116.49902343749999,\n 33.95247360616282\n ],\n [\n -116.49902343749999,\n 34.45674800347809\n ],\n [\n -118.795166015625,\n 34.45674800347809\n ],\n [\n -118.795166015625,\n 33.95247360616282\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.99615478515624,\n 33.35347332342168\n ],\n [\n -116.37268066406249,\n 33.35347332342168\n ],\n [\n -116.37268066406249,\n 33.95475186857191\n ],\n [\n -116.99615478515624,\n 33.95475186857191\n ],\n [\n -116.99615478515624,\n 33.35347332342168\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -116.96319580078125,\n 33.169743600216165\n ],\n [\n -116.74621582031249,\n 33.169743600216165\n ],\n [\n -116.74621582031249,\n 33.35347332342168\n ],\n [\n -116.96319580078125,\n 33.35347332342168\n ],\n [\n -116.96319580078125,\n 33.169743600216165\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"4","issue":"5","noUsgsAuthors":false,"publicationDate":"2022-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambert, Thierry 0000-0002-9450-9080 tchambert@usgs.gov","orcid":"https://orcid.org/0000-0002-9450-9080","contributorId":191979,"corporation":false,"usgs":false,"family":"Chambert","given":"Thierry","email":"tchambert@usgs.gov","affiliations":[],"preferred":false,"id":839217,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Backlin, Adam R. 0000-0001-5618-8426 abacklin@usgs.gov","orcid":"https://orcid.org/0000-0001-5618-8426","contributorId":3802,"corporation":false,"usgs":true,"family":"Backlin","given":"Adam","email":"abacklin@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839218,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gallegos, Elizabeth 0000-0002-8402-2631 egallegos@usgs.gov","orcid":"https://orcid.org/0000-0002-8402-2631","contributorId":1528,"corporation":false,"usgs":true,"family":"Gallegos","given":"Elizabeth","email":"egallegos@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baskerville-Bridges, Bradd","contributorId":289523,"corporation":false,"usgs":false,"family":"Baskerville-Bridges","given":"Bradd","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":839220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fisher, Robert N. 0000-0002-2956-3240","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":51675,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":839221,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228943,"text":"sir20215086 - 2022 - Hydrogeology of aquifers within the Fairport-Lyons channel system and adjacent areas in Wayne, Ontario, and Seneca Counties, New York","interactions":[],"lastModifiedDate":"2026-04-02T19:34:37.460842","indexId":"sir20215086","displayToPublicDate":"2022-03-02T10:40:00","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5086","displayTitle":"Hydrogeology of Aquifers Within the Fairport-Lyons Channel System and Adjacent Areas in Wayne, Ontario, and Seneca Counties, New York","title":"Hydrogeology of aquifers within the Fairport-Lyons channel system and adjacent areas in Wayne, Ontario, and Seneca Counties, New York","docAbstract":"A hydrogeologic investigation was undertaken by the U.S. Geological Survey, in cooperation with the New York State Department of Environmental Conservation, within the areas shown in the Macedon, Palmyra, Newark, and Lyons 7.5-minute quadrangle maps that include parts of Wayne, Ontario, and Seneca Counties in New York. The most productive zone of aquifers within the study area is associated with the Fairport-Lyons glacial-stream channel (hereinafter referred to as the “Fairport-Lyons channel”) in southern Wayne County and adjacent areas. The Fairport-Lyons channel is a west-east-oriented bedrock channel that once served as the outlet for glacial Lake Dawson, which occupied the Genesee Valley near Rochester during the Pleistocene. The Fairport-Lyons channel and intersecting subsidiary channels are hereinafter referred to as the “Fairport-Lyons channel system.” Glacial meltwater eroded this shallow channel network into the underlying bedrock, and the channels subsequently filled with interlayered glaciofluvial sand and gravel and fine-grained lacustrine deposits. These sand and gravel deposits provide the only large supplies of groundwater in Wayne County under unconfined and confined conditions and serve a population of over 20,000 through a combination of domestic and municipal water supply wells. The largest reported well yield, 1,200 gallons per minute, is from an industrial supply well near Newark, N.Y. Much of the sand and gravel within the Fairport-Lyons channel system is generally thinly saturated; however, in three areas—near Macedon, Newark, and Lyons, N.Y.—the saturated thickness of the aquifer is sufficient to support groundwater yields adequate for municipal and industrial use, in part because of induced infiltration from the Erie Canal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215086","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Reynolds, R.J., Heisig, P.M., and Linsey, K.S., 2022, Hydrogeology of aquifers within the Fairport-Lyons channel system and adjacent areas in Wayne, Ontario, and Seneca Counties, New York: U.S. Geological Survey Scientific Investigations Report 2021–5086, 15 p., 2 pls., https://doi.org/10.3133/sir20215086.","productDescription":"Report v, 15 p.; 2 Plates: 36.00 x 24.00 inches and 36.00 x 60.00 inches; Data Release","numberOfPages":"15","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070043","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":502111,"rank":9,"type":{"id":36,"text":"NGMDB Index 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E–E'"},{"id":396440,"rank":2,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5086/sir20215086_plate01.pdf","text":"Plate 1","size":"63.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5086 Plate 1","linkHelpText":"- Surficial geologic map of the Fairport-Lyons channel-system aquifer and adjacent areas in Wayne, Ontario, and Seneca Counties, New York [layered pdf; to toggle layers, download the file (right-click and select \"Save link as...\") and open it with Adobe Acrobat Reader]"},{"id":396653,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215086/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":396445,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5086/images/"},{"id":396439,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N3JVAQ","text":"USGS data release","description":"USGS data 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New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Understanding recruitment dynamics is an essential part of effective fisheries management, whether the focus is on conservation, harvest policy development, or invasive species control. We developed a model that estimates lake-wide Ricker stock-recruitment relations for invasive sea lampreys (Petromyzon marinus) in each of the five Laurentian Great Lakes to inform future control efforts. We fit adult-to-adult models, taking advantage of a long time series of lake-wide, adult, sea lamprey abundance estimates. We incorporated proportional contributions at age for the stock as well as additional explanatory variables sea lamprey weight, as a surrogate for fecundity, and lampricide quantity applied, as a surrogate for anthropogenic mortality, to explain residual recruitment variability. The best model incorporated equal cohort contributions from the adult stock (that matured 5, 6, and 7 years prior to recruitment), a single productivity parameter (
