Blue Lake crater (<3 ka) is monogenetic volcano that produced one of the youngest eruptions in the central Oregon Cascades. Understanding monogenetic volcano behavior – from storage through eruption – is imperative in planning for future eruptions. Here we combine physical volcanology and geochemistry to determine the pre-eruptive storage conditions, ascent rate, eruption style, and deposit distribution of this young eruption. We find that the eruption of Blue Lake was initially phreatomagmatic, producing lithic-rich fall deposits and thin surge deposits and excavating the maar crater, before transitioning rapidly to a final voluminous magmatic-volatile driven explosive eruption. The mapped fall deposit has an estimated volume of 3.9 × 107 m3 (2.2 × 107 m3 DRE) which suggests a VEI of 3. Although similar in magnitude (as measured by fall deposit volume) to many other recent cinder cone eruptions in the Cascades, the Blue Lake crater eruption lacks an effusive phase. The absence of lava flows may reflect the lack of evidence for syn-eruptive magma storage at shallow levels. Indeed, corrected volatile contents of olivine-hosted melt inclusions (2.9–4.2 wt% H2O, 910–1330 ppm CO2) are strikingly uniform and indicate storage and crystallization at a restricted pressure range (average ~ 235 MPa), equating to a depth of ~8.6 km. Melt inclusion geochemistry indicates that the basaltic andesite magma cooled and crystallized ~25% during storage at this pressure. Crystals in the Blue Lake magma show evidence of mixing with, or entrainment in, a more evolved magma. Feldspar crystals have large An-rich cores (An80–85) and abrupt An-poor rims (An60–70); olivine crystals have large, broad cores (~Fo82–84) and thin rims with lower Fo and NiO contents. Diffusion modeling of olivine zoning suggests that an intrusion event occurred ~10–60 days prior to eruption. Diffusive loss of H+ from melt inclusions was minimal (<1.3 wt% H2O) during magma ascent, from which we calculate minimum ascent times from 235 MPa of <1 day. Many inclusions indicate ascent times of <3 h, corresponding to ascent rates of ~1 to >13 m/s. This study illustrates the pre-eruptive and eruptive complexities of monogenetic volcanoes and highlights the minimal warning that may precede future eruptions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jvolgeores.2020.107103","usgsCitation":"Johnson, E.R., and Cashman, K., 2020, Understanding the storage conditions and fluctuating eruption style of a young monogenetic volcano: Blue Lake crater (<3 ka), High Cascades, Oregon: Journal of Volcanology and Geothermal Research, v. 408, 107103, 13 p., https://doi.org/10.1016/j.jvolgeores.2020.107103.","productDescription":"107103, 13 p.","ipdsId":"IP-123764","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":392386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Blue Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.79752349853516,\n 44.40202390088682\n ],\n [\n -121.7233657836914,\n 44.40202390088682\n ],\n [\n -121.7233657836914,\n 44.451183121531336\n ],\n [\n -121.79752349853516,\n 44.451183121531336\n ],\n [\n -121.79752349853516,\n 44.40202390088682\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"408","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Johnson, Emily Renee 0000-0002-7967-6913","orcid":"https://orcid.org/0000-0002-7967-6913","contributorId":269628,"corporation":false,"usgs":true,"family":"Johnson","given":"Emily","email":"","middleInitial":"Renee","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":827605,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cashman, Katharine V.","contributorId":40097,"corporation":false,"usgs":false,"family":"Cashman","given":"Katharine V.","affiliations":[],"preferred":false,"id":827606,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70216780,"text":"70216780 - 2020 - Assessing uranium and select trace elements associated with breccia pipe uranium deposits in the Colorado River and main tributaries in Grand Canyon, USA","interactions":[],"lastModifiedDate":"2020-12-10T13:27:37.377726","indexId":"70216780","displayToPublicDate":"2020-11-04T09:31:44","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Assessing uranium and select trace elements associated with breccia pipe uranium deposits in the Colorado River and main tributaries in Grand Canyon, USA","docAbstract":"Assessing chemical loading from streams in remote, difficult-to-access watersheds is challenging. The Grand Canyon area in northern Arizona, an international tourist destination and sacred place for many Native Americans, is characterized by broad plateaus divided by canyons as much as two-thousand meters deep and hosts some of the highest-grade uranium deposits in the U.S. From 2015–2018 major surface waters in Grand Canyon were monitored for select elements associated with breccia-pipe uranium deposits in the area, including uranium, arsenic, cadmium, and lead. Dissolved constituents in the Colorado River were monitored upstream (Lees Ferry), in the middle (Phantom Ranch), and downstream (Diamond Creek) of uranium mining areas. Concentrations of uranium, arsenic, cadmium, and lead at these main-stem sites varied little during the study period and were all well below human health and aquatic life benchmark criteria (30, 10, 5, and 15 μg/L maximum contaminant levels and 15, 150, 0.8, and 3.1 μg/L aquatic life criteria, respectively). Additionally, dissolved and sediment-bound constituents were monitored during a wide range of streamflow conditions at Little Colorado River, Kanab Creek, and Havasu Creek tributaries, whose watersheds have experienced different levels of uranium mining activities over time. Samples from the tributary sites contained ≤3.8 μg/L of dissolved cadmium and lead, and ≤17 μg/L of dissolved uranium. Dissolved arsenic also was mostly below human and aquatic life criteria at Little Colorado River and Kanab Creek; however, 63% of water samples from Havasu Creek were above the maximum contaminant level for arsenic. Arsenic in suspended sediment was greater than sediment quality guidelines in 9%, 35%, and 35% of samples from Little Colorado River, Kanab Creek, and Havasu Creek, respectively. At the concentrations observed during this study, tributaries contributed on average only about 0.12 μg/L of arsenic and 0.03 μg/L of uranium to the main-stem river. This study demonstrates how chemical loading from mined watersheds may be reliably assessed across a wide range of flow conditions in challenging locations.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0241502","usgsCitation":"Tillman, F.D., Anderson, J.R., Unema, J., and Chapin, T., 2020, Assessing uranium and select trace elements associated with breccia pipe uranium deposits in the Colorado River and main tributaries in Grand Canyon, USA: PLoS ONE, v. 15, no. 11, e0241502, 32 p., https://doi.org/10.1371/journal.pone.0241502.","productDescription":"e0241502, 32 p.","ipdsId":"IP-109483","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":454877,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0241502","text":"Publisher Index Page"},{"id":381032,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","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 -114.08203125,\n 35.44277092585766\n ],\n [\n -110.54443359375,\n 35.44277092585766\n ],\n [\n -110.54443359375,\n 36.94989178681327\n ],\n [\n -114.08203125,\n 36.94989178681327\n ],\n [\n -114.08203125,\n 35.44277092585766\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anderson, Jessica R. 0000-0002-3286-7552 jranderson@usgs.gov","orcid":"https://orcid.org/0000-0002-3286-7552","contributorId":193158,"corporation":false,"usgs":true,"family":"Anderson","given":"Jessica","email":"jranderson@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Unema, Joel A. 0000-0002-7428-219X","orcid":"https://orcid.org/0000-0002-7428-219X","contributorId":211449,"corporation":false,"usgs":true,"family":"Unema","given":"Joel A.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":806229,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chapin, Thomas 0000-0001-6587-0734 tchapin@usgs.gov","orcid":"https://orcid.org/0000-0001-6587-0734","contributorId":758,"corporation":false,"usgs":true,"family":"Chapin","given":"Thomas","email":"tchapin@usgs.gov","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":806230,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216761,"text":"70216761 - 2020 - An assessment of the thiamine status of Smallmouth Bass (Micropterus dolomieu) in the Susquehanna River watershed","interactions":[],"lastModifiedDate":"2020-12-04T14:50:36.985453","indexId":"70216761","displayToPublicDate":"2020-11-04T08:47:12","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2898,"text":"Northeastern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"An assessment of the thiamine status of Smallmouth Bass (Micropterus dolomieu) in the Susquehanna River watershed","docAbstract":"Unpredictable recruitment and physical abnormalities (sores and lesions) have been observed in populations of Micropterus dolomieu (Smallmouth Bass) throughout the Susquehanna River basin. Malnutrition has been proposed as one of among several potential stressors, yet little to no information was available to critically assess its feasibility as a causal factor. We measured thiamine profiles of Smallmouth Bass (free thiamine [T], thiamine monophosphate [TP], and thiamine pyrophosphate [TPP]) for 3 tissues (egg, liver, and muscle) collected at 13 sites in the Susquehanna River and compared the values to those in 2 neighboring drainages (Allegheny River and Delaware River). Mass-specific thiamine concentrations in eggs were comparable to published values for Micropterus salmoides (Largemouth Bass), but higher than those found in Sander vitreus (Walleye), and Salvelinus namaycush (Lake Trout) known to consume Alosa pseudoharengus (Alewife), a thiaminase positive forage fish. In general, Smallmouth Bass collected from sites within the Susquehanna River basin had thiamine concentrations comparable to fish at the site in the Allegheny River, yet average thiamine concentrations in fish from the Susquehanna and Allegheny sites were each considerably lower than the average value collected from the Smallmouth Bass in the Delaware River. Future studies should consider a more balanced sampling design among watersheds to assess spatial variability among sites and basins. Average site-specific thiamine concentrations measured in Smallmouth Bass exceeded published minimum threshold values for Lake Trout. Given that Smallmouth Bass appear to have distinct thiamine profiles, concentrations, and timing of egg development, threshold thiamine concentrations parameterized for salmonids may not apply to Smallmouth Bass. As such, empirical studies that parameterize species-specific thiamine thresholds are needed to formally evaluate if thiamine deficiency is an issue for Smallmouth Bass in the Susquehanna River basin. To our knowledge, these are the first data on thiamine concentrations published for Smallmouth Bass.