Glen Torridon is a topographic trough located on the slope of Aeolis Mons, Gale crater, Mars. It corresponds to what was previously referred to as the “clay-bearing unit”, due to the relatively strong spectral signatures of clay minerals (mainly ferric smectites) detected from orbit. Starting in January 2019, the Curiosity rover explored Glen Torridon for more than 700 sols (Martian days). The objectives of this campaign included acquiring a detailed understanding of the geologic context in which the clay minerals were formed and determining the intensity of aqueous alteration experienced by the sediments. Here, we present the major-element geochemistry of the bedrock as analyzed by the ChemCam instrument. Our results reveal that the two main types of bedrock exposures identified in the lower part of Glen Torridon are associated with distinct chemical compositions (K-rich and Mg-rich), for which we are able to propose mineralogical interpretations. Moreover, the topmost stratigraphic member exposed in the region displays a stronger diagenetic overprint, especially at two locations close to the unconformable contact with the overlying Stimson formation, where the bedrock composition significantly deviates from the rest of Glen Torridon. Overall, the values of the Chemical Index of Alteration determined with ChemCam are elevated by Martian standards, suggesting the formation of clay minerals through open system weathering. However, there is no indication that the alteration was stronger than in some terrains previously visited by Curiosity, which in turn implies that the enhanced orbital signatures are mostly controlled by non-compositional factors.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JE007103","usgsCitation":"Dehouck, E., Cousin, A., Mangold, N., Frydenvang, J., Gasnault, O., Forni, O., Rapin, W., Gasda, P.J., Caravaca, G., David, G., Bedford, C.C., Lasue, J., Meslin, P., Rammelkamp, K., Desjardins, M., Le Mouelic, S., Thorpe, M.T., Fox, V.K., Bennett, K.A., Bryk, A., Lanza, N.L., Maurice, S., and Wiens, R.C., 2022, Bedrock geochemistry and alteration history of the clay-bearing Glen Torridon region of Gale crater, Mars: Journal of Geophysical Research E: Planets, v. 127, no. 12, e2021JE007103, 29 p., https://doi.org/10.1029/2021JE007103.","productDescription":"e2021JE007103, 29 p.","ipdsId":"IP-134173","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":448627,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1029/2021je007103","text":"External 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We focused on Massachusetts and Connecticut, two states with only a few, small populations still extant, that likely need population restoration to prevent statewide extirpation. We identified that restoration actions aimed at redundancy (number of populations), representation (number of occupied basins), and resiliency (population size) were constrained by resource availability such as limited broodstock , staff time, and budgets. Optimal restoration locations depended on habitat conditions, the status (viability) of nearby mussel populations, population size (number of individuals), and the location within watersheds; all important considerations in addressing population persistence. Restoration actions also accounted for the risk of disease transmission among mussels and fish, and the genetic health and diversity of mussel populations. 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Resources","active":true,"usgs":false}],"preferred":false,"id":947068,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kalasz, Kevin","contributorId":7121,"corporation":false,"usgs":true,"family":"Kalasz","given":"Kevin","affiliations":[],"preferred":false,"id":947069,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Carmignani, Jason","contributorId":360465,"corporation":false,"usgs":false,"family":"Carmignani","given":"Jason","affiliations":[{"id":86008,"text":"Natural Heritage and Endangered Species Program","active":true,"usgs":false}],"preferred":false,"id":947070,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hazelton, Peter","contributorId":360449,"corporation":false,"usgs":false,"family":"Hazelton","given":"Peter","affiliations":[{"id":51525,"text":"Massachusetts Division of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":947071,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kern, Morgan","contributorId":360453,"corporation":false,"usgs":false,"family":"Kern","given":"Morgan","affiliations":[{"id":86005,"text":"SC Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":947072,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Perkins, David","contributorId":202066,"corporation":false,"usgs":true,"family":"Perkins","given":"David","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":947073,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Saucier, Laura","contributorId":360455,"corporation":false,"usgs":false,"family":"Saucier","given":"Laura","affiliations":[{"id":62986,"text":"Connecticut Department of Energy and Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":947074,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Skorupa, Ayla J.","contributorId":340492,"corporation":false,"usgs":false,"family":"Skorupa","given":"Ayla J.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":947075,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Katz, Rachel","contributorId":201422,"corporation":false,"usgs":false,"family":"Katz","given":"Rachel","affiliations":[{"id":6932,"text":"University of Massachusetts, Amherst","active":true,"usgs":false}],"preferred":false,"id":947076,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Coghlan, Christy C.","contributorId":360458,"corporation":false,"usgs":false,"family":"Coghlan","given":"Christy","middleInitial":"C.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":947077,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70232562,"text":"70232562 - 2022 - An efficient, analytic solution using order statistics for probabilistic seismic‐hazard assessment without the Poisson assumption","interactions":[],"lastModifiedDate":"2022-07-07T11:46:57.706851","indexId":"70232562","displayToPublicDate":"2022-03-02T06:44:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"An efficient, analytic solution using order statistics for probabilistic seismic‐hazard assessment without the Poisson assumption","docAbstract":"Standard approaches to probabilistic seismic‐hazard assessment (PSHA) assume that earthquakes are random, independent events that follow a Poisson distribution of occurrences in a given time period (Cornell, 1968). To overcome the limitations of the Poisson assumption, such as ignoring earthquake clustering, we introduce an analytic method for PSHA that uses order statistics to allow for arbitrary distributions of earthquake occurrence. Cornell (1968) used the Poisson assumption to achieve a computationally efficient method that enables users to explore the impact of parameters used in earthquake occurrence and ground‐motion models. We apply our order statistics method to the highly clustered seismicity associated with caldera collapses at Kīlauea and explore the general implications of non‐Poisson behavior for PSHA. We find that non‐Poisson behavior has the greatest impact for high probabilities of exceedance, low‐mean rates of occurrence, and multiple exceedances. Those conditions can be important for applications such as operating standards for buildings and infrastructure engineering, standards for temporary structures and during construction, the insurance industry, the design of earthquake early warning, and to assess hazards due to clustered processes such as aftershock sequences and earthquake swarms. The commonly used rate of exceedance hides the difference between the hazard due to a non‐Poisson distribution and a Poisson distribution with the same mean rate of earthquakes. Thus, including non‐Poisson behavior in PSHA means that we must plot and discuss PSHA results as the probability and not the rate of exceedance.
In arid and semiarid environments, evapotranspiration (ET) is the primary discharge component in the water balance, with potential ET exceeding precipitation. For this reason, reliable estimates of ET are needed to construct accurate water budgets in these environments. Remote sensing affords the ability to provide fast, accurate, field-scale ET estimates, but these methods have largely been restricted to deep rooted (phreatophytic) plant communities underlain by shallow groundwater. We used 13 years of data from a 3-ha drainage lysimeter in a semiarid sagebrush steppe and Landsat normalized difference vegetation index (NDVI) data to calibrate a generalized least squares model capable of predicting vadose zone ET in a high elevation upland ecosystem. Annual precipitation was the best predictor of annual ET, as they were nearly balanced every year analysed (mean difference = 3 mm). We incorporated reference crop ET and a linear combination of NDVI and precipitation to capably predict ET on a subannual, lag-determined interval of 48 days, with a mean error of only 9.92% across all observations. To our knowledge, this is the first vegetation index-ET algorithm calibrated in a semiarid upland plant community using field-scale lysimetry. Vadose zone ET is particularly important at waste disposal sites in the Desert Southwest, where accurate and spatially explicit ET estimates are needed for monitoring potential mobilization and transport of contaminants past the root zone into local aquifers and for monitoring and modelling effects of recharge on flow and transport of contaminants in underlying aquifers.
The National Energy Technology Laboratory, the Japan Oil, Gas and Metals National Corporation, and the U.S. Geological Survey are leading an effort to conduct an extended gas hydrate production test in northern Alaska. The proposed production test required the drilling of an initial stratigraphic test well (STW) to confirm the geologic conditions of the proposed test site. This well was completed in January 2019 in cooperation with the Prudhoe Bay Unit Working Interest Owners. The Prudhoe Bay Unit Hydrate-01 STW was spudded on 10-December-2018. Downhole data acquisition was completed on 25-December-2018, and the rig was released on 01-January-2019. The Hydrate-01 STW was drilled in two sections, including the surface hole that was drilled to a depth of 2248 ft measured depth (MD) (685 m MD) and cased, and the production hole section that was drilled to a depth of 3558 ft MD (1084 m MD) and also cased. A thermally chilled mineral-oil-based mud was used in the main (production) hole section of the well to maintain wellbore stability and quality of the wellbore acquired data. The primary wellbore data were acquired using logging-while-drilling tools. A sidewall pressure core system was also deployed to gather grain size and other data needed for the design of the future production test wells. In addition to confirming the geologic conditions at the test site, the Hydrate-01 STW was designed to serve as a monitoring well during future field operations. Therefore, two sets of fiber-optic cables, each including a bundled distributed acoustic sensor (DAS) and a distributed temperature sensor (DTS), were clamped to the outside of the production casing and cemented in place. In March 2019, the project team acquired three-dimensional (3D) DAS vertical seismic profiling data in the Hydrate-01 STW. Temperature surveys were also acquired with the DTS as deployed in the Hydrate-01 STW during the completion of the well and nearly continuously since March-2019.
U.S. coastal economies and communities are facing an unprecedented and growing number of impacts to coastal ecosystems including beach and fishery closures, harmful algal blooms, loss of critical habitat, as well as shoreline damage. This paper synthesizes our present understanding of the dynamics of human and ecosystem health in coastal systems with a focus on the need to better understand nearshore physical process interactions with coastal pollutants and ecosystems (e.g. fate and transport, circulation, depositional environment, climate change). It is organized around two major topical areas and six subtopic areas: 1) Identifying and mitigating coastal pollutants, including fecal pollution, nutrients and harmful algal blooms, and microplastics; and 2) Resilient coastal ecosystems, which focuses on coastal fisheries, shellfish and natural and nature-based features (NNBF). Societal needs and the tools and technologies needed to address them are discussed for each subtopic. Recommendations for scientific research, observations, community engagement, and policies aim to help prioritize future research and investments. A better understanding of coastal physical processes and interactions with coastal pollutants and resilient ecosystems (e.g. fate and transport, circulation, depositional environment, climate change) is a critical need. Other research recommendations include the need to quantify potential threats to human and ecosystem health through accurate risk assessments and to quantify the resulting hazard risk reduction of natural and nature-based features; improve pollutant and ecosystem impacts forecasting by integrating frequent and new data points into existing and novel models; collect environmental data to calibrate and validate models to predict future impacts on coastal ecosystems and their evolution due to anthropogenic stressors (land-based pollution, overfishing, coastal development), climate change, and sea level rise; and develop lower cost and rapid response tools to help coastal managers better respond to pollutant and ecosystem threats.
","language":"English","publisher":"American Shore and Beach Preservation Association (ASBPA)","doi":"10.34237/1009018","usgsCitation":"Elko, N., Foster, D., Kleinheinz, G., Raubenheimer, B., Brander, S., Kinzelman, J., Kritzer, J.P., Munroe, D., Storlazzi, C.D., Sutula, M., Mercer, A., Coffin, S., Fraioli, C., Ginger, L., Morrison, E., Parent-Doliner, G., Akan, C., Canestrelli, A., DiBenedetto, M., Lang, J., and Simm, J., 2022, Human and ecosystem health in coastal systems: Shore and Beach, v. 90, no. 1, p. 64-91, https://doi.org/10.34237/1009018.","productDescription":"28 p.","startPage":"64","endPage":"91","ipdsId":"IP-137023","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396764,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"90","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Elko, Nicole","contributorId":287920,"corporation":false,"usgs":false,"family":"Elko","given":"Nicole","affiliations":[{"id":61663,"text":"American Shore and Beach Preservation Association","active":true,"usgs":false}],"preferred":false,"id":837196,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Foster, Diane","contributorId":194421,"corporation":false,"usgs":false,"family":"Foster","given":"Diane","affiliations":[],"preferred":false,"id":837197,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kleinheinz, Gregory","contributorId":287921,"corporation":false,"usgs":false,"family":"Kleinheinz","given":"Gregory","affiliations":[{"id":7122,"text":"University of Wisconsin","active":true,"usgs":false}],"preferred":false,"id":837198,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Raubenheimer, Britt","contributorId":194340,"corporation":false,"usgs":false,"family":"Raubenheimer","given":"Britt","email":"","affiliations":[],"preferred":false,"id":837199,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brander, Suzanne","contributorId":287922,"corporation":false,"usgs":false,"family":"Brander","given":"Suzanne","email":"","affiliations":[{"id":25426,"text":"OSU","active":true,"usgs":false}],"preferred":false,"id":837200,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kinzelman, Julie","contributorId":207713,"corporation":false,"usgs":false,"family":"Kinzelman","given":"Julie","affiliations":[{"id":37612,"text":"City of Racine Health Department","active":true,"usgs":false}],"preferred":false,"id":837201,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kritzer, Jacob P.","contributorId":287923,"corporation":false,"usgs":false,"family":"Kritzer","given":"Jacob","email":"","middleInitial":"P.","affiliations":[{"id":61666,"text":"Northeastern Regional Association of Coastal Ocean Observing Systems","active":true,"usgs":false}],"preferred":false,"id":837202,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Munroe, Daphne","contributorId":268069,"corporation":false,"usgs":false,"family":"Munroe","given":"Daphne","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":837203,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Storlazzi, Curt D. 0000-0001-8057-4490","orcid":"https://orcid.org/0000-0001-8057-4490","contributorId":213610,"corporation":false,"usgs":true,"family":"Storlazzi","given":"Curt","middleInitial":"D.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":837204,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sutula, Marta","contributorId":287924,"corporation":false,"usgs":false,"family":"Sutula","given":"Marta","affiliations":[{"id":13211,"text":"Southern California Coastal Water Research Project Authority","active":true,"usgs":false}],"preferred":false,"id":837205,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mercer, Annie","contributorId":287925,"corporation":false,"usgs":false,"family":"Mercer","given":"Annie","email":"","affiliations":[{"id":61663,"text":"American Shore and Beach Preservation Association","active":true,"usgs":false}],"preferred":false,"id":837206,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Coffin, Scott","contributorId":287926,"corporation":false,"usgs":false,"family":"Coffin","given":"Scott","email":"","affiliations":[{"id":12702,"text":"California State Water Resources Control Board","active":true,"usgs":false}],"preferred":false,"id":837207,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Fraioli, Carolyn","contributorId":287927,"corporation":false,"usgs":false,"family":"Fraioli","given":"Carolyn","email":"","affiliations":[{"id":61667,"text":"New York State Department of State","active":true,"usgs":false}],"preferred":false,"id":837208,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ginger, Luke","contributorId":287928,"corporation":false,"usgs":false,"family":"Ginger","given":"Luke","email":"","affiliations":[{"id":61668,"text":"Heal