The processes controlling advance and retreat of outlet glaciers in fjords draining the Greenland Ice Sheet remain poorly known, undermining assessments of their dynamics and associated sea-level rise in a warming climate. Mass loss of the Greenland Ice Sheet has increased six-fold over the last four decades, with discharge and melt from outlet glaciers comprising key components of this loss. Here we acquired oceanographic data and multibeam bathymetry in the previously uncharted Sherard Osborn Fjord in northwest Greenland where Ryder Glacier drains into the Arctic Ocean. Our data show that warmer subsurface water of Atlantic origin enters the fjord, but Ryder Glacier’s floating tongue at its present location is partly protected from the inflow by a bathymetric sill located in the innermost fjord. This reduces under-ice melting of the glacier, providing insight into Ryder Glacier’s dynamics and its vulnerability to inflow of Atlantic warmer water.
Earthquake early warning (EEW) can be used to detect earthquakes and provide advanced notification of strong shaking, allowing pre-emptive actions to be taken that not only benefit infrastructure but reduce injuries and fatalities. Currently Aotearoa New Zealand does not have a nationwide EEW system, so a survey of the public was undertaken to understand whether EEW was considered useful and acceptable by the public, as well as perceptions of how and when such warnings should be communicated, before making an investment in such technology. We surveyed the public’s perspectives (N = 3084) on the usefulness of EEW, preferred system attributes, and what people anticipated doing on receipt of a warning. We found strong support for EEW, for the purposes of being able to undertake actions to protect oneself and others (e.g. family, friends, and pets), and to mentally prepare for shaking. In terms of system attributes, respondents expressed a desire for being warned at a threshold of shaking intensity MM5–6. They suggested a preference for receiving a warning via mobile phone, supported by other channels. In addition to being warned about impending shaking, respondents wanted to receive messages that alerted them to other attributes of the earthquake (including the possibility of additional hazards such as tsunami), and what actions to take. People’s anticipated actions on receipt of a warning varied depending on the time available from the warning to arrival of shaking. People were more likely to undertake quicker and easier actions for shorter timeframes of <10 s (e.g., stop, mentally prepare, take protective action), and more likely to move to a nearby safe area, help others, look for more information, or take safety actions as timeframes increased. Given the public endorsement for EEW, information from this survey can be used to guide future development in Aotearoa New Zealand and internationally with respect to system attributes, sources, channels and messages, in ways that promote effective action.
Ecological forecasts of the extent and impacts of invasive species can inform conservation management decisions. Such forecasts are hampered by ecological uncertainties associated with non-analog conditions resulting from the introduction of an invader to an ecosystem. We developed a state-and-transition simulation model tied to a fire behavior model to simulate the spread of buffelgrass (Cenchrus ciliaris) in Saguaro National Park, AZ, USA over a 30-year period. The simulation models forecast the potential extent and impact of a buffelgrass invasion including size and frequency of fire events and displacement of saguaro cacti and other native species. Using simulation models allowed us to evaluate how model uncertainties affected forecasted landscape outcomes. We compared scenarios covering a range of parameter uncertainties including model initialization (landscape susceptibility to invasion) and expert-identified ecological uncertainties (buffelgrass patch infill rates and precipitation). Our simulations showed substantial differences in the amount of buffelgrass on the landscape and the size and frequency of fires for dry years with slow patch infill scenarios compared to wet years with fast patch infill scenarios. We identified uncertainty in buffelgrass patch infill rates as a key area for research to improve forecasts. Our approach could be used to investigate novel processes in other invaded systems.
Ptilimnium nodosum (Rose) Mathias (harperella) is an endangered plant species found in Maryland, Virginia, and West Virginia, as well as in other locations throughout the southeastern United States. The narrow range of habitat characteristics for areas in which harperella has been found makes locating potential occurrence sites difficult and attempts at reintroduction of the plant relatively unsuccessful. Sightings of harperella have been made along the banks and in-channel bars of the Potomac River, along the Chesapeake and Ohio Canal National Historic Park, and within the Sideling Hill Wildlife Refuge near Hancock, Md. The large area covered by these sightings presents logistical challenges for repeat studies of harperella growth within the Park and in nearby areas. This study developed a geospatial method for characterizing harperella habitat through remote sensing, geospatial analysis, and field investigation. A geospatial prediction model was developed to model the habitat characteristics discussed in literature and found at harperella field observation sites in order to narrow the potential area for observation of the plant and its habitat. Analysis of historical aerial imagery was conducted within the space of the Potomac River to observe the persistence and flooding conditions of in-channel bars. The products of the geospatial prediction model and the historical aerial image analysis are a geospatial description of where harperella habitat is most likely to be found, as well as a map of in-channel bar locations and their persistence through time. From these two analyses, areas were identified that merited detailed observation. Very high resolution, unmanned aerial systems imagery was collected for 10 sites within this area in the Potomac River in June 2019. Unmanned aerial systems imagery has the potential to greatly improve detailed study of the harperella plant, as it provides the spatial resolution necessary to catalog detailed vegetation conditions (and potentially species identification). More importantly, the timing of imagery collection can be aligned carefully with the plant’s phenological patterns and local weather conditions to maximize cost-effectiveness of repeated imaging for specific areas.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201088","usgsCitation":"DeWitt, J.D., O’Pry, K.L., Chirico, P.G., and Young, J.A., 2020, Investigation of suitable habitat for the endangered plant Ptilimnium nodosum (Rose) Mathias (harperella) using remote sensing and field analysis—Documentation of methods and results: U.S. Geological Survey Open-File Report 2020–1088, 59 p., https://doi.org/10.3133/ofr20201088.","productDescription":"Report: vii, 59 p.; Data Release","numberOfPages":"59","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113590","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":380054,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1088/ofr20201088.pdf","text":"Report","size":"26.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1088"},{"id":380053,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1088/coverthb.jpg"},{"id":380055,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NG1QSQ","text":"USGS data release","linkHelpText":"Data associated with the investigation of suitable habitat for the endangered plant harperella (Ptilimnium nodosum Rose) in the Potomac River near Hancock, Maryland"}],"country":"United States","state":"Maryland, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78.27896118164061,\n 39.36190883564925\n ],\n [\n -77.69119262695312,\n 39.36190883564925\n ],\n [\n -77.69119262695312,\n 39.675484393594814\n ],\n [\n -78.27896118164061,\n 39.675484393594814\n ],\n [\n -78.27896118164061,\n 39.36190883564925\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Florence Bascom Geoscience Center
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The Earth Mapping Resources Initiative (Earth MRI) was developed by the U.S. Geological Survey in response to a Federal directive calling on various Federal agencies to address potential vulnerabilities in the Nation’s supply of critical mineral resources. The primary purpose of this initiative is to identify potentially mineralized areas containing critical minerals by gathering new basic geologic data about the United States and its territories and to make these data publicly available through the Earth MRI web portal (https://usgs.gov/earthmri). The gathering of data is accomplished through geophysical surveys, geologic mapping, and the collection of topographical (light detection and ranging, or lidar) data. In addition to identifying areas permissive for hosting critical minerals, the new data collected are likely to be helpful in addressing other societal needs as well, such as by helping to locate groundwater resources, providing information needed to mitigate effects of natural hazards, and identifying locations of the mineral resources useful for restoring and rebuilding aging infrastructure in the United States.