the Bay","active":true,"usgs":false}],"preferred":false,"id":837209,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Morrison, Elise","contributorId":287929,"corporation":false,"usgs":false,"family":"Morrison","given":"Elise","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":837210,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Parent-Doliner, Gabrielle","contributorId":287930,"corporation":false,"usgs":false,"family":"Parent-Doliner","given":"Gabrielle","email":"","affiliations":[{"id":61669,"text":"Water Rangers","active":true,"usgs":false}],"preferred":false,"id":837211,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Akan, Cigdem","contributorId":287931,"corporation":false,"usgs":false,"family":"Akan","given":"Cigdem","email":"","affiliations":[{"id":61670,"text":"University of Northern Florida","active":true,"usgs":false}],"preferred":false,"id":837212,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Canestrelli, Alberto","contributorId":287932,"corporation":false,"usgs":false,"family":"Canestrelli","given":"Alberto","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":837213,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"DiBenedetto, Michelle","contributorId":287933,"corporation":false,"usgs":false,"family":"DiBenedetto","given":"Michelle","email":"","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":837214,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Lang, Jackelyn","contributorId":287934,"corporation":false,"usgs":false,"family":"Lang","given":"Jackelyn","email":"","affiliations":[{"id":16975,"text":"University of California Davis","active":true,"usgs":false}],"preferred":false,"id":837215,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Simm, Jonathan","contributorId":287935,"corporation":false,"usgs":false,"family":"Simm","given":"Jonathan","email":"","affiliations":[{"id":61671,"text":"H.R. Wallingford","active":true,"usgs":false}],"preferred":false,"id":837216,"contributorType":{"id":1,"text":"Authors"},"rank":21}]}} ,{"id":70262486,"text":"70262486 - 2022 - Northern long-eared bats in the central Appalachians following white-nose syndrome: Failed maternity colonies?","interactions":[],"lastModifiedDate":"2025-01-23T21:34:17.142001","indexId":"70262486","displayToPublicDate":"2022-03-01T15:33:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Northern long-eared bats in the central Appalachians following white-nose syndrome: Failed maternity colonies?","docAbstract":"Northern long-eared bat (Myotis septentrionalis) populations have experienced severe declines in eastern North America from white-nose syndrome (WNS), yet potential secondary effects on maternity roosting and recruitment remain largely unknown. We documented female day- roosting at two locations in the central Appalachians of Virginia, Back Creek Mountain (BCM) and Rapidan Camp (RC), during 2015 and 2016, ap- proximately six years after the regional onset of WNS. We compared roost characteristics with available trees and roosts recorded prior to WNS at the Fernow Experimental Forest (FEF), West Virginia, in 2007 and 2008. Roosts at BCM were smaller than pre-WNS roosts but were otherwise similar in terms of stand condition and species use, though bats selected for red maple (Acer rubrum) at BCM rather than black locust (Robinia pseudoacacia) as at FEF. At RC, bats roosted almost exclusively in large eastern hemlock (Tsuga canadensis) snags (dbh x¯ = 50.13 cm, SD = 23.1) with high solar exposure that had been killed by the hemlock woolly adelgid (Adelges tsugae). The two observed strategies, selection of smaller, midstory trees at BCM and of dominant, exposed roosts at RC, correspond with pre-WNS observations of female northern long-eared bat roost use at similar sites. However, our re- sults suggest reliance on smaller roosts and canopy-dominant positions that better accommodate solitary individuals and small groups associated with smaller post-WNS colonies in terms of space and thermoregulatory benefits. Despite some observations of pregnant and lactating individuals, all three post-WNS colonies vacated roost networks in early June, and we observed no juveniles. Potential colony failure at BCM and RC is consistent with pre- dicted secondary physiological effects from WNS-induced population collapses, suggesting, if recruitment failed, northern long-eared bats may already be functionally extirpated in portions of the central Appalachians.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Kalen, N., Muthersbaugh, M.S., Johnson, J., Silvis, A., and Ford, W., 2022, Northern long-eared bats in the central Appalachians following white-nose syndrome: Failed maternity colonies?: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 9, p. 159-167.","productDescription":"9 p.","startPage":"159","endPage":"167","ipdsId":"IP-131875","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480719,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2022/northern-long-eared-bats-central-appalachians-following-white-nose-syndrome-failed","linkFileType":{"id":5,"text":"html"}},{"id":481111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Virginia, West Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.33428751101353,\n 39.425222908113994\n ],\n [\n -80.33428751101353,\n 37.77673487869805\n ],\n [\n -78.39513484183449,\n 37.77673487869805\n ],\n [\n -78.39513484183449,\n 39.425222908113994\n ],\n [\n -80.33428751101353,\n 39.425222908113994\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kalen, Nicholas J.","contributorId":286972,"corporation":false,"usgs":false,"family":"Kalen","given":"Nicholas J.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924340,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Muthersbaugh, Michael S.","contributorId":270636,"corporation":false,"usgs":false,"family":"Muthersbaugh","given":"Michael","email":"","middleInitial":"S.","affiliations":[{"id":36967,"text":"Virginia Tech University","active":true,"usgs":false}],"preferred":false,"id":924341,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Joshua B.","contributorId":270675,"corporation":false,"usgs":false,"family":"Johnson","given":"Joshua B.","affiliations":[{"id":12891,"text":"Pennsylvania Game Commission","active":true,"usgs":false}],"preferred":false,"id":924342,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silvis, Alexander","contributorId":270624,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":56186,"text":"WV DNR","active":true,"usgs":false}],"preferred":false,"id":924343,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":false,"id":924339,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232317,"text":"70232317 - 2022 - Multiple resource limitation of dryland soil microbial carbon cycling on the Colorado Plateau","interactions":[],"lastModifiedDate":"2022-06-27T18:50:56.591006","indexId":"70232317","displayToPublicDate":"2022-03-01T14:50:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Multiple resource limitation of dryland soil microbial carbon cycling on the Colorado Plateau","docAbstract":"Understanding interactions among biogeochemical cycles is increasingly important as anthropogenic alterations of global climate and of carbon (C), nitrogen (N), and phosphorus (P) cycles interactively affect the Earth system. Ecosystem processes in the dryland biome, which makes up over 40% of Earth's terrestrial surface, are often distinctively sensitive to small changes in resource availability, likely because levels of many resources are low. However, data also suggest that simultaneous changes in the availability of multiple resources may be necessary to affect a response in these low-resource systems, offering an opportunity to test patterns and controls of co-limitation, serial limitation, and individual limitation in soil environments. While drylands may play a governing role in key aspects of Earth's C cycle, and while an improved understanding of resource limitation could substantially improve our forecasts of dryland responses to change, our understanding of interacting controls on soil C cycle processes remains notably poor in these dry systems. Here, we address multiple fundamental hypotheses of resource controls over ecosystem function to test how water, C, N, and P regulate soil C cycling individually and interactively in a dryland ecosystem on the Colorado Plateau. Using a series of laboratory incubations, we found that, while water, C, and N limited C cycling through serial limitation, water alone resulted in an extremely small respiratory response from target organisms, whereas water + C resulted in a dramatic increase in soil C cycling, suggesting a degree of functional co-limitation. Nitrogen additions alone resulted in no changes to soil C cycling, but when N was added in concert with water and C, N greatly increased soil C cycling rates relative to additions of water and C without N. Phosphorus additions had no effect on the C cycle either alone or synergistically. These patterns were consistent with the stoichiometry of the system and interactions among resources were surprising in ways that inform our understanding of critical theories in ecology, such as the Transient Maxima Hypothesis, supporting the suggestion that multiple resource limitation explains pulse-dynamic C cycling in drylands better than water limitation alone.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3671","usgsCitation":"Choi, R.T., Reed, S.C., and Tucker, C., 2022, Multiple resource limitation of dryland soil microbial carbon cycling on the Colorado Plateau: Ecology, v. 103, no. 6, e3671; 17 p., https://doi.org/10.1002/ecy.3671.","productDescription":"e3671; 17 p.","ipdsId":"IP-110874","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":448642,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3671","text":"Publisher Index Page"},{"id":435940,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EEDPB0","text":"USGS data release","linkHelpText":"CO2 concentrations and microbial biomass data derived from incubation experiments on soils collected at Arches National Park in 2017 and 2018"},{"id":402544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Arches National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.71427917480467,\n 38.84719484808292\n ],\n [\n -109.71359252929686,\n 38.839707613545144\n ],\n [\n -109.70466613769531,\n 38.838103103640364\n ],\n [\n -109.70603942871092,\n 38.832219591146746\n ],\n [\n -109.69573974609375,\n 38.832754476016575\n ],\n [\n -109.6929931640625,\n 38.825265722073624\n ],\n [\n -109.68612670898438,\n 38.82473078093276\n ],\n [\n -109.68475341796875,\n 38.804935133335775\n ],\n [\n -109.69367980957031,\n 38.80332983969899\n ],\n [\n -109.69711303710938,\n 38.77603431465469\n ],\n [\n -109.6826934814453,\n 38.77442837007637\n ],\n [\n -109.68475341796875,\n 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University","active":true,"usgs":false}],"preferred":false,"id":845240,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reed, Sasha C. 0000-0002-8597-8619 screed@usgs.gov","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":462,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha","email":"screed@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845241,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tucker, Colin 0000-0002-4539-7780 ctucker@usgs.gov","orcid":"https://orcid.org/0000-0002-4539-7780","contributorId":167487,"corporation":false,"usgs":true,"family":"Tucker","given":"Colin","email":"ctucker@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":845242,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70229978,"text":"70229978 - 2022 - What drought means for southwestern landscapes","interactions":[],"lastModifiedDate":"2025-03-14T15:12:55.049862","indexId":"70229978","displayToPublicDate":"2022-03-01T11:48:27","publicationYear":"2022","noYear":false,"publicationType":{"id":25,"text":"Newsletter"},"publicationSubtype":{"id":30,"text":"Newsletter"},"seriesTitle":{"id":8569,"text":"Boatman's Quarterly Review","active":true,"publicationSubtype":{"id":30}},"title":"What drought means for southwestern landscapes","docAbstract":"Introduction Each year, more than 20,000 people raft the Grand Canyon, many of whom will experience this iconic landscape for the first and only time. Visitors to our region for their once-in-a-lifetime Grand Canyon experience might be surprised to see forests and wetlands in addition to deserts. While locals are seeing changes to the Colorado Plateau woodlands, many visitors may not be able to distinguish between our normal desert landscapes (we have cactus!) and the increasingly dry and hot conditions we have experienced in recent decades. Helping visitors see these drought impacts could help communicate that climate change is not a problem for future generations but something affecting us now. The southwestern US (“Southwest”) is one of many dry regions around the world located within about 30 degrees of the equator. As global temperatures rise, these dry zones are getting drier and are likely expanding1. Dryland expansion and aridification alters water availability, which touches our lives and ecosystem health in the Southwest. This essay focuses on drought impacts on ecosystems across the Four-Corners region and Grand Canyon, with particular attention to the forests and woodlands that contribute, in part, to Colorado River flows.
","language":"English","publisher":"Grand Canyon River Guides","usgsCitation":"Samuels-Crow, K., Ogle, K., and Palmquist, E.C., 2022, What drought means for southwestern landscapes: Boatman's Quarterly Review, v. 35, no. 1, p. 16-19.","productDescription":"4 p.","startPage":"16","endPage":"19","ipdsId":"IP-137479","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":407799,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":483349,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.gcrg.org/bqr","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Arizona, California, Nevada","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.85107421875,\n 34.415973384481866\n ],\n [\n -111.24755859375,\n 34.415973384481866\n ],\n [\n -111.24755859375,\n 36.94989178681327\n ],\n [\n -114.85107421875,\n 36.94989178681327\n ],\n [\n -114.85107421875,\n 34.415973384481866\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"35","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Samuels-Crow, Kimberly","contributorId":289104,"corporation":false,"usgs":false,"family":"Samuels-Crow","given":"Kimberly","email":"","affiliations":[{"id":62051,"text":"School of Informatics, Computing, and Cyber Systems; Northern Arizona University, Flagstaff, AZ","active":true,"usgs":false}],"preferred":false,"id":838550,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ogle, Kiona","contributorId":248351,"corporation":false,"usgs":false,"family":"Ogle","given":"Kiona","email":"","affiliations":[],"preferred":false,"id":838551,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Palmquist, Emily C. 0000-0003-1069-2154 epalmquist@usgs.gov","orcid":"https://orcid.org/0000-0003-1069-2154","contributorId":5669,"corporation":false,"usgs":true,"family":"Palmquist","given":"Emily","email":"epalmquist@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":838552,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70221717,"text":"70221717 - 2022 - Status and trends of the Lake Huron prey fish community, 1976-2019","interactions":[],"lastModifiedDate":"2022-04-20T16:44:39.860251","indexId":"70221717","displayToPublicDate":"2022-03-01T11:39:58","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Status and trends of the Lake Huron prey fish community, 1976-2019","docAbstract":"The USGS Great Lakes Science Center has assessed annual changes in the offshore prey fish community of Lake Huron since 1973. Assessments are based on a bottom trawl survey conducted in October of each year and an acoustics-midwater trawl survey, which began in 2004 and is conducted in September-October. Both surveys were completed in their entirety in 2019. Prey fish biomass in Lake Huron in 2019 was dominated by two species, Bloater (Coregonus hoyi) and Rainbow Smelt (Osmerus mordax). In the main basin, prey fish biomass remained below levels observed prior to community-wide declines that began in the early to mid 1990s. Bloater was the most abundant prey fish species in the main basin, whereas Rainbow Smelt was the most abundant prey species in the North Channel and in Georgian Bay. Both surveys suggested that Bloater biomass is increasing in the main basin. Low biomass of invasive species like Alewife (Alosa pseudoharengus) and Rainbow Smelt is consistent with fish community objectives focused on restoration of native fish communities. Abundance of invasive Round Goby (Neogobius melanostomus) in 2019 was low relative to 2018. Biomass of the native Cisco (Coregonus artedi) continued to increase in the North Channel and Georgian Bay. Biomass of slimy sculpin (Cottus cognatus) and Deepwater Sculpin (Myoxocephalus thompsoni) in 2019 was down from 2018 but within the range observed over the past decade. Reduced lake productivity, predation by a recovering piscivore community, and shifts in food web dynamics that favor fish production in nearshore environments may prevent prey fish biomass in offshore areas from returning to levels observed prior to the early 1990’s. However, increased biomass of Bloater and Cisco suggests that lake conditions may favor recovery of native coregonids.