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Mineral Resources Program
U.S. Geological Survey
913 National Center
12201 Sunrise Valley Drive
Reston, VA 20192
A crude-oil spill occurred in 1979 when a pipeline burst near Bemidji, Minnesota. More than 70 percent of the 1.7 million liters of spilled crude oil was removed shortly thereafter. In response to a requirement by the State regulatory agency to remove the remaining crude to a sheen in all wells, in 1998, the pipeline company installed a dual-pump recovery system at the site. This additional remediation from 1999 to 2003 resulted in removal of about 115,000 liters of crude oil, representing between 36 and 41 percent of the volume of oil (281,000–317,000 liters) estimated to be present in 1998. Effects of the 1999–2003 remediation on groundwater plumes and unsaturated-zone vapor concentrations were evaluated by the U.S. Geological Survey using several methods including measurements of oil thicknesses in wells; field water-quality properties of dissolved oxygen, specific conductance, temperature, and pH in groundwater; and vapor concentrations of methane, carbon dioxide, nitrogen, and oxygen in the unsaturated zone.
Although the recovery system decreased oil thicknesses near the remediation wells, average oil thicknesses measured in all wells at the site were not reduced substantially. Dissolved oxygen and specific conductance measurements indicate that a secondary plume was created during the remediation, caused by the disposal of pumped water from the remediation wells in an upgradient infiltration gallery. This plume expanded rapidly immediately after the start of the remediation in 1999, resulting in expansion of the anoxic zone of groundwater upgradient and beneath the existing natural attenuation plume. Beginning in 2000–1, for example, specific conductance concentrations noticeably increased in many wells at the north oil pool from about 400 to more than 700 microsiemens per centimeter. The rapid expansion of the anoxic and elevated specific conductance plume indicates that the remediation contributed substantial amounts of biodegradable dissolved organic carbon to groundwater through the infiltration gallery. The trends in vapor data collected before, during, and after the remediation generally support the research hypothesis that crude-oil removal would have an insignificant effect on vapor concentrations in the unsaturated zone. Although there were some small changes in the concentration of methane, carbon dioxide, nitrogen, and oxygen in the unsaturated zone, these changes were not coincident with the beginning or cessation of the remediation and are therefore thought to be the result of other factors affecting biodegradation rates. A decrease in methane concentrations in one representative well, for example, is thought to be the result of reduced rates of biodegradation and methane production from the increasingly more weathered crude oil. Oil-phase recovery at this site was determined to be challenging and resulted in considerable volumes of mobile and entrapped oil remaining in the subsurface despite remediation efforts.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205111","collaboration":"Toxic Substances Hydrology Program","usgsCitation":"Delin, G.N., Herkelrath, W.N., and Trost, J.J., 2020, Effects of a crude-oil recovery remediation system operated 1999–2003 on groundwater plumes and unsaturated-zone vapor concentrations at a crude-oil spill site near Bemidji, Minnesota: U.S. Geological Survey Scientific Investigations Report 2020–5111, 31 p., https://doi.org/10.3133/sir20205111.","productDescription":"Report: vii, 31 p.; Data Releases","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-112190","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5111/coverthb.jpg"},{"id":380006,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","description":"USGS Data Release","linkHelpText":"USGS water data for the Nation"},{"id":380005,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FJ8I0P","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data sets from the National Crude Oil Spill Fate and Natural Attenuation Research site near Bemidji, Minnesota, USA (ver 3.0, April 2020)"},{"id":380004,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5111/sir20205111.pdf","text":"Report","size":"1.60 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5111"}],"country":"United States","state":"Minnesota","city":"Bemidji","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.98779296875,\n 47.416937456635445\n ],\n [\n -94.7900390625,\n 47.416937456635445\n ],\n [\n -94.7900390625,\n 47.537601245618134\n ],\n [\n -94.98779296875,\n 47.537601245618134\n ],\n [\n -94.98779296875,\n 47.416937456635445\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
2280 Woodale Drive
Mounds View, MN 55112
Urbanization substantially alters the landscape in ways that can impact stream hydrology, water chemistry, and the health of aquatic communities. Stormwater best management practices (BMPs) are the primary tools used to mitigate the effects of urban stressors such as increased runoff, decreased baseflow, and increased nutrient and sediment transport. To date, Fairfax County Virginia’s stormwater management program has made substantial investments into the implementation of both structural and nonstructural BMPs aimed at restoring and protecting watersheds. The U.S. Geological Survey (USGS), in cooperation with Fairfax County, Virginia, established a long-term water-resources monitoring program to evaluate the watershed-scale effects of these investments. Monitoring began at 14 stations in 2007 and was expanded to 20 stations in 2013. This report utilized the first 10 years of data collection to (1) assess water quantity and quality, as well as ecological condition; (2) compute annual nutrient and sediment loads; and (3) evaluate trends in streamflow, water quality, and ecological condition. Efforts are underway to link the biotic and abiotic patterns described herein to watershed management practices as well as factors such as land use change, public works infrastructure, and climate.
Hydrologic, chemical, and benthic macroinvertebrate community conditions in the streams monitored were similar to those observed in other studies of urban streams. Multidecadal trends in baseflow indices and runoff ratios at long-term Chesapeake Bay Non-tidal Network streamgages (CB-NTN) indicate a decrease in groundwater recharge and increase in storm runoff as a result of urbanization. Streamflow yields varied spatially with land cover, geology, and soil characteristics, whereas flashiness was positively related to impervious area. Dissolved oxygen typically was lowest in the Coastal Plain and across all Triassic Lowlands streams, and highest in the Piedmont. Dissolved oxygen concentrations generally were above Virginia’s minimum criterion of 4.0 milligrams per liter (mg/L), most violations occurred at Paul Spring Branch in the Coastal Plain during the warmest months of the year owing to increased chemical and biological oxygen demand. Typical pH values of the monitored streams centered on neutrality (pH = 7); however, diurnal fluctuations were most prevalent in the continuous pH data at Flatlick Branch (FLAT; a Triassic Lowlands station), as a result of increased photosynthesis catalyzed by phosphorus-rich geology. Specific conductance (SC) varied spatially owing to geology (highest at Triassic Lowlands stations) and anthropogenic disturbance (watersheds with high impervious land cover). Specific conductance typically was inversely related to streamflow except in winter months following deicing road salt applications, when values increased by several orders of magnitude. A significant increase in SC of about 2 percent per year was observed from the combined trend result of all monitoring stations over the 10-year period. Significant SC increases occurred at nearly all monitoring stations. Increasing trends were observed during winter and nonwinter months, which suggests that salts applied to deice roadways and other impervious surfaces are stored in the environment and released year-round.
Suspended-sediment (SS) concentrations in monthly samples did not vary significantly between most stations, but typically were highest in the spring and lowest in the fall as a result of seasonal differences in streamflow and climate. Suspended-sediment yields ranged from 62 to 1,428 tons per square mile (ton/mi2), with a median of 302 ton/mi2. Annual loads were greatest during the wettest water years (October 1-September 30; 2008, 2011, and 2014), with the greatest interannual variability occurring at Difficult Run above Fox Lake (DIFF) and South Fork Little Difficult Run (SFLIL). Suspended sediment was primarily composed of silts and clays; however, the proportion of sand in suspended sediment was related positively to streamflow. Cross-correlation analyses suggested the dominant sources of SS were streambank erosion and resuspension of in-channel material at DIFF and FLAT; whereas, upland sources and erosion of upper streambanks were more common at Dead Run (DEAD), Long Branch (LONG), and SFLIL.
Median total phosphorus (TP) concentrations ranged from 0.016 to 0.077 mg/L, with a networkwide median of 0.022 mg/L, were highest in the warm season (April-September), and were composed primarily of dissolved phosphorous. Although TP concentrations were relatively low across the network, the highest concentrations were consistently at stations located in the Triassic Lowlands, owing to phosphorous-rich geology, and in the Coastal Plain, owing to the low-phosphorous sorptive capacity of those soils. A significant increase in TP concentration occurred in a few stations, but the combined trend results from all stations demonstrated a significant increase of about 4 percent per year. Networkwide increases were also observed in total dissolved phosphorus, orthophosphate, and total particulate phosphorus. The composition of TP shifted from dissolved to particulate as streamflow increased and for this reason loads primarily were composed of particulate phosphorous. Median annual TP loads were highest at FLAT and DEAD and ranged from 247 to 642 pounds per square mile (lbs/mi2) networkwide. Interannual variability in phosphorous yields was apparent at most stations; the highest loading years were also the wettest years during the study period and coincident with the highest peak annual flows.
Total nitrogen (TN) concentrations typically were low throughout the network with exceptions occurring at stations located in watersheds with a high density of septic infrastructure. Elevated TN concentrations also were observed in some watersheds without a high density of septic systems and may be attributable to geologic and soil properties that limit denitrification as well as other unknown anthropogenic inputs. Total nitrogen typically was dominated by nitrate during baseflows; however, the proportion of particulate nitrogen increased during stormflows. Total nitrogen yields were similar across stations, with medians ranging from about 3,600 to 6,300 lbs/mi2 and were related to annual streamflow volume. Total nitrogen concentrations and flow-normalized concentrations decreased over the 10-year period at 7 stations, with median reductions of about 2.5 percent. Increasing trends were observed at the two stations with the highest median TN concentration (Captain Hickory Run and SFLIL, 3–5 mg/L), both watersheds contain a high density of septic infrastructure. The combined trend results from all stations revealed no trend in TN and a declining trend in nitrate of about 2 percent per year.
Overall, benthic community metrics indicated that streams throughout Fairfax County were initially of poor health; however, many metrics show an improving trend (from poor to fair based on the Fairfax County Index of Biological Integrity [IBI]). Significant increasing trends in IBI occurred at the network-scale and at 4 individual stations; additionally, scores improved by at least 1 qualitative category (for example, poor to fair, fair to good) at 11 of the 14 stations between 2009 (the first year all 14 stations were sampled) and 2017. Changes in all metrics suggest that the biodiversity, function, and condition of streams in Fairfax County are improving, but some of these improvements are driven by increased diversity and percent composition of organisms that are tolerant of the urban environment.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205061","collaboration":"Prepared in cooperation with Fairfax County, Virginia","usgsCitation":"Porter, A.J., Webber, J.S., Witt, J.W., and Jastram, J.D., 2020, Spatial and temporal patterns in streamflow, water chemistry, and aquatic macroinvertebrates of selected streams in Fairfax County, Virginia, 2007–18: U.S. Geological Survey Scientific Investigations Report 2020–5061, 106 p., https://doi.org/10.3133/sir20205061.","productDescription":"Report: xii, 106 p.; Data Release","numberOfPages":"106","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113872","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":378258,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95S9RFV","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess spatial 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Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 E. Parham Road
Richmond, VA 23228
Seismic tomography of the crust is an essential tool for studying the three-dimensional structure of magmatic plumbing systems feeding active volcanoes, but it is often limited in resolution by the absence of deep local seismicity. Teleseismic receiver functions can be used to illuminate local structural variations, but typically do not account for the effects of three-dimensional velocity heterogeneities. Here we harness the complementary strengths of both techniques by processing Ps-P delay times derived from teleseismic receiver functions in a tomographic S wave inversion. Using our inversion technique, we produce the first tomographic crustal velocity model beneath Cleveland Volcano, identifying a vertically extensive high VP/VS anomaly beneath the volcano that likely signifies a middle-to-lower crustal magma reservoir. The observation is the first of its kind in the central Aleutians, illustrating the potential of our technique to advance our understanding of crustal magmatic systems without broad seismic networks or distributed local seismicity.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL090406","usgsCitation":"Portner, D.E., Wagner, L.S., Janiszewski, H., Roman, D., and Power, J., 2020, Ps-P tomography of a mid-crustal magma reservoir beneath Cleveland Volcano, Alaska: Geophysical Research Letters, v. 47, no. 22, e2020GL090406, 10 p., https://doi.org/10.1029/2020GL090406.","productDescription":"e2020GL090406, 10 p.","ipdsId":"IP-122139","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467273,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl090406","text":"Publisher Index Page"},{"id":463318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -137.62972271870777,\n 62.53157034065521\n ],\n [\n -166.0668168406847,\n 60.92923266584569\n ],\n [\n -172.4889739050872,\n 51.59695140768429\n ],\n [\n -129.83885301135155,\n 54.56061051314896\n ],\n [\n -137.62972271870777,\n 62.53157034065521\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"47","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Portner, Daniel E. 0000-0002-3478-6203","orcid":"https://orcid.org/0000-0002-3478-6203","contributorId":207877,"corporation":false,"usgs":false,"family":"Portner","given":"Daniel","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":917245,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wagner, Lara S.","contributorId":206726,"corporation":false,"usgs":false,"family":"Wagner","given":"Lara","email":"","middleInitial":"S.","affiliations":[{"id":30217,"text":"Carnegie Institution for Science","active":true,"usgs":false}],"preferred":false,"id":917246,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janiszewski, H.A.","contributorId":345682,"corporation":false,"usgs":false,"family":"Janiszewski","given":"H.A.","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":917247,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Roman, Diana","contributorId":237832,"corporation":false,"usgs":false,"family":"Roman","given":"Diana","affiliations":[{"id":47620,"text":"Dept. of Terrestrial Magnetism, Carnegie Institution for Science, Washington DC 20015","active":true,"usgs":false}],"preferred":false,"id":917248,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Power, John 0000-0002-7233-4398","orcid":"https://orcid.org/0000-0002-7233-4398","contributorId":215240,"corporation":false,"usgs":true,"family":"Power","given":"John","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":917249,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70222400,"text":"70222400 - 2020 - More fault connectivity Is needed in seismic hazard analysis","interactions":[],"lastModifiedDate":"2021-07-27T12:11:09.800683","indexId":"70222400","displayToPublicDate":"2020-11-03T07:02:03","publicationYear":"2020","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":"More fault connectivity Is needed in seismic hazard analysis","docAbstract":"Did the third Uniform California Earthquake Rupture Forecast (UCERF3) go overboard with multifault ruptures? Schwartz (2018) argues that there are too many long ruptures in the model. Here, I address his concern and show that the UCERF3 rupture‐length distribution matches empirical data. I also present evidence that, if anything, the UCERF3 model could be improved by adding more connectivity to the fault system. Adding more connectivity would improve model misfits with data, particularly with paleoseismic data on the southern San Andreas fault; make the model less characteristic on the faults; potentially improve aftershock forecasts; and reduce model sensitivity to inadequacies and unknowns in the modeled fault system.