","language":"English","publisher":"Great Lakes Fishery Commission","usgsCitation":"Hondorp, D.W., O’Brien, T.P., Esselman, P., and Roseman, E., 2022, Status and trends of the Lake Huron prey fish community, 1976-2019, 34 p.","productDescription":"34 p.","ipdsId":"IP-126930","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":399168,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399163,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.glfc.org/"}],"country":"Canada, United States","state":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.7705078125,\n 45.81348649679973\n ],\n [\n -84.4189453125,\n 45.5679096098613\n ],\n [\n -83.81469726562499,\n 45.390735154248894\n ],\n [\n -83.507080078125,\n 45.166547157856016\n ],\n [\n -83.38623046875,\n 44.73892994307368\n ],\n [\n -83.507080078125,\n 44.315987905196906\n ],\n [\n -83.9794921875,\n 44.02442151965934\n ],\n [\n -83.95751953125,\n 43.59630591596548\n ],\n [\n -83.60595703125,\n 43.61221676817573\n ],\n [\n -82.891845703125,\n 44.05601169578525\n ],\n [\n -82.46337890625,\n 42.94838139765314\n ],\n [\n -81.705322265625,\n 43.37311218382002\n ],\n [\n -81.683349609375,\n 44.11125397357155\n ],\n [\n -81.134033203125,\n 44.61393394730626\n ],\n [\n -81.49658203125,\n 45.205263456162385\n ],\n [\n -81.024169921875,\n 44.629573191951046\n ],\n [\n -79.95849609375,\n 44.41024041296011\n ],\n [\n -79.903564453125,\n 44.77793589631623\n ],\n [\n -79.56298828125,\n 44.72332018895825\n ],\n [\n -80.145263671875,\n 45.56021795715051\n ],\n [\n -80.804443359375,\n 46.03510927947334\n ],\n [\n -81.58447265624999,\n 46.18743678432541\n ],\n [\n -82.63916015625,\n 46.255846818480315\n ],\n [\n -84.122314453125,\n 46.39998810407942\n ],\n [\n -83.81469726562499,\n 46.17983040759436\n ],\n [\n -83.8916015625,\n 46.126556302418514\n ],\n [\n -83.968505859375,\n 45.98169518512228\n ],\n [\n -84.6826171875,\n 46.11132565729796\n ],\n [\n -84.7705078125,\n 45.81348649679973\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hondorp, Darryl W. 0000-0002-5182-1963 dhondorp@usgs.gov","orcid":"https://orcid.org/0000-0002-5182-1963","contributorId":5376,"corporation":false,"usgs":true,"family":"Hondorp","given":"Darryl","email":"dhondorp@usgs.gov","middleInitial":"W.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818508,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Brien, Timothy P. 0000-0003-4502-5204 tiobrien@usgs.gov","orcid":"https://orcid.org/0000-0003-4502-5204","contributorId":2662,"corporation":false,"usgs":true,"family":"O’Brien","given":"Timothy","email":"tiobrien@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818509,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Esselman, Peter C. 0000-0002-0085-903X","orcid":"https://orcid.org/0000-0002-0085-903X","contributorId":204291,"corporation":false,"usgs":true,"family":"Esselman","given":"Peter C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818510,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roseman, Edward F. 0000-0002-5315-9838","orcid":"https://orcid.org/0000-0002-5315-9838","contributorId":217909,"corporation":false,"usgs":true,"family":"Roseman","given":"Edward F.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":818511,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229221,"text":"70229221 - 2022 - Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire","interactions":[],"lastModifiedDate":"2022-03-03T17:03:57.559584","indexId":"70229221","displayToPublicDate":"2022-03-01T10:59:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3230,"text":"Rangelands","active":true,"publicationSubtype":{"id":10}},"title":"Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire","docAbstract":"• Use of adaptive management supported by robust monitoring is vital to solving severe rangeland problems, such as the exotic annual grass invasion and fire cycle in sagebrush-steppe rangelands.
• Uncertainty in post-fire plant-community composition and plant response to treatments poses a challenge to land management and research but can be addressed with a high density of observations over short time frames.
• The monitoring for adaptive management of the 2015 Soda Megafire area (113,000 Ha) sampled up to 2000 observation plots in each of five post-fire years, and provided important insights on challenges, solutions, and insights that can be applied to monitoring future burned areas.
No abstract available.