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120200119","usgsCitation":"Page, M.T., 2020, More fault connectivity Is needed in seismic hazard analysis: Bulletin of the Seismological Society of America, v. 111, no. 1, p. 391-397, https://doi.org/10.1785/0120200119.","productDescription":"7 p.","startPage":"391","endPage":"397","ipdsId":"IP-116797","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":387458,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"111","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-11-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Page, Morgan T. 0000-0001-9321-2990 mpage@usgs.gov","orcid":"https://orcid.org/0000-0001-9321-2990","contributorId":3762,"corporation":false,"usgs":true,"family":"Page","given":"Morgan","email":"mpage@usgs.gov","middleInitial":"T.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"preferred":true,"id":819937,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218485,"text":"70218485 - 2020 - Effects of snake fungal disease on short‐term survival, behavior, and movement in free‐ranging snakes","interactions":[],"lastModifiedDate":"2021-03-02T13:01:44.774067","indexId":"70218485","displayToPublicDate":"2020-11-03T06:58:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Effects of snake fungal disease on short‐term survival, behavior, and movement in free‐ranging snakes","docAbstract":"Pathogenic fungi are increasingly associated with epidemics in wildlife populations. Snake fungal disease (SFD, also referred to as Ophidiomycosis) is an emerging threat to snakes, taxa that are elusive and difficult to sample. Thus, assessments of the effects of SFD on populations have rarely occurred. We used a field technique to enhance detection, Passive Integrated Transponder (PIT) telemetry, and a multi‐state capture–mark–recapture model to assess SFD effects on short‐term (within‐season) survival, movement, and surface activity of two wild snake species, Regina septemvittata (Queensnake) and Nerodia sipedon (Common Watersnake). We were unable to detect an effect of disease state on short‐term survival for either species. However, we estimated Bayesian posterior probabilities of >0.99 that R. septemvittata with SFD spent more time surface‐active and were less likely to permanently emigrate from the study area. We also estimated probabilities of 0.98 and 0.87 that temporary immigration and temporary emigration rates, respectively, were lower in diseased R. septemvittata. We found evidence of elevated surface activity and lower temporary immigration rates in diseased N. sipedon, with estimated probabilities of 0.89, and found considerably less support for differences in permanent or temporary emigration rates. This study is the first to yield estimates for key demographic and behavioral parameters (survival, emigration, surface activity) of snakes in wild populations afflicted with SFD. Given the increase in surface activity of diseased snakes, future surveys of snake populations could benefit from exploring longer‐term demographic consequences of SFD and recognize that disease prevalence in surface‐active animals may exceed that of the population as a whole.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2251","usgsCitation":"McKenzie, J.M., Price, S.J., Connette, G.M., Bonner, S.J., and Lorch, J.M., 2020, Effects of snake fungal disease on short‐term survival, behavior, and movement in free‐ranging snakes: Ecological Applications, v. 31, no. 2, e02251, https://doi.org/10.1002/eap.2251.","productDescription":"e02251","ipdsId":"IP-123269","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":383707,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"31","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-15","publicationStatus":"PW","contributors":{"authors":[{"text":"McKenzie, Jennifer M.","contributorId":212841,"corporation":false,"usgs":false,"family":"McKenzie","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":811193,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Price, Steven J. 0000-0002-2388-0579","orcid":"https://orcid.org/0000-0002-2388-0579","contributorId":57738,"corporation":false,"usgs":false,"family":"Price","given":"Steven","email":"","middleInitial":"J.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":811194,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connette, Grant M.","contributorId":212844,"corporation":false,"usgs":false,"family":"Connette","given":"Grant","email":"","middleInitial":"M.","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":811195,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bonner, Simon J","contributorId":252946,"corporation":false,"usgs":false,"family":"Bonner","given":"Simon","email":"","middleInitial":"J","affiliations":[{"id":13255,"text":"University of Western Ontario","active":true,"usgs":false}],"preferred":false,"id":811196,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lorch, Jeffrey M. 0000-0003-2239-1252 jlorch@usgs.gov","orcid":"https://orcid.org/0000-0003-2239-1252","contributorId":5565,"corporation":false,"usgs":true,"family":"Lorch","given":"Jeffrey","email":"jlorch@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":811197,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70215074,"text":"70215074 - 2020 - Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada)","interactions":[],"lastModifiedDate":"2020-11-30T16:10:25.445752","indexId":"70215074","displayToPublicDate":"2020-11-02T16:29:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada)","docAbstract":"Fluid seepage along obliquely deforming plate boundaries can be an important indicator of crustal permeability and influence on fault-zone mechanics and hydrocarbon migration. The ~850-km-long Queen Charlotte fault (QCF) is the dominant structure along the right-lateral transform boundary that separates the Pacific and North American tectonic plates offshore southeastern Alaska (USA) and western British Columbia (Canada). Indications for fluid seepage along the QCF margin include gas bubbles originating from the seafloor and imaged in the water column, chemosynthetic communities, precipitates of authigenic carbonates, mud volcanoes, and changes in the acoustic character of seismic reflection data. Cold seeps sampled in this study preferentially occur along the crests of ridgelines associated with uplift and folding and between submarine canyons that incise the continental slope strata. With carbonate stable carbon isotope (δ13C) values ranging from −46‰ to −3‰, there is evidence of both microbial and thermal degradation of organic matter of continental-margin sediments along the QCF. Both active and dormant venting on ridge crests indicate that the development of anticlines is a key feature along the QCF that facilitates both trapping and focused fluid flow. Geochemical analyses of methane-derived authigenic carbonates are evidence of fluid seepage along the QCF since the Last Glacial Maximum. These cold seeps sustain vibrant chemosynthetic communities such as clams and bacterial mats, providing further evidence of venting of reduced chemical fluids such as methane and sulfide along the QCF.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02269.1","usgsCitation":"Prouty, N.G., Brothers, D.S., Kluesner, J.W., Barrie, J., Andrews, B.D., Lauer, R., Greene, G., Conrad, J.E., Lorenson, T., Law, M.D., Sahy, D., Conway, K., McGann, M., and Dartnell, P., 2020, Focused fluid flow and methane venting along the Queen Charlotte fault, offshore Alaska (USA) and British Columbia (Canada): Geosphere, v. 16, no. 6, p. 1336-1357, https://doi.org/10.1130/GES02269.1.","productDescription":"22 p.","startPage":"1336","endPage":"1357","ipdsId":"IP-111343","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454893,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02269.1","text":"Publisher Index Page"},{"id":380375,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","state":"Alaska, British Columbia","otherGeospatial":"Queen Charlotte Fault","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -138.9111328125,\n 51.83577752045248\n ],\n [\n -129.5068359375,\n 51.83577752045248\n ],\n [\n -129.5068359375,\n 58.07787626787517\n ],\n [\n -138.9111328125,\n 58.07787626787517\n ],\n [\n -138.9111328125,\n 51.83577752045248\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Prouty, Nancy G. 0000-0002-8922-0688 nprouty@usgs.gov","orcid":"https://orcid.org/0000-0002-8922-0688","contributorId":3350,"corporation":false,"usgs":true,"family":"Prouty","given":"Nancy","email":"nprouty@usgs.gov","middleInitial":"G.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800715,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brothers, Daniel S. 0000-0001-7702-157X dbrothers@usgs.gov","orcid":"https://orcid.org/0000-0001-7702-157X","contributorId":167089,"corporation":false,"usgs":true,"family":"Brothers","given":"Daniel","email":"dbrothers@usgs.gov","middleInitial":"S.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":800716,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kluesner, Jared W. 0000-0003-1701-8832 jkluesner@usgs.gov","orcid":"https://orcid.org/0000-0003-1701-8832","contributorId":201261,"corporation":false,"usgs":true,"family":"Kluesner","given":"Jared","email":"jkluesner@usgs.gov","middleInitial":"W.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800721,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Barrie, J. Vaughn","contributorId":242728,"corporation":false,"usgs":false,"family":"Barrie","given":"J. Vaughn","affiliations":[{"id":48497,"text":"2Geological Survey of Canada (Pacific,)","active":true,"usgs":false}],"preferred":false,"id":800717,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Andrews, Brian D. 0000-0003-1024-9400 bandrews@usgs.gov","orcid":"https://orcid.org/0000-0003-1024-9400","contributorId":201662,"corporation":false,"usgs":true,"family":"Andrews","given":"Brian","email":"bandrews@usgs.gov","middleInitial":"D.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800718,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lauer, Rachel","contributorId":242729,"corporation":false,"usgs":false,"family":"Lauer","given":"Rachel","affiliations":[{"id":39897,"text":"Department of Geoscience, University of Calgary","active":true,"usgs":false}],"preferred":false,"id":800719,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Greene, Gary","contributorId":242730,"corporation":false,"usgs":false,"family":"Greene","given":"Gary","affiliations":[{"id":48498,"text":"Moss Landing Marine Laboratories and Tombolo Mapping Laboratory","active":true,"usgs":false}],"preferred":false,"id":800720,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Conrad, James E. 0000-0001-6655-694X jconrad@usgs.gov","orcid":"https://orcid.org/0000-0001-6655-694X","contributorId":2316,"corporation":false,"usgs":true,"family":"Conrad","given":"James","email":"jconrad@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800722,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Lorenson, Thomas 0000-0001-7669-2873 tlorenson@usgs.gov","orcid":"https://orcid.org/0000-0001-7669-2873","contributorId":174599,"corporation":false,"usgs":true,"family":"Lorenson","given":"Thomas","email":"tlorenson@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800723,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Law, Michael D.","contributorId":218726,"corporation":false,"usgs":false,"family":"Law","given":"Michael","email":"","middleInitial":"D.","affiliations":[{"id":39897,"text":"Department of Geoscience, University of Calgary","active":true,"usgs":false}],"preferred":false,"id":800724,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sahy, Diana","contributorId":169649,"corporation":false,"usgs":false,"family":"Sahy","given":"Diana","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":804535,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Conway, Kim","contributorId":242731,"corporation":false,"usgs":false,"family":"Conway","given":"Kim","email":"","affiliations":[{"id":48501,"text":"Geological Survey of Canada (Pacific)","active":true,"usgs":false}],"preferred":false,"id":800725,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"McGann, Mary 0000-0002-3057-2945 mmcgann@usgs.gov","orcid":"https://orcid.org/0000-0002-3057-2945","contributorId":169540,"corporation":false,"usgs":true,"family":"McGann","given":"Mary","email":"mmcgann@usgs.gov","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":800726,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Dartnell, Peter 0000-0002-9554-729X","orcid":"https://orcid.org/0000-0002-9554-729X","contributorId":208208,"corporation":false,"usgs":true,"family":"Dartnell","given":"Peter","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":800727,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70215694,"text":"sir20205086 - 2020 - Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas","interactions":[],"lastModifiedDate":"2020-11-03T12:40:42.087231","indexId":"sir20205086","displayToPublicDate":"2020-11-02T14:07:21","publicationYear":"2020","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":"2020-5086","displayTitle":"Regional Regression Equations for Estimation of Four Hydraulic Properties of Streams at Approximate Bankfull Conditions for Different Ecoregions in Texas","title":"Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas","docAbstract":"The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, assessed statistical relations between hydraulic properties of streams at approximate bankfull conditions for different ecological regions (ecoregions) in Texas. Data from more than 103,000 records of measured discharge and ancillary hydraulic properties were assembled from summaries of discharge measurements for 424 U.S. Geological Survey streamgages in Texas. The data were subsequently subsetted at each streamgage for a streamgage-specific discharge interval centered on the estimated median annual peak discharge (0.5 annual exceedance probability) obtained from previously published regional regression equations in Texas in conjunction with the streamgage-specific sample median annual peak discharge for the period of record for each streamgage. Discharge measurements at gaged locations representing bankfull conditions (approximated from a discharge interval centered on the estimated median annual peak discharge at a given site) and associated watershed properties were subjected to rigorous statistical analysis. For most discharge measurements (where discharge is symbolically represented as Q), the following hydraulic properties are available: cross-section area (A), water-surface top width (B), and reported mean velocity (V). Statewide summary statistics were computed by using these four hydraulic properties (Q, A, B, and V) and the following five watershed properties: (1) watershed area (contributing drainage area), (2) a multiple of main-channel slope (1,000 times main-channel slope), (3) mean annual precipitation, (4) drainage density, and (5) sinuosity ratio. From the initial set of 424 streamgages, summary statistics were computed for 372 selected streamgages in Texas and constitute the subsetted measurements dataset described in this report. Eight of the 10 ecoregions in Texas are represented in the statewide summary statistics.
The resulting statistical relations, expressed as regression equations, can be used to estimate cross-section area, water-surface top width, discharge, and mean velocity of streams in different Texas ecoregions, at approximate bankfull conditions. In the regression equations, watershed properties were the independent variables for applicable watersheds, and predictions from the equations might be useful for estimating the four hydraulic properties at ungaged or unmonitored locations from selected characteristics measured at both the ungaged locations and gaged locations.
Four regression equations to estimate the four hydraulic properties were identified as the preferred equations from this study. The four preferred equations use watershed area, mean annual precipitation, and aggregated ecoregion (treated as a categorical variable) to estimate the hydraulic properties, and justification is provided for this preference. For the four equations, the proportions of variance explained by the regression equations as measured by Nash-Sutcliffe efficiency are about 71 percent for cross-section area, 36 percent for top width, 76 percent for discharge, and 25 percent for mean velocity. Residual standard error (RSEs) of the regression equations are 0.252 log10 square feet for cross-section area, 0.319 log10 feet for top width, 0.247 log10 cubic feet per second for discharge, and 0.190 log10 feet per second for mean velocity, and the corresponding standard deviations of response are 0.465 log10 square feet, 0.397 log10 feet, 0.507 log10 cubic feet per second, and 0.220 log10 feet per second, respectively. The residual standard errors are less than the standard deviations as anticipated but show that the uncertainty reduction (percent change) for cross-section area is about −46 percent, about −20 percent for top width, about −51 percent for discharge, and about −14 percent for mean velocity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205086","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers","usgsCitation":"Asquith, W.H., Gordon, J.D., and Wallace, D.S., 2020, Regional regression equations for estimation of four hydraulic properties of streams at approximate bankfull conditions for different ecoregions in Texas: U.S. Geological Survey Scientific Investigations Report 2020–5086, 45 p., https://doi.org/10.3133/sir20205086.","productDescription":"Report: vi, 45 p.; Companion File","numberOfPages":"54","onlineOnly":"Y","ipdsId":"IP-081456","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":436735,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96WY938","text":"USGS data release","linkHelpText":"Source code in R for creation of regional regression equations for estimation of four 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\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Ice wedge degradation is a widespread occurrence across the circumpolar Arctic causing extreme spatial heterogeneity in water distribution, vegetation, and energy balance across landscapes. These heterogeneities influence carbon dioxide (CO2) and methane (CH4) fluxes, yet there is little understanding of how they effect change in landscape‐level carbon (C) gas flux over time. We measured CO2 and CH4 fluxes in an area undergoing ice wedge degradation near Prudhoe Bay, Alaska, and combined with repeat imagery analysis to estimate seasonal landscape‐level C flux response to geomorphic change. Net CO2 and CH4 emissions changed by −25% and +42%, respectively, resulting in a 14% increase in seasonal CO2‐C equivalent emissions over 69 years as ice wedge degradation formed water‐filled troughs. The dynamic ice wedge degradation/stabilization process can cause significant changes in CO2 and CH4 fluxes over time, and the integration of this process is important to forecasting landscape‐level C fluxes in permafrost regions abundant in ice wedges.