","language":"English","publisher":"International Association for Bear Research and Management (IBA) and the IUCN/SSC Bear Specialist Group","usgsCitation":"Johnson, H.E., 2022, Why are human-black bear conflicts increasing? Assessing the mechanisms driving conflicts in Durango, Colorado: International Bear News, v. 31, no. 1, p. 22-24.","productDescription":"3 p.","startPage":"22","endPage":"24","ipdsId":"IP-136095","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":407861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":399732,"type":{"id":15,"text":"Index Page"},"url":"https://www.bearbiology.org/iba-publications/iba-newsletter/"}],"country":"United States","state":"Colorado","city":"Durango","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.95166015624999,\n 37.233061907375195\n ],\n [\n -107.80059814453125,\n 37.233061907375195\n ],\n [\n -107.80059814453125,\n 37.31447530414411\n ],\n [\n -107.95166015624999,\n 37.31447530414411\n ],\n [\n -107.95166015624999,\n 37.233061907375195\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Heather E. 0000-0001-5392-7676 hejohnson@usgs.gov","orcid":"https://orcid.org/0000-0001-5392-7676","contributorId":205919,"corporation":false,"usgs":true,"family":"Johnson","given":"Heather","email":"hejohnson@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":841509,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70262533,"text":"70262533 - 2022 - Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","interactions":[],"lastModifiedDate":"2025-01-23T17:03:28.007798","indexId":"70262533","displayToPublicDate":"2022-03-01T10:58:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains","docAbstract":"In the central Appalachians of Virginia and West Virginia, the Virginia northern flying squirrel (Glaucomys sabrinus fuscus; VNFS) is a subspecies of northern flying squirrel generally associated with red spruce (Picea rubens)-dominated forests at high elevations. Listed as endangered by the
U.S. Fish and Wildlife Service from 1985 to 2013, the VNFS currently is the subject of a 10-year post-delisting assessment. Still considered a state-listed species in Virginia and a species of greatest conservation need in West Virginia, the VNFS serves as a focal target for red spruce restoration activities in the High Allegheny Region (HAR) of the two states. Owing to the cryptic nature of VNFS and its low detection probability in live-capture surveys, managers in the region rely on habitat models to assess probable presence. Using long-term nest-box, live-trapping, and radio-telemetry data matched with updated high elevation forest-type coverage data for the region, we created a new VNFS resource selection function and spatial coverage map. Inputting red spruce cover, increasing elevation, and decreasing landform index (increasing site shelteredness) composed the best model explaining VNFS occurrence. The calculated amount of low-quality habitat was congruent with previous modeling efforts; however, inclusion of more VNFS occurrence records in the current effort indicated that previous efforts substantially underestimated the amount (>400%) of extant high quality VNFS habitat. We estimate the HAR to contain approximately 197,952 ha with ≥0.50 predicted probability of occurrence of VNFS. In addition to potentially improving current and future VNFS live-capture surveys, with this model managers may better target forests for red spruce restoration to increase high elevation forest ecological integrity and to improve habitat patch connectedness for VNFS.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., Diggins, C., De La Cruz, J., and Silvis, A., 2022, Distribution probability of the Virginia northern flying squirrel in the High Allegheny Mountains: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 9, p. 168-175.","productDescription":"8 p.","startPage":"168","endPage":"175","ipdsId":"IP-129565","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia, West Virginia","otherGeospatial":"High Allegheny Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.54094480023322,\n 36.94509861616578\n ],\n [\n -79.74649202772338,\n 36.97475579082641\n ],\n [\n -77.97606039422519,\n 38.33504579423493\n ],\n [\n -77.55782664323384,\n 39.373334368570056\n ],\n [\n -78.88106642468261,\n 39.62337985209078\n ],\n [\n -80.73280550358555,\n 38.17478880030487\n ],\n [\n -80.94924239314835,\n 37.25410993243544\n ],\n [\n -80.54094480023322,\n 36.94509861616578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. 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While expert decisions can add confidence to aspects of the modeling process by ensuring only reasonable models are employed, expert decisions also introduce human bias into assessments. This bias presents a source of error that may affect the performance of the models and resulting resource estimates. Our study aims to reduce expert input through robust data-driven analyses and better-suited data science techniques, with the goals of saving time, reducing bias, and improving predictive ability. We present six favorability maps for geothermal resources in the western United States created using two strategies applied to three modern machine learning algorithms (logistic regression, support-vector machines, and XGBoost). To provide a direct comparison to previous assessments, we use the same input data as the 2008 U.S. Geological Survey (USGS) conventional moderate- to high-temperature geothermal resource assessment. The six new favorability maps required far less expert decision-making, but broadly agree with the previous assessment. Despite the fact that the 2008 assessment results employed linear methods, the non-linear machine learning algorithms (i.e., support-vector machines and XGBoost) produced greater agreement with the previous assessment than the linear machine learning algorithm (i.e., logistic regression). It is not surprising that geothermal systems depend on non-linear combinations of features, and we postulate that the expert decisions during the 2008 assessment accounted for system non-linearities. Substantial challenges to applying machine learning algorithms to predict geothermal resource favorability include severe class imbalance (i.e., there are very few known geothermal systems compared to the large area considered), and while there are known geothermal systems (i.e., positive labels), all other sites have an unknown status (i.e., they are unlabeled), instead of receiving a negative label (i.e., the known/proven absence of a geothermal resource). We address both challenges through a custom undersampling strategy that can be used with any algorithm and then evaluated using F1 scores.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Proceedings, 47th workshop on geothermal reservoir engineering","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"47th Stanford Geothermal Workshop","conferenceDate":"Feb 7-9, 2022","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Mordensky, S.P., Lipor, J., DeAngelo, J., Burns, E., and Lindsey, C.R., 2022, Predicting geothermal favorability in the western United States by using machine learning: Addressing challenges and developing solutions, in Proceedings, 47th workshop on geothermal reservoir engineering, Stanford, CA, Feb 7-9, 2022, 18 p.","productDescription":"18 p.","ipdsId":"IP-135047","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":401053,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401039,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=35430"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.25976562499999,\n 31.728167146023935\n ],\n [\n -103.0078125,\n 31.952162238024975\n ],\n [\n -102.74414062499999,\n 36.80928470205937\n ],\n [\n -103.623046875,\n 41.902277040963696\n ],\n [\n -104.150390625,\n 49.15296965617042\n ],\n [\n -123.134765625,\n 48.980216985374994\n ],\n [\n -123.662109375,\n 48.22467264956519\n ],\n [\n -125.24414062499999,\n 48.22467264956519\n ],\n [\n -124.892578125,\n 42.68243539838623\n ],\n [\n -124.71679687499999,\n 39.774769485295465\n ],\n [\n -120.76171875,\n 34.45221847282654\n ],\n [\n -117.50976562499999,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.76880048488168\n ],\n [\n -110.830078125,\n 31.42866311735861\n ],\n [\n -107.9296875,\n 31.57853542647338\n ],\n [\n -106.25976562499999,\n 31.728167146023935\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mordensky, Stanley Paul 0000-0001-8607-303X","orcid":"https://orcid.org/0000-0001-8607-303X","contributorId":292014,"corporation":false,"usgs":true,"family":"Mordensky","given":"Stanley","email":"","middleInitial":"Paul","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843643,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lipor, John 0000-0002-0990-5493","orcid":"https://orcid.org/0000-0002-0990-5493","contributorId":292015,"corporation":false,"usgs":false,"family":"Lipor","given":"John","email":"","affiliations":[{"id":6929,"text":"Portland State University","active":true,"usgs":false}],"preferred":false,"id":843644,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeAngelo, Jacob 0000-0002-7348-7839 jdeangelo@usgs.gov","orcid":"https://orcid.org/0000-0002-7348-7839","contributorId":237879,"corporation":false,"usgs":true,"family":"DeAngelo","given":"Jacob","email":"jdeangelo@usgs.gov","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843645,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Burns, Erick R. 0000-0002-1747-0506","orcid":"https://orcid.org/0000-0002-1747-0506","contributorId":225412,"corporation":false,"usgs":true,"family":"Burns","given":"Erick R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843646,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lindsey, Cary Ruth 0000-0001-5693-9664","orcid":"https://orcid.org/0000-0001-5693-9664","contributorId":292016,"corporation":false,"usgs":true,"family":"Lindsey","given":"Cary","email":"","middleInitial":"Ruth","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":843647,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256683,"text":"70256683 - 2022 - Interannual colony exchange among breeding Eastern Brown Pelicans","interactions":[],"lastModifiedDate":"2024-08-30T15:49:16.568962","indexId":"70256683","displayToPublicDate":"2022-03-01T10:40:34","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2284,"text":"Journal of Field Ornithology","active":true,"publicationSubtype":{"id":10}},"title":"Interannual colony exchange among breeding Eastern Brown Pelicans","docAbstract":"Colonial seabirds often display high rates of interannual site fidelity to breeding locations, especially as adults. Species using more nearshore or coastal systems, however, may display comparatively less fidelity than highly pelagic species. We used long-term GPS tracking data to assess the frequency of interannual colony switching in Eastern Brown Pelicans (Pelecanus occidentalis carolinensis) in the South Atlantic Bight, a species about which adult site fidelity is poorly understood. By using recursive behavioral patterns to identify nest locations, we report 36% of reproductive attempts occurred at colonies that were different from the colony used in a prior year. Colony switching was especially prevalent in female pelicans, and distances between switched colonies ranged from 56 to 592 km. We suggest that long-term telemetry may be a reliable tool for assessing interannual breeding site fidelity, especially when compared to more traditional band resighting efforts.