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL089894","usgsCitation":"Wickland, K.P., Jorgenson, M., Koch, J.C., Kanevskiy, M.Z., and Striegl, R.G., 2020, Carbon dioxide and methane flux in a dynamic Arctic tundra landscape: Decadal‐scale impacts of ice wedge degradation and stabilization: Geophysical Research Letters, v. 47, no. 22, e2020GL089894, 10 p., https://doi.org/10.1029/2020GL089894.","productDescription":"e2020GL089894, 10 p.","ipdsId":"IP-120601","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":380783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Prudhoe Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -148.73428344726562,\n 70.00180966478055\n ],\n [\n -147.8704833984375,\n 70.00180966478055\n ],\n [\n -147.8704833984375,\n 70.35709062721314\n ],\n [\n -148.73428344726562,\n 70.35709062721314\n ],\n [\n -148.73428344726562,\n 70.00180966478055\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"47","issue":"22","noUsgsAuthors":false,"publicationDate":"2020-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wickland, Kimberly P. 0000-0002-6400-0590 kpwick@usgs.gov","orcid":"https://orcid.org/0000-0002-6400-0590","contributorId":1835,"corporation":false,"usgs":true,"family":"Wickland","given":"Kimberly","email":"kpwick@usgs.gov","middleInitial":"P.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":805612,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jorgenson, M.Torre 0000-0002-9834-8851","orcid":"https://orcid.org/0000-0002-9834-8851","contributorId":245200,"corporation":false,"usgs":false,"family":"Jorgenson","given":"M.Torre","affiliations":[{"id":13506,"text":"Alaska Ecoscience","active":true,"usgs":false}],"preferred":false,"id":805613,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":805614,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanevskiy, Mikhail Z.","contributorId":199153,"corporation":false,"usgs":false,"family":"Kanevskiy","given":"Mikhail","email":"","middleInitial":"Z.","affiliations":[],"preferred":false,"id":805615,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Striegl, Robert G. 0000-0002-8251-4659 rstriegl@usgs.gov","orcid":"https://orcid.org/0000-0002-8251-4659","contributorId":1630,"corporation":false,"usgs":true,"family":"Striegl","given":"Robert","email":"rstriegl@usgs.gov","middleInitial":"G.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":false,"id":805616,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70227004,"text":"70227004 - 2020 - Vegetation management on private forestland can increase avian species richness and abundance","interactions":[],"lastModifiedDate":"2021-12-27T14:37:29.778561","indexId":"70227004","displayToPublicDate":"2020-11-02T08:33:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Vegetation management on private forestland can increase avian species richness and abundance","docAbstract":"Conservation efforts on private lands are important for biodiversity conservation. On private lands in South Carolina, in the southeastern United States, forestry management practices (prescribed burning, thinning, herbicide application) are used to improve upland pine habitat for wildlife and timber harvest and are incentivized through U.S. Department of Agriculture Farm Bill cost-share programs. Because many forest-dependent avian species have habitat requirements created primarily through forest management, data are needed on the effectiveness of these management activities. We studied privately owned loblolly pine (Pinus taeda) stands in the South Carolina Piedmont region. Our objective was to understand how management practices influence avian species richness and abundance at local (forest stand) and landscape levels in relatively small stands (average ~28 ha). We surveyed 49 forest stands during 2 bird breeding seasons with traditional point counts and vegetation surveys. We evaluated the effects of management on pine stand characteristics, avian species richness, and abundance of state-designated bird species of concern. Repeated burning and thinning shifted stand conditions to open pine woodlands with reduced basal area and herbaceous understories. Stands with lower basal area supported greater avian species richness. Some species increased in abundance in response to active management (e.g., Brown-headed Nuthatch [Sitta pusilla] and Indigo Bunting [Passerina cyanea]), but relationships varied. Some species responded positively to increases in forest quantity at a landscape scale (1–5 km; e.g., Northern Bobwhite [Colinus virginianus]). We found species-rich avian communities and species of conservation concern on working timber lands, indicating that incentivized forest management on private lands can provide valuable habitat for wildlife.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duaa048","usgsCitation":"Wood, J., Tegeler, A., and Ross, B., 2020, Vegetation management on private forestland can increase avian species richness and abundance: Ornithological Applications, v. 122, no. 4, duaa048, 16 p., https://doi.org/10.1093/condor/duaa048.","productDescription":"duaa048, 16 p.","ipdsId":"IP-106496","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":454896,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duaa048","text":"Publisher Index Page"},{"id":393413,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Carolina\",\"nation\":\"USA \"}}]}","volume":"122","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Wood, J.M.","contributorId":270361,"corporation":false,"usgs":false,"family":"Wood","given":"J.M.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":829149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tegeler, A.K.","contributorId":270363,"corporation":false,"usgs":false,"family":"Tegeler","given":"A.K.","email":"","affiliations":[{"id":56153,"text":"2South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":829150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":829151,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70215885,"text":"ofr20201115 - 2020 - Southern (California) sea otter population status and trends at San Nicolas Island, 2017–2020","interactions":[],"lastModifiedDate":"2020-11-03T12:47:24.250221","indexId":"ofr20201115","displayToPublicDate":"2020-11-02T08:26:49","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-1115","displayTitle":"Southern (California) Sea Otter Population Status and Trends at San Nicolas Island, 2017–2020","title":"Southern (California) sea otter population status and trends at San Nicolas Island, 2017–2020","docAbstract":"The southern sea otter (Enhydra lutris nereis) population at San Nicolas Island, California, has been monitored annually since the translocation of 140 sea otters to the island was completed in 1990. Monitoring efforts have varied in frequency and type across years. In 2017, the U.S. Navy and the U.S. Fish and Wildlife Service initiated a sea otter monitoring and research plan to determine the effects of military readiness activities on the growth or decline of the southern sea otter population at San Nicolas Island. The monitoring program, at its basic level, includes quarterly seasonal surveys of population abundance, distribution, and foraging activity. From 2017 to 2020, we measured a 22-percent per annum increase in population abundance (95-percent confidence interval =11–34 percent) with 114 total individuals as of February 2020. Coinciding with recent population growth, the sea otter distribution, which previously tended to concentrate on the west side, appears to have shifted toward an expansion of use in the north and especially greater seasonal use in the north and south during winter and spring. Foraging data were collected on a total of 2,675 foraging dives in 167 foraging bouts, and the majority of identified prey on successful dives (n=1,335) were sea urchins (940) followed by snails (240) and crabs (78). Small numbers of lobsters (26), octopus (16), and abalone (5) also were identified. Estimates of energy intake rates averaged 17.3 kilocalories per minute (95-percent confidence interval =15.6–19.0 kilocalories per minute) and suggest possible variations across years and seasons, but confidence intervals based on specific years of data were relatively wide. In addition to abundance, trends, distribution, and forage energy intake across seasons and years, these replicated surveys provide information on the precision of data achieved by quarterly survey effort. We used precision estimates and conducted simulation analyses to assess the power of detecting 10-percent or greater decreases in population growth rates and how this power is likely to change with years of observation, survey effort, and the size of decrease. These results can be useful to the planning of future monitoring and research of sea otters at San Nicolas Island.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201115","collaboration":"Wildlife ProgramDirector, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Anticoagulant rodenticides (ARs) are commonly used to control rodent pests. However, worldwide, their use is associated with secondary and tertiary poisoning of nontarget species, especially predatory and scavenging birds. No medical device can rapidly test for AR exposure of avian wildlife. Prothrombin time (PT) is a useful biomarker for AR exposure, and multiple commercially available point-of-care (POC) devices measure PT of humans, and domestic and companion mammals. We evaluated the potential of one commercially available POC device, the Coag-Sense® PT/INR Monitoring System, to rapidly detect AR exposure of living birds of prey. The Coag-Sense device delivered repeatable PT measurements on avian blood samples collected from four species of raptors trapped during migration (Intraclass Correlation Coefficient > 0.9; overall intra-sample variation CV: 5.7%). However, PT measurements reported by the Coag-Sense system from 81 ferruginous hawk (Buteo regalis) nestlings were not correlated to those measured by a one-stage laboratory avian PT assay (r = − 0.017, p = 0.88). Although precise, the lack of agreement in PT estimates from the Coag-Sense device and the laboratory assay indicates that this device is not suitable for detecting potential AR exposure of birds of prey. The lack of suitability may be related to the use of a mammalian reagent in the clotting reaction, suggesting that the device may perform better in testing mammalian wildlife
The coupled biophysical interactions between submerged aquatic vegetation (SAV), hydrodynamics (currents and waves), sediment dynamics, and nutrient cycling have long been of interest in estuarine environments. Recent observational studies have addressed feedbacks between SAV meadows and their role in modifying current velocity, sedimentation, and nutrient cycling. To represent these dynamic processes in a numerical model, the presence of SAV and its effect on hydrodynamics (currents and waves) and sediment dynamics was incorporated into the open-source Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. In this study, we extend the COAWST modeling framework to account for dynamic changes of SAV and associated epiphyte biomass. Modeled SAV biomass is represented as a function of temperature, light, and nutrient availability. The modeled SAV community exchanges nutrients, detritus, dissolved inorganic carbon, and dissolved oxygen with the water-column biogeochemistry model. The dynamic simulation of SAV biomass allows the plants to both respond to and cause changes in the water column and sediment bed properties, hydrodynamics, and sediment transport (i.e., a two-way feedback). We demonstrate the behavior of these modeled processes through application to an idealized domain and then apply the model to a eutrophic harbor where SAV dieback is a result of anthropogenic nitrate loading and eutrophication. These cases demonstrate an advance in the deterministic modeling of coupled biophysical processes and will further our understanding of future ecosystem change.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/gmd-13-5211-2020","usgsCitation":"Kalra, T., Ganju, N., and Testa, J.M., 2020, Development of a submerged aquatic vegetation growth model in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST v3.4) model: Geoscientific Model Development, v. 13, no. 11, p. 5211-5228, https://doi.org/10.5194/gmd-13-5211-2020.","productDescription":"18 p.","startPage":"5211","endPage":"5228","ipdsId":"IP-102944","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":454901,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/gmd-13-5211-2020","text":"Publisher Index Page"},{"id":380185,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Kalra, Tarandeep S. 0000-0001-5468-248X tkalra@usgs.gov","orcid":"https://orcid.org/0000-0001-5468-248X","contributorId":178820,"corporation":false,"usgs":true,"family":"Kalra","given":"Tarandeep S.","email":"tkalra@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":false,"id":804107,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ganju, Neil K. 0000-0002-1096-0465","orcid":"https://orcid.org/0000-0002-1096-0465","contributorId":202878,"corporation":false,"usgs":true,"family":"Ganju","given":"Neil K.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":804108,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Testa, Jeremy M.","contributorId":244524,"corporation":false,"usgs":false,"family":"Testa","given":"Jeremy","email":"","middleInitial":"M.","affiliations":[{"id":37215,"text":"University of Maryland Center for Environmental Science","active":true,"usgs":false}],"preferred":false,"id":804109,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216069,"text":"70216069 - 2020 - Seasonality of biological and physical systems as indicators of climatic variation and change","interactions":[],"lastModifiedDate":"2024-05-16T15:21:12.860978","indexId":"70216069","displayToPublicDate":"2020-11-02T07:25:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1252,"text":"Climatic Change","active":true,"publicationSubtype":{"id":10}},"title":"Seasonality of biological and physical systems as indicators of climatic variation and change","docAbstract":"Evidence-based responses to climate change by society require operational and sustained information including biophysical indicator systems that provide up-to-date measures of trends and patterns against historical baselines. Two key components linking anthropogenic climate change to impacts on socio-ecological systems are the periodic inter- and intra-annual variations in physical climate systems (seasonality) and in plant and animal life cycles (phenology). We describe a set of national indicators that reflect sub-seasonal to seasonal drivers and responses of terrestrial physical and biological systems to climate change and variability at the national scale. Proposed indicators and metrics include seasonality of surface climate conditions (e.g., frost and freeze dates and durations), seasonality of freeze/thaw in freshwater systems (e.g., timing of stream runoff and durations of lake/river ice), seasonality in ecosystem disturbances (e.g., wildfire season timing and duration), seasonality in vegetated land surfaces (e.g., green-up and brown-down of landscapes), and seasonality of organismal life-history stages (e.g., timings of bird migration). Recommended indicators have strong linkages to variable and changing climates, include abiotic and biotic responses and feedback mechanisms, and are sufficiently simple to facilitate communication to broad audiences and stakeholders interested in understanding and adapting to climate change.
The COVID-19 pandemic has led to environmental recovery in some ecosystems from a global “anthropause,” yet such evidence for natural resources with extraction or production value (e.g., fisheries) is limited. This brief report provides a data-driven global snapshot of expert-perceived impacts of COVID-19 on inland fisheries. We distributed an online survey assessing perceptions of inland fishery pressures in June and July 2020 to basin-level inland fishery experts (i.e., identified by the Food and Agriculture Organization of the United Nations across the global North and South); 437 respondents from 79 countries addressed 93 unique hydrological basins, accounting for 82.1% of global inland fish catch. Based on the responses analyzed against extrinsic fish catch and human development index data, pandemic impacts on inland fisheries 1) add gradation to the largely positive environmental narrative of the global pandemic and 2) identify that basins of higher provisioning value are perceived to experience greater fishery pressures but may have limited compensatory capacity to mitigate COVID-19 impacts along with negative pressures already present.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2014016117","usgsCitation":"Stokes, G.L., Lynch, A., Lowe, B.S., Funge-Smith, S., Valbo-Jorgensen, J., and Smidt, S.J., 2020, COVID-19 pandemic impacts on global inland fisheries: PNAS, v. 117, no. 47, p. 29419-29421, https://doi.org/10.1073/pnas.2014016117.","productDescription":"3 p.","startPage":"29419","endPage":"29421","ipdsId":"IP-120698","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":454904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2014016117","text":"Publisher Index Page"},{"id":381214,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"117","issue":"47","noUsgsAuthors":false,"publicationDate":"2020-11-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Stokes, Gretchen L. 0000-0003-4202-6527","orcid":"https://orcid.org/0000-0003-4202-6527","contributorId":245640,"corporation":false,"usgs":false,"family":"Stokes","given":"Gretchen","email":"","middleInitial":"L.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":806705,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lynch, Abigail 0000-0001-8449-8392","orcid":"https://orcid.org/0000-0001-8449-8392","contributorId":220490,"corporation":false,"usgs":true,"family":"Lynch","given":"Abigail","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":806691,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lowe, Benjamin S. 0000-0002-1879-254X","orcid":"https://orcid.org/0000-0002-1879-254X","contributorId":245641,"corporation":false,"usgs":false,"family":"Lowe","given":"Benjamin","email":"","middleInitial":"S.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":806706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Funge-Smith, Simon 0000-0001-9974-5333","orcid":"https://orcid.org/0000-0001-9974-5333","contributorId":245642,"corporation":false,"usgs":false,"family":"Funge-Smith","given":"Simon","email":"","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":806707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Valbo-Jorgensen, John 0000-0002-1992-5682","orcid":"https://orcid.org/0000-0002-1992-5682","contributorId":220485,"corporation":false,"usgs":false,"family":"Valbo-Jorgensen","given":"John","affiliations":[{"id":32888,"text":"Food and Agriculture organization of the United Nations","active":true,"usgs":false}],"preferred":false,"id":806708,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Smidt, Samuel J. 0000-0001-7728-2083","orcid":"https://orcid.org/0000-0001-7728-2083","contributorId":192816,"corporation":false,"usgs":false,"family":"Smidt","given":"Samuel","email":"","middleInitial":"J.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":806709,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ]}