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While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world’s rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals.
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These data are available at 1-degree grids along the U.S. coastline and downscaled specifically at NOAA tide-gauge locations. Estimates of flood exposure are assessed using contemporary U.S. coastal flood-severity thresholds for current conditions (e.g., sea levels and infrastructure footprint) and for the next 30 years (out to year 2050), assuming no additional risk reduction measures are enacted.
This effort builds upon the 2017 Task Force report (Sweet et al., 2017). In particular, the set of global mean sea level rise scenarios from that report are updated and downscaled with output directly from the United Nations Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6; IPCC, 2021a), through the efforts of the NASA Sea Level Change Team; updates include adjustments to the temporal trajectories and exceedance probabilities of these scenarios based upon end-of-century global temperatures. As with the 2017 report, these global mean sea level rise scenarios are regionalized for the U.S. coastline. In addition, methodology supporting the U.S. Department of Defense Regional Sea Level (DRSL) database (Hall et al., 2016) is adapted for the extreme water level dataset newly developed for this report.
This report will be a key technical input for the Fifth National Climate Assessment (NCA5). These data and information are being incorporated into current and planned agency tools and services, such as NOAA’s Sea Level Rise Viewer and Inundation Dashboard, NASA’s Sea Level Change Portal, and others. Although the intent of this report is not to provide authoritative guidance or design specifications for a specific project, it is intended to help inform Federal agencies, state and local governments, and stakeholders in coastal communities about current and future sea level rise to help contextualize its effects for decision-making purposes.
","language":"English","publisher":"National Oceanic and Atmospheric Administration","usgsCitation":"Sweet, W., Hamlington, B., Kopp, R.E., Weaver, C., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A.S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K., and Zuzak, C., 2022, Global and regional sea level rise scenarios for the United States: Technical Report NOS.01, xiv, 95 p.","productDescription":"xiv, 95 p.","ipdsId":"IP-135630","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":396604,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":396585,"rank":1,"type":{"id":15,"text":"Index 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The deposit is hosted in the Taconian (approximately 480 – 450 Ma) Martinsville igneous complex, which consists of the Ordovician Leatherwood Granite (granodiorite) and Silurian Rich Acres Formation (diorite). The host rock was metamorphosed to orthogneiss during the Alleghanian orogeny (approximately 325 – 260 Ma) when it also underwent dextral strike-slip movement along the Brookneal shear zone. During the Triassic, extensional tectonics led to the development of the Dan River Basin that lies east of Coles Hill. The mineralized zone is hosted in brittle structures in the footwall of the Triassic Chatham fault that forms the western edge of the basin. Within brittle fracture zones, uranium silicate and uranium-bearing fluorapatite with traces of brannerite form veins and breccia fill with chlorite, quartz, calcite, titanium oxide, pyrite, and calcite. Uranium silicates also coat and replace primary titanite, zircon, ilmenite, and sulfides. Sodium metasomatism preceded and accompanied uranium mineralization, pervasively altering host rock, and forming albite from primary feldspar, depositing limpid albite rims on feldspar, altering titanite to titanium oxide and calcite and forming riebeckite. Various geothermometers suggest temperatures of less than approximately ~200°C during mineralization. In situ U-Pb analyses of titanite, Ti-oxide, and apatite, and Rb/Sr and U/Pb isotope-systematics of whole rock samples resolve the timing of geologic processes affecting Coles Hill. The host Leatherwood granite containing primary euhedral titanite is dated at 450 – 445 Ma, in agreement with previously obtained ages from zircon in the Martinsville igneous complex. A regional metamorphic event at 330 – 310 Ma formed anhedral titanite and some apatite, re-equilibrated whole rock Rb/Sr and U-Pb isotopes and is interpreted to have coincided with movement along the Brookneal shear zone. During shearing and metamorphism primary refractory uranium-bearing minerals including titanite, zircon, and uranothorite were recrystallized and uranium was liberated and incorporated locally into hematite, clay, and other fine-grained minerals. Uranium mineralization was accompanied by a metasomatic episode between 250 and 200 Ma that reset the Rb-Sr and U-Pb isotope systems, forming titanite and apatite that are associated and in places intimately intergrown with uranium silicate dating mineralization. This event coincides with rifting that formed the Dan River Basin and was a precursor to the breakup of Pangea. Based on the close spatial and temporal association of uranium with apatite, we conclude that uranium was carried as a uranyl-phosphate complex. The release of calcium during sodium metasomatic alteration of primary calcic feldspar and titanite in the host rock initiated successive reactions in which uranium and phosphate in mineralizing fluids combined with calcium to form U-enriched fluorapatite. Excess uranium was locally reduced by coupled redox reactions involving ferrous iron and sulfide minerals in the host rock, forming uranium silicates. Based on the deposit mineralogy, oxygen isotope geochemistry and trace element characteristics of uranium silicate and gangue minerals the primary mineralizing fluids likely included connate and/or meteoric water sourced from the local Dan River Basin. High heat flow related to Mesozoic rifting may have driven these (P-Na-F-rich) brines through local aquifers and into basin margin faults, transporting uranium from the basin or mobilizing uranium from previously formed U-minerals in the Brookneal shear zone, or from U-enriched older basement rock.
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Other fish will also be tracked to maintain a holistic view of the transmitter distribution in the Upper Illinois River Waterway. To facilitate these actions, a need to compile and analyze invasive carp-related data from all agencies exists. Invasive carp-related data include all data sources that could inform the MRWG objectives or projects. These data, often in disparate formats, must be integrated into a common format that allows all agencies the opportunity to assess invasive carp monitoring, control, and removal efforts. Ensuring the interoperability of these datasets allows for their use in various analyzes and modeling efforts. Implementing an interoperable data management framework provides the mechanisms for end users to find and use integrated data. Integrating data for use in modeling and analysis furthers the partnership’s collective understanding of bigheaded carp life history, distribution, and movement and can be used to facilitate adaptive management actions (e.g., directing monitoring, sampling, and removal efforts, assessing invasive carp abundance to support modeling efforts, informing deployment of control actions, etc.). An effective data management strategy will streamline the data update process, providing all agencies with timely data and analyses in support of informed decision-making processes.
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